FUNCTIONAL MAGNETIC RESONANCE IMAGING FOR AUTISM SPECTRUM DISORDER DETECTION USING DEEP LEARNING

Autism Spectrum Disorders (ASDs) define as a scope of disability in the development of certain conditions such as social communication, imagination, and patients' capabilities to make some connection. In Malaysia, the number of ASD cases diagnosed is increasing each year. Typically, ASD patients are analyzed by doctors based on history and behavior observation without the ability to diagnose instantaneously. This research intends to study the ASD biomarker based on neuroimaging functional Magnetic Resonance Imaging (fMRI) images, which can aid doctors in diagnosing ASD. This study applies a deep learning method from Convolutional Neural Network (CNN) variants to detect either the patients are ASD or non-ASD and extract the robust characteristics from neuroimages in fMRI. Then, it interprets the performance of pre-processed images in the form of accuracy to classify the neural patterns. The Autism Brain Imaging Data Exchange (ABIDE) dataset was used to research the brain imaging of ASD patients. The results achieved using CNN models namely VGG-16 and ResNet-50 are 63.4% and 87.0% accuracy, respectively. This method also assists doctors in detecting Autism from a quantifiable method that is not dependent on the behavioral observations of suspected autistic children.

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Diagnosis of Schizophrenia Based on Deep Learning Using fMRI

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/8437260 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

JinChi Zheng ◽

XiaoLan Wei ◽

JinYi Wang ◽

HuaSong Lin ◽

HongRun Pan ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Deep Learning ◽

Magnetic Resonance ◽

Early Diagnosis ◽

Functional Magnetic Resonance Imaging ◽

Fmri Data ◽

Imaging Data ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Functional Connection

Schizophrenia is a brain disease that frequently occurs in young people. Early diagnosis and treatment can reduce family burdens and reduce social costs. There is no objective evaluation index for schizophrenia. In order to improve the classification effect of traditional classification methods on magnetic resonance data, a method of classification of functional magnetic resonance imaging data is proposed in conjunction with the convolutional neural network algorithm. We take functional magnetic resonance imaging (fMRI) data for schizophrenia as an example, to extract effective time series from preprocessed fMRI data, and perform correlation analysis on regions of interest, using transfer learning and VGG16 net, and the functional connection between schizophrenia and healthy controls is classified. Experimental results show that the classification accuracy of fMRI based on VGG16 is up to 84.3%. On the one hand, it can improve the early diagnosis of schizophrenia, and on the other hand, it can solve the classification problem of small samples and high-dimensional data and effectively improve the generalization ability of deep learning models.

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Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network

Applied Sciences ◽

10.3390/app11136216 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6216

Author(s):

Aikaterini S. Karampasi ◽

Antonis D. Savva ◽

Vasileios Ch. Korfiatis ◽

Ioannis Kakkos ◽

George K. Matsopoulos

Keyword(s):

Magnetic Resonance Imaging ◽

Autism Spectrum Disorder ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Default Mode Network ◽

Autism Spectrum ◽

Fmri Data ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Dynamic Connectivity

Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.

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