Neuroimaging and biological markers of different paretic hand outcomes after stroke
Abstract Background Hand dysfunction significantly affects independence after stroke, with outcomes varying across individuals. Exploring biomarkers associated with the paretic hand can improve the prognosis and guide personalized rehabilitation. However, whether biomarkers derived from resting-stat...
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BMC
2025-07-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
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| Online Access: | https://doi.org/10.1186/s12984-025-01682-0 |
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| author | Zhujun Wang Manxu Zheng Binke Yuan Yingteng Zhang Wenjun Hong Chaozheng Tang Wen Wu |
| author_facet | Zhujun Wang Manxu Zheng Binke Yuan Yingteng Zhang Wenjun Hong Chaozheng Tang Wen Wu |
| author_sort | Zhujun Wang |
| collection | DOAJ |
| description | Abstract Background Hand dysfunction significantly affects independence after stroke, with outcomes varying across individuals. Exploring biomarkers associated with the paretic hand can improve the prognosis and guide personalized rehabilitation. However, whether biomarkers derived from resting-state fMRI (rs-fMRI) can effectively classify and predict different hand outcomes and their biological mechanisms remain unclear. Methods This study analyzed 65 patients with chronic subcortical stroke, including 32 patients with partially paretic hand (PPH) and 33 patients with completely paretic hand (CPH). For patients with PPH and CPH respectively, the age was 56.19 ± 10.53 and 55.60 ± 9.00 years, disease duration was 15.31 ± 14.87 and 14.12 ± 17.36 months, lesion volume was 9.45 ± 5.57 and 16.00 ± 11.33 ml, Fugl-Meyer Assessment for Hand and Wrist (FMA-HW) was 11.25 ± 6.15 and 1.24 ± 1.22. Four rs-fMRI metrics were analyzed, including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), degree centrality (DC), and voxel-mirrored homotopic connectivity (VMHC). Multivariate pattern analysis was used to classify and predict paretic hand performance. To explore the biological mechanisms of neuroimaging biomarkers, partial least squares regression was conducted to associate gene expression data (from Allen Human Brain Atlas), neurotransmitter maps, neuron types and developmental stages with the classification weight maps of rs-fMRI metrics. Results ALFF achieved a higher classification accuracy of 0.88 in differentiating PPH from CPH, outperforming the other three rs-fMRI metrics. Machine learning further identified the top contributing regions from the ALFF classification weight maps, such as the ipsilesional precentral gyrus, contralesional cerebellum posterior lobe, and ipsilesional parietal lobule. Neuroimaging-transcriptome analysis revealed that macroscopic biomarkers from the ALFF were associated with the G protein-coupled receptor signaling pathway and the detection of chemical stimuli involved in sensory perception. Additionally, these neuroimaging biomarkers from ALFF showed prominent expression in astrocytes and early fetal stages. Most importantly, the neurotransmitter noradrenaline positively correlated with the distribution of ALFF biomarkers. Conclusions The ALFF is an effective macroscopic biomarker for classifying and predicting paretic hand outcomes in individuals following chronic stroke. These neuroimaging biomarkers correspond to molecular transcriptional profiles and neurotransmitter distributions, offering insights into the potential of personalized stroke rehabilitation. |
| format | Article |
| id | doaj-art-47ec937d9f5e4916a436477644665593 |
| institution | DOAJ |
| issn | 1743-0003 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj-art-47ec937d9f5e4916a4364776446655932025-08-20T03:03:24ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032025-07-0122111510.1186/s12984-025-01682-0Neuroimaging and biological markers of different paretic hand outcomes after strokeZhujun Wang0Manxu Zheng1Binke Yuan2Yingteng Zhang3Wenjun Hong4Chaozheng Tang5Wen Wu6Rehabilitation Medicine Center, Zhujiang Hospital, Southern Medical UniversityRehabilitation Medicine Center, Zhujiang Hospital, Southern Medical UniversityPhilosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of EducationDepartment of Mathematics, Taizhou UniversityDepartment of Rehabilitation Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical SchoolRehabilitation Medicine Center, Zhujiang Hospital, Southern Medical UniversityRehabilitation Medicine Center, Zhujiang Hospital, Southern Medical UniversityAbstract Background Hand dysfunction significantly affects independence after stroke, with outcomes varying across individuals. Exploring biomarkers associated with the paretic hand can improve the prognosis and guide personalized rehabilitation. However, whether biomarkers derived from resting-state fMRI (rs-fMRI) can effectively classify and predict different hand outcomes and their biological mechanisms remain unclear. Methods This study analyzed 65 patients with chronic subcortical stroke, including 32 patients with partially paretic hand (PPH) and 33 patients with completely paretic hand (CPH). For patients with PPH and CPH respectively, the age was 56.19 ± 10.53 and 55.60 ± 9.00 years, disease duration was 15.31 ± 14.87 and 14.12 ± 17.36 months, lesion volume was 9.45 ± 5.57 and 16.00 ± 11.33 ml, Fugl-Meyer Assessment for Hand and Wrist (FMA-HW) was 11.25 ± 6.15 and 1.24 ± 1.22. Four rs-fMRI metrics were analyzed, including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), degree centrality (DC), and voxel-mirrored homotopic connectivity (VMHC). Multivariate pattern analysis was used to classify and predict paretic hand performance. To explore the biological mechanisms of neuroimaging biomarkers, partial least squares regression was conducted to associate gene expression data (from Allen Human Brain Atlas), neurotransmitter maps, neuron types and developmental stages with the classification weight maps of rs-fMRI metrics. Results ALFF achieved a higher classification accuracy of 0.88 in differentiating PPH from CPH, outperforming the other three rs-fMRI metrics. Machine learning further identified the top contributing regions from the ALFF classification weight maps, such as the ipsilesional precentral gyrus, contralesional cerebellum posterior lobe, and ipsilesional parietal lobule. Neuroimaging-transcriptome analysis revealed that macroscopic biomarkers from the ALFF were associated with the G protein-coupled receptor signaling pathway and the detection of chemical stimuli involved in sensory perception. Additionally, these neuroimaging biomarkers from ALFF showed prominent expression in astrocytes and early fetal stages. Most importantly, the neurotransmitter noradrenaline positively correlated with the distribution of ALFF biomarkers. Conclusions The ALFF is an effective macroscopic biomarker for classifying and predicting paretic hand outcomes in individuals following chronic stroke. These neuroimaging biomarkers correspond to molecular transcriptional profiles and neurotransmitter distributions, offering insights into the potential of personalized stroke rehabilitation.https://doi.org/10.1186/s12984-025-01682-0StrokeBiomarkerMachine learningResting-state fMRIALFF/ReHo/DC/VMHCGene expression |
| spellingShingle | Zhujun Wang Manxu Zheng Binke Yuan Yingteng Zhang Wenjun Hong Chaozheng Tang Wen Wu Neuroimaging and biological markers of different paretic hand outcomes after stroke Journal of NeuroEngineering and Rehabilitation Stroke Biomarker Machine learning Resting-state fMRI ALFF/ReHo/DC/VMHC Gene expression |
| title | Neuroimaging and biological markers of different paretic hand outcomes after stroke |
| title_full | Neuroimaging and biological markers of different paretic hand outcomes after stroke |
| title_fullStr | Neuroimaging and biological markers of different paretic hand outcomes after stroke |
| title_full_unstemmed | Neuroimaging and biological markers of different paretic hand outcomes after stroke |
| title_short | Neuroimaging and biological markers of different paretic hand outcomes after stroke |
| title_sort | neuroimaging and biological markers of different paretic hand outcomes after stroke |
| topic | Stroke Biomarker Machine learning Resting-state fMRI ALFF/ReHo/DC/VMHC Gene expression |
| url | https://doi.org/10.1186/s12984-025-01682-0 |
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