Abstract:
:Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2(⁎) weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Gonzalez-Castillo J,Panwar P,Buchanan LC,Caballero-Gaudes C,Handwerker DA,Jangraw DC,Zachariou V,Inati S,Roopchansingh V,Derbyshire JA,Bandettini PAdoi
10.1016/j.neuroimage.2016.07.049subject
Has Abstractpub_date
2016-11-01 00:00:00pages
452-468eissn
1053-8119issn
1095-9572pii
S1053-8119(16)30363-9journal_volume
141pub_type
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