Dataset Information

Optogenetic Approaches to Target Specific Neural Circuits in Post-stroke Recovery.

ABSTRACT: Stroke is a leading cause of death and disability in the USA, yet treatment options are very limited. Functional recovery can occur after stroke and is attributed, in part, to rewiring of neural connections in areas adjacent to or remotely connected to the infarct. A better understanding of neural circuit rewiring is thus an important step toward developing future therapeutic strategies for stroke recovery. Because stroke disrupts functional connections in peri-infarct and remotely connected regions, it is important to investigate brain-wide network dynamics during post-stroke recovery. Optogenetics is a revolutionary neuroscience tool that uses bioengineered light-sensitive proteins to selectively activate or inhibit specific cell types and neural circuits within milliseconds, allowing greater specificity and temporal precision for dissecting neural circuit mechanisms in diseases. In this review, we discuss the current view of post-stroke remapping and recovery, including recent studies that use optogenetics to investigate neural circuit remapping after stroke, as well as optogenetic stimulation to enhance stroke recovery. Multimodal approaches employing optogenetics in conjunction with other readouts (e.g., in vivo neuroimaging techniques, behavior assays, and next-generation sequencing) will advance our understanding of neural circuit reorganization during post-stroke recovery, as well as provide important insights into which brain circuits to target when designing brain stimulation strategies for future clinical studies.

SUBMITTER: Cheng MY

PROVIDER: S-EPMC4824024 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Optogenetic Approaches to Target Specific Neural Circuits in Post-stroke Recovery.

Cheng Michelle Y MY Aswendt Markus M Steinberg Gary K GK

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 20160401 2

Stroke is a leading cause of death and disability in the USA, yet treatment options are very limited. Functional recovery can occur after stroke and is attributed, in part, to rewiring of neural connections in areas adjacent to or remotely connected to the infarct. A better understanding of neural circuit rewiring is thus an important step toward developing future therapeutic strategies for stroke recovery. Because stroke disrupts functional connections in peri-infarct and remotely connected reg ...[more]

PMID: 26701667

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Project description:Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers’ ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application. Supplementary Information The online version contains supplementary material available at 10.1186/s40708-022-00160-w.

Project description:Identifying the neural changes that support recovery of cognitive functions after a brain lesion is important to advance our understanding of human neuroplasticity, which, in turn, forms the basis for the development of effective treatments. To date, the preponderance of neuroimaging studies has focused on localizing changes in average brain activity associated with functional recovery. Here, we took a novel approach by evaluating whether cognitive recovery in chronic stroke is related to increases in the differentiation of local neural response patterns. This approach is supported by research indicating that, in the intact brain, local neural representations become more differentiated (dissimilar) with learning (Glezer et al., 2015). We acquired fMRI data before and after 21 individuals received approximately 12 weeks of behavioral treatment for written language impairment due to a left-hemisphere stroke. We used Local-Heterogeneity Regression Analysis (Purcell and Rapp, 2018) to measure local neural response differentiation associated with written language processing, assuming that greater heterogeneity in the pattern of activity across adjacent neural areas indicates more well-differentiated neural representations. First, we observed pre to post-treatment increases in local neural differentiation (Local-Hreg) in the ventral occipital-temporal cortex of the left hemisphere. Second, we found that, in this region, higher local neural response differentiation prior to treatment was associated with less severe written language impairment, and that it also predicted greater future responsiveness to treatment. Third, we observed that changes in neural differentiation were systematically related to performance changes for trained and untrained items. Fourth, we did not observe these brain-behavior relationships for mean BOLD responses, only for Local-Hreg. Thus, this is the first investigation to quantify changes in local neural differentiation in the recovery of a cognitive function and the first to demonstrate the clear behavioral relevance of these changes. We conclude that the findings provide strong support for the novel hypothesis that the local re-differentiation of neural representations can play a significant role in functional recovery after brain lesion.

Dataset Information

Optogenetic Approaches to Target Specific Neural Circuits in Post-stroke Recovery.

Publications

Optogenetic Approaches to Target Specific Neural Circuits in Post-stroke Recovery.

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