Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression

Laboratory of Brain, Behavior, and Pharmacology, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, United States of America.
PLoS ONE (Impact Factor: 3.23). 02/2012; 7(2):e32508. DOI: 10.1371/journal.pone.0032508
Source: PubMed

ABSTRACT Symptoms of Major Depressive Disorder (MDD) are hypothesized to arise from dysfunction in brain networks linking the limbic system and cortical regions. Alterations in brain functional cortical connectivity in resting-state networks have been detected with functional imaging techniques, but neurophysiologic connectivity measures have not been systematically examined. We used weighted network analysis to examine resting state functional connectivity as measured by quantitative electroencephalographic (qEEG) coherence in 121 unmedicated subjects with MDD and 37 healthy controls. Subjects with MDD had significantly higher overall coherence as compared to controls in the delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-20 Hz) frequency bands. The frontopolar region contained the greatest number of "hub nodes" (surface recording locations) with high connectivity. MDD subjects expressed higher theta and alpha coherence primarily in longer distance connections between frontopolar and temporal or parietooccipital regions, and higher beta coherence primarily in connections within and between electrodes overlying the dorsolateral prefrontal cortical (DLPFC) or temporal regions. Nearest centroid analysis indicated that MDD subjects were best characterized by six alpha band connections primarily involving the prefrontal region. The present findings indicate a loss of selectivity in resting functional connectivity in MDD. The overall greater coherence observed in depressed subjects establishes a new context for the interpretation of previous studies showing differences in frontal alpha power and synchrony between subjects with MDD and normal controls. These results can inform the development of qEEG state and trait biomarkers for MDD.

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Available from: Andrew F Leuchter, Sep 29, 2015
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    • "However, while the BOLD signal is a surrogate marker of neuronal activity, the EEG provides direct evidence for altered neuronal interaction between frontal brain sites that are involved in emotional regulation. An increase of coherent BOLD signals between, for example, the SGPFC and other prefrontal areas and the shown increase of phase synchronization in MDD have been interpreted as complementary markers of altered CNS activity (Leuchter et al., 2012). While fMRI-based connectivity measures have been shown to be stable over time in healthy subjects (Fox et al., 2012b), the finding of unchanged EEGconnectivity measures in the subgroup of HC after 2 weeks in our study provides the first evidence of the state character of the electrophysiological measure, although longer retest intervals are needed for further validation. "
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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; 222(1). DOI:10.1016/j.pscychresns.2014.02.010 · 2.42 Impact Factor
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    • "MDD involves dysfunction in a number of cortical regions, such as dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), as well as deep gray matter structures, such as nuclei of the thalamus and hypothalamus. The illness is increasingly understood as a disorder of connectivity in brain networks linking these regions (Leuchter et al., 2012). Many of the mood and neurovegetative symptoms, as well as deficits in cognition and memory, have been hypothesized to arise from dysfunction in networks linking cortical and subcortical gray structures (Ottowitz et al., 2002; Savitz and Drevets, 2009). "
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    ABSTRACT: Major depressive disorder (MDD) is marked by disturbances in brain functional connectivity. This connectivity is modulated by rhythmic oscillations of brain electrical activity, which enable coordinated functions across brain regions. Oscillatory activity plays a central role in regulating thinking and memory, mood, cerebral blood flow, and neurotransmitter levels, and restoration of normal oscillatory patterns is associated with effective treatment of MDD. Repetitive transcranial magnetic stimulation (rTMS) is a robust treatment for MDD, but the mechanism of action (MOA) of its benefits for mood disorders remains incompletely understood. Benefits of rTMS have been tied to enhanced neuroplasticity in specific brain pathways. We summarize here the evidence that rTMS entrains and resets thalamocortical oscillators, normalizes regulation and facilitates reemergence of intrinsic cerebral rhythms, and through this mechanism restores normal brain function. This entrainment and resetting may be a critical step in engendering neuroplastic changes and the antidepressant effects of rTMS. It may be possible to modify the method of rTMS administration to enhance this MOA and achieve better antidepressant effectiveness. We propose that rTMS can be administered: (1) synchronized to a patient's individual alpha frequency (IAF), or synchronized rTMS (sTMS); (2) as a low magnetic field strength sinusoidal waveform; and, (3) broadly to multiple brain areas simultaneously. We present here the theory and evidence indicating that these modifications could enhance the therapeutic effectiveness of rTMS for the treatment of MDD.
    Frontiers in Human Neuroscience 02/2013; 7:37. DOI:10.3389/fnhum.2013.00037 · 2.99 Impact Factor
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    ABSTRACT: The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure. The classifier was realized as a kernelized partial least squares regression procedure, whose output is the predicted response. A low-dimensional kernelized principal component representation of the feature space was used for the purposes of visualization and clustering analysis. The overall method was evaluated using an 11-fold nested cross-validation procedure for which over 85% average prediction performance is obtained. The results indicate that ML methods hold considerable promise in predicting the efficacy of SSRI antidepressant therapy for major depression.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2010; 2010:6103-6. DOI:10.1109/IEMBS.2010.5627823
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