Differential brain network activity across mood states in bipolar disorder

Highlights

• We performed data driven analyses of functional connectivity in bipolar disorder.

• Whole brain connectivity patterns differentiate bipolar trait and mood state.

• Dorsal Attention Network connectivity differentiates mania and euthymia.

• Frontal connectivity in the Default Mode Network also differentiates state & trait.

Abstract

Background

This study aimed to identify how the activity of large-scale brain networks differs between mood states in bipolar disorder. The authors measured spontaneous brain activity in subjects with bipolar disorder in mania and euthymia and compared these states to a healthy comparison population.

Methods

23 subjects with bipolar disorder type I in a manic episode, 24 euthymic bipolar I subjects, and 23 matched healthy comparison (HC) subjects underwent resting state fMRI scans. Using an existing parcellation of the whole brain, we measured functional connectivity between brain regions and identified significant differences between groups.

Results

In unbiased whole-brain analyses, functional connectivity between parietal, occipital, and frontal nodes within the dorsal attention network (DAN) were significantly greater in mania than euthymia or HC subjects. In the default mode network (DMN), connectivity between dorsal frontal nodes and the rest of the DMN differentiated both mood state and diagnosis.

Limitations

The bipolar groups were separate cohorts rather than subjects imaged longitudinally across mood states.

Conclusions

Bipolar mood states are associated with highly significant alterations in connectivity in two large-scale brain networks. These same networks also differentiate bipolar mania and euthymia from a HC population. State related changes in DAN and DMN connectivity suggest a circuit based pathology underlying cognitive dysfunction as well as activity/reactivity in bipolar mania. Altered activities in neural networks may be biomarkers of bipolar disorder diagnosis and mood state that are accessible to neuromodulation and are promising novel targets for scientific investigation and possible clinical intervention.

Introduction

Bipolar disorder is a debilitating psychiatric disorder estimated to affect between 2% and 5% of the population (Merikangas et al., 2007). It may be instructive to examine the instability of neural activity in the varying mood states in bipolar disorder for clues into the mechanisms of specific mood states and the underlying physiology of bipolar disorder itself. A growing body of scientific inquiry has examined changes in neural activity associated with specific cognitive tasks such as emotion and reward processing (reviewed in Phillips and Swartz, (2014)); Strakowski et al.(2012). Drawing upon these findings from the primarily task-based fMRI literature, we recently examined the “resting state” (rsfMRI) functional connectivity of bipolar mania when compared to bipolar euthymia (Brady et al., 2016). That analysis examined functional connectivity to brain regions selected from a consensus model of the neurobiology of bipolar disorder (Strakowski et al., 2012). We observed mood state specific aberrant connectivity between the amygdala and brain regions implicated in emotion regulation even under rest (non-task) conditions.

We sought to complement our prior study of mood related connectivity of select cortical and subcortical regions with a more data-driven analysis of functional connectivity across the entire brain. The analysis of rsfMRI has demonstrated the presence of large-scale brain networks whose function is altered in psychiatric and neurologic diseases e.g. (Baker et al., 2014, Yeo et al., 2011, Zhou and Seeley, 2014). In bipolar disorder comparatively few studies have sought to examine whole brain measures of network activity and connectivity and there is a growing call to incorporate these studies into a bipolar imaging literature that has most often examined local networks (Chase and Phillips, 2016). Several recent studies have examined large scale brain networks to differentiate bipolar disorder from other diseases (reviewed in Chase and Phillips, 2016, Lois et al., 2014).

We examined spontaneous neural activity in bipolar disorder to understand how whole brain connectivity is altered in subjects with bipolar disorder in different mood states. We compared rsfMRI data from subjects diagnosed with bipolar disorder type I in a manic state to euthymic bipolar I subjects and a healthy comparison group. We then used a data driven approach to examine changes in brain network activity across and between these groups. We measured the low-frequency oscillations of the blood-oxygen-level dependent (BOLD) signal and conducted a whole-brain analysis of the functional connectivity (FC) between all grey matter regions. We then examined FC values for significant group differences across and between groups. We chose to use an existing atlas that parcellates grey matter into regions defined by functional connectivity (Craddock et al., 2012). In doing this we hoped to capture differences in large scale network activity that does not adhere to anatomical boundaries. While this can be accomplished by independent component analysis (ICA) (see Meda et al. (2012) for example), the method utilized here would also allow us to isolate differences in individual nodes of networks that may otherwise be broadly similar between groups.

Primarily, we hypothesized that FC measures would differentiate mood states in bipolar disorder as well as differentiating subjects diagnosed with bipolar disorder, whatever their mood state, from matched healthy comparison subjects.