Bipartite invariance in mouse primary visual cortex.

A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli. Here, we extended "inception loops" - a paradigm that iterates between large-scale recordings, neural predictive models, and in silico experiments followed by in vivo verification - to systematically characterize single neuron invariances in the mouse primary visual cortex. Using the predictive model we synthesized Diverse Exciting Inputs (DEIs), a set of inputs that differ substantially from each other while each driving a target neuron strongly, and verified these DEIs' efficacy in vivo. We discovered a novel bipartite invariance: one portion of the receptive field encoded phase-invariant texture-like patterns, while the other portion encoded a fixed spatial pattern. Our analysis revealed that the division between the fixed and invariant portions of the receptive fields aligns with object boundaries defined by spatial frequency differences present in highly activating natural images. These findings suggest that bipartite invariance might play a role in segmentation by detecting texture-defined object boundaries, independent of the phase of the texture. We also replicated these bipartite DEIs in the functional connectomics MICrONs data set, which opens the way towards a circuit-level mechanistic understanding of this novel type of invariance. Our study demonstrates the power of using a data-driven deep learning approach to systematically characterize neuronal invariances. By applying this method across the visual hierarchy, cell types, and sensory modalities, we can decipher how latent variables are robustly extracted from natural scenes, leading to a deeper understanding of generalization.

NEURD: automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Pattern completion and disruption characterize contextual modulation in mouse visual cortex.

A key role of sensory processing is integrating information across space. Neuronal responses in the visual system are influenced by both local features in the receptive field center and contextual information from the surround. While center-surround interactions have been extensively studied using simple stimuli like gratings, investigating these interactions with more complex, ecologically-relevant stimuli is challenging due to the high dimensionality of the stimulus space. We used large-scale neuronal recordings in mouse primary visual cortex to train convolutional neural network (CNN) models that accurately predicted center-surround interactions for natural stimuli. These models enabled us to synthesize surround stimuli that strongly suppressed or enhanced neuronal responses to the optimal center stimulus, as confirmed by in vivo experiments. In contrast to the common notion that congruent center and surround stimuli are suppressive, we found that excitatory surrounds appeared to complete spatial patterns in the center, while inhibitory surrounds disrupted them. We quantified this effect by demonstrating that CNN-optimized excitatory surround images have strong similarity in neuronal response space with surround images generated by extrapolating the statistical properties of the center, and with patches of natural scenes, which are known to exhibit high spatial correlations. Our findings cannot be explained by theories like redundancy reduction or predictive coding previously linked to contextual modulation in visual cortex. Instead, we demonstrated that a hierarchical probabilistic model incorporating Bayesian inference, and modulating neuronal responses based on prior knowledge of natural scene statistics, can explain our empirical results. We replicated these center-surround effects in the multi-area functional connectomics MICrONS dataset using natural movies as visual stimuli, which opens the way towards understanding circuit level mechanism, such as the contributions of lateral and feedback recurrent connections. Our data-driven modeling approach provides a new understanding of the role of contextual interactions in sensory processing and can be adapted across brain areas, sensory modalities, and species.

Functional connectomics reveals general wiring rule in mouse visual cortex.

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Towards a Foundation Model of the Mouse Visual Cortex.

Understanding the brain's perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain's nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models-capable of predicting large-scale neuronal activity in response to arbitrary sensory input-can be powerful tools for characterizing neuronal representations by enabling high-throughput in silico experiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models-trained on vast quantities of data-have demonstrated remarkable capabilities and generalization, we developed a "foundation model" of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ~1mm3 volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.

Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex.

Clones of excitatory neurons derived from a common progenitor have been proposed to serve as elementary information processing modules in the neocortex. To characterize the cell types and circuit diagram of clonally related excitatory neurons, we performed multi-cell patch clamp recordings and Patch-seq on neurons derived from Nestin-positive progenitors labeled by tamoxifen induction at embryonic day 10.5. The resulting clones are derived from two radial glia on average, span cortical layers 2-6, and are composed of a random sampling of transcriptomic cell types. We find an interaction between shared lineage and connection type: related neurons are more likely to be connected vertically across cortical layers, but not laterally within the same layer. These findings challenge the view that related neurons show uniformly increased connectivity and suggest that integration of vertical intra-clonal input with lateral inter-clonal input may represent a developmentally programmed connectivity motif supporting the emergence of functional circuits.

Author Correction: Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas.

Layer 4 (L4) of mammalian neocortex plays a crucial role in cortical information processing, yet a complete census of its cell types and connectivity remains elusive. Using whole-cell recordings with morphological recovery, we identified one major excitatory and seven inhibitory types of neurons in L4 of adult mouse visual cortex (V1). Nearly all excitatory neurons were pyramidal and all somatostatin-positive (SOM+) non-fast-spiking interneurons were Martinotti cells. In contrast, in somatosensory cortex (S1), excitatory neurons were mostly stellate and SOM+ interneurons were non-Martinotti. These morphologically distinct SOM+ interneurons corresponded to different transcriptomic cell types and were differentially integrated into the local circuit with only S1 neurons receiving local excitatory input. We propose that cell type specific circuit motifs, such as the Martinotti/pyramidal and non-Martinotti/stellate pairs, are used across the cortex as building blocks to assemble cortical circuits.

Docosahexaenoic Acid Inhibits Proliferation of EoL-1 Leukemia Cells and Induces Cell Cycle Arrest and Cell Differentiation.

Τhe effect of docosahexaenoic acid (DHA, an omega-3 polyunsaturated fatty acid) upon the proliferation of EoL-1 (Eosinophilic leukemia) cell line was assessed, while additional cellular events during the antiproliferative action were recorded. DHA inhibited EoL-1 cells growth dose-dependently by inducing growth arrest at G0/1 phase of the cell cycle. After DHA addition to the cells, the expression of MYC oncogene was decreased, PTAFR-mRNA overexpression was observed which was used as a marker of differentiation, and PLA2G4A-mRNA increase was recorded. The enzymatic activities of phospholipase A2 (PLA2), a group of hydrolytic enzymes, whose action precedes and leads to PAF biosynthesis through the remodeling pathway, as well as platelet activating factor acetylhydrolase (PAFAH) which hydrolyses and deactivates PAF, were also measured. DHA had an effect on the levels of both the intracellular and secreted activities of PLA2 and PAFAH. The inflammatory cytokines IL-6 and TNF-α were also detected in high levels. In conclusion, DHA-induced EoL-1 cells differentiation was correlated with downregulation of MYC oncogene, overexpression of PTAFR and PLA2G4A-mRNAs, increase of the inflammatory cytokines production, and alteration of the enzymatic activities that regulate PAF levels. DHA is a natural substance and the understanding of its action on EoL-1 cells on molecular level could be useful in further investigation as a future therapeutic tool against F/P ⁺ hypereosinophilic syndrome.

Aberrant Calcium Signaling in Astrocytes Inhibits Neuronal Excitability in a Human Down Syndrome Stem Cell Model.

Down syndrome (DS) is a genetic disorder that causes cognitive impairment. The staggering effects associated with an extra copy of human chromosome 21 (HSA21) complicates mechanistic understanding of DS pathophysiology. We examined the neuron-astrocyte interplay in a fully recapitulated HSA21 trisomy cellular model differentiated from DS-patient-derived induced pluripotent stem cells (iPSCs). By combining calcium imaging with genetic approaches, we discovered the functional defects of DS astroglia and their effects on neuronal excitability. Compared with control isogenic astroglia, DS astroglia exhibited more-frequent spontaneous calcium fluctuations, which reduced the excitability of co-cultured neurons. Furthermore, suppressed neuronal activity could be rescued by abolishing astrocytic spontaneous calcium activity either chemically by blocking adenosine-mediated signaling or genetically by knockdown of inositol triphosphate (IP3) receptors or S100B, a calcium binding protein coded on HSA21. Our results suggest a mechanism by which DS alters the function of astrocytes, which subsequently disturbs neuronal excitability.

CDK1 Enhances Mitochondrial Bioenergetics for Radiation-Induced DNA Repair.

Nuclear DNA repair capacity is a critical determinant of cell fate under genotoxic stress conditions. DNA repair is a well-defined energy-consuming process. However, it is unclear how DNA repair is fueled and whether mitochondrial energy production contributes to nuclear DNA repair. Here, we report a dynamic enhancement of oxygen consumption and mitochondrial ATP generation in irradiated normal cells, paralleled with increased mitochondrial relocation of the cell-cycle kinase CDK1 and nuclear DNA repair. The basal and radiation-induced mitochondrial ATP generation is reduced significantly in cells harboring CDK1 phosphorylation-deficient mutant complex I subunits. Similarly, mitochondrial ATP generation and nuclear DNA repair are also compromised severely in cells harboring mitochondrially targeted, kinase-deficient CDK1. These results demonstrate a mechanism governing the communication between mitochondria and the nucleus by which CDK1 boosts mitochondrial bioenergetics to meet the increased cellular fuel demand for DNA repair and cell survival under genotoxic stress conditions.

Academic-industrial partnerships in drug discovery in the age of genomics.

Many US FDA-approved drugs have been developed through productive interactions between the biotechnology industry and academia. Technological breakthroughs in genomics, in particular large-scale sequencing of human genomes, is creating new opportunities to understand the biology of disease and to identify high-value targets relevant to a broad range of disorders. However, the scale of the work required to appropriately analyze large genomic and clinical data sets is challenging industry to develop a broader view of what areas of work constitute precompetitive research.

Does varus alignment adversely affect implant survival and function six years after kinematically aligned total knee arthroplasty?

PURPOSE: We report the six year implant survivorship, tibial component alignment and knee and limb function measured by the Oxford Knee Score and Western Ontario and McMaster Universities Osteoarthritis Index ((WOMAC) score after kinematically aligned total knee arthroplasty (TKA) and tested the hypothesis that varus alignment of the tibial component, knee, or limb does not adversely affect implant survival and function. METHODS: We prospectively followed 214 consecutive patients (219 knees) treated with a kinematically aligned TKA in 2007. Kaplan-Meier survival analysis and revision rate per 100 component years determined implant failure. The Oxford Knee Score (0 worst, 48 best) and WOMAC score (0 worst, 100 best) were used to measure function. We categorised tibial component alignment as in-range (≤ 0°) or varus (>0°), knee alignment as in-range (between -2.5° and -7.4°), varus (>-2.5°), or valgus (<-7.4°), and limb alignment as in-range (0° ± 3°), varus (>3°) or valgus (<-3°). RESULTS: At a mean of 6.3 years (range, 5.8-7.2), implant survivorship was 97.5 % and revision-rate per 100 component years 0.40. Three implants had been revised (deep infection one, loose tibial component one and patella instability [1); two loose patella components were pending revision and considered failures. The average Oxford Knee Score was 43 and WOMAC 91. Function of tibial components (80 %), knees (31 %) and limbs (7 %) that were aligned in varus was similar to patients aligned in-range. CONCLUSIONS: At a mean of 6.3 years after kinematically aligned TKA, varus alignment of the tibial component, knee and limb did not adversely affect implant survival or function, which supports the consideration of kinematic alignment as an alternative to mechanical alignment for performing primary TKA. Level of evidence, III; therapeutic study.

Accurate alignment and high function after kinematically aligned TKA performed with generic instruments.

PURPOSE: Performing kinematically aligned total knee arthroplasty (TKA) with generic instruments is less costly than patient-specific instrumentation; however, the alignment and function with this new technique are unknown. METHODS: One hundred and one consecutive patients (101 knees) treated with kinematically aligned TKA, implanted with use of generic instruments, were prospectively followed. The medial collateral ligament was not released. The lateral collateral ligament was released in the 17 % of patients with a fixed valgus deformity. Six measures of alignment were categorized from a scanogram of the extremity, an axial scan of the knee, and an intraoperative measurement. Both the Oxford Knee and WOMAC™ scores were assessed as function. High function was a mean Oxford Knee score >41. RESULTS: The frequency that patients were categorized as in-range was 93 % for the mechanical alignment of the limb (0° ± 3°), 94 % for the joint line (-3° ± 3°), 57 % for the anatomic axis of the knee (-2.5° ± -7.4° valgus), 4 % for the varus-valgus rotation of the tibial component (≤0° valgus), 98 % for the rotation of the tibial component with respect to the femoral component (0° ± 10°), and 94 % for the intraoperative change in the anterior-posterior distance of the tibia with respect to the femur at 90° of flexion (0 ± 2 mm). The mean OKS score was 42, and WOMAC™ score was 89. For each alignment, the function was the same for patients categorized as an outlier or in-range. CONCLUSIONS: The authors prefer the use of generic instruments to perform kinematically aligned TKA in place of mechanically aligned TKA because five of six alignments were accurate and because high function was restored regardless of whether patients had an alignment categorized as an outlier or in-range. LEVEL OF EVIDENCE: IV.