Biomarker potential of spatial network organization

Studies from our lab and other groups have shown that spatial network organization may be a stronger predictor of behavior than between-region connectivity patterns. This invites the question whether spatial metrics may also offer more sensitive biomarkers of disease. We test this question in a variety of different disease cohorts including depression and addiction. 

Unique versus common cross-diagnostic connectivity abnormalities

There are many symptoms that are commonly seen across a number of different psychiatric disorders such as changes in sleep, mood, and motivation. The aim of this project is to differentiate between shared connectivity abnormalities (linked to common symptoms), and unique markers of disease. 

Using a variety of existing datasets (such as the UK Biobank and multiple Connectomes Related to Human Disease studies), this project involves the development of advanced data normalization tools.

Understanding overlap in resting state fMRI networks at the single cell level

Densely interconnected ‘hub’ regions play an important role in human cognition and behavior, however, the cellular architecture in such hub regions is currently unknown. This proposal bridges across species and across scales to study the cellular circuitry in hub regions. 

Using electrophysiology and oxygen polarography, we test whether neurons in hub regions are: (i) spatially interdigitated, (ii) temporally switching network allegiance, or (iii) truly integrating across networks. 

This project is a joint effort with Larry Snyder and funded by the NIH: R34NS118618


To get involved with any of these projects in the Personomics Research Group, please contact us here: