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      The Brain’s Topographical Organization Shapes Dynamic Interaction Patterns That Support Flexible Behavior Based on Rules and Long-Term Knowledge

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          Abstract

          Adaptive behavior relies both on specific rules that vary across situations and stable long-term knowledge gained from experience. The frontoparietal control network (FPCN) is implicated in the brain's ability to balance these different influences on action. Here, we investigate how the topographical organization of the cortex supports behavioral flexibility within the FPCN. Functional properties of this network might reflect its juxtaposition between the dorsal attention network (DAN) and the default mode network (DMN), two large-scale systems implicated in top-down attention and memory-guided cognition, respectively. Our study tests whether subnetworks of FPCN are topographically proximal to the DAN and the DMN, respectively, and how these topographical differences relate to functional differences: the proximity of each subnetwork is anticipated to play a pivotal role in generating distinct cognitive modes relevant to working memory and long-term memory. We show that FPCN subsystems share multiple anatomical and functional similarities with their neighboring systems (DAN and DMN) and that this topographical architecture supports distinct interaction patterns that give rise to different patterns of functional behavior. The FPCN acts as a unified system when long-term knowledge supports behavior but becomes segregated into discrete subsystems with different patterns of interaction when long-term memory is less relevant. In this way, our study suggests that the topographical organization of the FPCN and the connections it forms with distant regions of cortex are important influences on how this system supports flexible behavior.

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          Most cited references146

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          Array programming with NumPy

          Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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            The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

            Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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              Cortical surface-based analysis. I. Segmentation and surface reconstruction.

              Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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                Author and article information

                Journal
                J Neurosci
                J Neurosci
                jneuro
                J. Neurosci
                The Journal of Neuroscience
                Society for Neuroscience
                0270-6474
                1529-2401
                25 March 2024
                29 May 2024
                29 May 2024
                : 44
                : 22
                : e2223232024
                Affiliations
                [1] 1CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences , Beijing 100101, China
                [2] 2Department of Psychology, University of Chinese Academy of Sciences , Beijing 100049, China
                [3] 3Department of Psychology, University of York , Heslington, York YO10 5DD, United Kingdom
                [4] 4Department of Biomedical Engineering, University of California , Davis, California 95616
                [5] 5Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
                [6] 6Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104
                [7] 7McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University , Montreal, Quebec H3A 2B4, Canada
                [8] 8Chinese Institute for Brain Research , Beijing 102206, China
                [9] 9Department of Psychology, Queens University , Kingston, Ontario K7L 3N6, Canada
                [10] 10CAS Center for Excellence in Brain Science and Intelligence Technology , Shanghai 200031, China
                Author notes

                Author contributions: Xiuyi Wang, Y.D., and E.J. designed research; Xiuyi Wang, K.K.-R., and R.L. performed research; Xiuyi Wang, B.L., G.W., N.E.S., Xiaokang Wang, R.K., G.S., B.C.B., and Z.C. analyzed data; Xiuyi Wang, B.L., J.S., Y.D., and E.J. wrote the paper.

                We are grateful to Pradeepa Ruwan and Antonia De Freitas for piloting the experiment. We are grateful to Andrea I. Luppi and Emmanuel A. Stamatakis for providing code to calculate redundancy. We thank Ben D. Fulcher for providing codes and a guide to help us extract the features using hctsa. We thank Ting Xu and colleagues for making available their data pertaining cortical areal expansion and cross-species similarity. We thank Nan Lin and Yanni Cui for helpful feedback on an earlier draft of our manuscript. Xiuyi Wang discloses support for the research of this work from National Natural Science Foundation of China (Grant No. 32300881) and Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Grant Number E1CX4725CX). Y.D. discloses support for the publication of this work from the STI 2030—Major Projects (Grant Number 2021ZD0201500), the National Natural Science Foundation of China (Grant Number 31822024), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Number XDB32010300), and Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Grant Number E2CX3625CX). E.J. discloses support for the research of this work from a European Research Council Consolidator grant (Project ID: 771863 - FLEXSEM).

                The authors declare no competing interests.

                Correspondence should be addressed to Xiuyi Wang at wangxiuyi@ 123456psych.ac.cn or Yi Du at duyi@ 123456psych.ac.cn .
                Author information
                https://orcid.org/0000-0001-6097-4841
                https://orcid.org/0000-0003-4512-5221
                https://orcid.org/0000-0002-3826-4330
                Article
                jneuro-44-e2223232024
                10.1523/JNEUROSCI.2223-23.2024
                11140685
                38527807
                61eb7048-e6d2-4f02-b174-050bc5e3c218
                Copyright © 2024 Wang et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 15 November 2023
                : 14 March 2024
                : 17 March 2024
                Funding
                Funded by: MOST | National Natural Science Foundation of China (NSFC)
                Award ID: 32300881
                Award ID: 31822024
                Funded by: CAS | Institute of Psychology, Chinese Academy of Sciences (IPCAS)
                Award ID: E1CX4725CX
                Award ID: E2CX3625CX
                Funded by: STI 2030-Major Projects
                Award ID: 2021ZD0201500
                Funded by: Strategic Priority Research Program of Chinese Academy of Sciences
                Award ID: XDB32010300
                Funded by: European Research Council Consolidator
                Award ID: 771863 - FLEXSEM
                Categories
                Research Articles
                Behavioral/Cognitive

                cortical topography,default mode network,dorsal attention network,flexible cognition,frontoparietal control network

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