From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis.
From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis.
复制标题
DOI：
10.3389/fnhum.2022.940842
复制
发表时间：
2022
影响因子：
2.9
通讯作者：
中科院分区：
文献类型：
--
作者：
作为一个新兴的领域，连接组学极大地促进了我们对人类大脑接线图和组织特征的理解。尤其是基于生成模型的连接组分析，在解读健康认知功能和疾病功能障碍的神经机制方面发挥着至关重要的作用。本文回顾了功能磁共振成像（fMRI）的主要生成建模方法的基础和发展，并概述了它们在认知或临床神经科学问题上的应用。我们认为，传统的结构和功能连接（FC）分析本身不足以揭示观察到的神经成像数据背后的复杂电路相互作用，应该辅以基于生成模型的有效连接和模拟，这是一种富有成效的实践，我们称之为“机械连接组”。从描述性连接组到机械性连接组的转变将开辟有希望的途径，以获得对人类大脑微妙运作原理的机制见解及其在疾病中的潜在损害，这有助于开发有效的个性化治疗来抑制神经和精神疾病。
As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term “mechanistic connectome.” The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.
登录
查看更多内容
影响因子：
5.3
作者：
Deco, Gustavo;Ponce-Alvarez, Adrian;Corbetta, Maurizio
通讯作者：
Corbetta, Maurizio
影响因子：
82.9
作者：
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
影响因子：
5.7
作者：
Di X;Biswal BB
通讯作者：
Biswal BB
影响因子：
48
作者：
Craddock, R. Cameron;Jbabdi, Saad;Yan, Chao-Gan;Vogelstein, Joshua T.;Castellanos, F. Xavier;Di Martino, Adriana;Kelly, Clare;Heberlein, Keith;Colcombe, Stan;Milham, Michael P.
通讯作者：
Milham, Michael P.
影响因子：
2.5
作者：
Baliyan V;Das CJ;Sharma R;Gupta AK
通讯作者：
Gupta AK