Overview

ICA-PNM de-noising pipeline combines noise regressors identified by tools such as PNM and ICA (and FIX) and effectively prepares them for entry into a single FEAT model, thus allowing the user to clean their data in a single step. Note that it is essential to combine PNM and ICA into a single regression step, rather than performing them separately. The reason for this is that variance can be reintroduced into the “cleaned” data if regressions are performed separately.


Step 1. Pre-processing

Step 2. Stripping noise from ICA time series

The final line of your ICA-labels file contains the numbers of all noise components. This file is obtained either via manual classification using FSLeyes, or from automated classification using ICA-FIX or ICA-AROMA.

Red box highlights noise components

The fsl_ENTS tool can prepare this stage for you with the following usage:

fsl_ents <.ica directory> [-o outfile] <fixfile>

fsl_ents <.ica directory> [-o outfile] [-c conffile] [-c conffile] <fixfile>

Where

< -c = confound file > Any additional confounds such as noise associated with white matter, CSF or motion outliers can be included as a space separated text file

< -o = outfile > Naming convention not fixed, modify as needed

Step 3. Inputting the noise text files into your model

Red box highlights noise components

And one containing the time series information of ICA noise components along with any other sources of noise that you wish to model. This file should be space delimited and should be input into the Add additional confound EVs box. The input within the data tab should be your raw data and FEAT can be run from scratch, including all pre-processing steps.

Red box highlights noise components

The model

Red box highlights noise components


Referencing

If you use fsl_ENTS in your research, please select the appropriate publications from the list below pertaining to FIX and PNM for referencing.

1. G. Salimi-Khorshidi, G. Douaud, C.F. Beckmann, M.F. Glasser, L. Griffanti S.M. Smith. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage, 90:449-68, 2014

2. L. Griffanti, G. Salimi-Khorshidi, C.F. Beckmann, E.J. Auerbach, G. Douaud, C.E. Sexton, E. Zsoldos, K. Ebmeier, N. Filippini, C.E. Mackay, S. Moeller, J.G. Xu, E. Yacoub, G. Baselli, K. Ugurbil, K.L. Miller, and S.M. Smith. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage, 95:232-47, 2014

3. Brooks JC, Beckmann CF, Miller KL, Wise RG, Porro CA, Tracey I, Jenkinson M. Physiological noise modelling for spinal functional magnetic resonance imaging studies. Neuroimage, 39(2):680-92, 2008.

4. Harvey AK, Pattinson KT, Brooks JC, Mayhew SD, Jenkinson M, Wise RG. Brainstem functional magnetic resonance imaging: disentangling signal from physiological noise. J Magn Reson Imaging. 28(6):1337-44, 2008.

5. Birn RM, Diamond JB, Smith MA, Bandettini PA. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage, 31(4):1536-48, 2006.

6. Glover GH, Li TQ, Ress D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med., 44(1):162-7, 2000.

7. Shmueli K, van Gelderen P, de Zwart JA, Horovitz SG, Fukunaga M, Jansma JM, Duyn JH. Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. Neuroimage, 38(2):306-20, 2007.

8. Chang C, Cunningham JP, Glover GH. Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage, 44(3):857-69, 2009.

9. Cohen-Adad J, Gauthier CJ, Brooks JC, Slessarev M, Han J, Fisher JA, Rossignol S, Hoge RD. BOLD signal responses to controlled hypercapnia in human spinal cord. Neuroimage, 50(3):1074-84, 2010.


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ICA_PNM (last edited 13:43:55 30-09-2020 by MatthewWebster)