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      Functional connectivity modeling of consistent cortico-striatal degeneration in Huntington's disease

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          Abstract

          Huntington's disease (HD) is a progressive neurodegenerative disorder characterized by a complex neuropsychiatric phenotype. In a recent meta-analysis we identified core regions of consistent neurodegeneration in premanifest HD in the striatum and middle occipital gyrus (MOG). For early manifest HD convergent evidence of atrophy was most prominent in the striatum, motor cortex (M1) and inferior frontal junction (IFJ). The aim of the present study was to functionally characterize this topography of brain atrophy and to investigate differential connectivity patterns formed by consistent cortico-striatal atrophy regions in HD. Using areas of striatal and cortical atrophy at different disease stages as seeds, we performed task-free resting-state and task-based meta-analytic connectivity modeling (MACM). MACM utilizes the large data source of the BrainMap database and identifies significant areas of above-chance co-activation with the seed-region via the activation-likelihood-estimation approach. In order to delineate functional networks formed by cortical as well as striatal atrophy regions we computed the conjunction between the co-activation profiles of striatal and cortical seeds in the premanifest and manifest stages of HD, respectively. Functional characterization of the seeds was obtained using the behavioral meta-data of BrainMap. Cortico-striatal atrophy seeds of the premanifest stage of HD showed common co-activation with a rather cognitive network including the striatum, anterior insula, lateral prefrontal, premotor, supplementary motor and parietal regions. A similar but more pronounced co-activation pattern, additionally including the medial prefrontal cortex and thalamic nuclei was found with striatal and IFJ seeds at the manifest HD stage. The striatum and M1 were functionally connected mainly to premotor and sensorimotor areas, posterior insula, putamen and thalamus. Behavioral characterization of the seeds confirmed that experiments activating the MOG or IFJ in conjunction with the striatum were associated with cognitive functions, while the network formed by M1 and the striatum was driven by motor-related tasks. Thus, based on morphological changes in HD, we identified functionally distinct cortico-striatal networks resembling a cognitive and motor loop, which may be prone to early disruptions in different stages of the disease and underlie HD-related cognitive and motor symptom profiles. Our findings provide an important link between morphometrically defined seed-regions and corresponding functional circuits highlighting the functional and ensuing clinical relevance of structural damage in HD.

          Highlights

          • Pre-HD atrophy seeds showed common functional co-activation with a cognitive network.

          • Modeling of manifest-HD seeds delineated a segregation of a cognitive and motor loop.

          • Behavioral decoding of atrophy seeds confirmed functional segregation of networks.

          • Based on morphometric changes in HD distinct corticostriatal networks were identified.

          • Findings depict functional and ensuing clinical relevance of structural damage in HD.

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          Most cited references57

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          Parallel organization of functionally segregated circuits linking basal ganglia and cortex.

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            Unified segmentation

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              Assignment of functional activations to probabilistic cytoarchitectonic areas revisited.

              Probabilistic cytoarchitectonic maps in standard reference space provide a powerful tool for the analysis of structure-function relationships in the human brain. While these microstructurally defined maps have already been successfully used in the analysis of somatosensory, motor or language functions, several conceptual issues in the analysis of structure-function relationships still demand further clarification. In this paper, we demonstrate the principle approaches for anatomical localisation of functional activations based on probabilistic cytoarchitectonic maps by exemplary analysis of an anterior parietal activation evoked by visual presentation of hand gestures. After consideration of the conceptual basis and implementation of volume or local maxima labelling, we comment on some potential interpretational difficulties, limitations and caveats that could be encountered. Extending and supplementing these methods, we then propose a supplementary approach for quantification of structure-function correspondences based on distribution analysis. This approach relates the cytoarchitectonic probabilities observed at a particular functionally defined location to the areal specific null distribution of probabilities across the whole brain (i.e., the full probability map). Importantly, this method avoids the need for a unique classification of voxels to a single cortical area and may increase the comparability between results obtained for different areas. Moreover, as distribution-based labelling quantifies the "central tendency" of an activation with respect to anatomical areas, it will, in combination with the established methods, allow an advanced characterisation of the anatomical substrates of functional activations. Finally, the advantages and disadvantages of the various methods are discussed, focussing on the question of which approach is most appropriate for a particular situation.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                27 February 2015
                27 February 2015
                2015
                : 7
                : 640-652
                Affiliations
                [a ]Department of Neurology, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
                [b ]Institute of Neuroscience and Medicine (INM-1, INM-4), Research Center Jülich GmbH, 52425 Jülich, Germany
                [c ]JARA — Translational Brain Medicine, Aachen, Jülich, Germany
                [d ]Department of Psychiatry, Psychotherapy and Psychosomatic, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany
                [e ]Research Imaging Center, University of Texas Health Science Center, 7703 Floyd Curl Drive, San Antonio, TX 78284-7801, USA
                [f ]Department of Physics, Florida International University, Modesto A. Maidique Campus, CP 204, 11200 SW 8th Street, Miami, FL 33199, USA
                [g ]Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University, Universitätsstr. 1, 40225 Düsseldorf, Germany
                Author notes
                [* ]Corresponding author at: Department of Neurology, RWTH Aachen University, Pauwelsstr. 30, 52074 Aachen, Germany. Tel.: +49 241 80 85522; fax: +49 241 80 33 36516. kreetz@ 123456ukaachen.de
                [1]

                Contributed equally to this work.

                Article
                S2213-1582(15)00036-4
                10.1016/j.nicl.2015.02.018
                4375786
                25844318
                cd08b0a8-cb0b-455f-8e83-b52ac1e3a526
                © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

                History
                : 23 December 2014
                : 19 February 2015
                : 23 February 2015
                Categories
                Article

                neurodegeneration,atrophy,functional connectivity,resting-state,functional mri,meta-analytic connectivity modeling

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