Characterization of the Intrinsic Neural Timescale Indices as a Reliable Fingerprint of Brain Dynamics
Intrinsic neural timescales (INTs) characterize the persistence of neural activity over time and offer insight into the brain’s temporal hierarchy. This thesis evaluates and compares six autocorrelation-based INT metrics—AR(1) Coefficient (Autoregressive Coefficient at 1), First Zero Lag, Lag at ACF = 1/e, Sum of Positive ACFs, ACF at Half Lag, Exponential Decay Constant (τ)— using resting-state fMRI data from 400 cortical regions across a large, lifespan sample (ages 6– 85) from the NKI-Rockland dataset. A discriminability analysis revealed that the AR(1) coefficient provided the highest test-retest reliability and individual specificity across repeated scans, establishing it as the most reliable INT estimator. Using the AR(1) metric, we investigated how intrinsic timescales vary systematically with age, sex, across the cortical hierarchy. A linear mixedeffects model revealed nonlinear (inverted-U) age trends, sex interactions, and widespread motion sensitivity, particularly in sensory and salience networks. These findings support the existence of a developmentally structured gradient of cortical temporal dynamics and underscore the need for careful metric selection and motion correction in INT research. By combining comparative metric evaluation with a lifespan analysis, this study lays the groundwork for establishing normative baselines essential for future clinical investigations into neurodevelopmental disorders.
Intrinsic Neural Timescales (INTs), Brain Dynamics, Resting-State fMRI, Discriminability Analysis, Neural Biomarkers, Statistical Modeling, Bioelectrical and Neuroengineering, Biomedical Engineering and Bioengineering
City University of New York - City College
Networked Digital Library of Theses and Dissertations