Neuron populations across layer 2-6 in the mouse visual cortex exhibit different coding abilities in the awake mice.

Introduction: The visual cortex is a key region in the mouse brain, responsible for processing visual information. Comprised of six distinct layers, each with unique neuronal types and connections, the visual cortex exhibits diverse decoding properties across its layers. This study aimed to investigate the relationship between visual stimulus decoding properties and the cortical layers of the visual cortex while considering how this relationship varies across different decoders and brain regions. Methods: This study reached the above conclusions by analyzing two publicly available datasets obtained through two-photon microscopy of visual cortex neuronal responses. Various types of decoders were tested for visual cortex decoding. Results: Our findings indicate that the decoding accuracy of neuronal populations with consistent sizes varies among visual cortical layers for visual stimuli such as drift gratings and natural images. In particular, layer 4 neurons in VISp exhibited significantly higher decoding accuracy for visual stimulus identity compared to other layers. However, in VISm, the decoding accuracy of neuronal populations with the same size in layer 2/3 was higher than that in layer 4, despite the overall accuracy being lower than that in VISp and VISl. Furthermore, SVM surpassed other decoders in terms of accuracy, with the variation in decoding performance across layers being consistent among decoders. Additionally, we found that the difference in decoding accuracy across different imaging depths was not associated with the mean orientation selectivity index (OSI) and the mean direction selectivity index (DSI) neurons, but showed a significant positive correlation with the mean reliability and mean signal-to-noise ratio (SNR) of each layer's neuron population. Discussion: These findings lend new insights into the decoding properties of the visual cortex, highlighting the role of different cortical layers and decoders in determining decoding accuracy. The correlations identified between decoding accuracy and factors such as reliability and SNR pave the way for more nuanced understandings of visual cortex functioning.

Cable-free brain imaging for multiple free-moving animals with miniature wireless microscopes.

Significance: Although several miniature microscope systems have been developed to allow researchers to image brain neuron activities of free moving rodents, they generally require a long cable connecting to the miniature microscope. It not only limits the behavior of the animal, but also makes it challenging to study multiple animals simultaneously. Aim: The aim of this work is to develop a fully wireless miniature microscope that would break constraints from the connecting cables so that the animals could move completely freely, allowing neuroscience researchers to study more of animals' behaviors simultaneously, such as social behavior. Approach: We present a wireless mini-microscope (wScope) that enables simultaneously real-time brain imaging preview from multiple free-moving animals. The wScope has a mass of 2.7 g and a maximum frame rate of 25 Hz at 750 µ m × 450 µ m field of view with 1.8 - µ m resolution. Results: The performance of the wScope is validated via real-time imaging of the cerebral blood flow and the activity of neurons in the primary visual cortex (V1) of different mice. Conclusions: The wScope provides a powerful tool for brain imaging of multiple free moving animals in their much larger spaces and more naturalistic environments.

Rabphilin-3A undergoes phase separation to regulate GluN2A mobility and surface clustering.

N-methyl-D-aspartate receptors (NMDARs) are essential for excitatory neurotransmission and synaptic plasticity. GluN2A and GluN2B, two predominant Glu2N subunits of NMDARs in the hippocampus and the cortex, display distinct clustered distribution patterns and mobility at synaptic and extrasynaptic sites. However, how GluN2A clusters are specifically organized and stabilized remains poorly understood. Here, we found that the previously reported GluN2A-specific binding partner Rabphilin-3A (Rph3A) has the ability to undergo phase separation, which relies on arginine residues in its N-terminal domain. Rph3A phase separation promotes GluN2A clustering by binding GluN2A's C-terminal domain. A complex formed by Rph3A, GluN2A, and the scaffolding protein PSD95 promoted Rph3A phase separation. Disrupting Rph3A's phase separation suppressed the synaptic and extrasynaptic surface clustering, synaptic localization, stability, and synaptic response of GluN2A in hippocampal neurons. Together, our results reveal the critical role of Rph3A phase separation in determining the organization and stability of GluN2A in the neuronal surface.

Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning.

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social interactions. Traditional tracking methods (e.g., marking each animal with dye or surgically implanting microchips) can be invasive and may have an impact on the social behavior being measured. To overcome these shortcomings, video-based methods for tracking unmarked animals, such as fruit flies and zebrafish, have been developed. However, tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction patterns. In this study, we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network (R-CNN) deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion technology. The system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing behavior. The proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

Template-independent genome editing in the Pcdh15av-3j mouse, a model of human DFNB23 nonsyndromic deafness.

Although frameshift mutations lead to 22% of inherited Mendelian disorders in humans, there is no efficient in vivo gene therapy strategy available to date, particularly in nondividing cells. Here, we show that nonhomologous end-joining (NHEJ)-mediated nonrandom editing profiles compensate the frameshift mutation in the Pcdh15 gene and restore the lost mechanotransduction function in postmitotic hair cells of Pcdh15av-3J mice, an animal model of human nonsyndromic deafness DFNB23. Identified by an ex vivo evaluation system in cultured cochlear explants, the selected guide RNA restores reading frame in approximately 50% of indel products and recovers mechanotransduction in more than 70% of targeted hair cells. In vivo treatment shows that half of the animals gain improvements in auditory responses, and balance function is restored in the majority of injected mutant mice. These results demonstrate that NHEJ-mediated reading-frame restoration is a simple and efficient strategy in postmitotic systems.

Efficient and Highly Accurate Diagnosis of Malignant Hematological Diseases Based on Whole-Slide Images Using Deep Learning.

Hematopoietic disorders are serious diseases that threaten human health, and the diagnosis of these diseases is essential for treatment. However, traditional diagnosis methods rely on manual operation, which is time consuming and laborious, and examining entire slide is challenging. In this study, we developed a weakly supervised deep learning method for diagnosing malignant hematological diseases requiring only slide-level labels. The method improves efficiency by converting whole-slide image (WSI) patches into low-dimensional feature representations. Then the patch-level features of each WSI are aggregated into slide-level representations by an attention-based network. The model provides final diagnostic predictions based on these slide-level representations. By applying the proposed model to our collection of bone marrow WSIs at different magnifications, we found that an area under the receiver operating characteristic curve of 0.966 on an independent test set can be obtained at 10× magnification. Moreover, the performance on microscopy images can achieve an average accuracy of 94.2% on two publicly available datasets. In conclusion, we have developed a novel method that can achieve fast and accurate diagnosis in different scenarios of hematological disorders.

Restoration of FMRP expression in adult V1 neurons rescues visual deficits in a mouse model of fragile X syndrome.

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.

PORCN Negatively Regulates AMPAR Function Independently of Subunit Composition and the Amino-Terminal and Carboxy-Terminal Domains of AMPARs.

Most fast excitatory synaptic transmissions in the mammalian brain are mediated by α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptors (AMPARs), which are ligand-gated cation channels. The membrane expression level of AMPARs is largely determined by auxiliary subunits in AMPAR macromolecules, including porcupine O-acyltransferase (PORCN), which negatively regulates AMPAR trafficking to the plasma membrane. However, whether PORCN-mediated regulation depends on AMPAR subunit composition or particular regions of a subunit has not been determined. We systematically examined the effects of PORCN on the ligand-gated current and surface expression level of GluA1, GluA2, and GluA3 AMPAR subunits, alone and in combination, as well as the PORCN-GluA interaction in heterologous HEK293T cells. PORCN inhibited glutamate-induced currents and the surface expression of investigated GluA AMPAR subunits in a subunit-independent manner. These inhibitory effects required neither the amino-terminal domain (ATD) nor the carboxy-terminal domain (CTD) of GluA subunits. In addition, PORCN interacted with AMPARs independently of their ATD or CTD. Thus, the functional inhibition of AMPARs by PORCN in transfected heterologous cells was independent of the ATD, CTD, and subunit composition of AMPARs.

The Efficiency of Type-Specific High-Risk Human Papillomavirus Models in the Triage of Women with Atypical Squamous Cells of Undetermined Significance.

PURPOSE: To evaluate the performance of different high-risk human papillomavirus (HR-HPV) genotype models in triaging women with cytological diagnosis of atypical squamous cells of undetermined significance (ASCUS). PATIENTS AND METHODS: A total of 36,679 Chinese women who underwent cytology and HR-HPV genotyping assessments during cervical cancer screening were enrolled in this study. Women with cytology-proven ASCUS were referred for further screening by colposcopy and biopsy. The study endpoint was histological detection of cervical intraepithelial neoplasia grade 2 or worse (CIN2+) at any of the follow-up visits. The sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), positive likelihood ratio (PLR) and negative likelihood ratio (NLR) of different HR-HPV genotype combination models were estimated. RESULTS: In all, 1675 (4.9%) women were identified as having ASCUS, 1454 women underwent colposcopy and biopsy, and 6.0% (87/1454) women were identified as having CIN2+ lesions. Among those with ASCUS who were identified as having CIN2+, the HR-HPV infection rate was 97.7%, and the prevalence rates of HPV-16, -18, -31, -33, -35, -39, -45, -51, -52, -56, -58, -59, -66 and -68 were 48.3%, 8.0%, 6.9%, 4.6%, 1.1%, 2.3%, 3.4%, 3.4%, 26.4%, 1.1%, 17.2%, 2.3%, 0.0% and 0.0%, respectively. Compared to other HR-HPV-type combination models, the HPV16/18/31/33/52/58 model achieved a higher sensitivity [93.1 (87.8-98.4)], specificity [73.0 (70.7-75.4)], PPV [18.0 (14.5-21.5)], NPV [99.4 (98.9-99.9)], PLR [3.7 (3.1-3.8)] and NLR [0.06 (0.03-0.18)] for the triage of ASCUS patients, but the colposcopy referral rate (30.9%) was significantly lower than that of the recommended HR-HPV model (44.0%). CONCLUSION: This study confirms that the specific HR-HPV genotype HPV16/18/31/33/52/58 is an alternative strategy for ASCUS triage and can effectively reduce the high burden of colposcopy referrals in China.

Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging.

MOTIVATION: Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters. RESULTS: We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. AVAILABILITY AND IMPLEMENTATION: ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Excitatory Neural Assembly Encodes Short-Term Memory in the Prefrontal Cortex.

Short-term memory (STM) is crucial for animals to hold information for a small period of time. Persistent or recurrent neural activity, together with neural oscillations, is known to encode the STM at the cellular level. However, the coding mechanisms at the microcircuitry level remain a mystery. Here, we performed two-photon imaging on behaving mice to monitor the activity of neuronal microcircuitry. We discovered a neuronal subpopulation in the medial prefrontal cortex (mPFC) that exhibited emergent properties in a context-dependent manner underlying a STM-like behavior paradigm. These neuronal subpopulations exclusively comprise excitatory neurons and mainly represent a group of neurons with stronger functional connections. Microcircuitry plasticity was maintained for minutes and was absent in an animal model of Alzheimer's disease (AD). Thus, these results point to a functional coding mechanism that relies on the emergent behavior of a functionally defined neuronal assembly to encode STM.

The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm.

Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to construct fault-tolerant ANNs. The FT performance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algorithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR (XOR) problem and the overlapping classification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.