Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework

Chaitra, N; Vijaya, P. A.; Deshpande, Gopikrishna

doi:10.1016/j.bspc.2020.102099

Cited by 56 publications

(22 citation statements)

References 71 publications

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“…( Supekar et al, 2008 ) A recent study showed that network measures can be used as features in a machine learning framework to make diagnostic predictions of Autism Spectrum Disorder. ( Chaitra et al, 2020 ) These studies however all used a fixed atlas approach and did not investigate the possibility that some of the differences observed in the graph theory measures were due to voxel level connectivity changes and not simply due to changes in edges.…”

Section: Discussionmentioning

confidence: 99%

“…Here we show that many of these studies could come to different conclusions if they considered the changes in functional connectivity at the voxel level detectable either through fixed-node homogeneity measures, or through state- or group- dependent node reconfigurations. A sampling of several such studies covers a range of disorders including autism( Chaitra et al, 2020 ; Henry et al, 2018 ; Itahashi et al, 2014 ; Rudie et al, 2013 ), Alzheimer’s disease ( Brier et al, 2014 ; Khazaee et al, 2015 , 2016 ; Liu et al, 2014 ; Pereira et al, 2016 ; Sanz-Arigita et al, 2010 ; Supekar et al, 2008 ; Zhao et al, 2012 ), schizophrenia ( Alexander-Bloch et al, 2010 ; Karbasforoushan and Woodward, 2012 ; Liu et al, 2008 ; Lynall et al, 2010 ; Su et al, 2015 ; van den Heuvel et al, 2013 ), posttraumatic stress disorder ( Lei et al, 2015 ; Suo et al, 2015 ), Parkinson’s disease ( Göttlich et al, 2013 ; Luo et al, 2015 ), and many other disorders ( Agosta et al, 2013 ; Jiang et al, 2013 ; Lord et al, 2012 ; Rocca et al, 2016 ; Serra et al, 2020 ; Wang et al, 2014 ; Xu et al, 2013 ; Ye et al, 2015 ). Other studies that examined changes in graph measures with age ( Achard and Bullmore, 2007 ; Chan et al, 2014 ; Geerligs et al, 2015 ; Iordan et al, 2018 ; Meunier et al, 2009 ; Onoda and Yamaguchi, 2013 ; Sala-Llonch et al, 2014 ; Stanley et al, 2015 ; Wu et al, 2013 ), sex ( Satterthwaite et al, 2015 ; Tian et al, 2011 ; Wu et al, 2013 ; Zhang et al, 2016 ), cognitive states ( Cohen and D’Esposito, 2016 ; Hearne et al, 2017 ; Shine et al, 2016 ; Wang et al, 2012b ; Wen et al, 2015 ), and other conditions ( Bruno et al, 2012 ; Gard et al, 2014 ) also did not consider connectivity change...…”

Section: Introductionmentioning

confidence: 99%

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Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

2021

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Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nodes reconfigure with brain state. To date, all graph theory studies of functional connectivity in the brain have used fixed nodes. Here, using fixed-, group-, state-specific, and individualized- parcellations for defining nodes, we demonstrate that functional connectivity changes within the nodes significantly influence the findings at the network level. In some cases, state- or group-dependent changes of the sort typically reported do not persist, while in others, changes are only observed when node reconfigurations are considered. The findings suggest that graph theory investigations into connectivity contrasts between brain states and/or groups should consider the influence of voxel-level changes that lead to node reconfigurations; the fundamental building block of a graph.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

2021

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“…Many researches on brain FC are focussing on identifying the neurological biomarkers for ASD patients [ 15 ]. Application of SFC [ 10 , 12 , 16 , 17 , 18 ], and DFC [ 19 ] for detection of ASD in rs-fMRI has been investigated in the past papers. This section summarized the related works on ASD classification algorithms based on SFC and DFC as inputs to machine learning (ML) [ 16 , 17 , 19 ] or deep learning architecture [ 10 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Chaitra et al. [ 18 ] achieved 70.1% accuracy for ASD prediction using combination of Pearson correlation with complex brain network measurements as input features to the recursive-cluster-elimination-SVM (RCE-SVM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network

Al-Hiyali

Yahya

Faye

et al. 2021

Sensors

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The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.

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“…In our previous study [11] on the topological organization of the functional connectome of individuals with PTSD, we demonstrated that topological alterations predominantly involved the default-mode network (DMN) and the salience network (SN), which are associated with affective processing [12] and interoceptive-autonomic processing [13], respectively. Marked differences in network topology have also been found in various brain diseases, such as traumatic brain injury [14], Alzheimer's disease [15], autism spectrum disorder [16], schizophrenia [17], major depressive disorder [18], and attention-deficit/hyperactivity disorder [19] and may underlie the pathogenesis of these disorders. To better analyze such complex networks, the application of graph theory, which quantitatively examines all possible network connections and elucidates key topological properties of the overall network and subnetworks and the function of regions within local and global networks, has been increasingly and extensively applied [20].…”

Section: Introductionmentioning

confidence: 99%

Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level

et al. 2021

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Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.

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Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework

Cited by 56 publications

References 71 publications

Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network

Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level

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