10 research outputs found
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
Event-based models (EBM) are a class of disease progression models that can
be used to estimate temporal ordering of neuropathological changes from
cross-sectional data. Current EBMs only handle scalar biomarkers, such as
regional volumes, as inputs. However, regional aggregates are a crude summary
of the underlying high-resolution images, potentially limiting the accuracy of
EBM. Therefore, we propose a novel method that exploits high-dimensional
voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM
is based on an insight that mixture modeling, which is a key element of
conventional EBMs, can be replaced by a more scalable semi-supervised support
vector machine (SVM) approach. This SVM is used to estimate the degree of
abnormality of each region which is then used to obtain subject-specific
disease progression patterns. These patterns are in turn used for estimating
the mean ordering by fitting a generalized Mallows model. In order to validate
the biomarker ordering obtained using nDEBM, we also present a framework for
Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics
neurodegeneration in brain regions. SImBioTE trains variational auto-encoders
(VAE) in different brain regions independently to simulate images at varying
stages of disease progression. We also validate nDEBM clinically using data
from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both
experiments, nDEBM using high-dimensional features gave better performance than
state-of-the-art EBM methods using regional volume biomarkers. This suggests
that nDEBM is a promising approach for disease progression modeling.Comment: IPMI 201
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
We predicted residual fluid intelligence scores from T1-weighted MRI data
available as part of the ABCD NP Challenge 2019, using morphological similarity
of grey-matter regions across the cortex. Individual structural covariance
networks (SCN) were abstracted into graph-theory metrics averaged over nodes
across the brain and in data-driven communities/modules. Metrics included
degree, path length, clustering coefficient, centrality, rich club coefficient,
and small-worldness. These features derived from the training set were used to
build various regression models for predicting residual fluid intelligence
scores, with performance evaluated both using cross-validation within the
training set and using the held-out validation set. Our predictions on the test
set were generated with a support vector regression model trained on the
training set. We found minimal improvement over predicting a zero residual
fluid intelligence score across the sample population, implying that structural
covariance networks calculated from T1-weighted MR imaging data provide little
information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD
Neurocognitive Prediction Challenge at MICCAI 201
Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease
Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
We predicted fluid intelligence from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence
A pathophysiological framework of hippocampal dysfunction in ageing and disease
The hippocampal formation has been implicated in a growing number of disorders, from Alzheimer's disease and cognitive ageing to schizophrenia and depression. How can the hippocampal formation, a complex circuit that spans the temporal lobes, be involved in a range of such phenotypically diverse and mechanistically distinct disorders? Recent neuroimaging findings indicate that these disorders differentially target distinct subregions of the hippocampal circuit. In addition, some disorders are associated with hippocampal hypometabolism, whereas others show evidence of hypermetabolism. Interpreted in the context of the functional and molecular organization of the hippocampal circuit, these observations give rise to a unified pathophysiological framework of hippocampal dysfunction