Research
Brain Connectivity
Continuous Representations of Brain Connectivity Using Spatial Point Processes
We represent structural brain connectivity as a continuous Poisson point process of tractography streamlines, approximated with kernel density estimation.
Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering
There are many ways to divide the brain into parcels based on anatomy and connectivity. We propose a method to reconcile the many possible parcellations via an ensemble clustering algorithm.
Cortical Connectome Registration Using Spherical Demons
We develop a method to align different brains spatially based on their connectivity profiles, helping detect disease effects.
Imaging Genetics
Heritability of the Shape of Subcortical Brain Structures in the General Population
We determine the relative contribution of genetic factors to individual variation in the shape of seven bilateral subcortical structures. Our findings are replicated in two large independent cohorts: QTIM and the Rotterdam study.
Approximating Principal Genetic Components of Subcortical Shape
We develop a method to identify principal genetic factors of subcortical shape phenotypes using narrow-sense heritability in a twin cohort. The new genetic factors help identify correlations with two Alzheimer’s genes in an unrelated dataset.
Neurodegenerative Disease Progression
Empowering Imaging Biomarkers of Alzheimer’s Disease
We develop an unbiased univariate marker of Alzheimer’s neurodegeneration based on high-dimensional neuroimaging data.
Constraining Disease Progression Models Using Subject Specific Connectivity Priors
We extend the event-based model (EBM) of progressive neurodegeneration by exploiting the network diffusion hypothesis. The new model predicts Alzheimer’s progression more accurately than standard EBM.
Spatially Adaptive Morphometric Knowledge Transfer across Neurodegenerative Diseases
We present a method to simultaneously learn several linear discriminative models from imaging and geometry data with explicit information sharing. Sharing information about Alzheimer’s and Parkinson’s diseases significantly improves classification accuracy in both.
Shape Analysis
Shape Matching with Medial Curves and 1-D Group-wise Registration
We present a method for subcortical shape description and registration with medial curves.
A Family of Fast Spherical Registration Algorithms for Cortical Shapes
We introduce a family of fast spherical registration algorithms: a spherical fluid model and several modifications of spherical demons. Our algorithms are based on fast convolution of tangential spherical vector fields in the spectral domain.
A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes
We present a framework for intrinsic (parameterization-invariant) comparison of surface metric structures and curvatures. Here, instead of comparing the embedding of spherically parameterized surfaces in space, we focus on the first fundamental form.
Collaborations
ENIGMA Consortium
ENIGMA
The ENIGMA Consortium brings together researchers in imaging genomics to understand brain structure, function, and disease, based on brain imaging and genetic data. We welcome brain researchers, imagers, geneticists, methods developers, and others interested in cracking the neuro-genetic code.
Genetics INRIA
