Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism.
Deep learning identifies robust gender differences in functional brain organization and their dissociable links to clinical symptoms in autism.
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DOI：
10.1192/bjp.2022.13
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发表时间：
2022-02-15
影响因子：
10.5
通讯作者：
Menon, Vinod
中科院分区：
文献类型：
--
作者：
Supekar, Kaustubh;de los Angeles, Carlo;Ryali, Srikanth;Cao, Kaidi;Ma, Tengyu;Menon, Vinod
关键词：
自闭症谱系障碍（ASD）是一种高度异质性的疾病，女性每189人中有1人患病，男性每42人中有1人患病。然而，ASD中性别差异的神经生物学基础尚不清楚，因为大多数研究忽略了女性，并且使用了不适合捕捉这种差异的方法。识别区分女性和男性ASD患者并预测症状严重程度的强大功能性脑组织标记。我们利用多个神经成像队列（ASD n = 773）并开发了一种新的时空深度神经网络（stDNN），该网络使用功能磁共振成像数据的时空卷积来区分组。stDNN在区分女性和男性ASD方面取得了一致的高分类准确率。值得注意的是，经过训练来区分女性和男性自闭症患者的stDNN不能区分神经正常的女性和男性，这表明自闭症患者的功能性脑组织存在性别差异，而不是正常的性别差异。与运动、语言和视觉空间注意力系统相关的大脑特征可靠地区分了女性和男性ASD患者。至关重要的是，这些结果是在一个大型多地点队列中观察到的，并在一个完全独立的队列中得到了重复。此外，与运动网络初级运动皮层节点相关的大脑特征预测了女性自闭症患者限制性/重复性行为的严重程度，而不是男性自闭症患者。我们可重复的研究结果表明，患有自闭症谱系障碍的女性和男性的大脑在功能组织上是不同的，这以不同的方式导致了他们的临床症状。它们为自闭症谱系障碍的性别特异性诊断和治疗策略的发展提供了信息，并最终推动了精准精神病学的发展
Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences. To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity. We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups. stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network’s primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD. Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry
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DOI：
10.1016/j.jaac.2014.10.003
发表时间：
2015-01
影响因子：
13.3
作者：
Lai, Meng-Chuan;Lombardo, Michael V.;Auyeung, Bonnie;Chakrabarti, Bhismadev;Baron-Cohen, Simon
通讯作者：
Baron-Cohen, Simon
影响因子：
11
作者：
Di Martino, A.;Yan, C-G;Li, Q.;Denio, E.;Castellanos, F. X.;Alaerts, K.;Anderson, J. S.;Assaf, M.;Bookheimer, S. Y.;Dapretto, M.;Deen, B.;Delmonte, S.;Dinstein, I.;Ertl-Wagner, B.;Fair, D. A.;Gallagher, L.;Kennedy, D. P.;Keown, C. L.;Keysers, C.;Lainhart, J. E.;Lord, C.;Luna, B.;Menon, V.;Minshew, N. J.;Monk, C. S.;Mueller, S.;Mueller, R. A.;Nebel, M. B.;Nigg, J. T.;O'Hearn, K.;Pelphrey, K. A.;Peltier, S. J.;Rudie, J. D.;Sunaert, S.;Thioux, M.;Tyszka, J. M.;Uddin, L. Q.;Verhoeven, J. S.;Wenderoth, N.;Wiggins, J. L.;Mostofsky, S. H.;Milham, M. P.
通讯作者：
Milham, M. P.
DOI：
10.1016/j.nicl.2021.102719
发表时间：
2021
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Walsh MJM;Wallace GL;Gallegos SM;Braden BB
通讯作者：
Braden BB
影响因子：
--
作者：
Ruigrok, Amber N V;Lai, Meng-Chuan
通讯作者：
Lai, Meng-Chuan
自闭症谱系障碍。
DOI：
10.1038/s41572-019-0138-4
发表时间：
2020-01-16
期刊：
Nature reviews. Disease primers
影响因子：
--
作者：
Lord C;Brugha TS;Charman T;Cusack J;Dumas G;Frazier T;Jones EJH;Jones RM;Pickles A;State MW;Taylor JL;Veenstra-VanderWeele J
通讯作者：
Veenstra-VanderWeele J