The Neural Correlates of Emotional Lability in Children with Autism Spectrum Disorder

Bennett, Randi; Somandepalli, Krishna; Roy, Amy Krain; Martino, Adriana Di

doi:10.1089/brain.2016.0472

Cited by 16 publications

(17 citation statements)

References 76 publications

(88 reference statements)

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“…While node strength, betweenness centrality and clustering coefficient provide important information about affected areas, we should note that curvature identify differences in areas that are not detected by the other measures. In particular, curvature discovered differences in the following areas of the right hemisphere: inferior temporal gyrus (temporo-occipital), temporal occipital fusiform cortex, para-hippocampal gyrus (posterior), cuneus cortex, supra-marginal gyrus (posterior), insular cortex, frontal orbital cortex, and frontal pole (Figure 8, Panel A), which is in line with previous studies [56][57][58][59][60]. Interestingly, a morphometric analysis of asymmetry in volume/structure of cortical areas in individuals with ASD found a pronounced rightward bias [56], which appears to agree with our findings based on curvature.…”

Section: Change In Curvature and Autism Spectrum Disorders (Asd)supporting

confidence: 91%

Network Curvature as a Hallmark of Brain Structural Connectivity

Farooq

Chen

Georgiou

et al. 2017

Preprint

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Studies show that while brain networks are remarkably robust to a variety of adverse events, such as injuries and lesions due to accidents or disease, they may be fragile when the disturbance takes place in specific locations. This seems to be the case for diseases in which accumulated changes in network topology dramatically affect certain sensitive areas. To this end, previous attempts have been made to quantify robustness and fragility of brain functionality in two broadly defined ways: (i) utilizing model-based techniques to predict lesion effects, and (ii) studying empirical effects from brain lesions due to injury or disease. Both directions aim at assessing functional connectivity changes resulting from structural network variations. In the present work, we follow a more geometric viewpoint that is based on a notion of curvature of networks, the so-called Ollivier-Ricci curvature. A similar approach has been used in recent studies to quantify financial market robustness as well as to differentiate biological networks corresponding to cancer cells from normal cells. The same notion of curvature, defined at the node level for brain networks obtained from MRI data, may help identify and characterize the effects of diseases on specific brain regions. In the present paper, we apply the Ollivier-Ricci curvature to brain structural networks to: i) Demonstrate its unique ability to identify robust (or fragile) brain regions in healthy subjects. We compare our results to previously published work which identified a unique set of regions (called structural core) of the human cerebral cortex. This novel characterization of brain networks, complementary to measures such as degree, strength, clustering or efficiency, may be particularly useful to detect and monitor candidate areas for targeting by surgery (e.g. deep brain stimulation) or pharmaco-therapeutic agents; ii) Illustrate the power our curvature-derived measures to track changes in brain connectivity with healthy development/aging and; iii) Detect changes in brain structural connectivity in people with Autism Spectrum Disorders (ASD) which are in agreement with previous morphometric MRI studies.

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Section: Change In Curvature and Autism Spectrum Disorders (Asd)supporting

confidence: 91%

Network Curvature as a Hallmark of Brain Structural Connectivity

Farooq

Chen

Georgiou

et al. 2017

Preprint

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Section: Converging Findings Future Avenuesmentioning

confidence: 99%

Towards Neurosubtypes in Autism

Hong¹,

Vogelstein²,

Gozzi³

et al. 2019

Preprint

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There is a general consensus that substantial heterogeneity underlies the neurobiology in autism spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of variation at the molecular-, cellular-, and system-level domains is a prerequisite for identifying biomarkers and developing more targeted therapeutic strategies in ASD. Advances in neuroimaging approaches to characterizing atypical brain patterns have recently motivated their application as viable tools to delineate more homogenous ASD subgroups at the level of brain structure and function -i.e., neurosubtyping. This review assesses and critically discusses the current datadriven neurosubtyping in ASD. It breaks this pursuit into key methodological steps: the selection of diagnostic samples, neuroimaging features, algorithm and validation approaches. For each step, we appraise the current literature in terms of progress, as well as remaining challenges and potential solutions. Convergence across findings is discussed and biological implications are highlighted. Although preliminary and with limited methodological overlap, results from this literature illustrate the feasibility of neurosubtyping. Across studies, there is general agreement that distinct neurosubtypes exist, but the exact number and their definitions vary depending on the specific features and approach utilized in a given study. Results also suggest the utility of subtypes in predicting symptom severity and diagnostic labels above and beyond group-average comparison designs. This review concludes with a discussion of future avenues towards a comprehensive understanding of the mechanisms underlying ASD heterogeneity.

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“…Testing this hypothesis requires combining neuroimaging with phenotypic characterization of both ASD core and comorbid symptoms -a procedure not common in ASD neuroimaging, with few exemplary exceptions(76,(134)(135)(136)(137).The distributed nature of neural atypicalities in ASD neurosubtypes point towards mechanisms affecting large-scale brain organization. Thus, measures of brain connectivity (e.g., iFC, structural covariance, EEG coherence) may more directly guide towards the biology underlying ASD heterogeneity.…”

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confidence: 99%