Alzheimer’s Disease (AD) is a devastating neurodegenerative disease. Recent advances in tau-positron emission tomography (PET) imaging allow quantitating and mapping out the regional distribution of one important hallmark of AD across the brain. There is a need to develop machine learning (ML) algorithms to interrogate the utility of this new imaging modality. While there are some recent studies showing promise of using ML to differentiate AD patients from normal controls (NC) based on tau-PET images, there is limited work to investigate if tau-PET, with the help of ML, can facilitate predicting the risk of converting to AD while an individual is still at the early Mild Cognitive Impairment (MCI) stage. We developed an early AD risk predictor for subjects with MCI based on tau-PET using Machine Learning (ML). Our ML algorithms achieved good accuracy in predicting the risk of conversion to AD for a given MCI subject. Important features contributing to the prediction are consistent with literature reports of tau susceptible regions. This work demonstrated the feasibility of developing an early AD risk predictor for subjects with MCI based on tau-PET and ML.
Multi-modality images usually exist for diagnosis/prognosis of a disease, such as Alzheimer’s Disease (AD), but with different levels of accessibility and accuracy. MRI is used in the standard of care, thus having high accessibility to patients. On the other hand, imaging of pathologic hallmarks of AD such as amyloid-PET and tau-PET has low accessibility due to cost and other practical constraints, even though they are expected to provide higher diagnostic/prognostic accuracy than standard clinical MRI. We proposed Cross-Modality Transfer Learning (CMTL) for accurate diagnosis/prognosis based on standard imaging modality with high accessibility (mod_HA), with a novel training strategy of using not only data of mod_HA but also knowledge transferred from the model based on advanced imaging modality with low accessibility (mod_LA). We applied CMTL to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets, demonstrating improved performance of the MRI (mod_HA)-based model by leveraging the knowledge transferred from the model based on tau-PET (mod_LA).
Biomarker-assisted diagnosis and intervention in Alzheimer’s disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).
Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer’s disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.