{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T18:51:45Z","timestamp":1791571905626,"version":"4.3.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014821","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000}}],"reference-count":126,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Indian Institute of Technology Jodhpur","id":[{"id":"https:\/\/ror.org\/03yacj906","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Department of Biotechnology, Government of India","award":["BT\/HRD\/Dementia\/2017"],"award-info":[{"award-number":["BT\/HRD\/Dementia\/2017"]}],"id":[{"id":"https:\/\/ror.org\/036h6g940","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Ecological systems obey universal scaling laws such as Taylor\u2019s law, which links population variance to the sample mean. Whether other complex systems with integrated and segregated network topology, the human brain, in particular, follow analogous scaling laws, and how these laws behave under synchronized neural activity, remains unknown. To address this question, our initial attempt to implement the Taylor\u2019s law, has been hindered because neural signals at all measurement scales contain both positive and negative values, whereas Taylor\u2019s law was restricted to non-negative ecological data. To circumvent this limitation, we extend spatial Taylor\u2019s law by replacing the sample mean with the root-mean-square (RMS) of the measurements, and apply this generalized law to detrended multivariate neural time series. The detrending operation has been performed as a necessary step to remove the trends and temporal mean from the data. Within this framework, we address our next question how synchronous or coordinated activity of the brain network influences the scaling law. We quantify synchrony using a dedicated metric of functional coordination and show analytically, and via numerical simulations using multivariate Poisson, negative binomial, uniform and gamma distributions, that the scaling exponent is inversely related to synchrony. Applying this approach to human fMRI data from three large cohorts (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u2009840, ages 18\u201388 years) reveals distinct age-related trajectories of the scaling exponent, with healthy aging characterized by a substantial synchrony-induced reduction in this exponent. The synchrony\u2013scaling relationship remains stable during resting-state activity but progressively shifts during naturalistic tasks across the lifespan, and is pronounced in limbic, subcortical, and cerebellar subnetworks, indicating preservation at subnetwork levels. Finally, individuals with ADHD exhibit altered synchrony\u2013scaling coupling, highlighting the potential clinical and translational utility of this generalized scaling metric.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014821","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T17:46:22Z","timestamp":1791222382000},"page":"e1014821","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A generalized scaling law reveals synchrony-driven reorganization of brain dynamics across human lifespan"],"prefix":"10.1371","volume":"22","author":[{"given":"Suman","family":"Saha","sequence":"first","affiliation":[{"name":"Vellore Institute of Technology","place":["Chennai, Tamil Nadu, India"],"department":["School of Electronics 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Dynamics"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gustavo","family":"Deco","sequence":"additional","affiliation":[{"name":"Universitat Pompeu Fabra","place":["Barcelona, Spain"],"department":["Centre for Brain and Cognition, Computational Neuroscience Group"]},{"name":"Universitat Pompeu Fabra","place":["Barcelona, Spain"],"department":["Department of Information and Communication Technologies"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arpan","family":"Banerjee","sequence":"additional","affiliation":[{"name":"National Brain Research Centre","place":["Manesar, Haryana, India"],"department":["Cognitive Brain Dynamics Lab"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syamal Kumar","family":"Dana","sequence":"additional","affiliation":[{"name":"Technical University of Lodz","place":["Lodz, Poland"],"department":["Division of Dynamics"]},{"name":"Jadavpur University","place":["Kolkata, West Bengal, India"],"department":["Centre for Mathematical Biology and Ecology, Department of Mathematics"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1669-1083","authenticated-orcid":true,"given":"Dipanjan","family":"Roy","sequence":"additional","affiliation":[{"name":"National Brain Research Centre","place":["Manesar, Haryana, India"],"department":["Cognitive Brain Dynamics Lab"]},{"name":"Indian Institute of Technology","place":["Jodhpur, Rajasthan, India"],"department":["Department of Bioscience and Bioengineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"issue":"4766","key":"pcbi.1014821.ref001","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1038\/189732a0","article-title":"Aggregation, Variance and the Mean","volume":"189","author":"LR 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