Machine Learning-assisted Quantitative Mapping of Intracortical Axonal Plasticity Following a Focal Cortical Stroke in Rodents
- 주제(키워드) Ischemic stroke , Motor cortex , Neuronal plasticity , Machine learning , Support vector machine , Neuroanatomical tract-tracing techniques
- 주제(기타) Medicine, Research & Experimental; Neurosciences
- 설명문(일반) [Kim, Hyung Soon; Seo, Hyo Gyeong; Kim, Byung Gon] Ajou Univ, Dept Brain Sci, Sch Med, Suwon 16499, South Korea; [Kim, Hyung Soon; Seo, Hyo Gyeong; Kim, Byung Gon] Ajou Univ, Dept Biomed Sci, Neurosci Grad Program, Grad Sch Med, Suwon 16499, South Korea; [Kim, Byung Gon] Ajou Univ, Dept Neurol, Sch Med, Suwon 16499, South Korea; [Jhee, Jong Ho] Ajou Univ, Ctr KIURI Bioartificial Intelligence, Sch Med, Suwon 16499, South Korea; [Park, Chang Hyun] Ewha Womans Univ, Coll Engn, Div Artificial Intelligence & Software, Seoul 03760, South Korea; [Lee, Hyang Woon] Ewha Womans Univ, Sch Med, Dept Neurol & Med Sci, Seoul 03760, South Korea; [Lee, Hyang Woon] Ewha Med Res Inst, Seoul 03760, South Korea; [Lee, Hyang Woon] Ewha Womans Univ, Grad Programs Syst Hlth Sci err Engn & Artificial, Computat Med, Seoul 03760, South Korea; [Park, Bumhee] Ajou Univ, Dept Biomed Informat, Sch Med, Suwon 16499, South Korea; [Park, Bumhee] Ajou Univ, Ajou Res Inst Innovat Med, Med Res Collaborating Ctr, Off Biostat,Med Ctr, Suwon 16499, South Korea
- 등재 SCIE, SCOPUS, KCI등재
- OA유형 Green Published, gold
- 발행기관 KOREAN SOC BRAIN & NEURAL SCIENCE, KOREAN SOC NEURODEGENERATIVE DISEASE
- 발행년도 2023
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000211405
- 본문언어 영어
- Published As https://doi.org/10.5607/en23016
- PubMed 37403225
초록/요약
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 learning assisted quantitative axonal mapping developed in this study can be utilized to discover intracortical axonal plasticity that may mediate functional restoration following stroke.
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