Robust presurgical functional MRI at 7 T using response consistency.

Hum Brain Mapp

High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Lazarettgasse 14, A-1090, Vienna, Austria.

Published: June 2017

Functional MRI is valuable in presurgical planning due to its non-invasive nature, repeatability, and broad availability. Using ultra-high field MRI increases the specificity and sensitivity, increasing the localization reliability and reducing scan time. Ideally, fMRI analysis for this application should identify unreliable runs and work even if the patient deviates from the prescribed task timing or if there are changes to the hemodynamic response due to pathology. In this study, a model-free analysis method-UNBIASED-based on the consistency of fMRI responses over runs was applied, to ultra-high field fMRI localizations of the hand area. Ten patients with brain tumors and epilepsy underwent 7 Tesla fMRI with multiple runs of a hand motor task in a block design. FMRI data were analyzed with the proposed approach (UNBIASED) and the conventional General Linear Model (GLM) approach. UNBIASED correctly identified and excluded fMRI runs that contained little or no activation. Generally, less motion artifact contamination was present in UNBIASED than in GLM results. Some cortical regions were identified as activated in UNBIASED but not GLM results. These were confirmed to show reproducible delayed or transient activation, which was time-locked to the task. UNBIASED is a robust approach to generating activation maps without the need for assumptions about response timing or shape. In presurgical planning, UNBIASED can complement model-based methods to aid surgeons in making prudent choices about optimal surgical access and resection margins for each patient, even if the hemodynamic response is modified by pathology. Hum Brain Mapp 38:3163-3174, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5434844PMC
http://dx.doi.org/10.1002/hbm.23582DOI Listing

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