Home |  English version |  Mappa |  Commenti |  Sondaggio |  Staff |  Contattaci Cerca nel sito  
Istituto di scienze e tecnologie della cognizione

Torna all'elenco Contributi in rivista anno 2014

Contributo in rivista

Tipo: Articolo in rivista

Titolo: Volume of interest-based [(18)F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer's disease from healthy controls. A European Alzheimer's Disease Consortium (EADC) study.

Anno di pubblicazione: 2014

Autori: M. Pagani, F. De Carli, S. Morbelli, J. Öberg, A. Chincarini, G.B. Frisoni, S. Galluzzi, R. Perneczky, A. Drzezga, B.N.M. van Berckel, R. Ossenkoppele, M. Didic, E. Guedj, A. Brugnolo, A. Picco, D. Arnaldi, M. Ferrara, A. Buschiazzo, G. Sambuceti, F. Nobili

Affiliazioni autori: a Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy b Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden c Institute of Bioimaging and Molecular Physiology, Consiglio Nazionale delle Ricerche (CNR), Genoa, Italy d Nuclear Medicine, Department of Health Sciences (DISSAL), University of Genoa, IRCCS AOU San Martino-IST, Genoa, Italy e Department of Hospital Physics, Karolinska Hospital, Stockholm, Sweden f National Institute for Nuclear Physics (INFN), Genoa, Italy g LENITEM Laboratory of Epidemiology and Neuroimaging, IRCCS S. Giovanni di Dio-FBF, Brescia, Italy h University Hospitals and University of Geneva, Geneva, Switzerland i Neuroepidemiology and Ageing Research Unit, School of Public Health, Faculty of Medicine, The Imperial College London of Science, Technology and Medicine, London, UK j West London Cognitive Disorders Treatment and Research Unit, London, UK k Department of Psychiatry and Psychotherapy, Technische Universität, Munich, Germany l Department of Nuclear Medicine, Technische Universität, Munich, Germany m Department of Nuclear Medicine & PET Research, VU University Medical Center, Amsterdam, The Netherlands n APHM, CHU Timone, Service de Neurologie et Neuropsychologie, Aix-Marseille University, INSERM U 1106, Marseille, France o APHM, CHU Timone, Service de Médecine Nucléaire, CERIMED, INT CNRS UMR7289 , Aix-Marseille University, Marseille 13005, France p Clinical Neurology, Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child health (DINOGMI), University of Genoa, IRCCS AOU, San Martino-IST, Genoa, Italy

Autori CNR:

  • FABRIZIO DE CARLI
  • MARCO PAGANI

Lingua: inglese

Abstract: An emerging issue in neuroimaging is to assess the diagnostic reliability of PET and its application in clinical practice. We aimed at assessing the accuracy of brain FDG-PET in discriminating patients with MCI due to Alzheimer's disease and healthy controls. Sixty-two patients with amnestic MCI and 109 healthy subjects recruited in five centers of the European AD Consortium were enrolled. Group analysis was performed by SPM8 to confirm metabolic differences. Discriminant analyses were then carried out using the mean FDG uptake values normalized to the cerebellum computed in 45 anatomical volumes of interest (VOIs) in each hemisphere (90 VOIs) as defined in the Automated Anatomical Labeling (AAL) Atlas and on 12 meta-VOIs, bilaterally, obtained merging VOIs with similar anatomo-functional characteristics. Further, asymmetry indexes were calculated for both datasets. Accuracy of discrimination by a Support Vector Machine and the AAL VOIs was tested against a validated method (PALZ). At the voxel level SMP8 showed a relative hypometabolism in the bilateral precuneus, and posterior cingulate, temporo-parietal and frontal cortices. Discriminant analysis classified subjects with an accuracy ranging between .91 and .83 as a function of data organization. The best values were obtained from a subset of 6 meta-VOIs plus 6 asymmetry values reaching an area under the ROC curve of .947, significantly larger than the one obtained by the PALZ score. High accuracy in discriminating MCI converters from healthy controls was reached by a non-linear classifier based on SVM applied on predefined anatomo-functional regions and inter-hemispheric asymmetries. Data pre-processing was automated and simplified by an in-house created Matlab-based script encouraging its routine clinical use. Further validation toward nonconverter MCI patients with adequately long follow-up is needed

Lingua abstract: inglese

Pagine da: 34

Pagine a: 42

Rivista:

NeuroImage: Clinical Elsevier
Paese di pubblicazione:
Lingua: inglese
ISSN: 2213-1582

Numero volume: 7

DOI: 10.1016/j.nicl.2014.11.007

Stato della pubblicazione: Published version

Indicizzato da: PubMed [25610765]

Parole chiave:

  • MCI
  • FDG-PET
  • Volume of interest
  • Discriminant analysis
  • EADC

Strutture CNR:

Moduli:

 
Torna indietro Richiedi modifiche Invia per email Stampa
Home Il CNR  |  I servizi News |   Eventi | Istituti |  Focus