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Leveraging AAVs' versatile tropism and labeling capacity, we expanded the scale of in vivo CRISPR screening with single-cell transcriptomic phenotyping across embryonic to adult brains and peripheral nervous systems. Through extensive tests of 86 vectors across AAV serotypes combined with a transposon system, we substantially amplified labeling efficacy and accelerated in vivo gene delivery from weeks to days. Our proof-of-principle in utero screen identified the pleiotropic effects of Foxg1, highlighting its tight regulation of distinct networks essential for cell fate specification of Layer 6 corticothalamic neurons. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% by lentivirus, to achieve analysis of over 30,000 cells in one experiment and enable massively parallel in vivo Perturb-seq. Compatible with various phenotypic measurements (single-cell or spatial multi-omics), it presents a flexible approach to interrogate gene function across cell types in vivo, translating gene variants to their causal function., Competing Interests: Declaration of interests X.J. and X.Z. are co-inventors on in vivo AAV-based Perturb-seq and CRISPR inventions filed by Scripps Research relating to the work in this manuscript., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The oxytocin receptor has long been considered critical for social bonding and parenting in prairie voles. In this issue of Neuron, Berendzen et al. 1 show that oxytocin receptor-null prairie voles display normal bonding and parental behaviors, thus challenging the prevailing understanding of the receptor's role in these behaviors., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model aims to capture long range interactions by concatenating self-attention and convolution feature maps. This work proposes a novel attention-augmented convolution U-Net (AA-U-Net) that enables a more accurate spatial aggregation of contextual information by integrating attention-augmented convolution in the bottleneck of an encoder-decoder segmentation architecture. A deep segmentation network (U-Net) with this attention mechanism significantly improves the performance of semantic segmentation tasks on challenging COVID-19 lesion segmentation. The validation experiments show that the performance gain of the attention-augmented U-Net comes from their ability to capture dynamic and precise (wider) attention context. The AA-U-Net achieves Dice scores of 72.3% and 61.4% for ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.2% points against a baseline U-Net and 3.09% points compared to a baseline U-Net with matched parameters., Supplementary Information: The online version contains supplementary material available at 10.1007/s11760-022-02302-3., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.)

Good quality (annotated) data is one of the most important aspects of supervised deep learning. Tasks such as semantic segmentation have a huge data requirement in exchange for only satisfactory performance. Large-scale annotations spread across multiple annotators tends to create inconsistencies, as there are various manual and semi-automated techniques involved. This mandates an external evaluator or expert to check and narrow down the problematic annotations. Studies have shown that even marking a few instances wrong in classification can lead to a significant performance drop in the model (mislabeling only 10% of one class can degrade the total performance of all classes by up to 10%). It has been noticed that fault localization by a medical expert is one of the most expensive and time-consuming processes. In this paper, we propose a novel framework for detecting the inconsistencies in the annotation of every object/anatomy in a specific image. We leverage the power of semi-supervised deep learning models (STCN) to help produce high-quality data for AI segmentation algorithms. Evaluation using this algorithm has been shown to reduce annotation review time by at least 5 hours for just 1000 images, and the quality of ground truth data improved thereby increasing the performance of the model by almost 3%.

Deep learning based medical image segmentation is an important step within diagnosis, which relies strongly on capturing sufficient spatial context without requiring too complex models that are hard to train with limited labelled data. Training data is in particular scarce for segmenting infection regions of CT images of COVID-19 patients. Attention models help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent criss-cross-attention module aims to approximate global self-attention while remaining memory and time efficient by separating horizontal and vertical self-similarity computations. However, capturing attention from all non-local locations can adversely impact the accuracy of semantic segmentation networks. We propose a new Dynamic Deformable Attention Network (DDANet) that enables a more accurate contextual information computation in a similarly efficient way. Our novel technique is based on a deformable criss-cross attention block that learns both attention coefficients and attention offsets in a continuous way. A deep U-Net (Schlemper et al., 2019) segmentation network that employs this attention mechanism is able to capture attention from pertinent non-local locations and also improves the performance on semantic segmentation tasks compared to criss-cross attention within a U-Net on a challenging COVID-19 lesion segmentation task. Our validation experiments show that the performance gain of the recursively applied dynamic deformable attention blocks comes from their ability to capture dynamic and precise attention context. Our DDANet achieves Dice scores of 73.4% and 61.3% for Ground-glass opacity and consolidation lesions for COVID-19 segmentation and improves the accuracy by 4.9% points compared to a baseline U-Net and 24.4% points compared to current state of art methods (Fan et al., 2020)., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The neuropeptide oxytocin (OXT) is a crucial mediator of parturition and milk ejection and a major modulator of various social behaviors, including social recognition, aggression and parenting. In the past decade, there has been significant excitement around the possible use of OXT to treat behavioral deficits in neurodevelopmental disorders, including autism spectrum disorder (ASD). Yet, despite the fast move to clinical trials with OXT, little attention has been paid to the possibility that the OXT system in the brain is perturbed in these disorders and to what extent such perturbations may contribute to social behavior deficits. Large-scale whole-exome sequencing studies in subjects with ASD, along with biochemical and electrophysiological studies in animal models of the disorder, indicate several risk genes that play an essential role in brain synapses, suggesting that deficits in synaptic activity and plasticity underlie the pathophysiology in a considerable portion of these cases. OXT has been repeatedly shown, both in vitro and in vivo , to modify synaptic properties and plasticity and to modulate neural activity in circuits that regulate social behavior. Together, these findings led us to hypothesize that failure of the OXT system during early development, as a direct or indirect consequence of genetic mutations, may impact social behavior by altering synaptic activity and plasticity. In this article, we review the evidence that support our hypothesis.

Action potentials (APs) propagating along axons require the activation of voltage-gated Na(+) (Nav) channels. How Nav channels are transported into axons is unknown. We show that KIF5/kinesin-1 directly binds to ankyrin-G (AnkG) to transport Nav channels into axons. KIF5 and Nav1.2 channels bind to multiple sites in the AnkG N-terminal domain that contains 24 ankyrin repeats. Disrupting AnkG-KIF5 binding with small interfering RNA or dominant-negative constructs markedly reduced Nav channel levels at the axon initial segment (AIS) and along entire axons, thereby decreasing AP firing. Live-cell imaging showed that fluorescently tagged AnkG or Nav1.2 cotransported with KIF5 along axons. Deleting AnkG in vivo or virus-mediated expression of a dominant-negative KIF5 construct specifically decreased the axonal level of Nav, but not Kv1.2, channels in mouse cerebellum. These results indicate that AnkG functions as an adaptor to link Nav channels to KIF5 during axonal transport before anchoring them to the AIS and nodes of Ranvier., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Constructing a 3-D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1) leave-one-out experiment, 2) experiment on evaluating the present approach for handling pathology, 3) experiment on evaluating the present approach for handling outliers, and 4) experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

A majority of pre-operative planning and navigational guidance during computer assisted orthopaedic surgery routinely uses three-dimensional models of patient anatomy. These models enhance the surgeon's capability to decrease the invasiveness of surgical procedures and increase their accuracy and safety. A common approach for this is to use computed tomography (CT) or magnetic resonance imaging (MRI). These have the disadvantages that they are expensive and/or induce radiation to the patient. In this paper we propose a novel method to construct a patient-specific three-dimensional model that provides an appropriate intra-operative visualization without the need for a pre or intra-operative imaging. The 3D model is reconstructed by fitting a statistical deformable model to minimal sparse 3D data consisting of digitized landmarks and surface points that are obtained intra-operatively. The statistical model is constructed using Principal Component Analysis from training objects. Our deformation scheme efficiently and accurately computes a Mahalanobis distance weighted least square fit of the deformable model to the 3D data. Relaxing the Mahalanobis distance term as additional points are incorporated enables our method to handle small and large sets of digitized points efficiently. Formalizing the problem as a linear equation system helps us to provide real-time updates to the surgeons. Incorporation of M-estimator based weighting of the digitized points enables us to effectively reject outliers and compute stable models. We present here our evaluation results using leave-one-out experiments and extended validation of our method on nine dry cadaver bones.

This paper addresses the problem of surface reconstruction from partial data consisting of digitized landmarks and surface points that are obtained intraoperatively. The surface is derived by deforming a template so that the reconstructed surface matches the digitized points. Two techniques are employed to address such an ill-posed problem. First, a patient-specific template is used, which is computed by optimally fitting a statistical deformable model to partial data. Second, the estimated patient specific template is deformed using a regression technique by carefully designing a regularization term in kernel space. The proposed method is especially useful for accurate and stable surface construction from partial data when only a small sample size of training set is available. It adapts gradually to use more information derived from the statistical shape model when larger data are available. The proposed reconstruction method has been successfully tested on femoral heads, yielding very promising results.

The correspondence problem is of high relevance in the construction and use of statistical models. Statistical models are used for a variety of medical application, e.g. segmentation, registration and shape analysis. In this paper, we present comparative studies in three anatomical structures of four different correspondence establishing methods. The goal in all of the presented studies is a model-based application. We have analyzed both the direct correspondence via manually selected landmarks as well as the properties of the model implied by the correspondences, in regard to compactness, generalization and specificity. The studied methods include a manually initialized subdivision surface (MSS) method and three automatic methods that optimize the object parameterization: SPHARM, MDL and the covariance determinant (DetCov) method. In all studies, DetCov and MDL showed very similar results. The model properties of DetCov and MDL were better than SPHARM and MSS. The results suggest that for modeling purposes the best of the studied correspondence method are MDL and DetCov.