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      What do neuroanatomical networks reveal about the ontology of human cognitive abilities?

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          Summary

          Over the last decades, cognitive psychology has come to a fair consensus about the human intelligence ontological structure. However, it remains an open question whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance on 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology partly agrees with the cognitive performance-derived (psychometric) ontology complemented with interpretable differences. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and difference between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics, and treatment.

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          Highlights

          • •

            Psychometric and neurometric cognitive ontologies are partly equivalent

          • •

            Ability-related brain areas are ontologically segregated with little to no overlap

          • •

            However, ability-related brain areas are densely interconnected by fiber tracts

          Abstract

          Systems neuroscience; Cognitive neuroscience

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          Most cited references96

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

          Li‐tze Hu, Peter M. Bentler (1999)
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            The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

            B. Yeo, Fenna M. Krienen, Jorge Sepulcre … (2011)
            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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              The unity and diversity of executive functions and their contributions to complex "Frontal Lobe" tasks: a latent variable analysis.

              Akira Miyake, Naomi Friedman, Michael J Emerson … (2000)
              This individual differences study examined the separability of three often postulated executive functions-mental set shifting ("Shifting"), information updating and monitoring ("Updating"), and inhibition of prepotent responses ("Inhibition")-and their roles in complex "frontal lobe" or "executive" tasks. One hundred thirty-seven college students performed a set of relatively simple experimental tasks that are considered to predominantly tap each target executive function as well as a set of frequently used executive tasks: the Wisconsin Card Sorting Test (WCST), Tower of Hanoi (TOH), random number generation (RNG), operation span, and dual tasking. Confirmatory factor analysis indicated that the three target executive functions are moderately correlated with one another, but are clearly separable. Moreover, structural equation modeling suggested that the three functions contribute differentially to performance on complex executive tasks. Specifically, WCST performance was related most strongly to Shifting, TOH to Inhibition, RNG to Inhibition and Updating, and operation span to Updating. Dual task performance was not related to any of the three target functions. These results suggest that it is important to recognize both the unity and diversity of executive functions and that latent variable analysis is a useful approach to studying the organization and roles of executive functions. Copyright 2000 Academic Press.
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                Author and article information

                Contributors
                Andrea Hildebrandt
                Changsong Zhou
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 03 July 2022
                Publication date Collection: 19 August 2022
                Publication date (Electronic): 03 July 2022
                Volume: 25
                Issue: 8
                Electronic Location Identifier: 104706
                Affiliations
                [1 ]Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
                [2 ]Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
                [3 ]Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, 200062 Shanghai, China
                [4 ]Department of Psychology, Humboldt University at Berlin, 10117 Berlin, Germany
                [5 ]Department of Psychology, Zhejiang Normal University, Jin Hua, China
                [6 ]Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany
                [7 ]Department of Physics, Zhejiang University, 310000 Hangzhou, China
                Author notes
                [∗∗ ]Corresponding author cszhou@ 123456hkbu.edu.hk
                [8]

                Senior author

                [9]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(22)00978-6 Publisher ID: 104706
                DOI: 10.1016/j.isci.2022.104706
                PMC ID: 9293763
                SO-VID: 2e309dee-f206-4476-92bd-f5edb530fe90
                Copyright © © 2022 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 20 December 2021
                Date revision received : 15 May 2022
                Date accepted : 28 June 2022
                Categories
                Subject: Article

                Keywords: systems neuroscience,cognitive neuroscience
                Data availability:
                Keywords: systems neuroscience, cognitive neuroscience

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