A multimodal approach to capture post-stroke temporal dynamics of recovery

Pierella, Camilla; Pirondini, Elvira; Kinany, Nawal; Coscia, Martina; Giang, Christian; Miehlbradt, Jenifer; Magnin, Cécile; Nicolo, Pierre; Dalise, Stefania; Sgherri, Giada; Chisari, Carmelo; Van De Ville, Dimitri; Guggisberg, Adrian; Micera, Silvestro

doi:10.1088/1741-2552/ab9ada

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Title		A multimodal approach to capture post-stroke temporal dynamics of recovery
Authors		Pierella, Camilla Pirondini, Elvira Kinany, Nawal Coscia, Martina Giang, Christian Miehlbradt, Jenifer Magnin, Cécile Nicolo, Pierre Dalise, Stefania Sgherri, Giada Chisari, Carmelo Van De Ville, Dimitri Guggisberg, Adrian Micera, Silvestro show all authors [1 - 14]
Published in		Journal of Neural Engineering. 2020, vol. 17, no. 4, 045002
Abstract		Several training programs have been developed in the past to restore motor functions after stroke. Their efficacy strongly relies on the possibility to assess individual levels of impairment and recovery rate. However, commonly used clinical scales rely mainly on subjective functional assessments and are not able to provide a complete description of patients' neuro-biomechanical status. Therefore, current clinical tests should be integrated with specific physiological measurements, i.e. kinematic, muscular, and brain activities, to obtain a deep understanding of patients' condition and of its evolution through time and rehabilitative intervention.
Identifiers		DOI: 10.1088/1741-2552/ab9ada PMID: 32516757
Full text		Article (Published version) (2 MB) - Free access Supplemental data (148 Kb) - Free access
Structures		Faculté de médecine / Section de médecine clinique / Département des neurosciences cliniques Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale
Research groups		Neuroréhabilitation assistée par imagerie (910) Traitement d'images médicales (893)
Citation (ISO format)		PIERELLA, Camilla et al. A multimodal approach to capture post-stroke temporal dynamics of recovery. In: Journal of Neural Engineering, 2020, vol. 17, n° 4, p. 045002. doi: 10.1088/1741-2552/ab9ada https://archive-ouverte.unige.ch/unige:140115