Whole Brain Resting-State Analysis Reveals Decreased Functional Connectivity in Major Depression

By Ilya M. Veer, Christian F. Beckmann, M. van Tol, Luca Ferrarini, Julien Milles, Dick J. Veltman, André Aleman, M. van Buchem, N. J. van der Wee, Serge A. R. B. Rombouts. Frontiers in Systems Neuroscience, 2010

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Recently, both increases and decreases in resting-state functional connectivity have been found in major depression. However, these studies only assessed functional connectivity within a specific network or between a few regions of interest, while comorbidity and use of medication was not always controlled for. Therefore, the aim of the current study was to investigate whole-brain functional connectivity, unbiased by a priori definition of regions or networks of interest, in medication-free depressive patients without comorbidity. We analyzed resting-state fMRI data of 19 medication-free patients with a recent diagnosis of major depression (within 6 months before inclusion) and no comorbidity, and 19 age- and gender-matched controls. Independent component analysis was employed on the concatenated data sets of all participants. Thirteen functionally relevant networks were identified, describing the entire study sample. Next, individual representations of the networks were created using a dual regression method. Statistical inference was subsequently done on these spatial maps using voxel-wise permutation tests. Abnormal functional connectivity was found within three resting-state networks in depression: (1) decreased bilateral amygdala and left anterior insula connectivity in an affective network, (2) reduced connectivity of the left frontal pole in a network associated with attention and working memory, and (3) decreased bilateral lingual gyrus connectivity within ventromedial visual regions. None of these effects were associated with symptom severity or gray matter density. We found abnormal resting-state functional connectivity not previously associated with major depression, which might relate to abnormal affect regulation and mild cognitive deficits, both associated with the symptomatology of the disorder.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8fce73a49c83d5ba31dd27d56236ed8fd4d1fe4b

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Citations in the corpus

Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “…et al., 2006), dementia (Buckner et al., 2000; Rombouts et al., 2009), autism (Kennedy et al., 2006; Weng et al., 2010), multiple sclerosis (Bonavita et al., 2011), bipolar disorder (Lim et al., 2013) and major depression (Greicius et al., 2007; Veer et al., 2010; Wang et al., 2012) (see Figure 3).”
ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI (2015, methodology): “Analysis of group differences was performed using FSL’s dual regression, a regression technique that allows for voxel-wise comparisons of R-fMRI (Filippini et al., 2009; Veer et al., 2010).”
Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism (2013, positive): “DMN function encompasses a range of self-relevant and social cognitive processes.(58) The dorsal attention network is a bilateral system with nodes in the intraparietal sulcus and frontal eye fields and is involved in top-down orienting of attention.”
Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. (2012, methodology): “the lingual gyrus, fusiform gyrus, inferior occipital gyrus and calcarine gyrus), which are involved in visual processing (Beckmann et al., 2005; Damoiseaux et al., 2006; Veer et al., 2010).”
Reduced default mode network functional connectivity in patients with recurrent major depressive disorder (2019, positive): “R-fMRI studies have also found reduced VN FC in MDD patients (31), suggesting abnormal processing in the visual cortex in MDD.”