Optimizing brain connectivity networks for disease classification using EPIC

Abstract

We propose a method to adaptively select an optimal cortical segmentation for brain connectivity analysis that maximizes feature-based disease classification performance. In standard structural connectivity analysis, the cortex is typically subdivided (parcellated) into N anatomical regions. White matter fiber pathways from tractography are used to compute an N × N matrix, which represents the pairwise connectivity between those regions. We optimize this representation by sampling over the space of possible region combinations and represent each configuration as a set partition of the N anatomical regions. Each partition is assigned a score using accuracy from a support vector machine (SVM) classifier of connectivity matrices in a group of patients and controls. We then define a high-dimensional optimization problem using simulated annealing to identify an optimal partition for maximum classification accuracy. We evaluate the results separately on test data using cross-validation. Specifically, we demonstrate results on the ADNI-2 dataset, where we optimally parcellate the cortex to yield an 85% classification accuracy using connectivity information alone. We refer to our method as evolving partitions to improve connectomics (EPIC).

Brain connectivity and novel network measures for Alzheimer's disease classification

“…We recently used it to choose the architecture of the connectivity matrix by selecting the best nodes or regions of the cortex. This adaptive cortical parcellation was created based on a framework to evaluate different cortical parcellations by their accuracy from diagnostic classifiers, such as SVMs (Prasad et al, 2014). …”

Automatic clustering and population analysis of white matter tracts using maximum density paths

“…We could compute mean diffusivity (Le Bihan et al, 2001), generalized FA (Barmpoutis et al, 2009), or the tensor distribution function and interpolate them along each MDP. Our white matter analysis framework could even be scored by their capacity (Prasad et al, 2013b) and used as measures of connectivity to complement (Prasad et al, 2013a) and optimize our representation of brain connectivity networks (Prasad et al, 2014). …”

Genetics of the Connectome and the ENIGMA Project

“…In Prasad et al (2014), we introduced a method called "EPIC" (Evolving Partitions in Connectomics) to compute brain connectivity in such a way as to be optimally sensitive to statistical effects in a population, such as the effect of Alzheimer's disease or depression. Clearly, the brain can be divided into regions in many different ways, such as spectral clustering (Craddock et al 2012), hierarchical clustering (Blumensath et al 2013), or even genetic clustering (Chen et al 2012).…”