Search bioRxiv⌕ Search

Biology subjects

Tobyne, S. M.

Publications and source records attributed to Tobyne, S. M..

3 recordsLinked to original sources

Combined Auditory, Tactile, and Visual fMRI Reveals Sensory-Biased and Supramodal Working Memory Regions in Human Frontal Cortex

Selectivity for sensory modality characterizes distinct subregions of the human brain, well beyond the primary sensory cortices. We previously identified frontal and posterior cortical regions that are preferentially recruited for visual vs. auditory attention and working memory (WM). Here, we extend our approach to include tactile cognition and to characterize cortical regions recruited by WM in each of three sensory modalities. The joint organization of visual-selective, auditory-selective, tactile-selective, and supramodal WM recruitment within individual subjects has not been fully investigated previously. Male and female human subjects participated in a blocked fMRI task requiring them to perform N-back WM judgements in auditory, visual, or tactile (haptic) modalities. We confirmed our prior reports of multiple visual-biased and auditory-biased frontal lobe regions. We also observed several bilateral tactile-selective regions abutting previously described visual- and auditory-selective regions, including dorsal and ventral precentral sulcus, the postcentral sulcus, and the anterior intraparietal sulcus. Several cortical regions were recruited by WM in all three sensory modalities in individual subjects, including precentral sulcus, inferior frontal sulcus, intraparietal sulcus, anterior insula and pre-supplementary motor area. Supramodal regions exhibited substantial overlap with visual-biased regions in frontal and parietal cortex and comparatively little overlap with tactile- or auditory-biased regions. Lastly, resting-state analyses revealed that auditory-, visual- and tactile-selective WM regions segregate into modality-specific networks that span frontal and posterior cortex. Together, these results shed light on the functional organization of sensory-selective and supramodal regions supporting higher-order cognition. SIGNIFICANCE STATEMENTUsing within-subject fMRI analyses of three different sensory modalities, we identify the fine-scale architecture of sensory-biased and supramodal working memory structures within human frontal cortex. Sixteen bilateral frontal lobe regions are identified: five auditory-biased, four visual-biased, two tactile-biased, and five multiple-demand / supramodal regions. These parcels are largely distinct with the notable exception that the visual-biased regions each partially overlap with supramodal regions, suggesting a tight link between visual and supramodal working memory representations. Sensory-biased frontal lobe regions form modality-specific networks with traditionally-identified posterior sensory cortical regions. The findings demonstrate a pervasive role for sensory modality in the functional organization of frontal cortex and offer a fine-scale parcellation of the human frontal lobe regions supporting working memory.

neuroscience↗

Extended frontal networks for visual and auditory working memory

Working memory (WM) supports the persistent representation of transient sensory information. Visual and auditory stimuli place different demands on WM and recruit different brain networks. Separate auditory- and visual-biased WM networks extend into the frontal lobes, but several challenges confront attempts to parcellate human frontal cortex, including fine-grained organization and between-subject variability. Here, we use differential intrinsic functional connectivity from two visual-biased and two auditory-biased frontal structures to identify additional candidate sensory-biased regions in frontal cortex. We then examine direct contrasts of task fMRI during visual vs. auditory 2-back WM to validate those candidate regions. Three visual-biased and five auditory-biased regions are robustly activated bilaterally in the frontal lobes of individual subjects (N=14, 7 women). These regions exhibit a sensory preference during passive exposure to task stimuli, and that preference is stronger during WM. Hierarchical clustering analysis of intrinsic connectivity among novel and previously identified bilateral sensory-biased regions confirms that they functionally segregate into visual and auditory networks, even though the networks are anatomically interdigitated. We also observe that the fronto-temporal auditory WM network is highly selective and exhibits strong functional connectivity to structures serving non-WM functions, while the fronto-parietal visual WM network hierarchically merges into the multiple-demand cognitive system.

neuroscience↗

Individual heterogeneity in the functional network topography of medial prefrontal cortex

Regions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into \"DN\" and \"non-DN\" subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.\n\nHighlightsO_LIThe topography of Default Network cortical regions varies across individuals.\nC_LIO_LIA community detection algorithm, spectral partitioning, was applied to rs-fMRI data.\nC_LIO_LIThe algorithm identified individualized Default Network regions in mPFC and PCC.\nC_LIO_LIDefault Network topography varied across individuals in mPFC, moreso than in PCC.\nC_LIO_LIOverlap of task effects with DN regions should be assessed at the individual level.\nC_LI

neuroscience↗