Browsing by Subject "Brain networks"

A major component of most psychiatric disorders is that they affect the internal mental state of the individual.Modern psychiatric research primarily depends on self-report to measure internal state in humans and on behavior to measure internal state in animals. Both of these can be unreliable measures of internal mental state. In order to facilitate psychiatric research, we need models of mental state that are based on neural activity. Such models have proven challenging to design because they must be able to distill the complexity of neural activity distributed across many brain regions. This dissertation describes models that solve this problem, by representing such neural activity as a sum of contributions from many distinct sub-networks in the brain. We call these sub-network electrical functional connectome (electome) networks. Here, I show that electome networks can be used to distinguish between mental states in mice. One of these electome networks distinguishes mice that show depressive symptoms from those that do not, and can be used as a measure depressive phenotype. Electome networks represent a new class of tools that give brain researchers a new way to measure internal mental state and relate it back to brain activity.

In brain connectomics, it is common to divide the cortical surface into discrete regions of interest (ROIs), and then to use these regions to induce a graph. Nodes correspond to regions of interest and edges encode summaries of the strength of connections between pairs of regions. These spatial weighted graphs are often reduced to adjacency matrices, which are then used as inputs to downstream statistical analysis. The structure of these adjacency matrices depends critically on the chosen parcellation, with finer resolutions producing unique spare patterns. Consequently, both the available methods of analysis and the conclusions from analysis depend heavily on the chosen parcellation. To solve this problem we develop a multi-scale graph factorization model, which links together scale-specific factorizations through a common set of individual-specific latent factors. These scores combine information across from different parcellations to produce a single interpretable summary of an individuals brain structure. We develop a simple, efficient algorithm, and illustrate substantial advantages over comparable single-scale methods in both simulations and analyses of the Human Connectome Project dataset.