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oai:figshare.com:article/34027022 2026-09-29T17:40:26Z 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

Developmental patterns of 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 Generalized additive models (GAMs) were used to assess linear and nonlinear age effects in intrinsic timescale. (a) A whole-brain GAM was applied to model age-related differences in cortex-wide average timescale. GAM results demonstrated that average timescale increases during development in youth (partial R2 = 0.047, panova = 1.25 × 10−7). (b) Region-wise GAMs were used to examine developmental patterns in intrinsic timescale at the regional level. Age effects quantified as partial R2 are depicted across the cortex, displaying a heterogeneous spatial distribution. (c) Age effects on intrinsic timescale were compared to the S–A axis, identifying a hierarchical pattern of developmental patterns in intrinsic timescale along the S–A axis. Significance of the association between age effects and S–A axis rank was assessed using 10,000 spin tests. rs denotes Spearman’s rank correlation coefficient. Linear regression line is added for visualization purposes only. (d) Regional trajectories of developmental patterns in intrinsic timescale were obtained from region-wise GAM results (as shown in panel b). Each line corresponds to the model fit of each cortical region and is colored based on the region’s rank along the S–A axis. The results demonstrate that intrinsic timescale in association regions increases during development in youth while it remains relatively stable in sensorimotor regions. 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>. 2026-09-29T18:01:47Z Image Figure 10.1371/journal.pbio.3004037.g003 https://figshare.com/articles/figure/_p_Developmental_patterns_of_intrinsic_timescale_p_/34027022 CC BY 4.0