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Sadeghi MA, Stevens D, Kundu S, Sanghera R, Dagher R, Yedavalli V, Jones C, Sair H, Luna LP. Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01101-1. [PMID: 38780666 DOI: 10.1007/s10278-024-01101-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 05/25/2024]
Abstract
Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging when symptoms overlap or the conditions present atypically. Resting-state fMRI (rs-fMRI) studies have demonstrated condition-specific alterations in AD, FTD, and mild cognitive impairment (MCI) compared to healthy controls (HC). Here, we used machine learning to build a diagnostic classification model based on these alterations. We curated all rs-fMRIs and their corresponding clinical information from the ADNI and FTLDNI databases. Imaging data underwent preprocessing, time course extraction, and feature extraction in preparation for the analyses. The imaging features data and clinical variables were fed into gradient-boosted decision trees with fivefold nested cross-validation to build models that classified four groups: AD, FTD, HC, and MCI. The mean and 95% confidence intervals for model performance metrics were calculated using the unseen test sets in the cross-validation rounds. The model built using only imaging features achieved 74.4% mean balanced accuracy, 0.94 mean macro-averaged AUC, and 0.73 mean macro-averaged F1 score. It accurately classified FTD (F1 = 0.99), HC (F1 = 0.99), and MCI (F1 = 0.86) fMRIs but mostly misclassified AD scans as MCI (F1 = 0.08). Adding clinical variables to model inputs raised balanced accuracy to 91.1%, macro-averaged AUC to 0.99, macro-averaged F1 score to 0.92, and improved AD classification accuracy (F1 = 0.74). In conclusion, a multimodal model based on rs-fMRI and clinical data accurately differentiates AD-MCI vs. FTD vs. HC.
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Affiliation(s)
- Mohammad Amin Sadeghi
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA
| | - Daniel Stevens
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shinjini Kundu
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA
| | - Rohan Sanghera
- University of Cambridge, School of Clinical Medicine, Cambridge, UK
| | - Richard Dagher
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA
| | - Vivek Yedavalli
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA
| | - Craig Jones
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Haris Sair
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Licia P Luna
- Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 600 N Wolfe St, Phipps B100F, Baltimore, MD, 21287, USA.
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P L R, K S G. Revolutionizing dementia detection: Leveraging vision and Swin transformers for early diagnosis. Am J Med Genet B Neuropsychiatr Genet 2024:e32979. [PMID: 38619385 DOI: 10.1002/ajmg.b.32979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/01/2024] [Accepted: 03/04/2024] [Indexed: 04/16/2024]
Abstract
Dementia, an increasingly prevalent neurological disorder with a projected threefold rise globally by 2050, necessitates early detection for effective management. The risk notably increases after age 65. Dementia leads to a progressive decline in cognitive functions, affecting memory, reasoning, and problem-solving abilities. This decline can impact the individual's ability to perform daily tasks and make decisions, underscoring the crucial importance of timely identification. With the advent of technologies like computer vision and deep learning, the prospect of early detection becomes even more promising. Employing sophisticated algorithms on imaging data, such as positron emission tomography scans, facilitates the recognition of subtle structural brain changes, enabling diagnosis at an earlier stage for potentially more effective interventions. In an experimental study, the Swin transformer algorithm demonstrated superior overall accuracy compared to the vision transformer and convolutional neural network, emphasizing its efficiency. Detecting dementia early is essential for proactive management, personalized care, and implementing preventive measures, ultimately enhancing outcomes for individuals and lessening the overall burden on healthcare systems.
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Affiliation(s)
- Rini P L
- Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, India
| | - Gayathri K S
- Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, India
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Rogeau A, Hives F, Bordier C, Lahousse H, Roca V, Lebouvier T, Pasquier F, Huglo D, Semah F, Lopes R. A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET. Neuroimage 2024; 288:120530. [PMID: 38311126 DOI: 10.1016/j.neuroimage.2024.120530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/06/2024] Open
Abstract
With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.
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Affiliation(s)
- Antoine Rogeau
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK.
| | - Florent Hives
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France
| | - Cécile Bordier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
| | - Hélène Lahousse
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France
| | - Vincent Roca
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
| | - Thibaud Lebouvier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Memory Clinic, Lille University Hospitals, Lille, France
| | - Florence Pasquier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Memory Clinic, Lille University Hospitals, Lille, France
| | - Damien Huglo
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; Inserm, CHU Lille, University of Lille, U1189 OncoTHAI, Lille, France
| | - Franck Semah
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France
| | - Renaud Lopes
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
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Mattoli MV, Cocciolillo F, Chiacchiaretta P, Dotta F, Trevisi G, Carrarini C, Thomas A, Sensi S, Pizzi AD, Nicola ADD, Crosta AD, Mammarella N, Padovani A, Pilotto A, Moda F, Tiraboschi P, Martino G, Bonanni L. Combined 18F-FDG PET-CT markers in dementia with Lewy bodies. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12515. [PMID: 38145190 PMCID: PMC10746864 DOI: 10.1002/dad2.12515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/10/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023]
Abstract
INTRODUCTION 18F-Fluoro-deoxyglucose-positron emission tomography (FDG-PET) is a supportive biomarker in dementia with Lewy bodies (DLB) diagnosis and its advanced analysis methods, including radiomics and machine learning (ML), were developed recently. The aim of this study was to evaluate the FDG-PET diagnostic performance in predicting a DLB versus Alzheimer's disease (AD) diagnosis. METHODS FDG-PET scans were visually and semi-quantitatively analyzed in 61 patients. Radiomics and ML analyses were performed, building five ML models: (1) clinical features; (2) visual and semi-quantitative PET features; (3) radiomic features; (4) all PET features; and (5) overall features. RESULTS At follow-up, 34 patients had DLB and 27 had AD. At visual analysis, DLB PET signs were significantly more frequent in DLB, having the highest diagnostic accuracy (86.9%). At semi-quantitative analysis, the right precuneus, superior parietal, lateral occipital, and primary visual cortices showed significantly reduced uptake in DLB. The ML model 2 had the highest diagnostic accuracy (84.3%). DISCUSSION FDG-PET is a valuable tool in DLB diagnosis, having visual and semi-quantitative analyses with the highest diagnostic accuracy at ML analyses.
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Affiliation(s)
- Maria Vittoria Mattoli
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
- Nuclear Medicine UnitPresidio Ospedaliero Santo SpiritoPescaraItaly
| | - Fabrizio Cocciolillo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed EmatologiaUOC di Medicina Nucleare, Fondazione Policlinico Universitario Agostino Gemelli IRCCSRomeItaly
| | - Piero Chiacchiaretta
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
- Advanced Computing Core, Center for Advanced Studies and Technology ‐ C.A.S.TUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | - Francesco Dotta
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | - Gianluca Trevisi
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Claudia Carrarini
- Department of NeuroscienceCatholic University of Sacred HeartRomeItaly
- IRCCS San RaffaeleRomeItaly
| | - Astrid Thomas
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Stefano Sensi
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Andrea Delli Pizzi
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | | | - Adolfo Di Crosta
- Department of Psychological ScienceHumanities and TerritoryUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Department of Medicine and Aging SciencesUniversity G d'Annunzio of Chieti‐PescaraChietiItaly
| | - Nicola Mammarella
- Department of Psychological ScienceHumanities and TerritoryUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
- Parkinson's Disease Rehabilitation CentreFERB ONLUS‐S. Isidoro HospitalTrescore BalnearioBergamoItaly
| | - Fabio Moda
- Division of Neurology 5 and NeuropathologyFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Pietro Tiraboschi
- Division of Neurology 5 and NeuropathologyFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Gianluigi Martino
- Department of Radiological Sciences, Nuclear Medicine UniteSS. Annunziata HospitalVia dei Vestini 31ChietiItaly
| | - Laura Bonanni
- Department of Medicine and Aging SciencesUniversity G d'Annunzio of Chieti‐PescaraChietiItaly
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Barbero JA, Unadkat P, Choi YY, Eidelberg D. Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease. Neurotherapeutics 2023; 20:1653-1668. [PMID: 37684533 PMCID: PMC10684458 DOI: 10.1007/s13311-023-01433-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.
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Affiliation(s)
- János A Barbero
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | - Prashin Unadkat
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, 11030, USA
| | - Yoon Young Choi
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA.
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Perovnik M, Tang CC, Namías M, Eidelberg D. Longitudinal changes in metabolic network activity in early Alzheimer's disease. Alzheimers Dement 2023; 19:4061-4072. [PMID: 37204815 DOI: 10.1002/alz.13137] [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: 02/23/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/20/2023]
Abstract
INTRODUCTION The progression of Alzheimer's disease (AD) has been linked to two metabolic networks, the AD-related pattern (ADRP) and the default mode network (DMN). METHODS Converting and clinically stable cognitively normal subjects (n = 47) and individuals with mild cognitive impairment (n = 96) underwent 2-[18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) three or more times over 6 years (nscans = 705). Expression levels for ADRP and DMN were measured in each subject and time point, and the resulting changes were correlated with cognitive performance. The role of network expression in predicting conversion to dementia was also evaluated. RESULTS Longitudinal increases in ADRP expression were observed in converters, while age-related DMN loss was seen in converters and nonconverters. Cognitive decline correlated with increases in ADRP and declines in DMN, but conversion to dementia was predicted only by baseline ADRP levels. DISCUSSION The results point to the potential utility of ADRP as an imaging biomarker of AD progression.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Mauro Namías
- Fundación Centro Diagnóstico Nuclear, Buenos Aires, Argentina
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA
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