Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional‐magnetic resonance imaging: A spatial filtering approach

@article{Subbaraju2017IdentifyingDI,
  title={Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional‐magnetic resonance imaging: A spatial filtering approach},
  author={Vigneshwaran Subbaraju and Mahanand Belathur Suresh and Suresh Sundaram and Narasimhan Sundararajan},
  journal={Medical Image Analysis},
  year={2017},
  volume={35},
  pages={375–389}
}

Figures and Tables from this paper

Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI)
TLDR
A regularization framework to obtain a robust estimation of the covariance matrices such that the effect of atypical samples is reduced is presented and R-SFM provides an accurate and reliable tool for detection of ADHD from BOLD rs-fMRI time series data.
Identification of lateralized compensatory neural activities within the social brain due to autism spectrum disorder in adolescent males
TLDR
An MRI‐based study to identify the differences in brain activities due to ASD, verify whether such differences exist within the ‘social brain’ circuit, and uncover potential compensatory mechanisms are presented, showing that the most discriminative brain activities occur within a subset of the social brain involved with affective aspects of social processing.
Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored
Attentional Connectivity-based Prediction of Autism Using Heterogeneous rs-fMRI Data from CC200 Atlas
TLDR
Connectivity analysis on the optimal model highlighted informative regions strongly involved in the social cognition as well as interaction, and manifested lower correlation between the anterior and posterior default mode network (DMN) in autistic individuals than controls, which enables the proposed method to effectively identify the individuals with risk of ASD.
Detection of Autism Spectrum Disorder using fMRI Functional Connectivity with Feature Selection and Deep Learning
TLDR
A deep learning approach combined with the F-score feature selection method for ASD diagnosis using a functional magnetic resonance imaging (fMRI) dataset is proposed and the altered brain network may provide insight into the underlying pathology of ASD, and the functional connectivity features selected by the method may serve as biomarkers.
Ripplet II transform and higher order cumulants from R-fMRI data for diagnosis of autism
TLDR
Eickhoff-Zilles (EZ) atlas is presented to evaluate time courses for 20 ASDs and 16 HPs in 116 regions of interest (ROIs) and shows that the proposed method achieves 91.67% accuracy which outperforms previous works.
...
...

References

SHOWING 1-10 OF 76 REFERENCES
Functional connectivity magnetic resonance imaging classification of autism.
TLDR
Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age, indicating feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.
Multisite functional connectivity MRI classification of autism: ABIDE results
TLDR
Multisite functional connectivity classification of autism outperformed chance using a simple leave-one-out classifier, but exhibited poorer accuracy than for single site results.
Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study
TLDR
The results indicate common sites of structural and functional alterations in higher order association cortex areas and may provide multimodal imaging support to the long-standing hypothesis of autism as a disorder of impaired higher-order multisensory integration.
Sex Differences in the Default Mode Network with Regard to Autism Spectrum Traits: A Resting State fMRI Study
TLDR
This study draws on the extreme male brain theory to investigate the relationship between sex difference and the default mode network (DMN) and the relationship to autism spectrum traits as measured by autism-spectrum quotient (AQ) scores, finding significant differences between female and male subjects in DMN brain regions.
The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism
TLDR
W Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity.
...
...