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      On the role of the corpus callosum in interhemispheric functional connectivity in humans

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          Significance

          The relation between structural and functional connectivity has profound implications for our understanding of cerebral physiology and cognitive neuroscience. Yet, this relation remains incompletely understood. Cases in which the corpus callosum is sectioned for medical reasons provide a unique opportunity to study this question. We report functional connectivity assessed before and after surgical section of the corpus callosum, including multiyear follow-up in a limited subsample. Our results demonstrate a causal role for the corpus callosum in maintaining functional connectivity between the hemispheres. Additionally, comparison of results obtained in complete vs. partial callosotomy demonstrate that polysynaptic connections also play a role in maintaining interhemispheric functional connectivity.

          Abstract

          Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of functional connectivity correspond to structural (axonal) connectivity. However, this relation is not one-to-one. Interhemispheric functional connectivity in relation to the corpus callosum presents a case in point. Specifically, several reports have documented nearly intact interhemispheric functional connectivity in individuals in whom the corpus callosum (the major commissure between the hemispheres) never develops. To investigate this question, we assessed functional connectivity before and after surgical section of the corpus callosum in 22 patients with medically refractory epilepsy. Section of the corpus callosum markedly reduced interhemispheric functional connectivity. This effect was more profound in multimodal associative areas in the frontal and parietal lobe than primary regions of sensorimotor and visual function. Moreover, no evidence of recovery was observed in a limited sample in which multiyear, longitudinal follow-up was obtained. Comparison of partial vs. complete callosotomy revealed several effects implying the existence of polysynaptic functional connectivity between remote brain regions. Thus, our results demonstrate that callosal as well as extracallosal anatomical connections play a role in the maintenance of interhemispheric functional connectivity.

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          Neurophysiological architecture of functional magnetic resonance images of human brain.

          We investigated large-scale systems organization of the whole human brain using functional magnetic resonance imaging (fMRI) data acquired from healthy volunteers in a no-task or 'resting' state. Images were parcellated using a prior anatomical template, yielding regional mean time series for each of 90 regions (major cortical gyri and subcortical nuclei) in each subject. Significant pairwise functional connections, defined by the group mean inter-regional partial correlation matrix, were mostly either local and intrahemispheric or symmetrically interhemispheric. Low-frequency components in the time series subtended stronger inter-regional correlations than high-frequency components. Intrahemispheric connectivity was generally related to anatomical distance by an inverse square law; many symmetrical interhemispheric connections were stronger than predicted by the anatomical distance between bilaterally homologous regions. Strong interhemispheric connectivity was notably absent in data acquired from a single patient, minimally conscious following a brainstem lesion. Multivariate analysis by hierarchical clustering and multidimensional scaling consistently defined six major systems in healthy volunteers-- corresponding approximately to four neocortical lobes, medial temporal lobe and subcortical nuclei- - that could be further decomposed into anatomically and functionally plausible subsystems, e.g. dorsal and ventral divisions of occipital cortex. An undirected graph derived by thresholding the healthy group mean partial correlation matrix demonstrated local clustering or cliquishness of connectivity and short mean path length compatible with prior data on small world characteristics of non-human cortical anatomy. Functional MRI demonstrates a neurophysiological architecture of the normal human brain that is anatomically sensible, strongly symmetrical, disrupted by acute brain injury, subtended predominantly by low frequencies and consistent with a small world network topology.
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            Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity.

            It is commonly assumed that functional brain connectivity reflects structural brain connectivity. The exact relationship between structure and function, however, might not be straightforward. In this review we aim to examine how our understanding of the relationship between structure and function in the 'resting' brain has advanced over the last several years. We discuss eight articles that directly compare resting-state functional connectivity with structural connectivity and three clinical case studies of patients with limited white matter connections between the cerebral hemispheres. All studies examined show largely convergent results: the strength of resting-state functional connectivity is positively correlated with structural connectivity strength. However, functional connectivity is also observed between regions where there is little or no structural connectivity, which most likely indicates functional correlations mediated by indirect structural connections (i.e. via a third region). As the methodologies for measuring structural and functional connectivity continue to improve and their complementary strengths are applied in parallel, we can expect important advances in our diagnostic and prognostic capacities in diseases like Alzheimer's, multiple sclerosis, and stroke.
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              Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke.

              Deficits following stroke are classically attributed to focal damage, but recent evidence suggests a key role of distributed brain network disruption. We measured resting functional connectivity (FC), lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients, and used machine-learning models to predict neurological impairment in individual subjects. We found that visual memory and verbal memory were better predicted by FC, whereas visual and motor impairments were better predicted by lesion topography. Attention and language deficits were well predicted by both. Next, we identified a general pattern of physiological network dysfunction consisting of decrease of interhemispheric integration and intrahemispheric segregation, which strongly related to behavioral impairment in multiple domains. Network-specific patterns of dysfunction predicted specific behavioral deficits, and loss of interhemispheric communication across a set of regions was associated with impairment across multiple behavioral domains. These results link key organizational features of brain networks to brain-behavior relationships in stroke.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                12 December 2017
                28 November 2017
                28 November 2017
                : 114
                : 50
                : 13278-13283
                Affiliations
                [1] aDepartment of Neurological Surgery, Washington University in St. Louis , St. Louis, MO 63110;
                [2] bMallinckrodt Institute Radiology, Washington University in St. Louis , St. Louis, MO 63110;
                [3] cNeurology, Washington University in St. Louis , St. Louis, MO 63110;
                [4] dBiomedical Engineering, Washington University in St. Louis , St. Louis, MO 63110;
                [5] eNeuroscience, Washington University in St. Louis , St. Louis, MO 63110;
                [6] fMechanical Engineering and Materials Science, Washington University in St. Louis , St. Louis, MO 63110;
                [7] gCenter for Innovation in Neuroscience and Technology, Washington University in St. Louis , St. Louis, MO 63110;
                [8] hBrain Laser Center, Washington University in St. Louis , St. Louis, MO 63110
                Author notes
                1To whom correspondence should be addressed. Email: rolandj@ 123456wustl.edu .

                Edited by Michel Thiebaut de Schotten, Institut du Cerveau et de la Moelle Épinière, Paris, France, and accepted by Editorial Board Member Marlene Behrmann November 7, 2017 (received for review May 10, 2017)

                Author contributions: J.L.R., J.S.S., D.D.L., M.E.R., M.D.S., and E.C.L. designed research; J.L.R., A.Z.S., A.M., D.D.L., and M.D.S. performed research; J.L.R., A.Z.S., C.D.H., D.D.L., and M.D.S. contributed new reagents/analytic tools; J.L.R., A.Z.S., C.D.H., A.M., and E.C.L. analyzed data; and J.L.R., A.Z.S., and E.C.L. wrote the paper.

                Author information
                http://orcid.org/0000-0002-1312-8826
                http://orcid.org/0000-0002-3379-9627
                Article
                201707050
                10.1073/pnas.1707050114
                5740665
                29183973
                eac2b3cb-0c51-4a71-9ada-ce8e205591fc
                Copyright © 2017 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 6
                Funding
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: R25 NS090978
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: U54 HD087011
                Funded by: WUSTL | McDonnell Center for Systems Neuroscience 100009607
                Award ID: 22-3920-26239M
                Funded by: HHS | National Institutes of Health (NIH) 100000002
                Award ID: P30 NS098577
                Categories
                Biological Sciences
                Neuroscience

                corpus callosum,resting state,functional connectivity,structural connectivity,callosotomy

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