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Rumack A, Tibshirani RJ, Rosenfeld R. Recalibrating probabilistic forecasts of epidemics. PLoS Comput Biol 2022; 18:e1010771. [PMID: 36520949 PMCID: PMC9799311 DOI: 10.1371/journal.pcbi.1010771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 12/29/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Distributional forecasts are important for a wide variety of applications, including forecasting epidemics. Often, forecasts are miscalibrated, or unreliable in assigning uncertainty to future events. We present a recalibration method that can be applied to a black-box forecaster given retrospective forecasts and observations, as well as an extension to make this method more effective in recalibrating epidemic forecasts. This method is guaranteed to improve calibration and log score performance when trained and measured in-sample. We also prove that the increase in expected log score of a recalibrated forecaster is equal to the entropy of the PIT distribution. We apply this recalibration method to the 27 influenza forecasters in the FluSight Network and show that recalibration reliably improves forecast accuracy and calibration. This method, available on Github, is effective, robust, and easy to use as a post-processing tool to improve epidemic forecasts.
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Affiliation(s)
- Aaron Rumack
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Ryan J. Tibshirani
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Roni Rosenfeld
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
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Vega AR, Chkheidze R, Jarmale V, Shang P, Foong C, Diamond MI, White CL, Rajaram S. Deep learning reveals disease-specific signatures of white matter pathology in tauopathies. Acta Neuropathol Commun 2021; 9:170. [PMID: 34674762 PMCID: PMC8529809 DOI: 10.1186/s40478-021-01271-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/07/2021] [Indexed: 02/08/2023] Open
Abstract
Although pathology of tauopathies is characterized by abnormal tau protein aggregation in both gray and white matter regions of the brain, neuropathological investigations have generally focused on abnormalities in the cerebral cortex because the canonical aggregates that form the diagnostic criteria for these disorders predominate there. This corticocentric focus tends to deemphasize the relevance of the more complex white matter pathologies, which remain less well characterized and understood. We took a data-driven machine-learning approach to identify novel disease-specific morphologic signatures of white matter aggregates in three tauopathies: Alzheimer disease (AD), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). We developed automated approaches using whole slide images of tau immunostained sections from 49 human autopsy brains (16 AD,13 CBD, 20 PSP) to identify cortex/white matter regions and individual tau aggregates, and compared tau-aggregate morphology across these diseases. Tau burden in the gray and white matter for individual subjects strongly correlated in a highly disease-specific fashion. We discovered previously unrecognized tau morphologies for AD, CBD and PSP that may be of importance in disease classification. Intriguingly, our models classified diseases equally well based on either white or gray matter tau staining. Our results suggest that tau pathology in white matter is informative, disease-specific, and linked to gray matter pathology. Machine learning has the potential to reveal latent information in histologic images that may represent previously unrecognized patterns of neuropathology, and additional studies of tau pathology in white matter could improve diagnostic accuracy.
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Affiliation(s)
- Anthony R Vega
- Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, USA
- Center for Alzheimer's and Neurodegenerative Diseases, The University of Texas Southwestern Medical Center, Dallas, USA
| | - Rati Chkheidze
- Department of Pathology, University of Alabama at Birmingham, Birmingham, USA
| | - Vipul Jarmale
- Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, USA
| | - Ping Shang
- Department of Pathology, The University of Texas Southwestern Medical Center, Dallas, USA
| | - Chan Foong
- Department of Pathology, The University of Texas Southwestern Medical Center, Dallas, USA
| | - Marc I Diamond
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, USA
- Center for Alzheimer's and Neurodegenerative Diseases, The University of Texas Southwestern Medical Center, Dallas, USA
- Peter O'Donnell Jr. Brain Institute, The University of Texas Southwestern Medical Center, Dallas, USA
| | - Charles L White
- Department of Pathology, The University of Texas Southwestern Medical Center, Dallas, USA
- Center for Alzheimer's and Neurodegenerative Diseases, The University of Texas Southwestern Medical Center, Dallas, USA
- Peter O'Donnell Jr. Brain Institute, The University of Texas Southwestern Medical Center, Dallas, USA
| | - Satwik Rajaram
- Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, USA.
- Center for Alzheimer's and Neurodegenerative Diseases, The University of Texas Southwestern Medical Center, Dallas, USA.
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Iida MA, Farrell K, Walker JM, Richardson TE, Marx GA, Bryce CH, Purohit D, Ayalon G, Beach TG, Bigio EH, Cortes EP, Gearing M, Haroutunian V, McMillan CT, Lee EB, Dickson DW, McKee AC, Stein TD, Trojanowski JQ, Woltjer RL, Kovacs GG, Kofler JK, Kaye J, White CL, Crary JF. Predictors of cognitive impairment in primary age-related tauopathy: an autopsy study. Acta Neuropathol Commun 2021; 9:134. [PMID: 34353357 PMCID: PMC8340493 DOI: 10.1186/s40478-021-01233-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 07/16/2021] [Indexed: 12/14/2022] Open
Abstract
Primary age-related tauopathy (PART) is a form of Alzheimer-type neurofibrillary degeneration occurring in the absence of amyloid-beta (Aβ) plaques. While PART shares some features with Alzheimer disease (AD), such as progressive accumulation of neurofibrillary tangle pathology in the medial temporal lobe and other brain regions, it does not progress extensively to neocortical regions. Given this restricted pathoanatomical pattern and variable symptomatology, there is a need to reexamine and improve upon how PART is neuropathologically assessed and staged. We performed a retrospective autopsy study in a collection (n = 174) of post-mortem PART brains and used logistic regression to determine the extent to which a set of clinical and neuropathological features predict cognitive impairment. We compared Braak staging, which focuses on hierarchical neuroanatomical progression of AD tau and Aβ pathology, with quantitative assessments of neurofibrillary burden using computer-derived positive pixel counts on digitized whole slide images of sections stained immunohistochemically with antibodies targeting abnormal hyperphosphorylated tau (p-tau) in the entorhinal region and hippocampus. We also assessed other factors affecting cognition, including aging-related tau astrogliopathy (ARTAG) and atrophy. We found no association between Braak stage and cognitive impairment when controlling for age (p = 0.76). In contrast, p-tau burden was significantly correlated with cognitive impairment even when adjusting for age (p = 0.03). The strongest correlate of cognitive impairment was cerebrovascular disease, a well-known risk factor (p < 0.0001), but other features including ARTAG (p = 0.03) and hippocampal atrophy (p = 0.04) were also associated. In contrast, sex, APOE, psychiatric illness, education, argyrophilic grains, and incidental Lewy bodies were not. These findings support the hypothesis that comorbid pathologies contribute to cognitive impairment in subjects with PART. Quantitative approaches beyond Braak staging are critical for advancing our understanding of the extent to which age-related tauopathy changes impact cognitive function.
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Affiliation(s)
- Megan A Iida
- Department of Pathology, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine At Mount Sinai, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, 1 Gustave L. Levy Place Box 1194, New York, NY, 10029, USA
| | - Kurt Farrell
- Department of Pathology, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine At Mount Sinai, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, 1 Gustave L. Levy Place Box 1194, New York, NY, 10029, USA
| | - Jamie M Walker
- Department of Pathology and Laboratory Medicine and The Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Timothy E Richardson
- Department of Pathology and Laboratory Medicine and The Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Gabriel A Marx
- Department of Pathology, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine At Mount Sinai, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, 1 Gustave L. Levy Place Box 1194, New York, NY, 10029, USA
| | - Clare H Bryce
- Department of Pathology, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine At Mount Sinai, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, 1 Gustave L. Levy Place Box 1194, New York, NY, 10029, USA
| | - Dushyant Purohit
- Department of Pathology, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine At Mount Sinai, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, 1 Gustave L. Levy Place Box 1194, New York, NY, 10029, USA
| | - Gai Ayalon
- Ultragenyx Pharmaceuticals, Novato, CA, USA
| | | | - Eileen H Bigio
- Department of Pathology, Northwestern Cognitive Neurology and Alzheimer Disease Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Etty P Cortes
- Department of Pathology, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine At Mount Sinai, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, 1 Gustave L. Levy Place Box 1194, New York, NY, 10029, USA
| | - Marla Gearing
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Vahram Haroutunian
- Departments of Psychiatry and Neuroscience, Alzheimer's Disease Research Center, Icahn School of Medicine At Mount Sinai, New York, NY, USA
- JJ Peters VA Medical Center (MIRECC), Bronx, NY, USA
| | - Corey T McMillan
- Department of Neurology, Perelman School of Medicine, Penn FTD Center, Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Translational Neuropathology Research Laboratory, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | | | - Ann C McKee
- Department of Pathology, VA Medical Center & Boston University School of Medicine, Boston, MA, USA
| | - Thor D Stein
- Department of Pathology, VA Medical Center & Boston University School of Medicine, Boston, MA, USA
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randall L Woltjer
- Department of Pathology, Oregon Health Sciences University, Portland, OR, USA
| | - Gabor G Kovacs
- Laboratory Medicine Program, Krembil Brain Institute University Health Network Toronto Ontario, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, ON, Canada
- Institute of Neurology, Medical University of Vienna, Vienna, Austria
| | - Julia K Kofler
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Jeffrey Kaye
- Department of Neurology, Oregon Health & Science University, Portland, USA
| | - Charles L White
- Neuropathology Laboratory, Department of Pathology, University of Texas Southwestern Medical Center, Dallas, USA
| | - John F Crary
- Department of Pathology, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine At Mount Sinai, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, 1 Gustave L. Levy Place Box 1194, New York, NY, 10029, USA.
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