Multimodal brain age estimation using interpretable adaptive population-graph learning

• URL: http://arxiv.org/abs/2307.04639v2
• Date: Wed, 19 Jul 2023 12:08:51 GMT
• Title: Multimodal brain age estimation using interpretable adaptive population-graph learning
• Authors: Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Rolandos Alexandros Potamias, Alexander Hammers, Daniel Rueckert
• Abstract summary: We propose a framework that learns a population graph structure optimized for the downstream task. An attention mechanism assigns weights to a set of imaging and non-imaging features. By visualizing the attention weights that were the most important for the graph construction, we increase the interpretability of the graph.
• Score: 58.99653132076496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain age estimation is clinically important as it can provide valuable information in the context of neurodegenerative diseases such as Alzheimer's. Population graphs, which include multimodal imaging information of the subjects along with the relationships among the population, have been used in literature along with Graph Convolutional Networks (GCNs) and have proved beneficial for a variety of medical imaging tasks. A population graph is usually static and constructed manually using non-imaging information. However, graph construction is not a trivial task and might significantly affect the performance of the GCN, which is inherently very sensitive to the graph structure. In this work, we propose a framework that learns a population graph structure optimized for the downstream task. An attention mechanism assigns weights to a set of imaging and non-imaging features (phenotypes), which are then used for edge extraction. The resulting graph is used to train the GCN. The entire pipeline can be trained end-to-end. Additionally, by visualizing the attention weights that were the most important for the graph construction, we increase the interpretability of the graph. We use the UK Biobank, which provides a large variety of neuroimaging and non-imaging phenotypes, to evaluate our method on brain age regression and classification. The proposed method outperforms competing static graph approaches and other state-of-the-art adaptive methods. We further show that the assigned attention scores indicate that there are both imaging and non-imaging phenotypes that are informative for brain age estimation and are in agreement with the relevant literature.

Related papers

• Graph Neural Networks for Brain Graph Learning: A Survey [53.74244221027981]
  Graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs.
  arXiv  Detail & Related papers  (2024-06-01T02:47:39Z)
• Balanced Graph Structure Information for Brain Disease Detection [6.799894169098717]
  We propose Bargrain, which models two graph structures: filtered correlation matrix and optimal sample graph using graph convolution networks (GCNs) Based on our extensive experiment, Bargrain outperforms state-of-the-art methods in classification tasks on brain disease datasets, as measured by average F1 scores.
  arXiv  Detail & Related papers  (2023-12-30T06:50:52Z)
• Classification of developmental and brain disorders via graph convolutional aggregation [6.6356049194991815]
  We introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI)
  arXiv  Detail & Related papers  (2023-11-13T14:36:29Z)
• A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression [48.97251676778599]
  In medical imaging, population graphs have demonstrated promising results, mostly for classification tasks. extracting population graphs is a non-trivial task and can significantly impact the performance of Graph Neural Networks (GNNs) In this work, we highlight the importance of a meaningful graph construction and experiment with different population-graph construction methods.
  arXiv  Detail & Related papers  (2023-09-26T10:30:45Z)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model [64.29487107585665]
  Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning.
  arXiv  Detail & Related papers  (2022-07-14T20:03:52Z)
• Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph) [3.846749674808336]
  We present an extendable dataset of whole-brain vessel graphs based on specific imaging protocols. We benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification. Our work paves a path towards advancing graph learning research into the field of neuroscience.
  arXiv  Detail & Related papers  (2021-08-30T13:40:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.