Individual differences in retinotopic and network organization in Superior Parietal Lobule
Poster No:
2075
Submission Type:
Abstract Submission
Authors:
Vaibhav Tripathi1, David Somers2
Institutions:
1Harvard University, Cambridge, MA, 2Boston University, Boston, MA
First Author:
Co-Author:
Introduction:
Retinotopically organized visual, attention and working memory (WM) regions tile human superior parietal lobule (SPL). Early evidence (Swisher et al., 2007) reported individual differences in retinotopic organization, with some hemispheres exhibiting continuous retinotopic mapping along the medial bank of the intraparietal sulcus (IPS) from IPS0 to IPS4 and other hemispheres showing gaps between the retinotopic maps in the vicinity of IPS2; however, most current researchers (e.g. Wang et al., 2015) treat IPS retinotopy as continuous. Here, we investigate individual differences in SPL functional organization by examining retinotopy, resting-state connectivity, working memory task activation, and myelin density. We observe substantial individual differences in IPS retinotopy (continuous or fractured) that correlates with individual differences in the organization of resting-state subnetworks dorsal attention A (dATN-A: non-retinotopy) and dorsal attention B (dATN-B: retinotopy), working memory activation (stronger in the gaps / dATN-A), and myelin density (stronger in dATN-B).
Methods:
We utilized a subset of the Human Connectome Project (HCP) dataset (n=129; db.humanconnectome.org.) which had retinotopic data collected at 7T in addition to the full protocol at 3T (Glasser et al., 2013; Van Essen et al., 2013). Retinotopic maps (6 runs) were computed using population receptive field (pRF) modeling (Dumoulin & Wandell, 2008). Model fits are publicly available (Benson et al., 2018). A 2-Back visual working memory (WM) task (2 runs) was run to compute the 2-Back vs 0-Back task contrast for each individual. Fixation-based resting state conditions (15-min run, 4 runs) were used for the creation of resting state networks (RSN) using the Multi-session Hierarchical Bayesian Model (MS-HBM) method (Kong et al., 2019). Here, we used a 15 network separation (Du et al., 2024). Myelination was computed using the method described in using a ratio of T1w/T2w contrast across the cortical sheet (Glasser & Van Essen, 2011).
Results:
We computed individualized 15-network RSNs for each subject (Figure 1). Focusing on SPL (Figure 1b), we found that a majority of subjects have a discontinuity (91 out of 129) in dorsal attention network representations as highlighted in dark green (dorsal attention network A or dATN-A) breaking through light green (dorsal attention network B or dATN-B) regions. 38 had a continuous dATN-B map in at least one of the hemispheres (15: continuous in both).
Individual differences in dATN-A/B RSN organization aligned with individual differences in retinotopy and with individual differences in WM task activation patterns for subjects with and (Figure 1c). Retinotopic regions aligned with the dATN-B network, while WM preferred dATN-A regions (t(128) = 7.18, p < 0.001) (Figures 1c,d). Similarly we observed differences in myelination densities with dATN-B > dATN-A (Figure 1c,d). We found that subjects with a continuous dATN-B showed a similarly continuous organization of the retinotopic area from V1 to IPS3 (Figure 2) whereas subjects having discontinuous dATN-B showed a break in their retinotopic map after IPS1 where IPS2 and IPS3 were organized beyond the break (Figure 2).
Individual differences in dATN-A/B RSN organization aligned with individual differences in retinotopy and with individual differences in WM task activation patterns for subjects with and (Figure 1c). Retinotopic regions aligned with the dATN-B network, while WM preferred dATN-A regions (t(128) = 7.18, p < 0.001) (Figures 1c,d). Similarly we observed differences in myelination densities with dATN-B > dATN-A (Figure 1c,d). We found that subjects with a continuous dATN-B showed a similarly continuous organization of the retinotopic area from V1 to IPS3 (Figure 2) whereas subjects having discontinuous dATN-B showed a break in their retinotopic map after IPS1 where IPS2 and IPS3 were organized beyond the break (Figure 2).
·Figure 1: Multimodal estimation of juxtaposed retinotopic and parietal regions along parietal cortex.
·Figure 2: Break in dorsal parietal retinotopy/attention network.
Conclusions:
We observed substantial individual differences in functional organization in IPS/SPL. Many subject hemispheres exhibited a break in the retinotopic regions in IPS. Retinotopic gaps align with regions of strong WM task activation, rather than simply reflecting low-SNR voxels. This interleaved, complementary pattern of retinotopy and WM organization also aligned well with resting-state networks dATN-B and dATN-A, respectively, in individuals. These results indicate that two streams of functional organization - retinotopy (anterior-posterior) and WM (medial-lateral; also see Lefco et al, 2020) cross near retinotopic region IPS2 and that the precise functional organization at this junction differs across individuals.
Learning and Memory:
Working Memory
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Perception, Attention and Motor Behavior:
Perception: Visual 1
Keywords:
Cognition
FUNCTIONAL MRI
Myelin
Vision
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
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Lefco, R.W., Brissenden, J.A., Noyce, A.L., Tobyne, S.M, & Somers, D.C. (2020)., Gradients of Functional Organization in Posterior Parietal Cortex Revealed by Visual Attention, Visual Short-Term Memory, and Intrinsic Functional Connectivity. NeuroImage, 219:117029. 117029
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