Resting-state functional network connectivity in prefrontal regions differs between unmedicated patients with bipolar and major depressive disorders

Abstract

Background Differentiating bipolar disorder (BD) from major depressive disorder (MDD) often poses a major clinical challenge, and optimal clinical care can be hindered by misdiagnoses. This study investigated the differences between BD and MDD in resting-state functional network connectivity (FNC) using a data-driven image analysis method. Methods In this study, fMRI data were collected from unmedicated subjects including 13 BD, 40 MDD and 33 healthy controls (HC). The FNC was calculated between functional brain networks derived from fMRI using group independent component analysis (ICA). Group comparisons were performed on connectivity strengths and other graph measures of FNC matrices. Results Statistical tests showed that, compared to MDD, the FNC in BD was characterized by more closely connected and more efficient topological structures as assessed by graph theory. The differences were found at both the whole-brain-level and the functional-network-level in prefrontal networks located in the dorsolateral/ventrolateral prefrontal cortex (DLPFC, VLPFC) and anterior cingulate cortex (ACC). Furthermore, interconnected structures in these networks in both patient groups were negatively associated with symptom severity on depression rating scales. Limitations As patients were unmedicated, the sample sizes were relatively small, although they were comparable to those in previous fMRI studies comparing BD and MDD. Conclusions Our results suggest that the differences in FNC of the PFC reflect distinct pathophysiological mechanisms in BD and MDD. Such findings ultimately may elucidate the neural pathways in which distinct functional changes can give rise to the clinical differences observed between these syndromes.

Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders

“…Abnormal medial prefrontal cortex connectivity between ICA components were also found during the resting-state in the BD group in multiple previous studies (Calhoun et al 2011; Ongur et al 2010). Our findings with stronger FC in BD subjects within the prefrontal cortical areas highlighted in Module 1 not only replicated our recent results on the same dataset with different analysis approaches (He et al 2016), but also are in line with prior resting-state FC studies.…”

“…The magnitude (absolute value) of functional network connectivity strength was used as the fMRI data feature to input into the multimodal fusion analysis. For more details of FNC feature generation, please refer to our previous publication (He et al 2016). …”

“…We previously studies compared neuroimaging data between BD and MDD by constructing the functional network connectivity (FNC), composed of a whole brain graph with nodes defined by independent components analysis (ICA) (Jafri et al 2008). We found that the FNC in BD was characterized by more closely connected and more efficient topological structures compared to MDD (He et al 2016). …”

Elucidating Neural Network Functional Connectivity Abnormalities in Bipolar Disorder: Toward a Harmonized Methodological Approach

“…These have included whole-brain ICA studies, in which the interactions or spatial extent of major large-scale networks have been investigated (35, 61, 62). Importantly, some of these studies revealed findings compatible with the data previously reviewed, including alterations in left vlPFC/opercular regions (61), reduced interactions between right dlPFC- and amygdala/striatum-related independent components (35), and heightened connectivity between medial and lateral PFC subregions (63). By replicating similar networks to seed based studies, such findings also provide important validation of previous work, given that they may be less susceptible to bias.…”

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study

“…automated anatomical labeling, AAL template) to define the nodes based on brain structure. Alternatively, independent component analysis (ICA) can be run to detect independent components (ICs, spatial brain maps), which can be considered as graph nodes (He et al, 2016; Smith, 2012; Smith et al, 2011; Yu et al, 2015; Yu et al, 2011a; Yu et al, 2013a; Yu et al, 2013b; Yu et al, 2011b; Yu et al, 2016). While the “correct” method for defining the brain nodes remains an open question that deserves further extensive research (Stanley et al, 2013).…”