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Estimating fMRI timescale maps.

Gabriel Riegner¹, Samuel Davenport², Bradley Voytek^1,3,4

¹Halicioğlu Data Science Institute, University of California San Diego, La Jolla, CA, United States.

Imaging Neuroscience (Cambridge, Mass.)

|June 8, 2026

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This study introduces new methods for mapping brain activity timescales using resting-state functional MRI (fMRI). These advanced techniques offer more accurate estimates and enable statistical inference on brain dynamics.

Area of Science:

Neuroscience
Computational Neuroscience
Data Science

Background:

Brain activity occurs across hierarchical timescales, crucial for information processing.
Current methods for estimating brain timescales in fMRI have limitations, including restrictive assumptions and lack of statistical inference.

Purpose of the Study:

To develop and evaluate novel methods for mapping brain timescales in resting-state fMRI.
To overcome limitations of existing methods by relaxing assumptions and enabling statistical inference.

Main Methods:

Formalized and evaluated two methods: time-domain autoregressive (AR1) model fitting and autocorrelation-domain exponential decay model fitting.
Defined timescales by projecting fMRI time series onto approximating models, requiring only stationarity and mixing conditions.

Keywords:

autocorrelation-domain nonlinear model functional brain organization human connectome project statistical inference time-domain linear model uncertainty quantification

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Incorporated robust standard errors to account for model misspecification and provided theoretical properties of estimators.

Main Results:

The time-domain method demonstrated more accurate estimates, especially under model misspecification.
This method is computationally efficient for high-dimensional fMRI data.
Results yielded timescale maps consistent with known functional brain organization.

Conclusions:

The study successfully demonstrates valid statistical inference on fMRI timescale maps.
Introduced novel, more robust methods for analyzing brain dynamics.
Provided Python implementations for broader research application.