alff computes the amplitude of low-frequency fluctuations (ALFF) in each voxel of the processed image. Low-frequency fluctuations are of particular importance because functional connectivity is most typically computed on the basis of synchronous activations at low frequencies. It is possible that the magnitude (amplitude) of such activations has utility as a biomarker for pathologies or psychological variables.

alff_hipass and alff_lopass

The output of an ALFF analysis is dependent upon the precise definition of ‘low frequency’. ALFF is determined by computing a power spectrum at each voxel, then integrating over those frequencies of the power spectrum that correspond to the user-specified passband. The low-pass cutoff frequency corresponds to the upper limit of the passband; any frequencies lower than this cutoff are allowed to pass. Similarly, the high-pass cutoff frequency corresponds to the upper limit of the passband; any frequencies higher than this cutoff are allowed to pass. While the power-spectrum integral is probably most informative when the limits of integration encompass low frequencies, advanced users may elect to use this module to compute the amplitude of oscillations in any frequency range.:

# Low-frequency pass-band 0.01-0.08 Hz

# Low-frequency pass-band 0.008-0.12 Hz

alff_sptf and alff_smo

Spatial smoothing parameters.

Endemic noise, for instance due to physiological signals or scanner activity, can introduce spurious or artefactual results in single voxels. The effects of noise-related artefacts can be mitigated by spatially filtering the data, thus dramatically increasing the signal-to-noise ratio. However, spatial smoothing is not without its costs: it effectively reduces volumetric resolution by blurring signals from adjacent voxels. The spatial smoothing implemented in the alff module (i) keeps the unsmoothed analyte image for downstream use and (ii) creates a derivative image that is smoothed using the specified kernel. This allows either the smoothed or the unsmoothed version of the image to be used in any downstream modules as appropriate.:

# No smoothing

# Gaussian kernel (fslmaths) of FWHM 6 mm

# SUSAN kernel (FSL's SUSAN) of FWHM 4 mm

# Uniform kernel (AFNI's 3dBlurToFWHM) of FWHM 5 mm

alff_sptf specifies the type of spatial filter to apply for smoothing, while alff_smo specifies the full-width at half-maximum (FWHM) of the smoothing kernel in mm.

  • Gaussian smoothing applies the same Gaussian smoothing kernel across the entire volume.
  • SUSAN-based smoothing restricts mixing of signals from disparate tissue classes (Smith and Brady, 1997).
  • Uniform smoothing applies smoothing to all voxels until the smoothness computed at every voxel attains the target value.
  • Uniform smoothing may be used as a compensatory mechanism to reduce the effects of subject motion on the final processed image (Scheinost et al., 2014).


Ordinarily, each module will detect whether a particular analysis has run to completion before beginning it. If re-running is disabled, then the module will immediately skip to the next stage of analysis. Otherwise, any completed analyses will be repeated.If you change the run parameters, you should rerun any modules downstream of the change.:

# Skip processing steps if the pipeline detects the expected output

# Repeat all processing steps


Modules often produce numerous intermediate temporary files and images during the course of an analysis. In many cases, these temporary files are undesirable and unnecessarily consume disk space. If cleanup is enabled, any files stamped as temporary will be deleted when a module successfully runs to completion. If a module fails to detect the output that it expects, then temporary files will be retained to facilitate error diagnosis.:

# Remove temporary files

# Retain temporary files

Other derived outputs are the smoothed images if it is specify in design file.