Best method for analysis of brain oscillations in healthy subjects and neuropsychiatric diseases

Başar, Erol; Gölbaşı, Bilge Turp; Tülay, Elif; Aydın, Serap; Başar-Eroğlu, Canan

doi:10.1016/j.ijpsycho.2015.02.017

Cited by 29 publications

(19 citation statements)

References 114 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this study, spontaneous resting state EEG, sensory ERP and selective-attention ERP were used as three methods to obtain the important brain oscillations (Başar et al, 2016). Both EEG and ERP variables have been investigated as potential biomarkers to detect MCI and its progression to AD dementia, as well as to directly detect AD dementia (Herrmann and Demiralp, 2005;Uhlhaas and Singer, 2006;Jackson and Snyder, 2008).…”

Section: Discussionmentioning

confidence: 99%

Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

Doan

Choi

et al. 2021

Front. Aging Neurosci.

View full text Add to dashboard Cite

Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia.Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms.Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy.Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores.Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

Doan

Choi

et al. 2021

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…Oscillatory activity of neural populations has been suggested to represent a major communication mechanism of the brain (Buzsáki et al, 2013 ) and has furthermore been related to cognitive functions (Basar et al, 1999 ; Herrmann and Knight, 2001 ). Consequently, abnormal oscillatory activity has been associated with psychiatric and psychological disorders such as ADHD, Alzheimer’s disease, schizophrenia, bipolar disorder, or mild cognitive impairment (e.g., Başar, 2013 ; Başar and Güntekin, 2008 ; Başar et al, 2016 ). Electrophysiological studies of the normal functioning of basal ganglia-thalamocortical circuits and the pathophysiology of Parkinson’s disease provided insights into the functional role of neural oscillations (Schnitzler and Gross, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial

Enriquez‐Geppert

Huster

Herrmann

2017

Front. Hum. Neurosci.

235

204

View full text Add to dashboard Cite

Neurofeedback is attracting renewed interest as a method to self-regulate one’s own brain activity to directly alter the underlying neural mechanisms of cognition and behavior. It not only promises new avenues as a method for cognitive enhancement in healthy subjects, but also as a therapeutic tool. In the current article, we present a review tutorial discussing key aspects relevant to the development of electroencephalography (EEG) neurofeedback studies. In addition, the putative mechanisms underlying neurofeedback learning are considered. We highlight both aspects relevant for the practical application of neurofeedback as well as rather theoretical considerations related to the development of new generation protocols. Important characteristics regarding the set-up of a neurofeedback protocol are outlined in a step-by-step way. All these practical and theoretical considerations are illustrated based on a protocol and results of a frontal-midline theta up-regulation training for the improvement of executive functions. Not least, assessment criteria for the validation of neurofeedback studies as well as general guidelines for the evaluation of training efficacy are discussed.

show abstract

“…9 Of note, the behavior of local and long-range synchronized neural activities at different frequency bands have shed a spotlight on both healthy cognition and pathophysiological underpinnings of psychiatric diseases. 10,11 From another point of view, most of the existing brain neuroimaging studies reported neurological abnormalities of mood disorders at the group level with limited clinical translational value at the individual subject level. For this reason, advanced machine learning methods, which allow individual prediction, are increasingly used in the neuroimaging community.…”

mentioning

confidence: 99%

“…First, we aimed to characterize the spectral fingerprint discriminations of the MEG data during the resting-state between BD, UD, and HC. Spectral fingerprints are divided into the following classical frequency bands: delta band (1-4 Hz), theta band (4-8 Hz), alpha band (8)(9)(10)(11)(12)(13)(14), beta band (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma band . The second goal was to build a machine learning model to automatically distinguish BD, UD, and HC based on MEG resting-state brain activities.…”

mentioning

confidence: 99%

Magnetoencephalography resting‐state spectral fingerprints distinguish bipolar depression and unipolar depression

et al. 2019

View full text Add to dashboard Cite

Objectives In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level. Methods Eyes closed resting‐state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power. Results Both BD and UD showed decreased frontal‐central gamma/beta ratios comparing to HC, in which gamma power (30‐75 Hz) was decreased in BD while beta power (14‐30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P < .01). Conclusions In combination with resting‐state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.

show abstract

Best method for analysis of brain oscillations in healthy subjects and neuropsychiatric diseases

Cited by 29 publications

References 114 publications

Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial

Magnetoencephalography resting‐state spectral fingerprints distinguish bipolar depression and unipolar depression

Contact Info

Product

Resources

About