Abstract #2354

Deducing Resting-State fMRI Seeds with Manifold Learning and Distance Clustering.

Jian Ming Teo^1,2, Vinodh A. Kumar³, Kyle R. Noll⁴, Sherise D. Ferguson⁵, Chibawanye I. Ene⁵, Sujit S. Prabhu⁵, Max Wintermark³, and Ho-Ling Liu¹

¹Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ²The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States, ³Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁴Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁵Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: fMRI Analysis, fMRI (resting state)

Motivation: Canonical approaches for automating seed-based correlation analysis (SCA) forgo personalization.

Goal(s): Develop a personalized SCA algorithm that does not assume seed geometry or coordinate.

Approach: For 79 patients with presurgical resting-state fMRI (rs-fMRI), seeds in primary language areas were obtained by determining inter-voxel temporal similarity with PaCMAP and HDBSCAN (P-H). Per patient, binary seeds from 1000 P-H iterations were consolidated into probabilistic seeds for SCA. Maximum dice coefficients with task-based (tb-fMRI) localizations were compared to existing method.

Results: SCA with probabilistic seeds identified by this new method had significantly higher dice coefficients with tb-fMRI language localizations compared to existing method.

Impact: Personalized SCA with P-H method and probabilistic seeds improves the accuracy of detecting the rs-fMRI language network in brain tumor patients. This can facilitate clinical adoption of rs-fMRI for patients needing presurgical language localization but have limited tb-fMRI results.

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