Abstract #1581

Sensitivity, Specificity and Test-Retest Reliability Comparison of a Novel Real-Time and four Offline Resting-State fMRI Analysis Pipelines

Logan Dowdle¹, Jing Zhang², Curtis Tatsuoka³, Orrin Myers⁴, Kevin Rosenberg⁵, James Dilts⁶, Essa Yacoub⁷, and Stefan Posse⁸

¹Dept. Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands, ²Dept. of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States, ³Dept. of Medicine, Div. of Hematology/Oncology, University of Pittsburgh, Pittsburgh, PA, United States, ⁴Dept. of Family & Community Medicine, University of New Mexico, Albuquerque, NM, United States, ⁵NeurInsight LLC, Albuquerque, NM, United States, ⁶Stellar Science Inc., Albuquerque, NM, United States, ⁷Center for Magnetic Resonance Research, Dept. of Radiology, University of Minnesota, Minneapolis, MN, United States, ⁸Neurology & Phsyics and Astronomy, University of New Mexico, Albuquerque, NM, United States

Synopsis

Keywords: fMRI Analysis, fMRI (resting state), analysis, seed-based, real-time, offline

Motivation: Standardization of resting-state fMRI (rsfMRI) data analysis is an increasingly important goal for achieving clinical acceptance. A performance comparison of widely used seed-based rsfMRI analysis pipelines has not been reported.

Goal(s): We compare a real-time rsfMRI analysis pipeline (TurboFIRE) with AFNI, CONN, C-PAC and IClinfMRI.

Approach: Human Connectome Project rsfMRI data (25 subjects) and high-speed rsfMRI data (15 controls and 14 brain tumor patients) were analyzed to assess 8 performance metrics of sensitivity, specificity and test-rest reliability.

Results: Considerable variability of performance metrics was measured between pipelines with real-time rsfMRI analysis pipeline results being within the range of the offline analysis pipeline results.

Impact: This study emphasizes the need to further standardize rsfMRI analysis pipelines. It also shows that real-time rsfMRI analysis now approaches the sensitivity, specificity and test-retest reliability of state-of-the-art offline analysis pipelines.

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