Biology subjects

Haar, S.

Publications and source records attributed to Haar, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Delayed maturation of hippocampal trisynaptic circuitry underlies the protracted development of episodic-like memory

The ability to form high-resolution episodic memories emerges relatively late in development, but the circuit mechanisms underlying this transition are unknown. CA1 receives entorhinal input through a direct monosynaptic pathway and an indirect trisynaptic pathway via the dentate gyrus and CA3. In mice, CA3[->]CA1 innervation matured later than MEC[->]CA1 innervation, with pronounced maturation between P20 and P24. Trisynaptic pathway maturation was necessary for high-resolution memory and was shaped by neuronal activity and early-life experience. Mechanistically, trisynaptic activity promoted perineuronal net assembly around downstream parvalbumin-expressing interneurons, facilitating their maturation and subsequent recruitment during learning to constrain CA1 encoding ensembles. Thus, activity-dependent maturation of trisynaptic circuitry provides a unified circuit and cellular account of a sensitive period for the emergence of episodic-like memory.

neuroscience↗

bioRxiv2026

Latent dentate gyrus engrams formed in infancy scaffold adult learning

Infantile amnesia refers to the loss of event-based memories acquired early in life. In mice, however, these memories are not erased; selective optogenetic stimulation of engrams formed in infancy leads to memory recovery in adulthood, indicating that infant-formed engrams can persist in a latent form. We therefore asked whether these latent engrams, although not accessible through natural retrieval, nonetheless influence related adult learning. Using a contextual fear conditioning paradigm, we find that adult learning is facilitated by prior experience in infancy, both for the same and related contexts. Activity-dependent engram tagging revealed that adult learning reactivates infant-acquired dentate gyrus ensembles, and selective inactivation of these ensembles during adulthood abolished this facilitation. These findings indicate that memories formed in infancy persist as latent engrams that scaffold subsequent learning, consistent with a role in establishing schema-like representations.

neuroscience↗

bioRxiv2026

Spatiotemporal Modeling of GPCR Signaling: The Role of Endosomal Dynamics and Receptor Recycling

Cells communicate via extracellular ligands, such as hormones, which bind to plasma membrane receptors and trigger intracellular signaling cascades. G Protein-Coupled Receptors (GPCRs) exemplify this mechanism by initiating signaling both at the cell surface and, from intracellular compartments such as endosomes. The kinetics and spatial localization of these signals are critical determinants of cellular responses, yet receptor trafficking-including internalization, endosomal sorting, and recycling-remains a pivotal but often overlooked component of theoretical GPCR models. In this study, we present a mathematical framework that integrates receptor trafficking and signaling compartmentalization into generic GPCR dynamic models. Using a compartmentalized approach based on systems of ordinary differential equations (Chemical Reaction Networks), we analyze how receptor internalization and recycling modulate ligand-induced responses. Our results show that the balance between plasma membrane and endosomal signaling can significantly enhance or diminish ligand efficacy. Calibrated with high-throughput kinetic data, our model offers a refined tool for ligand pharmacological characterization and advances the understanding of GPCR signaling spatial organization.

systems biology↗

bioRxiv2025

Propagation of Beta bursts from the motor cortex to the motor units of multiple upper-limb muscles

Beta band (13-30 Hz) oscillations are crucial for motor control, though their functional significance remains debated. Recent research suggests that beta activity occurs in transient bursts, which may better capture its role in movement regulation than sustained oscillations. While cortical and sub- cortical beta bursts have been extensively studied, their transmission to muscles--particularly in the upper limb--remains poorly understood and has been limited by traditional bipolar EMG techniques. In this study, we used high-density surface electromyography (HDsEMG) and electroencephalography (EEG) to investigate the cortico-peripheral dynamics of beta bursts in forearm extensor muscles during isometric contractions at the motor unit (MU) level. We show that MU activity in the upper limb exhibits discrete beta bursts that are temporally aligned with cortical beta activity. Notably, beta bursts in the periphery were time-locked to cortical bursts, suggesting strong coordination and synchronisation of bursting across the corticospinal tract. We also found stronger beta synchronisation in the extensor carpi ulnaris compared to the extensor carpi radialis, indicating muscle-specific differences in shared neural drive. These findings provide the first demonstration of beta burst propagation from cortex to upper-limb MUs and show that HDsEMG can reliability detect such events in the upper limb. This work supports the cortical origin and structure of peripheral beta activity and demonstrates its potential as a neurophysiological biomarker for targeting corticospinal dynamics in motor disorders such as Parkinsons disease. Significance StatementWe provide the first evidence of cortical beta bursting in motor unit (MU) activity of forearm extensors across varying force levels. Our results show that MU beta bursting is time-locked to cortical beta bursts, supporting the idea of direct corticospinal transmission in the upper limb. Our findings highlight the value of MU level analysis in understanding beta burst transmission and indicate that burst timing is robust to changes in contraction levels. Unlike traditional bipolar EMG, MU-level analysis offers higher temporal precision and source specificity, enabling the reliable detection of beta bursts in individual muscles. These findings demonstrate a robust cortical-peripheral beta relationship during isometric contractions and reinforces the utility of HDsEMG for investigating burst dynamics and refining neurophysiological biomarkers.

neuroscience↗

bioRxiv2024

Anatomical Abnormalities Suggest a Compensatory Role of the Cerebellum in Early Parkinson's Disease

Brain atrophy is detected in early Parkinsons disease (PD) and accelerates over the first few years post-diagnosis. This was captured by multiple cross-sectional studies and a few longitudinal studies in early PD. Yet only a longitudinal study with a control group can capture accelerated atrophy in early PD and differentiate it from healthy ageing. Accordingly, we performed a multicohort longitudinal analysis between PD and healthy ageing, examining subcortical regions implicated in PD pathology, including the basal ganglia, thalamus, corpus callosum (CC), and cerebellum. Longitudinal volumetric analysis was performed on 56 early PD patients and 53 matched controls, with scans collected 2-3 years apart. At baseline, the PD group showed a greater volume in the pallidum, thalamus, and cerebellar white matter (WM), suggesting potential compensatory mechanisms in prodromal and early PD. After 2-3 years, accelerated atrophy in PD was observed in the putamen and cerebellar WM. Interestingly, healthy controls - but not PD patients - demonstrated a significant decline in Total Intracranial Volume (TIV), and atrophy in the thalamus and mid-CC. Between-group analysis revealed more severe atrophy in the right striatum and cerebellar WM in PD, and in the mid-posterior CC in controls. Using CEREbellum Segmentation (CERES) for lobule segmentation on the longitudinal PD cohort, we found a significant decline in the WM of non-motor regions in the cerebellum, specifically Crus I and lobule IX. Our results highlight an initial increase in cerebellar WM volume during prodromal PD, followed by significant degeneration over the first few years post-diagnosis.

neuroscience↗

bioRxiv2024

Motor Learning Mechanisms are not modified by Feedback Manipulations in a Real-World Task

Error-based and reward-based mechanisms of motor learning co-occur in real-world scenarios but are traditionally isolated in laboratory tasks via feedback manipulations. This study examines the distinctiveness of these mechanisms by applying a lab-based feedback manipulation to a real-world task. Using Embodied Virtual Reality (EVR) of pool billiards - allowing for full proprioception via interaction with the physical pool table, cue stick, and balls - we introduced visual perturbations to a real-world task. 32 participants (12 F) underwent two sessions learning a visuomotor rotation, once with error and once with reward feedback. While naive participants corrected the entire rotation with error feedback, only partial correction was observed with reward feedback, highlighting the influence of the feedback regime on learning. However, the reward-dependent motor variability, lag-1 autocorrelation decay, and inter-trial variability decay - all indicators of reward-based and skill learning - were higher in the error feedback session, suggesting that the provided visual feedback did not exclusively engage specific learning mechanisms. Analysis of post-movement beta rebound (PMBR), a brain activity marker of learning mechanisms, revealed a decrease in PMBR with reward feedback but no consistent trend during error feedback sessions. These findings support the behavioural results, suggesting that while reward feedback was absent in error conditions, participants still engaged in reward-based learning. This study underscores the complexity of motor learning processes and highlights that visual feedback by itself can not elucidate the interplay between error-based and reward-based mechanisms in real-world contexts.

neuroscience↗

bioRxiv2023

Modulatory Effect of Levodopa on the Basal Ganglia-Cerebellum Connectivity in Parkinson's Disease

Levodopa has remained the mainstay of medical therapy for Parkinsons disease since its development in the 1980s. However, long-term medication use is associated with declining clinical efficacy and the emergence of motor complications. Unveiling the effects of levodopa on brain functional reorganisation at a relatively early treatment phase is therefore imperative to inform the optimisation of Parkinsons therapeutics. In this study, we comprehensively investigated levodopas modulation on the resting-state functional connectivity in the cortico- basal ganglia-cerebellum system at regional and network levels, with dual cross-sectional and longitudinal designs. The data was extracted from the Parkinsons Progression Marker Initiative (PPMI) dataset. The cross-sectional patient groups comprised 17 Parkinsons patients on stable levodopa medication and 15 drug-naive patients, while the longitudinal set included 14 Parkinsons patients measured at both drug-naive and levodopa-medicated conditions. With nodes defined across cortical, basal ganglia, and cerebellar networks, we conducted univariate comparisons of the internodal connectivity strength between the medication conditions using nonparametric permutation. At the network level, we computed multivariate combinations of individual connections within and between the networks, followed by an assessment of their discriminative capabilities on patients medication classes using supervised machine learning. The univariate seed-based approach showed no statistically significant effect of levodopa in either dataset. However, the network connectivity pattern between basal ganglia and the cerebellum displayed a robust classification power in the longitudinal dataset and a similar trend was observed in the cross-sectional. The role of the cerebellum is often overlooked in previous functional integration investigations of Parkinsons disease and levodopa effects. Considering the recent evidence suggesting the bidirectional communications between the cerebellum and basal ganglia networks, our study provides further insight into the importance of inter-network functional connectivity in Parkinsons, as well as in the functional and plastic processes following levodopa medication.

neuroscience↗

bioRxiv2021

Reproducing Human Motor Adaptation in Spiking Neural Simulation and known Synaptic Learning Rules

Sensorimotor adaptation enables us to adjust our goal-oriented movements in response to external perturbations. These phenomena have been studied experimentally and computationally at the level of human and animals reaching movements, and have clear links to the cerebellum as evidenced by cerebellar lesions and neurodegeneration. Yet, despite our macroscopic understanding of the high-level computational mechanisms it is unclear how these are mapped and are implemented in the neural substrates of the cerebellum at a cellular-computational level. We present here a novel spiking neural circuit model of the sensorimotor system including a cerebellum which control physiological muscle models to reproduce behaviour experiments. Our cerebellar model is composed of spiking neuron populations reflecting cells in the cerebellar cortex and deep cerebellar nuclei, which generate motor correction to change behaviour in response to perturbations. The model proposes two learning mechanisms for adaptation: predictive learning and memory formation, which are implemented with synaptic updating rules. Our model is tested in a force-field sensorimotor adaptation task and successfully reproduce several phenomena arising from human adaptation, including well-known learning curves, aftereffects, savings and other multi-rate learning effects. This reveals the capability of our model to learn from perturbations and generate motor corrections while providing a bottom-up view for the neural basis of adaptation. Thus, it also shows the potential to predict how patients with specific types of cerebellar damage will perform in behavioural experiments. We explore this by in silico experiments where we selectively incapacitate selected cerebellar circuits of the model which generate and reproduce defined motor learning deficits. Author summaryA rich body of work in human motor neuroscience developed high-level computational theories of sensorimotor control, learning and adaptation. But there is a gap in understanding how this may be implemented and learn on the level of neurons, synapses and spikes. Conversely, studies of patients with cerebellar lesions or neurological disease highlight the essential role the cerebellum plays in our ability to perform motor learning. Yet, how these anatomical and molecular defects play out in terms of human movement have to date not been linked to a model that spans multiple level of biological organisation from neural circuits to reproducing human motor experiments. To address this gap, we present a spiking neuron of the sensorimotor system focused on the cerebellum, with which we can on the one side reproduce the high-level behaviour learning phenomena observed in healthy subjects, as well as quantitatively predicting the putative effects on human movement trajectories of cerebellar lesions implemented at the cellular level.

neuroscience↗

bioRxiv2021

Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls. Although these alterations affect multiple and widespread cortical regional asymmetries, the extent to which specific structural networks might be affected remains unknown. Inter-regional morphological covariance analysis can capture network connectivity relations between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1,455 individuals with ASD and 1,560 controls, across 43 independent datasets of the ENIGMA consortiums ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered asymmetry of hemispheric networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, driven by shifts toward higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve working memory, executive functions, language, reading, and sensorimotor processes. Taken together, these findings provide new insights into how altered brain left-right asymmetry in ASD affects specific structural and functional brain networks. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

neuroscience↗

bioRxiv2020

Learning to play the piano with the Supernumerary Robotic 3rd Thumb

Contemporary robotics gives us mechatronic capabilities for augmenting human bodies with extra limbs. However, how our motor control capabilities pose limits on such augmentation is an open question. We developed a Supernumerary Robotic 3rd Thumbs (SR3T) with two degrees-of-freedom controlled by the users body to endow them with an extra contralateral thumb on the hand. We demonstrate that a pianist can learn to play the piano with 11 fingers within an hour. We then evaluate 6 naive and 6 experienced piano players in their prior motor coordination and their capability in piano playing with the robotic augmentation. We show that individuals augmented performance could be predicted by our new custom motor coordination assessment, the Human Augmentation Motor Coordination Assessment (HAMCA) performed pre-augmentation. Our work demonstrates how supernumerary robotics can augment humans in skilled tasks and that individual differences in their augmentation capability are predictable by their individual motor coordination abilities.

neuroscience↗

bioRxiv2020

Reconciling Qualitative, Abstract, and Scalable Modeling of Biological Networks

Predicting the behaviors of complex biological systems, underpinning processes such as cellular differentiation, requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach which enables reasoning on the qualitative dynamics of networks accounting for many species. Several dynamical approaches have been proposed to interpret the logic of the regulations encoded by the BNs. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations are not adequate to represent quantitative systems, being able to both predict spurious behaviors and miss others. We introduce a new paradigm, the Most Permissive Boolean Networks (MPBNs), which offer the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.

systems biology↗

bioRxiv2020

Embodied virtual reality for the study of real-world motor learning

Motor-learning literature focuses on simple laboratory-tasks due to their controlled manner and the ease to apply manipulations to induce learning and adaptation. Recently, we introduced a billiards paradigm and demonstrated the feasibility of real-world-neuroscience using wearables for naturalistic full-body motion-tracking and mobile-brain-imaging. Here we developed an embodied virtual-reality (VR) environment to our real-world billiards paradigm, which allows to control the visual feedback for this complex real-world task, while maintaining sense of embodiment. The setup was validated by comparing real-world ball trajectories with the trajectories of the virtual balls, calculated by the physics engine. We then ran our learning protocol in the embodied VR. Subjects played billiard shots when they held the physical cue and hit a physical ball on the table while seeing it all in VR. We found comparable learning trends in the embodied VR to those we previously reported in the physical real-world task. Embodied VR can be used for learning real-world tasks in a highly controlled environment which enables applying visual manipulations, common in laboratory-tasks and rehabilitation, to a real-world full-body task. Embodied VR enables to manipulate feedback and apply perturbations to isolate and assess interactions between specific motor-learning components, thus enabling addressing the current questions of motor-learning in real-world tasks. Such a setup can be used for rehabilitation, where VR is gaining popularity but the transfer to the real-world is currently limited, presumably, due to the lack of embodiment.

neuroscience↗

bioRxiv2020

Neural biomarkers of multiple motor-learning mechanisms in a real-world task

Many recent studies found signatures of motor learning in neural Beta oscillations (13- 30Hz), and specifically in the post-movement Beta rebound (PMBR). All these studies were in controlled laboratory-tasks in which the task designed to induce the studied learning mechanism. Interestingly, these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based and reward-based tasks (increase versus decrease, respectively). Here we explored the PMBR dynamics during real-world motor-skill-learning in a billiards task using mobile-brain-imaging. Our EEG recordings highlight the opposing dynamics of PMBR magnitudes (increase versus decrease) between different subjects performing the same task. The groups of subjects, defined by their neural dynamics, also showed behavioural differences expected for different learning mechanisms. Our results suggest that when faced with the complexity of the real-world different subjects might use different learning mechanisms for the same complex task. We speculate that all subjects combine multi-modal mechanisms of learning, but different subjects have different predominant learning mechanisms.

neuroscience↗

bioRxiv2019

Neurobehavioural Signatures in Race Car Driving

Recent technological developments in mobile brain and body imaging are enabling new frontiers of real-world neuroscience. Simultaneous recordings of body movement and brain activity from highly skillful individuals as they demonstrate their exceptional skills in real-world settings, can shed new light on neurobehavioural structure of human expertise. Driving is a real-world skill which many of us acquire on different levels of expertise. Here we ran a case-study on a subject with the highest level of driving expertise - a Formula E Champion. We studied the expert drivers neural and motor patterns while he drove a sports car in the "Top Gear" race track under extreme conditions (high speed, low visibility, low temperature, wet track). His brain activity, eye movements and hand/foot movements were recorded. Brain activity in the delta, alpha, and beta frequency bands showed causal relation to hand movements. We demonstrate, here in summary, that even in extreme situations (race track driving) a method for conducting human ethomic (Ethology + Omics) data that encompasses information on the sensory inputs and motor outputs outputs of the brain as well as brain state to characterise complex human skills.

neuroscience↗

bioRxiv2019

Eye movements during visuomotor adaptation represent only part of the explicit learning

Visuomotor rotations are learned through a combination of explicit strategy and implicit recalibration. However, measuring the relative contribution of each remains a challenge and the possibility of multiple explicit and implicit components complicates the issue. Recent interest has focused on the possibility that eye movements reflect explicit strategy. Here we compared eye movements during adaptation to two accepted measures of explicit learning - verbal report and the exclusion test. We found that while reporting, all subjects showed a match between all three measures. However, when subjects did not report their intention, the eye movements of some subjects suggested less explicit adaptation than what was measured in an exclusion test. Interestingly, subjects whose eye movements did match their exclusion could be clustered into two subgroups: fully implicit learners showing no evidence of explicit adaptation and explicit learners with little implicit adaptation. Subjects showing a mix of both explicit and implicit adaptation were also those where eye movements showed less explicit adaptation than did exclusion. Thus, our results support the idea of multiple components of explicit learning as only part of the explicit learning is reflected in the eye movements. Individual subjects may use explicit components that are reflected in the eyes or those that are not or some mixture of the two. Analysis of reaction times suggests that the explicit components reflected in the eye-movements involve longer reaction times. This component, according to recent literature, may be related to mental rotation.\n\nSignificance StatementVisuomotor adaptation involves both explicit and implicit components: aware re-aiming and unaware error correction. Recent studies suggest that eye movements could be used to capture the explicit component, a method that would have significant advantages over other approaches. We show that eye movements capture only one component of explicit adaptation. This component scales with reaction time while the component unrelated to eye movements does not. Our finding has obvious practical implications for the use of eye movements as a proxy for explicit learning. However, our results also corroborate recent findings suggesting the existence of multiple explicit components, and, specifically, their decomposition into components correlated with reaction time and components that are not.

neuroscience↗

bioRxiv2019

Neurobehavioural signatures of learning that emerge in a real-world motor skill task

The neurobehavioral mechanisms of human motor-control and learning evolved in free behaving, real-life settings, yet this is studied mostly in reductionistic lab-based experiments. Here we take a step towards a more real-world motor neuroscience using wearables for naturalistic full-body motion-tracking and the sports of pool billiards to frame a real-world skill learning experiment. First, we asked if well-known features of motor learning in lab-based experiments generalize to a real-world task. We found similarities in many features such as multiple learning rates, and the relationship between task-related variability and motor learning. Our data-driven approach reveals the structure and complexity of movement, variability, and motor-learning, enabling an in-depth understanding of the structure of motor learning in three ways: First, while expecting most of the movement learning is done by the cue-wielding arm, we find that motor-learning affects the whole body, changing motor-control from head to toe. Second, during learning, all subjects decreased their movement variability and their variability in the outcome. Subjects who were initially more variable were also more variable after learning. Lastly, when screening the link across subjects between initial variability in individual joints and learning, we found that only the initial variability in the right forearm supination shows a significant correlation to the subjects learning rates. This is in-line with the relationship between learning and variability: while learning leads to an overall reduction in movement variability, only initial variability in specific task-relevant dimensions can facilitate faster learning.

neuroscience↗

bioRxiv2019

Altered structural brain asymmetry in autism spectrum disorder: large-scale analysis via the ENIGMA Consortium

BackgroundLeft-right asymmetry is an important organizing feature of the healthy brain. Various studies have reported altered structural brain asymmetry in autism spectrum disorder (ASD). However, findings have been inconsistent, likely due to limited sample sizes and low statistical power.\n\nMethodsWe investigated 1,774 subjects with ASD and 1,809 controls, from 54 datasets, for differences in the asymmetry of thickness and surface area of 34 cerebral cortical regions. We also examined global hemispheric measures of cortical thickness and area asymmetry, and volumetric asymmetries of subcortical structures. Data were obtained via the ASD Working Group of the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. T1-weighted MRI data were processed with a single protocol using FreeSurfer and the Desikan-Killiany atlas.\n\nResultsASD was significantly associated with reduced leftward asymmetry of total hemispheric average cortical thickness, compared to controls. Eight regional thickness asymmetries, distributed over the cortex, also showed significant associations with diagnosis after correction for multiple comparisons, for which asymmetry was again generally lower in ASD versus controls. In addition, the medial orbitofrontal surface area was less rightward asymmetric in ASD than controls, and the putamen volume was more leftward asymmetric in ASD than controls. The largest effect size had Cohens d = 0.15. Most effects did not depend on age, sex, IQ, or disorder severity.\n\nConclusionAltered lateralized neurodevelopment is suggested in ASD, affecting widespread cortical regions with diverse functions. Large-scale analysis was necessary to reliably detect, and accurately describe, subtle alterations of structural brain asymmetry in this disorder.

neuroscience↗

bioRxiv2017

No evidence of early head circumference enlargements in children later diagnosed with autism in Israel

BackgroundLarge controversy exists regarding the potential existence and clinical significance of larger brain volumes in toddlers who later develop autism. Assessing this relationship is important for determining the clinical utility of early head circumference (HC) measures and for assessing the validity of the early overgrowth hypothesis of autism, which suggests that early accelerated brain development may be a hallmark of the disorder\n\nMethodWe performed a retrospective comparison of HC, height, and weight measurements between 66 toddlers who were later diagnosed with autism and 66 matched controls. These toddlers represent an unbiased regional sample from a single health service provider in the southern district of Israel. Using 4-12 measurements between birth and the age of two, we were able to characterize individual HC, height, and weight development with high precision and fit a negative exponential growth model to the data of each toddler with exceptional accuracy\n\nResultsThe analyses revealed that HC sizes and growth rates were not significantly larger in toddlers with autism even when stratifying the autism group based on verbal capabilities at the time of diagnosis. In addition, there were no significant correlations between ADOS scores at the time of diagnosis and HC at any time-point during the first two years of life\n\nConclusionsThese negative results add to accumulating evidence, which suggest that brain volume is not necessarily larger in toddlers who develop autism. While early brain overgrowth may characterize specific individuals with autism, it is not likely to represent a common etiology of the entire autism population

neuroscience↗