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Research paper
Improving fMRI reliability in presurgical mapping for brain tumours
  1. M Tynan R Stevens1,2,
  2. David B Clarke3,4,
  3. Gerhard Stroink1,
  4. Steven D Beyea1,2,
  5. Ryan CN D'Arcy5
  1. 1Department of Physics, Dalhousie University, Halifax, Nova Scotia, Canada
  2. 2Biomedical Translational Imaging Centre, IWK Health Sciences Centre, Halifax, Nova Scotia, Canada
  3. 3Division of Neurosurgery, QEII Health Sciences Centre, Halifax, Nova Scotia, Canada
  4. 4Division of Surgery, QEII Health Sciences Centre, Halifax, Nova Scotia, Canada
  5. 5Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
  1. Correspondence to Ryan CN D'Arcy, Surrey Memorial Hospital, NeuroTech Lab, Barham Building, 13750-96th Avenue, Surrey, British Columbia, Canada V3V 1Z2; rdarcy{at}sfu.ca

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.

  • FUNCTIONAL IMAGING
  • ELECTRICAL STIMULATION
  • NEUROSURGERY
  • TUMOURS
  • BRAIN MAPPING

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