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Journal Abstract Search

187 related items for PubMed ID: 27146291

  • 1. Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches.
    Zhong J, Chen DQ, Nantes JC, Holmes SA, Hodaie M, Koski L.
    Brain Imaging Behav; 2017 Jun; 11(3):754-768. PubMed ID: 27146291
    [Abstract] [Full Text] [Related]

  • 2. Abnormal functional connectivity and cortical integrity influence dominant hand motor disability in multiple sclerosis: a multimodal analysis.
    Zhong J, Nantes JC, Holmes SA, Gallant S, Narayanan S, Koski L.
    Hum Brain Mapp; 2016 Dec; 37(12):4262-4275. PubMed ID: 27381089
    [Abstract] [Full Text] [Related]

  • 3. SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis.
    Wottschel V, Chard DT, Enzinger C, Filippi M, Frederiksen JL, Gasperini C, Giorgio A, Rocca MA, Rovira A, De Stefano N, Tintoré M, Alexander DC, Barkhof F, Ciccarelli O, MAGNIMS study group and the EuroPOND consortium.
    Neuroimage Clin; 2019 Dec; 24():102011. PubMed ID: 31734524
    [Abstract] [Full Text] [Related]

  • 4. Analysis of structural brain MRI and multi-parameter classification for Alzheimer's disease.
    Zhang Y, Liu S.
    Biomed Tech (Berl); 2018 Jul 26; 63(4):427-437. PubMed ID: 28622141
    [Abstract] [Full Text] [Related]

  • 5. Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups.
    Tozlu C, Jamison K, Gu Z, Gauthier SA, Kuceyeski A.
    Neuroimage Clin; 2021 Jul 26; 32():102827. PubMed ID: 34601310
    [Abstract] [Full Text] [Related]

  • 6. Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques.
    Rehme AK, Volz LJ, Feis DL, Bomilcar-Focke I, Liebig T, Eickhoff SB, Fink GR, Grefkes C.
    Cereb Cortex; 2015 Sep 26; 25(9):3046-56. PubMed ID: 24836690
    [Abstract] [Full Text] [Related]

  • 7. A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging.
    Long Z, Jing B, Yan H, Dong J, Liu H, Mo X, Han Y, Li H.
    Neuroscience; 2016 Sep 07; 331():169-76. PubMed ID: 27343830
    [Abstract] [Full Text] [Related]

  • 8. Classification of first-episode psychosis: a multi-modal multi-feature approach integrating structural and diffusion imaging.
    Peruzzo D, Castellani U, Perlini C, Bellani M, Marinelli V, Rambaldelli G, Lasalvia A, Tosato S, De Santi K, Murino V, Ruggeri M, Brambilla P, PICOS-Veneto Group.
    J Neural Transm (Vienna); 2015 Jun 07; 122(6):897-905. PubMed ID: 25344845
    [Abstract] [Full Text] [Related]

  • 9. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.
    Kim J, Calhoun VD, Shim E, Lee JH.
    Neuroimage; 2016 Jan 01; 124(Pt A):127-146. PubMed ID: 25987366
    [Abstract] [Full Text] [Related]

  • 10. Impairments in Walking Ability, Dexterity, and Cognitive Function in Multiple Sclerosis Are Associated with Different Regional Cerebellar Gray Matter Loss.
    Grothe M, Lotze M, Langner S, Dressel A.
    Cerebellum; 2017 Dec 01; 16(5-6):945-950. PubMed ID: 28612183
    [Abstract] [Full Text] [Related]

  • 11. Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy.
    Hu X, Liu Q, Li B, Tang W, Sun H, Li F, Yang Y, Gong Q, Huang X.
    Eur Neuropsychopharmacol; 2016 Feb 01; 26(2):246-254. PubMed ID: 26708318
    [Abstract] [Full Text] [Related]

  • 12. Functional and structural plasticity following action observation training in multiple sclerosis.
    Rocca MA, Meani A, Fumagalli S, Pagani E, Gatti R, Martinelli-Boneschi F, Esposito F, Preziosa P, Cordani C, Comi G, Filippi M.
    Mult Scler; 2019 Oct 01; 25(11):1472-1487. PubMed ID: 30084706
    [Abstract] [Full Text] [Related]

  • 13. MRI of Transcallosal White Matter Helps to Predict Motor Impairment in Multiple Sclerosis.
    Cordani C, Preziosa P, Valsasina P, Meani A, Pagani E, Morozumi T, Rocca MA, Filippi M.
    Radiology; 2022 Mar 01; 302(3):639-649. PubMed ID: 34846201
    [Abstract] [Full Text] [Related]

  • 14. Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data.
    Saccà V, Sarica A, Novellino F, Barone S, Tallarico T, Filippelli E, Granata A, Chiriaco C, Bruno Bossio R, Valentino P, Quattrone A.
    Brain Imaging Behav; 2019 Aug 01; 13(4):1103-1114. PubMed ID: 29992392
    [Abstract] [Full Text] [Related]

  • 15. Cortical adaptation in patients with MS: a cross-sectional functional MRI study of disease phenotypes.
    Rocca MA, Colombo B, Falini A, Ghezzi A, Martinelli V, Scotti G, Comi G, Filippi M.
    Lancet Neurol; 2005 Oct 01; 4(10):618-26. PubMed ID: 16168930
    [Abstract] [Full Text] [Related]

  • 16. Multivariate pattern classification of gray matter pathology in multiple sclerosis.
    Bendfeldt K, Klöppel S, Nichols TE, Smieskova R, Kuster P, Traud S, Mueller-Lenke N, Naegelin Y, Kappos L, Radue EW, Borgwardt SJ.
    Neuroimage; 2012 Mar 01; 60(1):400-8. PubMed ID: 22245259
    [Abstract] [Full Text] [Related]

  • 17. Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI.
    Xiao Y, Yan Z, Zhao Y, Tao B, Sun H, Li F, Yao L, Zhang W, Chandan S, Liu J, Gong Q, Sweeney JA, Lui S.
    Schizophr Res; 2019 Dec 01; 214():11-17. PubMed ID: 29208422
    [Abstract] [Full Text] [Related]

  • 18. Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.
    Bisenius S, Mueller K, Diehl-Schmid J, Fassbender K, Grimmer T, Jessen F, Kassubek J, Kornhuber J, Landwehrmeyer B, Ludolph A, Schneider A, Anderl-Straub S, Stuke K, Danek A, Otto M, Schroeter ML, FTLDc study group.
    Neuroimage Clin; 2017 Dec 01; 14():334-343. PubMed ID: 28229040
    [Abstract] [Full Text] [Related]

  • 19. Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.
    Yoo Y, Tang LYW, Brosch T, Li DKB, Kolind S, Vavasour I, Rauscher A, MacKay AL, Traboulsee A, Tam RC.
    Neuroimage Clin; 2018 Dec 01; 17():169-178. PubMed ID: 29071211
    [Abstract] [Full Text] [Related]

  • 20. Multimodal-neuroimaging machine-learning analysis of motor disability in multiple sclerosis.
    Rehák Bučková B, Mareš J, Škoch A, Kopal J, Tintěra J, Dineen R, Řasová K, Hlinka J.
    Brain Imaging Behav; 2023 Feb 01; 17(1):18-34. PubMed ID: 36396890
    [Abstract] [Full Text] [Related]

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