scholarly journals Comparing machine and deep learning‐based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging

2020 ◽  
Author(s):  
Jason Smucny ◽  
Ian Davidson ◽  
Cameron S. Carter
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Clinical Improvement ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Diagnosis of Schizophrenia Based on Deep Learning Using fMRI

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.

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

2021 ◽  
Vol 83 (3) ◽  
pp. 45-52
Author(s):  
R. Nur Syahindah Husna ◽  
A. R. Syafeeza ◽  
Norihan Abdul Hamid ◽  
Y. C. Wong ◽  
R. Atikah Raihan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

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.

Functional Magnetic Resonance Imaging Measures of Blood Flow Patterns in the Human Auditory Cortex in Response to Sound

1998 ◽  
Vol 41 (3) ◽  
pp. 538-548 ◽  
Author(s):  
Sean C. Huckins ◽  
Christopher W. Turner ◽  
Karen A. Doherty ◽  
Michael M. Fonte ◽  
Nikolaus M. Szeverenyi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Speech Stimuli ◽  
Complex Stimuli ◽  
Scanning Session ◽  
Auditory Research

Functional Magnetic Resonance Imaging (fMRI) holds exciting potential as a research and clinical tool for exploring the human auditory system. This noninvasive technique allows the measurement of discrete changes in cerebral cortical blood flow in response to sensory stimuli, allowing determination of precise neuroanatomical locations of the underlying brain parenchymal activity. Application of fMRI in auditory research, however, has been limited. One problem is that fMRI utilizing echo-planar imaging technology (EPI) generates intense noise that could potentially affect the results of auditory experiments. Also, issues relating to the reliability of fMRI for listeners with normal hearing need to be resolved before this technique can be used to study listeners with hearing loss. This preliminary study examines the feasibility of using fMRI in auditory research by performing a simple set of experiments to test the reliability of scanning parameters that use a high resolution and high signal-to-noise ratio unlike that presently reported in the literature. We used consonant-vowel (CV) speech stimuli to investigate whether or not we could observe reproducible and consistent changes in cortical blood flow in listeners during a single scanning session, across more than one scanning session, and in more than one listener. In addition, we wanted to determine if there were differences between CV speech and nonspeech complex stimuli across listeners. Our study shows reproducibility within and across listeners for CV speech stimuli. Results were reproducible for CV speech stimuli within fMRI scanning sessions for 5 out of 9 listeners and were reproducible for 6 out of 8 listeners across fMRI scanning sessions. Results of nonspeech complex stimuli across listeners showed activity in 4 out of 9 individuals tested.

Sign in / Sign up

Export Citation Format

Share Document