891 research outputs found
Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.
Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition
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Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets
The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to the unique context of MRIs of the brain. This thesis presents a theoretical underpinning for whole-brain classification of extremely large datasets of multi-site MRIs, including machine learning model architecture, dataset curation methods, machine learning visualization methods, encoding of MRI data, and feature extraction. To replicate large sample sizes typically applied to deep learning models, a dataset of over 50,000 functional and structural MRIs was amassed from nine different databases, and the undertaken analyses were conducted on three covariates commonly found across these collections: sex, resting state/task, and autism spectrum disorder. I find that deep learning is not only a method that has promise for clinical application in the future, but also a powerful statistical tool for analyzing complex, nonlinear relationships in brain data where conventional statistics may fail. However, results are also dependent on factors such as dataset imbalances, confounding factors such as motion and head size, selected methods of encoding MRI data, variability of machine learning models and selected methods of visualizing the machine learning results. In this thesis, I present the following methodological innovations: (1) a method of balancing datasets as a means of regressing out measurable confounding factors; (2) a means of removing spatial biases from deep learning visualization methods; (3) methods of encoding functional and structural datasets as connectivity matrices; (4) the use of ensemble models and convolutional neural network architectures to improve classification accuracy and consistency; (5) adaptation of deep learning visualization methods to study brain connections utilized in the classification process. Additionally, I discuss interpretations, limitations, and future directions of this research.Gates Cambridge Scholarshi
Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse
signs and symptoms that appear in early childhood. ASD is also associated with
communication deficits and repetitive behavior in affected individuals. Various
ASD detection methods have been developed, including neuroimaging modalities
and psychological tests. Among these methods, magnetic resonance imaging (MRI)
imaging modalities are of paramount importance to physicians. Clinicians rely
on MRI modalities to diagnose ASD accurately. The MRI modalities are
non-invasive methods that include functional (fMRI) and structural (sMRI)
neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI
for specialists is often laborious and time-consuming; therefore, several
computer-aided design systems (CADS) based on artificial intelligence (AI) have
been developed to assist the specialist physicians. Conventional machine
learning (ML) and deep learning (DL) are the most popular schemes of AI used
for diagnosing ASD. This study aims to review the automated detection of ASD
using AI. We review several CADS that have been developed using ML techniques
for the automated diagnosis of ASD using MRI modalities. There has been very
limited work on the use of DL techniques to develop automated diagnostic models
for ASD. A summary of the studies developed using DL is provided in the
appendix. Then, the challenges encountered during the automated diagnosis of
ASD using MRI and AI techniques are described in detail. Additionally, a
graphical comparison of studies using ML and DL to diagnose ASD automatically
is discussed. We conclude by suggesting future approaches to detecting ASDs
using AI techniques and MRI neuroimaging
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
Autism spectrum disorder (ASD) is a brain condition characterized by
diverse signs and symptoms that appear in early childhood. ASD is also
associated with communication deficits and repetitive behavior in affected
individuals. Various ASD detection methods have been developed, including
neuroimaging modalities and psychological tests. Among these methods,
magnetic resonance imaging (MRI) imaging modalities are of paramount
importance to physicians. Clinicians rely on MRI modalities to diagnose
ASD accurately. The MRI modalities are non-invasive methods that include
functional (fMRI) and structural (sMRI) neuroimaging methods. However,
diagnosing ASD with fMRI and sMRI for specialists is often laborious and
time-consuming; therefore, several computer-aided design systems (CADS)
based on artificial intelligence (AI) have been developed to assist specialist
physicians. Conventional machine learning (ML) and deep learning (DL) are
the most popular schemes of AI used for diagnosing ASD. This study aims to
review the automated detection of ASD using AI. We review several CADS that
have been developed using ML techniques for the automated diagnosis of
ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of
the studies developed using DL is provided in the Supplementary Appendix.
Then, the challenges encountered during the automated diagnosis of ASD
using MRI and AI techniques are described in detail. Additionally, a graphical
comparison of studies using ML and DL to diagnose ASD automatically
is discussed. We suggest future approaches to detecting ASDs using AI
techniques and MRI neuroimaging.Qatar National
Librar
Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity
Functional connectivity network (FCN) data from functional magnetic resonance
imaging (fMRI) is increasingly used for the diagnoses of brain disorders.
However, state-of-the-art studies used to build the FCN using a single brain
parcellation atlas at a certain spatial scale, which largely neglected
functional interactions across different spatial scales in hierarchical
manners. In this study, 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 across multiple spatial scales, namely
"Atlas-guided Pooling". Accordingly, we propose a Multiscale-Atlases-based
Hierarchical Graph Convolutional Network (MAHGCN), built on the stacked layers
of graph convolution and the atlas-guided pooling, for a comprehensive
extraction of diagnostic information from multiscale FCNs. Experiments on
neuroimaging data from 1792 subjects demonstrate the effectiveness of our
proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal
stage of AD (i.e., mild cognitive impairment [MCI]), as well as autism spectrum
disorder (ASD), with accuracy of 88.9%, 78.6%, and 72.7% respectively. All
results show significant advantages of our proposed method over other competing
methods. This study not only demonstrates the feasibility of brain disorder
diagnosis using resting-state fMRI empowered by deep learning, but also
highlights that the functional interactions in the multiscale brain hierarchy
are worth being explored and integrated into deep learning network
architectures for better understanding the neuropathology of brain disorders
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