1
|
Giehl K, Mutsaerts HJ, Aarts K, Barkhof F, Caspers S, Chetelat G, Colin ME, Düzel E, Frisoni GB, Ikram MA, Jovicich J, Morbelli S, Oertel W, Paret C, Perani D, Ritter P, Segura B, Wisse LEM, De Witte E, Cappa SF, van Eimeren T. Sharing brain imaging data in the Open Science era: how and why? Lancet Digit Health 2024; 6:e526-e535. [PMID: 38906618 DOI: 10.1016/s2589-7500(24)00069-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/18/2024] [Accepted: 04/02/2024] [Indexed: 06/23/2024]
Abstract
The sharing of human neuroimaging data has great potential to accelerate the development of imaging biomarkers in neurological and psychiatric disorders; however, major obstacles remain in terms of how and why to share data in the Open Science context. In this Health Policy by the European Cluster for Imaging Biomarkers, we outline the current main opportunities and challenges based on the results of an online survey disseminated among senior scientists in the field. Although the scientific community fully recognises the importance of data sharing, technical, legal, and motivational aspects often prevent active adoption. Therefore, we provide practical advice on how to overcome the technical barriers. We also call for a harmonised application of the General Data Protection Regulation across EU countries. Finally, we suggest the development of a system that makes data count by recognising the generation and sharing of data as a highly valuable contribution to the community.
Collapse
Affiliation(s)
- Kathrin Giehl
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Institute of Neurosciences and Medicine (INM-2), Research Center Jülich, Jülich, Germany
| | - Henk-Jan Mutsaerts
- Radiology and Nuclear Medicine, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, Netherlands; Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands
| | | | - Frederik Barkhof
- Radiology and Nuclear Medicine, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, Netherlands; Amsterdam Neuroscience, Brain Imaging, Amsterdam, Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gaël Chetelat
- Normandie université, UNICAEN, INSERM, U1237, NeuroPresage Team, Cyceron, Caen, France
| | | | - Emrah Düzel
- Faculty of Medicine, Institute for Cognitive Neurology and Dementia Research, University of Magdeburg, Magdeburg, Germany; Center for Neurodegenerative Diseases, Magdeburg, Germany
| | - Giovanni B Frisoni
- Department of Rehabilitation and Geriatrics, Memory Center, Geneva University and University Hospitals, Geneva, Switzerland
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Silvia Morbelli
- Nuclear Medicine Unit, AOU Città Della Salute e Della Scienza di Torino, Turin, Italy; Department of Medical Sciences, University of Turin, Turin, Italy
| | - Wolfgang Oertel
- European Brain Council, Brussels, Belgium; Department of Neurology, University of Marburg, Marburg, Germany
| | - Christian Paret
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Daniela Perani
- San Raffaele University and San Raffaele Scientific Institute, Milan, Italy
| | - Petra Ritter
- Berlin Institute of Health, Charité, Universitätsmedizin Berlin, Berlin, Germany; Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Berlin, Germany; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany; Einstein Center for Neuroscience Berlin, Berlin, Germany; Einstein Center Digital Future, Berlin, Germany
| | - Bàrbara Segura
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain; Hospital Clinic Foundation for Biomedical Research-August Pi i Sunyer Biomedical Research Institute, Barcelona, Spain; Biomedical Research Networking Center on Neurodegenerative Diseases Barcelona, Spain
| | - Laura E M Wisse
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Elke De Witte
- Neurosurgical Department, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Stefano F Cappa
- University Institute of Advanced Studies, Pavia, Italy; IRCCS Mondino Foundation, Pavia, Italy
| | - Thilo van Eimeren
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
| |
Collapse
|
2
|
Abd El Hamid MM, Omar YM, Shaheen M, Mabrouk MS. Discovering epistasis interactions in Alzheimer's disease using deep learning model. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
|
3
|
Liu L, Chang J, Wang Y, Liang G, Wang YP, Zhang H. Decomposition-Based Correlation Learning for Multi-Modal MRI-Based Classification of Neuropsychiatric Disorders. Front Neurosci 2022; 16:832276. [PMID: 35692429 PMCID: PMC9174798 DOI: 10.3389/fnins.2022.832276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Multi-modal magnetic resonance imaging (MRI) is widely used for diagnosing brain disease in clinical practice. However, the high-dimensionality of MRI images is challenging when training a convolution neural network. In addition, utilizing multiple MRI modalities jointly is even more challenging. We developed a method using decomposition-based correlation learning (DCL). To overcome the above challenges, we used a strategy to capture the complex relationship between structural MRI and functional MRI data. Under the guidance of matrix decomposition, DCL takes into account the spike magnitude of leading eigenvalues, the number of samples, and the dimensionality of the matrix. A canonical correlation analysis (CCA) was used to analyze the correlation and construct matrices. We evaluated DCL in the classification of multiple neuropsychiatric disorders listed in the Consortium for Neuropsychiatric Phenomics (CNP) dataset. In experiments, our method had a higher accuracy than several existing methods. Moreover, we found interesting feature connections from brain matrices based on DCL that can differentiate disease and normal cases and different subtypes of the disease. Furthermore, we extended experiments on a large sample size dataset and a small sample size dataset, compared with several other well-established methods that were designed for the multi neuropsychiatric disorder classification; our proposed method achieved state-of-the-art performance on all three datasets.
Collapse
Affiliation(s)
- Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Jing Chang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Ying Wang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
| | - Gongbo Liang
- Department of Computer Science, Eastern Kentucky University, Richmond, KY, United States
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Hui Zhang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, China
- *Correspondence: Hui Zhang
| |
Collapse
|
4
|
Feng J, Zhang SW, Chen L. Extracting ROI-Based Contourlet Subband Energy Feature From the sMRI Image for Alzheimer's Disease Classification. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1627-1639. [PMID: 33434134 DOI: 10.1109/tcbb.2021.3051177] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) classification and its prodromal stage-mild cognitive impairment (MCI) classification have attracted many attentions and been widely investigated in recent years. Owing to the high dimensionality, representation of the sMRI image becomes a difficult issue in AD classification. Furthermore, regions of interest (ROI) reflected in the sMRI image are not characterized properly by spatial analysis techniques, which has been a main cause of weakening the discriminating ability of the extracted spatial feature. In this study, we propose a ROI-based contourlet subband energy (ROICSE) feature to represent the sMRI image in the frequency domain for AD classification. Specifically, a preprocessed sMRI image is first segmented into 90 ROIs by a constructed brain mask. Instead of extracting features from the 90 ROIs in the spatial domain, the contourlet transform is performed on each of these ROIs to obtain their energy subbands. And then for an ROI, a subband energy (SE) feature vector is constructed to capture its energy distribution and contour information. Afterwards, SE feature vectors of the 90 ROIs are concatenated to form a ROICSE feature of the sMRI image. Finally, support vector machine (SVM) classifier is used to classify 880 subjects from ADNI and OASIS databases. Experimental results show that the ROICSE approach outperforms six other state-of-the-art methods, demonstrating that energy and contour information of the ROI are important to capture differences between the sMRI images of AD and HC subjects. Meanwhile, brain regions related to AD can also be found using the ROICSE feature, indicating that the ROICSE feature can be a promising assistant imaging marker for the AD diagnosis via the sMRI image. Code and Sample IDs of this paper can be downloaded at https://github.com/NWPU-903PR/ROICSE.git.
Collapse
|
5
|
Abd El Hamid MM, Shaheen M, Mabrouk MS, Omar YMK. MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2021; 33. [DOI: 10.4015/s1016237221500472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.
Collapse
Affiliation(s)
- Marwa M. Abd El Hamid
- The Higher Institute of Computer Science & Information Technology, El-Shorouk Academy, El Shorouk City, Cairo, Egypt
- College of Computing and Information Technology AASTMT, Egypt
| | - Mohamed Shaheen
- College of Computing and Information Technology AASTMT, Egypt
| | - Mai S. Mabrouk
- Biomedical Engineering Department Misr University for Science and Technology 6th of October City, Egypt
| | | |
Collapse
|
6
|
Abstract
In the medical field, Alzheimer’s disease (AD), as a neurodegenerative brain disease which is very difficult to diagnose, can cause cognitive impairment and memory decline. Many existing works include a variety of clinical neurological and psychological examinations, especially computer-aided diagnosis (CAD) methods based on electroencephalographic (EEG) recording or MRI images by using machine learning (ML) combined with different preprocessing steps such as hippocampus shape analysis, fusion of embedded features, and so on, where EEG dataset used for AD diagnosis is usually is large and complex, requiring extraction of a series of features like entropy features, spectral feature, etc., and it has seldom been applied in the AD detection based on deep learning (DL), while MRI images were suitable for both ML and DL. In terms of the structural MRI brain images, few differences could be found in brain atrophy among the three situations: AD, mild cognitive impairment (MCI), and Normal Control (NC). On the other hand, DL methods have been used to diagnose AD incorporating MRI images in recent years, but there have not yet been many selective models with very deep layers. In this article, the Gray Matter (GM) Magnetic Resonance Imaging (MRI) is automatically extracted, which could better distinguish among the three types of situations like AD, MCI, and NC, compared with Cerebro Spinal Fluid (CSF) and White Matter (WM). Firstly, FMRIB Software Library (FSL) software is utilized for batch processing to remove the skull, cerebellum and register the heterogeneous images, and the SPM + cat12 tool kits in MATLAB is used to segment MRI images for obtaining the standard GM MRI images. Next, the GM MRI images are trained by some new neural networks. The characteristics of the training process are as follows: (1) The Tresnet, as the network that achieves the best classification effect among several new networks in the experiment, is selected as the basic network. (2) A multi-receptive-field mechanism is integrated into the network, which is inspired by neurons that can dynamically adjust the receptive fields according to different stimuli. (3) The whole network is realized by adding multiple channels to the convolutional layer, and the size of the convolution kernel of each channel can be dynamically adjusted. (4) Transfer learning method is used to train the model for speeding up the learning and optimizing the learning efficiency. Finally, we achieve the accuracies of 86.9% for AD vs. NC, 63.2% for AD vs. MCI vs. NC respectively, which outperform the previous approaches. The results demonstrate the effectiveness of our approach.
Collapse
|
7
|
Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021; 8:ENEURO.0475-20.2021. [PMID: 34045210 PMCID: PMC8260273 DOI: 10.1523/eneuro.0475-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/08/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022] Open
Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
Collapse
|
8
|
Xu W, Tan CC, Zou JJ, Cao XP, Tan L. Insomnia Moderates the Relationship Between Amyloid-β and Cognitive Decline in Late-Life Adults without Dementia. J Alzheimers Dis 2021; 81:1701-1710. [PMID: 33967043 DOI: 10.3233/jad-201582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND It is suggested that not all individuals with elevated Aβ will develop dementia or cognitive impairment. Environment or lifestyle might modulate the association of amyloid pathology with cognition. Insomnia is a risk factor of cognitive disorders including Alzheimer's disease. OBJECTIVE To investigate if insomnia moderated the relationship between amyloid-β (Aβ) and longitudinal cognitive performance in non-demented elders. METHODS A total of 385 Alzheimer's Disease Neuroimaging Initiative participants (mean age = 73 years, 48% females) who completed 4 + neuropsychological evaluations and a [18F] florbetapir positron emission tomography scan were followed up to 8 years. Linear mixed-effects regression models were used to examine the interactions effect between insomnia and Aβ on longitudinal cognitive sores, including four domains (memory [MEM], executive function [EF], language [LAN], and visuospatial function [VS]). RESULTS The Aβ-positive status (A+) but not insomnia independently predicted faster cognitive decline in all domains. Furthermore, the relationship between Aβ and cognitive decline was moderated by insomnia (MEM: χ2 = 4.05, p = 0.044, EF: χ2 = 4.38, p = 0.036, LAN: χ2 = 4.56, p = 0.033, and VS: χ2 = 4.12, p = 0.042). Individuals with both elevated Aβ and insomnia experienced faster cognitive decline than those with only elevated Aβ or insomnia. CONCLUSION These data reinforced the values of insomnia management in preventing dementia, possibly by interacting Aβ metabolism. Future efforts are warranted to determine whether sleep improvement will postpone the onset of dementia, specifically among populations in stages of preclinical or prodromal AD.
Collapse
Affiliation(s)
- Wei Xu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Chen-Chen Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Juan-Juan Zou
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University; NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, China
| | - Xi-Peng Cao
- Clinical Research Center, Qingdao Municipal Hospital, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | | |
Collapse
|
9
|
Davis AM, Engkvist O, Fairclough RJ, Feierberg I, Freeman A, Iyer P. Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development. SLAS DISCOVERY 2021; 26:604-619. [PMID: 33586501 DOI: 10.1177/2472555220982268] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is public-private partnership (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process-from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases.
Collapse
Affiliation(s)
- Andrew M Davis
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Rebecca J Fairclough
- Emerging Innovations Unit, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Isabella Feierberg
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Boston, USA
| | - Adrian Freeman
- Emerging Innovations Unit, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Preeti Iyer
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| |
Collapse
|
10
|
Feng J, Zhang SW, Chen L, Xia J. Alzheimer’s disease classification using features extracted from nonsubsampled contourlet subband-based individual networks. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.09.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
11
|
Koola MM. Alpha7 nicotinic-N-methyl-D-aspartate hypothesis in the treatment of schizophrenia and beyond. Hum Psychopharmacol 2021; 36:1-16. [PMID: 32965756 DOI: 10.1002/hup.2758] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 12/12/2022]
Abstract
Development of novel treatments for positive, cognitive, and negative symptoms continue to be a high-priority area of schizophrenia research and a major unmet clinical need. Given that all randomized controlled trials (RCTs) conducted to date failed with one add-on medication/mechanism of action, future RCTs with the same approach are not warranted. Even if the field develops a medication for cognition, others are still needed to treat negative and positive symptoms. Therefore, fixing one domain does not completely solve the problem. Also, targeting the cholinergic system, glutamatergic system, and cholinergic plus alpha7 nicotinic and N-methyl-D-aspartate (NMDA) receptors failed independently. Hence, targeting other less important pathophysiological mechanisms/targets is unlikely to be successful. Meta-analyses of RCTs targeting major pathophysiological mechanisms have found some efficacy signal in schizophrenia; thus, combination treatments with different mechanisms of action may enhance the efficacy signal. The objective of this article is to highlight the importance of conducting RCTs with novel combination treatments in schizophrenia to develop antischizophrenia treatments. Positive RCTs with novel combination treatments that target the alpha7 nicotinic and NMDA receptors simultaneously may lead to a disease-modifying therapeutic armamentarium in schizophrenia. Novel combination treatments that concurrently improve the three domains of psychopathology and several prognostic and theranostic biomarkers may facilitate therapeutic discovery in schizophrenia.
Collapse
Affiliation(s)
- Maju Mathew Koola
- Department of Psychiatry and Behavioral Health, Stony Brook University Renaissance School of Medicine, Stony Brook, New York, USA
| |
Collapse
|
12
|
Liu J, Tan G, Lan W, Wang J. Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks. BMC Bioinformatics 2020; 21:123. [PMID: 33203351 PMCID: PMC7672960 DOI: 10.1186/s12859-020-3437-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 03/02/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer's disease (AD) and is associated with brain structural and functional changes, is still a challenging task. Recent studies show great promises for improving the performance of EMCI identification by combining multiple structural and functional features, such as grey matter volume and shortest path length. However, extracting which features and how to combine multiple features to improve the performance of EMCI identification have always been a challenging problem. To address this problem, in this study we propose a new EMCI identification framework using multi-modal data and graph convolutional networks (GCNs). Firstly, we extract grey matter volume and shortest path length of each brain region based on automated anatomical labeling (AAL) atlas as feature representation from T1w MRI and rs-fMRI data of each subject, respectively. Then, in order to obtain features that are more helpful in identifying EMCI, a common multi-task feature selection method is applied. Afterwards, we construct a non-fully labelled subject graph using imaging and non-imaging phenotypic measures of each subject. Finally, a GCN model is adopted to perform the EMCI identification task. RESULTS Our proposed EMCI identification method is evaluated on 210 subjects, including 105 subjects with EMCI and 105 normal controls (NCs), with both T1w MRI and rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed framework achieves an accuracy of 84.1% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.856 for EMCI/NC classification. In addition, by comparison, the accuracy and AUC values of our proposed framework are better than those of some existing methods in EMCI identification. CONCLUSION Our proposed EMCI identification framework is effective and promising for automatic diagnosis of EMCI in clinical practice.
Collapse
Affiliation(s)
- Jin Liu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 Lushan South Road, Changsha, 410083 China
| | - Guanxin Tan
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 Lushan South Road, Changsha, 410083 China
| | - Wei Lan
- School of Computer, Electronics and Information, Guangxi University, 100 Daxue East Road, Nanning, 530004 China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 Lushan South Road, Changsha, 410083 China
| |
Collapse
|
13
|
Ibanez A, Flichtentrei D, Hesse E, Dottori M, Tomio A, Slachevsky A, Serrano CM, Gonzalez‐Billaut C, Custodio N, Miranda C, Bustin J, Cetckovitch M, Torrente F, Olavarria L, Leon T, Beber BC, Bruki S, Suemoto CK, Nitrini R, Miller BL, Yokoyama JS. The power of knowledge about dementia in Latin America across health professionals working on aging. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12117. [PMID: 33088898 PMCID: PMC7560513 DOI: 10.1002/dad2.12117] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 09/01/2020] [Accepted: 09/16/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Expert knowledge is critical to fight dementia in inequitable regions like Latin American and Caribbean countries (LACs). However, the opinions of aging experts on public policies' accessibility and transmission, stigma, diagnostic manuals, data-sharing platforms, and use of behavioral insights (BIs) are not well known. METHODS We investigated opinions among health professionals working on aging in LACs (N = 3365) with regression models including expertise-related information (public policies, BI), individual differences (work, age, academic degree), and location. RESULTS Experts specified low public policy knowledge (X2 = 41.27, P < .001), high levels of stigma (X2 = 2636.37, P < .001), almost absent BI knowledge (X2 = 56.58, P < .001), and needs for regional diagnostic manuals (X2 = 2893.63, df = 3, P < .001) and data-sharing platforms (X2 = 1267.5, df = 3, P < .001). Lack of dementia knowledge was modulated by different factors. An implemented BI-based treatment for a proposed prevention program improved perception across experts. DISCUSSION Our findings help to prioritize future potential actions of governmental agencies and non-governmental organizations (NGOs) to improve LACs' dementia knowledge.
Collapse
Affiliation(s)
- Agustin Ibanez
- Global Brain Health Institute and the Memory and Aging Center, Weill Institute for Neurosciences, Department of NeurologyUniversity of California, San Francisco (UCSF)San FranciscoCaliforniaUSA
- Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Center for Social and Cognitive Neuroscience (CSCN), School of PsychologyUniversidad Adolfo IbáñezSantiago de ChileChile
- Universidad Autónoma del CaribeBarranquillaColombia
| | | | - Eugenia Hesse
- Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Martin Dottori
- Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Ailin Tomio
- Universidad de San AndrésBuenos AiresArgentina
| | - Andrea Slachevsky
- Memory and Neuropsychiatric Clinic (CMYN), Neurology DepartmentDel Salvador Hospital and University of Chile Faculty of MedicineSantiagoChile
- Geroscience Center for Brain Health and Metabolism (GERO), Faculty of MedicineUniversity of ChileSantiagoChile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department ‐ ICBM, Neuroscience and East Neuroscience Departments, Faculty of MedicineUniversity of ChileSantiagoChile
- Department of Neurology and PsychiatryClínica Alemana‐Universidad del DesarrolloSantiagoChile
| | - Cecilia M Serrano
- Cognitive Neurology, Neurology DepartmentDr César Milstein HospitalBuenos AiresArgentina
| | - Christian Gonzalez‐Billaut
- Geroscience Center for Brain Health and Metabolism (GERO), Faculty of MedicineUniversity of ChileSantiagoChile
| | - Nilton Custodio
- Unit Cognitive Impairment and Dementia Prevention, Cognitive Neurology CenterPeruvian Institute of NeurosciencesLimaPerú
| | - Claudia Miranda
- Faculty of NursingUniversidad Andres BelloSantiagoChile
- Millennium Institute for Research in Depression and PersonalitySantiagoChile
| | - Julian Bustin
- Institute of Translational and Cognitive Neuroscience (INCYT), INECO Foundation, Favaloro UniversityNational Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Marcelo Cetckovitch
- Institute of Translational and Cognitive Neuroscience (INCYT), INECO Foundation, Favaloro UniversityNational Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Fernando Torrente
- Institute of Translational and Cognitive Neuroscience (INCYT), INECO Foundation, Favaloro UniversityNational Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Loreto Olavarria
- Memory and Neuropsychiatric Clinic (CMYN), Neurology DepartmentDel Salvador Hospital and University of Chile Faculty of MedicineSantiagoChile
| | - Tomas Leon
- Memory and Neuropsychiatric Clinic (CMYN), Neurology DepartmentDel Salvador Hospital and University of Chile Faculty of MedicineSantiagoChile
| | - Barbara Costa Beber
- Department of Speech and Language Pathology, Federal University of Health Sciences of Porto Alegre (UFCSPA)Atlantic Fellow for Equity in Brain HealthPorto AlegreBrazil
| | - Sonia Bruki
- Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | | | - Ricardo Nitrini
- Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Bruce L. Miller
- Global Brain Health Institute and the Memory and Aging Center, Weill Institute for Neurosciences, Department of NeurologyUniversity of California, San Francisco (UCSF)San FranciscoCaliforniaUSA
| | - Jennifer S. Yokoyama
- Global Brain Health Institute and the Memory and Aging Center, Weill Institute for Neurosciences, Department of NeurologyUniversity of California, San Francisco (UCSF)San FranciscoCaliforniaUSA
| |
Collapse
|
14
|
Allegri RF. Moving from neurodegenerative dementias, to cognitive proteinopathies, replacing "where" by "what"…. Dement Neuropsychol 2020; 14:237-242. [PMID: 32973977 PMCID: PMC7500817 DOI: 10.1590/1980-57642020dn14-030005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Neurodegenerative dementias have been described based on their phenotype, in relation to selective degeneration occurring in a particular neuroanatomical system. More recently however, the term proteinopathy has been introduced to describe diseases in which one or more altered proteins can be detected. Neurodegenerative diseases can be produced by more than one abnormal protein and each proteinopathy can determine different clinical phenotypes. Specific biomarkers have now been linked to certain molecular pathologies in live patients. In 2016, a new biomarker-based classification, currently only approved for research in Alzheimer's disease, was introduced. It is based on the evaluation three biomarkers: amyloid (A) detected on amyloid-PET or amyloid- beta 42 assay in CSF; tau (T) measured in CSF as phosphorylated tau or on tau PET imaging; and neuronal injury/neurodegeneration (N), detected by total T-tau in CSF, FDG PET hypometabolism and on MRI brain scan. Results of clinical research using the ATN biomarkers at FLENI, a Neurological Institute in Buenos Aires, Argentina have, since 2011, contributed to ongoing efforts to move away from the concept of neurodegenerative dementias and more towards one of cognitive proteinopathies. Today, clinical diagnosis in dementia can only tell us "where" abnormal tissue is found but not "what" molecular mechanisms are involved.
Collapse
Affiliation(s)
- Ricardo Francisco Allegri
- Departament of Cognitive Neurology, Neuropsychology, and Neuropsychiatry, Instituto de Investigaciones Neurologicas Fleni, Buenos Aires, Argentina.,Department of Neurosciences, Universidad de la Costa, Barranquilla, Colombia
| |
Collapse
|
15
|
Enhancing the feature representation of multi-modal MRI data by combining multi-view information for MCI classification. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.03.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
16
|
Feng J, Zhang SW, Chen L. Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier. Artif Intell Med 2020; 108:101940. [DOI: 10.1016/j.artmed.2020.101940] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/01/2020] [Accepted: 08/07/2020] [Indexed: 02/07/2023]
|
17
|
Takamatsu Y, Ho G, Waragai M, Wada R, Sugama S, Takenouchi T, Masliah E, Hashimoto M. Transgenerational Interaction of Alzheimer's Disease with Schizophrenia through Amyloid Evolvability. J Alzheimers Dis 2020; 68:473-481. [PMID: 30741673 PMCID: PMC6484278 DOI: 10.3233/jad-180986] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Alzheimer's disease (AD), the most common neurodegenerative dementia, leads to memory dysfunction due to widespread neuronal loss associated with aggregation of amyloidogenic proteins (APs), while schizophrenia (SCZ) represents a major psychiatric disorder characterized by delusions, hallucinations, and other cognitive abnormalities, the underlying mechanisms of which remain obscure. Although AD and SCZ partially overlap in terms of psychiatric symptoms and some aspects of cognitive impairment, the causal relationship between AD and SCZ is unclear. Based on the similarity of APs with yeast prion in terms of stress-induced protein aggregation, we recently proposed that evolvability of APs might be an epigenetic phenomenon to transmit stress information of parental brain to cope with the stressors in offspring. Although amyloid evolvability may be beneficial in evolution, AD might be manifested during parental aging as the mechanism of antagonistic pleiotropy phenomenon. Provided that accumulating evidence implicates stress as an important factor in SCZ, the main objective of this paper is to better understand the possible connection of AD and SCZ through amyloid evolvability. Hypothetically, the delivery of information of stress by APs may be less efficient under the decreased evolvability conditions such as disease-modifying treatment, leading to SCZ in offspring. Conversely, the increased evolvability conditions including gene mutations of APs are supposed to be beneficial for offspring, but might lead to AD in parents. Collectively, AD and SCZ might transgenerationally interfere with each other through amyloid evolvability, and this could explain why both AD and SCZ have not been selected out through evolution.
Collapse
Affiliation(s)
- Yoshiki Takamatsu
- Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan
| | - Gilbert Ho
- PCND Neuroscience Research Institute, Poway, CA, USA
| | - Masaaki Waragai
- Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan
| | - Ryoko Wada
- Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan
| | - Shuei Sugama
- Department of Physiology, Nippon Medical School, Tokyo, Japan
| | - Takato Takenouchi
- Institute of Agrobiological Sciences, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Eliezer Masliah
- Division of Neurosciences, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Makoto Hashimoto
- Tokyo Metropolitan Institute of Medical Science, Setagaya-ku, Tokyo, Japan
| |
Collapse
|
18
|
Xu W, Sun FR, Tan CC, Tan L. Weight Loss is a Preclinical Signal of Cerebral Amyloid Deposition and Could Predict Cognitive Impairment in Elderly Adults. J Alzheimers Dis 2020; 77:449-456. [PMID: 32675417 DOI: 10.3233/jad-200524] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Higher late-life body mass index (BMI) was associated with reduced risk of Alzheimer's disease (AD), which might be explained by a reverse causal relationship. OBJECTIVE To investigate whether weight loss was a preclinical manifestation of AD pathologies and could be a predictor of cognitive impairment. METHODS A total of 1,194 participants (mean age = 73.2 [range: 54 to 91] years, female = 44.5%) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were grouped according to AD biomarker profile as indicated by amyloid (A) and tau (TN) status and clinical stage by clinical dementia rating (CDR). BMI across the biomarker-defined clinical stages was compared with Bonferroni correction. Pearson correlation analysis was performed to test the relationship between the amyloid change by PET and the BMI change. Multiple regression models were used to explore the influences of amyloid pathologies on BMI change as well as the effects of weight loss on longitudinal changes of global cognitive function. RESULTS BMI was significantly decreased in AD preclinical stage (amyloid positive [A+] and CDR = 0) and dementia stage (A+/TN+ and CDR = 0.5 or 1), compared with the healthy controls (A-/TN-and CDR = 0, p < 0.005), while no significant differences were observed between preclinical AD and AD dementia. Amyloid PET change was inversely correlated with BMI change (p = 0.023, β= -14). Individuals in amyloid positive group exhibited faster weight loss (time×group interaction p = 0.019, β= -0.20) compared to the amyloid negative group. Greater weight loss predicted higher risk of developing cognitive disorders. CONCLUSION Elders who experienced greater weight loss might belong to preclinical stage of AD and could be targeted for primary prevention of the disease.
Collapse
Affiliation(s)
- Wei Xu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Fu-Rong Sun
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Chen-Chen Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | | |
Collapse
|
19
|
Cao P, Gao J, Zhang Z. Multi-View Based Multi-Model Learning for MCI Diagnosis. Brain Sci 2020; 10:brainsci10030181. [PMID: 32244855 PMCID: PMC7139974 DOI: 10.3390/brainsci10030181] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 03/16/2020] [Indexed: 12/26/2022] Open
Abstract
Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).
Collapse
|
20
|
Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset. Sci Rep 2019; 9:16742. [PMID: 31727919 PMCID: PMC6856351 DOI: 10.1038/s41598-019-52966-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 10/22/2019] [Indexed: 11/23/2022] Open
Abstract
White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease, that is increasingly studied with large, pooled multicenter datasets. This data pooling increases statistical power, but poses challenges for automated WMH segmentation. Although there is extensive literature on the evaluation of automated WMH segmentation methods, such evaluations in a multicenter setting are lacking. We performed WMH segmentations in sixty patients scanned on six different magnetic resonance imaging (MRI) scanners (10 patients per scanner) using five freely available and fully-automated WMH segmentation methods (Cascade, kNN-TTP, Lesion-TOADS, LST-LGA and LST-LPA). Different MRI scanner vendors and field strengths were included. We compared these automated WMH segmentations with manual WMH segmentations as a reference. Performance of each method both within and across scanners was assessed using spatial and volumetric correspondence with the reference segmentations by Dice’s similarity coefficient (DSC) and intra-class correlation coefficient (ICC) respectively. We found the best performance, both within and across scanners, for kNN-TTP, followed by LST-LPA and LST-LGA, with worse performance for Lesion-TOADS and Cascade. Our findings can serve as a guide for choosing a method and also highlight the importance to further improve and evaluate consistency of methods in a multicenter setting.
Collapse
|
21
|
Han Q, Sun YA, Zong Y, Chen C, Wang HF, Tan L. Common Variants in PLXNA4 and Correlation to CSF-related Phenotypes in Alzheimer's Disease. Front Neurosci 2018; 12:946. [PMID: 30618575 PMCID: PMC6305543 DOI: 10.3389/fnins.2018.00946] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 11/29/2018] [Indexed: 01/21/2023] Open
Abstract
The Plexin-A 4 (PLXNA4) gene, has recently been identified in genome wide association studies (GWAS), as a novel genetic player associated with Alzheimer's disease (AD). Additionally, PLXNA4 genetic variations were also found to increase AD risk by tau pathology in vitro. However, the potential roles of PLXNA4 variants in the amyloid-β (Aβ) pathology, were not evaluated. Five targeted loci capturing the top common variations in PLXNA4, were extracted using tagger methods. Multiple linear regression models were used to explore whether these variations can affect the cerebrospinal fluid (CSF) (Aβ1−42, T-tau, and P-tau) phenotypes in the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset. We detected that two loci (rs6467431, rs67468325) were significantly associated with CSF Aβ1−42 levels in the hybrid population (rs6467431: P = 0.01376, rs67468325: P = 0.006536) and the significance remained after false discovery rate (FDR) correction (rs6467431: Pc = 0.03441, rs67468325: Pc = 0.03268). In the subgroup analysis, we further confirmed the association of rs6467431 in the cognitively normal (CN) subgroup (P = 0.01904, Pc = 0.04761). Furthermore, rs6467431-A carriers and rs67468325-G carriers showed higher CSF Aβ1−42 levels than non-carriers. Nevertheless, we did not detect any significant relationships between the levels of T-tau, P-tau and these PLXNA4 loci. Our findings provided preliminary evidence that PLXNA4 variants can confer AD risk through modulating the Aβ deposition.
Collapse
Affiliation(s)
- Qiu Han
- Department of Neurology, Qingdao Clinical Medical School, Qingdao Municipal Hospital, Nanjing Medical University, Qingdao, China.,Department of Neurology, The Affiliated Huaian Hosipital of Xuzhou Medical University, Huai'an, China
| | - Yong-An Sun
- Department of Neurology, Qingdao Clinical Medical School, Qingdao Municipal Hospital, Nanjing Medical University, Qingdao, China.,Department of Neurology, First Affiliated Hospital of Kangda School, Nanjing Medical University, Lianyungang, China
| | - Yu Zong
- Department of Neurology, School of Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Chun Chen
- Department of Neurology, Qingdao Clinical Medical School, Qingdao Municipal Hospital, Nanjing Medical University, Qingdao, China.,Department of Neurology, Hongze Huai'an District People's Hospital, Huai'an, China
| | - Hui-Fu Wang
- Department of Neurology, School of Medicine, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Clinical Medical School, Qingdao Municipal Hospital, Nanjing Medical University, Qingdao, China
| | | |
Collapse
|
22
|
Steinbach R, Gaur N, Stubendorff B, Witte OW, Grosskreutz J. Developing a Neuroimaging Biomarker for Amyotrophic Lateral Sclerosis: Multi-Center Data Sharing and the Road to a "Global Cohort". Front Neurol 2018; 9:1055. [PMID: 30564187 PMCID: PMC6288231 DOI: 10.3389/fneur.2018.01055] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 11/20/2018] [Indexed: 12/11/2022] Open
Abstract
Neuroimaging in Amyotrophic Lateral Sclerosis (ALS) has steadily evolved from an academic exercise to a powerful clinical tool for detecting and following pathological change. Nevertheless, significant challenges need to be addressed for the translation of neuroimaging as a robust outcome-metric and biomarker in quality-of-care assessments and pharmaceutical trials. Studies have been limited by small sample sizes, poor replication, incomplete patient characterization, and substantial differences in data collection and processing. This has been further exacerbated by the substantial heterogeneity associated with ALS. Multi-center transnational collaborations are needed to address these methodological limitations and achieve representation of rare phenotypes. This review will use the example of the Neuroimaging Society in ALS (NiSALS) to discuss the set-up of a multi-center data sharing ecosystem and the flow of information between various stakeholders. NiSALS' founding objective was to establish best practices for the acquisition and processing of MRI data and establish a structure that allows continuous data sharing and therefore augments the ability to fully describe patients. The practical challenges associated with such a system, including quality control, legal, ethical, and logistical constraints, will be discussed, as will be recommendations for future collaborative endeavors. We posit that “global cohorts” of well-characterized sub-populations within the disease spectrum are needed to fully understand the complex interplay between neuroimaging and other clinical metrics used to study ALS.
Collapse
Affiliation(s)
- Robert Steinbach
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | - Nayana Gaur
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | | | - Otto W Witte
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | - Julian Grosskreutz
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
| |
Collapse
|
23
|
Liu J, Wang J, Tang Z, Hu B, Wu FX, Pan Y. Improving Alzheimer's Disease Classification by Combining Multiple Measures. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1649-1659. [PMID: 28749356 DOI: 10.1109/tcbb.2017.2731849] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Several anatomical magnetic resonance imaging (MRI) markers for Alzheimer's disease (AD) have been identified. Cortical gray matter volume, cortical thickness, and subcortical volume have been used successfully to assist the diagnosis of Alzheimer's disease including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Currently, these anatomical MRI measures have mainly been used separately. Thus, the full potential of anatomical MRI scans for AD diagnosis might not yet have been used optimally. Meanwhile, most studies currently only focused on morphological features of regions of interest (ROIs) or interregional features without considering the combination of them. To further improve the diagnosis of AD, we propose a novel approach of extracting ROI features and interregional features based on multiple measures from MRI images to distinguish AD, MCI (including MCIc and MCInc), and health control (HC). First, we construct six individual networks based on six different anatomical measures (i.e., CGMV, CT, CSA, CC, CFI, and SV) and Automated Anatomical Labeling (AAL) atlas for each subject. Then, for each individual network, we extract all node (ROI) features and edge (interregional) features, and denoted as node feature set and edge feature set, respectively. Therefore, we can obtain six node feature sets and six edge feature sets from six different anatomical measures. Next, each feature within a feature set is ranked by -score in descending order, and the top ranked features of each feature set are applied to MKBoost algorithm to obtain the best classification accuracy. After obtaining the best classification accuracy, we can get the optimal feature subset and the corresponding classifier for each node or edge feature set. Afterwards, to investigate the classification performance with only node features, we proposed a weighted multiple kernel learning (wMKL) framework to combine these six optimal node feature subsets, and obtain a combined classifier to perform AD classification. Similarly, we can obtain the classification performance with only edge features. Finally, we combine both six optimal node feature subsets and six optimal edge feature subsets to further improve the classification performance. Experimental results show that the proposed method outperforms some state-of-the-art methods in AD classification, and demonstrate that different measures contain complementary information.
Collapse
|
24
|
Validation of prognostic biomarker scores for predicting progression of dementia in patients with amnestic mild cognitive impairment. Nucl Med Commun 2018; 39:297-303. [PMID: 29419659 PMCID: PMC5882247 DOI: 10.1097/mnm.0000000000000812] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Objective The objective of this study was to develop and validate a practical computerized prognostic model that uses baseline psychometric and imaging data, including results of PET imaging of amyloid deposition, to predict the progression to dementia in patients at risk for Alzheimer’s disease (AD). Patients and methods Data from patients in a phase II trial of [18F]flutemetamol for PET imaging of brain amyloid and from the Alzheimer’s Disease Neuroimaging Initiative were used to train the prognostic model to yield a disease state index (DSI), a measure of the similarity of an individual patient’s data to data from patients in specific diagnostic groups. Inputs to the model included amyloid PET results, MRI measurements of hippocampal volume, and the results of psychometric tests. The model was subsequently validated by using data from a prospective study of an independent cohort of patients with mild cognitive impairment. Results In total, data from 223 patients of the 233 enroled were suitable for analysis. The DSI predicted by the model and the risk of progression to AD dementia within 3 years were higher for patients with amyloid deposition and neurodegeneration than for patients with amyloid deposition without neurodegeneration. Rates of non-AD dementia among patients with neurodegeneration at baseline were consistent with the results of other studies. The results were consistent with the Jack model of AD progression. Conclusion The DSI from the model that included psychometric, MRI, and PET amyloid data provides useful prognostic information in cases of mild cognitive impairment.
Collapse
|
25
|
Liu J, Li M, Lan W, Wu FX, Pan Y, Wang J. Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:624-632. [PMID: 28114031 DOI: 10.1109/tcbb.2016.2635144] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Regions of interest (ROIs) based classification has been widely investigated for analysis of brain magnetic resonance imaging (MRI) images to assist the diagnosis of Alzheimer's disease (AD) including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Since an ROI representation of brain structures is obtained either by pre-definition or by adaptive parcellation, the corresponding ROI in different brains can be measured. However, due to noise and small sample size of MRI images, representations generated from single or multiple ROIs may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and health controls (HC). In this paper, we employ a whole brain hierarchical network (WBHN) to represent each subject. The whole brain of each subject is divided into 90, 54, 14, and 1 regions based on Automated Anatomical Labeling (AAL) atlas. The connectivity between each pair of regions is computed in terms of Pearson's correlation coefficient and used as classification feature. Then, to reduce the dimensionality of features, we select the features with higher scores. Finally, we use multiple kernel boosting (MKBoost) algorithm to perform the classification. Our proposed method is evaluated on MRI images of 710 subjects (200 AD, 120 MCIc, 160 MCInc, and 230 HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed method achieves an accuracy of 94.65 percent and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.954 for AD/HC classification, an accuracy of 89.63 percent and an AUC of 0.907 for AD/MCI classification, an accuracy of 85.79 percent and an AUC of 0.826 for MCI/HC classification, and an accuracy of 72.08 percent and an AUC of 0.716 for MCIc/MCInc classification, respectively. Our results demonstrate that our proposed method is efficient and promising for clinical applications for the diagnosis of AD via MRI images.
Collapse
|
26
|
Baldacci F, Lista S, O'Bryant SE, Ceravolo R, Toschi N, Hampel H. Blood-Based Biomarker Screening with Agnostic Biological Definitions for an Accurate Diagnosis Within the Dimensional Spectrum of Neurodegenerative Diseases. Methods Mol Biol 2018; 1750:139-155. [PMID: 29512070 DOI: 10.1007/978-1-4939-7704-8_9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The discovery, development, and validation of novel candidate biomarkers in Alzheimer's disease (AD) and other neurodegenerative diseases (NDs) are increasingly gaining momentum. As a result, evolving diagnostic research criteria of NDs are beginning to integrate biofluid and neuroimaging indicators of pathophysiological mechanisms. More than 10% of people aged over 65 suffer from NDs. There is an urgent need for a refined two-stage diagnostic model to first initiate an early, sensitive, and noninvasive process in primary care settings. Individuals that meet detection criteria will then be channeled to more specific, costly (positron-emission tomography), and invasive (cerebrospinal fluid) assessment methods for confirmatory biological characterization and diagnosis.A reliable and sensitive blood test for AD and other NDs is not yet established; however, it would provide the golden screening gate for an efficient primary care management. A limitation to the development of a large-scale blood-screening biomarker-based test is the traditional application of clinically descriptive criteria for the categorization of single late-stage ND constructs. These are genetically and biologically heterogeneous, reflected in multiple pathophysiological mechanisms and subsequent pathologies throughout a dimensional continuum. Evidence suggests that a shared, "open-source" integrated multilevel categorization of NDs that clusters individuals based on descriptive clinical phenotypes and pathophysiological biomarker signatures will provide the next incremental step toward an improved diagnostic process of NDs. This intermediate objective toward unbiased biomarker-guided early detection of individuals at risk for NDs is currently carried out by the international pilot Alzheimer Precision Medicine Initiative Cohort Program (APMI-CP).
Collapse
Affiliation(s)
- Filippo Baldacci
- AXA Research Fund & UPMC Chair, F-75013, Paris, France.,Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France.,Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France.,Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France.,Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Simone Lista
- AXA Research Fund & UPMC Chair, F-75013, Paris, France. .,Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France. .,Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France. .,Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France.
| | - Sid E O'Bryant
- Institute for Healthy Aging, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Roberto Ceravolo
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy.,Department of Radiology"Athinoula A. Martinos", Center for Biomedical Imaging, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Harald Hampel
- AXA Research Fund & UPMC Chair, F-75013, Paris, France.,Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France.,Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France.,Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France
| | | |
Collapse
|
27
|
de Vrueh RLA, Crommelin DJA. Reflections on the Future of Pharmaceutical Public-Private Partnerships: From Input to Impact. Pharm Res 2017; 34:1985-1999. [PMID: 28589444 PMCID: PMC5579142 DOI: 10.1007/s11095-017-2192-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 05/23/2017] [Indexed: 01/08/2023]
Abstract
Public Private Partnerships (PPPs) are multiple stakeholder partnerships designed to improve research efficacy. We focus on PPPs in the biomedical/pharmaceutical field, which emerged as a logical result of the open innovation model. Originally, a typical PPP was based on an academic and an industrial pillar, with governmental or other third party funding as an incentive. Over time, other players joined in, often health foundations, patient organizations, and regulatory scientists. This review discusses reasons for initiating a PPP, focusing on precompetitive research. It looks at typical expectations and challenges when starting such an endeavor, the characteristics of PPPs, and approaches to assessing the success of the concept. Finally, four case studies are presented, of PPPs differing in size, geographical spread, and research focus.
Collapse
Affiliation(s)
| | - Daan J A Crommelin
- Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, UIPS, Utrecht University, Utrecht, The Netherlands.
| |
Collapse
|
28
|
Liu J, Wang J, Hu B, Wu FX, Pan Y. Alzheimer’s Disease Classification Based on Individual Hierarchical Networks Constructed With 3-D Texture Features. IEEE Trans Nanobioscience 2017; 16:428-437. [DOI: 10.1109/tnb.2017.2707139] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
29
|
Edwards JD, Ramirez J, Callahan BL, Tobe SW, Oh P, Berezuk C, Lanctôt K, Swardfager W, Nestor S, Kiss A, Strother S, Black SE. Antihypertensive Treatment is associated with MRI-Derived Markers of Neurodegeneration and Impaired Cognition: A Propensity-Weighted Cohort Study. J Alzheimers Dis 2017; 59:1113-1122. [DOI: 10.3233/jad-170238] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jodi D. Edwards
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute>, University of Toronto, Toronto, Canada
- Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Site, Toronto, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute>, University of Toronto, Toronto, Canada
- Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Site, Toronto, Canada
| | - Brandy L. Callahan
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute>, University of Toronto, Toronto, Canada
| | | | - Paul Oh
- Toronto Rehabilitation Institute, Toronto, Canada
| | - Courtney Berezuk
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute>, University of Toronto, Toronto, Canada
- Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Site, Toronto, Canada
| | - Krista Lanctôt
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute>, University of Toronto, Toronto, Canada
- Geriatric Psychiatry, University of Toronto, Toronto, Canada
- Pharmacology and Toxicology, University of Toronto, Toronto, Canada
| | - Walter Swardfager
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute>, University of Toronto, Toronto, Canada
- Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Site, Toronto, Canada
- Pharmacology and Toxicology, University of Toronto, Toronto, Canada
| | - Sean Nestor
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Alexander Kiss
- Institute for Clinical Evaluative Sciences, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Stephen Strother
- Medical Biophysics, University of Toronto, Toronto, Canada
- Rotman Research Institute, Toronto, Canada
| | - Sandra E. Black
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute>, University of Toronto, Toronto, Canada
- Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Site, Toronto, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences and University of Toronto, Toronto, Canada
| | | |
Collapse
|
30
|
Son SJ, Kim J, Park H. Structural and functional connectional fingerprints in mild cognitive impairment and Alzheimer's disease patients. PLoS One 2017; 12:e0173426. [PMID: 28333946 PMCID: PMC5363868 DOI: 10.1371/journal.pone.0173426] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 02/19/2017] [Indexed: 02/04/2023] Open
Abstract
Regional volume atrophy and functional degeneration are key imaging hallmarks of Alzheimer's disease (AD) in structural and functional magnetic resonance imaging (MRI), respectively. We jointly explored regional volume atrophy and functional connectivity to better characterize neuroimaging data of AD and mild cognitive impairment (MCI). All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compared regional volume atrophy and functional connectivity in 10 subcortical regions using structural MRI and resting-state functional MRI (rs-fMRI). Neuroimaging data of normal controls (NC) (n = 35), MCI (n = 40), and AD (n = 30) were compared. Significant differences of regional volumes and functional connectivity measures between groups were assessed using permutation tests in 10 regions. The regional volume atrophy and functional connectivity of identified regions were used as features for the random forest classifier to distinguish among three groups. The features of the identified regions were also regarded as connectional fingerprints that could distinctively separate a given group from the others. We identified a few regions with distinctive regional atrophy and functional connectivity patterns for NC, MCI, and AD groups. A three label classifier using the information of regional volume atrophy and functional connectivity of identified regions achieved classification accuracy of 53.33% to distinguish among NC, MCI, and AD. We identified distinctive regional atrophy and functional connectivity patterns that could be regarded as a connectional fingerprint.
Collapse
Affiliation(s)
- Seong-Jin Son
- Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
| | - Jonghoon Kim
- Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
| |
Collapse
|
31
|
Sekiyama K, Takamatsu Y, Koike W, Waragai M, Takenouchi T, Sugama S, Hashimoto M. Insight into the Dissociation of Behavior from Histology in Synucleinopathies and in Related Neurodegenerative Diseases. J Alzheimers Dis 2017; 52:831-41. [PMID: 27031478 DOI: 10.3233/jad-151015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Recent clinical trials using immunization approaches against Alzheimer's disease (AD) have failed to demonstrate improved cognitive functions in patients, despite potent suppression in the formation of both senile plaques and other amyloid-β deposits in postmortem brains. Similarly, we observed that treatment with ibuprofen, a non-steroidal anti-inflammatory drug, was effective in improving the histopathology, such as reducing both protein aggregation and glial activation, in the brains of transgenic mice expressing dementia with Lewy bodies-linked P123H β-synuclein. In contrast, only a small improvement in cognitive functions was observed in these mice. Collectively, it is predicted that histology does not correlate with behavior that is resilient and resistant to therapeutic stimuli. Notably, such a 'discrepancy between histology and behavior' is reminiscent of AD-like pathologies and incidental Lewy bodies, which are frequently encountered in postmortem brains of the elderly who had been asymptomatic for memory loss and Parkinsonism during their lives. We suggest that 'the discrepancy between histology and behavior' may be a universal feature that is associated with various aspects of neurodegenerative diseases. Furthermore, given that the cognitive reserve is specifically observed in human brains, human behavior may be evolutionally distinct from that in other animals, thus, contributing to the differential efficiency of therapy between human and lower animals, an important issue in the therapy of neurodegenerative diseases. Overall, it is important to better understand 'the discrepancy between histology and behavior' in the mechanism of neurodegeneration for the development of effective therapies against neurodegenerative diseases.
Collapse
Affiliation(s)
- Kazunari Sekiyama
- Tokyo Metropolitan Institute of Medical Sciences, Setagaya-ku, Tokyo, Japan
| | - Yoshiki Takamatsu
- Tokyo Metropolitan Institute of Medical Sciences, Setagaya-ku, Tokyo, Japan
| | - Wakako Koike
- Tokyo Metropolitan Institute of Medical Sciences, Setagaya-ku, Tokyo, Japan
| | - Masaaki Waragai
- Tokyo Metropolitan Institute of Medical Sciences, Setagaya-ku, Tokyo, Japan
| | - Takato Takenouchi
- Division of Animal Sciences, National Institute of Agrobiological Sciences, Tsukuba, Ibaraki, Japan
| | - Shuei Sugama
- Department of Physiology, Nippon Medical School, Tokyo, Japan
| | - Makoto Hashimoto
- Tokyo Metropolitan Institute of Medical Sciences, Setagaya-ku, Tokyo, Japan
| |
Collapse
|
32
|
Sachdev PS, Lo JW, Crawford JD, Mellon L, Hickey A, Williams D, Bordet R, Mendyk AM, Gelé P, Deplanque D, Bae HJ, Lim JS, Brodtmann A, Werden E, Cumming T, Köhler S, Verhey FRJ, Dong YH, Tan HH, Chen C, Xin X, Kalaria RN, Allan LM, Akinyemi RO, Ogunniyi A, Klimkowicz-Mrowiec A, Dichgans M, Wollenweber FA, Zietemann V, Hoffmann M, Desmond DW, Linden T, Blomstrand C, Fagerberg B, Skoog I, Godefroy O, Barbay M, Roussel M, Lee BC, Yu KH, Wardlaw J, Makin SJ, Doubal FN, Chappell FM, Srikanth VK, Thrift AG, Donnan GA, Kandiah N, Chander RJ, Lin X, Cordonnier C, Moulin S, Rossi C, Sabayan B, Stott DJ, Jukema JW, Melkas S, Jokinen H, Erkinjuntti T, Mok VCT, Wong A, Lam BYK, Leys D, Hénon H, Bombois S, Lipnicki DM, Kochan NA. STROKOG (stroke and cognition consortium): An international consortium to examine the epidemiology, diagnosis, and treatment of neurocognitive disorders in relation to cerebrovascular disease. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2016; 7:11-23. [PMID: 28138511 PMCID: PMC5257024 DOI: 10.1016/j.dadm.2016.10.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
INTRODUCTION The Stroke and Cognition consortium (STROKOG) aims to facilitate a better understanding of the determinants of vascular contributions to cognitive disorders and help improve the diagnosis and treatment of vascular cognitive disorders (VCD). METHODS Longitudinal studies with ≥75 participants who had suffered or were at risk of stroke or TIA and which evaluated cognitive function were invited to join STROKOG. The consortium will facilitate projects investigating rates and patterns of cognitive decline, risk factors for VCD, and biomarkers of vascular dementia. RESULTS Currently, STROKOG includes 25 (21 published) studies, with 12,092 participants from five continents. The duration of follow-up ranges from 3 months to 21 years. DISCUSSION Although data harmonization will be a key challenge, STROKOG is in a unique position to reuse and combine international cohort data and fully explore patient level characteristics and outcomes. STROKOG could potentially transform our understanding of VCD and have a worldwide impact on promoting better vascular cognitive outcomes.
Collapse
Affiliation(s)
- Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, Australia; Dementia Collaborative Research Centre, University of New South Wales, Sydney, Australia
| | - Jessica W Lo
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, Australia
| | - John D Crawford
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, Australia
| | - Lisa Mellon
- Department of Psychology, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Anne Hickey
- Department of Psychology, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - David Williams
- Department of Stroke and Geriatric Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Régis Bordet
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Anne-Marie Mendyk
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Patrick Gelé
- University of Lille, Inserm, CHU Lille, CIC 1403 - Centre d'investigation clinique, Lille, France
| | - Dominique Deplanque
- University of Lille, Inserm, CHU Lille, CIC 1403 - Centre d'investigation clinique, Lille, France
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea; Clinical Neuroscience Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jae-Sung Lim
- Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - Amy Brodtmann
- Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Emilio Werden
- Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Toby Cumming
- Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Sebastian Köhler
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Frans R J Verhey
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Yan-Hong Dong
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, Australia; Dementia Collaborative Research Centre, University of New South Wales, Sydney, Australia; Memory Ageing and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Medicine (Neurology Division), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Hui Hui Tan
- Memory Ageing and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Medicine (Neurology Division), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Christopher Chen
- Memory Ageing and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Xu Xin
- Memory Ageing and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Raj N Kalaria
- Neurovascular Research Group, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Louise M Allan
- Neurovascular Research Group, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Rufus O Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Adesola Ogunniyi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training College of Medicine, University of Ibadan, Ibadan, Nigeria; Department of Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | | | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Frank A Wollenweber
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University, Munich, Germany
| | - Vera Zietemann
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-University, Munich, Germany
| | - Michael Hoffmann
- Cognitive Neurology and Stroke Programs, University of Central Florida, Orlando VA Medical Center, Orlando, Florida, USA
| | | | - Thomas Linden
- Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia; Institute of Neuroscience and Physiology, Centre of Brain Research and Rehabilitation, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Christian Blomstrand
- Institute of Neuroscience and Physiology, Centre of Brain Research and Rehabilitation, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Björn Fagerberg
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory for Cardiovascular and Metabolic Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Ingmar Skoog
- Institute of Neuroscience and Physiology, Center for Health and Ageing AGECAP, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Olivier Godefroy
- Department of Neurology and Laboratory of Functional Neurosciences, University Hospital of Amiens, France
| | - Mélanie Barbay
- Department of Neurology and Laboratory of Functional Neurosciences, University Hospital of Amiens, France
| | - Martine Roussel
- Department of Neurology and Laboratory of Functional Neurosciences, University Hospital of Amiens, France
| | - Byung-Chul Lee
- Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - Kyung-Ho Yu
- Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Stephen J Makin
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow Royal Infirmary, Glasgow, UK
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Velandai K Srikanth
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, Australia; Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - Amanda G Thrift
- Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea; Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, Australia
| | - Geoffrey A Donnan
- Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | | | | | - Xuling Lin
- Department of Neurology, National Neuroscience Institute, Singapore
| | - Charlotte Cordonnier
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Solene Moulin
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Costanza Rossi
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Behnam Sabayan
- Department of Gerontology and Geriatrics, Leiden University Medical Centre, Leiden, the Netherlands
| | - David J Stott
- Academic Section of Geriatrics, University of Glasgow, Glasgow, United Kingdom
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Susanna Melkas
- Clinical Neurosciences, Neurology, University of Helsinki and Helsinki University Hospital, Finland
| | - Hanna Jokinen
- Clinical Neurosciences, Neurology, University of Helsinki and Helsinki University Hospital, Finland
| | - Timo Erkinjuntti
- Clinical Neurosciences, Neurology, University of Helsinki and Helsinki University Hospital, Finland
| | - Vincent C T Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, Hong Kong SAR, China
| | - Adrian Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, Hong Kong SAR, China
| | - Bonnie Y K Lam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, Hong Kong SAR, China
| | - Didier Leys
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Hilde Hénon
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Stéphanie Bombois
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative & Vascular Cognitive Disorders, Lille, France
| | - Darren M Lipnicki
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, Australia
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, Australia
| | | |
Collapse
|
33
|
Robustness of Automated Methods for Brain Volume Measurements across Different MRI Field Strengths. PLoS One 2016; 11:e0165719. [PMID: 27798694 PMCID: PMC5087903 DOI: 10.1371/journal.pone.0165719] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/17/2016] [Indexed: 11/26/2022] Open
Abstract
Introduction Pooling of multicenter brain imaging data is a trend in studies on ageing related brain diseases. This poses challenges to MR-based brain segmentation. The performance across different field strengths of three widely used automated methods for brain volume measurements was assessed in the present study. Methods Ten subjects (mean age: 64 years) were scanned on 1.5T and 3T MRI on the same day. We determined robustness across field strength (i.e., whether measured volumes between 3T and 1.5T scans in the same subjects were similar) for SPM12, Freesurfer 5.3.0 and FSL 5.0.7. As a frame of reference, 3T MRI scans from 20 additional subjects (mean age: 71 years) were segmented manually to determine accuracy of the methods (i.e., whether measured volumes corresponded with expert-defined volumes). Results Total brain volume (TBV) measurements were robust across field strength for Freesurfer and FSL (mean absolute difference as % of mean volume ≤ 1%), but less so for SPM (4%). Gray matter (GM) and white matter (WM) volume measurements were robust for Freesurfer (1%; 2%) and FSL (2%; 3%) but less so for SPM (5%; 4%). For intracranial volume (ICV), SPM was more robust (2%) than FSL (3%) and Freesurfer (9%). TBV measurements were accurate for SPM and FSL, but less so for Freesurfer. For GM volume, SPM was accurate, but accuracy was lower for Freesurfer and FSL. For WM volume, Freesurfer was accurate, but SPM and FSL were less accurate. For ICV, FSL was accurate, while SPM and Freesurfer were less accurate. Conclusion Brain volumes and ICV could be measured quite robustly in scans acquired at different field strengths, but performance of the methods varied depending on the assessed compartment (e.g., TBV or ICV). Selection of an appropriate method in multicenter brain imaging studies therefore depends on the compartment of interest.
Collapse
|
34
|
Lista S, O'Bryant SE, Blennow K, Dubois B, Hugon J, Zetterberg H, Hampel H. Biomarkers in Sporadic and Familial Alzheimer's Disease. J Alzheimers Dis 2016; 47:291-317. [PMID: 26401553 DOI: 10.3233/jad-143006] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Most forms of Alzheimer's disease (AD) are sporadic (sAD) or inherited in a non-Mendelian fashion, and less than 1% of cases are autosomal-dominant. Forms of sAD do not exhibit familial aggregation and are characterized by complex genetic and environmental interactions. Recently, the expansion of genomic methodologies, in association with substantially larger combined cohorts, has resulted in various genome-wide association studies that have identified several novel genetic associations of AD. Currently, the most effective methods for establishing the diagnosis of AD are defined by multi-modal pathways, starting with clinical and neuropsychological assessment, cerebrospinal fluid (CSF) analysis, and brain-imaging procedures, all of which have significant cost- and access-to-care barriers. Consequently, research efforts have focused on the development and validation of non-invasive and generalizable blood-based biomarkers. Among the modalities conceptualized by the systems biology paradigm and utilized in the "exploratory biomarker discovery arena", proteome analysis has received the most attention. However, metabolomics, lipidomics, transcriptomics, and epigenomics have recently become key modalities in the search for AD biomarkers. Interestingly, biomarker changes for familial AD (fAD), in many but not all cases, seem similar to those for sAD. The integration of neurogenetics with systems biology/physiology-based strategies and high-throughput technologies for molecular profiling is expected to help identify the causes, mechanisms, and biomarkers associated with the various forms of AD. Moreover, in order to hypothesize the dynamic trajectories of biomarkers through disease stages and elucidate the mechanisms of biomarker alterations, updated and more sophisticated theoretical models have been proposed for both sAD and fAD.
Collapse
Affiliation(s)
- Simone Lista
- AXA Research Fund & UPMC Chair, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) & Institut du Cerveau et de la Moelle épinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpétrière, Paris, France
| | - Sid E O'Bryant
- Institute for Aging and Alzheimer's Disease Research & Department of Internal Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Bruno Dubois
- Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) & Institut du Cerveau et de la Moelle épinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpétrière, Paris, France
| | - Jacques Hugon
- Centre Mémoire de Ressources et de Recherche (CMRR) Paris Nord Ile-de-France, Groupe Hospitalier Saint Louis Lariboisière - Fernand Widal, Université Paris Diderot, Paris 07, Paris, France.,Institut du Fer à Moulin (IFM), Inserm UMR_S 839, Paris, France
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,University College London Institute of Neurology, Queen Square, London, UK
| | - Harald Hampel
- AXA Research Fund & UPMC Chair, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) & Institut du Cerveau et de la Moelle épinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpétrière, Paris, France
| |
Collapse
|
35
|
Rodríguez-Gómez O, Abdelnour C, Jessen F, Valero S, Boada M. Influence of Sampling and Recruitment Methods in Studies of Subjective Cognitive Decline. J Alzheimers Dis 2016; 48 Suppl 1:S99-S107. [PMID: 26402087 DOI: 10.3233/jad-150189] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Subjective cognitive decline (SCD) has been proposed as a marker of neurodegeneration in cognitively normal elderly. This idea is supported by the growing evidence that SCD is associated with Alzheimer's disease (AD) biomarkers and increases the risk of future cognitive impairment. Nevertheless, this evidence is not complete, since other studies have not found these associations. This discrepancy could have a methodological basis. It is well known that across the broad spectrum of degenerative disease from healthy controls to dementia, the research setting affects key characteristics of the sample such as age, educational level, or family history of dementia. However, virtually no studies have specifically tested the influence of sampling and recruitment methods in SCD research. Population-based samples are less biased and therefore they probably are more suitable for the study of memory complaints as a symptom at the population level. On the other hand, the memory clinic setting could introduce a set of biases that make these patients more likely to develop cognitive impairment. Thus, memory clinic would be the most cost-effective context in which to study the phenomenology of SCD due to AD and eventually recruit patients for secondary prevention trials. However, this general hypothesis needs to be tested. Studies that compare samples of patients with SCD from different settings are necessary. Sometimes it is difficult for patients with subtle forms of cognitive impairment to access specialized diagnostic centers. Based in our experience we state that Open House type initiatives may be useful for attracting these individuals to memory clinics.
Collapse
Affiliation(s)
| | - Carla Abdelnour
- Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain
| | - Frank Jessen
- Department of Psychiatry, Medical Faculty, University of Cologne, German Center for Neurodegenerative Diseases (DZNE), Cologne, Germany
| | - Sergi Valero
- Psychiatry Department, Hospital Universitari Vall d'Hebron, CIBERSAM, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Merçé Boada
- Fundació ACE, Institut Català de Neurociències Aplicades, Barcelona, Spain
| |
Collapse
|
36
|
Galluzzi S, Marizzoni M, Babiloni C, Albani D, Antelmi L, Bagnoli C, Bartres-Faz D, Cordone S, Didic M, Farotti L, Fiedler U, Forloni G, Girtler N, Hensch T, Jovicich J, Leeuwis A, Marra C, Molinuevo JL, Nobili F, Pariente J, Parnetti L, Payoux P, Del Percio C, Ranjeva JP, Rolandi E, Rossini PM, Schönknecht P, Soricelli A, Tsolaki M, Visser PJ, Wiltfang J, Richardson JC, Bordet R, Blin O, Frisoni GB. Clinical and biomarker profiling of prodromal Alzheimer's disease in workpackage 5 of the Innovative Medicines Initiative PharmaCog project: a 'European ADNI study'. J Intern Med 2016; 279:576-91. [PMID: 26940242 DOI: 10.1111/joim.12482] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND In the field of Alzheimer's disease (AD), the validation of biomarkers for early AD diagnosis and for use as a surrogate outcome in AD clinical trials is of considerable research interest. OBJECTIVE To characterize the clinical profile and genetic, neuroimaging and neurophysiological biomarkers of prodromal AD in amnestic mild cognitive impairment (aMCI) patients enrolled in the IMI WP5 PharmaCog (also referred to as the European ADNI study). METHODS A total of 147 aMCI patients were enrolled in 13 European memory clinics. Patients underwent clinical and neuropsychological evaluation, magnetic resonance imaging (MRI), electroencephalography (EEG) and lumbar puncture to assess the levels of amyloid β peptide 1-42 (Aβ42), tau and p-tau, and blood samples were collected. Genetic (APOE), neuroimaging (3T morphometry and diffusion MRI) and EEG (with resting-state and auditory oddball event-related potential (AO-ERP) paradigm) biomarkers were evaluated. RESULTS Prodromal AD was found in 55 aMCI patients defined by low Aβ42 in the cerebrospinal fluid (Aβ positive). Compared to the aMCI group with high Aβ42 levels (Aβ negative), Aβ positive patients showed poorer visual (P = 0.001), spatial recognition (P < 0.0005) and working (P = 0.024) memory, as well as a higher frequency of APOE4 (P < 0.0005), lower hippocampal volume (P = 0.04), reduced thickness of the parietal cortex (P < 0.009) and structural connectivity of the corpus callosum (P < 0.05), higher amplitude of delta rhythms at rest (P = 0.03) and lower amplitude of posterior cingulate sources of AO-ERP (P = 0.03). CONCLUSION These results suggest that, in aMCI patients, prodromal AD is characterized by a distinctive cognitive profile and genetic, neuroimaging and neurophysiological biomarkers. Longitudinal assessment will help to identify the role of these biomarkers in AD progression.
Collapse
Affiliation(s)
- S Galluzzi
- Laboratory of Alzheimer's Neuroimaging & Epidemiology, Saint John of God Clinical Research Centre, Brescia, Italy
| | - M Marizzoni
- Laboratory of Alzheimer's Neuroimaging & Epidemiology, Saint John of God Clinical Research Centre, Brescia, Italy
| | - C Babiloni
- Department of Physiology and Pharmacology, University of Rome 'La Sapienza', Rome, Italy.,IRCCS San Raffaele Pisana of Rome, Rome, Italy
| | - D Albani
- Department of Neuroscience, Mario Negri Institute for Pharmacological Research, Milan, Italy
| | - L Antelmi
- Laboratory of Alzheimer's Neuroimaging & Epidemiology, Saint John of God Clinical Research Centre, Brescia, Italy
| | - C Bagnoli
- Laboratory of Alzheimer's Neuroimaging & Epidemiology, Saint John of God Clinical Research Centre, Brescia, Italy
| | - D Bartres-Faz
- Department of Psychiatry and Clinical Psychobiology, Faculty of Medicine, University of Barcelona and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalunya, Spain
| | - S Cordone
- Department of Physiology and Pharmacology, University of Rome 'La Sapienza', Rome, Italy
| | - M Didic
- Aix-Marseille Université, INSERM, Marseille, France.,Service de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, France
| | - L Farotti
- Clinica Neurologica, Università di Perugia, Ospedale Santa Maria della Misericordia, Perugia, Italy
| | - U Fiedler
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, LVR-Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - G Forloni
- Department of Neuroscience, Mario Negri Institute for Pharmacological Research, Milan, Italy
| | - N Girtler
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology and Maternal-Fetal Medicine, University of Genoa, Genoa, Italy
| | - T Hensch
- Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany
| | - J Jovicich
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - A Leeuwis
- Department of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, the Netherlands
| | - C Marra
- Department of Gerontology, Neurosciences & Orthopedics, Catholic University, Rome, Italy
| | - J L Molinuevo
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic de Barcelona, and IDIBAPS, Barcelona, Catalunya, Spain
| | - F Nobili
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology and Maternal-Fetal Medicine, University of Genoa, Genoa, Italy
| | - J Pariente
- INSERM, Imagerie Cérébrale et Handicaps Neurologiques, Toulouse, France
| | - L Parnetti
- Clinica Neurologica, Università di Perugia, Ospedale Santa Maria della Misericordia, Perugia, Italy
| | - P Payoux
- INSERM, Imagerie Cérébrale et Handicaps Neurologiques, Toulouse, France
| | - C Del Percio
- SDN Istituto di Ricerca Diagnostica e Nucleare, Naples, Italy
| | - J-P Ranjeva
- Aix-Marseille Université, INSERM, Marseille, France.,Service de Neurologie et Neuropsychologie, APHM Hôpital Timone Adultes, Marseille, France
| | - E Rolandi
- Laboratory of Alzheimer's Neuroimaging & Epidemiology, Saint John of God Clinical Research Centre, Brescia, Italy
| | - P M Rossini
- Department of Gerontology, Neurosciences & Orthopedics, Catholic University, Rome, Italy
| | - P Schönknecht
- Department of Psychiatry and Psychotherapy, University of Leipzig, Leipzig, Germany
| | - A Soricelli
- SDN Istituto di Ricerca Diagnostica e Nucleare, Naples, Italy
| | - M Tsolaki
- Third Neurologic Clinic, Medical School, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - P J Visser
- Department of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, the Netherlands
| | - J Wiltfang
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, LVR-Hospital Essen, University of Duisburg-Essen, Essen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August-University, Goettingen, Germany
| | - J C Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, UK
| | - R Bordet
- University of Lille, Inserm, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - O Blin
- Mediterranean Institute of Cognitive Neurosciences, Aix Marseille University, Marseille, France
| | - G B Frisoni
- Laboratory of Alzheimer's Neuroimaging & Epidemiology, Saint John of God Clinical Research Centre, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | | |
Collapse
|
37
|
Clinical validity of CSF biomarkers for Alzheimer's disease: necessary indeed, but sufficient? Lancet Neurol 2016; 15:650-651. [DOI: 10.1016/s1474-4422(16)30040-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 04/13/2016] [Indexed: 11/17/2022]
|
38
|
The impact of PICALM genetic variations on reserve capacity of posterior cingulate in AD continuum. Sci Rep 2016; 6:24480. [PMID: 27117083 PMCID: PMC4846810 DOI: 10.1038/srep24480] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 03/24/2016] [Indexed: 11/25/2022] Open
Abstract
Phosphatidylinositolbinding clathrin assembly protein (PICALM) gene is one novel genetic player associated with late-onset Alzheimer’s disease (LOAD), based on recent genome wide association studies (GWAS). However, how it affects AD occurrence is still unknown. Brain reserve hypothesis highlights the tolerant capacities of brain as a passive means to fight against neurodegenerations. Here, we took the baseline volume and/or thickness of LOAD-associated brain regions as proxies of brain reserve capacities and investigated whether PICALM genetic variations can influence the baseline reserve capacities and the longitudinal atrophy rate of these specific regions using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In mixed population, we found that brain region significantly affected by PICALM genetic variations was majorly restricted to posterior cingulate. In sub-population analysis, we found that one PICALM variation (C allele of rs642949) was associated with larger baseline thickness of posterior cingulate in health. We found seven variations in health and two variations (rs543293 and rs592297) in individuals with mild cognitive impairment were associated with slower atrophy rate of posterior cingulate. Our study provided preliminary evidences supporting that PICALM variations render protections by facilitating reserve capacities of posterior cingulate in non-demented elderly.
Collapse
|
39
|
Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Donohue MC, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw L, Thompson PM, Toga AW, Trojanowski JQ. Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014. Alzheimers Dement 2016; 11:865-84. [PMID: 26194320 DOI: 10.1016/j.jalz.2015.04.005] [Citation(s) in RCA: 140] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 03/04/2015] [Accepted: 04/23/2015] [Indexed: 01/18/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. METHODS We searched for ADNI publications using established methods. RESULTS ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. DISCUSSION ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials.
Collapse
Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California- San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, CA, USA
| | - Nigel J Cairns
- Department of Neurology, Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Michael C Donohue
- Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute and the School of Public Health, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Andrew J Saykin
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Marina Del Rey, CA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | |
Collapse
|
40
|
Hopkins C, Sydes M, Murray G, Woolfall K, Clarke M, Williamson P, Tudur Smith C. UK publicly funded Clinical Trials Units supported a controlled access approach to share individual participant data but highlighted concerns. J Clin Epidemiol 2016; 70:17-25. [PMID: 26169841 PMCID: PMC4742521 DOI: 10.1016/j.jclinepi.2015.07.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Revised: 05/22/2015] [Accepted: 07/06/2015] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Evaluate current data sharing activities of UK publicly funded Clinical Trial Units (CTUs) and identify good practices and barriers. STUDY DESIGN AND SETTING Web-based survey of Directors of 45 UK Clinical Research Collaboration (UKCRC)-registered CTUs. RESULTS Twenty-three (51%) CTUs responded: Five (22%) of these had an established data sharing policy and eight (35%) specifically requested consent to use patient data beyond the scope of the original trial. Fifteen (65%) CTUs had received requests for data, and seven (30%) had made external requests for data in the previous 12 months. CTUs supported the need for increased data sharing activities although concerns were raised about patient identification, misuse of data, and financial burden. Custodianship of clinical trial data and requirements for a CTU to align its policy to their parent institutes were also raised. No CTUs supported the use of an open access model for data sharing. CONCLUSION There is support within the publicly funded UKCRC-registered CTUs for data sharing, but many perceived barriers remain. CTUs are currently using a variety of approaches and procedures for sharing data. This survey has informed further work, including development of guidance for publicly funded CTUs, to promote good practice and facilitate data sharing.
Collapse
Affiliation(s)
- Carolyn Hopkins
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
| | - Matthew Sydes
- MRC Clinical Trials Unit, University College London, Aviation House, 125 Kingsway, London, WC2B 6NH, UK
| | - Gordon Murray
- Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK
| | - Kerry Woolfall
- MRC North West Hub for Trials Methodology Research, Department of Psychological Sciences, Block B Waterhouse Building, Brownlow Street, Liverpool L69 3GL, UK
| | - Mike Clarke
- All-Ireland Hub for Trials Methodology Research, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Health Sciences Building, 97 Lisburn Road, Belfast, BT9 7BL, UK
| | - Paula Williamson
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK
| | - Catrin Tudur Smith
- MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, 1-5 Brownlow Street, Liverpool, L69 3GL, UK.
| |
Collapse
|
41
|
Mattsson N, Carrillo MC, Dean RA, Devous MD, Nikolcheva T, Pesini P, Salter H, Potter WZ, Sperling RS, Bateman RJ, Bain LJ, Liu E. Revolutionizing Alzheimer's disease and clinical trials through biomarkers. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2015; 1:412-9. [PMID: 27239522 PMCID: PMC4879481 DOI: 10.1016/j.dadm.2015.09.001] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The Alzheimer's Association's Research Roundtable met in May 2014 to explore recent progress in developing biomarkers to improve understanding of disease pathogenesis and expedite drug development. Although existing biomarkers have proved extremely useful for enrichment of subjects in clinical trials, there is a clear need to develop novel biomarkers that are minimally invasive and that more broadly characterize underlying pathogenic mechanisms, including neurodegeneration, neuroinflammation, and synaptic dysfunction. These may include blood-based assays and new neuropsychological testing protocols, as well as novel ligands for positron emission tomography imaging, and advanced magnetic resonance imaging methodologies. In addition, there is a need for biomarkers that can serve as theragnostic markers of response to treatment. Standardization remains a challenge, although international consortia have made substantial progress in this area and provide lessons for future standardization efforts.
Collapse
Affiliation(s)
- Niklas Mattsson
- Clinical Memory Research Unit, Lund University, Sweden
- Corresponding author. Tel.: +46-(0)-40-33-50-36; Fax: +46-(0)-40-33-56-57.
| | | | | | | | | | | | - Hugh Salter
- AztraZeneca, Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Sweden
| | | | | | | | | | - Enchi Liu
- Janssen Research and Development, LLC., San Diego, CA, USA
| |
Collapse
|
42
|
Son SJ, Kim J, Seo J, Lee JM, Park H. Connectivity analysis of normal and mild cognitive impairment patients based on FDG and PiB-PET images. Neurosci Res 2015; 98:50-8. [DOI: 10.1016/j.neures.2015.04.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Revised: 04/02/2015] [Accepted: 04/08/2015] [Indexed: 01/18/2023]
|
43
|
Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks. Adv Bioinformatics 2015; 2015:639367. [PMID: 26366461 PMCID: PMC4561111 DOI: 10.1155/2015/639367] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 07/26/2015] [Indexed: 12/11/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer's disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer's disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively.
Collapse
|
44
|
Sugino H, Watanabe A, Amada N, Yamamoto M, Ohgi Y, Kostic D, Sanchez R. Global Trends in Alzheimer Disease Clinical Development: Increasing the Probability of Success. Clin Ther 2015; 37:1632-42. [PMID: 26243073 DOI: 10.1016/j.clinthera.2015.07.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Revised: 07/03/2015] [Accepted: 07/06/2015] [Indexed: 02/03/2023]
Abstract
PURPOSE Alzheimer disease (AD) is a growing global health and economic issue as elderly populations increase dramatically across the world. Despite the many clinical trials conducted, currently no approved disease-modifying treatment exists. In this commentary, the present status of AD drug development and the grounds for collaborations between government, academia, and industry to accelerate the development of disease-modifying AD therapies are discussed. METHODS Official government documents, literature, and news releases were surveyed by MEDLINE and website research. FINDINGS Currently approved anti-AD drugs provide only short-lived symptomatic improvements, which have no effect on the underlying pathogenic mechanisms or progression of the disease. The failure to approve a disease-modifying drug for AD may be because the progression of AD in the patient populations enrolled in clinical studies was too advanced for drugs to demonstrate cognitive and functional improvements. The US Food and Drug Administration and the European Medicines Agency recently published draft guidance for industry which discusses approaches for conducting clinical studies with patients in early AD stages. For successful clinical trials in early-stage AD, however, it will be necessary to identify biomarkers highly correlated with the clinical onset and the longitudinal progress of AD. In addition, because of the high cost and length of clinical AD studies, support in the form of global initiatives and collaborations between government, industry, and academia is needed. IMPLICATIONS In response to this situation, national guidance and international collaborations have been established. Global initiatives are focusing on 2025 as a goal to provide new treatment options, and early signs of success in biomarker and drug development are already emerging.
Collapse
Affiliation(s)
- Haruhiko Sugino
- Global CNS Business, Otsuka Pharmaceutical Development and Commercialization, Ltd (OPDC), Princeton, New Jersey.
| | - Akihito Watanabe
- Global Pharmaceutical Business, Otsuka Pharmaceutical Co Ltd, Tokyo, Japan
| | - Naoki Amada
- Qs' Research Institute, Otsuka Pharmaceutical Co Ltd, Tokushima, Japan
| | - Miho Yamamoto
- Global Pharmaceutical Business, Otsuka Pharmaceutical Co Ltd, Tokyo, Japan
| | - Yuta Ohgi
- Qs' Research Institute, Otsuka Pharmaceutical Co Ltd, Tokushima, Japan
| | | | - Raymond Sanchez
- Global Clinical Development, OPDC, USA, Princeton, New Jersey
| |
Collapse
|
45
|
Jones-Davis DM, Buckholtz N. The impact of the Alzheimer's Disease Neuroimaging Initiative 2: What role do public-private partnerships have in pushing the boundaries of clinical and basic science research on Alzheimer's disease? Alzheimers Dement 2015; 11:860-4. [PMID: 26194319 PMCID: PMC4513361 DOI: 10.1016/j.jalz.2015.05.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 05/06/2015] [Accepted: 05/07/2015] [Indexed: 11/26/2022]
Abstract
In the growing landscape of biomedical public-private-partnerships, particularly for Alzheimer's disease, the question is posed as to their value. What impacts do public-private-partnerships have on clinical and basic science research in Alzheimer's disease? The authors answer the question using the Alzheimer's Disease Neuroimaging Initiative (ADNI) as a test case and example. ADNI is an exemplar of how public-private-partnerships can make an impact not only on clinical and basic science research and practice (including clinical trials), but also of how similar partnerships using ADNI as an example, can be designed to create a maximal impact within their fields.
Collapse
Affiliation(s)
| | - Neil Buckholtz
- National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
46
|
Weiner MW, Veitch DP. Introduction to special issue: Overview of Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement 2015; 11:730-3. [PMID: 26194308 PMCID: PMC5536175 DOI: 10.1016/j.jalz.2015.05.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2015] [Revised: 04/24/2015] [Accepted: 05/05/2015] [Indexed: 02/06/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI), designed as a naturalistic longitudinal study to develop and validate magnetic resonance, positron emission tomography, cerebrospinal fluid, and genetic biomarkers for use in AD clinical trials, has made many impacts in the decade since its inception. The initial 5-year study, ADNI-1, enrolled cognitively normal, mild cognitive impairment (MCI) and AD subjects, and the subsequent studies (ADNI-GO and ADNI-2) added early- and late-MCI cohorts. The development of standardized methods allowed comparison of data gathered across multiple sites, and these data are available to qualified researchers without embargo. ADNI data have been used in >600 publications including those describing relationships between biomarkers, improved methods for disease diagnosis and the prediction of future decline, and identifying novel genetic AD risk loci. ADNI has provided a framework for similar initiatives worldwide.
Collapse
Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| |
Collapse
|
47
|
Snyder HM, Hendrix J, Bain LJ, Carrillo MC. Alzheimer's disease research in the context of the national plan to address Alzheimer's disease. Mol Aspects Med 2015; 43-44:16-24. [PMID: 26096321 DOI: 10.1016/j.mam.2015.06.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 06/10/2015] [Accepted: 06/10/2015] [Indexed: 12/22/2022]
Abstract
In 2012, the first National Plan to Address Alzheimer's Disease in the United States (U.S.) was released, a component of the National Alzheimer's Project Act legislation. Since that time, there have been incremental increases in U.S. federal funding for Alzheimer's disease and related dementia research, particularly in the areas of biomarker discovery, genetic link and related biological underpinnings, and prevention studies for Alzheimer's. A central theme in each of these areas has been the emphasis of cross-sector collaboration and private-public partnerships between government, non-profit organizations and for-profit organizations. This paper will highlight multiple private-public partnerships supporting the advancement of Alzheimer's research in the context of the National Plan to Address Alzheimer's.
Collapse
Affiliation(s)
- Heather M Snyder
- Alzheimer's Association, Medical & Scientific Relations, Chicago, IL, USA.
| | - James Hendrix
- Alzheimer's Association, Medical & Scientific Relations, Chicago, IL, USA
| | - Lisa J Bain
- Independent Science Writer, Philadelphia, PA, USA
| | - Maria C Carrillo
- Alzheimer's Association, Medical & Scientific Relations, Chicago, IL, USA
| |
Collapse
|
48
|
Redolfi A, Manset D, Barkhof F, Wahlund LO, Glatard T, Mangin JF, Frisoni GB. Head-to-head comparison of two popular cortical thickness extraction algorithms: a cross-sectional and longitudinal study. PLoS One 2015; 10:e0117692. [PMID: 25781983 PMCID: PMC4364123 DOI: 10.1371/journal.pone.0117692] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 12/29/2014] [Indexed: 11/19/2022] Open
Abstract
Background and Purpose The measurement of cortical shrinkage is a candidate marker of disease progression in Alzheimer’s. This study evaluated the performance of two pipelines: Civet-CLASP (v1.1.9) and Freesurfer (v5.3.0). Methods Images from 185 ADNI1 cases (69 elderly controls (CTR), 37 stable MCI (sMCI), 27 progressive MCI (pMCI), and 52 Alzheimer (AD) patients) scanned at baseline, month 12, and month 24 were processed using the two pipelines and two interconnected e-infrastructures: neuGRID (https://neugrid4you.eu) and VIP (http://vip.creatis.insa-lyon.fr). The vertex-by-vertex cross-algorithm comparison was made possible applying the 3D gradient vector flow (GVF) and closest point search (CPS) techniques. Results The cortical thickness measured with Freesurfer was systematically lower by one third if compared to Civet’s. Cross-sectionally, Freesurfer’s effect size was significantly different in the posterior division of the temporal fusiform cortex. Both pipelines were weakly or mildly correlated with the Mini Mental State Examination score (MMSE) and the hippocampal volumetry. Civet differed significantly from Freesurfer in large frontal, parietal, temporal and occipital regions (p<0.05). In a discriminant analysis with cortical ROIs having effect size larger than 0.8, both pipelines gave no significant differences in area under the curve (AUC). Longitudinally, effect sizes were not significantly different in any of the 28 ROIs tested. Both pipelines weakly correlated with MMSE decay, showing no significant differences. Freesurfer mildly correlated with hippocampal thinning rate and differed in the supramarginal gyrus, temporal gyrus, and in the lateral occipital cortex compared to Civet (p<0.05). In a discriminant analysis with ROIs having effect size larger than 0.6, both pipelines yielded no significant differences in the AUC. Conclusions Civet appears slightly more sensitive to the typical AD atrophic pattern at the MCI stage, but both pipelines can accurately characterize the topography of cortical thinning at the dementia stage.
Collapse
Affiliation(s)
- Alberto Redolfi
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- * E-mail:
| | - David Manset
- Gnúbila France, Imp Pres d’en Bas, Argonay, France
| | - Frederik Barkhof
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Lars-Olof Wahlund
- Department of Neurobiology, Caring Sciences & Society, Division of Clinical Geriatrics Novum, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Tristan Glatard
- CREATIS, CNRS, INSERM, University of Lyon, Lyon, France
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Giovanni B. Frisoni
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Laboratory of Neuroimaging of Aging, Memory Clinic and LANVIE, University Hospitals and University of Geneva, Geneva, Switzerland
| | | |
Collapse
|
49
|
Lista S, Dubois B, Hampel H. Paths to Alzheimer's disease prevention: from modifiable risk factors to biomarker enrichment strategies. J Nutr Health Aging 2015; 19:154-63. [PMID: 25651440 DOI: 10.1007/s12603-014-0515-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Alzheimer's disease (AD) represents an increasing worldwide healthcare epidemic. Secondary preventive disease-modifying treatments under clinical development are considered most effective when initiated as early as possible in the pathophysiological course and progression of the disease. Major targets are to enhance clearance and to reduce cerebral accumulation of amyloid, decrease hyperphosphorylation of tau and the generation of neurofibrillary tangles, reduce inflammation, and finally progressive neurodegeneration. Comprehensive sets of biological markers are needed to characterize the pathophysiological mechanisms, indicate effects of treatment and to facilitate early characterisation and detection of AD during the prodromal or even at asymptomatic stages. No primary or secondary preventive treatments for AD have been approved. Epidemiological research, however, has provided evidence of specifically modifiable risk and protective factors. Among them are vascular, lifestyle and psychological risk factors that may act both independently and by potentiating each other. These factors may be substantially impacted by single or multi-domain strategies to prevent or postpone the onset of AD-related pathophysiology. Researchers have recently started the European Dementia Prevention Initiative (EDPI), an international consortium to improve strategies for preventing dementia. EDPI, in particular, includes the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) which aims at optimizing the early identification of subjects at increased risk of late-life cognitive deterioration, and at the evaluation of multi-domain intervention strategies. The ongoing discussion on new diagnostic criteria provided by the International Working Group (IWG), as well as by the recommendations summoned by the National Institute on Aging and Alzheimer's Association (NIA-AA) initiative, has inspired the creation of novel study designs and the definition of earlier target populations for trials in pre- and asymptomatic at-risk and prodromal stages of AD. As a result, a number of promising international prevention trials are currently ongoing. In this review, we critically discuss the main paths to AD prevention through control of modifiable risk factors and lifestyle changes. We will also review the role of biomarkers to identify subgroups of patients who would most likely benefit from secondary prevention strategies, and to evaluate the benefit of treatment in such patients.
Collapse
Affiliation(s)
- S Lista
- S. Lista, Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) and Institut du Cerveau et de la Moelle épinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpétrière, Paris, France,
| | | | | |
Collapse
|
50
|
Utility of Autoantibodies as Biomarkers for Diagnosis and Staging of Neurodegenerative Diseases. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2015; 122:1-51. [DOI: 10.1016/bs.irn.2015.05.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|