Francesco Di Gregorio , Giada Lullini , Silvia Orlandi , Valeria Petrone , Enrico Ferrucci , Emanuela Casanova , Vincenzo Romei , Fabio La Porta
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At discharge, participants were re-tested with the FIM</div></div><div><h3>Results</h3><div>Powers in the delta, theta, alpha, and beta bands and connectivity within the fronto-parietal network were compared between groups. Then, the between-group discriminative EEG measures and the motor/cognitive scales were used to feed a machine learning algorithm to predict FIM scores at discharge and the length of hospitalization (LoH). Higher delta, theta, and beta and impaired connectivity were found in PS compared to UC. Moreover, motor/cognitive functioning, beta power, and fronto-parietal connectivity predicted the FIM score at discharge and the LoH (accuracy=73.2 % and 85.2 % respectively).</div></div><div><h3>Conclusions</h3><div>Results show that the integration of motor/cognitive scales and EEG measures can reveal the rehabilitative potentials of PS predicting their functional outcome and LoH.</div></div><div><h3>Significance</h3><div>Synergistic clinical and electrophysiological models can support rehabilitative decision-making.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"308 ","pages":"Article 121059"},"PeriodicalIF":5.3000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical and neurophysiological predictors of the functional outcome in right-hemisphere stroke\",\"authors\":\"Francesco Di Gregorio , Giada Lullini , Silvia Orlandi , Valeria Petrone , Enrico Ferrucci , Emanuela Casanova , Vincenzo Romei , Fabio La Porta\",\"doi\":\"10.1016/j.neuroimage.2025.121059\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>The aim of the present study is to examine the relationship between EEG measures and functional recovery in right-hemisphere stroke patients.</div></div><div><h3>Methods</h3><div>Participants with stroke (PS) and neurologically unimpaired controls (UC) were enrolled. At enrolment, all participants were assessed for motor and cognitive functioning with specific scales (motricity index, trunk control test, Level of Cognitive Functioning, and Functional Independence Measure (FIM). Moreover, EEG data were recorded. At discharge, participants were re-tested with the FIM</div></div><div><h3>Results</h3><div>Powers in the delta, theta, alpha, and beta bands and connectivity within the fronto-parietal network were compared between groups. Then, the between-group discriminative EEG measures and the motor/cognitive scales were used to feed a machine learning algorithm to predict FIM scores at discharge and the length of hospitalization (LoH). Higher delta, theta, and beta and impaired connectivity were found in PS compared to UC. Moreover, motor/cognitive functioning, beta power, and fronto-parietal connectivity predicted the FIM score at discharge and the LoH (accuracy=73.2 % and 85.2 % respectively).</div></div><div><h3>Conclusions</h3><div>Results show that the integration of motor/cognitive scales and EEG measures can reveal the rehabilitative potentials of PS predicting their functional outcome and LoH.</div></div><div><h3>Significance</h3><div>Synergistic clinical and electrophysiological models can support rehabilitative decision-making.</div></div>\",\"PeriodicalId\":19299,\"journal\":{\"name\":\"NeuroImage\",\"volume\":\"308 \",\"pages\":\"Article 121059\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NeuroImage\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1053811925000618\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROIMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NeuroImage","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1053811925000618","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"NEUROIMAGING","Score":null,"Total":0}
Clinical and neurophysiological predictors of the functional outcome in right-hemisphere stroke
Objective
The aim of the present study is to examine the relationship between EEG measures and functional recovery in right-hemisphere stroke patients.
Methods
Participants with stroke (PS) and neurologically unimpaired controls (UC) were enrolled. At enrolment, all participants were assessed for motor and cognitive functioning with specific scales (motricity index, trunk control test, Level of Cognitive Functioning, and Functional Independence Measure (FIM). Moreover, EEG data were recorded. At discharge, participants were re-tested with the FIM
Results
Powers in the delta, theta, alpha, and beta bands and connectivity within the fronto-parietal network were compared between groups. Then, the between-group discriminative EEG measures and the motor/cognitive scales were used to feed a machine learning algorithm to predict FIM scores at discharge and the length of hospitalization (LoH). Higher delta, theta, and beta and impaired connectivity were found in PS compared to UC. Moreover, motor/cognitive functioning, beta power, and fronto-parietal connectivity predicted the FIM score at discharge and the LoH (accuracy=73.2 % and 85.2 % respectively).
Conclusions
Results show that the integration of motor/cognitive scales and EEG measures can reveal the rehabilitative potentials of PS predicting their functional outcome and LoH.
Significance
Synergistic clinical and electrophysiological models can support rehabilitative decision-making.
期刊介绍：
NeuroImage, a Journal of Brain Function provides a vehicle for communicating important advances in acquiring, analyzing, and modelling neuroimaging data and in applying these techniques to the study of structure-function and brain-behavior relationships. Though the emphasis is on the macroscopic level of human brain organization, meso-and microscopic neuroimaging across all species will be considered if informative for understanding the aforementioned relationships.
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