Mapping causal functional contributions in the brain from the game-theoretical analysis of stroke lesions
Professor Claus Hilgetag, University of Hamburg, Germany
Strokes affect multiple brain regions and produce multiple functional deficits, acting as a natural experiment in brain perturbation. However, the interpretation of such perturbation data and the inference of regional functional contributions to brain function is made difficult by the multivariate interactions between different brain regions. To address this problem, we used a multivariate, game-theoretical approach to objectively quantify the causal functional contributions of brain regions, and applied it to animal model data, ground-truth simulations, as well as clinical data of stroke lesions and corresponding functional deficits. The approach reliably inferred regional contributions to brain function and revealed a wide quantitative range of contributions of multiple regions to multiple functions. It also indicated functional synergies and redundancies among brain regions and helped to identify potential targets for clinical rehabilitation
Using The Virtual Brain as an 'informatics microscope' to uncover biophysical parameters of stroke recovery
Dr Ana Solodkin, UC Irvine School of Medicine, USA
An exciting advance in the field of neuroimaging is the acquisition and processing of very large data sets (so called “big data”), permitting large-scale inferences that foster a greater understanding of brain function in health and disease. Yet what we are clearly lacking are quantitative integrative tools to translate this understanding to the individual level to lay the basis for personalized medicine.
This challenge will be addressed through a new neuroInformatics modelling platform that has the capacity to track brain network function at different levels of inquiry, from microscopic to macroscopic and from the localized to the distributed. The multi-scale approach, The Virtual Brain (TVB), can effectively model individualized brain activity, provides unique biological interpretable data in stroke.
The talk will show how our results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease including on the individual level via personalized therapeutic interventions.
Stroke dys-connectome: behaviour, structure and function
Dr Maurizio Corbetta, Washington University, USA
Since the early days of neuroscience the relative merit of structural vs. functional network accounts in explaining neurological deficits has been intensely debated. Using a large stroke cohort and a machine learning approach, we show that visual memory, and verbal memory deficits are better predicted by functional connectivity than by lesion location, while visual and motor deficits are better predicted by lesion location than functional connectivity. In addition, we show that disruption to a subset of cortical areas predicts general cognitive deficit (spanning multiple behavior domains). In a separate set of computational studies we show that these deficits affects the integration/segregation of brain regions. These results shed light on the complementary value of structural vs. functional accounts of stroke, and provide a physiological mechanism for general multi-domain deficits seen after stroke.
Predicting outcome and recovery after stroke
Professor Cathy Price, University College London, UK
Predicting outcome and recovery after stroke is notoriously difficult because the consequences of seemingly similar brain damage are inconsistent across patients. Clearly factors other than lesion site influence the level of recovery. To investigate the main causes of variability, machine learning on a wide variety of data acquired from large cohorts of stroke survivors has been used. This indicates the importance of lesion site, lesion size and years post stroke as the most informative variables for recovery. Although machine learning does not indicate how the predictions can be improved, it does place constraints on the variables that need to be controlled and considered in models of recovery. Hence, this talk will show how predicting outcome after brain damage can be optimised by a combination of model-based and data-led approaches.
Panel discussion/Overview (future directions)