Imperial College London

Dr Shlomi Haar

Faculty of MedicineDepartment of Brain Sciences

Edmond and Lily Safra Research Fellow and UK DRI Fellow
 
 
 
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Contact

 

s.haar Website

 
 
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Location

 

Building E - Sir Michael UrenWhite City Campus

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Summary

 

Publications

Publication Type
Year
to

37 results found

Shafti SA, Haar Millo S, Mio Zaldivar R, Guilleminot P, Faisal Aet al., 2021, Playing the piano with a robotic third thumb: Assessing constraints of human augmentation, Scientific Reports, ISSN: 2045-2322

Journal article

Wu Y, Haar S, Faisal A, 2021, Reproducing Human Motor Adaptation in Spiking Neural Simulation and known Synaptic Learning Rules

<jats:title>Abstract</jats:title><jats:p>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 <jats:italic>in silico</jats:italic> experiments where we selectively incapacitate selected cerebellar circuits of the model which generate and reproduce defined motor learning deficits.</jats:p><jats:sec><jats:title>Author summary</jats:t

Journal article

Patel BV, Haar S, Handslip R, Auepanwiriyakul C, Lee TM-L, Patel S, Harston JA, Hosking-Jervis F, Kelly D, Sanderson B, Borgatta B, Tatham K, Welters I, Camporota L, Gordon AC, Komorowski M, Antcliffe D, Prowle JR, Puthucheary Z, Faisal AAet 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

Journal article

Lannou EL, Post B, Haar S, Brett SJ, Kadirvelu B, Faisal AAet al., 2021, Clustering of patient comorbidities within electronic medical records enables high-precision COVID-19 mortality prediction

<jats:title>Abstract</jats:title><jats:p>We present an explainable AI framework to predict mortality after a positive COVID-19 diagnosis based solely on data routinely collected in electronic healthcare records (EHRs) obtained prior to diagnosis. We grounded our analysis on the ½ Million people UK Biobank and linked NHS COVID-19 records. We developed a method to capture the complexities and large variety of clinical codes present in EHRs, and we show that these have a larger impact on risk than all other patient data but age. We use a form of clustering for natural language processing of the clinical codes, specifically, topic modelling by Latent Dirichlet Allocation (LDA), to generate a succinct digital fingerprint of a patient’s full secondary care clinical history, i.e. their comorbidities and past interventions. These digital comorbidity fingerprints offer immediately interpretable clinical descriptions that are meaningful, e.g. grouping cardiovascular disorders with common risk factors but also novel groupings that are not obvious. The comorbidity fingerprints differ in both their breadth and depth from existing observational disease associations in the COVID-19 literature. Taking this data-driven approach allows us to avoid human-induction bias and confirmation bias during selection of what are important potential predictors of COVID-19 mortality. Together with age, these digital fingerprints are the single most important factor in our predictor. This holds the potential for improving individual risk profiling for clinical decisions and the identification of groups for public health interventions such as vaccine programmes. Combining our digital precondition fingerprints with demographic characteristics allow us to match or exceed the performance of existing state-of-the-art COVID-19 mortality predictors (EHCF) which have been developed through expert consensus. Our precondition fingerprinting and entire mortality prediction anal

Journal article

Haar Millo S, Sundar G, Faisal A, 2021, Embodied virtual reality for the study of real-world motor learning, PLoS One, Vol: 16, ISSN: 1932-6203

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 short-term motor 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 short-term motor 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 potentially 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.

Journal article

Haar Millo S, van Assel C, Faisal A, 2020, Motor learning in real-world pool billiards, Scientific Reports, Vol: 10, Pages: 1-13, ISSN: 2045-2322

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.

Journal article

Patel BV, Haar S, Handslip R, Lee TM-L, Patel S, Harston JA, Hosking-Jervis F, Kelly D, Sanderson B, Bogatta B, Tatham K, Welters I, Camporota L, Gordon AC, Komorowski M, Antcliffe D, Prowle JR, Puthucheary Z, Faisal AAet al., 2020, Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom, Publisher: Cold Spring Harbor Laboratory

Background To date the description of mechanically ventilated patients with Coronavirus Disease 2019 (COVID-19) has focussed on admission characteristics with no consideration of the dynamic course of the disease. Here, we present a data-driven analysis of granular, daily data from a representative proportion of patients undergoing invasive mechanical ventilation (IMV) within the United Kingdom (UK) to evaluate the complete natural history of COVID-19.Methods We included adult patients undergoing IMV within 48 hours of ICU admission with complete clinical data until death or ICU discharge. We examined factors and trajectories that determined disease progression and responsiveness to ARDS interventions. Our data visualisation tool is available as a web-based widget (https://www.CovidUK.ICU).Findings Data for 623 adults with COVID-19 who were mechanically ventilated between 01 March 2020 and 31 August 2020 were analysed. Mortality, intensity of mechanical ventilation and severity of organ injury increased with severity of hypoxaemia. Median tidal volume per kg across all mandatory breaths was 5.6 [IQR 4.7-6.6] mL/kg based on reported body weight, but 7.0 [IQR 6.0-8.4] mL/kg based on calculated ideal body weight. Non-resolution of hypoxaemia over the first week of IMV was associated with higher ICU mortality (59.4% versus 16.3%; P<0.001). Of patients ventilated in prone position only 44% showed a positive oxygenation response. Non-responders to prone position show higher D-Dimers, troponin, cardiovascular SOFA, and higher ICU mortality (68.9% versus 29.7%; P<0.001). Multivariate analysis showed prone non-responsiveness being independently associated with higher lactate (hazard ratio 1.41, 95% CI 1.03–1.93), respiratory SOFA (hazard ratio 3.59, 95% CI 1.83–7.04); and cardiovascular SOFA score (hazard ratio 1.37, 95% CI 1.05–1.80).Interpretation A sizeable proportion of patients with progressive worsening of hypoxaemia were also refractory to evid

Working paper

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.

Journal article

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>

Journal article

Haar Millo S, Faisal A, 2020, Brain activity reveals multiple motor-learning mechanisms in a real-world task, Frontiers in Human Neuroscience, Vol: 14, ISSN: 1662-5161

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.

Journal article

Boedhoe PSW, van Rooij D, Hoogman M, Twisk JWR, Schmaal L, Abe Y, Alonso P, Ameis SH, Anikin A, Anticevic A, Arango C, Arnold PD, Asherson P, Assogna F, Auzias G, Banaschewski T, Baranov A, Batistuzzo MC, Baumeister S, Baur-Streubel R, Behrmann M, Bellgrove MA, Benedetti F, Beucke JC, Biederman J, Bollettini I, Bose A, Bralten J, Bramati IE, Brandeis D, Brem S, Brennan BP, Busatto GF, Calderoni S, Calvo A, Calvo R, Castellanos FX, Cercignani M, Chaim-Avancini TM, Chantiluke KC, Cheng Y, Cho KIK, Christakou A, Coghill D, Conzelmann A, Cubillo A, Dale AM, Dallaspezia S, Daly E, Denys D, Deruelle C, Di Martino A, Dinstein I, Doyle AE, Durston S, Earl EA, Ecker C, Ehrlich S, Ely BA, Epstein JN, Ethofer T, Fair DA, Fallgatter AJ, Faraone S, Fedor J, Feng X, Feusner JD, Fitzgerald J, Fitzgerald KD, Fouche J-P, Freitag CM, Fridgeirsson EA, Frodl T, Gabel MC, Gallagher L, Gogberashvili T, Gori I, Gruner P, Gursel DA, Haar S, Haavik J, Hall GB, Harrison NA, Hartman CA, Heslenfeld DJ, Hirano Y, Hoekstra PJ, Hoexter MQ, Hohmann S, Hovik MF, Hu H, Huyser C, Jahanshad N, Jalbrzikowski M, James A, Janssen J, Jaspers-Fayer F, Jernigan TL, Kapilushniy D, Kardatzki B, Karkashadze G, Kathmann N, Kaufmann C, Kelly C, Khadka S, King JA, Koch K, Kohls G, Konrad K, Kuno M, Kuntsi J, Kvale G, Kwon JS, Lazaro L, Lera-Miguel S, Lesch K-P, Hoekstra L, Liu Y, Lochner C, Louza MR, Luna B, Lundervold AJ, Malpas CB, Marques P, Marsh R, Martinez-Zalacain I, Mataix-Cols D, Mattos P, McCarthy H, McGrath J, Mehta MA, Menchon JM, Mennes M, Martinho MM, Moreira PS, Morer A, Morgado P, Muratori F, Murphy CM, Murphy DGM, Nakagawa A, Nakamae T, Nakao T, Namazova-Baranova L, Narayanaswamy JC, Nicolau R, Nigg JT, Novotny SE, Nurmi EL, Weiss EO, Tuura RLO, O'Hearn K, O'Neill J, Oosterlaan J, Oranje B, Paloyelis Y, Parellada M, Pauli P, Perriello C, Piacentini J, Piras F, Piras F, Plessen KJ, Puig O, Ramos-Quiroga JA, Reddy YCJ, Reif A, Reneman L, Retico A, Rosa PGP, Rubia K, Rus OG, Sakai Y, Schrantee A, Scet 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

Journal article

Writing Committee for the Attention-DeficitHyperactivity Disorder, Autism Spectrum Disorder, Bipolar Disorder, Major Depressive Disorder, Obsessive-Compulsive Disorder, and Schizophrenia ENIGMA Working Groups, Patel Y, Parker N, Shin J, Howard D, French L, Thomopoulos SI, Pozzi E, Abe Y, Abé C, Anticevic A, Alda M, Aleman A, Alloza C, Alonso-Lana S, Ameis SH, Anagnostou E, McIntosh AA, Arango C, Arnold PD, Asherson P, Assogna F, Auzias G, Ayesa-Arriola R, Bakker G, Banaj N, Banaschewski T, Bandeira CE, Baranov A, Bargalló N, Bau CHD, Baumeister S, Baune BT, Bellgrove MA, Benedetti F, Bertolino A, Boedhoe PSW, Boks M, Bollettini I, Del Mar Bonnin C, Borgers T, Borgwardt S, Brandeis D, Brennan BP, Bruggemann JM, Bülow R, Busatto GF, Calderoni S, Calhoun VD, Calvo R, Canales-Rodríguez EJ, Cannon DM, Carr VJ, Cascella N, Cercignani M, Chaim-Avancini TM, Christakou A, Coghill D, Conzelmann A, Crespo-Facorro B, Cubillo AI, Cullen KR, Cupertino RB, Daly E, Dannlowski U, Davey CG, Denys D, Deruelle C, Di Giorgio A, Dickie EW, Dima D, Dohm K, Ehrlich S, Ely BA, Erwin-Grabner T, Ethofer T, Fair DA, Fallgatter AJ, Faraone SV, Fatjó-Vilas M, Fedor JM, Fitzgerald KD, Ford JM, Frodl T, Fu CHY, Fullerton JM, Gabel MC, Glahn DC, Roberts G, Gogberashvili T, Goikolea JM, Gotlib IH, Goya-Maldonado R, Grabe HJ, Green MJ, Grevet EH, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Guerrero-Pedraza A, Gur RE, Gur RC, Haar S, Haarman BCM, Haavik J, Hahn T, Hajek T, Harrison BJ, Harrison NA, Hartman CA, Whalley HC, Heslenfeld DJ, Hibar DP, Hilland E, Hirano Y, Ho TC, Hoekstra PJ, Hoekstra L, Hohmann S, Hong LE, Höschl C, Høvik MF, Howells FM, Nenadic I, Jalbrzikowski M, James AC, Janssen J, Jaspers-Fayer F, Xu J, Jonassen R, Karkashadze G, King JA, Kircher T, Kirschner M, Koch K, Kochunov P, Kohls G, Konrad K, Krämer B, Krug A, Kuntsi J, Kwon JS, Landén M, Landrø NI, Lazaro L, Lebedeva IS, Leehr EJ, Lera-Miguel S, Lesch K-P, Lochner C, Louza MR, Luna B, Lundervold AJ, MacMaster FP Met 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

Journal article

Rito Lima I, Haar Millo S, Di Grassi L, Faisal Aet al., 2020, Neurobehavioural signatures in race car driving: a case study, Scientific Reports, Vol: 10, Pages: 1-9, ISSN: 2045-2322

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 skilled individuals as they demonstrate their exceptional skills in real-world settings, can shed new light on the neurobehavioural structure of human expertise. Driving is a real-world skill which many of us acquire to 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 driver’s neural and motor patterns while he drove a sports car on 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 herein demonstrate the feasibility of using mobile brain and body imaging even in very extreme conditions (race car driving) to study the sensory inputs, motor outputs, and brain states which characterise complex human skills.

Journal article

Shafti A, Haar S, Zaldivar RM, Guilleminot P, Faisal AAet al., 2020, Learning to play the piano with the Supernumerary Robotic 3rd Thumb, Publisher: Cold Spring Harbor Laboratory

We wanted to study the ability of our brains and bodies to be augmented by supernumerary robot limbs, here extra fingers. We developed a mechanically highly functional supernumerary robotic 3rd thumb actuator, the SR3T, and interfaced it with human users enabling them to play the piano with 11 fingers. We devised a set of measurement protocols and behavioural “biomarkers”, the Human Augmentation Motor Coordination Assessment (HAMCA), which allowed us a priori to predict how well each individual human user could, after training, play the piano with a two-thumbs-hand. To evaluate augmented music playing ability we devised a simple musical score, as well as metrics for assessing the accuracy of playing the score. We evaluated the SR3T (supernumerary robotic 3rd thumb) on 12 human subjects including 6 naïve and 6 experienced piano players. We demonstrated that humans can learn to play the piano with a 6-fingered hand within one hour of training. For each subject we could predict individually, based solely on their HAMCA performance before training, how well they were able to perform with the extra robotic thumb, after training (training end-point performance). Our work demonstrates the feasibility of robotic human augmentation with supernumerary robotic limbs within short time scales. We show how linking the neuroscience of motor learning with dexterous robotics and human-robot interfacing can be used to inform a priori how far individual motor impaired patients or healthy manual workers could benefit from robotic augmentation solutions.

Working paper

Haar S, Sundar G, Faisal A, 2020, Embodied virtual reality for the study of real-world motor learning, Publisher: bioRxiv

Abstract Background The motor learning literature focuses on relatively simple laboratory-tasks due to their highly controlled manner and the ease to apply different manipulations to induce learning and adaptation. In recent work 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 us to control the visual feedback for this complex real-world task, while maintaining the sense of embodiment. Methods The setup was validated by comparing real-world ball trajectories with the embodied VR trajectories, calculated by the physics engine. We then ran our real-world learning protocol in the embodied VR. 10 healthy human subjects played repeated trials of the same billiard shot when they held the physical cue and hit a physical ball on the table while seeing it all in VR. Results We found comparable learning trends in the embodied VR to those we previously reported in the real-world task. Conclusions Embodied VR can be used for learning real-world tasks in a highly controlled VR environment which enables applying visual manipulations, common in laboratory-tasks and in rehabilitation, to a real-world full-body task. Such a setup can be used for rehabilitation, where the use of VR is gaining popularity but the transfer to the real-world is currently limited, presumably, due to the lack of embodiment. The embodied VR enables to manipulate feedback and apply perturbations to isolate and assess interactions between specific motor learning components mechanisms, thus enabling addressing the current questions of motor-learning in real-world tasks.

Working paper

Haar S, Faisal A, 2020, Neural biomarkers of multiple motor-learning mechanisms in a real-world task, Publisher: bioRxiv

Abstract 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 simplified laboratory-tasks in which learning was either error-based or reward-based. Interestingly, these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based and reward-based tasks (increase verses 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 opposing dynamics of PMBR magnitudes between different subjects performing the same task. The groups of subjects, defined by their neural-dynamics, also showed behavioral differences expected for error-based verses reward-based learning. 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.

Working paper

Lima IR, Haar S, Di Grassi L, Faisal Aet al., 2019, Neurobehavioural signatures in race car driving

ABSTRACT 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 driver’s 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.

Working paper

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.

Journal article

Postema MC, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Busatto Filho G, Calderoni S, Calvo R, Daly E, Deruelle C, Di Martino A, Dinstein I, Duran FLS, Durston S, Ecker C, Ehrlich S, Fair D, Fedor J, Feng X, Fitzgerald J, Floris DL, Freitag CM, Gallagher L, Glahn DC, Gori I, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, King JA, Kong XZ, Lazaro L, Lerch JP, Luna B, Martinho MM, McGrath J, Medland SE, Muratori F, Murphy CM, Murphy DGM, O'Hearn K, Oranje B, Parellada M, Puig O, Retico A, Rosa P, Rubia K, Shook D, Taylor MJ, Tosetti M, Wallace GL, Zhou F, Thompson PM, Fisher SE, Buitelaar JK, Francks Cet 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.

Journal article

Bromberg Z, Donchin O, Haar S, 2019, Eye movements during visuomotor adaptation represent only part of the explicit learning, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:p>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.</jats:p><jats:sec><jats:title>Significance Statement</jats:title><jats:p>Visuomotor 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 metho

Working paper

Haar S, van Assel C, Faisal A, 2019, Neurobehavioural signatures of learning that emerge in a real-world motor skill task

Summary The behavioral and neural processes of real-world motor learning remain largely unknown. We demonstrate the feasibility of real-world neuroscience, using wearables for naturalistic full-body motion tracking and mobile brain imaging, to study motor learning in billiards. We highlight the similarities between motor learning in-the-wild and classic toy-tasks in well-known features, such as multiple learning rates, and the relationship between task-related variability and motor learning. However, we found that real-world motor learning affects the whole body, changing motor control from head to toe. Moreover, with a data-driven approach, based on the relationship between variability and learning, we found the arm supination to be the task relevant joint angle. Our EEG recordings highlight groups of subjects with opposing dynamics of post-movement Beta rebound (PMBR), not resolved before in toy-tasks. The first group increased PMBR over learning while the second decreased. These opposite trends were previously reported in error-based learning and skill learning tasks respectively. Behaviorally, the PMBR decreasers better controlled task-relevant variability dynamically leading to lower variability and smaller errors in the learning plateau. We speculate that these PMBR dynamics emerge because subjects must combine multi-modal mechanisms of learning in new ways when faced with the complexity of the real-world.

Working paper

Ricotti V, Haar S, Selby V, Voit T, Faisal Aet 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

Conference paper

van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Busatto GF, Calderoni S, Daly E, Deruelle C, Di Martino A, Dinstein I, Duran FLS, Durston S, Ecker C, Fair D, Fedor J, Fitzgerald J, Freitag CM, Gallagher L, Gori I, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, Lerch J, Luna B, Martinho MM, McGrath J, Muratori F, Murphy CM, Murphy DGM, O'Hearn K, Oranje B, Parellada M, Retico A, Rosa P, Rubia K, Shook D, Taylor M, Thompson PM, Tosetti M, Wallace GL, Zhou F, Buitelaar JKet 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

Journal article

Fingher N, Dinstein I, Ben-Shachar M, Haar S, Dale AM, Eyler L, Pierce K, Courchesne Eet 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.

Journal article

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.

Journal article

Haar S, Dinstein I, Shelef I, Donchin Oet 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.

Journal article

Givon-Mayo R, Haar S, Aminov Y, Simons E, Donchin Oet al., 2017, Long Pauses in Cerebellar Interneurons in Anesthetized Animals, CEREBELLUM, Vol: 16, Pages: 293-305, ISSN: 1473-4222

Journal article

Dinstein I, Haar S, Atsmon S, Schtaerman Het 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

Journal article

Gonen-Yaacovi G, Arazi A, Shahar N, Karmon A, Haar S, Meiran N, Dinstein Iet al., 2016, Increased ongoing neural variability in ADHD, CORTEX, Vol: 81, Pages: 50-63, ISSN: 0010-9452

Journal article

Haar S, Berman S, Behrmann M, Dinstein Iet al., 2016, Anatomical Abnormalities in Autism?, CEREBRAL CORTEX, Vol: 26, Pages: 1440-1452, ISSN: 1047-3211

Journal article

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