{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T22:50:35Z","timestamp":1778107835652,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013714","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000}}],"reference-count":50,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Johns Hopkins Catalyst Award"},{"name":"Institute for Data Intensive Engineering and Science"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF PHY-2309135"],"award-info":[{"award-number":["NSF PHY-2309135"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>How does the human brain encode complex visual information? While previous research has characterized individual dimensions of visual representation in cortex, we still lack a comprehensive understanding of how visual information is organized across the full range of neural population activity. Here, analyzing fMRI responses to natural scenes across multiple individuals, we discover that neural representations in human visual cortex follow a remarkably consistent scale-free organization\u2014their variance decay is consistent with a power-law distribution, detected across four orders of magnitude of latent dimensions. This scale-free structure appears consistently across multiple visual regions and across individuals, suggesting it reflects a fundamental organizing principle of visual processing. Critically, when we align neural responses across individuals using hyperalignment, we find that these representational dimensions are largely shared between people, revealing a universal high-dimensional spectrum of visual information that emerges despite individual differences in brain anatomy and visual experience. Traditional analysis approaches in cognitive neuroscience have focused primarily on a small number of high-variance dimensions, potentially missing crucial aspects of visual representation. Our results demonstrate that visual information is distributed across the full dimensionality of cortical activity in a systematic way, thus revealing a key property of neural coding in visual cortex. These findings suggest that we need to move beyond low-dimensional characterizations to fully understand how the brain represents the visual world.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013714","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T18:54:01Z","timestamp":1763751241000},"page":"e1013714","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Universal scale-free representations in human visual cortex"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7121-1532","authenticated-orcid":true,"given":"Raj Magesh","family":"Gauthaman","sequence":"first","affiliation":[]},{"given":"Brice","family":"M\u00e9nard","sequence":"additional","affiliation":[]},{"given":"Michael F.","family":"Bonner","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"issue":"19","key":"pcbi.1013714.ref001","doi-asserted-by":"crossref","DOI":"10.1016\/j.cub.2022.08.009","article-title":"A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition","volume":"32","author":"M Khosla","year":"2022","journal-title":"Curr Biol."},{"issue":"1","key":"pcbi.1013714.ref002","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1038\/s41467-020-16846-w","article-title":"Sociality and interaction envelope organize visual action representations","volume":"11","author":"L Tarhan","year":"2020","journal-title":"Nat Commun."},{"issue":"2","key":"pcbi.1013714.ref003","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.neuron.2011.08.026","article-title":"A common, high-dimensional model of the representational space in human ventral temporal cortex","volume":"72","author":"JV Haxby","year":"2011","journal-title":"Neuron."},{"issue":"6","key":"pcbi.1013714.ref004","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1016\/j.neuron.2012.10.014","article-title":"A continuous semantic space describes the representation of thousands of object and action categories across the human brain","volume":"76","author":"AG Huth","year":"2012","journal-title":"Neuron."},{"issue":"10","key":"pcbi.1013714.ref005","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1162\/NECO_a_00648","article-title":"Dimensionality of object representations in monkey inferotemporal cortex","volume":"26","author":"SR Lehky","year":"2014","journal-title":"Neural Comput."},{"key":"pcbi.1013714.ref006","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.conb.2015.12.001","article-title":"Neural representation for object recognition in inferotemporal cortex","volume":"37","author":"SR Lehky","year":"2016","journal-title":"Curr Opin Neurobiol."},{"key":"pcbi.1013714.ref007","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.conb.2016.01.010","article-title":"Why neurons mix: high dimensionality for higher cognition","volume":"37","author":"S Fusi","year":"2016","journal-title":"Curr Opin Neurobiol."},{"key":"pcbi.1013714.ref008","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.47142","article-title":"The nature of the animacy organization in human ventral temporal cortex","volume":"8","author":"S Thorat","year":"2019","journal-title":"Elife."},{"issue":"8","key":"pcbi.1013714.ref009","doi-asserted-by":"crossref","first-page":"2608","DOI":"10.1523\/JNEUROSCI.5547-11.2012","article-title":"The representation of biological classes in the human brain","volume":"32","author":"AC Connolly","year":"2012","journal-title":"J Neurosci."},{"issue":"7765","key":"pcbi.1013714.ref010","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1038\/s41586-019-1346-5","article-title":"High-dimensional geometry of population responses in visual cortex","volume":"571","author":"C Stringer","year":"2019","journal-title":"Nature."},{"key":"pcbi.1013714.ref011","first-page":"4","article-title":"Representational similarity analysis - connecting the branches of systems neuroscience","volume":"2","author":"N Kriegeskorte","year":"2008","journal-title":"Front Syst Neurosci."},{"key":"pcbi.1013714.ref012","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.56601","article-title":"Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies","volume":"9","author":"JV Haxby","year":"2020","journal-title":"Elife."},{"issue":"1","key":"pcbi.1013714.ref013","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41593-021-00962-x","article-title":"A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence","volume":"25","author":"EJ Allen","year":"2022","journal-title":"Nat Neurosci."},{"key":"pcbi.1013714.ref014","doi-asserted-by":"crossref","unstructured":"Wandell BA, Dumoulin SO, Brewer AA. 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