The implication of functional connectivity strength in predicting treatment response of major depressive disorder: A resting EEG study
ABSTRACT Predicting treatment response in major depressive disorder (MDD) has been an important clinical issue given that the initial intent-to-treat response rate is only 50 to 60%. This study was designed to examine whether functional connectivity strengths of resting EEG could be potential biomarkers in predicting treatment response at 8 weeks of treatment. Resting state 3-min eyes-closed EEG activity was recorded at baseline and compared in 108 depressed patients. All patients were being treated with selective serotonin-reuptake inhibitors. Baseline coherence and power series correlation were compared between responders and non-responders evaluated at the 8th week by Hamilton Depression Rating Scale. Pearson correlation and receiver operating characteristic (ROC) analyses were applied to evaluate the performance of connectivity strengths in predicting/classifying treatment responses. The connectivity strengths of right fronto-temporal network at delta/theta frequencies differentiated responders and non-responders at the 8th week of treatment, such that the stronger the connectivity strengths, the poorer the treatment response. ROC analyses supported the value of these measures in classifying responders/non-responders. Our results suggest that fronto-temporal connectivity strengths could be potential biomarkers to differentiate responders and slow responders or non-responders in MDD.
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ABSTRACT: To illuminate candidate neural working mech-anisms of Mindfulness-Based Cognitive Therapy (MBCT) in the treatment of recurrent depressive disorder, parallel to the potential interplays between modulations in electro-cortical dynamics and depressive symptom severity and self-compassionate experience. Linear and nonlinear α and γ EEG oscillatory dynamics were examined concomitant to an affective Go/NoGo paradigm, pre-to-post MBCT or natural wait-list, in 51 recurrent depressive patients. Spe-cific EEG variables investigated were; (1) induced event-related (de-) synchronisation (ERD/ERS), (2) evoked power, and (3) inter-/intra-hemispheric coherence. Sec-ondary clinical measures included depressive severity and experiences of self-compassion. MBCT significantly downregulated α and γ power, reflecting increased cortical excitability. Enhanced α-desynchronisation/ERD was observed for negative material opposed to attenuated α-ERD towards positively valenced stimuli, suggesting acti-vation of neural networks usually hypoactive in depression, related to positive emotion regulation. MBCT-related increase in left-intra-hemispheric α-coherence of the fron-to-parietal circuit aligned with these synchronisation dynamics. Ameliorated depressive severity and increased self-compassionate experience pre-to-post MBCT corre-lated with α-ERD change. The multi-dimensional neural mechanisms of MBCT pertain to task-specific linear and non-linear neural synchronisation and connectivity network dynamics. We propose MBCT-related modulations in dif-fering cortical oscillatory bands have discrete excitatory (enacting positive emotionality) and inhibitory (disengag-ing from negative material) effects, where mediation in the α and γ bands relates to the former. Keywords Major Depressive Disorder (MDD) · Mindfulness-Based Cognitive Therapy (MBCT) · ERD/ERS · Oscillatory EEG · α-Band coherence · γ-Band · EEG power IntroductionCognitive Neurodynamics 02/2015; 9(1):13-29. DOI:10.1007/s11571-014-9308-y · 1.77 Impact Factor
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ABSTRACT: Temporal and spectral perspectives are two fundamental facets in deciphering fluctuating signals. In resting state, the dynamics of blood oxygen level-dependent (BOLD) signals recorded by functional magnetic resonance imaging (fMRI) has been proven to be strikingly informative (0.01-0.1 Hz). The distinction between slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) has been described, but the pertinent data have never been systematically investigated. This study used fMRI to measure spontaneous brain activity and to explore systemically the different spectral characteristics of slow-4 and slow-5 at regional, inter-regional, and network levels, respectively assessed by regional homogeneity (ReHo) and mean amplitude of low-frequency fluctuation (mALFF), functional connectivity patterns, and graph theory. Results of paired t-tests supported/replicated recent research dividing low-frequency BOLD fluctuation into slow-4 and slow-5 for ReHo and mALFF. Inter-regional analyses showed that for brain regions reaching statistical significance, functional connectivity strengths at slow-4 were always weaker than those at slow-5. Community detection algorithm was applied to functional connectivity data and unveiled two modules sensitive to frequency effects: one comprised sensorimotor structure, and the other encompassed limbic/paralimbic system. Graph theoretical analysis verified that slow-4 and slow-5 differed in local segregation measures. Although the manifestation of frequency differences seemed complicated, the associated brain regions can be grossly categorized into limbic/paralimbic, midline, and sensorimotor systems. Our results suggest that future resting fMRI research addressing the three above systems either from neuropsychiatric or psychological perspectives may consider using spectrum-specific analytical strategies.01/2014; DOI:10.1089/brain.2013.0182
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ABSTRACT: Structural and metabolic alterations in prefrontal brain areas, including the subgenual (SGPFC), medial (MPFC) and dorsolateral prefrontal cortex (DLPFC), have been shown in major depressive disorder (MDD). Still it remains largely unknown how brain connectivity within these regions is altered at the level of neuronal oscillations. Therefore, the goal was to analyze prefrontal electroencephalographic phase synchronization in MDD and its changes after antidepressant treatment. In 60 unmedicated patients and 60 healthy controls (HC), a 15-min resting electroencephalogram (EEG) was recorded in subjects at baseline and in a subgroup of patients after 2 weeks of antidepressant medication. EEG functional connectivity between the SGPFC and the MPFC/DLPFC was assessed with eLORETA (low resolution brain electromagnetic tomography) by means of lagged phase synchronization. At baseline, patients revealed increased prefrontal connectivity at the alpha frequency between the SGPFC and the left DLPFC/MPFC. After treatment, an increased connectivity between the SGPFC and the right DLPFC/MPFC at the beta frequency was found for MDD. A positive correlation was found for baseline beta connectivity and reduction in scores on the Hamilton Depression Rating Scale. MDD is characterized by increased EEG functional connectivity within frontal brain areas. These EEG markers of disturbed neuronal communication might have potential value as biomarkers.Psychiatry Research Neuroimaging 03/2014; DOI:10.1016/j.pscychresns.2014.02.010 · 2.83 Impact Factor