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Devanarayan V, Ye Y, Charil A, Andreozzi E, Sachdev P, Llano DA, Tian L, Zhu L, Hampel H, Kramer L, Dhadda S, Irizarry M. Predicting clinical progression trajectories of early Alzheimer's disease patients. Alzheimers Dement 2024; 20:1725-1738. [PMID: 38087949 PMCID: PMC10984448 DOI: 10.1002/alz.13565] [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: 04/26/2023] [Revised: 09/06/2023] [Accepted: 11/07/2023] [Indexed: 03/16/2024]
Abstract
BACKGROUND Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring. METHODS Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures. RESULTS The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%. DISCUSSION Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.
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Affiliation(s)
- Viswanath Devanarayan
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
- Department of MathematicsStatistics and Computer ScienceUniversity of Illinois ChicagoChicagoIllinoisUSA
| | - Yuanqing Ye
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Arnaud Charil
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | | | | | - Daniel A. Llano
- Carle Illinois College of MedicineUrbanaIllinoisUSA
- Department of Molecular and Integrative PhysiologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Lu Tian
- Department of Biomedical Data ScienceStanford University School of MedicinePalo AltoCaliforniaUSA
| | - Liang Zhu
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Harald Hampel
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Lynn Kramer
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
| | - Shobha Dhadda
- Clinical Evidence GenerationEisai Inc.NutleyNew JerseyUSA
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Blanco K, Salcidua S, Orellana P, Sauma-Pérez T, León T, Steinmetz LCL, Ibañez A, Duran-Aniotz C, de la Cruz R. Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease. Alzheimers Res Ther 2023; 15:176. [PMID: 37838690 PMCID: PMC10576366 DOI: 10.1186/s13195-023-01304-8] [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: 06/02/2023] [Accepted: 09/15/2023] [Indexed: 10/16/2023]
Abstract
Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.
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Affiliation(s)
- Kevin Blanco
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile
| | - Stefanny Salcidua
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Diagonal Las Torres 2700, Building D, Peñalolén, Santiago, Chile
| | - Paulina Orellana
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tania Sauma-Pérez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tomás León
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- Memory and Neuropsychiatric Center (CMYN) Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile
| | - Lorena Cecilia López Steinmetz
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Technische Universität Berlin, Berlin, Deutschland
- Instituto de Investigaciones Psicológicas (IIPsi), Universidad Nacional de Córdoba (UNC) y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
| | - Agustín Ibañez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- Global Brain Health Institute, University of California San Francisco (UCSF), San Francisco, CA, USA
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, & National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Claudia Duran-Aniotz
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile.
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Rolando de la Cruz
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
- Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Diagonal Las Torres 2700, Building D, Peñalolén, Santiago, Chile.
- Data Observatory Foundation, ANID Technology Center No. DO210001, Santiago, Chile.
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Elhady MM, Adly SA, Elshebiny HA, Moselhy SS. Detection the severity of organophosphate intoxication using sensitive serum biomarkers S100B and amyloid β (Aβ) in Egyptian subjects. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:105194-105201. [PMID: 37713081 PMCID: PMC10579114 DOI: 10.1007/s11356-023-29748-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 09/03/2023] [Indexed: 09/16/2023]
Abstract
Organophosphate (OP) is a compound considered the main leading cause of morbidity and mortality from poisoning worldwide. Serum pseudocholinesterase was evaluated as a diagnostic indicator; it cannot be used to monitor therapy or severity of the intoxication. The rationale of the current study was to evaluate sensitivity, specificity, and cut-off values of serum S100B and amyloid β for neurological affection severity. This study was carried out on sixty OP-impaired patients; in addition, 20 normal controls were included. Serum liver and kidney function tests, malondialdehyde, pseudocholinesterase, and the levels of S100B and amyloid β (Aβ) were determined. Data showed that Pearson's analysis indicated that the serum level of S100B was positively correlated with Aβ. On the contrary, the activity of pseudocholinesterase was negatively correlated with both of S100B and Aβ. Serum ALT, AST, creatinine, urea, acetylcholine, and MDA levels were elevated while pseudocholinesterase activity was reduced in moderate and severe OP intoxication versus control. A drastic elevation (p<0.001) in the levels of S100B and Aβ was performed in the patient group suffering from OP intoxication versus the normal group. The diagnostic statistical validation of targeted parameters in distinguishing between moderate OP intoxication and control clarifies that S100B displayed the best AUC (0.997) followed by Aβ (AUC=0.992), while the diagnostic veracity of S100B and Aβ in setting apart severe OP-intoxicated and normal subjects stated the symmetric efficacy of potential markers. It was concluded that the significant changes in the levels of S100B and Aβ were directly proportional to the degree of severity of OP intoxication.
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Affiliation(s)
- Mostafa M Elhady
- Biochemistry Department, Faculty of Science, Ain Shams University, Cairo, Egypt
| | - Sara A Adly
- Poison Control Center, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Husam A Elshebiny
- Poison Control Center, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Said S Moselhy
- Biochemistry Department, Faculty of Science, Ain Shams University, Cairo, Egypt.
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Saleh H, Elrashidy N, Elaziz MA, Aseeri AO, El-sappagh S. Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data.. [DOI: 10.21203/rs.3.rs-3250006/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Abstract
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression.
We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.
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Affiliation(s)
- Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt
| | - Nora ElRashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh, 13518, Egypt
| | - Mohamed Abd Elaziz
- Faculty of Computer Science and Engineerings, Galala University, Suez, 435611, Egypt, Egypt
| | - Ahmad O. Aseeri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineerings, Galala University, Suez, 435611, Egypt, Egypt
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Salmanpour MR, Rezaeijo SM, Hosseinzadeh M, Rahmim A. Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques. Diagnostics (Basel) 2023; 13:diagnostics13101696. [PMID: 37238180 DOI: 10.3390/diagnostics13101696] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/22/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Although handcrafted radiomics features (RF) are commonly extracted via radiomics software, employing deep features (DF) extracted from deep learning (DL) algorithms merits significant investigation. Moreover, a "tensor'' radiomics paradigm where various flavours of a given feature are generated and explored can provide added value. We aimed to employ conventional and tensor DFs, and compare their outcome prediction performance to conventional and tensor RFs. METHODS 408 patients with head and neck cancer were selected from TCIA. PET images were first registered to CT, enhanced, normalized, and cropped. We employed 15 image-level fusion techniques (e.g., dual tree complex wavelet transform (DTCWT)) to combine PET and CT images. Subsequently, 215 RFs were extracted from each tumor in 17 images (or flavours) including CT only, PET only, and 15 fused PET-CT images through the standardized-SERA radiomics software. Furthermore, a 3 dimensional autoencoder was used to extract DFs. To predict the binary progression-free-survival-outcome, first, an end-to-end CNN algorithm was employed. Subsequently, we applied conventional and tensor DFs vs. RFs as extracted from each image to three sole classifiers, namely multilayer perceptron (MLP), random-forest, and logistic regression (LR), linked with dimension reduction algorithms. RESULTS DTCWT fusion linked with CNN resulted in accuracies of 75.6 ± 7.0% and 63.4 ± 6.7% in five-fold cross-validation and external-nested-testing, respectively. For the tensor RF-framework, polynomial transform algorithms + analysis of variance feature selector (ANOVA) + LR enabled 76.67 ± 3.3% and 70.6 ± 6.7% in the mentioned tests. For the tensor DF framework, PCA + ANOVA + MLP arrived at 87.0 ± 3.5% and 85.3 ± 5.2% in both tests. CONCLUSIONS This study showed that tensor DF combined with proper machine learning approaches enhanced survival prediction performance compared to conventional DF, tensor and conventional RF, and end-to-end CNN frameworks.
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Affiliation(s)
- Mohammad R Salmanpour
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Technological Virtual Collaboration (TECVICO CORP.), Vancouver, BC V5E 3J7, Canada
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 6135715794, Iran
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO CORP.), Vancouver, BC V5E 3J7, Canada
- Department of Electrical & Computer Engineering, University of Tarbiat Modares, Tehran 14115111, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Maheux E, Koval I, Ortholand J, Birkenbihl C, Archetti D, Bouteloup V, Epelbaum S, Dufouil C, Hofmann-Apitius M, Durrleman S. Forecasting individual progression trajectories in Alzheimer's disease. Nat Commun 2023; 14:761. [PMID: 36765056 PMCID: PMC9918533 DOI: 10.1038/s41467-022-35712-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 12/19/2022] [Indexed: 02/12/2023] Open
Abstract
The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.
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Affiliation(s)
- Etienne Maheux
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Igor Koval
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Juliette Ortholand
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Colin Birkenbihl
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Damiano Archetti
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Vincent Bouteloup
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Stéphane Epelbaum
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), center of excellence of neurodegenerative diseases (CoEN), department of Neurology, DMU Neurosciences, Paris, France
| | - Carole Dufouil
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Martin Hofmann-Apitius
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France.
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Haulath K, Mohamed Basheer KP. TT self-weighted Deep-AD 3-Net: An AD stage and risk prediction. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2023. [DOI: 10.1080/20479700.2023.2175414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- K. Haulath
- Department of Computer Science, EMEA College of Arts and Science, Kondotty, India
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Marcisz A, Polanska J. Can T1-Weighted Magnetic Resonance Imaging Significantly Improve Mini-Mental State Examination-Based Distinguishing Between Mild Cognitive Impairment and Early-Stage Alzheimer's Disease? J Alzheimers Dis 2023; 92:941-957. [PMID: 36806505 PMCID: PMC10116132 DOI: 10.3233/jad-220806] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2023] [Indexed: 02/19/2023]
Abstract
BACKGROUND Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process. OBJECTIVE Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine. METHODS The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model. RESULTS The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis. CONCLUSION The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.
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Affiliation(s)
- Anna Marcisz
- Department of Data Science and Engineering, The Silesian University of Technology, Gliwice, Poland
| | | | - Joanna Polanska
- Department of Data Science and Engineering, The Silesian University of Technology, Gliwice, Poland
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Golovanevsky M, Eickhoff C, Singh R. Multimodal attention-based deep learning for Alzheimer's disease diagnosis. J Am Med Inform Assoc 2022; 29:2014-2022. [PMID: 36149257 PMCID: PMC9667156 DOI: 10.1093/jamia/ocac168] [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: 06/15/2022] [Revised: 08/10/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis. MATERIALS AND METHODS We present a Multimodal Alzheimer's Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in that we use cross-modal attention, which captures interactions between modalities-a method not previously explored in this domain. We perform multi-class classification, a challenging task considering the strong similarities between MCI and AD. We compare with previous state-of-the-art models, evaluate the importance of attention, and examine the contribution of each modality to the model's performance. RESULTS MADDi classifies MCI, AD, and controls with 96.88% accuracy on a held-out test set. When examining the contribution of different attention schemes, we found that the combination of cross-modal attention with self-attention performed the best, and no attention layers in the model performed the worst, with a 7.9% difference in F1-scores. DISCUSSION Our experiments underlined the importance of structured clinical data to help machine learning models contextualize and interpret the remaining modalities. Extensive ablation studies showed that any multimodal mixture of input features without access to structured clinical information suffered marked performance losses. CONCLUSION This study demonstrates the merit of combining multiple input modalities via cross-modal attention to deliver highly accurate AD diagnostic decision support.
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Affiliation(s)
- Michal Golovanevsky
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Carsten Eickhoff
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
- Center for Biomedical Informatics, Brown University, Providence, Rhode Island, USA
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
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10
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Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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11
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Zheng H, Petrella JR, Doraiswamy PM, Lin G, Hao W. Data-driven causal model discovery and personalized prediction in Alzheimer's disease. NPJ Digit Med 2022; 5:137. [PMID: 36076010 PMCID: PMC9458727 DOI: 10.1038/s41746-022-00632-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 06/16/2022] [Indexed: 12/03/2022] Open
Abstract
With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.
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Affiliation(s)
- Haoyang Zheng
- School of Mechanical Engineering, Purdue University, West Lafayette, 47907, IN, USA
| | - Jeffrey R Petrella
- Department of Radiology, Duke University Health System, Durham, 27710, NC, USA
| | - P Murali Doraiswamy
- Departments of Psychiatry and Medicine, Duke University School of Medicine and Duke Institute for Brain Sciences, Durham, 27710, NC, USA
| | - Guang Lin
- School of Mechanical Engineering, Purdue University, West Lafayette, 47907, IN, USA. .,Department of Mathematics, Purdue University, West Lafayette, 47907, IN, USA.
| | - Wenrui Hao
- Department of Mathematics, Penn State University, University Park, 16802, PA, USA
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Lombardi A, Diacono D, Amoroso N, Biecek P, Monaco A, Bellantuono L, Pantaleo E, Logroscino G, De Blasi R, Tangaro S, Bellotti R. A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease. Brain Inform 2022; 9:17. [PMID: 35882684 PMCID: PMC9325942 DOI: 10.1186/s40708-022-00165-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/03/2022] [Indexed: 11/11/2022] Open
Abstract
In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer's disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient's cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer's disease progression.
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Affiliation(s)
- Angela Lombardi
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Przemysław Biecek
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Ester Pantaleo
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Giancarlo Logroscino
- Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | | | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
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13
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Kohli M, Kar AK, Bangalore A, AP P. Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study. Brain Inform 2022; 9:16. [PMID: 35879626 PMCID: PMC9311349 DOI: 10.1186/s40708-022-00164-6] [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: 04/21/2022] [Accepted: 06/25/2022] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum is a brain development condition that impairs an individual's capacity to communicate socially and manifests through strict routines and obsessive-compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81-84%, with a normalized discounted cumulative gain of 79-81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models' treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.
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Affiliation(s)
- Manu Kohli
- Indian Institute of Technology-Delhi, Department of Management Studies, IV Floor, Vishwakarma Bhavan, Shaheed Jeet Singh Marg, Hauz Khas, New Delhi, 110016 India
| | - Arpan Kumar Kar
- Indian Institute of Technology-Delhi, Department of Management Studies, IV Floor, Vishwakarma Bhavan, Shaheed Jeet Singh Marg, Hauz Khas, New Delhi, 110016 India
| | - Anjali Bangalore
- ICON Centre, K. M. Chavan chawk, Shivajinagar Road, Garkheda, Aurangabad, 431005 India
| | - Prathosh AP
- Indian Institute of Science, CV Raman Rd, Bengaluru, 560012 Karnataka India
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14
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Platero C. Categorical predictive and disease progression modeling in the early stage of Alzheimer's disease. J Neurosci Methods 2022; 374:109581. [PMID: 35346695 DOI: 10.1016/j.jneumeth.2022.109581] [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: 11/10/2021] [Revised: 03/02/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND A preclinical stage of Alzheimer's disease (AD) precedes the symptomatic phases of mild cognitive impairment (MCI) and dementia, which constitutes a window of opportunities for preventive therapies or delaying dementia onset. NEW METHOD We propose to use categorical predictive models based on survival analysis with longitudinal data which are capable of determining subsets of markers to classify cognitively unimpaired (CU) subjects who progress into MCI/dementia or not. Subsequently, the proposed combination of markers was used to construct disease progression models (DPMs), which reveal long-term pathological trajectories from short-term clinical data. The proposed methodology was applied to a population recruited by the ADNI. RESULTS A very small subset of standard MRI-based data, CSF markers and cognitive measures was used to predict CU-to-MCI/dementia progression. The longitudinal data of these selected markers were used to construct DPMs using the algorithms of growth models by alternating conditional expectation (GRACE) and the latent time joint mixed effects model (LTJMM). The results show that the natural history of the proposed cognitive decline classifies the subjects well according to the clinical groups and shows a moderate correlation between the conversion times and their estimates by the algorithms. COMPARISON WITH EXISTING METHODS Unlike the training of the DPM algorithms without preselection of the markers, here, it is proposed to construct and evaluate the DPMs using the subsets of markers defined by the categorical predictive models. CONCLUSIONS The estimates of the natural history of the proposed cognitive decline from GRACE were more robust than those using LTJMM. The transition from normal to cognitive decline is mostly associated with an increase in temporal atrophy, worsening of clinical scores and pTAU/Aβ. Furthermore, pTAU/Aβ, Everyday Cognition score and the normalized volume of the entorhinal cortex show alterations of more than 20% fifteen years before the onset of cognitive decline.
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Affiliation(s)
- Carlos Platero
- Health Science Technology Group, Technical University of Madrid, Ronda de Valencia 3, 28012 Madrid, Spain
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15
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El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T. Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07263-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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16
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Howlett J, Hill SM, Ritchie CW, Tom BDM. Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort. Front Big Data 2021; 4:676168. [PMID: 34490422 PMCID: PMC8417903 DOI: 10.3389/fdata.2021.676168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/30/2021] [Indexed: 12/04/2022] Open
Abstract
A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.
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Affiliation(s)
- James Howlett
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Steven M Hill
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Craig W Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
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17
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Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021; 8:ENEURO.0475-20.2021. [PMID: 34045210 PMCID: PMC8260273 DOI: 10.1523/eneuro.0475-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/08/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022] Open
Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
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18
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K. P. MN, P. T. Alzheimer's classification using dynamic ensemble of classifiers selection algorithms: A performance analysis. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102729] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Jung W, Jun E, Suk HI. Deep recurrent model for individualized prediction of Alzheimer's disease progression. Neuroimage 2021; 237:118143. [PMID: 33991694 DOI: 10.1016/j.neuroimage.2021.118143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 03/15/2021] [Accepted: 04/13/2021] [Indexed: 01/27/2023] Open
Abstract
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developing AD in its earliest time. While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling. Under the same problem settings, in this work, we propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status along with cognitive scores at multiple future time points. However, in handling time series data, it generally faces many unexpected missing observations. In regard to such an unfavorable situation, we define a secondary problem of estimating those missing values and tackle it in a systematic way by taking account of temporal and multivariate relations inherent in time series data. Concretely, we propose a deep recurrent network that jointly tackles the four problems of (i) missing value imputation, (ii) phenotypic measurements forecasting, (iii) trajectory estimation of a cognitive score, and (iv) clinical status prediction of a subject based on his/her longitudinal imaging biomarkers. Notably, the learnable parameters of all the modules in our predictive models are trained in an end-to-end manner by taking the morphological features and cognitive scores as input, with our circumspectly defined loss function. In our experiments over The Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge cohort, we measured performance for various metrics and compared our method to competing methods in the literature. Exhaustive analyses and ablation studies were also conducted to better confirm the effectiveness of our method.
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Affiliation(s)
- Wonsik Jung
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Eunji Jun
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea; Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea.
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20
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Velazquez M, Lee Y. Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects. PLoS One 2021; 16:e0244773. [PMID: 33914757 PMCID: PMC8084194 DOI: 10.1371/journal.pone.0244773] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/12/2021] [Indexed: 12/01/2022] Open
Abstract
Alzheimer's Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.
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Affiliation(s)
- Matthew Velazquez
- Department of Computer Science, University of Missouri - Kansas City, Kansas City, MO, United States of America
| | - Yugyung Lee
- Department of Computer Science, University of Missouri - Kansas City, Kansas City, MO, United States of America
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21
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Koval I, Bône A, Louis M, Lartigue T, Bottani S, Marcoux A, Samper-González J, Burgos N, Charlier B, Bertrand A, Epelbaum S, Colliot O, Allassonnière S, Durrleman S. AD Course Map charts Alzheimer's disease progression. Sci Rep 2021; 11:8020. [PMID: 33850174 PMCID: PMC8044144 DOI: 10.1038/s41598-021-87434-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 03/22/2021] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer's disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient's cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer's disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.
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Affiliation(s)
- Igor Koval
- 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
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Alexandre Bône
- 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
| | - Maxime Louis
- 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
| | - Thomas Lartigue
- 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
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Simona Bottani
- 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
| | - Arnaud Marcoux
- 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
| | - 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
| | - Ninon Burgos
- 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
| | - Benjamin Charlier
- 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
- Laboratoire Alexandre Grotendieck, Université de Montpellier, Montpellier, France
| | - Anne Bertrand
- 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
- AP-HP, Hôpital de la Pitié Salpêtrière, 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
- AP-HP, Hôpital de la Pitié Salpêtrière, 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
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphanie Allassonnière
- Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Stanley Durrleman
- 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.
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22
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Abuhmed T, El-Sappagh S, Alonso JM. Robust hybrid deep learning models for Alzheimer’s progression detection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106688] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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23
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Ansart M, Epelbaum S, Bassignana G, Bône A, Bottani S, Cattai T, Couronné R, Faouzi J, Koval I, Louis M, Thibeau-Sutre E, Wen J, Wild A, Burgos N, Dormont D, Colliot O, Durrleman S. Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Med Image Anal 2020; 67:101848. [PMID: 33091740 DOI: 10.1016/j.media.2020.101848] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 08/17/2020] [Accepted: 08/31/2020] [Indexed: 11/25/2022]
Abstract
We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues. The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. We found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments. We also identified several methodological issues, including the absence of a test set, or its use for feature selection or parameter tuning in nearly a fourth of the papers. Other issues, found in 15% of the studies, cast doubts on the relevance of the method to clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. These issues highlight the importance of adhering to good practices for the use of machine learning as a decision support system for the clinical practice.
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Affiliation(s)
- Manon Ansart
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France.
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, F-75013, France
| | - Giulia Bassignana
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Alexandre Bône
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Simona Bottani
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Tiziana Cattai
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Dept. of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Italy
| | - Raphaël Couronné
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Johann Faouzi
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Igor Koval
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Maxime Louis
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Elina Thibeau-Sutre
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Junhao Wen
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Adam Wild
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Didier Dormont
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, F-75013, France; AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Stanley Durrleman
- Inria, Aramis project-team, Paris, F-75013, France; Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France
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Rallabandi VS, Tulpule K, Gattu M. Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100305] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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