ArticlePloS one2015
A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis.PloS one · 2017Trial
- Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.Network neuroscience (Cambridge, Mass.) · 2024Article
- MapperPlus: Agnostic clustering of high-dimension data for precision medicine.PLOS digital health · 2023Article
- The topology of data: opportunities for cancer research.Bioinformatics (Oxford, England) · 2021Article
- A Novel Knowledge Distillation-Based Feature Selection for the Classification of ADHD.Biomolecules · 2021Article
- Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis.Network neuroscience (Cambridge, Mass.) · 2019Article
- Topological gene expression networks recapitulate brain anatomy and function.Network neuroscience (Cambridge, Mass.) · 2019Article
- Article
- A personality trait contributes to the occurrence of postoperative delirium: a prospective study.BMC psychiatry · 2016Article
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6 authors.
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Abstract
backgroundAttention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients.
methodsTo quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214).
resultsIn the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset.
conclusionsThe application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.
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