Primary Tactile Thalamus Spiking Reflects Cognitive Signals

The 6-OHDA Parkinson's Disease Mouse Model Shows Deficits in Sensory Behavior

Parkinson's disease (PD) is characterized by the degeneration of dopaminergic (DA) neurons in the substantia nigra pars compacta, leading to dopamine depletion in the striatum and the hallmark motor symptoms of the disease. However, non-motor deficits, particularly sensory symptoms, often precede motor manifestations, offering a potential early diagnostic window. The impact of non-motor deficits on sensation behavior and the underlying mechanisms remain poorly understood. In this study, we examined changes in tactile sensation within a parkinsonian state by employing a mouse model of PD induced by 6-hydroxydopamine (6-OHDA) to deplete striatal DA. Leveraging the conserved mouse whisker system as a model for tactile-sensory stimulation, we conducted psychophysical experiments to assess sensory-driven behavioral performance during a tactile detection task in both the healthy and PD-like state. Our findings reveal a range of deficits across subjects following 6-OHDA lesion, including DA loss, motor asymmetry, weight loss, and varying levels of altered tactile sensation behavior. Behavioral changes ranged from no impairments in minor cases to isolated sensory-behavioral deficits in moderate cases and severe motor dysfunction in advanced stages. These results underscore the complex relationship between DA imbalance and sensory-motor processing, emphasizing the need for precise and multifaceted behavioral measurements to accurately capture the diverse manifestations of PD.

A machine learning approach for real-time cortical state estimation

Objective.Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as 'cortical state'. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation.Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments.Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data.Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

Cognitive and affective interaction with somatosensory afference in acupuncture-a specific brain response to compound stimulus

Introduction

Acupuncture is a clinical intervention consisting of multiple stimulus components, including somatosensory stimulation and manipulation of therapeutic context. Existing findings in neuroscience consolidated cognitive modulation to somatosensory afferent process, which could differ from placebo mechanism in brain. Here, we aimed to identify intrinsic process of brain interactions induced by compound stimulus of acupuncture treatment.

Methods

To separately and comprehensively investigate somatosensory afferent and cognitive/affective processes in brain, we implemented a novel experimental protocol of contextual manipulation with somatosensory stimulation (real acupuncture: REAL) and only contextual manipulation (phantom acupuncture: PHNT) for fMRI scan, and conducted independent component (IC)-wise assessment with the concatenated fMRI data.

Results

By our double (experimentally and analytically) dissociation, two ICs (CA1: executive control, CA2: goal-directed sensory process) for cognitive/affective modulation (associated with both REAL and PHNT) and other two ICs (SA1: interoceptive attention and motor-reaction, SA2: somatosensory representation) for somatosensory afference (associated with only REAL) were identified. Moreover, coupling between SA1 and SA2 was associated with a decreased heart rate during stimulation, whereas CA1 was associated with a delayed heart rate decrease post-stimulation. Furthermore, partial correlation network for these components demonstrated a bi-directional interaction between CA1 and SA1/SA2, suggesting the cognitive modulation to somatosensory process. The expectation for the treatment negatively affected CA1 but positively affected SA1 in REAL, whereas the expectation positively affected CA1 in PHNT.

Discussion

These specific cognitive-somatosensory interaction in REAL were differed from vicarious sensation mechanism in PHNT; and might be associated with a characteristic of acupuncture, which induces voluntary attention for interoception. Our findings on brain interactions in acupuncture treatment elucidated the underlying brain mechanisms for compound stimulus of somatosensory afferent and therapeutic contextual manipulation, which might be a specific response to acupuncture.

Emerging experience-dependent dynamics in primary somatosensory cortex reflect behavioral adaptation

Behavioral experience and flexibility are crucial for survival in a constantly changing environment. Despite evolutionary pressures to develop adaptive behavioral strategies in a dynamically changing sensory landscape, the underlying neural correlates have not been well explored. Here, we use genetically encoded voltage imaging to measure signals in primary somatosensory cortex (S1) during sensory learning and behavioral adaptation in the mouse. In response to changing stimulus statistics, mice adopt a strategy that modifies their detection behavior in a context dependent manner as to maintain reward expectation. Surprisingly, neuronal activity in S1 shifts from simply representing stimulus properties to transducing signals necessary for adaptive behavior in an experience dependent manner. Our results suggest that neuronal signals in S1 are part of an adaptive framework that facilitates flexible behavior as individuals gain experience, which could be part of a general scheme that dynamically distributes the neural correlates of behavior during learning.

Waiblinger et al. investigate the role of primary sensory cortex in flexible behaviors. They show that neuronal signals in S1 are part of an adaptive and dynamic framework that facilitates flexible behavior as an individual gains experience, indicating a role for S1 in long-term adaptive strategies.

Sensorimotor strategies and neuronal representations for shape discrimination

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.

Rapid Cortical Adaptation and the Role of Thalamic Synchrony during Wakefulness

Rapid sensory adaptation is observed across all sensory systems, and strongly shapes sensory percepts in complex sensory environments. Yet despite its ubiquity and likely necessity for survival, the mechanistic basis is poorly understood. A wide range of primarily in vitro and anesthetized studies have demonstrated the emergence of adaptation at the level of primary sensory cortex, with only modest signatures in earlier stages of processing. The nature of rapid adaptation and how it shapes sensory representations during wakefulness, and thus the potential role in perceptual adaptation, is underexplored, as are the mechanisms that underlie this phenomenon. To address these knowledge gaps, we recorded spiking activity in primary somatosensory cortex (S1) and the upstream ventral posteromedial (VPm) thalamic nucleus in the vibrissa pathway of awake male and female mice, and quantified responses to whisker stimuli delivered in isolation and embedded in an adapting sensory background. We found that cortical sensory responses were indeed adapted by persistent sensory stimulation; putative excitatory neurons were profoundly adapted, and inhibitory neurons only modestly so. Further optogenetic manipulation experiments and network modeling suggest this largely reflects adaptive changes in synchronous thalamic firing combined with robust engagement of feedforward inhibition, with little contribution from synaptic depression. Taken together, these results suggest that cortical adaptation in the regime explored here results from changes in the timing of thalamic input, and the way in which this differentially impacts cortical excitation and feedforward inhibition, pointing to a prominent role of thalamic gating in rapid adaptation of primary sensory cortex.SIGNIFICANCE STATEMENT Rapid adaptation of sensory activity strongly shapes representations of sensory inputs across all sensory pathways over the timescale of seconds, and has profound effects on sensory perception. Despite its ubiquity and theoretical role in the efficient encoding of complex sensory environments, the mechanistic basis is poorly understood, particularly during wakefulness. In this study in the vibrissa pathway of awake mice, we show that cortical representations of sensory inputs are strongly shaped by rapid adaptation, and that this is mediated primarily by adaptive gating of the thalamic inputs to primary sensory cortex and the differential way in which these inputs engage cortical subpopulations of neurons.

Functional analysis of information rates conveyed by rat whisker-related trigeminal nuclei neurons

The rat whisker system connects the tactile environment with the somatosensory thalamocortical system using only two synaptic stages. Encoding properties of the first stage, the primary afferents with somas in the trigeminal ganglion (TG), has been well studied, whereas much less is known from the second stage, the brainstem trigeminal nuclei (TN). The TN are a computational hub giving rise to parallel ascending tactile pathways and receiving feedback from many brain sites. We asked the question, whether encoding properties of TG neurons are kept by two trigeminal nuclei, the principalis (Pr5) and the spinalis interpolaris (Sp5i), respectively giving rise to two "lemniscal" and two "nonlemniscal" pathways. Single units were recorded in anesthetized rats while a single whisker was deflected on a band-limited white noise trajectory. Using information theoretic methods and spike-triggered mixture models (STM), we found that both nuclei encode the stimulus locally in time, i.e., stimulus features more than 10 ms in the past do not significantly influence spike generation. They further encode stimulus kinematics in multiple, distinct response fields, indicating encoding characteristics beyond previously described directional responses. Compared with TG, Pr5 and Sp5i gave rise to lower spike and information rates, but information rate per spike was on par with TG. Importantly, both brainstem nuclei were found to largely keep encoding properties of primary afferents, i.e. local encoding and kinematic response fields. The preservation of encoding properties in channels assumed to serve different functions seems surprising. We discuss the possibility that it might reflect specific constraints of frictional whisker contact with object surfaces.NEW & NOTEWORTHY We studied two trigeminal nuclei containing the second neuron on the tactile pathway of whisker-related tactile information in rats. We found that the subnuclei, traditionally assumed to give rise to functional tactile channels, nevertheless transfer primary afferent information with quite similar properties in terms of integration time and kinematic profile. We discuss whether such commonality may be due the requirement to adapt to physical constraints of frictional whisker contact.

Astrocytes Control Sensory Acuity via Tonic Inhibition in the Thalamus

Sensory discrimination is essential for survival. However, how sensory information is finely controlled in the brain is not well defined. Here, we show that astrocytes control tactile acuity via tonic inhibition in the thalamus. Mechanistically, diamine oxidase (DAO) and the subsequent aldehyde dehydrogenase 1a1 (Aldh1a1) convert putrescine into GABA, which is released via Best1. The GABA from astrocytes inhibits synaptically evoked firing at the lemniscal synapses to fine-tune the dynamic range of the stimulation-response relationship, the precision of spike timing, and tactile discrimination. Our findings reveal a novel role of astrocytes in the control of sensory acuity through tonic GABA release.

State-aware detection of sensory stimuli in the cortex of the awake mouse

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states.

Establishing the link between neural activity and behavior is a central goal of neuroscience. One context in which to examine this link is in a sensory detection task, in which an animal is trained to report the presence of a barely perceptible sensory stimulus. In such tasks, both sensory responses in the brain and behavioral responses are highly variable. A simple hypothesis, originating in signal detection theory, is that perceived inputs generate neural activity that cross some threshold for detection. According to this hypothesis, sensory response variability would predict behavioral variability, but previous studies have not born out this prediction. Further complicating the picture, sensory response variability is partially dependent on the ongoing state of cortical activity, and we wondered whether this could resolve the mismatch between response variability and behavioral variability. Here, we use a computational approach to study an adaptive observer that utilizes an ongoing prediction of sensory responsiveness to detect sensory inputs. This observer has higher overall accuracy than the standard ideal observer. Moreover, because of the adaptation, the observer breaks the direct link between neural and behavioral variability, which could resolve discrepancies arising in past studies. We suggest new experiments to test our theory.

Stimulus Context and Reward Contingency Induce Behavioral Adaptation in a Rodent Tactile Detection Task

Behavioral adaptation is a prerequisite for survival in a constantly changing sensory environment, but the underlying strategies and relevant variables driving adaptive behavior are not well understood. Many learning models and neural theories consider probabilistic computations as an efficient way to solve a variety of tasks, especially if uncertainty is involved. Although this suggests a possible role for probabilistic inference and expectation in adaptive behaviors, there is little if any evidence of this relationship experimentally. Here, we investigated adaptive behavior in the rat model by using a well controlled behavioral paradigm within a psychophysical framework to predict and quantify changes in performance of animals trained on a simple whisker-based detection task. The sensory environment of the task was changed by transforming the probabilistic distribution of whisker deflection amplitudes systematically while measuring the animal's detection performance and corresponding rate of accumulated reward. We show that the psychometric function deviates significantly and reversibly depending on the probabilistic distribution of stimuli. This change in performance relates to accumulating a constant reward count across trials, yet it is exempt from changes in reward volume. Our simple model of reward accumulation captures the observed change in psychometric sensitivity and predicts a strategy seeking to maintain reward expectation across trials in the face of the changing stimulus distribution. We conclude that rats are able maintain a constant payoff under changing sensory conditions by flexibly adjusting their behavioral strategy. Our findings suggest the existence of an internal probabilistic model that facilitates behavioral adaptation when sensory demands change.SIGNIFICANCE STATEMENT The strategy animals use to deal with a complex and ever-changing world is a key to understanding natural behavior. This study provides evidence that rodent behavioral performance is highly flexible in the face of a changing stimulus distribution, consistent with a strategy to maintain a desired accumulation of reward.