Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder.
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ABSTRACT:
SUBMITTER: Xie Q
PROVIDER: S-EPMC8269664 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Autism spectrum disorder (ASD) is a neurodevelopmental disorder that can cause significant social, communication, and behavioral challenges. Diagnosis of ASD is complicated and there is an urgent need to identify ASD-associated biomarkers and features to help automate diagnostics and develop predictive ASD models. The present study adopts a novel evolutionary algorithm, the conjunctive clause evolutionary algorithm (CCEA), to select features most significant for distinguishing individuals with and without ASD, and is able to accommodate datasets having a small number of samples with a large number of feature measurements. The dataset is unique and comprises both behavioral and neuroimaging measurements from a total of 28 children from 7 to 14 years old. Potential biomarker candidates identified include brain volume, area, cortical thickness, and mean curvature in specific regions around the cingulate cortex, frontal cortex, and temporal-parietal junction, as well as behavioral features associated with theory of mind. A separate machine learning classifier (i.e., k-nearest neighbors algorithm) was used to validate the CCEA feature selection and for ASD prediction. Study findings demonstrate how machine learning tools might help move the needle on improving diagnostic and predictive models of ASD.
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Project description:Imitation has been considered as one of the precursors for sociocommunicative development. Impairments of imitation in autism spectrum disorder (ASD) could be indicative of dysfunctional underlying neural processes. Neuroimaging studies have found reduced activation in areas associated with imitation, but a functional connectivity MRI network perspective of these regions in autism is unavailable. Functional and effective connectivity was examined in 14 male participants with ASD and 14 matched typically developing (TD) participants. We analyzed intrinsic, low-frequency blood oxygen level dependent (BOLD) fluctuations of three regions in literature found to be associated with imitation (inferior frontal gyrus [IFG], inferior parietal lobule [IPL], superior temporal sulcus [STS]). Direct group comparisons did not show significantly reduced functional connectivity within the imitation network in ASD. Conversely, we observed greater connectivity with frontal regions, particularly superior frontal and anterior cingulate gyri, in the ASD compared to TD group. Structural equation modeling of effective connectivity revealed a significantly reduced effect of IPL on IFG together with an increased influence of a region in dorsal prefrontal cortex (dPFC) on IFG in the ASD group. Our results suggest atypical connectivity of the imitation network with an enhanced role of dPFC, which may relate to behavioral impairments.
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Project description:The cerebral cortex and the cerebellum are spatially remote areas that are connected by complex circuits that link both primary and associative areas. Previous studies have revealed abnormalities in autism spectrum disorder (ASD); however, it is not clear whether cortico-cerebellar connectivity is differentially manifested in the disorder. To explore this issue, we investigated differences in intrinsic cortico-cerebellar functional connectivity between individuals with typical development (TD) and those with ASD. To this end, we used functional magnetic resonance imaging (fMRI) of 708 subjects under a resting state protocol provided by the ABIDE I Consortium. We found that people with ASD had diminished functional connectivity between the cerebellum and the following cortical regions: (i) right fusiform gyrus, (ii) right postcentral gyrus, (iii) right superior temporal gyrus, (iv) right middle temporal gyrus, and (v) left middle temporal gyrus. All of these regions are involved in many cognitive systems that contribute to commonly affected functions in ASD. For right fusiform gyrus, right superior temporal gyrus, and left middle temporal gyrus, we reproduced the results in an independent cohort composed of 585 subjects of the ABIDE II Consortium. Our results points toward a consistent atypical cortico-cerebellar connectivity in ASD.
| S-EPMC6341229 | biostudies-literature
Project description:Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi-view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l2,1 -norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group-sparse network at local level. We then linearly fuse the selected features from each individual network via a multi-kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.
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Project description:The increase in the number of children with autism and the importance of early autism intervention has prompted researchers to perform automatic and early autism screening. Consequently, in the present paper, a cry-based screening approach for children with Autism Spectrum Disorder (ASD) is introduced which would provide both early and automatic screening. During the study, we realized that ASD specific features are not necessarily observable in all children with ASD and in all instances collected from each child. Therefore, we proposed a new classification approach to be able to determine such features and their corresponding instances. To test the proposed approach a set of data relating to children between 18 to 53 months which had been recorded using high-quality voice recording devices and typical smartphones at various locations such as homes and daycares was studied. Then, after preprocessing, the approach was used to train a classifier, using data for 10 boys with ASD and 10 Typically Developed (TD) boys. The trained classifier was tested on the data of 14 boys and 7 girls with ASD and 14 TD boys and 7 TD girls. The sensitivity, specificity, and precision of the proposed approach for boys were 85.71%, 100%, and 92.85%, respectively. These measures were 71.42%, 100%, and 85.71% for girls, respectively. It was shown that the proposed approach outperforms the common classification methods. Furthermore, it demonstrated better results than the studies which used voice features for screening ASD. To pilot the practicality of the proposed approach for early autism screening, the trained classifier was tested on 57 participants between 10 to 18 months. These 57 participants consisted of 28 boys and 29 girls and the results were very encouraging for the use of the approach in early ASD screening.
| S-EPMC7728261 | biostudies-literature
Project description:Germline mutations in PTEN, which encodes a widely expressed phosphatase, was mapped to 10q23 and identified as the susceptibility gene for Cowden syndrome, characterized by macrocephaly and high risks of breast, thyroid, and other cancers. The phenotypic spectrum of PTEN mutations expanded to include autism with macrocephaly only 10 years ago. Neurological studies of patients with PTEN-associated autism spectrum disorder (ASD) show increases in cortical white matter and a distinctive cognitive profile, including delayed language development with poor working memory and processing speed. Once a germline PTEN mutation is found, and a diagnosis of phosphatase and tensin homolog (PTEN) hamartoma tumor syndrome made, the clinical outlook broadens to include higher lifetime risks for multiple cancers, beginning in childhood with thyroid cancer. First described as a tumor suppressor, PTEN is a major negative regulator of the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (mTOR) signaling pathway-controlling growth, protein synthesis, and proliferation. This canonical function combines with less well-understood mechanisms to influence synaptic plasticity and neuronal cytoarchitecture. Several excellent mouse models of Pten loss or dysfunction link these neural functions to autism-like behavioral abnormalities, such as altered sociability, repetitive behaviors, and phenotypes like anxiety that are often associated with ASD in humans. These models also show the promise of mTOR inhibitors as therapeutic agents capable of reversing phenotypes ranging from overgrowth to low social behavior. Based on these findings, therapeutic options for patients with PTEN hamartoma tumor syndrome and ASD are coming into view, even as new discoveries in PTEN biology add complexity to our understanding of this master regulator.
| S-EPMC4489960 | biostudies-literature
Project description:Autism spectrum disorder (ASD) is associated with disrupted brain networks. Neuroimaging techniques provide noninvasive methods of investigating abnormal connectivity patterns in ASD. In the present study, we compare functional connectivity networks in people with ASD with those in typical controls, using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE) project. Specifically, we focus on the characteristics of intrinsic functional connectivity based on data collected by resting-state functional magnetic resonance imaging (rs-fMRI). Our aim was to identify disrupted brain connectivity patterns across all networks, instead of in individual edges, by using advanced statistical methods. Unlike many brain connectome studies, in which networks are prespecified before the edge connectivity in each network is compared between clinical groups, we detected the latent differentially expressed networks automatically. Our network-level analysis identified abnormal connectome networks that (i) included a high proportion of edges that were differentially expressed between people with ASD and typical controls; and (ii) showed highly-organized graph topology. These findings provide new insight into the study of the underlying neuropsychiatric mechanism of ASD.
| S-EPMC5359252 | biostudies-literature
Project description:BACKGROUND: Introduction of proposed criteria for DSM-5 Autism Spectrum Disorder (ASD) has raised concerns that some individuals currently meeting diagnostic criteria for Pervasive Developmental Disorder (PDD; DSM-IV-TR/ICD-10) will not qualify for a diagnosis under the proposed changes. To date, reports of sensitivity and specificity of the new criteria have been inconsistent across studies. No study has yet considered how changes at the 'sub domain' level might affect overall sensitivity and specificity, and few have included individuals of different ages and ability levels. METHODS: A set of DSM-5 ASD algorithms were developed using items from the Diagnostic Interview for Social and Communication Disorders (DISCO). The number of items required for each DSM-5 subdomain was defined either according to criteria specified by DSM-5 (Initial Algorithm), a statistical approach (Youden J Algorithm), or to minimise the number of false positives while maximising sensitivity (Modified Algorithm). The algorithms were designed, tested and compared in two independent samples (Sample 1, N = 82; Sample 2, N = 115), while sensitivity was assessed across age and ability levels in an additional dataset of individuals with an ICD-10 PDD diagnosis (Sample 3, N = 190). RESULTS: Sensitivity was highest in the Initial Algorithm, which had the poorest specificity. Although Youden J had excellent specificity, sensitivity was significantly lower than in the Modified Algorithm, which had both good sensitivity and specificity. Relaxing the domain A rules improved sensitivity of the Youden J Algorithm, but it remained less sensitive than the Modified Algorithm. Moreover, this was the only algorithm with variable sensitivity across age. All versions of the algorithm performed well across ability level. CONCLUSIONS: This study demonstrates that good levels of both sensitivity and specificity can be achieved for a diagnostic algorithm adhering to the DSM-5 criteria that is suitable across age and ability level.
| S-EPMC4098079 | biostudies-other
Project description:Autism Spectrum Disorder (ASD) is phenotypically and genetically heterogeneous, but genomic analyses have identified candidate susceptibility genes. We present a CRISPR gene editing strategy to insert a protein tag and premature termination sites creating an induced pluripotent stem cell (iPSC) knockout resource for functional studies of 10 ASD-relevant genes (AFF2/FMR2, ANOS1, ASTN2, ATRX, CACNA1C, CHD8, DLGAP2, KCNQ2, SCN2A, TENM1). Neurogenin 2 (NEUROG2)-directed differentiation of iPSCs allowed production of cortical excitatory neurons, and mutant proteins were not detectable. Using both patch-clamp and multi-electrode array approaches, the electrophysiological deficits measured were distinct for different mutations. However, they culminated in a consistent reduction in synaptic activity, including reduced spontaneous excitatory post-synaptic current frequencies in AFF2/FMR2-, ASTN2-, ATRX-, KCNQ2- and SCN2A-null neurons. RNAseq revealed convergence of several neuronal networks. Despite ASD susceptibility genes belonging to different gene ontologies, isogenic stem cell resources can reveal common functional phenotypes, such as reduced functional connectivity.
2018-11-01 | GSE107878 | GEO
Project description:While there is increasing evidence of altered brain connectivity in autism, the degree and direction of these alterations in connectivity and their uniqueness to autism has not been established. The aim of the present study was to compare connectivity in children with autism to that of typically developing controls and children with developmental delay without autism.We assessed EEG spectral power, coherence, phase lag, Pearson and partial correlations, and epileptiform activity during the awake, slow wave sleep, and REM sleep states in 137 children aged 2 to 6 years with autism (n = 87), developmental delay without autism (n = 21), or typical development (n = 29).We found that brain connectivity, as measured by coherence, phase lag, and Pearson and partial correlations distinguished children with autism from both neurotypical and developmentally delayed children. In general, children with autism had increased coherence which was most prominent during slow wave sleep.Functional connectivity is distinctly different in children with autism compared to samples with typical development and developmental delay without autism. Differences in connectivity in autism are state and region related. In this study, children with autism were characterized by a dynamically evolving pattern of altered connectivity.
| S-EPMC4703709 | biostudies-other