1 citations · 2 across the 12 of their papers we have counts for
14 papers
Predicting Brain Morphometry with MT-GNN: Mesh Evolution in Continuous Time with Graph-Based Metric Tensor Embeddings
Hao Ding, Daniel Semchin, Paul M. Thompson +1
Predicting how a subcortical structure's shape will evolve from a few prior scans could support prognosis and clinical-trial enrichment. Existing longitudinal mesh predictors eithe…
Predicting Future Cognitive Decline with Hyperbolic Stochastic Coding
J. Zhang, Q. Dong, J. Shi +9
Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However such approaches, similar to other…
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
Santiago Silva, Boris Gutman, Eduardo Romero +3
At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to…
Deep Learning for Quality Control of Subcortical Brain 3D Shape Models
Dmitry Petrov, Boris A. Gutman Egor Kuznetsov, Theo G. M. van Erp +70
We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional ne…
Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms
Ayagoz Mussabayeva, Alexey Kroshnin, Anvar Kurmukov +5
We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffe…
Connectivity-Driven Brain Parcellation via Consensus Clustering
Anvar Kurmukov, Ayagoz Mussabayeva, Yulia Denisova +2
We present two related methods for deriving connectivity-based brain atlases from individual connectomes. The proposed methods exploit a previously proposed dense connectivity repr…
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
Nikita Mokrov, Maxim Panov, Boris A. Gutman +3
This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classif…
Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
Dmitry Petrov, Boris A. Gutman, Shih-Hua +72
As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts ha…
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz +5
There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to disting…
A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
Daniel Moyer, Boris A Gutman, Neda Jahanshad +1
One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i.e. parcellation. While it is generally agreed tha…
Structural Connectome Validation Using Pairwise Classification
Dmitry Petrov, Boris Gutman, Alexander Ivanov +4
In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains. To do so, we classify st…
Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016
Sarah Parisot, Jonathan Passerat-Palmbach, Markus D. Schirmer +1
Understanding brain connectivity in a network-theoretic context has shown much promise in recent years. This type of analysis identifies brain organisational principles, bringing a…
An Empirical Study of Continuous Connectivity Degree Sequence Equivalents
Daniel Moyer, Boris A. Gutman, Joshua Faskowitz +2
In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes. This model produces for each subject a continuous bivariate i…
A Continuous Model of Cortical Connectivity
Daniel Moyer, Boris A. Gutman, Joshua Faskowitz +2
We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in…