{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wei L"],"funding":["Research Foundation - Flanders","National Natural Science Foundation of China"],"pagination":["161-174"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8959121"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6(1)"],"pubmed_abstract":["Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem disorder accompanied by cognitive changes. To date, no effective therapy is available for ALS patients, partly due to disease heterogeneity and an imperfect understanding of the underlying pathophysiological processes. Reliable models that can predict cognitive and motor deficits are needed to improve symptomatic treatment and slow down disease progression. This study aimed to identify individualized functional connectivity-based predictors of cognitive and motor function in ALS by using multiple kernel learning (MKL) regression. Resting-state fMRI scanning was performed on 34 riluzole-naive ALS patients. Motor severity and global cognition were separately measured with the revised ALS functional rating scale ("],"journal":["Network neuroscience (Cambridge, Mass.)"],"pubmed_title":["Functional connectivity-based prediction of global cognition and motor function in riluzole-naive amyotrophic lateral sclerosis patients."],"pmcid":["PMC8959121"],"funding_grant_id":["T000720N","82071883","61876156","31900764"],"pubmed_authors":["Wu GR","Zhang J","Baeken C","Liu D","Wei L"],"additional_accession":[]},"is_claimable":false,"name":"Functional connectivity-based prediction of global cognition and motor function in riluzole-naive amyotrophic lateral sclerosis patients.","description":"Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem disorder accompanied by cognitive changes. To date, no effective therapy is available for ALS patients, partly due to disease heterogeneity and an imperfect understanding of the underlying pathophysiological processes. Reliable models that can predict cognitive and motor deficits are needed to improve symptomatic treatment and slow down disease progression. This study aimed to identify individualized functional connectivity-based predictors of cognitive and motor function in ALS by using multiple kernel learning (MKL) regression. Resting-state fMRI scanning was performed on 34 riluzole-naive ALS patients. Motor severity and global cognition were separately measured with the revised ALS functional rating scale (","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2025-04-05T15:53:00.474Z","creation":"2025-04-05T15:53:00.474Z"},"accession":"S-EPMC8959121","cross_references":{"pubmed":["35356196"],"doi":["10.1162/netn_a_00217"]}}