ReviewLocalized connectivity in depression: A meta-analysis of resting state functional imaging studies
Introduction
Depressive disorder is one of the most prevalent of serious mental disorders that contribute to a significant portion of illness-related disability across the world (Eaton et al., 2008). Despite its high prevalence and the resultant burden, the pathophysiology of depression is yet to be fully understood. The ability to measure brain function using non-invasive neuroimaging techniques has greatly enhanced our understanding of this illness (Rigucci et al., 2010). Patients with depression show impairments in the coordinated activity of several brain regions considered to be important for several domains of mental functioning such as emotional processing (amygdala, subgenual anterior cingulate and pallidum) (Disner et al., 2011, Sheline et al., 2010), self-referential processes (medial prefrontal cortex (MPFC), precuneus and posterior cingulate cortex) (Kühn and Gallinat, 2013, Price and Drevets, 2009, Sheline et al., 2010), cognitive functions such as memory (hippocampus, parahippocampal cortex) (Lorenzetti et al., 2009), visual processing (fusiform gyrus, lingual gyrus and lateral temporal cortex) (Veer et al., 2010), and attention processing (dorsolateral prefrontal cortex, anterior cingulate cortex [ACC], thalamus and insula) (Hamilton et al., 2012). In particular, emerging notions from functional imaging literature argue for the presence of prominent cross-network disconnections mediated by key connector hubs (e.g. MPFC/ACC) that enable smooth integration of these functional domains across several sub-systems in depressed individuals (Sheline et al., 2010, Treadway and Pizzagalli, 2014).
Results from functional imaging studies appear to be highly heterogeneous (Fitzgerald et al., 2008), in part due to the varied nature of the clinical population studied, which often comprises of patients with various levels of exposure to antidepressants, and at various stages of clinical severity, age of onset, gender composition and duration of illness, all of which affect the results of neuroimaging studies (Fitzgerald et al., 2008, Price and Drevets, 2009). Further, inferences made from functional activation studies are significantly constrained by the paradigm employed in the studies, and the task performance differences related to varied levels of motivation, attention and information processing found in patients and controls.
Resting-state fMRI provides a task-free approach that removes some performance-related confounds, and provides a reliable measure of ‘baseline’ brain activity and connectivity (Gusnard and Raichle, 2001). In addition to the study of integration among spatially distinct brain regions using conventional functional connectivity metrics, we can also study the concordance in the fluctuating BOLD signal amplitude (across time) between a single voxel and its nearest neighbours, using a measure termed as regional homogeneity (ReHo) (Zang et al., 2004). Distinct from functional connectivity approaches which measure the temporal correlation of low frequency fluctuations (LFF) between remote brain regions, ReHo is based on the assumption that LFFs within a functional cluster will synchronize with its neighbouring voxels. Though ReHo has been interpreted as a measure of localized synchrony (Liu et al., 2012) or regional neural coherence (Guo et al., 2011b), given the computational basis of this metric, it is best described as a measure of ‘local connectivity’ (Chen et al., 2012). In healthy individuals, ReHo from resting fMRI acquisitions appears to be higher in brain regions that display a relative high level of baseline activity when not engaging in an explicit cognitive task, the so-called default mode network (Greicius et al., 2003). Due to the voxelwise nature of its measurement, ReHo provides an opportunity to discover localized functional disruptions in patient groups without a priori constraints, thus enabling the discovery of previously unconsidered regional brain abnormalities. Abnormalities in functional connectivity between distinct brain regions may arise despite the absence of regional pathological changes in these brain regions, as well as vice versa. Therapeutically useful neural-network models for depression can only emerge from a careful consideration of both regionally specialized and integrative aspects of brain function. In particular, if regional spontaneous activity is disrupted in key cross-network connector hubs such as MPFC, this may reveal valuable insights into the pathophysiology of depression.
Several studies have employed ReHo to investigate the pathophysiology of depression. ReHo abnormalities relate to the symptom burden in depression (Yao et al., 2009), and are observed in both antidepressant-naïve (Guo et al., 2011a) and treated (Wang et al., 2013) samples. Certain regional abnormalities in ReHo change with antidepressant therapy (Wang et al., 2013), while the others relate to the presence of treatment-resistance (Guo et al., 2011b). Nevertheless, a consistent picture regarding the anatomical distribution of the aberrant regional homogeneity in depression is still lacking (Supplementary Table 1). In part this can be attributed to the use of small sized samples in most studies and the presence of notable clinical heterogeneity with respect to age, gender, duration of illness, treatment exposure status and the number of previous episodes of depression in the various published reports.
In the present study, we systematically reviewed and synthesized the ReHo literature in depression using a meta-analytic framework aimed at detecting the most consistent loci of distributed anatomical changes from the individual studies. To this end, we used the Anisotropic Effect Size version of Signed Differential Mapping (AES-SDM) (Radua et al., 2011, Radua et al., 2014), a meta-analytical procedure to identify the spatially consistent fMRI changes in patient groups. Further, we also estimated the spatial heterogeneity of the findings, and investigated the effect of clinical/demographic variables such as age, gender distribution, duration of illness, and treatment status on ReHo changes in depression. Given the emerging findings from the vast fMRI literature in depression, we expected ReHo changes to be clustered around the MPFC region but extending to involves attentional, visual processing and emotion regulation networks as well. On the basis of the meta-analytical results, we relate regional ‘neural dysfunction’ to the wider network-level abnormalities in depression.
Section snippets
Search
Two authors (LP, RK) independently searched several databases for articles published until 30 November 2013 through PubMed and OVID online for EMBASE, PsycINFO, PsycArticles, MEDLINE In-Process & Other Non-Indexed Citations and Google Scholar using the following key words in various combinations: ‘regional homogeneity’, ‘functional MRI’, ‘ReHo’ ‘local connectivity’, ‘coherence’, ‘concordance’, ‘depress*’, ‘affective’, ‘mood’ and ‘depression’. The following inclusion criteria was used to select
Results
As shown in Fig. 1, the literature search yielded 10 studies, with 11 relevant patients vs. controls comparisons, with a total sample size of n = 455 (225 patients, 230 controls). During the search process we identified three studies on late-onset depression (Chen et al., 2012, Yuan et al., 2008, Yue et al., 2013). Late-onset depression has a strong association with white matter vascular lesions and neurodegenerative changes, suggesting pathophysiological pathways that are unique when compared to
Medial prefrontal ReHo
For the first time using a meta-analytic approach to locate the most consistent changes in ‘local connectivity’ in depressed patients, we note that the medial prefrontal cortex shows the most robust and reliable increase in the regional homogeneity during paradigm-free resting state. This increase in MPFC ReHo was robust and unaffected by either the statistical heterogeneity among the studies or the demographic variables such as age and gender. MPFC is one of the most important brain regions
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