Post-stroke outcome prediction based on lesion-derived features
Stroke-induced deficits result from both focal structural damage and widespread network disruption. This study investigated whether simulated measures of network disruption, derived from structural lesions, could predict functional impairments in stroke patients. We extracted four lesion-derived fea...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158225000178 |
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| author | Maedeh Khalilian Olivier Godefroy Martine Roussel Amir Mousavi Ardalan Aarabi |
| author_facet | Maedeh Khalilian Olivier Godefroy Martine Roussel Amir Mousavi Ardalan Aarabi |
| author_sort | Maedeh Khalilian |
| collection | DOAJ |
| description | Stroke-induced deficits result from both focal structural damage and widespread network disruption. This study investigated whether simulated measures of network disruption, derived from structural lesions, could predict functional impairments in stroke patients. We extracted four lesion-derived feature sets: lesion masks, probabilistic structural disconnection maps (pSDMs), structural and indirectly estimated functional connectivity strengths between brain regions, and topological properties of functional and structural brain networks to predict motor, executive, and processing speed deficits in 340 S patients, employing PCA-based ridge regression with leave-one-out cross validation.The findings revealed that both structural disconnection map patterns and lesion masks were strong predictors of functional deficits. Lesion masks exhibited superior predictive performance relative to unthresholded pSDMs. Furthermore, applying a probability threshold to the pSDMs − retaining only disconnections present in a sufficient proportion of healthy subjects − significantly improved predictive performance. For motor deficits, thresholded SDMs (tSDMs) with thresholds of 0.9 and 0.5 produced the highest R2 values, 0.95 for left motor deficits and 0.69 for right motor deficits, respectively. In the case of executive function and processing speed, the highest R2 values were 0.58 and 0.64, achieved with tSDM thresholds of 0.3 and 0.5, respectively. Connectome-based features exhibited lower R2 values, with structural connection strength alterations showing stronger associations with post-stroke scores compared to changes in functional connectivity. Nodal parameters (degree and clustering coefficient) had lower explanatory power than the SDM features and lesion masks.Our findings underscore the effectiveness of lesion masks and thresholded SDMs in predicting post-stroke deficits. This study contributes to the growing body of evidence supporting the reliability of simulated structural networks as a complementary approach to lesion patterns and structural disconnection in predicting post-stroke outcomes. |
| format | Article |
| id | doaj-art-be47f75d116c4fd8b83f906bc49b99aa |
| institution | Kabale University |
| issn | 2213-1582 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj-art-be47f75d116c4fd8b83f906bc49b99aa2025-02-06T05:11:35ZengElsevierNeuroImage: Clinical2213-15822025-01-0145103747Post-stroke outcome prediction based on lesion-derived featuresMaedeh Khalilian0Olivier Godefroy1Martine Roussel2Amir Mousavi3Ardalan Aarabi4Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, FranceLaboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France; Faculty of Medicine, University of Picardy Jules Verne, Amiens, France; Neurology Department, Amiens University Hospital, Amiens, FranceLaboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, FranceLaboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, FranceLaboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France; Neurology Department, Amiens University Hospital, Amiens, France; Corresponding author.Stroke-induced deficits result from both focal structural damage and widespread network disruption. This study investigated whether simulated measures of network disruption, derived from structural lesions, could predict functional impairments in stroke patients. We extracted four lesion-derived feature sets: lesion masks, probabilistic structural disconnection maps (pSDMs), structural and indirectly estimated functional connectivity strengths between brain regions, and topological properties of functional and structural brain networks to predict motor, executive, and processing speed deficits in 340 S patients, employing PCA-based ridge regression with leave-one-out cross validation.The findings revealed that both structural disconnection map patterns and lesion masks were strong predictors of functional deficits. Lesion masks exhibited superior predictive performance relative to unthresholded pSDMs. Furthermore, applying a probability threshold to the pSDMs − retaining only disconnections present in a sufficient proportion of healthy subjects − significantly improved predictive performance. For motor deficits, thresholded SDMs (tSDMs) with thresholds of 0.9 and 0.5 produced the highest R2 values, 0.95 for left motor deficits and 0.69 for right motor deficits, respectively. In the case of executive function and processing speed, the highest R2 values were 0.58 and 0.64, achieved with tSDM thresholds of 0.3 and 0.5, respectively. Connectome-based features exhibited lower R2 values, with structural connection strength alterations showing stronger associations with post-stroke scores compared to changes in functional connectivity. Nodal parameters (degree and clustering coefficient) had lower explanatory power than the SDM features and lesion masks.Our findings underscore the effectiveness of lesion masks and thresholded SDMs in predicting post-stroke deficits. This study contributes to the growing body of evidence supporting the reliability of simulated structural networks as a complementary approach to lesion patterns and structural disconnection in predicting post-stroke outcomes.http://www.sciencedirect.com/science/article/pii/S2213158225000178LesionStructural disconnection mapNetwork connectivityStroke |
| spellingShingle | Maedeh Khalilian Olivier Godefroy Martine Roussel Amir Mousavi Ardalan Aarabi Post-stroke outcome prediction based on lesion-derived features NeuroImage: Clinical Lesion Structural disconnection map Network connectivity Stroke |
| title | Post-stroke outcome prediction based on lesion-derived features |
| title_full | Post-stroke outcome prediction based on lesion-derived features |
| title_fullStr | Post-stroke outcome prediction based on lesion-derived features |
| title_full_unstemmed | Post-stroke outcome prediction based on lesion-derived features |
| title_short | Post-stroke outcome prediction based on lesion-derived features |
| title_sort | post stroke outcome prediction based on lesion derived features |
| topic | Lesion Structural disconnection map Network connectivity Stroke |
| url | http://www.sciencedirect.com/science/article/pii/S2213158225000178 |
| work_keys_str_mv | AT maedehkhalilian poststrokeoutcomepredictionbasedonlesionderivedfeatures AT oliviergodefroy poststrokeoutcomepredictionbasedonlesionderivedfeatures AT martineroussel poststrokeoutcomepredictionbasedonlesionderivedfeatures AT amirmousavi poststrokeoutcomepredictionbasedonlesionderivedfeatures AT ardalanaarabi poststrokeoutcomepredictionbasedonlesionderivedfeatures |