ISMRM25 - fMRI Analysis: Software, Pipelines & Methods

ISMRM & ISMRT Annual Meeting & Exhibition • 10-15 May 2025 • Honolulu, Hawai'i

ISMRM & ISMRT 2025 Annual Meeting & Exhibition

Digital Poster

fMRI Analysis: Software, Pipelines & Methods

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fMRI Analysis: Software, Pipelines & Methods

Digital Poster

fMRI

Tuesday, 13 May 2025

Exhibition Hall

08:15 - 09:15

Session Number: D-123

No CME/CE Credit

		Computer Number: 81 2341. A Software Platform for High-Performance Real-Time Resting-State fMRI Analysis View Presentation Video S. Posse, K. Rosenberg, k. Talaat, J. Zhang, C. Tatsuoka, J. Dilts University of New Mexico, Albuquerque, United States 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.
		Computer Number: 82 2342. Developing an Efficient Pipeline for Accurate ASL MRI Functional Connectivity Analysis View Presentation Video H. Rahimzadeh, J. Guo University of California, Riverside, Riverside, United States Impact: This optimized ASL MRI pipeline enables more accurate functional connectivity analysis, benefiting neuroscience research and potential clinical applications by providing improved brain connectivity measurements for studying network abnormalities in neurodegenerative and psychiatric conditions.
		Computer Number: 83 2343. A Self-Supervised Voxel Shuffling Framework for Kernel-Based fMRI activation detection View Presentation Video C. Han, Z. Yang, X. Zhuang, D. Cordes Cleveland Clinic, Las Vegas, United States Impact: The proposed augmentation method effectively finds the optimal kernel mapping and mitigates overfitting in activation detection without relying on spatial ground truth information. This could be used in real fMRI data and broaden the potential for modeling more complex relationships.
		Computer Number: 84 2344. BRNet: Ballistocardiogram Artifact Removal in EEG Acquired Inside MRI via Deep Learning View Presentation Video I-C WANG, T-Y Huang, H-J Lee, F-H Lin Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan Impact: Enhances EEG data quality in MRI environments, improving reliability in neuroscience research and clinical diagnostics.
		Computer Number: 85 2345. Multimodal Analysis of Autonomic Regression on Neuronal and Physiological Correlates View Presentation Video K. Eren, L. Alqam, B. Tavashi, C. Karakuzu, K. Yildirim, E. Can, A. Dincer, P. Ozbay Boğaziçi University, Istanbul, Turkey Impact: By integrating autonomic corrections into fMRI workflows, this study enhances the accuracy of brain activity measurements, providing a clearer understanding of neural dynamics during cognitive tasks.
		Computer Number: 86 2346. Voxel And Vicinity-based algOrithm for nOise reductiOn of fMri data sets (VAVOOOM). View Presentation Video A-M Oros-Peusquens, P. Pais-Roldan, S. D. Yun, N. J. Shah Forschungszentrum Jülich, Jülich, Germany Impact: The presented denoising method (VAVOOOM) can be implemented in pre-processing pipelines to improve the tSNR and facilitate accurate characterization of fMRI metrics in high-resolution data such as laminar resolution fMRI at ultra-high fields.
		Computer Number: 87 2347. Introducing ME-DUNE, a denoising U-network applied in a task based multi-echo fMRI study. View Presentation Video P. Van Schuerbeek, M. Roose, H. Raeymaekers, M. Naeyaert UZ Brussel (VUB), Brussel, Belgium Impact: As a first step in the development of a new denoising technique that is not ICA-dependent, this work showed the potential of using an U-convolutional network to denoise multi-echo fMRI data as an alternative to ICA-based methods.
		Computer Number: 88 2348. Class-Aware Hidden Markov Model for simultaneous functional connectivity estimation and classification View Presentation Video C. Han, P. Pattiam Giriprakash, R. Nandy, D. Cordes Cleveland Clinic, Las Vegas, United States Impact: For fMRI classification problems, the one-step Class-Aware HMM is simpler and more accurate compared to two-step classification while maintaining model interpretability. This could help in understanding brain connectivity and disease diagnosis.
		Computer Number: 89 2349. Decoding Task-Based Functional Connectivity: Exploring Kolmogorov-Arnold Networks for Task-Evoked fMRI Classification View Presentation Video Y. Chen, Z. Tang, T. Chen, X. Wang, Y. Sun, C. Tjoean, J. Lv, M. Barnett, F. Calamante, M. Irish, W. Cai, C. Wang The University of Sydney, Sydney, Australia Impact: This study demonstrates that the Kolmogorov-Arnold Network offers strong interpretability for fMRI task-condition classification, aligning with literature on the functional roles of specific brain regions. This advances task-based fMRI analysis and supports the development of explainable neural networks in neuroscience.
		Computer Number: 90 2350. Precision Functional Gradients via Optimal Shrinkage Denoising View Presentation Video J. Mao, K. Huynh, K-H Thung, H. P. Taylor, G. Lin, S. Ahmad, P-T Yap University of North Carolina at Chapel Hil, Chapel Hill, United States Impact: Denoising enables high-quality individual gradients, even with temporally undersampled data, allowing precise assessment of individual gradient changes.
		Computer Number: 91 2351. Implementation of automated resting-state presurgical fMRI analysis pipelines with patient-based probabilistic functional atlases View Presentation Video K. Tran, J. M. Teo, V. Kumar, M-L Jen, C. Elsinger, H-L Liu NordicNeuroLab Inc., Milwaukee, United States Impact: This work developed software that automates rs-fMRI analysis by implementing probabilistic functional atlases in SBC and ICA pipelines. It supports routine clinical use of rs-fMRI for presurgical mapping of motor, visual, and language areas for patients undergoing brain tumor resection.
		Computer Number: 92 2352. Consistency and stability of individualized cortical functional networks parcellation at 3.0 T and 5.0 T MRI M. Yu, B. Rao, Y. Cao, L. Gao, H. Li, X. Song, H. Xu Zhongnan Hospital of Wuhan University, Wuhan, China Impact: Obtaining functional atlases at individual level is important to explore individual brain function and for precision personalized medicine. This study laid the foundation for future application of individualized cortical functional networks parcellation at 5.0 T and other ultra-high field MR.
		Computer Number: 93 2353. Dynamic autocorrelation as a tool to measure time-dependent changes in neuronal time-scale along the hippocampus axis during resting-state. View Presentation Video A. Golestani, L. Homann, N. Bouffard, M. Moscovitch, M. Barense University of Calgary, Calgary, Canada Impact: We introduced dynamic autocorrelation (dAC) as a measure to investigate dynamic changes in AC patterns and demonstrated that the AC pattern is dynamic during resting-state. The dAC provides a promising approach to examine how neural timescales adapt to environmental changes.
		Computer Number: 94 2354. Deducing Resting-State fMRI Seeds with Manifold Learning and Distance Clustering. View Presentation Video J. M. Teo, V. Kumar, K. Noll, S. Ferguson, C. Ene, S. Prabhu, M. Wintermark, H-L Liu The University of Texas MD Anderson Cancer Center, Houston, United States 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.
		Computer Number: 95 2355. Brain-to-brain communication channels and information content View Presentation Video R. Lee, P. Sajda, N. Tottenham University of Texas Health Science Center at San Antonio, San Antonio, United States Impact: A novel approach was developed to identify both brain-to-brain network communication channels and the information content in each channel. The characteristics of the channels and the information flows in parent-child eye-contact quantify both social interaction and children’s brain developmental stages.
		Computer Number: 96 2356. Adapting the Tedana Multi-Echo Denoising Pipeline for Echo Planar Time-Resolved Imaging (EPTI) View Presentation Video K. Lamar-Bruno, F. Wang, Z. Dong, R. Barnes, C. Chen, D. Handwerker, T. Liu University of California, San Diego, La Jolla, United States Impact: We adapted the Tedana pipeline to ensure accurate denoising of EPTI data and leveraged EPTI’s high echo count by no longer assuming that BOLD and non-BOLD related signal changes are uncoupled thus improving Tedana’s accuracy for EPTI.

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