eNeuro最新文献

A possible mechanism of temporal lobe epilepsy is insufficient inhibition of hippocampal dentate granule cells. Precipitating injuries that kill interneurons in the dentate gyrus might result in fewer inhibitory synapses with granule cells. To test this hypothesis, previous studies evaluated numbers or densities of interneurons, γ-amino butyric acid (GABA)ergic boutons, and inhibitory synapses in tissue from human patients with temporal lobe epilepsy and rodent models. However, those studies have limitations. Some of those limitations can be addressed by a large animal model. Sea lions (Zalophus californianus) can develop temporal lobe epilepsy naturally. Like humans, epileptic sea lions exhibit bilateral or unilateral hippocampal sclerosis (neuron loss) with granule cell vulnerability, but sea lions permit optimal tissue preservation and sampling, and good control subjects. To label interneuron cell bodies and GABAergic synaptic boutons, sea lion hippocampal tissue from both sexes was processed with immunohistochemistry for glutamic acid decarboxylase (GAD) and vesicular GABA transporter. Stereological techniques were used to evaluate the dentate gyrus of the entire hippocampus. Numbers of granule cells, GAD cells, and GABAergic boutons were substantially reduced in shrunken, sclerotic hippocampi. However, numbers of GABAergic boutons and granule cells were correlated. These findings indicate that, despite losses, numbers of GABAergic boutons scale with numbers of surviving granule cells.

Ras-related C3 botulinum toxin substrate 1 (Rac1) is a small GTPase that regulates actin cytoskeleton dynamics and synaptic plasticity. Rac1 has been implicated in active forgetting, but whether it also constrains the consolidation of new memories remains unclear. Here we show that systemic administration of the Rac1 inhibitor 1A-116 after training in the novel object recognition task markedly extends memory persistence in rats. A single post-training injection of 1A-116 enhanced recognition memory for at least 28 days without altering locomotor- or anxiety-related behaviors. When given after a brief, sub-threshold training session that normally supports only short-term memory, 1A-116 enabled long-term retention that required hippocampal protein synthesis. This promnesic effect was time-dependent, independent of sex, and consistent with Rac1 acting as a negative regulator of memory consolidation rather than merely promoting forgetting. These findings indicate that Rac1 activity after learning limits the consolidation process itself, functioning as a molecular brake on recognition memory stabilization, and suggest that its inhibition may represent a therapeutic avenue to enhance cognitive durability in both healthy and pathological conditions.Significance Statement Memory persistence is shaped by both consolidation and active forgetting, yet the molecular constraints that determine how long a memory lasts remain partially understood. We demonstrate that Rac1, a small GTPase involved in actin remodeling, serves as a negative regulator of hippocampal-dependent recognition memory consolidation. Pharmacological inhibition of Rac1 after learning not only enhances retention but also enables long-term memory formation from sub-threshold training through a hippocampal protein synthesis-dependent mechanism. These findings identify Rac1 activity as a molecular brake on memory stabilization and suggest that its inhibition may enhance cognitive persistence and resilience against age- or disease-related decline.

Charcot-Marie-Tooth disease (CMT) is an inherited peripheral neuropathy characterized by sensory dysfunction and muscle weakness, manifesting in the most distal limbs first and progressing more proximal. Over a hundred genes are currently linked to CMT with enrichment for activities in myelination, axon transport, and protein synthesis. Mutations in tRNA synthetases cause dominantly inherited forms of CMT and animal models with CMT-linked mutations in these enzymes display defects in neuronal protein synthesis. Rescuing protein synthesis in CMT mutant neurons could offer exciting therapeutic options beyond symptom management. To address this need, we expressed CMT-linked variants in tyrosyl tRNA synthetase (YARS-CMT) in primary mouse sensory neurons derived from both male and female embryos and evaluated impacts on protein synthesis and cell viability. YARS-CMT expression reduced protein synthesis in these neurons prior to the onset of caspase-dependent axon degeneration and cell death. To determine how YARS-CMT expression affects axon outgrowth, we dissociated and replated these neurons to stimulate axon regeneration. To our surprise, axonal regrowth occurred normally in replated YARS-CMT neurons. Moreover, replating YARS-CMT neurons rescued protein synthesis. Inhibiting mTOR suppressed rescue of protein synthesis after replating, consistent with its significant role in protein synthesis during axon regeneration. These discoveries identify new avenues for augmenting protein synthesis in diseased neurons and restoring protein synthesis in CMT or other neurological disorders.Significance statement Peripheral neuropathies represent a challenging threat to human health, impacting quality of life for millions with limited treatment options beyond symptom management. Charcot-Marie-Tooth Disease (CMT) is the most common inherited peripheral neuropathy with some causative mutations identified in an enzyme family of tRNA synthetases. We use a cellular model of this disease to understand the mechanistic basis of CMT and identify novel ways to protect neuron function. We observe severe defects in protein synthesis in our model followed by axon degeneration. Most importantly, we rescue protein synthesis by stimulating a regenerative growth program in these neurons which promotes normal axon elongation despite CMT mutations. Restoring protein synthesis will have broad relevance to many neurological disorders and warrants additional investigation.

Science education is traditionally framed as a driver of scientific literacy and economic growth. However, emerging evidence suggests that it may also function as a contributor to public health by shaping brain health across the lifespan. In this invited commentary, I synthesize findings from human and animal studies to examine how enriched, inquiry-based educational experiences intersect with neural processes underlying cognitive development, stress regulation, executive function, and social-emotional well-being. This synthesis is guided by the principle of cognitive compassion, which emphasizes the design of learning environments that support both cognitive and emotional needs. Research on neuroplasticity, stress biology, and motivation indicates that learning contexts characterized by curiosity, emotional safety, and active engagement are associated with adaptive neural function and long-term cognitive resilience. Drawing on empirical literature and illustrative translational observations from educational and community science contexts, I propose that science education can be conceptualized as a population-level contributor to brain health. Framing education through a brain health lens has implications for educational policy, teacher professional development, and public investment in learning environments, particularly in underserved settings. This perspective positions education not only as a mechanism for knowledge transmission but also as a modifiable environmental factor that supports neural and societal resilience.

We lack a mechanistic understanding of how cortical contributions to balance control change in aging and Parkinson's disease (PD). Balance is governed by brainstem circuits, with higher-order centers like the cortex or basal ganglia becoming engaged as challenge increases or balance health declines. We previously showed that parallel sensorimotor feedback loops engaging brainstem and cortical circuitry contribute to muscle activity for balance control in young adults (YAs). Here, we analyze data from male and female older adults (OAs) with and without PD, decomposing perturbation-evoked tibialis anterior and medial gastrocnemius muscle activity into hierarchical components based on latencies of feedback control loops. We found that balance-correcting muscle activity followed a stereotypical waveform of long-latency responses (LLRs): LLR1 began ∼120ms and LLR2 occurred ∼210ms, respectively, consistent with subcortical and cortical feedback latencies. Both LLRs increased with balance challenge and could be explained by center of mass kinematics. Perturbation-evoked antagonist muscle activity consisted of destabilizing and stabilizing components categorized based on whether they resist the kinematic errors that drive their activation. The destabilizing component occurred at ∼180ms and was negatively correlated with clinical measures of balance ability in the OA but not PD group. Exploratory comparisons showed OA and PD groups had larger LLR2s at lower challenge levels than YAs, consistent with greater cortical engagement during balance with aging. These findings demonstrate that a neuromechanical model can decompose perturbation-evoked muscle activity into hierarchical components related to clinical balance ability and identify mechanistic changes in the neural control of balance without direct brain measurements.Significance Statement We show that reactive balance recovery in older adults with and without Parkinson's disease can be decomposed into distinct components that reflect hierarchical brainstem, cortical, and basal ganglia feedback loops. Using a neuromechanical model of delayed task-level feedback control, we link these components to perturbation difficulty and clinical balance ability in older adults. This framework connects specific features of agonist and antagonist muscle activity to underlying neural control processes without requiring direct brain recordings. Our findings provide a mechanistic basis for age- and disease-related changes in balance control that can inform individualized assessment and future rehabilitation strategies.

The retina encodes a broad range of stimuli, adapting its computations to features like brightness, contrast, and motion. However, it is unclear whether it also adapts when switching between natural scenes and white noise. To address this, we analyzed the neural activity of male marmoset retinal ganglion cells (RGCs) in response to white noise and naturalistic movies. We trained linear-nonlinear models on both stimuli, evaluated their performance, and compared their receptive fields across stimulus domains. We found that models with spatial filters trained on one stimulus ensemble were less accurate when predicting neural activity on the other compared to models trained directly on the target stimulus. This suggests that spatial processing adapts to stimulus statistics. Different RGC types exhibited distinct changes: The OFF midget cells' receptive fields became enlarged under natural movies, resulting in a lower cutoff frequency. Parasol cells and large OFF cells did not significantly change their receptive field sizes. All cell types exhibited stronger surrounds under natural movies, resembling the whitening filters predicted by efficient coding for stimulus decorrelation, prompting us to test whether these changes were related to the different spectral content of the two stimulus types. Quantifying the effects of the filters' enhanced surrounds on the stimulus power spectrum showed a significant contribution towards whitening only in ON parasol cells, where a whitening effect emerged regardless of the training stimulus. These results suggest that while RGCs adapt to the differences between white noise and natural movie stimuli, efficient coding can only partially account for this adaptation.Significance statement Natural scenes differ from artificial stimuli in many properties, including spatial frequency structure. How the retina adapts to these differences remains unclear. To explore this, we studied responses of four primate retinal ganglion cell types to white noise and natural stimuli and compared their receptive field properties. We found that midget cells enlarge their receptive field centers and strengthen their surrounds under natural stimulation, whereas others show enhanced surrounds without center size changes. These modifications qualitatively match predictions of efficient coding based on differences in stimulus power spectra. However, in three of four cell types, stronger surrounds did not substantially whiten responses to natural movies, contrary to theoretical expectations. Thus, efficient coding alone cannot fully account for retinal adaptation mechanisms.

Implicit adaptation recalibrates movements based on sensory prediction errors. It is often characterized as automatic and resource-independent, suggesting that it is insulated from cognitive influence. Here, we asked whether implicit adaptation is sensitive to goal-directed attentional demands imposed by a concurrent visual task. Across two experiments, we used clamped visual feedback to measure implicit adaptation while human adults (49 females, 23 males) monitored a rapidly changing visual stream for targets. In Experiment 1, participants performing the visual task showed modest early enhancement in implicit adaptation relative to a single-task control condition. In Experiment 2, adding response-contingent feedback to the visual task led to stronger and more sustained enhancement. Visual task accuracy and implicit adaptation were uncorrelated, arguing against resource competition. Model-based analyses revealed elevated error sensitivity under dual-task conditions, with individual differences reflecting an inverse relationship between error sensitivity and retention. These patterns are compatible with arousal-mediated modulation of cerebellar error processing and hierarchical models of cerebellar learning. Together, these findings suggest that implicit adaptation is automatic but not autonomous: while it operates outside voluntary control, it appears open to the physiological states in which errors are experienced.

Harmonicity is a property of complex sounds such as vocalizations or music, but it remains unclear how harmonicity is processed in the auditory cortex (ACtx). Subregions of ACtx are thought to process harmonic stimuli differently. Selective responses to sound features in ACtx emerge hierarchically from primary ACtx (A1) L4 and secondary ACtx (A2) layer (L)2/3, which is believed to be the most responsive to harmonic sounds. Since harmonic stacks can range from 2 to >10 components, being more similar to naturalistic vocalizations, harmonic sensitivity might also arise hierarchically across layers and areas. We studied responses to harmonic stacks of 2-10 frequencies across A1 L4, A1 L2/3, and A2 L2/3 in adult male and female mice using in vivo two-photon microscopy. We found harmonic-sensitive neurons (HNs) responding only to harmonic stacks but not to individual frequencies in all areas at similar proportions. HNs showed highly nonlinear spectral integration of harmonic frequencies that decreased as the harmonic stacks became more complex. Specifically, onset-biased HNs showed greater nonlinearity than offset-biased HNs only in A1 L4. Moreover, HNs in A1 L4 exhibited higher signal correlation than A2 L2/3. Sound-responsive neurons in A1 L4 have the weakest noise correlation compared with A1 L2/3 and A2 L2/3. Together, harmonic sensitivity is not a unique feature of A2 L2/3 but is already established in A1 L4, where neurons robustly encode harmonic sounds through sparse connections.

The current treatment for drug-resistant epilepsy is surgical intervention, which relies on accurate identification of the seizure onset zone (SOZ) using intracranial electroencephalography (iEEG) data. iEEG analysis with computational epileptogenic zone identification algorithms (CEZIAs) is a promising step toward better SOZ localization and surgical outcomes. A key step in validation and adoption of CEZIAs is to allow for widespread shared development and validation of code and data. To achieve this, we developed an ecosystem of seizure localization methods that includes a straightforward analysis pipeline, standardized data formatting and storage, and completely documented and open-source code. The TableContainer package provides standardized storage of tabular data and serves as a foundational data structure for the ecosystem. Building on this, the Epoch package enables cropping, resampling, and visualization of iEEG data and provides publicly downloadable datasets for reproducibility. The public iEEG dataset includes eight females and six males, with a total of 47 iEEG recordings. Finally, the EZFragility package uses these two foundational packages to analyze iEEGs for SOZ localization using the Neural Fragility method described by Li et al. (2021) Additionally, EZFragility provides improvements in computational efficiency and user experience. It accurately reproduces neural fragility results for both sample patients used in the original paper. This project serves as the first step toward building an open-source, reproducible ecosystem of seizure localization methods in R. Future steps include the addition of other CEZIAs using the framework and sample data already made available by these packages.

Rapid advancements in artificial intelligence (AI) have enabled text-to-speech (TTS) systems to produce voices increasingly indistinguishable from humans, posing significant societal risks, particularly through potential misuse in fraud and deception. To address this concern, this study combined behavioral assessments and neural measures using electroencephalography (EEG) to examine whether short-term perceptual training enhances people's ability to distinguish AI-generated from human speech. Thirty participants (of either sex) listened to sentences produced by human speakers and corresponding AI-generated clones, judging each sentence as either human or AI-generated before and after a brief (∼12 min) training session, during which voices were explicitly labeled as "human" or "AI." Behaviorally, participants showed consistently poor discrimination before and after training, with only minimal improvement. However, neural analyses revealed substantial training-induced changes. Specifically, temporal response function (TRF) analysis identified significant neural differentiation between speech types at early (∼55 ms, ∼210 ms) and later (∼455 ms) auditory processing stages following training. Additional EEG analyses, including spectral power and decoding, were conducted to further investigate training effects, but these measures revealed limited differentiation. The findings here highlight a dissociation between behavioral and neural sensitivity: while listeners struggle to behaviorally discriminate sophisticated AI-generated voices, their auditory systems rapidly adapt to subtle acoustic differences following short-term exposure. Understanding this neural-behavioral dissociation is crucial for developing effective perceptual training protocols and informing policies to mitigate societal threats posed by increasingly realistic synthetic voices.