Further results on why a point process is effective for estimating correlation between brain regions

I. Cifre; M. Zarepour; S. G.  Horovitz; S. A. Cannas; D. R.  Chialvo

doi:10.4279/pip.120003

Authors

I. Cifre Facultat de Psicologia, Ciències de l'educació i de l'Esport, Blanquerna, Universitat Ramon Llull, C. Císter 34. Barcelona, (08022), Spain.
M. Zarepour Instituto de Física Enrique Gaviola (IFEG), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Ciudad Universitaria, (5000), Córdoba, Argentina.
S. G. Horovitz National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
S. A. Cannas Instituto de Física Enrique Gaviola (IFEG), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Ciudad Universitaria, (5000), Córdoba, Argentina. https://orcid.org/0000-0001-7331-3532
D. R. Chialvo Center for Complex Systems & Brain Sciences (CEMSC 3), Universidad Nacional de San Martín, 25 de Mayo 1169, San Martín, (1650), Buenos Aires, Argentina. https://orcid.org/0000-0002-1038-3637

DOI:

https://doi.org/10.4279/pip.120003

Keywords:

time series, point processes, functional connectivity, resting states, dynamics

Abstract

Signals from brain functional magnetic resonance imaging (fMRI) can be efficiently represented by a sparse spatiotemporal point process, according to a recently introduced heuristic signal processing scheme. This approach has already been validated for relevant conditions, demonstrating that it preserves and compresses a surprisingly large fraction of the signal information. Here we investigated the conditions necessary for such an approach to succeed, as well as the underlying reasons, using real fMRI data and a simulated dataset. The results show that the key lies in the temporal correlation properties of the time series under consideration. It was found that signals with slowly decaying autocorrelations are particularly suitable for this type of compression, where inflection points contain most of the information.

Author Biography

S. A. Cannas, Instituto de Física Enrique Gaviola (IFEG), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Ciudad Universitaria, (5000), Córdoba, Argentina.

Sergio studied Physics in the National University of Córdoba (UNC, Argentina) and earned his PhD at the Centro Brasileiro de Pesquisas Físicas (Brazil) in 1992. He got a permanent position at the UNC next year and became a researcher of CONICET (Argentina) in 1995. He worked on, listed at random, phase transitions and critical phenomena, pattern formation in magnetic systems, mathematical ecology, complex networks and neural networks. Current research interests are mainly focused on critical phenomena on biological systems, specially in neuroscience.