Abnormal dynamic functional network connectivity in unmedicated bipolar and major depressive disorders based on the triple-network model

Abstract

BackgroundPrevious studies have analyzed brain functional connectivity to reveal the neural physiopathology of bipolar disorder (BD) and major depressive disorder (MDD) based on the triple-network model [involving the salience network, default mode network (DMN), and central executive network (CEN)]. However, most studies assumed that the brain intrinsic fluctuations throughout the entire scan are static. Thus, we aimed to reveal the dynamic functional network connectivity (dFNC) in the triple networks of BD and MDD.MethodsWe collected resting state fMRI data from 51 unmedicated depressed BD II patients, 51 unmedicated depressed MDD patients, and 52 healthy controls. We analyzed the dFNC by using an independent component analysis, sliding window correlation and k-means clustering, and used the parameters of dFNC state properties and dFNC variability for group comparisons.ResultsThe dFNC within the triple networks could be clustered into four configuration states, three of them showing dense connections (States 1, 2, and 4) and the other one showing sparse connections (State 3). Both BD and MDD patients spent more time in State 3 and showed decreased dFNC variability between posterior DMN and right CEN (rCEN) compared with controls. The MDD patients showed specific decreased dFNC variability between anterior DMN and rCEN compared with controls.ConclusionsThis study revealed more common but less specific dFNC alterations within the triple networks in unmedicated depressed BD II and MDD patients, which indicated their decreased information processing and communication ability and may help us to understand their abnormal affective and cognitive functions clinically.

Default mode network activity in bipolar disorder

“…Detailed neurophysiological correlates of resting-state activity are currently the object of speculation and only specific analysis techniques (i.e. dynamic functional connectivity) can account for the complex dynamics in the interactions between brain regions as suggested by some studies (Brady et al, 2017;Wang et al, 2020). Furthermore, since the BOLD contrast reflects hemodynamic/metabolic processes in the brain, the interpretation of rs-fMRI findings in terms of the underlying neuronal activity is not straightforward and would require the integration of electrophysiological information (Maggioni et al, 2016).…”

Disturbances of Dynamic Function in Patients With Bipolar Disorder I and Its Relationship With Executive-Function Deficit

“…Kucyi et al suggested dFC within DMN is positively associated with emotion control (55). Abnormal intra-and internetwork connectivity variability are linked with mood disorders (56). Inter-network connectivity dynamics also contribute to cognitive flexibility (57), which is a key element of executive function.…”

“…Indeed, as two related but distinct diseases, major depression and BD patients may present disparate brain-function temporal dynamics. However, there is no uniform conclusion about the common or specific alterations of brain-function temporal dynamics (56,59). Further studies included BD I and II type and major depression to explore the differentiating signatures of brainfunction temporal dynamics.…”

Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach

“…Zhi et al (2018) used the sliding-window algorithm to identify three types of node damage which were related to the severity of depressive symptoms and cognitive ability. Wang et al (2019) found decreased DFC variability between the anterior DMN and the right CEN compared with controls (Wang et al, 2019). It is noteworthy that initial attempts have been made to validate that the accuracy of a machine learning-based diagnosis system could be largely improved by using DFC metrics, instead of traditional SFC measures .…”