bioRxiv Subject Collection: Neuroscience's Journal

Synapse-related protein alterations and estradiol deficiency associate with early Parkinsonism in female A53T-α-synuclein transgenic mice fed on a high-fat diet
Substantial evidence highlights the detrimental impact of a fat-rich diet on cognitive and emotional behaviour. Epidemiological studies have linked the consumption of saturated fat with an increased risk of Parkinson's disease (PD), whereas a low-fat or ketogenic diet is reported to improve both motor and non-motor symptoms. Several animal model studies further support these associations. However, the impact of a high-fat diet (HFD) on sex-specific behavioural alterations and the underlying molecular mechanism in PD remains poorly studied. In the present study, we investigated the impact of HFD on PD progression in a sex-specific manner using the A53T transgenic mouse model of PD. Behavioural and pathophysiological analyses revealed a faster onset and progression of PD-like phenotype in female mice exposed to HFD compared with the male mice. Proteomics profiling of brain tissues demonstrated positive enrichment of immune system-related pathways in males, while females exhibited considerable downregulation of synapse-associated pathways under HFD conditions. The reduced estradiol level was identified as a potential factor contributing to synaptic dysfunction and the subsequent early onset of PD in female mice. These findings provide novel insights into the sex-specific consequences of HFD on PD pathogenesis and highlight the role of estrogen-linked synaptic vulnerability in mediating diet-induced PD onset.

Aβ-42 sidechain deamidation at Q15 and N27 modulate protein aggregation and microglial responses via altered cytokine production and CD68 expression
The progressive aggregation of amyloid beta (A{beta}) monomers into oligomers is a critical factor in Alzheimers disease (AD) pathogenesis. Although mutated forms of A{beta} have been shown to display altered aggregation dynamics, the specific effects of deamidated A{beta} on microglial function remain understudied. Our research group previously found that the deamidated variant A{beta}-42-N27D modified A{beta} aggregation, reduced neurotoxicity, and reduced microglial reactivity, but the impact of A{beta}-42 side chain deamidation in general on such parameters remained unclear. Here, we expanded on our prior work by investigating how two site-specific A{beta}-42 mutations (Q15E & N27D), where neutral amide side chains are replaced with negatively charged carboxylic acids, affect aggregation and microglial immune response using a mouse microglial cell line. Size exclusion chromatography revealed that A{beta}-42-Q15E and A{beta}-42-N27D exhibit distinct aggregation profiles compared to A{beta}-42 wild type (WT). Multiplexed analysis of 8 cytokines secreted into the culture medium revealed that A{beta}-42-Q15E and A{beta}-42-N27D decrease the expression of inflammatory cytokines such as IL-6, IP-10, and MIP-1 relative to A{beta}-42-WT. Immunocytochemistry revealed that A{beta}-42-Q15E and A{beta}-42-N27D decrease CD68 expression relative to A{beta}-42-WT. These findings demonstrate that deamidation significantly alters A{beta}-42 aggregation and microglial activation, suggesting structural modifications to A{beta}-42 modulate inflammatory signaling in AD. This work provides a foundation for future studies on A{beta}-42 post-translational modifications as potential therapeutic targets in AD.

Exacerbation of a Subset of Behavioral Phenotypes by Early Treatment with AAV FOXG1 Gene Replacement Therapy in a Mouse Model of FOXG1 Syndrome
FOXG1 syndrome is a severe neurodevelopmental disorder characterized by microcephaly, profound intellectual disability with communication deficits including lack of speech, impaired social interaction, increased anxiety, hyperkinetic/dyskinetic movements, seizures and abnormal sleep patterns. Mutations in a single allele of the FOXG1 gene cause disease, likely due to loss-of-function. However, current therapies do not target this root cause of FOXG1 syndrome and have little to modest therapeutic benefit on only a small subset of symptoms. Recently, we reported the beneficial effects of adeno-associated virus (AAV) human FOXG1 gene replacement therapy administered by intracerebroventricular (ICV) injection at postnatal day 6 (P6) on several behavioral deficits that are relevant to key features of human FOXG1 syndrome, in male FOXG1 mice that were engineered with a highly prevalent, patient-specific Q84P mutation. Here, we report the behavioral effects of AAV human FOXG1 gene replacement therapy administered by ICV injection in female as well as male mice and at an earlier age - postnatal day 2 (P2). Although the earlier studies had suggested that AAV FOXG1 gene replacement therapy is a promising approach for the treatment of a subset of functional deficits in human FOXG1 syndrome with no toxicity observations, our current study shows that certain motor behaviors can be negatively impacted or exacerbated by P2 treatment with AAV FOXG1 gene replacement therapy, in female but not male FOXG1 mice. Given these results, the risk-benefit balance of AAV FOXG1 gene replacement therapy in patients with FOXG1 syndrome should be carefully considered, especially in female patients.

Proteomic characterization of neuronal extracellular vesicle interactomes in Alzheimer's disease mouse model through TurboID-based proximity labeling
Extracellular vesicles (EVs) are critical mediators of neuronal communication and have been implicated in propagating pathological processes in neurodegenerative diseases, including Alzheimer's disease (AD). However, the molecular interactome of neuronal EVs in vivo remains poorly defined. Here, we employed TurboID-CD9-based proximity biotinylation to label and capture EV-interacting proteins in the hippocampus of wild-type (WT) and APPNLGF knock-in AD mouse models. Adeno-associated viral delivery of hSyn1 promoter-driven TurboID-CD9 enabled neuron-specific EV tagging, followed by in vivo biotinylation and affinity purification of labeled proteins. Proteomic analysis using data independent acquisition liquid chromatography - mass spectrometry identified 5,502 proteins, with enriched pathways involving synaptic transmission, vesicle trafficking, and inhibitory neurotransmission. Comparative analyses revealed robust enrichment of GABAergic signaling components, including GABAA receptor subunits (Gabrb3, Gabra1, Gabbr2), Ncam1, and chloride transporters, in both WT and APPNLGF EV interactomes, with additional disease-associated proteins (Mapt, Snca) and potassium channel enrichment observed in APPNLGF mice. Proximity ligation assays validated direct EV-associated biotinylation of Ncam1, Gabrb3, and Gad1, with Gad1 showing significant upregulation in the APPNLGF cohort. In silico HADDOCK docking supported stable interactions between CD9 and these target proteins, revealing plausible EV-protein interfaces. These findings define the in vivo neuronal EV interactome and its remodeling in amyloid pathology, implicating EV-associated GABAergic and ion channel proteins in network excitability regulation. This work establishes a proteomic and structural framework for understanding EV-mediated signaling in health and disease, providing candidate targets for therapeutic modulation of excitatory / inhibitory balance in AD.

Neurally-Informed Models of Protracted Sequential Sampling of Long, Noise-free Stimuli
Sequential sampling models have provided a rich theoretical framework in the study of perceptual decision-making and quantitatively capture diverse behavioural data. However, research has increasingly highlighted that for the long-duration, statistically stationary stimuli commonly studied, it is difficult to definitively establish the operation of protracted sampling or temporal integration as opposed to non-integration strategies like extremum-detection, especially when based on behaviour alone. Here, we attempted to resolve such mechanistic details in the interesting case of judging subtle feature-differences (contrast) in a long (1.6-sec), noiseless stimulus, by jointly analysing the accuracy of delayed reports and the dynamics of a centroparietal electroencephalographic signal (CPP) reflecting decision formation. Accuracy steadily increased across four covertly-manipulated evidence durations and the CPP remained elevated throughout the stimulus period, together indicating protracted sampling. Models fit to CPP dynamics as well as accuracy resolved more details than those fit to accuracy alone, such as the setting of a bound. However, a convincing joint neural-behaviour fit could be achieved not only by a temporal integration model, but also by an extremum detection model that evoked a stereotyped signal flagging the first single-sample bound crossing. This illuminates interesting, testable alternatives to integration, and the limited extent to which combining neural decision dynamics with behavioural information can uniquely identify underlying mechanisms in some behavioural scenarios.

Persistent Adaptation through Dual-Timescale Regulation of Ion Channel Properties
Neurons are terminally differentiated cells that adapt to maintain stable function over years, despite encountering a wide range of environmental perturbations. Some adaptations are transient, fading once the perturbation ends. Others are persistent, continuing to influence a neuron's responses to future challenges even after baseline conditions are restored. These persistent adaptations are especially intriguing because some remain undetectable under normal conditions -- only becoming apparent upon re-exposure to a perturbation. Among the many mechanisms that may contribute to persistent adaptation, we investigate one based on the regulation of intrinsic currents. Using a computational model of activity-dependent homeostasis, we show that slow changes in channel density can encode the influence of past experience and shape future responses while rapid shifts in ion channel voltage-dependence provide immediate compensation during perturbations. Together, these dual processes tune a neuron's intrinsic excitability, enabling persistent adaptation.

A mean-field model of neural networks with PV and SOM interneurons reveals connectivity-based mechanisms of gamma oscillations
Classic theoretical models of cortical oscillations are based on the interactions between two populations of excitatory and inhibitory neurons. Nevertheless, experimental studies and network simulations suggest that interneuron subclasses such as parvalbumin (PV) and somatostatin (SOM) exert distinct control over oscillatory dynamics. Yet, we lack a theoretical understanding of the mechanisms underlying oscillations in E-PV-SOM circuits and of the differences with respect to the classical mechanisms for oscillations in simpler E-I networks. Here, we derive a biologically realistic mean-field model of a canonical three-population E-PV-SOM circuit. This model robustly generates oscillations whose features are consistent with experimental observations, including the relative timing of PV and SOM activity and the effects of optogenetic perturbations. By reducing the model to a linear analytical form, we demonstrate that gamma oscillations emerge directly from the cell-specific connectivity of the three-population circuit. This connectivity motif alone accounts for experimentally observed phase relationships, with PV activity consistently leading that of SOM neurons. Together, this mean field model identifies a distinct structural mechanism giving rise to oscillations in canonical E-PV-SOM circuits and provides theoretical primitives for constructing large-scale, cell-type-specific models of cortical dynamics.

Systemic infections alter cortical transcriptional signatures in Alzheimer's disease
Alzheimer's disease (AD) is characterized by neuroinflammation, yet the impact of concurrent systemic infections on the AD brain remains poorly understood. We investigated the molecular mechanisms underlying the central nervous system response to systemic infections in AD by analyzing RNA sequencing data generated in the prefrontal cortex from 202 post-mortem donors (113 AD, 89 controls), where we stratified by the presence of a respiratory infection at the time of death. We identified 763 significantly differentially expressed genes (DEGs) between AD and controls without infection, which were enriched for oxidative phosphorylation and neurodegenerative pathways. In contrast, 122 DEGs distinguished AD from controls during infection, with 57 genes uniquely altered in AD in the presence of infection, including MAPK4, VAV3, and POU3F4, implicating infection-dependent mechanisms of vascular and immune regulation. Pathway activity analysis revealed that infection in AD suppresses some immune and vascular pathways, while enhancing transcriptional and developmental programs. Weighted gene co-expression network analysis uncovered three key modules: one module strongly associated with AD, enriched for aging and signal transduction; one module linked to both AD and infection, highlighting cytoskeletal remodelling and host-pathogen interactions; and one module specific to infection, enriched in astrocytes, pericytes, and endothelial cells, implicating blood-brain-barrier dysfunction. These findings suggest that systemic respiratory infections reshape transcriptional programs in the AD brain, dampening immune effector pathways and engaging vascular and host-pathogen processes in blood-brain-barrier-associated cell types. Our results highlight the complex interplay between systemic infection, neuroinflammation, and vascular responses in AD.

Prenatal Cannabinoids Produce Sex Specific Changes in Risk Assessment and Shared Increases in Repetitive Behavior in Adult Offspring
The widespread perception of cannabinoids as harmless remedies has led to increasing use of both cannabidiol (CBD) and delta9--tetrahydrocannabinol (THC) during pregnancy, yet their long term impact on offspring behavior remains incompletely characterized. To directly compare these compounds, pregnant mice received daily CBD or THC (3 mg/kg, gestational days 5-18) and adult progeny of both sexes were evaluated (P90-120). To capture complementary dimensions of emotional and defensive responding, we employed the elevated plus maze (EPM) to quantify anxiety related measures and ethological risk assessment postures (e.g., stretch attend) and the marble burying (MB) test to index repetitive/defensive behavior; together these assays provide convergent but distinct readouts that increase sensitivity to cannabinoid induced effects. In the EPM, prenatal CBD exposure selectively increased stretch-attend postures in females, indicating heightened risk assessment; males were unaffected, and THC produced no comparable effect. Classical EPM indices-time in open or closed arms, preference for open-arms, and total distance traveled-were unchanged across groups, except that THC-exposed females exhibited hyperlocomotion. In contrast, marble burying was increased in both sexes following prenatal exposure to either CBD or THC, indicating a shared enhancement of repetitive/defensive responses. These results show that prenatal cannabinoid exposure yields enduring, sex dependent alterations in adult behavior, with CBD selectively heightening female risk assessment and both cannabinoids increasing repetitive behavior across sexes, challenging the notion that CBD is a benign alternative to THC during gestation.

LRRK2 G2019S disrupts GABAergic signaling and shifts excitatory/inhibitory balance in the striatum
The excitatory/inhibitory (E/I) balance within neural circuits is essential for proper brain function, and its disruption is a hallmark of several neurodegenerative diseases. In Parkinson disease (PD), widespread alterations in the basal ganglia circuitry lead to an E/I imbalance in the striatum, contributing to excitotoxicity. Leucine rich repeat kinase 2 (LRRK2) has recently emerged as a key contributor to both familial and sporadic forms of PD, with the pathogenic Gly2019Ser (G2019S) mutation representing one of the most frequently observed variants. This mutation is known to exacerbate excitotoxicity by impairing glutamate reuptake mechanisms, particularly through dysregulation of EAAT2 activity and its membrane localization. In contrast, the role of LRRK2 in GABAergic transmission remains poorly understood. Here, we reveal a clear modulation of inhibitory signaling by LRRK2 through a comprehensive approach combining mouse striatal slices and Xenopus laevis oocytes. Our results demonstrate, for the first time, that LRRK2 G2019S induces a significant reduction in GABA- evoked current amplitudes. Moreover, we identified an altered distribution of receptor isoforms in pathological tissue, affecting both tonic and phasic GABA currents. Specifically, synaptic GABAA receptors containing the {gamma}2 subunit were functionally modulated by LRRK2 G2019S. The reduced availability of gephyrin in the presence of the G2019S variant may impair the gephyrin- GABAA receptor complex, leading to decreased receptor surface expression and further shifting the glutamate/GABA current ratio toward excitatory dominance. This is supported by the increased activity of AMPA and NMDA receptors observed in the pathological striatum. Overall, our findings highlight a previously underappreciated role of LRRK2 G2019S in impairing GABAergic transmission and disrupting the E/I balance. These insights point to novel circuit-level mechanisms underlying LRRK2-linked PD and suggest new avenues for the development of disease-modifying therapies targeting inhibitory dysfunction.

Cortico-cerebellar effective connectivity during adapting to vs ignoring delayed visual movement feedback
We modelled hemodynamic responses acquired during a virtual reality based hand-target matching task with conditions in which delayed visual movement feedback was behaviorally relevant (requiring visuomotor adaptation) vs irrelevant (i.e., needed to be ignored). We had observed increased hemodynamic responses in the cerebellum, V5, and intraparietal sulcus linked to delay-dependent adaptation. Here, we used dynamic causal modeling to test if these regional activity changes could be explained in terms of network interactions among those nodes. We found a strong excitatory influence of the right cerebellum on the bilateral V5, which increased during the visuomotor adaptation > no adaptation tasks. Furthermore, there was an increased mutual excitation among the cerebellar hemispheres, and an inhibition of the cerebella by the V5, during visuomotor adaptation. These results are consistent with the idea that the cerebellum implements forward models predicting the sensory consequences of actions, and communicates these predictions to other regions of the visuomotor control hierarchy.

Normative models of individualized functional brain networks reveal language network expansion in autism
Autism spectrum disorder is a highly heterogeneous neurodevelopmental disorder, hindering mechanistic insights and the identification of biomarkers for clinical diagnosis. Recently, precision functional mapping has been developed to identify abnormalities in brain network topologies associated with various psychiatric disorders, yet its application in autism remains limited. Here, we utilized precision functional mapping and a large, multisite neuroimaging dataset (N = 1,182) to construct individualized functional networks in individuals with autism. We developed normative models using network surface area from healthy controls (n = 628) to characterize typical brain network organization across age, allowing for the quantification of individual-specific deviations in individuals with autism (n = 554). We found widespread and heterogeneous deviations from the normative model, with the language network emerging as the most significantly altered region, thereby emerging as an epicenter of functional disruption in autism. Individuals with autism were clustered into three subtypes involving distinct functional network topologies, associated with behavioral profiles marked by impairments in perception, language processing, or socio-emotional functioning. We further linked these atypical brain features to cortical gene expression patterns, revealing enriched pathways related to neurodevelopment, language, and signaling processes. Together, these findings reveal autism-specific deviations in individualized functional brain networks, offering potential clinical relevance for understanding and stratifying autism.

Pharmacologic NLRP3 Inhibition Modulates Parkinson's Disease-Associated Microglial Transcriptomic Signatures and Mitigates α-Synuclein-Triggered Neurodegeneration
Background: Parkinson's disease (PD), the second most common neurodegenerative disorder after Alzheimer's disease, and the rare disorder multiple system atrophy (MSA), are both characterized by intracellular accumulation of -synuclein fibrils and early, sustained microglial reactivity in parallel to the neurodegeneration. Activation of the NLRP3 inflammasome in disease-associated reactive microglia is increasingly recognized as a key pathogenic driver and a promising therapeutic target in synucleinopathies. Dapansutrile (OLT1177(R)) is a selective, orally bioavailable NLRP3 inhibitor with a favorable safety profile in clinical trials for non-neurological indications. Here, we evaluated the therapeutic potential of dapansutrile in preclinical models of PD and MSA and explored the predictive and translational value of its effects. Methods: Two established mouse models of synucleinopathy with nigral neurodegeneration were employed: the -synuclein preformed fibril (PFF) propagation model and the transgenic PLP--syn model expressing human wild-type -synuclein in oligodendrocytes. Pharmacokinetic analyses assessed plasma and brain exposure after oral administration. The efficacy of six-month dapansutrile treatment was examined in both preventive (post-PFF injection) and therapeutic (PLP--syn mice) paradigms, using behavioral, histopathological, and molecular readouts. Transcriptomic profiling of striatal and midbrain microglia identified differentially expressed genes (DEGs) associated with treatment and compared them with post-mortem transcriptomic signatures of disease-associated microglia in PD patients. Plasma IL-18 and neurofilament light chain (NfL) levels were evaluated as translational biomarkers. Results: Chronic oral dapansutrile treatment at clinically relevant doses improved motor performance, reduced -synuclein inclusions, attenuated gliosis, and mitigated nigral neurodegeneration in both models. Microglial transcriptomic analyses revealed that dapansutrile reversed key transcriptional signatures characteristic of PD-associated reactive microglia. Moreover, plasma IL-18 and NfL levels correlated with neuropathological and functional outcomes, supporting their potential as biomarkers of target engagement and treatment efficacy. Conclusions: These data identify chronic NLRP3 activation as a shared and targetable mechanism in PD and MSA and highlight dapansutrile as a CNS-penetrant, clinically advanced candidate for disease modification in -synucleinopathies. The observed transcriptomic reprogramming of microglia and the parallel changes in blood biomarkers provide a strong translational bridge to clinical development.

A Meta-Analysis of the Effects of Chronic Stress on the Prefrontal Transcriptome in Animal Models and Convergence with Existing Human Data
Background: Chronic stress is a major risk factor for psychiatric disorders, including anxiety, depression, and post-traumatic stress disorder. Chronic stress can cause structural alterations like grey matter atrophy in key emotion-related areas such as the prefrontal cortex (PFC). To identify biological pathways affected by chronic stress in the PFC, researchers have performed transcriptional profiling (RNA-sequencing, microarray) to measure gene expression in rodent models. However, transcriptional signatures in the PFC that are shared across different chronic stress paradigms and laboratories remain relatively unexplored. Methods: We performed a meta-analysis of publicly available transcriptional profiling datasets within the Gemma database. We identified six datasets that characterized the effects of either chronic social defeat stress (CSDS) or chronic unpredictable mild stress (CUMS) on gene expression in the PFC or medial prefrontal cortex (mPFC) in mice (n=117). We fit a random effects meta-analysis model to the chronic stress effect sizes (log(2) fold changes) for each transcript (n=21,379) measured in most datasets. We then compared our results with two other published chronic stress meta-analyses, as well as transcriptional signatures associated with psychiatric disorders. Results: We identified 133 genes that were consistently differentially expressed across chronic stress studies and paradigms (false discovery rate (FDR)<0.05). Fast Gene Set Enrichment Analysis (fGSEA) revealed 53 gene sets enriched with differential expression (FDR <0.05), dominated by glial and neurovascular markers (e.g., oligodendrocyte, astrocyte, endothelial/vascular) and stress-related signatures (e.g., major depressive disorder, hormonal responses). Immediate-early gene markers of neuronal activity (Fos, Junb, Arc, Dusp1) were consistently suppressed. Many of the identified effects resembled those seen in previous meta-analyses characterizing stress effects (CSDS, early life stress), despite minimal overlap in included samples. Moreover, some of the differential gene expression resembled previous observations from stress-related psychiatric disorders , including alcohol abuse disorder, major depressive disorder, bipolar disorder, and schizophrenia. Conclusion: Our study demonstrates that chronic stress induces a robust, cross-paradigm PFC signature characterized by down-regulation of glia/myelin and vascular pathways and suppression of immediate-early gene activity, highlighting cellular processes linking chronic stress exposure, PFC dysfunction, and the onset of psychiatric disorders.

Dynamic and task-dependent decoding of the human attentional spotlight from MEG
Attention is a fundamental mechanism enabling the brain to overcome its limited capacity for parallel processing. In non-human primates, invasive electrophysiology has shown that attentional selection operates rhythmically, primarily within the alpha (~8-12 Hz) and theta (~4-5 Hz) bands. Whether such finely resolved control signals can be captured non-invasively in humans, and how they adapt to changing task demands, remains unclear. Using high-precision magnetoencephalography (MEG) combined with machine learning, we decoded the spatial locus of covert attention in humans performing three variants of a spatial cueing task that manipulated cue validity as well invalid trial switching rules. Spatial attention could be decoded from whole-brain MEG activity at both static and time-resolved scales, with accuracies significantly above chance (N = 30). Decoding performance decreased as cue validity was reduced, indicating that task structure shapes attentional engagement. Analysis of decoding trajectories revealed rhythmic fluctuations at ~8-12 Hz across all tasks, demonstrating alpha-band sampling of attention. Pre-target attention became increasingly focused on the cued side, especially in the 100% Valid condition, consistent with proactive orienting. Furthermore, individual and task-specific differences in decoding strength correlated with task-variations in behavioral performance, linking the accuracy of neural attention codes to both discrimination accuracy and reaction time. These findings demonstrate that MEG can non-invasively capture dynamic, task-dependent fluctuations in spatial attention that parallel those observed in non-human primates. They reveal that attentional demands reshape the neural code for attention, modulate rhythmic sampling, and influence behavioral efficiency. This work bridges invasive primate and non-invasive human research and establishes MEG-based decoding of attention as a promising tool for mechanistic and clinical applications, including neurofeedback and attention-related interventions.

Turquoise killifish naturally develop hallmarks of age-related macular degeneration with advancing age
Ageing is a major risk factor for developing vision loss diseases such as age-related macular degeneration (AMD). Unfortunately, we do not have the ability to effectively prevent, slow, or stop onset and progression of AMD long term. These challenges with therapeutic development result from poor understanding of disease mechanism and pathogenesis due to a lack of animal models that manifest the hallmark features of disease. Here, we investigated the rapidly ageing turquoise killifish (Nothobranchius furzeri) retina for features of human ageing and AMD. We report that the ageing killifish retina expresses genes associated with human retinal disease in the photoreceptors and retinal pigment epithelium (RPE). Our characterisation of the retina identified that killifish spontaneously develop many hallmark features of AMD and human ageing, including photoreceptor deterioration, lipid deposits, outer retinal inflammation, and ceramide accumulation in the RPE with advancing age. Further, we identify a sex-specific difference in the severity of phenotypes. We propose that the turquoise killifish is a highly suitable model for investigating ageing and AMD-related disease mechanisms across the lifespan.

Generating Synthetic MR Perfusion Maps from DWI and FLAIR in Acute Ischemic Stroke using Deep Learning
Background: Magnetic resonance imaging (MRI) is critical for acute stroke triage, but time-consuming, and often requires contrast injection for perfusion imaging. This study aimed to synthesize T-map perfusion maps from routinely available, non-contrast DWI and FLAIR sequences by means of deep generative models. We hypothesized that relevant perfusion information could be inferred from these modalities that would streamline imaging and reduce reliance on dynamic susceptibility contrast perfusion. Methods: Acute MRI data from 355 patients with anterior circulation stroke, including dynamic susceptibility contrast perfusion, were retrospectively collected from two European centers. Six versions of a denoising diffusion probabilistic model (DDPM) and a GAN architecture were trained to generate synthetic T-max perfusion maps from DWI and FLAIR imaging and infarct core mask as input. Performance was assessed by comparing synthetic and ground truth T-max perfusion maps using image similarity metrics. Regions with T-max >6s were compared using Dice coefficients, and mismatch volume distributions were analyzed. An ablation study quantified the contribution of each input. Results: The best performance was achieved by a DDPM with a 2.5D architecture using DWI, FLAIR, infarct core mask, and a specified loss function. It produced synthetic perfusion T-max maps with high similarity to ground truth in an inference time of under 110 seconds. The model showed strong spatial overlap for clinically relevant T-max >6s regions in internal validation (average Dice = 0.82, SD = 0.08), and external validation average (Dice 0.59, SD = 0.13), respectively. Synthetic maps also closely matched ground-truth mismatch distributions, capturing key perfusion patterns. The infarct core mask played a critical role in model performance, alongside DWI and FLAIR inputs. Conclusions: We propose a robust, non-invasive, and scalable framework to generate synthetic T-max perfusion maps from standard non-contrast MRI. This approach has the potential to expand access to perfusion data in acute stroke, shorten imaging protocols, and accelerate treatment decisions by eliminating the need for contrast-enhanced acquisition.

Pupillary dynamics reflect age-related changes in memory encoding
Pupillary dynamics are closely dependent on tonic and phasic activity of the brainstem locus coeruleus (LC), a key neuromodulatory nucleus. LC neurons are the earliest site of hyper-phosphorylated tau accumulation, leading to a decline in nucleus structural integrity in older age that likely impacts long-term memory. While several studies have explored the link between pupil dilation and successful memory encoding, little is known about the effects of aging on this relationship. This study investigated task-evoked pupillary responses (TEPRs) in young and older adults during incidental encoding of neutral visual scenes. Memory performance was assessed 24 hours later using a Remember-Know-Guess recognition task. TEPRs were compared based on recognition performance. Our findings notably revealed attenuated TEPRs in older individuals, supporting the hypothesis of impaired LC-driven modulation with age. Pupil dilation was associated with memory performance in young adults only. In this group, subsequently recognized stimuli elicited greater dilation during encoding compared to forgotten ones. Moreover, among recognized stimuli, recollected ones evoked larger pupillary responses than those remembered with a feeling of familiarity. Importantly, this effect was absent in older adults, suggesting that the benefit provided by LC involvement during encoding declines with advancing age. These findings highlight the crucial role of LC-mediated neuromodulation in episodic memory, and suggest that age-related LC decline can be evidenced using pupillometry.

Augmented Feedback Training for Overcoming the Learning Plateau of Motor Expertise
Skilled performers often encounter a plateau in which further practice yields little improvement of intricate sensorimotor skills. Overcoming such limits requires novel training paradigms that can engage performers in novel ways of exploring and refining their actions. Here, we introduce a training pipeline that integrates high-precision motion sensing with augmented feedback to enhance expert-level motor learning. Using piano performance as a model, trained pianists practiced imitating a prize-winning expert's performance of a technically demanding movement sequence. While conventional auditory-based learning offered limited benefit, augmenting practice with trial-by-trial visualizations of discrepancies between the pianist's own movements and those of the expert enabled learners to refine their performance. This training induced richer movement exploration, facilitated closer convergence toward expert motion patterns, and produced perceptible improvements in sound quality evaluated by professional pianists. These findings demonstrate that augmented feedback can break performance plateaus in experts by providing externally-sensed, task-specific information that expands exploration beyond habitual training strategies. Such augmented learning promises new applications across domains where expertise is constrained by entrenched motor habits, including musical performance, athletics, and surgical training.

Molecular Characterization of Parabrachial Neurons in Xenopus laevis and Silurana tropicalis: Evolutionary Conservation and Sex-Specific Differences
Clawed frogs communicate acoustically to coordinate reproduction, with males producing species-specific advertisement calls to attract females. In Xenopus laevis, males generate fast trills composed of clicks repeated at 60 Hz, a feature absent in both Silurana tropicalis males and X. laevis females, whose calls consist of slower click rates (30 Hz and 7 Hz, respectively). In male X. laevis, fast trills are generated by premotor neurons in the parabrachial nucleus (PBN), known as Fast Trill Neurons (FTNs). We hypothesized that FTNs are unique in male X. laevis, and either absent or molecularly distinct in clawed frogs that do not produce fast trills. To test this, we used constellation pharmacology to profile receptor expression of neurons via intracellular Ca2+; responses to pharmacological agents in PBN neurons from male X. laevis, male S. tropicalis, and female X. laevis. Surprisingly, we found putative FTNs in all three groups, including those that do not produce fast trills. Furthermore, a similar proportion of FTNs across groups expressed fast-kinetic voltage-gated potassium channels known to support rapid firing, indicating that the presence of these channels does not correlate with the ability to produce fast trills. Instead, some of these channels were more prevalent in males of both species compared to female X. laevis, suggesting a potential sex-specific, non-vocal function. The discovery of FTNs with similar molecular profiles in non-fast-trilling individuals suggests that these neurons are conserved across species and sexes, and may serve other functions. In male X. laevis, FTNs may have been repurposed for fast trill production during speciation. These findings provide new insight into understanding how neural circuits evolve and diversify across species and sexes.

Seasonal plasticity in neuroendocrine mechanisms relevant to year-round territorial aggression in a wild teleost fish
Animals experience cyclical environmental changes, such as seasons, that require physiological adjustments to support different behaviors. Although many behaviors occur only during specific periods, some species, like Gymnotus omarorum, display territorial aggression year-round, making them valuable models to study seasonal plasticity in the mechanisms maintaining stable behavioral outputs. G. omarorum is a teleost fish in which neuroestrogens have been shown to play a key role in non-breeding aggression. Here, we quantified circulating hormone levels and gene expression in the social behavior network of wild breeding and non-breeding individuals. During the non-breeding season, both sexes exhibited elevated circulating androgen levels, providing potential substrates for local estrogen synthesis. Consistently, brain aromatase and estrogen receptor expression were also upregulated, suggesting an increased capacity for local estrogen synthesis and signaling. Our findings provide the first evidence in a teleost of seasonal plasticity in the mechanisms underlying territorial aggression. Comparisons with birds and mammals reveal both shared and lineage-specific strategies, highlighting common endocrine principles while revealing the evolutionary diversity of solutions to maintain a stable behavioral phenotype across changing seasonal contexts.

Spatial entropy of brain network landscapes: a novel method to assess spatial disorder in brain networks
In this work, we introduce a method for mapping the spatial entropy of functional brain network community structure images in brain space. Entropy maps indicate the extent to which the network communities present in a local area are ordered or disordered. We demonstrate how spatial entropy can be quantified for each voxel in the brain according to the network community affiliations of surrounding voxels. This process results in interpretable maps of brain network entropy. We show that local entropy decreases in predictable brain regions during working memory and music-listening tasks. We suggest that these regional entropy reductions reflect self-organization of neural processes in support of functionally localized cognitive tasks. Analyses in this work provide a framework for future analyses of spatial entropy in complex networks that can be mapped to Euclidean space, both within the brain and in other contexts.