Human assembloid model of the ascending neural sensory pathway.

The ascending somatosensory pathways convey crucial information about pain, touch, itch, and body part movement from peripheral organs to the central nervous system. Despite a significant need for effective therapeutics modulating pain and other somatosensory modalities, clinical translation remains challenging, which is likely related to species-specific features and the lack of in vitro models to directly probe and manipulate this polysynaptic pathway. Here, we established human ascending somatosensory assembloids (hASA)- a four-part assembloid completely generated from human pluripotent stem cells that integrates somatosensory, spinal, diencephalic, and cortical organoids to model the human ascending spinothalamic pathway. Transcriptomic profiling confirmed the presence of key cell types in this circuit. Rabies tracing and calcium imaging showed that sensory neurons connected with dorsal spinal cord projection neurons, which ascending axons further connected to thalamic neurons. Following noxious chemical stimulation, single neuron calcium imaging of intact hASA demonstrated coordinated response, while four-part concomitant extracellular recordings and calcium imaging revealed synchronized activity across the assembloid. Loss of the sodium channel SCN9A, which causes pain insensitivity in humans, disrupted synchrony across the four-part hASA. Taken together, these experiments demonstrate the ability to functionally assemble the essential components of the human sensory pathway. These findings could both accelerate our understanding of human sensory circuits and facilitate therapeutic development.

Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue.

Breast conserving resection with free margins is the gold standard treatment for early breast cancer recommended by guidelines worldwide. Therefore, reliable discrimination between normal and malignant tissue at the resection margins is essential. In this study, normal and abnormal tissue samples from breast cancer patients were characterized ex vivo by optical emission spectroscopy (OES) based on ionized atoms and molecules generated during electrosurgical treatment. The aim of the study was to determine spectroscopic features which are typical for healthy and neoplastic breast tissue allowing for future real-time tissue differentiation and margin assessment during breast cancer surgery. A total of 972 spectra generated by electrosurgical sparking on normal and abnormal tissue were used for support vector classifier (SVC) training. Specific spectroscopic features were selected for the classification of tissues in the included breast cancer patients. The average classification accuracy for all patients was 96.9%. Normal and abnormal breast tissue could be differentiated with a mean sensitivity of 94.8%, a specificity of 99.0%, a positive predictive value (PPV) of 99.1% and a negative predictive value (NPV) of 96.1%. For 66.6% patients all classifications reached 100%. Based on this convincing data, a future clinical application of OES-based tissue differentiation in breast cancer surgery seems to be feasible.

Risk assessment of neuromuscular stimulation by energy-based transurethral resection devices: an ex vivo test standard.

BACKGROUND: During transurethral resection of bladder tumours (TURB), radio-frequency (RF) currents can lead to adverse neuromuscular stimulation (NMS). Here we present a novel ex vivo method to determine the risk of RF generators and their bipolar TURB modes to cause NMS. We aimed to develop an experimental platform for safety evaluation of new RF generators and their modes with a newly established test standard, suitable for replacement or reduction of animal testing. METHODS: We tested four contemporary RF generators with their bipolar modes for TURB in saline. A two-stage ex vivo approach was pursued: First, we recorded voltages at possible positions of the obturator nerve behind a porcine bladder wall in a TURB model using 18 RF applications per generator. Second, these voltage records were used as stimuli to evoke nerve compound action potentials (CAPs) in isolated porcine axillary nerves. The NMS potential was defined as the ratio between the observed area under the CAPs and the theoretical CAP area at maximum response and a firing rate of 250 Hz, which would reliably induce tetanic muscle responses in most human subjects. The measurement protocol was tailored to optimise reproducibility of the obtained NMS potentials and longevity of the nerve specimens. RESULTS: As prerequisite for the clinical translation of our results, the robustness of our test method and reproducibility of the NMS potential are demonstrated with an excellent correlation (r = 0.93) between two sets of identical stimuli (n = 72 each) obtained from 16 nerve segments with similar diameters (4.2 ± 0.37 mm) in the nerve model. The RF generators differed significantly (p < 0.0001) regarding NMS potential (medians: 0-3%). CONCLUSIONS: Our test method is suitable for quantifying the NMS potential of different electrosurgical systems ex vivo with high selectivity at a reasonable degree of standardization and with justifiable effort. Our results suggest that the clinical incidence of NMS is considerably influenced by the type of RF generator. Future generations of RF generators take advantage from the proposed test standard through higher safety and less animal testing. Health professionals and treated patients will benefit most from improved RF surgery using generators with a low NMS risk.

Hold Your Methods! How Multineuronal Firing Ensembles Can Be Studied Using Classical Spike-Train Analysis Techniques.

Responses of neuronal populations play an important role in the encoding of stimulus related information. However, the inherent multidimensionality required to describe population activity has imposed significant challenges and has limited the applicability of classical spike train analysis techniques. Here, we show that these limitations can be overcome. We first quantify the collective activity of neurons as multidimensional vectors (patterns). Then we characterize the behavior of these patterns by applying classical spike train analysis techniques: peri-stimulus time histograms, tuning curves and auto- and cross-correlation histograms. We find that patterns can exhibit a broad spectrum of properties, some resembling and others substantially differing from those of their component neurons. We show that in some cases pattern behavior cannot be intuitively inferred from the activity of component neurons. Importantly, silent neurons play a critical role in shaping pattern expression. By correlating pattern timing with local-field potentials, we show that the method can reveal fine temporal coordination of cortical circuits at the mesoscale. Because of its simplicity and reliance on well understood classical analysis methods the proposed approach is valuable for the study of neuronal population dynamics.

Learning Enhances Sensory Processing in Mouse V1 before Improving Behavior.

A fundamental property of visual cortex is to enhance the representation of those stimuli that are relevant for behavior, but it remains poorly understood how such enhanced representations arise during learning. Using classical conditioning in adult mice of either sex, we show that orientation discrimination is learned in a sequence of distinct behavioral stages, in which animals first rely on stimulus appearance before exploiting its orientation to guide behavior. After confirming that orientation discrimination under classical conditioning requires primary visual cortex (V1), we measured, during learning, response properties of V1 neurons. Learning improved neural discriminability, sharpened orientation tuning, and led to higher contrast sensitivity. Remarkably, these learning-related improvements in the V1 representation were fully expressed before successful orientation discrimination was evident in the animals' behavior. We propose that V1 plays a key role early in discrimination learning to enhance behaviorally relevant sensory information.SIGNIFICANCE STATEMENT Decades of research have documented that responses of neurons in visual cortex can reflect the behavioral relevance of visual information. The behavioral relevance of any stimulus needs to be learned, though, and little is known how visual sensory processing changes, as the significance of a stimulus becomes clear. Here, we trained mice to discriminate two visual stimuli, precisely quantified when learning happened, and measured, during learning, the neural representation of these stimuli in V1. We observed learning-related improvements in V1 processing, which were fully expressed before discrimination was evident in the animals' behavior. These findings indicate that sensory and behavioral improvements can follow different time courses and point toward a key role of V1 at early stages in discrimination learning.

Mice Can Use Second-Order, Contrast-Modulated Stimuli to Guide Visual Perception.

Visual processing along the primate ventral stream takes place in a hierarchy of areas, characterized by an increase in both complexity of neuronal preferences and invariance to changes of low-level stimulus attributes. A basic type of invariance is form-cue invariance, where neurons have similar preferences in response to first-order stimuli, defined by changes in luminance, and global features of second-order stimuli, defined by changes in texture or contrast. Whether in mice, a now popular model system for early visual processing, visual perception can be guided by second-order stimuli is currently unknown. Here, we probed mouse visual perception and neural responses in areas V1 and LM using various types of second-order, contrast-modulated gratings with static noise carriers. These gratings differ in their spatial frequency composition and thus in their ability to invoke first-order mechanisms exploiting local luminance features. We show that mice can transfer learning of a coarse orientation discrimination task involving first-order, luminance-modulated gratings to the contrast-modulated gratings, albeit with markedly reduced discrimination performance. Consistent with these behavioral results, we demonstrate that neurons in area V1 and LM are less responsive and less selective to contrast-modulated than to luminance-modulated gratings, but respond with broadly similar preferred orientations. We conclude that mice can, at least in a rudimentary form, use second-order stimuli to guide visual perception. SIGNIFICANCE STATEMENT: To extract object boundaries in natural scenes, the primate visual system does not only rely on differences in local luminance but can also take into account differences in texture or contrast. Whether the mouse, which has a much simpler visual system, can use such second-order information to guide visual perception is unknown. Here we tested mouse perception of second-order, contrast-defined stimuli and measured their neural representations in two areas of visual cortex. We find that mice can use contrast-defined stimuli to guide visual perception, although behavioral performance and neural representations were less robust than for luminance-defined stimuli. These findings shed light on basic steps of feature extraction along the mouse visual cortical hierarchy, which may ultimately lead to object recognition.

Effects of locomotion extend throughout the mouse early visual system.

BACKGROUND: Neural responses in visual cortex depend not only on sensory input but also on behavioral context. One such context is locomotion, which modulates single-neuron activity in primary visual cortex (V1). How locomotion affects neuronal populations across cortical layers and in precortical structures is not well understood. RESULTS: We performed extracellular multielectrode recordings in the visual system of mice during locomotion and stationary periods. We found that locomotion influenced activity of V1 neurons with a characteristic laminar profile and shaped the population response by reducing pairwise correlations. Although the reduction of pairwise correlations was restricted to cortex, locomotion slightly but consistently increased firing rates and controlled tuning selectivity already in the dorsolateral geniculate nucleus (dLGN) of the thalamus. At the level of the eye, increases in locomotion speed were associated with pupil dilation. CONCLUSIONS: These findings document further, nonmultiplicative effects of locomotion, reaching earlier processing stages than cortex.

Timescales of multineuronal activity patterns reflect temporal structure of visual stimuli.

The investigation of distributed coding across multiple neurons in the cortex remains to this date a challenge. Our current understanding of collective encoding of information and the relevant timescales is still limited. Most results are restricted to disparate timescales, focused on either very fast, e.g., spike-synchrony, or slow timescales, e.g., firing rate. Here, we investigated systematically multineuronal activity patterns evolving on different timescales, spanning the whole range from spike-synchrony to mean firing rate. Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space. Patterns evolve on a continuum of coexisting timescales that strongly relate to the temporal properties of stimuli. Timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (5-20 ms) play a particularly salient role in encoding a large amount of stimulus-related information. Thus, to faithfully encode the properties of visual stimuli the brain engages multiple neurons into activity patterns evolving on multiple timescales.

A color-based visualization technique for multielectrode spike trains.

Multi electrode recordings of neuronal activity provide an overwhelming amount of data that is often difficult to analyze and interpret. Although various methods exist for treating multielectrode datasets quantitatively, there is a particularly prominent lack of techniques that enable a quick visual exploration of such datasets. Here, by using Kohonen self-organizing maps, we propose a simple technique that allows for the representation of multiple spike trains through a sequence of color-coded population activity vectors. When multiple color sequences are grouped according to a certain criterion, e.g., by stimulation condition or recording time, one can inspect an entire dataset visually and extract quickly information about the identity, stimulus-locking and temporal distribution of multi-neuron activity patterns. Color sequences can be computed on various time scales revealing different aspects of the temporal dynamics and can emphasize high-order correlation patterns that are not detectable with pairwise techniques. Furthermore, this technique is useful for determining the stability of neuronal responses during a recording session. Due to its simplicity and reliance on perceptual grouping, the method is useful for both quick on-line visualization of incoming data and for more detailed post hoc analyses.

The oscillation score: an efficient method for estimating oscillation strength in neuronal activity.

We present a method that estimates the strength of neuronal oscillations at the cellular level, relying on autocorrelation histograms computed on spike trains. The method delivers a number, termed oscillation score, that estimates the degree to which a neuron is oscillating in a given frequency band. Moreover, it can also reliably identify the oscillation frequency and strength in the given band, independently of the oscillation in other frequency bands, and thus it can handle superimposed oscillations on multiple scales (theta, alpha, beta, gamma, etc.). The method is relatively simple and fast. It can cope with a low number of spikes, converging exponentially fast with the number of spikes, to a stable estimation of the oscillation strength. It thus lends itself to the analysis of spike-sorted single-unit activity from electrophysiological recordings. We show that the method performs well on experimental data recorded from cat visual cortex and also compares favorably to other methods. In addition, we provide a measure, termed confidence score, that determines the stability of the oscillation score estimate over trials.