Computer tomography measurements of the airway and thoracic cavity do not provide support for bronchial conformation as a predisposing factor of left cranial lung lobe torsion in pugs.

The objective of this retrospective clinical study was to determine if airway or thoracic cavity measurements in pugs, particularly the left cranial lung lobe, were significantly different from brachycephalic and mesocephalic control. Thoracic computed tomographic studies of 10 pugs, French bulldogs (FB), and Jack Russell Terriers (JRT) were analyzed. Thoracic height: width ratio (H:W), cross-sectional areas of the left mainstem bronchus (CSA LMB), left cranial lung lobe bronchus (CSA LCrBr), left caudal lung lobe bronchus (CSA LCauBr), CSA LCrBr relative to length (CSA LCrBr/length) and CSA LCauBr/length were measured and adjusted to body weight (/kg). CSA LMB/kg, CSA LCauBr/length/kg, and CSA LCrBr/length /kg were smaller in pugs and FB compared with JRT (P < .05), but no differences were found between pugs and FB. Cross-sectional areas of left cranial lung lobe bronchus /kg and CSA LCauBr/kg were smaller in pugs than JRT (P < .05), but no differences were found between pugs and FB or FB and JRT. No difference was found in thoracic H:W between any breeds. This demonstrated that pugs and FB had significantly narrower bronchi CSA/lengths ratios compared with JRT, but this was not limited to the LCBr. Airway measurements were not significantly different between brachycephalic breeds; therefore, the pugs' predisposition to left cranial lung lobe torsion cannot be solely explained by narrower lower airways.

Comparison of three radiographic assessment methods for detecting slipped capital femoral epiphyses in cats: Klein's line, modified Klein's line and the S-sign.

OBJECTIVES: The aim of the present study was to investigate whether diagnostic assessment methods used on radiographs in humans with slipped capital femoral epiphysis (SCFE) can be used in cats. METHODS: The ventrodorsal (VD) extended-leg and VD frog-leg pelvic radiographs of 20 cats with SCFE without fully displaced femoral capital epiphyses (FCE), eight cats with fully displaced FCE and five control cats with normal pelvic anatomy were assessed by five observers on two separate occasions 3 months apart. The Klein's line and modified Klein's line were assessed on each VD extended-leg radiograph, and the S-sign was assessed on each VD extended-leg and VD frog-leg radiograph. RESULTS: Excluding cases of fully displaced FCE, the S-sign on the VD frog-leg radiographs more accurately diagnosed SCFE than the S-sign on the VD extended-leg radiographs and the Klein's line (92.4% vs 88.8% vs 60.6%, respectively), and had the greatest sensitivity (93.9% vs 79.2% vs 30.6%, respectively). The S-sign on the VD extended-leg radiographs had greater specificity than the Klein's line and S-sign on the VD frog-leg radiographs (99.2% vs 97.9% vs 90.9%, respectively). The modified Klein's line detected SCFE in 40.2% of cases that were negative for the Klein's line. CONCLUSIONS AND RELEVANCE: The S-sign in both VD extended-leg and VD frog-leg views successfully detected SCFE in cats and can be used to increase early diagnosis and treatment in cats with SCFE that have only subtle radiographic changes.

Activity in primate visual cortex is minimally driven by spontaneous movements.

Organisms process sensory information in the context of their own moving bodies, an idea referred to as embodiment. This idea is important for developmental neuroscience, robotics and systems neuroscience. The mechanisms supporting embodiment are unknown, but a manifestation could be the observation in mice of brain-wide neuromodulation, including in the primary visual cortex, driven by task-irrelevant spontaneous body movements. We tested this hypothesis in macaque monkeys (Macaca mulatta), a primate model for human vision, by simultaneously recording visual cortex activity and facial and body movements. We also sought a direct comparison using an analogous approach to those used in mouse studies. Here we found that activity in the primate visual cortex (V1, V2 and V3/V3A) was associated with the animals' own movements, but this modulation was largely explained by the impact of the movements on the retinal image, that is, by changes in visual input. These results indicate that visual cortex in primates is minimally driven by spontaneous movements and may reflect species-specific sensorimotor strategies.

Hierarchical VAEs provide a normative account of motion processing in the primate brain.

The relationship between perception and inference, as postulated by Helmholtz in the 19th century, is paralleled in modern machine learning by generative models like Variational Autoencoders (VAEs) and their hierarchical variants. Here, we evaluate the role of hierarchical inference and its alignment with brain function in the domain of motion perception. We first introduce a novel synthetic data framework, Retinal Optic Flow Learning (ROFL), which enables control over motion statistics and their causes. We then present a new hierarchical VAE and test it against alternative models on two downstream tasks: (i) predicting ground truth causes of retinal optic flow (e.g., self-motion); and (ii) predicting the responses of neurons in the motion processing pathway of primates. We manipulate the model architectures (hierarchical versus non-hierarchical), loss functions, and the causal structure of the motion stimuli. We find that hierarchical latent structure in the model leads to several improvements. First, it improves the linear decodability of ground truth factors and does so in a sparse and disentangled manner. Second, our hierarchical VAE outperforms previous state-of-the-art models in predicting neuronal responses and exhibits sparse latent-to-neuron relationships. These results depend on the causal structure of the world, indicating that alignment between brains and artificial neural networks depends not only on architecture but also on matching ecologically relevant stimulus statistics. Taken together, our results suggest that hierarchical Bayesian inference underlines the brain's understanding of the world, and hierarchical VAEs can effectively model this understanding.

Detailed characterization of neural selectivity in free viewing primates.

Fixation constraints in visual tasks are ubiquitous in visual and cognitive neuroscience. Despite its widespread use, fixation requires trained subjects, is limited by the accuracy of fixational eye movements, and ignores the role of eye movements in shaping visual input. To overcome these limitations, we developed a suite of hardware and software tools to study vision during natural behavior in untrained subjects. We measured visual receptive fields and tuning properties from multiple cortical areas of marmoset monkeys who freely viewed full-field noise stimuli. The resulting receptive fields and tuning curves from primary visual cortex (V1) and area MT match reported selectivity from the literature which was measured using conventional approaches. We then combined free viewing with high-resolution eye tracking to make the first detailed 2D spatiotemporal measurements of foveal receptive fields in V1. These findings demonstrate the power of free viewing to characterize neural responses in untrained animals while simultaneously studying the dynamics of natural behavior.

Model-based characterization of the selectivity of neurons in primary visual cortex.

Statistical models are increasingly being used to understand the complexity of stimulus selectivity in primary visual cortex (V1) in the context of complex time-varying stimuli, replacing averaging responses to simple parametric stimuli. Although such models often can more accurately reflect the computations performed by V1 neurons in more natural visual environments, they do not by themselves provide insight into V1 neural selectivity to basic stimulus features such as receptive field size, spatial frequency tuning, and phase invariance. Here, we present a battery of analyses that can be directly applied to encoding models to link complex encoding models to more interpretable aspects of stimulus selectivity. We apply this battery to nonlinear models of V1 neurons recorded in awake macaque during random bar stimuli. In linking model properties to more classical measurements, we demonstrate several novel aspects of V1 selectivity not available to simpler experimental measurements. For example, this approach reveals that individual spatiotemporal elements of the V1 models often have a smaller spatial scale than the neuron as a whole, resulting in nontrivial tuning to spatial frequencies. In addition, we propose measures of nonlinear integration that suggest that classical classifications of V1 neurons into simple versus complex cells will be spatial-frequency dependent. In total, rather than obfuscate classical characterizations of V1 neurons, model-based characterizations offer a means to more fully understand their selectivity, and link their classical tuning properties to their roles in more complex, natural, visual processing.NEW & NOTEWORTHY Visual neurons are increasingly being studied with more complex, natural visual stimuli, and increasingly complex models are necessary to characterize their response properties. Here, we describe a battery of analyses that relate these more complex models to classical characterizations. Using such model-based characterizations of V1 neurons furthermore yields several new insights into V1 processing not possible to capture in more classical means to measure their visual selectivity.

Decision-related feedback in visual cortex lacks spatial selectivity.

Feedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity.

Lameness, generalised myopathy and myalgia in an adult cat with toxoplasmosis.

CASE SUMMARY: A 2-year-old female neutered domestic shorthair cat presented with an 18-month history of intermittent lameness on all four limbs. The cat was markedly lame on all four limbs. There was alternation between toe-walking on the forelimbs with a hunched posture and using the dorsal aspect of the carpi to walk on. The cat would hiss repeatedly when walking and would lie down tentatively, appearing happier and less painful when lying. When not lying, the cat preferred to sit back on the hindlimbs and non-weightbear on the forelimbs (the so-called kangaroo stance). Physical examination detected pain on palpation of the calcaneus bone and Achilles tendon bilaterally, and general resentment to handling. Investigations revealed an elevated creatine kinase, a positive Toxoplasma gondii IgG titre, toxic neutrophilic inflammation within the Achilles tendon bursae, electromyography and nerve conduction velocity studies consistent with a diffuse muscular disease, and histopathology of the muscle consistent with a chronic and diffuse myopathy. Arthrocentesis samples and an antinuclear antibodies titre were normal. Prior treatment with meloxicam had been ineffective. A 6-week course of clindamycin was prescribed; an improvement was seen within 3 days and clinical resolution at 3 months. The cat remained clinically normal after 20 months. RELEVANCE AND NOVEL INFORMATION: To our knowledge, there have been no previously published reports of histopathologically confirmed myopathy as a result of T gondii in cats. This report suggests toxoplasmosis should be considered as a differential diagnosis in cats with myopathies or lameness in the absence of other causes.

Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models.

Sensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the stimulus information contained in those responses. This information can in principle be preserved if variability is shared across many neurons, but depends on the structure of the shared variability and its relationship to sensory encoding at the population level. The structure of this shared variability in neural activity can be characterized by latent variable models, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron's stimulus selectivity and a set of latent variables that modulate these stimulus-driven responses both additively and multiplicatively. While these approaches can detect very general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an "affine model". In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.

The quest for interpretable models of neural population activity.

Many aspects of brain function arise from the coordinated activity of large populations of neurons. Recent developments in neural recording technologies are providing unprecedented access to the activity of such populations during increasingly complex experimental contexts; however, extracting scientific insights from such recordings requires the concurrent development of analytical tools that relate this population activity to system-level function. This is a primary motivation for latent variable models, which seek to provide a low-dimensional description of population activity that can be related to experimentally controlled variables, as well as uncontrolled variables such as internal states (e.g. attention and arousal) and elements of behavior. While deriving an understanding of function from traditional latent variable methods relies on low-dimensional visualizations, new approaches are targeting more interpretable descriptions of the components underlying system-level function.

Data-Driven Approaches to Understanding Visual Neuron Activity.

With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

Functional characterization of retinal ganglion cells using tailored nonlinear modeling.

The mammalian retina encodes the visual world in action potentials generated by 20-50 functionally and anatomically-distinct types of retinal ganglion cell (RGC). Individual RGC types receive synaptic input from distinct presynaptic circuits; therefore, their responsiveness to specific features in the visual scene arises from the information encoded in synaptic input and shaped by postsynaptic signal integration and spike generation. Unfortunately, there is a dearth of tools for characterizing the computations reflected in RGC spike output. Therefore, we developed a statistical model, the separable Nonlinear Input Model, to characterize the excitatory and suppressive components of RGC receptive fields. We recorded RGC responses to a correlated noise ("cloud") stimulus in an in vitro preparation of mouse retina and found that our model accurately predicted RGC responses at high spatiotemporal resolution. It identified multiple receptive fields reflecting the main excitatory and suppressive components of the response of each neuron. Significantly, our model accurately identified ON-OFF cells and distinguished their distinct ON and OFF receptive fields, and it demonstrated a diversity of suppressive receptive fields in the RGC population. In total, our method offers a rich description of RGC computation and sets a foundation for relating it to retinal circuitry.

Parallel Processing of Sound Dynamics across Mouse Auditory Cortex via Spatially Patterned Thalamic Inputs and Distinct Areal Intracortical Circuits.

Natural sounds have rich spectrotemporal dynamics. Spectral information is spatially represented in the auditory cortex (ACX) via large-scale maps. However, the representation of temporal information, e.g., sound offset, is unclear. We perform multiscale imaging of neuronal and thalamic activity evoked by sound onset and offset in awake mouse ACX. ACX areas differed in onset responses (On-Rs) and offset responses (Off-Rs). Most excitatory L2/3 neurons show either On-Rs or Off-Rs, and ACX areas are characterized by differing fractions of On and Off-R neurons. Somatostatin and parvalbumin interneurons show distinct temporal dynamics, potentially amplifying Off-Rs. Functional network analysis shows that ACX areas contain distinct parallel onset and offset networks. Thalamic (MGB) terminals show either On-Rs or Off-Rs, indicating a thalamic origin of On and Off-R pathways. Thus, ACX areas spatially represent temporal features, and this representation is created by spatial convergence and co-activation of distinct MGB inputs and is refined by specific intracortical connectivity.

Cortical computations via transient attractors.

The ability of sensory networks to transiently store information on the scale of seconds can confer many advantages in processing time-varying stimuli. How a network could store information on such intermediate time scales, between typical neurophysiological time scales and those of long-term memory, is typically attributed to persistent neural activity. An alternative mechanism which might allow for such information storage is through temporary modifications to the neural connectivity which decay on the same second-long time scale as the underlying memories. Earlier work that has explored this method has done so by emphasizing one attractor from a limited, pre-defined set. Here, we describe an alternative, a Transient Attractor network, which can learn any pattern presented to it, store several simultaneously, and robustly recall them on demand using targeted probes in a manner reminiscent of Hopfield networks. We hypothesize that such functionality could be usefully embedded within sensory cortex, and allow for a flexibly-gated short-term memory, as well as conferring the ability of the network to perform automatic de-noising, and separation of input signals into distinct perceptual objects. We demonstrate that the stored information can be refreshed to extend storage time, is not sensitive to noise in the system, and can be turned on or off by simple neuromodulation. The diverse capabilities of transient attractors, as well as their resemblance to many features observed in sensory cortex, suggest the possibility that their actions might underlie neural processing in many sensory areas.

Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.

The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not "noise" and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end.NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are related to behavioral contexts outside of direct experimental control.

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells.

Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.

Variability and Correlations in Primary Visual Cortical Neurons Driven by Fixational Eye Movements.

UNLABELLED: The ability to distinguish between elements of a sensory neuron's activity that are stimulus independent versus driven by the stimulus is critical for addressing many questions in systems neuroscience. This is typically accomplished by measuring neural responses to repeated presentations of identical stimuli and identifying the trial-variable components of the response as noise. In awake primates, however, small "fixational" eye movements (FEMs) introduce uncontrolled trial-to-trial differences in the visual stimulus itself, potentially confounding this distinction. Here, we describe novel analytical methods that directly quantify the stimulus-driven and stimulus-independent components of visual neuron responses in the presence of FEMs. We apply this approach, combined with precise model-based eye tracking, to recordings from primary visual cortex (V1), finding that standard approaches that ignore FEMs typically miss more than half of the stimulus-driven neural response variance, creating substantial biases in measures of response reliability. We show that these effects are likely not isolated to the particular experimental conditions used here, such as the choice of visual stimulus or spike measurement time window, and thus will be a more general problem for V1 recordings in awake primates. We also demonstrate that measurements of the stimulus-driven and stimulus-independent correlations among pairs of V1 neurons can be greatly biased by FEMs. These results thus illustrate the potentially dramatic impact of FEMs on measures of signal and noise in visual neuron activity and also demonstrate a novel approach for controlling for these eye-movement-induced effects. SIGNIFICANCE STATEMENT: Distinguishing between the signal and noise in a sensory neuron's activity is typically accomplished by measuring neural responses to repeated presentations of an identical stimulus. For recordings from the visual cortex of awake animals, small "fixational" eye movements (FEMs) inevitably introduce trial-to-trial variability in the visual stimulus, potentially confounding such measures. Here, we show that FEMs often have a dramatic impact on several important measures of response variability for neurons in primary visual cortex. We also present an analytical approach for quantifying signal and noise in visual neuron activity in the presence of FEMs. These results thus highlight the importance of controlling for FEMs in studies of visual neuron function, and demonstrate novel methods for doing so.

Nonlinear computations shaping temporal processing of precortical vision.

Computations performed by the visual pathway are constructed by neural circuits distributed over multiple stages of processing, and thus it is challenging to determine how different stages contribute on the basis of recordings from single areas. In the current article, we address this problem in the lateral geniculate nucleus (LGN), using experiments combined with nonlinear modeling capable of isolating various circuit contributions. We recorded cat LGN neurons presented with temporally modulated spots of various sizes, which drove temporally precise LGN responses. We utilized simultaneously recorded S-potentials, corresponding to the primary retinal ganglion cell (RGC) input to each LGN cell, to distinguish the computations underlying temporal precision in the retina from those in the LGN. Nonlinear models with excitatory and delayed suppressive terms were sufficient to explain temporal precision in the LGN, and we found that models of the S-potentials were nearly identical, although with a lower threshold. To determine whether additional influences shaped the response at the level of the LGN, we extended this model to use the S-potential input in combination with stimulus-driven terms to predict the LGN response. We found that the S-potential input "explained away" the major excitatory and delayed suppressive terms responsible for temporal patterning of LGN spike trains but revealed additional contributions, largely PULL suppression, to the LGN response. Using this novel combination of recordings and modeling, we were thus able to dissect multiple circuit contributions to LGN temporal responses across retina and LGN, and set the foundation for targeted study of each stage.

Inferring Cortical Variability from Local Field Potentials.

The responses of sensory neurons can be quite different to repeated presentations of the same stimulus. Here, we demonstrate a direct link between the trial-to-trial variability of cortical neuron responses and network activity that is reflected in local field potentials (LFPs). Spikes and LFPs were recorded with a multielectrode array from the middle temporal (MT) area of the visual cortex of macaques during the presentation of continuous optic flow stimuli. A maximum likelihood-based modeling framework was used to predict single-neuron spiking responses using the stimulus, the LFPs, and the activity of other recorded neurons. MT neuron responses were strongly linked to gamma oscillations (maximum at 40 Hz) as well as to lower-frequency delta oscillations (1-4 Hz), with consistent phase preferences across neurons. The predicted modulation associated with the LFP was largely complementary to that driven by visual stimulation, as well as the activity of other neurons, and accounted for nearly half of the trial-to-trial variability in the spiking responses. Moreover, the LFP model predictions accurately captured the temporal structure of noise correlations between pairs of simultaneously recorded neurons, and explained the variation in correlation magnitudes observed across the population. These results therefore identify signatures of network activity related to the variability of cortical neuron responses, and suggest their central role in sensory cortical function. SIGNIFICANCE STATEMENT: The function of sensory neurons is nearly always cast in terms of representing sensory stimuli. However, recordings from visual cortex in awake animals show that a large fraction of neural activity is not predictable from the stimulus. We show that this variability is predictable given the simultaneously recorded measures of network activity, local field potentials. A model that combines elements of these signals with the stimulus processing of the neuron can predict neural responses dramatically better than current models, and can predict the structure of correlations across the cortical population. In identifying ways to understand stimulus processing in the context of ongoing network activity, this work thus provides a foundation to understand the role of sensory cortex in combining sensory and cognitive variables.

Saccadic modulation of stimulus processing in primary visual cortex.

Saccadic eye movements play a central role in primate vision. Yet, relatively little is known about their effects on the neural processing of visual inputs. Here we examine this question in primary visual cortex (V1) using receptive-field-based models, combined with an experimental design that leaves the retinal stimulus unaffected by saccades. This approach allows us to analyse V1 stimulus processing during saccades with unprecedented detail, revealing robust perisaccadic modulation. In particular, saccades produce biphasic firing rate changes that are composed of divisive gain suppression followed by an additive rate increase. Microsaccades produce similar, though smaller, modulations. We furthermore demonstrate that this modulation is likely inherited from the LGN, and is driven largely by extra-retinal signals. These results establish a foundation for integrating saccades into existing models of visual cortical stimulus processing, and highlight the importance of studying visual neuron function in the context of eye movements.