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| Hauptseite > Publikationsdatenbank > Dynamic connectivity predicts acute motor impairment and recovery post-stroke > print |
| Hauptseite > Publikationsdatenbank > Dynamic connectivity predicts acute motor impairment and recovery post-stroke > print |
| 001 | 905340 | ||
| 005 | 20240506205521.0 | ||
| 024 | 7 | _ | |a 10.1093/braincomms/fcab227 |2 doi |
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| 037 | _ | _ | |a FZJ-2022-00608 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Bonkhoff, Anna K |0 P:(DE-Juel1)162183 |b 0 |
| 245 | _ | _ | |a Dynamic connectivity predicts acute motor impairment and recovery post-stroke |
| 260 | _ | _ | |a [Großbritannien] |c 2021 |b Guarantors of Brain |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1714997172_17176 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We herebuilt random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied onstructural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset. Functional connectivitywas estimated via static and dynamic approaches. Motor performance was phenotyped in the acute phase and 6 months later.A model based on the time spent in specific dynamic connectivity configurations achieved the best discrimination between patientswith and without motor impairments (out-of-sample area under the curve, 95% confidence interval: 0.6760.01). In contrast,patients with moderate-to-severe impairments could be differentiated from patients with mild deficits using a model based on thevariability of dynamic connectivity (0.8360.01). Here, the variability of the connectivity between ipsilesional sensorimotor cortexand putamen discriminated the most between patients. Finally, motor recovery was best predicted by the time spent in specific connectivityconfigurations (0.8960.01) in combination with the initial impairment. Here, better recovery was linked to a shortertime spent in a functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors of acuteimpairment and recovery, which, in the future, might inform personalized therapy regimens to promote stroke recovery. |
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| 700 | 1 | _ | |a Rehme, Anne K |0 P:(DE-Juel1)165784 |b 1 |
| 700 | 1 | _ | |a Hensel, Lukas |0 P:(DE-Juel1)142144 |b 2 |
| 700 | 1 | _ | |a Tscherpel, Caroline |b 3 |
| 700 | 1 | _ | |a Volz, Lukas J |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Espinoza, Flor A |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Gazula, Harshvardhan |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Vergara, Victor M |0 P:(DE-HGF)0 |b 7 |
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| 700 | 1 | _ | |a Calhoun, Vince D |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Rost, Natalia S |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Grefkes, Christian |0 P:(DE-Juel1)161406 |b 11 |e Corresponding author |
| 773 | _ | _ | |a 10.1093/braincomms/fcab227 |g Vol. 3, no. 4, p. fcab227 |0 PERI:(DE-600)3020013-1 |n 4 |p fcab227 |t Brain communications |v 3 |y 2021 |x 2632-1297 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/905340/files/Bonkhoff_2021a_Brain_comm_stroke.pdf |y OpenAccess |
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