DWT–CEM: an algorithm for scale-temporal clustering in fMRI

  • Authors:

  • João Ricardo Sato;André Fujita;Edson Amaro, Jr;Janaina Mourão Miranda;Pedro Alberto Morettin;Michal John Brammer

  • Affiliations:

  • University of São Paulo, Institute of Mathematics and Statistics, Rua do Matão, 1010, Cidade Universitria, CEP 05508 −090, São Paulo, S.P., Brazil;University of São Paulo, Institute of Mathematics and Statistics, Rua do Matão, 1010, Cidade Universitria, CEP 05508 −090, São Paulo, S.P., Brazil;University of São Paulo, LIM44-NIF, Department of Radiology, Av. Dr. Enéas de Carvalho Aguiar, 255, 3o. andar, Cerqueira César, CEP 05403-001, São Paulo, S.P., Brazil;King’s College, London, Brain Image Analysis Unit, Institute of Psychiatry, De Crespigny Park, SE5 8AF, London, S.P., UK;University of São Paulo, Institute of Mathematics and Statistics, Rua do Matão, 1010, Cidade Universitria, CEP 05508 −090, São Paulo, S.P., Brazil;King’s College, London, Brain Image Analysis Unit, Institute of Psychiatry, De Crespigny Park, SE5 8AF, London, S.P., UK

  • Venue:
  • Biological Cybernetics
  • Year:
  • 2007

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Abstract

The number of studies using functional magnetic resonance imaging (fMRI) has grown very rapidly since the first description of the technique in the early 1990s. Most published studies have utilized data analysis methods based on voxel-wise application of general linear models (GLM). On the other hand, temporal clustering analysis (TCA) focuses on the identification of relationships between cortical areas by measuring temporal common properties. In its most general form, TCA is sensitive to the low signal-to-noise ratio of BOLD and is dependent on subjective choices of filtering parameters. In this paper, we introduce a method for wavelet-based clustering of time-series data and show that it may be useful in data sets with low signal-to-noise ratios, allowing the automatic selection of the optimum number of clusters. We also provide examples of the technique applied to simulated and real fMRI datasets.