2026-10-12T14:37:30Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34027016 2026-09-29T17:40:24Z 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

<p>Quantifying intrinsic timescale using autocorrelation function.</p> 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 <p>Fully processed parcellated fMRI time series were used to estimate intrinsic timescale for each brain region (i.e., parcel) and participant. We estimated an autocorrelation function (ACF) for the normalized (i.e., z-scored) time series. Intrinsic timescale was then quantified as the sum of positive autocorrelation values (ac  >  0; colored parts of the ACFs above) multiplied by TR for each cortical region and individual. This is approximately equivalent to calculating the area under the curve of the positive part of the ACF curve (i.e., shaded areas). As demonstrated in the example plots, a fast-decaying ACF will have shorter memory, and thus a shorter timescale (orange region and ACF plot), compared to a slow-decaying ACF (purple region and ACF plot). 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>.</p> 2026-09-29T18:01:47Z Image Figure 10.1371/journal.pbio.3004037.g001 https://figshare.com/articles/figure/_p_Quantifying_intrinsic_timescale_using_autocorrelation_function_p_/34027016 CC BY 4.0