Erin Burke

Biomarkers and Predictors of Restorative Therapy Effects After Stroke

Many restorative therapies that promote brain repair are under development. Stroke is very heterogeneous, highlighting the need to identify target populations and to understand intersubject differences in treatment response. Several neuroimaging measures have shown promise as biomarkers and predictors, including measures of structure and function, in gray matter and white matter. The choice of biomarker and predictor can differ with the content of therapy and with the population under study, for example, contralesional hemisphere measures may be of particular importance in patients with more severe injury. Studies of training effects in healthy subjects provide insights useful to brain repair. Limitations of published studies include a focus on chronic stroke, however the brain is most galvanized to respond to restorative therapies in the early days after stroke. Multimodal approaches might be the most robust approach for stratifying patients and so for optimizing prescription of restorative therapies after stroke.

The influence of genetic factors on brain plasticity and recovery after neural injury.

PURPOSE OF REVIEW The fields of clinical genetics and pharmacogenetics are rapidly expanding. Genetic factors have numerous associations with injury and with treatment effects in the setting of neural plasticity and recovery. RECENT FINDINGS Evidence is reviewed that established genetic variants, as well as some more recently described variants, are related to outcome after neural injury and in some cases are useful for predicting clinical course. In many cases, the interaction of genetics with clinical factors such as experience and therapy may be important. As an extension of this, genetic factors have been associated with differential response to a number of forms of therapy, including pharmacological, brain stimulation, psychotherapy, and meditation. Genetic variation might also have a significant effect on plasticity and recovery through key covariates such as depression or stress. A key point is that genetic associations might be most accurately identified when studied in relation to distinct forms of a disorder rather than in relation to broad clinical syndromes. SUMMARY Understanding genetic variation gives clinicians a biological signal that could be used to predict who is most likely to recover from neural injury, to choose the optimal treatment for a patient, or to supplement rehabilitation therapy.

Predictors and Biomarkers of Treatment Gains in a Clinical Stroke Trial Targeting the Lower Extremity

Background and Purpose— Behavioral measures are often used to distinguish subgroups of patients with stroke (eg, to predict treatment gains, stratify clinical trial enrollees, or select rehabilitation therapy). In studies of the upper extremity, measures of brain function using functional magnetic resonance imaging (fMRI) have also been found useful, but this approach has not been examined for the lower extremity. The current study hypothesized that an fMRI-based measure of cortical function would significantly improve prediction of treatment-induced lower extremity behavioral gains. Biomarkers of treatment gains were also explored. Methods— Patients with hemiparesis 1 to 12 months after stroke were enrolled in a double-blind, placebo-controlled, randomized clinical trial of ropinirole+physical therapy versus placebo+physical therapy, results of which have previously been reported (NCT00221390).15 Primary end point was change in gait velocity. Enrollees underwent baseline multimodal assessment that included 19 measures spanning 5 assessment categories (medical history, impairment, disability, brain injury, and brain function), and also underwent reassessment 3 weeks after end of therapy. Results— In bivariate analysis, 8 baseline measures belonging to 4 categories (medical history, impairment, disability, and brain function) significantly predicted change in gait velocity. Prediction was strongest, however, using a multivariate model containing 2 measures (leg Fugl–Meyer score and fMRI activation volume within ipsilesional foot sensorimotor cortex). Increased activation volume within bilateral foot primary sensorimotor cortex correlated positively with treatment-induced leg motor gains. Conclusions— A multimodal model incorporating behavioral and fMRI measures best predicted treatment-induced changes in gait velocity in a clinical trial setting. Results also suggest potential use of fMRI measures as biomarkers of treatment gains.

Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity

Vector auto-regressive (VAR) models typically form the basis for constructing directed graphical models for investigating connectivity in a brain network with brain regions of interest (ROIs) as nodes. There are limitations in the standard VAR models. The number of parameters in the VAR model increases quadratically with the number of ROIs and linearly with the order of the model and thus due to the large number of parameters, the model could pose serious estimation problems. Moreover, when applied to imaging data, the standard VAR model does not account for variability in the connectivity structure across all subjects. In this paper, we develop a novel generalization of the VAR model that overcomes these limitations. To deal with the high dimensionality of the parameter space, we propose a Bayesian hierarchical framework for the VAR model that will account for both temporal correlation within a subject and between subject variation. Our approach uses prior distributions that give rise to estimates that correspond to penalized least squares criterion with the elastic net penalty. We apply the proposed model to investigate differences in effective connectivity during a hand grasp experiment between healthy controls and patients with residual motor deficit following a stroke.