Brain Mapping: QEEG & Peak Agency
Quantitative EEG analysis, brain mapping techniques, and assessment protocols.
Research Library
We've curated 126 research papers for this use case. Dr. Hill and the Peak Brain team are reviewing and summarizing these papers to provide accessible, actionable insights.
Citations and abstracts shown below. Detailed summaries, key findings, and clinical applications will be added as reviews are completed.
Research Citations
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 âEEG-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 50Â 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 âRelationship 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 âBibliometric 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 âQuantitative 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 âCognitive 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 âDetection 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 âOne-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 âAttempted 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 âA 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 âNeural 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 15Â 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 âElectrophysiological 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 âCharacterizing the ASDâADHD 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 ASDâADHD 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 3â20 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 ASDâADHD 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 âComparison 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 âReward 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 âOnline 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 âMedical 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 âAlpha oscillatory activity during attentional control in children with Autism Spectrum Disorder (ASD), AttentionâDeficit/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 (âFast taskâ) 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, 100â600 ms) was also examined. ANOVA was used to test differences across diagnostics groups on these measures. Spearmanâs 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 âLong-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 âUsing 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 Friedmanâs Test and correlated with changes in pain scores using Pearsonâs 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 = â0.45, 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 âComparison 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 ”m3 and that, for our data, the optimal gray matter sub-voxel size is between 62.5 ”m3 and 125 ”m3. The example experiments presented here represent an important advancement towards robust multi-modal MRI-CLARITY comparisons.
View Full Paper âInhibitory 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 âUnified 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 âResting-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 âTrial 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 âModeling 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 âMapping 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 âThe Potential of Functional Near-Infrared Spectroscopy-Based NeurofeedbackâA 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: (1â2) 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 âMicrostates-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 HLIâs 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 underpinningâs 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 âReliability 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 âIdentifying 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 âInterpretable, 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 âBeta 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: râ=â0.746, Pâ=â.001). The threshold value for gaining positive feedback from pre-NF baseline on beta power significantly increased (Spearman rank correlation test: râ=â0.805, Pâ=â.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 âBrain 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 âVirtual RealityâBased 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 physicianâdiagnosed 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 âno pain at allâ and âpain as bad as it could be.â 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 âDominance 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 Όm 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 Ă 55 Όm2 in-plane resolution and a 500-ÎŒm thickness at 9.4 T, and found that the fMRI signal peaked predominantly in the inner half of EPL (136 ± 54 Όm anatomical thickness). The mean full-width at half-maximum of these fMRI peaks was 347 ± 102 Όm and the functional spread was approximately 100 or 200 Όm 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 Όm accounted for 64.3% of the total vascular volume within EPL and had a mean segment length of 55 ± 40 Όm (n = 472). Additionally, vessels that crossed the EPL border had a mean segment length outside of EPL equal to 73 ± 61 Όm (n = 28), which is comparable to half of the functional spread (50-100 Όm). 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 âComorbid 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 (nâ=â69). In contrast, women with a history of childhood onset depression and pathological levels of anxious apprehension (nâ=â18)-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 âPeak 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 (6-12Â 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 (NÂ =Â 59) and age-matched typically developing (TD) children (NÂ =Â 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 âThe 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 âElectromyogram 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 âCognitive Improvement and Brain Changes after Real-Time Functional MRI Neurofeedback Training in Healthy Elderly and Prodromal Alzheimerâs 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 âResting-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 âEndophenotype 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, inïŹuenced 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 veriïŹed associations with molecular genetic variants. To move the ïŹeld 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 beneïŹt from successful molecular genetic studies of psychopathology by examining the degree to which these veriïŹed psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional signiïŹcance 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 âElectrophysiological 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)Ă2 (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 âReady to Apply This Research?
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