Characterization and Classification of ADHD Subtypes: An Approach Based on the Nodal Distribution of Eigenvector Centrality and Classification Tree Model.
In recent times, the complex network theory is increasingly applied to characterize, classify, and diagnose a broad spectrum of neuropathological conditions, including attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, and many others. Nevertheless, the diagnosis...
| Publicado en: | Child Psychiatry & Human Development Vol. 55; no. 3; pp. 622 - 635 |
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| Autores principales: | , |
| Formato: | Artículo |
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Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=177002199&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177002199 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0009398X CPH jtl: Child Psychiatry & Human Development issn: 0009398X maglogo: N pubinfo: dt: Jun2024 vid: 55 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 177002199 10.1007/s10578-022-01432-6 ppf: 622 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P size: 14.6MB tig: atl: Characterization and Classification of ADHD Subtypes: An Approach Based on the Nodal Distribution of Eigenvector Centrality and Classification Tree Model. aug: au: Saha, Papri Sarkar, Debasish affil: Department of Computer Science, Derozio Memorial College, 700136, Kolkata, India https://ror.org/01e7v7w47 Department of Chemical Engineering, University of Calcutta, 700009, Kolkata, India su: Attention-deficit hyperactivity disorder Alzheimer's disease Functional magnetic resonance imaging sug: subj: Attention-deficit hyperactivity disorder Alzheimer's disease Functional magnetic resonance imaging keyword: Brain networks Correlation matrix Functional connectivity Machine learning Region of interest Brain networks Correlation matrix Functional connectivity Machine learning Region of interest ab: In recent times, the complex network theory is increasingly applied to characterize, classify, and diagnose a broad spectrum of neuropathological conditions, including attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, and many others. Nevertheless, the diagnosis and associated subtype identification majorly rely on the baseline correlation matrix obtained from the functional MRI scan. Thus, the existing protocols are either full of personalized bias or computationally expensive as network complexity-based simple but deterministic protocols are yet to be developed and formalized. This article proposes a deterministic method to identify and differentiate the common ADHD subtypes, which is based on a single complexity measure, namely the eigenvector centrality. The node-wise centrality differences were explored using a classification tree model (p < 0.05) to diagnose the subtypes. Identification of marker nodes from default mode, visual, frontoparietal, limbic, and cerebellar networks strongly vouch for the involvement of multiple brain regions in ADHD neuropathology. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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