Directed Transfer Function: Unified Asymptotic Theory and Some of Its Implications

Autor(es)

Baccala, Luiz A. ; Takahashi, Daniel Y. ; Sameshima, Koichi

• Tipo de documento: Artigo Científico
• Fonte: IEEE Transactions on Biomedical Engineering; v. 63, n. 12, p. 2450-2460, DEC 2016.
• Citações Web of Science: 5

Resumo

Objective: To present a unified mathematical derivation of the frequency-dependent asymptotic behavior of the three main forms of directed transfer function (DTF). Methods: A synthesis of the results (proved in an extended Appendix) is followed by a series of Monte Carlo simulations of representative examples. Results: DTF estimators are asymptotically normal when the true values are different from zero. Under the null hypothesis H-0 : DTF = 0, the estimator is distributed as a linear combination of independent chi(2)(1) variables. Conclusions: Null DTF rejection is shown to be achievable with identical performance irrespective of which DTF form is adopted. Significance: Together with recent allied partial directed coherence results, this paper rounds up connectivity inference tools for a class of frequency-domain connectivity estimators. (AU)

• Processo FAPESP: 08/08171-0 - Modelagem de populações de neurônios por sistemas markovianos de muitos componentes com interações de alcance variável
• Beneficiário: Daniel Yasumasa Takahashi
• Linha de fomento: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 05/56464-9 - Centro de Imagem em Neurociências da Faculdade de Medicina da Universidade de São Paulo
• Beneficiário: Giovanni Guido Cerri
• Linha de fomento: Auxílio à Pesquisa - Programa CINAPCE - Temático