Labeling subtypes in a Parkinson's Cohort using Multifeatures in MRI - Integrating Grey and White Matter Information

• URL: http://arxiv.org/abs/2403.17332v1
• Date: Tue, 26 Mar 2024 02:32:52 GMT
• Title: Labeling subtypes in a Parkinson's Cohort using Multifeatures in MRI - Integrating Grey and White Matter Information
• Authors: Tanmayee Samantaray, Jitender Saini, Pramod Kumar Pal, Bithiah Grace Jaganathan, Vijaya V Saradhi, Gupta CN,
• Abstract summary: Mutual K-Nearest Neighbor (MKNN)-based thresholding for brain network analysis. Structural MRI data from 180 Parkinsons patients and 70 controls from the NIMHANS, India were analyzed.
• Score: 0.05311301767110321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Thresholding of networks has long posed a challenge in brain connectivity analysis. Weighted networks are typically binarized using threshold measures to facilitate network analysis. Previous studies on MRI-based brain networks have predominantly utilized density or sparsity-based thresholding techniques, optimized within specific ranges derived from network metrics such as path length, clustering coefficient, and small-world index. Thus, determination of a single threshold value for facilitating comparative analysis of networks remains elusive. To address this, our study introduces Mutual K-Nearest Neighbor (MKNN)-based thresholding for brain network analysis. Here, nearest neighbor selection is based on the highest correlation between features of brain regions. Construction of brain networks was accomplished by computing Pearson correlations between grey matter volume and white matter volume for each pair of brain regions. Structural MRI data from 180 Parkinsons patients and 70 controls from the NIMHANS, India were analyzed. Subtypes within Parkinsons disease were identified based on grey and white matter volume atrophy using source-based morphometric decomposition. The loading coefficients were correlated with clinical features to discern clinical relationship with the deciphered subtypes. Our data-mining approach revealed: Subtype A (N = 51, intermediate type), Subtype B (N = 57, mild-severe type with mild motor symptoms), and Subtype AB (N = 36, most-severe type with predominance in motor impairment). Subtype-specific weighted matrices were binarized using MKNN-based thresholding for brain network analysis. Permutation tests on network metrics of resulting bipartite graphs demonstrated significant group differences in betweenness centrality and participation coefficient. The identified hubs were specific to each subtype, with some hubs conserved across different subtypes.

Related papers

• Adaptive Critical Subgraph Mining for Cognitive Impairment Conversion Prediction with T1-MRI-based Brain Network [4.835051121929712]
  Prediction the conversion to early-stage dementia is critical for mitigating its progression. Traditional T1-weighted magnetic resonance imaging (T1-MRI) research focus on identifying brain atrophy regions. Brain-SubGNN is a novel graph representation network to mine and enhance critical subgraphs based on T1-MRI.
  arXiv  Detail & Related papers  (2024-03-20T06:46:01Z)
• Unique Brain Network Identification Number for Parkinson's Individuals Using Structural MRI [0.060379119983736775]
  We propose a novel algorithm called Unique Brain Network Identification Number, UBNIN for encoding the brain networks of individual subjects. We parcellated each subjects brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The numerical representation UBNIN was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities.
  arXiv  Detail & Related papers  (2023-06-02T17:03:39Z)
• 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)
• Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions [68.41088365582831]
  White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. Most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient parcellation of 198 SWM clusters from whole-brain tractography.
  arXiv  Detail & Related papers  (2022-07-18T23:07:53Z)
• White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning [68.5548609642999]
  We propose a deep-learning-based framework for neuropsychological score prediction using white matter tract data. We represent the arcuate fasciculus (AF) as a point cloud with microstructure measurements at each point. We improve prediction performance with the proposed Paired-Siamese Loss that utilizes information about differences between continuous neuropsychological scores.
  arXiv  Detail & Related papers  (2022-07-06T02:03:28Z)
• CNN-based fully automatic wrist cartilage volume quantification in MR Image [55.41644538483948]
  The U-net convolutional neural network with additional attention layers provides the best wrist cartilage segmentation performance. The error of cartilage volume measurement should be assessed independently using a non-MRI method.
  arXiv  Detail & Related papers  (2022-06-22T14:19:06Z)
• Automatic Semantic Segmentation of the Lumbar Spine. Clinical Applicability in a Multi-parametric and Multi-centre MRI study [0.0]
  This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different strategies.
  arXiv  Detail & Related papers  (2021-11-16T17:33:05Z)
• Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry [55.57953219617467]
  We show that the learnability of a neural network is directly related to its size. We use Betti numbers to measure the topological geometric complexity of input data and the neural network. We perform the experiments on a real-world dataset MNIST and the results verify our analysis and conclusion.
  arXiv  Detail & Related papers  (2021-09-03T11:45:51Z)
• Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification [13.907981019956832]
  We propose a graph deep learning framework to incorporate the non-Euclidean information about graph structure for classifying brain networks in major depressive disorder (MDD) We design a novel graph autoencoder (GAE) architecture based on the graph convolutional networks (GCNs) to embed the topological structure and node content of large-sized fMRI networks into low-dimensional latent representations. Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.
  arXiv  Detail & Related papers  (2021-07-27T14:12:39Z)
• BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification [9.312868504719193]
  Alzheimer's disease is the most common age-related dementia. Brain MRI offers a noninvasive biomarker to detect brain aging. Alzheimer's disease is the most common age-related dementia.
  arXiv  Detail & Related papers  (2021-03-10T23:44:53Z)
• 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)

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.