The auditory brainstem response (ABR) is a measure of the neurophysiological response to sound, widely used in clinical and research settings. Here, we describe OpenSABR (“Open-source System for ABR”), an open-source system for measuring the ABR that is more flexible and about fifty times cheaper than closed-source alternatives. The heart of our system is the commercially available Texas Instruments ADS1299 chip for multi-channel differential recording. We designed auxiliary hardware and software to record and visualize the ABR. To demonstrate high-quality recording, we measured the response in mice to free-field auditory clicks from the left and right. We show that OpenSABR yields data consistent with prior work, introduce new methods for analyzing ABR waveforms, benchmark these methods against existing tools, compare variability within and between individuals, and detect the decreased auditory sensitivity associated with conductive hearing loss. Unlike proprietary alternatives, our system is highly flexible and could be customized for recording other biosignals like electromyography (EMG), electrocardiography (ECG), and sensory or motor evoked potentials. The low cost of OpenSABR (about $400) could broaden access to biosignal recording in research and education. This system is intended for research purposes and is not evaluated or approved for clinical use in humans, but if validated its low cost could broaden access to diagnosis of hearing and balance disorders.

Humans and animals learn about the world through targeted and deliberate movement of the head, eyes, hands, and other sensors. A new study demonstrates how the geometry of the rat whisker system simplifies the motor control of active sensing.

Mouse models of Fragile X Syndrome (FXS) have demonstrated impairments in sensory-evoked neuronal firing of excitatory and inhibitory neurons. Homeostatic plasticity does not compensate for these changes in activity. Previous work has shown that impairments in homeostatic plasticity mechanisms are observed in FXS, including deficits in synaptic scaling and intrinsic excitability. Here, we aimed to examine how sensory integration changes in vivo following a homeostatic perturbation, unilateral whisker deprivation (WD), in an Fmr1 knock out (KO) mouse model. We used multi-electrode array recordings of neurons in the lightly anesthetized juvenile mouse somatosensory cortex, and found that whisker-evoked responses in layer 5/6 (L5/6) excitatory neurons were weaker in the KO compared to the wild-type (WT). We show that WD in the WT leads to a compensatory increase in the proportion of L5/6 somatosensory neurons that were recruited following whisker stimulation, but this did not occur in the KO. On the other hand, certain compensatory responses were observed in the KO following WD; the firing rate of the whisker-responsive neurons was increased following both a 2- and 7-day WD. Similar to excitatory neurons, we observed increased recruitment of fast spiking (presumed inhibitory) neurons following WD in the WT, but not KO. Our results suggest that certain homeostatic mechanisms are impaired in the KO, while others appear to remain intact. Compromised homeostatic plasticity in development could influence adult sensory processing and long-term cortical organization.

The auditory brainstem response (ABR) is a measure of the neurophysiological response to sound, widely used in clinical and research settings. In this manuscript, we describe an open-source and low-cost system suitable for measuring the ABR in mice. (This system is not evaluated or approved for clinical use in humans.) The heart of the system is the commercially available Texas Instruments ADS1299 chip, which is designed for multi-channel differential recording of biosignals. We designed auxiliary hardware and software to record and visualize the ABR. To demonstrate that this system is capable of high-quality and low-noise measurements, we recorded the response to free-field auditory clicks from the left and right. Next, we compared different electrode configurations, quantified the most consistent aspects of the ABR, and assessed variability within and across mice. Finally, we demonstrated how to detect the decreased auditory sensitivity caused by surgically induced conductive hearing loss. Users can deploy this open-source system at low cost and customize it for different applications, such as recording other biosignals like electromyography (EMG) or electrocardiography (ECG).

Mouse models of Fragile X Syndrome (FXS) have demonstrated impairments in excitatory and inhibitory sensory-evoked neuronal firing. Homeostatic plasticity, which encompasses a set of mechanisms to stabilize baseline activity levels, does not compensate for these changes in activity. Previous work has shown that impairments in homeostatic plasticity are observed in FXS, including deficits in synaptic scaling and intrinsic excitability. Here, we aimed to examine how homeostatic plasticity is altered in vivo in an Fmr1 KO mouse model following unilateral whisker deprivation (WD). We show that WD in the wild type leads to an increase in the proportion of L5/6 somatosensory neurons that are recruited, but this does not occur in the KO. In addition, we observed a change in the threshold of excitatory neurons at a later developmental stage in the KO. Compromised homeostatic plasticity in development could influence sensory processing and long-term cortical organization.

Exposing meaningful and interpretable neural interactions is critical to understanding neural circuits. Inferred neural interactions from neural signals primarily reflect functional interactions. In a long experiment, subject animals may experience different stages defined by the experiment, stimuli, or behavioral states, and hence functional interactions can change over time. To model dynamically changing functional interactions, prior work employs state-switching generalized linear models with hidden Markov models (i.e., HMM-GLMs). However, we argue they lack biological plausibility, as functional interactions are shaped and confined by the underlying anatomical connectome. Here, we propose a novel prior-informed state-switching GLM. We introduce both a Gaussian prior and a one-hot prior over the GLM in each state. The priors are learnable. We will show that the learned prior should capture the state-constant interaction, shedding light on the underlying anatomical connectome and revealing more likely physical neuron interactions. The state-dependent interaction modeled by each GLM offers traceability to capture functional variations across multiple brain states. Our methods effectively recover true interaction structures in simulated data, achieve the highest predictive likelihood with real neural datasets, and render interaction structures and hidden states more interpretable when applied to real neural data.

How we move our bodies affects how we perceive sound. For instance, head movements help us to better localize the source of a sound and to compensate for asymmetric hearing loss. However, many auditory experiments are designed to restrict head and body movements. To study the role of movement in hearing, we developed a behavioral task called sound-seeking that rewarded freely moving mice for tracking down an ongoing sound source. Over the course of learning, mice more efficiently navigated to the sound. Next, we asked how sound-seeking was affected by hearing loss induced by surgical removal of the malleus from the middle ear. After bilateral hearing loss sound-seeking performance drastically declined and did not recover. In striking contrast, after unilateral hearing loss mice were only transiently impaired and then recovered their sound-seek ability over about a week. Throughout recovery, unilateral mice increasingly relied on a movement strategy of sequentially checking potential locations for the sound source. In contrast, the startle reflex (an innate auditory behavior) was preserved after unilateral hearing loss and abolished by bilateral hearing loss without recovery over time. In sum, mice compensate with body movement for permanent unilateral damage to the peripheral auditory system. Looking forward, this paradigm provides an opportunity to examine how movement enhances perception and enables resilient adaptation to sensory disorders.

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

Neurons often encode highly heterogeneous non-linear functions of multiple task variables, a signature of a high-dimensional geometry. We studied the representational geometry in the somatosensory cortex of mice trained to report the curvature of objects touched by their whiskers. High-speed videos of the whiskers revealed that the task can be solved by linearly integrating multiple whisker contacts over time. However, the neural activity in somatosensory cortex reflects non-linear integration of spatio-temporal features of the sensory inputs. Although the responses at first appeared disorganized, we identified an interesting structure in the representational geometry: different whisker contacts are disentangled variables represented in approximately, but not fully, orthogonal subspaces of the neural activity space. This geometry allows linear readouts to perform a broad class of tasks of different complexities without compromising the ability to generalize to novel situations.

7T MRI is affected by inhomogeneous transmit and receive B1 field that can impede the inherent gains in signal-to-noise ratio. pTx provides excellent results in correcting the transmit field and showed feasibility in a clinical setting as well 1,2 . Although multiple algorithms have been developed to correct for the receive profile or signal homogeneities in general, each algorithm has its own shortcomings. Here, we suggest combining prospective correction of the transmit field by pTx with a deep learning network to retrospectively correct for the remaining signal inhomogeneities (mainly receive field variations) in a generalized fashion .

Mice adeptly use their whiskers to touch, recognize, and learn about objects in their environment. This behavior is enabled by computations performed by populations of neurons in the somatosensory cortex. To understand these computations, we trained mice to use their whiskers to recognize different shapes while we recorded activity in the barrel cortex, which processes whisker input. Here, we present a large dataset of high-speed video of the whiskers, along with rigorous tracking of the entire extent of multiple whiskers and every contact they made on the shape. We used spike sorting to identify individual neurons, which responded with precise timing to whisker contacts and motion. These data will be useful for understanding the behavioral strategies mice use to explore objects, as well as the neuronal dynamics that mediate those strategies. In addition, our carefully curated labeled data could be used to develop new computer vision algorithms for tracking body posture, or for extracting responses of individual neurons from large-scale neural recordings.

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within A1 but controlled by top-down inputs from mPFC, can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

Decisions can be made internally and implicitly, without being expressed explicitly. A new study reveals how implicit decisions might engage the enigmatic ‘non-sensory’ neurons in sensory cortex.

Adaptive behavior in humans, rodents, and other animals often requires the integration over time of multiple sensory inputs. Here we studied the behavior and the neural activity of mice trained to actively integrate information from different whiskers to report the curvature of an object. The analysis of high speed videos of the whiskers revealed that the task could be solved by integrating linearly the whisker contacts on the object. However, recordings from the mouse barrel cortex revealed that the neural representations are high dimensional as the inputs from multiple whiskers are mixed non-linearly to produce the observed neural activity. The observed representation enables the animal to perform a broad class of significantly more complex tasks, with minimal disruption of the ability to generalize to novel situations in simpler tasks. Simulated recurrent neural networks trained to perform similar tasks reproduced both the behavioral and neuronal experimental observations. Our work suggests that the somatosensory cortex operates in a regime that represents an efficient compromise between generalization, which typically requires pure and linear mixed selectivity representations, and the ability to perform complex discrimination tasks, which is granted by non-linear mixed representations.

SUMMARY The remarkable cognitive abilities characterizing humans are thought to emerge from our unique features of cortical circuit architecture, including increased feedforward and feedback connectivity. However, our understanding of the evolutionary origin and nature of these changes in circuit connectivity, and how they impact cortical circuit function and behavior is currently lacking. Here, we demonstrate that expression of the human-specific gene duplication SRGAP2C leads to a specific increase in feedforward and feedback cortico-cortical connectivity. Moreover, humanized SRGAP2C mice display improved cortical sensory coding, and an enhanced ability to learn a cortex-dependent sensory discrimination task. Our results identify a novel substrate for human brain evolution whereby the emergence of SRGAP2C led to increased feedforward and feedback cortico-cortical connectivity, improved cortical sensory processing and enhanced behavioral performance.

Humans and other animals can identify objects by active touch, requiring the coordination of exploratory motion and tactile sensation. Both the motor strategies and neural representations employed could depend on the subject's goals. We developed a shape discrimination task that challenged head-fixed mice to discriminate concave from convex shapes. Behavioral decoding revealed that mice did this by comparing contacts across whiskers. In contrast, a separate group of mice performing a shape detection task simply summed up contacts over whiskers. We recorded populations of neurons in the barrel cortex, which processes whisker input, and found that individual neurons across the cortical layers encoded touch, whisker motion, and task-related signals. Sensory representations were task-specific: during shape discrimination, but not detection, neurons responded most to behaviorally relevant whiskers, overriding somatotopy. Thus, sensory cortex employs task-specific representations compatible with behaviorally relevant computations.

1 Massachusetts General Hospital 2 Harvard Medical School 3 University of Pennsylvania 4 Loyola University 5 University of Syracuse 6 Northwestern University 7 Sonat Consulting 8 Princeton University 9 NTT DATA Mathematical Systems Inc 10 605 11 Rockets of Awesome 12 Epoch Capital 13 University of Washington 14 IDLab-AIRO – Ghent University – imec 15 Clean Power Research 16 University of Trento 17 Columbia University 18 Georgia Tech 19 System1 Biosciences Inc 20 Research Institute for Agriculture, Fisheries and Food DOI: 10.21105/joss.01959

SummaryThe neocortex, comprised of multiple distinct layers, processes sensory input from the periphery, makes decisions, and executes actions. Despite extensive investigation of cortical anatomy and physiology, the contributions of different cortical layers to sensory guided behaviors remain unknown. Here, we developed a two-alternative forced choice (2AFC) paradigm in which head-fixed mice use a single whisker to either discriminate textures of parametrically varied roughness or detect the same textured surfaces. Lesioning the barrel cortex revealed that 2AFC texture discrimination, but not detection, was cortex-dependent. Paralyzing the whisker pad had little effect on performance, demonstrating that passive can rival active perception and cortical dependence is not movement-related. Transgenic Cre lines were used to target inhibitory opsins to excitatory cortical neurons of specific layers for selective perturbations. Both deep and superficial layers were critical for texture discrimination. We conclude that even basic cortical computations require coordinated transformation of sensory information across layers.Abstract Figure

Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These non-classically responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here, we describe a trial-by-trial, spike-timing-based algorithm to reveal the coding capacities of these neurons in auditory and frontal cortex of behaving rats. Classically responsive and non-classically responsive cells contained significant information about sensory stimuli and behavioral decisions. Stimulus category was more accurately represented in frontal cortex than auditory cortex, via ensembles of non-classically responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons – particularly those without obvious task-related, trial-averaged firing rate modulation – to be assessed for behavioral relevance on single trials.