Nonlinear Dynamics

In previous studies by the author on binocular vision with the asymmetric eye (AE), which models a healthy human eye with misaligned optical components, the results were primarily presented in the Rodrigues vector (RV) framework and supported by simulations and 3D visualizations in GeoGebras dynamic geometry environment. In this paper, the novel geometric kinematics of the human eye, that is, the eye with misaligned optics, and simplified assumptions about the eye rotations (the eyes translational movements are disregarded), are developed within the framework of rigid-body rotations. The originality of the analysis lies in a precise geometric decomposition of a full rotation of the eyes posture into a torsion-free rotation (the geodesic part) and a torsional rotation (the non-geodesic extension of the geodesic part). This decomposition is extended to the corresponding decomposition of the angular velocity. A novel derivation of the eyes angular velocity from the RV formulation of the eye kinematics is proposed.

This work focuses on the global and partial identification problem for fractional differential equations. We provide a general numerical procedure based on global and local optimization algorithms with two refinements for biological systems that ensure solution positivity and homogeneous parameter units. The method is applied to a new fractional model of Dengue outbreak called the Fractional Homogeneous Nishiura (FHN) model, calibrated using data of newly infected people in Cape Verde. We show that our identification method yields a better fit between data and model solutions than previous approaches and that our FHN model captures the dynamics of Dengue more closely than existing systems.

Sex-related differences in the aging of the human brain were studied using large array of experimental data. The open archive CamCan was used as a source of data: the magnetic encephalograms, co-registered with magnetic resonance images of the head, were obtained for each of 434 subjects (ages 18-87 years, mean age 54.7 {+/-}18.4): 217 females (ages 18-87 years, mean age 54.5 {+/-}18.4) and 217 males (ages 18-84 years, mean age 54.8 {+/-}18.3). Recordings were split in 10-year age cohorts, each cohort consisted of equal number of men and women to calculate average intersex characteristics correctly. By massively solving the inverse problem, functional tomograms were calculated - the spatial distribution of elementary spectral components. Physiological noise was eliminated by joint analysis of MEG-based functional tomogram and magnetic resonance image for each subject. Then multichannel spectra were transformed into time series of the power of elementary current dipoles. Summary electric powers were calculated in six conventional frequency bands (1-4 Hz - delta; 4-8 Hz - theta; 8-13 Hz - alpha; 13-21 Hz - beta1; 21-30 Hz - beta2; 30-48 Hz - gamma), and sex differences in age-related changes were examined. It was found that in the youngest age cohort (18-29 years) the summary electrical power of the brain for males is 1.5 times greater than such power for females. For adults (30-69 years), male and female powers are approximately equal, while in older cohorts (70-87 years), male total brain power is greater. Age dependencies in various frequency bands are generally different for men and women, excluding higher frequencies 21-48 Hz. Basic conclusion can be made that after intersex averaging total electric power of the human brain is invariant through the lifespan from 18 to 87 years. The proposed method of joint MEG and MRI analysis can be used for further study of the sex-related details of brain sources in their connection with age changes.

The phenomenon of splitting was originally observed in hamsters which, after prolonged exposure to constant light, exhibit two rest/wake cycles within a subjective day. Splitting is a consequence of the left and right suprachiasmatic nuclei (SCN) falling out of synchrony. While it is known that split activity is characterized by an antiphase relationship between the left and right SCN and between the core and shell within each hemisphere, the role of the commissural projections that connect the right and left SCN is not known. In the present study, we investigate the impact of the inter-hemispheric connections on the split and unsplit dynamics of a computational model of the bilateral SCN. Our model has 4 nodes corresponding to each right and left core and shell. We simulated our bilateral model under different lighting conditions and measured its period and the phase relationships among the 4 nodes. To further characterize the dynamics of the system, we performed a bifurcation analysis. We found that the bilateral model automatically splits unless entrained by bright light/dark cycles, or unless it has excitatory inter-hemispheric connections. This suggests that excitatory cross-connections may be important for freerunning behavior. We found that constant light of varying intensities transitions the model between split and unsplit activity only in very limited conditions, but the strength and polarity of the contralateral connections play a much greater role in this dynamical transition. These findings suggest that splitting may involve plasticity of the inter-hemispheric connections of the SCN.

Canine rabies persists in NDjamena (Chad) despite vaccination campaigns exceeding 70% coverage, suggesting a role for dog mobility and spatial heterogeneity. We propose a metapopulation SEIR model incorporating distance-modulated dog movements and an explicit vaccinated class. Analysis of the isolated patch establishes global stability of the disease-free equilibrium via a Lyapunov function. For the metapopulation, a composite Lyapunov function shows that elimination is governed by a reproduction number [R]v. Calibrated with field data (2012-2022), simulations reveal that uniform vaccination of both patches reduces [R]v by 46% (from 2.84 to 1.52) but does not achieve elimination, while targeted strategies are less effective. These results demonstrate that exhaustive vaccination coverage across the entire urban network and increased vaccination intensity are necessary to eliminate canine rabies in NDjamena. Our model provides a quantitative framework for planning effective control strategies.

Growing interest to describe the electrical behavior of glial cells, mainly astrocytes, in intact brain tissue poses more and more challenges to commonly accepted belief they only respond in a linear manner in uptake of the excess of extracellular potassium and maintenance of their network equipotentiality. Their highly conductive mutual interconnections via gap junction (GJ) connections introduce yet another class of nonlinear elements. As more studies report nonlinearities in membrane voltage Vm dependence of both, the membrane and junctional conductances, the need to formulate minimal dynamical models of their transient behavior is getting more acute. Since ODE models of coupled cells, even in simplest 1-d arrays, require simplified descriptions and small set of parameters, rare quantitative studies on glia makes the task even more difficult. This study attempts to qualify a self-coupled cell, or a glial cell coupled to fixed voltage as useful system for detecting the nature of instabilities and transitions coming from coupling. In a novel biophysical model of coupled astrocyte, we introduce nonlinear kinetics of deactivation for large junctional voltages for the first time. We found that N-shaped nonlinearities and corresponding fold structure in the vector field of isolated cell serves as a baseline on top of which coupling nonlinearities enrich the bifurcation picture. Numerical simulations of 1-d array of coupled astrocytes show that coupling increases the propensity of astrocytic Vm to bistability and front propagation. We believe that presented illustrations of possible effects of coupling nonlinearities will motivate neurobiologists to further explore their impact in disease. Significance statementTransient changes in membrane voltage of glial cells may produce significant transient voltage difference between directly coupled cells. Nonlinear steady-state conductance of their interconnection elements, the gap junctions, introduce nonlinear current profiles which are very difficult to measure and quantitate using the available methods due to marked permeability of the junctions and leakiness of glial membrane in general. We propose a minimal model of glial membrane extended with a self-coupled feedback loop, which under realistic simplifying assumptions could serve for qualitative analysis of the impact of coupling, on the stability of resting membrane voltage. Neuronal cells of the brain and spinal cord cannot exist and function without supportive and neuromodulatory functions of the diverse population of glial cells. This applies to virtually all physiological processes on cell level - from cell development, metabolic support, membrane signaling, slow molecular signal transduction, ion homeostasis, neurovascular coupling, myelination, to mention only a few, manifest neuro-glial interaction. Even though all glial cell types are interconnected, the most abundant ones, the astrocytes are massively interconnected by gap junctions to form ordered networks. Electrically, astrocytic networks display membrane voltage equipotentiality, which is considered system-wide resting state for given neuro-glial circuit or unit. With molecular and cellular substrates of glial connectivity being slowly elucidated, network science and dynamical modeling are slowly "invading" that area with many important issues left open. In this study using classical dynamical systems approaches we give indications how nonlinear intercellular coupling between astrocytes affects physiological resting state and its instabilities compared to isolated, uncoupled cell. We strongly believe the suggested minimal model could fill the gap in ODE modeling of neuro-glial circuits, within broadest scope of hypothesis-driven research in cell-level neuroscience.

This study presents a proof-of-concept cyber-physical architecture integrating a SEIR epidemiological model (Susceptible-Exposed-Infectious-Recovered), implemented in MATLAB, with a simulated Internet of Things (IoT) acquisition and transmission stage based on the ESP32 microcontroller and the ThingSpeak platform. The system generates synthetic biomedical signals of body temperature and peripheral oxygen saturation (SpO2), structured across three levels: circadian variation, scheduled pathological episodes, and Gaussian noise. These signals feed a dual parametric coupling function that dynamically updates the SEIR transmission parameter as a combined function of body temperature and oxygen saturation deviations from their clinical reference values. The proposed architecture is organized into four functional phases: measurement, communication, computational processing, and feedback. Five simulated clinical scenarios were evaluated, ranging from normal conditions (T = 36.5 {degrees}C, SpO2 = 97%) to fever with severe hypoxia (T = 38.5 {degrees}C, SpO2 = 88%), yielding basic reproduction number (R0) values between 4.20 and 5.38, and peak infected proportions between 29.9% and 35.2% of the simulated population (N = 1,000). A sensitivity analysis on the coupling coefficients, with {+/-}50% variation from nominal values, showed that the oxygen saturation coefficient is the most influential parameter on R0 (range = 0.76) compared to the thermal coefficient (range = 0.42), with monotonic and predictable behavior across the entire evaluated parametric space. The primary contribution of this work is system integration: we propose a reproducible platform connecting biomedical simulation, IoT communication, and epidemiological modeling through parametric coupling in a controlled environment. All data used are entirely synthetic; a retrospective calibration with real Colombian data from the first epidemic wave of 2020 confirmed the epidemiological consistency of the model, with a calibrated R0 of 1.85 and a Pearson correlation of 0.930. Results should be interpreted as evidence of architectural feasibility, not as clinical or epidemiological validation. Author SummaryThe COVID-19 pandemic made it clear that epidemiological surveillance systems need tools that combine accessible technology with mathematical models capable of anticipating disease spread. In this work, we built a proof-of-concept platform connecting three elements: a low-cost electronic sensor based on the ESP32 microcontroller, a cloud communication platform (ThingSpeak), and a mathematical model that simulates how an epidemic spreads through a population. The sensor generates synthetic data on body temperature and oxygen saturation that, through a mathematical formula we designed, dynamically modify the rate of contagion in the model. We evaluated five clinical scenarios, ranging from normal conditions to fever with severe hypoxia, and analyzed how sensitive the results are to changes in the system parameters. We found that oxygen saturation has a greater influence on the estimated contagion potential than body temperature. Although all data are synthetic, this platform demonstrates that it is possible to integrate low-cost sensors with epidemiological models in real time, opening a viable pathway for early warning systems in resource-limited settings.

BackgroundThe relevance of spontaneous activity has been unlocked thanks to recent large scale recordings that revealed, via Shared Variance Component Analysis (SVCA), the high-dimensional nature of the ongoing activity. A fundamental problem is how the dimension modifies when more neurons are included in the analysis. Contradictory results have been reported on this subject based on SVCA and Principal Component Analysis (PCA). New MethodWe investigate pro et contra of SVCA and PCA for the identification of reliable responses encoding underlying state variables. We focus on common features of the spectra of the reliable variances (RVs) and on their dimensionality. The analysis is demonstrated on previously published Ca2+ data from the visual and the dorsal cortex in head fixed mice during spontaneous behavior. ResultsRVs grow proportionally to the number N of neurons and show a power-law decay k- with the k-th SVC dimension over a range bounded by a maximal dimension kc, initially diverging as N 1/ and then saturating at sufficiently large N. The reliable dimensionality, estimated with different methodologies, also shows a clear saturation to an asymptotic value for large N. Furthermore, its value decreases when becomes larger, as demonstrated by employing experimental data as well as theoretical predictions. ConclusionWe have shown that SVCA is an extremely effective tool to extract reliable features from the neural signals, and that the exponent represents a biomarker able to reveal the level of correlation of the neurons as well as the dimensionality of the reliable space. HighlightsO_LIAdvantages and drawbacks of Shared Variance Component Analysis to extract reliable signals from neural data C_LIO_LIComparison of different methods to estimate reliable neural dimensionality associated to spontaneous activity C_LIO_LIAnalytical expressions of embedding dimensionality for power-law decaying reliable variances C_LIO_LIBounded growth of the dimensionality with the number of neurons C_LI

We find that multi-site temporal control of optogenetic photostimulation in peripheral nerves can enhance firing rates by overcoming the intrinsic limitation of opsin photophysics. The benefits of multi-site optogenetic stimulation were demonstrated with three approaches: (1) in silico modeling, (2) ex vivo in the sciatic nerve, and (3) in vivo in the vagus nerve. An in silico model of multi-site optogenetic stimulation was developed in two Hodgkin and Huxley type neuron models, that supported our hypothesis. The ex vivo sciatic nerve showed an increase in firing frequency that is physiologically relevant for functional control. The technique was then applied in vivo for optogenetic vagus nerve stimulation resulting in significant changes in heart rate compared with standard methods of single-site stimulation. Improving the control of optogenetically induced neural firing will have broad impacts for future developments in optical nerve interfaces and brain-machine interfaces.

The emergence of a hantavirus variant aboard a commercial cruise ship presents a significant public health concern. This study develops a discrete-time stochastic Susceptible-Exposed-Infectious-Recovered-Dead model to estimate transmission dynamics, hidden exposed infections, and outbreak risk among passengers and crew. Epidemiological parameters and latent disease states were inferred using an Ensemble Adjustment Kalman Filter calibrated to reported case data from WHO and ECDC situation reports. The estimated basic reproduction number was 2.76, with a 95% confidence interval of 2.52-2.99, indicating substantial potential for sustained onboard transmission before strict quarantine measures. Simulations further suggest that several exposed individuals may remain unidentified during the early outbreak phase, creating a hidden reservoir that symptom-based surveillance alone may fail to detect. These findings highlight the importance of rapid surveillance, widespread testing, targeted quarantine, and active monitoring of exposed individuals in confined travel settings. The proposed modeling framework can support timely outbreak assessment and intervention planning for infectious-disease events in similarly dense and spatially constrained populations.

Introduction-India's immunization initiatives are among the largest globally, characterized by a substantial birth cohort of 27 million children annually, and have achieved significant progress in increasing coverage through the UIP. However, there are still challenges that persist, and multiple determinants contribute to the existing challenges; parental migration is one of them. Migration has always been a key driver of socio-economic and demographic changes, particularly in low and middle-income countries (LMICs). Specifically, there is a need to better understand the vulnerabilities of immunization among recent migrants. To examine this, the study explores the association between a mother's recent migration and the full immunization coverage of children aged 12-23 months in India. Data & Methods-Our study utilized data from the National Family Health Survey-5 (2019-21). The outcome variable of interest in this study is the receipt of all basic vaccinations (full immunization) for children. The primary predictor variable in this study is the children's migration status. We used a series of multivariate logistic regression models to examine the relationship between full Immunization and recent migration of children, with some data restrictions in the models. Results - The results show a 17% difference in full immunization between migrant and non-migrant children. The odds ratios for children who had recently migrated were lower for full immunization (OR: 0.39, 95% CI: 0.35-0.43) compared to children who had not recently migrated. Even across the household wealth quintile and social groups, the recent migration of children was associated with being less likely to be fully immunized among children 12-23 months. Conclusion- The findings of this study provide significant quantitative evidence that recent migration (less than 3 years) of children is a key factor influencing Immunization coverage and is a predictor of full vaccination among children aged 12-23 months in India. The recent migration was consistently linked to a lower likelihood of full immunization coverage across different household wealth levels and social groups. This study suggests that recently migrated children are a vulnerable subgroup of the population at risk of not receiving all basic vaccinations by their first birthday.

Malaria transmission in Nigeria is highly seasonal and climate-sensitive, yet routine surveillance and meteorological datasets remain underutilized for predictive modelling at subnational levels. This study modelled seasonal malaria incidence trends in Nasarawa State, Nigeria using routine surveillance and climatic data. A retrospective ecological time-series study was conducted using monthly confirmed malaria incidence data from all 13 Local Government Areas of Nasarawa State between 2021 and 2025. Rainfall and temperature were examined as the climatic predictors. Seasonal decomposition and cross-correlation analyses were performed to identify the temporal patterns and lag structures. Seasonal Autoregressive Integrated Moving Average (SARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models were developed using the Box-Jenkins framework. Model performance was evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Malaria incidence showed pronounced seasonal peaks, with the highest transmission occurring during the rainy season. Cross-correlation analysis identified rainfall at a one-month lag and contemporaneous temperature as significant predictors of malaria incidence. The SARIMAX model outperformed the univariate SARIMA model, achieving strong predictive accuracy (MAPE = 8.7%). Forecast projections indicate sustained transmission with a peak incidence expected between June and August 2026. Malaria transmission in Nasarawa follows a predictable seasonal pattern that is influenced by climatic variability. Incorporating rainfall and temperature into SARIMAX models improves the forecasting performance and provides evidence supporting climate-informed malaria surveillance and preparedness in endemic settings.

Understanding intracranial pressure (ICP) dynamics is essential for interpreting clinical infusion tests used in the diagnosis of cerebrospinal fluid circulation disorders. However, the complex coupling between vascular pulsations, cerebrospinal fluid flow, and intracranial compliance makes quantitative interpretation of these tests challenging. Here, I present a patient specific simulation framework based on an extended electrical analog model that reproduces intracranial pressure dynamics observed during clinical infusion tests. The model integrates physiological inputs including arterial blood pressure, heart rate, respiratory rhythm, and resistance to cerebrospinal fluid outflow derived from clinical data. Built upon the classical Ursino framework, the model incorporates several modifications enabling realistic representation of physiological pulsations and infusion test conditions. The resulting system functions as a hybrid electrical-numerical simulation model representing a simplified digital electrical twin of intracranial hydrodynamics. The model was validated using data from 21 clinical infusion tests performed in patients with suspected normal pressure hydrocephalus. Simulated intracranial pressure recordings were compared with clinical measurements using regression and residual analysis. The simulations demonstrated strong agreement with measured data, with a mean correlation coefficient of r = 0.95 (95% CI 0.94 - 0.96), mean residual values within -1.71 to +1.68 mmHg, and a mean root mean square error (RMSE) of 2.07 mmHg. These results demonstrate that the proposed model accurately reproduces the dynamic behavior of intracranial pressure observed during clinical infusion tests. The framework provides a physiologically grounded computational tool for studying patient specific intracranial dynamics and may support improved interpretation of infusion test results in clinical practice.

The awake brain is known to display asynchronous (AS) states during periods of attention and arousal, but the responsiveness properties of such states remain unclear. Here, we investigate this question using computational models of spiking networks of excitatory and inhibitory neurons, mimicking recurrently-connected networks in layer 2/3 of the cerebral cortex. The networks can generate a continuum of AS states, but with different responsiveness characteristics. By using a mean-field model to infer the dynamic properties of the system, we find that there are two families of AS states, which we call "underdamped" (UD) and "overdamped" (OD). Responsiveness is maximised at the transition between OD and UD states, which identifies a "working point" that may present advantageous computational properties.

Hybridisation between wild and domestic taxa can favour the spread of domestic alleles into wild populations through backcrossing. The complex interplay of random genetic drift, recombination, and selection can shape the fate of introgressed alleles. Maladaptive domestic variants are likely to be purged by natural selection, but others may persist across generations. It has long been known that the Apennine Italian wolf population, exposed to large numbers of free-ranging dogs, has experienced extensive introgression. The unusually high frequency of black wolves observed in Italy, compared to other European populations, may parallel patterns documented in North American wolves, where the melanistic KB allele at the CBD103 gene, of domestic origin, has spread over thousands of years of introgression. We tested whether the KB mutation entered the peninsular Italian wolf population via hybridisation and spread through adaptive introgression. Genome-wide analyses of black and wild-type (grey-coated) Apennine wolves showed no clear signatures of recent dog ancestry in most melanistic animals. Our ancestry reconstruction approaches identified two distinct KB haplogroups of domestic origin, suggesting multiple introgression events. Notably, we found molecular evidence consistent with balancing selection on the KB haplotypes, whose functional role, nonetheless, warrants further research. Therefore, the microevolutionary genomic and ecological consequences of wolf-dog hybridisation in Italy should be carefully investigated to inform appropriate science-based conservation management strategies.

IntroductionAdolescence is a sensitive developmental period during which chronic stress can induce lasting adaptations in corticolimbic circuits involved in stress regulation, cognition, and emotional behavior. We examined the long-term behavioral, endocrine, and molecular consequences of adolescent chronic variable stress (CVS) in male and female rats, focusing on the infralimbic cortex (IL) and basolateral amygdala (BLA) MethodsSprague Dawley rats of both sexes were exposed to CVS during late adolescence and evaluated in adulthood after an extensive recovery period. Behavioral testing included cued fear conditioning and extinction recall, delayed spatial win-shift, novel object recognition, Morris water maze, three-chamber social behavior, and passive avoidance. HPA-axis reactivity to acute restraint was assessed. Targeted qPCR was used to measure stress-related gene expression in the IL and BLA immediately after stress or after a 5-week recovery period ResultsAdolescent CVS did not cause generalized cognitive impairment, but instead produced selective, sex-specific effects. Females had reduced HPA responses to acute stress and mild deficits in delayed spatial win-shift performance, together with long-term IL changes in genes related to adrenergic signaling, plasticity, and GABA clearance. Males showed enhanced Morris water maze probe retention, weaker novel object discrimination, altered passive avoidance with marked inter-individual variability, and enhanced social preference. At the molecular level, males exhibited long-term upregulation of Fkbp5 in IL and downregulation of PACAP, 1D adrenergic receptor, and proenkephalin in BLA, whereas females showed delayed PACAP upregulation in BLA DiscussionAdolescent CVS induces persistent, sex- and region-specific recalibration of corticolimbic function, supporting distinct patterns of vulnerability and resilience, rather than uniform stress pathology.

The application of transcranial magnetic stimulation (TMS) to lateral parietal cortex has shown promise in improving episodic memory in older adults with Mild Cognitive Impairment (MCI). Previous work has suggested that such improvements are achieved by activating hippocampus at a distance with TMS, though this explanation is incomplete. We hypothesized that the mnemonic benefits arise from an additional mechanism: the modulation of semantic representations. Nineteen participants with amnestic MCI received either active intermittent theta-burst stimulation (iTBS) to angular gyrus or control vertex stimulation over three consecutive days while viewing object stimuli and completing relational memory encoding tasks during fMRI, followed by conceptual and perceptual recognition memory tests. We found that active TMS (relative to control TMS) significantly modulated conceptual memory performance. Using Representational Similarity Analysis with semantic embeddings derived from a large language model, we examined how TMS affects neural representations in inferior parietal lobule and hippocampus. We found that TMS enhanced semantic representational strength in inferior parietal lobule and reduced representational strength in hippocampus. Surprisingly, both effects supported successful memory. Neural pattern similarity analyses suggested that reduced hippocampal similarity supported successful memory, perhaps by promoting pattern separation mechanisms. These findings demonstrate that parietal TMS modulates semantic processing in a region-specific manner, by strengthening semantic integration at the stimulation site while promoting representational differentiation in medial temporal regions. This work advances our mechanistic understanding of memory neuromodulation and has implications for the optimization of therapeutic interventions in age-related memory disorders. Significance StatementTranscranial Magnetic Stimulation (TMS) applied to parietal cortex can improve memory in patients with Mild Cognitive Impairment (MCI), a population at high risk for Alzheimers Disease, yet the mechanisms underlying this benefit remain poorly understood. Using fMRI and Representational Similarity Analysis (RSA), we examined how TMS alters the neural representation of semantic stimulus information in parietal cortex and hippocampus during memory encoding. Our results show that TMS selectively modulates semantic representations at the stimulation site and in hippocampus, and that these representational changes predict memory improvement. These findings advance our mechanistic understanding of parietal memory neuromodulation and lay the groundwork for more targeted and effective TMS-based interventions for age-related memory disorders.

Learning to read requires linking auditory and visual information, yet how the developing brain maps information across sensory modalities remains poorly understood. To shed light on this topic we employed functional MRI to investigate hemodynamic brain responses during spoken and written word or pseudoword recognition in 61 primary school children with different levels of reading experience. Audiovisual representational similarity of activation patterns in the inferior frontal gyrus, inferior parietal lobule, superior temporal gyrus, and temporo-occipital cortex, increased linearly with school grade and this effect was largest in the left posterior superior temporal gyrus. Our results suggest that learning to read is related to a progressively increasing similarity of auditory and visual word representations within canonical language areas.

Autism affects males four times more often than females, yet the basis of this sex bias remains unclear. One hypothesis is that hypomorphic variants in X-linked genes--genes where loss-of-function alleles cause syndromic neurodevelopmental disorders (NDDs) predominantly in females--produce milder, non-syndromic phenotypes in hemizygous males. We tested this by investigating cis-regulatory elements (CREs) of MECP2, a dosage-sensitive X-linked gene. Using a massively parallel reporter assay in human neurons, we mapped transcription factor binding sites within MECP2 CREs and tested autism-associated variants for functional impact. We identified two noncoding variants that change CRE activity, each with a male-biased phenotype. One of these, a promoter variant, disrupts NFY binding and reduces MECP2 expression by [~]30%, a magnitude that produces autism-like phenotypes in mice. These findings suggest noncoding MECP2 variants can cause non-syndromic, male-biased autism, and provide a framework for uncovering regulatory variants in other X-linked NDD genes that may contribute to autisms missing heritability.

Visual scene analysis relies on a set of scene-selective regions in posterior cerebral cortex (OPA, PPA, MPA), each paired with an anterior memory-responsive counterpart (LPMA, VPMA, MPMA). The interaction between these pairs of regions is thought to integrate visual input with mnemonic context. Recently, a fourth scene-perception area in superior parietal cortex (SPPA/PIGS) was identified, with a proposed role in visually-guided navigation. Whether this region also has an anterior paired memory region is currently unknown. Across two independent fMRI datasets (total N=24, 14 females) using static or dynamic stimuli and distinct memory tasks, we show that recalling visual scenes evokes robust responses in a region (referred to here as SPMA/PIGS-mem) immediately anterior and dorsal to SPPA/PIGS. During resting-state fMRI, SPPA/PIGS preferentially coupled with the other scene-perception areas, while SPMA/PIGS-mem preferentially coupled with the other place memory areas. At the whole-brain level, seed-based connectivity revealed that SPPA/PIGS sits at the confluence of four processing streams spanning regions implicated in egocentric scene perception, map-based navigation, perspective taking, and goal-directed movement. These findings extend the perception-memory motif associated with visual scene processing to a fourth cortical surface. The ubiquitous anatomical coupling between scene-perception and memory processes reflects the importance of this interaction for flexible, context-grounded navigation.