基于fMRI时变特征的大脑状态研究综述

doi:10.11896/jsjkx.230700059

摘要/Abstract

摘要： 功能磁共振成像技术已被广泛应用于人脑功能活动的研究,使用大脑状态(Brain State)研究大脑动力学得到了研究人员的广泛关注。以往关于大脑状态的综述,通常从状态定义方法的角度进行比较和总结,忽略了底层数据形式的不一致,可能导致对大脑状态的解读多样化。此外,现有综述也缺少对大脑状态分析应用方法的探讨。基于不同的数据形式,回顾了大脑状态的不同定义方法,总结了基于大脑状态进行大脑动力学分析的不同方法,并从大脑状态应用于认知、精神疾病、生理状态等方面的研究,总结出典型的研究方法。最后,发现了大脑元状态的定义与深度学习在特征提取方面具有相似性,从而提出将深度学习应用于大脑状态的识别以及大脑动力学的研究,这是一个有希望的未来方向。

关键词: 大脑状态, 元状态, 动态功能连接, 大脑动力学, 大脑网络

Abstract: Functional magnetic resonance imaging(fMRI) has been widely applied in the study of human brain activity.Recently,the use of brain states to investigate brain dynamics has attracted extensive attention from researchers.Previous reviews on brain states typically compare and summarize from the perspective of state definition methods,neglecting the inconsistency in under-lying data formats,which may results in diverse interpretations of brain states.Furthermore,these reviews also lack discussions on the analytical approaches for brain states.Here,we review various methods for defining brain states based on different data formats,provide an overview of different approaches for analyzing brain dynamics based on brain states,and summarize typical research methods in the application of brain states to cognition,psychiatric disorders,physiological states,and other aspects.Fina-lly,we find similarities between the definition of brain meta-states and feature extraction in deep learning.Therefore,we believe that deep learning is a promising approach for studying brain states.

Key words: Brain state, Meta-states, Dynamic functional connectivity, Brain dynamic, Brain networks

中图分类号:

TP181

林祺业, 夏佳楠, 周雪忠. 基于fMRI时变特征的大脑状态研究综述[J]. 计算机科学, 2024, 51(4): 182-192. https://doi.org/10.11896/jsjkx.230700059

LIN Qiye, XIA Jianan, ZHOU Xuezhong. Study on Time-varying Brain State Based on fMRI Data－A Review[J]. Computer Science, 2024, 51(4): 182-192. https://doi.org/10.11896/jsjkx.230700059

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