Caroline E. Jagella
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Caroline E. Jagella has a total of 24 co-authors. Here are the top co-authors by number of shared publications:
- Andreas Mühl
Clinique Romande de Réadaptation, Switzerland (B.L., A.M., P.V., J.-L.T.).
1 shared publication - Andéol Cadic-Melchior
Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (INX), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
1 shared publication - Bertrand Léger
Department of Medical Research Suva clinics, Clinique Romande de Réadaptation, Sion, Switzerland
1 shared publication - Christophe Bonvin
Division of Neurology and Stroke Unit, Hôpital du Valais, Sion, Switzerland
1 shared publication - Christophe Constantin
Department of Neurology, Hôpital du Valais, Sion, Switzerland (C.C., V.A., C.B.).
1 shared publication - Diego San Millán
Department of Neurology, Hôpital du Valais, Switzerland (C.C., V.A., D.d.L.R., D.S.M., C.B.).
1 shared publication
Latest Publications
- Open Access
- 1 April 2023
- Published by Wolters Kluwer Health in Stroke
- Vol. 54 (4) , 955-963
Differential Impact of Brain Network Efficiency on Poststroke Motor and Attentional Deficits
Background: Most studies on stroke have been designed to examine one deficit in isolation; yet, survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretical methods may open new avenues of understanding. Methods: Fifty subacute stroke patients (7±3days poststroke) underwent diffusion-weighted magnetic resonance imaging and a battery of clinical tests of motor and cognitive functions. We defined indices of impairment in strength, dexterity, and attention. We also computed imaging-based probabilistic tractography and whole-brain connectomes. To efficiently integrate inputs from different sources, brain networks rely on a rich-club of a few hub nodes. Lesions harm efficiency, particularly when they target the rich-club. Overlaying individual lesion masks onto the tractograms enabled us to split the connectomes into their affected and unaffected parts and associate them to impairment. Results: We computed efficiency of the unaffected connectome and found it was more strongly correlated to impairment in strength, dexterity, and attention than efficiency of the total connectome. The magnitude of the correlation between efficiency and impairment followed the order attention>dexterity ≈ strength (strength: | r |=.03, P =0.02, dexterity: | r |=.30, P =0.05, attention: | r |=.55, P <0.001). Network weights associated with the rich-club were more strongly correlated to efficiency than non-rich-club weights. Conclusions: Attentional impairment is more sensitive to disruption of coordinated networks between brain regions than motor impairment, which is sensitive to disruption of localized networks. Providing more accurate reflections of actually functioning parts of the network enables the incorporation of information about the impact of brain lesions on connectomics contributing to a better understanding of underlying stroke mechanisms.
- 1 April 2023
- Published by Wolters Kluwer Health in Stroke
- Vol. 54 (4) , 955-963
Open AccessDifferential Impact of Brain Network Efficiency on Poststroke Motor and Attentional Deficits
Background: Most studies on stroke have been designed to examine one deficit in isolation; yet, survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretical methods may open new avenues of understanding. Methods: Fifty subacute stroke patients (7±3days poststroke) underwent diffusion-weighted magnetic resonance imaging and a battery of clinical tests of motor and cognitive functions. We defined indices of impairment in strength, dexterity, and attention. We also computed imaging-based probabilistic tractography and whole-brain connectomes. To efficiently integrate inputs from different sources, brain networks rely on a rich-club of a few hub nodes. Lesions harm efficiency, particularly when they target the rich-club. Overlaying individual lesion masks onto the tractograms enabled us to split the connectomes into their affected and unaffected parts and associate them to impairment. Results: We computed efficiency of the unaffected connectome and found it was more strongly correlated to impairment in strength, dexterity, and attention than efficiency of the total connectome. The magnitude of the correlation between efficiency and impairment followed the order attention>dexterity ≈ strength (strength: | r |=.03, P =0.02, dexterity: | r |=.30, P =0.05, attention: | r |=.55, P <0.001). Network weights associated with the rich-club were more strongly correlated to efficiency than non-rich-club weights. Conclusions: Attentional impairment is more sensitive to disruption of coordinated networks between brain regions than motor impairment, which is sensitive to disruption of localized networks. Providing more accurate reflections of actually functioning parts of the network enables the incorporation of information about the impact of brain lesions on connectomics contributing to a better understanding of underlying stroke mechanisms.