Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data

• URL: http://arxiv.org/abs/2104.04672v1
• Date: Sat, 10 Apr 2021 03:16:33 GMT
• Title: Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data
• Authors: Nanyan Zhu, Chen Liu, Xinyang Feng, Dipika Sikka, Sabrina Gjerswold-Selleck, Scott A. Small, Jia Guo
• Abstract summary: We propose a potential solution by first learning a structural-to-functional transformation in brain MRI. We then synthesize spatially matched functional images from large-scale structural scans. We identify the temporal lobe to be the most predictive structural-region and the parieto-occipital lobe to be the most predictive functional-region of our model.
• Score: 8.388888908045406
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Current neuroimaging techniques provide paths to investigate the structure and function of the brain in vivo and have made great advances in understanding Alzheimer's disease (AD). However, the group-level analyses prevalently used for investigation and understanding of the disease are not applicable for diagnosis of individuals. More recently, deep learning, which can efficiently analyze large-scale complex patterns in 3D brain images, has helped pave the way for computer-aided individual diagnosis by providing accurate and automated disease classification. Great progress has been made in classifying AD with deep learning models developed upon increasingly available structural MRI data. The lack of scale-matched functional neuroimaging data prevents such models from being further improved by observing functional changes in pathophysiology. Here we propose a potential solution by first learning a structural-to-functional transformation in brain MRI, and further synthesizing spatially matched functional images from large-scale structural scans. We evaluated our approach by building computational models to discriminate patients with AD from healthy normal subjects and demonstrated a performance boost after combining the structural and synthesized functional brain images into the same model. Furthermore, our regional analyses identified the temporal lobe to be the most predictive structural-region and the parieto-occipital lobe to be the most predictive functional-region of our model, which are both in concordance with previous group-level neuroimaging findings. Together, we demonstrate the potential of deep learning with large-scale structural and synthesized functional MRI to impact AD classification and to identify AD's neuroimaging signatures.

Related papers

• Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification [3.144057505325736]
  Part-prototype neural networks integrate the computational advantages of Deep Learning (DL) in an interpretable-by-design architecture. We present PIMPNet, the first interpretable multimodal model for 3D images and demographics applied to the binary classification of Alzheimer's Disease (AD) from 3D sMRI and patient's age.
  arXiv  Detail & Related papers  (2024-07-19T12:58:18Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis [11.295734491885682]
  This study aims to classify MRI images using deep learning models to identify different stages of Alzheimer Disease. Our experimental results show that the deep learning framework based on the Xception model achieved a 99.6% accuracy rate in the multi-class MRI image classification task.
  arXiv  Detail & Related papers  (2024-03-24T16:11:27Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity [48.75665245214903]
  We propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling.
  arXiv  Detail & Related papers  (2022-09-22T04:17:57Z)
• BrainFormer: A Hybrid CNN-Transformer Model for Brain fMRI Data Classification [31.83866719445596]
  BrainFormer is a general hybrid Transformer architecture for brain disease classification with single fMRI volume. BrainFormer is constructed by modeling the local cues within each voxel with 3D convolutions. We evaluate BrainFormer on five independently acquired datasets including ABIDE, ADNI, MPILMBB, ADHD-200 and ECHO.
  arXiv  Detail & Related papers  (2022-08-05T07:54:10Z)
• Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learning [12.128463028063146]
  Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. Deep learning is capable of almost perfectly distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Subcortical regions and ventricles are the most predictive brain regions.
  arXiv  Detail & Related papers  (2022-06-26T21:44:33Z)
• Functional2Structural: Cross-Modality Brain Networks Representation Learning [55.24969686433101]
  Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
  arXiv  Detail & Related papers  (2022-05-06T03:45:36Z)
• DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning [125.99533416395765]
  We developed a deep learning model capable of exploiting the structure of the cortex to learn the complex relationship between brain function and anatomy from structural and functional MRI data. Our model was able to predict the functional organization of human visual cortex from anatomical properties alone, and it was also able to predict nuanced variations across individuals.
  arXiv  Detail & Related papers  (2020-05-26T04:54:31Z)
• Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis [5.23143327587266]
  We review a few recently developed algorithms in various domains of fMRI research. These algorithms all tackle the challenges in fMRI similarly. We advocate wider adoption of explicit model construction in cognitive neuroscience.
  arXiv  Detail & Related papers  (2020-05-11T06:32:54Z)

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.