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Smaczny S, Sperber C, Jung S, Moeller K, Karnath HO, Klein E. Disconnection in a left-hemispheric temporo-parietal network impairs multiplication fact retrieval. Neuroimage 2023; 268:119840. [PMID: 36621582 DOI: 10.1016/j.neuroimage.2022.119840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/16/2022] [Accepted: 12/25/2022] [Indexed: 01/07/2023] Open
Abstract
Arithmetic fact retrieval has been suggested to recruit a left-lateralized network comprising perisylvian language areas, parietal areas such as the angular gyrus (AG), and non-neocortical structures such as the hippocampus. However, the underlying white matter connectivity of these areas has not been evaluated systematically so far. Using simple multiplication problems, we evaluated how disconnections in parietal brain areas affected arithmetic fact retrieval following stroke. We derived disconnectivity measures by jointly considering data from n = 73 patients with acute unilateral lesions in either hemisphere and a white-matter tractography atlas (HCP-842) using the Lesion Quantification Toolbox (LQT). Whole-brain voxel-based analysis indicated a left-hemispheric cluster of white matter fibers connecting the AG and superior temporal areas to be associated with a fact retrieval deficit. Subsequent analyses of direct gray-to-gray matter disconnections revealed that disconnections of additional left-hemispheric areas (e.g., between the superior temporal gyrus and parietal areas) were significantly associated with the observed fact retrieval deficit. Results imply that disconnections of parietal areas (i.e., the AG) with language-related areas (i.e., superior and middle temporal gyri) seem specifically detrimental to arithmetic fact retrieval. This suggests that arithmetic fact retrieval recruits a widespread left-hemispheric network and emphasizes the relevance of white matter connectivity for number processing.
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Affiliation(s)
- S Smaczny
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - C Sperber
- Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland
| | - S Jung
- Department of Computer Science/Therapy Science, Trier University of Applied Science, Trier, Germany; Leibniz Institut fuer Wissensmedien, Tuebingen, Germany
| | - K Moeller
- Leibniz Institut fuer Wissensmedien, Tuebingen, Germany; Centre for Individual Development and Adaptive Education of Children at Risk (IDeA), Frankfurt, Germany; Centre for Mathematical Cognition, School of Science, Loughborough University, United Kingdom
| | - H O Karnath
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany; Department of Psychology, University of South Carolina, Columbia, SC, USA.
| | - E Klein
- Leibniz Institut fuer Wissensmedien, Tuebingen, Germany; University of Paris, LaPsyDÉ, CNRS, Sorbonne Paris Cité, Paris, France.
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Dulyan L, Talozzi L, Pacella V, Corbetta M, Forkel SJ, Thiebaut de Schotten M. Longitudinal prediction of motor dysfunction after stroke: a disconnectome study. Brain Struct Funct 2022; 227:3085-3098. [PMID: 36334132 PMCID: PMC9653357 DOI: 10.1007/s00429-022-02589-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 10/20/2022] [Indexed: 06/01/2023]
Abstract
Motricity is the most commonly affected ability after a stroke. While many clinical studies attempt to predict motor symptoms at different chronic time points after a stroke, longitudinal acute-to-chronic studies remain scarce. Taking advantage of recent advances in mapping brain disconnections, we predict motor outcomes in 62 patients assessed longitudinally two weeks, three months, and one year after their stroke. Results indicate that brain disconnection patterns accurately predict motor impairments. However, disconnection patterns leading to impairment differ between the three-time points and between left and right motor impairments. These results were cross-validated using resampling techniques. In sum, we demonstrated that while some neuroplasticity mechanisms exist changing the structure-function relationship, disconnection patterns prevail when predicting motor impairment at different time points after stroke.
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Affiliation(s)
- Lilit Dulyan
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France.
- Donders Centre for Brain Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - Lia Talozzi
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
| | - Valentina Pacella
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
| | - Maurizio Corbetta
- Clinica Neurologica, Department of Neuroscience, University of Padova, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padua, Italy
- Venetian Institute of Molecular Medicine, VIMM, Padua, Italy
| | - Stephanie J Forkel
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France.
- Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Donders Centre for Brain Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
- Department of Neurosurgery, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France.
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3
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Conrad J, Boegle R, Ruehl RM, Dieterich M. Evaluating the rare cases of cortical vertigo using disconnectome mapping. Brain Struct Funct 2022; 227:3063-3073. [PMID: 35838791 DOI: 10.1007/s00429-022-02530-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 06/26/2022] [Indexed: 12/26/2022]
Abstract
In rare cases, cortical infarcts lead to vertigo. We evaluated structural and functional disconnection in patients with acute vertigo due to unilateral ischemic cortical infarcts compared to infarcts without vertigo in a similar location with a focus on the connectivity of the vestibular cortex, i.e., the parieto-opercular (retro-)insular cortex (PIVC). Using lesion maps from the ten published case reports, we computed lesion-functional connectivity networks in a set of healthy individuals from the human connectome project. The probability of lesion disconnection was evaluated by white matter disconnectome mapping. In all ten cases with rotational vertigo, disconnections of interhemispheric connections via the corpus callosum were present but were spared in lesions of the PIVC without vertigo. Further, the arcuate fascicle was affected in 90% of the lesions that led to vertigo and spared in lesions that did not lead to vertigo. The lesion-functional connectivity network included vestibulo-cerebellar hubs, the vestibular nuclei, the PIVC, the retro-insular and posterior insular cortex, the multisensory vestibular ventral intraparietal area, motion-sensitive areas (temporal area MT+ and cingulate visual sulcus) as well as hubs for ocular motor control (lateral intraparietal area, cingulate and frontal eye fields). However, this was not sufficient to differentiate between lesions with and without vertigo. Disruption of interhemispheric connections of both PIVC via the corpus callosum and intra-hemispheric disconnection via the arcuate fascicle might be the distinguishing factor between vestibular cortical network lesions that manifest with vertigo compared to those without vertigo.
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Affiliation(s)
- Julian Conrad
- Department of Neurology, Munich University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany. .,German Center for Vertigo and Balance Disorders (DSGZ), Munich University Hospital, LMU Munich, Munich, Germany.
| | - Rainer Boegle
- Department of Neurology, Munich University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.,Graduate School for Systemic Neuroscience (GSN-LMU), LMU Munich, Munich, Germany
| | - Ria Maxine Ruehl
- Department of Neurology, Munich University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.,German Center for Vertigo and Balance Disorders (DSGZ), Munich University Hospital, LMU Munich, Munich, Germany
| | - Marianne Dieterich
- Department of Neurology, Munich University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.,German Center for Vertigo and Balance Disorders (DSGZ), Munich University Hospital, LMU Munich, Munich, Germany.,Graduate School for Systemic Neuroscience (GSN-LMU), LMU Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
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Ravano V, Andelova M, Fartaria MJ, Mahdi MFAW, Maréchal B, Meuli R, Uher T, Krasensky J, Vaneckova M, Horakova D, Kober T, Richiardi J. Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study. Neuroimage Clin 2022; 32:102817. [PMID: 34500427 PMCID: PMC8429972 DOI: 10.1016/j.nicl.2021.102817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/30/2021] [Accepted: 08/30/2021] [Indexed: 12/01/2022]
Abstract
Structural disconnectomes can be modelled without diffusion using tractography atlases. Atlas-based and DTI-derived disconnectome topological metrics correlate strongly. MS patient disconnectomes relate to clinical scores.
The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision. Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.
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Affiliation(s)
- Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Michaela Andelova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Jan Krasensky
- MR unit, Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Manuela Vaneckova
- MR unit, Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Griffis JC, Metcalf NV, Corbetta M, Shulman GL. Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions. Neuroimage Clin 2021; 30:102639. [PMID: 33813262 PMCID: PMC8053805 DOI: 10.1016/j.nicl.2021.102639] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 12/19/2022]
Abstract
Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain's structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.
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Affiliation(s)
- Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neuroscience, University of Padua, Padua, Italy; Padua Neuroscience Center, Padua, Italy
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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Greene C, Cieslak M, Volz LJ, Hensel L, Grefkes C, Rose K, Grafton ST. Finding maximally disconnected subnetworks with shortest path tractography. Neuroimage Clin 2019; 23:101903. [PMID: 31491834 PMCID: PMC6627647 DOI: 10.1016/j.nicl.2019.101903] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 05/16/2019] [Accepted: 06/16/2019] [Indexed: 11/25/2022]
Abstract
Connectome-based lesion symptom mapping (CLSM) can be used to relate disruptions of brain network connectivity with clinical measures. We present a novel method that extends current CLSM approaches by introducing a fast reliable and accurate way for computing disconnectomes, i.e. identifying damaged or lesioned connections. We introduce a new algorithm that finds the maximally disconnected subgraph containing regions and region pairs with the greatest shared connectivity loss. After normalizing a stroke patient's segmented MRI lesion into template space, probability weighted structural connectivity matrices are constructed from shortest paths found in white matter voxel graphs of 210 subjects from the Human Connectome Project. Percent connectivity loss matrices are constructed by measuring the proportion of shortest-path probability weighted connections that are lost because of an intersection with the patient's lesion. Maximally disconnected subgraphs of the overall connectivity loss matrix are then derived using a computationally fast greedy algorithm that closely approximates the exact solution. We illustrate the approach in eleven stroke patients with hemiparesis by identifying expected disconnections of the corticospinal tract (CST) with cortical sensorimotor regions. Major disconnections are found in the thalamus, basal ganglia, and inferior parietal cortex. Moreover, the size of the maximally disconnected subgraph quantifies the extent of cortical disconnection and strongly correlates with multiple clinical measures. The methods provide a fast, reliable approach for both visualizing and quantifying the disconnected portion of a patient's structural connectome based on their routine clinical MRI, without reliance on concomitant diffusion weighted imaging. The method can be extended to large databases of stroke patients, multiple sclerosis or other diseases causing focal white matter injuries helping to better characterize clinically relevant white matter lesions and to identify biomarkers for the recovery potential of individual patients. Significantly accelerated shortest path tractography approach for constructing connectomes and disconnectomes. New algorithm extracts the subnetwork containing cortical connections and regions with maximal shared connectivity loss. The size of the maximally disconnected subnetwork quantifies the extent of disconnection and correlates with stroke measures. Fast and accurate approach for visualizing and analyzing the disconnected portion of a patient's structural connectome.
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Affiliation(s)
- Clint Greene
- Signal Compression Lab, Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA.
| | - Matthew Cieslak
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Lukas J Volz
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Lukas Hensel
- Department of Neurology, University of Cologne, Cologne, Germany
| | | | - Ken Rose
- Signal Compression Lab, Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
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Langen CD, Cremers LGM, de Groot M, White T, Ikram MA, Niessen WJ, Vernooij MW. Disconnection due to white matter hyperintensities is associated with lower cognitive scores. Neuroimage 2018; 183:745-756. [PMID: 30144572 DOI: 10.1016/j.neuroimage.2018.08.037] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 07/27/2018] [Accepted: 08/16/2018] [Indexed: 11/17/2022] Open
Abstract
Previous studies have linked global burden of age-related white matter hyperintensities (WMHs) to cognitive impairment. We aimed to determine how WMHs in individual white matter connections relate to measures of cognitive function relative to measures of connectivity which do not take WMHs into account. Brain connectivity and WMH-related disconnectivity were derived from 3714 participants of the population-based Rotterdam Study. Connectivity was represented by the structural connectome, which was defined using diffusion tensor data, whereas the disconnectome represented disconnectivity due to WMH. The relationship between (dis)connectivity and cognitive measures was estimated using linear regression. We found that lower disconnectivity and higher connectivity corresponded to better cognitive function. There were many more significant associations with cognitive function in the disconnectome than in the connectome. Most connectome associations attenuated when disconnection was included in the model. WMH-related disconnectivity was especially related to worse executive functioning. Better cognitive speed corresponded to higher connectivity in specific connections independent of WMH presence. We conclude that WMH-related disconnectivity explains more variation in cognitive function than does connectivity. Efficient wiring in specific connections is important to information processing speed independent of WMH presence.
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Affiliation(s)
- Carolyn D Langen
- Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Medical Informatics, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands.
| | - Lotte G M Cremers
- Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands.
| | - Marius de Groot
- Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Medical Informatics, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands.
| | - Tonya White
- Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands.
| | - M Arfan Ikram
- Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands.
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Medical Informatics, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Imaging Physics, Faculty of Applied Sciences, PO Box 5046, 2600GA, Delft University of Technology, Delft, the Netherlands.
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC, PO Box 2040, 3000CA, Rotterdam, the Netherlands.
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