A comparison of diffusion tractography techniques in simulating the generalized Ising model to predict the intrinsic activity of the brain.

Autor: Abeyasinghe PM; Department of Physics and Astronomy, Western University, London, ON, Canada. pubu.abeyasinghe@monash.edu.; Brain and Mind Institute, Western University, London, ON, Canada. pubu.abeyasinghe@monash.edu.; School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia. pubu.abeyasinghe@monash.edu., Aiello M; IRCCS SDN, Istituto Di Ricerca Diagnostica E Nucleare, Via E. Gianturco 113, 80143, Naples, Italy., Cavaliere C; IRCCS SDN, Istituto Di Ricerca Diagnostica E Nucleare, Via E. Gianturco 113, 80143, Naples, Italy., Owen AM; Brain and Mind Institute, Western University, London, ON, Canada.; Department of Psychology, Western University, London, ON, Canada., Soddu A; Department of Physics and Astronomy, Western University, London, ON, Canada.; Brain and Mind Institute, Western University, London, ON, Canada.
Jazyk: angličtina
Zdroj: Brain structure & function [Brain Struct Funct] 2021 Apr; Vol. 226 (3), pp. 817-832. Date of Electronic Publication: 2021 Feb 01.
DOI: 10.1007/s00429-020-02211-6
Abstrakt: Diffusion tractography is a non-invasive technique that is being used to estimate the location and direction of white matter tracts in the brain. Identifying the characteristics of white matter plays an important role in research as well as in clinical practice that relies on finding the relationship between the structure and function of the brain. An Ising model implemented on a structural connectivity (SC) has proven to explain the spontaneous fluctuations in the brain at criticality using brain's structure depicted by white matter tracts. Since the SC is the only input of the model, identifying the tractography technique which provides a SC that delivers the highest prediction of the brain's intrinsic activity via the generalized Ising model (GIM) is essential. Hence an Ising model is simulated on SCs generated using two different acquisition schemes (single and multi-shell) and two different tractography approaches (deterministic and probabilistic) and analyzed at criticality across 69 healthy subjects. Results showed that by introducing the GIM, predictability of the empirical correlation matrix increases on average from 0.2 to 0.6 compared to the predictability using the empirical connectivity matrix directly. It is also observed that the SC generated using deterministic tractography without fractional anisotropy resulted in the highest correlation coefficient value of 0.65 between the simulated and empirical correlation matrices. Additionally, calculated dimensionalities per simulation illustrated that the dimensionality depends upon the method of tractography that has been used to extract the SC.
Databáze: MEDLINE
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