Common and unique alterations of functional connectivity in major depressive disorder and bipolar disorder

Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity

BACKGROUND

Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients' IQ scores at the individual level using a prediction model.

METHODS

We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients' IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains.

RESULTS

The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance.

CONCLUSIONS

The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.

Common and disease-specific patterns of functional connectivity and topology alterations across unipolar and bipolar disorder during depressive episodes: a transdiagnostic study

Bipolar disorder (BD) and unipolar depression (UD) are defined as distinct diagnostic categories. However, due to some common clinical and pathophysiological features, it is a clinical challenge to distinguish them, especially in the early stages of BD. This study aimed to explore the common and disease-specific connectivity patterns in BD and UD. This study was constructed over 181 BD, 265 UD and 204 healthy controls. In addition, an independent group of 90 patients initially diagnosed with major depressive disorder at the baseline and then transferred to BD with the episodes of mania/hypomania during follow-up, was identified as initial depressive episode BD (IDE-BD). All participants completed resting-state functional magnetic resonance imaging (R-fMRI) at recruitment. Both network-based analysis and graph theory analysis were applied. Both BD and UD showed decreased functional connectivity (FC) in the whole brain network. The shared aberrant network across groups of patients with depressive episode (BD, IDE-BD and UD) mainly involves the visual network (VN), somatomotor networks (SMN) and default mode network (DMN). Analysis of the topological properties over the three networks showed that decreased clustering coefficient was found in BD, IDE-BD and UD, however, decreased shortest path length and increased global efficiency were only found in BD and IDE-BD but not in UD. The study indicate that VN, SMN, and DMN, which involve stimuli reception and abstraction, emotion processing, and guiding external movements, are common abnormalities in affective disorders. The network separation dysfunction in these networks is shared by BD and UD, however, the network integration dysfunction is specific to BD. The aberrant network integration functions in BD and IDE-BD might be valuable diagnostic biomarkers.

Cortical structural degeneration and functional network connectivity changes in patients with subcortical vascular cognitive impairment

PURPOSE

To explore the structural basis of functional network connectivity (FNC) changes and early cortical degenerative patterns in subcortical vascular cognitive impairment (SVCI).

METHODS

We prospectively included SVCI cases and healthy controls (HCs). FNC alterations were evaluated using group-independent component analysis of resting-state functional MRI data. Cortical microstructural and macrostructural alterations were assessed using gray matter-based spatial statistics analysis with neurite orientation dispersion and density imaging and cortical thickness analysis with FreeSurfer software on T1-weighted images, respectively. Spearman correlation analyses were performed to assess relationships between FNC alterations and cortical microstructural/macrostructural alterations and between FNC, cortical thickness, or neurite density index (NDI)/orientation dispersion index (ODI) alterations and cognitive performance.

RESULTS

Forty-six SVCI patients and 73 HCs were recruited. FNC analysis showed lower network connectivity between the visual network (VN) and sensorimotor network (SMN) in SVCI, positively correlated with information processing speed (p=0.008) and negatively with summary SVD score (p = 0.037). Cortical microstructural analyses exhibited a lower NDI, mainly in the VN and default mode network (DMN) areas (PFWE < 0.05, cluster > 100 voxels), and lower ODI, mainly in the SMN and DMN areas (PFWE < 0.05, cluster > 100 voxels) in SVCI, both of which were related to cognitive function (p < 0.05). However, cortical thickness did not differ between groups. Lower NDI in the lateral occipital cortex was linked to lower VN-SMN connectivity in SVCI (p = 0.002).

CONCLUSION

Cortical microstructural alterations may serve as the basis for FNC changes in SVCI.

Exploring causal association between functional/structural connectivity and major depression disorder: A bidirectional Mendelian randomization study

OBJECTIVES

Prior observational studies have suggested a correlation between major depressive disorder (MDD) and communication imbalances within the resting-state brain network (RSN), but the causal relationship remains unclear. This research uses Mendelian randomization (MR) analysis to explore the potential causal effects between functional connectivity (FC), structural connectivity (SC) and MDD.

METHODS

Two-sample bidirectional MR analysis was employed in this study. Inverse variance weighted (IVW) was used to explore the causal relationship between the FC/SC and MDD, with various methods such as MR-Egger to conduct sensitivity analyses.

RESULTS

The IVW analysis results showed that higher genetic predicted dorsal attention network FC, limbic network SC, and dorsal attention network SC were associated with an increased risk of MDD (β: 15.08, 95%CI: 5.89-24.27, p = 0.001; β: 3.79, 95%CI: -0.22-7.8, p = 0.034; β: 9.89, 95%CI: 0.88-18.90, p = 0.031). Reverse MR analysis demonstrated that a genetically predicted elevated risk of MDD was associated with reduced frontal parietal network FC (β: -0.00046, p = 0.041).

CONCLUSIONS

The study suggests a causal relationship between the FC and SC within specific RSNs and the risk of MDD. Abnormalities in the dorsal attention network FC/SC and the limbic network SC were risk factors for MDD. The FC abnormality of the frontal parietal network may be the downstream influence following the MDD onset. Further investigation is needed to determine the potential utility of these neuroimaging markers in the prevention of MDD or the evaluation of treatment efficacy.

Abnormal Dynamic Reconfiguration of Multilayer Temporal Networks in Patients with Bipolar Disorder

BACKGROUND

Multilayer networks have been used to identify abnormal dynamic reconfiguration in bipolar disorder (BD). However, these studies ignore the differences in information interactions between adjacent layers when constructing multilayer networks, and the analysis of dynamic reconfiguration is not comprehensive enough; Methods: Resting-state functional magnetic resonance imaging data were collected from 46 BD patients and 54 normal controls. A multilayer temporal network was constructed for each subject, and inter-layer coupling of different nodes was considered using network similarity. The promiscuity, recruitment, and integration coefficients were calculated to quantify the different dynamic reconfigurations between the two groups; Results: The global inter-layer coupling, recruitment, and integration coefficients were significantly lower in BD patients. These results were further observed in the attention network and the limbic/paralimbic and subcortical network, reflecting reduced temporal stability, intra- and inter-subnetwork communication abilities in BD patients. The whole-brain promiscuity was increased in BD patients. The same results were observed in the somatosensory/motor and auditory network, reflecting more functional interactions; Conclusions: This study discovered abnormal dynamic interactions of BD from the perspective of dynamic reconfiguration, which can help to understand the pathological mechanisms of BD.

Shared and unique alterations of large-scale network connectivity in drug-free adolescent-onset and adult-onset major depressive disorder

Differences in clinical manifestations and biological underpinnings between Major Depressive Disorder (MDD) onset during adolescence and adulthood have been posited in previous studies, implying an influential role of age of onset (AOO) in the clinical subtyping and therapeutic approaches to MDD. However, direct comparisons between the two cohorts and their age-matched controls have been lacking in extant investigations. In this investigation, 156 volunteers participated, comprising 46 adolescents with MDD (adolescent-onset group), 35 adults with MDD (adult-onset group), 19 healthy adolescents, and 56 healthy adults. Resting-state functional MRI scans were undergone by all participants. Large-scale network analyses were applied. Subsequently, a 2 × 2 ANOVA was employed to analyze the main effects of diagnosis, age, and their interaction effect on functional connectivity (FC). Furthermore, regression analysis was employed to scrutinize the association between anomalous FC and HAMD sub-scores. Increased FC in visual network (VN), limbic network (LN), VN-dorsal attention network (DAN), VN-LN, and LN-Default Mode (DMN) was found in both adolescent-onset and adult-onset MDD; however, the increased FC in DAN and LN were only found in adult-onset MDD and the decreased FC in DAN was only found in adolescent-onset MDD. Additionally, the relationship between HAMD factor 1 anxiety somatization and altered FC of DAN, VN, and VN-DAN was moderated by AOO. In conclusion, shared and distinctive large-scale network alterations in adolescent-onset and adult-onset MDD patients were suggested by our findings, providing valuable contributions towards refining clinical subtyping and treatment approaches for MDD.