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Abstract #2341

A Software Platform for High-Performance Real-Time Resting-State fMRI Analysis

Stefan Posse1, Kevin Rosenberg2, khaled Talaat2, Jing Zhang3, Curtis Tatsuoka4, and James Dilts5
1Neurology & Phsyics and Astronomy, University of New Mexico, Albuquerque, NM, United States, 2NeurInsight LLC, Albuquerque, NM, United States, 3Dept. of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States, 4Dept. of Medicine, Div. of Hematology/Oncology, University of Pittsburgh, Pittsburgh, PA, United States, 5Stellar Science Inc., Albuquerque, NM, United States

Synopsis

Keywords: fMRI Analysis, Brain Connectivity, Real-time, resting-state, high-speed, denoising

Motivation: Real-time resting-state fMRI (rsfMRI) is a novel modality with considerable clinical potential. However, denoising and statistics approaches lack the performance of widely used offline rsfMRI analysis approaches.

Goal(s): To enhance sensitivity and specificity of real-time rsfMRI analysis by integrating advanced signal processing approaches.

Approach: Dual sliding window partial correlation with PCA-based confound regression and spectral segmentation of regression vectors and correction for nonstationary autocorrelations enabled simultaneous mapping of static and dynamic connectivity.

Results: Frequency segmented regression substantially reduced false-positive connectivity in motion corrupted data. Ten networks with whole-band regression of motion parameters, white matter and CSF signals were mapped with 400 ms TR.

Impact: This study demonstrates advances in seed-based real-time resting-state fMRI analysis for high-speed data acquisition that approach the performance and utility of conventional offline resting-state fMRI analysis toolboxes.

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Keywords