Shared and distinct patterns of dFC variability of thalamo-cortical circuit in BDD and MDD patients

585 

Abstract Submission 

Fengmei Lu¹, Wei Luo¹, Yue Yu¹, Zongling He¹, Huafu Chen¹

¹The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, UESTC, Chengdu, China

Fengmei Lu  
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, UESTC
Chengdu, China

Wei Luo  
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, UESTC
Chengdu, China

Yue Yu  
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, UESTC
Chengdu, China

Zongling He  
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, UESTC
Chengdu, China

Huafu Chen  
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, UESTC
Chengdu, China

Bipolar disorder (BD) is a chronic, severe and lifelong psychiatric disorder with a prevalence of 1~2% (Kessing & Andersen, 2017). Though BD has a manic episode, depressive episode is the most common feature of BD that overlaps with major depressive disorder (MDD) and is mainly manifested as depressed mood, loss of interest, insomnia or hypersomnia. Thus, it is difficult to distinguish between BD during a depressive episode (BDD) and MDD due to similar clinical symptoms (Chen et al., 2022; Evans, 2000; Redlich et al., 2015; Smith & Craddock, 2011). Evidence has indicated abnormalities of thalamo-cortical functional connectivity (FC) in bipolar disorder during a depressive episode (BDD) and major depressive disorder (MDD). However, the dynamic FC (dFC) within this system is poorly understood. This study aims to provide more precise thalamic subregions to explore the disrupted thalamo-cortical dFC patterns between BDD and MDD, using the Human Brainnetome Atlas combined with a sliding-window approach.

A total of 250 subjects including 95 MDD patients, 58 BDD patients and 97 healthy controls (HCs) were recruited. The rs-fMRI data were obtained with a 3T GE Discovery MR750 scanner. Data were preprocessed by the Data Processing and Analysis of Brain Imaging (DPABI) toolkit (v5.1, http://rfmri.org/dpabi). After preprocessing, we defined 16 seeds of the thalamus according to the Human Brainnetome Atlas (HBA, http://atlas.brainnetome.org) (L. Fan et al., 2016; Y. Fan et al., 2015). To identify the dFC variability, a sliding-window dFC approach was applied with the Dynamic Brain Connectome (DynamicBC) toolbox. To explore the differences in dFC variability patterns of each thalamus subregions with the rest of the brain among the three groups, one-way ANOVA model was conducted on the dFC variance in z value at each voxel, with age, gender, educational level, mean FD and the variance of FD as covariates. Next, to examine the differences among the three groups (i.e. BDD vs. MDD, BDD vs. HCs, MDD vs. HCs), the brain clusters showing significant group differences were defined as regions of interest (ROIs) for post-hoc analysis, with age, gender, educational level, mean FD and the variance of FD as covariates. Correlation analysis was performed between altered dFC variability and clinical data (including course of illness, age of first onset, number of depression episodes, duration of single episode, number of mania episodes, HAMD score, and BRMS score) in MDD and BDD, regressing out the age, gender, educational level, mean FD, the variance of FD and total medication load index. Further, classification analysis with a linear support vector machine model was conducted.

Compared with HCs, both patients revealed increased dFC variability between thalamus subregions with hippocampus (HIP), angular gyrus and caudate, and only BDD showed increased dFC variability of the thalamus with superior frontal gyrus (SFG), HIP, insula, middle cingulate gyrus and postcentral gyrus (Figure 1). Compared with MDD and HCs, only BDD exhibited enhanced dFC variability of the thalamus with SFG and superior temporal gyrus (Figure 1). Further, the number of depressive episodes in MDD was significantly positively associated with altered dFC variability (Figure 2A). Finally, the disrupted dFC variability could distinguish BDD from MDD with 83.44% classification accuracy (Figure 2B).

Our findings support and extend the role of thalamo-cortical circuit in the shared neuropathological mechanisms of the two mood disorders. Discriminative disorder-specific altered dFC in thalamo-cortical circuit were found in BDD. Notably, excesssive varibalitiy in thalamus-related salience and sensory perception system was observed in BDD. Our findings give further evidence to suggest the deficits in the cognitive, emotional as well as sensory and perception processes in BDD.

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ¹

Connectivity (eg. functional, effective, structural) ²

Affective Disorders

FUNCTIONAL MRI

Psychiatric Disorders