eneuro

The main olfactory epithelium (MOE) is the primary site of olfaction and consists of multiple cell types including olfactory sensory neurons (OSNs), sustentacular cells, and immune cells. Neuroimmune interactions in epithelial tissues are critical in maintaining tissue function, but how OSNs and immune cells interact in the MOE in healthy and diseased states is largely unknown. Cellular responses in the MOE determine how and whether OSNs maintain olfactory function and are repaired or replenished following inflammatory environmental exposures. We hypothesized that acute nasal aeroallergen exposure alters immune cell function in the MOE to elicit a neuroprotective response, thereby preserving OSN function. We developed an environmental aeroallergen exposure consisting of one week of daily intranasal house dust mite extract (HDM) instillations. Spectral flow cytometry indicated only subtle changes in resident immune cells proportions and phenotypes in the MOE. Immunohistochemical evaluation did not reveal extensive changes in immune cell distribution in the sensory epithelium or lamina propria, but instead we observed increases in axonal olfactory marker protein (OMP) expression in the lamina propria, where resident immune cells are most abundant. To evaluate the effects of HDM exposure on OSN function, we performed live ex vivo Ca2+ imaging of MOEs from HDM- and sham-exposed transgenic mice using objective-coupled planar illumination (OCPI) microscopy. OSN responses to multiple odorants revealed increased chemosensory sensitivity and decreased across-trial adaptation in HDM-treated epithelia. These results indicate that short-term nasal aeroallergen exposure minimally alters immune cell phenotypes, and instead induces functional changes in OSN physiology that preserve olfactory function.

Extensive experiments document that SPARCL1, a secreted protein that is produced primarily by astrocytes in brain and endothelia throughout the body and that is also known as Hevin, enhances synapse formation. However, the mode of action of SPARCL1 at synapses remains unclear owing to divergent results in the literature. Here, we use cultured neurons from newborn male and female mouse embryos to show that the C-terminal follistatin-like and Ca2+-binding domains of SPARCL1, which account for only 35% of the total SPARCL1 sequence, are sufficient to potently enhance synapse numbers. SPARCL1 acts at nanomolar concentrations at which SPARCL1 does not robustly bind to neurexins, neuroligins or neurexin/neuroligin complexes but avidly interacts with all teneurins. Strikingly, the follistatin-like domain of SPARCL1 on its own strongly binds to teneurins but is unable to stimulate synapse formation. Only when combined with the SPARCL1 Ca2+- binding domain does the follistatin-like domain induce synapses, suggesting that SPARCL1 enhances synapse numbers by binding to teneurins via its C-terminal follistatin-like domain and by activating synapse formation via its Ca2+-binding domain. SIGNIFICANCE STATEMENTSPARCL1 (also known as Hevin) is a synaptogenic factor that is produced primarily by astrocytes in brain, and that enhances synapse formation. How SPARCL1 acts at synapses, however, remains unclear because divergent results describe its binding partners at synapses and the sequences involved in its synaptogenic activity remain unclear. In the present study, we show that SPARCL1 avidly binds to the presynaptic teneurins adhesion molecules, that this binding is mediated by its small follistatin-like domain, and that its synaptogenic activity requires both its follistatin-like and its Ca2+-binding EC domains. Thus, our results suggest that SPARCL1 is recruited to developing synapses by binding of its follistatin-like domain to teneurins and then induces synapse assembly via its Ca2+-binding domain.

In noisy environments, visible speech articulations improve listening comprehension. The benefit derives from several sources, including articulatory timing and shape. Recent research has shown that visual cortex encodes a categorical representation of articulatory features and that visual speech can benefit both acoustic and phonetic feature processing separately. The present study advances the hypothesis that the shape of the articulators specifically influences the categorization of auditory speech in terms of its phonetic features. We tested this by linearly modeling electroencephalographic responses to natural, continuous speech (in noise) in terms of the acoustic and articulatory features of the speech. We compared the performance of these models in conditions where the speech was accompanied by a natural video of the speaker with their mouth visible, and a video where their mouth was covered by a dynamic ellipse obscuring articulatory shape but preserving dynamics. The dynamic mask reduced comprehension, neural processing of phonetic features, the associated multisensory benefits, and indices of visual-only linguistic processing over occipital scalp. Our findings support substantial visual involvement in speech comprehension, derived largely from the shape of the articulators. They also corroborate several proposals involving audiovisual speech processing hierarchy and the nature of the information contained in visible speech. HighlightsO_LIVisual speech provides at least two forms of information to enhance acoustic speech processing: redundant temporal dynamics and complementary articulatory information C_LIO_LICovering the mouth with a dynamic mask preserves horizontal and vertical lip movement information, but largely removes articulatory detail C_LIO_LIVisual speech with a mask preserves some general multisensory benefits but removes visual linguistic information and its ability to enhance auditory processing at the level of phonetic features. C_LI

Interception of moving objects requires the nervous system to compensate for sensory delays and uncertainty, yet how behavior is controlled remains debated. Key questions concern whether predictive processes play any role at all and, if so, whether they rely on simple motion extrapolation or incorporate internalized physical priors, such as gravity. Another open question is whether observers adopt a single control strategy or flexibly switch between predictive and reactive control - or between different predictive strategies - depending on task demands. To address these questions, we developed a virtual interception task in which participants intercepted moving targets under systematically varied conditions. We manipulated gravity (1g vs. 0g), visual availability (occluded vs. non-occluded), target velocity, and the initial spatial configuration of the ball and paddle (same vs. opposite side). Results indicate that interception is supported by predictive mechanisms across conditions. Behavioral patterns during occluded 0g trials suggest that participants extrapolate target motion using expectations consistent with gravity. Target velocity, visual occlusion, and task geometry modulated movement strategies, indicating that predictive control is flexibly adapted to task demands. These findings support the view that interception relies on predictive internal models incorporating structured physical priors while revealing flexible, context-dependent adaptations to sensory and task constraints.

Cortisol, our stress hormone, exerts widespread influence on neural activity. However, its influence on the aperiodic component of the electroencephalography power spectrum remains to be investigated. Given individual differences in the capacity to cope with stress and adversity, it also remains unclear whether trait resilience moderates this relationship. Hence, the present study examined whether individual differences in trait resilience moderates the association between resting cortisol and aperiodic activity. Participants (N=145) completed various self-report questionnaires (e.g., trait resilience). Electroencephalography was recorded over a 20-minute baseline period, followed by salivary cortisol collection. The results revealed a significant moderating effect of trait resilience in the occipital scalp region. Specifically, higher cortisol concentration was associated with flatter 1/f slopes amongst individuals with low trait resilience, whereas this association was reversed amongst those with high trait resilience. Overall, our findings highlight the role of individual differences in trait resilience in shaping hypothalamic-pituitary-adrenal axis-related neural dynamics.

Hippocampal place cell activity represents an animals location in space; yet, how hippocampal neuronal population dynamics change with spatial learning and the mechanisms underlying these activity changes, which drive allocentric navigation to a learned goal, are poorly understood. To address these questions, we performed calcium imaging with a novel wire-free waterproof miniaturized microscope to image the activity of large populations of hippocampal CA1 neurons during spatial learning of a two-dimensional navigational task, the Morris water maze. We followed the same cells during learning and were able to directly examine how each neuron in the ensemble, and the ensemble as a whole, changes its response properties. We found that neuronal spatial selectivity increased and population decoding of spatial location improved as mice learned to navigate to the goal. Viral CRISPR knock out of Grin1 (encoding the essential GluN1 NMDA receptor subunit) in dorsal hippocampal neurons, dramatically reduced long-term potentiation in CA1. This manipulation also prevented the increase in spatial selectivity and improvement of population decoding with spatial learning and resulted in learning deficits in the Morris water maze. Together, our results show that dorsal hippocampus NMDAR-dependent synaptic plasticity is essential for the learning-dependent refinement of CA1 place selectivity and improvement in population decoding of space.

Intracranial neurophysiology studies have typically ignored signals from electrodes located in white matter (WM), assuming that their information content is artifactual or related to nearby gray matter (GM). Here, we tested the electrophysiological and functional features of signals recorded from different WM locations. Signals were recorded from 19 patients undergoing intracranial monitoring for drug-resistant epilepsy by means of stereo-electroencephalography (sEEG). Each sEEG electrode was classified into WM or GM based on the surrounding tissue. We obtained recordings from a total of 1,717 sEEG electrode contacts, 36% in WM, while the patients were in awake resting state (5 minutes). For each sEEG electrode, we employed a model-based spectral decomposition to separate periodic and aperiodic components, and we computed signal complexity metrics. For a subset of participants, we computed WM structural information from diffusion-weighted magnetic resonance imaging and we evaluated functional signals during a cognitive control task. Our results show that signals recorded from WM have different spectral features and higher complexity than GM. Complexity correlates positively with fractional anisotropy, and modulations related to behavior during the task were detected in WM. Overall, this indicates that WM signals carry information that may reflect signal propagation across WM fiber tracts.

Brain-computer interfaces (BCIs) have achieved impressive performance by decoding motor and articulatory signals associated with speech production. However, considerably less is known about whether higher-level semantic representations can be decoded from human cortical activity. Demonstrating semantic decoding would advance both our understanding of language organization and the development of BCIs that rely on conceptual rather than purely articulatory information. We recorded intracranial neural activity from patients undergoing stereotactic electroencephalography (sEEG) for clinical epilepsy monitoring while they performed language tasks requiring semantic processing. High-gamma power was extracted from local field potentials and used to generate trial-level features for supervised machine-learning classification. Classification performance was evaluated using cross-validation. Semantic category information was decoded significantly above chance, with mean classification accuracy reaching 29.8% across 15 semantic categories (chance = 6.7%). These findings demonstrate that high-gamma activity contains information about conceptual category membership that can be extracted on individual trials. These results provide evidence that semantic information is accessible from intracranial population recordings and support the feasibility of semantic decoding as a complementary direction for future language BCIs. Beyond neuroprosthetic applications, this work contributes to understanding how conceptual knowledge is represented in the distributed human language network.

Objective: Working memory (WM) deficits are a co-occurring feature to numerous neuropsychiatric disorders, particularly attention-deficit/hyperactivity disorder (ADHD), and there remain no treatments that directly target WM. The coupling between the phase of theta band activity and amplitude of gamma band activity (i.e., TGC) is an established neural correlate of WM. However, no studies have examined WM-related TGC in ADHD or whether neuromodulation can modulate these oscillatory dynamics in youth. This set of studies examined the effects of intermittent theta burst stimulation (iTBS) to the left dorsolateral prefrontal cortex (DLPFC) and left posterior parietal cortex (PPC) on TGC in youth with ADHD. Methods: In two randomized, double-blind, sham-controlled crossover trials, adolescents with ADHD and clinically significant parent-reported WM symptoms first completed a single-session study comparing DLPFC versus PPC iTBS targeting (n = 47) and then a multi-session clinical trial comparing 10 sessions of active versus sham left DLPFC iTBS (n = 29). Participants completed a computerized visuospatial Sternberg WM task with concurrent electroencephalography (EEG) before and after the single sessions, as well as at baseline, midway through treatment, and approximately 24 hours after the final session within the multi-session trial. Phase-amplitude coupling between theta phase and gamma amplitude was quantified using the Kullback Leibler modulation index at frontoparietal electrodes. Linear mixed-effects models examined treatment effects and associations between change in TGC and WM status (including accuracy, reaction time, and clinical symptoms). Results: Across participants, lower TGC was associated with lower symptoms and better WM performance, including higher accuracy, faster and more consistent RT. Active iTBS increased frontoparietal TGC relative to sham stimulation, with effects observed both acutely after a single session and ~24 hours after multiple sessions. DLPFC-targeted iTBS increased TGC, whereas PPC-iTBS had no measurable effect. Change in TGC was associated with change in WM, such that a decrease in TGC was associated with faster RT and decreased RT variability. Higher baseline TGC was associated with greater improvement in WM. Active iTBS decoupled the TGC-WM association observed during sham iTBS, and greater electric field intensity of iTBS was associated with greater improvement in WM accuracy and greater decrease in TGC. Conclusions: Active iTBS to the left DLPFC modulated WM-related TGC in youth with ADHD. These findings provide preliminary evidence that neuromodulation may improve WW by modifying oscillatory dynamics within frontoparietal networks. Larger clinical trials with higher stimulation doses are needed to determine whether targeting oscillatory coupling represents a potential therapeutic strategy for WM deficits.

Cognitive flexibility is an executive function that allows individuals to adjust behaviour in response to changing environmental demands. We assessed volitional switching under uncertainty, without rule-based learning, in the Change Your Mind task. Nineteen patients with obsessive-compulsive disorder (OCD), 19 patients with attention-deficit hyperactivity disorder (ADHD) and matched control participants (20 per group) completed the task whilst undergoing a functional MRI scan. The task was a two-alternative forced choice paradigm where each stimulus was presented twice successively, with spurious feedback following the first presentation. This allowed participants the opportunity to repeat or change their response. Participants with ADHD changed their response more frequently than controls following a previously correct response, associated with reduced accuracy on the second trial. This was accompanied with smaller differences between change and repeat trials in the superior frontal gyrus, paracingulate gyrus and frontal pole compared to controls. Participants with OCD did not differ from healthy controls in their performance but exhibited greater activity on both change and repeat trials in the pre- and postcentral gyri than controls. These results point to distinct neurobehavioural differences in patients with ADHD and OCD underlying what is often termed more broadly inflexible behaviour.

Traumatic brain injury (TBI) is one of the leading causes of emergency room visits in children under 10. Children are potentially more vulnerable to the adverse effects of TBI, given that their brains are still developing at the time of injury. Indeed, early life TBI has been linked to cognitive, social, and mood-related impairments later in life. The neuroimmune system has been implicated in adult TBI mechanisms and plays numerous key roles in brain development, making it an interesting candidate for linking pediatric TBI and prolonged behavioral alterations. Here we establish a rat model of mild pediatric TBI to investigate the relationship between early life TBI, acute responses of neuroimmune cells, and chronic behavioral dysregulation. At postnatal day 15, which is roughly equivalent to toddler age, male and female rat pups received a TBI via lateral fluid percussion injury. At 3 days post injury, TBI increased microglia and astrocyte coverage locally in the Perilesional Cortex but not in more distant corticolimbic regions. However, the hippocampus and prefrontal cortex did exhibit increased expression of the phagocytic marker CD68 in microglia, suggesting widespread glial activation even in the absence of gross coverage change. TBI also impacted mast cells, early-response innate immune cells, increasing their number and degranulation in multiple regions. In the juvenile and early adult periods, TBI impaired cognitive function, reduced sociability, and increased avoidance, with no change in anxiety-like behavior. Later in adulthood, TBI continued to impact cognitive behavior, increasing risky decision-making and impairing optimization months after injury. Together, these results suggest that pediatric TBI causes lasting cognitive and social dysregulation, possibly via acute neuroimmune alterations following injury at a critical period of brain development.

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

Speech perception requires transforming acoustic input into neural representations that support linguistic understanding, yet its underlying computational principles remain unclear. Classical efficient coding theories posit optimal compression of sensory input, whereas alternative accounts propose that neural systems preferentially encode information that supports prediction. A key open question is whether such predictive encoding operates on fixed inputs or on flexible internal representations. We instantiated three hypothesis models of speech processing: (i) optimal compression with deep autoencoders, (ii) predictive reconstruction with predictive autoencoders, and (iii) predictive information representation via latent-space prediction using contrastive learning. We compared resulting speech latent representations to electroencephalographic (EEG) activity during speech listening. Representations learned under the predictive information objective best explained neural latents. Crucially, only representations that selectively compressed predictive information predicted behavioral performance, suggesting that neural speech representations are structured to encode predictive information in latent space rather than to maximize compression or input prediction.

Cooling the skin may increase its tolerance to mechanical loading and decrease the risk of developing pressure ulcers. Yet, the mechanisms of action (e.g. cooling-modulation of cytotoxic, post-occlusive hyperaemia), and their individual variability, remain unclear. We investigated the effects of different cooling levels (24{degrees}C and 16{degrees}C) on microvascular, inflammatory and perceptual responses to mechanical loading of the sacrum in healthy young (N=23) and older adults (N=19), and in spinal cord injury patients (SCI; N=10). Healthy participants underwent 45-min loading (~60 mmHg) and 20-min unloading of the sacrum, using an instrumented indenter probe set at either 38{degrees}C (control condition), 24{degrees}C or 16{degrees}C. SCI participants completed a more conservative protocol (i.e. 25min, ~45mmHg loading, 38{degrees}C and 16{degrees}C conditions). Pre-insult skin structure was characterised with optical coherence tomography; skin blood flow (SkBF) at the loading site was continuously measured, alongside thermal acceptability; and post-insult inflammatory responses were determined via skin-sebum cytokines analyses. Compared to control, 24{degrees}C- and 16{degrees}C-cooling induced a similar ~8-fold decrease in peak post-occlusive reactive hyperaemia in healthy participants, with similar temperature-related differences observed in SCI. Pro-inflammatory cytokines decreased post-insult; yet this occurred similarly across all temperatures and groups. The majority of participants ([≥]70%) rated both 24{degrees}C- and 16{degrees}C-cooling as thermally acceptable. We conclude that cooling is a potent modulator of the skin microvascular response to mechanical loading in younger, older, and vulnerable skin (SCI). These findings can inform design parameters for thermal technology aimed at preventing the loss of skin integrity (e.g. integrating 24{degrees}C-cooling in support surfaces and skin wearables).

Efferocytosis, the recognition, engulfment, and degradation of apoptotic cells by phagocytes, is essential for tissue homeostasis and development, and its failure contributes to chronic inflammation and neurodegeneration. The amyloid precursor protein (APP), a central pathogenic factor in Alzheimers disease, retains physiological functions independent of amyloid production that remain poorly understood. Here, we identify a conserved, non-amyloidogenic role for APP in regulating apoptotic cell degradation via the endolysosomal pathway. Using Drosophila APPL as a model, structure-function analysis demonstrated that the intracellular internalization domain of APPL, but not its secreted ectodomain, is required for efficient apoptotic cell degradation. Immunoprecipitation coupled with mass spectrometry revealed a physical interaction between APPL and the microtubule severing ATPase Spastin, mediated by the microtubule-interacting and trafficking domain of Spastin. APPL interacts with Spastin on endosomal microtubules and modulates the dynamics of the Spastin-ESCRT-III complex, enabling Spastin to sever microtubules and promote endosomal tubule fission. Loss of APPL disrupts this process, causing aberrant endosomal tubulation and impaired lysosome biogenesis. Furthermore, it compromises the function of residual lysosomes, characterized by reduced acidity, diminished proteolytic activity, and increased lysosomal damage, which ultimately impairs the degradation of engulfed apoptotic cells. Critically, this phenotype is evolutionarily conserved in C. elegans and mice. Together, these findings establish a conserved APP-Spastin axis that regulates endolysosomal homeostasis and apoptotic cargo digestion. This reveals a critical non-amyloidogenic function of APP in maintaining tissue homeostasis through efficient efferocytosis, with broad implications for inflammatory and neurodegenerative disorders that warrant further investigation.

Multimodal imaging approaches that combine structural and functional neuroimaging provide a robust framework for examining neuroplastic adaptations that may not be captured by any single modality. The present study investigated the effects of a four-week expiratory muscle strength training (EMST) program on structural and resting-state functional connectivity in healthy young adults. Five healthy young adult males (aged 19-35 years) completed a standard four-week EMST protocol and underwent pre- and post-training imaging assessments. Structural neuroimaging included T1-weighted and diffusion-weighted MRI, which were analyzed using voxel-based morphometry, surface-based morphometry, and white-matter structural connectivity. Functional neuroimaging consisted of resting-state fMRI to assess training-related changes in functional architecture, network connectivity, and global network measures. Structural MRI analyses revealed no significant changes in gray or white matter volume, cortical morphology, or white-matter structural connectivity following EMST (all FWE- or FDR-corrected p > .05). In contrast, resting-state fMRI demonstrated a significant increase in whole-brain functional connectivity (FDR-corrected p = .036), accompanied by greater network integration, reflected in increased local efficiency and transitivity and reduced modularity. Network-level analyses showed enhanced within- and between-network connectivity in sensorimotor and cognitive circuits. Our findings demonstrate robust functional reorganization following EMST, despite the absence of detectable macrostructural or large-scale white-matter connectivity changes, at least within the timescale and sample characteristics of the current study. These results reflect early-stage neuroplasticity, both globally and within the networks underlying speech and swallowing control and suggest that functional reorganization occurs early in training and likely precedes longer-term structural modifications in these networks.

Background The global prevalence of autism spectrum disorder (ASD) has significantly increased over the past two decades. Despite substantial research advances, critical aspects, including etiology, diagnostic biomarkers, and pharmacological interventions, remain incompletely elucidated. This persistent knowledge gap warrants systematic mapping of the field's evolution to inform future research priorities. Methods A bibliometric analysis of ASD-related publications indexed in Web of Science was conducted from January 2020 to May 2025. Following a systematic deduplication process, original articles, reviews, case reports, and clinical trials were included in the analysis. The analytical framework comprised co-authorship networks, institutional collaboration patterns, national research contributions, and keyword co-occurrence structures, all of which were examined using CiteSpace (version 5.8.R3) and VOSviewer. Results After deduplication, 8,162 publications (January 2020-May 2025) were analyzed. The annual output grew steadily, confirming ASD as a sustained priority in neuroscience. Research remains academia-driven, led by the United States, with China as the second-largest contributor. Chinese institutions place greater emphasis on mechanistic and developmental phenotyping, which aligns with national priorities. These studies maintain strong methodological rigor, and their growing volume underscores the central role of ASD in translational neuroscience. Conclusion Future research on ASD should focus on strengthening case identification, refining clinical phenotyping, and expanding large-scale cohort studies to advance our understanding of its etiology and identify reliable diagnostic biomarkers. It is equally important to develop and evaluate targeted interventions for core symptoms and integrate telemedicine into service delivery models. A critical yet understudied priority is improving the quality of life for autistic individuals and their families, an area in which research globally, including in China, requires greater depth and consistency. With China's growing investment in autism research, it is well-positioned to contribute to these pressing international challenges.

AO_SCPLOWBSTRACTC_SCPLOWThere is a gap between neuroscientific theories of learning and the speed of learning observed in many experiments. Since the Cognitive Revolution of the 1950s, compositionality has played a central role in efforts to bridge this gap. Roughly, a compositional system is one where distinct modules are combined according to a set of rules in order to accomplish complex tasks. Recently, significant progress has been made in understanding the emergence of modules in both biological and artificial neural systems. How, and under what conditions, the rules of module recombination are represented in these systems remains an open question. Here we present a neural model that can leverage these rules to dramatically speed up learning. We first show that when faced with multiple tasks which share subcomponents, models learn a low-dimensional representation that captures how subcomponents are reused across the task set. These low-dimensional spaces encode the structure that governs how modules should be recombined. Restricting learning to these subspaces greatly reduces the amount of experience needed to acquire a novel task, even when learning from reinforcement on single trials. In some cases, we can leverage the geometric regularities of these representations to reduce learning to a form of hypothesis testing over a small set of discrete points. Finally, we use this theory to model both behavioral and neural data from non-human primates performing a compositional task, and show that key features in this data are consistent with a model in which exploration during learning is restricted to these low-dimensional spaces. Overall, this work shows that the advantages of modularity in neural systems can be greatly improved upon when models represent the structure of module reuse. Both these features working in tandem lead to learning on timescales similar to biological intelligences, and hence provide a model for how such fast, adaptable behavior can emerge from systems of neurons.

Automated interpretation of neuroimaging studies requires simultaneous assessment of multiple imaging evidence variables, each tied to distinct anatomical structures. Vision-language models (VLMs) offer a unified framework for multi-task analysis, but adapting pre-trained VLMs remains challenging. Full fine-tuning is computationally prohibitive, and joint multi-task training requires simultaneous access to all task data, which is often infeasible in clinical settings. Although model merging enables multi-task composition without joint re-training, existing methods focus on post-hoc algorithms with limited extension to VLMs and minimal application to neuroimaging. Here, we present GRadient-guided Adapter Merging (GRAM), a layer-selective low-rank adaptation (LoRA)-based fine-tuning and merging framework for multi-task neuroimaging visual question-answering (VQA). GRAM uses a gradient ratio that contrasts class-specific gradients to identify task-discriminative layers, and applies subspace-constrained projected gradient descent to restrict LoRA updates to directions consistent with the geometry of the pre-trained model. We leveraged a structured VQA benchmark, developed from the National Alzheimer's Coordinating Center (NACC) dataset, that pairs multi-sequence brain MRI studies with question-answer pairs across clinically relevant imaging evidence variables. Experiments on the VQA benchmark showed that GRAM outperformed or matched all-layer LoRA fine-tuning and a standard merging baseline while reducing inter-task interference during merging, and approached or surpassed the performance of joint multi-task training without joint re-training.

Alzheimers disease (AD) causes progressive memory loss and disorientation. It is preceded by a prolonged preclinical phase marked by pathological changes in medial temporal lobe regions involved in spatial navigation. Spatial navigation tasks have been proposed for early AD detection, and altered navigation performance was reported in older carriers of the apolipoprotein E (APOE) {varepsilon}4 allele, the major genetic risk factor for sporadic AD. However, whether spatial navigation or other cognitive abilities are affected in younger {varepsilon}4 carriers remains unclear. Here, we genotyped 1000 healthy young adults (18-35 years) who completed the app-based navigation game "Sea Hero Quest" and several tasks assessing working memory, processing speed, executive functioning, and face recognition. {varepsilon}4 carriers ({varepsilon}3{varepsilon}4, N = 88) showed no significant differences from non-carriers ({varepsilon}3{varepsilon}3, N = 327) in spatial navigation or other cognitive abilities, supported by equivalence testing and Bayesian analyses. Exploratory findings suggested altered spatial navigation in {varepsilon}2 carriers ({varepsilon}2{varepsilon}2/{varepsilon}2{varepsilon}3/{varepsilon}2{varepsilon}4, Ns = 7/51/7) versus {varepsilon}3{varepsilon}3 controls, who stayed closer to environmental borders and showed better memory updating, face recognition, and processing speed. Therefore, APOE-related behavioural differences in young adults appear small at best, highlighting the need for paradigms sensitive to very subtle changes decades before potential dementia onset.