In clinical neuroscience, there are considerable difficulties in translating basic research into clinical applications such as diagnostic tools or therapeutic interventions. This gap, known as the “valley of death,” was mainly attributed to the problem of “small numbers” in clinical neuroscience research, i.e. sample sizes that are too small (Hutson et al., 2017). As a possible solution, it has been repeatedly suggested to systematically manage research data to provide long-term storage, accessibility, and federate data. This goal is supported by a current call of the DFG for a national research data infrastructure (NFDI). This article will review current challenges and possible solutions specific to clinical neuroscience and discuss them in the context of other national and international health data initiatives. A successful NFDI consortium will help to overcome not only the “valley of death” but also promises a path to individualized medicine by enabling big data to produce generalizable results based on artificial intelligence and other methods.
In den klinischen Neurowissenschaften gibt es erhebliche Schwierigkeiten, Erkenntnisse aus der Grundlagenforschung in therapeutische klinische Strategien umzusetzen. Diese Lücke wurde als „Tal des Todes” (Hutson et al., 2017) bezeichnet und hat zu der Ansicht geführt, dass die klinisch-neurowissenschaftliche Forschung nicht optimal aufgestellt ist. Als mögliche Lösung wurde vorgeschlagen, Forschungsdaten systematisch zu verwalten, um eine langfristige Speicherung, Zugänglichkeit und Vernetzung der Daten bereitzustellen. Dieses Ziel wird durch einen aktuellen Aufruf der DFG für eine nationale Forschungsdateninfrastruktur (NFDI) unterstützt. In diesem Artikel werden aktuelle Probleme und mögliche Lösungen der NFDI für die klinische Neurowissenschaft beschrieben. Ein erfolgreiches NFDI-Konsortium wird dazu beitragen, nicht nur das „Tal des Todes“ zu überwinden, sondern verspricht auch einen Weg zur individualisierten Medizin, indem die daraus resultierenden „Big Data“ zusammen mit Methoden des maschinellen Lernens genutzt werden können.
Funding source: Deutsche Forschungsgemeinschaft
Award Identifier / Grant number: 1738 B2CRC CRC1315 936CRC TRR-CRC 295 RI 2073/6-1 WI 830 12-1
Funding source: Bundesministerium für Bildung und Forschung
Award Identifier / Grant number: 031 5581B FKZ 01ZZ1803C
About the authors
Carsten M. Klingner studied computer science and medicine at the Technical University Berlin and Charité Berlin. He received a diploma in computer science and a doctorate after his state examination in medicine. Then he moved to Jena where he received his neurology education with Prof. O.W. Witte. Since 2015, he is the head of the Biomagnetic Center at the Hans Berger Department of Neurology in Jena. His scientific interests include brain plasticity and the interaction between sensory and motor signals in the human brain. He performed multiple studies in the field of CNS that combined functional imaging and behavioral experiments. In the course of these studies, he developed a strong interest for research data management in CNS.
Petra Ritter studied medicine at the Charité University Medicine Berlin. She spent a large part of her clinical traineeships and practical year abroad: at the universities UCLA and UCSD in Los Angeles and San Diego, the Mount Sinai School of Medicine in New York, and the Harvard Medical School in Boston. In 2002, she received her license to practice medicine. In 2004, she completed her doctoral thesis at the Charité, and in 2010, she received habilitation in experimental neurology. After being Max Planck Minerva research group leader from 2011 to 2015, she assumed the lifetime position of BIH Johanna Quandt Professor for Brain Simulation at Berlin Institute of Health (BIH) and Charité Universitätsmedizin Berlin, one of Europe’s largest university hospitals. Since 2017, she is the Director of the Brain Simulation Section at Charité Universitätsmedizin Berlin. Ritter holds an ERC Consolidator grant and serves in the leadership of several national and international neuroinformatics consortia.
Stefan Brodoehl studied medicine and mathematics in Jena. In 2006, he received his license to practice medicine. In 2007, he completed her doctoral thesis at the Charité, and in 2017, he received habilitation in experimental neurology. Since 2007, he worked in the Hans Berger Department of Neurology in Jena where he received his neurology education. Since 2019, he is the head of the dementia center of the university hospital in Jena. His scientific interests include brain plasticity and brain aging, as well as experimental and clinical brain imaging. He has applied these methodological interests in many studies to investigate healthy and nonhealthy cerebral aging processes.
Christian Gaser studied electrical engineering and technical acoustics in Chemnitz and Dresden. He received his PhD degree in neuroscience from the University of Magdeburg, Germany, in 2001. He was a postdoctoral fellow at Harvard Medical School in Boston in 2001 and was a visiting postdoctoral fellow at UCLA in Los Angeles, Mount Sinai School of Medicine in New York, Australian National University in Canberra, and Auckland University. Currently he is an associate Professor of Computational Neuroscience and Neuroimaging and the Head of the Structural Brain Mapping Group at the University of Jena. His research program is directed toward the development of advanced computation tools for the analysis of structural brain data. More specifically, he is heavily involved in the development of algorithms and tools for processing of voxel- and surface-based imaging data which encompasses segmentation, surface reconstruction, and disease prediction. He has developed several software tools, including the Computational Anatomy Toolbox for performing voxel- and surface-based morphometry, which is widely used by the scientific community (>40000 downloads).
André Scherag is the director of the Institute for Medical Statistics, Informatics and Data Sciences and deputy scientific director of the Center for Clinical Studies Jena. He studied psychology and biostatistics in Marburg and Heidelberg. After obtaining his doctorate in Marburg in biostatistics, he moved to the University Hospital Essen in 2007. In Essen, he was the head of Biometry at the Center for Clinical Trials Essen and Head of the Research Group Biometry and Bioinformatics. After his habilitation and Venia legendi for “Medical Informatics, Biometry and Epidemiology,” he moved to Jena in 2013 as a full Professor of Clinical Epidemiology. His research interests are diverse and cover topics from clinical, genetic, and translational epidemiology, as well as clinical trial statistics, biometry, bioinformatics, biostatistics, and digital medicine. He is particularly interested in methodological challenges connected to translating and communicating results from basis and translational research to clinical research and even evidence-based clinical practice (and thus to put some of the promises made by precision medicine into effect). In addition, he is the deputy speaker of consortium SMITH (“Smart Medical Information Technology for Healthcare”; www.smith.care) which is funded within the German medical informatics funding scheme. Interoperable solutions for (research) data management in medicine and the healthcare system are currently implemented as part of the data integration centers within SMITH.
Daniel Güllmar received a diploma in media technology and a doctoral degree in biomedical engineering at the Ilmenau University of Technology. He has started his scientific work in the field of EEG/MEG source localization in combination with advanced volume conductor models at the Biomagnetic Center Jena. During this research phase, he worked also as a visiting scientist at the A. Martinos Center for Biomedical Imaging in Boston, MA, USA, in 2005/2006. After his PhD phase, he shifted his focus on diffusion MRI methods to study neurological and psychiatric disease, as well as psychological mechanisms in interdisciplinary collaborations. Currently, he analyzes the strengths and weaknesses of deep learning methods in biomedical imaging.
Felix Rosenow studied medicine at the Free University of Berlin and received a research grant from the Max-Planck Institute for Neurological Research Cologne and subsequently an MD degree at the University of Cologne, where he also was trained as a board-certified neurologist. He received postdoctoral training at the Cleveland Clinic Foundation (Department of Neurology, Section of Epilepsy and Sleep). He was Professor for Neurology/Epileptology at the Philipps University Marburg and is now Professor and Director of the Epilepsy Center Frankfurt Rhine Main at the University Hospital Frankfurt. His research interests include clinical and translational epilepsy research, clinical neurophysiology, and genetics. He currently serves as a member of the Research Strategy Committee of the Medial Faculty and as a speaker of the LOEWE Center for Personalized Translational Epilepsy Research (CePTER) of the Goethe-University Frankfurt and as the President of the German Society of Clinical Neurophysiology and Functional Imaging (www.DGKN.de).
Ulf Ziemann has research expertise in human motor cortex, excitability, plasticity, motor learning, TMS, brain state–dependent stimulation, neuropharmacology, and TMS-EEG and clinical expertise in stroke, neurointensive care, neuroimmunology, and clinical neurophysiology; holds current positions as the Editor-in-Chief of “Clinical Neurophysiology,” ExCo member of the International Federation of Clinical Neurophysiology, Deputy Editor of “Brain Stimulation,” Associate Editor of “Journal of Neuroscience,” and President of the German Society for Clinical Neurophysiology and Functional Imaging (DGKN); has received awards including Richard-Jung Prize of the German Society of Clinical Neurophysiology and Functional Neuroimaging (DGKN), National Institutes of Health (NIH, Bethesda, USA) Merit Award, and NIH Fellowship Award for Research Excellence; and has published 350 peer-reviewed publications, 40 book chapters, and 7 books, with cumulative IF of 2.070, ISI citations of 32.000, ISI h-index of 90, and Google Scholar h-index of 103.
Otto W. Witte studied medicine, psychology, and mathematics in Münster and London. He worked as a postdoc in Neurophysiology in Münster with E.-J. Speckmann before he moved to Düsseldorf where he received his neurology education with H.-J. Freund. Since 2001, he is the director of the Hans Berger Department of Neurology in Jena. His scientific interests include brain plasticity and brain aging, as well as experimental and clinical brain imaging. As the secretary of the DGKN, he heads the office of the society which supports research and innovation in the exciting field of clinical neurophysiology and functional brain imaging, and is engaged in establishing standard procedures and quality control measures.
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: The authors received support from German Research Foundation (DFG) for 1738 B2; Wi 830 12-1; CRC 1315; CRC 936, CRC-TRR 295 and RI 2073/6-1; BMBF Gerontosys JenAge (FKZ 031 5581B); BMBF SMITH (FKZ 01ZZ1803C); EU EpimiRNA (FP7 GA No. 602130), EU H2020 826421, 785907; 945539, ERC 683049; Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, LOEWE Hessen (Center for Personalized Translational Epilepsy Research – CePTER).
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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