https://actu.epfl.ch/news/machine-learning-improves-the-prediction-of-stroke/ * Homepage of the site * Skip to content * Skip to main navigation * Skip to side navigation * Skip to search * Contact us Go to main site Logo EPFL, Ecole polytechnique federale de Lausanne News * EPFL * ENAC * SB * STI * IC * SV * CDM * CDH * All Show / Hide search form Search [ ] Hide search form Search [ ] Search EN * FR * EN * DE [EN] Menu 1. 2. News * EPFL * ENAC * SB * STI * IC * SV * CDM * CDH * All Machine-learning improves the prediction of stroke recovery Dr Philip Koch and Professor Friedhelm Hummel performing an MRI. Credit: F. Hummel (EPFL) Dr Philip Koch and Professor Friedhelm Hummel performing an MRI. Credit: F. Hummel (EPFL) An international team of scientists led by EPFL has developed a system that combines information from the brain's connectome - the "wiring" between neurons - and machine learning to assess and predict the outcome of stroke victims. When blood flow to the brain is somehow reduced or restricted, a person can suffer what we know as a stroke (from "ischemic stroke" in medical jargon). Stroke is one of those conditions that seems fairly common. This isn't a misperception: just in Europe, there are over 1.5 million new cases each year. Some strokes can be lethal, and when they're not they often result in serious damage to the victim's ability to move. In fact, stroke is one of the major causes of long-term disability today. Recovery can be a long and arduous road. Again, in Europe, under 15% of the patients achieve full recovery, leaving 3.7 million patients with persistent impairments. Clearly, this is a medical problem that needs urgent addressing. But rehabilitation is a complicated problem to solve. Strokes can occur in different parts of the brain, affecting different brain systems, and patients who undergo rehabilitation show a "heterogeneity in outcome", which is the medical way of saying that recovery can be very different between individual stroke victims. "The key is to find the optimal neuro-rehabilitative strategy to maximize individual treatment outcome," says Professor Friedhelm Hummel, a neuroscientist and Director of the Defitech Chair for Clinical Neuroengineering at EPFL's School of Life Sciences. "If we want to address these challenges in everyday clinical practice, we have to first enhance our ability to predict the individual courses of recovery," adds Dr Philipp J. Koch, the study's first author. Hummel has now led an international team of scientists into a new approach for outcome prediction that can significantly improve stroke treatment. Publishing in the journal Brain, they demonstrate a predictive method based on two powerful, cutting-edge tools: connectomes and machine learning. The team included scientists from Sungkyunkwan University School of Medicine (Professor Y.-H. Kim), University Medical School of Geneva (Professor A. Guggisberg), Inserm Paris (Professor C. Rosso), Santa Lucia Foundation IRCCS, Rome (Professor G. Koch), and EPFL (Professor Thiran). What is a connectome? Simply put, it's a map of a brain's wiring. The term itself was coined independently in 2005 by two scientists (one from Lausanne's University Hospital) to describe the "blueprint" of how a brain's neurons connect to each other, evoking the concept of the genome - hence, "connectome". [66c011b0]MRI-based techniques are used to determine the individual structural wiring of the brain (left) and the underlying connectome (middle). Features from this complex information is used to classify patients with high precision in the group who does show natural recovery or who does not show natural recovery (right). Credit: F. Hummel (EPFL).(c) 2021 EPFL Connectomes are generated by analyzing multiple images taken from magnetic resonance imaging and reconstructing the brain's structural or functional wiring non-invasively and in vivo. Today, connectomes are indispensable tools for neuroscientists, especially when they want to interpret structural or dynamic brain data and associate them with functions, functional deficits, or recovery processes. In short, the connectome shows how the brain is wired to control the body and its functions, which makes them important for working out the best recovery approach for a stroke victim. In the study, Hummel's group analyzed connectomes from 92 patients two weeks after the stroke, tracking connectome changes up to three months later while assessing motor impairment with a standardized scale. This allowed them to monitor connection changes in the individual brains of the patients while they underwent recovery. The scientists input the connectome information into a "support-vector machine", or SVM, which is a type of machine-learning model that uses examples to map an input onto an output. SVMs are particularly useful for classification, where they tell things apart and categorize them appropriately, e.g. spam and non-spam email. In this study, the researchers trained the SVMs to distinguish between patients with natural recovery from those without based on their whole-brain structural connectomes. The SVMs then defined the underlying brain-network pattern of each patient, focusing on those who were severely impaired to make predictions about their recovery potential, with the accuracy of each prediction cross-validated internally and externally with independent datasets. The result is a cutting-edge tool of personalized medicine: a machine-learning system that can identify neuronal network patterns to make high-accuracy predictions on the outcome of recovery for stroke patients. "This tool can support the prediction of individual courses of recovery early on and will have an important impact on clinical management, translational research, and treatment choice," says Hummel. Other contributors * Clinique Romande de Readaptation (Switzerland) * University of Lubeck (Germany) * University of Geneva Medical School * EPFL Signal Processing Laboratory * Centre Hospitalier Universitaire Vaudois (CHUV) * University of Lausanne (UniL) * University School of Medicine (Republic of Korea) * Santa Lucia Foundation IRCCS (Italy) * Geneva University Hospitals * Stroke Unit, Pitie-Salpetriere Hospital (France) * Sungkyunkwan University (Republic of Korea) Funding ETH domain - Strategic Focus Area Personalized Health and Related Technologies (PHRT) Defitech Foundation Wyss Foundation Bertarelli Foundation EPFL/UniL/UniGe Center for Biomedical Imaging (CIBM) Leenaards Foundation Louis-Jeantet Foundation National Research Foundation of Korea (NRF) References Philipp J. Koch, Chang-Hyun Park, Gabriel Girard, Elena Beanato, Philip Egger, Giorgia Giulia Evangelista, Jungsoo Lee, Maximilian J. Wessel, Takuya Morishita, Giacomo Koch, Jean-Philippe Thiran, Adrian Guggisberg, Charlotte Rosso, Yun-Hee Kim, Friedhelm C. Hummel. The structural connectome and motor recovery after stroke: predicting natural recovery. Brain 08 July 2021. DOI: 10.1093/brain/awab082 --------------------------------------------------------------------- Author: Nik Papageorgiou Source: EPFL 09.07.21 --------------------------------------------------------------------- Tags Brain Mind InstituteFriedhelm Christoph Hummellife sciences neuroscienceProf. Hummel Group (UPHUMMEL)School of Life SciencesSV Related articles 21.06.2019 EPFL awarded joint Chan Zuckerberg grant for the Human Cell Atlas 10.02.2021 How the brain makes sense of touch 13.08.2020 The (neuro)science of getting and staying motivated 22.04.2021 "Molecular Tomographer" algorithm maps gene expression in space News * All EPFL news * All news Subscription Receive an email for each new article Share on Login Logo EPFL, Ecole polytechnique federale de Lausanne * Contact * EPFL CH-1015 Lausanne * +41 21 693 11 11 Follow the pulses of EPFL on social networks Follow us on Facebook. Follow us on Twitter. Follow us on Instagram. Follow us on Youtube. Follow us on LinkedIn. 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