Abstract
Fine-grained functional organization of cortex is not well-conserved across individuals. As a result, individual differences in cortical functional architecture are confounded by topographic idiosyncrasies—i.e., differences in functional–anatomical correspondence. In this study, we used hyperalignment to align information encoded in topographically variable patterns to study individual differences in fine-grained cortical functional architecture in a common representational space. We characterized the structure of individual differences using three common functional indices, and assessed the reliability of this structure across independent samples of data in a natural vision paradigm. Hyperalignment markedly improved the reliability of individual differences across all three indices by resolving topographic idiosyncrasies and accommodating information encoded in spatially fine-grained response patterns. Our results demonstrate that substantial individual differences in cortical functional architecture exist at fine spatial scales, but are inaccessible with anatomical normalization alone.
Highlights
Functional architecture of the human brain is relatively consistent across individuals at a coarse scale, but idiosyncrasies in functional topography become increasingly apparent at finer scales
Our results suggest that substantial individual differences exist at a fine spatial scale, but are obscured by idiosyncratic functional–anatomical correspondence; hyperalignment can reveal these fine-scale individual differences, and make observed individual differences in cortical functional architecture more reliable
In this study, we measured individual differences in local cortical functional architecture based on three functional indices—response profiles, functional connectivity, and representational geometry—using dynamic, naturalistic movie stimuli
Summary
Functional architecture of the human brain is relatively consistent across individuals at a coarse scale, but idiosyncrasies in functional topography become increasingly apparent at finer scales. Hyperalignment projects features (voxels or surface vertices) from a brain to a common high-dimensional space through linear transformations In this common space, the same features from different individuals will share similar functional properties instead of the same anatomical locations or topographic conformations. Hyperalignment decomposes the original fMRI data of each individual into two parts: a transformation matrix, which reflects topographic properties of the individual’s functional activations; and a new data matrix in the common space, which reflects shared, stimulus-driven responses. This hyperaligned data matrix provides an opportunity to study brain functions without confounds from topographic variability. Our results suggest that substantial individual differences exist at a fine spatial scale, but are obscured by idiosyncratic functional–anatomical correspondence; hyperalignment can reveal these fine-scale individual differences, and make observed individual differences in cortical functional architecture more reliable
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