Overexpression of the autism candidate gene Cyfip1 pathologically enhances olivo-cerebellar signaling in mice

Cyfip1, the gene encoding cytoplasmic FMR1 interacting protein 1, has been of interest as an autism candidate gene for years. A potential role in autism spectrum disorder (ASD) is suggested by its location on human chromosome 15q11-13, an instable region that gives rise to a variety of copy number variations associated with syndromic autism. In addition, the CYFIP1 protein acts as a binding partner to Fragile X Messenger Ribonucleoprotein (FMRP) in the regulation of translation initiation. Mutation of FMR1, the gene encoding FMRP, causes Fragile X syndrome, another form of syndromic autism. Here, in mice overexpressing CYFIP1, we study response properties of cerebellar Purkinje cells to activity of the climbing fiber input that originates from the inferior olive and provides an instructive signal in sensorimotor input analysis and plasticity. We find that CYFIP1 overexpression results in enhanced localization of the synaptic organizer neurexin 1 (NRXN1) at climbing fiber synaptic input sites on Purkinje cell primary dendrites and concomitant enhanced climbing fiber synaptic transmission (CF-EPSCs) measured using whole-cell patch-clamp recordings from Purkinje cells in vitro. Moreover, using two-photon measurements of GCaMP6f-encoded climbing fiber signals in Purkinje cells of intact mice, we observe enhanced responses to air puff stimuli applied to the whisker field. These findings resemble our previous phenotypic observations in a mouse model for the human 15q11-13 duplication, which does not extend to the Cyfip1 locus. Thus, our study demonstrates that CYFIP1 overexpression shares a limited set of olivo-cerebellar phenotypes as those resulting from an increased number of copies of non-overlapping genes located on chromosome 15q11-13.

NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca2+ imaging

The development of two-photon microscopy and Ca²⁺ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca²⁺ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca²⁺ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca²⁺ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca²⁺ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process.

Fast and Accurate Motion Correction for Two-Photon Ca2+ Imaging in Behaving Mice

Two-photon Ca2+ imaging is a widely used technique for investigating brain functions across multiple spatial scales. However, the recording of neuronal activities is affected by movement of the brain during tasks in which the animal is behaving normally. Although post-hoc image registration is the commonly used approach, the recent developments of online neuroscience experiments require real-time image processing with efficient motion correction performance, posing new challenges in neuroinformatics. We propose a fast and accurate image density feature-based motion correction method to address the problem of imaging animal during behaviors. This method is implemented by first robustly estimating and clustering the density features from two-photon images. Then, it takes advantage of the temporal correlation in imaging data to update features of consecutive imaging frames with efficient calculations. Thus, motion artifacts can be quickly and accurately corrected by matching the features and obtaining the transformation parameters for the raw images. Based on this efficient motion correction strategy, our algorithm yields promising computational efficiency on imaging datasets with scales ranging from dendritic spines to neuronal populations. Furthermore, we show that the proposed motion correction method outperforms other methods by evaluating not only computational speed but also the quality of the correction performance. Specifically, we provide a powerful tool to perform motion correction for two-photon Ca2+ imaging data, which may facilitate online imaging experiments in the future.

Network instability dynamics drive a transient bursting period in the developing hippocampus in vivo

Spontaneous correlated activity is a universal hallmark of immature neural circuits. However, the cellular dynamics and intrinsic mechanisms underlying network burstiness in the intact developing brain are largely unknown. Here, we use two-photon Ca²⁺ imaging to comprehensively map the developmental trajectories of spontaneous network activity in the hippocampal area CA1 of mice in vivo. We unexpectedly find that network burstiness peaks after the developmental emergence of effective synaptic inhibition in the second postnatal week. We demonstrate that the enhanced network burstiness reflects an increased functional coupling of individual neurons to local population activity. However, pairwise neuronal correlations are low, and network bursts (NBs) recruit CA1 pyramidal cells in a virtually random manner. Using a dynamic systems modeling approach, we reconcile these experimental findings and identify network bi-stability as a potential regime underlying network burstiness at this age. Our analyses reveal an important role of synaptic input characteristics and network instability dynamics for NB generation. Collectively, our data suggest a mechanism, whereby developing CA1 performs extensive input-discrimination learning prior to the onset of environmental exploration.

FARCI: Fast and Robust Connectome Inference

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling.

Arctigenin Exerts Neuroprotective Effect by Ameliorating Cortical Activities in Experimental Autoimmune Encephalomyelitis In Vivo

Multiple sclerosis (MS) is a chronic disease in the central nervous system (CNS), characterized by inflammatory cells that invade into the brain and the spinal cord. Among a bulk of different MS models, the most widely used and best understood rodent model is experimental autoimmune encephalomyelitis (EAE). Arctigenin, a botanical extract from Arctium lappa, is reported to exhibit pharmacological properties, including anti-inflammation and neuroprotection. However, the effects of arctigenin on neural activity attacked by inflammation in MS are still unclear. Here, we use two-photon calcium imaging to observe the activity of somatosensory cortex neurons in awake EAE mice in vivo and found added hyperactive cells, calcium influx, network connectivity, and synchronization, mainly at preclinical stage of EAE model. Besides, more silent cells and decreased calcium influx and reduced network synchronization accompanied by a compensatory rise in functional connectivity are found at the remission stage. Arctigenin treatment not only restricts inordinate individually neural spiking, calcium influx, and network activity at preclinical stage but also restores neuronal activity and communication at remission stage. In addition, we confirm that the frequency of AMPA receptor-mediated spontaneous excitatory postsynaptic current (sEPSC) is also increased at preclinical stage and can be blunted by arctigenin. These findings suggest that excitotoxicity characterized by calcium influx is involved in EAE at preclinical stage. What is more, arctigenin exerts neuroprotective effect by limiting hyperactivity at preclinical stage and ameliorates EAE symptoms, indicating that arctigenin could be a potential therapeutic drug for neuroprotection in MS-related neuropsychological disorders.

The relationship between birth timing, circuit wiring, and physiological response properties of cerebellar granule cells

Cerebellar granule cells (GrCs) are usually regarded as a uniform cell type that collectively expands the coding space of the cerebellum by integrating diverse combinations of mossy fiber inputs. Accordingly, stable molecularly or physiologically defined GrC subtypes within a single cerebellar region have not been reported. The only known cellular property that distinguishes otherwise homogeneous GrCs is the correspondence between GrC birth timing and the depth of the molecular layer to which their axons project. To determine the role birth timing plays in GrC wiring and function, we developed genetic strategies to access early- and late-born GrCs. We initiated retrograde monosynaptic rabies virus tracing from control (birth timing unrestricted), early-born, and late-born GrCs, revealing the different patterns of mossy fiber input to GrCs in vermis lobule 6 and simplex, as well as to early- and late-born GrCs of vermis lobule 6: sensory and motor nuclei provide more input to early-born GrCs, while basal pontine and cerebellar nuclei provide more input to late-born GrCs. In vivo multidepth two-photon Ca2+ imaging of axons of early- and late-born GrCs revealed representations of diverse task variables and stimuli by both populations, with modest differences in the proportions encoding movement, reward anticipation, and reward consumption. Our results suggest neither organized parallel processing nor completely random organization of mossy fiber→GrC circuitry but instead a moderate influence of birth timing on GrC wiring and encoding. Our imaging data also provide evidence that GrCs can represent generalized responses to aversive stimuli, in addition to recently described reward representations.

Restoration of Two-Photon Ca2+ Imaging Data Through Model Blind Spatiotemporal Filtering

Two-photon Ca²⁺ imaging is a leading technique for recording neuronal activities in vivo with cellular or subcellular resolution. However, during experiments, the images often suffer from corruption due to complex noises. Therefore, the analysis of Ca²⁺ imaging data requires preprocessing steps, such as denoising, to extract biologically relevant information. We present an approach that facilitates imaging data restoration through image denoising performed by a neural network combining spatiotemporal filtering and model blind learning. Tests with synthetic and real two-photon Ca²⁺ imaging datasets demonstrate that the proposed approach enables efficient restoration of imaging data. In addition, we demonstrate that the proposed approach outperforms the current state-of-the-art methods by evaluating the qualities of the denoising performance of the models quantitatively. Therefore, our method provides an invaluable tool for denoising two-photon Ca²⁺ imaging data by model blind spatiotemporal processing.

S1 Employs Feature-Dependent Differential Selectivity of Single Cells and Distributed Patterns of Populations to Encode Mechanosensations

The primary somatosensory (S1) cortex plays an important role in the perception and discrimination of touch and pain mechanosensations. Conventionally, neurons in the somatosensory system including S1 cortex have been classified into low/high threshold (HT; non-nociceptive/nociceptive) or wide dynamic range (WDR; convergent) neurons by their electrophysiological responses to innocuous brush-stroke and noxious forceps-pinch stimuli. Besides this “noxiousness” (innocuous/noxious) feature, each stimulus also includes other stimulus features: “texture” (brush hairs/forceps-steel arm), “dynamics” (dynamic stroke/static press) and “intensity” (weak/strong). However, it remains unknown how S1 neurons inclusively process such diverse features of brushing and pinch at the single-cell and population levels. Using in vivo two-photon Ca²⁺ imaging in the layer 2/3 neurons of the mouse S1 cortex, we identified clearly separated response patterns of the S1 neural population with distinct tuning properties of individual cells to texture, dynamics and noxiousness features of cutaneous mechanical stimuli. Among cells other than broadly tuned neurons, the majority of the cells showed a highly selective response to the difference in texture, but low selectivity to the difference in dynamics or noxiousness. Between the two low selectivity features, the difference in dynamics was slightly more specific, yet both could be decoded using the response patterns of neural populations. In addition, more neurons are recruited and stronger Ca²⁺ responses are evoked as the intensity of forceps-pinch is gradually increased. Our results suggest that S1 neurons encode various features of mechanosensations with feature-dependent differential selectivity of single cells and distributed response patterns of populations. Moreover, we raise a caution about describing neurons by a single stimulus feature ignoring other aspects of the sensory stimuli.

Mouse dLGN Receives Functional Input from a Diverse Population of Retinal Ganglion Cells with Limited Convergence

Mouse vision is based on the parallel output of more than 30 functional types of retinal ganglion cells (RGCs). Little is known about how representations of visual information change between retina and dorsolateral geniculate nucleus (dLGN) of the thalamus, the main relay between retina and cortex. Here, we functionally characterized responses of retrogradely labeled dLGN-projecting RGCs and dLGN neurons to the same set of visual stimuli. We found that many of the previously identified functional RGC types innervate dLGN, which maintained a high degree of functional diversity. Using a linear model to assess functional connectivity between RGC types and dLGN neurons, we found that responses of dLGN neurons could be predicted as linear combination of inputs from on average five RGC types, but only two of those had the strongest functional impact. Thus, mouse dLGN receives functional input from a diverse population of RGC types with limited convergence.

Two-Photon Functional Imaging of the Auditory Cortex in Behaving Mice: From Neural Networks to Single Spines

In vivo two-photon Ca²⁺ imaging is a powerful tool for recording neuronal activities during perceptual tasks and has been increasingly applied to behaving animals for acute or chronic experiments. However, the auditory cortex is not easily accessible to imaging because of the abundant temporal muscles, arteries around the ears and their lateral locations. Here, we report a protocol for two-photon Ca²⁺ imaging in the auditory cortex of head-fixed behaving mice. By using a custom-made head fixation apparatus and a head-rotated fixation procedure, we achieved two-photon imaging and in combination with targeted cell-attached recordings of auditory cortical neurons in behaving mice. Using synthetic Ca²⁺ indicators, we recorded the Ca²⁺ transients at multiple scales, including neuronal populations, single neurons, dendrites and single spines, in auditory cortex during behavior. Furthermore, using genetically encoded Ca²⁺ indicators (GECIs), we monitored the neuronal dynamics over days throughout the process of associative learning. Therefore, we achieved two-photon functional imaging at multiple scales in auditory cortex of behaving mice, which extends the tool box for investigating the neural basis of audition-related behaviors.

Tonotopic Variation of the T-Type Ca2+ Current in Avian Auditory Coincidence Detector Neurons

Neurons in avian nucleus laminaris (NL) are binaural coincidence detectors for sound localization and are characterized by striking structural variations in dendrites and axon initial segment (AIS) according to their acoustic tuning [characteristic frequency (CF)]. T-type Ca²⁺ (CaT) channels regulate synaptic integration and firing behavior at these neuronal structures. However, whether or how CaT channels contribute to the signal processing in NL neurons is not known. In this study, we addressed this issue with whole-cell recording and two-photon Ca²⁺ imaging in brain slices of posthatch chicks of both sexes. We found that the CaT current was prominent in low-CF neurons, whereas it was almost absent in higher-CF neurons. In addition, a large Ca²⁺ transient occurred at the dendrites and the AIS of low-CF neurons, indicating a localization of CaT channels at these structures in the neurons. Because low-CF neurons have long dendrites, dendritic CaT channels may compensate for the attenuation of EPSPs at dendrites. Furthermore, the short distance of AIS from the soma may accelerate activation of axonal CaT current in the neurons and help EPSPs reach spike threshold. Indeed, the CaT current was activated by EPSPs and augmented the synaptic response and spike generation of the neurons. Notably, the CaT current was inactivated during repetitive inputs, and these augmenting effects predominated at the initial phase of synaptic activity. These results suggested that dendritic and axonal CaT channels increase the sensitivity to sound at its onset, which may expand the dynamic range for binaural computation in low-CF NL neurons.SIGNIFICANCE STATEMENT Neurons in nucleus laminaris are binaural coincidence detectors for sound localization. We report that T-type Ca²⁺ (CaT) current was prominent at dendrites and the axonal trigger zone in neurons tuned to low-frequency sound. Because these neurons have long dendrites and a closer trigger zone compared with those tuned to higher-frequency sound, the CaT current augmented EPSPs at dendrites and accelerated spike triggers in the neurons, implying a strategic arrangement of the current within the nucleus. This effect was limited to the onset of repetitive inputs due to progressive inactivation of CaT current. The results suggested that the CaT current increases the sensitivity to sound at its onset, which may expand the dynamic range for binaural computation of low-frequency sound.

Sensory Response of Transplanted Astrocytes in Adult Mammalian Cortex In Vivo

Glial precursor transplantation provides a potential therapy for brain disorders. Before its clinical application, experimental evidence needs to indicate that engrafted glial cells are functionally incorporated into the existing circuits and become essential partners of neurons for executing fundamental brain functions. While previous experiments supporting for their functional integration have been obtained under in vitro conditions using slice preparations, in vivo evidence for such integration is still lacking. Here, we utilized in vivo two-photon Ca²⁺ imaging along with immunohistochemistry, fluorescent indicator labeling-based axon tracing and correlated light/electron microscopy to analyze the profiles and the functional status of glial precursor cell-derived astrocytes in adult mouse neocortex. We show that after being transplanted into somatosensory cortex, precursor-derived astrocytes are able to survive for more than a year and respond with Ca²⁺ signals to sensory stimulation. These sensory-evoked responses are mediated by functionally-expressed nicotinic receptors and newly-established synaptic contacts with the host cholinergic afferents. Our results provide in vivo evidence for a functional integration of transplanted astrocytes into adult mammalian neocortex, representing a proof-of-principle for sensory cortex remodeling through addition of essential neural elements. Moreover, we provide strong support for the use of glial precursor transplantation to understand glia-related neural development in vivo.