Small World derived index to distinguish Alzheimer's type dementia and healthy subjects.
Background This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns. Methods Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200...
| Publicado en: | Age & Ageing Vol. 53; no. 6; pp. 1 - 7 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Oxford University Press / USA
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=178158896&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 178158896 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Jun2024 vid: 53 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 178158896 10.1093/ageing/afae121 ppf: 1 ppct: 6 formats: tig: atl: Small World derived index to distinguish Alzheimer's type dementia and healthy subjects. aug: au: Vecchio, Fabrizio Miraglia, Francesca Pappalettera, Chiara Nucci, Lorenzo Cacciotti, Alessia Rossini, Paolo Maria affil: Brain Connectivity Laboratory , Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome , Italy Department of Theoretical and Applied Sciences, eCampus University, Novedrate , Como , Italy su: Brain physiology Risk assessment Alzheimer's disease Electroencephalography Descriptive statistics Psychology of movement Neuropsychology Neuropsychological tests One-way analysis of variance Dementia Data analysis software Machine learning Biomarkers sug: subj: Brain physiology Risk assessment Alzheimer's disease Electroencephalography Descriptive statistics Psychology of movement Neuropsychology Neuropsychological tests One-way analysis of variance Dementia Data analysis software Machine learning Biomarkers keyword: Alzheimer's electroencephalographic (EEG) graph theory neurorehabilitation older people Small World Alzheimer's electroencephalographic (EEG) graph theory neurorehabilitation older people Small World ab: Background This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns. Methods Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones. Results Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones. Conclusions These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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