Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning

• URL: http://arxiv.org/abs/2109.03723v1
• Date: Wed, 8 Sep 2021 15:33:29 GMT
• Title: Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning
• Abstract summary: Neuroimaging biomarkers distinguish between typical brain aging and Alzheimer's disease (AD) We present a methodology toward disentangling the two. Disentangled SPARE models derived brain patterns that were more specific to the two types of the brain changes.
• Score: 2.388738275105558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neuroimaging biomarkers that distinguish between typical brain aging and Alzheimer's disease (AD) are valuable for determining how much each contributes to cognitive decline. Machine learning models can derive multi-variate brain change patterns related to the two processes, including the SPARE-AD (Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease) and SPARE-BA (of Brain Aging) investigated herein. However, substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology toward disentangling the two. T1-weighted MRI images of 4,054 participants (48-95 years) with AD, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the iSTAGING (Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases) consortium were analyzed. First, a subset of AD patients and CN adults were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus AD). Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2: amyloid-positive (A+) AD continuum group (consisting of A+AD, A+MCI, and A+ and tau-positive CN individuals) and amyloid-negative (A-) CN group. Finally, the combined group of the AD continuum and A-/CN individuals was used to train SPARE-BA3, with the intention to estimate brain age regardless of AD-related brain changes. Disentangled SPARE models derived brain patterns that were more specific to the two types of the brain changes. Correlation between the SPARE-BA and SPARE-AD was significantly reduced. Correlation of disentangled SPARE-AD was non-inferior to the molecular measurements and to the number of APOE4 alleles, but was less to AD-related psychometric test scores, suggesting contribution of advanced brain aging to these scores.

Related papers

• Neuropsychiatric Deviations From Normative Profiles: An MRI-Derived Marker for Early Alzheimer's Disease Detection [3.4046018979004695]
  Neuropsychiatric symptoms (NPS) such as depression and apathy are common in Alzheimer's disease (AD)<n>Current tools cannot distinguish whether NPS are part of aging or early signs of AD, limiting their utility.<n>We present a deep learning-based normative modelling framework to identify atypical NPS burden from structural MRI.
  arXiv  Detail & Related papers  (2026-04-01T06:41:53Z)
• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• Multi-Resolution Graph Analysis of Dynamic Brain Network for Classification of Alzheimer's Disease and Mild Cognitive Impairment [0.0]
  Alzheimer's disease (AD) is a neurodegenerative disorder marked by memory loss and cognitive decline. Traditional methods, such as Pearson's correlation, have been used to calculate association matrices. We introduce a novel method that integrates discrete wavelet transform (DWT) and graph theory to model the dynamic behavior of brain networks.
  arXiv  Detail & Related papers  (2024-09-06T07:26:14Z)
• 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)
• An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease [13.213387075528017]
  Alzheimer's disease (AD) is the most prevalent form of dementia with a progressive decline in cognitive abilities. We leveraged structural and functional MRI to investigate the disease-induced GM and functional network connectivity changes. We propose a novel DL-based classification framework where a generative module employing Cycle GAN was adopted for imputing missing data.
  arXiv  Detail & Related papers  (2024-06-19T07:31:47Z)
• A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates [0.6417777780911224]
  Alzheimer's Disease and normal aging are both characterized by brain atrophy. We propose a deformation-based morphometry framework to estimate normal aging and AD-specific atrophy patterns.
  arXiv  Detail & Related papers  (2023-11-14T14:04:35Z)
• A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry [18.36168858563601]
  Alzheimer's Disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits.
  arXiv  Detail & Related papers  (2023-03-31T21:17:54Z)
• Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction [53.122045119395594]
  Unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results. We propose deep learning for UAD in 3D brain MRI considering additional age information. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction.
  arXiv  Detail & Related papers  (2022-01-31T09:39:52Z)
• 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)
• MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases [3.955454029331185]
  We introduce a novel method, MAGIC, to uncover disease heterogeneity by leveraging multi-scale clustering. We validate MAGIC using simulated heterogeneous neuroanatomical data and demonstrate its clinical potential by exploring the heterogeneity of Alzheimers Disease (AD) Our results indicate two main subtypes of AD with distinct atrophy patterns that consist of both fine-scale atrophy in the hippocampus as well as large-scale atrophy in cortical regions.
  arXiv  Detail & Related papers  (2020-07-01T23:42:37Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)
• 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.