Predictable navigation through spontaneous brain states with cognitive-map-like representations

IF 6.7 2区 医学 Q1 NEUROSCIENCES Progress in Neurobiology Pub Date : 2024-01-15 DOI:10.1016/j.pneurobio.2024.102570
Siyang Li , Zhipeng Li , Qiuyi Liu , Peng Ren , Lili Sun , Zaixu Cui , Xia Liang
{"title":"Predictable navigation through spontaneous brain states with cognitive-map-like representations","authors":"Siyang Li ,&nbsp;Zhipeng Li ,&nbsp;Qiuyi Liu ,&nbsp;Peng Ren ,&nbsp;Lili Sun ,&nbsp;Zaixu Cui ,&nbsp;Xia Liang","doi":"10.1016/j.pneurobio.2024.102570","DOIUrl":null,"url":null,"abstract":"<div><p>Just as navigating a physical environment, navigating through the landscapes of spontaneous brain states may also require an internal cognitive map. Contemporary computation theories propose modeling a cognitive map from a reinforcement learning perspective and argue that the map would be predictive in nature, representing each state as its upcoming states. Here, we used resting-state fMRI to test the hypothesis that the spaces of spontaneously reoccurring brain states are cognitive map-like, and may exhibit future-oriented predictivity. We identified two discrete brain states of the navigation-related brain networks during rest. By combining pattern similarity and dimensional reduction analysis, we embedded the occurrences of each brain state in a two-dimensional space. Successor representation modeling analysis recognized that these brain state occurrences exhibit place cell-like representations, akin to those observed in a physical space. Moreover, we observed predictive transitions of reoccurring brain states, which strongly covaried with individual cognitive and emotional assessments. Our findings offer a novel perspective on the cognitive significance of spontaneous brain activity and support the theory of cognitive map as a unifying framework for mental navigation.</p></div>","PeriodicalId":20851,"journal":{"name":"Progress in Neurobiology","volume":"233 ","pages":"Article 102570"},"PeriodicalIF":6.7000,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Neurobiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0301008224000066","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Just as navigating a physical environment, navigating through the landscapes of spontaneous brain states may also require an internal cognitive map. Contemporary computation theories propose modeling a cognitive map from a reinforcement learning perspective and argue that the map would be predictive in nature, representing each state as its upcoming states. Here, we used resting-state fMRI to test the hypothesis that the spaces of spontaneously reoccurring brain states are cognitive map-like, and may exhibit future-oriented predictivity. We identified two discrete brain states of the navigation-related brain networks during rest. By combining pattern similarity and dimensional reduction analysis, we embedded the occurrences of each brain state in a two-dimensional space. Successor representation modeling analysis recognized that these brain state occurrences exhibit place cell-like representations, akin to those observed in a physical space. Moreover, we observed predictive transitions of reoccurring brain states, which strongly covaried with individual cognitive and emotional assessments. Our findings offer a novel perspective on the cognitive significance of spontaneous brain activity and support the theory of cognitive map as a unifying framework for mental navigation.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用类似认知地图的表征在自发大脑状态中进行可预测导航
正如在物理环境中导航一样,在大脑自发状态的景观中导航也可能需要一个内部认知地图。当代计算理论建议从强化学习的角度对认知地图进行建模,并认为该地图具有预测性,可将每种状态表示为其即将出现的状态。在这里,我们利用静息态 fMRI 验证了一个假设,即自发重复出现的大脑状态空间类似于认知地图,并可能表现出面向未来的预测性。我们确定了静息状态下导航相关脑网络的两种离散脑状态。通过结合模式相似性和降维分析,我们将每种脑状态的出现嵌入了一个二维空间。后续的表征建模分析表明,这些大脑状态的出现呈现出类似于物理空间中观察到的场所细胞的表征。此外,我们还观察到重复出现的大脑状态的预测性转换,这与个体的认知和情绪评估密切相关。我们的研究结果为自发大脑活动的认知意义提供了一个新的视角,并支持认知地图理论作为心理导航的统一框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Progress in Neurobiology
Progress in Neurobiology 医学-神经科学
CiteScore
12.80
自引率
1.50%
发文量
107
审稿时长
33 days
期刊介绍: Progress in Neurobiology is an international journal that publishes groundbreaking original research, comprehensive review articles and opinion pieces written by leading researchers. The journal welcomes contributions from the broad field of neuroscience that apply neurophysiological, biochemical, pharmacological, molecular biological, anatomical, computational and behavioral analyses to problems of molecular, cellular, developmental, systems, and clinical neuroscience.
期刊最新文献
Receptor-dependent influence of R7 RGS proteins on neuronal GIRK channel signaling dynamics CB1 receptors in NG2 cells mediate cannabinoid-evoked functional myelin regeneration Alterations of synaptic plasticity in Angelman syndrome model mice are rescued by 5-HT7R stimulation Opposing effects of nicotine on hypothalamic arcuate nucleus POMC and NPY neurons Cerebellar impairments in genetic models of autism spectrum disorders: A neurobiological perspective
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1