Publications
37 results found
Nardi F, Haar S, Faisal AA, 2023, Bill-EVR: an embodied virtual reality framework for reward-and-error-based motor rehab-learning, 2023 International Conference on Rehabilitation Robotics (ICORR), Publisher: Institute of Electrical and Electronics Engineers Inc., Pages: 1-6, ISSN: 1945-7901
VR rehabilitation is an established field by now, however, it often refers to computer screen-based interactive rehabilitation activities. In recent years, there was an increased use of VR-headsets, which can provide an immersive virtual environment for real-world tasks, but they are lacking any physical interaction with the task objects and any proprioceptive feedback. Here, we focus on Embodied Virtual Reality (EVR), an emerging field where not only the visual input via VR-headset but also the haptic feedback is physically correct. This happens because subjects interact with physical objects that are veridically aligned in Virtual Reality. This technology lets us manipulate motor performance and motor learning through visual feedback perturbations. Bill-EVR is a framework that allows interventions in the performance of real-world tasks, such as playing pool billiard, engaging end-users in motivating life-like situations to trigger motor (re)learning - subjects see in VR and handle the real-world cue stick, the pool table and shoot physical balls. Specifically, we developed our platform to isolate and evaluate different mechanisms of motor learning to investigate its two main components, error-based and reward-based motor adaptation. This understanding can provide insights for improvements in neurorehabilitation: indeed, reward-based mechanisms are putatively impaired by degradation of the dopaminergic system, such as in Parkinson's disease, while error-based mechanisms are essential for recovering from stroke-induced movement errors. Due to its fully customisable features, our EVR framework can be used to facilitate the improvement of several conditions, providing a valid extension of VR-based implementations and constituting a motor learning tool that can be completely tailored to the individual needs of patients.
Carpio Chicote A, Jeyasingh-Jacob J, Abulikemu S, et al., 2023, Computational tracking of Parkinsonian motor fluctuations in a real-world setting: a case study, 2023 Conference on Cognitive Computational Neuroscience, Publisher: Cognitive Computational Neuroscience, Pages: 198-200
Digital biomarkers based on accurate tracking of motor behaviour can provide a cost-effective, objective, and robust measure for Parkinson’s Disease progression, changes in care needs, and the effect of interventions. Markerless motion capture technology offers a promising approach for running it in the home. This technology uses depth sensors to capture movement unobtrusively and generate objective and quantifiable movement features. Here we present a 4-month long case study during which the patient visits our lab every month to perform mobility tasks and daily living tasks. Our data suggest accurate tracking of symptom fluctuations during both task types. This is a promising proof-of-concept towards passive tracking in-the-home of Parkinsonian symptom fluctuations.
Haugland MR, Borovykh A, Tai Y, et al., 2023, Explainable deep learning for arm classification during deep brain stimulation - towards digital biomarkers for closed-loop stimulation, 2023 Conference on Cognitive Computational Neuroscience, Publisher: Cognitive Computational Neuroscience, Pages: 59-61
Deep brain stimulation (DBS) is an effective technique for treating motor symptoms in neurological conditions like Parkinson’s disease and dystonic and essential tremor (DT and ET). The DBS delivery could be improved if reliable biomarkers could be found. We propose a deep learning (DL) framework based on EEGNet to search for digital biomarkers in EEG recordings for discriminating neural response from changes in DBS parameters. Here we present a proof-of-concept by distinguishing left and right arm movement in raw EEG recorded during a DBS programming session of a DT patient. Based on the classification of 1s segments from six-channel EEG, we achieve an average accuracy of up to 93.8%. In addition, we propose a simple, yet effective model-agnostic filtering strategy for explaining the network’s performance, showing which frequency band features it mostly uses to classify the EEG.
Crook-Rumsey M, Daniels S, Abulikemu S, et al., 2023, Multicohort cross-sectional study of cognitive and behavioural digital biomarkers in neurodegeneration: the Living Lab study protocol, BMJ Open, Vol: 13, Pages: 1-9, ISSN: 2044-6055
Introduction and aimsDigital biomarkers can provide a cost-effective, objective, and robust measure forneurological disease progression, changes in care needs, and the effect of interventions.Motor function, physiology and behaviour can provide informative measures of neurologicalconditions and neurodegenerative decline. New digital technologies present an opportunityto provide remote, high-frequency monitoring of patients from within their homes. Thepurpose of the Living Lab study is to develop novel digital biomarkers of functionalimpairment in those living with neurodegenerative disease (NDD) and neurologicalconditions.Methods and analysisThe Living Lab Study is a cross-sectional observational study of cognition and behaviour inpeople living with NDDs and other, non-degenerative neurological conditions. Patients (n≥25for each patient group) with Dementia, Parkinson’s disease, Amyotrophic Lateral Sclerosis, Mild Cognitive Impairment, Traumatic Brain Injury, and Stroke along with controls (n≥60) willbe pragmatically recruited. Patients will carry out activities of daily living and functionalassessments within the living lab. The living lab is an apartment-laboratory containing afunctional kitchen, bathroom, bed and living area to provide a controlled environment todevelop novel digital biomarkers. The living lab provides an important intermediary stagebetween the conventional laboratory and the home. Multiple passive environmental sensors,internet-enabled medical devices, wearables, and EEG will be used to characterise functionalimpairments of NDDs and non-NDD conditions. We will also relate these digital technologymeasures to clinical and cognitive outcomes.Ethics and disseminationEthical approvals have been granted by the Imperial College Research Ethics Committee(reference number: 21IC6992). Results from the study will be disseminated at conferencesand within peer-reviewed journals.
Kutuzova A, Graef C, Lonergan B, et al., 2023, Linking volume of tissue activated to neural oscillations in deep brain stimulation, International Congress of Parkinson's Disease and Movement Disorders, Publisher: Wiley, ISSN: 0885-3185
Graef C, Bocum A, Ciocca M, et al., 2022, Digital biomarkers for deep brain stimulation programming in PD, 2022 MDS International Congress, Publisher: Wiley, ISSN: 0885-3185
Graef C, Bocum A, Ciocca M, et al., 2022, Digital Biomarkers for Deep Brain Stimulation Programming in PD, Publisher: WILEY, Pages: S602-S602, ISSN: 0885-3185
Sha Z, van Rooij D, Anagnostou E, et al., 2022, Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium, Molecular Psychiatry, Vol: 27, Pages: 2114-2125, ISSN: 1359-4184
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, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture network connectivity between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1455 individuals with ASD and 1560 controls, across 43 independent datasets of the ENIGMA consortium’s 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 average asymmetry of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, involving 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 executive functions, language-related and sensorimotor processes. These findings provide a network-level characterization of altered left-right brain asymmetry in ASD, based on a large combined sample. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.
Nardi F, Ziman M, Haar S, et al., 2022, Isolating Motor Learning Mechanisms in Embodied Virtual Reality, 2022 Conference on Cognitive Computational Neuroscience, Publisher: Cognitive Computational Neuroscience
Postema MC, van Rooij D, Anagnostou E, et al., 2021, Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets (Oct, 10.1038/s41467-019-13005-8, 2019), Nature Communications, Vol: 12, Pages: 1-1, ISSN: 2041-1723
Shafti A, Haar S, Mio R, et al., 2021, Playing the piano with a robotic third thumb: assessing constraints of human augmentation, SCIENTIFIC REPORTS, Vol: 11, ISSN: 2045-2322
Patel BV, Haar S, Handslip R, et al., 2021, Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom, Intensive Care Medicine, Vol: 47, Pages: 549-565, ISSN: 0342-4642
PurposeThe trajectory of mechanically ventilated patients with coronavirus disease 2019 (COVID-19) is essential for clinical decisions, yet the focus so far has been on admission characteristics without consideration of the dynamic course of the disease in the context of applied therapeutic interventions.MethodsWe included adult patients undergoing invasive mechanical ventilation (IMV) within 48 h of intensive care unit (ICU) admission with complete clinical data until ICU death or discharge. We examined the importance of factors associated with disease progression over the first week, implementation and responsiveness to interventions used in acute respiratory distress syndrome (ARDS), and ICU outcome. We used machine learning (ML) and Explainable Artificial Intelligence (XAI) methods to characterise the evolution of clinical parameters and our ICU data visualisation tool is available as a web-based widget (https://www.CovidUK.ICU).ResultsData for 633 adults with COVID-19 who underwent IMV between 01 March 2020 and 31 August 2020 were analysed. Overall mortality was 43.3% and highest with non-resolution of hypoxaemia [60.4% vs17.6%; P < 0.001; median PaO2/FiO2 on the day of death was 12.3(8.9–18.4) kPa] and non-response to proning (69.5% vs.31.1%; P < 0.001). Two ML models using weeklong data demonstrated an increased predictive accuracy for mortality compared to admission data (74.5% and 76.3% vs 60%, respectively). XAI models highlighted the increasing importance, over the first week, of PaO2/FiO2 in predicting mortality. Prone positioning improved oxygenation only in 45% of patients. A higher peak pressure (OR 1.42[1.06–1.91]; P < 0.05), raised respiratory component (OR 1.71[ 1.17–2.5]; P < 0.01) and cardiovascular component (OR 1.36 [1.04–1.75]; P < 0.05) of the sequential organ failure assessment (SOFA) score and raised lactate (OR 1.33 [0.99–1.79
Haar S, Sundar G, Faisal AA, 2021, Embodied virtual reality for the study of real-world motor learning, PLOS ONE, Vol: 16, ISSN: 1932-6203
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- Citations: 11
Haar S, van Assel CM, Faisal AA, 2020, Motor learning in real-world pool billiards, SCIENTIFIC REPORTS, Vol: 10, ISSN: 2045-2322
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- Citations: 17
Haar S, Donchin O, 2020, A revised computational neuroanatomy for motor control, Journal of Cognitive Neuroscience, Vol: 32, Pages: 1823-1836, ISSN: 0898-929X
We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical-subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and basal ganglia. These subcortical areas are thus engaged in domain appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modelled? We suggest that one fundamental division is between modelling of task and body while another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices.
Haar S, Donchin O, 2020, A Revised Computational Neuroanatomy for Motor Control, Journal of Cognitive Neuroscience, Vol: 32, Pages: 1823-1836, ISSN: 0898-929X
<jats:p> We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical–subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes that each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and BG. These subcortical areas are thus engaged in domain-appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modeled? We suggest that one fundamental division is between modeling of task and body whereas another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices. </jats:p>
Haar S, Faisal AA, 2020, Brain Activity Reveals Multiple Motor-Learning Mechanisms in a Real-World Task, FRONTIERS IN HUMAN NEUROSCIENCE, Vol: 14, ISSN: 1662-5161
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- Citations: 19
Boedhoe PSW, van Rooij D, Hoogman M, et al., 2020, Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups, AMERICAN JOURNAL OF PSYCHIATRY, Vol: 177, Pages: 834-843, ISSN: 0002-953X
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- Citations: 78
Writing Committee for the Attention-DeficitHyperactivity Disorder, Autism Spectrum Disorder, Bipolar Disorder, et al., 2020, Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders., JAMA Psychiatry, Pages: E1-E17, ISSN: 2168-622X
Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood. Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. Design, Setting, and Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244. Main Outcomes and Measures: Interregional profiles of group difference in cortical thickness between cases and controls. Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene
Lima IR, Haar S, Di Grassi L, et al., 2020, Neurobehavioural signatures in race car driving: a case study, SCIENTIFIC REPORTS, Vol: 10, ISSN: 2045-2322
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- Citations: 9
Bromberg Z, Donchin O, Haar S, 2019, Eye movements during visuomotor adaptation represent only part of the explicit learning, eNeuro, Vol: 6, Pages: 1-12, ISSN: 2373-2822
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.
Postema MC, van Rooij D, Anagnostou E, et al., 2019, Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets, Nature Communications, Vol: 10, Pages: 1-12, ISSN: 2041-1723
Altered structural brain asymmetry in autism spectrum disorder (ASD) has been reported. However, findings have been inconsistent, likely due to limited sample sizes. Here we investigated 1,774 individuals with ASD and 1,809 controls, from 54 independent data sets of the ENIGMA consortium. ASD was significantly associated with alterations of cortical thickness asymmetry in mostly medial frontal, orbitofrontal, cingulate and inferior temporal areas, and also with asymmetry of orbitofrontal surface area. These differences generally involved reduced asymmetry in individuals with ASD compared to controls. Furthermore, putamen volume asymmetry was significantly increased in ASD. The largest case-control effect size was Cohen’s d = −0.13, for asymmetry of superior frontal cortical thickness. Most effects did not depend on age, sex, IQ, severity or medication use. Altered lateralized neurodevelopment may therefore be a feature of ASD, affecting widespread brain regions with diverse functions. Large-scale analysis was necessary to quantify subtle alterations of brain structural asymmetry in ASD.
Ricotti V, Haar S, Selby V, et al., 2018, Kinematic/behavioural fingerprints in Duchenne muscular dystrophy and their clinical applications, 23rd International Annual Congress of the World-Muscle-Society (WMS), Publisher: PERGAMON-ELSEVIER SCIENCE LTD, Pages: S125-S125, ISSN: 0960-8966
van Rooij D, Anagnostou E, Arango C, et al., 2018, Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results From the ENIGMA ASD Working Group, AMERICAN JOURNAL OF PSYCHIATRY, Vol: 175, Pages: 359-369, ISSN: 0002-953X
Fingher N, Dinstein I, Ben-Shachar M, et al., 2017, Toddlers later diagnosed with autism exhibit multiple structural abnormalities in temporal corpus callosum fibers, Cortex, Vol: 97, Pages: 291-305, ISSN: 0010-9452
Interhemispheric functional connectivity abnormalities are often reported in autism and it is thus not surprising that structural defects of the corpus callosum (CC) are consistently found using both traditional MRI and DTI techniques. Past DTI studies however, have subdivided the CC into 2 or 3 segments without regard for where fibers may project to within the cortex, thus placing limitations on our ability to understand the nature, timing and neurobehavioral impact of early CC abnormalities in autism. Leveraging a unique cohort of 97 toddlers (68 autism; 29 typical) we utilized a novel technique that identified seven CC tracts according to their cortical projections. Results revealed that younger (<2.5 years old), but not older toddlers with autism exhibited abnormally low mean, radial, and axial diffusivity values in the CC tracts connecting the occipital lobes and the temporal lobes. Fractional anisotropy and the cross sectional area of the temporal CC tract were significantly larger in young toddlers with autism. These findings indicate that water diffusion is more restricted and unidirectional in the temporal CC tract of young toddlers who develop autism. Such results may be explained by a potential overabundance of small caliber axons generated by excessive prenatal neural proliferation as proposed by previous genetic, animal model, and postmortem studies of autism. Furthermore, early diffusion measures in the temporal CC tract of the young toddlers were correlated with outcome measures of autism severity at later ages. These findings regarding the potential nature, timing, and location of early CC abnormalities in autism add to accumulating evidence, which suggests that altered inter-hemispheric connectivity, particularly across the temporal lobes, is a hallmark of the disorder.
Haar S, Donchin O, Dinstein I, 2017, Individual movement variability magnitudes are explained by cortical neural variability, Journal of Neuroscience, Vol: 37, Pages: 9076-9085, ISSN: 0270-6474
Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior–parietal cortex of individual subjects explained their movement–extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities.
Haar S, Dinstein I, Shelef I, et al., 2017, Effector-invariant movement encoding in the human motor system, Journal of Neuroscience, Vol: 37, Pages: 9054-9063, ISSN: 0270-6474
Ipsilateral motor areas of cerebral cortex are active during arm movements and even reliably predict movement direction. Is coding similar during ipsilateral and contralateral movements? If so, is it in extrinsic (world-centered) or intrinsic (joint-configuration) coordinates? We addressed these questions by examining the similarity of multivoxel fMRI patterns in visuomotor cortical regions during unilateral reaching movements with both arms. The results of three complementary analyses revealed that fMRI response patterns were similar across right and left arm movements to identical targets (extrinsic coordinates) in visual cortices, and across movements with equivalent joint-angles (intrinsic coordinates) in motor cortices. We interpret this as evidence for the existence of distributed neural populations in multiple motor system areas that encode ipsilateral and contralateral movements in a similar manner: according to their intrinsic/joint coordinates.
Haar S, Donchin O, Dinstein I, 2017, Individual movement variability magnitudes are predicted by cortical neural variability, The Journal of Neuroscience, Vol: 37, Pages: 9076-9085, ISSN: 0270-6474
Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior–parietal cortex of individual subjects explained their movement–extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities.
Givon-Mayo R, Haar S, Aminov Y, et al., 2017, Long Pauses in Cerebellar Interneurons in Anesthetized Animals, CEREBELLUM, Vol: 16, Pages: 293-305, ISSN: 1473-4222
Dinstein I, Haar S, Atsmon S, et al., 2017, No evidence of early head circumference enlargements in children later diagnosed with autism in Israel, Molecular Autism, Vol: 8, Pages: 1-9, ISSN: 2040-2392
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.MethodsWe 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. On average, participating toddlers had >8 measurements between birth and the age of two, which enabled us 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.ResultsThe 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 2 years of life.ConclusionsThese negative results add to accumulating evidence, which suggest that brain volume is not necessarily larger in toddlers who develop autism. We believe that conflicting results reported in other studies are due to small sample sizes, use of misleading population norms, changes in the clinical definition of autism over time, and/or inclusion of individuals with syndromic autism. While abnormally large brains may be evident in some individuals with autism and more clearly visible in MRI scans, converging evidence from this and other studies suggests that enlarged HC is not a common etiology
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