The implication of functional connectivity strength in predicting treatment response of major depressive disorder: A resting EEG study

Lee, Tien Wen; Wu, Yu Te; Yu, Younger W.‐Y.; Chen, Ming Chao; Chen, Tai Jui

doi:10.1016/j.pscychresns.2011.02.009

Cited by 51 publications

(36 citation statements)

References 55 publications

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“…More recent studies used a huge variety of connectivity measures like partial directed coherence, Granger causality, structural synchrony index and phase synchrony index. Some found decreased EEG connectivity in MDD [122,132,133,134] while others report of increased EEG connectivity in MDD, mainly in the alpha band [135,136,137,138]. More studies are needed to disentangle the complex relationship between the different connectivity measures and their physiological interpretation and to estimate the value for treatment prediction.…”

Section: Biomarkersmentioning

confidence: 99%

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

2015

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Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography (EEG) during sleep or resting-state conditions and event-related potentials (ERPs) have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual. This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.

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Section: Biomarkersmentioning

confidence: 99%

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

2015

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“…Furthermore, these studies have used different approaches and methodologies to investigate differences in brain dynamics between depressed patients and healthy controls making it difficult to compare outcomes. For example, Lee et al (2011) used correlations between power series of channel pairs as a measure of connectivity; Leistedt et al (2009) used synchronization likelihood (Stam et al, 2003); whereas Fingelkurts et al (2007) used an in-house synchronization measure termed index of structural synchronization (Fingelkurts and Kahkonen, 2005). Although these and other EEG/MEG measures of connectivity (phase coherency, phase lag index, imaginary coherency, etc) have been shown to capture aspects of correlations/synchronization between two time series, they are known to perform differently.…”

Section: Synchronization Asymmetrymentioning

confidence: 99%

EEG Power Asymmetry and Functional Connectivity as a Marker of Treatment Effectiveness in DBS Surgery for Depression

Quraan

Protzner

Daskalakis

et al. 2013

Neuropsychopharmacol

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Recently, deep brain stimulation (DBS) has been evaluated as an experimental therapy for treatment-resistant depression. Although there have been encouraging results in open-label trials, about half of the patients fail to achieve meaningful benefit. Although progress has been made in understanding the neurobiology of MDD, the ability to characterize differences in brain dynamics between those who do and do not benefit from DBS is lacking. In this study, we investigated EEG resting-state data recorded from 12 patients that have undergone DBS surgery. Of those, six patients were classified as responders to DBS, defined as an improvement of 50% or more on the 17-item Hamilton Rating Scale for Depression (HAMD-17). We compared hemispheric frontal theta and parietal alpha power asymmetry and synchronization asymmetry between responders and non-responders. Hemispheric power asymmetry showed statistically significant differences between responders and non-responders with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry was characterized by an increase in frontal theta in the right hemisphere relative to the left combined with an increase in parietal alpha in the left hemisphere relative to the right in non-responders compared with responders. Hemispheric mean synchronization asymmetry showed a statistically significant difference between responders and non-responders in the theta band, with healthy controls showing an asymmetry similar to responders but opposite to non-responders. This asymmetry resulted from an increase in frontal synchronization in the right hemisphere relative to the left combined with an increase in parietal synchronization in the left hemisphere relative to the right in non-responders compared with responders. Connectivity diagrams revealed long-range differences in frontal/central-parietal connectivity between the two groups in the theta band. This pattern was observed irrespective of whether EEG data were collected with active DBS or with the DBS stimulation turned off, suggesting stable functional and possibly structural modifications that may be attributed to plasticity.

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“…With respect to this, novel methods to quantify functional connectivity among different brain areas have been proposed with the aim of discovering new insights about the brain's response to different emotional processes [15,16]. Thus, these algorithms have been used to identify different emotions elicited by audiovisual stimuli [16][17][18], as well as to characterize mental disorders, such as major depression [19,20], consciousness problems [21], epilepsy [22], Alzheimer's [23] or schizophrenia [24].…”

Section: Introductionmentioning

confidence: 99%

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

García-Martínez

Martínez-Rodrigo

Cantabrana

et al. 2016

Entropy

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Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE), quadratic SE (QSE) and distribution entropy (DE) to discern between emotional states of calm and negative stress (also called distress). In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG) recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain's behavior under this negative emotion.

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The implication of functional connectivity strength in predicting treatment response of major depressive disorder: A resting EEG study

Cited by 51 publications

References 55 publications

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

EEG Power Asymmetry and Functional Connectivity as a Marker of Treatment Effectiveness in DBS Surgery for Depression

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

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