Hyperplane navigation: A method to set individual scores in fMRI group datasets

Autor(es)

Sato, Joao Ricardo ^{[1, 2]} ; Thomaz, Carlos Eduardo ^[3] ; Cardoso, Ellison Fernando ^[1] ; Fujita, Andre ^[4] ; Morais Martin, Maria da Graca ^[1] ; Amaro, Jr., Edson ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Radiol, Hosp Clin, Sch Med, NIF LIM 44, BR-05403001 Sao Paulo - Brazil

^[2] Univ Sao Paulo, Inst Math & Stat, BR-05403001 Sao Paulo - Brazil

^[3] Ctr Univ FEI, Sao Paulo - Brazil

^[4] Univ Tokyo, Inst Med Sci, Tokyo 1138654 - Japan

• Tipo de documento: Artigo Científico
• Fonte: NeuroImage; v. 42, n. 4, p. 1473-1480, OCT 1 2008.
• Citações Web of Science: 23

Resumo

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups' patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 05/02899-4 - Imagem, estatística e data mining: métodos computacionais para análise do cérebro humano
• Beneficiário: Carlos Eduardo Thomaz
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores