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Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis.

ABSTRACT: 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 ( l_2,1 -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.

SUBMITTER: Huang H

PROVIDER: S-EPMC6865533 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis.

Huang Huifang H Liu Xingdan X Jin Yan Y Lee Seong-Whan SW Wee Chong-Yaw CY Shen Dinggang D

Human brain mapping 20181025 3

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 in ...[more]

PMID: 30357998

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Project description:Introduction: Clinicians working with children with autism spectrum disorder (ASD) occupy an important position between parents and the wide-ranging research findings. However, it is not widely known how clinicians view and experience ASD in children, even though their perspective has been shown to significantly influence their work. Material and methods: Sixteen physicians working with preschool children without a diagnosis of (intellectual or other) disability with a (presumed) diagnosis of ASD participated in a semistructured interview. They described their professional views on ASD, and how they experienced its use in their clinical practice. The data were analyzed by applying the qualitative research method of interpretative phenomenological analysis of the data through Nvivo 11. Results: The main topic of the interviewed physicians' views and experiences of ASD in a young child comprised three inductively established themes: 1) physicians' views on ASD are multifaceted but fit within their personal clinical styles; 2) the ASD diagnosis is a "descriptive" part of a clinical trajectory; and 3) ASD treatment is a mix of "standard" approaches and a personalized search. These physicians' perspectives on ASD are composed of multiple and sometimes ambiguous facets. However, their views are embedded in their personal clinical styles in general (i.e., beyond ASD) and are experienced as clinically "workable." With the aim of finding an adequate approach to the problems parents bring to their office, many interviewed physicians say that-complementary to or rather than a classificatory diagnosis-they prefer using a personalized "profile" of a child in a therapeutic "process." Conclusions: The interviewed physicians consider doubts and concerns to be an inherent part of their clinical work with ASD in young children, but do not experience this ambiguity as an obstacle to clinical care. These physicians deal with the multiplicity of their views on ASD by basing their eclectic views on their generally adopted clinical styles, and by selecting what works for them, and for the parents and child, from what they regard as the 'textbook knowledge' on ASD. We discuss the implications of these findings for translating research results to the clinic.

Project description:IMPORTANCE:Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. OBJECTIVES:To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. DESIGN, SETTING, AND PARTICIPANTS:We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. MAIN OUTCOMES AND MEASURES:We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. RESULTS:Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t???3.13, P?<?.001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r???0.21, P?<?.0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r???-0.09, P?<?.012 corrected), although this association was significant only in typically developing participants (all r???-0.13, P?<?.009 corrected). CONCLUSIONS AND RELEVANCE:Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior.

Dataset Information

Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis.

Publications

Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis.

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