Parsing Heterogeneity in Mood Disorders: The Challenges of Modeling Stable Mood Disorder-Related Functional Connectomes.

Parsing Heterogeneity in Mood Disorders: The Challenges of Modeling Stable Mood Disorder-Related Functional Connectomes.
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DOI:
10.1016/j.bpsc.2021.10.009
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发表时间:
2022-01
期刊:
Biological psychiatry. Cognitive neuroscience and neuroimaging
影响因子:
--
通讯作者:
Belleau EL
Belleau EL
中科院分区:
其他
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作者:
Belleau EL

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尽管有报道称情绪障碍和神经网络异常之间存在联系,但研究结果的差异比比皆是。鉴于抑郁症的异质性,尝试使用静息状态连接来识别不同的抑郁症亚组有时无法复制(1,2)。在抑郁症的活跃临床状态期间检查功能性神经网络,涉及个体之间数千种不同症状的组合,可能会导致识别情绪障碍的不稳定生物标志物。专注于缓解或正常的临床阶段,减少症状的变异性,可能会阐明更稳定和特质样的情绪障碍生物标志物。
Despite reported links between mood disorders and neural network abnormalities, discrepancies in findings abound. Given the heterogeneous nature of depression, attempts to use resting state connectivity to identify different depression subgroups have sometimes failed to replicate (1, 2). Examining functional neural networks during active clinical states of depression involving thousands of combinations of different symptoms across individuals may result in the identification of unstable biomarkers of mood disorders. Focusing on remitted or euthymic clinical phases that reduce symptom variability, may elucidate more stable and trait-like mood disorder biomarkers.
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