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1.
Protein & Cell ; (12): 203-219, 2022.
Artigo em Inglês | WPRIM | ID: wpr-929177

RESUMO

Many people affected by fragile X syndrome (FXS) and autism spectrum disorders have sensory processing deficits, such as hypersensitivity to auditory, tactile, and visual stimuli. Like FXS in humans, loss of Fmr1 in rodents also cause sensory, behavioral, and cognitive deficits. However, the neural mechanisms underlying sensory impairment, especially vision impairment, remain unclear. It remains elusive whether the visual processing deficits originate from corrupted inputs, impaired perception in the primary sensory cortex, or altered integration in the higher cortex, and there is no effective treatment. In this study, we used a genetic knockout mouse model (Fmr1KO), in vivo imaging, and behavioral measurements to show that the loss of Fmr1 impaired signal processing in the primary visual cortex (V1). Specifically, Fmr1KO mice showed enhanced responses to low-intensity stimuli but normal responses to high-intensity stimuli. This abnormality was accompanied by enhancements in local network connectivity in V1 microcircuits and increased dendritic complexity of V1 neurons. These effects were ameliorated by the acute application of GABAA receptor activators, which enhanced the activity of inhibitory neurons, or by reintroducing Fmr1 gene expression in knockout V1 neurons in both juvenile and young-adult mice. Overall, V1 plays an important role in the visual abnormalities of Fmr1KO mice and it could be possible to rescue the sensory disturbances in developed FXS and autism patients.


Assuntos
Animais , Humanos , Camundongos , Modelos Animais de Doenças , Proteína do X Frágil da Deficiência Intelectual/metabolismo , Síndrome do Cromossomo X Frágil/metabolismo , Camundongos Knockout , Neurônios/metabolismo
2.
Neuroscience Bulletin ; (6): 576-590, 2022.
Artigo em Inglês | WPRIM | ID: wpr-929120

RESUMO

Neurons in the primary auditory area (AUDp) innervate multiple brain regions with long-range projections while receiving informative inputs for diverse functions. However, the brain-wide connections of these neurons have not been comprehensively investigated. Here, we simultaneously applied virus-based anterograde and retrograde tracing, labeled the connections of excitatory and inhibitory neurons in the mouse AUDp, and acquired whole-brain information using a dual-channel fluorescence micro-optical sectioning tomography system. Quantified results showed that the two types of neurons received inputs with similar patterns but sent heterogeneous projections to downstream regions. In the isocortex, functionally different areas consistently sent feedback-dominated projections to these neurons, with concomitant laterally-dominated projections from the sensory and limbic cortices to inhibitory neurons. In subcortical regions, the dorsal and medial parts of the non-lemniscal auditory thalamus (AT) were reciprocally connected to the AUDp, while the ventral part contained the most fibers of passage from the excitatory neurons and barely sent projections back, indicating the regional heterogeneity of the AUDp-AT circuit. Our results reveal details of the whole-brain network and provide new insights for further physiological and functional studies of the AUDp.

3.
Neuroscience Bulletin ; (6): 493-505, 2020.
Artigo em Inglês | WPRIM | ID: wpr-826803

RESUMO

The caudal forelimb area (CFA) of the mouse cortex is essential in many forelimb movements, and diverse types of GABAergic interneuron in the CFA are distinct in the mediation of cortical inhibition in motor information processing. However, their long-range inputs remain unclear. In the present study, we combined the monosynaptic rabies virus system with Cre driver mouse lines to generate a whole-brain map of the inputs to three major inhibitory interneuron types in the CFA. We discovered that each type was innervated by the same upstream areas, but there were quantitative differences in the inputs from the cortex, thalamus, and pallidum. Comparing the locations of the interneurons in two sub-regions of the CFA, we discovered that their long-range inputs were remarkably different in distribution and proportion. This whole-brain mapping indicates the existence of parallel pathway organization in the forelimb subnetwork and provides insight into the inhibitory processes in forelimb movement to reveal the structural architecture underlying the functions of the CFA.

4.
International Journal of Biomedical Engineering ; (6): 154-159, 2018.
Artigo em Chinês | WPRIM | ID: wpr-693100

RESUMO

Objective To design and implement a universal multi-channel software for neural electrophysiological stimulation experimental platforms. Method The layered design of software and hardware was adopted for the logical architecture to avoid excessive reliance on specific hardware. On the premise of ensuring compatibility with existing devices, an extensible control algorithm based on the .NET Frameworks platform was developed to realize multi-channel, feedback-controlled program-controlled stimulus output. The proposed software was designed with a user-friendly interface and stimulating/recording switch function, and could dynamically change stimulation programs and switch electrodes during the experiment process. Results The results showed that the software could control the stimulators steadily and generate random stimulation protocols and synchronization control signals according to the user-supplied dynamical parameters, including electrodes, amplitudes, and intervals. In the stimulation sequence, the switching delay between two electrodes was around 600 ms level. Conclusion The software has good compatibility with existing equipment systems. It can achieve multi-channel, real-time, feedback-controlled program-controlled stimulation according to the characteristics and needs of multi-lead neural electrophysiological stimulation researches. It has the functions of dynamically changing the stimulation program and switching electrodes during operation. This software provides tools for the study of the mechanism of network-level neural network feedback loops.

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