bioRxiv Subject Collection: Neuroscience's Journal

The deubiquitinase OTULIN regulates tau expression and RNA metabolism in neurons
The degradation of aggregation-prone tau is regulated by the ubiquitin-proteasome system (UPS) and autophagy, which are impaired in Alzheimers disease (AD) and related tauopathies causing tau aggregation. Protein ubiquitination with linkage specificity determines the fate of proteins that can be either degradative or stabilization signals. While the linear M1-linked ubiquitination on protein aggregates is a signaling hub that recruits various ubiquitin-binding proteins for coordinated actions of protein aggregates turnover and inflammatory NF-kB activation, the deubiquitinase OTULIN counteracts with the M1-linked ubiquitin signaling. However, the exact role of OTULIN on tau aggregate clearance in AD is unknown. Based on our bulk RNA sequence analysis, human inducible pluripotent stem cell (iPSC)-derived neurons (iPSNs) from an individual with late-onset sporadic AD (sAD2.1) show downregulation of ubiquitin ligase activating factors (MAGEA2B and MAGEA) and OTULIN long non-coding RNA (lncRNA-OTULIN) compared to healthy control WTC11 iPSNs. In sAD2.1 iPSNs, downregulated lncRNA-OTULIN is inversely correlated with increased levels of OTULIN protein and phosphorylated tau at p-S202/p-T205 (AT8), p-T231 (AT180), and p-S396/p-S404 (PHF-1). Loss of OTULIN deubiquitinase function using pharmacological inhibitor UC495 or CRISPR-Cas9-mediated OTULIN gene knockout causes a significant reduction of total tau and phosphorylated tau at AT8 epitope in sAD2.1 iPSNs. Whereas in SH-SY5Y neuroblastoma cells, either treating with the UC495 compound or knocking out of the OTULIN gene causes a significant reduction of total tau at both mRNA and protein levels and consequently decreases phosphorylated tau at AT8, AT180, and PHF-1 epitopes. An additional bulk RNA sequence analysis of OUTLIN knockout SH-SY5Y shows a 14-fold down-regulation of tau mRNA levels and differential expression of many other genes associated with autophagy, UPS, NF-kB pathway, and RNA metabolism. Together, our results suggest for the first time a non-canonical function for OTULIN in regulating gene expression and RNA metabolism, which may have a significant pathogenic role in AD and related tauopathies.

Multi-Metric Quantitative MRI Identifies Spatially Distinct Age-Related Brain Differences in Female Bonnet Macaques
This study employs a data-driven, voxelwise analysis of high-resolution ex vivo quantitative MRI (qMRI) to examine age-related differences in brain morphometry and microstructure in female bonnet macaques. A binary classifier differentiated mid- and late-age groups, achieving the highest accuracy when integrating all MRI metrics rather than using diffusion or relaxometry alone. Diffusion-only and relaxometry-only classifiers revealed distinct, minimally overlapping spatial patterns, while the multi-metric approach captured a broader range of age-related differences. Tensor-based morphometry (TBM) differences were most pronounced in the neocortex, whereas the thalamus showed the highest classification accuracy despite minimal morphometric differences, suggesting unique tissue composition alterations. These findings highlight the complementary nature of diffusion, relaxometry, and morphometry qMRI metrics in aging research. Our results support the use of multi-parametric qMRI to identify age-vulnerable brain regions and highlight its potential for improving qMRI biomarkers in larger, longitudinal aging studies.

Amyloid fibrils in Alzheimer's disease differently modulate sleep and cortical oscillations in mice depending on the type of amyloid
Alzheimers disease (AD) is characterized by aggregation and deposition of the amyloid-beta (A{beta}) protein in patient brains, with aging playing a crucial role through oxidative stress and neuroinflammation. Sleep disturbances are common in patients with AD, and contribute to their cognitive impairment. However, the association between the aggregation of specific A{beta} species in particular brain regions and its effect on sleep impairment remains unclear. Here, we investigated the effect of A{beta}1-40 (A{beta}40) and A{beta}1-42 (A{beta}42) amyloid fibrils on sleep/wakefulness and cortical oscillations in 3-month old wild-type mice. A{beta}42 aggregated faster and demonstrated distinct structural properties compared with A{beta}40. Bilateral injections into the dentate gyrus of the hippocampus showed that A{beta}42 amyloid fibrils significantly disrupted sleep and cortical activity as well as caused neuronal death, whereas A{beta}40 amyloid fibrils mainly affected cortical oscillations and caused minimal neuronal death. These findings shed light on AD-associated sleep disorders, which are differentially affected by the distinct properties of A{beta}40 and A{beta}42 aggregates.

Suzetrigine (VX-548) exhibits activity-dependent effects on human dorsal root ganglion neurons
Non-selective antagonists of voltage-gated sodium channels (VGSCs) provide effective local analgesia, but systemic administration is fraught with unwanted cardiovascular and central nervous system toxicity. The development of antagonists targeting VGSCs preferentially expressed in peripheral neurons, such as NaV1.7, 1.8, and 1.9, could mitigate these risks. One such new small molecule that selectively inhibits NaV1.8, is orally bioavailable, and has recent FDA approval for the treatment of acute pain is suzetrigine (VX-548). Here we tested the effects of this agent on human dorsal root ganglion (hDRG) neurons obtained from either patients undergoing surgical thoracic vertebrectomy or from organ donors in parallel electrophysiology studies across 2 laboratories. Bath application of 10 nM suzetrigine abolished spontaneous discharges previously shown to be associated with on-going neuropathic pain within minutes. In contrast, suppression of discharges evoked by intracellular current injection required more prolonged application times. These results suggest that suzetrigine will likely show efficacy versus spontaneous pain but may have less robust effects on evoked or breakthrough pain.

Superior colliculus coordinates whole-brain dynamics for sudden unconscious visual insight
Recognition of Mooney images, highly degraded black-and-white stimuli, provides a classic paradigm for studying unconscious, sudden generative insight often accompanied by a "Eureka!" experience. This spontaneous recognition is believed to rely on top-down modulation by widespread cortical networks, a conclusion primarily drawn from contrasts between recognition and non-recognition. However, how the brain transitions from non-recognition to recognition remains unclear, particularly regarding the role of non-cortical regions such as the midbrain superior colliculus. Here we show that this transition can be captured by three data-driven large-scale functional clusters, reflecting stimulus-driven activation, suppression, and recognition-associated dynamics. Within these whole-brain dynamics, the superior colliculus displayed a distinct activation peak just before the behavioral response indicating recognition, and was the only region exhibiting mutual positive interactions with all three clusters. Although the superior colliculus has been recognized as a key node in binary perceptual decision-making in non-human primates, its contribution to human insight has remained largely unexplored. Our findings suggest that the superior colliculus coordinates large-scale neural activity for unconscious generative insight, revealing that evolutionarily conserved non-cortical structures may contribute directly to higher-level cognitive processes.

Lost in a Large EEG Multiverse? Comparing Sampling Approaches for Representative Pipeline Selection
The multiplicity of defensible strategies for processing and analysing data has been implicated as a core contributor to the replicability crisis, creating uncertainty about the robustness of a result to variations in data processing choices. This issue is exacerbated where a large number of data processing pipelines are defensible, and where there is great heterogeneity in the pipelines applied in the literature, such as in processing and analysing electroencephalography (EEG) signals. In a multiverse analysis, equally defensible pipelines are computed and the robustness of the result to these variations is reported. However, a large number of defensible pipelines is sometimes infeasible to compute exhaustively, and researchers rely on sampling approaches. In these cases, pipelines are sampled from the full multiverse and the robustness is reported across these samples, assuming that they are representative for the entire multiverse. However, different sampling methods may yield different robustness results, introducing what we term multiverse sampling uncertainty. To illustrate, we computed a 528-pipeline multiverse analysis on EEG-recordings during an emotion classification task aiming to predict extraversion scores from the Late Positive Potential. We applied three sampling methods (random, stratified, and active learning) to sample 26 pipelines (5%), and evaluated the results in terms of the representativeness of the distribution of model fits to that of the full multiverse. Our results highlight variability in the representativeness of the distribution of model fits between samples. The active learning sample most closely represented the median model fit of the full multiverse. The need for representative pipeline sampling to mitigate bias in large multiverse analyses is discussed.

CLOOSE: An open-source platform for optical closed-loop experiments
Brain-Computer Interfaces (BCI) have catalyzed advancements in both clinical applications and basic neuroscience research. However, technical barriers such as steep learning curves and complex synchronization requirements often impede their widespread adoption. In response to the increasing demand for optical BCI experiments and to the technical challenges associated with their implementation, we introduce CLOOSE (Closed loop Optical Open-Source Experiments), a versatile platform designed to standardize, simplify, and accelerate closed-loop experiments with functional imaging data. CLOOSE interfaces easily with any frame-based imaging system via TCP/IP, allowing for real-time data streaming and distributed computation. Benchmark tests validate CLOOSE real-time accuracy in image registration, signal processing, and analysis at 1 kHz imaging frequencies, making it the first optical BCI compatible with voltage indicators. Throughout the paper we showcase CLOOSE versatility in supporting several neurofeedback paradigms: from single neuron to population dynamics, multiplane imaging, and online (and offline) z-tracking. CLOOSE functionality is easily accessible to users with minimal coding experience through an intuitive graphical interface for experiment setup and real-time performance monitoring. By significantly lowering the barrier to performing technically demanding closed-loop experiments, CLOOSE advances the streamlining, standardization, and democratization of online image analysis and eurofeedback BCI paradigms, opening new avenues for precise manipulation and real-time readout of neuronal activity in health and disease.

The Maternal Effect of SKN-1B and DAF-7 on Intergenerational Pathogen Avoidance Learning in C. elegans.
Parental health strongly influences the development of embryos, and can ultimately influence behavior post-birth. When exposed to pathogenic Pseudomonas aeruginosa (PA14), C. elegans produces a robust immune response, as well as a learned behavior to avoid PA14 in the future. This learned avoidance of PA14 can be inherited by the F1 generation. An important trigger of the worms immune response to infection is the Nrf2 homologue, SKN-1. We find that the SKN-1B isoform strongly influences the intergenerational inheritance of PA14 avoidance in F1 animals via DAF-7 (TGF-{beta}) signaling from ASI. Functional SKN-1B is required in both the P0 and F1 generations to facilitate F1 avoidance of PA14 due to parental exposure. While inherited avoidance of PA14 was not observed in F2+ generations, the single generation learning investigated here may provide a parallel pathway to improve the fitness of F1 animals in a dynamic environment.

Brain Signal Variability During Rest as a Neural Mechanism Underlying Cognitive Reserve
BackgroundResting-state brain signal variability has been found to vary with age and cognitive function. Neural flexibility has been suggested as a neural mechanism underlying cognitive reserve (CR), a construct that describes better than expected cognition given brain status. Thus, we examined the associations between age, resting-state brain signal variability, cognition, and CR.

MethodAnalysis was based on resting-state functional neuroimaging data from 470 participants (aged 20-80 years) from the Reference Ability Neural Networks and the CR studies. Brain signal variability was quantified for each brain region as the log-transformed standard deviation of the time-varying blood-oxygen-dependent (BOLD) signal. We then derived variability patterns related to age, perceptual speed, fluid reasoning, episodic memory, and vocabulary using Scaled Subprofile Modelling principal component analysis. To perform the formal test whether these patterns fulfill the requirements for CR, we examined whether they explained additional variance in cognition beyond brain status, age, sex, and education, or moderated the brain status-cognition relationship. We additionally stratified all regression models by age (cutoff: 60 years) and sex.

ResultsBOLD signal variability showed an age-related increase in subcortical/medial brain regions, and an age-related decrease in cortical regions. It also met the CR test for speed (standardized regression coefficient ({beta})=0.251, 95% confidence interval (CI): 0.118-0.384, pFDR<0.001), episodic memory ({beta}=0.344, CI: 0.200-0.489, pFDR<0.001), reasoning ({beta}=0.316, CI: 0.197-0.436, pFDR<0.001), and vocabulary ({beta}=0.270, CI: 0.167-0.373, pFDR<0.001). Associations were stronger in women for vocabulary and in young individuals for reasoning.

ConclusionsBOLD signal variability plays a role in aging and cognition and underlies CR.

Unpacking similarity effects in visual memory search: categorical, semantic, and visual contributions
Visual memory search involves comparing a probe item against multiple memorized items. Previous work has shown that distractor probes from a different object category than the objects in the memory set are rejected more quickly than distractor probes from the same category. Because objects belonging to the same category usually share both visual and semantic features compared with objects of different categories, it is unclear whether the category effects reported in previous studies reflected category-level selection, visual similarity, and/or semantic target-distractor similarity. Here, we employed old/new recognition tasks to examine the role of categorical, semantic, and visual similarity in short- and long-term memory search. Participants (N=64) performed visual long-term memory (LTM) or short-term memory (STM) search tasks involving animate and inanimate objects. Trial-wise RT variability to distractor probes in LTM and STM search was modelled using regression analyses that included predictors capturing categorical target-distractor similarity (same or different category), semantic target-distractor similarity (from a distributional semantic model), and visual target-distractor similarity (from a deep neural network). We found that for both memory tasks, categorical, semantic, and visual similarity all explained unique variance in trial-wise memory search performance. However, their respective contributions varied with memory set size and task, with STM performance being relatively more strongly influenced by visual and categorical similarity and LTM performance being relatively more strongly influenced by semantic similarity. These results clarify the nature of the representations used in memory search and reveal both similarities and differences between search in STM and LTM.

Alzheimer Disease: The proposed role of tanycytes in the formation of tau tangles and amyloid beta plaques in human brain
Currently, the causes for Alzheimer Disease (AD) are thought to lie in the formation of abnormal protein deposits including tau tangles and Amyloid {beta} (A{beta}) plaques in the human cortex. These proteins are believed to accumulate in the brain due to impaired waste removal resulting in neurodegeneration. In an alternative hypothesis we have recently proposed the existence of an aquaporin-4 aqua channel (AQP4)-expressing tanycyte-derived canal network that likely internalizes waste for removal from the brain. We propose that both A{beta} and tau protein may play important structural roles in this canal system. In support of this hypothesis, we demonstrate the formation of waste-internalizing receptacles by AQP4-expressing myelinated tanycytes. Using RNA-scope in situ hybridization, immunohistochemistry, ultrastructural, and histological approaches, we demonstrate receptacle differentiation in tanycyte-derived swell-bodies. We show that these receptacles are AQP4- and A{beta}-immunoreactive and that related gene expression is observed in swell-bodies where receptacle differentiation is observed. Through correlative light- and electron-microscopy in human and mouse brain, we demonstrate that both A{beta} and tau protein are associated with these waste-internalizing structures and likely play important roles in the waste internalization process. Based on our findings, we postulate that the functional significance of A{beta} may be structural stabilization of waste-internalizing structures and canals to prevent their collapse during the uptake process. We propose that tau protein may govern the quantitatively appropriate release of waste-internalizing structures. We postulate that AD-related A{beta}-plaques and tau tangles likely show hypertrophic pathologies of this glial-canal-system and associated waste-internalizing receptacles.

It matters who you are: Biography modulates the neural dynamics of facial identity representation
Recognizing the face of a person is an essential capacity to our social life. However, to interact properly with others we also need to know to which person a face belongs. Here we tested how person knowledge modulates face processing. Participants were familiarized with highly variable faces, associated with artificial biographies. Crucially, participants were allocated randomly into two groups, trained on the identical faces of the same persons, but with reversed person knowledge associated with the faces. Multivariate pattern analyses were used to examine the time course of identity representations in the EEG data. We estimated cross-participant, leave-one-participant- out pairwise identity classifications within the same face-person association groups and compared them to the those performed across association groups. We observed that face-person knowledge associations led to a robust familiarity signal from 300 ms and to a rapidly emerging identity representation, starting from 80 ms. Importantly, the shared associations within participant groups led to a longer-lasting and stronger face identity representation over the right posterior electrode cluster when compared to cross-group comparisons with reversed associations. The direct comparison of within and cross-group classifications showed that an early stage of identity representation, between 250 and 370 ms, is significantly modulated by face-biography associations. Our findings suggest that top-down, person recognition memory related information modulates visual face identity representation already at an early processing stage. Our study provides new insights into the spatiotemporal dynamics of how person-related conceptual, biographical knowledge modulates familiar identity representation.

Integration of Nanomaterials and DBS to Improve Basal Ganglia Oscillations Using Delayed Van der Pol Model
Basal ganglia play a crucial role in motor control and the challenges posed by pathological oscillations, especially in Parkinsons disease. Although deep brain stimulation (DBS) is effective in attenuating pathological frequencies, it often leads to side effects. This study presents a comprehensive framework for understanding and improving neural oscillation dynamics by integrating nanomaterials with DBS. Nanomaterials with their controllable frequencies through length, width, and structure could stabilize oscillations and maintain normal neural frequencies. This study introduces three mathematical models: the DBS model, the nanomaterials model, and a combined model of both, which use the Van der Pol delay model to demonstrate how the periodic force and nanomaterials collaborate to stabilize brain activity. Numerical simulations indicate that DBS by itself lowers harmful frequencies but might make the motor cortex unstable because it increases the strength of signals. In contrast, the nanomaterial models reduce the amplitude and activate the motor cortex. The combination of DBS and nanomaterials significantly improves stability, reduces pathological oscillations, and decreases hyperactivity. The use of nanomaterials must be carefully monitored to ensure that they do not suppress normal neural activity, highlighting the need for experimental validation. This study provides a promising direction for the development of personalized therapeutic technologies that balance the suppression of pathological activity with the preservation of normal neural function.

Cell type-specific associations with Alzheimer's Disease conserved across racial and ethnic groups
Genomic studies at single-cell resolution have implicated multiple cell types associated with clinical and pathological traits in Alzheimers Disease (AD), but have not examined common features across broad, multi-ethnic populations, and across multiple regions. To bridge this gap, we performed single-nucleus RNA-seq and ATAC-seq profiling of cortical and subcortical brain regions from post-mortem samples across Non-Latin White, African American, and Latin donors (the latter of any race). Using discrete and continuous dissection of molecular programs, we elucidate cell-type-specific glial and neuronal signatures associated with AD across multiple population groups. Notably, we found that multiple microglial (GPNMB+, CD74+, and CR1+ subgroups) and astrocyte (SERPINH1+ and WIF1+ subgroups) signatures are associated with worse clinical and pathological phenotypes across all three population groups. We also report continuous gene expression factors in oligodendrocytes that are not captured by discrete clusters, yet still show strong associations with disease phenotypes. Finally, we observe these discrete cellular identities and continuous gene programs separate cognitively impaired donors into 6 molecularly distinct subgroups that span racial and ethnic population groups. Overall, our study identifies key cell types and gene programs implicated in AD that are shared across population groups, and provides an initial data set that underscores how representative sampling can capture conserved signatures as well as disease heterogeneity, leading to better prioritization of key cell types for further investigation.

Regulation of input excitability in human and mouse parvalbumin interneurons by Kir potassium channels
Compared to rodents, inhibitory interneurons in the human neocortex exhibit high input excitability because of reduced passive ion leakage across their extracellular membrane. However, the regulation of intrinsic excitability by voltage-gated ion channels activated over a wide range of membrane potentials in human interneurons remains poorly understood. We performed whole-cell patch-clamp microelectrode recordings in mouse and human neocortical slices obtained from surgically resected non-pathological brain tissue finding that Kir channels control the electrical resistance of parvalbumin (Pvalb) neurons in an identical manner in the human and mouse. Molecular analyses revealed predominantly Kir3.1 and Kir3.2 channels in Pvalb neurons in both species. Using whole-cell recordings from synaptically connected neuron pairs and a computational model, we demonstrated that physiological Kir activation inhibits human Pvalb interneurons during postsynaptic potentials evoked by presynaptic neurogliaform cells. The similarity of Kir-mediated inhibition across species suggests that it is an archetypal property of Pvalb neurons.

Functional validation of human SK channels variants causing NEDMAB and Zimmermann-Laband syndrome-3 in C. elegans
Small-conductance Ca2+-activated K+ channels (SK channels) are widely expressed in the central nervous system, where they play a crucial role in modulating neuronal excitability. Recent studies have identified missense variants in the genes encoding SK2 and SK3 channels as the cause of two rare neurodevelopmental disorders: NEDMAB and ZLS3, respectively. Here, we used C. elegans as an in vivo model to investigate the functional consequences of these patient variants. The C. elegans ortholog KCNL-1 regulates neuronal and muscle excitability in the egg-laying system, a well-characterized model circuit. To visualize KCNL-1 expression and localization, we generated a fluorescent translational reporter at the endogenous kcnl-1 locus. We then introduced eight point mutations corresponding to pathogenic variants reported in NEDMAB or ZLS3 patients. Our study confirmed the molecular pathogenicity of the ZLS3-associated mutations, revealing a gain-of-function effect that led to increased in utero egg retention, likely due to electrical silencing of the egg-laying circuitry. NEDMAB mutations exhibited more complex phenotypic effects. Most caused a loss-of-function phenotype, indistinguishable from null mutants, while one displayed a clear gain-of-function effect. Additionally, a subset of NEDMAB variants altered KCNL-1 localization, suggesting an impairment in channel biosynthesis, trafficking or stability. These findings provide new insights into the molecular mechanisms underlying NEDMAB and ZLS3 physiopathology, enhancing our understanding of SK channel dysfunction in human disease. Moreover, they establish C. elegans as a robust and cost-effective in vivo model for rapid functional validation of new SK channel mutations, paving the way for future investigations.

Non-linearity of spatial integration varies across layers of primary visual cortex
The receptive field (RF) of visual cortical neurons is highly dynamic and context-dependent, shaped by both the spatial and temporal properties of stimuli and the complex architecture of cortical circuits. While classical RF mapping through extracellular recordings reveals only the area triggering spiking responses, intracellular recordings reveal a much broader region of subthreshold synaptic input. We investigated how neurons in different cortical layers integrate visual input across space, with a focus on the linearity of spatial summation. Using intracellular recordings, we found that supragranular complex cells integrate input in a highly sublinear manner, in contrast to infragranular complex cells and simple cells, which exhibited near-linear summation. To understand the underlying mechanisms, we employed a large-scale recurrent spiking model of cat primary visual cortex (V1). Modeling results point to the differential patterning of long-range horizontal connections--particularly their targeting of excitatory versus inhibitory neurons--as a potential source of the observed layer-specific integration properties. These findings suggest that RFs emerge from interaction of feedforward, horizontal, and possibly feedback inputs, that are continuous in space, challenging the conventional notions of fixed spatial RF boundaries in early visual processing.

Artemin sensitises mouse (Mus musculus) and naked mole-rat (Heterocephalus glaber) sensory neurons
The naked mole-rat (NMR, Heterocephalus glaber) is a subterranean rodent that exhibits a range of unusual physiological traits, including diminished inflammatory pain. For example, nerve growth factor (NGF), a key inflammatory mediator, fails to induce sensitization of sensory neurons and thermal hyperalgesia in NMRs. This lack of NGF-induced neuronal sensitization and thermal hyperalgesia results from hypofunctional signaling of the NGF receptor, tropomyosin receptor kinase A (TrkA). Like NGF-TrkA signaling, the neurotrophic factor artemin, a member of the glial cell line-derived neurotrophic factor (GDNF) family, is implicated in mediating inflammatory pain through its receptor, GDNF family receptor 3 (GFR3), which is expressed by a subset of dorsal root ganglia (DRG) sensory neurons. Here we investigated GFR3 expression in DRG neurons of mice and NMRs, as well as measuring the impact of artemin on DRG sensory neuron function in both species. Using immunohistochemistry, we observed a similar abundance of GFR3 in mouse and NMR DRG sensory neurons, high coexpression with the transient receptor potential vanilloid 1 (TRPV1) ion channel suggesting that these neurons are nociceptors. Using electrophysiology, we observed that artemin induced depolarization of the resting membrane potential and decreased the rheobase of sensory neurons in both species, as well as diminishing the degree of TRPV1 desensitization to multiple capsaicin stimuli. Overall, results indicate that artemin sensitizes sensory neurons in both mice and NMRs, suggesting a conserved mechanism of sensory modulation.

Domain-general neural effects of associative learning and expectations on pain and hedonic taste perception
Predictive cues significantly influence perception through associative learning. However, it is unknown whether circuits are conserved across domains. We investigated how associative learning influences perceived intensity and valence of pain and hedonic taste and whether the mechanisms that support expectancy-based modulation vary as a function of aversiveness and modality. Sixty participants were randomly assigned to receive either painful heat, unpleasant liquid saline, or pleasant liquid sucrose during fMRI scanning. Following conditioning, cues that were initially associated with low or high intensity outcomes were intermittently followed by stimuli calibrated to elicit medium intensity ratings. Learned cues modulated expectations and subjective outcomes similarly across domains. Consistent with this, the orbitofrontal cortex exhibited domain-general anticipatory activation. The left anterior insula mediated domain-general cue effects on subjective intensity, whereas the thalamus mediated cue effects on subjective valence. Notably, these two regions were nearly identical to those previously implicated in mediating cue effects on pain (nearly identical to those previously implicated in cue effects on mediating pain (Atlas et al., 2010). Pain specificity was evident when we measured variations in stimulus intensity, whether we used univariate or multivariate approaches, but there was minimal evidence of specificity by modality or aversiveness when we examined cue effects on medium trials. These findings suggest that shared neural circuits mediate the effects of learned expectations on perception, linking pain with other areas of affective processing and perception across domains.

Juvenile mice are susceptible to infarct-induced neurodegeneration that causes delayed cognitive decline in a new model of pediatric ischemic stroke
Over half of pediatric stroke survivors have permanent neurologic and cognitive deficits, and some children develop new or worsening cognitive impairment late after stroke. Their cognitive symptoms are associated with chronic alterations of brain structure in regions distant from the infarct, including in the uninjured hemisphere. However, the mechanisms that cause childrens late cognitive symptoms and disrupted brain growth outside the infarcted tissue remain unknown. We therefore developed and validated a mouse model of pediatric ischemic stroke that causes infarct-induced, delayed cognitive decline accompanied by chronic innate and adaptive neuroinflammation in the infarct and in uninjured subcortical regions that are connected to the infarct.

MethodsMale and female C57BL/6J mice were randomized to stroke or sham surgery at p28 to model stroke in late childhood and compared to adult 6-month-old mice. We used permanent distal MCAO followed by 60 minutes of hypoxia, an established model for focal ischemia that generates a purely cortical stroke. Mice underwent behavioral testing at 1- and 7-weeks post stroke Barnes Maze protocol adapted for reversal learning. We performed immunostaining to quantify stroke size, atrophy, and innate and adaptive neuroinflammation at 3 days and 7 weeks after surgery.

ResultsAt 7 weeks post-stroke, juvenile stroke mice performed significantly worse on reversal learning on Barnes Maze testing than sham mice (n = 7 stroke, 7 sham, p = 0.0265 2-way ANOVA with repeated measures). At 3 days after stroke, juvenile mice had greater innate immune cell activation at subcortical sites of secondary neurodegeneration; however, by 7 weeks after surgery, this relationship was reversed, and adults had greater chronic innate immune cell activation at these uninjured subcortical sites (corpus callosum, corticospinal tract, thalamus). Like adult mice, pediatric mice exhibited chronic innate and adaptive neuroimmunity in the infarct scar; the inflammation in the stroke scar did not differ by age.

ConclusionsIn a model of pediatric ischemic stroke, juvenile mice develop a delayed cognitive deficit that is associated with chronic innate and adaptive neuroinflammation in uninjured subcortical structures that undergo secondary neurodegeneration. Our results suggest that infarct-induced neurodegeneration occurs after stroke in juvenile mice and that juvenile and adult mice have divergent trajectories in the innate immune response to stroke.

Design and Biological Activity of a Novel Brain Penetrant Urea Compound Against Glioblastoma
Glioblastoma (GBM) remains the most lethal primary brain tumor, largely due to therapy-resistant glioma stem cells (GSCs) and the ability of non-stem cells to dedifferentiate under therapeutic pressure. We developed MXC-017, a novel urea-based compound that crosses the blood-brain-barrier, directly targets GSCs, and prevents radiation-induced GSC formation. Using click chemistry pull-down and mass spectrometry, we identified vimentin as the target of MXC-017, further validated by in silico docking. Global transcriptomic profiling (bulk RNA-seq) and single-cell RNA-seq analyses revealed MXC-017s efficacy with minimal off-target effects, supported by metabolic and kinome assays. Normal cell toxicity was negligible in fibroblasts, microglia, astrocytes, and murine neural progenitors. Maximum tolerated dose was identified and we observed significantly extended median survival in 17 PDOX GBM models when treated with MXC-017 plus radiation, benchmarked against standard-of-care temozolomide. These findings underscore the therapeutic potential of vimentin-targeting agents to overcome radiation resistance and improve outcomes for GBM patients.

Statement of SignificanceGlioblastomas distinctive nature and the blood-brain barrier hamper therapies targeting therapy-resistant GSCs. We developed a novel urea-based agent that crosses the barrier, targets GSCs, and prevents radiation-induced GSC formation. With minimal off-target effects, reduced toxicity, and superior survival in PDOX models, it offers potential to improve outcome in GBM.

Posterior parietal cortex activity during visually cued gait: a preliminary study
Safe gait requires visually cued (VC) step adjustments for negotiating targets and obstacles. Effective step adjustments rely on good visuospatial processing. The posterior parietal cortex (PPC) is implicated in visuospatial processing, yet empirical evidence is limited for the PPCs role during gait in humans. Increased cortical control of gait is associated with higher gait variability, a marker of gait performance and fall risk among older adults. However, the cortical underpinnings of gait variability in visually complex environments are not well established. The primary aim of this preliminary study was to assess PPC activity during VC gait and VC gait with perturbations (VCP). A secondary aim was to determine how PPC activity relates to gait variability during VC and VCP gait. Twenty-one healthy young adults completed three treadmill gait conditions at preferred speed: non-cued (NC) gait, VC gait, where stepping targets were presented in a regular pattern, and VCP gait, where stepping target positions were pseudorandomly shifted. Functional near-infrared spectroscopy quantified relative changes in deoxygenated and oxygenated hemoglobin ({Delta}HbO2) concentrations in the PPC. Inertial measurement units quantified gait variability. Moderate effects were observed for more positive {Delta}HbO2 from NC to both VC and VCP gait, likely reflecting the increased visuospatial processing demands. Stride time variability was positively correlated with PPC {Delta}HbO2 during VC gait, suggesting a potential role for the PPC in modulating temporal components of VC gait. Extending these findings to older adults will help to elucidate the PPCs role in gait adaptability and fall risk with aging.

Finite element model predicts micromotion-induced strain profiles that correlate with the functional performance of Utah arrays in humans and non-human primates
ObjectiveUtah arrays are widely used in both humans and non-human primates (NHPs) for intracortical brain-computer interfaces (BCIs), primarily for detecting electrical signals from cortical tissue to decode motor commands. Recently, these arrays have also been applied to deliver electrical stimulation aimed at restoring sensory functions. A key challenge limiting their longevity is the micromotion between the array and cortical tissue, which may induce mechanical strain in surrounding tissue and contribute to performance decline. This strain, due to mechanical mismatch, can exacerbate glial scarring around the implant, reducing the efficacy of Utah arrays in recording neuronal activity and delivering electrical stimulation.

ApproachTo investigate this, we employed a finite element model (FEM) to predict tissue strains resulting from micromotion.

Main ResultsOur findings indicated that strain profiles around edge and corner electrodes were greater than those around interior shanks, affecting both maximum and average strains within 50 {micro}m of the electrode tip. We then correlated these predicted tissue strains with in-vivo electrode performance metrics. We found negative correlations between 1 kHz impedance and tissue strains in human motor arrays and NHP area V4 arrays at 1-mo, 1-yr, and 2-yrs post-implantation. In human motor arrays, the peak-to-peak waveform voltage (PTPV) and signal-to-noise ratio (SNR) of spontaneous activity were also negatively correlated with strain. Conversely, we observed a positive correlation between the evoked SNR of multi-unit activity and strain in NHP area V4 arrays.

SignificanceThis study establishes a spatial dependence of electrode performance in Utah arrays that correlates with tissue strain.

Enhanced Tactile Coding in Rat Neocortex Under Darkness
Sensory systems are known for their adaptability, responding dynamically to changes in environmental conditions. A key example of this adaptability is the enhancement of tactile perception in the absence of visual input. Despite behavioral studies showing visual deprivation can improve tactile discrimination, the underlying neural mechanisms, particularly how tactile neural representations are reorganized during visual deprivation, remain unclear. In this study, we explore how the absence of visual input alters tactile neural encoding in the rat somatosensory cortex (S1). Rats were trained on a custom-designed treadmill with distinct tactile textures (rough and smooth), and local field potentials (LFPs) were recorded from S1 under light and dark conditions. Machine learning techniques, specifically a convolutional neural network, were used to decode the high-dimensional LFP signals. We found that the neural representations of tactile stimuli became more distinct in the dark, indicating a reorganization of sensory processing in S1 when visual input was removed. Notably, conventional amplitude-based analyses failed to capture these changes, highlighting the power of machine learning in uncovering subtle neural patterns. These findings offer new insights into how the brain rapidly adapts tactile processing in response to the loss of visual input, with implications for multisensory integration and potential strategies for sensory rehabilitation.