estimators.rst

Estimators

When fitting hierarchical models, there are two broad classes of estimators: time-series estimators, which accept a time series and a design matrix and produce statistical maps; and summary-statistic estimators, which accept a collection of statistical maps and a design matrix, and produce statistical maps. Time-series estimators fit models that are often called "first-level" or "run-level" models, and summary-statistic estimators fit models that are often called "second-level" or perhaps "subject-level" or "group-level".

Time-series estimators

Fitlins supports two estimators for first-level models: Nilearn's :py:class:`~nilearn.glm.first_level.FirstLevelModel` and AFNI's :ref:`3dREMLfit<afni:ahelp_3dremlfit>`. In both cases, the design matrix is generated by PyBIDS and Nilearn. You can select the AFNI estimator by passing --estimator afni to FitLins.

Nilearn

Fits a normal general linear model (GLM) assuming that the residuals are not autocorrelated. This is quick and no-frills.

AFNI

Fits a "prewhitened" GLM with an ARMA(1,1) model to each voxel to account for autocorrelated time series noise. The GLM and the ARMA(1,1) model are simultaneously optimized with a restricted maximum likelihood approach. The downside of the approach is that it's a bit slower as it has to iteratively fit the GLM and ARMA model at each voxel.

Summary statistic estimators

For "glm" model types, FitLins currently only supports Nilearn's :py:class:`~nilearn.glm.second_level.SecondLevelModel` estimator.

For "meta" model types, FitLins uses Nilearn's :py:func:`~nilearn.glm.compute_fixed_effects` to compute a fixed-effects combination.