resting-state
Research Papers
Showing 6 of 10Neurofeedback-guided kinesthetic motor imagery training in Parkinson's disease: Randomized trial
BACKGROUND: Parkinson's disease (PD) causes difficulty with maintaining the speed, size, and vigor of movements, especially when they are internally generated. We previously proposed that the insula is important in motivating intentional movement via its connections with the dorsomedial frontal cortex (dmFC). We demonstrated that subjects with PD can increase the right insula-dmFC functional connectivity using fMRI-based neurofeedback (NF) combined with kinesthetic motor imagery (MI). The current study is a randomized clinical trial testing whether NF-guided kinesthetic MI training can improve motor performance and increase task-based and resting-state right insula-dmFC functional connectivity in subjects with PD. METHODS: We assigned nondemented subjects with mild PD (Hoehn & Yahr stage ≤ 3) to the experimental kinesthetic MI with NF (MI-NF, n = 22) and active control visual imagery (VI, n = 22) groups. Only the MI-NF group received NF-guided MI training (10-12 runs). The NF signal was based on the right insula-dmFC functional connectivity strength. All subjects also practiced their respective imagery tasks at home daily for 4 weeks. Post-training changes in 1) task-based and resting-state right insula-dmFC functional connectivity were the primary imaging outcomes, and 2) MDS-UPDRS motor exam and motor function scores were the primary and secondary clinical outcomes, respectively. RESULTS: The MI-NF group was not significantly different from the VI group in any of the primary imaging or clinical outcome measures. The MI-NF group reported subjective improvement in kinesthetic body awareness. There was significant and comparable improvement only in motor function scores in both groups (secondary clinical outcome). This improvement correlated with NF regulation of the right insula-dmFC functional connectivity only in the MI-NF group. Both groups showed specific training effects in whole-brain functional connectivity with distinct neural circuits supporting kinesthetic motor and visual imagery (exploratory imaging outcome). CONCLUSIONS: The functional connectivity-based NF regulation was unsuccessful, however, both kinesthetic MI and VI practice improved motor function in our cohort with mild PD.
View Full Paper →Resting-state quantitative spectral patterns in migraine during ictal phase reveal deviant brain oscillations: Potential role of density spectral array
Background. Migraine headache may have a substantial bearing on the brain functions and rhythms. Electrophysiological methods can detect changes in brain oscillation. The present work examined the frequency band power through quantitative electroencephalogram (qEEG) and density spectral array (DSA) to elucidate the resting state neuronal oscillations in migraine. Methods. Clinical details were inquired, and EEG was recorded in migraineurs and healthy controls. The acquired data were analyzed to determine power spectral density values and obtain DSA graphs. The absolute and relative powers for the alpha, theta, and delta frequencies in frontocentral, parieto-occipital, and temporal regions were determined. A correlation of significant EEG findings with clinical features of migraine was sought. Results. Forty-five participants were enrolled in the study. The spectrum analysis revealed an increase in the relative theta power (P < .001) and a reduction in relative alpha power (P < .001) in the observed cortical areas among the migraineurs as compared to the healthy controls. Relative delta power was increased over the frontocentral region (P = .001), slightly more on the symptomatic side of the head. In addition, frontocentral delta power had a moderate positive correlation (r = .697, n = 22, P = .000) with migraine severity. Conclusion. The study supports the evidence of a neuronal dysfunction existing in the resting state during the ictal phase of migraine. qEEG can reveal these aberrant oscillations. Utility of DSA to depict the changes in brain activity in migraine is a potential area for research. The information can help formulate new therapeutic strategies towards alteration in cortical excitability using brain stimulation techniques.
View Full Paper →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 →Altered task-based and resting-state amygdala functional connectivity following real-time fMRI amygdala neurofeedback training in major depressive disorder
Background We have previously shown that in participants with major depressive disorder (MDD) trained to upregulate their amygdala hemodynamic response during positive autobiographical memory (AM) recall with real-time fMRI neurofeedback (rtfMRI-nf) training, depressive symptoms diminish. Here, we assessed the effect of rtfMRI-nf on amygdala functional connectivity during both positive AM recall and rest. Method The current manuscript consists of a secondary analysis on data from our published clinical trial of neurofeedback. Patients with MDD completed two rtfMRI-nf sessions (18 received amygdala rtfMRI-nf, 16 received control parietal rtfMRI-nf). One-week prior-to and following training participants also completed a resting-state fMRI scan. A GLM-based functional connectivity analysis was applied using a seed ROI in the left amygdala. We compared amygdala functional connectivity changes while recalling positive AMs from the baseline run to the final transfer run during rtfMRI-nf training, as well during rest from the baseline to the one-week follow-up visit. Finally, we assessed the correlation between change in depression scores and change in amygdala connectivity, as well as correlations between amygdala regulation success and connectivity changes. Results Following training, amygdala connectivity during positive AM recall increased with widespread regions in the frontal and limbic network. During rest, amygdala connectivity increased following training within the fronto-temporal-limbic network. During both task and resting-state analyses, amygdala-temporal pole connectivity decreased. We identified increased amygdala-precuneus and amygdala-inferior frontal gyrus connectivity during positive memory recall and increased amygdala-precuneus and amygdala-thalamus connectivity during rest as functional connectivity changes that explained significant variance in symptom improvement. Amygdala-precuneus connectivity changes also explain a significant amount of variance in neurofeedback regulation success. Conclusions Neurofeedback training to increase amygdala hemodynamic activity during positive AM recall increased amygdala connectivity with regions involved in self-referential, salience, and reward processing. Results suggest future targets for neurofeedback interventions, particularly interventions involving the precuneus.
View Full Paper →Low Motivational Incongruence Predicts Successful EEG Resting-state Neurofeedback Performance in Healthy Adults
Neurofeedback is becoming increasingly sophisticated and widespread, although predictors of successful performance still remain scarce. Here, we explored the possible predictive value of psychological factors and report the results obtained from a neurofeedback training study designed to enhance the self-regulation of spontaneous EEG microstates of a particular type (microstate class D). Specifically, we were interested in life satisfaction (including motivational incongruence), body awareness, personality and trait anxiety. These variables were quantified with questionnaires before neurofeedback. Individual neurofeedback success was established by means of linear mixed models that accounted for the amount of observed target state (microstate class D contribution) as a function of time and training condition: baseline, training and transfer (results shown in Diaz Hernandez et al.). We found a series of significant negative correlations between motivational incongruence and mean percentage increase of microstate D during the condition transfer, across-sessions (36% of common variance) and mean percentage increase of microstate D during the condition training, within-session (42% of common variance). There were no significant correlations related to other questionnaires, besides a trend in a sub-scale of the Life Satisfaction questionnaire. We conclude that motivational incongruence may be a potential predictor for neurofeedback success, at least in the current protocol. The finding may be explained by the interfering effect on neurofeedback performance produced by incompatible simultaneously active psychological processes, which are indirectly measured by the Motivational Incongruence questionnaire.
View Full Paper →Parsing heterogeneity in attention-deficit hyperactivity disorder using EEG-based subgroups.
Background: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous condition for which multiple efforts to characterize brain state differences are underway. The objective of this study was to identify distinct subgroups of resting electroencephalography (EEG) profiles among children with and without ADHD and subsequently provide extensive clinical characterization of the subgroups. Methods: Latent class analysis was used with resting state EEG recorded from a large sample of 781 children with and without ADHD (N = 620 ADHD, N = 161 Control), aged 618 years old. Behavioral and cognitive characteristics of the latent classes were derived from semistructured diagnostic interviews, parent completed behavior rating scales, and cognitive test performance. Results: A five-class solution was the best fit for the data, of which four classes had a defining spectral power elevation. The distribution of ADHD and control subjects was similar across classes suggesting there is no one resting state EEG profile for children with or without ADHD. Specific latent classes demonstrated distinct behavioral and cognitive profiles. Those with elevated slow-wave activity (i.e. delta and theta band) had higher levels of externalizing behaviors and cognitive deficits. Latent subgroups with elevated alpha and beta power had higher levels of internalizing behaviors, emotion dysregulation, and intact cognitive functioning. Conclusions: There is population-level heterogeneity in resting state EEG subgroups, which are associated with distinct behavioral and cognitive profiles. EEG measures may be more useful biomarkers of ADHD outcome or treatment response rather than diagnosis. Keywords: Electrophysiology; ADHD; resting state; latent class analysis.
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