Homotopic contralesional excitation suppresses spontaneous circuit repair and global network reconnections following ischemic stroke
Abstract
Understanding circuit-level manipulations that affect the brain's capacity for plasticity will inform the design of targeted interventions that enhance recovery after stroke. Following stroke, increased contralesional activity (e.g. use of the unaffected limb) can negatively influence recovery, but it is unknown which specific neural connections exert this influence, and to what extent increased contralesional activity affects systems- and molecular-level biomarkers of recovery. Here, we combine optogenetic photostimulation with optical intrinsic signal imaging (OISI) to examine how contralesional excitatory activity affects cortical remodeling after stroke in mice. Following photothrombosis of left primary somatosensory forepaw (S1FP) cortex, mice either recovered spontaneously or received chronic optogenetic excitation of right S1FP over the course of 4 weeks. Contralesional excitation suppressed perilesional S1FP remapping and was associated with abnormal patterns of stimulus-evoked activity in the unaffected limb. This maneuver also prevented the restoration of resting-state functional connectivity (RSFC) within the S1FP network, RSFC in several networks functionally-distinct from somatomotor regions, and resulted in persistent limb-use asymmetry. In stimulated mice, perilesional tissue exhibited transcriptional changes in several genes relevant for recovery. Our results suggest that contralesional excitation impedes local and global circuit reconnection through suppression of cortical activity and several neuroplasticity-related genes after stroke, and highlight the importance of site selection for therapeutic intervention after focal ischemia.
Data availability
Data reported in Figures 1, 6, 7 are publicly available:Fig. 1: https://figshare.com/articles/dataset/Cylinder_Rearing_Scores/19773487Fig. 6: https://figshare.com/articles/dataset/Neuroimaging_Data_Pre_Post_Stroke_for_26-03-2021-RA-eLife-68852/19773244Fig. 7: https://figshare.com/articles/dataset/RT-PCR_Data/19773364Data reported in Figures 2, 3, 4, 5 are unavailable due to technical issues with storage hard drives.Analysis code is available at https://github.com/BauerLabCodebase
Article and author information
Author details
Funding
National Institutes of Health (R01NS102870)
- Adam Q Bauer
National Institutes of Health (F31NS103275)
- Zachary Pollack Rosenthal
McDonnell Center for Systems Neuroscience
- Adam Q Bauer
The Alborada Trust
- Tadeusz Wieloch
The Wachtmeister Foundation
- Tadeusz Wieloch
Swedish Research Council
- Tadeusz Wieloch
National Institutes of Health (K25NS083754)
- Adam Q Bauer
National Institutes of Health (R37NS110699)
- Jin-Moo Lee
National Institutes of Health (R01NS084028)
- Jin-Moo Lee
National Institutes of Health (R01NS094692)
- Jin-Moo Lee
National Institutes of Health (R01NS078223)
- Joseph P Culver
National Institutes of Health (P01NS080675)
- Joseph P Culver
National Institutes of Health (R01NS099429)
- Joseph P Culver
National Institutes of Health (F31NS089135)
- Andrew W Kraft
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures described below were approved by theWashington University Animal Studies Committee in compliance with theAmerican Association for Accreditation of Laboratory Animal Care guidelines (Protocol #20-0022)
Copyright
© 2022, Bice et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,271
- views
-
- 306
- downloads
-
- 13
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
Large vesicle extrusion from neurons may contribute to spreading pathogenic protein aggregates and promoting inflammatory responses, two mechanisms leading to neurodegenerative disease. Factors that regulate the extrusion of large vesicles, such as exophers produced by proteostressed C. elegans touch neurons, are poorly understood. Here, we document that mechanical force can significantly potentiate exopher extrusion from proteostressed neurons. Exopher production from the C. elegans ALMR neuron peaks at adult day 2 or 3, coinciding with the C. elegans reproductive peak. Genetic disruption of C. elegans germline, sperm, oocytes, or egg/early embryo production can strongly suppress exopher extrusion from the ALMR neurons during the peak period. Conversely, restoring egg production at the late reproductive phase through mating with males or inducing egg retention via genetic interventions that block egg-laying can strongly increase ALMR exopher production. Overall, genetic interventions that promote ALMR exopher production are associated with expanded uterus lengths and genetic interventions that suppress ALMR exopher production are associated with shorter uterus lengths. In addition to the impact of fertilized eggs, ALMR exopher production can be enhanced by filling the uterus with oocytes, dead eggs, or even fluid, supporting that distention consequences, rather than the presence of fertilized eggs, constitute the exopher-inducing stimulus. We conclude that the mechanical force of uterine occupation potentiates exopher extrusion from proximal proteostressed maternal neurons. Our observations draw attention to the potential importance of mechanical signaling in extracellular vesicle production and in aggregate spreading mechanisms, making a case for enhanced attention to mechanobiology in neurodegenerative disease.
-
- Neuroscience
Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.