xcp_d.workflows.bold.metrics module

Workflows for calculating BOLD metrics (ALFF and ReHo).

xcp_d.workflows.bold.metrics.init_alff_wf(name_source, TR, mem_gb, name='alff_wf')

Compute alff for both nifti and cifti.

Workflow Graph

(Source code, png, svg, pdf)

Parameters:

• name_source

• TR (float) – Repetition time of the BOLD run, in seconds.

• mem_gb (dict) – Memory allocation dictionary

• name (str, optional) – Name of the workflow. This is used for working directories and workflow graphs. Default is “compute_alff_wf”.

Inputs:

• denoised_bold – This is the filtered, interpolated, denoised BOLD, although interpolation is not necessary if the data were not originally censored.

• bold_mask – bold mask if bold is nifti

• temporal_mask

• name_source

Outputs:

• alff – alff output

• smoothed_alff – smoothed alff output

Notes

The ALFF implementation is based on Yu-Feng et al.[1], although the ALFF values are not scaled by the mean ALFF value across the brain.

If censoring is applied (i.e., fd_thresh > 0), then the power spectrum will be estimated using a Lomb-Scargle periodogram [2][3][4][5].

This workflow will also generate a plot of the ALFF map. For CIFTI data, the plot will be overlaid on the midthickness surface- either the subject’s surface warped to fsLR space (when the anatomical workflow is enabled) or the fsLR 32k midthickness surface template.

References

[1]

Zang Yu-Feng, He Yong, Zhu Chao-Zhe, Cao Qing-Jiu, Sui Man-Qiu, Liang Meng, Tian Li-Xia, Jiang Tian-Zi, and Wang Yu-Feng. Altered baseline brain activity in children with adhd revealed by resting-state functional mri. Brain and Development, 29(2):83–91, 2007.

[2]

Nicholas R Lomb. Least-squares frequency analysis of unequally spaced data. Astrophysics and space science, 39:447–462, 1976.

[3]

Jeffrey D Scargle. Studies in astronomical time series analysis. ii-statistical aspects of spectral analysis of unevenly spaced data. Astrophysical Journal, Part 1, vol. 263, Dec. 15, 1982, p. 835-853., 263:835–853, 1982.

[4]

RHD Townsend. Fast calculation of the lomb–scargle periodogram using graphics processing units. The Astrophysical Journal Supplement Series, 191(2):247, 2010.

[5]

Paul A Taylor, Gang Chen, Daniel R Glen, Richard C Reynolds, and Robert W Cox. Lomb-scargle your way to rsfc parameter estimation in afni-fatcat. International Society for Magnetic Resonance in Medicine, 2018. Description of Lomb-Scargle applied to scrubbed fMRI data in AFNI.

xcp_d.workflows.bold.metrics.init_reho_cifti_wf(name_source, mem_gb, name='cifti_reho_wf')

Compute ReHo from surface+volumetric (CIFTI) data.

Workflow Graph

(Source code, png, svg, pdf)

Parameters:

• name_source

• mem_gb (dict) – Memory allocation dictionary

• name (str, optional) – Name of the workflow. This is used for working directories and workflow graphs. Default is “cifti_reho_wf”.

Inputs:

• denoised_bold – residual and filtered, cifti

• name_source

Outputs:

reho – ReHo in a CIFTI file.

Notes

This workflow will also generate a plot of the ReHo map. The plot will be overlaid on the midthickness surface- either the subject’s surface warped to fsLR space (when the anatomical workflow is enabled) or the fsLR 32k midthickness surface template.

xcp_d.workflows.bold.metrics.init_reho_nifti_wf(name_source, mem_gb, name='reho_nifti_wf')

Compute ReHo on volumetric (NIFTI) data.

Workflow Graph

(Source code, png, svg, pdf)

Parameters:

• name_source

• mem_gb (dict) – Memory allocation dictionary

• name (str, optional) – Name of the workflow. This is used for working directories and workflow graphs. Default is “nifti_reho_wf”.

Inputs:

• denoised_bold – residual and filtered, nifti

• bold_mask – bold mask

• name_source

Outputs:

reho – reho output