Lynch Charles J, Elbau Immanuel, Ng Tommy, Ayaz Aliza, Zhu Shasha, Manfredi Nicola, Johnson Megan, Wolk Danielle, Power Jonathan D, Gordon Evan M, Kay Kendrick, Aloysi Amy, Moia Stefano, Caballero-Gaudes Cesar, Victoria Lindsay W, Solomonov Nili, Goldwaser Eric, Zebley Benjamin, Grosenick Logan, Downar Jonathan, Vila-Rodriguez Fidel, Daskalakis Zafiris J, Blumberger Daniel M, Williams Nolan, Gunning Faith M, Liston Conor
bioRxiv. 2023 Aug 14:2023.08.09.551651. doi: 10.1101/2023.08.09.551651.
Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.
二十年来，数百项神经影像学研究揭示了抑郁症患者大脑结构和功能连接的差异，但效应量较小，这使得获取机制性病理生理学见解或开发生物标志物的努力变得复杂。此外，尽管抑郁症从根本上说是一种发作性疾病，但很少有神经影像学研究采用纵向研究方法，而这种方法对于理解因果关系以及描绘随时间推移驱动情绪状态转变的机制至关重要。利用密集采样的纵向神经影像学数据进行精确功能映射的新兴领域，揭示了健康个体大脑网络拓扑中意想不到的、具有功能意义的个体差异，但这些方法从未应用于抑郁症患者。在这里，我们使用精确功能映射技术和11个数据集（包括n = 187名重复采样个体和超过21000分钟的功能磁共振成像数据），表明前额叶纹状体突显网络在大多数抑郁症患者中扩大了两倍。这种效应在多个样本中都可重复，包括大规模的组平均数据（N = 1231名受试者），并且主要是由影响特定功能系统的网络边界移动引起的，不同个体出现三种不同的侵入模式。突显网络扩张出乎意料地随时间稳定，不受情绪状态变化的影响，并且在青少年出现抑郁症状之前的儿童中就可检测到。对在1.5年内扫描多达62次的个体进行纵向分析，确定了特定前额叶纹状体回路中的连接变化，这些变化跟踪特定症状领域的波动，并在未来快感缺失症状出现之前进行预测。总之，这些发现确定了一种稳定的特质样大脑网络拓扑结构，它可能赋予抑郁症风险，以及前额叶纹状体回路中依赖情绪状态的连接变化，这些变化可预测抑郁症状随时间的出现和缓解。
