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Iyappan A, Younesi E, Redolfi A, Vrooman H, Khanna S, Frisoni GB, Hofmann-Apitius M. Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features. J Alzheimers Dis 2017; 59:1153-1169. [PMID: 28731430 PMCID: PMC5611802 DOI: 10.3233/jad-161148] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.
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Affiliation(s)
- Anandhi Iyappan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
| | - Alberto Redolfi
- Laboratory of Epidemiology and Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Henri Vrooman
- Departments of Radiology and Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC University Medical Center, The Netherlands
| | - Shashank Khanna
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Giovanni B Frisoni
- Laboratory of Epidemiology and Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and Laboratoire de Neuroimagerie du Vieillissement (LANVIE), University Hospitals and University of Geneva, Geneva, Switzerland
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
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Zimmermann J, Ritter P, Shen K, Rothmeier S, Schirner M, McIntosh AR. Structural architecture supports functional organization in the human aging brain at a regionwise and network level. Hum Brain Mapp 2016; 37:2645-61. [PMID: 27041212 PMCID: PMC6867479 DOI: 10.1002/hbm.23200] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 03/18/2016] [Accepted: 03/20/2016] [Indexed: 12/13/2022] Open
Abstract
Functional interactions in the brain are constrained by the underlying anatomical architecture, and structural and functional networks share network features such as modularity. Accordingly, age-related changes of structural connectivity (SC) may be paralleled by changes in functional connectivity (FC). We provide a detailed qualitative and quantitative characterization of the SC-FC coupling in human aging as inferred from resting-state blood oxygen-level dependent functional magnetic resonance imaging and diffusion-weighted imaging in a sample of 47 adults with an age range of 18-82. We revealed that SC and FC decrease with age across most parts of the brain and there is a distinct age-dependency of regionwise SC-FC coupling and network-level SC-FC relations. A specific pattern of SC-FC coupling predicts age more reliably than does regionwise SC or FC alone (r = 0.73, 95% CI = [0.7093, 0.8522]). Hence, our data propose that regionwise SC-FC coupling can be used to characterize brain changes in aging. Hum Brain Mapp 37:2645-2661, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Joelle Zimmermann
- Baycrest Health SciencesRotman Research Institute3560 Bathurst StTorontoOntarioM6A 2E1Canada
| | - Petra Ritter
- Department of NeurologyCharité ‐ University MedicineCharitéplatz 1Berlin13353Germany
- BrainModes Minerva Research Group Max‐Planck Institute for Cognitive and Brain Science LeipzigCharitéplatz 1Berlin13353Germany
- Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational NeuroscienceCharitéplatz 1Berlin10117Germany
- Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt UniversityLuisenstraße 56, Haus 110117BerlinGermany
| | - Kelly Shen
- Baycrest Health SciencesRotman Research Institute3560 Bathurst StTorontoOntarioM6A 2E1Canada
| | - Simon Rothmeier
- Department of NeurologyCharité ‐ University MedicineCharitéplatz 1Berlin13353Germany
- BrainModes Minerva Research Group Max‐Planck Institute for Cognitive and Brain Science LeipzigCharitéplatz 1Berlin13353Germany
| | - Michael Schirner
- Department of NeurologyCharité ‐ University MedicineCharitéplatz 1Berlin13353Germany
| | - Anthony R. McIntosh
- Baycrest Health SciencesRotman Research Institute3560 Bathurst StTorontoOntarioM6A 2E1Canada
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Ikram MA, van der Lugt A, Niessen WJ, Koudstaal PJ, Krestin GP, Hofman A, Bos D, Vernooij MW. The Rotterdam Scan Study: design update 2016 and main findings. Eur J Epidemiol 2015; 30:1299-315. [PMID: 26650042 PMCID: PMC4690838 DOI: 10.1007/s10654-015-0105-7] [Citation(s) in RCA: 156] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 11/25/2015] [Indexed: 12/20/2022]
Abstract
Imaging plays an essential role in research on neurological diseases in the elderly. The Rotterdam Scan Study was initiated as part of the ongoing Rotterdam Study with the aim to elucidate the causes of neurological disease by performing imaging of the brain in a prospective population-based setting. Initially, in 1995 and 1999, random subsamples of participants from the Rotterdam Study underwent neuroimaging, whereas from 2005 onwards MRI has been implemented into the core protocol of the Rotterdam Study. In this paper, we discuss the background and rationale of the Rotterdam Scan Study. Moreover, we describe the imaging protocol, image post-processing techniques, and the main findings to date. Finally, we provide recommendations for future research, which will also be topics of investigation in the Rotterdam Scan Study.
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Affiliation(s)
- M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
| | - Aad van der Lugt
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Biomedical Imaging Group Rotterdam, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Peter J Koudstaal
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Gabriel P Krestin
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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Langen CD, White T, Ikram MA, Vernooij MW, Niessen WJ. Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia. PLoS One 2015; 10:e0137484. [PMID: 26331844 PMCID: PMC4557994 DOI: 10.1371/journal.pone.0137484] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 08/16/2015] [Indexed: 11/18/2022] Open
Abstract
Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, “bi-modal comparison plots” show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using “worm plots”. Group differences in connections are examined with an existing visualization, the “connectogram”. These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the “Statistical Analysis of Minimum cost path based Structural Connectivity” method and the average fractional anisotropy along the fiber. The functional measures were Pearson’s correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.
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Affiliation(s)
- Carolyn D. Langen
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- * E-mail:
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus Medical Centre, Rotterdam, Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Meike W. Vernooij
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro J. Niessen
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
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5
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Duarte-Carvajalino JM, Jahanshad N, Lenglet C, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Thompson PM, Sapiro G. Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship. Neuroimage 2012; 59:3784-804. [PMID: 22108644 PMCID: PMC3551467 DOI: 10.1016/j.neuroimage.2011.10.096] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Revised: 10/20/2011] [Accepted: 10/26/2011] [Indexed: 10/15/2022] Open
Abstract
Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.
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Affiliation(s)
| | - Neda Jahanshad
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
- Medical Imaging Informatics, Department of Radiology, UCLA School of Medicine, Los Angeles, CA, USA
| | | | - Katie L. McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | | | | | - Margaret J. Wright
- School of Psychology, University of Queensland, Brisbane, Australia
- Queensland Institute of Medical Research, Brisbane, Australia
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
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Ikram MA, van der Lugt A, Niessen WJ, Krestin GP, Koudstaal PJ, Hofman A, Breteler MMB, Vernooij MW. The Rotterdam Scan Study: design and update up to 2012. Eur J Epidemiol 2011; 26:811-24. [PMID: 22002080 PMCID: PMC3218266 DOI: 10.1007/s10654-011-9624-z] [Citation(s) in RCA: 106] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2011] [Accepted: 10/06/2011] [Indexed: 02/06/2023]
Abstract
Neuroimaging plays an important role in etiologic research on neurological diseases in the elderly. The Rotterdam Scan Study was initiated as part of the ongoing Rotterdam Study with the aim to unravel causes of neurological disease by performing neuroimaging in a population-based longitudinal setting. In 1995 and 1999 random subsets of the Rotterdam Study underwent neuroimaging, whereas from 2005 onwards MRI has been implemented into the core protocol of the Rotterdam Study. In this paper, we discuss the background and rationale of the Rotterdam Scan Study. We also describe the imaging protocol and post-processing techniques, and highlight the main findings to date. Finally, we make recommendations for future research, which will also be the main focus of investigation in the Rotterdam Scan Study.
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Affiliation(s)
- M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
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Hofman A, van Duijn CM, Franco OH, Ikram MA, Janssen HLA, Klaver CCW, Kuipers EJ, Nijsten TEC, Stricker BHC, Tiemeier H, Uitterlinden AG, Vernooij MW, Witteman JCM. The Rotterdam Study: 2012 objectives and design update. Eur J Epidemiol 2011; 26:657-86. [PMID: 21877163 PMCID: PMC3168750 DOI: 10.1007/s10654-011-9610-5] [Citation(s) in RCA: 263] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2011] [Accepted: 08/08/2011] [Indexed: 01/09/2023]
Abstract
The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.
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Affiliation(s)
- Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.
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