Group differences in MEG-ICA derived resting state networks: Application to major depressive disorder

Abstract

Functional magnetic resonance imaging (fMRI) studies have revealed the existence of robust, interconnected brain networks exhibiting correlated low frequency fluctuations during rest, which can be derived by examining inherent spatio-temporal patterns in functional scans independent of any a priori model. In order to explore the electrophysiological underpinnings of these networks, analogous techniques have recently been applied to magnetoencephalography (MEG) data, revealing similar networks that exhibit correlated low frequency fluctuations in the power envelope of beta band (14–30Hz) power. However, studies to date using this technique have concentrated on healthy subjects, and no method has yet been presented for group comparisons. We extended the ICA resting state MEG method to enable group comparisons, and demonstrate the technique in a sample of subjects with major depressive disorder (MDD). We found that the intrinsic resting state networks evident in fMRI appeared to be disrupted in individuals with MDD compared to healthy participants, particularly in the subgenual cingulate, although the electrophysiological correlates of this are unknown. Networks extracted from a combined group of healthy and MDD participants were examined for differences between groups. Individuals with MDD showed reduced correlations between the subgenual anterior cingulate (sgACC) and hippocampus in a network with primary nodes in the precentral and middle frontal gyri. Individuals with MDD also showed increased correlations between insulo-temporal nodes and amygdala compared to healthy controls. To further support our methods and findings, we present test/re-test reliability on independent recordings acquired within the same session. Our results demonstrate that group analyses are possible with the resting state MEG-independent components analysis (ICA) technique, highlighting a new pathway for analysis and discovery. This study also provides the first evidence of altered sgACC connectivity with a motor network. This finding, reliable across multiple sessions, suggests that the sgACC may partially mediate the psychomotor symptoms of MDD via synchronized changes in beta-band power, and expands the idea of the sgACC as a hub region mediating cognitive and emotional symptomatic domains in MDD. Findings of increased connectivity between the amygdala and cortical nodes further supports the role of amygdalar networks in mediated depressive symptomatology.

Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology

“…The state of a given emotional network along this curve is modulated by PFC inputs to AMY since this PFC-AMY circuit is altered by chronic stress and direct stimulation of this circuitry using DREADDs. Given that deficits in functional connectivity across PFC-dependent networks have been described in major depressive disorder (Greicius et al, 2007; Nugent et al, 2015), activity across the P-AV network may serve as a novel translational measure of face validity in preclinical models of major depressive disorder.…”

Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

“…1(B). Firstly, preprocessed data from individuals was normalized to z values (to account for inter-subject variations) and temporally concatenated across all subjects, which has been widely used in ICA to identify networked brain activity in group analysis (Chen et al, 2013; Ding et al, 2014; Nugent et al, 2015; Ponomarev et al, 2014; Ramkumar et al, 2014; Yuan et al, 2012), assuming spatial similarity across subjects. Secondly, power spectra in the range of 2 Hz to 30 Hz at a 1 Hz resolution were calculated via a short-time Fourier transform (STFT) on individual 1-s EEG epochs, and then obtained time-frequency EEG (epochs × power spectra) data were decomposed into 32 ICs using tfICA (Bingham and Hyvarinen, 2000; Shou et al, 2012) 1 , after an initial data reduction using principal component analysis (PCA).…”

Preliminary differences in resting state MEG functional connectivity pre- and post-ketamine in major depressive disorder

“…The primary aim of this preliminary study was to assess the overall effects of ketamine on connectivity in MDD subjects, as our sample size was too limited to assess responders versus non-responders. Although we already reported the baseline effects of ketamine in a larger cohort that overlapped with this one (Nugent et al, 2015b), we felt that the additional scans acquired post-ketamine infusion justified further analysis, albeit preliminary and hypothesis-generating in nature. The formulation of hypotheses in this area is difficult given the dearth of research using resting state MEG to study MDD, as well as the lack of literature investigating the effects of ketamine on connectivity in subjects with MDD at a time point corresponding to its antidepressant effects.…”

“…We previously found that this method can be extended to group analyses (Nugent et al, 2015b). Comparing healthy subjects to subjects with MDD at baseline, we found reduced connectivity between the sgACC and a bilateral precentral independent component (IC), as well as increased amygdalar connectivity with insulo-temporal ICs in individuals with MDD (Nugent et al, 2015b). …”

“…Building on this work, in the present study we applied the same ICA methodology to a group of 13 subjects with MDD who received a single open-label dose of ketamine; 11 had been examined at baseline in our prior study (Nugent et al, 2015b). While there are advantages and disadvantages to ICA methods for resting state data analysis, we chose the ICA method to maximize the interpretability of the current paper in the context of our prior publication.…”