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

Author(s)

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

• Document type: Journal article
• Source: IEEE Transactions on Biomedical Engineering; v. 63, n. 12, p. 2450-2460, DEC 2016.
• Web of Science Citations: 5

Abstract

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)

• FAPESP's process: 08/08171-0 - Modeling populations of neurons with multicomponent systems with variable range interactions
• Grantee: Daniel Yasumasa Takahashi
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 05/56464-9 - Neuroscience Imaging Center at University of São Paulo Medical School
• Grantee: Giovanni Guido Cerri
• Support Opportunities: Inter-institutional Cooperation in Support of Brain Research (CINAPCE) - Thematic Grants