title: Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination.
abstract: Brain connectivity analyses using voxels as features are not robust enough for single-patient classification because of the inter-subject anatomical and functional variability. To construct more robust features, voxels can be aggregated into clusters that are maximally coherent across subjects. Moreover, combining multi-modal neuroimaging and multi-view data integration techniques allows generating multiple independent connectivity features for the same patient. Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration by an ensemble learning method (random forest). Two different multi-view models (intermediate and late data integration) were trained on, and tested for the classification of, individual whole-brain default-mode network (DMN) and fractional anisotropy (FA) maps, from 41 amyotrophic lateral sclerosis (ALS) patients, 37 Parkinson's disease (PD) patients and 43 healthy control (HC) subjects. Both multi-view data models exhibited ensemble classification accuracies significantly above chance. In ALS patients, multi-view models exhibited the best performances (intermediate: 82.9%, late: 80.5% correct classification) and were more discriminative than each single-view model. In PD patients and controls, multi-view models' performances were lower (PD: 59.5%, 62.2%; HC: 56.8%, 59.1%) but higher than at least one single-view model. Training the models only on patients, produced more than 85% patients correctly discriminated as ALS or PD type and maximal performances for multi-view models. These results highlight the potentials of mining complementary information from the integration of multiple data views in the classification of connectivity patterns from multi-modal brain images in the study of neurodegenerative diseases.
authors.affiliation: Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Fratello
authors.affiliation: Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, Italy.
authors.forename: Giuseppina
authors.initials: G
authors.lastname: Caiazzo
authors.affiliation: Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, Italy.
authors.forename: Francesca
authors.initials: F
authors.lastname: Trojsi
authors.affiliation: Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, Italy.
authors.forename: Antonio
authors.initials: A
authors.lastname: Russo
authors.affiliation: Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, Italy.
authors.forename: Gioacchino
authors.initials: G
authors.lastname: Tedeschi
authors.affiliation: Department of Medicine Surgery and Dentistry Scuola Medica Salernitana, University of Salerno, Baronissi, Salerno, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Tagliaferri
authors.affiliation: Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via S. Allende, 84081, Baronissi, Salerno, Italy. faesposito@unisa.it.
authors.forename: Fabrizio
authors.identifier: http://orcid.org/0000-0002-5099-9786
authors.initials: F
authors.lastname: Esposito
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016880:Anisotropy; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D003663:Decision Trees; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009434:Neural Pathways / Q000000981:diagnostic imaging* / Q000502:physiology; D019636:Neurodegenerative Diseases / Q000145:classification / Q000473:pathology*
keywords: Amyotrophic lateral sclerosis; Default mode network; Fractional anisotropy; Multi-modality; Multi-view; Parkinson’s disease; Random forests