Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
463-02-013 ISMRM Abstract

Functional Imaging Enriches Lesion Connectome Models by Integrating Graph Theory and Disconnectome for Stroke Recovery

Primary: Neuro - Stroke and Cerebrovascular Disorders
Secondary: Brain Function and fMRI - fMRI Analysis
463-02-013 · Imaging Stroke Recovery: From Microstructure to Personalized Therapy · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row D
Keywords: Diagnosis/Prediction fMRI (resting state) Functional Connectivity Clinical Decision-Making Stroke recovery
Accepted
Himanshu Singh 1, Sparsh Singh2, Vishnu V. Y.2, S Senthil Kumaran1, Leve Joseph Devarajan Sebastian3, Ajay Garg3
1Department of Nuclear Magnetic Resonance (NMR), All India Institute of Medical Sciences, New Delhi, India
2Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
3Department of Neuroimaging & Interventional Neuroradiology, All India Institute of Medical Sciences, New Delhi, India
Presenting Author: Himanshu Singh

Synopsis

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References

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4. 4. Schmidt P. et al. LST-AI: a deep learning ensemble for accurate lesion segmentation. NeuroImage: Clinical 42, 103611 (2024). https://doi.org/10.1016/j.nicl.2024.103611 [doi]
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8. 8. Lamouroux, A., Coloigner, J., Maurel, P., Farrugia, N. & Lioi, G. Evaluating lesion-specific preprocessing pipelines for rs-fMRI in stroke patients: Impact on functional connectivity and behavioral prediction. Imaging Neurosci. 3, IMAG.a.6 (2025). https://doi.org/10.1162/IMAG.a.6 [doi]
9. 9. Whitfield-Gabrieli S. & Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–141 (2012). https://doi.org/10.1089/brain.2012.0073 [doi]
10. 10. Craddock, C. et al. Towards automated analysis of connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC). Front. Neuroinform. Conf. Abstr. Neuroinformatics 2013 (2013). https://doi.org/10.3389/conf.fninf.2013.09.00042 [doi]
11. 11. Mehta, K. et al. XCP-D: A robust pipeline for the post-processing of fMRI data.Imaging Neurosci. 2, IMAG.a.257 (2024). https://doi.org/10.1162/imag_a_00257 [doi]
12. 12. Gong, J. et al. Hemodynamic timing in resting-state and breathing-task BOLD fMRI. NeuroImage 274, 120120 (2023). https://doi.org/10.1016/j.neuroimage.2023.120120 [doi]

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http://echo.ismrm.org/p/ISMRM2026/463-02-013