Spatiotemporal multiscale ICA could invariantly extract task (motor) modes from wavelet subbands of fMRI data. (September 2021)

• Record Type: Journal Article
• Authors: Chen, Zeyuan; Chen, Zikuan
• Is Part Of: Computer methods and programs in biomedicine. Volume 208(2021)
• Publication Date: 2021-09
• Subjects: Task fMRI -- Independent component analysis (ICA) -- Wavelet transform (WT) -- Spatiotemporal multiscales -- Subbands -- Shift and scale invariance; fMRI functional magnetic resonance imaging -- ICA independent component analysis -- tasktcorr temporal correlation between task paradigm and a component timecourse -- 'db1' Daubechies wavelet with 1 vanishing moment -- 'db2' Daubechies wavelet with 2 vanishing moments -- 'sym4" symlet wavelet with 4 vanishing moments -- Lt low-pass subband of temporal wavelet transform -- Ht high-pass subband of temporal wavelet transform -- {LxLyLz} low-pass subbands of spatial wavelet transform along {x, y, z} dimensions -- {HxHyHz} high-pass subbands of spatial wavelet transform along {x, y, z} dimensions; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106249
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Independent component analysis (ICA) can extract task mode from fMRI data. Wavelet transform (WT) can decompose 4D fMRI data into multiscale subbands. ICA can extract task modes from WT-decomposed subbands (multiscale ICA). Multiscale ICA for task fMRI informs finite spatial extent and duration of brain function. Abstract: Background and objective: . Given a timeseries of task-evoked functional MRI (fMRI) images (4D spatiotemporal data), we can extract the task mode by statistical independent component analysis (ICA). If the 4D data are spatiotemporally decomposed into subbands (multiresolutions in both time and space), is ICA still capable of extracting the task modes at multiscales? We answer this question using the well-established fingertapping motor-task experiments at 3T and 7T. The positive answer informs that a brain task is spatiotemporal separable at ICA decomposition and shift invariant at multiscales during activation over a finite region. Methods: . We collected a set of task fMRI datasets from sixteen subjects performing fingertapping at 3T and one single dataset from a different subject at 7T. For each 4D fMRI dataset, we first performed temporal wavelet transform (1D WT) at 3 levels using different wavelets (e.g. 'db1', 'db2', and 'sym4'), then extracted the task modes from the WT subbands via ICA (as called multi-timescale ICA). Meanwhile, we also performed task mode extraction by applying ICA to 3D spatial WT subbands (as called multi-spacescale ICA). Upon the multiscale ICA results, we identified the primary motor task modes in the motor cortex, in comparison to the raw fMRI data analysis (at level 0). Results: . In the 7T experiment, the multiscale ICA across 3 timescale levels and 2 spacescale levels could extract the primary task modes at a tasktcorr of 0.90 and 0.86, respectively, compared to 0.87 for the ICA task extraction from raw data. In the 3T experiment, the multiscale could extract the primary task mode with 0.92 and 0.91, while the ICA task extraction from raw data was 0.91. Conclusion: . ICA could extract the primary motor task modes from wavelet-decomposed multi-timescale and multi-spacescale subbands, construing the broad spatial activation (extent >>voxel size) of the brain motor task performed in a long duration (>>TR). Our experimental results show the brain functional activity signal is spatiotemporal separable as well as shift invariant at multiscales in both time and space.