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Song Z, Li H, Zhang Y, Zhu C, Jiang M, Song L, Wang Y, Ouyang M, Hu F, Zheng Q. s 2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01178-3. [PMID: 38869733 DOI: 10.1007/s10334-024-01178-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/19/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVE To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. METHODS A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. RESULTS The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. CONCLUSION The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.
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Affiliation(s)
- Zhiwei Song
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Honglun Li
- Department of Radiology, Yantai Yuhuangding Hospital Affiliated with Qingdao University Medical College, Yantai, 264099, China
| | - Yiyu Zhang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Chuanzhen Zhu
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Minbo Jiang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Limei Song
- School of Medical Imaging, Weifang Medical University, Weifang, 261000, China
| | - Yi Wang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Fang Hu
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
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Kang X, Wang D, Lin J, Yao H, Zhao K, Song C, Chen P, Qu Y, Yang H, Zhang Z, Zhou B, Han T, Liao Z, Chen Y, Lu J, Yu C, Wang P, Zhang X, Li M, Zhang X, Jiang T, Zhou Y, Liu B, Han Y, Liu Y. Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118. Neurosci Bull 2024:10.1007/s12264-024-01218-x. [PMID: 38824231 DOI: 10.1007/s12264-024-01218-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 11/24/2023] [Indexed: 06/03/2024] Open
Abstract
The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.
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Affiliation(s)
- Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Jiaji Lin
- Department of Neurology, the Second Affiliated Hospital of Air Force Medical University, Xi'an, 710032, China
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100191, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yida Qu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572013, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Zhengluan Liao
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Yan Chen
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300070, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Xinqing Zhang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tianzi Jiang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Bing Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100875, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China.
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China.
| | - Yong Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100191, China.
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Wegner P, Balabin H, Ay MC, Bauermeister S, Killin L, Gallacher J, Hofmann-Apitius M, Salimi Y. Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper. J Alzheimers Dis 2024; 99:1409-1423. [PMID: 38759012 PMCID: PMC11191441 DOI: 10.3233/jad-240116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2024] [Indexed: 05/19/2024]
Abstract
Background Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed. Objective As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool. Methods We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model. Results Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables. Conclusion AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.
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Affiliation(s)
- Philipp Wegner
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Helena Balabin
- Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium
- Department of Computer Science, Language Intelligence and Information Retrieval Lab, KU Leuven, Leuven, Belgium
| | - Mehmet Can Ay
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Sarah Bauermeister
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Lewis Killin
- SYNAPSE Research Management Partners, Barcelona, Spain
| | - John Gallacher
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Heng NYW, Rittman T. Understanding ethnic diversity in open dementia neuroimaging data sets. Brain Commun 2023; 5:fcad308. [PMID: 38025280 PMCID: PMC10667030 DOI: 10.1093/braincomms/fcad308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/22/2023] [Accepted: 11/07/2023] [Indexed: 12/01/2023] Open
Abstract
Ethnic differences in dementia are increasingly recognized in epidemiological measures and diagnostic biomarkers. Nonetheless, ethnic diversity remains limited in many study populations. Here, we provide insights into ethnic diversity in open-access neuroimaging dementia data sets. Data sets comprising dementia populations with available data on ethnicity were included. Statistical analyses of sample and effect sizes were based on the Cochrane Handbook. Nineteen databases were included, with 17 studies of healthy groups or a combination of diagnostic groups if breakdown was unavailable and 12 of mild cognitive impairment and dementia groups. Combining all studies on dementia patients, the largest ethnic group was Caucasian (20 547 participants), with the next most common being Afro-Caribbean (1958), followed by Asian (1211). The smallest effect size detectable within the Caucasian group was 0.03, compared to Afro-Caribbean (0.1) and Asian (0.13). Our findings quantify the lack of ethnic diversity in openly available dementia data sets. More representative data would facilitate the development and validation of biomarkers relevant across ethnicities.
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Affiliation(s)
- Nicholas Yew Wei Heng
- Department of Neurosciences, University of Cambridge, Herchel Smith building, Cambridge Biomedical Campus, Robinson Way, Cambridge CB2 0SZ, UK
| | - Timothy Rittman
- Department of Neurosciences, University of Cambridge, Herchel Smith building, Cambridge Biomedical Campus, Robinson Way, Cambridge CB2 0SZ, UK
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5
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Marcisz A, Polanska J. Can T1-Weighted Magnetic Resonance Imaging Significantly Improve Mini-Mental State Examination-Based Distinguishing Between Mild Cognitive Impairment and Early-Stage Alzheimer's Disease? J Alzheimers Dis 2023; 92:941-957. [PMID: 36806505 PMCID: PMC10116132 DOI: 10.3233/jad-220806] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2023] [Indexed: 02/19/2023]
Abstract
BACKGROUND Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process. OBJECTIVE Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine. METHODS The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model. RESULTS The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis. CONCLUSION The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.
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Affiliation(s)
- Anna Marcisz
- Department of Data Science and Engineering, The Silesian University of Technology, Gliwice, Poland
| | | | - Joanna Polanska
- Department of Data Science and Engineering, The Silesian University of Technology, Gliwice, Poland
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Safri AA, Nassir CMNCM, Iman IN, Mohd Taib NH, Achuthan A, Mustapha M. Diffusion tensor imaging pipeline measures of cerebral white matter integrity: An overview of recent advances and prospects. World J Clin Cases 2022; 10:8450-8462. [PMID: 36157806 PMCID: PMC9453345 DOI: 10.12998/wjcc.v10.i24.8450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/20/2022] [Accepted: 07/17/2022] [Indexed: 02/05/2023] Open
Abstract
Cerebral small vessel disease (CSVD) is a leading cause of age-related microvascular cognitive decline, resulting in significant morbidity and decreased quality of life. Despite a progress on its key pathophysiological bases and general acceptance of key terms from neuroimaging findings as observed on the magnetic resonance imaging (MRI), key questions on CSVD remain elusive. Enhanced relationships and reliable lesion studies, such as white matter tractography using diffusion-based MRI (dMRI) are necessary in order to improve the assessment of white matter architecture and connectivity in CSVD. Diffusion tensor imaging (DTI) and tractography is an application of dMRI that provides data that can be used to non-invasively appraise the brain white matter connections via fiber tracking and enable visualization of individual patient-specific white matter fiber tracts to reflect the extent of CSVD-associated white matter damage. However, due to a lack of standardization on various sets of software or image pipeline processing utilized in this technique that driven mostly from research setting, interpreting the findings remain contentious, especially to inform an improved diagnosis and/or prognosis of CSVD for routine clinical use. In this minireview, we highlight the advances in DTI pipeline processing and the prospect of this DTI metrics as potential imaging biomarker for CSVD, even for subclinical CSVD in at-risk individuals.
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Affiliation(s)
- Amanina Ahmad Safri
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Che Mohd Nasril Che Mohd Nassir
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Ismail Nurul Iman
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Nur Hartini Mohd Taib
- Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Anusha Achuthan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | - Muzaimi Mustapha
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
- Department of Neurosciences, Hospital Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
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Salimi Y, Domingo-Fernández D, Bobis-Álvarez C, Hofmann-Apitius M, Birkenbihl C. ADataViewer: exploring semantically harmonized Alzheimer's disease cohort datasets. Alzheimers Res Ther 2022; 14:69. [PMID: 35598021 PMCID: PMC9123725 DOI: 10.1186/s13195-022-01009-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/27/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Currently, Alzheimer's disease (AD) cohort datasets are difficult to find and lack across-cohort interoperability, and the actual content of publicly available datasets often only becomes clear to third-party researchers once data access has been granted. These aspects severely hinder the advancement of AD research through emerging data-driven approaches such as machine learning and artificial intelligence and bias current data-driven findings towards the few commonly used, well-explored AD cohorts. To achieve robust and generalizable results, validation across multiple datasets is crucial. METHODS We accessed and systematically investigated the content of 20 major AD cohort datasets at the data level. Both, a medical professional and a data specialist, manually curated and semantically harmonized the acquired datasets. Finally, we developed a platform that displays vital information about the available datasets. RESULTS Here, we present ADataViewer, an interactive platform that facilitates the exploration of 20 cohort datasets with respect to longitudinal follow-up, demographics, ethnoracial diversity, measured modalities, and statistical properties of individual variables. It allows researchers to quickly identify AD cohorts that meet user-specified requirements for discovery and validation studies regarding available variables, sample sizes, and longitudinal follow-up. Additionally, we publish the underlying variable mapping catalog that harmonizes 1196 unique variables across the 20 cohorts and paves the way for interoperable AD datasets. CONCLUSIONS In conclusion, ADataViewer facilitates fast, robust data-driven research by transparently displaying cohort dataset content and supporting researchers in selecting datasets that are suited for their envisioned study. The platform is available at https://adata.scai.fraunhofer.de/ .
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Affiliation(s)
- Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany.
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany.
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
| | - Carlos Bobis-Álvarez
- University Hospital Ntra. Sra. de Candelaria, Santa Cruz de Tenerife, 38010, Spain
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
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Golriz Khatami S, Salimi Y, Hofmann-Apitius M, Oxtoby NP, Birkenbihl C. Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease. Alzheimers Res Ther 2022; 14:55. [PMID: 35443691 PMCID: PMC9020023 DOI: 10.1186/s13195-022-01001-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/06/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged. METHODS We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort. RESULTS We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory. CONCLUSION Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany.
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Neil P Oxtoby
- Centre for Medical Image Computing and Department of Computer Science, University College London, Gower St, London, WC1E 6BT, UK
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
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Frau-Pascual A, Augustinack J, Varadarajan D, Yendiki A, Salat DH, Fischl B, Aganj I. Conductance-Based Structural Brain Connectivity in Aging and Dementia. Brain Connect 2021; 11:566-583. [PMID: 34042511 PMCID: PMC8558081 DOI: 10.1089/brain.2020.0903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.
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Affiliation(s)
- Aina Frau-Pascual
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Divya Varadarajan
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David H. Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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10
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Parra MA, Baez S, Sedeño L, Gonzalez Campo C, Santamaría‐García H, Aprahamian I, Bertolucci PHF, Bustin J, Camargos Bicalho MA, Cano‐Gutierrez C, Caramelli P, Chaves MLF, Cogram P, Beber BC, Court FA, de Souza LC, Custodio N, Damian A, de la Cruz M, Diehl Rodriguez R, Brucki SMD, Fajersztajn L, Farías GA, De Felice FG, Ferrari R, de Oliveira FF, Ferreira ST, Ferretti C, Figueredo Balthazar ML, Ferreira Frota NA, Fuentes P, García AM, Garcia PJ, de Gobbi Porto FH, Duque Peñailillo L, Engler HW, Maier I, Mata IF, Gonzalez‐Billault C, Lopez OL, Morelli L, Nitrini R, Quiroz YT, Guerrero Barragan A, Huepe D, Pio FJ, Suemoto CK, Kochhann R, Kochen S, Kumfor F, Lanata S, Miller B, Mansur LL, Hosogi ML, Lillo P, Llibre Guerra J, Lira D, Lopera F, Comas A, Avila‐Funes JA, Sosa AL, Ramos C, Resende EDPF, Snyder HM, Tarnanas I, Yokoyama J, Llibre J, Cardona JF, Possin K, Kosik KS, Montesinos R, Moguilner S, Solis PCL, Ferretti‐Rebustini REDL, Ramirez JM, Matallana D, Mbakile‐Mahlanza L, Marques Ton AM, Tavares RM, Miotto EC, Muniz‐Terrera G, Muñoz‐Nevárez LA, Orozco D, Okada de Oliveira M, Piguet O, Pintado Caipa M, Piña Escudero SD, Schilling LP, Rodrigues Palmeira AL, Yassuda MS, Santacruz‐Escudero JM, Serafim RB, Smid J, Slachevsky A, Serrano C, Soto‐Añari M, Takada LT, Grinberg LT, Teixeira AL, Barbosa MT, Trépel D, Ibanez A. Dementia in Latin America: Paving the way toward a regional action plan. Alzheimers Dement 2021; 17:295-313. [PMID: 33634602 PMCID: PMC7984223 DOI: 10.1002/alz.12202] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/28/2020] [Accepted: 08/30/2020] [Indexed: 12/12/2022]
Abstract
Across Latin American and Caribbean countries (LACs), the fight against dementia faces pressing challenges, such as heterogeneity, diversity, political instability, and socioeconomic disparities. These can be addressed more effectively in a collaborative setting that fosters open exchange of knowledge. In this work, the Latin American and Caribbean Consortium on Dementia (LAC-CD) proposes an agenda for integration to deliver a Knowledge to Action Framework (KtAF). First, we summarize evidence-based strategies (epidemiology, genetics, biomarkers, clinical trials, nonpharmacological interventions, networking, and translational research) and align them to current global strategies to translate regional knowledge into transformative actions. Then we characterize key sources of complexity (genetic isolates, admixture in populations, environmental factors, and barriers to effective interventions), map them to the above challenges, and provide the basic mosaics of knowledge toward a KtAF. Finally, we describe strategies supporting the knowledge creation stage that underpins the translational impact of KtAF.
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Affiliation(s)
- Mario Alfredo Parra
- School of Psychological Sciences and HealthGraham Hills BuildingGlasgow, G1 1QE, UK, Universidad Autónoma del CaribePrograma de PsicologíaUniversity of StrathclydeBarranquillaColombia
| | | | - Lucas Sedeño
- Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET)Buenos AiresArgentina
| | - Cecilia Gonzalez Campo
- Cognitive Neuroscience Center (CNC)Universidad de San AndresConsejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET)Buenos AiresArgentina
| | - Hernando Santamaría‐García
- Pontificia Universidad JaverianaMedical School, Physiology and Psychiatry DepartmentsMemory and Cognition Center IntellectusHospital Universitario San IgnacioBogotáColombia
| | - Ivan Aprahamian
- Department of Internal MedicineFaculty of Medicine of JundiaíGroup of Investigation on Multimorbidity and Mental Health in Aging (GIMMA)JundiaíState of São PauloBrazil
| | - Paulo HF Bertolucci
- Department of Neurology and NeurosurgeryEscola Paulista de MedicinaFederal University of São Paulo ‐ UNIFESPSão PauloBrazil
| | - Julian Bustin
- INECO FoundationInstitute of Cognitive and Translational Neuroscience (INCYT)Favaloro UniversityBuenos AiresArgentina
| | | | - Carlos Cano‐Gutierrez
- Medical SchoolGeriatric Unit, Memory and Cognition Center‐IntellectusAging InstituteHospital Universitario San IgnacioPontificia Universidad JaverianaBogotáColombia
| | - Paulo Caramelli
- Faculdade de MedicinaUniversidade Federal de Minas GeraisBelo HorizonteBrazil
| | - Marcia L. F. Chaves
- Neurology ServiceHospital de Clínicas de Porto Alegre e Universidade Federal do Rio Grande do SulBrazil
| | - Patricia Cogram
- Laboratory of Molecular NeuropsychiatryINECO FoundationNational Scientific and Technical Research CouncilInstitute of Cognitive and Translational Neuroscience (INCyT)Favaloro UniversityBuenos AiresArgentina
| | - Bárbara Costa Beber
- Department of Speech and Language PathologyAtlantic Fellow for Equity in Brain HealthFederal University of Health Sciences of Porto Alegre (UFCSPA)Porto AlegreBrazil
| | - Felipe A. Court
- Center for Integrative BiologyFaculty of SciencesFONDAP Center for GeroscienceBrain Health and Metabolism, Santiago, Chile, The Buck Institute for Research on AgingUniversidad Mayor, ChileNovatoCAUSA
| | | | - Nilton Custodio
- Unit Cognitive Impairment and Dementia PreventionCognitive Neurology CenterPeruvian Institute of NeurosciencesLimaPerú
| | - Andres Damian
- Centro Uruguayo de Imagenología Molecular (CUDIM)Centro de Medicina Nuclear e Imagenología MolecularHospital de ClínicasUniversidad de la RepúblicaMontevideoUruguay
| | - Myriam de la Cruz
- Global Brain Health Institute, University of CaliforniaSan FranciscoUSA
| | - Roberta Diehl Rodriguez
- Behavioral and Cognitive Neurology UnitDepartment of Neurology and LIM 22University of São PauloSão PauloBrazil
| | | | - Lais Fajersztajn
- Laboratory of Experimental Air Pollution (LIM05)Department of PathologySchool of MedicineGlobal Brain Health Institute, University of CaliforniaSan Francisco (UCSF)University of São PauloSão PauloSao PauloBrazil
| | - Gonzalo A. Farías
- Department Neurology and Neurosurgery North/Department of NeurosciencesCenter for Advanced Clinical Research (CICA)Faculty of MedicineUniversidad de ChileSantiagoChile
| | | | - Raffaele Ferrari
- Department of Neurodegenerative DiseaseUniversity College LondonLondonESUK
| | - Fabricio Ferreira de Oliveira
- Department of Neurology and NeurosurgeryEscola Paulista de MedicinaFederal University of São Paulo ‐ UNIFESPSão PauloBrazil
| | - Sergio T. Ferreira
- Institute of Medical Biochemistry Leopoldo de Meis & Institute of Biophysics Carlos Chagas FilhoFederal University of Rio de JaneiroRio de JaneiroRJBrazil
| | - Ceres Ferretti
- Division of NeurologyUniversity of São PauloSão PauloBrazil
| | | | | | - Patricio Fuentes
- Geriatrics Section Clinical Hospital University of Chile, Santos Dumont 999 IndependenciaSantiagoChile
| | - Adolfo M. García
- Cognitive Neuroscience Center (CNC)Faculty of EducationNational University of Cuyo (UNCuyo)Universidad de San Andres. National Scientific and Technical Research Council (CONICET)MendozaArgentina
| | | | - Fábio Henrique de Gobbi Porto
- Laboratory of Psychiatric Neuroimaging (LIM‐21)Instituto de PsiquiatriaHospital das Clinicas HCFMUSPFaculdade de MedicinaUniversidade de Sao PauloSao PauloSao PauloBrazil
| | | | | | | | - Ignacio F. Mata
- Department of Genomic MedicineLerner Research InstituteCleveland ClinicOHUSA
| | - Christian Gonzalez‐Billault
- Center for GeroscienceBrain Health and Metabolism (GERO), Santiago, Chile, and Department of Biology, Faculty of SciencesUniversity of ChileSantiagoChile
| | - Oscar L. Lopez
- Alzheimer's Disease Research CenterUniversity of PittsburghPittsburghPAUSA
| | - Laura Morelli
- Fundacion Instituto Leloir‐IIBBA‐CONICET. AveArgentina
| | - Ricardo Nitrini
- Department of NeurologyUniversity of São Paulo Medical SchoolSão PauloBrazil
| | | | - Alejandra Guerrero Barragan
- Trinity College Dublin, Dublin, Departamento de Neurologia Hospital Occidente de KennedyGlobal Brain Health InstituteUniversidad de la SabanaBogotaColombia
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN)School of PsychologyUniversidad Adolfo IbañezSantiagoChile
| | - Fabricio Joao Pio
- Department of NeurologyHospital Governador Celso RamosFlorianopolisBrazil
| | | | - Renata Kochhann
- Graduate Program in PsychologySchool of Health SciencesHospital Moinhos de VentoPontifical Catholic University of Rio Grande do Sul—PUCRS and Researcher OfficePorto AlegreBrazil
| | - Silvia Kochen
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp, El Cruce “N. Kirchner”, Univ. National A, Jauretche (UNAJ), F. Varela, Prov. Buenos Aires. Fac. MedicineUniv Nacional de Buenos Aires (UBA)Buenos AiresArgentina
| | - Fiona Kumfor
- Brain and Mind Centre and School of PsychologyUniversity of SydneySydneyNSWAustralia
| | - Serggio Lanata
- UCSF Department of NeurologyMemory and Aging CenterUCSFSan FranciscoCaliforniaUS
| | - Bruce Miller
- UCSF Department of NeurologyMemory and Aging CenterUCSFSan FranciscoCaliforniaUS
| | | | - Mirna Lie Hosogi
- Behavioral and Cognitive Unit of Department of NeurologyUniversity of São Paulo School of MedicineSao PauloBrazil
| | - Patricia Lillo
- Geroscience Center for Brain Health and Metabolism, Santiago, Chile, Departamento de Neurología Sur/Departamento de Neurociencia, Facultad de MedicinaUniversidad de ChileSantiagoChile
| | | | - David Lira
- Unit Cognitive Impairment and Dementia PreventionCognitive Neurology CenterPeruvian Institute of NeurosciencesLimaPerú
| | - Francisco Lopera
- Neuroscience Research GroupUniversidad de AntioquiaMedellínColombia
| | - Adelina Comas
- Department of Health Policy at the London School of Economics and Political ScienceLondonUK
| | | | - Ana Luisa Sosa
- Instituto Nacional de Neurología y NeurocirugíaCiudad de MéxicoMéxico
| | - Claudia Ramos
- Global Brain Health Institute, University of California, San Francisco (UCSF)San FranciscoUSA
| | | | | | - Ioannis Tarnanas
- Global Brain Health Institute, University of CaliforniaSan FranciscoUSA
- Altoida Inc.HoustonTexasUSA
| | - Jenifer Yokoyama
- UCSF Department of NeurologyMemory and Aging CenterUCSFSan FranciscoCaliforniaUS
| | | | | | - Kate Possin
- UCSF Department of NeurologyMemory and Aging CenterUCSFSan FranciscoCaliforniaUS
| | - Kenneth S. Kosik
- Neuroscience Research Institute and Dept of Molecular Cellular and Developmental BiologyUniversity of California SantaBarbaraCaliforniaUSA
| | - Rosa Montesinos
- Unit Cognitive Impairment and Dementia PreventionCognitive Neurology CenterPeruvian Institute of NeurosciencesLimaPerú
| | - Sebastian Moguilner
- Global Brain Health Institute, University of California, San Francisco (UCSF)San FranciscoUSA
| | - Patricia Cristina Lourdes Solis
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp, El Cruce “N. Kirchner”, Univ. National A, Jauretche (UNAJ), F. Varela, Prov. Buenos Aires. Fac. MedicineUniv Nacional de Buenos Aires (UBA)Buenos AiresArgentina
| | | | - Jeronimo Martin Ramirez
- Departamen de Admision Continua Adultos Hospital General La Raza Instituto Mexicano del Seguro SocialGlobal Brain Health Institute, Trinity College Dublin, DublinCiudad de MexicoMexico
| | - Diana Matallana
- Medical SchoolAging Institute and Psychiatry DepartmentPontificia Universidad Javeriana. Memory and Cognition Center‐IntellectusHospital Universitario San IgnacioBogotáColombia
| | - Lingani Mbakile‐Mahlanza
- Global Brain Health InstituteUniversity of California San Francisco, University of BotswanaGaboroneBotswana
| | | | | | - Eliane C Miotto
- Department of NeurologyUniversity of Sao PauloSao PauloBrazil
| | | | | | - David Orozco
- Cognitive Neuroscience Development LaboratoryAxis NeurocienciasUniversidad Nacional del Sur, Cognitive Impairment and Behavior Disorders UnitBahía BlancaArgentina
| | - Maira Okada de Oliveira
- Global Brain Health Institute, University of California, San Francisco (UCSF)San FranciscoUSA
| | - Olivier Piguet
- School of Psychology and Brain and Mind CentreUniversity of SydneyCamperdownNSWAustralia
| | - Maritza Pintado Caipa
- Global Brain Health Institute, University of California, San Francisco (UCSF)San FranciscoUSA
| | | | - Lucas Porcello Schilling
- Department of NeurologyPontificia Universidade Catolica do Rio Grande do Sul (PUCRS)Porto AlegreBrazil
| | - André Luiz Rodrigues Palmeira
- Santa Casa de Misericórdia de Porto Alegre, Serviço de Neurologia, Porto Alegre, BrazilHospital Ernesto DornellesServiço de Neurologia e NeurocirurgiaPorto AlegreBrazil
| | | | - Jose Manuel Santacruz‐Escudero
- Medical School and Psychiatry DepartmentMemory and Cognition Center‐ IntellectusPontificia Universidad JaverianaHospital Universitario San IgnacioBogotáColombia
| | | | - Jerusa Smid
- Department of NeurologyUniversity of Sao PauloSão PauloBrazil
| | - Andrea Slachevsky
- Neurology DepartmentGeroscience Center for Brain Health and Metabolism, Santiago, Chile, Laboratory of Neuropsychology and Clinical Neuroscience (LANNEC), Physiopathology Program‐ICBM, East Neurologic and Neurosciences Departments, Faculty of MedicineHospital del Salvador and Faculty of Medicine University of Chile. Servicio de NeurologíaDepartamento de MedicinaClínica Alemana—Universidad del DesarrolloUniversity of Chile, Neuropsychiatry and Memory Disorders clinic (CMYN)SantiagoChile
| | | | | | | | - Lea Tenenholz Grinberg
- Departments of NeurologyPathology and Global Brain Health InstituteUCSF ‐ USA, Department of PathologyUniversity of São Paulo Medical SchoolSão PauloBrazil
| | - Antonio Lucio Teixeira
- Laboratório Interdisciplinar de Investigação MédicaFaculdade de MedicinaAv. Alfredo Balena, 110Universidade Federal de Minas GeraisBelo HorizonteBrazil
| | - Maira Tonidandel Barbosa
- Faculdade de Medicina da Universidade Federal de Minas Gerais e Faculdade deCiências Médicas de Minas GeraisBelo HorizonteBrazil
| | - Dominic Trépel
- Global Brain Health Institute (GBHI)Trinity College DublinDublin
| | - Agustin Ibanez
- Cognitive Neuroscience Center (CNC) Buenos Aires, Argentina; Universidad Autonoma del Caribe, Barranquilla, Colombia; Global Brain Health Institute (GBHI), USUniversidad de San AndresCONICETUniversidad Autonoma del CaribeUniversidad Adolfo IbanezUCSFUSA
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11
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Teipel SJ, Kuper-Smith JO, Bartels C, Brosseron F, Buchmann M, Buerger K, Catak C, Janowitz D, Dechent P, Dobisch L, Ertl-Wagner B, Fließbach K, Haynes JD, Heneka MT, Kilimann I, Laske C, Li S, Menne F, Metzger CD, Priller J, Pross V, Ramirez A, Scheffler K, Schneider A, Spottke A, Spruth EJ, Wagner M, Wiltfang J, Wolfsgruber S, Düzel E, Jessen F, Dyrba M. Multicenter Tract-Based Analysis of Microstructural Lesions within the Alzheimer's Disease Spectrum: Association with Amyloid Pathology and Diagnostic Usefulness. J Alzheimers Dis 2020; 72:455-465. [PMID: 31594223 PMCID: PMC6918918 DOI: 10.3233/jad-190446] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Diffusion changes as determined by diffusion tensor imaging are potential indicators of microstructural lesions in people with mild cognitive impairment (MCI), prodromal Alzheimer’s disease (AD), and AD dementia. Here we extended the scope of analysis toward subjective cognitive complaints as a pre-MCI at risk stage of AD. In a cohort of 271 participants of the prospective DELCODE study, including 93 healthy controls and 98 subjective cognitive decline (SCD), 45 MCI, and 35 AD dementia cases, we found reductions of fiber tract integrity in limbic and association fiber tracts in MCI and AD dementia compared with controls in a tract-based analysis (p < 0.05, family wise error corrected). In contrast, people with SCD showed spatially restricted white matter alterations only for the mode of anisotropy and only at an uncorrected level of significance. DTI parameters yielded a high cross-validated diagnostic accuracy of almost 80% for the clinical diagnosis of MCI and the discrimination of Aβ positive MCI cases from Aβ negative controls. In contrast, DTI parameters reached only random level accuracy for the discrimination between Aβ positive SCD and control cases from Aβ negative controls. These findings suggest that in prodromal stages of AD, such as in Aβ positive MCI, multicenter DTI with prospectively harmonized acquisition parameters yields diagnostic accuracy meeting the criteria for a useful biomarker. In contrast, automated tract-based analysis of DTI parameters is not useful for the identification of preclinical AD, including Aβ positive SCD and control cases.
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Affiliation(s)
- Stefan J Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany.,German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Jan O Kuper-Smith
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Claudia Bartels
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Martina Buchmann
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Cihan Catak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Peter Dechent
- MR-Research in Neurology and Psychiatry, Georg-August-University Göttingen, Göttingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig-Maximilians-University, Munich, Germany.,Division of Neuroradiology, Department of Medical Imaging, The Hospital for Sick Children, Toronto, Canada
| | - Klaus Fließbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Ingo Kilimann
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany.,German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Siyao Li
- Institute of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Menne
- Institute of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Josef Priller
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Verena Pross
- Study Center Bonn, Medical Faculty, Bonn, Germany
| | - Alfredo Ramirez
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University of Bonn, Bonn, Germany
| | - Eike J Spruth
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
| | | | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
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Redolfi A, De Francesco S, Palesi F, Galluzzi S, Muscio C, Castellazzi G, Tiraboschi P, Savini G, Nigri A, Bottini G, Bruzzone MG, Ramusino MC, Ferraro S, Gandini Wheeler-Kingshott CAM, Tagliavini F, Frisoni GB, Ryvlin P, Demonet JF, Kherif F, Cappa SF, D'Angelo E. Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts. Front Neurol 2020; 11:1021. [PMID: 33071930 PMCID: PMC7538836 DOI: 10.3389/fneur.2020.01021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Samantha Galluzzi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Cristina Muscio
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gloria Castellazzi
- IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom
- Department of Computer, Electrical and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Pietro Tiraboschi
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gabriella Bottini
- Neuropsychology Center, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Matteo Cotta Ramusino
- IRCCS Mondino Foundation, Pavia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Stefania Ferraro
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom
| | - Fabrizio Tagliavini
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Giovanni B. Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Jean-François Demonet
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Stefano F. Cappa
- IRCCS Mondino Foundation, Pavia, Italy
- University School of Advanced Studies, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
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13
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Dou X, Yao H, Feng F, Wang P, Zhou B, Jin D, Yang Z, Li J, Zhao C, Wang L, An N, Liu B, Zhang X, Liu Y. Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets. Cortex 2020; 129:390-405. [PMID: 32574842 DOI: 10.1016/j.cortex.2020.03.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 12/13/2019] [Accepted: 03/31/2020] [Indexed: 12/28/2022]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.
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Affiliation(s)
- Xuejiao Dou
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Hongxiang Yao
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Feng Feng
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300350, China; Department of Neurology, Nankai University Huanhu Hospital, Tianjin, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Dan Jin
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Zhengyi Yang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jin Li
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Cui Zhao
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Luning Wang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Ningyu An
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China.
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
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14
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Shi Y, Zeng W, Deng J, Nie W, Zhang Y. The Identification of Alzheimer's Disease Using Functional Connectivity Between Activity Voxels in Resting-State fMRI Data. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2020; 8:1400211. [PMID: 32355582 PMCID: PMC7186217 DOI: 10.1109/jtehm.2020.2985022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 01/04/2020] [Accepted: 03/28/2020] [Indexed: 01/06/2023]
Abstract
Background: Alzheimer’s disease (AD) is a common neurodegenerative disease occurring in the elderly population. The effective and accurate classification of AD symptoms by using functional magnetic resonance imaging (fMRI) has a great significance for the clinical diagnosis and prediction of AD patients. Methods: Therefore, this paper proposes a new method for identifying AD patients from healthy subjects by using functional connectivities (FCs) between the activity voxels in the brain based on fMRI data analysis. Firstly, independent component analysis is used to detect the activity voxels in the fMRI signals of AD patients and healthy subjects; Secondly, the FCs between the common activity voxels of the two groups are calculated, and then the FCs with significant differences are further identified by statistical analysis between them; Finally, the classification of AD patients from healthy subjects is realized by using FCs with significant differences as the feature samples in support vector machine. Results: The results show that the proposed identification method can obtain higher classification accuracy, and the FCs between activity voxels within prefrontal lobe as well as those between prefrontal and parietal lobes play an important role in the prediction of AD patients. Furthermore, we also find that more brain regions and much more voxels in some regions are activity in AD group compared with health control group. Conclusion: It has a great potential value for the AD pathogenesis mechanism study.
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Affiliation(s)
- Yuhu Shi
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
| | - Weiming Zeng
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
| | - Jin Deng
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
| | - Weifang Nie
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
| | - Yifei Zhang
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
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15
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Archetti D, Ingala S, Venkatraghavan V, Wottschel V, Young AL, Bellio M, Bron EE, Klein S, Barkhof F, Alexander DC, Oxtoby NP, Frisoni GB, Redolfi A. Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease. NEUROIMAGE-CLINICAL 2019; 24:101954. [PMID: 31362149 PMCID: PMC6675943 DOI: 10.1016/j.nicl.2019.101954] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 06/24/2019] [Accepted: 07/19/2019] [Indexed: 11/18/2022]
Abstract
Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2EBM = 0.866; R2DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain. Data-driven event sequences describe evolution of relevant biomarkers in AD. Agreement between event sequences and heuristic AD progression models Accuracy in classifying subjects from clinical cohorts up to 91% Staging of subjects and MMSE scores of individuals show linear relation. Transferability of AD progression models based on research data to clinical cohorts
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Affiliation(s)
- Damiano Archetti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Vikram Venkatraghavan
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Maura Bellio
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands; Institutes of Neurology and Healthcare Engineering, UCL, London, UK.
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Giovanni B Frisoni
- University of Geneva, Geneva, Switzerland; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Alberto Redolfi
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
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16
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Brueggen K, Dyrba M, Cardenas-Blanco A, Schneider A, Fliessbach K, Buerger K, Janowitz D, Peters O, Menne F, Priller J, Spruth E, Wiltfang J, Vukovich R, Laske C, Buchmann M, Wagner M, Röske S, Spottke A, Rudolph J, Metzger CD, Kilimann I, Dobisch L, Düzel E, Jessen F, Teipel SJ. Structural integrity in subjective cognitive decline, mild cognitive impairment and Alzheimer's disease based on multicenter diffusion tensor imaging. J Neurol 2019; 266:2465-2474. [PMID: 31227891 DOI: 10.1007/s00415-019-09429-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 05/18/2019] [Accepted: 06/11/2019] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Subjective cognitive decline (SCD) can represent a preclinical stage of Alzheimer's disease. Diffusion tensor imaging (DTI) could aid an early diagnosis, yet only few monocentric DTI studies in SCD have been conducted, reporting heterogeneous results. We investigated microstructural changes in SCD in a larger, multicentric cohort. METHODS 271 participants with SCD, mild cognitive impairment (MCI) or Alzheimer's dementia (AD) and healthy controls (CON) were included, recruited prospectively at nine centers of the observational DELCODE study. DTI was acquired using identical protocols. Using voxel-based analyses, we investigated fractional anisotropy (FA), mean diffusivity (MD) and mode (MO) in the white matter (WM). Discrimination accuracy was determined by cross-validated elastic-net penalized regression. Center effects were explored using variance analyses. RESULTS MO and FA were lower in SCD compared to CON in several anterior and posterior WM regions, including the anterior corona radiata, superior and inferior longitudinal fasciculus, cingulum and splenium of the corpus callosum (p < 0.01, uncorrected). MD was higher in the superior and inferior longitudinal fasciculus, cingulum and superior corona radiata (p < 0.01, uncorrected). The cross-validated accuracy for discriminating SCD from CON was 67% (p < 0.01). As expected, the AD and MCI groups had higher MD and lower FA and MO in extensive regions, including the corpus callosum and temporal brain regions. Within these regions, center accounted for 3-15% of the variance. CONCLUSIONS DTI revealed subtle WM alterations in SCD that were intermediate between those in MCI and CON and may be useful to detect individuals with an increased risk for AD in clinical studies.
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Affiliation(s)
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | | | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Klaus Fliessbach
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Katharina Buerger
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Oliver Peters
- Institute of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany.,German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Felix Menne
- Institute of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Eike Spruth
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Göttingen, Germany.,German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
| | - Ruth Vukovich
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Göttingen, Germany
| | - Christoph Laske
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Martina Buchmann
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Sandra Röske
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University of Bonn, Bonn, Germany
| | - Janna Rudolph
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto-Von-Guericke University, Magdeburg, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Laura Dobisch
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-Von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
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17
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Mannheim JG, Kara F, Doorduin J, Fuchs K, Reischl G, Liang S, Verhoye M, Gremse F, Mezzanotte L, Huisman MC. Standardization of Small Animal Imaging-Current Status and Future Prospects. Mol Imaging Biol 2019; 20:716-731. [PMID: 28971332 DOI: 10.1007/s11307-017-1126-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The benefit of small animal imaging is directly linked to the validity and reliability of the collected data. If the data (regardless of the modality used) are not reproducible and/or reliable, then the outcome of the data is rather questionable. Therefore, standardization of the use of small animal imaging equipment, as well as of animal handling in general, is of paramount importance. In a recent paper, guidance for efficient small animal imaging quality control was offered and discussed, among others, the use of phantoms in setting up a quality control program (Osborne et al. 2016). The same phantoms can be used to standardize image quality parameters for multi-center studies or multi-scanners within center studies. In animal experiments, the additional complexity due to animal handling needs to be addressed to ensure standardized imaging procedures. In this review, we will address the current status of standardization in preclinical imaging, as well as potential benefits from increased levels of standardization.
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Affiliation(s)
- Julia G Mannheim
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany.
| | - Firat Kara
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
| | - Janine Doorduin
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Kerstin Fuchs
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany
| | - Gerald Reischl
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany
| | - Sayuan Liang
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
| | | | - Felix Gremse
- Institute for Experimental Molecular Imaging, RWTH Aachen University Clinic, Aachen, Germany
| | - Laura Mezzanotte
- Optical Molecular Imaging, Department of Radiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Marc C Huisman
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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18
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ten Kate M, Ingala S, Schwarz AJ, Fox NC, Chételat G, van Berckel BNM, Ewers M, Foley C, Gispert JD, Hill D, Irizarry MC, Lammertsma AA, Molinuevo JL, Ritchie C, Scheltens P, Schmidt ME, Visser PJ, Waldman A, Wardlaw J, Haller S, Barkhof F. Secondary prevention of Alzheimer's dementia: neuroimaging contributions. Alzheimers Res Ther 2018; 10:112. [PMID: 30376881 PMCID: PMC6208183 DOI: 10.1186/s13195-018-0438-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 10/10/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations. MAIN BODY Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation. CONCLUSION We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.
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Affiliation(s)
- Mara ten Kate
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Adam J. Schwarz
- Takeda Pharmaceuticals Comparny, Cambridge, MA USA
- Eli Lilly and Company, Indianapolis, Indiana USA
| | - Nick C. Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Gaël Chételat
- Institut National de la Santé et de la Recherche Médicale, Inserm UMR-S U1237, Université de Caen-Normandie, GIP Cyceron, Caen, France
| | - Bart N. M. van Berckel
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | | | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | | | | | - Adriaan A. Lammertsma
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Craig Ritchie
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | | | - Pieter Jelle Visser
- Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, PO Box 7056, 1007 MB Amsterdam, the Netherlands
| | - Adam Waldman
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Sven Haller
- Affidea Centre de Diagnostic Radiologique de Carouge, Geneva, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
- Insititutes of Neurology and Healthcare Engineering, University College London, London, UK
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19
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Menke RAL, Agosta F, Grosskreutz J, Filippi M, Turner MR. Neuroimaging Endpoints in Amyotrophic Lateral Sclerosis. Neurotherapeutics 2017; 14:11-23. [PMID: 27752938 PMCID: PMC5233627 DOI: 10.1007/s13311-016-0484-9] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative, clinically heterogeneous syndrome pathologically overlapping with frontotemporal dementia. To date, therapeutic trials in animal models have not been able to predict treatment response in humans, and the revised ALS Functional Rating Scale, which is based on coarse disability measures, remains the gold-standard measure of disease progression. Advances in neuroimaging have enabled mapping of functional, structural, and molecular aspects of ALS pathology, and these objective measures may be uniquely sensitive to the detection of propagation of pathology in vivo. Abnormalities are detectable before clinical symptoms develop, offering the potential for neuroprotective intervention in familial cases. Although promising neuroimaging biomarker candidates for diagnosis, prognosis, and disease progression have emerged, these have been from the study of necessarily select patient cohorts identified in specialized referral centers. Further multicenter research is now needed to establish their validity as therapeutic outcome measures.
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Affiliation(s)
- Ricarda A L Menke
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Julian Grosskreutz
- Hans-Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
- Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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