bioRxiv Subject Collection: Neuroscience's Journal

Temporal fMRI Dynamics Map Dopamine Physiology
Spatial variations in dopamine function are linked to cognition and substance use disorders but are challenging to characterize with current methods. Because dopamine influences blood vessel dilation, we hypothesized that hemodynamic latency, which reflects BOLD signal timing, could serve as an indirect marker of dopamine physiology. Across four datasets, we found a topography of hemodynamic latencies that precisely distinguished the nucleus accumbens, a dopaminergic region implicated in motivation and substance abuse, from other striatal regions. Using PET, genetics, and pharmacology, we found that hemodynamic latencies are robustly related to dopamine function and dopamine-linked behavior. In individuals with cocaine use disorder, we observed a spatial gradient of altered hemodynamic latencies in the striatum. This pattern independently predicted nicotine use, revealing a conserved physiological profile associated with addictive substance use. Hemodynamic latencies map regional, individual, and pathological differences linked to dopamine, opening new avenues for indirectly assessing the role of dopamine in healthy cognition and disease.

MLIB: an easy-to-use Matlab toolbox for the analysis of extracellular spike data
The analysis of neurophysiological data obtained from extracellular recordings is usually performed using a number of standard techniques. These include a) the extraction of action potentials from voltage traces and their subsequent classification, i.e., spike sorting, b) the visualization of activity, e.g., by constructing raster plots, peri-stimulus time histograms (PSTHs), and spike density functions, and c) the quantification of neuronal responses according to experimental variables such as stimulation or movement. Here I present a Matlab toolbox containing functions for the visualization and analysis of neuronal spike data. The toolbox consists entirely of one-liners that operate on vector or matrix inputs, i.e., spike and event timestamps or waveform samples. The toolbox functions provide both basic (constructing PSTHs, computing waveform characteristics etc.) and more advanced functionality, such as dimensionality reduction of multi-neuron recordings. While offering a high degree of versatility, the toolbox should also be accessible to newcomers to neurophysiology, such as (under)graduate students or PhD students. The functions are easy to use, and each function is extensively introduced with several examples using real or simulated data. In addition, many functions provide fully formatted plots on request, even with minimal Matlab knowledge. The toolbox is available from https://github.com/maikstue/mlib-spike-data.

Synaptopathy in the TDP43ΔNLS Mouse Model of Sporadic Amyotrophic Lateral Sclerosis
Sporadic cases of Amyotrophic Lateral Sclerosis (sALS) represent the most common form of motor neuron disease. sALS is characterised by pathological cytoplasmic inclusions of TDP-43 , so-called reactive astrocyte pathology, and motor neuron degeneration. Early-stage alterations in certain subpopulations of synapses between neurons are thought to be a key driver of the early pathological mechanisms of ALS. However, we do not have a clear understanding of which types of synapses are impacted in ALS. Identifying vulnerable synapses affected in sALS models may provide insights into the key sites of disease pathogenesis. In this study we have performed quantitative high-resolution microscopy to survey different synapse subtypes, including excitatory (glutamatergic), inhibitory (glycinergic) and modulatory (cholinergic C-Boutons) synapses, in the spinal cord of a mouse model of sALS showing inducible TDP-43 pathology (TDP43{Delta}NLS) restricted to neurons. We have identified changes in cholinergic synapses and a subpopulation of excitatory synapses. Mice display robust neuronal TDP-43 pathology and evidence of TDP-43 changes at cholinergic C-boutons. We also observe no evidence of astrocytic pathology nor changes in the fraction of synapses that are contacted by astrocytes. Overall, our findings highlight the selective vulnerability of distinct synapse populations in ALS.

A Software for Identification and Characterization of Theta Rhythms in the Hippocampus
Characterizing theta rhythms in the hippocampus provides a window into understanding memory processing. An inquiry that arises when an animal sustains a pathological state is how theta rhythms are affected. In pathological states like epilepsy or Alzheimer's, these rhythms change in specific ways. Statistically robust changes in these rhythms could serve as potential biomarkers, indicating the severity of the animal's condition and the effectiveness of a drug. However, this understanding depends on how the data is analyzed. There are currently no standard criteria for recognizing theta dominance in experimental recordings. To address this, we have developed novel MATLAB-based software with an easy-to-use graphical user interface which enables identifying and analyzing theta rhythms in a standard way. We discuss the software's functionality and its underlying algorithms. The algorithms were developed using previously acquired EEG/LFP data recorded from the hippocampus of a mouse kindling model of epilepsy. Two primary analyses were conducted to test the software's functionality: first, comparing theta rhythms during the baseline period versus during spontaneous recurrent seizures; second, analyzing the timing of theta rhythms relative to the seizure event. Our illustrative results indicate that our developed software can robustly identify theta events with statistically significant feature differences. Further, the examination presented here with two mice shows that while theta events can occur just before seizures, it takes tens of minutes post-seizure before theta rhythms occur again. Our software thus provides the user with the ability to robustly identify and characterize theta rhythms and their feature changes.

Temporal coding enables hyperacuity in event based vision
The fact that the eyes are constantly in motion, even during `fixation`, entails that the spike times of retinal outputs carry information about the visual scene even when the scene is static. Moreover, this motion implies that fine details of the visual scene could not be decoded from pure spatial retinal representations due to smearing. Understanding the interplay of temporal and spatial information in visual processing is thus pivotal for both biological research and bio-inspired computer-vision applications. In this study, we consider data from a popular event-based camera that was designed to emulate the function of a biological retina in hardware. Similarly to biological eye, and in contrast to standard frame-based cameras, this camera outputs an asynchronous sequence of "spike" events. We used this camera to obtain dataset of event streams of tiny images, i.e., images whose recognition is impaired by photosensor's pixelization and thus their recognition requires hyperacuity. Using these datasets we demonstrate here the superiority of event-based spatio-temporal coding over frame-based spatial coding in the recognition of tiny images by artificial neural networks (ANNs). We further demonstrate the benefits of event sequences for unsupervised learning. Interestingly, Vernier hyperacuity, which is a standard measure of shape hyperacuity, emerged in ANNs following training on tiny images, resembling the natural hyperacuity observed in humans. Our findings underscore the essential role of precise temporal information in visual processing, offering insights for advancing both biological understanding and bio-inspired engineering of visual perception.

Highly selective visual receptive fields in mouse frontal cortex
A hallmark of the mammalian visual system is spatial information processing. This relies on feedforward activity spanning multiple brain areas, and on interconnected neurons with spatial receptive fields (RFs) aligned across these areas. This organization allows neurons to iteratively analyze information from the same point of the visual field. It remains unclear if this framework extends beyond the visual system, especially into cognitive areas of frontal cortex that exert feedback control over early sensory areas. Here, we surveyed the mouse frontal cortex (anterior cingulate and secondary motor areas), and discovered neurons with low latency, highly localized visual RFs. Just like in visual cortex, responses were often highly selective for bright or dark stimuli. The responses lagged visual cortical areas by only ~25 ms, and their RFs were comparable in size. Further, the representation of visual space in frontal cortex showed a strong bias for the central (binocular) visual field, but there was no evidence of a topographically organized retinotopic map. Importantly, these visual responses were abolished by optogenetic silencing of visual cortex, revealing a causal role for feedforward hierarchical connectivity that extends visual spatial processing directly into motor and cognitive regions of mouse frontal cortex.

Fast Interneuron Dysfunction in Laminar Neural Mass Model Reproduces Alzheimer's Oscillatory Biomarkers
Alzheimer's disease (AD) is characterized by a progressive cognitive decline underpinned by disruptions in neural circuit dynamics. Early-stage AD is associated with cortical hyperexcitability, whereas later stages exhibit oscillatory slowing and hypoactivity, a progression observable in electrophysiological spectral characteristics. While previous studies have linked these changes to the dysfunction of fast-spiking parvalbumin-positive (PV) interneurons and neuronal loss associated with amyloid-beta (Abeta) and hyperphosphorylated tau (hptau) pathology, the precise mechanistic relationship between cellular and altered electrophysiology remains unclear. To study this relationship, we employed a Laminar Neural Mass Model that integrates excitatory and inhibitory neural populations within a biophysically informed columnar framework. The connectivity constant from PV cells to pyramidal neurons was gradually reduced to simulate the progressive neurotoxic effects of Abeta oligomers. Other model parameters were systematically varied to compare with existing modeling literature and also to simulate the effects of hptau. All model predictions were compared to empirical M/EEG findings in the literature. Our simulations of PV interneuron dysfunction successfully reproduced the biphasic electrophysiological progression observed in AD: an early phase of hyperexcitability with increased gamma and alpha power, followed by oscillatory slowing and reduced spectral power. Alternative mechanisms and model parameters, such as increased excitatory drive, failed to replicate the observed biomarker trajectory. Additionally, to reconcile the hypoactivity and decreased firing rates observed in advanced AD stages, we combined the PV dysfunction model with a disruption of the pyramidal cell populations that reflects the neurotoxicity induced by hptau. Although this additional mechanism is not necessary to reproduce oscillatory changes in the isolated neural mass, it is crucial for aligning the model with evidence of reduced firing rates, metabolic activity, and cell loss and will enhance its applicability in future whole-brain modeling studies. These results support the hypothesis that at the local level, PV interneuron dysfunction is a primary driver of cortical electrophysiological alterations, while pyramidal neuron loss underlies later-stage severe hypoactivity. Our model provides a mechanistic framework for interpreting excitation-inhibition imbalance across AD progression, demonstrating the value of biophysically constrained models for interpreting electrophysiological biomarkers.

Cold Receptor TRPM8 as a target for Migraine-associated Pain and Affective Comorbidities
Background: Genetic variations in the Trpm8 gene that encodes the cold receptor TRPM8 have been linked to protection against polygenic migraine, a disabling condition primarily affecting women. Noteworthy, TRPM8 has been recently found in brain areas related to emotional processing, suggesting an unrecognized role in migraine comorbidities. Here, we use mouse behavioural models to investigate the role of Trpm8 in migraine-related phenotypes. Subsequently, we test the efficacy of rapamycin, a clinically relevant TRPM8 agonist, in these behavioural traits and in human induced pluripotent stem cell (iPSC)-derived sensory neurons. Findings: We report that Trpm8 null mice exhibited impulsive and depressive-like behaviours, while also showing frequent pain-like facial expressions detected by an artificial intelligence algorithm. In a nitroglycerin-induced migraine model, Trpm8 knockout mice of both sexes developed anxiety and mechanical hypersensitivity, whereas wild-type females also displayed depressive-like phenotype and hypernociception. Notably, rapamycin alleviated pain-related behaviour through both TRPM8-dependent and independent mechanisms but lacked antidepressant activity, consistent with a peripheral action. The macrolide ionotropically activated TRPM8 signalling in human sensory neurons, emerging as a new candidate for intervention. Significance: Together, our findings underscore the potential of TRPM8 for migraine relief and its involvement in affective comorbidities, emphasizing the importance of addressing emotional symptoms to improve clinical outcomes for migraine sufferers, especially in females.

A trace-based analysis pipeline for coherent and optimized electrophysiological data analysis
The development of large-scale neuronal networks notably relies on the use of point-neuron models to reduce the computational cost of simulations while focusing on integrative neuronal properties. However, the precise tuning of these neuron models remains a major aspect of modeling work to accurately reproduce neuronal properties and understand their implications in network activity. To this end, the precise characterization of neuronal electrophysiological properties, from linear properties to the input-output (I/O) relationship and spike frequency adaptation, from intracellular recordings is a crucial step. Furthermore, the increasing availability of publicly accessible databases opens the possibility of deriving I/O properties for point-neuron models from multiple datasets studying different neuronal populations. However, despite recent advancements in establishing universal data formats for electrophysiological studies, challenges persist due to the absence of standardized protocols (notably for current-clamp experiments) and unified data analysis methods, hindering cross-database comparisons of electrophysiological features. To address these limitations, we developed the TACO pipeline, a free, Python-based pipeline for analyzing databases of current-clamp recordings. The TACO pipeline is designed to be user-friendly, minimizing the need for manual implementation of database-specific data extraction methods and enabling the application of user-defined quality control criteria. The pipeline incorporates robust methods for characterizing neuronal I/O relationships, spike-related feature adaptation, and estimating common experimental artifacts such as bridge errors. These methods have been designed to accommodate variability in database-specific experimental design, the sampling of the input space being of particular importance. We validated the utility of this approach by demonstrating performance comparable to or exceeding that of machine learning models reported in the literature for neuronal type classification, using protocol-agnostic features extracted by the pipeline. This work highlights the potential of database-independent data analysis tools to enhance crossdatabase comparability and interoperability, advancing research sustainability and promoting the principles of Open Science.

Corticospinal excitability during timed interception depends on the speed of the moving target
Successfully intercepting a moving object requires precisely timing the optimal moment to act by integrating information about the targets visual motion properties. Neurophysiological evidence indicates that activity in the primary motor cortex (M1) during interception preparation is sensitive to both the targets kinematic features and motor planning. However, how visual motion signals are integrated within M1 to guide interception timing is unclear. In the present study, we applied single-pulse transcranial magnetic stimulation (TMS) over M1 to examine how a targets kinematics influence corticospinal excitability during interception preparation. Participants were instructed to abduct their right index finger to intercept a target moving horizontally at a constant speed toward a fixed interception zone. Target speed (Fast or Slow) and travel distance (Far or Close) were manipulated while controlling motion duration across conditions. Motor-evoked potentials (MEPs) were elicited at five latencies before target arrival at the interception zone. Consistent with previous behavioral findings, movement initiation occurred earlier for faster targets and was delayed when TMS was applied closer to the targets arrival. Though MEPs were generally suppressed relative to baseline at earlier timepoints and facilitated closer to movement initiation, we observed that target speed--but not distance--influenced the time course of MEP modulation. When adjusting for movement initiation times, there was an overall reduced suppression and increased facilitation for faster-moving targets, possibly reflecting a heightened urgency to move. These results suggest M1 activity during interception preparation integrates internal estimates of target motion, which may serve to optimize interception timing and performance.

New & NoteworthyWhen intercepting a moving object, like catching a ball, we need to continuously combine visual motion signals to predict the objects future location and enable accurate movement. Here, we show that preparatory suppression and facilitation of corticospinal excitability depends on the speed, but not the distance, of the moving target. These findings reveal that differences in interception timing are closely linked to changes in motor system excitability.

In Situ Detection of α-Synuclein Seeding Activity Using Quiescent Seed Amplification Assay
Accurate detection of -synuclein (Syn) seeding activity is crucial for understanding synucleinopathies like Parkinson's disease. Traditional seed amplification assays lack spatial resolution, while antibody-based methods only capture pre-formed aggregates. To overcome this limitation, we developed the Quiescent Seed Amplification Assay (QSAA), enabling the in situ amplification and visualization of Syn seeding activity directly within tissue sections. Using QSAA, we generated the first brain-wide map of seeding activity in mice injected with Syn pre-formed fibrils and identified endogenous Syn aggregates in human brain samples. Additionally, by combined QSAA with immunofluorescence, IF-QSAA revealed that a portion of Syn seeding occurs independently of pathological aggregates detected by antibodies, such as pS129. This highlights the complementary value of seeding activity mapping alongside antibody-based assessments of Lewy body pathology. Overall, QSAA and IF-QSAA provide valuable tools for investigating Syn seeding and propagation, offering new insights into synucleinopathy pathogenesis and potential therapeutic strategies.

Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing
Single-nucleus RNA sequencing (snRNA-seq) enables resolving cellular heterogeneity in complex tissues. snRNA-seq overcomes limitations of traditional single-cell RNA-seq by using nuclei instead of cells, allowing to utilize frozen tissues and difficult-to-isolate cell types. Although various nuclei isolation methods have been developed, systematic evaluations of their effects on nuclear integrity and subsequent data quality remain lacking, a critical gap with profound implications for the rigor and reproducibility. To address this, we compared three mechanistically distinct nuclei isolation strategies with brain tissues: a sucrose gradient centrifugation-based method, a spin column-based method, and a machine-assisted platform. All methods successfully captured diverse cell types but revealed considerable protocol-dependent differences in cell type proportions, transcriptional homogeneity, and the preservation of cell-type-specific and cell-state-specific markers. Moreover, isolation workflows differentially influenced contamination levels from ambient, mitochondrial, and ribosomal RNAs. Our findings establish nuclei isolation methodology as a critical experimental variable shaping snRNA-seq data quality and biological interpretation.

MOTIVATIONSingle-nucleus RNA sequencing (snRNA-seq) has become an essential tool for transcriptomic analysis of complex tissues. However, the quality and efficiency of data generation depend heavily on the method used for nuclear isolation. The existing isolation techniques vary in their ability to preserve nuclear integrity, minimize ambient RNA contamination, and optimize recovery rates. Despite these differences in quality, a systematic comparison of these methods, specifically for brain tissue, is lacking. This gap poses a challenge for researchers in choosing the most suitable approach for their particular experimental requirements. To address this critical issue, our study directly compared three nuclei isolation methods and evaluated their performance in terms of yield, purity, and downstream sequencing quality. By providing a comprehensive assessment, we aim to guide researchers in selecting the most appropriate isolation protocol for their snRNA-seq experiments, ensuring optimal results and advancing the study of complex brain tissues at the single-nucleus level.

Beyond ROIs: Laminar fMRI Mapping with Cylarim
Laminar fMRI offers a powerful approach to investigate brain function by resolving neural activity across the distinct layers of the cortical gray matter. However, current laminar fMRI analysis methods often rely on predefined regions of interest (ROIs), which introduce subjectivity and bias due to arbitrary spatial delineation. To address this limitation, we present "Cylarim", a novel software package that eliminates dependence on rigid ROI definitions. Cylarim employs small, overlapping cylinders that systematically traverse the cortical ribbon, enabling unbiased, large-scale mapping of laminar-specific hemodynamic activity. This approach supports comprehensive sampling of cortical depth-resolved signals across extensive regions. By circumventing the need for manual or atlas-driven ROI selection, Cylarim facilitates the study of feedforward and feedback pathways in diverse cortical areas without compromising spatial coverage. Preliminary results demonstrate Cylarims ability to accurately localize layer-dependent responses, providing a robust framework for advancing ultrahigh-resolution fMRI research in neuroscience.

Unexpected mechanisms of sex-specific memory vulnerabilities to acute traumatic stress
It is increasingly recognized that severe acute traumatic events (e.g., mass shooting, natural disasters) can provoke enduring memory disturbances, and these problems are more common in women. We probed the fundamental sex differences underlying memory vulnerability to acute traumatic stress (ATS), focusing on the role of the sex hormone, estrogen (17{beta}-estradiol) and its receptor signaling in hippocampus. Surprisingly, high physiological hippocampal estrogen levels were required for ATS-induced episodic memory disruption and the concurrent sensitization and generalization of fear memories in both male and female mice. Pharmacological and transgenic approaches demonstrated signaling via estrogen receptor (ER) in males and, in contrast, ER{beta} in females, as the mechanisms for these memory problems. Finally, identify distinct hippocampal chromatin states governed by sex and estrogen levels, which may confer an enduring vulnerability to post-traumatic memory disturbances in females.

Heterogeneity of functional cellular properties for neurons in mouse cerebral cortex
1Biological systems are known to exhibit a high degree of heterogeneity in their constituent components and their organization, and neuronal systems are no exceptions. To understand the functional impact of this heterogeneity in the brain, network models need to consider how this is manifested at the level of cellular properties, and thus how to consider variability in reported experimental data. Many studies have pointed out the variability in neuronal density, structural organization or synaptic connectivity across different neuronal networks and populations. Similarly, neuronal physiological properties are known to greatly vary across neuronal populations. Yet, the characterization of electrophysiological diversity has mainly relied on descriptions of firing properties (e.g. bursting, spike frequency adaptation) with various quantitative definitions of the boundaries between neuronal classes (e.g. fast spiking, regular spiking). Furthermore, lab specific implementations of experimental design and data analysis are an obstacle for comparisons between studies. In this context, the quantitative consideration of neuronal variability across commonly accepted neuronal classes provides an objective approach to describe neuronal physiological heterogeneity.

We analyzed several publicly available databases to characterize the variability of linear and input/output properties of cortical neurons, according to multiple factors covering the entire cortical neuronal population. We assessed the variability of the main cortical neuron types (Excitatory, PValb, Sst, Htr3a, Vip), revealing their heterogeneity as function of cortical area (primary visual, motor and somato-sensory areas), including between layers within a given area. Our comparative database study revealed that different experimental conditions (e.g., in-vitro vs. in-vivo, recording temperature) can influence the properties of any given cell type, while preserving overall differences between types. We find that considering the input to a given neuron in terms of the effective voltage response of a linear model can account for some of the heterogeneity of I/O properties, and suggest that these properties are directly linked to cell input resistance, thus cell size. This works constitute a strong foundation for the consideration of detailed neuronal electrophysiological heterogeneity in future large-scale modeling works.

Non-linear microglial, inflammatory and oligodendrocyte dynamics across stages of Alzheimer's disease
Alzheimers disease (AD) is characterized by cognitive decline and neuropathological hallmarks including A{beta} plaques and Tau tangles. Emerging evidence indicates oligodendrocyte (OL) dysfunction and demyelination also contribute to disease progression. Here, we analyzed OL markers and inflammatory gene expression in human hippocampal samples at early and late AD stages. In early AD, we observed OL and myelinating pathways downregulation, alongside microglial and astrocytic activation, as well as upregulated chemokine CCL2 and peripheral immune infiltration markers. In late stages, expression of OL-related genes and myelination pathways increase, with a higher NG2/MBP ratio, coinciding with decreased microglial coverage and peripheral immune markers. These findings indicate that early neuroinflammation may impair OL function, while attenuated immune activity in late AD allows partial OL recovery. This study provides insights into stage-specific inflammatory and myelin-related changes in AD, supporting the relevance of understanding oligodendrocyte dynamics and potential regenerative responses for future therapeutic strategies.

HighlightsO_LIEarly AD: heightened microglial activation and peripheral infiltration.
C_LIO_LILate AD: reduced microglial presence and oligodendrocyte partial recovery.
C_LIO_LINeuroinflammation shifts toward remyelination-supporting conditions.
C_LI

Graphical abstract

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SARM1 base-exchange inhibitors induce SARM1 activation and neurodegeneration at low doses
SARM1 has emerged as a promising therapeutic target in neurology due to its central role in axonal degeneration and its amenability to different modes of small molecule inhibition. One chemical approach to modulate SARM1 involves orthosteric inhibition via a SARM1-mediated base-exchange reaction between a small molecule and nicotinamide adenine dinucleotide (NAD+), the substrate of SARM1, to generate the active inhibitor. Here, we report that subinhibitory concentrations of SARM1 base-exchange inhibitors (BEIs) paradoxically increase SARM1 activity and worsen SARM1-induced cell death and neuronal damage in vitro. Low dose administration of RO-7529, a SARM1 BEI, exacerbated experimental autoimmune encephalomyelitis (EAE)-induced neurodegeneration in vivo. Our data highlight a unique pharmacological feature of SARM1 BEIs that may limit their therapeutic application in disorders associated with SARM1 activation and axonal degeneration.

Combination of Cas9 and adeno-associated vectors (AAVs) enables efficient in vivo knockdown of precise miRNAs in the rodent brain
Although the advent of Cas9 technology has expanded our ability to precisely edit the genome, manipulating microRNAs in vivo has been shown to be particularly challenging, especially in the brain. Here, we sought to generate novel tools aiming at targeting and efficiently downregulating defined microRNAs species in a cell-specific manner so that their function in discrete neuronal networks could be investigated. Focusing on miR-124, a microRNA highly expressed in the mammalian brain and transcribed from three independent chromosomal loci, we designed and validated different guide RNAs directed against this miRNA. In vitro, our Cas9 designs show not only a significant reduction in miR-124 levels but also a functional effect on miR-124 silencing. Similarly, when packed into AAV vectors and injected into the mouse cortex, miR-124-Cas9 vectors strongly downregulate miR-124 levels without affecting the expression of other miRNAs. In parallel, levels of endogenous miR-124 targets exhibit a significant increase supporting the release of its silencing activity. To functionally validate our tools, we provide evidences that deletion of miR-124 in the subventricular zone altered migration of newly generated neurons into the olfactory bulb. Finally, we also showed that our vectors modified the Ca2+ permeability of AMPA receptors, a robust functional output downstream of miR-124. These tools are expected to help elucidating miRNA function in complex experimental settings such as brain networks in vivo.

Learning new perceptual skills: Individual differences in the computations that integrate novel sensory cues into depth perception
Sensory substitution and augmentation rely on the brains ability to integrate novel sensory cues into its perceptual repertoire. However, the flexibility of the computations that support augmented perception is still not fully known. Here, we contrasted how a novel depth cue is processed, as compared with familiar depth cues, following one hour of training. Observers (N=78) made forced-choice comparisons of surface distances (depths), while we assessed three markers of integration with familiar and newly learned cues: (1) cue combination, predicting precision benefits; (2) re-weighting, predicting reliability-weighted biases; and (3) congruence sensitivity, predicting increased sensitivity for the learned cue mapping. We found that, (1) while familiar cues (size and binocular disparity) were combined near-optimally, there was little evidence for a novel cue (auditory pitch) being combined with binocular disparity. Measures of repeatability across two sessions suggest that this was due to reliable inter-individual differences in combination with the novel cue. In contrast, (2) both familiar and novel cue pairs were re-weighted by their relative reliabilities and (3) showed sensitivity to incongruence. These direct comparisons show that, while a novel cue was rapidly mapped onto depth and weighted in line with its relative reliability, it was not integrated into the native perceptual repertoire by everyone. Reliable individual differences suggest that abilities to combine novel sensory cues may vary between people. These findings provide insight into the flexibility of human sensory processing and suggest that learning to interpret new sensory information depends in part on individual flexibility in perceptual computations.

Highlights- We evaluated evidence for integration of novel and familiar cues to depth
- After short training, direct comparisons show dissociations in integration
- At a group level, novel cues, unlike familiar, were not combined overall
- Strong individual differences suggest varied abilities to combine new cues
- Individual differences seem to play an important role in integration of new cues

Growth charts of infant visual neurodevelopment generalize across global contexts
Normative brain growth charts in early life hold great promise for furthering basic and clinical science. We leverage the rapid, substantial development of visual cortex function that is indexed by visual-evoked potentials (VEP) in electroencephalography to create longitudinal normative growth curves of task-related brain function with 1374 observations contributed by 802 infants (57 to 579 days old) from South Africa, Brazil, and the United States. Site-specific models were cross-validated and showed excellent fits to other sites samples, demonstrating functional growth curves generalize across contexts robustly. Deviations from the normative growth models associated with early environmental and behavioral measures such as prenatal exposures and postnatal cognition. These findings demonstrate the utility of using functional growth charts to understand and potentially act on individual neurodevelopmental trajectories. VEP brain function growth charts represent a new direction for EEG research to serve public health and early identification efforts to support healthy brain development globally.

Autism spectrum disorder risk genes have convergent effects on transcription and neuronal firing patterns in primary neurons
Autism spectrum disorder (ASD) is a highly heterogenous neurodevelopmental disorder with numerous genetic risk factors. Notably, a disproportionate number of risk genes encode transcription regulators including transcription factors and proteins that regulate chromatin. Here, we tested the function of nine such ASD-linked transcription regulators by depleting them in primary cultured neurons. We then defined the resulting gene expression disruptions using RNA-sequencing and tested effects on neuronal firing using multielectrode array recordings. We identified shared gene expression signatures across many ASD risk genes that converged on disruption of critical synaptic genes. Fitting with this, we detected drastic disruptions to neuronal firing throughout neuronal maturation. Together, these findings provide evidence that multiple ASD-linked transcriptional regulators disrupt transcription of synaptic genes and have convergent effects on neuronal firing that may contribute to enhanced ASD risk.

Long-term editing of brain circuits in mice using an engineered electrical synapse
Electrical signaling across distinct populations of brain cells underpins cognitive and emotional function; however, approaches that selectively regulate electrical signaling between two cellular components of a mammalian neural circuit remain sparse. Here, we engineered an electrical synapse composed of two connexin proteins found in Morone americana (white perch fish) - connexin34.7 and connexin35 - to accomplish mammalian circuit modulation. By exploiting protein mutagenesis, devising a new in vitro system for assaying connexin hemichannel docking, and performing computational modeling of hemichannel interactions, we uncovered a structural motif that contributes to electrical synapse formation. Targeting these motifs, we designed connexin34.7 and connexin35 hemichannels that dock with each other to form an electrical synapse, but not with other major connexins expressed in the mammalian central nervous system. We validated this electrical synapse in vivo using C. elegans and mice, demonstrating that it can strengthen communication across neural circuits composed of pairs of distinct cell types and modify behavior accordingly. Thus, we establish Long-term integration of Circuits using connexins (LinCx) for precision circuit-editing in mammals.

Cell-type specific sensory and motor activity in the cuneiform nucleus and pedunculopontine nucleus in mice
The activity of neurotransmitter-based cell types in the cuneiform and pedunculopontine nuclei during locomotion, non-locomotor behaviors, and following sensory stimulation is not fully understood. Using fiber photometry in mice, we found cell-type specific responses to sensory stimuli. Glutamatergic and GABAergic cells responded to sound, visual looming, and air puffs, except for pedunculopontine GABAergic cells, which did not respond to visual looming. Cholinergic cells responded to air puffs. When a stimulus triggered high-speed locomotion, activity increased in cuneiform glutamatergic neurons. Conversely, when low-speed locomotion was triggered, activity increased in pedunculopontine glutamatergic neurons. During spontaneous low-speed locomotion, activity increased in pedunculopontine glutamatergic cells. Activity also increased in a cell type-specific manner during grooming or rearing. Our study shows cell type-specific activity in the cuneiform or pedunculopontine nuclei during locomotion, non-locomotor behaviors, and following sensory stimulation. Sensory responsiveness likely has relevance in Parkinsons disease, where sensory circuits are increasingly targeted to improve walking.

Male and Female Mice Are Similarly Susceptible to Chronic Nondiscriminatory Social Defeat Stress Despite Differences in Attack Frequency from Aggressor
RationaleMood disorders are often precipitated by chronic stress and can result in an inability to adapt to the environment and increased vulnerability to challenging experiences. While diagnoses of mood disorders are diagnosed twice as frequently in women than in men, most preclinical chronic social defeat stress mouse models exclude females due to decreased aggression toward female intruders.

ObjectivesWe previously reported that the chronic non-discriminatory social defeat stress (CNSDS) paradigm is effective in both sexes, allowing for comparisons between male and female mice. We aimed to improve the screening protocol to identify CD-1 aggressors for use in CNSDS and the method for determining susceptibility to CNSDS. Finally, we aimed to determine whether susceptibility to CNSDS correlated with impaired performance in a satiety- based outcome devaluation task.

MethodsWe analyzed CNSDS screening and social defeat sessions to determine appropriate parameters for selecting CD-1 aggressors and investigated aggressions toward male and female intruder mice. We also investigated CNSDS effects on a reward valuation task.

ResultsWe observed that despite receiving fewer attacks, female mice are equally susceptible to CNSDS as males and that CNSDS abolished satiety-based outcome devaluation in susceptible male and female mice, but not in resilient male and female mice.

ConclusionsThese data suggest that CNSDS-defined susceptible and resilient phenotypes extend to reward behaviors.

Lipidomics profiling identifies β-oxidation as a key process in noise-induced hearing loss
Noise-induced hearing loss (NIHL) is the second leading cause of hearing loss worldwide, and the most common cause in young adults. Despite this burden, the molecular mechanisms by which noise causes damage are poorly understood, and there are no pharmacologic therapies to prevent or reduce noise-induced damage to the inner ear. Here, using targeted and untargeted lipidomics, we show that noise exposure induces changes in fatty acid (FA) and acylcarnitine (CAR) species in the inner ear, a metabolic profile indicative of noise-induced increases in {beta}- oxidation. This conclusion is validated through treatment with Etomoxir, an inhibitor of carnitine palmitoyltransferase 1A, the rate-limiting enzyme of long-chain {beta}-oxidation. Furthermore, we demonstrate that blocking {beta}-oxidation with Etomoxir does not affect hearing in a normal acoustic environment but reduces the extent of hearing loss induced by an intense noise exposure (2 hours, 112 dB SPL, 8-16kHz). Together, our findings provide insights into cochlear energy metabolism and suggest that its modulation could be targeted to reduce NIHL.

Temporal dynamics of proteome and phosphorproteome during neuronal differentiation in the reference KOLF2.1J iPSC line
Induced pluripotent stem cell (iPSC)-derived neurons have emerged as a powerful model to investigate both neuronal development and neurodegenerative diseases. Although transcriptomics and imaging have been applied to characterize neuronal development signatures, comprehensive datasets of protein and post-translational modifications (PTMs) are not readily available. Here, we applied quantitative proteomics and phosphoproteomics to profile the differentiation of the KOLF2.1J iPSC line, the first reference line of the iPSC Neurodegenerative Disease Initiative (iNDI) project. We developed an automated workflow enabling high-coverage enrichment of proteins and phosphoproteins. Our results revealed molecular signatures across proteomic and phosphoproteomic landscapes during differentiation of iPSC-derived neurons. Proteomic data highlighted distinct changes in mitochondrial pathways throughout the course of differentiation, while phosphoproteomics revealed specific regulatory dynamics in GTPase signaling pathways and microtubule proteins. Additionally, phosphosite dynamics exhibited discordant trends compared to protein expression, particularly in processes related to axon functions and RNA transport. Furthermore, we mapped the kinase dynamic changes that are critical for neuronal development and maturation. We developed an interactive Web app (https://niacard.shinyapps.io/Phosphoproteome/) to visualize temporal landscape dynamics of protein and phosphosite expression. By establishing baselines of proteomic and phosphoproteomic profiles for neuronal differentiation, this dataset offers a valuable resource for future research into neuronal development and neurodegenerative diseases using this reference iPSC line.

HighlightsO_LITemporal dynamics of proteome and phosphoproteome profiles in KOLF2.1J iPSC derived neurons.
C_LIO_LIPhosphoproteomics highlights GTPase signaling and microtubule regulation in neuronal differentiation.
C_LIO_LIKinome mapping reveals a shift in kinase activity patterns from early to late differentiation.
C_LIO_LIShinyapp for visualizing the trajectory of protein and phosphosite expression during neuronal differentiation.
C_LI

Unraveling Temporal Dynamics of SPSD matrices viaRiemmanian Multi-Scale Decomposition
Understanding the temporal dynamics of high-dimensional, time-varying systems remains a fundamental challenge across scientific disciplines. In many real-world systems, the interactions between sensors are also time-varying, which presents an additional level of complexity in extracting a meaningful representation and subsequent modeling. In this work, we introduce a novel multi-resolution frame-work for analyzing the temporal dynamics of Symmetric Positive Semi-Definite (SPSD) matrices, such as correlation matrices. A key innovation of our framework is a difference operator that respects the symmetry of the Riemannian manifold. We apply this operator recursively and extract a hierarchical representation of the temporal dynamics of SPSD matrices with a fine frequency resolution. We employ spectral analysis to select informative frequency components and extract key structural patterns. Specifically, our approach reveals which sensors of the original signal drive the correlational dynamics, thus enhancing both denoising and the interoperability of SPSD matrix evolution over time. We validate our method using synthetic data and real neural recordings from mice trained to perform a motor task. Our results demonstrate its ability to extract meaningful structures from complex temporal datasets and provide deeper insights into evolving network connectivity.

Enhancing Brain Age Prediction and Neurodegeneration Detection with Contrastive Learning on Regional Biomechanical Properties
The aging process affects brain structure and function, yet its biomechanical properties remain underexplored. Magnetic Resonance Elastography (MRE) provides a unique perspective by mapping brain tissue stiffness and damping ratio, observables that correlate with age and disease. Using a self-supervised contrastive regression framework, we demonstrate that MRE surpasses conventional structural magnetic resonance imaging (MRI) in sensitivity. Specifically, stiffness captures Alzheimers disease (AD), while damping ratio detects subtle changes associated with mild cognitive impairment (MCI). Our regional analysis identifies deep brain structures, particularly the caudate and thalamus, as key biomarkers of aging. The greater age sensitivity of MRE translates to superior differentiation of AD and MCI from healthy individuals, pinpointing regions where significant biomechanical alterations occur, notably the thalamus in AD and hippocampus in MCI. Furthermore, our results reveal biomechanical alterations in cognitively healthy individuals whose aging profiles closely resemble patients with MCI and AD. These findings highlight MREs potential as a biomarker for early neurodegenerative changes, aiding dementia risk detection and early intervention.

Fruit flies actively restart their circadian clock by proactively shaping their environment
Circadian clocks are prevalent on Earth and are generally believed to provide adaptive advantage to organisms. Functional circadian clocks, and their synchronization with the outside world, has also been implicated to provide health benefits for humans. However, experimental evidence for the benefits of possessing a circadian clock is sparse and largely restricted to prokaryotic organisms. Here, we provide evidence for the benefits of circadian clocks and temporally organized life in the fruit fly Drosophila melanogaster. We demonstrate that flies prefer and actively choose to live under circadian clock regulation: Exposure to constant light breaks down the circadian clock and leads to arrhythmic locomotor activity patterns. When given the choice to move between dark and illuminated areas in a constant light environment, flies were able to maintain, or even re-gain, rhythmic behavioural patterns. These rhythms were mirrored by regular positional changes between the two areas, demonstrating that flies actively contribute to creating an environment allowing circadian clock function and temporal organisation. The self-inflicted rhythms were accompanied by molecular rhythms in the majority of the clock neurons known to drive behavioural rhythms in flies, showing that they are indeed controlled by the circadian clock. Finally, behavioural rhythmicity was correlated with restoration of rhythmic sleep patterns and less-fragmented sleep compared to arrhythmic flies. While life is possible without a circadian clock, we show that if given the choice, animals prefer to live in a temporally organized manner and actively contribute to make this possible. This provides strong arguments for the benefits of possessing and using a circadian clock, for example by ensuring a better quality of sleep.

Human sensory neurons exhibit cell-type-specific, pain-associated differences in intrinsic excitability and expression of SCN9A and SCN10A
Despite the prevalence of chronic pain, the approval of novel, non-opioid therapeutics has been slow. A major translational challenge in analgesic development is the difference in gene expression and functional properties between human and rodent dorsal root ganglia (DRG) sensory neurons. Extensive work in rodents suggests that sensitization of nociceptors in the DRG is essential for the pathogenesis and persistence of pain; however, direct evidence demonstrating similar physiological sensitization in humans is limited. Here, we examine whether pain history is associated with nociceptor hyperexcitability in human DRG (hDRG). We identified three electrophysiologically distinct clusters (E-types) of hDRG neurons based on firing properties and membrane excitability. Combining electrophysiological recordings and single-cell RNA-sequencing ("Patch-seq"), we linked these E-types to specific transcriptionally defined nociceptor subpopulations. Comparing hDRG neurons from donors with and without evident pain history revealed cluster-specific, pain history-associated differences in hDRG excitability. Finally, we found that hDRG from donors with pain history express higher levels of transcripts encoding voltage-gated sodium channel 1.7 (NaV1.7) and 1.8 (NaV1.8) which specifically regulate nociceptor excitability. These findings suggest that donors with pain history exhibit distinct hDRG electrophysiological profiles compared to those without pain history and further validate NaV1.7 and 1.8 as targets for analgesic development.

Salient objects in a scene trigger enhanced perceptual selection and memory encoding
Rapidly detecting salient objects from surrounding environments is crucial for survival. Our study demonstrates that salient objects in visual search arrays trigger von Restorff-like effects. In a search task, participants detected a tilted target bar among distractors with EEG recordings. The results revealed that salient objects elicited the largest and earliest N2pc component, reflecting early attentional selection, which enhanced multivariate decoding of target location. Importantly, early selection of highly salient targets (25{degrees} tilt) triggered a cascade of preferential processing downstream, marked by stronger P3b components, neural synchronization, and phase-amplitude coupling between low- and high-frequency activity, along with better recall performance of target orientation. The strength of memory-related activity on the current trial predicted the vigor of the next selection event, indicating that salience-driven learning influences future attentional control. Overall, object salience in spatial arrays drives a cascade of processing, facilitating rapid learning of object relevance while humans search their environment.

Systematic bias in surface area asymmetry measurements from automatic cortical parcellations
Anatomical asymmetry is a hallmark of the human brain and may reflect hemispheric differences in its functional organization. Widely used software like FreeSurfer can automate neuroanatomical measurements and facilitate studies of hemispheric asymmetry. However, patterns of surface area lateralization measured using FreeSurfer are curiously consistent across diverse samples. Here, we demonstrate systematic biases in these measurements obtained from the default processing pipeline. We compared surface area asymmetry measured from reconstructions of original brains vs. the same scans after flipping their left-right orientation. The default pipeline returned implausible asymmetry patterns between the original and flipped brains: Many structures were always left- or right-lateralized. Notably, these biases occur prominently in key speech and language regions. In contrast, manual labeling and curvature-based parcellations of key structures both yielded the expected reversals of left/right lateralization in flipped brains. We determined that these biases result from discrepancies in how regional labels are defined in the left vs. right hemisphere in the default cortical parcellation atlases. These biases are carried into individual parcellations because the FreeSurfer parcellation algorithm prioritizes vertex correspondence to the template atlas relative to individual neuroanatomical variation. We further demonstrate several straightforward, bias-free approaches to measuring surface area asymmetry, including using symmetric registration templates and parcellation atlases, vertex-wise analyses, and within-subject curvature-based parcellations. These results highlight theoretical concerns about using only the default processing stream to make inferences about population-level brain asymmetry and underscore the need for validating bias-free neuroanatomical measurements, particularly when studying regions where structural lateralization may underlie functional lateralization.

Personalized mapping of inhibitory spinal circuits via neural decoding of high-density electromyography and in silico modelling
Studying human motoneuron activity through electromyography (EMG) can yield insights into the operation of fundamental spinal cord microcircuits. Traditional surface and needle electromyography (EMG) methodologies have limited capacity to shed light on the diversity of motor unit (MU) control strategies that may be unique to each individual. Here, we employed high-density surface EMG (HDsEMG) to sample multiple MUs per subject to investigate the dynamics of inhibitory spinal microcircuits in both upper and lower limb control. We characterised the net inhibition as a function of individual MU firing rates, revealing subject-specific relationships. In silico modelling replicated these experimental characteristics and suggested that properties of the inhibitory currents rather than motoneuron size are responsible for net functional inhibition. Our results show that HDsEMG can highlight distinct control strategies across circuits and motor pools, revealing subject-specific properties of inhibitory spinal microcircuits.

TEASERHigh-density surface EMG electrodes can reveal the functional properties of inhibitory spinal circuits

iGABASnFR2: Improved genetically encoded protein sensors of GABA
Monitoring GABAergic inhibition in the nervous system has been enabled by development of an intensiometric molecular sensor that directly detects GABA. However the first generation iGABASnFR exhibits low signal-to-noise and suboptimal kinetics, making in vivo experiments challenging. To improve sensor performance, we targeted several sites in the protein for near-saturation mutagenesis, and evaluated the resulting sensor variants in a high throughput screening system using evoked synaptic release in primary cultured neurons. This identified a sensor variant, iGABASnFR2, with 4.2-fold improved sensitivity and 20% faster kinetics, and binding affinity that remained in a range sensitive to changes in GABA concentration at synapses. We also identified sensors with an inverted response, decreasing fluorescence intensity upon GABA binding. We termed the best such negative-going sensor iGABASnFR2n, which can be used to corroborate observations with the positive-going sensor. These improvements yielded a qualitative enhancement of in vivo performance, enabling us to make the first measurements of direction selective GABA release in the retina and confirm a longstanding hypothesis for how sensitivity to motion arises in the visual system.

The gut microbiome in early age-related macular degeneration
The gut microbiome is implicated in the development of advanced age-related macular degeneration (AMD) but no study has investigated microbial composition in early AMD nor controlled for important microbiome modulators such as diet and light. This is crucial as diet can change the microbiome rapidly as well as affects disease progression. In addition, the light signalling photoreceptors are dysfunctional in AMD. Here we determined the gut microbiota by conducting 16S DNA metagenomic sequencing of 40 faecal samples from 20 participants with and without early AMD. We normalised both groups to the same diet over 5 days and determined gut microbial composition before and after the diet. To control for light, we assessed habitual light exposure with actigraphy and the post-illumination pupil light response (PIPR) quantified photoreceptor signalling. Clostridium termitidis, Bacteroides gallinarum, and Bacteroides finegoldii were significantly abundant in early AMD compared to controls at both time points. Interestingly, pro-inflammatory species such as Sutterella were only significantly abundant in AMD before the diet. After the diet, the gut microbiome shifted to a significantly greater abundance of commensal bacteria in AMD. Controls and AMD patients had a similar ambient light exposure, however the PIPR was significantly reduced in AMD suggesting impaired light signalling. We are the first to show a distinct gut microbiome in early AMD and infer that dietary changes may positively affect the gut microbiome at early stage of the disease.

TNF-α disrupts the malate-aspartate shuttle, driving metabolic rewiring in iPSC-derived enteric neural lineages from Parkinson's Disease patients
Gastrointestinal (GI) dysfunction emerges years before motor symptoms in Parkinsons disease (PD), implicating the enteric nervous system (ENS) in early disease progression. However, the mechanisms linking the PD hallmark protein, -synuclein (-syn), to ENS dysfunction - and whether these mechanisms are influenced by inflammation - remains elusive. Using iPSC-derived enteric neural lineages from patients with -syn triplications, we reveal that TNF- increases mitochondrial--syn interactions, disrupts the malate-aspartate shuttle, and forces a metabolic shift toward glutamine oxidation. These alterations drive mitochondrial dysfunction, characterizing metabolic impairment under cytokine stress. Interestingly, targeting glutamate metabolism with Chicago Sky Blue 6B restores mitochondrial function, reversing TNF--driven metabolic disruption. Our findings position the ENS as a central player in PD pathogenesis, establishing a direct link between cytokines, -syn accumulation, metabolic stress and mitochondrial dysfunction. By uncovering a previously unrecognized metabolic vulnerability in the ENS, we highlight its potential as a therapeutic target for early PD intervention.

Sleep-Modulated Cross-Frequency Coupling Between δ Phase and β-γ Bistability: A System-Level Modulation of Epileptic Activity
ObjectiveTo investigate the mechanistic link between large-scale {delta}-band (0.5-4 Hz) synchrony during sleep and local bistability of the {beta}-{gamma} band (15-200 Hz), a biomarker of the epileptogenic zone.

Methods7-9-hour stereo-EEG sleep recordings were obtained from 14 subjects (22.3 {+/-} 10.8 years old; 7 males) with sleep-related hypermotor epilepsy. These recordings were segmented into 10-minute epochs of uninterrupted interictal N2 and N3 stages of NREM sleep. We assessed phase synchrony, phase-amplitude coupling (PAC), and {beta}-{gamma} band (15-200 Hz) bistability. Canonical correlation analysis was used to explore whether PAC links {delta}-synchrony to {beta}-{gamma} band bistability.

ResultsCompared to non-epileptogenic regions (nEZ), the epileptogenic zone (EZ) exhibited stronger 15-200 Hz bistability and greater 2-8 Hz and 15-100 Hz phase synchrony. Compared to N3, N2 showed stronger PAC between 2-30 Hz phases in nEZ and 5-150 Hz amplitudes in EZ. Canonical correlations between direction-specific {delta}-modulated PAC and both bistability and synchrony were identified during N2 (r = 0.86 and 0.82) and N3 (r = 0.84 and 0.80), with the strongest contributors being 2-4 Hz synchrony and bistability in 2-4 Hz and 15-200 Hz bands. Correlations between interictal spikes and canonical covariates of bistability and PAC (r2=0.62 for N2 and 0.56 for N3) validated their relevance to epileptogenicity.

Interpretation{delta}-band synchrony and {beta}-{gamma} band bistability play a pivotal role in epileptogenic mechanisms, likely through the coupling of {delta} phases and {beta}-{gamma} amplitudes across large-scale networks--with a significant contribution from the nEZ tissues.

Multi-center Translational Trial of Remote Ischemic Conditioning in Acute Ischemic Stroke (TRICS BASIC)
BackgroundBasic science studies have reported remote ischemic conditioning (RIC) as neuroprotective in acute ischemic stroke, while clinical evidence remains conflicting. The TRICS BASIC study investigated the efficacy and safety of RIC in experimental ischemic stroke using a rigorous clinical trial methodology.

MethodsMulti-center, multi-species, parallel group, randomized, controlled, preclinical trial of transient femoral artery clipping to induce RIC in female and male rats and mice subjected to transient endovascular occlusion of the middle cerebral artery. Animals were randomized to receive RIC, or sham surgery, after reperfusion. The primary endpoint was good functional outcome at 48 hours, assessed using a composite functional neuroscore. Secondary endpoints was infarct volume at 48 hours and safety, assessed using a standardized health report at 24 and 48 hours. Pre-enrollment harmonization, centralized monitoring, allocation concealment, blinded outcome assessment and intention-to-treat analysis were applied.

ResultsThe trial enrolled 164 rodents (82 mice and 82 rats) of both sexes (53% females), across seven laboratories. A greater proportion of RIC-treated rodents achieved a favorable functional outcome compared to controls, at 48 hours post-ischemia (55% versus 36%; OR 2.2, 95% CI [1.23-4.4], p=0.009). RIC was associated with a small reduction in infarct volume (standardized mean difference -0.38, 95% CI [-0.70, -0.05], p=0.024). Health monitoring indicated no major safety concerns, and post-operative analgesia requirements were lower in RIC-treated mice.

ConclusionsSurgically-induced RIC provided a modest but evident neuroprotective effect in experimental ischemic stroke, underscoring the potential of this strategy as an adjunctive treatment in stroke care. The findings of the TRICS BASIC study highlighted the importance of multicenter preclinical trials in addressing variability and enhancing translational validity.

Registrationregistered at preclinicaltrials.eu, identifier PCTE0000177.