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Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome.

ABSTRACT: Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.

SUBMITTER: Yang Z

PROVIDER: S-EPMC8513388 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome.

Yang Zhiyun Z Jian Lingyu L Qiu Hui H Zhang Chaoqing C Cheng Song S Ji Junjun J Li Ting T Wang Yu Y Li Junfeng J Li Kefeng K

Translational psychiatry 20211013 1

Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating tre ...[more]

PMID: 34645783

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Project description:BackgroundCognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks.MethodsCognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength.ResultsAll cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course.ConclusionsOur study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity.

Project description:A cardinal symptom of Major Depressive Disorder (MDD) is the disruption of circadian patterns. Yet, to date, there is no direct evidence of circadian clock dysregulation in the brains of MDD patients. Circadian rhythmicity of gene expression has been observed in animals and peripheral human tissues, but its presence and variability in the human brain was difficult to characterize. Here we applied time-of-death analysis to gene expression data from high-quality postmortem brains, examining 24-hour cyclic patterns in six cortical and limbic regions of 55 subjects with no history of psychiatric or neurological illnesses ('Controls') and 34 MDD patients. Our dataset covered ~12,000 transcripts in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (AnCg), hippocampus (HC), amygdala (AMY), nucleus accumbens (NAcc) and cerebellum (CB). Several hundred transcripts in each region showed 24-hour cyclic patterns in Controls, and >100 transcripts exhibited consistent rhythmicity and phase-synchrony across regions. Among the top ranked rhythmic genes were the canonical clock genes BMAL1(ARNTL), PER1-2-3, NR1D1(REV-ERB), DBP, BHLHE40(DEC1), and BHLHE41(DEC2). The phasing of known circadian genes was consistent with data derived from other diurnal mammals. Cyclic patterns were much weaker in MDD brains, due to shifted peak timing and potentially disrupted phase relationships between individual circadian genes. This is the first transcriptome-wide analysis of cyclic patterns in the human brain and demonstrates a rhythmic rise and fall of gene expression in regions outside of the suprachiasmatic nucleus in control subjects. The description of its breakdown in MDD suggest novel molecular targets for treatment of mood disorders. Sample collection, including human subject recruitment and characterization, tissue dissection, and RNA extraction, was described previously (see Evans 2003 Neurobiol Dis 14:240-250, Li 2004 Biol Psychiatry 55:346-352). RNA samples for different regions came from the same set of brains from 86 control subjects. Sample size varied by region: AnCg (n=70 controls), DLPFC (n=83), CB (n=51), AMY (n=32), HC (n=63) and NAcc (n=66). Many of the samples were run on two or more chips, resulting in the following chip counts for each region: AnCg = 124, DLPFC = 161, CB = 79, AMY = 62, HC = 108 and NaCC = 136. We ran each sample on at least two microarrays using the Affymetrix U133-A or U133Plus-v2 GeneChips, however, we only used the U133A part of the U133Plus-v2 GeneChip in our analysis. We applied RMA (Robust Multi-array Analysis) (see Irizarry 2003 Biostatistics 4:249-264, Irizarry 2003 Nucleic Acids Res 31:e15) to summarize probe set expression levels, using a custom Chip Definition File, resulting in expression data for 11,911 ENTREZ transcripts. Microarray data for each region were analyzed separately. All downstream analyses were performed in R. Details of the data processing, including data cleaning and normalization, are located in the Supplementary Materials (section 1). After data filtering, 1,424 microarrays remained, corresponding to 776 unique RNA samples in six regions.

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Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome.

Publications

Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome.

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