Summary
Shlomi Haar is an Edmond and Lily Safra Research Fellow at the Department of Brain Sciences and a UK DRI Emerging Leader at the UK Dementia Research Institute Care Research & Technology Centre.
Dr Haar studies the neurobehavioural mechanisms of human movement in health and disease: motor control, motor learning, motor decline, and their neural correlates. He is leading interdisciplinary research between engineering and neuroscience, using novel sensors, and developing novel data science approaches to enable Real-World Motor Neuroscience – studying body movement and brain activity during free behaviour and real-world tasks.
His research programme focuses on improving our understanding of the neural network of human motor control and the effects of neurodegeneration on it, predominantly in Parkinson’s disease. His research programme aims to improve disease progression and symptom fluctuation tracking in neurodegeneration to enable better care and robust outcome measurements for clinical trials in new disease-modifying interventions. Of specific interest is a better understanding of the neurobehavioural mechanisms of Deep Brain Stimulation (DBS) for Parkinson's disease. This would enable better treatment delivery and leverage smart sensing and AI toward personalized medicine using adaptive closed-loop therapies.
BIO
Shlomi has a BSc and MSc in Biomedical Engineering, and a PhD in Brain and Cognitive Sciences from Ben-Gurion University of the Negev, Israel. In his PhD research, he studied the Encoding of Arm Movement in the Human Brain, by running motor experiments with human subjects using fMRI.
Dr Haar first joined Imperial College London, in 2017 as a Royal Society – Kohn International Fellow with the Brain and Behaviour Lab at the Department of Bioengineering. His fellowship research, entitled "The Dynamic of Real-World Motor Skill Learning and its Neural Correlates", focused on the relationship between changes in brain and body to the learning of novel real-life complex motor skills in an attempt to uncover what makes some of us better learners.
During the first few months of the COVID19 pandemic, Shlomi led data science in the COVID-ICU National Service Evaluation.
In November 2020, Dr Haar started his current position as an Edmond and Lily Safra Research Fellow at the UK Dementia Research Institute Care Research & Technology Centre and the Department of Brain Sciences.
Selected Publications
Journal Articles
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, ISSN:2044-6055, Pages:1-9
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
Haar S, van Assel CM, Faisal AA, 2020, Motor learning in real-world pool billiards, Scientific Reports, Vol:10, ISSN:2045-2322
Haar S, Donchin O, 2020, A revised computational neuroanatomy for motor control, Journal of Cognitive Neuroscience, Vol:32, ISSN:0898-929X, Pages:1823-1836
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
Bromberg Z, Donchin O, Haar S, 2019, Eye movements during visuomotor adaptation represent only part of the explicit learning, Eneuro, Vol:6, ISSN:2373-2822, Pages:1-12
Haar S, Dinstein I, Shelef I, et al., 2017, Effector-invariant movement encoding in the human motor system, Journal of Neuroscience, Vol:37, ISSN:0270-6474, Pages:9054-9063
Haar S, Donchin O, Dinstein I, 2017, Individual movement variability magnitudes are explained by cortical neural variability, Journal of Neuroscience, Vol:37, ISSN:0270-6474, Pages:9076-9085
Conference
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, Cognitive Computational Neuroscience, Pages:198-200
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, Cognitive Computational Neuroscience, Pages:59-61
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, 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, Wiley, ISSN:0885-3185