Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in
- Award ID(s):
- 1631112
- NSF-PAR ID:
- 10196095
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 117
- Issue:
- 32
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- 19556 to 19565
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ex vivo cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations.SIGNIFICANCE STATEMENT The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used anex vivo approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles. -
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Introduction The medical and recreational use of cannabis has increased in the United States. Its chronic use can have detrimental effects on the neurobiology of the brain—effects that are age-dependent. This was an exploratory study looking at the effects of chronically inhaled vaporized cannabis on brain structure in adult female mice.
Methods Adult mice were exposed daily to vaporized cannabis (10.3% THC and 0.05% CBD) or placebo for 21 days. Following cessation of treatment mice were examined for changes in brain structure using voxel-based morphometry and diffusion weighted imaging MRI. Data from each imaging modality were registered to a 3D mouse MRI atlas with 139 brain areas.
Results Mice showed volumetric changes in the forebrain particularly the prefrontal cortex, accumbens, ventral pallidum, and limbic cortex. Many of these same brain areas showed changes in water diffusivity suggesting alterations in gray matter microarchitecture.
Discussion These data are consistent with much of the clinical findings on cannabis use disorder. The sensitivity of the dopaminergic system to the daily exposure of vaporized cannabis raises concerns for abuse liability in drug naïve adult females that initiate chronic cannabis use.
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Ethanol tolerance is the first type of behavioral plasticity and neural plasticity that is induced by ethanol intake, and yet its molecular and circuit bases remain largely unexplored. Here, we characterize the following three distinct forms of ethanol tolerance in male
Drosophila : rapid, chronic, and repeated. Rapid tolerance is composed of two short-lived memory-like states, one that is labile and one that is consolidated. Chronic tolerance, induced by continuous exposure, lasts for 2 d, induces ethanol preference, and hinders the development of rapid tolerance through the activity of histone deacetylases (HDACs). Unlike rapid tolerance, chronic tolerance is independent of the immediate early geneHr38 /Nr4a . Chronic tolerance is suppressed by the sirtuin HDACSirt1 , whereas rapid tolerance is enhanced bySirt1 . Moreover, rapid and chronic tolerance map to anatomically distinct regions of the mushroom body learning and memory centers. Chronic tolerance, like long-term memory, is dependent on new protein synthesis and it induces thekayak /c-fos immediate early gene, but it depends on CREB signaling outside the mushroom bodies, and it does not require the Radish GTPase. Thus, chronic ethanol exposure creates an ethanol-specific memory-like state that is molecularly and anatomically different from other forms of ethanol tolerance.SIGNIFICANCE STATEMENT The pattern and concentration of initial ethanol exposure causes operationally distinct types of ethanol tolerance to form. We identify separate molecular and neural circuit mechanisms for two forms of ethanol tolerance, rapid and chronic. We also discover that chronic tolerance forms an ethanol-specific long-term memory-like state that localizes to learning and memory circuits, but it is different from appetitive and aversive long-term memories. By contrast, rapid tolerance is composed of labile and consolidated short-term memory-like states. The multiple forms of ethanol memory-like states are genetically tractable for understanding how initial forms of ethanol-induced neural plasticity form a substrate for the longer-term brain changes associated with alcohol use disorder. -
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Abstract Objective. Intracortical brain interfaces are an ever evolving technology with growing potential for clinical and research applications. The chronic tissue response to these devices traditionally has been characterized by glial scarring, inflammation, oxidative stress, neuronal loss, and blood-brain barrier disruptions. The full complexity of the tissue response to implanted devices is still under investigation.Approach. In this study, we have utilized RNA-sequencing to identify the spatiotemporal gene expression patterns in interfacial (within 100µ m) and distal (500µ m from implant) brain tissue around implanted silicon microelectrode arrays. Naïve, unimplanted tissue served as a control.Main results. The data revealed significant overall differential expression (DE) in contrasts comparing interfacial tissue vs naïve (157 DE genes), interfacial vs distal (94 DE genes), and distal vs naïve tissues (21 DE genes). Our results captured previously characterized mechanisms of the foreign body response, such as astroglial encapsulation, as well as novel mechanisms which have not yet been characterized in the context of indwelling neurotechnologies. In particular, we have observed perturbations in multiple neuron-associated genes which potentially impact the intrinsic function and structure of neurons at the device interface. In addition to neuron-associated genes, the results presented in this study identified significant DE in genes which are associated with oligodendrocyte, microglia, and astrocyte involvement in the chronic tissue response.Significance . The results of this study increase the fundamental understanding of the complexity of tissue response in the brain and provide an expanded toolkit for future investigation into the bio-integration of implanted electronics with tissues in the central nervous system. -
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Abstract Children who experience a traumatic brain injury (TBI) are at elevated risk for a range of negative cognitive and neuropsychological outcomes. Identifying which children are at greatest risk for negative outcomes can be difficult due to the heterogeneity of TBI. To address this barrier, the current study applied a novel method of characterizing brain connectivity networks, Bayesian multi‐subject vector autoregressive modelling (BVAR‐connect), which used white matter integrity as priors to evaluate effective connectivity—the time‐dependent relationship in functional magnetic resonance imaging (fMRI) activity between two brain regions—within the default mode network (DMN). In a prospective longitudinal study, children ages 8–15 years with mild to severe TBI underwent diffusion tensor imaging and resting state fMRI 7 weeks after injury; post‐concussion and anxiety symptoms were assessed 7 months after injury. The goals of this study were to (1) characterize differences in positive effective connectivity of resting‐state DMN circuitry between healthy controls and children with TBI, (2) determine if severity of TBI was associated with differences in DMN connectivity and (3) evaluate whether patterns of DMN effective connectivity predicted persistent post‐concussion symptoms and anxiety. Healthy controls had unique positive connectivity that mostly emerged from the inferior temporal lobes. In contrast, children with TBI had unique effective connectivity among orbitofrontal and parietal regions. These positive orbitofrontal‐parietal DMN effective connectivity patterns also differed by TBI severity and were associated with persisting behavioural outcomes. Effective connectivity may be a sensitive neuroimaging marker of TBI severity as well as a predictor of chronic post‐concussion symptoms and anxiety.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2003601117
