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Maurizio Corbetta

Alterations of functional connectivity in the human brain after focal lesions, and cognitive functions: empirical and modeling studies (Brainsynch-HIT). Maurizio Corbetta. Andrea Brovelli. Gustavo Deco. Rainer Goebel. 1. Widely known facts about stroke and neurological impairment.

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Maurizio Corbetta

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  1. Alterations of functional connectivity in the human brain after focal lesions, and cognitive functions: empirical and modeling studies (Brainsynch-HIT) Maurizio Corbetta Andrea Brovelli Gustavo Deco Rainer Goebel 1

  2. Widely known facts about stroke andneurological impairment

  3. Stroke in segregated and integrated networks Network of specialized modules A new framework for brain-behavior relationships? Local Function Networks of segregated and integrated nodes writing motor motor attention attention arithmetic executive executive vision vision speech lesion disruption reading Vision Language Motor Attention language repetition comprehension Deficits Sustained Attention Language comp/prod Motor deficits 3

  4. Mapping the effects of stroke on behavior and the structural and functional organization of the brain

  5. Low dimensional functional connectivity changes predict behavior and recovery

  6. Whole brain computational modeling gustavo deco Deco & Corbetta, The Neuroscientist, 2010; Deco et al. J.Neuroscience 2013, 2014; Adrián Ponce-Alvarez et al. PLOS Comput.Biology 2015

  7. 2. Compute network integration (empirical, model) and segregation (model only) 1. Lesion structural connectivity in each subject, then optimize based on functional connectivity

  8. Decrements of integration (empirical, model) and segregation (model) in stroke Adhikari et al. Brain 2017

  9. STROKE Stimulate stroke patients to improve connectivity and potentially behavior Low dimensional behavior, structural & functional connectivity Model stimulation patterns that return higher entropy in model Model lesion and related connectivity changes in whole brain models at the individual level

  10. Many limitations to whole brain modeling • Accuracy of structural connectivity (e.g. inter-hemispheric fibers are not well estimated). • Modeling is based on total correlation, not directional interactions. • Total correlation does not rule the influence of third parties (e.g. thalamus) driving the correlation. • Mainly static symmetric correlation, not effective connectivity, not dynamics. • Next steps: • Directional interactions between regions that show abnormal functional connectivity. • Are there common inputs? • Is there information in dynamic connectivity?

  11. BrainSynch-HIT FLAG-ERA 2018Specific aims Aim 1: develop and test methods for the analysis of directional interactions among brain regions in resting state and task-evoked fMRI data in healthy controls and stroke damaged individuals (Andrea Brovelli). Aim 2: develop a computational model of an injured brain that replicates both empirically measured patterns of connectivity abnormalities and behavioral deficits. This aim will employ: whole-brain computational dynamic models developed in SP4 (Gustavo Deco and Rainer Goebel).

  12. BrainSynch-HIT FLAG-ERA 2018Preliminary work plan • 3.1 Work plan and work packages • The stroke data, collected by the PI at Washington University in St.Louis, will be transferred to CNRS and UniPD. • The CNRS team will adapt the hierarchical pipeline of algorithms for the estimation of directional interactions to the R-fMRI data (Work Package 1, WP1). This will be also confirmed in a subset of patients with task data. • The UniPD post-doc will spend time in the Netherlands in Dr Goebel’s and in Spain in Dr. Deco’s laboratory to learn process and whole brain modeling, and will apply these two models to the stroke data (WP2). • Directional interactions will be tested on time series obtained from cortical parcels derived from healthy subjects parcellation scheme both in single subject and groups (CNRS, WP3). • After the directional pipelines are completed, we will apply the directional parameters to the whole brain and process oriented modeling (UniPD, WP4-B).

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