The long-term goal of our research program is to develop new treatments for improving behavioral deficits post-stroke based on a formal theory of brain function and organization. However, the immediate goal of this competitive renewal is to identify patterns of disrupted structural and functional connectivity that are related to stroke-induced deficits, determine how they change during recovery, and see if they predict outcome. Computational theories have proposed that lesions to the brain will have different effects based on the underlying network architecture. Mechanisms controlling recovery of function may similarly depend on network architecture.
Specific Aim 1 measures changes in whole brain functional connectivity (FC) after damage to peripheral networks that are mainly connected within the network vs. central networks that communicate more broadly with other brain networks. We measure separately the effect of cortical vs. white matter pathway damage. We predict that peripheral lesions (either to gray or white matter) will produce FC changes that are mainly limited to the affected network, while central lesions (either gray or white matter) will produce multi- network FC changes. Peripheral networks are connected to input/output pathways and mediate predominantly sensory or motor functions. Central networks are connected through association pathways and mediate predominantly attention and memory functions.
Specific Aim 2 uses a machine learning ridge regression method to test the idea that sensory-motor deficits and their recovery are more dependent on structural damage and anatomical disconnection, whereas cognitive impairments are better captured by multi-network functional connectivity dysfunction. An important clinical goal of the project is to determine whether advanced neuroimaging techniques, such as structural imaging, DTI, and fMRI can provide useful clinical information on an individual patient's recovery and outcome, over and above that provided by measures of acute behavior, demographic variables, and amount of treatment.
In Specific Aim 3 we enter neuroimaging data into a classifier to predict outcome in individual patients, as well as to predict the effect of rehabilitation interventions. Finally, in Specific Aim 4 we move from correlation and prediction to mechanism. Our basic hypothesis is that changes in functional connectivity after focal injury reflect alterations in brain dynamics, specifically a decrease in the variability of inter-regional phase differences. This hypothesis is tested empirically by phase measurements, but also computationally, using a biophysically based model that can simulate the FC changes observed in patients. Importantly, the model also estimates the information capacity of the damaged brain, which we will correlate with the patient's empirically measured degree of modularity and profile of behavioral deficits.
The goal of this project is to use advanced neuroimaging techniques to understand how a stroke changes the large-scale physiology of the brain, and how those changes in turn relate to the behavioral problems caused by the stroke. We use techniques that allow us to measure how a stroke damages the connectivity of the brain, both by damaging the fiber bundles that transmit information between brain regions, but also by changing the functional communication across many brain regions. We test hypotheses concerning: 1) how damage to different parts of the brain affect these measures of connectivity; 2) how measures of connectivity can help us to predict the outcome of individual stroke patients; and, 3) whether computational modeling of connectivity can help us understand the behavioral deficits caused by a stroke.
|Lin, Leanne Y; Ramsey, Lenny; Metcalf, Nicholas V et al. (2018) Stronger prediction of motor recovery and outcome post-stroke by cortico-spinal tract integrity than functional connectivity. PLoS One 13:e0202504|
|Tu, Chengyi; Rocha, Rodrigo P; Corbetta, Maurizio et al. (2018) Warnings and caveats in brain controllability. Neuroimage 176:83-91|
|Saenger, Victor M; Ponce-Alvarez, Adrián; Adhikari, Mohit et al. (2018) Linking Entropy at Rest with the Underlying Structural Connectivity in the Healthy and Lesioned Brain. Cereb Cortex 28:2948-2958|
|Siegel, Joshua S; Seitzman, Benjamin A; Ramsey, Lenny E et al. (2018) Re-emergence of modular brain networks in stroke recovery. Cortex 101:44-59|
|Corbetta, Maurizio; Siegel, Joshua S; Shulman, Gordon L (2018) On the low dimensionality of behavioral deficits and alterations of brain network connectivity after focal injury. Cortex 107:229-237|
|Betti, Viviana; Corbetta, Maurizio; de Pasquale, Francesco et al. (2018) Topology of Functional Connectivity and Hub Dynamics in the Beta Band As Temporal Prior for Natural Vision in the Human Brain. J Neurosci 38:3858-3871|
|Siegel, Joshua S; Shulman, Gordon L; Corbetta, Maurizio (2017) Measuring functional connectivity in stroke: Approaches and considerations. J Cereb Blood Flow Metab 37:2665-2678|
|Adhikari, Mohit H; Deco, Gustavo; Corbetta, Maurizio (2017) Reply: Defining a functional network homeostasis after stroke: EEG-based approach is complementary to functional MRI. Brain 140:e72|
|Ramsey, L E; Siegel, J S; Lang, C E et al. (2017) Behavioural clusters and predictors of performance during recovery from stroke. Nat Hum Behav 1:|
|Adhikari, Mohit H; Hacker, Carl D; Siegel, Josh S et al. (2017) Decreased integration and information capacity in stroke measured by whole brain models of resting state activity. Brain 140:1068-1085|
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