Systems-level decoding reveals the cognitive and behavioral profile of the human intraparietal sulcus

Abstract

IntroductionThe human intraparietal sulcus (IPS) covers large portions of the posterior cortical surface and has been implicated in a variety of cognitive functions. It is, however, unclear how cognitive functions dissociate between the IPS's heterogeneous subdivisions, particularly in perspective to their connectivity profile.MethodsWe applied a neuroinformatics driven system-level decoding on three cytoarchitectural distinct subdivisions (hIP1, hIP2, hIP3) per hemisphere, with the aim to disentangle the cognitive profile of the IPS in conjunction with functionally connected cortical regions.ResultsThe system-level decoding revealed nine functional systems based on meta-analytical associations of IPS subdivisions and their cortical coactivations: Two systems–working memory and numeric cognition–which are centered on all IPS subdivisions, and seven systems–attention, language, grasping, recognition memory, rotation, detection of motions/shapes and navigation–with varying degrees of dissociation across subdivisions and hemispheres. By probing the spatial overlap between systems-level co-activations of the IPS and seven canonical intrinsic resting state networks, we observed a trend toward more co-activation between hIP1 and the front parietal network, between hIP2 and hIP3 and the dorsal attention network, and between hIP3 and the visual and somatomotor network.DiscussionOur results confirm previous findings on the IPS's role in cognition but also point to previously unknown differentiation along the IPS, which present viable starting points for future work. We also present the systems-level decoding as promising approach toward functional decoding of the human connectome.

Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences

“…Functional characterization of all 82 regions of interests was achieved via meta‐analytic reverse inference decoding, using the NeurosynthDecoder as implemented in the Neuroimaging Meta‐Analysis Research Environment (NiMARE; Salo et al, 2022), following our standard protocols (Boeken et al, 2022; Boeken & Markett, 2023). Neurosynth is a comprehensive data base of the functional neuroimaging literature and contains functional activation foci from 14,371 functional neuroimaging together with relevant meta data (called terms) which can be queried to gain insights under which conditions these activation foci have been obtained.…”