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oai:figshare.com:article/34027004 2026-09-29T17:40:16Z 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

Convergent results with methodological sensitivity analysis in quantifying 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 test whether intrinsic timescale estimates were sensitive to methodological choices, we quantified the intrinsic timescale in HCPD using two other approaches. (a) The intrinsic timescale was quantified as the first zero-crossing time point of the fMRI time series autocorrelation functions (ACF). (b) The intrinsic timescale was quantified as the exponential decay constant from an exponential fit to ACF. Both approaches generated intrinsic timescale maps consistent with the original method. Additionally, the developmental patterns were consistent with the original analysis. 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. 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.s008 https://figshare.com/articles/figure/_p_Convergent_results_with_methodological_sensitivity_analysis_in_quantifying_intrinsic_timescale_p_/34027004 CC BY 4.0