Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting.

Calvetti, Daniela; Pascarella, Annalisa; Pitolli, Francesca; Somersalo, Erkki; Vantaggi, Barbara

Calvetti, Daniela; Pascarella, Annalisa; Pitolli, Francesca; Somersalo, Erkki; Vantaggi, Barbara.

Afiliação

Calvetti D; Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA. dxc57@case.edu.
Pascarella A; Istituto per le Applicazioni del Calcolo "Mauro Picone", CNR - National Research Council, Via dei Taurini 19, 00185, Rome, Italy.
Pitolli F; Department of Basic and Applied Science for Engineering, University of Rome "La Sapienza", Via Scarpa 16, 00161, Rome, Italy.
Somersalo E; Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
Vantaggi B; Department of Basic and Applied Science for Engineering, University of Rome "La Sapienza", Via Scarpa 16, 00161, Rome, Italy.

Brain Topogr ; 32(3): 363-393, 2019 05.

Article em En | MEDLINE | ID: mdl-30121834

Assuntos

Algoritmos; Mapeamento Encefálico/métodos; Encéfalo/fisiologia; Magnetoencefalografia/métodos; Teorema de Bayes; Humanos; Modelos Neurológicos; Método de Monte Carlo

Palavras-chave

Activity map; Bayes factor; Brain region; Deep sources; MEG inverse problem

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Encéfalo / Mapeamento Encefálico / Magnetoencefalografia Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

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