Neuron最新文献

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

Hypothalamic VMHdm^SF1 neurons are activated by predator cues and are necessary and sufficient for instinctive defensive responses. However, such data do not distinguish which features of a predator encounter are encoded by VMHdm^SF1 neural activity. To address this issue, we imaged VMHdm^SF1 neurons at single-cell resolution in freely behaving mice exposed to a natural predator in varying contexts. Our results reveal that VMHdm^SF1 neurons do not encode different defensive behaviors but rather represent predator identity and multiple predator-evoked internal states, including threat-evoked fear/anxiety, arousal or neophobia, predator imminence, and safety. Notably, threat and safety are encoded bi-directionally by anti-correlated subpopulations. Strikingly, individual differences in predator defensiveness are correlated with individual differences in VMHdm^SF1 response dynamics. Thus, different threat-related internal state variables are encoded by distinct neuronal subpopulations within a genetically defined, anatomically restricted hypothalamic cell class.

The widespread adoption of deep learning to model neural activity often relies on "black-box" approaches that lack an interpretable connection between neural activity and network parameters. Here, we propose using algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We introduce our method, deconvolutional unrolled neural learning (DUNL), and demonstrate its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. We uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the heterogeneity of neural responses in the piriform cortex and across striatum during unstructured, naturalistic experiments. Our work leverages advances in interpretable deep learning to provide a mechanistic understanding of neural activity.

Changes in β-amyloid (Aβ) and hyperphosphorylated tau (T) in brain and cerebrospinal fluid (CSF) precede Alzheimer's disease (AD) symptoms, making the CSF proteome a potential avenue to understand disease pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 analytes (2,029 unique proteins) dysregulated in AD. Of these, 865 (43%) were previously reported, and 1,164 (57%) are novel. The identified proteins cluster in four different pseudo-trajectories groups spanning the AD continuum and were enriched in pathways including neuronal death, apoptosis, and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfunction (mid stages), brain plasticity and longevity (mid stages), and microglia-neuron crosstalk (late stages). Using machine learning, we created and validated highly accurate and replicable (area under the curve [AUC] > 0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD.

Enteric infections often cause long-term sequelae, including persistent gastrointestinal symptoms, such as pain, discomfort, or irritable bowel syndrome. The plethora of sensory symptoms indicates that gut-innervating neurons might be directly affected by inflammation. However, sequencing studies of neurons in the gastrointestinal tract are hampered by difficulties in purifying neurons, especially during inflammation. Activating a nuclear GFP tag selectively in neurons enabled sort purification of intrinsic and extrinsic neurons of the gastrointestinal tract in models of intestinal inflammation. Using bulk and single-nucleus RNA sequencing, we mapped the whole transcriptomic landscape and identified a conserved neuronal response to inflammation, which included the interferon signaling and ferroptosis pathway. Deletion of the interferon receptor 1 in neurons regulated ferroptosis, neuronal loss, and consequently gut-transit time. Collectively, this study offers a resource documenting neuronal adaptation to inflammatory conditions and exposes the interferon and ferroptosis pathways as signaling cascades activated in neurons during inflammation.

Because early-life stress is common and constitutes a strong risk factor for cognitive and mental health disorders, it has been the focus of a multitude of studies in humans and experimental models. Yet, we have an incomplete understanding of what is perceived as stressful by the developing brain, what aspects of stress influence brain maturation, what developmental ages are particularly vulnerable to stress, which molecules mediate the effects of stress on brain operations, and how transient stressful experiences can lead to enduring emotional and cognitive dysfunctions. Here, we discuss these themes, highlight the challenges and progress in resolving them, and propose new concepts and avenues for future research.

The most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is the repeat expansion in C9ORF72. Dipeptide repeat (DPR) proteins translated from both sense and antisense repeats, especially arginine-rich DPRs (R-DPRs), contribute to neurodegeneration. Through CRISPR interference (CRISPRi) screening in human-derived neurons, we identified receptor-type tyrosine-protein phosphatase S (PTPσ) as a strong modifier of poly-GR-mediated toxicity. We showed that reducing PTPσ promotes the survival of both poly-GR- and poly-PR-expressing neurons by elevating phosphatidylinositol 3-phosphate (PI3P), accompanied by restored early endosomes and lysosomes. Remarkably, PTPσ knockdown or inhibition substantially rescues the PI3P-endolysosomal defects and improves the survival of C9ORF72-ALS/FTD patient-derived neurons. Furthermore, the PTPσ inhibitor diminishes GR toxicity and rescues pathological and behavioral phenotypes in mice. Overall, these findings emphasize the critical role of PI3P-mediated endolysosomal deficits induced by R-DPRs in disease pathogenesis and reveal the therapeutic potential of targeting PTPσ in C9ORF72-ALS/FTD.

Pain relief is the most frequently reported motivation for opioid misuse, but it remains unclear how pain alters reward pathway function contributing to maladaptive opioid use and whether these neuroadaptations occur in a sex-specific manner. Here, we show that persistent inflammatory pain leads to augmented fentanyl self-administration in male, not female, rats. Wireless in vivo fiber photometry recordings and chemogenetic manipulations indicate that pain-facilitated fentanyl use is mediated by enhanced ventral tegmental area dopamine (VTA^DA) neuron responses during self-administration. In females, ovariectomy enhances fentanyl self-administration, but the protective effects of ovarian hormones are not solely mediated by estradiol per se. Instead, pain and high estradiol states-naturally occurring in intact females or artificially produced in males-suppress fentanyl self-administration and associated VTA^DA activity through VTA estrogen receptor beta signaling. These findings highlight the importance of assessing hormonal factors in opioid misuse liability in the context of pain.

Systems consolidation relies on coordination between hippocampal sharp-wave ripples (SWRs) and neocortical UP/DOWN states during sleep. However, whether this coupling exists across the neocortex and the mechanisms enabling it remains unknown. By combining electrophysiology in mouse hippocampus (HPC) and retrosplenial cortex (RSC) with wide-field imaging of the dorsal neocortex, we found spatially and temporally precise bi-directional hippocampo-neocortical interaction. HPC multi-unit activity and SWR probability were correlated with UP/DOWN states in the default mode network (DMN), with the highest modulation by the RSC in deep sleep. Further, some SWRs were preceded by the high rebound excitation accompanying DMN DOWN → UP transitions, whereas large-amplitude SWRs were often followed by DOWN states originating in the RSC. We explain these electrophysiological results with a model in which the HPC and RSC are weakly coupled excitable systems capable of bi-directional perturbation and suggest that the RSC may act as a gateway through which SWRs can perturb downstream cortical regions via cortico-cortical propagation of DOWN states.

In this issue of Neuron, Kong et al.¹ identify targetable natural killer-like T cells that seed the intact aged human and murine spinal cords and increase further after injury. These cells impede myeloid-cell-dependent wound healing in the aged injured cord through expressing natural killer cell granule protein 7 (NKG7).