This study proposes a new approach to diagnose Alzheimer's disease by using a generative adversarial network (GAN) applied to T1-weighted scans to predict tau pathology on positron emission tomography (PET) images. We used a cohort of 259 participants across different stages stages of Alzheimer’s disease from the Alzheimer's Disease Neuroimaging Initiative. The proposed 3D pix2pix GAN model was more successful than other models in synthesizing regional tau-PET signals from structural brain scans, holding great promise as a tool for multi-modal diagnosis and allowing to assess the underlying disease’s pathology without the need of exposing patients to radiation.
Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study.
Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts.
Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer's disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.
Implementation of graph theory for novel approaches to analyze the brain in an easy, accurate, and reproducible manner requires a modern solution tool. Here, we present BRAPH 2.0 (BRain Analysis using graPH theory version 2.0, www.braph.org), a comprehensive extension of the first version of this software that includes these novel approaches.
The MatLab-based BRAPH 2.0 uses object-oriented programming and a completely new software engine to provide clear, robust, clean, modular, maintainable, and testable code. The core of BRAPH 2.0 consists of a set of functions that can automatically transform a user-provided script into an object that is intertwined with the rest of the code. In this way, BRAPH 2.0 provides a scaffold on which users can define custom analysis pipelines with alternative network measures, additional statistical tests, or different options for network visualization.
Recent advances in network neuroscience have provided new insights into brain organization in health and disease. In particular, graph theory analyses of brain networks have shown that the human brain is characterized by a high level of integration between distant brain regions and good local communication between neighboring areas. However, these brain networks are normally analyzed using single neuroimaging modalities such as functional magnetic resonance or diffusion tensor imaging. Machine learning techniques for graph structures, such as Graph Neural Networks (GNN), are used to infer and predict from the graph data.
Here we present BRAPH 2.0 (BRain Analysis using graPH theory version 2.0 ), which is a major update of the first version. BRAPH 2..0 Genesis utilizes the capability of an object-oriented programming paradigm and a new engine to provide clear, robust, clean, modular, maintainable, testable, and machine learning ready code.
A growing number of studies suggest that detection of Alzheimer’s disease can be improved by using information derived from distinct neuroimaging modalities. However, so far it remains unresolved how these modalities can be combined within a deep learning model approach. In this study, we proposed a deep-neural-network model GapNet that can work with incomplete dataset including baseline and longitudinal MR, amyloid-PET, and FDG-PET data. We verified the effectiveness of GapNet by comparing it to the conventional Vanilla neural networks and specifically testing their performance in discriminating between healthy controls and individuals with amyloid changes, which is an important early pathological marker in Alzheimer’s Disease. Results showed that, compared to the Vanilla networks, GapNet achieved higher classification accuracy. In sum, our finding suggested that the GapNet model is a promising deep learning approach for detecting Alzheimer’s disease with multi-modal neuroimaging
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