Data were analyzed using Nilearn (version= {{ version }}; RRID:SCR_001362).
{{if model_type == "First Level Model"}}At the subject level, a mass univariate analysis was performed with a linear regression at each voxel of the brain, using generalized least squares with a global {{ reporting_data.noise_model }} noise model to account for temporal auto-correlation {{ reporting_data.drift_model }}.
{{if reporting_data.trial_types}}Regressors were entered into run-specific design matrices and onsets were convolved with a {{ reporting_data.hrf_model }} canonical hemodynamic response function for the following conditions:
At the group level, a mass univariate analysis was performed with a linear regression at each voxel of the brain.
{{endif}} {{if smoothing_fwhm }}Input images were smoothed with gaussian kernel (full-width at half maximum={{ smoothing_fwhm }} mm).
{{endif}} {{if contrasts }}The following contrasts were computed {{if model_type == "First Level Model"}} using a fixed-effect approach across runs {{endif}}: