Measuring robustness of brain networks in autism spectrum disorder with Ricci curvature

Simhal, Anish K.; Carpenter, Kimberly L. H.; Nadeem, Saad; Kurtzberg, Joanne; Song, Allen W.; Tannenbaum, Allen; Sapiro, Guillermo; Dawson, Geraldine

doi:10.1038/s41598-020-67474-9

Cited by 12 publications

(15 citation statements)

References 55 publications

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“…In addition to our application of curvature to cluster various cancerous datasets, the concept of curvature also finds its way in numerous applications such as computer vision, signal processing, and machine learning [2, 4, 20]. Anish et al used curvature to measure changes in robustness of brain networks in children with autism spectrum disorder [16]. Elkin et al found correlation between Ricci curvature and distinct survival outcomes for a set of patients with ovarian cancer using protein-protein interactive (PPI) networks and gene copy number alterations (CNAs) [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-omic integrated curvature study on pan-cancer genomic data

Zhu

Tran

Deasy

et al. 2022

Preprint

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In this work, we introduce a new mathematical framework based on network curvature to extract significant cancer subtypes from multi-omics data. This extends our previous work that was based on analyzing a fixed single -omics data class (e,g,, CNA, gene expression, etc.). Notably, we are able to show that this new methodology provided us with significant survival differences on Kaplan-Meier curves across almost every cancer that we considered. Moreover, the variances in Ollivier-Ricci curvature was explored to investigate its usefulness in network topology analysis as this curvature may be capturing subtle functional changes between various cancer subtypes.

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Section: Introductionmentioning

confidence: 99%

Multi-omic integrated curvature study on pan-cancer genomic data

Zhu

Tran

Deasy

et al. 2022

Preprint

Self Cite

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“…Another related work 25 characterized the brain networks affected by Autism Spectrum Disorder ( asd ) using Ollivier–Ricci curvature. Individuals with asd have altered white matter developmental patterns compared to individuals without asd .…”

Section: Introductionmentioning

confidence: 99%

“…Autologous cord blood infusion, a potential therapy, is believed to reduce neuro-inflammation and promote white matter development, thus triggering a reconfiguration of connectivity patterns in the brain. Simhal et al 25 used the Ollivier–Ricci network curvature (based on the mass transportation distance) to quantify the changes in the brain network after administering asd patients a single infusion of autologous umbilical cord blood. They calculated the Spearman correlation between changes in clinical behavioral scores and changes in curvature following treatment, and identified a relationship between clinical improvement and altered curvature in three white matter pathways that are implicated in social and communication abilities.…”

Section: Introductionmentioning

confidence: 99%

Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks

Chatterjee

Albert

Thapliyal

et al. 2021

Sci Rep

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We analyze networks of functional correlations between brain regions to identify changes in their structure caused by Attention Deficit Hyperactivity Disorder (adhd). We express the task for finding changes as a network anomaly detection problem on temporal networks. We propose the use of a curvature measure based on the Forman–Ricci curvature, which expresses higher-order correlations among two connected nodes. Our theoretical result on comparing this Forman–Ricci curvature with another well-known notion of network curvature, namely the Ollivier–Ricci curvature, lends further justification to the assertions that these two notions of network curvatures are not well correlated and therefore one of these curvature measures cannot be used as an universal substitute for the other measure. Our experimental results indicate nine critical edges whose curvature differs dramatically in brains of adhd patients compared to healthy brains. The importance of these edges is supported by existing neuroscience evidence. We demonstrate that comparative analysis of curvature identifies changes that more traditional approaches, for example analysis of edge weights, would not be able to identify.

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“…They also show that ORC can be used to identify changes in brain structural connectivity related to ASD and healthy aging. Simhal et al (Simhal et al, 2020) used ORC to measure changes in brain structural connectivity of individuals with ASD before and after the infusion of autologous umbilical cord blood. ORC has also been used to study differences in brain structural connectivity networks of cognitively impaired and non-impaired multiple sclerosis patients (Farooq et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Graph Ricci Curvatures Reveal Atypical Functional Connectivity in Autism Spectrum Disorder

Elumalai

Yadav

Williams

et al. 2021

Preprint

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Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

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Measuring robustness of brain networks in autism spectrum disorder with Ricci curvature

Cited by 12 publications

References 55 publications

Multi-omic integrated curvature study on pan-cancer genomic data

Multi-omic integrated curvature study on pan-cancer genomic data

Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks

Graph Ricci Curvatures Reveal Atypical Functional Connectivity in Autism Spectrum Disorder

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