1
|
Wang X, Zhou S, Ye N, Li Y, Zhou P, Chen G, Hu H. Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal. BMC Geriatr 2024; 24:531. [PMID: 38898411 PMCID: PMC11188292 DOI: 10.1186/s12877-024-05044-8] [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: 11/24/2023] [Accepted: 05/06/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Mild cognitive impairment has received widespread attention as a high-risk population for Alzheimer's disease, and many studies have developed or validated predictive models to assess it. However, the performance of the model development remains unknown. OBJECTIVE The objective of this review was to provide an overview of prediction models for the risk of Alzheimer's disease dementia in older adults with mild cognitive impairment. METHOD PubMed, EMBASE, Web of Science, and MEDLINE were systematically searched up to October 19, 2023. We included cohort studies in which risk prediction models for Alzheimer's disease dementia in older adults with mild cognitive impairment were developed or validated. The Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed to assess model bias and applicability. Random-effects models combined model AUCs and calculated (approximate) 95% prediction intervals for estimations. Heterogeneity across studies was evaluated using the I2 statistic, and subgroup analyses were conducted to investigate sources of heterogeneity. Additionally, funnel plot analysis was utilized to identify publication bias. RESULTS The analysis included 16 studies involving 9290 participants. Frequency analysis of predictors showed that 14 appeared at least twice and more, with age, functional activities questionnaire, and Mini-mental State Examination scores of cognitive functioning being the most common predictors. From the studies, only two models were externally validated. Eleven studies ultimately used machine learning, and four used traditional modelling methods. However, we found that in many of the studies, there were problems with insufficient sample sizes, missing important methodological information, lack of model presentation, and all of the models were rated as having a high or unclear risk of bias. The average AUC of the 15 best-developed predictive models was 0.87 (95% CI: 0.83, 0.90). DISCUSSION Most published predictive modelling studies are deficient in rigour, resulting in a high risk of bias. Upcoming research should concentrate on enhancing methodological rigour and conducting external validation of models predicting Alzheimer's disease dementia. We also emphasize the importance of following the scientific method and transparent reporting to improve the accuracy, generalizability and reproducibility of study results. REGISTRATION This systematic review was registered in PROSPERO (Registration ID: CRD42023468780).
Collapse
Affiliation(s)
- Xiaotong Wang
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Shi Zhou
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Niansi Ye
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Yucan Li
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Pengjun Zhou
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Gao Chen
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Hui Hu
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China.
- Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Wuhan, China.
- Hubei Shizhen Laboratory, Wuhan, China.
| |
Collapse
|
2
|
Wang X, Qi C, Li X, Li D, Ding H, Shen J, Liu Y, Xi Y. The role of dietary fats on cognition and sarcopenia in the elderly. Asia Pac J Clin Nutr 2024; 33:272-282. [PMID: 38794985 PMCID: PMC11170001 DOI: 10.6133/apjcn.202406_33(2).0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/09/2024] [Accepted: 03/11/2024] [Indexed: 05/27/2024]
Abstract
BACKGROUND AND OBJECTIVES To elucidate the role of dietary fats on the relationship between mild cognitive impairment and sarcopenia and help identifying and preventing the decline of cognitive and muscle function in elderly individuals. METHODS AND STUDY DESIGN The study conducted involving a group of 1812 individuals between the ages of 61 and 92. Body composition and BMR were assessed by bioelectrical impedance analysis. Cognitive function and dietary nutrition were evaluated by neuropsychological assessments and questionnaire of food intake frequency. Lipidomics analysis was performed using UHPLC-Qtrap-MS/MS. RESULTS MCI and SA are mutual influencing factors, lower intake of MUFA, PUFA and higher intake of fat was associated with cognitive dysfunction and/or SA (p < 0.05). PUFA was important for MCI combined with SA (Compared with Q1, Q4 OR: 0.176, 95%CI: 0.058,0.533). Lipidomics analysis revealed that triacylglycerol (TAG) contain more carbon chains with saturated double bonds may be closely related to cognitive impairment and the progression of SA (p < 0.05). While, DAG with carbon chains of unsaturated double bonds is opposite. CONCLUSIONS Insufficient intake of unsaturated fatty acids was associated with the development of cognitive decline and the progression of SA. MUFA affecting muscle health, fats and PUFA has a greater impact on MCI combined with SA. Less MUFA intake and increasing saturated double-bonded fatty acid intake might be the key factors on promoting cognitive impairment and SA in the elderly. They have the potential to serve as prospective biomarkers indicating a higher risk of cognitive decline and/or SA in the elderly population.
Collapse
Affiliation(s)
- Xianyun Wang
- School of Public Health, Capital Medical University, Beijing, China
| | - Chengyan Qi
- School of Public Health, Capital Medical University, Beijing, China
| | - Xiaoying Li
- Department of Geriatrics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Dajun Li
- School of Public Health, Capital Medical University, Beijing, China
| | - Huini Ding
- School of Public Health, Capital Medical University, Beijing, China
| | - Jing Shen
- School of Public Health, Capital Medical University, Beijing, China
| | - Yijia Liu
- School of Public Health, Capital Medical University, Beijing, China
| | - Yuandi Xi
- School of Public Health, Capital Medical University, Beijing, China.
| |
Collapse
|
3
|
Liu H, Zhang X, Liu H, Chong ST. Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study. Int J Public Health 2023; 68:1605322. [PMID: 36798738 PMCID: PMC9926933 DOI: 10.3389/ijph.2023.1605322] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023] Open
Abstract
Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment. Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.
Collapse
Affiliation(s)
- Haihong Liu
- Centre for Research in Psychology and Human Well-being, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, Malaysia,Department of Psychology, Chengde Medical University, Chengde, China
| | - Xiaolei Zhang
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China,Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
| | - Haining Liu
- Department of Psychology, Chengde Medical University, Chengde, China,Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, China,Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde, China,*Correspondence: Haining Liu, ; Sheau Tsuey Chong,
| | - Sheau Tsuey Chong
- Centre for Research in Psychology and Human Well-being, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, Malaysia,Counselling Psychology Programme, Secretariat of Postgraduate Studies, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, Malaysia,*Correspondence: Haining Liu, ; Sheau Tsuey Chong,
| |
Collapse
|
4
|
Wang X, Ezeana CF, Wang L, Puppala M, Huang Y, He Y, Yu X, Yin Z, Zhao H, Lai EC, Wong STC. Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia. ALZHEIMER'S & DEMENTIA: TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS 2022; 8:e12351. [PMID: 36204350 PMCID: PMC9520763 DOI: 10.1002/trc2.12351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 11/26/2022]
Abstract
Introduction Geriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. We aimed to identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life. Methods We identified risk factors, that is, demographics, hospital complications, pre‐admission, and post‐admission risk factors including medical history and comorbidities, affecting hospitalization outcomes determined by hospital stays and discharge dispositions. Over 150 clinical and demographic factors of 15,678 encounters (8407 patients) were retrieved from our institution's data warehouse. We further narrowed them down to twenty factors through feature selection engineering by using analysis of variance (ANOVA) and Glmnet. We developed an explainable machine‐learning model to predict hospitalization outcomes among geriatric patients with dementia. Results Our model is based on stacking ensemble learning and achieved accuracy of 95.6% and area under the curve (AUC) of 0.757. It outperformed prevalent methods of risk assessment for encounters of patients with Alzheimer's disease dementia (ADD) (4993), vascular dementia (VD) (4173), Parkinson's disease with dementia (PDD) (3735), and other unspecified dementias (OUD) (2777). Top identified hospitalization outcome risk factors, mostly from medical history, include encephalopathy, number of medical problems at admission, pressure ulcers, urinary tract infections, falls, admission source, age, race, anemia, etc., with several overlaps in multi‐dementia groups. Discussion Our model identified several predictive factors that can be modified or intervened so that efforts can be made to prevent recurrence or mitigate their adverse effects. Knowledge of the modifiable risk factors would help guide early interventions for patients at high risk for poor hospitalization outcome as defined by hospital stays longer than seven days, undesirable discharge disposition, or both. The interventions include starting specific protocols on modifiable risk factors like encephalopathy, falls, and infections, where non‐existent or not routine, to improve hospitalization outcomes of geriatric patients with dementia. Highlights A total 15,678 encounters of Geriatrics with dementia with a final 20 risk factors. Developed a predictive model for hospitalization outcomes for multi‐dementia types. Risk factors for each type were identified including those amenable to interventions. Top factors are encephalopathy, pressure ulcers, urinary tract infection (UTI), falls, and admission source. With accuracy of 95.6%, our ensemble predictive model outperforms other models.
Collapse
Affiliation(s)
- Xin Wang
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
| | - Chika F. Ezeana
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
| | - Lin Wang
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
| | - Mamta Puppala
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
| | | | - Yunjie He
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
| | - Xiaohui Yu
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
| | - Zheng Yin
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
| | - Hong Zhao
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
| | - Eugene C. Lai
- Neurological Institute Houston Methodist Hospital Houston Texas USA
| | - Stephen T. C. Wong
- T.T. & W.F. Chao Center for BRAIN Houston Methodist Academic Institute Houston Methodist Hospital Houston Texas USA
- Brain and Mind Research Institute Weill Cornell Medical College New York USA
| |
Collapse
|
5
|
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.
Collapse
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
| | | |
Collapse
|
6
|
Bae J, Stocks J, Heywood A, Jung Y, Jenkins L, Hill V, Katsaggelos A, Popuri K, Rosen H, Beg MF, Wang L. Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network. Neurobiol Aging 2021; 99:53-64. [PMID: 33422894 PMCID: PMC7902477 DOI: 10.1016/j.neurobiolaging.2020.12.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 11/09/2020] [Accepted: 12/05/2020] [Indexed: 01/02/2023]
Abstract
Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learning model to predict conversion from MCI to DAT. Structural magnetic resonance imaging scans were used as input to a 3-dimensional convolutional neural network. The 3-dimensional convolutional neural network was trained using transfer learning; in the source task, normal control and DAT scans were used to pretrain the model. This pretrained model was then retrained on the target task of classifying which MCI patients converted to DAT. Our model resulted in 82.4% classification accuracy at the target task, outperforming current models in the field. Next, we visualized brain regions that significantly contribute to the prediction of MCI conversion using an occlusion map approach. Contributory regions included the pons, amygdala, and hippocampus. Finally, we showed that the model's prediction value is significantly correlated with rates of change in clinical assessment scores, indicating that the model is able to predict an individual patient's future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI.
Collapse
Affiliation(s)
- Jinhyeong Bae
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Jane Stocks
- Department of Psychology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ashley Heywood
- Department of Psychology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Lisanne Jenkins
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Virginia Hill
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Burnaby, Canada
| | - Howie Rosen
- School of Medicine, University of California, San Francisco, CA, USA
| | - M Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, Canada
| | - Lei Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| |
Collapse
|
7
|
Sendi MSE, Zendehrouh E, Miller RL, Fu Z, Du Y, Liu J, Mormino EC, Salat DH, Calhoun VD. Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study. Front Neural Circuits 2021; 14:593263. [PMID: 33551754 PMCID: PMC7859281 DOI: 10.3389/fncir.2020.593263] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 12/15/2020] [Indexed: 12/24/2022] Open
Abstract
Background Alzheimer's disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart. Method We used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM. Results All states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks. Conclusion Our results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.
Collapse
Affiliation(s)
- Mohammad S. E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Elizabeth C. Mormino
- School of Medicine, Stanford University, Palo Alto, CA, United States
- Department of Neurology and Neurological Sciences, School of Medicine, Stanford University, Stanford, CA, United States
| | - David H. Salat
- Harvard Medical School, Cambridge, MA, United States
- Massachusetts General Hospital, Boston, MA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| |
Collapse
|
8
|
Jiang X, Wang L, Morgenstern LB, Cigolle CT, Claflin ES, Lisabeth LD. New Index for Multiple Chronic Conditions Predicts Functional Outcome in Ischemic Stroke. Neurology 2021; 96:e42-e53. [PMID: 33024024 PMCID: PMC7884978 DOI: 10.1212/wnl.0000000000010992] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 08/20/2020] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE To determine whether a new index for multiple chronic conditions (MCCs) predicts poststroke functional outcome (FO), we developed and internally validated the new MCC index in patients with ischemic stroke. METHODS A prospective cohort of patients with ischemic stroke (2008-2017) was interviewed at baseline and 90 days in the Brain Attack Surveillance in Corpus Christi Project. An average of 22 activities of daily living (ADL)/instrumental ADL (IADL) items measured the FO score (range 1-4) at 90 days. A FO score >3 (representing a lot of difficulty with ADL/IADLs) was considered unfavorable FO. A new index was developed using machine learning techniques to select and weight conditions and prestroke impairments. RESULTS Prestroke modified Rankin Scale (mRS) score, age, congestive heart failure (CHF), weight loss, diabetes, other neurologic disorders, and synergistic effects (dementia × age, CHF × renal failure, and prestroke mRS × prior stroke/TIA) were identified as important predictors in the MCC index. In the validation dataset, the index alone explained 31% of the variability in the FO score, was well-calibrated (p = 0.41), predicted unfavorable FO well (area under the receiver operating characteristic curve 0.81), and outperformed the modified Charlson Comorbidity Index in predicting the FO score and poststroke mRS. CONCLUSIONS A new MCC index was developed and internally validated to improve the prediction of poststroke FO. Novel predictors and synergistic interactions were identified. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that in patients with ischemic stroke, an index for MCC predicts FO at 90 days.
Collapse
Affiliation(s)
- Xiaqing Jiang
- From the Departments of Epidemiology (X.J., L.B.M., L.D.L.) and Biostatistics (L.W.), School of Public Health, University of Michigan; Stroke Program (L.B.M., E.S.C., L.D.L.), Department of Family Medicine (C.T.C.), Department of Internal Medicine (C.T.C.), and Ann Arbor Healthcare System, Department of Physical Medicine and Rehabilitation (E.S.C.), University of Michigan Medical School; and VA Geriatric Research Education and Clinical Center (C.T.C.), Ann Arbor, MI
| | - Lu Wang
- From the Departments of Epidemiology (X.J., L.B.M., L.D.L.) and Biostatistics (L.W.), School of Public Health, University of Michigan; Stroke Program (L.B.M., E.S.C., L.D.L.), Department of Family Medicine (C.T.C.), Department of Internal Medicine (C.T.C.), and Ann Arbor Healthcare System, Department of Physical Medicine and Rehabilitation (E.S.C.), University of Michigan Medical School; and VA Geriatric Research Education and Clinical Center (C.T.C.), Ann Arbor, MI
| | - Lewis B Morgenstern
- From the Departments of Epidemiology (X.J., L.B.M., L.D.L.) and Biostatistics (L.W.), School of Public Health, University of Michigan; Stroke Program (L.B.M., E.S.C., L.D.L.), Department of Family Medicine (C.T.C.), Department of Internal Medicine (C.T.C.), and Ann Arbor Healthcare System, Department of Physical Medicine and Rehabilitation (E.S.C.), University of Michigan Medical School; and VA Geriatric Research Education and Clinical Center (C.T.C.), Ann Arbor, MI
| | - Christine T Cigolle
- From the Departments of Epidemiology (X.J., L.B.M., L.D.L.) and Biostatistics (L.W.), School of Public Health, University of Michigan; Stroke Program (L.B.M., E.S.C., L.D.L.), Department of Family Medicine (C.T.C.), Department of Internal Medicine (C.T.C.), and Ann Arbor Healthcare System, Department of Physical Medicine and Rehabilitation (E.S.C.), University of Michigan Medical School; and VA Geriatric Research Education and Clinical Center (C.T.C.), Ann Arbor, MI
| | - Edward S Claflin
- From the Departments of Epidemiology (X.J., L.B.M., L.D.L.) and Biostatistics (L.W.), School of Public Health, University of Michigan; Stroke Program (L.B.M., E.S.C., L.D.L.), Department of Family Medicine (C.T.C.), Department of Internal Medicine (C.T.C.), and Ann Arbor Healthcare System, Department of Physical Medicine and Rehabilitation (E.S.C.), University of Michigan Medical School; and VA Geriatric Research Education and Clinical Center (C.T.C.), Ann Arbor, MI
| | - Lynda D Lisabeth
- From the Departments of Epidemiology (X.J., L.B.M., L.D.L.) and Biostatistics (L.W.), School of Public Health, University of Michigan; Stroke Program (L.B.M., E.S.C., L.D.L.), Department of Family Medicine (C.T.C.), Department of Internal Medicine (C.T.C.), and Ann Arbor Healthcare System, Department of Physical Medicine and Rehabilitation (E.S.C.), University of Michigan Medical School; and VA Geriatric Research Education and Clinical Center (C.T.C.), Ann Arbor, MI.
| |
Collapse
|
9
|
Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Sci Rep 2020; 10:20630. [PMID: 33244011 PMCID: PMC7692490 DOI: 10.1038/s41598-020-77296-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 11/09/2020] [Indexed: 12/11/2022] Open
Abstract
Alzheimer's Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.
Collapse
|
10
|
Popescu SG, Whittington A, Gunn RN, Matthews PM, Glocker B, Sharp DJ, Cole JH. Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease. Hum Brain Mapp 2020; 41:4406-4418. [PMID: 32643852 PMCID: PMC7502835 DOI: 10.1002/hbm.25133] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 06/11/2020] [Accepted: 06/25/2020] [Indexed: 12/20/2022] Open
Abstract
Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid‐β42, total tau, phosphorylated tau), [18F]florbetapir, hippocampal volume and brain‐age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R2 = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two‐way interaction models. The best performing model included an interaction between amyloid‐β‐PET and P‐tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker ‘space', providing information for personalised or stratified healthcare or clinical trial design.
Collapse
Affiliation(s)
- Sebastian G Popescu
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London, UK.,Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Alex Whittington
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London, UK.,Invicro Ltd, London, UK
| | - Roger N Gunn
- Invicro Ltd, London, UK.,Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK.,Department of Brain Sciences, Imperial College London, London, UK
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London, London, UK.,Care Research & Technology Centre, UK Dementia Research Institute, London, UK
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - David J Sharp
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London, UK.,Care Research & Technology Centre, UK Dementia Research Institute, London, UK
| | - James H Cole
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London, UK.,Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Centre for Medical Imaging Computing, Computer Science, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | | |
Collapse
|
11
|
Lombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre A, Lista C, Costantino G, Frisoni G, Virgili G, Filippini G. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Cochrane Database Syst Rev 2020; 3:CD009628. [PMID: 32119112 PMCID: PMC7059964 DOI: 10.1002/14651858.cd009628.pub2] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year. OBJECTIVES To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI. SEARCH METHODS On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches. SELECTION CRITERIA We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter. DATA COLLECTION AND ANALYSIS Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available. MAIN RESULTS We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate. AUTHORS' CONCLUSIONS The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.
Collapse
Affiliation(s)
- Gemma Lombardi
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Giada Crescioli
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Enrica Cavedo
- Pitie‐Salpetriere Hospital, Sorbonne UniversityAlzheimer Precision Medicine (APM), AP‐HP47 boulevard de l'HopitalParisFrance75013
| | - Ersilia Lucenteforte
- University of PisaDepartment of Clinical and Experimental MedicineVia Savi 10PisaItaly56126
| | - Giovanni Casazza
- Università degli Studi di MilanoDipartimento di Scienze Biomediche e Cliniche "L. Sacco"via GB Grassi 74MilanItaly20157
| | | | - Chiara Lista
- Fondazione I.R.C.C.S. Istituto Neurologico Carlo BestaNeuroepidemiology UnitVia Celoria, 11MilanoItaly20133
| | - Giorgio Costantino
- Ospedale Maggiore Policlinico, Università degli Studi di MilanoUOC Pronto Soccorso e Medicina D'Urgenza, Fondazione IRCCS Ca' GrandaMilanItaly
| | | | - Gianni Virgili
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Graziella Filippini
- Carlo Besta Foundation and Neurological InstituteScientific Director’s Officevia Celoria, 11MilanItaly20133
| | | |
Collapse
|
12
|
Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS, AND APPLICATIONS 2020; 16:1-35. [DOI: 10.1145/3344998] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 07/01/2019] [Indexed: 08/30/2023]
Abstract
Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
Collapse
Affiliation(s)
- M. Tanveer
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - B. Richhariya
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - R. U. Khan
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - A. H. Rashid
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore 8 School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India
| | - P. Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - M. Prasad
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| | - C. T. Lin
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| |
Collapse
|
13
|
Wang L, Heywood A, Stocks J, Bae J, Ma D, Popuri K, Toga AW, Kantarci K, Younes L, Mackenzie IR, Zhang F, Beg MF, Rosen H. Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2019; 4:e190017. [PMID: 31754634 PMCID: PMC6868780 DOI: 10.20900/jpbs.20190017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We report on the ongoing project "PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis" describing completed and future work supported by this grant. This project is a multi-site, multi-study collaboration effort with research spanning seven sites across the US and Canada. The overall goal of the project is to study neurodegeneration within Alzheimer's Disease, Frontotemporal Dementia, and related neurodegenerative disorders, using a variety of brain imaging and computational techniques to develop methods for the early and accurate prediction of disease and its course. The overarching goal of the project is to develop the earliest and most accurate biomarker that can differentiate clinical diagnoses to inform clinical trials and patient care. In its third year, this project has already completed several projects to achieve this goal, focusing on (1) structural MRI (2) machine learning and (3) FDG-PET and multimodal imaging. Studies utilizing structural MRI have identified key features of underlying pathology by studying hippocampal deformation that is unique to clinical diagnosis and also post-mortem confirmed neuropathology. Several machine learning experiments have shown high classification accuracy in the prediction of disease based on Convolutional Neural Networks utilizing MRI images as input. In addition, we have also achieved high accuracy in predicting conversion to DAT up to five years in the future. Further, we evaluated multimodal models that combine structural and FDG-PET imaging, in order to compare the predictive power of multimodal to unimodal models. Studies utilizing FDG-PET have shown significant predictive ability in the prediction and progression of disease.
Collapse
Affiliation(s)
- Lei Wang
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Ashley Heywood
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Jane Stocks
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Jinhyeong Bae
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Da Ma
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Arthur W. Toga
- Keck School of Medicine of University of Southern California, Los Angeles, 90033 CA, USA
| | - Kejal Kantarci
- Departments of Neurology and Radiology, Mayo Clinic, Rochester, 55905 MN, USA
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, 21218 MD, USA
| | - Ian R. Mackenzie
- Department of Pathology and Lab Medicine, University of British Columbia, Vancouver, B6T1Z4 BC, Canada
| | - Fengqing Zhang
- Department of Psychology, Drexel University, Philadelphia, 19104 PA, USA
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Howard Rosen
- Department of Neurology, University of California, San Francisco, 94143 CA, USA
| | - Alzheimer’s Disease Neuroimaging Initiative
- Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNIAcknowledgement_List.pdf
| |
Collapse
|
14
|
Moore PJ, Lyons TJ, Gallacher J. Using path signatures to predict a diagnosis of Alzheimer's disease. PLoS One 2019; 14:e0222212. [PMID: 31536538 PMCID: PMC6752804 DOI: 10.1371/journal.pone.0222212] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 08/23/2019] [Indexed: 11/18/2022] Open
Abstract
The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides interpretable features and its output is a fixed length vector irrespective of the number of input points or their sample times. In this paper we use the path signature to provide features for identifying people whose diagnosis subsequently converts to Alzheimer's disease. In two separate classification tasks we distinguish converters from 1) healthy individuals, and 2) individuals with mild cognitive impairment. The data used are time-ordered measurements of the whole brain, ventricles and hippocampus from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We find two nonlinear interactions which are predictive in both cases. The first interaction is change of hippocampal volume with time, and the second is a change of hippocampal volume relative to the volume of the whole brain. While hippocampal and brain volume changes are well known in Alzheimer's disease, we demonstrate the power of the path signature in their identification and analysis without manual feature selection. Sequential data is becoming increasingly available as monitoring technology is applied, and the path signature method is shown to be a useful tool in the processing of this data.
Collapse
Affiliation(s)
- P. J. Moore
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - T. J. Lyons
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - J. Gallacher
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | | |
Collapse
|
15
|
Random forest prediction of Alzheimer's disease using pairwise selection from time series data. PLoS One 2019; 14:e0211558. [PMID: 30763336 PMCID: PMC6375557 DOI: 10.1371/journal.pone.0211558] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 01/16/2019] [Indexed: 01/31/2023] Open
Abstract
Time-dependent data collected in studies of Alzheimer’s disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer’s disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods.
Collapse
|
16
|
Rasero J, Amoroso N, La Rocca M, Tangaro S, Bellotti R, Stramaglia S. Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer's disease. PLoS One 2017; 12:e0187281. [PMID: 29135998 PMCID: PMC5685585 DOI: 10.1371/journal.pone.0187281] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 10/17/2017] [Indexed: 01/21/2023] Open
Abstract
Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.
Collapse
Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute. Hospital Universitario de Cruces. E-48903, Barakaldo, Spain
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | - Marianna La Rocca
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | | | - Roberto Bellotti
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro, Bari, Italy
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro, Bari, Italy
- * E-mail:
| | | |
Collapse
|
17
|
Rasero J, Alonso-Montes C, Diez I, Olabarrieta-Landa L, Remaki L, Escudero I, Mateos B, Bonifazi P, Fernandez M, Arango-Lasprilla JC, Stramaglia S, Cortes JM. Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer's Disease. Front Aging Neurosci 2017; 9:215. [PMID: 28736521 PMCID: PMC5500648 DOI: 10.3389/fnagi.2017.00215] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 06/20/2017] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1 = 36, healthy control subjects, Control), G2 (N2 = 36, early mild cognitive impairment, EMCI), G3 (N3 = 36, late mild cognitive impairment, LMCI) and G4 (N4 = 36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I (Control vs. EMCI), stage II (Control vs. LMCI) and stage III (Control vs. AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were threefold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks, including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.
Collapse
Affiliation(s)
- Javier Rasero
- Dipartimento Interateneo di Fisica, Istituto Nazionale di Fisica Nucleare, Universita degli Studi di BariBari, Italy
- Biocruces Health Research InstituteBarakaldo, Spain
| | | | - Ibai Diez
- Biocruces Health Research InstituteBarakaldo, Spain
| | | | | | - Iñaki Escudero
- Biocruces Health Research InstituteBarakaldo, Spain
- Radiology Service, Cruces University HospitalBarakaldo, Spain
| | - Beatriz Mateos
- Biocruces Health Research InstituteBarakaldo, Spain
- Radiology Service, Cruces University HospitalBarakaldo, Spain
| | - Paolo Bonifazi
- Biocruces Health Research InstituteBarakaldo, Spain
- IKERBASQUE: The Basque Foundation for ScienceBilbao, Spain
| | - Manuel Fernandez
- Biocruces Health Research InstituteBarakaldo, Spain
- Neurology Service, Cruces University HospitalBarakaldo, Spain
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Istituto Nazionale di Fisica Nucleare, Universita degli Studi di BariBari, Italy
- Basque Center for Applied MathematicsBilbao, Spain
| | - Jesus M. Cortes
- Biocruces Health Research InstituteBarakaldo, Spain
- IKERBASQUE: The Basque Foundation for ScienceBilbao, Spain
- Department of Cell Biology and Histology, University of the Basque CountryLeioa, Spain
| | | |
Collapse
|
18
|
Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review. PLoS One 2017; 12:e0179804. [PMID: 28662070 PMCID: PMC5491044 DOI: 10.1371/journal.pone.0179804] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 06/05/2017] [Indexed: 11/19/2022] Open
Abstract
Background Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. Objective The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. Method To achieve our goal we carried out a systematic literature review, in which three large databases—Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. Results In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer’s disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Conclusions Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies’ different contexts.
Collapse
|
19
|
Tangaro S, Fanizzi A, Amoroso N, Bellotti R. A fuzzy-based system reveals Alzheimer’s Disease onset in subjects with Mild Cognitive Impairment. Phys Med 2017; 38:36-44. [DOI: 10.1016/j.ejmp.2017.04.027] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 03/18/2017] [Accepted: 04/27/2017] [Indexed: 01/18/2023] Open
|
20
|
Predictors That a Diagnosis of Mild Cognitive Impairment Will Remain Stable 3 Years Later. Cogn Behav Neurol 2017; 30:8-15. [PMID: 28323681 DOI: 10.1097/wnn.0000000000000119] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND OBJECTIVE In half to two thirds of patients who are diagnosed with mild cognitive impairment (MCI), the diagnosis neither converts to dementia nor reverts to normal cognition; however, little is known about predictors of MCI stability. Our study aimed to identify those predictors. METHODS We obtained 3-year longitudinal data from the National Alzheimer's Coordinating Center Uniform Data Set for patients with a baseline diagnosis of MCI. To predict MCI stability, we used the patients' baseline data to conduct three logistic regression models: demographics, global function, and neuropsychological performance. RESULTS Our final sample had 1059 patients. At the end of 3 years, 596 still had MCI and 463 had converted to dementia. The most reliable predictors of stable MCI were higher baseline scores on delayed recall, processing speed, and global function; younger age; and absence of apolipoprotein E4 alleles. CONCLUSIONS Not all patients with MCI progress to dementia. Of the protective factors that we identified from demographic, functional, and cognitive data, the absence of apolipoprotein E4 alleles best predicted MCI stability. Our predictors may help clinicians better evaluate and treat patients, and may help researchers recruit more homogeneous samples for clinical trials.
Collapse
|
21
|
Cheng B, Liu M, Shen D, Li Z, Zhang D. Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease. Neuroinformatics 2017; 15:115-132. [PMID: 27928657 PMCID: PMC5444948 DOI: 10.1007/s12021-016-9318-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer's Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD. Specifically, the proposed MDTL framework consists of two key components: 1) a multi-domain transfer feature selection (MDTFS) model that selects the most informative feature subset from multi-domain data, and 2) a multi-domain transfer classification (MDTC) model that can identify disease status for early AD detection. We evaluate our method on 807 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline magnetic resonance imaging (MRI) data. The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods.
Collapse
Affiliation(s)
- Bo Cheng
- Key Laboratory of Advanced Network and Intellectual Technology, Chongqing Three Gorges University, Chongqing, 404120, China
| | - Mingxia Liu
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China
| | - Daoqiang Zhang
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China.
| |
Collapse
|
22
|
Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 165] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
Collapse
Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | |
Collapse
|
23
|
Kong D, Giovanello KS, Wang Y, Lin W, Lee E, Fan Y, Murali Doraiswamy P, Zhu H. Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data. J Alzheimers Dis 2016; 46:695-702. [PMID: 25869783 DOI: 10.3233/jad-150164] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The growing public threat of Alzheimer's disease (AD) has raised the urgency to discover and validate prognostic biomarkers in order to predicting time to onset of AD. It is anticipated that both whole genome single nucleotide polymorphism (SNP) data and high dimensional whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. The aim of this paper is to test whether both whole genome SNP data and whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. In 343 subjects with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), we extracted high dimensional MR imaging (volumetric data on 93 brain regions plus a surface fluid registration based hippocampal subregion and surface data), and whole genome data (504,095 SNPs from GWAS), as well as routine neurocognitive and clinical data at baseline. MCI patients were then followed over 48 months, with 150 participants progressing to AD. Combining information from whole brain MR imaging and whole genome data was substantially superior to the standard model for predicting time to onset of AD in a 48-month national study of subjects at risk. Our findings demonstrate the promise of combined imaging-whole genome prognostic markers in people with mild memory impairment.
Collapse
Affiliation(s)
- Dehan Kong
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Kelly S Giovanello
- Department of Psychology, University of North Carolina, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Eunjee Lee
- Department of Statistics, University of North Carolina, Chapel Hill, NC, USA
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - P Murali Doraiswamy
- Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| |
Collapse
|
24
|
Nesteruk M, Nesteruk T, Styczyńska M, Barczak A, Mandecka M, Walecki J, Barcikowska-Kotowicz M. Predicting the conversion of mild cognitive impairment to Alzheimer's disease based on the volumetric measurements of the selected brain structures in magnetic resonance imaging. Neurol Neurochir Pol 2015; 49:349-53. [PMID: 26652867 DOI: 10.1016/j.pjnns.2015.09.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 09/03/2015] [Accepted: 09/04/2015] [Indexed: 10/23/2022]
Abstract
INTRODUCTION Mild cognitive impairment (MCI) is defined as abnormal cognitive state, but does not meet the criteria for the diagnosis of dementia. According to the new guidelines Alzheimer's disease (AD) involves not only dementia's phase but also predementia phase which is asymptomatic and pathological process in the brain is already present. For this reason it is very important to determine the suitability of markers which should be positive before onset of the first symptoms. One of these biomarkers is a structural magnetic resonance imaging with hippocampal volumetric assessment. The aim of this study was to investigate the usefulness of structural brain magnetic resonance imaging with volumetric assessment of the hippocampus and entorhinal cortex, posterior cingulate gyrus, parahippocampal gyrus, temporal gyri: superior, medial and inferior, to predict the conversion of MCI to AD. MATERIAL AND METHODS Magnetic resonance imaging of brain was performed at the baseline visit in 101 patients diagnosed with MCI. Clinic follow-ups were scheduled after 6.12 and 24 months. RESULTS Amongst 101 patients with MCI, 17 (16.8%) converted into AD within two years of observation. All measured volumes were lower in converters than non-converters. Discriminant analysis was conducted and sensitivity for MCI conversion to AD was 64.7%, specificity 96.4%. 91% of patients were correctly classified (converter or non-converter). CONCLUSIONS Volumetric measurements may help clinicians to predict MCI conversion to AD but due to low sensitivity it cannot be use separately. The study group requires further observation.
Collapse
Affiliation(s)
- Marta Nesteruk
- Department of Neurology, Central Clinical Hospital of the Ministry of Interior, Warsaw, Poland.
| | - Tomasz Nesteruk
- Department of Radiology, Central Clinical Hospital of the Ministry of Interior, Warsaw, Poland
| | - Maria Styczyńska
- Department of Neurodegenerative Disorders, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| | - Anna Barczak
- Department of Neurodegenerative Disorders, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| | - Monika Mandecka
- Department of Neurodegenerative Disorders, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| | - Jerzy Walecki
- Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| | - Maria Barcikowska-Kotowicz
- Department of Neurology, Central Clinical Hospital of the Ministry of Interior, Warsaw, Poland; Department of Neurodegenerative Disorders, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland
| |
Collapse
|
25
|
Ritter K, Schumacher J, Weygandt M, Buchert R, Allefeld C, Haynes JD. Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2015; 1:206-15. [PMID: 27239505 PMCID: PMC4877756 DOI: 10.1016/j.dadm.2015.01.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Background This study investigates the prediction of mild cognitive impairment-to-Alzheimer's disease (MCI-to-AD) conversion based on extensive multimodal data with varying degrees of missing values. Methods Based on Alzheimer's Disease Neuroimaging Initiative data from MCI-patients including all available modalities, we predicted the conversion to AD within 3 years. Different ways of replacing missing data in combination with different classification algorithms are compared. The performance was evaluated on features prioritized by experts and automatically selected features. Results The conversion to AD could be predicted with a maximal accuracy of 73% using support vector machines and features chosen by experts. Among data modalities, neuropsychological, magnetic resonance imaging, and positron emission tomography data were most informative. The best single feature was the functional activities questionnaire. Conclusion Extensive multimodal and incomplete data can be adequately handled by a combination of missing data substitution, feature selection, and classification.
Collapse
Affiliation(s)
- Kerstin Ritter
- Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany
| | - Julia Schumacher
- Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany
| | - Martin Weygandt
- Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany
| | - Ralph Buchert
- Department of Nuclear Medicine, Charité - University Medicine Berlin, Berlin, Germany
| | - Carsten Allefeld
- Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany
| | - John-Dylan Haynes
- Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany
| |
Collapse
|
26
|
Cheng B, Liu M, Zhang D, Munsell BC, Shen D. Domain Transfer Learning for MCI Conversion Prediction. IEEE Trans Biomed Eng 2015; 62:1805-1817. [PMID: 25751861 DOI: 10.1109/tbme.2015.2404809] [Citation(s) in RCA: 96] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI nonconverters (MCI-NC). However, most existing methods construct classifiers using data from one particular target domain (e.g., MCI), and ignore data in other related domains (e.g., AD and normal control (NC)) that may provide valuable information to improve MCI conversion prediction performance. To address is limitation, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection component that selects the most informative feature-subset from both target domain and auxiliary domains from different imaging modalities; 2) a domain transfer sample selection component that selects the most informative sample-subset from the same target and auxiliary domains from different data modalities; and 3) a domain transfer support vector machine classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that have MRI, FDG-PET, and CSF data. The experimental results show the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC.
Collapse
Affiliation(s)
- Bo Cheng
- Nanjing University of Aeronautics and Astronautics
| | - Mingxia Liu
- Nanjing University of Aeronautics and Astronautics
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | | | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA
| |
Collapse
|
27
|
Xu Y, Pan X, Zhou Z, Yang Z, Zhang Y. Structural least square twin support vector machine for classification. APPL INTELL 2014. [DOI: 10.1007/s10489-014-0611-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
28
|
Kehoe EG, McNulty JP, Mullins PG, Bokde ALW. Advances in MRI biomarkers for the diagnosis of Alzheimer's disease. Biomark Med 2014; 8:1151-69. [DOI: 10.2217/bmm.14.42] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
With the prevalence of Alzheimer's disease (AD) predicted to increase substantially over the coming decades, the development of effective biomarkers for the early detection of the disease is paramount. In this short review, the main neuroimaging techniques which have shown potential as biomarkers for AD are introduced, with a focus on MRI. Structural MRI measures of the hippocampus and medial temporal lobe are still the most clinically validated biomarkers for AD, but newer techniques such as functional MRI and diffusion tensor imaging offer great scope in tracking changes in the brain, particularly in functional and structural connectivity, which may precede gray matter atrophy. These new advances in neuroimaging methods require further development and crucially, standardization; however, before they are used as biomarkers to aid in the diagnosis of AD.
Collapse
Affiliation(s)
- Elizabeth G Kehoe
- The Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
- Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Jonathan P McNulty
- School of Medicine & Medical Science, University College Dublin, Dublin, Ireland
| | | | - Arun L W Bokde
- The Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
- Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| |
Collapse
|