About

The Vision

A balanced interplay among the different parameters of neural circuits, such as the balance between excitation (E) and inhibition (I), is important for brain function, and disruption of this balance plays an important role in clinical conditions such as autism and Alzheimer's disease. Thus, developing mechanistic accounts of healthy brain function and its aberrations requires being able to robustly track parameters of the neural circuit. Noninvasive neuroimaging techniques (e.g., EEG and MEG) reflect the integration of large-scale electromagnetic fluctuations generated and propagated within and among different classes of cells, different layers within the cortex, and different columns across the cortical sheet. Thus, current neuroimaging measures are inherently ambiguous about the underlying individual neural microcircuit processes and alone have limited utility for tracking variations of circuit parameters.

The interdisciplinary approach of the MS-BrainMarker & HPEEC-BRAINMOD projects will be at the convergence of recent developments in computational modelling, brain biomarker development and machine learning that together will provide the opportunity for new and breakthrough progress in linking microcircuit attributes to macroscopic neuroimaging markers. Our tools will have the potential of identifying alterations of circuit parameters (focusing on the E/I ratio) at a preclinical stage of brain pathologies, in the asymptomatic period in which cognitive deviations are not visible at all, and during progression of disease. In a similar way, our inference tools may be used for monitoring changes in the cortical circuit in response to different types of E/I-modulating interventions, such as pharmacological drugs, for a better characterization and individualization of therapeutic techniques.

Objectives

Project Descriptions