Improving fMRI reliability in presurgical mapping for brain tumours. Issue 3 (26th March 2015)

• Record Type: Journal Article
• Authors: Stevens, M Tynan R; Clarke, David B; Stroink, Gerhard; Beyea, Steven D; D'Arcy, Ryan CN
• Is Part Of: Journal of neurology, neurosurgery and psychiatry. Volume 87:Issue 3(2016)
• Page Start: 267
• Page End: 274
• Publication Date: 2015-03-26
• Subjects: FUNCTIONAL IMAGING -- ELECTRICAL STIMULATION -- NEUROSURGERY -- TUMOURS -- BRAIN MAPPING; Neurology -- Periodicals; Nervous system -- Surgery -- Periodicals; Psychiatry -- Periodicals; 616.8
• DOI: 10.1136/jnnp-2015-310307
• Languages: English
• ISSNs: 0022-3050

Abstract

Abstract : Purpose: Functional MRI (fMRI) is becoming increasingly integrated into clinical practice for presurgical mapping. Current efforts are focused on validating data quality, with reliability being a major factor. In this paper, we demonstrate the utility of a recently developed approach that uses receiver operating characteristic-reliability (ROC-r) to: (1) identify reliable versus unreliable data sets; (2) automatically select processing options to enhance data quality; and (3) automatically select individualised thresholds for activation maps. Methods: Presurgical fMRI was conducted in 16 patients undergoing surgical treatment for brain tumours. Within-session test–retest fMRI was conducted, and ROC-reliability of the patient group was compared to a previous healthy control cohort. Individually optimised preprocessing pipelines were determined to improve reliability. Spatial correspondence was assessed by comparing the fMRI results to intraoperative cortical stimulation mapping, in terms of the distance to the nearest active fMRI voxel. Results: The average ROC-r reliability for the patients was 0.58±0.03, as compared to 0.72±0.02 in healthy controls. For the patient group, this increased significantly to 0.65±0.02 by adopting optimised preprocessing pipelines. Co-localisation of the fMRI maps with cortical stimulation was significantly better for more reliable versus less reliable data sets (8.3±0.9 vs 29±3 mm, respectively). Conclusions: We demonstrated ROC-r analysis for identifying reliable fMRI data sets, choosing optimal postprocessing pipelines, and selecting patient-specific thresholds. Data sets with higher reliability also showed closer spatial correspondence to cortical stimulation. ROC-r can thus identify poor fMRI data at time of scanning, allowing for repeat scans when necessary. ROC-r analysis provides optimised and automated fMRI processing for improved presurgical mapping.