利用深度卷积-递归模型,可从基于任务的动态功能连接中预测个体认知特征。

IF 2.9 2区 医学 Q2 NEUROSCIENCES Cerebral cortex Pub Date : 2024-10-03 DOI:10.1093/cercor/bhae412
Erick Almeida de Souza, Bruno Hebling Vieira, Carlos Ernesto Garrido Salmon
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引用次数: 0

摘要

人们越来越关注从神经成像数据中了解智力的神经基质和人类的一些特征。深度学习可用于从不同的功能磁共振成像实验中预测不同的认知指标,如一般智能和流体智能,并提供有关这些预测所涉及的主要脑区的信息。利用人类连接组计划(Human Connectome Project)提供的 874 名受试者的神经成像和行为数据,我们使用 360 区域多模态图集,利用从语言和工作记忆功能磁共振成像任务状态中获得的动态功能连接来预测各种认知得分。该深度模型结合了多尺度卷积层和长短期记忆层,并在 10 倍分层交叉验证下进行了训练。我们使用多元线性回归剔除了性别、年龄、脑容量、运动和多波段重建算法的混杂影响。在工作记忆和语言任务中,我们可以分别解释 17.1% 和 16% 的一般智力变异。我们的研究表明,与文献相比,基于任务的动态功能连接比静息态动态功能连接具有更强的预测能力,而且剔除混杂因素会显著降低预测性能。在预测一般智力和流体智力方面,没有特定的皮层网络显示出明显的相关性,这表明智力结构在大脑中的空间分布是均匀的。
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Individual cognitive traits can be predicted from task-based dynamic functional connectivity with a deep convolutional-recurrent model.

There has been increased interest in understanding the neural substrates of intelligence and several human traits from neuroimaging data. Deep learning can be used to predict different cognitive measures, such as general and fluid intelligence, from different functional magnetic resonance imaging experiments providing information about the main brain areas involved in these predictions. Using neuroimaging and behavioral data from 874 subjects provided by the Human Connectome Project, we predicted various cognitive scores using dynamic functional connectivity derived from language and working memory functional magnetic resonance imaging task states, using a 360-region multimodal atlas. The deep model joins multiscale convolutional and long short-term memory layers and was trained under a 10-fold stratified cross-validation. We removed the confounding effects of gender, age, total brain volume, motion and the multiband reconstruction algorithm using multiple linear regression. We can explain 17.1% and 16% of general intelligence variance for working memory and language tasks, respectively. We showed that task-based dynamic functional connectivity has more predictive power than resting-state dynamic functional connectivity when compared to the literature and that removing confounders significantly reduces the prediction performance. No specific cortical network showed significant relevance in the prediction of general and fluid intelligence, suggesting a spatial homogeneous distribution of the intelligence construct in the brain.

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来源期刊
Cerebral cortex
Cerebral cortex 医学-神经科学
CiteScore
6.30
自引率
8.10%
发文量
510
审稿时长
2 months
期刊介绍: Cerebral Cortex publishes papers on the development, organization, plasticity, and function of the cerebral cortex, including the hippocampus. Studies with clear relevance to the cerebral cortex, such as the thalamocortical relationship or cortico-subcortical interactions, are also included. The journal is multidisciplinary and covers the large variety of modern neurobiological and neuropsychological techniques, including anatomy, biochemistry, molecular neurobiology, electrophysiology, behavior, artificial intelligence, and theoretical modeling. In addition to research articles, special features such as brief reviews, book reviews, and commentaries are included.
期刊最新文献
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