no code implementations • 29 Nov 2022 • Dale Zhou, Jason Z. Kim, Adam R. Pines, Valerie J. Sydnor, David R. Roalf, John A. Detre, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, Dani S. Bassett
Using a large sample of youth ($n=1, 040$), we test predictions in two ways: by measuring the dimensionality of spontaneous activity from sensorimotor to association cortex, and by assessing the representational capacity for 24 behaviors in neural circuits and 20 cognitive variables in recurrent neural networks.
For each individual, we measured the TMS-induced change in FC between the FPS and DMS (the FC network), and the structural coupling between the stimulated area and the FPS and DMS (the structural context network (SCN)).
no code implementations • 11 Oct 2020 • Vishnu M. Bashyam, Jimit Doshi, Guray Erus, Dhivya Srinivasan, Ahmed Abdulkadir, Mohamad Habes, Yong Fan, Colin L. Masters, Paul Maruff, Chuanjun Zhuo, Henry Völzke, Sterling C. Johnson, Jurgen Fripp, Nikolaos Koutsouleris, Theodore D. Satterthwaite, Daniel H. Wolf, Raquel E. Gur, Ruben C. Gur, John C. Morris, Marilyn S. Albert, Hans J. Grabe, Susan M. Resnick, R. Nick Bryan, David A. Wolk, Haochang Shou, Ilya M. Nasrallah, Christos Davatzikos
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine.
1 code implementation • 14 Jan 2020 • Dale Zhou, Christopher W. Lynn, Zaixu Cui, Rastko Ciric, Graham L. Baum, Tyler M. Moore, David R. Roalf, John A. Detre, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, Danielle S. Bassett
In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks.
This paper provides a novel method for the extraction of hierarchical connectivity components in the human brain using resting-state fMRI.
Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly.
We quantitatively characterize this topology using a local measure of network segregation known as the clustering coefficient, and find that it accounts for a greater degree of SES-associated variance than meso-scale segregation captured by modularity.
Neurons and Cognition
Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance.