Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism

Uddin, Lucina Q.; Supekar, Kaustubh; Lynch, Charles J.; Khouzam, Amirah; Phillips, Jennifer M.; Feinstein, Carl; Ryali, Srikanth; Menon, Vinod

doi:10.1001/jamapsychiatry.2013.104

Cited by 522 publications

(526 citation statements)

References 89 publications

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“…Critically, the degree of engagement of these functional systems represents a biological signature of individual differences in social communication abilities. Our findings provide a novel neurobiological template for the investigation of normal social development as well as clinical disorders such as autism (37), in which perception of biologically salient voices may be impaired (40).…”

Section: Discussionmentioning

confidence: 93%

Neural circuits underlying mother’s voice perception predict social communication abilities in children

Abrams

Chen

Odriozola

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child's life is mother's voice: Infants discriminate their mother's voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother's voice or whether this brain activity is related to children's social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (<1 s) nonsense words produced by their biological mother and two female control voices and explored relationships between speech-evoked neural activity and social function. Compared to female control voices, mother's voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother's voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired.

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Section: Discussionmentioning

confidence: 93%

Neural circuits underlying mother’s voice perception predict social communication abilities in children

Abrams

Chen

Odriozola

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…Assaf et al, 2010). Many studies have reported decreased FC between DMN regions in those with ASD (Assaf et al, 2010;Kennedy & Courchesne, 2008;Monk et al, 2009;Weng et al, 2010), although hyperconnectivity within this network has also been reported (Monk et al, 2009;Uddin et al, 2013a). One study found that hypo-or hyperconnectivity of the DMN may be region-specific (Doyle-Thomas et al, 2015).…”

Section: Cc-by-nc-nd 40 International License Peer-reviewed) Is Thementioning

confidence: 99%

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

Easson

Fatima

McIntosh

2017

Preprint

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Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder that is characterized by impairments in social communication and restricted and repetitive behaviours.Neuroimaging studies of individuals with ASD have shown complex patterns of functional connectivity (FC), with no clear consensus on defining brain-behaviour attributes of different subtypes of the disorder. In these studies, "static FC", a metric that considers correlations across the entire time series for a given region, was used. Emerging evidence shows that FC changes over time; this "dynamic FC" can allow for unique detection of the temporal variability of functional connections over time. Here, we used k-means clustering, an unsupervised machine learning algorithm, to characterize two subtypes of ASD based on distinct patterns of static and dynamic FC. A multivariate statistical approach was then implemented to determine optimal relationships between FC patterns and group membership or behaviour. The main objective was to characterize differences in static and dynamic FC between subtypes of ASD defined in a fully data-driven manner, and between subtypes and typically developing individuals. Subtype 2 was defined by increased FC within resting-state networks and decreased FC across networks for static FC, global increases in temporal stability of dynamic FC, and robust relationships between FC and several behaviours, relative to Subtype 1. Further, both ASD subtypes exhibited different patterns of static and dynamic FC compared to controls, particularly within default mode, sensorimotor, and occipital networks. Static and dynamic FC metrics provided both overlapping and unique information about the nature of FC differences between subtypes, and between both subtypes and controls. Our results demonstrate the value of considering FC-based subtypes of ASD to elucidate different relationships between brain and behaviour among individuals with this disorder, and have important clinical implications for catering treatments and behavioural interventions to specific subtypes.

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“…Recently, interest in understanding how ASD alters connectivity between different brain regions has grown with the development of important technological and methodological neuroimaging tools. Most of connectomic studies on ASD in the literature [1,2,3] have mainly focused on structural and functional connectivity (FC) derived from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), respectively. For example, [4] used functional MRI to quantify consistent spatial temporal FC patterns to distinguish between ASD subjects and normal controls (NC).…”

Section: Introductionmentioning

confidence: 99%

High-order Connectomic Manifold Learning for Autistic Brain State Identification

Soussia

Rekik

2017

Connectomics in NeuroImaging

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Abstract. Previous studies have identified disordered functional (from fMRI) and structural (from diffusion MRI) brain connectivities in Autism Spectrum Disorder (ASD). However, 'shape connections' between brain regions were rarely investigated in ASD -e.g., how morphological attributes of a specific brain region (e.g., sulcal depth) change in relation to morphological attributes in other regions. In this paper, we use conventional T1-w MRI to define morphological connectivity networks, each quantifying shape similarity between different cortical regions for a specific cortical attribute at both low-order and high-order levels. For ASD identification, we present a connectomic manifold learning framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectomic features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high-and low-order relationships between morphological regions in the left and right hemispheres.

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Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism

Cited by 522 publications

References 89 publications

Neural circuits underlying mother’s voice perception predict social communication abilities in children

Neural circuits underlying mother’s voice perception predict social communication abilities in children

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

High-order Connectomic Manifold Learning for Autistic Brain State Identification

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