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Register a new userThe Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
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We present the findings of The Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimers disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimers Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimers disease.
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The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimers disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team EMC1), which was based on disease progression modelling and spline regression, obtained mean absolute error of 0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was considerably better than the benchmark mixed effects model (BenchmarkME), provided to participants before the submission deadline. Further analysis can help understand which input features and algorithms are most suitable for Alzheimers disease prediction and for aiding patient stratification in clinical trials.
Recent evidence has shown that structural magnetic resonance imaging (MRI) is an effective tool for Alzheimers disease (AD) prediction and diagnosis. While traditional MRI-based diagnosis uses images acquired at a single time point, a longitudinal study is more sensitive and accurate in detecting early pathological changes of the AD. Two main difficulties arise in longitudinal MRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects (i.e., different scanning time and different total number of scans); (2) the heterogeneous progressions of high-dimensional regions of interest (ROIs) in MRI. In this work, we propose a novel feature selection and estimation method which can be applied to extract features from the heterogeneous longitudinal MRI. A key ingredient of our method is the combination of smoothing splines and the $l_1$-penalty. We perform experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) database. The results corroborate the advantages of the proposed method for AD prediction in longitudinal studies.
With the resurgence of tick-borne diseases such as Lyme disease and the emergence of new pathogens such as Powassan virus, understanding what distinguishes vector from non-vector species, and predicting undiscovered tick vectors is an important step towards mitigating human disease risk. We apply generalized boosted regression to interrogate over 90 features for over 240 species of Ixodes ticks. Our model predicted vector status with ~97% accuracy and implicated 14 tick species whose intrinsic trait profiles confer high probabilities (~80%) that they are capable of transmitting infections from animal hosts to humans. Distinguishing characteristics of zoonotic tick vectors include several anatomical structures that facilitate efficient host seeking and blood-feeding from a wide variety of host species. Boosted regression analysis produced both actionable predictions to guide ongoing surveillance as well as testable hypotheses about the biological underpinnings of vectorial capacity across tick species.
Accurate diagnosis of Alzheimers Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimers Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patients cognitive state such as learning, memory, and language production/comprehension. Although computer-aided diagnostic techniques for classification of a patients current disease state exist, they provide little insight into the relationship between changes in brain structure and different aspects of a patients cognitive state that occur over time in AD. We have developed a Convolutional Neural Network architecture that can concurrently predict the trajectories of the 13 subscores comprised by a subjects ADAS-cog examination results from a current minimally preprocessed structural MRI scan up to 36 months from image acquisition time without resorting to manual feature extraction. Mean performance metrics are within range of those of existing techniques that require manual feature selection and are limited to predicting aggregate scores.
Three major biomarkers: beta-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimers disease. While there are extensive studies on cerebrospinal fluids biomarkers (amyloid, tau), the spatial propagation pattern across brain is missing and their interactive mechanisms with neurodegeneration are still unclear. To this end, we aim to analyze the spatiotemporal associations between ATN biomarkers using large-scale neuroimaging data. We first investigate the temporal appearances of amyloid plaques, tau tangles, and neuronal loss by modeling the longitudinal transition trajectories. Second, we propose linear mixed-effects models to quantify the pathological interactions and propagation of ATN biomarkers at each brain region. Our analysis of the current data shows that there exists a temporal latency in the build-up of amyloid to the onset of tau pathology and neurodegeneration. The propagation pattern of amyloid can be characterized by its diffusion along the topological brain network. Our models provide sufficient evidence that the progression of pathological tau and neurodegeneration share a strong regional association, which is different from amyloid.
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