• J Neuroimaging · Mar 2024

    Meta Analysis

    A parcellation-based connectomic model of hemispatial neglect.

    • Syed A Ahsan, Nicholas B Dadario, Jasneet Dhaliwal, Robert G Briggs, Karol Osipowicz, Syed M Ahsan, Kassem Chendeb, Andrew K Conner, Christen M O'Neal, Chad A Glenn, and Michael E Sughrue.
    • Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia.
    • J Neuroimaging. 2024 Mar 1; 34 (2): 267279267-279.

    Background And PurposeHemispatial neglect is characterized by a reduced awareness to stimuli on the contralateral side. Current literature suggesting that damage to the right parietal lobe and attention networks may cause hemispatial neglect is conflicting and can be improved by investigating a connectomic model of the "neglect system" and the anatomical specificity of regions involved in it.MethodsA meta-analysis of voxel-based morphometry magnetic resonance imaging (MRI) studies of hemispatial neglect was used to identify regions associated with neglect. We applied parcellation schemes to these regions and performed diffusion spectrum imaging (DSI) tractography to determine their connectivity. By overlaying neglect areas and maps of the attention networks, we studied the relationship between them.ResultsThe meta-analysis generated a list of 13 right hemisphere parcellations. These 13 neglect-related parcellations were predominantly linked by the superior longitudinal fasciculus (SLF) throughout a fronto-parietal-temporal network. We found that the dorsal and ventral attention networks showed partial overlap with the neglect system and included various other higher-order networks.ConclusionsWe provide an anatomically specific connectomic model of the neurobehavioral substrates underlying hemispatial neglect. Our model suggests a fronto-parietal-temporal network linked via the SLF supports the functions impaired in neglect and implicates various higher-order networks which are not limited to the attention networks.© 2023 American Society of Neuroimaging.

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