Enhancement of endogenous opioid signaling through inhibition of enkephalin degradation has emerged as a potential strategy for pain control, yet its role in migraine remains unclear. This study investigated whether spinorphin, an endogenous inhibitor of multiple enkephalinases, attenuates trigeminovascular activation and migraine-like responses in experimental migraine. Migraine-like pain was induced in male Wistar rats by nitroglycerin. The effects of spinorphin were evaluated using periorbital and plantar von Frey tests, followed by assessment of c-Fos levels in the trigeminal nucleus caudalis (TNC), calcitonin gene-related peptide (CGRP) levels in the trigeminal ganglion (TG) and TNC, and meningeal mast cell degranulation. Opioid receptor involvement was examined using nalmefene. In complementary ex vivo hemiskull preparations, capsaicin-evoked CGRP release was quantified, and the interaction between spinorphin and sumatriptan was investigated. Spinorphin dose-dependently alleviated cephalic and extracephalic mechanical hypersensitivity, reduced c-Fos levels, decreased CGRP concentrations in the TG and TNC, and inhibited meningeal mast cell degranulation. These protective effects were markedly attenuated by nalmefene, supporting the involvement of opioid receptor-mediated mechanisms. In ex vivo preparations, spinorphin selectively reduced capsaicin-induced CGRP levels in the superfusates without affecting basal CGRP levels, and this effect was abolished by nalmefene. Moreover, spinorphin enhanced the inhibitory effect of sumatriptan on capsaicin-stimulated CGRP levels. Spinorphin attenuates behavioral, neurochemical, and neuroinflammatory features of experimental migraine, at least partly through mechanisms involving endogenous opioid signaling. These findings suggest enkephalinase inhibition as a promising therapeutic approach for migraine and support further investigation of spinorphin as a potential adjunct to established antimigraine therapies.

Although faces are homogeneous stimuli, individuals adopt different eye-movement patterns when looking at them. Do such differences in eye movements reflect distinct patterns of neural processing of faces? We conducted two experiments to investigate how face-viewing preferences affect face-sensitive event-related potentials (ERPs). In both experiments, participants were separated into two groups: an upper-focused group who fixated mainly on the eyes of faces and a lower-focused group who fixated more on the nose and mouth. Participants then completed two fixation number-controlled blocks in Experiment 1 or two fixation duration-controlled blocks in Experiment 2, in which the initial fixation location was restricted to the left eye or the nose. In both fixation number-controlled and duration-controlled blocks, the upper-focused group performed better and elicited a larger P1-to-N170 peak-to-peak amplitude for the eye-fixation condition than the nose-fixation condition. In contrast, the lower-focused group performed evenly and elicited comparable P1-to-N170 amplitudes between fixation conditions. In addition, the P1 component was larger for the nose-fixation condition than the eye-fixation condition, and this difference was bigger for the lower-focused group than the upper-focused group. On the other hand, the P1 appeared to have no relation to behavioral performance. These findings show how individual differences in fixation patterns for faces map to differences in neural processing of those faces, and in turn provide evidence for the intricate relationship between sensory and neural processing of visual information.

Organization and specification of the nervous system start in the embryo and are largely controlled by a cluster of transcription factors known as HOX genes, which control body patterning and morphogenesis at the transcriptional level. However, this entire process requires the influence of multiple pathways, including cell signalling, genomic and post-genomic pathways. These include the Wnt (wingless) pathway, which controls neuronal development and polarity, and the small miRNA pathway, which regulates genes post-transcriptionally. In this study, we investigated mir-57, the homolog of human miR-10 in Caenorhabditis elegans and its role in influencing motor neuron function. mir-57 is expressed posteriorly throughout development and enriched in motor neurons. Absence of mir-57 results in decreased movement and abnormal axonal projections of posterior motor neurons. Moreover, the Wnt pathway genes cam-1 and mom-5 are mir-57 targets and show elevated expression at the post-transcriptional level in mir-57 deletion mutants. Taken together this study suggests that mir-57 influences Wnt receptor expression to maintain posterior motor neuron axon morphology.

Motor learning is accompanied by adaptive changes in neural processing, yet the extent to which feedback-based practice alters the complexity of cortical activity during movement execution remains unclear. The present study examined learning-associated changes in brain complexity during the execution of a sequential arm movement following augmented-feedback-based practice. Thirty-eight healthy young adults practiced a sequential arm movement with augmented feedback across five sessions. Cortical activity was recorded with electroencephalography during the first and last practice sessions. Multiscale entropy was used to quantify brain complexity across 50 time scales. Behavioral measures indicated improved movement accuracy and reduced dual-task costs across practice. At the cortical level, a significant global decrease in entropy was observed from the first to the last practice session. Further analyses revealed that the differences were confined to coarse temporal window and midline frontal electrodes Fz and FCz. In addition, changes in coarse-scale entropy at FCz were negatively correlated with reductions in absolute error. Together, the present findings suggest that augmented-feedback-based practice is accompanied by a refinement of cortical complexity during movement execution, particularly in large-scale frontal dynamics. Multiscale entropy may therefore provide a sensitive framework for capturing learning-related neural adaptations that complement conventional electroencephalographic measures and advance the understanding of motor learning at the systems level.

Neuroscience increasingly informs education, mental health, and public policy, yet its real-world impact remains limited, with misunderstandings and neuromyths persisting. We argue that the societal impact of neuroscience communication depends less on the volume of information disseminated than on whether it cultivates critical science literacy, namely, the ability to evaluate how neuroscientific knowledge is generated, modeled, and constrained and to understand the inferential steps linking data to conclusions and their limits of generalization. In the context of neuroscience, we refer to this as critical neuroscience literacy to emphasize the domain-specific challenges of interpreting brain-based evidence. The primary barrier is not insufficient dissemination alone, but systematic misinterpretation of neuroscientific inference once findings leave the laboratory. We advance three claims: first, that public misunderstandings often reflect limited insight into scientific reasoning and practice rather than simple information deficits about scientific results; second, that citizen science and dialogical engagement function as mechanisms for practicing such reasoning rather than as outreach add-ons; and third, that neuroscientists share responsibility for shaping the interpretive conditions under which findings are translated, particularly in education, health, and policy. Importantly, this self-referentiality is not merely descriptive. Interpretations are associated with beliefs, expectations, and environments that may themselves be linked to neural and behavioral change. We outline implications for training, incentives, and professional norms within the neuroscience community.

Statistical learning is the brain's primary strategy for transforming continuous, noisy, and ambiguous sensory inputs into structured internal representations. This process is studied through a diverse array of perspectives spanning different species, sensory systems, levels of analysis, and methodological frameworks. Here, we present four such perspectives. Two of them approach the problem from the bottom, examining the neural substrates of the biological implementation. The first line describes implicit contextual learning in the rodent auditory midbrain, showing how neuronal frequency tuning adapts to predictable statistical regularities over days. The second line shows that explicit knowledge modulates belief updating as early as the human auditory midbrain and thalamus, revealing a functionally specialized subcortical inferential hierarchy. The other two research lines approach it from the top, examining the computations the brain performs. The third line compares statistical computations across species and human development, uncovering fundamental differences in probabilistic chunking strategies between honeybees, human infants, and adults. The fourth line provides a computational framework for continual learning in which experience is organized into structured internal models through ongoing contextual inference. We then examine what each line can contribute to the others. We argue that, if the brain evolved statistical learning under pressure to behave optimally, this optimality must have been achieved globally through the constraints and synergies that emerge from the interplay between subsystems and levels of analysis. The neuronal mechanisms implementing a computation are shaped by the computation they perform, while that computation is itself constrained by the available physiology: a mutual dependence that can only be understood by bridging across perspectives.

Microglia are the immune cells resident in the central nervous system (CNS) and are involved in CNS homeostasis and in responses to injury and disease. Microglia, upon activation, become pro-inflammatory (M1) or anti-inflammatory (M2), with M1 being neurotoxic and M2 participating in tissue repair. The activation of microglia in neuroinflammation and pain hypersensitivity is important in the neurodegenerative disorders such as Parkinson's (PD) and Alzheimer's disease (AD). Recent investigations have identified GLP-1 receptor agonists (GLP-1 RAs) as potent pharmacological agents that can regulate the activity of the microglia. GLP-1RA attaches to the GLP-1 receptor found on the microglia to cause a phenotypic shift to the anti-inflammatory M2 form, which, in turn, reduces neuroinflammation and increases neuronal protection. Experimental evidence shows that GLP-1 RAs are effective at reducing hypersensitivity to pain, improving cognitive performance, and attenuating inflammatory effects in preclinical models of AD, PD, and multiple sclerosis (MS). All these facts suggest that GLP-1 RAs are a significant treatment option in chronic pain and neurodegenerative disease management, as they can change microglial polarization and suppress neuroinflammation.

Alzheimer's disease is marked by progressive synaptic loss and cognitive decline, with amyloid-beta accumulation playing a central pathological role. Although the molecular effects of amyloid-beta are well-characterized, the cascade from local synaptic dysfunction to large-scale network collapse remains poorly understood. We present a biophysically grounded computational model that integrates small-world network topology, Izhikevich neuron dynamics, short-term plasticity, and amyloid-beta-induced synaptic pruning to investigate how amyloid burden degrades functional brain connectivity over time. Crucially, we simulate the evolving network dynamics across disease stages, revealing how firing rate, synchrony, variability, and Fano factor evolve in response to progressive structural degradation. Our results demonstrate a two-phase deterioration: Initial compensatory dynamics followed by abrupt disintegration of coordinated activity. This temporal dissociation mirrors clinical observations and offers mechanistic insight into Alzheimer's disease progression. We further identify a critical accumulated amyloid dose beyond which network collapse becomes inevitable, preceded by early-warning indicators such as declining global efficiency and the spectral radius of the network connections. Network topology, synaptic time constants, and repair mechanisms strongly influence resilience, with small-world networks showing delayed collapse and greater functional compensation. Finite-size scaling reveals that larger networks experience earlier and sharper phase transitions. Ultimately, by establishing a mathematical link between microscopic synaptic degradation and macroscopic network failure, our findings suggest that if experimental proxies for the spectral radius can be identified, they would serve as highly sensitive early-warning indicators of impending network collapse. This theoretical framework offers new mechanistic targets for the early detection and therapeutic monitoring of neurodegenerative diseases.

Autistic individuals are often thought to exhibit reduced tolerance for change, which may stem from atypicalities in sensory processing, particularly in the detection of environmental changes. Within the predictive coding framework, this intolerance may reflect altered generation and updating internal predictions in response to sensory input, leading to increased prediction errors. Mismatch negativity and the P3a component are event-related potentials (ERPs) indexing automatic auditory change detection and involuntary attention shift. While studied in autistic children and young adults, little is known about how these components evolve during aging, an important gap as 21 million autistic adults over 60 are expected by 2050. This study examined mismatch negativity and P3a responses in 43 autistic adults (aged 21-64) and 60 nonautistic adults (aged 20-69) using a passive auditory oddball paradigm with standard (1000 Hz) and deviant (1100 Hz; p = 0.15) tones. Electroencephalography recording revealed no significant differences between autistic and nonautistic individuals on mismatch negativity and P3a amplitude or latency. However, mismatch negativity amplitude decreased with age over frontal site, similarly across both groups. No age-related effects were found for mismatch negativity latency or P3a measures in either group. These findings suggest that, from adulthood onward, the neural mechanisms underlying automatic change detection and preattentional processes are similar in both autistic and nonautistic adults, and that their overall time course of aging appears comparable, supporting the parallel aging hypothesis. Nonetheless, future research should explore how these preserved neural responses relate to behavioral reactivity to change in autism.

The development and evaluation of novel treatment options for freezing of gait (FOG) in Parkinson's disease have been limited by an ambiguous definition and lack of standardized clinical outcome assessments (COAs) that 1) reliably assess FOG severity, 2) are responsive to change, and 3) capture patient-relevant impact. To address these gaps, working groups from the International Consortium on FOG (ICFOG) developed a clinician-rated outcome-the Giladi Protocol for assessing FOG severity (GP-FOG), and a patient-reported outcome-the FOG Quality of Life questionnaire (FOG-QOL). The FOG-COA project will evaluate the conceptual framework and psychometric properties of these COAs to determine their fit-for-purpose in clinical trials. A total of 105 freezers and 35 non-freezers from seven international centers will complete the GP-FOG in the dopaminergic OFF and ON states during two separate visits (7-14 days apart). FOG annotations from video recordings will be used to assess criterion validity. OFF and ON GP-FOG scores from two trained raters at Visit 1, as well as within-rater scores across visits, will examine reliability. Responsiveness will be evaluated by comparing effect sizes between OFF and ON GP-FOG scores. Patient meaningfulness will be assessed through the association between patient-perceived FOG severity and GP-FOG scores. Participants will complete the FOG-QOL at each visit to assess test-retest reliability and validity against the NFOGQ and PDQ-8. A subset of freezers will undergo cognitive interviews to identify relevant daily life FOG impacts and evaluate FOG-QOL meaningfulness. Collectively, this work will advance standardized, sensitive, and meaningful FOG-COAs, supporting regulatory-grade endpoints and the development of a multi-dimensional outcome framework.

Emerging studies report that information decoded from brain activity may pulse at specific frequencies. As such inferences take root, I clarify that trial-level decoding time courses inherit background brain rhythms even when information content is stable. This cautions against conflating rhythms in decoding with rhythms in information content.

Previous studies suggest that when a tone-conditioned stimulus (CS) predicting an airpuff towards the eye is presented at diastole during expiration, both sensory responses to the CS and trace eyeblink conditioning (TEBC) are enhanced. However, not all participants learn and individual variation in the body-brain relationship remains overlooked. We studied whether timing the CS based on an individually optimized bodily state enhances sensory responses and TEBC. We assumed so, as sensory response magnitude reflects attention and stimulus salience: A salient CS should facilitate TEBC. Forty-one young adults and 19 older adults completed a passive auditory task while electrocardiogram, respiration and electroencephalogram were recorded. The cardiorespiratory state associated with the largest N1-P2 response was identified individually. Optimal states varied across participants and did not differ between age groups. Participants underwent TEBC at their optimal state (Personalized) or at diastole during expiration (Control). As expected, the CS evoked a larger N1-P2 response in the Personalized than the Control group. However, conditioned responding, peak performance, and learning rate, or the proportion of good learners did not differ between groups. Older adults learned more slowly, made fewer conditioned responses, and were less aware of the tone-airpuff association than young adults. These findings tentatively suggest that enhancing early sensory processing of the CS may not improve TEBC, although the low reliability of the individually determined optimal state limits this conclusion. Future studies should examine whether timing external stimuli to specific bodily states effectively influences behaviour in tasks that rely on rapid sensory processing.

Parkinson's disease (PD) affects voice and speech production, often resulting in reduced vocal intensity and monotonous speech. Recent studies suggest that these changes can be partially explained by altered sensory feedback processing in speech production: individuals with PD (IwPD) may fail to monitor and adapt sensory feedback when producing speech. We investigated PD voice production using a voice playback paradigm and focused on the sensory attenuation typically observed in event-related responses (ERPs) to the self-generated voice. When sensory feedback is intact, the P50, N100, and P200 responses to a self-generated voice are attenuated compared to an externally generated voice. Twenty-three IwPD and 23 matched healthy controls (HCs) completed the voice playback study with three conditions: self-generated voice (auditory-motor [AMC]), externally generated voice (auditory-only [AOC]), and motor-only. Linear mixed models assessed group differences in ERP morphology. Groups differed in the N100 latency response: IwPD showed a greater shift in latency between AMC and AOC than healthy controls. Regarding the P200, a significant group-by-condition interaction indicated that IwPD, unlike healthy controls, produced larger amplitudes for the self-generated than the passively listened to voice. Taken together, these findings suggest that the processing of one's own voice in IwPD is altered in terms of temporal precision rather than in the N100 suppression effect per se. This effect was accompanied by an enhanced P200 attentional response. These response patterns in IwPD are indicative of likely progressing difficulties in regulating one's own voice, characteristic of Parkinson's disease.

Virtual reality (VR) provides a highly controllable, immersive, and interactive platform for investigating hippocampal mechanisms underlying spatial learning and memory. In rodent models, VR-based paradigms have elucidated how hippocampal subregions including CA1, CA3, and the dentate gyrus support spatial representation, pattern separation, and memory consolidation. Within VR environments, place cells, grid cells, head-direction cells, and boundary vector cells exhibit characteristic remapping dynamics, reflecting both the experimental advantages and sensory constraints inherent to VR. Although VR enhances reproducibility and integrates seamlessly with advanced methodologies such as two-photon calcium imaging and optogenetics, reduced vestibular and multisensory inputs can limit the fidelity of spatial coding. This review synthesizes current evidence on hippocampal-retrosplenial interactions, synaptic plasticity, and multisensory integration in VR, highlighting mechanistic insights into spatial cognition.

Lick microstructure is a term used in behavioural neuroscience to describe the information that can be obtained from a detailed examination of rodent drinking behaviour. Rather than simply recording total intake (volume consumed), lick microstructure examines how licks are grouped and the spacing of these groups of licks. This type of analysis can provide important insights into why an animal is drinking, for example, whether it is influenced by taste or affected by the consequences of consumption (e.g., feeling 'full'). Here, we present a software package, lickcalc, that allows detailed microstructural analysis of licking patterns. The software is browser based and is hosted at https://lickcalc.uit.no, or the repository can be downloaded and installed locally. Lick timestamps can be loaded from a variety of formats, and different analysis and plotting options allow quality control of data and the determination of critical parameters for microstructural analysis, including the number and size of lick bursts. Data can be divided into epochs for detailed examination of changes across the session. Batch processing and custom configurations are supported. In this manuscript, we demonstrate the use of the functions exposed by lickcalc by analysing data comparing lick patterns between mice on a protein-restricted and a control (non-restricted) diet. We show that lickcalc allows quality control of the data and the uncovering of subtle differences in lick behaviour that are not apparent when just considering the total number of licks. This software makes microstructural analysis accessible to any researcher who wishes to employ it while providing sophisticated analyses with high scientific value.