Analysis of cortical function

National Institute Of Diabetes And Digestive And Kidney Diseases

Investigators

Linked publications & trials

Abstract

My lab has been exploring how genetic and anatomical perturbations can give rise to conditions such as mental illness and overeating. In particular, we used a local cortical circuit at the sub-millimeter level as a bridge between genotype and phenotype. We have found that synaptic imbalance and changes in the mini-column structure of cortex can effect performance in visual saccade tasks that match experiments. The model also makes predictions for possible pharmacological therapeutics. Cognitive phenomena that may be usefully exploited to probe cortical circuit function involve visual illusions such as binocular rivalry, where each eye is presented with a different image and the brain's perception alternates between the two images. For the past two decades, I have been developing a physiologically-based cortical circuit model that can account for a comprehensive set of psychophysical and electrophysiological data pertaining to many neural phenomena including perceptual rivalry, stimulus disambiguation, and neural activity normalization. The model is quantified by a small number of parameters that can be mapped back to molecular and genetic sources. We have made progress towards our goal of designing a suite of cognitive tasks where performance measurements combined with neural recordings from MEG and fMRI can be combined to estimate the parameters, which would then give an objective measure or nosology for cognitive function and mental illness. These parameters could be used for defining illnesses, quantifying progression of illnesses, quantifying the effects of treatments and medication. For example, illnesses could be defined in terms of deviations from the mean and treatments could be assessed in terms of how they affected the parameters. The parameters could be used in personalized medicine for treatments of diseases by determining optimal drugs to use, dosing regimens, and drug combinations. They could also be used to help design novel therapies. This could work could lead to a new paradigm for the diagnosis and treatment of diseases. I have also been using machine learning methods to model circuits pertaining to decision making.