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Ezzati A, Abdulkadir A, Jack CR, Thompson PM, Harvey DJ, Truelove-Hill M, Sreepada LP, Davatzikos C, Lipton RB. Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia. Alzheimers Dement 2021; 17:1855-1867. [PMID: 34870371 DOI: 10.1002/alz.12491] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 07/15/2021] [Accepted: 09/07/2021] [Indexed: 01/18/2023]
Abstract
We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge-based cut-offs for each component of ATN and compared it to two data-driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data-driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia-like and normal groups. Both data-driven classification methods performed better than the empirical cut-offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data-driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.
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Affiliation(s)
- Ali Ezzati
- Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical center, Bronx, New York, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, University of California-Davis, Davis, California, USA
| | - Monica Truelove-Hill
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Lasya P Sreepada
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Richard B Lipton
- Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical center, Bronx, New York, USA.,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
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Ezzati A, Harvey DJ, Habeck C, Golzar A, Qureshi IA, Zammit AR, Hyun J, Truelove-Hill M, Hall CB, Davatzikos C, Lipton RB. Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques. J Alzheimers Dis 2021; 73:1211-1219. [PMID: 31884486 DOI: 10.3233/jad-191038] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. OBJECTIVE The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. METHODS We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. RESULTS The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92. CONCLUSIONS Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.
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Affiliation(s)
- Ali Ezzati
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Neurology, Montefiore Medical Center, Bronx, NY, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, University of California-Davis, Davis, CA, USA
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | | | - Irfan A Qureshi
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Biohaven Pharmaceuticals, New Haven, CT, USA
| | - Andrea R Zammit
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jinshil Hyun
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | | | | | - Richard B Lipton
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Neurology, Montefiore Medical Center, Bronx, NY, USA
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Bashyam VM, Erus G, Doshi J, Habes M, Nasrallah IM, Truelove-Hill M, Srinivasan D, Mamourian L, Pomponio R, Fan Y, Launer LJ, Masters CL, Maruff P, Zhuo C, Völzke H, Johnson SC, Fripp J, Koutsouleris N, Satterthwaite TD, Wolf D, Gur RE, Gur RC, Morris J, Albert MS, Grabe HJ, Resnick S, Bryan RN, Wolk DA, Shou H, Davatzikos C. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain 2020; 143:2312-2324. [PMID: 32591831 PMCID: PMC7364766 DOI: 10.1093/brain/awaa160] [Citation(s) in RCA: 126] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 03/17/2020] [Accepted: 03/31/2020] [Indexed: 01/21/2023] Open
Abstract
Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.
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Affiliation(s)
- Vishnu M Bashyam
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Correspondence to: Vishnu Bashyam3700 Hamilton Walk, 7th FloorCenter of Biomedical Image Computing and Analytics, University of PennsylvaniaPhiladelphia, PA 19104, USA E-mail: Website: https://www.med.upenn.edu/cbica/
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Monica Truelove-Hill
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Liz Mamourian
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, USA
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Chuanjun Zhuo
- Tianjin Mental Health Center, Nankai University Affiliated Tianjin Anding Hospital, Tianjin, China,Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Germany,German Centre for Cardiovascular Research, Partner Sit Greifswald, Germany
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, USA
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Daniel Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Raquel E Gur
- Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ruben C Gur
- Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - John Morris
- Department of Neurology, Washington University in St. Louis, St Louis, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, Ernst-Moritz-Arndt University, Greifswald, Mecklenburg-Vorpommern, Germany
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Bethesda, USA
| | - R Nick Bryan
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadephia, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Correspondence may also be addressed to: Christos DavatzikosE-mail:
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Erickson B, Truelove-Hill M, Oh Y, Anderson J, Zhang FZ, Kounios J. Resting-state brain oscillations predict trait-like cognitive styles. Neuropsychologia 2018; 120:1-8. [PMID: 30261163 DOI: 10.1016/j.neuropsychologia.2018.09.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 09/19/2018] [Accepted: 09/23/2018] [Indexed: 11/26/2022]
Abstract
Anecdotal reports suggest the existence of individual differences in peoples' cognitive styles for solving problems, in particular, the tendency to rely on insight (the "aha" phenomenon) versus deliberate analytical thought. We hypothesized that such stable individual differences exist and are associated with trait-like individual differences in resting-state brain activity. We tested this idea by recording participants' resting-state electroencephalograms (RS-EEGs) on 4 occasions over approximately 7 weeks and then tasking them with solving anagrams and compound remote associates problems that are solvable by either strategy. We found that peoples' tendency to solve problems consistently by insight or by analysis spans both tasks and time. Moreover, we discovered trait-like individual differences in the balance between frontal and posterior resting-state brain activity and in temporal-lobe hemispheric asymmetries that predict, at least weeks in advance, the tendency to solve by insight versus analysis. The discovery of an insight-analytic dimension of cognitive style and its neural basis in resting state brain activity suggests new avenues for the development of neuroscience-based methods for intellectual, educational, and vocational assessment.
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Affiliation(s)
- Brian Erickson
- Department of Psychology, Drexel University, Stratton Hall, 3201 Chestnut Street, Philadelphia, PA 19104, USA.
| | - Monica Truelove-Hill
- Department of Psychology, Drexel University, Stratton Hall, 3201 Chestnut Street, Philadelphia, PA 19104, USA
| | - Yongtaek Oh
- Department of Psychology, Drexel University, Stratton Hall, 3201 Chestnut Street, Philadelphia, PA 19104, USA
| | - Julia Anderson
- Department of Psychology, Drexel University, Stratton Hall, 3201 Chestnut Street, Philadelphia, PA 19104, USA
| | - Fengqing Zoe Zhang
- Department of Psychology, Drexel University, Stratton Hall, 3201 Chestnut Street, Philadelphia, PA 19104, USA
| | - John Kounios
- Department of Psychology, Drexel University, Stratton Hall, 3201 Chestnut Street, Philadelphia, PA 19104, USA
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