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Article
Original Article
Exp Neurobiol 2023; 32(3): 170-180
Published online June 30, 2023
https://doi.org/10.5607/en23016
© The Korean Society for Brain and Neural Sciences
Machine Learning-assisted Quantitative Mapping of Intracortical Axonal Plasticity Following a Focal Cortical Stroke in Rodents
Hyung Soon Kim1,2, Hyo Gyeong Seo1,2, Jong Ho Jhee4, Chang Hyun Park5, Hyang Woon Lee6,7, Bumhee Park8,9 and Byung Gon Kim1,2,3*
1Department of Brain Science, Ajou University School of Medicine, Suwon 16499, 2Neuroscience Graduate Program, Department of Biomedical Science, Ajou University Graduate School of Medicine, Suwon 16499, 3Department of Neurology, Ajou University School of Medicine, Suwon 16499, 4Center for KIURI Bio-Artificial Intelligence, Ajou University School of Medicine, Suwon 16499, 5Division of Artificial Intelligence and Software, College of Engineering, Ewha Womans University, Seoul 03760, 6Department of Neurology and Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul 03760, 7Computational Medicine, Graduate Programs in System Health Science & Engineering and Artificial Intelligence Convergence, Ewha Womans University, Seoul 03760, 8Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 16499, 9Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon 16499, Korea
Correspondence to: *To whom correspondence should be addressed.
TEL: 82-31-219-4495, FAX: 82-31-219-4444
e-mail: kimbg@ajou.ac.kr
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Stroke destroys neurons and their connections leading to focal neurological deficits. Although limited, many patients exhibit a certain degree of spontaneous functional recovery. Structural remodeling of the intracortical axonal connections is implicated in the reorganization of cortical motor representation maps, which is considered to be an underlying mechanism of the improvement in motor function. Therefore, an accurate assessment of intracortical axonal plasticity would be necessary to develop strategies to facilitate functional recovery following a stroke. The present study developed a machine learning-assisted image analysis tool based on multi-voxel pattern analysis in fMRI imaging. Intracortical axons originating from the rostral forelimb area (RFA) were anterogradely traced using biotinylated dextran amine (BDA) following a photothrombotic stroke in the mouse motor cortex. BDA-traced axons were visualized in tangentially sectioned cortical tissues, digitally marked, and converted to pixelated axon density maps. Application of the machine learning algorithm enabled sensitive comparison of the quantitative differences and the precise spatial mapping of the post-stroke axonal reorganization even in the regions with dense axonal projections. Using this method, we observed a substantial extent of the axonal sprouting from the RFA to the premotor cortex and the peri-infarct region caudal to the RFA. Therefore, the machine learningassisted quantitative axonal mapping developed in this study can be utilized to discover intracortical axonal plasticity that may mediate functional restoration following stroke.
Graphical Abstract
Keywords: Ischemic stroke, Motor cortex, Neuronal plasticity, Machine learning, Support vector machine, Neuroanatomical tract-tracing techniques
