TY - JOUR
T1 - A comparison of diffusion tractography techniques in simulating the generalized Ising model to predict the intrinsic activity of the brain
AU - Abeyasinghe, Pubuditha M.
AU - Aiello, Marco
AU - Cavaliere, Carlo
AU - Owen, Adrian M.
AU - Soddu, Andrea
N1 - Funding Information:
The authors acknowledge the Human Connectome Project (HCP) for providing the data. In particular, the data were provided by the HCP, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. We thank the Natural Science and Engineering Research Council of Canada (NSERC) for the discovery grant and also the Canada Excellence Research Chairs (CERC).?AMO is a Fellow of the CIFAR Brain, Mind, and Consciousness Program.
Funding Information:
The authors acknowledge the Human Connectome Project (HCP) for providing the data. In particular, the data were provided by the HCP, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. We thank the Natural Science and Engineering Research Council of Canada (NSERC) for the discovery grant and also the Canada Excellence Research Chairs (CERC). AMO is a Fellow of the CIFAR Brain, Mind, and Consciousness Program.
Funding Information:
This study was also funded by the discovery grant awarded by Science and Engineering Research Council of Canada (NSERC) (#05578-2014 RGPIN). This study was also funded by Canada Excellence Research Chair (CERC) Award (#215063).
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Deterministic tractography
KW - Dimensionality of the brain
KW - Generalized Ising model
KW - Probabilistic tractography
KW - Structure–function relationship
UR - https://www.scopus.com/pages/publications/85100244815
U2 - 10.1007/s00429-020-02211-6
DO - 10.1007/s00429-020-02211-6
M3 - Article
C2 - 33523294
AN - SCOPUS:85100244815
SN - 1863-2653
VL - 226
SP - 817
EP - 832
JO - Brain Structure and Function
JF - Brain Structure and Function
IS - 3
ER -