Brain Mapping: QEEG & Peak Agency
Quantitative EEG analysis, brain mapping techniques, and assessment protocols.
This collection includes research on quantitative EEG, brain mapping, assessment protocols, and normative databases. The literature covers QEEG methodology, interpretation, clinical applications, and validity studies.
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We've curated 126 research papers for this use case.
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Research Citations (100 of 126)
Wall shear stress and pressure patterns in aortic stenosis patients with and without aortic dilation captured by high-performance image-based computational fluid dynamics
Spatial patterns of elevated wall shear stress and pressure due to blood flow past aortic stenosis (AS) are studied using GPU-accelerated patient-specific computational fluid dynamics. Three cases of moderate to severe AS, one with a dilated ascending aorta and two within the normal range (root diameter less than 4cm) are simulated for physiological waveforms obtained from echocardiography. The computational framework is built based on sharp-interface Immersed Boundary Method, where aortic geometries segmented from CT angiograms are integrated into a high-order incompressible Navier-Stokes solver. The key question addressed here is, given the presence of turbulence due to AS which increases wall shear stress (WSS) levels, why some AS patients undergo much less aortic dilation. Recent case studies of AS have linked the existence of an elevated WSS hotspot (due to impingement of AS on the aortic wall) to the dilation process. Herein we further investigate the WSS distribution for cases with and without dilation to understand the possible hemodynamics which may impact the dilation process. We show that the spatial distribution of elevated WSS is significantly more focused for the case with dilation than those without dilation. We further show that this focal area accommodates a persistent pocket of high pressure, which may have contributed to the dilation process through an increased wall-normal forcing. The cases without dilation, on the contrary, showed a rather oscillatory pressure behaviour, with no persistent pressure "buildup" effect. We further argue that a more proximal branching of the aortic arch could explain the lack of a focal area of elevated WSS and pressure, because it interferes with the impingement process due to fluid suction effects. These phenomena are further illustrated using an idealized aortic geometry. We finally show that a restored inflow eliminates the focal area of elevated WSS and pressure zone from the ascending aorta.
View Full Paper →EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI
Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source artifact removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50 ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. A limitation of the method is its use of a reference layer, a piece of EEG equipment which is not commercially available, but can be assembled in-house. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.
View Full Paper →Relationship between psychosocial stress-induced prefrontal cortex activity and gut microbiota in healthy Participants-A functional near-infrared spectroscopy study
Brain and gut microbes communicate in a bidirectional manner with each affecting a person's response to psychosocial stress. Although human studies demonstrated that the intake of probiotics can alter stress-related behavior in both patients and healthy participants, the association between stress-related brain functions and the gut microbiota has mostly been investigated in patients with depression. However, the response to psychosocial stress differs, even among healthy individuals, and elucidating the natural state of the gut microbiota would broaden the understanding of responses to psychosocial stress. We investigated the relationship between psychosocial stress response in the prefrontal cortex and the abundance of gut microbes in healthy male participants. The participants were exposed to psychosocial stress during a task while brain activation data were recorded using functional near-infrared spectroscopy. The heart rate and subjective stress were recorded, and fecal samples were collected. The stressful condition was accompanied by high subjective stress, high heart rate, and higher prefrontal activation in the right pre-motor cortex/supplementary motor area, right dorsolateral prefrontal cortex, right frontal pole, and right inferior prefrontal gyrus. The psychosocial stress response in the prefrontal cortex was also associated with changes in the gut microbiota abundance. The abundance of Alistipes, Clostridium IV, Clostridium XI, Faecalibacterium, and Blautia in healthy participants who had high psychosocial stress resembled that noted in patients with depression. These results suggest that the gut microbiota differs, among healthy participants, depending on the psychosocial stress response. We believe that this study is the first to report a direct relationship between brain function and the gut microbiota in healthy participants, and our findings would shed a new light on this field in the near future.
Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function
Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.
View Full Paper →Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021
Background With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis. Methods QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform ( bibliometric.com ) were employed to perform bibliographic and visualized analysis. Results A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends. Conclusion The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.
View Full Paper →Quantitative Electroencephalography (QEEG) as an Innovative Diagnostic Tool in Mental Disorders
Quantitative electroencephalography (QEEG) is becoming an increasingly common method of diagnosing neurological disorders and, following the recommendations of The American Academy of Neurology (AAN) and the American Clinical Neurophysiology Society (ACNS), it can be used as a complementary method in the diagnosis of epilepsy, vascular diseases, dementia, and encephalopathy. However, few studies are confirming the importance of QEEG in the diagnosis of mental disorders and changes occurring as a result of therapy; hence, there is a need for analyses in this area. The aim of the study is analysis of the usefulness of QEEG in the diagnosis of people with generalized anxiety disorders. Our research takes the form of case studies. The paper presents an in-depth analysis of the QEEG results of five recently studied people with a psychiatric diagnosis: generalized anxiety disorder. The results show specific pattern amplitudes at C3 and C4. In all of the examined patients, two dependencies are repeated: low contribution of the sensorimotor rhythm (SMR) wave amplitudes and high beta2 wave amplitudes, higher or equal to the alpha amplitudes. The QEEG study provides important information about the specificity of brain waves of people with generalized anxiety disorder; therefore, it enables the preliminary and quick diagnosis of dysfunction. It is also possible to monitor changes due to QEEG, occurring as a result of psychotherapy, pharmacological therapy and EEG-biofeedback.
View Full Paper →Cognitive behavioral therapy with and without biofeedback in fibromyalgia: Assessment of functional and clinical change
The study compared the effectiveness of Cognitive Behavioral Therapy (CBT) with biofeedback or with emotional expression in individuals with fibromyalgia, and a waiting list control group. 88 women participated in a naturalistic study with random assignment. The Fibromyalgia Impact Questionnaire, SCL-90R, and a visual analog quality of life scale were used. Both intervention groups improved, but differed in physical and emotional control response. Using the reliable change index procedure, clinical improvement occurred in 18.8% of participants, and 4.8% achieved scores comparable with clinical recovery. Greater specificity on therapeutic objectives is warranted.
View Full Paper →Detection of Stroke-Induced Visual Neglect and Target Response Prediction Using Augmented Reality and Electroencephalography
We aim to build a system incorporating electroencephalography (EEG) and augmented reality (AR) that is capable of identifying the presence of visual spatial neglect (SN) and mapping the estimated neglected visual field. An EEG-based brain-computer interface (BCI) was used to identify those spatiospectral features that best detect participants with SN among stroke survivors using their EEG responses to ipsilesional and contralesional visual stimuli. Frontal-central delta and alpha, frontal-parietal theta, Fp1 beta, and left frontal gamma were found to be important features for neglect detection. Additionally, temporal analysis of the responses shows that the proposed model is accurate in detecting potentially neglected targets. These targets were predicted using common spatial patterns as the feature extraction algorithm and regularized discriminant analysis combined with kernel density estimation for classification. With our preliminary results, our system shows promise for reliably detecting the presence of SN and predicting visual target responses in stroke patients with SN.
View Full Paper →One-Year Follow-Up of Healthy Older Adults with Electroencephalographic Risk for Neurocognitive Disorder After Neurofeedback Training
Background: In healthy older adults, excess theta activity is an electroencephalographic (EEG) predictor of cognitive impairment. In a previous study, neurofeedback (NFB) treatment reinforcing reductions theta activity resulted in EEG reorganization and cognitive improvement. Objective: To explore the clinical applicability of this NFB treatment, the present study performed a 1-year follow-up to determine its lasting effects. Methods: Twenty seniors with excessive theta activity in their EEG were randomly assigned to the experimental or control group. The experimental group received an auditory reward when the theta absolute power (AP) was reduced. The control group received the reward randomly. Results: Both groups showed a significant decrease in theta activity at the training electrode. However, the EEG results showed that only the experimental group underwent global changes after treatment. These changes consisted of delta and theta decreases and beta increases. Although no changes were found in any group during the period between the posttreatment evaluation and follow-up, more pronounced theta decreases and beta increases were observed in the experimental group when the follow-up and pretreatment measures were compared. Executive functions showed a tendency to improve two months after treatment which became significant one year later. Conclusion: These results suggest that the EEG and behavioral benefits of this NFB treatment persist for at least one year, which adds up to the available evidence contributing to identifying factors that increase its efficacy level. The relevance of this study lies in its prophylactic features of addressing a clinically healthy population with EEG risk of cognitive decline.
View Full Paper →Attempted brain wave modelling in participants under severe chronic stress using quantitative electroencephalogram
The paper primarily focuses on differences in electroencephalogram (EEG) brain wave frequencies in the presence of symptoms of severe, chronic stress. In the case of a constant increase of stress triggers, it is important to quickly diagnose people who reveal difficulties coping with difficult situations in order to prevent the occurrence of mental disorders. One way to do this is to diagnose brainwave patterns. The study aimed to identify differences in the brainwave levels of participants reporting intense stress compared to the control group. Differences in brainwave frequency between the right and left hemisphere were also investigated in the study group. The study consisted of two stages. Initially, the study group was enrolled based on their level of stress intensity criterion determined by means of an interview (in which participants declared a sense of chronic stress) and high scores on the Perceived Stress Scale (PSS). The control group consisted of subjects with a low score. In the next stage brainwave frequencies were analyzed using quantitative analysis of EEG (electroencephalography, QEEG) recordings. QEEG is a quantitative analysis of the EEG record, in which the data is digitally coded and statistically analyzed using the Fourier transform algorithm. The results demonstrated that people reporting intense, chronic stress statistically significantly more often had higher frequencies of theta, alpha, and beta 2 waves, and a lower level of SMR. Significant differences in the frequencies of the waves in both hemispheres were also noted.
View Full Paper →A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders
Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.
View Full Paper →Neural correlates in functional brain mapping among breast cancer survivors receiving different chemotherapy regimens: a qEEG/HEG-based investigation
BACKGROUND: Post-chemotherapy cognitive impairment commonly known as 'chemobrain' or 'chemofog' is a well-established clinical disorder affecting various cognitive domains including attention, visuospatial working memory, executive function, etc. Although several studies have confirmed the chemobrain in recent years, scant experiments have evaluated the potential neurotoxicity of different chemotherapy regimens and agents. In this study, we aimed to evaluate the extent of attention deficits, one of the commonly affected cognitive domains, among breast cancer patients treated with different chemotherapy regimens through neuroimaging techniques. METHODS: Breast cancer patients treated with two commonly prescribed chemotherapy regimens, Adriamycin, Cyclophosphamide and Taxol and Taxotere, Adriamycin and Cyclophosphamide, and healthy volunteers were recruited. Near-infrared hemoencephalography and quantitative electroencephalography assessments were recorded for each participant at rest and during task performance to compare the functional cortical changes associated with each chemotherapy regimen. RESULTS: Although no differences were observed in hemoencephalography results across groups, the quantitative electroencephalography analysis revealed increased power of high alpha/low beta in left fronto-centro-parietal regions involved in dorsal and ventral attention networks in the Adriamycin, Cyclophosphamide and Taxol-treated group compared with the Taxotere, Adriamycin and Cyclophosphamide and control group. The Adriamycin, Cyclophosphamide and Taxol-treated cases had the highest current source density values in dorsal attention network and ventral attention network and ventral attention network-related centers in 10 and 15 Hz associated with the lowest Z-scored Fast Fourier Transform coherence in the mentioned regions. CONCLUSIONS: The negatively affected neurocognitive profile in breast cancer patients treated with the Adriamycin, Cyclophosphamide and Taxol regimen proposes presumably neurotoxic sequelae of this chemotherapy regimen as compared with the Taxotere, Adriamycin and Cyclophosphamide regimen.
fMRI-based validation of continuous-wave fNIRS of supplementary motor area activation during motor execution and motor imagery
Compared to functional magnetic resonance imaging (fMRI), functional near infrared spectroscopy (fNIRS) has several advantages that make it particularly interesting for neurofeedback (NFB). A pre-requisite for NFB applications is that with fNIRS, signals from the brain region of interest can be measured. This study focused on the supplementary motor area (SMA). Healthy older participants (N = 16) completed separate continuous-wave (CW-) fNIRS and (f)MRI sessions. Data were collected for executed and imagined hand movements (motor imagery, MI), and for MI of whole body movements. Individual anatomical data were used to (i) define the regions of interest for fMRI analysis, to (ii) extract the fMRI BOLD response from the cortical regions corresponding to the fNIRS channels, and (iii) to select fNIRS channels. Concentration changes in oxygenated ([Formula: see text]) and deoxygenated ([Formula: see text]) hemoglobin were considered in the analyses. Results revealed subtle differences between the different MI tasks, indicating that for whole body MI movements as well as for MI of hand movements [Formula: see text] is the more specific signal. Selection of the fNIRS channel set based on individual anatomy did not improve the results. Overall, the study indicates that in terms of spatial specificity and task sensitivity SMA activation can be reliably measured with CW-fNIRS.
View Full Paper →Electrophysiological signatures of brain aging in autism spectrum disorder
Recent evidence suggests that structural and functional brain aging is atypical in adults with autism spectrum disorder (ASD). However, it remains unclear if oscillatory slowing, a key marker of neurophysiological aging, follows an atypical trajectory in this population. This study examines patterns of age-related oscillatory slowing in adults with ASD, captured by reductions in the brain's peak alpha frequency (PAF). Resting-state electroencephalography (EEG) data from adults (18-70 years) with ASD (N = 93) and non-ASD controls (N = 87) were pooled from three independent datasets. A robust curve-fitting procedure quantified the peak frequency of alpha oscillations (7-13 Hz) across all brain regions. Associations between PAF and age were assessed and compared between groups. Consistent with characteristic patterns of oscillatory slowing, PAF was negatively associated with age across the entire sample (p < .0001). A significant group-by-age interaction revealed that this relationship was more pronounced in adults with ASD (p < .01). These findings invite further longitudinal investigations of PAF in adults with ASD to confirm if age-related oscillatory slowing is accelerated.
View Full Paper →Characterizing the ASD–ADHD phenotype: measurement structure and invariance in a clinical sample
Background: Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) have considerable overlap, supporting the need for a dimensional framework that examines neurodevelopmental domains which cross traditional diagnostic boundaries. In the following study, we use factor analysis to deconstruct the ASD–ADHD phenotype into its underlying phenotypic domains and test for measurement invariance across adaptive functioning, age, gender and ASD/ADHD clinical diagnoses. Methods: Participants included children and youth (aged 3–20 years) with a clinical diagnosis of ASD (n = 727) or ADHD (n = 770) for a total of 1,497 participants. Parents of these children completed the Social Communication Questionnaire (SCQ), a measure of autism symptoms, and the Strengths and Weaknesses of ADHD and Normal Behaviour (SWAN) questionnaire, a measure of ADHD symptoms. An exploratory factor analysis (EFA) was performed on combined SCQ and SWAN items. This was followed by a confirmatory factor analysis (CFA) and tests of measurement invariance. Results: EFA revealed a four-factor solution (inattention, hyperactivity/impulsivity, social-communication, and restricted, repetitive, behaviours and interests (RRBI)) and a CFA confirmed good model fit. This solution also showed good model fit across subgroups of interest. Conclusions: Our study shows that a combined ASD–ADHD phenotype is characterized by two latent ASD domains (social communication and RRBIs) and two latent ADHD domains (inattention and hyperactivity/impulsivity). We established measurement invariance of the derived measurement model across adaptive functioning, age, gender and ASD/ADHD diagnoses
View Full Paper →Comparison of QEEG Findings before and after Onset of Post-COVID-19 Brain Fog Symptoms
Previous research and clinical reports have shown that some individuals after COVID-19 infection may demonstrate symptoms of so-called brain fog, manifested by cognitive impairment and disorganization in behavior. Meanwhile, in several other conditions, related to intellectual function, a specific pattern of changes in electric brain activity, as recorded by quantitative electroencephalography (QEEG) has been documented. We hypothesized, that in post-COVID brain fog, the subjective complaints may be accompanied by objective changes in the QEEG profile. In order to test this hypothesis, we have performed an exploratory study on the academic staff of our University with previous records of QEEG originating in the pre-COVID-19 era. Among them, 20 subjects who revealed neurological problems in the cognitive sphere (confirmed as covid fog/brain fog by a clinical specialist) after COVID-19 infection were identified. In those individuals, QEEG was performed. We observed, that opposite to baseline QEEG records, increased Theta and Alpha activity, as well as more intensive sensimotor rhythm (SMR) in C4 (right hemisphere) in relation to C3 (left hemisphere). Moreover, a visible increase in Beta 2 in relation to SMR in both hemispheres could be documented. Summarizing, we could demonstrate a clear change in QEEG activity patterns in individuals previously not affected by COVID-19 and now suffering from post-COVID-19 brain fog. These preliminary results warrant further interest in delineating their background. Here, both neuroinflammation and psychological stress, related to Sars-CoV2-infection may be considered. Based on our observation, the relevance of QEEG examination as a supportive tool for post-COVID clinical workup and for monitoring the treatment effects is also to be explored.
View Full Paper →Reward sensitivity modulates the brain reward pathway in stress resilience via the inherent neuroendocrine system
In the previous 10 years, researchers have suggested a critical role for the brain reward system in stress resilience. However, no study has provided an empirical link between activity in the mesostriatal reward regions during stress and the recovery of cortisol stress response. Moreover, although reward sensitivity as a trait has been demonstrated to promote stress resilience, it remains unclear whether it modulates the brain reward system in stress resilience and how this effect is achieved by the inherent neuroendocrine system. To investigate these uncertainties, 70 young adults were recruited to participate in a ScanSTRESS task, and their brain imaging data and saliva samples (for cortisol assay) were collected during the task. In addition, we assessed reward sensitivity, cortisol awakening response, and intrinsic functional connectivity of the brain in all the participants. We found that left putamen activation during stress exposure positively predicted cortisol recovery. In addition, reward sensitivity was positively linked with activation of the left putamen, and this relationship was serially mediated by the cortisol awakening response and right hippocampus-left inferior frontal gyrus intrinsic connectivity. These findings suggest that reward sensitivity modulates reward pathways in stress resilience through the interplay of the diurnal stress response system and network of the hippocampus-prefrontal circuitry. Summarily, the current study built a model to highlight the dynamic and multifaceted interaction between pertinent allostatic factors in the reward-resilience pathway and uncovered new insight into the resilience function of the mesostriatal reward system during stress.
View Full Paper →Online detection and removal of eye blink artifacts from electroencephalogram
The most prominent type of artifact contaminating electroencephalogram (EEG) signals are the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG signal. Online identification and elimination of eye blink artifacts are crucial in applications such a Brain-Computer Interfaces (BCI), neurofeedback, and epilepsy diagnosis. In this paper, algorithms that combine unsupervised eye blink artifact detection (eADA) with modified Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed, i.e., FastEMD-CCA2 and FastCCA, to automatically identify eye blink artifacts and remove them in an online setting. The average accuracy, sensitivity, specificity, and error rate for eye blink artifact removal with FastEMD-CCA2 is 97.9%, 97.65%, 99.22%, and 2.1%, respectively, validated on a Hitachi dataset with 60 EEG signals, consisting of more than 5600 eye blink artifacts. FastCCA achieved an average of 99.47%, 99.44%, 99.74%, and 0.53% artifact removal accuracy, sensitivity, specificity, and error rate, respectively, validated on the Hitachi dataset too. FastEMD-CCA2 and FastCCA algorithms are developed and implemented in the C++ programming language, mainly to investigate the processing speed that these algorithms could achieve in a different medium. Analysis has shown that FastEMD-CCA2 and FastCCA took about 10.7 and 12.7 ms, respectively, on average to clean a 1-s length of EEG segment. As a result, they're a viable option for applications that require online removal of eye blink objects from EEG signals.
View Full Paper →Medical education and distrust modulate the response of insular-cingulate network and ventral striatum in pain diagnosis
Healthcare providers often underestimate patients' pain, sometimes even when aware of their reports. This could be the effect of experience reducing sensitivity to others pain, or distrust toward patients' self-evaluations. Across multiple experiments (375 participants), we tested whether senior medical students differed from younger colleagues and lay controls in the way they assess people's pain and take into consideration their feedback. We found that medical training affected the sensitivity to pain faces, an effect shown by the lower ratings and highlighted by a decrease in neural response of the insula and cingulate cortex. Instead, distrust toward the expressions' authenticity affected the processing of feedbacks, by decreasing activity in the ventral striatum whenever patients' self-reports matched participants' evaluations, and by promoting strong reliance on the opinion of other doctors. Overall, our study underscores the multiple processes which might influence the evaluation of others' pain at the early stages of medical career.
View Full Paper →Alpha oscillatory activity during attentional control in children with Autism Spectrum Disorder (ASD), Attention‐Deficit/Hyperactivity Disorder (ADHD), and ASD+ADHD
Background: Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) share impairments in top-down and bottom-up modulation of attention. However, it is not yet well understood if co-occurrence of ASD and ADHD reflects a distinct or additive profile of attention deficits. We aimed to characterise alpha oscillatory activity (stimulus-locked alpha desynchronisation and prestimulus alpha) as an index of integration of top-down and bottom-up attentional processes in ASD and ADHD. Methods: Children with ASD, ADHD, comorbid ASD+ADHD, and typically-developing children completed a fixed-choice reaction-time task (‘Fast task’) while neurophysiological activity was recorded. Outcome measures were derived from source-decomposed neurophysiological data. Main measures of interest were prestimulus alpha power and alpha desynchronisation (difference between poststimulus and prestimulus alpha). Poststimulus activity linked to attention allocation (P1, P3), attentional control (N2), and cognitive control (theta synchronisation, 100–600 ms) was also examined. ANOVA was used to test differences across diagnostics groups on these measures. Spearman’s correlations were used to investigate the relationship between attentional control processes (alpha oscillations), central executive functions (theta synchronisation), early visual processing (P1), and behavioural performance. Results: Children with ADHD (ADHD and ASD+ADHD) showed attenuated alpha desynchronisation, indicating poor integration of top-down and bottom-up attentional processes. Children with ADHD showed reduced N2 and P3 amplitudes, while children with ASD (ASD and ASD+ADHD) showed greater N2 amplitude, indicating atypical attentional control and attention allocation across ASD and ADHD. In the ASD group, prestimulus alpha and theta synchronisation were negatively correlated, and alpha desynchronisation and theta synchronisation were positively correlated, suggesting an atypical association between attentional control processes and executive functions. Conclusions: ASD and ADHD are associated with disorder-specific impairments, while children with ASD+ADHD overall presented an additive profile with attentional deficits of both disorders. Importantly, these findings may inform the improvement of transdiagnostic procedures and optimisation of personalised intervention approaches. Keywords: Autism Spectrum Disorder; ADHD; attention; comorbidity.
The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG
An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85-100% for intra-experiment dataset, and were 80-100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.
View Full Paper →Long-term stability of resting state EEG-based linear and nonlinear measures
This preliminary study is aimed to evaluate the stability of various linear and nonlinear EEG measures over three years on healthy adults. The linear measures, relative powers of EEG frequency bands, interhemispheric (IHAS) and spectral (SASI) asymmetries plus nonlinear Higuchi's fractal dimension (HFD) and detrended fluctuation analyses (DFA), have been calculated from the resting state eyes closed EEG of 17 participants during two sessions separated over three years. Our results indicate that the stability is highest for the nonlinear (HFD and DFA) and the linear (relative powers of EEG frequency bands) EEG measures that use the signal from a single EEG channel and frequency band, followed by the SASI employing signals from a single channel and two frequency bands and lowest for the IHAS employing signals from two channels. The result support the prospect of using EEG-based measures in clinical practice.
View Full Paper →Using EEG Alpha States to Understand Learning During Alpha Neurofeedback Training for Chronic Pain
Objective Alpha-neurofeedback (α-NFB) is a novel therapy which trains individuals to volitionally increase their alpha power to improve pain. Learning during NFB is commonly measured using static parameters such as mean alpha power. Considering the biphasic nature of alpha rhythm (high and low alpha), dynamic parameters describing the time spent by individuals in high alpha state and the pattern of transitioning between states might be more useful. Here, we quantify the changes during α-NFB for chronic pain in terms of dynamic changes in alpha states. Methods Four chronic pain and four healthy participants received five NFB sessions designed to increase frontal alpha power. Changes in pain resilience were measured using visual analogue scale (VAS) during repeated cold-pressor tests (CPT). Changes in alpha state static and dynamic parameters such as fractional occupancy (time in high alpha state), dwell time (length of high alpha state) and transition probability (probability of moving from low to high alpha state) were analyzed using Friedman’s Test and correlated with changes in pain scores using Pearson’s correlation. Results There was no significant change in mean frontal alpha power during NFB. There was a trend of an increase in fractional occupancy, mean dwell duration and transition probability of high alpha state over the five sessions in chronic pain patients only. Significant correlations were observed between change in pain scores and fractional occupancy ( r = −0.45, p = 0.03), mean dwell time ( r = -0.48, p = 0.04) and transition probability from a low to high state ( r = -0.47, p = 0.03) in chronic pain patients but not in healthy participants. Conclusion There is a differential effect between patients and healthy participants in terms of correlation between change in pain scores and alpha state parameters. Parameters providing a more precise description of the alpha power dynamics than the mean may help understand the therapeutic effect of neurofeedback on chronic pain.
View Full Paper →Comparison of diffusion MRI and CLARITY fiber orientation estimates in both gray and white matter regions of human and primate brain
Diffusion MRI (dMRI) represents one of the few methods for mapping brain fiber orientations non-invasively. Unfortunately, dMRI fiber mapping is an indirect method that relies on inference from measured diffusion patterns. Comparing dMRI results with other modalities is a way to improve the interpretation of dMRI data and help advance dMRI technologies. Here, we present methods for comparing dMRI fiber orientation estimates with optical imaging of fluorescently labeled neurofilaments and vasculature in 3D human and primate brain tissue cuboids cleared using CLARITY. The recent advancements in tissue clearing provide a new opportunity to histologically map fibers projecting in 3D, which represents a captivating complement to dMRI measurements. In this work, we demonstrate the capability to directly compare dMRI and CLARITY in the same human brain tissue and assess multiple approaches for extracting fiber orientation estimates from CLARITY data. We estimate the three-dimensional neuronal fiber and vasculature orientations from neurofilament and vasculature stained CLARITY images by calculating the tertiary eigenvector of structure tensors. We then extend CLARITY orientation estimates to an orientation distribution function (ODF) formalism by summing multiple sub-voxel structure tensor orientation estimates. In a sample containing part of the human thalamus, there is a mean angular difference of 19o±15o between the primary eigenvectors of the dMRI tensors and the tertiary eigenvectors from the CLARITY neurofilament stain. We also demonstrate evidence that vascular compartments do not affect the dMRI orientation estimates by showing an apparent lack of correspondence (mean angular difference = 49o±23o) between the orientation of the dMRI tensors and the structure tensors in the vasculature stained CLARITY images. In a macaque brain dataset, we examine how the CLARITY feature extraction depends on the chosen feature extraction parameters. By varying the volume of tissue over which the structure tensor estimates are derived, we show that orientation estimates are noisier with more spurious ODF peaks for sub-voxels below 30 µm3 and that, for our data, the optimal gray matter sub-voxel size is between 62.5 µm3 and 125 µm3. The example experiments presented here represent an important advancement towards robust multi-modal MRI-CLARITY comparisons.
View Full Paper →Inhibitory effects of biofeedback electrostimulation therapy on pain and cortisol levels in chronic neuropathic pain: A randomized-controlled trial
Objectives: This study aims to investigate the effectiveness of biofeedback electrostimulation therapy (BEST) in chronic neuropathic pain and to evaluate changes in perceived level of pain and level of blood cortisol before and after treatment. Patients and methods: This single-blind, prospective, randomized-controlled study included a total of 20 patients (8 males, 12 females; mean age: 53.5-13.8; range, 31 to 82 years) with chronic neuropathic pain between January 2014 and June 2014. The patients were randomized to BEST (n=10) or placebo (n=10) group. Pain was measured using the Visual Analog Scale, and serum cortisol levels were measured before and after treatment. Results: There was no significant difference in the baseline demographics, diagnosis, and treatment modalities between the groups. Approximately 50% patients in the treatment group reported that the treatment was effective, compared to 30% in the placebo group. Pain score reduction after treatment in the BEST group was significant (p 0.05), while it was not significant in the placebo group (p=0.4). Cortisol levels significantly reduced only in the BEST group after treatment (p=0.013). Conclusion: The BEST yields reduction in pain severity and cortisol levels. Based on these results, it seems to be effective in the treatment of chronic neuropathic pain after a single treatment and may be more effective for long-Term management.
Neuroscience Education as Therapy for Migraine and Overlapping Pain Conditions: A Scoping Review
BACKGROUND: Neuroscience education therapy (NET) has been successfully used for numerous overlapping pain conditions, but few studies have investigated NET for migraine. OBJECTIVE: We sought to 1) review the literature on NET used for the treatment of various pain conditions to assess how NET has been studied thus far and 2) recommend considerations for future research of NET for the treatment of migraine. DESIGN/METHODS: Following the PRISMA guideline for scoping reviews, co-author (TR), a medical librarian, searched the MEDLINE, PsychInfo, Embase, and Cochrane Central Clinical Trials Registry databases for peer-reviewed articles describing NET to treat migraine and other chronic pain conditions. Each citation was reviewed by two trained independent reviewers. Conflicts were resolved through consensus. RESULTS: Overall, a NET curriculum consists of the following topics: pain does not equate to injury, pain is generated in the brain, perception, genetics, reward systems, fear, brain plasticity, and placebo/nocebo effects. Delivered through individual, group, or a combination of individual and group sessions, NET treatments often incorporate exercise programs and/or components of other evidence-based behavioral treatments. NET has significantly reduced catastrophizing, kinesiophobia, pain intensity, and disability in overlapping pain conditions. In migraine-specific studies, when implemented together with traditional pharmacological treatments, NET has emerged as a promising therapy by reducing migraine days, pain intensity and duration, and acute medication intake. CONCLUSION: NET is an established treatment for pain conditions, and future research should focus on refining NET for migraine, examining delivery modality, dosage, components of other behavioral therapies to integrate, and migraine-specific NET curricula.
View Full Paper →Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion
The data quality of simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) can be strongly affected by motion. Recent work has shown that the quality of fMRI data can be improved by using a Moiré-Phase-Tracker (MPT)-camera system for prospective motion correction. The use of the head position acquired by the MPT-camera-system has also been shown to correct motion-induced voltages, ballistocardiogram (BCG) and gradient artefact residuals separately. In this work we show the concept of an integrated framework based on the general linear model to provide a unified motion informed model of in-MRI artefacts. This model (retrospective EEG motion educated gradient artefact suppression, REEG-MEGAS) is capable of correcting voltage-induced, BCG and gradient artefact residuals of EEG data acquired simultaneously with prospective motion corrected fMRI. In our results, we have verified that applying REEG-MEGAS correction to EEG data acquired during subject motion improves the data quality in terms of motion induced voltages and also GA residuals in comparison to standard Artefact Averaging Subtraction and Retrospective EEG Motion Artefact Suppression. Besides that, we provide preliminary evidence that although adding more regressors to a model may slightly affect the power of physiological signals such as the alpha-rhythm, its application may increase the overall quality of a dataset, particularly when strongly affected by motion. This was verified by analysing the EEG traces, power spectra density and the topographic distribution from two healthy subjects. We also have verified that the correction by REEG-MEGAS improves higher frequency artefact correction by decreasing the power of Gradient Artefact harmonics. Our method showed promising results for decreasing the power of artefacts for frequencies up to 250 Hz. Additionally, REEG-MEGAS is a hybrid framework that can be implemented for real time prospective motion correction of EEG and fMRI data. Among other EEG-fMRI applications, the approach described here may benefit applications such as EEG-fMRI neurofeedback and brain computer interface, which strongly rely on the prospective acquisition and application of motion artefact removal.
View Full Paper →Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training
Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.
View Full Paper →Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).
View Full Paper →Modeling Development of Frontal Electroencephalogram (EEG) Asymmetry: Sex Differences and Links with Temperament
Asymmetric patterns of frontal brain electrical activity reflect approach and avoidance tendencies, with stability of relative right activation associated with withdrawal emotions/motivation and left hemisphere activation linked with approach and positive affect. However, considerable shifts in approach/avoidance-related lateralization have been reported for children not targeted because of extreme temperament. In this study, dynamic effects of frontal electroencephalogram (EEG) power within and across hemispheres were examined throughout early childhood. Specifically, EEG indicators at 5, 10, 24, 36, 48, and 72 months-of-age (n=410) were analyzed via a hybrid of difference score and panel design models, with baseline measures and subsequent time-to-time differences modeled as potentially influencing all subsequent amounts of time-to-time change (i.e., predictively saturated). Infant sex was considered as a moderator of dynamic developmental effects, with temperament attributes measured at 5 months examined as predictors of EEG hemisphere development. Overall, change in left and right frontal EEG power predicted declining subsequent change in the same hemisphere, with effects on the opposing neurobehavioral system enhancing later growth. Infant sex moderated the pattern of within and across-hemisphere effects, wherein for girls more prominent left hemisphere influences on the right hemisphere EEG changes were noted and right hemisphere effects were more salient for boys. Largely similar patterns of temperament prediction were observed for the left and the right EEG power changes, with limited sex differences in links between temperament and growth parameters. Results were interpreted in the context of comparable analyses using parietal power values, which provided evidence for unique frontal effects.
EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD
Adults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behaviour (mediational endophenotypes). Resting-state eyes-open and eyes-closed EEG was measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45 Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis. Eyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = 0.71-0.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not successfully classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic = 0.68-0.72), suggesting that these features may be a mediational endophenotype for adult ADHD. Resting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.
View Full Paper →Mapping typical and hypokinetic dysarthric speech production network using a connected speech paradigm in functional MRI
We developed a task paradigm whereby subjects spoke aloud while minimizing head motion during functional MRI (fMRI) in order to better understand the neural circuitry involved in motor speech disorders due to dysfunction of the central nervous system. To validate our overt continuous speech paradigm, we mapped the speech production network (SPN) in typical speakers (n = 19, 10 females) and speakers with hypokinetic dysarthria as a manifestation of Parkinson disease (HKD; n = 21, 8 females) in fMRI. We then compared it with the SPN derived during overt speech production by 15O-water PET in the same group of typical speakers and another HKD cohort (n = 10, 2 females). The fMRI overt connected speech paradigm did not result in excessive motion artifacts and successfully identified the same brain areas demonstrated in the PET studies in the two cohorts. The SPN derived in fMRI demonstrated significant spatial overlap with the corresponding PET derived maps (typical speakers: r = 0.52; speakers with HKD: r = 0.43) and identified the components of the neural circuit of speech production belonging to the feedforward and feedback subsystems. The fMRI study in speakers with HKD identified significantly decreased activity in critical feedforward (bilateral dorsal premotor and motor cortices) and feedback (auditory and somatosensory areas) subsystems replicating previous PET study findings in this cohort. These results demonstrate that the overt connected speech paradigm is feasible during fMRI and can accurately localize the neural substrates of typical and disordered speech production. Our fMRI paradigm should prove useful for study of motor speech and voice disorders, including stuttering, apraxia of speech, dysarthria, and spasmodic dysphonia.
View Full Paper →The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback—A Systematic Review and Recommendations for Best Practice
Background: The effects of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)-neurofeedback on brain activation and behaviors have been studied extensively in the past. More recently, researchers have begun to investigate the effects of functional near-infrared spectroscopy-based neurofeedback (fNIRS-neurofeedback). FNIRS is a functional neuroimaging technique based on brain hemodynamics, which is easy to use, portable, inexpensive, and has reduced sensitivity to movement artifacts. Method: We provide the first systematic review and database of fNIRS-neurofeedback studies, synthesizing findings from 22 peer-reviewed studies (including a total of N = 441 participants; 337 healthy, 104 patients). We (1) give a comprehensive overview of how fNIRS-neurofeedback training protocols were implemented, (2) review the online signal-processing methods used, (3) evaluate the quality of studies using pre-set methodological and reporting quality criteria and also present statistical sensitivity/power analyses, (4) investigate the effectiveness of fNIRS-neurofeedback in modulating brain activation, and (5) review its effectiveness in changing behavior in healthy and pathological populations. Results and discussion: (1–2) Published studies are heterogeneous (e.g., neurofeedback targets, investigated populations, applied training protocols, and methods). (3) Large randomized controlled trials are still lacking. In view of the novelty of the field, the quality of the published studies is moderate. We identified room for improvement in reporting important information and statistical power to detect realistic effects. (4) Several studies show that people can regulate hemodynamic signals from cortical brain regions with fNIRS-neurofeedback and (5) these studies indicate the feasibility of modulating motor control and prefrontal brain functioning in healthy participants and ameliorating symptoms in clinical populations (stroke, ADHD, autism, and social anxiety). However, valid conclusions about specificity or potential clinical utility are premature. Conclusion: Due to the advantages of practicability and relatively low cost, fNIRS-neurofeedback might provide a suitable and powerful alternative to EEG and fMRI neurofeedback and has great potential for clinical translation of neurofeedback. Together with more rigorous research and reporting practices, further methodological improvements may lead to a more solid understanding of fNIRS-neurofeedback. Future research will benefit from exploiting the advantages of fNIRS, which offers unique opportunities for neurofeedback research.
View Full Paper →Microstates-based resting frontal alpha asymmetry approach for understanding affect and approach/withdrawal behavior
The role of resting frontal alpha-asymmetry in explaining neural-mechanisms of affect and approach/withdrawal behavior is still debatable. The present study explores the ability of the quasi-stable resting EEG asymmetry information and the associated neurovascular synchronization/desynchronization in bringing more insight into the understanding of neural-mechanisms of affect and approach/withdrawal behavior. For this purpose, a novel frontal alpha-asymmetry based on microstates, that assess quasi-stable EEG scalp topography information, is proposed and compared against standard frontal-asymmetry. Both proposed and standard frontal alpha-asymmetries were estimated from thirty-nine healthy volunteers resting-EEG simultaneously acquired with resting-fMRI. Further, neurovascular mechanisms of these asymmetry measures were estimated through EEG-informed fMRI. Subsequently, the Hemodynamic Lateralization Index (HLI) of the neural-underpinnings of both asymmetry measures was assessed. Finally, the robust correlation of both asymmetry-measures and their HLI’s with PANAS, BIS/BAS was carried out. The standard resting frontal-asymmetry and its HLI yielded no significant correlation with any psychological-measures. However, the microstate resting frontal-asymmetry correlated significantly with negative affect and its neural underpinning’s HLI significantly correlated with Positive/Negative affect and BIS/BAS measures. Finally, alpha-BOLD desynchronization was observed in neural-underpinning whose HLI correlated significantly with negative affect and BIS. Hence, the proposed resting microstate-frontal asymmetry better assesses the neural-mechanisms of affect, approach/withdrawal behavior.
View Full Paper →Reliability of Electroencephalogram-Based Individual Markers - Case Study
The aim of this study was to evaluate individual level of natural variability of electroencephalogram (EEG) based markers. Three linear: alpha power variability, spectral asymmetry index, relative gamma power and three nonlinear methods: Higuchi's fractal dimension, detrended fluctuation analysis, and Lempel-Ziv complexity were selected. The markers were evaluated over 15 sessions acquired in 14 months. The results indicate that individual natural variability for five of the selected markers is lower compared to differences between healthy and depressed groups of subjects in our previous studies. The results of the current study suggest that EEG based markers can be applied for evaluation of disturbances in brain activity at individual level.Clinical Relevance-The indicated stability in the current study of widely used EEG-based markers at individual level suggests a promising opportunity to apply EEG as a novel method in diagnoses of brain mental disorders in clinical practice.
View Full Paper →Identifying Stable Patterns over Time for Emotion Recognition from EEG
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. We systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset called SEED for this study. Discriminative Graph regularized Extreme Learning Machine with differential entropy features achieves the best average accuracies of 69.67 and 91.07 percent on the DEAP and SEED datasets, respectively. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotions than negative emotions in beta and gamma bands; the neural patterns of neutral emotions have higher alpha responses at parietal and occipital sites; and for negative emotions, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition models shows that the neural patterns are relatively stable within and between sessions.
View Full Paper →Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks
Decoding brain functional states underlying cognitive processes from functional MRI (fMRI) data using multivariate pattern analysis (MVPA) techniques has achieved promising performance for characterizing brain activation patterns and providing neurofeedback signals. However, it remains challenging to decode subtly distinct brain states for individual fMRI data points due to varying temporal durations and dependency among different cognitive processes. In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in intrinsic functional network modeling and sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, subject-specific intrinsic functional networks (FNs) are computed from resting-state fMRI data and are used to characterize functional signals of task fMRI data with a compact representation for building brain decoding models, and LSTM RNNs are adopted to learn brain decoding mappings between functional profiles and brain states. Validation results on fMRI data from the HCP dataset have demonstrated that brain decoding models built on training data using the proposed method could learn discriminative latent feature representations and effectively distinguish subtly distinct working memory tasks of different subjects with significantly higher accuracy than conventional decoding models. Informative FNs of the brain decoding models identified as brain activation patterns of working memory tasks were largely consistent with the literature. The method also obtained promising decoding performance on motor and social cognition tasks. Our results suggest that LSTM RNNs in conjunction with FNs could build interpretable, highly accurate brain decoding models.
View Full Paper →Beta wave enhancement neurofeedback improves cognitive functions in patients with mild cognitive impairment: A preliminary pilot study
BACKGROUND: Mild cognitive impairment (MCI) is a symptom characterizing cognitive decline and a transitional state between normal aging and dementia; however, there is no definitive diagnosis and treatment for MCI. Neurofeedback (NF), which is a training mechanism that employs operant conditioning to regulate brain activity, has been increasingly investigated concerning its beneficial effects for dementia and MCI. METHODS: This study investigated cognitive improvement and hemodynamic changes in the prefrontal cortex (PFC) following NF training in patients with MCI. Five patients with MCI received NF training for enhanced beta band activity in the dorsolateral PFC-16 sessions for 8 weeks-with each session divided into 9 5-minute trials. The primary outcome measure was a cognitive assessment tool: the Korean version of the Montreal Cognitive Assessment. The secondary outcome measures were the Central Nervous System Vital Signs for neurocognitive testing, hemodynamic changes using functional near-infrared spectroscopy in the PFC during a working-memory task, and Beck Depression Inventory scores. RESULTS: After completing the training, patients' cognitive function significantly improved in domains such as composite memory, cognitive flexibility, complex attention, reaction time, and executive function. Increased electroencephalogram beta power was observed over NF training sessions (Spearman rank correlation test: r = 0.746, P = .001). The threshold value for gaining positive feedback from pre-NF baseline on beta power significantly increased (Spearman rank correlation test: r = 0.805, P = .001). Hemodynamic response in PFC changed after NF training, and individual differences were identified. Specifically, hypoactivation of the hemodynamic response by emotional distraction recovered following NF training. CONCLUSION: We suggest that patients' cognitive processing efficiency was improved by the NF training. These beneficial results suggest that NF training may have potential therapeutic applications to prevent the progression from MCI to dementia. TRIAL REGISTRATION NUMBER: Clinical Research Information Service (KCT0003433).
View Full Paper →Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography
Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically exploited the potential of electroencephalography to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity, and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4-8 Hz) and gamma (>60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results suggest that increased theta and gamma synchrony in frontal brain areas are involved in the pathophysiology of chronic pain. Although substantial challenges concerning the reproducibility of the findings and the accuracy, specificity, and validity of potential electroencephalography-based disease markers remain to be overcome, our study indicates that abnormal frontal synchrony at theta and gamma frequencies might be promising targets for noninvasive brain stimulation and/or neurofeedback approaches.
View Full Paper →Virtual Reality–Based Biofeedback and Guided Meditation in Rheumatology: A Pilot Study
Objective As technology continues to improve, it plays an increasingly vital role in the practice of medicine. This study aimed to assess the feasibility of the implementation of virtual reality ( VR ) in a rheumatology clinic as a platform to administer guided meditation and biofeedback as a means of reducing chronic pain. Methods Twenty participants were recruited from a rheumatology clinic. These participants included adults with physician‐diagnosed autoimmune disorders who were on a stable regimen of medication and had a score of at least 5 on the pain Visual Analog Scale ( VAS ) for a minimum of 4 days during the prior 30 days. VAS , part of most composite outcome measurements in rheumatology, is an instrument used to assess pain that consists of a straight line with the endpoints ranging from “no pain at all” and “pain as bad as it could be.” Patients were randomized into two groups that differed in the order in which they experienced the two VR modules. One module consisted of a guided meditation ( GM ) environment, whereas the other module consisted of a respiratory biofeedback ( BFD ) environment. Data on pain and anxiety levels were gathered before, during, and after the two modules. Results The three most common diagnoses among participants were rheumatoid arthiritis ( RA ), lupus, and fibromyalgia. There was a significant reduction in VAS scores after BFD and GM ( P values = 0.01 and 0.04, respectively). There was a significant reduction in Facial Anxiety Scale after the GM compared with the BFD ( P values = 0.02 and 0.08, respectively). Conclusion This novel study demonstrated that VR could be a feasible solution for the management of pain and anxiety in rheumatology patients. Further trials with varying treatment exposures and durations are required to solidify the viability of VR as a treatment option in rheumatology clinics.
View Full Paper →Dominance of layer-specific microvessel dilation in contrast-enhanced high-resolution fMRI: Comparison between hemodynamic spread and vascular architecture with CLARITY
Contrast-enhanced cerebral blood volume-weighted (CBVw) fMRI response peaks are specific to the layer of evoked synaptic activity (Poplawsky et al., 2015), but the spatial resolution limit of CBVw fMRI is unknown. In this study, we measured the laminar spread of the CBVw fMRI evoked response in the external plexiform layer (EPL, 265 ± 65 μm anatomical thickness, mean ± SD, n = 30 locations from 5 rats) of the rat olfactory bulb during electrical stimulation of the lateral olfactory tract and examined its potential vascular source. First, we obtained the evoked CBVw fMRI responses with a 55 × 55 μm2 in-plane resolution and a 500-μm thickness at 9.4 T, and found that the fMRI signal peaked predominantly in the inner half of EPL (136 ± 54 μm anatomical thickness). The mean full-width at half-maximum of these fMRI peaks was 347 ± 102 μm and the functional spread was approximately 100 or 200 μm when the effects of the laminar thicknesses of EPL or inner EPL were removed, respectively. Second, we visualized the vascular architecture of EPL from a different rat using a Clear Lipid-exchanged Anatomically Rigid Imaging/immunostaining-compatible Tissue hYdrogel (CLARITY)-based tissue preparation method and confocal microscopy. Microvascular segments with an outer diameter of <11 μm accounted for 64.3% of the total vascular volume within EPL and had a mean segment length of 55 ± 40 μm (n = 472). Additionally, vessels that crossed the EPL border had a mean segment length outside of EPL equal to 73 ± 61 μm (n = 28), which is comparable to half of the functional spread (50-100 μm). Therefore, we conclude that dilation of these microvessels, including capillaries, likely dominate the CBVw fMRI response and that the biological limit of the fMRI spatial resolution is approximately the average length of 1-2 microvessel segments, which may be sufficient for examining sublaminar circuits.
View Full Paper →Comorbid anxiety moderates the relationship between depression history and prefrontal EEG asymmetry
The internalizing spectrum of psychiatric disorders-depression and anxiety-are common, highly comorbid, and challenging to treat. Individuals with childhood onset depression have a particularly poor prognosis. There is compelling evidence that individuals with depression display reduced resting-state EEG activity at sensors overlying the left prefrontal cortex, even during periods of remission, but it remains unknown whether this asymmetry is evident among individuals with a comorbid anxiety disorder. Here, we demonstrate that women with a history of childhood onset depression and no anxiety disorder (n = 37) show reduced left lateral frontal activity compared to psychiatrically healthy controls (n = 69). In contrast, women with a history of childhood onset depression and pathological levels of anxious apprehension (n = 18)-as indexed by a current generalized anxiety disorder, obsessive compulsive disorder, or separation anxiety disorder diagnosis-were statistically indistinguishable from healthy controls. Collectively, these observations suggest that anxious apprehension can mask the relationship between prefrontal EEG asymmetry and depression. These findings have implications for understanding (a) prefrontal EEG asymmetry as a neurophysiological marker of depression, (b) the comorbidity of depression and anxiety, and (c) failures to replicate the relationship between prefrontal EEG asymmetry and depression. More broadly, they set the stage for developing refined interventions for internalizing psychopathology.
View Full Paper →Peak alpha frequency is a neural marker of cognitive function across the autism spectrum
Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks, which underlie cognitive function, and therefore, it holds promise as a developmentally sensitive neural marker of cognitive function in ASD. Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N = 59) and age-matched typically developing (TD) children (N = 38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants.
View Full Paper →The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository
This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.
View Full Paper →Electromyogram Biofeedback in Patients with Fibromyalgia: A Randomized Controlled Trial
BACKGROUND/AIM: Electromyogram (EMG) biofeedback is used in chronic pain but its effectiveness in patients with fibromyalgia is unclear. The objective of this randomized controlled clinical trial was to evaluate the effectiveness of EMG biofeedback in patients with fibromyalgia. METHODS: Patients were recruited from a waiting list at the fibromyalgia day care clinic at the University Hospital Munich. The study intervention comprised 14 sessions of EMG biofeedback during 8 weeks in addition to the usual care. The control intervention was usual care alone. Assessments were scheduled before intervention (T0), after intervention (T1), and 3 months after the end of intervention (T2). The primary outcome measure was the Fibromyalgia Impact Questionnaire (FIQ). Secondary outcome measures included additional patient-oriented measures and the pressure-pain threshold in the trapezius muscles. Effectiveness was analyzed by significance tests and standardized effect sizes (ES). RESULTS: 36 patients completed the study. EMG biofeedback did not improve the health status (FIQ, T1: p = 0.95, ES = 0.02; T2: p = 0.52, ES = 0.26). Among the secondary outcome measures, only the pressure-pain threshold at the trapezius muscles showed an improvement in the intervention group (T1: p = 0.016, ES = 0.84). CONCLUSION: EMG biofeedback showed no health status improvement in patients with fibromyalgia.
View Full Paper →Cognitive Improvement and Brain Changes after Real-Time Functional MRI Neurofeedback Training in Healthy Elderly and Prodromal Alzheimer’s Disease
Background: Cognitive decline is characteristic for Alzheimer's disease (AD) and also for healthy ageing. As a proof-of-concept study, we examined whether this decline can be counteracted using real-time fMRI neurofeedback training. Visuospatial memory and the parahippocampal gyrus (PHG) were targeted. Methods: Sixteen healthy elderly subjects (mean age 63.5 years, SD = 6.663) and 10 patients with prodromal AD (mean age 66.2 years, SD = 8.930) completed the experiment. Four additional healthy subjects formed a sham-feedback condition to validate the paradigm. The protocol spanned five examination days (T1-T5). T1 contained a neuropsychological pre-test, the encoding of a real-world footpath, and an anatomical MRI scan of the brain. T2-T4 included the fMRI neurofeedback training paradigm, in which subjects learned to enhance activation of the left PHG while recalling the path encoded on T1. At T5, the neuropsychological post-test and another anatomical MRI brain scan were performed. The neuropsychological battery included the Montreal Cognitive Assessment (MoCA); the Visual and Verbal Memory Test (VVM); subtests of the Wechsler Memory Scale (WMS); the Visual Patterns Test; and Trail Making Tests (TMT) A and B. results: Healthy elderly and patients with prodromal AD showed improved visuospatial memory performance after neurofeedback training. Healthy subjects also performed better in a working-memory task (WMS backward digit-span) and in the MoCA. Both groups were able to elicit parahippocampal activation during training, but no significant changes in brain activation were found over the course of the training. However, Granger-causality-analysis revealed changes in cerebral connectivity over the course of the training, involving the parahippocampus and identifying the precuneus as main driver of activation in both groups. Voxel-based morphometry showed increases in grey matter volumes in the precuneus and frontal cortex. Neither cognitive enhancements, nor parahippocampal activation were found in the control group undergoing sham-feedback.conclusion: These fndings suggest that cognitive decline, either related to prodromal AD or healthy ageing, could be counteracted using fMRI-based neurofeedback. Future research needs to determine the potential of this method as a treatment tool.
The Utility of EEG in Attention Deficit Hyperactivity Disorder: A Replication Study
The routine use of stimulants in pediatrics has increased dramatically over the past 3 decades and the long-term consequences have yet to be fully studied. Since 1978 there have been 7 articles identifying electroencephalogram (EEG) abnormalities, particularly epileptiform discharges in children with attention deficit hyperactivity disorder (ADHD). Many have studied the prevalence of these discharges in this population with varying results. An article published in 2011 suggests that EEG technology should be considered prior to prescribing stimulants to children diagnosed with ADHD due to a high prevalence of epileptiform discharges. The 2011 study found a higher prevalence (26%) of epileptiform discharges when using 23-hour and sleep-deprived EEGs in comparison with other methods of activation (hyperventilation or photostimulation) and conventional EEG. We sought to replicate the 2011 results using conventional EEG with the added qEEG technologies of automatic spike detection and low-resolution electromagnetic tomography analysis (LORETA) brain mapping. Our results showed 32% prevalence of epileptiform discharges, which suggests that an EEG should be considered prior to prescribing stimulant medications.
View Full Paper →Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers
Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.
View Full Paper →Endophenotype best practices
This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.
View Full Paper →Electrophysiological correlates of reinforcement learning in young people with Tourette syndrome with and without co-occurring ADHD symptoms
Altered reinforcement learning is implicated in the causes of Tourette syndrome (TS) and attention-deficit/hyperactivity disorder (ADHD). TS and ADHD frequently co-occur but how this affects reinforcement learning has not been investigated. We examined the ability of young people with TS (n=18), TS+ADHD (N=17), ADHD (n=13) and typically developing controls (n=20) to learn and reverse stimulus-response (S-R) associations based on positive and negative reinforcement feedback. We used a 2 (TS-yes, TS-no)×2 (ADHD-yes, ADHD-no) factorial design to assess the effects of TS, ADHD, and their interaction on behavioural (accuracy, RT) and event-related potential (stimulus-locked P3, feedback-locked P2, feedback-related negativity, FRN) indices of learning and reversing the S-R associations. TS was associated with intact learning and reversal performance and largely typical ERP amplitudes. ADHD was associated with lower accuracy during S-R learning and impaired reversal learning (significantly reduced accuracy and a trend for smaller P3 amplitude). The results indicate that co-occurring ADHD symptoms impair reversal learning in TS+ADHD. The implications of these findings for behavioural tic therapies are discussed.
View Full Paper →Real-Time fMRI in Neuroscience Research and Its Use in Studying the Aging Brain
Cognitive decline is a major concern in the aging population. It is normative to experience some deterioration in cognitive abilities with advanced age such as related to memory performance, attention distraction to interference, task switching, and processing speed. However, intact cognitive functioning in old age is important for leading an independent day-to-day life. Thus, studying ways to counteract or delay the onset of cognitive decline in aging is crucial. The literature offers various explanations for the decline in cognitive performance in aging; among those are age-related gray and white matter atrophy, synaptic degeneration, blood flow reduction, neurochemical alterations, and change in connectivity patterns with advanced age. An emerging literature on neurofeedback and Brain Computer Interface (BCI) reports exciting results supporting the benefits of volitional modulation of brain activity on cognition and behavior. Neurofeedback studies based on real-time functional magnetic resonance imaging (rtfMRI) have shown behavioral changes in schizophrenia and behavioral benefits in nicotine addiction. This article integrates research on cognitive and brain aging with evidence of brain and behavioral modification due to rtfMRI neurofeedback. We offer a state-of-the-art description of the rtfMRI technique with an eye towards its application in aging. We present preliminary results of a feasibility study exploring the possibility of using rtfMRI to train older adults to volitionally control brain activity. Based on these first findings, we discuss possible implementations of rtfMRI neurofeedback as a novel technique to study and alleviate cognitive decline in healthy and pathological aging.
View Full Paper →High-Definition tDCS of Noun and Verb Retrieval in Naming and Lexical Decision
High-definition transcranial direct current stimulation (HD-tDCS) is a novel brain stimulation method that has high potential for use in language therapy for speakers with aphasia, due to its safety and focality. This study aimed to obtain foundational data on using HD-tDCS to modulate language processing in healthy speakers. Participants received stimulation either of Broca's area or of the left angular gyrus (20 min of anodal, cathodal, and sham stimulation on separate days), followed by naming and lexical decision tasks with single-word verb and noun stimuli. We found that cathodal stimulation over both Broca's area and the left angular gyrus increased naming speed for both verbs and nouns, challenging the traditional view of cathodal stimulation as suppressive or leading to decreased performance. The effect did not extend to the lexical decision task. Additionally, effects of specific stimulation types depended on the order of their administration, suggesting possible physiological carry-over and/or task novelty effects. These results are relevant to the application of HD-tDCS to enhance and direct neural plasticity in patients with neurogenic language disorders.
View Full Paper →Area-specific information processing in prefrontal cortex during a probabilistic inference task: a multivariate fMRI BOLD time series analysis
INTRODUCTION: Discriminating spatiotemporal stages of information processing involved in complex cognitive processes remains a challenge for neuroscience. This is especially so in prefrontal cortex whose subregions, such as the dorsolateral prefrontal (DLPFC), anterior cingulate (ACC) and orbitofrontal (OFC) cortices are known to have differentiable roles in cognition. Yet it is much less clear how these subregions contribute to different cognitive processes required by a given task. To investigate this, we use functional MRI data recorded from a group of healthy adults during a "Jumping to Conclusions" probabilistic reasoning task. METHODS: We used a novel approach combining multivariate test statistics with bootstrap-based procedures to discriminate between different task stages reflected in the fMRI blood oxygenation level dependent signal pattern and to unravel differences in task-related information encoded by these regions. Furthermore, we implemented a new feature extraction algorithm that selects voxels from any set of brain regions that are jointly maximally predictive about specific task stages. RESULTS: Using both the multivariate statistics approach and the algorithm that searches for maximally informative voxels we show that during the Jumping to Conclusions task, the DLPFC and ACC contribute more to the decision making phase comprising the accumulation of evidence and probabilistic reasoning, while the OFC is more involved in choice evaluation and uncertainty feedback. Moreover, we show that in presumably non-task-related regions (temporal cortices) all information there was about task processing could be extracted from just one voxel (indicating the unspecific nature of that information), while for prefrontal areas a wider multivariate pattern of activity was maximally informative. CONCLUSIONS/SIGNIFICANCE: We present a new approach to reveal the different roles of brain regions during the processing of one task from multivariate activity patterns measured by fMRI. This method can be a valuable tool to assess how area-specific processing is altered in psychiatric disorders such as schizophrenia, and in healthy subjects carrying different genetic polymorphisms.
View Full Paper →Fusion and Fission of Cognitive Functions in the Human Parietal Cortex
How is higher cognitive function organized in the human parietal cortex? A century of neuropsychology and 30 years of functional neuroimaging has implicated the parietal lobe in many different verbal and nonverbal cognitive domains. There is little clarity, however, on how these functions are organized, that is, where do these functions coalesce (implying a shared, underpinning neurocomputation) and where do they divide (indicating different underlying neural functions). Until now, there has been no multi-domain synthesis in order to reveal where there is fusion or fission of functions in the parietal cortex. This aim was achieved through a large-scale activation likelihood estimation (ALE) analysis of 386 studies (3952 activation peaks) covering 8 cognitive domains. A tripartite, domain-general neuroanatomical division and 5 principles of cognitive organization were established, and these are discussed with respect to a unified theory of parietal functional organization.
View Full Paper →Bluetooth Communication Interface for EEG Signal Recording in Hyperbaric Chambers
Recording biological signals inside a hyperbaric chamber poses technical challenges (the steel walls enclosing it greatly attenuate or completely block the signals as in a Faraday cage), practical (lengthy cables creating eddy currents), and safety (sparks hazard from power supply to the electronic apparatus inside the chamber) which can be overcome with new wireless technologies. In this technical report we present the design and implementation of a Bluetooth system for electroencephalographic (EEG) recording inside a hyperbaric chamber and describe the feasibility of EEG signal transmission outside the chamber. Differently from older systems, this technology allows the online recording of amplified signals, without interference from eddy currents. In an application of this technology, we measured EEG activity in professional divers under three experimental conditions in a hyperbaric chamber to determine how oxygen, assumed at a constant hyperbaric pressure of 2.8 ATA , affects the bioelectrical activity. The EEG spectral power estimated by fast Fourier transform and the cortical sources of the EEG rhythms estimated by low-resolution brain electromagnetic analysis were analyzed in three different EEG acquisitions: breathing air at sea level; breathing oxygen at a simulated depth of 18 msw, and breathing air at sea level after decompression.
View Full Paper →Z-score LORETA Neurofeedback as a Potential Therapy in Depression/Anxiety and Cognitive Dysfunction1www.TallahasseeNeuroBalanceCenter.com
Introduction of quantitative electroencephalogram low-resolution electromagnetic tomography (QEEG/LORETA) electrical brain imaging has improved our diagnostic ability in neuropsychiatric practice by enhancing identification of dysregulated (defined as two standard deviations above or below the norm) cortical areas implicated in patient symptoms. Additional use of LORETA Z-score neurofeedback (NFB) enables us to directly target these areas of dysregulation in order to improve associated symptoms. Out of 235 neuropsychiatric patients treated in our clinic with Z-score LORETA NFB, a detailed analysis of 35 cases of depression, anxiety, and cognitive dysfunction is presented. Specific areas of dysregulation attributed to particular conditions identified by LORETA are discussed. Follow-up findings of QEEG/LORETA electrical imaging after NFB therapy (including computerized cognitive testing results) are shown. This chapter summarizes our experience with LORETA Z-score NFB as a tool for therapy of depression and associated anxiety. In addition, this form of NFB is able to improve cognitive functions of individuals suffering from memory, information processing, and other cognitive dysfunctions. Extensive presentations of selected cases are used for demonstration of results from our practice. 25 out of 35 patients (71%) were identified as having an objective improvement (on average 10 points) through cognitive testing. In addition, with NFB subjective cognitive improvement and an objective reduction of QEEG abnormalities were also achieved in most of the patients. Detailed analysis of our patients diagnosed with depression and/or anxiety showed that out of 31 included in the study, 24 (77%) were found to have both subjective and objective (improvement of QEEG abnormalities) improvement of the symptoms within 10 sessions of LORETA Z-score NFB. These results are very promising and indicate good effectiveness of LORETA Z-score NFB in therapy of depression, anxiety, and cognitive enhancement.
Real-time fMRI processing with physiological noise correction - Comparison with off-line analysis
BACKGROUND: While applications of real-time functional magnetic resonance imaging (rtfMRI) are growing rapidly, there are still limitations in real-time data processing compared to off-line analysis. NEW METHODS: We developed a proof-of-concept real-time fMRI processing (rtfMRIp) system utilizing a personal computer (PC) with a dedicated graphic processing unit (GPU) to demonstrate that it is now possible to perform intensive whole-brain fMRI data processing in real-time. The rtfMRIp performs slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis with multiple noise regressors including physiological noise modeled with cardiac (RETROICOR) and respiration volume per time (RVT). RESULTS: The whole-brain data analysis with more than 100,000voxels and more than 250volumes is completed in less than 300ms, much faster than the time required to acquire the fMRI volume. Real-time processing implementation cannot be identical to off-line analysis when time-course information is used, such as in slice-timing correction, signal scaling, and GLM. We verified that reduced slice-timing correction for real-time analysis had comparable output with off-line analysis. The real-time GLM analysis, however, showed over-fitting when the number of sampled volumes was small. COMPARISON WITH EXISTING METHODS: Our system implemented real-time RETROICOR and RVT physiological noise corrections for the first time and it is capable of processing these steps on all available data at a given time, without need for recursive algorithms. CONCLUSIONS: Comprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM.
View Full Paper →The Coordinated Allocation of Resource (CAR) Electrophysiological Patterns of Recalling Names of Faces in Children, Adolescents and Adults and the Central Processing Unit (CPU) of the Brain
The quantitative EEG (QEEG) has proven to be an important methodology in the understanding of brain functioning. The Coordinated Allocation of Resource (CAR) model maintains that cognitive effectiveness depends on the employment of a specific set of resources for specific cognitive tasks, which overlap in some situations. The model employs the flashlight metaphor in understanding the coherence and phase relations between locations. The metaphor asserts that each location can function as a flashlight that sends out a “beam” to the other locations within a frequency. The “beam” can involve all the other locations or be a mini-flashlight that involves only selected locations. The task of recalling names of faces was examined in the context of the CAR model. The developmental changes that occur during the encoding of names of faces include increases in diffusely located communication connections involving theta (4–8 Hz) and alpha (8–13 Hz), increases in the relative power values of the beta variables (13–64 Hz), peak frequency of beta1 (13–32 Hz) and alpha, decreases in communication patterns involving the beta2 (32–64 Hz) and delta (0–4 Hz) frequencies as well as decreasing values of variables involving the lower frequencies (delta, theta), relative power values of alpha and magnitudes of alpha, beta2 and peak amplitudes of beta2.The face-name task is both a verbal and visual task as the participant is hearing the name while he looks at the photograph. Variables that relate to success during the encoding task involve diffuse increases in flashlight activity from F7 and T3 across all frequencies to and between central locations. The QEEG variables that relate to immediate and delayed recall success involve flashlights from T3 across 4 frequencies, F7 involving 3 frequencies and the appearance of a heuristic “central processing unit” involving frontal (F3, Fz, F4), central (C3, Cz, C4) and posterior (P3, Pz, P4) locations.
View Full Paper →Resting state functional connectivity predicts neurofeedback response
Tailoring treatments to the specific needs and biology of individual patients—personalized medicine—requires delineation of reliable predictors of response. Unfortunately, these have been slow to emerge, especially in neuropsychiatric disorders. We have recently described a real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback protocol that can reduce contamination-related anxiety, a prominent symptom of many cases of obsessive-compulsive disorder (OCD). Individual response to this intervention is variable. Here we used patterns of brain functional connectivity, as measured by baseline resting-state fMRI (rs-fMRI), to predict improvements in contamination anxiety after neurofeedback training. Activity of a region of the orbitofrontal cortex (OFC) and anterior prefrontal cortex, Brodmann area (BA) 10, associated with contamination anxiety in each subject was measured in real time and presented as a neurofeedback signal, permitting subjects to learn to modulate this target brain region. We have previously reported both enhanced OFC/BA 10 control and improved anxiety in a group of subclinically anxious subjects after neurofeedback. Five individuals with contamination-related OCD who underwent the same protocol also showed improved clinical symptomatology. In both groups, these behavioral improvements were strongly correlated with baseline whole-brain connectivity in the OFC/BA 10, computed from rs-fMRI collected several days prior to neurofeedback training. These pilot data suggest that rs-fMRI can be used to identify individuals likely to benefit from rt-fMRI neurofeedback training to control contamination anxiety
EEG alpha power and creative ideation
► EEG Alpha activity is sensitive to different creativity-related demands. ► Creativity is associated with alpha increases at frontal and right parietal sites. ► Alpha increases during creative cognition reflect internal processing demands., Neuroscientific studies revealed first insights into neural mechanisms underlying creativity, but existing findings are highly variegated and often inconsistent. Despite the disappointing picture on the neuroscience of creativity drawn in recent reviews, there appears to be robust evidence that EEG alpha power is particularly sensitive to various creativity-related demands involved in creative ideation. Alpha power varies as a function of creativity-related task demands and the originality of ideas, is positively related to an individuals’ creativity level, and has been observed to increase as a result of creativity interventions. Alpha increases during creative ideation could reflect more internally oriented attention that is characterized by the absence of external bottom-up stimulation and, thus, a form of top-down activity. Moreover, they could indicate the involvement of specific memory processes such as the efficient (re-)combination of unrelated semantic information. We conclude that increased alpha power during creative ideation is among the most consistent findings in neuroscientific research on creativity and discuss possible future directions to better understand the manifold brain mechanisms involved in creativity.
View Full Paper →QEEG and 19-Channel Neurofeedback as a Clinical Evaluation Tool for Children with Attention, Learning and Emotional Problems
Attention, learning and emotional problems can have different causes that cannot be easily and clearly distinguished by clinical testing methods. But, QEEG and, even more so, live 19-channel Z-score training under different task conditions can both give very detailed insights about the specific functioning and dysregulations of an individual’s brain. The clinical intake evaluation of the child is optimized by including a quantitative, neurometric analysis of an eyes open (EO) and eyes closed (EC) EEG acquisition combined with a real-time analysis of the child’s (in vivo) brain functioning during a specific set of conditions, as described below. This method was developed and refined with more than 300 children who were tested between June 2012 and April 2014. The goal is to get as much information as possible in only one session lasting 45 to 60 minutes. The different parts of the evaluation consist of: eyes open (EO) and eyes closed (EC) collection of data, display of the actual brain waves, listing of the Z-score values (also presented as plots or instant brain maps with different task conditions), followed by games to play with a challenge condition. In addition, current source density (CSD) sLORETA of the different wave frequencies (usually delta, theta, alpha, beta, and gamma bands), distribution and velocity are shown as they change, as well as when the brain evaluates emotions. The session ends with a brief, individual 19-channel training with video feedback. Because of the usefulness of the information obtained from using this QEEG method, the author recommends that QEEG and an interactive neurofeedback session be included as a standard component in the diagnosis of and treatment planning for children with attention, learning and emotional problems.
View Full Paper →Near-Infrared Spectroscopy in Schizophrenia: A Possible Biomarker for Predicting Clinical Outcome and Treatment Response
Functional near-infrared spectroscopy (fNIRS) is a relatively new technique that can measure hemoglobin changes in brain tissues, and its use in psychiatry has been progressing rapidly. Although it has several disadvantages (e.g., relatively low spatial resolution and the possibility of shallow coverage in the depth of brain regions) compared with other functional neuroimaging techniques (e.g., functional magnetic resonance imaging and positron emission tomography), fNIRS may be a candidate instrument for clinical use in psychiatry, as it can measure brain activity in naturalistic position easily and non-invasively. fNIRS instruments are also small and work silently, and can be moved almost everywhere including schools and care units. Previous fNIRS studies have shown that patients with schizophrenia have impaired activity and characteristic waveform patterns in the prefrontal cortex during the letter version of the verbal fluency task, and part of these results have been approved as one of the Advanced Medical Technologies as an aid for the differential diagnosis of depressive symptoms by the Ministry of Health, Labor and Welfare of Japan in 2009, which was the first such approval in the field of psychiatry. Moreover, previous studies suggest that the activity in the frontopolar prefrontal cortex is associated with their functions in chronic schizophrenia and is its next candidate biomarker. Future studies aimed at exploring fNIRS differences in various clinical stages, longitudinal changes, drug effects, and variations during different task paradigms will be needed to develop more accurate biomarkers that can be used to aid differential diagnosis, the comprehension of the present condition, the prediction of outcome, and the decision regarding treatment options in schizophrenia. Future fNIRS researches will require standardized measurement procedures, probe settings, analytical methods and tools, manuscript description, and database systems in an fNIRS community.
View Full Paper →Brain's reward circuits mediate itch relief. a functional MRI study of active scratching
Previous brain imaging studies investigating the brain processing of scratching used an exogenous intervention mimicking scratching, performed not by the subjects themselves, but delivered by an investigator. In real life, scratching is a conscious, voluntary, controlled motor response to itching, which is directed to the perceived site of distress. In this study we aimed to visualize in real-time by brain imaging the core mechanisms of the itch-scratch cycle when scratching was performed by subjects themselves. Secondly, we aimed to assess the correlations between brain patterns of activation and psychophysical ratings of itch relief or pleasurability of scratching. We also compared the patterns of brain activity evoked by self-scratching vs. passive scratching. We used a robust tridimensional Arterial Spin Labeling fMRI technique that is less sensitive to motion artifacts: 3D gradient echo and spin echo (GRASE)--Propeller. Active scratching was accompanied by a higher pleasurability and induced a more pronounced deactivation of the anterior cingulate cortex and insula, in comparison with passive scratching. A significant involvement of the reward system including the ventral tegmentum of the midbrain, coupled with a mechanism deactivating the periaqueductal gray matter (PAG), suggests that itch modulation operates in reverse to the mechanism known to suppress pain. Our findings not only confirm a role for the central networks processing reward in the pleasurable aspects of scratching, but also suggest they play a role in mediating itch relief.
View Full Paper →Meditation states and traits: EEG, ERP, and neuroimaging studies.
Neuroelectric and imaging studies of meditation are reviewed. Electroencephalographic measures indicate an overall slowing subsequent to meditation, with theta and alpha activation related to proficiency of practice. Sensory evoked potential assessment of concentrative meditation yields amplitude and latency changes for some components and practices. Cognitive event-related potential evaluation of meditation implies that practice changes attentional allocation. Neuroimaging studies indicate increased regional cerebral blood flow measures during meditation. Taken together, meditation appears to reflect changes in anterior cingulate cortex and dorsolateral prefrontal areas. Neurophysiological meditative state and trait effects are variable but are beginning to demonstrate consistent outcomes for research and clinical applications. Psychological and clinical effects of meditation are summarized, integrated, and discussed with respect to neuroimaging data.
View Full Paper →Latest Developments in Live Z-Score Training: Symptom Check List, Phase Reset, and Loreta Z-Score Biofeedback
Advances in neuroscience are applied to the clinical applications of EEG neurofeedback by linking symptoms to functional networks in the brain. This is achieved by reviews of the last 20 years of functional neuroimaging studies of brain networks related to clinical disorders based on positron emission tomography, functional MRI, diffusion tensor imaging, and EEG/MEG inverse solutions. Considerable consistency exists between different imaging modalities because of the property of functional localization and the existence of large clusters of connections in the brain representing network modules and hubs. Reviewed here is new method of EEG neurofeedback called Z-Score Neurofeedback, and it is demonstrated how real-time comparison to an age-matched population of healthy subjects simplifies protocol generation and allows clinicians to target modules and hubs that indicate dysregulation and instability in networks related to symptoms. Z-score neurofeedback, by measuring the distance from the center of the healthy age-matched population, increases specificity in operant conditioning and provides a guide by which extreme Z-score outliers are linked to symptoms and then reinforced toward states of greater homeostasis and stability. The goal is increased efficiency of information processing in brain networks related to the patient's symptoms. The unique advantage of EEG over other neuroimaging methods is high temporal resolution in which the fine temporal details of phase lock and phase shift between large masses of neurons is quantified and can be modified by Z-score neurofeedback to address the patient's symptoms. The latest developments in Z-score neurofeedback are a harbinger of a bright future for clinicians and, most important, patients that suffer from a variety of brain dysfunctions.
Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas
In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation.
View Full Paper →The Relation Between Memory Improvement and QEEG Changes in Three Clinical Groups as a Result of EEG Biofeedback Treatment
It is important to understand the relation between changes in the quantitative EEG (QEEG) variables and memory changes as a result of the EEG biofeedback treatment. With this goal in mind, the senior author reviewed his clinical files from the last 5 years and examined the QEEG data addressing relative power and coherence changes and memory (auditory and reading) improvements. The groups involved included (a) normal individuals wanting to improve their cognitive functioning, (b) traumatic brain injured (TBI) subjects, and (c) + (d) subjects who can best be classified as having a specific learning disability (SLD). The SLD group was divided between those who are (c) older than 14 (adults) and those who are (d) younger than 14 (children) in order to reference the appropriate age-related normative group values. The analysis revealed significant improvements in auditory and reading memory across all groups as well as changes on the QEEG variables. All of the groups were performing above the normative reference group on measures of auditory and reading memory in terms of percentage differences (24-97%) and standard deviations (+1.28-1.85). The average auditory memory SD improvement was +1.52, whereas the average percentage change was 82%. For the reading task the average memory standard deviation improvement was 1.38, whereas the percentage improvement was 154%. The experimental group was performing 1.66 SD (68%) above the control group on auditory memory and.90 SD (52%) above the control group on reading memory measures. For the QEEG variables, the average raw value of the Spectral Correlation Coefficient (SCC) change for alpha was 6.1 points (2.09 SD), for SCC beta1 (13-32 Hz) 6.53 points (1.81 SD), and for beta2 (32-64 Hz) 7.5 points (1.77 SD). The changes on the relative power measures were less dramatic, albeit significant. These results underlie the importance of addressing the SCC values in EEG biofeedback treatment protocols.
View Full Paper →Clinical Advantages of Quantitative Electroencephalogram (QEEG)–Electrical Neuroimaging Application in General Neurology Practice
QEEG-electrical neuroimaging has been underutilized in general neurology practice for uncertain reasons. Recent advances in computer technology have made this electrophysiological testing relatively inexpensive. Therefore, this study was conducted to evaluate the clinical usefulness of QEEG/electrical neuroimaging in neurological practice. Over the period of approximately 6 months, 100 consecutive QEEG recordings were analyzed for potential clinical benefits. The patients who completed QEEG were divided into 5 groups based on their initial clinical presentation. The main groups included patients with seizures, headaches, post-concussion syndrome, cognitive problems, and behavioral dysfunctions. Subsequently, cases were reviewed and a decision was made as to whether QEEG analysis contributed to the diagnosis and/or furthered patient’s treatment. Selected and representative cases from each group are presented in more detail, including electrical neuroimaging with additional low-resolution electromagnetic tomography analysis or using computerized cognitive testing. Statistical analysis showed that QEEG analysis contributed to 95% of neurological cases, which indicates great potential for wider application of this modality in general neurology. Many patients also began neurotherapy, depending on the patient’s desire to be involved in this treatment modality.
View Full Paper →EEG-Based Personalized Medicine in ADHD: Individual Alpha Peak Frequency as an Endophenotype Associated with Nonresponse
This review article summarizes some recent developments in psychiatry such as personalized medicine, employing biomarkers and endophenotypes, and developments collectively referred to as neuromodulation with a focus on ADHD. Several neurophysiological subtypes in ADHD and their relation to treatment outcome are reviewed. In older research the existence of an "abnormal EEG" or "paroxysmal EEG" was often reported, most likely explained by the high occurrence of this EEG subtype in autism, as the diagnosis of autism was not coined until 1980. This subgroup might respond best to anticonvulsant treatments, which requires more specific research. A second subgroup is a beta-excess or beta-spindling subgroup. This group responds well to stimulant medication, albeit several studies suggesting that neurophysiologically this might represent a different subgroup. The third subgroup consists of the "impaired vigilance" subgroup with the often-reported excess frontal theta or excess frontal alpha. This subgroup responds well to stimulant medication. Finally, it is proposed that a slow individual alpha peak frequency is an endophenotype related to treatment resistance in ADHD. Future studies should incorporate this endophenotype in clinical trials to further investigate new treatments for this substantial subgroup of patients, such as NIRS-biofeedback, transcranial Doppler sonography biofeedback, hyperbaric oxygen therapy, or medications such as nicotine and piracetam
View Full Paper →Application of Neurofeedback in General Neurology Practice
Neurofeedback (NFB), also called EEG biofeedback, is infrequently applied in general neurology practice. Therefore, this study was conducted to evaluate the clinical usefulness of NFB in neurological settings. Over the period of approximately 15 months, 25 subsequent patients who were interested in NFB therapy and completed at least 20 sessions of NFB treatment were analyzed for potential clinical benefits. Patients’ subjective responses were collected after NFB treatment to see if any improvement of symptoms was accomplished with NFB therapy. Quantitative electroencephalography (QEEG) was completed before and after NFB therapy initiation and analyzed for any major changes in frequency bands expression. Patients’ analysis revealed 84% subjective improvement rate and 75% objective QEEG improvement after completion of NFB therapy. These encouraging results indicate the need for more broad utilization of NFB in general neurology practice.
View Full Paper →Neurofeedback to improve neurocognitive functioning of children treated for a brain tumor: design of a randomized controlled double-blind trial
BACKGROUND: Neurotoxicity caused by treatment for a brain tumor is a major cause of neurocognitive decline in survivors. Studies have shown that neurofeedback may enhance neurocognitive functioning. This paper describes the protocol of the PRISMA study, a randomized controlled trial to investigate the efficacy of neurofeedback to improve neurocognitive functioning in children treated for a brain tumor. METHODS/DESIGN: Efficacy of neurofeedback will be compared to placebo training in a randomized controlled double-blind trial. A total of 70 brain tumor survivors in the age range of 8 to 18 years will be recruited. Inclusion also requires caregiver-reported neurocognitive problems and being off treatment for more than two years. A group of 35 healthy siblings will be included as the control group. On the basis of a qEEG patients will be assigned to one of three treatment protocols. Thereafter patients will be randomized to receive either neurofeedback training (n=35) or placebo training (n=35). Neurocognitive tests, and questionnaires administered to the patient, caregivers, and teacher, will be used to evaluate pre- and post-intervention functioning, as well as at 6-month follow-up. Siblings will be administered the same tests and questionnaires once. DISCUSSION: If neurofeedback proves to be effective for pediatric brain tumor survivors, this can be a valuable addition to the scarce interventions available to improve neurocognitive and psychosocial functioning. TRIAL REGISTRATION: ClinicalTrials.gov NCT00961922.
View Full Paper →Evaluating Prefrontal Activation and Its Relationship with Cognitive and Emotional Processes by Means of Hemoencephalography (HEG)
The main aim of this study is to determine the efficacy of the method of diagnosis known as hemoencephalography (HEG), which measures hemodynamic changes in the prefrontal cortex by determining differences in oxygen flow to show patterns of neuronal activity. Of the 5 tests designed for this purpose, 2 are strictly cognitive, while the other 3 have primarily emotional or sensitive content. The tests were applied to a sample of 70 university students. The Wilcoxon nonparametric signed rank test was applied to test the paired differences between the HEG baseline result and the HEG result of the task. Results show, first, that the HEG method successfully determines oxygen flow to the prefrontal cortex and clearly differentiates the subject's baseline from HEG activation during the task (Wilcoxon, p < .05); second, that HEG results vary depending on the type of activity, whether cognitive (low emotional load) or emotional (high emotional load) in such a way that cognitive areas, those located higher in the cortex (dorsolateral prefrontal), show less activity during emotional tests and more activity during cognitive tests, thus associating higher areas (dorsolateral prefrontal) with cognition and deeper areas (medial temporal, medial prefrontal, and cingulate) with emotion. The HEG procedure is effective in detecting states or situations of ailment or suffering not always accompanied by evident external manifestations. Furthermore, the procedure can differentiate between cognitive and emotional processing. The HEG method can help diagnosis in clinical settings due to its ability to detect painful-feeling processing independently of both body and verbal language.
View Full Paper →Amygdala lesions disrupt modulation of functional MRI activity evoked by facial expression in the monkey inferior temporal cortex
We previously showed that facial expressions modulate functional MRI activity in the face-processing regions of the macaque monkey’s amygdala and inferior temporal (IT) cortex. Specifically, we showed that faces expressing emotion yield greater activation than neutral faces; we term this difference the “valence effect.” We hypothesized that amygdala lesions would disrupt the valence effect by eliminating the modulatory feedback from the amygdala to the IT cortex. We compared the valence effects within the IT cortex in monkeys with excitotoxic amygdala lesions (n = 3) with those in intact control animals (n = 3) using contrast agent-based functional MRI at 3 T. Images of four distinct monkey facial expressions--neutral, aggressive (open mouth threat), fearful (fear grin), and appeasing (lip smack)--were presented to the subjects in a blocked design. Our results showed that in monkeys with amygdala lesions the valence effects were strongly disrupted within the IT cortex, whereas face responsivity (neutral faces > scrambled faces) and face selectivity (neutral faces > non-face objects) were unaffected. Furthermore, sparing of the anterior amygdala led to intact valence effects in the anterior IT cortex (which included the anterior face-selective regions), whereas sparing of the posterior amygdala led to intact valence effects in the posterior IT cortex (which included the posterior face-selective regions). Overall, our data demonstrate that the feedback projections from the amygdala to the IT cortex mediate the valence effect found there. Moreover, these modulatory effects are consistent with an anterior-to-posterior gradient of projections, as suggested by classical tracer studies.
View Full Paper →Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth
It has recently been reported (Van Dijk et al., 2011) that in-scanner head motion can have a substantial impact on MRI measurements of resting-state functional connectivity. This finding may be of particular relevance for studies of neurodevelopment in youth, confounding analyses to the extent that motion and subject age are related. Furthermore, while Van Dijk et al. demonstrated the effect of motion on seed-based connectivity analyses, it is not known how motion impacts other common measures of connectivity. Here we expand on the findings of Van Dijk et al. by examining the effect of motion on multiple types of resting-state connectivity analyses in a large sample of children and adolescents (n=456). Following replication of the effect of motion on seed-based analyses, we examine the influence of motion on graphical measures of network modularity, dual-regression of independent component analysis, as well as the amplitude and fractional amplitude of low frequency fluctuation. In the entire sample, subject age was highly related to motion. Using a subsample where age and motion were unrelated, we demonstrate that motion has marked effects on connectivity in every analysis examined. While subject age was associated with increased within-network connectivity even when motion was accounted for, controlling for motion substantially attenuated the strength of this relationship. The results demonstrate the pervasive influence of motion on multiple types functional connectivity analysis, and underline the importance of accounting for motion in studies of neurodevelopment.
View Full Paper →Clinical Efficacy of a New Automated Hemoencefalographic Neurofeedback Protocol
Among the ongoing attempts to enhance cognitive performance, an emergent and yet underrepresented venue is brought by hemoencefalographic neurofeedback (HEG). This paper presents three related advances in HEG neurofeedback for cognitive enhancement: a) a new HEG protocol for cognitive enhancement, as well as b) the results of independent measures of biological efficacy (EEG brain maps) extracted in three phases, during a one year follow up case study; c) the results of the first controlled clinical trial of HEG, designed to assess the efficacy of the technique for cognitive enhancement of an adult and neurologically intact population. The new protocol was developed in the environment of a software that organizes digital signal algorithms in a flowchart format. Brain maps were produced through 10 brain recordings. The clinical trial used a working memory test as its independent measure of achievement. The main conclusion of this study is that the technique appears to be clinically promising. Approaches to cognitive performance from a metabolic viewpoint should be explored further. However, it is particularly important to note that, to our knowledge, this is the world's first controlled clinical study on the matter and it is still early for an ultimate evaluation of the technique., Entre los intentos en curso para mejorar el rendimiento cognitivo, uno emergente y todavía insuficientemente representado es el neurofeedback hemoencefalográphico (HEG). Este trabajo presenta tres avances relacionados con HEG neurofeedback para la mejora cognitiva: a) un nuevo protocolo HEG para la mejora cognitiva, así como b) los resultados de las medidas independientes de la eficacia biológica (mapas cerebrales EEG) extraídos en tres fases durante un año estudio de seguimiento de casos; c) los resultados del primer ensayo clínico controlado de HEG, diseñado para evaluar la eficacia de la técnica para la mejora cognitiva de población adulta y neurológicamente sana. El nuevo protocolo fue desarrollado en el marco de un software que organiza algoritmos de señales digitales en un formato de diagrama de flujo. Los mapas de cerebro fueron producidos a través de 10 registros cerebrales. El ensayo clínico utilizó un test de memoria de trabajo como medida independiente de sus logros. La principal conclusión de este estudio es que la técnica parece ser clínicamente prometedora. Los enfoques para el rendimiento cognitivo desde un punto de vista metabólico deben investigarse más a fondo. Sin embargo, es particularmente importante tener en cuenta que, a nuestro entender, este es el primer estudio clínico controlado sobre el tema en el mundo, y aún es pronto para una evaluación final de la técnica.
View Full Paper →Symbol Digit and the Quantitative EEG
The coordination of allocation resource model of brain functioning examines the relations between quantitative EEG (QEEG) variables and cognitive performance on specific tasks. The Digit Symbol (DS) subtest of the Wechsler Adult Intelligence Scales has proven to be a sensitive measure in a variety of clinical conditions. A conceptually and empirically similar task (Symbol Digit [SD]) was employed to examine the QEEG correlates of successful functioning. A sample of 119 participants engaged in a modified SD test for 200 seconds while QEEG data were obtained. The participant verbally provided the matching number to the examiner to avoid any motor component of the task. There were negative relations between performance and magnitudes across almost all locations and across a wide bandwidth (0-64 Hz). Negative relations to SD performance were also observed for increased relative power of beta1, whereas positive relations were found for absolute values of coherences of alpha, beta1 (13-32 Hz), and beta2 (32-64 Hz). The results showed the importance of spectral correlation coefficients (SCC) in cognitive functioning, in particular the SCC values within the frontal region and in the 13-64 frequency range.
EVENT-RELATED POTENTIAL STUDY OF ATTENTION REGULATION DURING ILLUSORY FIGURE CATEGORIZATION TASK IN ADHD, AUTISM SPECTRUM DISORDER, AND TYPICAL CHILDREN
Autism spectrum disorders (ASD) and attention deficit/hyperactivity disorder (ADHD) are very common developmental disorders which share some similar symptoms of social, emotional, and attentional deficits. This study is aimed to help understand the differences and similarities of these deficits using analysis of dense-array event-related potentials (ERP) during an illusory figure recognition task. Although ADHD and ASD seem very distinct, they have been shown to share some similarities in their symptoms. Our hypothesis was that children with ASD will show less pronounced differences in ERP responses to target and non-target stimuli as compared to typical children, and to a lesser extent, ADHD. Participants were children with ASD (N=16), ADHD (N=16), and controls (N=16). EEG was collected using a 128 channel EEG system. The task involved the recognition of a specific illusory shape, in this case a square or triangle, created by three or four inducer disks. There were no between group differences in reaction time (RT) to target stimuli, but both ASD and ADHD committed more errors, specifically the ASD group had statistically higher commission error rate than controls. Post-error RT in ASD group was exhibited in a post-error speeding rather than corrective RT slowing typical for the controls. The ASD group also demonstrated an attenuated error-related negativity (ERN) as compared to ADHD and controls. The fronto-central P200, N200, and P300 were enhanced and less differentiated in response to target and non-target figures in the ASD group. The same ERP components were marked by more prolonged latencies in the ADHD group as compared to both ASD and typical controls. The findings are interpreted according to the “minicolumnar” hypothesis proposing existence of neuropathological differences in ASD and ADHD, specifically minicolumnar number/width morphometry spectrum differences. In autism, a model of local hyperconnectivity and long-range hypoconnectivity explains many of the behavioral and cognitive deficits present in the condition, while the inverse arrangement of local hypoconnectivity and long-range hyperconnectivity in ADHD explains some deficits typical for this disorder. The current ERP study supports the proposed suggestion that some between group differences could be manifested in the frontal ERP indices of executive functions during performance on an illusory figure categorization task.
The use of functional neuroimaging to evaluate psychological and other non-pharmacological treatments for clinical pain
A large number of studies have provided evidence for the efficacy of psychological and other non-pharmacological interventions in the treatment of chronic pain. While these methods are increasingly used to treat pain, remarkably few studies focused on the exploration of their neural correlates. The aim of this article was to review the findings from neuroimaging studies that evaluated the neural response to distraction-based techniques, cognitive behavioral therapy (CBT), clinical hypnosis, mental imagery, physical therapy/exercise, biofeedback, and mirror therapy. To date, the results from studies that used neuroimaging to evaluate these methods have not been conclusive and the experimental methods have been suboptimal for assessing clinical pain. Still, several different psychological and non-pharmacological treatment modalities were associated with increased pain-related activations of executive cognitive brain regions, such as the ventral- and dorsolateral prefrontal cortex. There was also evidence for decreased pain-related activations in afferent pain regions and limbic structures. If future studies will address the technical and methodological challenges of today's experiments, neuroimaging might have the potential of segregating the neural mechanisms of different treatment interventions and elucidate predictive and mediating factors for successful treatment outcomes. Evaluations of treatment-related brain changes (functional and structural) might also allow for sub-grouping of patients and help to develop individualized treatments.
View Full Paper →Covert waking brain activity reveals instantaneous sleep depth
The neural correlates of the wake-sleep continuum remain incompletely understood, limiting the development of adaptive drug delivery systems for promoting sleep maintenance. The most useful measure for resolving early positions along this continuum is the alpha oscillation, an 8-13 Hz electroencephalographic rhythm prominent over posterior scalp locations. The brain activation signature of wakefulness, alpha expression discloses immediate levels of alertness and dissipates in concert with fading awareness as sleep begins. This brain activity pattern, however, is largely ignored once sleep begins. Here we show that the intensity of spectral power in the alpha band actually continues to disclose instantaneous responsiveness to noise--a measure of sleep depth--throughout a night of sleep. By systematically challenging sleep with realistic and varied acoustic disruption, we found that sleepers exhibited markedly greater sensitivity to sounds during moments of elevated alpha expression. This result demonstrates that alpha power is not a binary marker of the transition between sleep and wakefulness, but carries rich information about immediate sleep stability. Further, it shows that an empirical and ecologically relevant form of sleep depth is revealed in real-time by EEG spectral content in the alpha band, a measure that affords prediction on the order of minutes. This signal, which transcends the boundaries of classical sleep stages, could potentially be used for real-time feedback to novel, adaptive drug delivery systems for inducing sleep.
View Full Paper →QEEG-guided neurofeedback for recurrent migraine headaches
Seventy-one patients with recurrent migraine headaches, aged 17-62, from one neurological practice, completed a quantitative electroencephalogram (QEEG) procedure. All QEEG results indicated an excess of high-frequency beta activity (21-30 Hz) in 1-4 cortical areas. Forty-six of the 71 patients selected neurofeedback training while the remaining 25 chose to continue on drug therapy. Neurofeedback protocols consisted of reducing 21-30 Hz activity and increasing 10 Hz activity (5 sessions for each affected site). All the patients were classified as migraine without aura. For the neurofeedback group the majority (54%) experienced complete cessation of their migraines, and many others (39%) experienced a reduction in migraine frequency of greater than 50%. Four percent experienced a decrease in headache frequency of < 50%. Only one patient did not experience a reduction in headache frequency. The control group of subjects who chose to continue drug therapy as opposed to neurofeedback experienced no change in headache frequency (68%), a reduction of less than 50% (20%), or a reduction greater than 50% (8%). QEEG-guided neurofeedback appears to be dramatically effective in abolishing or significantly reducing headache frequency in patients with recurrent migraine.
View Full Paper →A continuous mapping of sleep states through association of EEG with a mesoscale cortical model
Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.
View Full Paper →Validity and Reliability of Quantitative Electroencephalography
Reliability and validity are statistical concepts that are reviewed and then applied to the field of quantitative electroencephalography (qEEG). The review of the scientific literature demonstrated high levels of split-half and test–retest reliability of qEEG and convincing content and predictive validity as well as other forms of validity. QEEG is distinguished fromnonquantitative EEG (“eyeball” examination of EEG traces), with the latter showing low reliability (e.g., 0.2–0.29) and poor interrater agreement for nonepilepsy evaluation. In contrast, qEEG is greater than 0.9 reliable with as little as 40-s epochs and remains stable with high test–retest reliability over many days and weeks. Predictive validity of qEEG is established by significant and replicable correlations with clinical measures and accurate predictions of outcome and performance on neuropsychological tests. In contrast, non-qEEG or eyeball visual examination of the EEG traces in cases of nonepilepsy has essentially zero predictive validity. Content validity of qEEG is established by correlations with independent measures such as the MRI, PET and SPECT, the Glasgow Coma Score, neuropsychological tests, and so on, where the scientific literature again demonstrates significant correlations between qEEG and independent measures known to be related to various clinical disorders. The ability to test and evaluate the concepts of reliability and validity are demonstrated by mathematical proof and simulation where one can demonstrate test–retest reliability as well as zero physiological validity of coherence and phase differences when using an average reference and Laplacian montage.
View Full Paper →Post WISC-R and TOVA Improvement with QEEG Guided Neurofeedback Training in Mentally Retarded: A Clinical Case Series of Behavioral Problems
According to the DSM-IV, Mental Retardation is significantly subaverage general intellectual functioning accompanied by significant limitations in adaptive functioning in at least two of the following skill areas: communication, self-care, home living, social/interpersonal skills, use of community resources, self-direction, functional academic skills, work, leisure, health and safety. In pilot work, we have seen positive clinical effects of Neurofeedback (NF) applied to children with Trisomy 21 (Down Syndrome) and other forms of mental retardation. Given that many clinicians use NF in Attention Deficit Hyperactivity Disorder and Generalized Learning Disability cases, we studied the outcomes of a clinical case series using Quantitative EEG (QEEG) guided NF in the treatment of mental retardation. All 23 subjects received NF training. The QEEG data for most subjects had increased theta, alpha, and coherence abnormalities. A few showed increased delta over the cortex. Some of the subjects were very poor in reading and some had illegible handwriting, and most subjects had academic failures, impulsive behavior, and very poor attention, concentration, memory problems, and social skills. This case series shows the impact of QEEG-guided NF training on these clients' clinical outcomes. Fourteen out of 23 subjects formerly took medications without any improvement. Twenty-three subjects ranging from 7–16 years old attending private learning centers were previously diagnosed with mental retardation (severity of degree: from moderate to mild) at various university hospitals. Evaluation measures included QEEG analysis, WISC-R (Wechsler Intelligence Scale for Children-Revised) IQ test, TOVA (Test of Variables of Attention) test, and DPC-P (Developmental Behaviour Checklist) were filled out by the parents. NF trainings were performed by Lexicor Biolex software. NX-Link was the commercial software reference database used to target the treatment protocols, along with the clinical judgment of the first author. QEEG signals were sampled at 128 samples per second per channel and electrodes were placed according to the International 10–20 system. Between 80 and 160 NF training sessions were completed, depending on the case. None of the subjects received any special education during NF treatment. Two subjects with the etiology of epilepsy were taking medication, and the other 21 subjects were medication-free at the baseline. Twenty-two out of 23 patients who received NF training showed clinical improvement according to the DPC-P with QEEG reports. Nineteen out of 23 patients showed significant improvement on the WISC-R, and the TOVA. For the WISC-R test, 2 showed decline on total IQ due to the decline on some of the subtests, 2 showed no improvement on total IQ although improvement was seen on some of the subtests, however even these cases showed improvement on QEEG and DPC-P. This study provides the first evidence for positive effects of NF treatment in mental retardation. The results of this study encourage further research.
View Full Paper →Neurotherapy of fibromyalgia?
OBJECTIVE: To evaluate the efficacy of a novel variant of electroencephalograph biofeedback, the Low Energy Neurofeedback System (LENS), that utilizes minute pulses of electromagnetic stimulation to change brainwave activity for the amelioration of fibromyalgia (FM) symptoms. DESIGN: Randomized, double-blind, placebo-controlled clinical trial. SETTING: Tertiary referral academic medical center, outpatient. PATIENTS: Thirty-four patients diagnosed with FM according to 1990 American College of Rheumatology classification criteria. INTERVENTIONS: Active or sham LENS, depending on randomization, for 22 treatment sessions. OUTCOME MEASURES: Primary outcome measure was the Fibromyalgia Impact Questionnaire total score. Secondary outcome measures included number of tender points (TPs) and pressure required to elicit TPs on physical examination, quantitative sensory testing heat pain threshold, and self-reported cognitive dysfunction, fatigue, sleep problems, global psychological distress, and depression obtained at baseline, immediate post-treatment, and 3- and 6-month follow-up. RESULTS: Participants who received the active or sham interventions improved (Ps < 0.05) on the primary and a variety of secondary outcome measures, without statistically significant between group differences in evidence at post-treatment or 3- or 6-month follow-up. Individual session self-reported ratings of specific symptoms (cognitive dysfunction, fatigue, pain, and sleep, and overall activity level) over the course of the 22 intervention sessions indicated significant linear trends for improvement for the active intervention condition only (Ps < 0.05). CONCLUSION: LENS cannot be recommended as a single modality treatment for FM. However, further study is warranted to investigate the potential of LENS to interact synergistically with other pharmacologic and nonpharmacologic therapies for improving symptoms in FM.
View Full Paper →The clinical use of quantitative EEG in cognitive disorders
The primary diagnosis of most cognitive disorders is clinically based, but the EEG plays a role in evaluating, classifying and following some of these disorders. There is an ongoing debate over routine use of qEEG. Although many findings regarding the clinical use of quantitative EEG are awaiting validation by independent investigators while confirmatory clinical follow-up studies are also needed, qEEG can be cautiously used by a skilled neurophysiologist in cognitive dysfunctions to improve the analysis of background activity, slow/fast focal activity, subtle asymmetries, spikes and waves, as well as in longitudinal follow-ups.
View Full Paper →Functional Neuroanatomy and the Rationale for Using EEG Biofeedback for Clients with Asperger’s Syndrome
This paper reviews the symptoms of Asperger’s Syndrome (AS), a disorder along the autism continuum, and highlights research findings with an emphasis on brain differences. Existing theories concerning AS are described, including theory of mind (Hill and Frith in Phil Trans Royal Soc Lond, Bull 358:281–289, 2003), mirror neuron system (Ramachandran and Oberman in Sci Am 295(5):62–69, 2006), and Porges’ (Ann N Y Acad Sci 1008:31–47, 2003, The neurobiology of autism, Johns Hopkins University Press, Baltimore, 2004) polyvagal theory. (A second paper, Outcomes using EEG Biofeedback Training in Clients with Asperger’s Syndrome, summarizes clinical outcomes obtained with more than 150 clients.) Patterns seen with QEEG assessment are then presented. Single channel assessment at the vertex (CZ) reveals patterns similar to those found in Attention-Deficit/Hyperactivity Disorder. Using 19-channel data, significant differences (z-scores > 2) were found in the amplitude of both slow waves (excess theta and/or alpha) and fast waves (beta) at various locations. Differences from the norm were most often found in mirror neuron areas (frontal, temporal and temporal-parietal). There were also differences in coherence patterns, as compared to a normative database (Neuroguide). Low Resolution Electromagnetic Tomography Analysis (Pascual-Marqui et al. in Methods Find Exp Clin Pharmacol 24C:91–95, 2002) suggested the source of the abnormal activity was most often the anterior cingulate. Other areas involved included the amygdala, uncus, insula, hippocampal gyrus, parahippocampal gyrus, fusiform gyrus, and the orbito-frontal and/or ventromedial areas of the prefrontal cortex. Correspondence between symptoms and the functions of the areas found to have abnormalities is evident and those observations are used to develop a rationale for using EEG biofeedback, called neurofeedback (NFB), intervention. NFB training is targeted to improve symptoms that include difficulty reading and mirroring emotions, poor attention to the outside world, poor self-regulation skills, and anxiety. Porges’ polyvagal theory is used to emphasize the need to integrate NFB with biofeedback (BFB), particularly heart rate variability training. We term this emerging understanding the Systems Theory of Neural Synergy. The name underscores the fact that NFB and BFB influence dynamic circuits and emphasizes that, no matter where we enter the nervous system with an intervention, it will seek its own new balance and equilibrium.
View Full Paper →QEEG-Based Protocol Selection: A Study of Level of Agreement on Sites, Sequences, and Rationales Among a Group of Experienced QEEG-Based Neurofeedback Practitioners
Background. The history of neurofeedback is marked by a diversity of theoretical bases and specific protocol development approaches, including standard protocols based on research, symptom/neurophysiological function-based approaches, and approaches based on quantitative electroencephalography (QEEG) assessment (Budzynski, 1999; Demos, 2005). Although this diversity of approaches currently characterizes clinical practice within the field, one might assume that a certain degree of uniformity exists among practitioners who follow one particular treatment model. That is, clinicians who follow a symptom/function-based approach might be expected to select similar protocols for a given client, and practitioners who base their protocols largely on QEEG likewise would develop similar protocols for the same client. Method. To test this latter assumption, 13 neurofeedback practitioners having 5 to 20 years of experience using QEEG and neurofeedback were sent the same QEEG data and presenting problems of a female adult who had previously sought neurofeedback treatment. The participant's data were edited in both NeuroReport and NeuroGuide, and both edits were provided to the survey participants. The practitioners were asked to provide treatment protocols covering sites, frequencies, sequences, and so on, as well as rationales that supported their protocol selections. Results. Ten of the 13 professionals contacted responded to the survey. Respondents were in general agreement as to which sites and frequencies to treat. However, they diverged in their sequencing of treatment sites; in whether to inhibit, reinforce, or both; in cautioning about reference contamination in the QEEG record; and in their theoretical rationales for their protocol selections. Conclusions. Although further research will have to document the efficacy of the various protocols recommended by the experienced QEEG-based practitioners surveyed for this study, it can be assumed that these practitioners are finding some consistent success using them in their practices. Therefore, the primary implication of this study appears to be that as long as appropriate treatment sites and frequencies are addressed for a given client, competently applied neurofeedback seems to be robust enough to tolerate a relatively wide diversity in specific protocol configurations.
View Full Paper →Eyes-Closed and Activation QEEG Databases in Predicting Cognitive Effectiveness and the Inefficiency Hypothesis
Background. Quantitative electroencephalography (QEEG) databases have been developed for the eyes closed (EC) condition. The development of a cognitive activation database is a logical and necessary development for the field. Method. Brain activation was examined by QEEG during several tasks including EC rest, visual attention (VA), auditory attention (AA), listening to paragraphs presented auditorily and reading silently. The QEEG measures obtained in the EC and simple, non-cognitive attention task that were significantly related to subsequent cognitive performance were not the same variables which accounted for success during the cognitive task. Results. There were clear differences between relative power, microvolt, coherence and phase values across these different tasks. Conclusions. The conclusions reached are (1) the associations among QEEG variables are complex and vary by task; (2) the QEEG variables which predict cognitive performance under task demands are not the same as the variables which predict to subsequent performance from the EC or simple, non-cognitive attention tasks; (3) a cognitive activation database is clinically useful; and (4) an hypothesis of brain functioning is proposed to explain the findings. The coordinated allocation of resources (CAR) hypothesis states that cognitive effectiveness is a product of multiple specific activities in the brain, which vary according to the task; and (5) the average response pattern does not involve the variables that are critical to success at the task, thus indicating an inefficiency of the normal human brain.
Introduction to Advances in EEG Connectivity
This special issue of the Journal of Neurotherapy has been devoted to Advances in EEG Connectivities. These purposes include providing education to our readers and collaboration among the scientists and authors. Multiple connectivity metrics have been defined with an emphasis on coherence and multivariate connectivity measures. The goals of connectivity measurements should include accuracy compared to known neurological networks and utility in assessment and application for intervention (e.g., EEG coherence training). It is hoped that the information contained in this special issue will form the basis for future advancements in EEG connectivity assessment and intervention.
View Full Paper →Connectivity Assessment and Training: A Partial Directed Coherence Approach
Background. The multivariate autoregressive (MVAR) method to generate a linear model of multichannel signal processes has been employed in many fields but not applied to the assessment of quantitative electroencephalographic (QEEG) connectivity neurofeedback. A measure known as Partial Directed Coherence (PDC) derived in the MVAR framework can offer insensitivity to volume conduction and ability to provide information relating to the direction of information flow between electrode locations, as a function of frequency during QEEG assessment and neurofeedback. Method. This article outlines a variety of reasons why PDC and other related metrics could play a more fundamental role in elucidating the causal relationships underlying EEG connectivity than can be provided though a multivariate analysis of coherence alone. Results. Real-time PDC neurofeedback implementation issues are discussed, technical challenges are outlined, and research questions are proposed. Conclusion. MVAR-based methods are an additional means of relating global to local EEG activity as well as helping to bridge QEEG assessment and neurofeedback protocol generation and treatment.
View Full Paper →Infrared Imaging and Neurofeedback: Initial Reliability and Validity
Introduction. The neurological correlates underlying positive treatment outcomes for neurofeedback have been either unavailable or difficult to demonstrate. Assessment of brain-related changes associated with neurofeedback is needed to further establish its empirical basis. Infrared (IR) imaging is a noninvasive assessment of brain activity with high spatial and temporal resolution. Method. Study 1, a reliability study, assessed the test-retest stability of IR imaging. In Validity Study 2 and 3, IR imaging assessed brain-related changes prior to and following neurofeedback and passive infrared hemoencephalography (pir HEG) training, respectively. Results. In Study 1, high correlations occurred in pre-post comparisons for IR measures unrelated to treatment. Lower correlation between measures of IR imaging indicated changes in brain activation associated with thermoregulation following neurofeedback training. In Study 2, changes in thermal regulation occurred both within and across sessions. The change in metabolic regulation was enduring and associated with a reduction in core Autistic Spectrum Disorder symptomatology and improved cerebral connectivity. In Study 3, a significant percentage of patients with Traumatic Brain Injury increased thermal readings following pir HEG training and the change in thermal readings was associated with EEG connectivity. Conclusion. Findings indicated that IR imaging may be a reliable and valid measure of treatment outcomes with clinical utility and sensitivity.
Atlas-based multichannel monitoring of functional MRI signals in real-time: automated approach
We report an automated method to simultaneously monitor blood-oxygenation-level-dependent (BOLD) MR signals from multiple cortical areas in real-time. Individual brain anatomy was normalized and registered to a pre-segmented atlas in standardized anatomical space. Subsequently, using real-time fMRI (rtfMRI) data acquisition, localized BOLD signals were measured and displayed from user-selected areas labeled with anatomical and Brodmann's Area (BA) nomenclature. The method was tested on healthy volunteers during the performance of hand motor and internal speech generation tasks employing a trial-based design. Our data normalization and registration algorithm, along with image reconstruction, movement correction and a data display routine were executed with enough processing and communication bandwidth necessary for real-time operation. Task-specific BOLD signals were observed from the hand motor and language areas. One of the study participants was allowed to freely engage in hand clenching tasks, and associated brain activities were detected from the motor-related neural substrates without prior knowledge of the task onset time. The proposed method may be applied to various applications such as neurofeedback, brain-computer-interface, and functional mapping for surgical planning where real-time monitoring of region-specific brain activity is needed.
View Full Paper →Functional Connectivity and Aging: Comodulation and Coherence Differences
Introduction. Misconceptions about coherence and comodulation has hindered their simultaneous use in assessing electroencephalography (EEG). Coherence refers to phase synchrony, whereas comodulation refers to magnitude synchrony. Child and adult EEG data were analyzed for age functions to demonstrate coherence and comodulation differences. Method. Eyes closed resting EEG was analyzed for 101 children and adults between ages of 5 and 35 years (34 female, 67 male; M age = 17.5 years). Spectral analysis focused on site-centered connectivity of 10 frequency bands. Site-centered connectivity refers to averaged coherence or comodulation associated with a site, an estimate of its network traffic. Results. Site-centered coherence and comodulation increased with age for frequencies below 30 Hz in most sites. Age-related changes in anterior connectivity occurred for adults but not for children. The strongest age function was found for alpha comodulation at electrode site T5. Differences in coherence and comodulation results are also reported. Conclusion. Functional connectivity increases steadily with age. Anterior EEG connectivity increased during adulthood but not during childhood. This finding parallels previous research on anterior callosal myelination and suggests that EEG connectivity measures may in part reflect myelination patterns. A model that associates coherence and comodulation with feedforward and feedback activity of the brain is proposed. A Periodicity Table for creating new and potentially relevant psychophysiological coefficients was described.
View Full Paper →An open-source hardware and software system for acquisition and real-time processing of electrophysiology during high field MRI
Simultaneous recording of electrophysiology and functional magnetic resonance imaging (fMRI) is a technique of growing importance in neuroscience. Rapidly evolving clinical and scientific requirements have created a need for hardware and software that can be customized for specific applications. Hardware may require customization to enable a variety of recording types (e.g., electroencephalogram, local field potentials, or multi-unit activity) while meeting the stringent and costly requirements of MRI safety and compatibility. Real-time signal processing tools are an enabling technology for studies of learning, attention, sleep, epilepsy, neurofeedback, and neuropharmacology, yet real-time signal processing tools are difficult to develop. We describe an open-source system for simultaneous electrophysiology and fMRI featuring low-noise (<0.6microV p-p input noise), electromagnetic compatibility for MRI (tested up to 7T), and user-programmable real-time signal processing. The hardware distribution provides the complete specifications required to build an MRI-compatible electrophysiological data acquisition system, including circuit schematics, print circuit board (PCB) layouts, Gerber files for PCB fabrication and robotic assembly, a bill of materials with part numbers, data sheets, and vendor information, and test procedures. The software facilitates rapid implementation of real-time signal processing algorithms. This system has been used in human EEG/fMRI studies at 3 and 7T examining the auditory system, visual system, sleep physiology, and anesthesia, as well as in intracranial electrophysiological studies of the non-human primate visual system during 3T fMRI, and in human hyperbaric physiology studies at depths of up to 300 feet below sea level.
View Full Paper →Towards a Coherent View of Brain Connectivity
Background. The electroencephalogram provides a myriad of opportunities to detect and assess brain function and brain connectivity. Method. This article describes the relationship between local and non-local brain activation and synchrony, and discusses the use of appropriate connectivity measures to study and train functional brain connectivity. Specific connectivity measures are described including coherence, phase, synchrony, correlation, and comodulation. The measures are contrasted and compared in terms of their ability to detect particular aspects of connectivity and their usefulness for neurofeedback training. Results. Connectivity metrics for example EEG data are calculated and shown graphically, to illustrate relevant principles. Conclusion. It is possible to assess brain connectivity and integrated function for both assessment and training, through the use of appropriate metrics and display methods.
View Full Paper →Test–retest reliability of resting EEG spectra validates a statistical signature of persons
Objective When EEG is recorded in humans, the question arises whether the resting EEG remains stable. We compared the inter-individual variation in spectral observables to the intra-individual stability over more than a year. Methods We recorded resting EEG in 55 healthy adults with eyes closed. In 20 persons EEG was recorded in a second session with retest intervals 12–40 months. For electrodes AFz, Cz and Pz α peak frequency and α peak height were transformed into Z-scores. We compared the curve shape of power spectra by first aligning α peaks to 10Hz and then regressing spectra pairwise onto each other to calculate a t-value. The t-value and differences of Z-scores for all pairs of sessions were entered into a generalized linear model (GLM) where binary output represents the recognition probability. The results were cross-validated by out-of-sample testing. Results Of the 40 sessions, 35 were correctly matched. The shape of power spectra contributed most to recognition. Out of all 2960 pairwise comparisons 99.5% were correct, with sensitivity 88% and specificity 99.5%. Conclusions Our statistical apparatus allows to identify those spectral EEG observables which qualify as statistical signature of a person. Significance The effect of external factors on EEG observables can be contrasted against their normal variability over time.
View Full Paper →What Is Quantitative EEG?
This article provides a basic description of quantitative electroencephalography (EEG) in the context of neurotherapeutic application. Issues associated with spectral analysis of human EEG are discussed and an example quantitative EEG assessment report is provided.
View Full Paper →A Modular Activation/Coherence Approach to Evaluating Clinical/QEEG Correlations and for Guiding Neurofeedback Training: Modular Insufficiencies, Modular Excesses, Disconnections, and Hyperconnections
Current approaches to QEEG-guided neurofeedback involve efforts to normalize the abnormalities seen, without reference to the functional localization of the cortical areas involved. Recent advances in cortical neurophysiology indicate that specific brain areas are developed to perform certain functions (cortical modules). Complex brain functions require cooperation between modules, particularly during a learning situation. For example, the left prefrontal “activation module” must cooperate with one or both occipital “visual modules” to attend and see something on a chalkboard. To remember what has been seen, both temporal “memory modules” must cooperate with the visual modules for the image to be retained in short-term memory. If the connections between these modules are not functioning optimally, visual learning will be impaired. Decreased coherence (hypocoherence) indicates a decrease in functional connectivity between these modules, and increased coherence (hypercoherence) indicates an increase in functional connectivity between the modules. Neurofeedback can be used to normalize coherence between these modules, thereby improving the efficiency of their cooperation in the learning process. If coherence is less than normal, it is trained up. If coherence is more than normal, it is trained down. Three cases are presented where this approach has succeeded in remediating the client's symptoms.
View Full Paper →Effects of Electrode Placement Upon EEG Biofeedback Training: The Monopolar-Bipolar Controversy
Roles of tradition, convenience, and noise or artifact rejection are discussed with regard to the referential versus bipolar electrode placement controversy in electroencephalography (EEG). Particular emphasis is placed on the relevance to neurofeedback. The crucial interactions between the differential amplifier, brain waves, and referential/bipolar placements are discussed. Through logical analysis and empirical observation, it is demonstrated how the very nature of the EEG differential amplifier must destroy those elements of brain activity which are common (synchronous) to the recording electrodes. Controlled experiments further illustrate the critical importance of electrode placements. Various methods, including preferred electrode placements, are presented to help resolve recording problems that frequently arise. It is concluded that there are serious implications for researchers, EEG clinicians, neurofeedback providers, and their clients in preferring one type of electrode placement technique over another. EEG recording information is affected by this choice.
View Full Paper →EEG alpha oscillations: The inhibition–timing hypothesis
The traditional belief is that the event-related alpha response can solely be described in terms of suppression or event-related desynchronization (ERD). Recent research, however, has shown that under certain conditions alpha responds reliably with an increase in amplitudes (event-related synchronization or ERS). ERS is elicited in situations, where subjects withhold or control the execution of a response and is obtained over sites that probably are under, or exert top-down control. Thus, we assume that alpha ERS reflects top-down, inhibitory control processes. This assumption leads over to the timing aspect of our hypothesis. By the very nature of an oscillation, rhythmic amplitude changes reflect rhythmic changes in excitation of a population of neurons. Thus, the time and direction of a change – described by phase – is functionally related to the timing of neuronal activation processes. A variety of findings supports this view and shows, e.g., that alpha phase coherence increases between task-relevant sites and that phase lag lies within a time range that is consistent with neuronal transmission speed. Another implication is that phase reset will be a powerful mechanism for the event-related timing of cortical processes. Empirical evidence suggests that the extent of phase locking is a functionally sensitive measure that is related to cognitive performance. Our general conclusion is that alpha ERS plays an active role for the inhibitory control and timing of cortical processing whereas ERD reflects the gradual release of inhibition associated with the emergence of complex spreading activation processes.
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