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Ahmadzadeh M, Christie GJ, Cosco TD, Arab A, Mansouri M, Wagner KR, DiPaola S, Moreno S. Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review. BMC Neurol 2023; 23:309. [PMID: 37608251 PMCID: PMC10463866 DOI: 10.1186/s12883-023-03323-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 07/08/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia. METHODS We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines. RESULTS Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia. CONCLUSION Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.
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Affiliation(s)
- Maryam Ahmadzadeh
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
| | - Gregory J Christie
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
| | - Theodore D Cosco
- Gerontology Research Center, Simon Fraser University, Vancouver, BC, Canada
- Oxford Institute of Population Ageing, University of Oxford, Oxford, UK
| | - Ali Arab
- Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Mehrdad Mansouri
- Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Kevin R Wagner
- Gerontology Research Center, Simon Fraser University, Vancouver, BC, Canada
| | - Steve DiPaola
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada.
| | - Sylvain Moreno
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
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2
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Deatsch A, Perovnik M, Namías M, Trošt M, Jeraj R. Development of a deep learning network for Alzheimer’s disease classification with evaluation of imaging modality and longitudinal data. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac8f10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 09/02/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer’s disease (AD), there remains a clear clinical need for reliable tools to extract diagnoses from neuroimages. Significant work has been done to develop deep learning (DL) networks using neuroimaging for AD diagnosis. However, no particular model has emerged as optimal. Due to a lack of direct comparisons and evaluations on independent data, there is no consensus on which modality is best for diagnostic models or whether longitudinal information enhances performance. The purpose of this work was (1) to develop a generalizable DL model to distinguish neuroimaging scans of AD patients from controls and (2) to evaluate the influence of imaging modality and longitudinal data on performance. Approach. We trained a 2-class convolutional neural network (CNN) with and without a cascaded recurrent neural network (RNN). We used datasets of 772 (N
AD = 364, N
control = 408) 3D 18F-FDG PET scans and 780 (N
AD = 280, N
control = 500) T1-weighted volumetric-3D MR images (containing 131 and 144 patients with multiple timepoints) from the Alzheimer’s Disease Neuroimaging Initiative, plus an independent set of 104 (N
AD = 63, N
NC = 41) 18F-FDG PET scans (one per patient) for validation. Main Results. ROC analysis showed that PET-trained models outperformed MRI-trained, achieving maximum AUC with the CNN + RNN model of 0.93 ± 0.08, with accuracy 82.5 ± 8.9%. Adding longitudinal information offered significant improvement to performance on 18F-FDG PET, but not on T1-MRI. CNN model validation with an independent 18F-FDG PET dataset achieved AUC of 0.99. Layer-wise relevance propagation heatmaps added CNN interpretability. Significance. The development of a high-performing tool for AD diagnosis, with the direct evaluation of key influences, reveals the advantage of using 18F-FDG PET and longitudinal data over MRI and single timepoint analysis. This has significant implications for the potential of neuroimaging for future research on AD diagnosis and clinical management of suspected AD patients.
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3
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Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study. MATHEMATICS 2022. [DOI: 10.3390/math10101767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Alzheimer’s Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process. The diagnosis is currently established following an evaluation of the protein biomarkers in cerebrospinal fluid (CSF), brain imaging, cognitive tests, and the medical history of the individuals. While diagnostic tools based on CSF collections are invasive, the tools used for acquiring brain scans are expensive. Taking these into account, an early predictive system, based on Artificial Intelligence (AI) approaches, targeting the diagnosis of this condition, as well as the identification of lead biomarkers becomes an important research direction. In this survey, we review the state-of-the-art research on machine learning (ML) techniques used for the detection of AD and Mild Cognitive Impairment (MCI). We attempt to identify the most accurate and efficient diagnostic approaches, which employ ML techniques and therefore, the ones most suitable to be used in practice. Research is still ongoing to determine the best biomarkers for the task of AD classification. At the beginning of this survey, after an introductory part, we enumerate several available resources, which can be used to build ML models targeting the diagnosis and classification of AD, as well as their main characteristics. After that, we discuss the candidate markers which were used to build AI models with the best results in terms of diagnostic accuracy, as well as their limitations.
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4
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Jang I, Li B, Riphagen JM, Dickerson BC, Salat DH. Multiscale structural mapping of Alzheimer's disease neurodegeneration. Neuroimage Clin 2022; 33:102948. [PMID: 35121307 PMCID: PMC8814667 DOI: 10.1016/j.nicl.2022.102948] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/09/2021] [Accepted: 01/19/2022] [Indexed: 01/25/2023]
Abstract
A multiscale structural mapping (MSSM) procedure is proposed for the quantification of neurodegeneration in Alzheimer's disease using a single structural brain image. The MSSM procedure captures both macrostructural properties and indirect index of tissue microstructure throughout the cerebral cortex. The MSSM procedure provides enhanced ability for the detection of degeneration in Alzheimer’s disease and mild cognitive impairment compared to traditional measures such as cortical thickness and hippocampal volume and therefore may provide a sensitive measure of Alzheimer’s disease neurodegeneration.
The recently described biological framework of Alzheimer’s disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the ‘amyloid/tau/neurodegeneration’ (A-T-N) status. The ‘neurodegenerative’ component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the ‘cortical signature of AD’ provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as ‘multi-scale structural mapping’; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment.
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Affiliation(s)
- Ikbeom Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Binyin Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Joost M Riphagen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, the Netherlands
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
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5
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Pena D, Suescun J, Schiess M, Ellmore TM, Giancardo L. Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion. Front Neurosci 2022; 15:744190. [PMID: 35046766 PMCID: PMC8761739 DOI: 10.3389/fnins.2021.744190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/09/2021] [Indexed: 01/21/2023] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.
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6
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Warren SL, Moustafa AA, Alashwal H. Harnessing forgetfulness: can episodic-memory tests predict early Alzheimer's disease? Exp Brain Res 2021; 239:2925-2937. [PMID: 34313791 DOI: 10.1007/s00221-021-06182-w] [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] [Received: 03/25/2021] [Accepted: 07/16/2021] [Indexed: 01/04/2023]
Abstract
A rapid increase in the number of patients with Alzheimer's disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD. For our analysis, we studied 307 participants with MCI across four years using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using a binary logistic regression, we compared episodic-memory tests to each other and to prominent neuroimaging methods in MCI converter (MCI participants who developed AD) and MCI non-converter groups (MCI participants who did not develop AD). We also combined variables to test the accuracy of mixed-predictor models. Our results indicated that the best predictors of MCI to AD conversion were the following: a combined episodic-memory and neuroimaging model in year one (59.8%), the Rey Auditory Verbal Learning Test in year two (71.7%), a mixed episodic-memory predictor model in year three (77.7%) and the Logical Memory Test in year four (77.2%) of ADNI. Overall, we found that individual episodic-memory measure and mixed models performed similarly when predicting MCI to AD conversion. Comparatively, individual neuroimaging measures predicted MCI conversion worse than chance. Accordingly, our results indicate that episodic-memory tests could be instrumental in detecting early-stage AD and enabling effective treatment.
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Affiliation(s)
- Samuel L Warren
- School of Psychology, Western Sydney University, Sydney, Australia.
| | - Ahmed A Moustafa
- School of Psychology, Western Sydney University, Sydney, Australia.,MARCS Institute for Brain and Behaviour, Western Sydney University, Sydney, Australia
| | - Hany Alashwal
- College of Information Technology, United Arab Emirates University, Al-Ain, 15551, United Arab Emirates
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7
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Shi H, Ma D, Nie Y, Faisal Beg M, Pei J, Cao J, Neuroimaging Initiative TAD. Early diagnosis of Alzheimer's disease on ADNI data using novel longitudinal score based on functional principal component analysis. J Med Imaging (Bellingham) 2021; 8:024502. [PMID: 33898638 DOI: 10.1117/1.jmi.8.2.024502] [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] [Received: 05/22/2020] [Accepted: 03/12/2021] [Indexed: 11/14/2022] Open
Abstract
Methods: Alzheimer's disease (AD) is a worldwide prevalent age-related neurodegenerative disease with no available cure yet. Early prognosis is therefore crucial for planning proper clinical intervention. It is especially true for people diagnosed with mild cognitive impairment, to whom the prediction of whether and when the future disease onset would happen is particularly valuable. However, such prognostic prediction has been proven to be challenging, and previous studies have only achieved limited success. Approach: In this study, we seek to extract the principal component of the longitudinal disease progression trajectory in the early stage of AD, measured as the magnetic resonance imaging (MRI)-derived structural volume, to predict the onset of AD for mild cognitive impaired patients two years ahead. Results: Cross-validation results of LASSO regression using the longitudinal functional principal component (FPC) features show significant improved predictive power compared to training using the baseline volume 12 months before AD conversion [area under the receiver operating characteristic curve (AUC) of 0.802 versus 0.732] and 24 months before AD conversion (AUC of 0.816 versus 0.717). Conclusions: We present a framework using the FPCA to extract features from MRI-derived information collected from multiple timepoints. The results of our study demonstrate the advantageous predictive power of the population-based longitudinal features to predict the disease onset compared with using only cross-sectional data-based on volumetric features extracted from a single timepoint, demonstrating the improved prediction power using FPC-derived longitudinal features.
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Affiliation(s)
- Haolun Shi
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada
| | - Da Ma
- Simon Fraser University, School of Engineering Science, Burnaby, BC, Canada
| | - Yunlong Nie
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada
| | - Mirza Faisal Beg
- Simon Fraser University, School of Engineering Science, Burnaby, BC, Canada
| | - Jian Pei
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada.,Simon Fraser University, School of Computing Science, Burnaby, BC, Canada
| | - Jiguo Cao
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada.,Simon Fraser University, School of Computing Science, Burnaby, BC, Canada
| | - The Alzheimer's Disease Neuroimaging Initiative
- Simon Fraser University, Department of Statistics and Actuarial Science, Burnaby, BC, Canada.,Simon Fraser University, School of Engineering Science, Burnaby, BC, Canada.,Simon Fraser University, School of Computing Science, Burnaby, BC, Canada
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8
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Vogrinc D, Goričar K, Dolžan V. Genetic Variability in Molecular Pathways Implicated in Alzheimer's Disease: A Comprehensive Review. Front Aging Neurosci 2021; 13:646901. [PMID: 33815092 PMCID: PMC8012500 DOI: 10.3389/fnagi.2021.646901] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/16/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting a significant part of the population. The majority of AD cases occur in the elderly with a typical age of onset of the disease above 65 years. AD presents a major burden for the healthcare system and since population is rapidly aging, the burden of the disease will increase in the future. However, no effective drug treatment for a full-blown disease has been developed to date. The genetic background of AD is extensively studied; numerous genome-wide association studies (GWAS) identified significant genes associated with increased risk of AD development. This review summarizes more than 100 risk loci. Many of them may serve as biomarkers of AD progression, even in the preclinical stage of the disease. Furthermore, we used GWAS data to identify key pathways of AD pathogenesis: cellular processes, metabolic processes, biological regulation, localization, transport, regulation of cellular processes, and neurological system processes. Gene clustering into molecular pathways can provide background for identification of novel molecular targets and may support the development of tailored and personalized treatment of AD.
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Affiliation(s)
| | | | - Vita Dolžan
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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9
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Moradi K, Faghani S, Abdolalizadeh A, Khomeijani-Farahani M, Ashraf-Ganjouei A. Biological Features of Reversion from Mild Cognitive Impairment to Normal Cognition: A Study of Cerebrospinal Fluid Markers and Brain Volume. J Alzheimers Dis Rep 2021; 5:179-186. [PMID: 33981955 PMCID: PMC8075565 DOI: 10.3233/adr-200229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a transitional condition between normal cognition and dementia. Although a significant proportion of the population with MCI experience reversion to normal cognition, it is still poorly understood. OBJECTIVE This study was designed to extend the present evidence regarding the difference between stable and reverting MCI by including whole brain atrophy measures as possible parameters involved. METHODS 405 patients diagnosed with MCI at baseline were selected. After one-year follow-up period, 337 patients (83.2%) were categorized as stable MCI and 68 patients (16.8%) reverted to cognitively normal status (reversion group). Several baseline biomarkers including cerebrospinal fluid (CSF) biomarkers of AD, including Aβ42, t-tau, and p-tau and MRI-based atrophy measurements were compared. RESULTS Participants with stable MCI demonstrated greater brain atrophy as well as lower Aβ and higher tau proteins in the CSF. The atrophy rate was found to be associated with CSF biomarkers merely in the stable group, after adjustment for confounding variables. CONCLUSION These findings provide novel evidence regarding the biological perspective of the reversion phenomenon in individuals with MCI.
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Affiliation(s)
- Kamyar Moradi
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | - Shahriar Faghani
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | - AmirHussein Abdolalizadeh
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Khomeijani-Farahani
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Ashraf-Ganjouei
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
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10
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Popuri K, Ma D, Wang L, Beg MF. Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases. Hum Brain Mapp 2020; 41:4127-4147. [PMID: 32614505 PMCID: PMC7469784 DOI: 10.1002/hbm.25115] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 04/15/2020] [Accepted: 06/08/2020] [Indexed: 12/29/2022] Open
Abstract
Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1-weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume-based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble-learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time-to-conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state-of-the-art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.
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Affiliation(s)
- Karteek Popuri
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
| | - Da Ma
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
| | - Lei Wang
- Feinberg School of MedicineNorthwestern UniversityEvanstonIllinoisUSA
| | - Mirza Faisal Beg
- School of Engineering ScienceSimon Fraser UniversityBarnabyBritish ColumbiaCanada
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11
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Mancuso V, Stramba-Badiale C, Cavedoni S, Pedroli E, Cipresso P, Riva G. Virtual Reality Meets Non-invasive Brain Stimulation: Integrating Two Methods for Cognitive Rehabilitation of Mild Cognitive Impairment. Front Neurol 2020; 11:566731. [PMID: 33117261 PMCID: PMC7561425 DOI: 10.3389/fneur.2020.566731] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 08/24/2020] [Indexed: 11/17/2022] Open
Abstract
Mild cognitive impairment (MCI) refers to a subtle, general cognitive decline with a detrimental impact on elderlies' independent living and quality of life. Without a timely diagnosis, this condition can evolve into dementia over time, hence the crucial need for early detection, prevention, and rehabilitation. For this purpose, current neuropsychological interventions have been integrated with (i) virtual reality, which immerses the user in a controlled, ecological, and safe environment (so far, both virtual reality-based cognitive and motor rehabilitation have revealed promising positive outcomes); and (ii) non-invasive brain stimulation, i.e., transcranial magnetic or electric brain stimulation, which has emerged as a promising cognitive treatment for MCI and Alzheimer's dementia. To date, these two methods have been employed separately; only a few studies (limited to motor rehabilitation) have suggested their integration. The present paper suggests to extend this integration to cognitive rehabilitation as well as to provide a multimodal stimulation that could enhance cognitive training, resulting in a more efficient rehabilitation.
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Affiliation(s)
- Valentina Mancuso
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy
| | - Chiara Stramba-Badiale
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy
| | - Silvia Cavedoni
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy
| | - Elisa Pedroli
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy.,Department of Psychology, E-Campus University, Novedrate, Italy
| | - Pietro Cipresso
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy.,Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
| | - Giuseppe Riva
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy.,Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
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12
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A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs. J Neurol 2020; 267:2983-2997. [PMID: 32500373 DOI: 10.1007/s00415-020-09890-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/03/2020] [Accepted: 05/05/2020] [Indexed: 12/23/2022]
Abstract
Mild cognitive impairment (MCI) is a pre-existing state of Alzheimer's disease (AD). An accurate prediction on the conversion from MCI to AD is of vital clinical significance for potential prevention and treatment of AD. Longitudinal studies received widespread attention for investigating the disease progression, though most studies did not sufficiently utilize the evolution information. In this paper, we proposed a cerebral similarity network with more progression information to predict the conversion from MCI to AD efficiently. First, we defined the new dynamic morphological feature to mine longitudinal information sufficiently. Second, based on the multiple dynamic morphological features the cerebral similarity network was constructed by sparse regression algorithm with optimized parameters to obtain better prediction performance. Then, leave-one-out cross-validation and support vector machine (SVM) were employed for the training and evaluation of the classifiers. The proposed methodology obtained a high accuracy of 92.31% (Sensitivity = 100%, Specificity = 82.86%) in a three-year ahead prediction of MCI to AD conversion. Experiment results suggest the effectiveness of the dynamic morphological feature, serving as a more sensitive biomarker in the prediction of MCI conversion.
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13
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Martí-Juan G, Sanroma-Guell G, Piella G. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105348. [PMID: 31995745 DOI: 10.1016/j.cmpb.2020.105348] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 01/10/2020] [Accepted: 01/18/2020] [Indexed: 05/02/2023]
Abstract
BACKGROUND AND OBJECTIVES Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging. METHODS We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines. RESULTS After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions. CONCLUSIONS Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.
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Affiliation(s)
- Gerard Martí-Juan
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | | | - Gemma Piella
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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14
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Femir-Gurtuna B, Kurt E, Ulasoglu-Yildiz C, Bayram A, Yildirim E, Soncu-Buyukiscan E, Bilgic B. White-matter changes in early and late stages of mild cognitive impairment. J Clin Neurosci 2020; 78:181-184. [PMID: 32334962 DOI: 10.1016/j.jocn.2020.04.078] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 04/13/2020] [Indexed: 01/03/2023]
Abstract
Mild Cognitive Impairment (MCI) is characterized by cognitive deficits that exceed age-related decline, but not interfering with daily living activities. Amnestic type of the disorder (aMCI) is known to have a high risk to progress to Alzheimer's Disease (AD), the most common type of dementia. Identification of very early structural changes in the brain related to the cognitive decline in MCI patients would further contribute to the understanding of the dementias. In the current study, we target to investigate whether the white-matter changes are related to structural changes, as well as the cognitive performance of MCI patients. Forty-nine MCI patients were classified as Early MCI (E-MCI, n = 24) and Late MCI (L-MCI, n = 25) due to their performance on The Free and Cued Selective Reminding Test (FCSRT). Age-Related White-Matter Changes (ARWMC) scale was used to evaluate the white-matter changes in the brain. Volumes of specific brain regions were calculated with the FreeSurfer program. Both group and correlation analyses were conducted to show if there was any association between white-matter hyperintensities (WMHs) and structural changes and cognitive performance. Our results indicate that, L-MCI patients had significantly more WMHs not in all but only in the frontal regions compared to E-MCI patients. Besides, ARWMC scores were not correlated with total hippocampal and white-matter volumes. It can be concluded that WMHs play an important role in MCI and cognitive functions are affected by white-matter changes of MCI patients, especially in the frontal regions.
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Affiliation(s)
- Banu Femir-Gurtuna
- Graduate School of Health Sciences, Istanbul University, 34126, Fatih-Istanbul, Turkey; Department of Psychology, Faculty of Social Sciences, Beykoz University, 34810, Beykoz-Istanbul, Turkey.
| | - Elif Kurt
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093, Capa-Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093, Capa-Istanbul, Turkey.
| | - Cigdem Ulasoglu-Yildiz
- Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093, Capa-Istanbul, Turkey.
| | - Ali Bayram
- Department of Neuroscience, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093, Capa-Istanbul, Turkey; Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093, Capa-Istanbul, Turkey.
| | - Elif Yildirim
- Department of Psychology, Faculty of Arts and Science, Isik University, 34980, Sile-Istanbul, Turkey.
| | - Ezgi Soncu-Buyukiscan
- Department of Psychology, Faculty of Arts and Science, Yeditepe University, 34755, Atasehir-Istanbul, Turkey.
| | - Basar Bilgic
- Hulusi Behcet Life Sciences Research Laboratory, Neuroimaging Unit, Istanbul University, 34093, Capa-Istanbul, Turkey; Department of Neurology, Behavioral Neurology and Movement Disorders Unit, Istanbul Faculty of Medicine, Istanbul University, 34093, Capa-Istanbul, Turkey.
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15
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Pena D, Barman A, Suescun J, Jiang X, Schiess MC, Giancardo L. Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach. Front Neurosci 2019; 13:1053. [PMID: 31636533 PMCID: PMC6788344 DOI: 10.3389/fnins.2019.01053] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 09/19/2019] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide and is one of the leading sources of morbidity and mortality in the aging population. There is a long preclinical period followed by mild cognitive impairment (MCI). Clinical diagnosis and the rate of decline is variable. Progression monitoring remains a challenge in AD, and it is imperative to create better tools to quantify this progression. Brain magnetic resonance imaging (MRI) is commonly used for patient assessment. However, current approaches for analysis require strong a priori assumptions about regions of interest used and complex preprocessing pipelines including computationally expensive non-linear registrations and iterative surface deformations. These preprocessing steps are composed of many stacked processing layers. Any error or bias in an upstream layer will be propagated throughout the pipeline. Failures or biases in the non-linear subject registration and the subjective choice of atlases of specific regions are common in medical neuroimaging analysis and may hinder the translation of many approaches to the clinical practice. Here we propose a data-driven method based on an extension of a deep learning architecture, DeepSymNet, that identifies longitudinal changes without relying on prior brain regions of interest, an atlas, or non-linear registration steps. Our approach is trained end-to-end and learns how a patient's brain structure dynamically changes between two-time points directly from the raw voxels. We compare our approach with Freesurfer longitudinal pipelines and voxel-based methods using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model can identify AD progression with comparable results to existing Freesurfer longitudinal pipelines without the need of predefined regions of interest, non-rigid registration algorithms, or iterative surface deformation at a fraction of the processing time. When compared to other voxel-based methods which share some of the same benefits, our model showed a statistically significant performance improvement. Additionally, we show that our model can differentiate between healthy subjects and patients with MCI. The model's decision was investigated using the epsilon layer-wise propagation algorithm. We found that the predictions were driven by the pallidum, putamen, and the superior temporal gyrus. Our novel longitudinal based, deep learning approach has the potential to diagnose patients earlier and enable new computational tools to monitor neurodegeneration in clinical practice.
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Affiliation(s)
- Danilo Pena
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
- Center for Precision Health, UTHealth, Houston, TX, United States
| | - Arko Barman
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
- Center for Precision Health, UTHealth, Houston, TX, United States
| | - Jessika Suescun
- Department of Neurology, McGovern Medical School, UTHealth, Houston, TX, United States
| | - Xiaoqian Jiang
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
| | - Mya C. Schiess
- Department of Neurology, McGovern Medical School, UTHealth, Houston, TX, United States
| | - Luca Giancardo
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, United States
- Center for Precision Health, UTHealth Diagnostic and Interventional Imaging, McGovern Medical School, UTHealth Institute for Stroke and Cerebrovascular Diseases, UTHealth, Houston, TX, United States
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16
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Niemantsverdriet E, Ribbens A, Bastin C, Benoit F, Bergmans B, Bier JC, Bladt R, Claes L, De Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Lemper JC, Mormont E, Picard G, Salmon E, Segers K, Sieben A, Smeets D, Struyfs H, Thiery E, Tournoy J, Triau E, Vanbinst AM, Versijpt J, Bjerke M, Engelborghs S. A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER). J Alzheimers Dis 2019; 63:1509-1522. [PMID: 29782314 PMCID: PMC6004934 DOI: 10.3233/jad-171140] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer’s disease (AD). Objective: We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression. Methods: The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (n = 887) and follow-up (FU, n = 95) T1-weighted brain MRIs and time-linked neuropsychological data were available. Results: The cohort consisted of cognitively healthy controls (HC, n = 93), subjective cognitive decline (n = 102), mild cognitive impairment (MCI, n = 379), and AD dementia (n = 313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (n = 95) compared to HC (FU>24months, p = 0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment. Conclusion: WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.
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Affiliation(s)
- Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | | | - Christine Bastin
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium
| | - Florence Benoit
- Department of Geriatrics, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Bruno Bergmans
- Department of Neurology and Center for Cognitive Disorders, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium
| | | | - Roxanne Bladt
- Department of Radiology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | | | - Peter Paul De Deyn
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Olivier Deryck
- Department of Neurology and Center for Cognitive Disorders, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium
| | - Bernard Hanseeuw
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Jean-Claude Lemper
- Department of Geriatrics, UZ Brussel, Brussels, Belgium.,Silva medical Scheutbos, Molenbeek-Saint-Jean (Brussels), Belgium
| | - Eric Mormont
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Namur, Université catholique de Louvain, Yvoir, Belgium.,Université catholique de Louvain, Institute of Neuroscience (IoNS), Louvain-la-Neuve (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | - Eric Salmon
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Anne Sieben
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | | | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Gerontology and Geriatrics, Department of Clinical and Experimental Medicine, KU Leuven, Leuven, Belgium.,Geriatric Medicine and Memory Clinic, University Hospital Leuven, Leuven, Belgium
| | | | - Anne-Marie Vanbinst
- Department of Radiology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Vrije Universiteit Brussel (VUB), UZ Brussel, Brussels, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
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17
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Sun Z, Qiao Y, Lelieveldt BPF, Staring M. Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification. Neuroimage 2018; 178:445-460. [DOI: 10.1016/j.neuroimage.2018.05.051] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 04/10/2018] [Accepted: 05/21/2018] [Indexed: 12/21/2022] Open
Affiliation(s)
- Zhuo Sun
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300, RC, Leiden, The Netherlands
| | - Yuchuan Qiao
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300, RC, Leiden, The Netherlands
| | - Boudewijn P F Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300, RC, Leiden, The Netherlands; Intelligent System Group, Faculty of EEMCS, Delft University of Technology, 2600, GA, Delft, The Netherlands
| | - Marius Staring
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300, RC, Leiden, The Netherlands; Intelligent System Group, Faculty of EEMCS, Delft University of Technology, 2600, GA, Delft, The Netherlands.
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18
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Hampel H, Toschi N, Babiloni C, Baldacci F, Black KL, Bokde AL, Bun RS, Cacciola F, Cavedo E, Chiesa PA, Colliot O, Coman CM, Dubois B, Duggento A, Durrleman S, Ferretti MT, George N, Genthon R, Habert MO, Herholz K, Koronyo Y, Koronyo-Hamaoui M, Lamari F, Langevin T, Lehéricy S, Lorenceau J, Neri C, Nisticò R, Nyasse-Messene F, Ritchie C, Rossi S, Santarnecchi E, Sporns O, Verdooner SR, Vergallo A, Villain N, Younesi E, Garaci F, Lista S. Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. J Alzheimers Dis 2018; 64:S47-S105. [PMID: 29562524 PMCID: PMC6008221 DOI: 10.3233/jad-179932] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.
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Affiliation(s)
- Harald Hampel
- AXA Research Fund & Sorbonne Université Chair, 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
| | - 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
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, University of Rome “La Sapienza”, Rome, Italy
- Institute for Research and Medical Care, IRCCS “San Raffaele Pisana”, Rome, Italy
| | - Filippo Baldacci
- AXA Research Fund & Sorbonne Université Chair, 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
| | - Keith L. Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland
| | - René S. Bun
- AXA Research Fund & Sorbonne Université Chair, 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
| | - Francesco Cacciola
- Unit of Neurosurgery, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Enrica Cavedo
- AXA Research Fund & Sorbonne Université Chair, 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
- IRCCS “San Giovanni di Dio-Fatebenefratelli”, Brescia, Italy
| | - Patrizia A. Chiesa
- AXA Research Fund & Sorbonne Université Chair, 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
| | - Olivier Colliot
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Paris, France
| | - Cristina-Maria Coman
- AXA Research Fund & Sorbonne Université Chair, 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
| | - Bruno Dubois
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
| | - Stanley Durrleman
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France
| | - Maria-Teresa Ferretti
- IREM, Institute for Regenerative Medicine, University of Zurich, Zürich, Switzerland
- ZNZ Neuroscience Center Zurich, Zürich, Switzerland
| | - Nathalie George
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle Épinière, ICM, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris, France
| | - Remy Genthon
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Marie-Odile Habert
- Département de Médecine Nucléaire, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France
- Laboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Karl Herholz
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Wolfson Molecular Imaging Centre, Manchester, UK
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Foudil Lamari
- AP-HP, UF Biochimie des Maladies Neuro-métaboliques, Service de Biochimie Métabolique, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | | | - Stéphane Lehéricy
- Centre de NeuroImagerie de Recherche - CENIR, Institut du Cerveau et de la Moelle Épinière - ICM, F-75013, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, F-75013, Paris, France
| | - Jean Lorenceau
- Institut de la Vision, INSERM, Sorbonne Universités, UPMC Univ Paris 06, UMR_S968, CNRS UMR7210, Paris, France
| | - Christian Neri
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, CNRS UMR 8256, Institut de Biologie Paris-Seine (IBPS), Place Jussieu, F-75005, Paris, France
| | - Robert Nisticò
- Department of Biology, University of Rome “Tor Vergata” & Pharmacology of Synaptic Disease Lab, European Brain Research Institute (E.B.R.I.), Rome, Italy
| | - Francis Nyasse-Messene
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Simone Rossi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Department of Medicine, Surgery and Neurosciences, Section of Human Physiology University of Siena, Siena, Italy
| | - Emiliano Santarnecchi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- IU Network Science Institute, Indiana University, Bloomington, IN, USA
| | | | - Andrea Vergallo
- AXA Research Fund & Sorbonne Université Chair, 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
| | - Nicolas Villain
- 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
| | | | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Casa di Cura “San Raffaele Cassino”, Cassino, Italy
| | - Simone Lista
- AXA Research Fund & Sorbonne Université Chair, 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
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