S1 Employs Feature-Dependent Differential Selectivity of Single Cells and Distributed Patterns of Populations to Encode Mechanosensations

From nociception to pain perception, possible implications of astrocytes

Astrocytes are determinants for the functioning of the CNS. They respond to neuronal activity with calcium increases and can in turn modulate synaptic transmission, brain plasticity as well as cognitive processes. Astrocytes display sensory-evoked calcium responses in different brain structures related to the discriminative system of most sensory modalities. In particular, noxious stimulation evoked calcium responses in astrocytes in the spinal cord, the hippocampus, and the somatosensory cortex. However, it is not clear if astrocytes are involved in pain. Pain is a private, personal, and complex experience that warns us about potential tissue damage. It is a perception that is not linearly associated with the amount of tissue damage or nociception; instead, it is constructed with sensory, cognitive, and affective components and depends on our previous experiences. However, it is not fully understood how pain is created from nociception. In this perspective article, we provide an overview of the mechanisms and neuronal networks that underlie the perception of pain. Then we proposed that coherent activity of astrocytes in the spinal cord and pain-related brain areas could be important in binding sensory, affective, and cognitive information on a slower time scale.

Development of a spontaneous pain indicator based on brain cellular calcium using deep learning

Chronic pain remains an intractable condition in millions of patients worldwide. Spontaneous ongoing pain is a major clinical problem of chronic pain and is extremely challenging to diagnose and treat compared to stimulus-evoked pain. Although extensive efforts have been made in preclinical studies, there still exists a mismatch in pain type between the animal model and humans (i.e., evoked vs. spontaneous), which obstructs the translation of knowledge from preclinical animal models into objective diagnosis and effective new treatments. Here, we developed a deep learning algorithm, designated AI-bRNN (Average training, Individual test-bidirectional Recurrent Neural Network), to detect spontaneous pain information from brain cellular Ca²⁺ activity recorded by two-photon microscopy imaging in awake, head-fixed mice. AI-bRNN robustly determines the intensity and time points of spontaneous pain even in chronic pain models and evaluates the efficacy of analgesics in real time. Furthermore, AI-bRNN can be applied to various cell types (neurons and glia), brain areas (cerebral cortex and cerebellum) and forms of somatosensory input (itch and pain), proving its versatile performance. These results suggest that our approach offers a clinically relevant, quantitative, real-time preclinical evaluation platform for pain medicine, thereby accelerating the development of new methods for diagnosing and treating human patients with chronic pain.

A microscopy technique coupled with an artificial intelligence (AI) platform could help researchers discover new types of pain-relief medicines. A team from South Korea led by Sun Kwang Kim of Kyung Hee University and Sang Jeong Kim of Seoul National University created a machine-learning algorithm that converts calcium signaling data in the brain, as estimated via imaging on genetically engineered mice, into a measurement of pain intensity. The researchers applied the technique to several mouse models of chronic pain and showed that it accurately captured the analgesic effects of known painkillers. They also extended the system to multiple brain regions, cell types and another brain-controlled sensory process, itch. The researchers propose using the AI-based tool to evaluate candidate anti-pain and anti-itch medicines ahead of human trials.

Are Grid-Like Representations a Component of All Perception and Cognition?

Grid cells or grid-like responses have been reported in the rodent, bat and human brains during various spatial and non-spatial tasks. However, the functions of grid-like representations beyond the classical hippocampal formation remain elusive. Based on accumulating evidence from recent rodent recordings and human fMRI data, we make speculative accounts regarding the mechanisms and functional significance of the sensory cortical grid cells and further make theory-driven predictions. We argue and reason the rationale why grid responses may be universal in the brain for a wide range of perceptual and cognitive tasks that involve locomotion and mental navigation. Computational modeling may provide an alternative and complementary means to investigate the grid code or grid-like map. We hope that the new discussion will lead to experimentally testable hypotheses and drive future experimental data collection.

Altered synaptic connections and inhibitory network of the primary somatosensory cortex in chronic pain

Chronic pain is induced by tissue or nerve damage and is accompanied by pain hypersensitivity (i.e., allodynia and hyperalgesia). Previous studies using in vivo two-photon microscopy have shown functional and structural changes in the primary somatosensory (S1) cortex at the cellular and synaptic levels in inflammatory and neuropathic chronic pain. Furthermore, alterations in local cortical circuits were revealed during the development of chronic pain. In this review, we summarize recent findings regarding functional and structural plastic changes of the S1 cortex and alteration of the S1 inhibitory network in chronic pain. Finally, we discuss potential neuromodulators driving modified cortical circuits and suggest further studies to understand the cortical mechanisms that induce pain hypersensitivity.

Neural Plasticity in the Brain during Neuropathic Pain

Neuropathic pain is an intractable chronic pain, caused by damage to the somatosensory nervous system. To date, treatment for neuropathic pain has limited effects. For the development of efficient therapeutic methods, it is essential to fully understand the pathological mechanisms of neuropathic pain. Besides abnormal sensitization in the periphery and spinal cord, accumulating evidence suggests that neural plasticity in the brain is also critical for the development and maintenance of this pain. Recent technological advances in the measurement and manipulation of neuronal activity allow us to understand maladaptive plastic changes in the brain during neuropathic pain more precisely and modulate brain activity to reverse pain states at the preclinical and clinical levels. In this review paper, we discuss the current understanding of pathological neural plasticity in the four pain-related brain areas: the primary somatosensory cortex, the anterior cingulate cortex, the periaqueductal gray, and the basal ganglia. We also discuss potential treatments for neuropathic pain based on the modulation of neural plasticity in these brain areas.

Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks

One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain.

Multiplexed Processing of Vibrotactile Information in the Mouse Primary Somatosensory Cortex

The primary somatosensory (S1) cortex plays a key role in distinguishing different sensory stimuli. Vibrotactile touch information is conveyed from the periphery to the S1 cortex through three major classes of mechanoreceptors: slowly adapting type 1 (SA1), rapidly adapting (RA), and Pacinian (PC) afferents. It has been a long-standing question whether specific populations in the S1 cortex preserve the peripheral segregation by the afferent submodalities. Here, we investigated whether S1 neurons exhibit specific responses to two distinct vibrotactile stimuli, which excite different types of mechanoreceptors (e.g., SA1 and PC afferents). Using in vivo two-photon microscopy and genetically encoded calcium indicator, GCaMP6s, we recorded calcium activities of S1 L2/3 neurons. At the same time, static (<1 Hz) and dynamic (150 Hz) vibrotactile stimuli, which are known to excite SA1 and PC, respectively, were pseudorandomly applied to the right hind paw in lightly anesthetized mice. We found that most active S1 neurons responded to both static and dynamic stimuli, but more than half of them showed preferred responses to either type of stimulus. Only a small fraction of the active neurons exhibited specific responses to either static or dynamic stimuli. However, the S1 population activity patterns by the two stimuli were markedly distinguished. These results indicate that the vibrotactile inputs driven by excitation of distinct submodalities are converged on the single cells of the S1 cortex, but are well discriminated by population activity patterns composed of neurons that have a weighted preference for each type of stimulus.