Purpose The purpose of this study was to observe, using Footscan analysis, the effect of electromyographic feedback functional electrical stimulation (FES) on the changes in the plantar pressure of drop foot patients. Methods This case–control study enrolled 34 stroke patients with foot drop. There were 17 cases received FES for 20 min per day, 5 days per week for 4 weeks (the FES group) and the other 17 cases only received basic rehabilitations (the control group). Before and after 4 weeks, the walking speed, spatiotemporal parameters and plantar pressure were measured. Results After 4 weeks treatments, Both the FES and control groups had increased walking speed and single stance phase percentage, decreased step length symmetry index (SI), double stance phase percentage and start time of the heel after 4 weeks (p < 0.05). The increase in walking speed and decrease in step length SI in the FES group were more significant than the control group after 4 weeks (p < 0.05). The FES group had an increased initial contact phase, decreased SI of the maximal force (Max F) and impulse in the medial heel after 4 weeks (p < 0.05). Conclusion The advantages of FES were: the improvement of gait speed, step length SI, and the enhancement of propulsion force were more significant. The initial contact phase was closer to the normal range, which implies that the control of ankle dorsiflexion was improved. The plantar dynamic parameters between the two sides of the foot were more balanced than the control group. FES is more effective than basic rehabilitations for stroke patients with foot drop based on current spatiotemporal parameters and plantar pressure results.
{"title":"The effect of electromyographic feedback functional electrical stimulation on the plantar pressure in stroke patients with foot drop","authors":"Xiaoting Li, Hanting Li, Yu Liu, Weidi Liang, Lixin Zhang, Fenghua Zhou, Zhiqiang Zhang, Xiangnan Yuan","doi":"10.3389/fnins.2024.1377702","DOIUrl":"https://doi.org/10.3389/fnins.2024.1377702","url":null,"abstract":"Purpose The purpose of this study was to observe, using Footscan analysis, the effect of electromyographic feedback functional electrical stimulation (FES) on the changes in the plantar pressure of drop foot patients. Methods This case–control study enrolled 34 stroke patients with foot drop. There were 17 cases received FES for 20 min per day, 5 days per week for 4 weeks (the FES group) and the other 17 cases only received basic rehabilitations (the control group). Before and after 4 weeks, the walking speed, spatiotemporal parameters and plantar pressure were measured. Results After 4 weeks treatments, Both the FES and control groups had increased walking speed and single stance phase percentage, decreased step length symmetry index (SI), double stance phase percentage and start time of the heel after 4 weeks (p < 0.05). The increase in walking speed and decrease in step length SI in the FES group were more significant than the control group after 4 weeks (p < 0.05). The FES group had an increased initial contact phase, decreased SI of the maximal force (Max F) and impulse in the medial heel after 4 weeks (p < 0.05). Conclusion The advantages of FES were: the improvement of gait speed, step length SI, and the enhancement of propulsion force were more significant. The initial contact phase was closer to the normal range, which implies that the control of ankle dorsiflexion was improved. The plantar dynamic parameters between the two sides of the foot were more balanced than the control group. FES is more effective than basic rehabilitations for stroke patients with foot drop based on current spatiotemporal parameters and plantar pressure results.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"317 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140754432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20DOI: 10.3389/fnins.2024.1397293
Bin Hu
{"title":"Editorial: New insights into brain imaging methods for rehabilitation of brain diseases","authors":"Bin Hu","doi":"10.3389/fnins.2024.1397293","DOIUrl":"https://doi.org/10.3389/fnins.2024.1397293","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"32 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.3389/fnins.2024.1388520
Sukanya Saha, Marija Cvetanovic
{"title":"Editorial: Women in neurodegeneration","authors":"Sukanya Saha, Marija Cvetanovic","doi":"10.3389/fnins.2024.1388520","DOIUrl":"https://doi.org/10.3389/fnins.2024.1388520","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"194 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.3389/fnins.2024.1396702
Nizhuan Wang, Lei Chen, Wei Kong, Chung Y. Hsu, I-Shiang Tzeng
{"title":"Editorial: Data-driven clinical biosignatures and treatment for neurodegenerative diseases, volume II","authors":"Nizhuan Wang, Lei Chen, Wei Kong, Chung Y. Hsu, I-Shiang Tzeng","doi":"10.3389/fnins.2024.1396702","DOIUrl":"https://doi.org/10.3389/fnins.2024.1396702","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"213 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-13DOI: 10.3389/fnins.2024.1379619
Conor Wall, Y. Çelik, Victoria Hetherington, Peter McMeekin, Richard Walker, Lisa Graham, Rodrigo Vitorio, Alan Godfrey
{"title":"Considering and understanding developmental and deployment barriers for wearable technologies in neurosciences","authors":"Conor Wall, Y. Çelik, Victoria Hetherington, Peter McMeekin, Richard Walker, Lisa Graham, Rodrigo Vitorio, Alan Godfrey","doi":"10.3389/fnins.2024.1379619","DOIUrl":"https://doi.org/10.3389/fnins.2024.1379619","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140245604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.3389/fnins.2024.1377665
Shugeng Chen, Lin Yao, Lei Cao, Marco Caimmi, Jie Jia
{"title":"Editorial: Exploration of the non-invasive brain-computer interface and neurorehabilitation","authors":"Shugeng Chen, Lin Yao, Lei Cao, Marco Caimmi, Jie Jia","doi":"10.3389/fnins.2024.1377665","DOIUrl":"https://doi.org/10.3389/fnins.2024.1377665","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"68 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.3389/fnins.2024.1373451
João Filipe Ribeiro, Kenneth L. Shepard, Patrick Ruther
{"title":"Editorial: Problems, strategies, and developments for high-density long-term chronic intracortical neural interfaces and their application","authors":"João Filipe Ribeiro, Kenneth L. Shepard, Patrick Ruther","doi":"10.3389/fnins.2024.1373451","DOIUrl":"https://doi.org/10.3389/fnins.2024.1373451","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"48 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.3389/fnins.2024.1373451
João Filipe Ribeiro, Kenneth L. Shepard, Patrick Ruther
{"title":"Editorial: Problems, strategies, and developments for high-density long-term chronic intracortical neural interfaces and their application","authors":"João Filipe Ribeiro, Kenneth L. Shepard, Patrick Ruther","doi":"10.3389/fnins.2024.1373451","DOIUrl":"https://doi.org/10.3389/fnins.2024.1373451","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"306 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.3389/fnins.2024.1377665
Shugeng Chen, Lin Yao, Lei Cao, Marco Caimmi, Jie Jia
{"title":"Editorial: Exploration of the non-invasive brain-computer interface and neurorehabilitation","authors":"Shugeng Chen, Lin Yao, Lei Cao, Marco Caimmi, Jie Jia","doi":"10.3389/fnins.2024.1377665","DOIUrl":"https://doi.org/10.3389/fnins.2024.1377665","url":null,"abstract":"","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"14 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.3389/fnins.2024.1331677
Da Ma, Jane K Stocks, Howie Rosen, K. Kantarci, Samuel N Lockhart, James R Bateman, Suzanne Craft, Metin N. Gurcan, K. Popuri, M. Faisal Beg, Lei Wang
Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN).Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach.The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping.In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.
{"title":"Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI","authors":"Da Ma, Jane K Stocks, Howie Rosen, K. Kantarci, Samuel N Lockhart, James R Bateman, Suzanne Craft, Metin N. Gurcan, K. Popuri, M. Faisal Beg, Lei Wang","doi":"10.3389/fnins.2024.1331677","DOIUrl":"https://doi.org/10.3389/fnins.2024.1331677","url":null,"abstract":"Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN).Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach.The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping.In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.","PeriodicalId":509131,"journal":{"name":"Frontiers in Neuroscience","volume":"29 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139795813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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