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Panigrahy A, Schmithorst V, Ceschin R, Lee V, Beluk N, Wallace J, Wheaton O, Chenevert T, Qiu D, Lee JN, Nencka A, Gagoski B, Berman JI, Yuan W, Macgowan C, Coatsworth J, Fleysher L, Cannistraci C, Sleeper LA, Hoskoppal A, Silversides C, Radhakrishnan R, Markham L, Rhodes JF, Dugan LM, Brown N, Ermis P, Fuller S, Cotts TB, Rodriguez FH, Lindsay I, Beers S, Aizenstein H, Bellinger DC, Newburger JW, Umfleet LG, Cohen S, Zaidi A, Gurvitz M. Design and Harmonization Approach for the Multi-Institutional Neurocognitive Discovery Study (MINDS) of Adult Congenital Heart Disease (ACHD) Neuroimaging Ancillary Study: A Technical Note. J Cardiovasc Dev Dis 2023; 10:381. [PMID: 37754810 PMCID: PMC10532244 DOI: 10.3390/jcdd10090381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
Dramatic advances in the management of congenital heart disease (CHD) have improved survival to adulthood from less than 10% in the 1960s to over 90% in the current era, such that adult CHD (ACHD) patients now outnumber their pediatric counterparts. ACHD patients demonstrate domain-specific neurocognitive deficits associated with reduced quality of life that include deficits in educational attainment and social interaction. Our hypothesis is that ACHD patients exhibit vascular brain injury and structural/physiological brain alterations that are predictive of specific neurocognitive deficits modified by behavioral and environmental enrichment proxies of cognitive reserve (e.g., level of education and lifestyle/social habits). This technical note describes an ancillary study to the National Heart, Lung, and Blood Institute (NHLBI)-funded Pediatric Heart Network (PHN) "Multi-Institutional Neurocognitive Discovery Study (MINDS) in Adult Congenital Heart Disease (ACHD)". Leveraging clinical, neuropsychological, and biospecimen data from the parent study, our study will provide structural-physiological correlates of neurocognitive outcomes, representing the first multi-center neuroimaging initiative to be performed in ACHD patients. Limitations of the study include recruitment challenges inherent to an ancillary study, implantable cardiac devices, and harmonization of neuroimaging biomarkers. Results from this research will help shape the care of ACHD patients and further our understanding of the interplay between brain injury and cognitive reserve.
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Affiliation(s)
- Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
- Department of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, 45th Str., Penn Ave., Pittsburgh, PA 15201, USA
| | - Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Vince Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Nancy Beluk
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Olivia Wheaton
- HealthCore Inc., 480 Pleasant Str., Watertown, MA 02472, USA;
| | - Thomas Chenevert
- Department of Radiology, Michigan Medicine University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA;
- Congenital Heart Center, C. S. Mott Children’s Hospital, 1540 E Hospital Dr., Ann Arbor, MI 48109, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory School of Medicine, 1364 Clifton Rd., Atlanta, GA 30322, USA;
| | - James N Lee
- Department of Radiology, The University of Utah, 50 2030 E, Salt Lake City, UT 84112, USA;
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave., Milwaukee, WI 53226, USA;
| | - Borjan Gagoski
- Department of Radiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA;
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA;
| | - Weihong Yuan
- Department of Radiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA;
- Department of Radiology, University of Cincinnati College of Medicine, 3230 Eden Ave., Cincinnati, OH 45267, USA
| | - Christopher Macgowan
- Department of Medical Biophysics, University of Toronto, 101 College Str. Suite 15-701, Toronto, ON M5G 1L7, Canada;
- The Hospital for Sick Children Division of Translational Medicine, 555 University Ave., Toronto, ON M5G 1X8, Canada
| | - James Coatsworth
- Department of Radiology, Medical University of South Carolina, 171 Ashley Ave., Room 372, Charleston, SC 29425, USA;
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Christopher Cannistraci
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
| | - Arvind Hoskoppal
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Candice Silversides
- Department of Cardiology, University of Toronto, C. David Naylor Building, 6 Queen’s Park Crescent West, Third Floor, Toronto, ON M5S 3H2, Canada;
| | - Rupa Radhakrishnan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 University Blvd., Indianapolis, IN 46202, USA;
| | - Larry Markham
- Department of Cardiology, University of Indiana School of Medicine, 545 Barnhill Dr., Indianapolis, IN 46202, USA;
| | - John F. Rhodes
- Department of Cardiology, Medical University of South Carolina, 96 Jonathan Lucas Str. Ste. 601, MSC 617, Charleston, SC 29425, USA;
| | - Lauryn M. Dugan
- Department of Cardiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA; (L.M.D.); (N.B.)
| | - Nicole Brown
- Department of Cardiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA; (L.M.D.); (N.B.)
| | - Peter Ermis
- Department of Radiology, Texas Children’s Hospital, Houston, TX 77030, USA; (P.E.); (S.F.)
| | - Stephanie Fuller
- Department of Radiology, Texas Children’s Hospital, Houston, TX 77030, USA; (P.E.); (S.F.)
| | - Timothy Brett Cotts
- Departments of Internal Medicine and Pediatrics, Michigan Medicine University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA;
| | - Fred Henry Rodriguez
- Department of Cardiology, Emory School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322, USA;
| | - Ian Lindsay
- Department of Cardiology, The University of Utah, 95 S 2000 E, Salt Lake City, UT 84112, USA;
| | - Sue Beers
- Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O’Hara Str., Pittsburgh, PA 15213, USA; (S.B.); (H.A.)
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O’Hara Str., Pittsburgh, PA 15213, USA; (S.B.); (H.A.)
| | - David C. Bellinger
- Cardiac Neurodevelopmental Program, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA;
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
| | - Laura Glass Umfleet
- Department of Neuropsychology, Medical College of Wisconsin, 9200 W Wisconsin Ave., Milwaukee, WI 53226, USA;
| | - Scott Cohen
- Heart and Vascular Center, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Ali Zaidi
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Michelle Gurvitz
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
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Chen X, Lin S, Zheng Y, He L, Fang Y. Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults:Evidence from the China Health and Retirement Longitudinal Study (CHARLS). Arch Gerontol Geriatr 2023; 111:105012. [PMID: 37030148 DOI: 10.1016/j.archger.2023.105012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND Falls are the most common adverse outcome of depression in older adults, yet a accurate risk prediction model for falls stratified by distinct long-term trajectories of depressive symptoms is still lacking. METHODS We collected the data of 1617 participants from the China Health and Retirement Longitudinal Study register, spanning between 2011 and 2018. The 36 input variables included in the baseline survey were regarded as candidate features. The trajectories of depressive symptoms were classified by the latent class growth model and growth mixture model. Three data balancing technologies and four machine learning algorithms were utilized to develop predictive models for fall classification of depressive prognosis. RESULTS Depressive symptom trajectories were divided into four categories, i.e., non-symptoms, new-onset increasing symptoms, slowly decreasing symptoms, and persistent high symptoms. The random forest-TomekLinks model achieved the best performance among the case and incident models with an AUC-ROC of 0.844 and 0.731, respectively. In the chronic model, the gradient boosting decision tree-synthetic minority oversampling technique obtained an AUC-ROC of 0.783. In the three models, the depressive symptom score was the most crucial component. The lung function was a common and significant feature in both the case and the chronic models. CONCLUSIONS This study suggests that the ideal model has a good chance of identifying older persons with a high risk of falling stratified by long-term trajectories of depressive symptoms. Baseline depressive symptom score, lung function, income, and injury experience are influential factors associated with falls of depression evolution.
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Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst 2023; 47:17. [PMID: 36720727 PMCID: PMC9889464 DOI: 10.1007/s10916-023-01906-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/03/2023] [Indexed: 02/02/2023]
Abstract
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Tomtebodavagen, Stockholm, 17165, Solna, Sweden
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Johan Sanmartin Berglund
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden.
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Liaqata Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
- School of Health Sciences, University of Skovde, Högskolevägen 1, Skövde, SE-541 28, Skövde, Sweden
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Lin S, Wu Y, He L, Fang Y. Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques. Aging Ment Health 2023; 27:8-17. [PMID: 35118924 DOI: 10.1080/13607863.2022.2031868] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVES Our aim was to explore the possibility of using machine learning (ML) in predicting the onset and trajectories of depressive symptom in home-based older adults over a 7-year period. METHODS Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2650) recruited in the China Health and Retirement Longitudinal Study (CHARLS) were included in the current analysis. The latent class growth modeling (LCGM) and growth mixture modeling (GMM) were used to classify different trajectory classes. Based on the identified trajectory patterns, three ML classification algorithms (i.e. gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUC). RESULTS Four trajectories were identified for the depressive symptoms: no symptoms (63.9%), depressive symptoms onset {incident increasing symptoms [new-onset increasing (16.8%)], chronic symptoms [slowly decreasing (12.5%), persistent high (6.8%)]}. Among the analyzed baseline variables, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) score, cognition, sleep time, self-reported memory were the top five important predictors across all trajectories. The mean AUCs of the three predictive models had a range from 0.661 to 0.892. CONCLUSIONS ML techniques can be robust in predicting depressive symptom onset and trajectories over a 7-year period with easily accessible sociodemographic and health information. UNLABELLED Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2022.2031868.
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Affiliation(s)
- Shaowu Lin
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Yafei Wu
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Lingxiao He
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Ya Fang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
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Lin S, Wu Y, Fang Y. A hybrid machine learning model of depression estimation in home-based older adults: a 7-year follow-up study. BMC Psychiatry 2022; 22:816. [PMID: 36544119 PMCID: PMC9768728 DOI: 10.1186/s12888-022-04439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Our aim was to explore whether a two-step hybrid machine learning model has the potential to discover the onset of depression in home-based older adults. METHODS Depression data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2,548) recruited in the China Health and Retirement Longitudinal Study were included in the current analysis. The long short-term memory network (LSTM) was applied to identify the risk factors of participants in 2015 utilizing the first 2 waves of data. Based on the identified predictors, three ML classification algorithms (i.e., gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUROC) to estimate the depressive outcome. RESULTS Time-varying predictors of the depression were successfully identified by LSTM (mean squared error =0.8). The mean AUCs of the three predictive models had a range from 0.703 to 0.749. Among the prediction variables, self-reported health status, cognition, sleep time, self-reported memory and ADL (activities of daily living) disorder were the top five important variables. CONCLUSIONS A two-step hybrid model based on "LSTM+ML" framework can be robust in predicting depression over a 5-year period with easily accessible sociodemographic and health information.
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Affiliation(s)
- Shaowu Lin
- grid.12955.3a0000 0001 2264 7233The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102 China
| | - Yafei Wu
- grid.12955.3a0000 0001 2264 7233The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102 China
| | - Ya Fang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, China. .,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China. .,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, China.
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Leist AK, Klee M, Kim JH, Rehkopf DH, Bordas SPA, Muniz-Terrera G, Wade S. Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences. SCIENCE ADVANCES 2022; 8:eabk1942. [PMID: 36260666 PMCID: PMC9581488 DOI: 10.1126/sciadv.abk1942] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/01/2022] [Indexed: 05/20/2023]
Abstract
Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.
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Affiliation(s)
- Anja K. Leist
- Department of Social Sciences, Institute for Research on Socio-Economic Inequality (IRSEI), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Corresponding author.
| | - Matthias Klee
- Department of Social Sciences, Institute for Research on Socio-Economic Inequality (IRSEI), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jung Hyun Kim
- Department of Social Sciences, Institute for Research on Socio-Economic Inequality (IRSEI), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - David H. Rehkopf
- Department of Epidemiology and Population Health, Stanford University, Palo Alto, CA, USA
| | | | - Graciela Muniz-Terrera
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Ohio University, Athens, OH, USA
| | - Sara Wade
- School of Mathematics, University of Edinburgh, Edinburgh, UK
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Yan YH, Chen TB, Yang CP, Tsai IJ, Yu HL, Wu YS, Huang WJ, Tseng ST, Peng TY, Chou EP. Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods. Sci Rep 2022; 12:17130. [PMID: 36224306 PMCID: PMC9556552 DOI: 10.1038/s41598-022-22100-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/10/2022] [Indexed: 02/05/2023] Open
Abstract
Air pollution exposure has been linked to various diseases, including dementia. However, a novel method for investigating the associations between air pollution exposure and disease is lacking. The objective of this study was to investigate whether long-term exposure to ambient particulate air pollution increases dementia risk using both the traditional Cox model approach and a novel machine learning (ML) with random forest (RF) method. We used health data from a national population-based cohort in Taiwan from 2000 to 2017. We collected the following ambient air pollution data from the Taiwan Environmental Protection Administration (EPA): fine particulate matter (PM2.5) and gaseous pollutants, including sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), and nitrogen dioxide (NO2). Spatiotemporal-estimated air quality data calculated based on a geostatistical approach, namely, the Bayesian maximum entropy method, were collected. Each subject's residential county and township were reviewed monthly and linked to air quality data based on the corresponding township and month of the year for each subject. The Cox model approach and the ML with RF method were used. Increasing the concentration of PM2.5 by one interquartile range (IQR) increased the risk of dementia by approximately 5% (HR = 1.05 with 95% CI = 1.04-1.05). The comparison of the performance of the extended Cox model approach with the RF method showed that the prediction accuracy was approximately 0.7 by the RF method, but the AUC was lower than that of the Cox model approach. This national cohort study over an 18-year period provides supporting evidence that long-term particulate air pollution exposure is associated with increased dementia risk in Taiwan. The ML with RF method appears to be an acceptable approach for exploring associations between air pollutant exposure and disease.
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Affiliation(s)
- Yuan-Horng Yan
- grid.415517.30000 0004 0572 8068Department of Endocrinology and Metabolism, Kuang Tien General Hospital, Taichung, Taiwan ,grid.415517.30000 0004 0572 8068Department of Medical Research, Kuang Tien General Hospital, Taichung, Taiwan ,grid.411432.10000 0004 1770 3722Institute of Biomedical Nutrition, Hungkuang University, Taichung, Taiwan
| | - Ting-Bin Chen
- grid.410764.00000 0004 0573 0731Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, Taichung, Taiwan ,grid.411432.10000 0004 1770 3722Department of Applied Cosmetology, Hungkuang University, Taichung, Taiwan
| | - Chun-Pai Yang
- grid.415517.30000 0004 0572 8068Department of Medical Research, Kuang Tien General Hospital, Taichung, Taiwan ,grid.411432.10000 0004 1770 3722Institute of Biomedical Nutrition, Hungkuang University, Taichung, Taiwan ,grid.415517.30000 0004 0572 8068Department of Neurology, Kuang Tien General Hospital, Taichung, Taiwan
| | - I-Ju Tsai
- grid.415517.30000 0004 0572 8068Department of Medical Research, Kuang Tien General Hospital, Taichung, Taiwan
| | - Hwa-Lung Yu
- grid.19188.390000 0004 0546 0241Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan
| | - Yuh-Shen Wu
- grid.411432.10000 0004 1770 3722Department of Safety, Health, and Environmental Engineering, Hungkuang University, Taichung, Taiwan
| | - Winn-Jung Huang
- grid.411432.10000 0004 1770 3722Department of Safety, Health, and Environmental Engineering, Hungkuang University, Taichung, Taiwan
| | - Shih-Ting Tseng
- grid.415517.30000 0004 0572 8068Division of Endocrinology and Metabolism, Department of Internal Medicine, Kuang Tien General Hospital, Taichung, Taiwan ,Jenteh Junior College of Medicine, Nursing and Management, Miaoli County, Taiwan
| | - Tzu-Yu Peng
- grid.412042.10000 0001 2106 6277Department of Statistics, National Chengchi University, No. 64, Sec. 2, Zhinan Rd., Wenshan Dist., Taipei City, 116 Taiwan
| | - Elizabeth P. Chou
- grid.412042.10000 0001 2106 6277Department of Statistics, National Chengchi University, No. 64, Sec. 2, Zhinan Rd., Wenshan Dist., Taipei City, 116 Taiwan
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Schmithorst VJ, Adams PS, Badaly D, Lee VK, Wallace J, Beluk N, Votava-Smith JK, Weinberg JG, Beers SR, Detterich J, Wood JC, Lo CW, Panigrahy A. Impaired Neurovascular Function Underlies Poor Neurocognitive Outcomes and Is Associated with Nitric Oxide Bioavailability in Congenital Heart Disease. Metabolites 2022; 12:metabo12090882. [PMID: 36144286 PMCID: PMC9504090 DOI: 10.3390/metabo12090882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 12/03/2022] Open
Abstract
We use a non-invasive MRI proxy of neurovascular function (pnvf) to assess the ability of the vasculature to supply baseline metabolic demand, to compare pediatric and young adult congenital heart disease (CHD) patients to normal referents and relate the proxy to neurocognitive outcomes and nitric oxide bioavailability. In a prospective single-center study, resting-state blood-oxygen-level-dependent (BOLD) and arterial spin labeling (ASL) MRI scans were successfully obtained from 24 CHD patients (age = 15.4 ± 4.06 years) and 63 normal referents (age = 14.1 ± 3.49) years. Pnvf was computed on a voxelwise basis as the negative of the ratio of functional connectivity strength (FCS) estimated from the resting-state BOLD acquisition to regional cerebral blood flow (rCBF) as estimated from the ASL acquisition. Pnvf was used to predict end-tidal CO2 (PETCO2) levels and compared to those estimated from the BOLD data. Nitric oxide availability was obtained via nasal measurements (nNO). Pnvf was compared on a voxelwise basis between CHD patients and normal referents and correlated with nitric oxide availability and neurocognitive outcomes as assessed via the NIH Toolbox. Pnvf was shown as highly predictive of PETCO2 using theoretical modeling. Pnvf was found to be significantly reduced in CHD patients in default mode network (DMN, comprising the ventromedial prefrontal cortex and posterior cingulate/precuneus), salience network (SN, comprising the insula and dorsal anterior cingulate), and central executive network (CEN, comprising posterior parietal and dorsolateral prefrontal cortex) regions with similar findings noted in single cardiac ventricle patients. Positive correlations of Pnvf in these brain regions, as well as the hippocampus, were found with neurocognitive outcomes. Similarly, positive correlations between Pnvf and nitric oxide availability were found in frontal DMN and CEN regions, with particularly strong correlations in subcortical regions (putamen). Reduced Pnvf in CHD patients was found to be mediated by nNO. Mediation analyses further supported that reduced Pnvf in these regions underlies worse neurocognitive outcome in CHD patients and is associated with nitric oxide bioavailability. Impaired neuro-vascular function, which may be non-invasively estimated via combined arterial-spin label and BOLD MR imaging, is a nitric oxide bioavailability dependent factor implicated in adverse neurocognitive outcomes in pediatric and young adult CHD.
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Affiliation(s)
| | - Phillip S. Adams
- Department of Pediatric Anesthesiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | - Daryaneh Badaly
- Learning and Development Center, Child Mind Institute, New York, NY 10022, USA
| | - Vincent K. Lee
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Julia Wallace
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | - Nancy Beluk
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | | | | | - Sue R. Beers
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Jon Detterich
- Heart Institute, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
| | - John C. Wood
- Heart Institute, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
| | - Cecilia W. Lo
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ashok Panigrahy
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Correspondence: ; Tel.: +1-412-692-5510; Fax: +1-412-692-6929
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9
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Asada T, Kakuma T, Ota M, Jon Lebowitz A, Shibata N, Niizato K, Izumimoto N, Abe Y, Tanaka M, Arai T. Development of informant-reported or self-reported MCI-J questionnaire. Int J Geriatr Psychiatry 2022; 37. [PMID: 35775722 DOI: 10.1002/gps.5773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/13/2022] [Indexed: 11/06/2022]
Abstract
UNLABELLED Several studies have reported a high prevalence of missed and delayed mild cognitive impairment (MCI) or mild dementia diagnosis, which could lead to delayed treatment and increased patient and caregiver burden. OBJECTIVES This study aimed to develop a new questionnaire for nonprofessionals to help detect early signs of MCI and dementia. Respondents included patients, family caregivers, or health professionals. Scores are calculated based on the respondent type and age of subject. METHODS This study consisted of four steps and included 461 respondents. Steps 1-3 were conducted by a working group, and step 4, by 67 specialist members of the Japanese Society of Geriatric Psychiatry. A scoring algorithm was created and predictive diagnostic probability was analyzed using misdiscrimination rate and cross-validation after item selection to establish a cut-off value for MCI or dementia symptoms. Alzheimer's disease, Lewy body dementia, and frontotemporal dementia were diagnosed. RESULTS The prediction error rate for patient or informant respondents was confirmed from the evaluation results of 13 items. Sensitivity and specificity were 90.6% and 56.6%, respectively, with a cut-off score of 2. Overall, 82% (61 pairs) of respondents received a definitive diagnosis following a diagnosis from the questionnaire. CONCLUSIONS This questionnaire could promote earlier presentation to clinical settings for treatment. The high sensitivity indicates the utility of this instrument, but it is not meant as a definitive diagnostic tool and should be followed with a professional assessment.
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Affiliation(s)
- Takashi Asada
- Memory Clinic Ochanomizu, Tokyo, Japan.,Tokyo Medical and Dental University, Tokyo, Japan
| | - Tatsuyuki Kakuma
- Biostatistics Center, Kurume University School of Medicine, Kurume, Japan
| | - Miho Ota
- Department of Psychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Adam Jon Lebowitz
- Department of General Education, Jichi Medical University, Shimotsuke, Japan
| | - Nobuto Shibata
- Mental Clinic, Juntendo Tokyo Koto Geriatric Medical Center, Koto-ku, Japan
| | - Kazuhiro Niizato
- Department of Psychiatry, Tokyo Metropolitan Matsuzawa Hospital, Setagaya-ku, Japan
| | | | - Yasuko Abe
- Division of Internal Medicine, Yokufukai Hospital, Tokyo, Japan
| | | | - Tetsuaki Arai
- Department of Psychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
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10
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Bruni F, Borghesi F, Mancuso V, Riva G, Stramba-Badiale M, Pedroli E, Cipresso P. Cognition Meets Gait: Where and How Mind and Body Weave Each Other in a Computational Psychometrics Approach in Aging. Front Aging Neurosci 2022; 14:909029. [PMID: 35875804 PMCID: PMC9304933 DOI: 10.3389/fnagi.2022.909029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/16/2022] [Indexed: 11/26/2022] Open
Abstract
Aging may be associated with conditions characterized by motor and cognitive alterations, which could have a detrimental impact on daily life. Although motors and cognitive aspects have always been treated as separate entities, recent literature highlights their relationship, stressing a strong association between locomotion and executive functions. Thus, designing interventions targeting the risks deriving from both components’ impairments is crucial: the dual-task represents a starting point. Although its role in targeting and decreasing difficulties in aging is well known, most interventions are focused on a single domain, proposing a vertical model in which patients emerge only for a single aspect per time during assessment and rehabilitation. In this perspective, we propose a view of the individual as a whole between mind and body, suggesting a multicomponent and multidomain approach that could integrate different domains at the same time retracing lifelike situations. Virtual Reality, thanks to the possibility to develop daily environments with engaging challenges for patients, as well as to manage different devices to collect multiple data, provides the optimal scenario in which the integration could occur. Artificial Intelligence, otherwise, offers the best methodologies to integrate a great amount of various data to create a predictive model and identify appropriate and individualized interventions. Based on these assumptions the present perspective aims to propose the development of a new approach to an integrated, multimethod, multidimensional training in order to enhance cognition and physical aspects based on behavioral data, incorporating consolidated technologies in an innovative approach to neurology.
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Affiliation(s)
| | - Francesca Borghesi
- Applied Technology for Neuropsychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | | | - Giuseppe Riva
- Applied Technology for Neuropsychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Human Technology Lab, Catholic University of the Sacred Heart, Milan, Italy
| | - Marco Stramba-Badiale
- Department of Geriatrics and Cardiovascular Medicine, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Elisa Pedroli
- Faculty of Psychology, eCampus University, Novedrate, Italy
- Applied Technology for Neuropsychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Pietro Cipresso
- Applied Technology for Neuropsychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Department of Psychology, University of Turin, Turin, Italy
- *Correspondence: Pietro Cipresso,
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11
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Wu Y, Lin S, Shi K, Ye Z, Fang Y. Seasonal prediction of daily PM 2.5 concentrations with interpretable machine learning: a case study of Beijing, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:45821-45836. [PMID: 35150424 DOI: 10.1007/s11356-022-18913-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Machine learning (ML) has shown high predictive ability in environmental research. Accurate estimation of daily PM2.5 concentrations is a prerequisite to address environmental public health issues. However, studies on the interpretability of ML algorithms were limited. In this study, we aimed to estimate the daily concentrations of PM2.5 at a seasonal level, and to understand the potential mechanisms of ML algorithms' decisions with SHapley Additive exPlanations (SHAP). Daily ground PM2.5 concentrations and meteorological data were obtained from the Beijing Municipal Ecological and Environmental Monitoring Center, and China Meteorological Data Service Centre between December 2013 and 2019 November. We calculated correlation coefficient and variance inflation factor (VIF) to eliminate the variables with collinearity, and recursive feature elimination (RFE) was further used to selected more important predictors. A series of ML algorithms, including linear regression, the variants of linear regression (Ridge, Lasso, Elasticnet), decision tree (DT), k-nearest neighbor (KNN), support vector regression (SVR), ensemble methods (random forest: RF, eXtreme Gradient Boosting: XGBoost), and deep learning (long short-term memory network: LSTM), were developed to estimate seasonal-level daily PM2.5 concentrations. A 10-fold cross validation was used to tune hyperparameters, and root mean square error (RMSE), mean absolute error (MAE), ratio of performance to deviation (RPD), and Lin's concordance correlation coefficient (LCCC) were used to evaluate models' performance. SHAP was performed for local and global interpretability analysis. The results showed that the distribution of PM2.5 concentrations in Beijing showed obvious seasonal patterns. A total of five variables (Precipitation, Mean wind speed, Sunshine duration, Mean surface temperature, Mean relative humidity) were selected for final prediction. LSTM showed much higher accuracy than other traditional ML models, achieved the smallest RMSE of 19.58 µg/m3 and MAE of 15.11 µg/m3. In terms of selected data set, there was acceptable (LCCC = 0.41 ~ 0.52) agreement and accuracy (RPD = 0.97 ~ 1.92) for LSTM. The SHAP analyses revealed that the meteorological factors had different influences in specific predictions, and the complex interactions were also illustrated. These results enhance our understanding of meteorological factors-PM2.5 relationships and explain the mechanisms of ML algorithms' decisions.
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Affiliation(s)
- Yafei Wu
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, China
| | - Shaowu Lin
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, China
| | - Kewei Shi
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, China
| | - Zirong Ye
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, China
| | - Ya Fang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China.
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, China.
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12
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Varela-López B, Cruz-Gómez ÁJ, Lojo-Seoane C, Díaz F, Pereiro A, Zurrón M, Lindín M, Galdo-Álvarez S. Cognitive reserve, neurocognitive performance, and high-order resting-state networks in cognitively unimpaired aging. Neurobiol Aging 2022; 117:151-164. [DOI: 10.1016/j.neurobiolaging.2022.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 10/18/2022]
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13
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Dementia classification using MR imaging and clinical data with voting based machine learning models. MULTIMEDIA TOOLS AND APPLICATIONS 2022. [DOI: 10.1007/s11042-022-12754-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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14
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Rivas-Fernández MÁ, Lindín M, Zurrón M, Díaz F, Aldrey-Vázquez JM, Pías-Peleteiro JM, Vázquez-Vázquez L, Pereiro AX, Lojo-Seoane C, Nieto-Vieites A, Galdo-Álvarez S. Brain Atrophy and Clinical Characterization of Adults With Mild Cognitive Impairment and Different Cerebrospinal Fluid Biomarker Profiles According to the AT(N) Research Framework of Alzheimer’s Disease. Front Hum Neurosci 2022; 16:799347. [PMID: 35280203 PMCID: PMC8914376 DOI: 10.3389/fnhum.2022.799347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/10/2022] [Indexed: 12/05/2022] Open
Abstract
Introduction This study aimed to evaluate, in adults with mild cognitive impairment (MCI), the brain atrophy that may distinguish between three AT(N) biomarker-based profiles, and to determine its clinical value. Methods Structural MRI (sMRI) was employed to evaluate the volume and cortical thickness differences in MCI patients with different AT(N) profiles, namely, A−T−(N)−: normal AD biomarkers; A+T−(N)−: AD pathologic change; and A+T+(N)+: prodromal AD. Sensitivity and specificity of these changes were also estimated. Results An initial atrophy in medial temporal lobe (MTL) areas was found in the A+T−(N)− and A+T+(N)+ groups, spreading toward the parietal and frontal regions in A+T+(N)+ patients. These structural changes allowed distinguishing AT(N) profiles within the AD continuum; however, the profiles and their pattern of neurodegeneration were unsuccessful to determine the current clinical status. Conclusion sMRI is useful in the determination of the specific brain structural changes of AT(N) profiles along the AD continuum, allowing differentiation between MCI adults with or without pathological AD biomarkers.
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Affiliation(s)
- Miguel Ángel Rivas-Fernández
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- *Correspondence: Miguel Ángel Rivas-Fernández,
| | - Mónica Lindín
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Montserrat Zurrón
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Fernando Díaz
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - José Manuel Aldrey-Vázquez
- Neurology Service, Santiago Clinic Hospital (CHUS), Santiago de Compostela, Spain
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Juan Manuel Pías-Peleteiro
- Neurology Service, Santiago Clinic Hospital (CHUS), Santiago de Compostela, Spain
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Laura Vázquez-Vázquez
- NeuroAging Group (NEURAL), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Arturo Xosé Pereiro
- Department of Developmental and Educational Psychology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Cristina Lojo-Seoane
- Department of Developmental and Educational Psychology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Ana Nieto-Vieites
- Department of Developmental and Educational Psychology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Santiago Galdo-Álvarez
- Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
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15
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Khajehpiri B, Moghaddam HA, Forouzanfar M, Lashgari R, Ramos-Cejudo J, Osorio RS, Ardekani BA. Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression. J Alzheimers Dis 2021; 85:837-850. [PMID: 34864679 DOI: 10.3233/jad-215266] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. OBJECTIVE The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. METHODS We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. RESULTS In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. CONCLUSION Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.
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Affiliation(s)
- Boshra Khajehpiri
- Machine Vision and Medical Image Processing(MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Hamid Abrishami Moghaddam
- Machine Vision and Medical Image Processing(MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mohamad Forouzanfar
- Machine Vision and Medical Image Processing(MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Department of Systems Engineering, École deTechnologie Supérieure, Université duQuébec, Montreal, Quebec, Canada
| | - Reza Lashgari
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Jaime Ramos-Cejudo
- Department of Psychiatry, New York University(NYU) Grossman School of Medicine, New York, NY, USA
| | - Ricardo S Osorio
- TheNathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.,Department of Psychiatry, New York University(NYU) Grossman School of Medicine, New York, NY, USA
| | - Babak A Ardekani
- TheNathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
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16
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Battineni G, Hossain MA, Chintalapudi N, Traini E, Dhulipalla VR, Ramasamy M, Amenta F. Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms. Diagnostics (Basel) 2021; 11:diagnostics11112103. [PMID: 34829450 PMCID: PMC8623867 DOI: 10.3390/diagnostics11112103] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 11/16/2022] Open
Abstract
Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer's disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.
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Affiliation(s)
- Gopi Battineni
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
- Correspondence: ; Tel.: +39-3331728206
| | - Mohmmad Amran Hossain
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
| | - Nalini Chintalapudi
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
| | - Enea Traini
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
| | - Venkata Rao Dhulipalla
- The Research Centre of the ECE Department, V.R. Siddhartha Engineering College, Vijayawada 521002, Andhra Pradesh, India; (V.R.D.); (M.R.)
| | - Mariappan Ramasamy
- The Research Centre of the ECE Department, V.R. Siddhartha Engineering College, Vijayawada 521002, Andhra Pradesh, India; (V.R.D.); (M.R.)
| | - Francesco Amenta
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
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17
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Danso SO, Zeng Z, Muniz-Terrera G, Ritchie CW. Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms. Front Big Data 2021; 4:613047. [PMID: 34124650 PMCID: PMC8187875 DOI: 10.3389/fdata.2021.613047] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 04/13/2021] [Indexed: 02/02/2023] Open
Abstract
Alzheimer's disease (AD) has its onset many decades before dementia develops, and work is ongoing to characterise individuals at risk of decline on the basis of early detection through biomarker and cognitive testing as well as the presence/absence of identified risk factors. Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results. However, these approaches have been criticised as they are unable to generalise due to over-reliance on one data source, poor internal and external validations, and lack of understanding of prediction models, thereby limiting the clinical utility of these prediction models. We propose a framework that employs a transfer-learning paradigm with ensemble learning algorithms to develop explainable personalised risk prediction models for dementia. Our prediction models, known as source models, are initially trained and tested using a publicly available dataset (n = 84,856, mean age = 69 years) with 14 years of follow-up samples to predict the individual risk of developing dementia. The decision boundaries of the best source model are further updated by using an alternative dataset from a different and much younger population (n = 473, mean age = 52 years) to obtain an additional prediction model known as the target model. We further apply the SHapely Additive exPlanation (SHAP) algorithm to visualise the risk factors responsible for the prediction at both population and individual levels. The best source model achieves a geometric accuracy of 87%, specificity of 99%, and sensitivity of 76%. In comparison to a baseline model, our target model achieves better performance across several performance metrics, within an increase in geometric accuracy of 16.9%, specificity of 2.7%, and sensitivity of 19.1%, an area under the receiver operating curve (AUROC) of 11% and a transfer learning efficacy rate of 20.6%. The strength of our approach is the large sample size used in training the source model, transferring and applying the "knowledge" to another dataset from a different and undiagnosed population for the early detection and prediction of dementia risk, and the ability to visualise the interaction of the risk factors that drive the prediction. This approach has direct clinical utility.
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Affiliation(s)
- Samuel O Danso
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Zhanhang Zeng
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh Medical School, Edinburgh, United Kingdom
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18
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Navarro-Pardo E, Facal D, Campos-Magdaleno M, Pereiro AX, Juncos-Rabadán O. Prevalence of Cognitive Frailty, Do Psychosocial-Related Factors Matter? Brain Sci 2020; 10:brainsci10120968. [PMID: 33322251 PMCID: PMC7763872 DOI: 10.3390/brainsci10120968] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/07/2020] [Accepted: 12/09/2020] [Indexed: 01/12/2023] Open
Abstract
Cognitive frailty (CF) is a topic of growing interest with implications for the study of preventive interventions in aging. Nevertheless, little research has been done to assess the influence of psychosocial variables on the risk of CF. Our objectives were to estimate the prevalence of CF in a Spanish sample and to explore the influence of psychosocial variables in this prevalence. Physical frailty and cognitive, functional, psychosocial, and socio-demographic aspects were assessed in a sample of 285 participants over 60 years. Univariate and multivariate logistic regression models were carried out. A prevalence of 21.8% (95% CI 17.4-26.9) was established when both frail and pre-frail conditions were included, and a prevalence of 3.2% (95% CI 1.7-5.9) if only frail individuals were considered. Age, educational level, profession and psychological well-being variables significantly predicted CF. Frailty and pre-frailty are high-prevalence health conditions in older adults influenced by socio-demographic, socio-educative and affective factors.
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Affiliation(s)
- Esperanza Navarro-Pardo
- Department of Developmental and Educational Psychology, Universitat de Valencia, 46010 Valencia, Spain;
| | - David Facal
- Department of Developmental Psychology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (M.C.-M.); (A.X.P.); (O.J.-R.)
- Correspondence:
| | - María Campos-Magdaleno
- Department of Developmental Psychology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (M.C.-M.); (A.X.P.); (O.J.-R.)
| | - Arturo X. Pereiro
- Department of Developmental Psychology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (M.C.-M.); (A.X.P.); (O.J.-R.)
| | - Onésimo Juncos-Rabadán
- Department of Developmental Psychology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (M.C.-M.); (A.X.P.); (O.J.-R.)
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Consonni M, Dalla Bella E, Bersano E, Telesca A, Lauria G. Cognitive reserve is associated with altered clinical expression in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener 2020; 22:237-247. [PMID: 33263428 DOI: 10.1080/21678421.2020.1849306] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Objective: Long-term life experiences, such as education, occupational attainment, leisure activities, and bilingualism, have been considered proxies of cognitive reserve (CR). In neurodegenerative disease, CR is considered as a modulator of a more favorable cognitive trajectory and motor functions. Our study investigated the role of CR on cognitive and motor involvement in a large cohort of incident patients with amyotrophic lateral sclerosis (ALS). Methods: Cognition assessment and clinical and demographic information were obtained in 101 incident ALS patients. CR was measured based on years of education, occupational attainment, amount of leisure activities, and bilingualism. Correlation and regression analyses were performed to test the association between CR and the clinical expression of ALS. Results: We found that all proxies of CR were positively associated with executive functions, verbal fluency, and memory domains. Motor impairment was inversely related to educational level and occupational attainment. Regression analysis documented the association between CR and cognitive performances in all patients and the predictive role of CR in modulating motor functional disability in patients with bulbar-onset. Conclusion: Our findings showed that CR mediates the extent of cognitive decline and that of functional bulbar impairment, suggesting that the concept of reserve applied to ALS should encompass cognitive and motor domains.
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Affiliation(s)
- Monica Consonni
- 3rd Neurology Unit and Motor Neuron Diseases Centre, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy and
| | - Eleonora Dalla Bella
- 3rd Neurology Unit and Motor Neuron Diseases Centre, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy and
| | - Enrica Bersano
- 3rd Neurology Unit and Motor Neuron Diseases Centre, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy and
| | - Alessandra Telesca
- 3rd Neurology Unit and Motor Neuron Diseases Centre, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy and
| | - Giuseppe Lauria
- 3rd Neurology Unit and Motor Neuron Diseases Centre, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy and.,Department of Biomedical and Clinical Sciences "Luigi Sacco", University of Milan, Milan, Italy
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20
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Hu B. Trajectories of informal care intensity among the oldest-old Chinese. Soc Sci Med 2020; 266:113338. [DOI: 10.1016/j.socscimed.2020.113338] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 08/24/2020] [Accepted: 08/28/2020] [Indexed: 11/16/2022]
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21
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Qin FY, Lv ZQ, Wang DN, Hu B, Wu C. Health status prediction for the elderly based on machine learning. Arch Gerontol Geriatr 2020; 90:104121. [DOI: 10.1016/j.archger.2020.104121] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/25/2020] [Accepted: 04/20/2020] [Indexed: 10/24/2022]
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22
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Cavedoni S, Chirico A, Pedroli E, Cipresso P, Riva G. Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality. Front Hum Neurosci 2020; 14:245. [PMID: 32848660 PMCID: PMC7396670 DOI: 10.3389/fnhum.2020.00245] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 06/02/2020] [Indexed: 01/16/2023] Open
Abstract
Elderly people affected by Mild Cognitive Impairment (MCI) usually report a perceived decline in cognitive functions that deeply impacts their quality of life. This subtle waning, although it cannot be diagnosable as dementia, is noted by caregivers on the basis of their relative’s behaviors. Crucially, if this condition is also not detected in time by clinicians, it can easily turn into dementia. Thus, early detection of MCI is strongly needed. Classical neuropsychological measures – underlying a categorical model of diagnosis - could be integrated with a dimensional assessment approach involving Virtual Reality (VR) and Artificial Intelligence (AI). VR can be used to create highly ecologically controlled simulations resembling the daily life contexts in which patients’ daily instrumental activities (IADL) usually take place. Clinicians can record patients’ kinematics, particularly gait, while performing IADL (Digital Biomarkers). Then, Artificial Intelligence employs Machine Learning (ML) to analyze them in combination with clinical and neuropsychological data. This integrated computational approach would enable the creation of a predictive model to identify specific patterns of cognitive and motor impairment in MCI. Therefore, this new dimensional cognitive-behavioral assessment would reveal elderly people’s neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time-sensitive interventions.
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Affiliation(s)
- Silvia Cavedoni
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy
| | - Alice Chirico
- Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
| | - Elisa Pedroli
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Faculty of Psychology, eCampus University, Novedrate, Italy
| | - Pietro Cipresso
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
| | - Giuseppe Riva
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
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Battineni G, Chintalapudi N, Amenta F, Traini E. A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer's Disease (AD) in Older Subjects. J Clin Med 2020; 9:jcm9072146. [PMID: 32650363 PMCID: PMC7408873 DOI: 10.3390/jcm9072146] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 05/27/2020] [Accepted: 05/29/2020] [Indexed: 01/24/2023] Open
Abstract
Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.
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Xu H, Yang R, Dintica C, Qi X, Song R, Bennett DA, Xu W. Association of lifespan cognitive reserve indicator with the risk of mild cognitive impairment and its progression to dementia. Alzheimers Dement 2020; 16:873-882. [PMID: 32342664 PMCID: PMC10321560 DOI: 10.1002/alz.12085] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 02/02/2020] [Accepted: 02/21/2020] [Indexed: 11/08/2022]
Abstract
INTRODUCTION The association of lifespan cognitive reserve (CR) with mild cognitive impairment (MCI) remains controversial. We aimed to examine the association of lifespan CR indicator with the risk of MCI and its progression to dementia, taking brain pathologies into account. METHODS In a community-based cohort study (mean age, 79 years) with annual follow-up (median, 5.16 years; maximum, 20 years), a cognitively intact group (n = 1182) and an MCI group (n = 420) were identified at baseline. During the follow-up, 611 participants died and underwent autopsies. CR indicator encompassing education, early life to late-life cognitive and social activities were obtained and tertiled. RESULTS The multi-adjusted hazard ratio (HR) of MCI was 0.72 (95% confidence interval [CI] 0.58 to 0.90) in the cognitively intact group, and the HR of dementia was 0.66 (95% CI 0.45 to 0.97) in the MCI group for participants with the highest CR indicator (reference: the lowest CR indicator). Among MCI participants with brain pathologies, dementia incidence was about 50% lower in people with the highest CR indicator than the lowest CR indicator. DISCUSSION High lifespan CR indicator reduces risk of MCI, and delays its progression to dementia.
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Affiliation(s)
- Hui Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Tianjin Center for International Collaborative Research in Environment, Nutrition and Public Health, Tianjin, China
| | - Rongrong Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Tianjin Center for International Collaborative Research in Environment, Nutrition and Public Health, Tianjin, China
| | - Christina Dintica
- Dept. of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Xiuying Qi
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Tianjin Center for International Collaborative Research in Environment, Nutrition and Public Health, Tianjin, China
| | - Ruixue Song
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Tianjin Center for International Collaborative Research in Environment, Nutrition and Public Health, Tianjin, China
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, 60612
| | - Weili Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Tianjin Center for International Collaborative Research in Environment, Nutrition and Public Health, Tianjin, China
- Dept. of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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Ahmadzadeh M, Christie GJ, Cosco TD, Moreno S. Neuroimaging and analytical methods for studying the pathways from mild cognitive impairment to Alzheimer's disease: protocol for a rapid systematic review. Syst Rev 2020; 9:71. [PMID: 32241302 PMCID: PMC7118884 DOI: 10.1186/s13643-020-01332-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 03/15/2020] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder commonly associated with deficits of cognition and changes in behavior. Mild cognitive impairment (MCI) is the prodromal stage of AD that is defined by slight cognitive decline. Not all with MCI progress to AD dementia. Thus, the accurate prediction of progression to Alzheimer's, particularly in the stage of MCI could potentially offer developing treatments to delay or prevent the transition process. The objective of the present study is to investigate the most recent neuroimaging procedures in the domain of prediction of transition from MCI to AD dementia for clinical applications and to systematically discuss the machine learning techniques used for the prediction of MCI conversion. METHODS Electronic databases including PubMed, SCOPUS, and Web of Science will be searched from January 1, 2017, to the date of search commencement to provide a rapid review of the most recent studies that have investigated the prediction of conversion from MCI to Alzheimer's using neuroimaging modalities in randomized trial or observational studies. Two reviewers will screen full texts of included papers using predefined eligibility criteria. Studies will be included if addressed research on AD dementia and MCI, explained the results in a way that would be able to report the performance measures such as the accuracy, sensitivity, and specificity. Only studies addressed Alzheimer's type of dementia and its early-stage MCI using neuroimaging modalities will be included. We will exclude other forms of dementia such as vascular dementia, frontotemporal dementia, and Parkinson's disease. The risk of bias in individual studies will be appraised using an appropriate tool. If feasible, we will conduct a random effects meta-analysis. Sensitivity analyses will be conducted to explore the potential sources of heterogeneity. DISCUSSION The information gathered in our study will establish the extent of the evidence underlying the prediction of conversion to AD dementia from its early stage and will provide a rigorous and updated synthesis of neuroimaging modalities allied with the data analysis techniques used to measure the brain changes during the conversion process. SYSTEMATIC REVIEW REGISTRATION PROSPERO,CRD42019133402.
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Affiliation(s)
- Maryam Ahmadzadeh
- Digital Health Hub, Simon Fraser University, 4190 Galleria 4, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
- School of Interactive Arts and Technology, Simon Fraser University, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
- Science and Technology for Aging Research Institute, Simon Fraser University, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
| | - Gregory J. Christie
- Digital Health Hub, Simon Fraser University, 4190 Galleria 4, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
- School of Interactive Arts and Technology, Simon Fraser University, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
- Science and Technology for Aging Research Institute, Simon Fraser University, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
| | - Theodore D. Cosco
- Gerontology Research Center, Simon Fraser University, 2800-515 West Hastings St, Vancouver, V6B 5 K3 Canada
- Oxford Institute of Population Ageing, University of Oxford, 66 Banbury Road, Oxford, OX2 6PR UK
| | - Sylvain Moreno
- Digital Health Hub, Simon Fraser University, 4190 Galleria 4, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
- School of Interactive Arts and Technology, Simon Fraser University, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
- Science and Technology for Aging Research Institute, Simon Fraser University, 250 – 13450 102 Ave, Surrey, BC V3T 0A3 Canada
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Past Life Experiences and Neurological Recovery: The Role of Cognitive Reserve in the Rehabilitation of Severe Post-Anoxic Encephalopathy and Traumatic Brain Injury. J Int Neuropsychol Soc 2020; 26:394-406. [PMID: 31727184 DOI: 10.1017/s1355617719001231] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Patients with an equivalent clinical background may show unexpected interindividual differences in their outcome. The cognitive reserve (CR) model has been proposed to account for such discrepancies, but its role after acquired severe injuries is still being debated. We hypothesize that inappropriate investigative methods might have been used when dealing with severe patients, which have very likely reduced the possibility of observing meaningful influences in recovery from severe traumas. METHODS To overcome this issue, the potential neuroprotective role of CR was investigated, considering a wider spectrum of clinical symptoms ranging from low-level brain stem functions necessary for life to more complex motor and cognitive skills. In the present study, data from 50 severe patients, 20 suffering from post-anoxic encephalopathy (PAE) and 30 with traumatic brain injury (TBI), were collected and retrospectively analyzed. RESULTS We found that CR, diagnosis, time of hospitalization, and their interaction had an effect on the clinical indexes. When the predictive power of CR was investigated by means of two machine learning classifier algorithms, CR, together with age, emerged as the strongest factor in discriminating between patients who reached or did not reach successful recovery. CONCLUSIONS Overall, the present study highlights a possible role of CR in shaping the recovery of severe patients suffering from either PAE or TBI. The practical implications underlying the need to routinely considered CR in the clinical practice are discussed.
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Cognitive reserve and working memory in cognitive performance of adults with subjective cognitive complaints: longitudinal structural equation modeling. Int Psychogeriatr 2020; 32:515-524. [PMID: 31547899 DOI: 10.1017/s1041610219001248] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE To study the influence of cognitive reserve (CR) on cognitive performance of individuals with subjective cognitive complaints (SCCs) within a period of 36 months. DESIGN We used a general linear model repeated measures procedure to analyze the differences in performance between three assessments. We used a longitudinal structural equation modeling to analyze the relationship between CR and cognitive performance at baseline and at two follow-up assessments. SETTING Participants with SCCs were recruited and assessed in primary care health centers. PARTICIPANTS A total of 212 participants older than 50 years with SCCs. MEASUREMENTS Cognitive reserve data were collected with an ad hoc questionnaire administered to the subjects in an interview. General cognitive performance (GCP), episodic memory (EM), and working memory (WM) have been evaluated. The Mini-Mental State Examination and the total score of Spanish version of the Cambridge Cognitive Examination evaluated the GCP. Episodic memory was assessed with the Spanish version of the California Verbal Learning. Working memory was evaluated by the counting span task and the listening span task. RESULTS The satisfactory fit of the proposed model confirmed the direct effects of CR on WM and GCP at baseline, as well as indirect effects on EM and WM at first and second follow-up. Indirect effects of CR on other cognitive constructs via WM were observed over time. CONCLUSION The proposed model is useful for measuring the influence of CR on cognitive performance over time. Cognitive response acquired throughout life may influence cognitive performance in old age and prevent cognitive deterioration, thus increasing processing resources via WM.
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Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach. Int Psychogeriatr 2020; 32:381-392. [PMID: 31455461 DOI: 10.1017/s1041610219001030] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVES To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not. DESIGN Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1 score, and Cohen's kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm. SETTING Primary care health centers. PARTICIPANTS 128 participants: 78 cognitively unimpaired and 50 with MCI. MEASUREMENTS Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score. RESULTS 30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen's kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score. CONCLUSIONS ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI.
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