Nature Neuroscience

With dense axonal connectivity to every region of cortex, the thalamus plays a central role in the nervous system from sensory processing to cognitive functions. Yet how tissue maturation of the thalamus unfolds during childhood and contributes to typical or atypical development is not clear. Through several large datasets, we provide here a thalamic portrait of fine-scale structural development whose nuclei develop along unique trajectories, some of which diverge from predictions of developmental theory. We find that those thalamic nuclei which show the most protracted development are at the greatest risk for later clinical differences in schizophrenia. The spatial pattern across thalamic nuclei for early psychosis risk is associated with a unique neuroreceptor fingerprint with implications for symptom severity.

Preprints now disseminate a large share of biomedical research before peer review. Because they have not yet passed peer review, some scientists regard preprint claims as unverified or potentially unreliable, yet how much those claims change before publication has so far been quantified only in smaller cohorts, with results that vary by field and topic. Here, we compiled every bioRxiv preprint posted between 2018 and 2025 that we could match by DOI to a peer-reviewed published version, yielding 72,644 preprint-publication pairs. Using a large language model (Claude Sonnet 4.6), we parsed every preprint-publication abstract pair into one primary and two secondary claims, and classified each pair for content change (unchanged, minor, major) and hedging shift (more cautious, more confident, unchanged). On a validation subsample, the model agreed with two independent domain experts about as well as the experts agreed with each other (Cohens kappa 0.63 to 0.66). The primary claim was unchanged in 39.9% of abstracts, minorly revised in 50.0%, and substantially revised in only 10.2%. Hedging shifts were uncommon and asymmetric, with twice as many claims becoming more cautious as more confident (8.4% vs 4.2%). Major revisions were more frequent after long peer review (14.1% in the slowest versus 7.0% in the fastest tertile of review time) and declined over the study period (17.0% in 2019 to 5.7% in 2024). Over the same period, biomedical papers that were never posted as preprints were retracted at roughly twice the rate of those that were. Together, these data show that the move from preprint to peer-reviewed publication leaves the central claims of most biomedical abstracts intact, indicating that preprints are a reliable source of biomedical research.

Chronic pain engages distributed cortical and subcortical circuits, and large-scale intracranial recordings in humans offer a valuable opportunity to characterize its neural signatures. Here, we recorded multi-day stereoelectroencephalography (sEEG) from six participants with refractory chronic neuropathic pain, each implanted with sEEG electrodes spanning dozens of cortical and subcortical structures. Using simultaneous chronic pain ratings, we decoded spontaneous high versus low pain states within individuals (median area under the curve = 0.72; five of six participants performed above chance). Pain-predictive signals were broadly distributed and highly participant-specific. However, mapping the spatial distribution of pain-predictive features revealed preferential representation within canonical macroscale networks: beta-band activity in the default mode network and high-gamma activity in the salience network. These results demonstrate that intracranial recordings can capture distributed, network-organized representations of spontaneous chronic pain states.

Metabolic psychiatry has recently achieved unprecedented clinical rescue in treatment-resistant Schizophrenia (SCZ) utilizing targeted ketogenic interventions. However, the field has operated without a defined genomic anchor, leaving the biophysical mechanism of these therapies largely unexplained. Here, we report the discovery of the definitive metabolic sensor array driving this pathology. By integrating high-resolution topological mapping of SCZ GWAS summary statistics, 3D chromatin conformation (Hi-C), and multi-tissue transcriptomics, we identify massive, non-coding structural variances flanking the HCAR2/HCAR1 tandem locus--the brain's master thermodynamic governor. We demonstrate that while the protein-coding hardware of these receptors remains intact, their shared 3D Topologically Associating Domain (TAD) is fundamentally fractured. This structural collapse drives a perfect transcriptomic double dissociation in the human cortex: the 3' mutational "skyscraper" severely downregulates the HCAR1 lactate emergency brake, while the 5' mutational cluster selectively paralyzes the HCAR2 beta-hydroxybutyrate (BHB) and niacin cooling switch. This dual-flank enhancer failure elegantly provides a definitive genomic etiology for historical SCZ biomarkers, physically explaining both chronic cerebrospinal fluid lactate pooling and the infamous "absent niacin flush." Furthermore, peripheral eQTL mapping reveals profound antagonistic pleiotropy, characterized by a hyper-activation of the HCAR1 lactate shuttle in the testis, explaining the evolutionary conservation of this metabolically catastrophic architecture. Ultimately, we reframe Schizophrenia not as an intrinsic neurological defect, but as an evolutionary "fuel mismatch." The high-performance cognitive architecture of the hominid brain, evolved for ancestral ketogenic environments, experiences a catastrophic thermodynamic crash when deprived of its requisite BHB coolant by modern, high-glycemic diets.

The ability to delay gratification emerges early in life and is linked to long-term health and economic success. Conversely, high impulsivity, marked by a preference for immediate rewards, can be associated with psychiatric disorders. Although processes underlying human delay discounting have been studied at behavioural and macroscopic neural levels, they remain elusive at the single-neuron level. Specifically, it is unknown how human neurons encode extended delays and predict intertemporal choices, and how these processes are impacted by impulsivity. Here, we record single-neuron activity in the human medial temporal lobe (MTL) to explore decision and delay coding. We identify neurons that predict upcoming decisions in the amygdala and hippocampus. Neurons in the entorhinal cortex and hippocampus encode reward delays, with particularly hippocampal population activity coding prospective temporal periods. Importantly, neuronal activity in impulsive individuals shows diminished prospective temporal coding and predicts decisions only shortly before choices are reported. Our findings reveal how distinct MTL regions contribute to intertemporal decisions and provide insight into the neuronal signatures underlying impulsivity.

How the spatial organization of synapses contributes to stable learning remains a fundamental question in neuroscience. Using the H01 electron microscopy connectome of human temporal cortex, we found that dendritic spines clustered morphologically, whereas synaptic weights followed a center-elevated, surround-suppressed arrangement along dendrites. A regularized Hebbian model formalized this spatial signature, showing that strong synapses lower the probability that neighboring synapses reach high-weight states. Translating this principle into Spatial Synaptic Regularization (SSR) reduced forgetting and stabilized learning across diverse artificial networks and tasks, including continual visual learning, large language-model knowledge editing, and parameter-efficient adaptation of vision-language models, by preserving high-rank, low-overlap representations. These findings identify spatial synaptic organization as an unrecognized dimension for stabilizing learning and show that structural connectomics can yield actionable AI methods.

Dense electron-microscopy connectomes provide synaptic-resolution maps of neuronal structure and wiring, but learning scalable representations that integrate structure and connectivity for connectome discovery with minimal human intervention remains difficult. Here we present a self-supervised framework for structure-connectivity representation learning in dense connectomes. A hierarchical graph neural network with skeleton decomposition enables contrastive learning from finely sampled FlyWire neuronal skeletons, showing that fine skeletons preserve substantially richer identity information than coarse representations. Coordinate-free topology reduces developmental and geometric confounds, improving clustering and label-efficient inference. We then use learned structural embeddings as continuous descriptors of synaptic partners to construct structure-driven connectivity representations, improving subtype discrimination without predefined partner-type labels. Iterative multi-hop learning further reveals higher-order organization, including hemispheric connectivity lateralization and connectivity-defined subgroups. Attention analysis links these differences to specific synaptic partners. Together, these results establish a self-supervised and scalable framework for discovering neuronal identity and connectome organization in a large-scale dense connectome.

The ability to derive neural-level language coding models holds great scientific and clinical potential. Current approaches are limited by the scale and ethological validity of input data; applications requiring large, rare, or naturalistic samples in particular would benefit from the ability to infer neural coding from incidental everyday speech. Here we present a novel pipeline designed to leverage spontaneous and incidental naturalistic speech. This pipeline performs transcription, segmentation, and video-assisted diarization, as well as alignment and spike detection of neural data. We apply this pipeline to a dataset derived from 21 patients (6+ days each, over 800 hours and 5 million words total). We benchmark both encoding and decoding models against extensive and rare ground-truth control datasets consisting of human-curated word-level temporal alignment and manually sorted spikes. We further validate our approach by quantifying representational drift, effect of dataset size, and differences between six brain areas. Together, these findings demonstrate that incidental natural speech is sufficiently processed in the brain to enable the estimation neural-level embeddings.

Many neural recordings have revealed low-dimensional sets of behaviorally relevant variables encoded within large-scale neural activity patterns. However, dimensionality reduction analyses alone cannot yield causal explanations for how networks stably implement computations that are resilient to the substantial variability of single neuron dynamics. Further, existing methods for dimensionality reduction often rely on simplifying assumptions about network structure that limit their applicability and explanatory power. To provide a theoretical framework describing the dynamics of low-dimensional computation in high-dimensional neural networks, here we introduce the concept of latent processing units (LPUs), which are architecture-agnostic computational elements operating within biological neural circuitry. Six theorems governing coding and computation by LPUs collectively provide explanations for a range of common biological findings: low-dimensional sets of coding variables can generate high-dimensional neural dynamics; many neurons have activity patterns that represent behaviorally relevant variables but exert little influence on downstream circuits; linear readouts of neural population activity commonly permit near-optimal decoding; the drift of neural representations is often substantial even while network computations remain intact. Overall, our treatment of LPUs, as enacted in network dynamics, unifies the geometric and dynamical views of neural computation under a joint framework and provides systems neuroscience with a causal account of how the brain executes reliable computations.

Episodic memory formation engages hippocampal oscillations that vary along the anterior-posterior axis, but the molecular programs supporting this physiological specialization remain unclear. Here, we leveraged a rare neurosurgical dataset in which patients performed verbal episodic memory tasks during intrahippocampal intracranial EEG recordings prior to en bloc hippocampal resection, enabling integration of encoding-related oscillatory signatures with matched cell-type-resolved transcriptomics from the same individuals. Subsequent memory effects (SMEs) spanned delta/theta, gamma, and hippocampal ripple activity across anterior and posterior hippocampus. Single-nucleus RNA sequencing from anatomically matched anterior and posterior tissue revealed longitudinal transcriptional gradients, most prominent in excitatory neurons. Spatial transcriptomic maps validated axis-enriched transcripts and their localization. Linking subject-specific SMEs to gene expression identified distinct molecular programs: anterior low frequency SMEs associated with synaptic and chromatin-regulatory pathways, and posterior high-frequency SMEs associated with metabolic and protein synthesis processes. Gene regulatory network inference further revealed axis-specific hub architectures. Together, these results define a cell-type-specific genetic architecture linking longitudinal molecular specialization to the human hippocampal encoding dynamics.

We utilize internal representations of meaning for two purposes: to understand the words we hear and to generate our own speech. This dual requirement necessitates abstract, modality-agnostic representations. Building on work identifying it as a hub for relational mapping, we hypothesized that the hippocampus supports abstract, cross-person representations, and uses shared semantic geometries to do so. We tested this hypothesis by examining hippocampal activity in a remarkable single-neuron dataset derived from conversational speech. Neurons robustly encoded meanings of both spoken and heard words, and used common geometric embeddings for both, leading to abstract meaning performance. Speaker identity was aligned with meaning via partial subspace alignment, which affords speaker-meaning binding by partitioning meaning by speaker while maintaining cross-speaker generalization. Degrees of subspace rotation varied on a single word level and depended systematically on semantic category. Together, these findings indicate how geometric principles allow for abstract cross-personal meanings while preserving binding to speaker identity.

Brain areas differ in their inherent susceptibility to focal seizures, but the principles governing this risk remain unclear. While prior work has focused on anatomical and physiological factors, here we observed a fundamental contribution from the computations performed by the underlying neural network. Handcrafted and trained recurrent neural networks supporting continuous representations respond to seizure perturbations with higher activity and earlier performance decline relative to matched networks stabilizing discrete, well-separated states. Consistent with this prediction, in vivo recordings revealed that medial entorhinal cortex, whose grid cells exhibit continuous attractor dynamics, drives acute epileptiform discharges with stronger involvement and smoother state trajectories compared to CA3, a hippocampal subfield associated with discrete memory storage. Moreover, selective synaptic silencing demonstrated that this difference in seizure responses depends on intact entorhinal connectivity. Thus, the computations that enable neural networks to process information also influence their vulnerability to pathological transitions.

The brain refines its predictions of the world by updating its internal model whenever sensory input differs from expectation. The sign of this prediction error matters: an unexpected event signals that the model under-predicted (positive error), while a predicted event that fails to occur indicates that the model over-predicted (negative error), and the two should drive opposite synaptic changes. How cortical circuits represent error sign in spiking activity, and how that representation translates into synaptic learning, remain unresolved. We propose the Signed Error by Timing Asymmetry (SETA) model, in which the sign of a prediction error is encoded by when layer 2/3 neurons fire relative to a brief plasticity window in their layer 5 targets. Chandelier cells, an inhibitory cell type recruited by the prediction, impose a temporal clamp on layer 2/3 output: positive errors escape the clamp and arrive within the synaptic potentiation window, while negative errors are released only after the clamp decays and arrive later, during the synaptic depression window. The same circuit, therefore, biases downstream synapses toward either potentiation or depression depending on the prediction-error sign. We demonstrate this signed-error computation in a reduced two-compartment model, test SETA-specific predictions using in vivo recordings from mouse visual cortex, and examine how E/I imbalance leads to pathological consequences in predictive coding.

Adaptive behavior requires sensory systems to prioritize cues that predict meaningful outcomes while suppressing irrelevant stimuli, but how relevance-based filtering is implemented along early sensory pathways remains unclear. Using deep brain two-photon imaging and causal circuit manipulations in mice performing an audiovisual detection task, we show that inhibition from thalamic reticular nucleus dynamically tunes sensory thalamus according to learned value. As animals learned stimulus-outcome associations, neurons in medial geniculate body developed biased responses favoring reward-predicting cues and suppressing non-rewarded stimuli. Silencing inhibitory input from thalamic reticular nucleus broadly disinhibited thalamic responses and abolished this value bias. Notably, stimulus identity decoding was unaffected by loss of inhibition. However, the geometry of MGB population activity was reorganized: coding axes rotated, action-related coding was strongly impaired, and thalamic representations became misaligned with learned behavioral readout. This altered population code impaired behavioral performance despite preserved sensory separability. Thus, inhibition of sensory thalamus by the reticular nucleus does not simply gate sensory throughput; it acts as a subcortical coding mechanism that aligns neuronal representations with learned value and behavioral goals, with implications for disorders of perception and cognition.

The infralimbic cortex (IL) has been described as both facilitator and terminator of reward-seeking, yet both functions appear mutually exclusive. We reconcile this discrepancy by proposing a dual-ensemble model: one for action-outcome prediction and another for prediction correction. Using longitudinal miniscope imaging in rats, we show that during training, the action-outcome predictor ensemble sharpens its activity by suppressing the majority of the IL neuronal population. As contingencies stabilize, entropy decreases, reflecting a refined predictive model. During extinction, the prediction correction ensemble activates in response to reward omission. A computational simulation based on these two ensembles successfully recapitulated our experimental results. This framework reconciles conflicting models, where IL manipulation can either accelerate or abolish extinction by demonstrating that IL activity dynamically encodes predictive accuracy. Our findings indicate that IL functions as an action-outcome predictor that elegantly guides behavioural flexibility.

How much machinery does a network need to memorize and recall discrete sequences when constrained to a biologically plausible substrate? We address this question using 50 short monophonic melodies in 4/4, used only as a controlled sequence-memory benchmark. Each beat is encoded with two clean one-hot populations - a 12-way pitch code and a separate 2-way {onset, sustain} code - and the decoder emits the same 14-dimensional code, so the autoregressive loop closes in a single neural format. The model obeys Dales law: latent units are excitatory or inhibitory, synaptic weights are non-negative, the decoder is implemented as explicit multi-contact bundles, and the encoder is a frozen sparse random projection wired at cortical ([~]10%) density. On this substrate, a local ENGRAMMER signed-XOR read-out rule combined with a sparse k-winner-take-all code stores the training corpus exactly. With a modest latent expansion (L = 512), the model reaches 100% teacher-forced and autoregressive pitch accuracy, recognizes all training melodies, and separates all held-out melodies as novel with zero overlap. Ablations show that the signed error, sparse code, explicit E/I routing, and multi-contact synapses are the main load-bearing ingredients, whereas learning the encoder is strongly detrimental and dense input wiring does not help. Capacity sweeps show that Dales law mainly increases the capacity required for stable autoregressive recall: teacher-forced storage saturates between L = 128 and L = 256, while free-running recall becomes perfect by L = 512. A matched random corpus reaches the same final fidelity and is recalled at least as well at every capacity, indicating that musical structure does not improve recall on this benchmark and that final fidelity is set by capacity rather than by structure. The result is a Dale-compliant, gradient-free sparse associative memory rather than a general sequence learner.

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.

Exogenous opioids that activate mu-opioid receptors (MORs) in nociceptive circuits mediate transient pain relief lasting minutes to hours but have more limited utility for treating chronic pain. By comparison, electrical or magnetic stimulation of the motor cortex can induce pain relief lasting weeks, for which the underlying mechanisms have remained unclear. Here we report an unconventional role for endogenous opioidergic signaling in the rapid induction of long-lasting analgesia from motor cortical stimulation, which triggers opioid-peptide-dependent neural plasticity in the rostral ventromedial medulla (RVM), a key node in the brain's descending pain control pathways. To dissect the circuit and cellular bases for these effects, we created a miniaturized, millimeter-sized device allowing focal, non-invasive transcranial magnetic stimulation (TMS) of the mouse motor cortex. In mice with chronic neuropathic pain, reflexive and affective pain behaviors diminished for 1-2 weeks after one session of TMS treatment. Chemogenetic and optogenetic manipulations showed that motor cortical layer 5 pyramidal neurons with axonal projections to the RVM mediated TMS-induced pain relief. High-density electrophysiological recordings revealed that TMS treatment shifted the balance of RVM activity between pain-ON and pain-OFF neurons to a state promoting greater suppression of pain. Genetic and neuropharmacological manipulations revealed that NMDA-receptor-dependent signaling and MOR activation by endogenous opioid peptides in the RVM jointly mediate the long-lasting analgesia induced by a transient bout of TMS. Strikingly, enkephalinase inhibition in the RVM during TMS treatment enhanced the amplitude and duration of analgesia, showing that transiently boosting endogenous opioidergic signaling during TMS increases analgesia-conferring plasticity. In accord, re-analyses of data from human subjects with chronic pain support the idea that opioid administration amplifies analgesia from motor cortical TMS. Overall, our results showcase miniaturized TMS devices as versatile tools for basic and translational neuroscience and detail a hybrid, long-range neural network and NMDA- and opioid-receptor-dependent plasticity mechanism for durable pain relief. These findings point the way to mechanistically grounded, synergistic neurostimulation and drug therapies for brain diseases and disorders that jointly target neural circuit and molecular signaling pathways.

Chronic sleep disruption and cognitive deficits are debilitating long-term consequences of traumatic brain injury (TBI), yet the endogenous mechanisms that drive network-level recovery remain poorly understood. Here, we demonstrate that during the chronic phase post-injury, network oscillations critical for memory, including slow oscillations, delta waves, and sleep spindles, undergo precise adaptive tuning that sustains sleep-dependent memory consolidation. This functional circuit resilience requires Semaphorin-3A (Sema3a); loss of Sema3a function prevents the protective reorganization of non-rapid eye movement (NREM) sleep architecture, impairs sleep-dependent memory consolidation, and exacerbates cortical lesion size. Together, these findings identify Sema3a as an innate protective mechanism that preserves sleep architecture and cognitive function following TBI.

Reward predictions are critical to both adaptive learning and decision making. Such predictions are supported by environmental cues that signal the availability and identity of rewarding events. Here we used fiber photometry, cell-type and pathway-specific optogenetic inhibition, Pavlovian cue-reward conditioning, and decision-making tests in male and female rats to reveal that ventral tegmental area dopamine (VTADA) projections to the basolateral amygdala (BLA) support cue-reward predictions. Reward-predictive cues trigger dopamine release in the BLA that encodes the value of the predicted reward. This cue-evoked VTADA[->]BLA activity mediates the ability of cue-reward predictions to bias action selection and adapt cue-response decisions based on the current value of the predicted reward. Cue-evoked VTADA[->]BLA activity also mediates the constraining influence of cue-reward predictions on new learning. Thus, cue-evoked BLA dopamine supports the reward predictions that both enable adaptive decision making and constrain learning.