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De Francesco S, Crema C, Archetti D, Muscio C, Reid RI, Nigri A, Bruzzone MG, Tagliavini F, Lodi R, D'Angelo E, Boeve B, Kantarci K, Firbank M, Taylor JP, Tiraboschi P, Redolfi A. Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA. Sci Rep 2023; 13:17355. [PMID: 37833302 PMCID: PMC10575864 DOI: 10.1038/s41598-023-43706-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.
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Affiliation(s)
- Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Cristina Muscio
- ASST Bergamo Ovest, Bergamo, Italy
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, Minnesota, USA
| | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Brad Boeve
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael Firbank
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, UK
| | - Pietro Tiraboschi
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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2
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Chouliaras L, O'Brien JT. The use of neuroimaging techniques in the early and differential diagnosis of dementia. Mol Psychiatry 2023; 28:4084-4097. [PMID: 37608222 PMCID: PMC10827668 DOI: 10.1038/s41380-023-02215-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/24/2023]
Abstract
Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.
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Affiliation(s)
- Leonidas Chouliaras
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Specialist Dementia and Frailty Service, Essex Partnership University NHS Foundation Trust, St Margaret's Hospital, Epping, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
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3
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Nguyen HD, Clément M, Planche V, Mansencal B, Coupé P. Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia. Artif Intell Med 2023; 144:102636. [PMID: 37783553 PMCID: PMC10904714 DOI: 10.1016/j.artmed.2023.102636] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 06/22/2023] [Accepted: 08/11/2023] [Indexed: 10/04/2023]
Abstract
Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Behavioral alterations and cognitive impairments are found in the clinical courses of both diseases, and their differential diagnosis can sometimes pose challenges for physicians. Therefore, an accurate tool dedicated to this diagnostic challenge can be valuable in clinical practice. However, current structural imaging methods mainly focus on the detection of each disease but rarely on their differential diagnosis. In this paper, we propose a deep learning-based approach for both disease detection and differential diagnosis. We suggest utilizing two types of biomarkers for this application: structure grading and structure atrophy. First, we propose to train a large ensemble of 3D U-Nets to locally determine the anatomical patterns of healthy people, patients with Alzheimer's disease and patients with Frontotemporal dementia using structural MRI as input. The output of the ensemble is a 2-channel disease's coordinate map, which can be transformed into a 3D grading map that is easily interpretable for clinicians. This 2-channel disease's coordinate map is coupled with a multi-layer perceptron classifier for different classification tasks. Second, we propose to combine our deep learning framework with a traditional machine learning strategy based on volume to improve the model discriminative capacity and robustness. After both cross-validation and external validation, our experiments, based on 3319 MRIs, demonstrated that our method produces competitive results compared to state-of-the-art methods for both disease detection and differential diagnosis.
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Affiliation(s)
- Huy-Dung Nguyen
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France.
| | - Michaël Clément
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
| | - Vincent Planche
- Univ. Bordeaux, CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France; Centre Mémoire Ressources Recherches, Pôle de Neurosciences Cliniques, CHU de Bordeaux, 33000 Bordeaux, France
| | - Boris Mansencal
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
| | - Pierrick Coupé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
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4
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Pérez-Millan A, Contador J, Juncà-Parella J, Bosch B, Borrell L, Tort-Merino A, Falgàs N, Borrego-Écija S, Bargalló N, Rami L, Balasa M, Lladó A, Sánchez-Valle R, Sala-Llonch R. Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data. Hum Brain Mapp 2023; 44:2234-2244. [PMID: 36661219 PMCID: PMC10028671 DOI: 10.1002/hbm.26205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 12/01/2022] [Accepted: 01/04/2023] [Indexed: 01/21/2023] Open
Abstract
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.
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Affiliation(s)
- Agnès Pérez-Millan
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
- Department of Biomedicine, Faculty of Medicine, Institute of Neurosciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - José Contador
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Jordi Juncà-Parella
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Beatriz Bosch
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Laia Borrell
- Department of Biomedicine, Faculty of Medicine, Institute of Neurosciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Adrià Tort-Merino
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Neus Falgàs
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
- Atlantic Fellow for Equity in Brain Health, Global Brain Heath Institute, University of California San Francisco, Trinity College Dublin, San Francisco, California, USA
| | - Sergi Borrego-Écija
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Nuria Bargalló
- Image Diagnostic Centre, Hospital Clínic de Barcelona, CIBER de Salud Mental, Instituto de Salud Carlos III. Magnetic Resonance Image Core Facility, IDIBAPS, Barcelona, Spain
| | - Lorena Rami
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Mircea Balasa
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
- Atlantic Fellow for Equity in Brain Health, Global Brain Heath Institute, University of California San Francisco, Trinity College Dublin, San Francisco, California, USA
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Fundació Clínic per a la Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Roser Sala-Llonch
- Department of Biomedicine, Faculty of Medicine, Institute of Neurosciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
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5
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Mao C, You H, Hou B, Chu S, Jin W, Huang X, Shang L, Feng F, Peng B, Gao J. Differentiation of Alzheimer’s Disease from Frontotemporal Dementia and Mild Cognitive Impairment Based on Arterial Spin Labeling Magnetic Resonance Imaging: A Pilot Cross-Sectional Study from PUMCH Dementia Cohort. J Alzheimers Dis 2023; 93:509-519. [PMID: 37038812 DOI: 10.3233/jad-221023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Background: Arterial spin labeling (ASL) is helpful in early diagnosis and differential diagnosis of Alzheimer’s disease (AD), with advantages including no exposure to radioactivity, no injection of a contrast agent, more accessible, and relatively less expensive. Objective: To establish the perfusion pattern of different dementia in Chinese population and evaluate the effectiveness of ASL in differentiating AD from cognitive unimpaired (CU), mild cognitive impairment (MCI), and frontotemporal dementia (FTD). Methods: Four groups of participants were enrolled, including AD, FTD, MCI, and CU based on clinical diagnosis from PUMCH dementia cohort. ASL image was collected using 3D spiral fast spin echo–based pseudo-continuous ASL pulse sequence with background suppression and a high resolution T1-weighted scan covering the whole brain. Data processing was performed using Dr. Brain Platform to get cerebral blood flow (ml/100g/min) in every region of interest cortices. Results: Participants included 66 AD, 26 FTD, 21 MCI, and 21 CU. Statistically, widespread hypoperfusion neocortices, most significantly in temporal-parietal-occipital cortices, but not hippocampus and subcortical nucleus were found in AD. Hypoperfusion in parietal lobe was most significantly associated with cognitive decline in AD. Hypoperfusion in parietal lobe was found in MCI and extended to adjacent temporal, occipital and posterior cingulate cortices in AD. Significant reduced perfusion in frontal and temporal cortices, including subcortical nucleus and anterior cingulate cortex were found in FTD. Hypoperfusion regions were relatively symmetrical in AD and left predominant especially in FTD. Conclusion: Specific patterns of ASL hypoperfusion were helpful in differentiating AD from CU, MCI, and FTD.
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Affiliation(s)
- Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Shanshan Chu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Wei Jin
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Xinying Huang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Li Shang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of MedicalScience/ Peking Union Medical College, Beijing, China
| | - Bin Peng
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
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Taylor JA, Greenhaff PL, Bartlett DB, Jackson TA, Duggal NA, Lord JM. Multisystem physiological perspective of human frailty and its modulation by physical activity. Physiol Rev 2023; 103:1137-1191. [PMID: 36239451 PMCID: PMC9886361 DOI: 10.1152/physrev.00037.2021] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
"Frailty" is a term used to refer to a state characterized by enhanced vulnerability to, and impaired recovery from, stressors compared with a nonfrail state, which is increasingly viewed as a loss of resilience. With increasing life expectancy and the associated rise in years spent with physical frailty, there is a need to understand the clinical and physiological features of frailty and the factors driving it. We describe the clinical definitions of age-related frailty and their limitations in allowing us to understand the pathogenesis of this prevalent condition. Given that age-related frailty manifests in the form of functional declines such as poor balance, falls, and immobility, as an alternative we view frailty from a physiological viewpoint and describe what is known of the organ-based components of frailty, including adiposity, the brain, and neuromuscular, skeletal muscle, immune, and cardiovascular systems, as individual systems and as components in multisystem dysregulation. By doing so we aim to highlight current understanding of the physiological phenotype of frailty and reveal key knowledge gaps and potential mechanistic drivers of the trajectory to frailty. We also review the studies in humans that have intervened with exercise to reduce frailty. We conclude that more longitudinal and interventional clinical studies are required in older adults. Such observational studies should interrogate the progression from a nonfrail to a frail state, assessing individual elements of frailty to produce a deep physiological phenotype of the syndrome. The findings will identify mechanistic drivers of frailty and allow targeted interventions to diminish frailty progression.
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Affiliation(s)
- Joseph A Taylor
- MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, School of Life Sciences, University of Nottingham, Queen's Medical Centre, Nottingham, United Kingdom
| | - Paul L Greenhaff
- MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, School of Life Sciences, University of Nottingham, Queen's Medical Centre, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, University of Nottingham, Queen's Medical Centre, Nottingham, United Kingdom
| | - David B Bartlett
- Division of Medical Oncology, Department of Medicine, Duke University, Durham, North Carolina.,Department of Nutritional Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Thomas A Jackson
- MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, Institute of Inflammation and Ageing, https://ror.org/03angcq70University of Birmingham, Birmingham, United Kingdom
| | - Niharika A Duggal
- MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, Institute of Inflammation and Ageing, https://ror.org/03angcq70University of Birmingham, Birmingham, United Kingdom
| | - Janet M Lord
- MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, Institute of Inflammation and Ageing, https://ror.org/03angcq70University of Birmingham, Birmingham, United Kingdom.,NIHR Birmingham Biomedical Research Centre, University Hospital Birmingham and University of Birmingham, Birmingham, United Kingdom
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7
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Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev 2023; 87:101911. [PMID: 36931328 DOI: 10.1016/j.arr.2023.101911] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 03/01/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
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Affiliation(s)
- Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
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8
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Nguyen HD, Clément M, Mansencal B, Coupé P. Towards better interpretable and generalizable AD detection using collective artificial intelligence. Comput Med Imaging Graph 2023; 104:102171. [PMID: 36640484 DOI: 10.1016/j.compmedimag.2022.102171] [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: 05/23/2022] [Revised: 12/24/2022] [Accepted: 12/24/2022] [Indexed: 01/03/2023]
Abstract
Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the life expectancy of the patient. Intense research has been conducted on the use of machine learning to identify Alzheimer's Disease from neuroimaging data, such as structural magnetic resonance imaging. In recent years, advances of deep learning in computer vision suggest a new research direction for this problem. Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques. In this paper, we design a two-stage framework to overcome these limitations. In the first stage, an ensemble of 125 U-Nets is used to grade the input image, producing a 3D map that reflects the disease severity at voxel-level. This map can help to localize abnormal brain areas caused by the disease. In the second stage, we model a graph per individual using the generated grading map and other information about the subject. We propose to use a graph convolutional neural network classifier for the final classification. As a result, our framework demonstrates comparative performance to the state-of-the-art methods in different datasets for both diagnosis and prognosis. We also demonstrate that the use of a large ensemble of U-Nets offers a better generalization capacity for our framework.
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Affiliation(s)
- Huy-Dung Nguyen
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France.
| | - Michaël Clément
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
| | - Boris Mansencal
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
| | - Pierrick Coupé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
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9
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Poos JM, Grandpierre LDM, van der Ende EL, Panman JL, Papma JM, Seelaar H, van den Berg E, van 't Klooster R, Bron E, Steketee R, Vernooij MW, Pijnenburg YAL, Rombouts SARB, van Swieten J, Jiskoot LC. Longitudinal Brain Atrophy Rates in Presymptomatic Carriers of Genetic Frontotemporal Dementia. Neurology 2022; 99:e2661-e2671. [PMID: 36288997 PMCID: PMC9757869 DOI: 10.1212/wnl.0000000000201292] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 08/10/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES It is important to identify at what age brain atrophy rates in genetic frontotemporal dementia (FTD) start to accelerate and deviate from normal aging effects to find the optimal starting point for treatment. We investigated longitudinal brain atrophy rates in the presymptomatic stage of genetic FTD using normative brain volumetry software. METHODS Presymptomatic GRN, MAPT, and C9orf72 pathogenic variant carriers underwent longitudinal volumetric T1-weighted magnetic resonance imaging of the brain as part of a prospective cohort study. Images were automatically analyzed with Quantib® ND, which consisted of volume measurements (CSF and sum of gray and white matter) of lobes, cerebellum, and hippocampus. All volumes were compared with reference centile curves based on a large population-derived sample of nondemented individuals (n = 4,951). Mixed-effects models were fitted to analyze atrophy rates of the different gene groups as a function of age. RESULTS Thirty-four GRN, 8 MAPT, and 14 C9orf72 pathogenic variant carriers were included (mean age = 52.1, standard deviation = 7.2; 66% female). The mean follow-up duration of the study was 64 ± 33 months (median = 52; range 13-108). GRN pathogenic variant carriers showed a faster decline than the reference centile curves for all brain areas, though relative volumes remained between the 5th and 75th percentiles between the ages of 45 and 70 years. In MAPT pathogenic variant carriers, frontal lobe volume was already at the 5th percentile at age 45 years and showed a further decline between the ages 50 and 60 years. Temporal lobe volume started in the 50th percentile at age 45 years but showed fastest decline over time compared with other brain structures. Frontal, temporal, parietal, and cerebellar volume already started below the 5th percentile compared with the reference centile curves at age 45 years for C9orf72 pathogenic variant carriers, but there was minimal decline over time until the age of 60 years. DISCUSSION We provide evidence for longitudinal brain atrophy in the presymptomatic stage of genetic FTD. The affected brain areas and the age after which atrophy rates start to accelerate and diverge from normal aging slopes differed between gene groups. These results highlight the value of normative volumetry software for disease tracking and staging biomarkers in genetic FTD. These techniques could help in identifying the optimal time window for starting treatment and monitoring treatment response.
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Affiliation(s)
- Jackie M Poos
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Leonie D M Grandpierre
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Emma L van der Ende
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Jessica L Panman
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Janne M Papma
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Harro Seelaar
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Esther van den Berg
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Ronald van 't Klooster
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Esther Bron
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Rebecca Steketee
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Meike W Vernooij
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Yolande A L Pijnenburg
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Serge A R B Rombouts
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - John van Swieten
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Lize C Jiskoot
- From the Department of Neurology and Alzheimer Center Erasmus MC (Jackie M. Poos, L.D.M.G., E.L.E., J.L.P., Janne M. Papma, H.S., Esther van den Berg, J.S., L.C.J.), Erasmus MC University Medical Center; Quantib B.V. (R.K.), Rotterdam; Departments of Radiology and Nuclear Medicine (Esther Bron, R.S., M.W.V.) and Epidemiology (M.W.V.), Erasmus MC University Medical Center Rotterdam; Department of Neurology (Y.A.L.P.), Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center; Department of Radiology (S.A.R.B.R.), Leiden University Medical Center; Institute of Psychology (S.A.R.B.R.) and Leiden Institute for Brain and Cognition (S.A.R.B.R.), Leiden University, The Netherlands; and Dementia Research Centre (L.C.J.), Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom.
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Li C, Li W, Liu C, Zheng H, Cai J, Wang S. Artificial intelligence in multi-parametric magnetic resonance imaging: A review. Med Phys 2022; 49:e1024-e1054. [PMID: 35980348 DOI: 10.1002/mp.15936] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022] Open
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Cheng Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,Peng Cheng Laboratory, Shenzhen, 518066, China.,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
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11
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Termine A, Fabrizio C, Caltagirone C, Petrosini L. A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks. Life (Basel) 2022; 12:947. [PMID: 35888037 PMCID: PMC9323676 DOI: 10.3390/life12070947] [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: 05/25/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 12/16/2022] Open
Abstract
Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorithm was trained to detect frontotemporal dementia (FTD) on data from the NIFD database to ensure reproducibility. The DL model yielded 0.80 accuracy (95% confidence intervals: 0.64, 0.91), 1 sensitivity, 0.6 specificity, 0.83 F1-score, and 0.86 AUC, achieving a comparable performance with other FTD classification approaches. Explainable AI methods were applied to understand AI behavior and to identify regions of the images where the DL model misbehaves. Attention maps highlighted that its decision was driven by hallmarking brain areas for FTD and helped us to understand how to improve FTD detection. The proposed standardized methodology could be useful for benchmark comparison in FTD classification. AI-based CAD tools should be developed with the goal of standardizing pipelines, as varying pre-processing and training methods, along with the absence of model behavior explanations, negatively impact regulators' attitudes towards CAD. The adoption of common best practices for neuroimaging data analysis is a step toward fast evaluation of efficacy and safety of CAD and may accelerate the adoption of AI products in the healthcare system.
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Affiliation(s)
- Andrea Termine
- Data Science Unit, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (A.T.); (C.F.)
| | - Carlo Fabrizio
- Data Science Unit, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (A.T.); (C.F.)
| | - Carlo Caltagirone
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy;
| | - Laura Petrosini
- Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy
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Ssali T, Narciso L, Hicks J, Liu L, Jesso S, Richardson L, Günther M, Konstandin S, Eickel K, Prato F, Anazodo UC, Finger E, St Lawrence K. Concordance of regional hypoperfusion by pCASL MRI and 15O-water PET in frontotemporal dementia: Is pCASL an efficacious alternative? Neuroimage Clin 2022; 33:102950. [PMID: 35134705 PMCID: PMC8829802 DOI: 10.1016/j.nicl.2022.102950] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/21/2022] [Accepted: 01/25/2022] [Indexed: 12/11/2022]
Abstract
ASL is an alternative to 15O-water for identifying hypoperfusion in FTD patients. ROI-based perfusion by ASL and 15O-water were strongly correlated (R > 0.75). Hypoperfusion patterns identified by 15O-water and ASL were in good agreement. Careful selection of the reference region is required to avoid erroneous results.
Background Clinical diagnosis of frontotemporal dementia (FTD) remains a challenge due to the overlap of symptoms among FTD subtypes and with other psychiatric disorders. Perfusion imaging by arterial spin labeling (ASL) is a promising non-invasive alternative to established PET techniques; however, its sensitivity to imaging parameters can hinder its ability to detect perfusion abnormalities. Purpose This study evaluated the similarity of regional hypoperfusion patterns detected by ASL relative to the gold standard for imaging perfusion, PET with radiolabeled water (15O-water). Methods and materials Perfusion by single-delay pseudo continuous ASL (SD-pCASL), free-lunch Hadamard encoded pCASL (FL_TE-pCASL), and 15O-water data were acquired on a hybrid PET/MR scanner in 13 controls and 9 FTD patients. Cerebral blood flow (CBF) by 15O-water was quantified by a non-invasive approach (PMRFlow). Regional hypoperfusion was determined by comparing individual patients to the control group. This was performed using absolute (aCBF) and CBF normalized to whole-brain perfusion (rCBF). Agreement was assessed based on the fraction of overlapping voxels. Sensitivity and specificity of pCASL was estimated using hypoperfused regions of interest identified by 15O-water. Results Region of interest (ROI) based perfusion measured by 15O-water strongly correlated with SD-pCASL (R = 0.85 ± 0.1) and FL_TE-pCASL (R = 0.81 ± 0.14). Good agreement in terms of regional hypoperfusion patterns was found between 15O-water and SD-pCASL (sensitivity = 70%, specificity = 78%) and between 15O-water and FL_TE-pCASL (sensitivity = 71%, specificity = 73%). However, SD-pCASL showed greater overlap (43.4 ± 21.3%) with 15O-water than FL_TE-pCASL (29.9 ± 21.3%). Although aCBF and rCBF showed no significant differences regarding spatial overlap and metrics of agreement with 15O-water, rCBF showed considerable variability across subtypes, indicating that care must be taken when selecting a reference region. Conclusions This study demonstrates the potential of pCASL for assessing regional hypoperfusion related to FTD and supports its use as a cost-effective alternative to PET.
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Affiliation(s)
- Tracy Ssali
- Lawson Health Research Institute, London, Canada; Department of Medical Biophysics, Western University, London, Canada.
| | - Lucas Narciso
- Lawson Health Research Institute, London, Canada; Department of Medical Biophysics, Western University, London, Canada
| | - Justin Hicks
- Lawson Health Research Institute, London, Canada; Department of Medical Biophysics, Western University, London, Canada
| | - Linshan Liu
- Lawson Health Research Institute, London, Canada; Department of Medical Biophysics, Western University, London, Canada
| | - Sarah Jesso
- Lawson Health Research Institute, London, Canada; St. Joseph's Health Care, London, Canada
| | - Lauryn Richardson
- Lawson Health Research Institute, London, Canada; St. Joseph's Health Care, London, Canada
| | - Matthias Günther
- Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany; University Bremen, Bremen, Germany
| | - Simon Konstandin
- Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany; Mediri GmbH, Heidelberg, Germany
| | | | - Frank Prato
- Lawson Health Research Institute, London, Canada; Department of Medical Biophysics, Western University, London, Canada
| | - Udunna C Anazodo
- Lawson Health Research Institute, London, Canada; Department of Medical Biophysics, Western University, London, Canada
| | - Elizabeth Finger
- Lawson Health Research Institute, London, Canada; Department of Medical Biophysics, Western University, London, Canada; Department of Clinical Neurological Sciences, Western University, London, Canada
| | - Keith St Lawrence
- Lawson Health Research Institute, London, Canada; Department of Medical Biophysics, Western University, London, Canada
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McKenna MC, Murad A, Huynh W, Lope J, Bede P. The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation. Expert Rev Neurother 2022; 22:179-207. [PMID: 35227146 DOI: 10.1080/14737175.2022.2048648] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterised based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorise individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories. AREAS COVERED This article reviews (1) the neuroimaging studies that propose single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) potential practical implications and (3) the limitations of current single-subject data interpretation models. EXPERT OPINION Classification studies in FTLD have demonstrated the feasibility of categorising individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.
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Affiliation(s)
| | - Aizuri Murad
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - William Huynh
- Brain and Mind Centre, University of Sydney, Australia
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Ireland.,Pitié-Salpêtrière University Hospital, Sorbonne University, France
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Sato R, Kudo K, Udo N, Matsushima M, Yabe I, Yamaguchi A, Tha KK, Sasaki M, Harada M, Matsukawa N, Amemiya T, Kawata Y, Bito Y, Ochi H, Shirai T. A diagnostic index based on quantitative susceptibility mapping and voxel-based morphometry may improve early diagnosis of Alzheimer's disease. Eur Radiol 2022; 32:4479-4488. [PMID: 35137303 DOI: 10.1007/s00330-022-08547-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/25/2021] [Accepted: 12/14/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Voxel-based morphometry (VBM) is widely used to quantify the progression of Alzheimer's disease (AD), but improvement is still needed for accurate early diagnosis. We evaluated the feasibility of a novel diagnosis index for early diagnosis of AD based on quantitative susceptibility mapping (QSM) and VBM. METHODS Thirty-seven patients with AD, 24 patients with mild cognitive impairment (MCI) due to AD, and 36 cognitively normal (NC) subjects from four centers were included. A hybrid sequence was performed by using 3-T MRI with a 3D multi-echo GRE sequence to obtain both a T1-weighted image for VBM and phase images for QSM. The index was calculated from specific voxels in QSM and VBM images by using a linear support vector machine. The method of voxel extraction was optimized to maximize diagnostic accuracy, and the optimized index was compared with the conventional VBM-based index using receiver operating characteristic analysis. RESULTS The index was optimal when voxels were extracted as increased susceptibility (AD > NC) in the parietal lobe and decreased gray matter volume (AD < NC) in the limbic system. The optimized proposed index showed excellent performance for discrimination between AD and NC (AUC = 0.94, p = 1.1 × 10-10) and good performance for MCI and NC (AUC = 0.87, p = 1.8 × 10-6), but poor performance for AD and MCI (AUC = 0.68, p = 0.018). Compared with the conventional index, AUCs were improved for all cases, especially for MCI and NC (p < 0.05). CONCLUSIONS In this preliminary study, the proposed index based on QSM and VBM improved the diagnostic performance between MCI and NC groups compared with the VBM-based index. KEY POINTS • We developed a novel diagnostic index for Alzheimer's disease based on quantitative susceptibility mapping (QSM) and voxel-based morphometry (VBM). • QSM and VBM images can be acquired simultaneously in a single sequence with little increasing scan time. • In this preliminary study, the proposed diagnostic index improved the discriminative performance between mild cognitive impairment and normal control groups compared with the conventional VBM-based index.
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Affiliation(s)
- Ryota Sato
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan.
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan.
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
| | - Niki Udo
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
| | - Masaaki Matsushima
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Ichiro Yabe
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Akinori Yamaguchi
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
| | - Khin Khin Tha
- Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Sapporo, Hokkaido, Japan
| | - Makoto Sasaki
- Institute for Biomedical Sciences, Iwate Medical University, Morioka, Iwate, Japan
| | - Masafumi Harada
- Department of Radiology, Tokushima University, Tokushima, Japan
| | | | - Tomoki Amemiya
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Yasuo Kawata
- Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Yoshitaka Bito
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
- Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Hisaaki Ochi
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
| | - Toru Shirai
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
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15
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Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
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16
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McKenna MC, Tahedl M, Murad A, Lope J, Hardiman O, Hutchinson S, Bede P. White matter microstructure alterations in frontotemporal dementia: Phenotype-associated signatures and single-subject interpretation. Brain Behav 2022; 12:e2500. [PMID: 35072974 PMCID: PMC8865163 DOI: 10.1002/brb3.2500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/22/2021] [Accepted: 01/01/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Frontotemporal dementias (FTD) include a genetically heterogeneous group of conditions with distinctive molecular, radiological and clinical features. The majority of radiology studies in FTD compare FTD subgroups to healthy controls to describe phenotype- or genotype-associated imaging signatures. While the characterization of group-specific imaging traits is academically important, the priority of clinical imaging is the meaningful interpretation of individual datasets. METHODS To demonstrate the feasibility of single-subject magnetic resonance imaging (MRI) interpretation, we have evaluated the white matter profile of 60 patients across the clinical spectrum of FTD. A z-score-based approach was implemented, where the diffusivity metrics of individual patients were appraised with reference to demographically matched healthy controls. Fifty white matter tracts were systematically evaluated in each subject with reference to normative data. RESULTS The z-score-based approach successfully detected white matter pathology in single subjects, and group-level inferences were analogous to the outputs of standard track-based spatial statistics. CONCLUSIONS Our findings suggest that it is possible to meaningfully evaluate the diffusion profile of single FTD patients if large normative datasets are available. In contrast to the visual review of FLAIR and T2-weighted images, computational imaging offers objective, quantitative insights into white matter integrity changes even at single-subject level.
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Affiliation(s)
- Mary Clare McKenna
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Marlene Tahedl
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Aizuri Murad
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | | | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
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17
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Custodia A, Ouro A, Romaus-Sanjurjo D, Pías-Peleteiro JM, de Vries HE, Castillo J, Sobrino T. Endothelial Progenitor Cells and Vascular Alterations in Alzheimer’s Disease. Front Aging Neurosci 2022; 13:811210. [PMID: 35153724 PMCID: PMC8825416 DOI: 10.3389/fnagi.2021.811210] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/14/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease representing the most common type of dementia worldwide. The early diagnosis of AD is very difficult to achieve due to its complexity and the practically unknown etiology. Therefore, this is one of the greatest challenges in the field in order to develop an accurate therapy. Within the different etiological hypotheses proposed for AD, we will focus on the two-hit vascular hypothesis and vascular alterations occurring in the disease. According to this hypothesis, the accumulation of β-amyloid protein in the brain starts as a consequence of damage in the cerebral vasculature. Given that there are several vascular and angiogenic alterations in AD, and that endothelial progenitor cells (EPCs) play a key role in endothelial repair processes, the study of EPCs in AD may be relevant to the disease etiology and perhaps a biomarker and/or therapeutic target. This review focuses on the involvement of endothelial dysfunction in the onset and progression of AD with special emphasis on EPCs as a biomarker and potential therapeutic target.
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Affiliation(s)
- Antía Custodia
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Alberto Ouro
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
- *Correspondence: Alberto Ouro,
| | - Daniel Romaus-Sanjurjo
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Juan Manuel Pías-Peleteiro
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Helga E. de Vries
- Neuroimmunology Research Group, Department of Molecular Cell Biology and Immunology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
| | - José Castillo
- Neuroimaging and Biotechnology Laboratory (NOBEL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Tomás Sobrino
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
- Tomás Sobrino,
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18
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Xia N, Li Y, Xue Y, Li W, Zhang Z, Wen C, Li J, Ye Q. Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease. Brain Imaging Behav 2021; 16:617-626. [PMID: 34480258 DOI: 10.1007/s11682-021-00538-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/08/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES Alzheimer's disease (AD) is the most common type of dementia, and characterizing brain changes in AD is important for clinical diagnosis and prognosis. This study was designed to evaluate the classification performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in differentiating between AD patients and normal control (NC) subjects and to explore its potential effectiveness as a neuroimaging biomarker. METHODS Thirty-one patients with probable AD and twenty NC subjects were included in the prospective study. IVIM data were subjected to postprocessing, and parameters including the apparent diffusion coefficient (ADC), slow diffusion coefficient (Ds), fast diffusion coefficient (Df), perfusion fraction (fp) and Df*fp were calculated. The classification model was developed and confirmed with cross-validation (group A/B) using Support Vector Machine (SVM). Correlations between IVIM parameters and Mini-Mental State Examination (MMSE) scores in AD patients were investigated using partial correlation analysis. RESULTS Diffusion MRI revealed significant region-specific differences that aided in differentiating AD patients from controls. Among the analyzed regions and parameters, the Df of the right precuneus (PreR) (ρ = 0.515; P = 0.006) and the left cerebellum (CL) (ρ = 0.429; P = 0.026) demonstrated significant associations with the cognitive function of AD patients. An area under the receiver operating characteristics curve (AUC) of 0.84 (95% CI: 0.66, 0.99) was calculated for the validation in dataset B after the prediction model was trained on dataset A. When the datasets were reversed, an AUC of 0.90 (95% CI: 0.75, 1.00) was calculated for the validation in dataset A, after the prediction model trained in dataset B. CONCLUSION IVIM imaging is a promising method for the classification of AD and NC subjects, and IVIM parameters of precuneus and cerebellum might be effective biomarker for the diagnosis of AD.
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Affiliation(s)
- Nengzhi Xia
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Yanxuan Li
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Yingnan Xue
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Weikang Li
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Zhenhua Zhang
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Caiyun Wen
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Jiance Li
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Qiong Ye
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, People's Republic of China. .,High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, People's Republic of China.
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19
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Li R, Qi Y, Shi L, Wang W, Zhang A, Luo Y, Kung WK, Jiao Z, Liu G, Li H, Zhang L. Brain Volumetric Alterations in Preclinical HIV-Associated Neurocognitive Disorder Using Automatic Brain Quantification and Segmentation Tool. Front Neurosci 2021; 15:713760. [PMID: 34456678 PMCID: PMC8385127 DOI: 10.3389/fnins.2021.713760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose This study aimed to determine if people living with HIV (PLWH) in preclinical human immunodeficiency virus (HIV)-associated neurocognitive disorder (HAND), with no clinical symptoms and without decreased daily functioning, suffer from brain volumetric alterations and its patterns. Method Fifty-nine male PLWH at the HAND preclinical stage were evaluated, including 19 subjects with asymptomatic neurocognitive impairment (ANI), 17 subjects with cognitive abnormality that does not reach ANI (Not reach ANI), and 23 subjects with cognitive integrity. Moreover, 23 healthy volunteers were set as the seronegative normal controls (NCs). These individuals underwent sagittal three-dimensional T1-weighted imaging (3D T1WI). Quantified data and volumetric measures of brain structures were automatically segmented and extracted using AccuBrain®. In addition, the multiple linear regression analysis was performed to analyze the relationship of volumes of brain structures and clinical variables in preclinical HAND, and the correlations of the brain volume parameters with different cognitive function states were assessed by Pearson's correlation analysis. Results The significant difference was shown in the relative volumes of the ventricular system, bilateral lateral ventricle, thalamus, caudate, and left parietal lobe gray matter between the preclinical HAND and NCs. Furthermore, the relative volumes of the bilateral thalamus in preclinical HAND were negatively correlated with attention/working memory (left: r = -0.271, p = 0.042; right: r = -0.273, p = 0.040). Higher age was associated with increased relative volumes of the bilateral lateral ventricle and ventricular system and reduced relative volumes of the left thalamus and parietal lobe gray matter. The lower CD4+/CD8+ ratio was associated with increased relative volumes of the left lateral ventricle and ventricular system. Longer disease course was associated with increased relative volumes of the bilateral thalamus. No significant difference was found among preclinical HAND subgroups in all indices, and the difference between the individual groups (Not reach ANI and Cognitive integrity groups) and NCs was also insignificant. However, there was a significant difference between ANI and NCs in the relative volumes of the bilateral caudate and lateral ventricle. Conclusion Male PLWH at the HAND preclinical stage suffer from brain volumetric alterations. AccuBrain® provides potential value in evaluating HIV-related neurocognitive dysfunction.
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Affiliation(s)
- Ruili Li
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.,Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Yu Qi
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, China.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Wei Wang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Aidong Zhang
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | | | - Zengxin Jiao
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Guangxue Liu
- Department of Natural Medicines, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, China
| | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
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20
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Bron EE, Klein S, Papma JM, Jiskoot LC, Venkatraghavan V, Linders J, Aalten P, De Deyn PP, Biessels GJ, Claassen JAHR, Middelkoop HAM, Smits M, Niessen WJ, van Swieten JC, van der Flier WM, Ramakers IHGB, van der Lugt A. Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease. NEUROIMAGE-CLINICAL 2021; 31:102712. [PMID: 34118592 PMCID: PMC8203808 DOI: 10.1016/j.nicl.2021.102712] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 11/18/2022]
Abstract
This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.
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Affiliation(s)
- Esther E Bron
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Lize C Jiskoot
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Vikram Venkatraghavan
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Jara Linders
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Pauline Aalten
- Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Peter Paul De Deyn
- Department of Neurology and Alzheimer Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Huub A M Middelkoop
- Department of Neurology & Neuropsychology, Leiden University Medical Center, Leiden, The Netherlands; Institute of Psychology, Health, Medical and Neuropsychology Unit, Leiden University, The Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Imaging Physics, Applied Sciences, Delft University of Technology, The Netherlands
| | | | | | - Inez H G B Ramakers
- Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
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21
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Soni N, Ora M, Bathla G, Nagaraj C, Boles Ponto LL, Graham MM, Saini J, Menda Y. Multiparametric magnetic resonance imaging and positron emission tomography findings in neurodegenerative diseases: Current status and future directions. Neuroradiol J 2021; 34:263-288. [PMID: 33666110 DOI: 10.1177/1971400921998968] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Neurodegenerative diseases (NDDs) are characterized by progressive neuronal loss, leading to dementia and movement disorders. NDDs broadly include Alzheimer's disease, frontotemporal lobar degeneration, parkinsonian syndromes, and prion diseases. There is an ever-increasing prevalence of mild cognitive impairment and dementia, with an accompanying immense economic impact, prompting efforts aimed at early identification and effective interventions. Neuroimaging is an essential tool for the early diagnosis of NDDs in both clinical and research settings. Structural, functional, and metabolic imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are widely available. They show encouraging results for diagnosis, monitoring, and treatment response evaluation. The current review focuses on the complementary role of various imaging modalities in relation to NDDs, the qualitative and quantitative utility of newer MRI techniques, novel radiopharmaceuticals, and integrated PET/MRI in the setting of NDDs.
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Affiliation(s)
- Neetu Soni
- University of Iowa Hospitals and Clinics, USA
| | - Manish Ora
- Department of Nuclear Medicine, SGPGIMS, India
| | - Girish Bathla
- Neuroradiology Department, University of Iowa Hospitals and Clinics, USA
| | - Chandana Nagaraj
- Department of Neuro Imaging and Interventional Radiology, NIMHANS, India
| | | | - Michael M Graham
- Division of Nuclear Medicine, University of Iowa Hospitals and Clinics, USA
| | - Jitender Saini
- Department of Neuro Imaging and Interventional Radiology, NIMHANS, India
| | - Yusuf Menda
- University of Iowa Hospitals and Clinics, USA
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22
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Feis RA, van der Grond J, Bouts MJRJ, Panman JL, Poos JM, Schouten TM, de Vos F, Jiskoot LC, Dopper EGP, van Buchem MA, van Swieten JC, Rombouts SARB. Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept. Brain Commun 2021; 2:fcaa079. [PMID: 33543126 PMCID: PMC7846185 DOI: 10.1093/braincomms/fcaa079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 04/29/2020] [Accepted: 05/11/2020] [Indexed: 11/14/2022] Open
Abstract
Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10–20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms (‘convert’) within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia (‘converters’), while 35 had not (‘non-converters’). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials.
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Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Jackie M Poos
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands.,Dementia Research Centre, University College London, London, WC1N 3AR, UK
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Mark A van Buchem
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, 3015 GD, Rotterdam, the Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, 2333 ZA, Leiden, the Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, 2333 ZA, Leiden, the Netherlands.,Institute of Psychology, Leiden University, 2333 AK, Leiden, the Netherlands
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23
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Hu J, Qing Z, Liu R, Zhang X, Lv P, Wang M, Wang Y, He K, Gao Y, Zhang B. Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer's Disease. Front Neurosci 2021; 14:626154. [PMID: 33551735 PMCID: PMC7858673 DOI: 10.3389/fnins.2020.626154] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 12/28/2020] [Indexed: 11/13/2022] Open
Abstract
Frontotemporal dementia (FTD) and Alzheimer's disease (AD) have overlapping symptoms, and accurate differential diagnosis is important for targeted intervention and treatment. Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear. In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing. On the one hand, it imposes high requirements on clinicians and data themselves; On the other hand, it hinders the backtracking of classification results to the original image data, resulting in the classification results cannot be interpreted intuitively. In the current study, a large cohort of 3D T1-weighted structural magnetic resonance imaging (MRI) volumes (n = 4,099) was collected from two publicly available databases, i.e., the ADNI and the NIFD. We trained a DL-based network directly based on raw T1 images to classify FTD, AD and corresponding NCs. And we evaluated the convergence speed, differential diagnosis ability, robustness and generalizability under nine scenarios. The proposed network yielded an accuracy of 91.83% based on the most common T1-weighted sequence [magnetization-prepared rapid acquisition with gradient echo (MPRAGE)]. The knowledge learned by the DL network through multiple classification tasks can also be used to solve subproblems, and the knowledge is generalizable and not limited to a specified dataset. Furthermore, we applied a gradient visualization algorithm based on guided backpropagation to calculate the contribution graph, which tells us intuitively why the DL-based networks make each decision. The regions making valuable contributions to FTD were more widespread in the right frontal white matter regions, while the left temporal, bilateral inferior frontal and parahippocampal regions were contributors to the classification of AD. Our results demonstrated that DL-based networks have the ability to solve the enigma of differential diagnosis of diseases without any hypothesis-based preprocessing. Moreover, they may mine the potential patterns that may be different from human clinicians, which may provide new insight into the understanding of FTD and AD.
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Affiliation(s)
- Jingjing Hu
- National Institute of Healthcare Data Science at Nanjing University, Nanjing, China.,State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Zhao Qing
- Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Renyuan Liu
- Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Pin Lv
- Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Maoxue Wang
- Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yang Wang
- Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Kelei He
- National Institute of Healthcare Data Science at Nanjing University, Nanjing, China.,Medical School of Nanjing University, Nanjing, China
| | - Yang Gao
- National Institute of Healthcare Data Science at Nanjing University, Nanjing, China.,State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
| | - Bing Zhang
- National Institute of Healthcare Data Science at Nanjing University, Nanjing, China.,State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.,Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.,Medical School of Nanjing University, Nanjing, China
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24
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Yu Q, Mai Y, Ruan Y, Luo Y, Zhao L, Fang W, Cao Z, Li Y, Liao W, Xiao S, Mok VCT, Shi L, Liu J. An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease. Alzheimers Res Ther 2021; 13:23. [PMID: 33436059 PMCID: PMC7805212 DOI: 10.1186/s13195-020-00757-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/21/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer's disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD. METHODS In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in "FTD dominant" structures over that obtained from "AD dominant" structures. The weights and the identification of "FTD/AD dominant" structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases. RESULTS AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p < 0.001 and AUC = 0.88) as we reported before, while no significant difference was found between AD and FTD (p = 0.647). FTDI showed excellent accuracy in identifying FTD from AD (AUC = 0.90; SEN = 89%, SPE = 75% with threshold value = 1.08). The validation using independent data from ADNI and FTLDNI datasets also confirmed the efficacy of FTDI (AUC = 0.93; SEN = 96%, SPE = 70% with threshold value = 1.08). CONCLUSIONS Brain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice.
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Affiliation(s)
- Qun Yu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Yingren Mai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Yuting Ruan
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | - Lei Zhao
- BrainNow Research Institute, Shenzhen, China
| | - Wenli Fang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Zhiyu Cao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Yi Li
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Wang Liao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Songhua Xiao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China
| | - Vincent C T Mok
- BrainNow Research Institute, Shenzhen, China
- Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, China.
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Jun Liu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, Guangzhou, Guangdong, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
- Laboratory of RNA and Major Diseases of Brain and Heart, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
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25
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An intelligent multimodal medical diagnosis system based on patients’ medical questions and structured symptoms for telemedicine. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100513] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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26
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Leocadi M, Canu E, Calderaro D, Corbetta D, Filippi M, Agosta F. An update on magnetic resonance imaging markers in AD. Ther Adv Neurol Disord 2020; 13:1756286420947986. [PMID: 33747128 PMCID: PMC7903819 DOI: 10.1177/1756286420947986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 07/09/2020] [Indexed: 12/22/2022] Open
Abstract
The purpose of the present review is to provide an update of the available recent scientific literature on the use of magnetic resonance imaging (MRI) in Alzheimer's disease (AD). MRI is playing an increasingly important role in the characterization of the AD signatures, which can be useful in both the diagnostic process and monitoring of disease progression. Furthermore, this technique is unique in assessing brain structure and function and provides a deep understanding of in vivo evolution of cerebral pathology. In the reviewing process, we established a priori criteria and we thoroughly searched the very recent scientific literature (January 2018-March 2020) for relevant articles on this topic. In summary, we selected 73 articles out of 1654 publications retrieved from PubMed. Based on this selection, this review summarizes the recent application of MRI in clinical trials, defining the predementia stages of AD, the clinical utility of MRI, proposal of novel biomarkers and brain regions of interest, and assessing the relationship between MRI and cognitive features, risk and protective factors of AD. Finally, the value of a multiparametric approach in clinical and preclinical stages of AD is discussed.
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Affiliation(s)
- Michela Leocadi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisa Canu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Davide Calderaro
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Davide Corbetta
- Laboratory of Movement Analysis, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, Neurology and Neurophysiology Units, IRCCS San Raffaele Scientific Institute, and Vita-Salute San Raffaele University, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, and Vita-Salute San Raffaele University, Via Olgettina 60, Milan 20132, Italy
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27
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Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes. Sci Rep 2020; 10:11237. [PMID: 32641807 PMCID: PMC7343779 DOI: 10.1038/s41598-020-68118-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 05/21/2020] [Indexed: 11/23/2022] Open
Abstract
Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main subtypes: behavioural-variant FTD, non-fluent/agrammatic variant primary progressive aphasia and semantic variant PPA. To better understand the FTD subtypes and develop more specific treatments, correct diagnosis is essential. This study aimed to test the discrimination power of a novel set of cortical Diffusion Tensor Imaging measures (DTI), on FTD subtypes. A total of 96 subjects with FTD and 84 healthy subjects (HS) were included in the study. A “selection cohort” was used to determine the set of features (measurements) and to use them to select the “best” machine learning classifier from a range of seven main models. The selected classifier was trained on a “training cohort” and tested on a third cohort (“test cohort”). The classifier was used to assess the classification power for binary (HS vs. FTD), and multiclass (HS and FTD subtypes) classification problems. In the binary classification, one of the new DTI features obtained the highest accuracy (85%) as a single feature, and when it was combined with other DTI features and two other common clinical measures (grey matter fraction and MMSE), obtained an accuracy of 88%. The new DTI features can distinguish between HS and FTD subgroups with an accuracy of 76%. These results suggest that DTI measures could support differential diagnosis in a clinical setting, potentially improve efficacy of new innovative drug treatments through effective patient selection, stratification and measurement of outcomes.
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28
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Ducharme S, Dols A, Laforce R, Devenney E, Kumfor F, van den Stock J, Dallaire-Théroux C, Seelaar H, Gossink F, Vijverberg E, Huey E, Vandenbulcke M, Masellis M, Trieu C, Onyike C, Caramelli P, de Souza LC, Santillo A, Waldö ML, Landin-Romero R, Piguet O, Kelso W, Eratne D, Velakoulis D, Ikeda M, Perry D, Pressman P, Boeve B, Vandenberghe R, Mendez M, Azuar C, Levy R, Le Ber I, Baez S, Lerner A, Ellajosyula R, Pasquier F, Galimberti D, Scarpini E, van Swieten J, Hornberger M, Rosen H, Hodges J, Diehl-Schmid J, Pijnenburg Y. Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders. Brain 2020; 143:1632-1650. [PMID: 32129844 PMCID: PMC7849953 DOI: 10.1093/brain/awaa018] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 11/27/2019] [Accepted: 12/08/2019] [Indexed: 12/12/2022] Open
Abstract
The behavioural variant of frontotemporal dementia (bvFTD) is a frequent cause of early-onset dementia. The diagnosis of bvFTD remains challenging because of the limited accuracy of neuroimaging in the early disease stages and the absence of molecular biomarkers, and therefore relies predominantly on clinical assessment. BvFTD shows significant symptomatic overlap with non-degenerative primary psychiatric disorders including major depressive disorder, bipolar disorder, schizophrenia, obsessive-compulsive disorder, autism spectrum disorders and even personality disorders. To date, ∼50% of patients with bvFTD receive a prior psychiatric diagnosis, and average diagnostic delay is up to 5-6 years from symptom onset. It is also not uncommon for patients with primary psychiatric disorders to be wrongly diagnosed with bvFTD. The Neuropsychiatric International Consortium for Frontotemporal Dementia was recently established to determine the current best clinical practice and set up an international collaboration to share a common dataset for future research. The goal of the present paper was to review the existing literature on the diagnosis of bvFTD and its differential diagnosis with primary psychiatric disorders to provide consensus recommendations on the clinical assessment. A systematic literature search with a narrative review was performed to determine all bvFTD-related diagnostic evidence for the following topics: bvFTD history taking, psychiatric assessment, clinical scales, physical and neurological examination, bedside cognitive tests, neuropsychological assessment, social cognition, structural neuroimaging, functional neuroimaging, CSF and genetic testing. For each topic, responsible team members proposed a set of minimal requirements, optimal clinical recommendations, and tools requiring further research or those that should be developed. Recommendations were listed if they reached a ≥ 85% expert consensus based on an online survey among all consortium participants. New recommendations include performing at least one formal social cognition test in the standard neuropsychological battery for bvFTD. We emphasize the importance of 3D-T1 brain MRI with a standardized review protocol including validated visual atrophy rating scales, and to consider volumetric analyses if available. We clarify the role of 18F-fluorodeoxyglucose PET for the exclusion of bvFTD when normal, whereas non-specific regional metabolism abnormalities should not be over-interpreted in the case of a psychiatric differential diagnosis. We highlight the potential role of serum or CSF neurofilament light chain to differentiate bvFTD from primary psychiatric disorders. Finally, based on the increasing literature and clinical experience, the consortium determined that screening for C9orf72 mutation should be performed in all possible/probable bvFTD cases or suspected cases with strong psychiatric features.
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Affiliation(s)
- Simon Ducharme
- Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Str., Montreal, Quebec, H3A 2B4, Canada
| | - Annemiek Dols
- Department of Old Age Psychiatry, GGZ InGeest, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire (CIME), Laval University, Quebec, Canada
| | - Emma Devenney
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Fiona Kumfor
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Jan van den Stock
- Laboratory for Translational Neuropsychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | | | - Harro Seelaar
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Flora Gossink
- Department of Old Age Psychiatry, GGZ InGeest, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Everard Vijverberg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Edward Huey
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Psychiatry, Colombia University, New York, USA
| | - Mathieu Vandenbulcke
- Department of Geriatric Psychiatry, University Hospitals Leuven, Leuven, Belgium
| | - Mario Masellis
- Department of Neurology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Calvin Trieu
- Department of Old Age Psychiatry, GGZ InGeest, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Chiadi Onyike
- Division of Geriatric Psychiatry and Neuropsychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Paulo Caramelli
- Behavioral and Cognitive Neurology Research Group, Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Leonardo Cruz de Souza
- Behavioral and Cognitive Neurology Research Group, Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Maria Landqvist Waldö
- Division of Clinical Sciences Helsingborg, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | | | - Olivier Piguet
- Division of Clinical Sciences Helsingborg, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Wendy Kelso
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia
| | - Dhamidhu Eratne
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia
| | - Dennis Velakoulis
- Neuropsychiatry Unit, Royal Melbourne Hospital, Melbourne, Australia
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - David Perry
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, USA
| | - Peter Pressman
- Department of Neurology, University of Colorado Denver, Aurora, USA
| | - Bradley Boeve
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Rik Vandenberghe
- Department of Neurology, University Hospital Leuven, Leuven, Belgium
| | - Mario Mendez
- Department of Neurology, UCLA Medical Centre, University of California Los Angeles, Los Angeles, USA
| | - Carole Azuar
- Department of Neurology, Hôpital La Pitié Salpêtrière, Paris, France
| | - Richard Levy
- Department of Neurology, Hôpital La Pitié Salpêtrière, Paris, France
| | - Isabelle Le Ber
- Department of Neurology, Hôpital La Pitié Salpêtrière, Paris, France
| | - Sandra Baez
- Department of Psychology, Andes University, Bogota, Colombia
| | - Alan Lerner
- Department of Neurology, University Hospital Cleveland Medical Center, Cleveland, USA
| | - Ratnavalli Ellajosyula
- Department of Neurology, Manipal Hospital and Annasawmy Mudaliar Hospital, Bangalore, India
| | - Florence Pasquier
- Univ Lille, Inserm U1171, Memory Center, CHU Lille, DISTAlz, Lille, France
| | - Daniela Galimberti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Centro Dino Ferrari, Milan, Italy
- Fondazione IRCCS Ca’ Granda, Ospedale Policlinico, Neurodegenerative Diseases Unit Milan, Italy
| | - Elio Scarpini
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Centro Dino Ferrari, Milan, Italy
- Fondazione IRCCS Ca’ Granda, Ospedale Policlinico, Neurodegenerative Diseases Unit Milan, Italy
| | - John van Swieten
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | | | - Howard Rosen
- Memory and Aging Center, University of California San Francisco, San Francisco, USA
| | - John Hodges
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Technical University of Munich, School of Medicine, Munich, Germany
| | - Yolande Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Chagué P, Marro B, Fadili S, Houot M, Morin A, Samper-González J, Beunon P, Arrivé L, Dormont D, Dubois B, Teichmann M, Epelbaum S, Colliot O. Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps. J Neuroradiol 2020; 48:412-418. [PMID: 32407907 DOI: 10.1016/j.neurad.2020.04.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 04/25/2020] [Accepted: 04/26/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND AND PURPOSE Many artificial intelligence tools are currently being developed to assist diagnosis of dementia from magnetic resonance imaging (MRI). However, these tools have so far been difficult to integrate in the clinical routine workflow. In this work, we propose a new simple way to use them and assess their utility for improving diagnostic accuracy. MATERIALS AND METHODS We studied 34 patients with early-onset Alzheimer's disease (EOAD), 49 with late-onset AD (LOAD), 39 with frontotemporal dementia (FTD) and 24 with depression from the pre-existing cohort CLIN-AD. Support vector machine (SVM) automatic classifiers using 3D T1 MRI were trained to distinguish: LOAD vs. Depression, FTD vs. LOAD, EOAD vs. Depression, EOAD vs. FTD. We extracted SVM weight maps, which are tridimensional representations of discriminant atrophy patterns used by the classifier to take its decisions and we printed posters of these maps. Four radiologists (2 senior neuroradiologists and 2 unspecialized junior radiologists) performed a visual classification of the 4 diagnostic pairs using 3D T1 MRI. Classifications were performed twice: first with standard radiological reading and then using SVM weight maps as a guide. RESULTS Diagnostic performance was significantly improved by the use of the weight maps for the two junior radiologists in the case of FTD vs. EOAD. Improvement was over 10 points of diagnostic accuracy. CONCLUSION This tool can improve the diagnostic accuracy of junior radiologists and could be integrated in the clinical routine workflow.
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Affiliation(s)
- Pierre Chagué
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France; Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France
| | - Béatrice Marro
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Sarah Fadili
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Marion Houot
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France
| | - Alexandre Morin
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France
| | - Jorge Samper-González
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France
| | - Paul Beunon
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Lionel Arrivé
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Didier Dormont
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France
| | - Marc Teichmann
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France.
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Bachli MB, Sedeño L, Ochab JK, Piguet O, Kumfor F, Reyes P, Torralva T, Roca M, Cardona JF, Campo CG, Herrera E, Slachevsky A, Matallana D, Manes F, García AM, Ibáñez A, Chialvo DR. Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach. Neuroimage 2019; 208:116456. [PMID: 31841681 PMCID: PMC7008715 DOI: 10.1016/j.neuroimage.2019.116456] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 10/29/2019] [Accepted: 12/09/2019] [Indexed: 12/12/2022] Open
Abstract
Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.
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Affiliation(s)
- M Belen Bachli
- Center for Complex Systems & Brain Sciences (CEMSC(3)), Escuela de Ciencia y Tecnologia (ECyT), Universidad Nacional de San Martín, 25 de Mayo 1169, San Martín, (1650), Buenos Aires, Argentina
| | - Lucas Sedeño
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina.
| | - Jeremi K Ochab
- Marian Smoluchowski Institute of Physics, Mark Kac Complex Systems Research Center Jagiellonian University, Ul. Łojasiewicza 11, PL30-348, Kraków, Poland
| | - Olivier Piguet
- ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Fiona Kumfor
- ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Pablo Reyes
- Radiology, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia; Medical School, Physiology Sciences, Psychiatry and Mental Health Pontificia Universidad Javeriana (PUJ) - Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia
| | - Teresa Torralva
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
| | - María Roca
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
| | | | - Cecilia Gonzalez Campo
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad Icesi, Cali, Colombia
| | - Andrea Slachevsky
- Gerosciences Center for Brain Health and Metabolism, Santiago, Chile; Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department, ICBM, Neurosciences Department, East Neuroscience Department, Faculty of Medicine, University of Chile, Avenida Salvador 486, Providencia, Santiago, Chile; Memory and Neuropsychiatric Clinic (CMYN) Neurology Department- Hospital del Salvador & University of Chile, Av. Salvador 364, Providencia, Santiago, Chile; Servicio de Neurología, Departamento de Medicina, Clínica Alemana-Universidad del Desarrollo, Chile
| | - Diana Matallana
- Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana (PUJ) - Centro de Memoria y Cognición Intellectus. Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia
| | - Facundo Manes
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia
| | - Adolfo M García
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; Faculty of Education, National University of Cuyo (UNCuyo), Sobremonte 74, C5500, Mendoza, Argentina
| | - Agustín Ibáñez
- Institute of Cognitive and Translational Neuroscience (INCYyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina; ARC Centre of Excellence in Cognition and Its Disorders, Sydney, Australia; Universidad Autónoma del Caribe, Calle 90, No 46-112, C2754, Barranquilla, Colombia; Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Diagonal Las Torres, 2640, Santiago, Chile
| | - Dante R Chialvo
- Center for Complex Systems & Brain Sciences (CEMSC(3)), Escuela de Ciencia y Tecnologia (ECyT), Universidad Nacional de San Martín, 25 de Mayo 1169, San Martín, (1650), Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Godoy Cruz 2290, Buenos Aires, Argentina
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Donnelly-Kehoe PA, Pascariello GO, García AM, Hodges JR, Miller B, Rosen H, Manes F, Landin-Romero R, Matallana D, Serrano C, Herrera E, Reyes P, Santamaria-Garcia H, Kumfor F, Piguet O, Ibanez A, Sedeño L. Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:588-598. [PMID: 31497638 PMCID: PMC6719282 DOI: 10.1016/j.dadm.2019.06.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem. METHODS We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier. RESULTS Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%). DISCUSSION This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.
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Affiliation(s)
- Patricio Andres Donnelly-Kehoe
- Multimedia Signal Processing Group - Neuroimage Division, French-Argentine International Center for Information and Systems Sciences (CIFASIS) - National Scientific and Technical Research Council (CONICET), Rosario, Argentina
- Laboratory of Neuroimaging and Neuroscience (LANEN), INECO Foundation Rosario, Rosario, Argentina
| | - Guido Orlando Pascariello
- Multimedia Signal Processing Group - Neuroimage Division, French-Argentine International Center for Information and Systems Sciences (CIFASIS) - National Scientific and Technical Research Council (CONICET), Rosario, Argentina
- Laboratory of Neuroimaging and Neuroscience (LANEN), INECO Foundation Rosario, Rosario, Argentina
| | - Adolfo M. García
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Faculty of Education, National University of Cuyo (UNCuyo), Mendoza, Argentina
| | - John R. Hodges
- Centre of Excellence in Cognition and its Disorders, Australian Research Council (ARC), Sydney, Australia
- The University of Sydney, Brain and Mind Centre and Clinical Medical School, Sydney, Australia
| | - Bruce Miller
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Howie Rosen
- Department of Neurology, Memory Aging Center, University of California, San Francisco, CA, USA
| | - Facundo Manes
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Centre of Excellence in Cognition and its Disorders, Australian Research Council (ARC), Sydney, Australia
| | - Ramon Landin-Romero
- Centre of Excellence in Cognition and its Disorders, Australian Research Council (ARC), Sydney, Australia
- The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Diana Matallana
- Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana (PUJ), Bogotá, Colombia
| | - Cecilia Serrano
- Memory and Balance Clinic, Buenos Aires, Argentina
- Department of Neurology, Dr Cesar Milstein Hospital, Buenos Aires, Argentina
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad Icesi, Cali, Colombia (eduar)
| | - Pablo Reyes
- Radiology, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia
- Pontificia Universidad Javeriana, Departments of Physiology and Psychiatry – Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Hernando Santamaria-Garcia
- Radiology, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia
- Pontificia Universidad Javeriana, Departments of Physiology and Psychiatry – Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Fiona Kumfor
- Centre of Excellence in Cognition and its Disorders, Australian Research Council (ARC), Sydney, Australia
- The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Olivier Piguet
- Centre of Excellence in Cognition and its Disorders, Australian Research Council (ARC), Sydney, Australia
- The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia
| | - Agustin Ibanez
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Centre of Excellence in Cognition and its Disorders, Australian Research Council (ARC), Sydney, Australia
- Universidad Autónoma del Caribe, Barranquilla, Colombia
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Lucas Sedeño
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
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Feis RA, Bouts MJRJ, de Vos F, Schouten TM, Panman JL, Jiskoot LC, Dopper EGP, van der Grond J, van Swieten JC, Rombouts SARB. A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers. J Neurol Neurosurg Psychiatry 2019; 90:1207-1214. [PMID: 31203211 DOI: 10.1136/jnnp-2019-320774] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/26/2019] [Accepted: 05/12/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between presymptomatic FTD mutation carriers and controls. Furthermore, we contrasted carriers that converted during follow-up ('converters') and non-converting carriers ('non-converters'). METHODS We acquired anatomical MRI, diffusion tensor imaging and resting-state functional MRI in 55 presymptomatic FTD mutation carriers and 48 healthy controls at baseline, and at 2, 4, and 6 years of follow-up as available. At each time point, FTD classification scores were calculated using a behavioural variant FTD classification model. Classification scores were tested in a mixed-effects model for mean differences and differences over time. RESULTS Presymptomatic mutation carriers did not have higher classification score increase over time than controls (p=0.15), although carriers had higher FTD classification scores than controls on average (p=0.032). However, converters (n=6) showed a stronger classification score increase over time than non-converters (p<0.001). CONCLUSIONS Our findings imply that presymptomatic FTD mutation carriers may remain similar to controls in terms of MRI-based classification scores until they are close to symptom onset. This proof-of-concept study shows the promise of longitudinal MRI data acquisition in combination with machine learning to contribute to early FTD diagnosis.
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Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands .,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Elise G P Dopper
- Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, Rotterdam, The Netherlands.,Department of Clinical Genetics, VU University Medical Centre, Amsterdam, The Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Institute of Psychology, Leiden University, Leiden, The Netherlands
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33
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Gupta Y, Lama RK, Kwon GR. Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Front Comput Neurosci 2019; 13:72. [PMID: 31680923 PMCID: PMC6805777 DOI: 10.3389/fncom.2019.00072] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 10/01/2019] [Indexed: 01/15/2023] Open
Abstract
Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.
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Bouts MJRJ, van der Grond J, Vernooij MW, Koini M, Schouten TM, de Vos F, Feis RA, Cremers LGM, Lechner A, Schmidt R, de Rooij M, Niessen WJ, Ikram MA, Rombouts SARB. Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. Hum Brain Mapp 2019; 40:2711-2722. [PMID: 30803110 PMCID: PMC6563478 DOI: 10.1002/hbm.24554] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 01/30/2019] [Accepted: 02/09/2019] [Indexed: 01/18/2023] Open
Abstract
Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population.
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Affiliation(s)
- Mark J. R. J. Bouts
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | | | - Meike W. Vernooij
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Marisa Koini
- Department of NeurologyMedical University of GrazAustria
| | - Tijn M. Schouten
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | - Frank de Vos
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | - Rogier A. Feis
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | - Lotte G. M. Cremers
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Anita Lechner
- Department of NeurologyMedical University of GrazAustria
| | | | - Mark de Rooij
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
| | - Wiro J. Niessen
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Medical InformaticsErasmus MC University Medical CenterRotterdamthe Netherlands
- Faculty of Applied SciencesDelft University of TechnologyDelftthe Netherlands
| | - M. Arfan Ikram
- Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of Radiology and Nuclear MedicineErasmus MC University Medical CenterRotterdamthe Netherlands
- Department of NeurologyErasmus MC University Medical CenterRotterdamthe Netherlands
| | - Serge A. R. B. Rombouts
- Institute of PsychologyLeiden UniversityLeidenthe Netherlands
- Department of RadiologyLeiden University Medical CenterLeidenthe Netherlands
- Leiden Institute for Brain and CognitionLeiden UniversityLeidenthe Netherlands
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Bouts MJRJ, Möller C, Hafkemeijer A, van Swieten JC, Dopper E, van der Flier WM, Vrenken H, Wink AM, Pijnenburg YAL, Scheltens P, Barkhof F, Schouten TM, de Vos F, Feis RA, van der Grond J, de Rooij M, Rombouts SARB. Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging. J Alzheimers Dis 2019; 62:1827-1839. [PMID: 29614652 DOI: 10.3233/jad-170893] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND/OBJECTIVE Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. METHODS Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). RESULTS Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). CONCLUSION Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.
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Affiliation(s)
- Mark J R J Bouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Christiane Möller
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Anne Hafkemeijer
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - John C van Swieten
- Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Elise Dopper
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands.,Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, London, UK
| | - Tijn M Schouten
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Frank de Vos
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Rogier A Feis
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Mark de Rooij
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Serge A R B Rombouts
- Institute of Psychology, Leiden University, Leiden, The Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
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Kim JP, Kim J, Park YH, Park SB, Lee JS, Yoo S, Kim EJ, Kim HJ, Na DL, Brown JA, Lockhart SN, Seo SW, Seong JK. Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease. NEUROIMAGE-CLINICAL 2019; 23:101811. [PMID: 30981204 PMCID: PMC6458431 DOI: 10.1016/j.nicl.2019.101811] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Revised: 03/30/2019] [Accepted: 04/01/2019] [Indexed: 01/18/2023]
Abstract
Background In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method. Methods We recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability. Results The classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1–4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA. Conclusions In the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions. We developed a machine learning-based automated classifier for differential diagnosis of FTD clinical syndromes and AD. Our classifier achieved good to excellent accuracy for each classification step. Discriminative regions are similar to previously known cortical atrophic patterns in each clinical syndrome.
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Affiliation(s)
- Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeonghun Kim
- Department of Bio-convergence Engineering, Korea University, Seoul, Republic of Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seong Beom Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jin San Lee
- Department of Neurology, Kyunghee University Medical Center, Seoul, Republic of Korea
| | - Sole Yoo
- Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea
| | - Eun-Joo Kim
- Department of Neurology, Busan National University Hospital, Busan, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jesse A Brown
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Samuel N Lockhart
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea; Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea; Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea; Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
| | - Joon-Kyung Seong
- Department of Bio-convergence Engineering, Korea University, Seoul, Republic of Korea; School of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
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Zhutovsky P, Vijverberg EGB, Bruin WB, Thomas RM, Wattjes MP, Pijnenburg YAL, van Wingen GA, Dols A. Individual Prediction of Behavioral Variant Frontotemporal Dementia Development Using Multivariate Pattern Analysis of Magnetic Resonance Imaging Data. J Alzheimers Dis 2019; 68:1229-1241. [PMID: 30909224 DOI: 10.3233/jad-181004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND Patients with behavioral variant of frontotemporal dementia (bvFTD) initially may only show behavioral and/or cognitive symptoms that overlap with other neurological and psychiatric disorders. The diagnostic accuracy is dependent on progressive symptoms worsening and frontotemporal abnormalities on neuroimaging findings. Predictive biomarkers could facilitate the early detection of bvFTD. OBJECTIVE To determine the prognostic accuracy of clinical and structural MRI data using a support vector machine (SVM) classification to predict the 2-year clinical follow-up diagnosis in a group of patients presenting late-onset behavioral changes. METHODS Data from 73 patients were included and divided into probable/definite bvFTD (n = 18), neurological (n = 28), and psychiatric (n = 27) groups based on 2-year follow-up diagnosis. Grey-matter volumes were extracted from baseline structural MRI scans. SVM classifiers were used to perform three binary classifications: bvFTD versus neurological and psychiatric, bvFTD versus neurological, and bvFTD versus psychiatric group(s), and one multi-class classification. Classification performance was determined for clinical and neuroimaging data separately and their combination using 5-fold cross-validation. RESULTS Accuracy of the binary classification tasks ranged from 72-82% (p < 0.001) with adequate sensitivity (67-79%), specificity (77-88%), and area-under-the-receiver-operator-curve (0.80-0.9). Multi-class accuracy ranged between 55-59% (p < 0.001). The combination of clinical and voxel-wise whole brain data showed the best performance overall. CONCLUSION These results show the potential for automated early confirmation of diagnosis for bvFTD using machine learning analysis of clinical and neuroimaging data in a diverse and clinically relevant sample of patients.
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Affiliation(s)
- Paul Zhutovsky
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Everard G B Vijverberg
- Amsterdam UMC, VU University Medical Center, Department of Neurology and Alzheimer Centre, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Department of Neurology, HagaZiekenhuis, The Hague, The Netherlands
| | - Willem B Bruin
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Rajat M Thomas
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Mike P Wattjes
- Amsterdam UMC, VU University Medical Center, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Yolande A L Pijnenburg
- Amsterdam UMC, VU University Medical Center, Department of Neurology and Alzheimer Centre, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Guido A van Wingen
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Annemiek Dols
- Amsterdam UMC, VU University Medical Center, Department of Neurology and Alzheimer Centre, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Department of Old Age Psychiatry, GGZ InGeest, Amsterdam, The Netherlands
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38
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Bruun M, Frederiksen KS, Rhodius-Meester HFM, Baroni M, Gjerum L, Koikkalainen J, Urhemaa T, Tolonen A, van Gils M, Rueckert D, Dyremose N, Andersen BB, Lemstra AW, Hallikainen M, Kurl S, Herukka SK, Remes AM, Waldemar G, Soininen H, Mecocci P, van der Flier WM, Lötjönen J, Hasselbalch SG. Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study. ALZHEIMERS RESEARCH & THERAPY 2019; 11:25. [PMID: 30894218 PMCID: PMC6425602 DOI: 10.1186/s13195-019-0482-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 03/11/2019] [Indexed: 12/19/2022]
Abstract
Background In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics. Methods In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0–100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy. Results After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI − 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI − 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (∆VAS = 4%, p < .0001). Conclusions Adding the PredictND tool to the clinical evaluation increased clinicians’ confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications. Electronic supplementary material The online version of this article (10.1186/s13195-019-0482-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Kristian S Frederiksen
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | | | - Timo Urhemaa
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Antti Tolonen
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
| | - Nadia Dyremose
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Birgitte B Andersen
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Merja Hallikainen
- Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Sudhir Kurl
- Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Sanna-Kaisa Herukka
- Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Anne M Remes
- Neurology, Neuro Center, Kuopio University Hospital, Kuopio, Finland.,Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Hilkka Soininen
- Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | | | - Steen G Hasselbalch
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
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Feis RA, Bouts MJRJ, Panman JL, Jiskoot LC, Dopper EGP, Schouten TM, de Vos F, van der Grond J, van Swieten JC, Rombouts SARB. Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI. Neuroimage Clin 2019; 22:101718. [PMID: 30827922 PMCID: PMC6543025 DOI: 10.1016/j.nicl.2019.101718] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
BACKGROUND Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. METHODS Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). RESULTS The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004). CONCLUSIONS FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.
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Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands.
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands; Institute of Psychology, Leiden University, Leiden, Netherlands.
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands.
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands.
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands; Alzheimer Centre & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, Netherlands.
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands; Institute of Psychology, Leiden University, Leiden, Netherlands.
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands; Institute of Psychology, Leiden University, Leiden, Netherlands.
| | | | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands; Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, Netherlands.
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands; Institute of Psychology, Leiden University, Leiden, Netherlands.
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40
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Bruun M, Koikkalainen J, Rhodius-Meester HFM, Baroni M, Gjerum L, van Gils M, Soininen H, Remes AM, Hartikainen P, Waldemar G, Mecocci P, Barkhof F, Pijnenburg Y, van der Flier WM, Hasselbalch SG, Lötjönen J, Frederiksen KS. Detecting frontotemporal dementia syndromes using MRI biomarkers. NEUROIMAGE-CLINICAL 2019; 22:101711. [PMID: 30743135 PMCID: PMC6369219 DOI: 10.1016/j.nicl.2019.101711] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 02/01/2019] [Accepted: 02/03/2019] [Indexed: 12/20/2022]
Abstract
Background Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another. Methods In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200). Results The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index. Conclusion This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia. Quantitative MRI biomarkers (API, ASI, and TPL) for detection of FTD and its subtypes. API differentiated FTD from other diagnostic groups with AUC of 83%. ASI and TPL showed highest performance for PPA subtypes. A subcortical bvFTD subtype resembling AD atrophy pattern seems undetectable for MRI.
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Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark.
| | | | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Mark van Gils
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland; Neurocenter, neurology, Kuopio University Hospital, Kuopio, Finland
| | - Anne M Remes
- Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland; Medical Research Center, Oulu University Hospital, Oulu, Finland
| | | | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; UCL institutes of Neurology and Healthcare Engineering, London, UK
| | - Yolande Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | | | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
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Ahmed MR, Zhang Y, Feng Z, Lo B, Inan OT, Liao H. Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects. IEEE Rev Biomed Eng 2018; 12:19-33. [PMID: 30561351 DOI: 10.1109/rbme.2018.2886237] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.
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42
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McCarthy J, Collins DL, Ducharme S. Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability. Neuroimage Clin 2018; 20:685-696. [PMID: 30218900 PMCID: PMC6140291 DOI: 10.1016/j.nicl.2018.08.028] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 07/31/2018] [Accepted: 08/28/2018] [Indexed: 01/21/2023]
Abstract
Frontotemporal dementia (FTD) is difficult to diagnose, due to its heterogeneous nature and overlap in symptoms with primary psychiatric disorders. Brain MRI for atrophy is a key biomarker but lacks sensitivity in the early stage. Morphometric MRI-based measures and machine learning techniques are a promising tool to improve diagnostic accuracy. Our aim was to review the current state of the literature using morphometric MRI to classify FTD and assess its applicability for clinical practice. A search was completed using Pubmed and PsychInfo of studies which conducted a classification of subjects with FTD from non-FTD (controls or another disorder) using morphometric MRI metrics on an individual level, using single or combined approaches. 28 relevant articles were included and systematically reviewed following PRISMA guidelines. The studies were categorized based on the type of FTD subjects included and the group(s) against which they were classified. Studies varied considerably in subject selection, MRI methodology, and classification approach, and results are highly heterogeneous. Overall many studies indicate good diagnostic accuracy, with higher performance when differentiating FTD from controls (highest result was accuracy of 100%) than other dementias (highest result was AUC of 0.874). Very few machine learning algorithms have been tested in prospective replication. In conclusion, morphometric MRI with machine learning shows potential as an early diagnostic biomarker of FTD, however studies which use rigorous methodology and validate findings in an independent real-life cohort are necessary before this method can be recommended for use clinically.
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Affiliation(s)
- Jillian McCarthy
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
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43
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Feis RA, Bouts MJRJ, Panman JL, Jiskoot LC, Dopper EGP, Schouten TM, de Vos F, van der Grond J, van Swieten JC, Rombouts SARB. Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI. Neuroimage Clin 2018; 20:188-196. [PMID: 30094168 PMCID: PMC6072645 DOI: 10.1016/j.nicl.2018.07.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/29/2018] [Accepted: 07/15/2018] [Indexed: 11/30/2022]
Abstract
Background Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005). Conclusions FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.
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Key Words
- (bv)FTD, (behavioural variant) Frontotemporal dementia
- (rs-f)MRI, (resting-state functional) Magnetic resonance imaging
- 3DT1w, 3-dimensional T1-weighted
- AUC, Area under the receiver operating characteristics curve
- AxD, Axial diffusivity
- C9orf72, Chromosome 9 open reading frame 72
- C9orf72, human
- DTI, Diffusion tensor imaging
- DWI, Diffusion-weighted imaging
- Diffusion Tensor Imaging
- FA, Fractional anisotropy
- FCor, Full correlations
- Frontotemporal dementia
- GM, Grey matter
- GMD, Grey matter density
- GRN protein, human
- GRN, Progranulin
- ICA, Independent component analysis
- MAPT protein, human
- MAPT, Microtubule-associated protein Tau
- MD, Mean diffusivity
- MMSE, Mini-mental state examination
- Multimodal MRI
- Pcor, Sparse L1-regularised partial correlations
- RD, Radial diffusivity
- ROC, Receiver operating characteristics
- Resting-state functional MRI
- TBSS, Tract-based spatial statistics
- WM, White matter
- WMD, White matter density
- classification
- machine learning
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Affiliation(s)
- Rogier A Feis
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands.
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Jessica L Panman
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands.
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands.
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands; Alzheimer Centre & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, the Netherlands.
| | - Tijn M Schouten
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Frank de Vos
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands.
| | - John C van Swieten
- Department of Neurology, Erasmus Medical Centre, Rotterdam, the Netherlands; Department of Clinical Genetics, Neuroscience Campus Amsterdam, VU University Medical Centre, Amsterdam, the Netherlands.
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands.
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Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks. Biosci Rep 2018; 38:BSR20180289. [PMID: 29572387 PMCID: PMC5938423 DOI: 10.1042/bsr20180289] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 03/20/2018] [Accepted: 03/20/2018] [Indexed: 12/29/2022] Open
Abstract
Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.
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Zhuo Z, Mo X, Ma X, Han Y, Li H. Identifying aMCI with functional connectivity network characteristics based on subtle AAL atlas. Brain Res 2018; 1696:81-90. [PMID: 29729253 DOI: 10.1016/j.brainres.2018.04.042] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 04/28/2018] [Accepted: 04/30/2018] [Indexed: 11/17/2022]
Abstract
PURPOSE To investigate the subtle functional connectivity alterations of aMCI based on AAL atlas with 1024 regions (AAL_1024 atlas). MATERIALS AND METHODS Functional MRI images of 32 aMCI patients (Male/Female: 15/17, Ages: 66.8 ± 8.36 y) and 35 normal controls (Male/Female:13/22, Ages: 62.4 ± 8.14 y) were obtained in this study. Firstly, functional connectivity networks were constructed by Pearson's Correlation based on the subtle AAL_1024 atlas. Then, local and global network parameters were calculated from the thresholding functional connectivity matrices. Finally, multiple-comparison analysis was performed on these parameters to find the functional network alterations of aMCI. And furtherly, a couple of classifiers were adopted to identify the aMCI by using the network parameters. RESULTS More subtle local brain functional alterations were detected by using AAL_1024 atlas. And the predominate nodes including hippocampus, inferior temporal gyrus, inferior parietal gyrus were identified which was not detected by AAL_90 atlas. The identification of aMCI from normal controls were significantly improved with the highest accuracy (98.51%), sensitivity (100%) and specificity (97.14%) compared to those (88.06%, 84.38% and 91.43% for the highest accuracy, sensitivity and specificity respectively) obtained by using AAL_90 atlas. CONCLUSION More subtle functional connectivity alterations of aMCI could be found based on AAL_1024 atlas than those based on AAL_90 atlas. Besides, the identification of aMCI could also be improved.
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Affiliation(s)
- Zhizheng Zhuo
- Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Xiao Mo
- Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Xiangyu Ma
- Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, China.
| | - Haiyun Li
- Lab of Computer Simulation and Medical Imaging Processing, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
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Tolonen A, Rhodius-Meester HFM, Bruun M, Koikkalainen J, Barkhof F, Lemstra AW, Koene T, Scheltens P, Teunissen CE, Tong T, Guerrero R, Schuh A, Ledig C, Baroni M, Rueckert D, Soininen H, Remes AM, Waldemar G, Hasselbalch SG, Mecocci P, van der Flier WM, Lötjönen J. Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier. Front Aging Neurosci 2018; 10:111. [PMID: 29922145 PMCID: PMC5996907 DOI: 10.3389/fnagi.2018.00111] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 04/03/2018] [Indexed: 01/18/2023] Open
Abstract
Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer's disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.
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Affiliation(s)
- Antti Tolonen
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Marie Bruun
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen, Denmark
| | | | - Frederik Barkhof
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands.,Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Afina W Lemstra
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Teddy Koene
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Charlotte E Teunissen
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Tong Tong
- Imperial College London, London, United Kingdom
| | | | | | | | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | | | - Hilkka Soininen
- Institute of Clinical Medicine and Department of Neurology, University of Eastern Finland, Kuopio, Finland.,Neurology, Neurocenter, Kuopio University Hospital, Kuopio, Finland
| | - Anne M Remes
- Institute of Clinical Medicine and Department of Neurology, University of Eastern Finland, Kuopio, Finland.,Neurology, Neurocenter, Kuopio University Hospital, Kuopio, Finland
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen, Denmark
| | | | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, Netherlands
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47
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Shi M, Tang L, Toledo JB, Ginghina C, Wang H, Aro P, Jensen PH, Weintraub D, Chen-Plotkin AS, Irwin DJ, Grossman M, McCluskey L, Elman LB, Wolk DA, Lee EB, Shaw LM, Trojanowski JQ, Zhang J. Cerebrospinal fluid α-synuclein contributes to the differential diagnosis of Alzheimer's disease. Alzheimers Dement 2018; 14:1052-1062. [PMID: 29604263 DOI: 10.1016/j.jalz.2018.02.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 11/08/2017] [Accepted: 02/07/2018] [Indexed: 10/17/2022]
Abstract
INTRODUCTION The ability of Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers (amyloid β peptide 1-42, total tau, and phosphorylated tau) to discriminate AD from related disorders is limited. Biomarkers for other concomitant pathologies (e.g., CSF α-synuclein [α-syn] for Lewy body pathology) may be needed to further improve the differential diagnosis. METHODS CSF total α-syn, phosphorylated α-syn at Ser129, and AD CSF biomarkers were evaluated with Luminex immunoassays in 367 participants, followed by validation in 74 different neuropathologically confirmed cases. RESULTS CSF total α-syn, when combined with amyloid β peptide 1-42 and either total tau or phosphorylated tau, improved the differential diagnosis of AD versus frontotemporal dementia, Lewy body disorders, or other neurological disorders. The diagnostic accuracy of the combined models attained clinical relevance (area under curve ∼0.9) and was largely validated in neuropathologically confirmed cases. DISCUSSION Combining CSF biomarkers representing AD and Lewy body pathologies may have clinical value in the differential diagnosis of AD.
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Affiliation(s)
- Min Shi
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Lu Tang
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA; Department of Neurology, Peking University Third Hospital, Beijing, China
| | - Jon B Toledo
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Carmen Ginghina
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Hua Wang
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA; Department of Pathology, Peking University Health Science Centre and Third Hospital, Beijing, China
| | - Patrick Aro
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Poul H Jensen
- DANDRITE-Danish Research Institute of Translational Neuroscience & Department of Biomedicine, University of Aarhus, Aarhus, Denmark
| | - Daniel Weintraub
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Alice S Chen-Plotkin
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David J Irwin
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Murray Grossman
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Leo McCluskey
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren B Elman
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Leslie M Shaw
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - John Q Trojanowski
- Departments of Pathology and Laboratory Medicine, Psychiatry, Neurology and Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Jing Zhang
- Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA; Department of Pathology, Peking University Health Science Centre and Third Hospital, Beijing, China.
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Abstract
Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.
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Nimmy John T, D Puthankattil S, Menon R. Analysis of long range dependence in the EEG signals of Alzheimer patients. Cogn Neurodyn 2018; 12:183-199. [PMID: 29564027 DOI: 10.1007/s11571-017-9467-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 11/14/2017] [Accepted: 12/19/2017] [Indexed: 11/28/2022] Open
Abstract
Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.
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Affiliation(s)
- T Nimmy John
- 1Department of Electrical Engineering, National Institute of Technology Calicut, Kozhikode, India
| | - Subha D Puthankattil
- 1Department of Electrical Engineering, National Institute of Technology Calicut, Kozhikode, India
| | - Ramshekhar Menon
- 2Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
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50
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Anazodo UC, Finger E, Kwan BYM, Pavlosky W, Warrington JC, Günther M, Prato FS, Thiessen JD, St Lawrence KS. Using simultaneous PET/MRI to compare the accuracy of diagnosing frontotemporal dementia by arterial spin labelling MRI and FDG-PET. NEUROIMAGE-CLINICAL 2017; 17:405-414. [PMID: 29159053 PMCID: PMC5683801 DOI: 10.1016/j.nicl.2017.10.033] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/24/2017] [Accepted: 10/28/2017] [Indexed: 11/30/2022]
Abstract
Purpose The clinical utility of FDG-PET in diagnosing frontotemporal dementia (FTD) has been well demonstrated over the past decades. On the contrary, the diagnostic value of arterial spin labelling (ASL) MRI - a relatively new technique - in clinical diagnosis of FTD has yet to be confirmed. Using simultaneous PET/MRI, we evaluated the diagnostic performance of ASL in identifying pathological abnormalities in FTD (FTD) to determine whether ASL can provide similar diagnostic value as FDG-PET. Methods ASL and FDG-PET images were compared in 10 patients with FTD and 10 healthy older adults. Qualitative and quantitative measures of diagnostic equivalency were used to determine the diagnostic utility of ASL compared to FDG-PET. Sensitivity, specificity, and inter-rater reliability were calculated for each modality from scores of subjective visual ratings and from analysis of regional mean values in thirteen a priori regions of interest (ROI). To determine the extent of concordance between modalities in each patient, individual statistical maps generated from comparison of each patient to controls were compared between modalities using the Jaccard similarity index (JI). Results Visual assessments revealed lower sensitivity, specificity and inter-rater reliability for ASL (66.67%/62.12%/0.2) compared to FDG-PET (88.43%/90.91%/0.61). Across all regions, ASL performed lower than FDG-PET in discriminating patients from controls (areas under the receiver operating curve: ASL = 0.75 and FDG-PET = 0.87). In all patients, ASL identified patterns of reduced perfusion consistent with FTD, but areas of hypometabolism exceeded hypoperfused areas (group-mean JI = 0.30 ± 0.22). Conclusion This pilot study demonstrated that ASL can detect similar spatial patterns of abnormalities in individual FTD patients compared to FDG-PET, but its sensitivity and specificity for discriminant diagnosis of a patient from healthy individuals remained unmatched to FDG-PET. Further studies at the individual level are required to confirm the clinical role of ASL in FTD management.
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Affiliation(s)
- Udunna C Anazodo
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, Medical Sciences Building, Rm M407, London, Ontario N6A 5C1, Canada.
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, Western University, 339 Windermere Road, London, Ontario N6A 5A5, Canada.
| | - Benjamin Yin Ming Kwan
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5W9, Canada
| | - William Pavlosky
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5W9, Canada.
| | - James Claude Warrington
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5W9, Canada.
| | - Matthias Günther
- Fraunhofer Institute for Medical Image Computing MEVIS, Am Fallturm 1, 28359 Bremen, Germany.; University Bremen, Faculty 1, Otto-Hahn-Allee 1, 28359 Bremen, Germany.
| | - Frank S Prato
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, Medical Sciences Building, Rm M407, London, Ontario N6A 5C1, Canada.
| | - Jonathan D Thiessen
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, Medical Sciences Building, Rm M407, London, Ontario N6A 5C1, Canada.
| | - Keith S St Lawrence
- Lawson Health Research Institute, St Joseph's Health Care, 268 Grosvenor St., London, Ontario N6A 4V2, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, Medical Sciences Building, Rm M407, London, Ontario N6A 5C1, Canada.
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