Age-Stratified Differences in Morphological Connectivity Patterns in ASD: An sMRI and Machine Learning Approach

• URL: http://arxiv.org/abs/2308.07356v1
• Date: Mon, 14 Aug 2023 12:11:25 GMT
• Title: Age-Stratified Differences in Morphological Connectivity Patterns in ASD: An sMRI and Machine Learning Approach
• Authors: Gokul Manoj, Sandeep Singh Sengar, Jac Fredo Agastinose Ronickom
• Abstract summary: The objective of this study was to compare the effect of different age groups in classifying ASD using morphological features (MF) and morphological connectivity features (MCF) The MCF with RF in the 6 to 11 age group performed better in the classification than the other groups and produced an accuracy, F1 score, recall, and precision of 75.8%, 83.1%, 86%, and 80.4%, respectively.
• Score: 1.3436368800886478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Age biases have been identified as an essential factor in the diagnosis of ASD. The objective of this study was to compare the effect of different age groups in classifying ASD using morphological features (MF) and morphological connectivity features (MCF). Methods: The structural magnetic resonance imaging (sMRI) data for the study was obtained from the two publicly available databases, ABIDE-I and ABIDE-II. We considered three age groups, 6 to 11, 11 to 18, and 6 to 18, for our analysis. The sMRI data was pre-processed using a standard pipeline and was then parcellated into 148 different regions according to the Destrieux atlas. The area, thickness, volume, and mean curvature information was then extracted for each region which was used to create a total of 592 MF and 10,878 MCF for each subject. Significant features were identified using a statistical t-test (p<0.05) which was then used to train a random forest (RF) classifier. Results: The results of our study suggested that the performance of the 6 to 11 age group was the highest, followed by the 6 to 18 and 11 to 18 ages in both MF and MCF. Overall, the MCF with RF in the 6 to 11 age group performed better in the classification than the other groups and produced an accuracy, F1 score, recall, and precision of 75.8%, 83.1%, 86%, and 80.4%, respectively. Conclusion: Our study thus demonstrates that morphological connectivity and age-related diagnostic model could be an effective approach to discriminating ASD.

Related papers

• OpenMAP-BrainAge: Generalizable and Interpretable Brain Age Predictor [0.0]
  We develop an age prediction model which is interpretable and robust to demographic and technological MRI scans.<n>Our model processes T1-dimensional pseudo-3D MRI scans from three views and incorporates linear brain information.
  arXiv  Detail & Related papers  (2025-06-21T05:24:42Z)
• Naturalistic Language-related Movie-Watching fMRI Task for Detecting Neurocognitive Decline and Disorder [60.84344168388442]
  Language-related functional magnetic resonance imaging (fMRI) may be a promising approach for detecting cognitive decline and early NCD.<n>We examined the effectiveness of this task among 97 non-demented Chinese older adults from Hong Kong.<n>The study demonstrated the potential of the naturalistic language-related fMRI task for early detection of aging-related cognitive decline and NCD.
  arXiv  Detail & Related papers  (2025-06-10T16:58:47Z)
• Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV [49.1574468325115]
  This study explores multiple ML approaches for predicting LOS in ICU specifically for the patients with neurological diseases based on the MIMIC-IV dataset.<n>The evaluated models include classic ML algorithms (K-Nearest Neighbors, Random Forest, XGBoost and CatBoost) and Neural Networks (LSTM, BERT and Temporal Fusion Transformer)
  arXiv  Detail & Related papers  (2025-05-23T14:06:42Z)
• Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths [0.43163184307789293]
  Multidimensional breast MRI data might improve tumor diagnostics, characterization, and treatment planning. Accurate alignment and delineation of images acquired at different field strengths such as 3T and 7T, remain challenging research tasks. The proposed method may be feasible in providing joint tumor segmentation and registration of MRI data acquired at different field strengths.
  arXiv  Detail & Related papers  (2025-01-23T14:15:54Z)
• Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy [0.0]
  The aim was to develop a CT-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors. Performance was assessed with Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and mean surface distance (MSD) Model-PMC-UMCU achieved mean DSC values above 0.95 for five of nine OARs, while spleen and heart ranged between 0.90 and 0.95. The stomach-bowel and pancreas exhibited DSC values below 0.90.
  arXiv  Detail & Related papers  (2024-11-01T13:54:31Z)
• Leveraging Persistent Homology for Differential Diagnosis of Mild Cognitive Impairment [2.474908349649168]
  Mild cognitive impairment (MCI) is characterized by subtle changes in cognitive functions, often associated with disruptions in brain connectivity. The present study introduces a novel fine-grained analysis to examine topological alterations in neurodegeneration pertaining to six different brain networks of subjects.
  arXiv  Detail & Related papers  (2024-08-28T09:01:55Z)
• A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
  We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics. We generate high-quality synthetic fMRI data based on user-supplied demographics.
  arXiv  Detail & Related papers  (2024-05-13T17:49:20Z)
• A Federated Learning Framework for Stenosis Detection [70.27581181445329]
  This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA) Two heterogeneous datasets from two institutions were considered: dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy) dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature.
  arXiv  Detail & Related papers  (2023-10-30T11:13:40Z)
• Comparative study of machine learning and deep learning methods on ASD classification [4.826988182025783]
  The autism dataset is studied to identify the differences between autistic and healthy groups. Our best model can classify autistic and healthy patients on the multisite ABIDE I data with 71% accuracy.
  arXiv  Detail & Related papers  (2022-09-18T16:39:10Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model [0.3904666078483698]
  Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk. We develop and apply a data-driven, machine learning based method to identify AF cases. Our model is the first machine learning based method for identification of AF cases in HCM.
  arXiv  Detail & Related papers  (2021-09-19T20:00:49Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks [0.0]
  We leverage crowdsourcing to build a pool of machine learning pipelines for neurological disorder diagnosis with application to autism spectrum disorder (ASD) diagnosis. The first-ranked team achieved 70% accuracy, 72.5% sensitivity, and 67.5% specificity, while the second-ranked team achieved 63.8%, 62.5%, 65% respectively.
  arXiv  Detail & Related papers  (2020-04-28T06:23:29Z)

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.