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Neuroimage. 2017 Jan 15;145(Pt B):365-376. doi: 10.1016/j.neuroimage.2016.03.038. Epub 2016 Mar 23.

The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA.

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The Mind Research Network, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA. Electronic address:
The Mind Research Network, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA; Neurology and Psychiatry Departments, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA; Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA.
The Mind Research Network, 1101 Yale Blvd. NE, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA.
Departments of Psychology and Neuroscience, University of Colorado, Boulder, CO 80302, USA.


Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker to identify several brain disorders. Recently it has been pointed out that methods used to preprocess head motion variance might not fully remove all unwanted effects in the data. Proposed processing pipelines locate the treatment of head motion effects either close to the beginning or as one of the final steps. In this work, we assess several preprocessing pipelines applied in group independent component analysis (gICA) methods to study the rsFNC of the brain. The evaluation method utilizes patient/control classification performance based on linear support vector machines and leave-one-out cross validation. In addition, we explored group tests and correlation with severity measures in the patient population. We also tested the effect of removing high frequencies via filtering. Two real data cohorts were used: one consisting of 48 mTBI and one composed of 21 smokers, both with their corresponding matched controls. A simulation procedure was designed to test the classification power of each pipeline. Results show that data preprocessing can change the classification performance. In real data, regressing motion variance before gICA produced clearer group differences and stronger correlation with nicotine dependence.

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