Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia
Abstract Background 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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-06-01
|
| Series: | Alzheimer’s Research & Therapy |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13195-019-0502-3 |
| _version_ | 1828493675435393024 |
|---|---|
| author | David López-Sanz Ricardo Bruña María Luisa Delgado-Losada Ramón López-Higes Alberto Marcos-Dolado Fernando Maestú Stefan Walter |
| author_facet | David López-Sanz Ricardo Bruña María Luisa Delgado-Losada Ramón López-Higes Alberto Marcos-Dolado Fernando Maestú Stefan Walter |
| author_sort | David López-Sanz |
| collection | DOAJ |
| description | Abstract Background 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. |
| first_indexed | 2024-12-11T11:42:01Z |
| format | Article |
| id | doaj.art-039fc85824844c3db9a5382dace2a371 |
| institution | Directory Open Access Journal |
| issn | 1758-9193 |
| language | English |
| last_indexed | 2024-12-11T11:42:01Z |
| publishDate | 2019-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj.art-039fc85824844c3db9a5382dace2a3712022-12-22T01:08:35ZengBMCAlzheimer’s Research & Therapy1758-91932019-06-0111111010.1186/s13195-019-0502-3Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementiaDavid López-Sanz0Ricardo Bruña1María Luisa Delgado-Losada2Ramón López-Higes3Alberto Marcos-Dolado4Fernando Maestú5Stefan Walter6Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM)Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM)Department of Experimental Psychology, Complutense University of Madrid (UCM)Department of Experimental Psychology, Complutense University of Madrid (UCM)Neurology Department, Clinico San Carlos HospitalLaboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM)Department of Epidemiology and Biostatistics, University of California San FranciscoAbstract Background 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.http://link.springer.com/article/10.1186/s13195-019-0502-3NeuroimagingMagnetoencephalographyAlpha bandSubjective cognitive declineAlzheimer’s disease |
| spellingShingle | David López-Sanz Ricardo Bruña María Luisa Delgado-Losada Ramón López-Higes Alberto Marcos-Dolado Fernando Maestú Stefan Walter Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia Alzheimer’s Research & Therapy Neuroimaging Magnetoencephalography Alpha band Subjective cognitive decline Alzheimer’s disease |
| title | Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia |
| title_full | Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia |
| title_fullStr | Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia |
| title_full_unstemmed | Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia |
| title_short | Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia |
| title_sort | electrophysiological brain signatures for the classification of subjective cognitive decline towards an individual detection in the preclinical stages of dementia |
| topic | Neuroimaging Magnetoencephalography Alpha band Subjective cognitive decline Alzheimer’s disease |
| url | http://link.springer.com/article/10.1186/s13195-019-0502-3 |
| work_keys_str_mv | AT davidlopezsanz electrophysiologicalbrainsignaturesfortheclassificationofsubjectivecognitivedeclinetowardsanindividualdetectioninthepreclinicalstagesofdementia AT ricardobruna electrophysiologicalbrainsignaturesfortheclassificationofsubjectivecognitivedeclinetowardsanindividualdetectioninthepreclinicalstagesofdementia AT marialuisadelgadolosada electrophysiologicalbrainsignaturesfortheclassificationofsubjectivecognitivedeclinetowardsanindividualdetectioninthepreclinicalstagesofdementia AT ramonlopezhiges electrophysiologicalbrainsignaturesfortheclassificationofsubjectivecognitivedeclinetowardsanindividualdetectioninthepreclinicalstagesofdementia AT albertomarcosdolado electrophysiologicalbrainsignaturesfortheclassificationofsubjectivecognitivedeclinetowardsanindividualdetectioninthepreclinicalstagesofdementia AT fernandomaestu electrophysiologicalbrainsignaturesfortheclassificationofsubjectivecognitivedeclinetowardsanindividualdetectioninthepreclinicalstagesofdementia AT stefanwalter electrophysiologicalbrainsignaturesfortheclassificationofsubjectivecognitivedeclinetowardsanindividualdetectioninthepreclinicalstagesofdementia |