Neuron最新文献

Neuronal firing patterns have significant spatiotemporal variability with no agreed-upon theoretical framework. Using a combined experimental and modeling approach, we found that spike interval statistics of excitatory neurons in the mammalian forebrain are dominated by a universal low-rate ("ground state"; GS) mode, with irregular spiking at neuron-specific rates. In contrast, when firing rates are increased during intrinsic network patterns or in response to stimuli, spiking across neurons is temporally coordinated with more regular spiking patterns in a region- and brain-state-specific manner. We demonstrate the generality of this distinction in six forebrain areas and show that the majority of spikes in all regions are emitted in the GS mode, emphasizing its physiological importance. We hypothesize that GS spiking maintains persistent neuronal dynamics.

Past work has reported inverted-U relationships between arousal and auditory task performance, but the underlying neural network mechanisms remain unclear. To make progress, we recorded auditory cortex activity from behaving mice during passive tone presentation and simultaneously monitored pupil-indexed arousal. In these experiments, the neural discriminability of tones was maximized at intermediate arousal, revealing a neural correlate of the inverted-U. We explained this arousal-dependent sound processing using a spiking model with clusters. In the model, stimulus discriminability peaked as the network transitioned from a multi-attractor phase exhibiting slow switching between metastable cluster activations (low arousal) to a single-attractor phase with uniform activity (high arousal). This transition also qualitatively captured arousal-induced reductions of neural variability observed in the data. Altogether, this study elucidates computational principles to explain interactions between arousal, neural discriminability, and variability and suggests that transitions in the dynamical regime of cortical networks could underlie nonlinear modulations of sensory processing.

The core use of human language is to send complex ideas from one mind to another. In everyday conversations, comprehension and production are intertwined, as speakers and listeners alternate roles. Nonetheless, the neural systems underlying these faculties are typically studied in isolation, using paradigms that cannot capture interactive communication. Here, we used fMRI hyperscanning to simultaneously record dyads engaged in real-time conversations. We used language model embeddings to quantify the degree to which production and comprehension systems rely on shared neural representations, both within and across brains. We found that both processes key into overlapping neural systems, with similar neural tuning for both processes, spanning the cortical language network. Speaker-listener coupling extended beyond the language network into areas associated with social cognition. Our results suggest that the neural systems for speech comprehension and production align with common linguistic features encoded in a broad cortical network for language and communication.

Ralhan et al.¹ describe how lipidated particles of apolipoprotein E (ApoE) isoforms ApoE2 and ApoE3-Christchurch protect neurons from oxidative stress through the efflux of unsaturated and oxidized lipids via ABCA7. This mechanism ameliorated multiple dysfunctions observed in ApoE4 models.

Recent advances in three-dimensional single-cell-resolution imaging have begun to link organ-wide and cellular-level research in development and disease. Although powerful, whole-organ imaging remains limited by the inability to stain a broad range of molecular markers and by the lack of an analytical scheme to precisely quantify cell populations. Here, we present a highly multiplexed whole-mount staining technique, utilizing the repeated application of fluorescence in situ hybridization. This technique, termed mFISH3D, enables the visualization of 10 types of mRNAs in an intact mouse brain and has been demonstrated in various biological specimens, including the human brain. To achieve higher levels of accuracy in spatial cell mapping, we developed an artificial intelligence (AI)-driven workflow that reduces the need for extensive manual annotations. This integration provides a systematic framework for analyzing complex cellular ecosystems across large tissue volumes and enables the comprehensive investigation of selective cellular vulnerabilities in disease.

In this issue of Neuron, Papadopoulos et al.¹ demonstrate that stimulus encoding accuracy in auditory cortex rises and then falls with increasing arousal. Their model of stimulus-induced transitions between discrete, arousal-dependent attractor states of spiking neurons successfully accounts for their data.

Calneuron-1 is a Ca²⁺ sensor that has been linked in several genome-wide association studies to schizophrenia (SCZ). We show that calneuron-1 expression is elevated in the dorsolateral prefrontal cortex of SCZ patients and that overexpression in the medial prefrontal cortex (mPFC) of mice elicits SCZ-related behavioral disabilities, disrupts rhythmogenesis within the mPFC, impairs functional connectivity between the hippocampus and the mPFC, and causes deficits in muscarinic synaptic plasticity. These neurophysiological signatures of SCZ are linked to the role of calneuron-1 as an accessory subunit of muscarinic M1 receptors (M1Rs). Calneuron-1 displaces Gαq₁₁ from the third intracellular loop of M1R at elevated [Ca²⁺]_i, thereby disrupting downstream signaling. The M1R agonist xanomeline, shown to reduce positive and negative symptoms of SCZ and recently approved for clinical use, impedes this calneuron-1/M1R interaction, which leads to restoration of G-protein coupling, muscarinic synaptic plasticity, and network communication. Collectively, our data indicate a potential causative pathomechanism of SCZ.

Sensory processing enables adaptive behavior by accurately encoding dynamic environmental stimuli. Within thalamocortical (TC) circuits, the thalamic reticular nucleus (TRN) functions as a key inhibitory gate that regulates cortical access to sensory input. While classical models posit that sensory circuits stabilize after early critical periods, we uncover a previously unrecognized phase of synaptic refinement in TRN circuitry extending from the juvenile period into adulthood. This late-stage remodeling is driven by a progressive reduction in corticothalamic (CT) excitatory input and is essential for enhancing sensory gain, response linearity, and stimulus discriminability. We identify LRRTM3, a TRN-enriched synaptic adhesion molecule, as a molecular gatekeeper of this process. TRN-specific deletion of LRRTM3 disrupts CT-TRN refinement, elevates TRN-mediated inhibition, and impairs fine tactile discrimination. These findings revise canonical views of sensory circuit maturation, revealing that LRRTM3-mediated juvenile-to-adult TRN plasticity is essential for the emergence of high-resolution sensory encoding in the adult brain.