Brain Structural Saliency Over The Ages

• URL: http://arxiv.org/abs/2202.11690v3
• Date: Sat, 23 Jul 2022 12:00:21 GMT
• Title: Brain Structural Saliency Over The Ages
• Authors: Daniel Taylor, Jonathan Shock, Deshendran Moodley, Jonathan Ipser, Matthias Treder
• Abstract summary: We trained a ResNet model as a BA regressor on T1 structural MRI volumes from a small cross-sectional cohort of 524 individuals. We show the change in attribution of relevance to different brain regions through the course of ageing. Some regions increase in relevance with age; some decrease in relevance with age; and others are consistently relevant across ages.
• Score: 0.41998444721319217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain Age (BA) estimation via Deep Learning has become a strong and reliable bio-marker for brain health, but the black-box nature of Neural Networks does not easily allow insight into the features of brain ageing.We trained a ResNet model as a BA regressor on T1 structural MRI volumes from a small cross-sectional cohort of 524 individuals. Using Layer-wise Relevance Propagation (LRP) and DeepLIFT saliency mapping techniques, we analysed the trained model to determine the most relevant structures for brain ageing for the network, and compare these between the saliency mapping techniques. We show the change in attribution of relevance to different brain regions through the course of ageing. A tripartite pattern of relevance attribution to brain regions emerges. Some regions increase in relevance with age (e.g. the right Transverse Temporal Gyrus); some decrease in relevance with age (e.g. the right Fourth Ventricle); and others are consistently relevant across ages. We also examine the effect of the Brain Age Gap (BAG) on the distribution of relevance within the brain volume. It is hoped that these findings will provide clinically relevant region-wise trajectories for normal brain ageing, and a baseline against which to compare brain ageing trajectories.

Related papers

• Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
  We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
  arXiv  Detail & Related papers  (2024-07-31T04:32:43Z)
• Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks [102.75954614946258]
  Increasing brain age with respect to chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. NeuroVNN is pre-trained as a regression model on healthy population to predict chronological age. NeuroVNN adds anatomical interpretability to brain age and has a scale-free' characteristic that allows its transference to datasets curated according to any arbitrary brain atlas.
  arXiv  Detail & Related papers  (2024-02-12T14:46:31Z)
• Explainable Brain Age Prediction using coVariance Neural Networks [94.81523881951397]
  We propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) We make two important observations: VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions.
  arXiv  Detail & Related papers  (2023-05-27T22:28:25Z)
• Voxel-level Importance Maps for Interpretable Brain Age Estimation [70.5330922395729]
  We focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature.
  arXiv  Detail & Related papers  (2021-08-11T18:08:09Z)
• Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss [75.03117866578913]
  A novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Experiments with $6586$ MRIs showed that TSAN could provide accurate brain age estimation.
  arXiv  Detail & Related papers  (2021-06-06T07:11:25Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)
• Local semi-supervised approach to brain tissue classification in child brain MRI [0.0]
  Most segmentation methods in child brain MRI are supervised and are based on global intensity probabilistic computation of major brain structures. In this paper, we consider classification into major tissue classes (white matter and grey matter) and the cerebrospinal fluid. We show that our method improves detection of the tissue classes by its comparison to state-of-the-art classification techniques known as Partial Volume Estimation.
  arXiv  Detail & Related papers  (2020-05-20T06:43:41Z)
• Causality based Feature Fusion for Brain Neuro-Developmental Analysis [26.218572787292427]
  We propose to add the directional flow of information during brain maturation. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups. Our findings indicated that the strength of connections was significantly higher in young adults relative to children.
  arXiv  Detail & Related papers  (2020-01-22T17:38:42Z)

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.