{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Troisi Lopez E"],"funding":["SNSF Ambizione Project","Swiss National Science Foundation","European Union's Horizon 2020 Research and Innovation Program","Bando Ricerca Competitiva 2017","Virtual Brain Cloud"],"pagination":["1239-1250"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9875937"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["44(3)"],"pubmed_abstract":["The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source-reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated t"],"journal":["Human brain mapping"],"pubmed_title":["Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment."],"pmcid":["PMC9875937"],"funding_grant_id":["PZ00P2_185716","185716","945539","D.R. 289/2017","826421"],"pubmed_authors":["Troisi Lopez E","Jirsa V","Lucidi F","Minino R","De Micco R","Sorrentino G","Amico E","Liparoti M","Polverino A","Sorrentino P","Tessitore A","Romano A"],"additional_accession":[]},"is_claimable":false,"name":"Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment.","description":"The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cognitive decline in preclinical Alzheimer's disease. In this article, we explore the performance of CCF in 47 Parkinson's disease (PD) patients and 47 healthy controls, under the hypothesis that patients would show reduced identifiability as compared to controls, and that such reduction could be used to predict motor impairment. We used source-reconstructed magnetoencephalography signals to build two functional connectomes for 47 patients with PD and 47 healthy controls. Then, exploiting the two connectomes per individual, we investigated t","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2025-04-21T23:10:47.544Z","creation":"2025-04-05T19:07:00.353Z"},"accession":"S-EPMC9875937","cross_references":{"pubmed":["36413043"],"doi":["10.1002/hbm.26156"]}}