1 code implementation • 8 Dec 2022 • Yizi Zhang, Meimei Liu, Zhengwu Zhang, David Dunson
We applied the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition.
1 code implementation • 10 Oct 2022 • Haoming Yang, Steven Winter, Zhengwu Zhang, David Dunson
One of the central problems in neuroscience is understanding how brain structure relates to function.
1 code implementation • 18 Dec 2021 • Yang Li, Gonzalo Mateos, Zhengwu Zhang
Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level.
1 code implementation • 29 Jul 2021 • Zhengwu Zhang, Bayan Saparbayeva
Manifold-valued functional data analysis (FDA) recently becomes an active area of research motivated by the raising availability of trajectories or longitudinal data observed on non-linear manifolds.
no code implementations • 7 Nov 2019 • Meimei Liu, Zhengwu Zhang, David B. Dunson
In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer the relationships between brain structural connectomes and human traits.
no code implementations • 26 Apr 2019 • Mengyu Dai, Zhengwu Zhang, Anuj Srivastava
This paper studies change-points in human brain functional connectivity (FC) and seeks patterns that are common across multiple subjects under identical external stimulus.
1 code implementation • 10 Apr 2019 • Mengyu Dai, Zhengwu Zhang, Anuj Srivastava
Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task.
no code implementations • 5 Jan 2016 • Stephen Tierney, Junbin Gao, Yi Guo, Zhengwu Zhang
However the data may actually be functional i. e.\ each data point is a function of some variable such as time and the function is discretely sampled.
1 code implementation • 1 Apr 2015 • Zhengwu Zhang, Debdeep Pati, Anuj Srivastava
The elastic-inner product matrix obtained from the data is modeled using a Wishart distribution whose parameters are assigned carefully chosen prior distributions to allow for automatic inference on the number of clusters.
no code implementations • 23 Mar 2015 • Zhengwu Zhang, Jingyong Su, Eric Klassen, Huiling Le, Anuj Srivastava
Using a natural Riemannain metric on vector bundles of SPDMs, we compute geodesic paths and geodesic distances between trajectories in the quotient space of this vector bundle, with respect to the re-parameterization group.