Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts

Lisa Hemforth1, Baptiste Couvy-Duchesne1,2, Kevin De Matos1, Camille Brianceau1, Matthieu Joulot1, Tobias Banaschewski3, Arun L.W. Bokde4, Sylvane Desrivières5, Herta Florg6, Antoine Grigis7, Hugh Garavan8, Penny Gowland9, Andreas Heinz10, Rüdiger Brühl11, Jean-Luc Martinot12, Marie-Laure Paillère Martinot12,13, Eric Artigesn14, Dimitri Papadopoulos7, Herve Lemaitrei15, Tomas Pausr16, Luise Poustka17, Sarah Hohmana1, Nathalie Holz3, Juliane H. Fröhner18, Michael N. Smolka18, Nilakshi Vaidya19, Henrik Walter10, Robert Whelan20, Gunter Schumann19,21, Christian Büchel22, JB Poline23, Bernd Itterman11, Vincent Frouin7, Alexandre Martin1, Claire Cury24, IMAGEN study group , Olivier Colliot1
1: Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, F-75013, Paris, France, 2: Institute for Molecular Bioscience, the University of Queensland, Brisbane, 4072, Australia , 3: Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68159, Germany , 4: Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland , 5: Centre for Popula tion Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King’s College London, London, United Kingdom , 6: Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, 68131, Germany , 7: NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France , 8: Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont, 05405, USA , 9: Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, Univer sity of Nottingham, University Park, Nottingham, United Kingdom , 10: Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany , 11: Physikalisch-Technische Bundesanstalt (PTB), Braun schweig and Berlin, Germany , 12: Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 ”Trajectoires développementales & psychiatrie”, University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France , 13: Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France , 14: Psychiatry Department, EPS Barthélémy Durand, Etampes, France , 15: Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, Bordeaux, France , 16: Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada , 17: Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany , 18: Department of Psychiatry and Neuroimaging Center, Technische Univer sität Dresden, Dresden, Germany , 19: Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany , 20: School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland , 21: Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan Uni versity, Shanghai, China , 22: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany , 23: Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada aa Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 24: University of Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, 35000, France
Publication date: 2024/07/30
https://doi.org/10.59275/j.melba.2024-3d4e
PDF · arXiv

Abstract

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (”conv5-FC3”, ResNet and ”SECNN”) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM and QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the ”conv5-FC3” network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization (acceptable performances on all tested cohorts including some that are not included in training). The trained models will be made publicly available should the manuscript be accepted.

Keywords

Deep Learning · MRI · Hippocampus · Machine Learning · Incomplete Hippocampal Inversion

Bibtex @article{melba:2024:016:hemforth, title = "Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts", author = "Hemforth, Lisa and Couvy-Duchesne, Baptiste and De Matos, Kevin and Brianceau, Camille and Joulot, Matthieu and Banaschewski, Tobias and Bokde, Arun L.W. and Desrivières, Sylvane and Florg, Herta and Grigis, Antoine and Garavan, Hugh and Gowland, Penny and Heinz, Andreas and Brühl, Rüdiger and Martinot, Jean-Luc and Paillère Martinot, Marie-Laure and Artigesn, Eric and Papadopoulos, Dimitri and Lemaitrei, Herve and Pausr, Tomas and Poustka, Luise and Hohmana, Sarah and Holz, Nathalie and Fröhner, Juliane H. and Smolka, Michael N. and Vaidya, Nilakshi and Walter, Henrik and Whelan, Robert and Schumann, Gunter and Büchel, Christian and Poline, JB and Itterman, Bernd and Frouin, Vincent and Martin, Alexandre and Cury, Claire and , IMAGEN study group and Colliot, Olivier", journal = "Machine Learning for Biomedical Imaging", volume = "2", issue = "July 2024 issue", year = "2024", pages = "1004--1029", issn = "2766-905X", doi = "https://doi.org/10.59275/j.melba.2024-3d4e", url = "https://melba-journal.org/2024:016" }
RISTY - JOUR AU - Hemforth, Lisa AU - Couvy-Duchesne, Baptiste AU - De Matos, Kevin AU - Brianceau, Camille AU - Joulot, Matthieu AU - Banaschewski, Tobias AU - Bokde, Arun L.W. AU - Desrivières, Sylvane AU - Florg, Herta AU - Grigis, Antoine AU - Garavan, Hugh AU - Gowland, Penny AU - Heinz, Andreas AU - Brühl, Rüdiger AU - Martinot, Jean-Luc AU - Paillère Martinot, Marie-Laure AU - Artigesn, Eric AU - Papadopoulos, Dimitri AU - Lemaitrei, Herve AU - Pausr, Tomas AU - Poustka, Luise AU - Hohmana, Sarah AU - Holz, Nathalie AU - Fröhner, Juliane H. AU - Smolka, Michael N. AU - Vaidya, Nilakshi AU - Walter, Henrik AU - Whelan, Robert AU - Schumann, Gunter AU - Büchel, Christian AU - Poline, JB AU - Itterman, Bernd AU - Frouin, Vincent AU - Martin, Alexandre AU - Cury, Claire AU - , IMAGEN study group AU - Colliot, Olivier PY - 2024 TI - Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts T2 - Machine Learning for Biomedical Imaging VL - 2 IS - July 2024 issue SP - 1004 EP - 1029 SN - 2766-905X DO - https://doi.org/10.59275/j.melba.2024-3d4e UR - https://melba-journal.org/2024:016 ER -
2024:016 cover