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oai:figshare.com:article/34027013 2026-09-29T17:40:22Z category_1 category_12 category_15 category_272 category_734 category_811 category_61 category_122 portal_5 item_type_1 month_year_09_2026

Short- and long-lag contributions to ACF-sum intrinsic timescale. Golia Shafiei (3191169) Joëlle Bagautdinova (25138348) Valerie J. Sydnor (25138351) Dani S. Bassett (13104131) Deanna M. Barch (6759263) Matthew Cieslak (3112521) Yong Fan (212624) Elizabeth Flook (25138354) Alexandre R. Franco (6564785) Gregory Kiar (785219) Audrey C. Luo (25138357) Michael P. Milham (8895014) Linden Parkes (6617396) Taylor Salo (5277443) Leah H. Somerville (5352008) Tien T. Tong (25138360) Russell T. Shinohara (9019436) Theodore D. Satterthwaite (11006319) Biophysics Cell Biology Neuroscience Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Developmental Biology Mental Health two independent cross spontaneous neural dynamics results reveal convergence commonly used measure hierarchical brain maturation independent developmental samples div >< p intrinsic timescale follow intrinsic timescale evolves intrinsic timescale displays highlighting intrinsic timescale human brain development sensorimotor cortex compared axis spanning sensorimotor adulthood </ p intrinsic timescale hierarchical pattern developmental patterns developmental findings association cortex n </ temporal window shorter timescales sectional datasets principled marker neuronal populations neurodevelopmental patterns major axes imaging modalities cortical organization axis ). association cortices To examine whether ACF-sum intrinsic timescale was differentially driven by short- versus longer-lag autocorrelation structure, we decomposed the positive ACF area into short-lag and long-lag components. (a) The short-lag component was modestly associated with the intrinsic timescale estimated using the original ACF-sum method. The regional age effects from the short-lag component were moderately associated with age effects from the original method. (b) In contrast, the short-lag component was highly associated with lag-1 temporal autocorrelation (lag-1 TA), both for regional timescale maps and regional age effects. (c) The long-lag component and its age effects were strongly associated with the original findings estimated using the ACF-sum method. (d) In contrast, the long-lag component was not significantly associated with lag-1 TA in regional timescale maps and was only weakly associated with lag-1 TA regional age effects. Each data point in the scatter plot represents a brain region from the Schaefer 400 atlas. The data points are colored based on their rankings on the S–A axis. rs denotes Spearman’s rank correlation coefficient. Significance was assessed using 10,000 spin tests. Linear regression lines are added for visualization purposes only. Data and code needed to regenerate this figure can be found at <a href="https://doi.org/10.5281/zenodo.22799416" target="_blank">https://doi.org/10.5281/zenodo.22799416</a>. (TIFF) 2026-09-29T18:01:47Z Image Figure 10.1371/journal.pbio.3004037.s011 https://figshare.com/articles/figure/_p_Short-_and_long-lag_contributions_to_ACF-sum_intrinsic_timescale_p_/34027013 CC BY 4.0