Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia

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dc.contributor.authorLópez Sanz, David
dc.contributor.authorBruña Fernández, Ricardo
dc.contributor.authorDelgado Losada, María Luisa
dc.contributor.authorLópez Sánchez, Ramón
dc.contributor.authorMarcos Dolado, Alberto
dc.contributor.authorMaestu Unturbe, Fernando
dc.contributor.authorWalter, Stefan
dc.date.accessioned2024-01-17T13:05:18Z
dc.date.available2024-01-17T13:05:18Z
dc.date.issued2019-06-01
dc.description.abstractBackground Alzheimer’s disease (AD) prevalence is rapidly growing as worldwide populations grow older. Available treatments have failed to slow down disease progression, thus increasing research focus towards early or preclinical stages of the disease. Subjective cognitive decline (SCD) is known to increase the risk of developing AD and several other negative outcomes. However, it is still very scarcely characterized and there is no neurophysiological study devoted to its individual classification which could improve targeted sample recruitment for clinical trials. Methods Two hundred fifty-two older adults (70 healthy controls, 91 SCD, and 91 MCI) underwent a magnetoencephalography scan. Alpha relative power in the source space was employed to train a LASSO classifier and applied to distinguish between healthy controls and SCD. Moreover, MCI participants were used to further validate the previously trained algorithm. Results The classifier was significantly associated to SCD with an AUC of 0.81 in the whole sample. After randomly splitting the sample in 2/3 for discovery and 1/3 for validation, the newly trained classifier was also able to correctly classify SCD individuals with an AUC of 0.75 in the validation sample. The regions selected by the algorithm included medial frontal, temporal, and occipital areas. The algorithm trained to select SCD individuals was also significantly associated to MCI diagnostic. Conclusions According to our results, magnetoencephalography could be a useful tool for distinguishing individuals with SCD and healthy older adults without cognitive concerns. Furthermore, our classifier showed good external validity, being not only successful for an unseen SCD sample, but also in a different population with MCI cases. This supports its utility in the context of preclinical dementia. These findings highlight the potential applications of electrophysiological techniques to improve sample recruitment at the individual level in the context of clinical trials.
dc.description.departmentDepto. de Psicología Experimental, Procesos Cognitivos y Logopedia
dc.description.facultyFac. de Psicología
dc.description.refereedTRUE
dc.description.statuspub
dc.identifier.citationLópez-Sanz, D., Bruña, R., Delgado-Losada, M.L. et al. Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia. Alz Res Therapy 11, 49 (2019). https://doi.org/10.1186/s13195-019-0502-3
dc.identifier.doi10.1186/s13195-019-0502-3
dc.identifier.issn1758-9193
dc.identifier.officialurlhttps://alzres.biomedcentral.com/articles/10.1186/s13195-019-0502-3
dc.identifier.urihttps://hdl.handle.net/20.500.14352/93607
dc.journal.titleAlzheimer's Research and Therapy
dc.language.isoeng
dc.publisherBMC
dc.rights.accessRightsopen access
dc.subject.keywordNeuroimaging
dc.subject.keywordMagnetoencephalography
dc.subject.keywordAlphaband
dc.subject.keywordSubjective cognitive decline
dc.subject.keywordAlzheimer’s disease
dc.subject.ucmPsicología experimental
dc.subject.unesco6106.01 Actividad Cerebral
dc.titleElectrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number11
dspace.entity.typePublication
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