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Gray Matter Alterations in Young Children with Autism Spectrum Disorders: Comparing Morphometry at the Voxel and Regional Level.
Gori, Ilaria; Giuliano, Alessia; Muratori, Filippo; Saviozzi, Irene; Oliva, Piernicola; Tancredi, Raffaella; Cosenza, Angela; Tosetti, Michela; Calderoni, Sara; Retico, Alessandra.
Afiliación
  • Gori I; Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.
  • Giuliano A; Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.
  • Muratori F; Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.
  • Saviozzi I; Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.
  • Oliva P; Dipartimento di Fisica, Università di Pisa, Italy.
  • Tancredi R; IRCCS Fondazione Stella Maris, Pisa, Italy.
  • Cosenza A; Dipartimento di Medicina Clinica e Sperimentale, Università of Pisa, Italy.
  • Tosetti M; IRCCS Fondazione Stella Maris, Pisa, Italy.
  • Calderoni S; Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.
  • Retico A; Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Italy.
J Neuroimaging ; 25(6): 866-74, 2015.
Article en En | MEDLINE | ID: mdl-26214066
BACKGROUND AND PURPOSE: Sophisticated algorithms to infer disease diagnosis, pathology progression and patient outcome are increasingly being developed to analyze brain MRI data. They have been successfully implemented in a variety of diseases and are currently investigated in the field of neuropsychiatric disorders, including autism spectrum disorder (ASD). We aim to test the ability to predict ASD from subtle morphological changes in structural magnetic resonance imaging (sMRI). METHODS: The analysis of sMRI of a cohort of male ASD children and controls matched for age and nonverbal intelligence quotient (NVIQ) has been carried out with two widely used preprocessing software packages (SPM and Freesurfer) to extract brain morphometric information at different spatial scales. Then, support vector machines have been implemented to classify the brain features and to localize which brain regions contribute most to the ASD-control separation. RESULTS: The features extracted from the gray matter subregions provide the best classification performance, reaching an area under the receiver operating characteristic curve (AUC) of 74%. This value is enhanced to 80% when considering only subjects with NVIQ over 70. CONCLUSIONS: Despite the subtle impact of ASD on brain morphology and a limited cohort size, results from sMRI-based classifiers suggest a consistent network of altered brain regions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Imagen por Resonancia Magnética / Sustancia Gris / Trastorno del Espectro Autista Tipo de estudio: Prognostic_studies Límite: Child, preschool / Humans / Male Idioma: En Revista: J Neuroimaging Asunto de la revista: DIAGNOSTICO POR IMAGEM / NEUROLOGIA Año: 2015 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Imagen por Resonancia Magnética / Sustancia Gris / Trastorno del Espectro Autista Tipo de estudio: Prognostic_studies Límite: Child, preschool / Humans / Male Idioma: En Revista: J Neuroimaging Asunto de la revista: DIAGNOSTICO POR IMAGEM / NEUROLOGIA Año: 2015 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos