Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and neurotypical aging
Liao, K.; Martin, L. E.; Fakorede, S.; Brooks, W. M.; Burns, J. M.; Devos, H.
Show abstract
There is increasing evidence of the usefulness of electroencephalography (EEG) as an early neurophysiological marker of preclinical AD. Our objective was to apply machine learning approaches on event-related oscillations to discriminate preclinical AD from neurotypical controls. Twenty-two preclinical AD participants who were cognitively normal with elevated amyloid and 21 cognitively normal with no elevated amyloid controls completed n-back working memory tasks (n= 0, 1, 2). EEG signals were recorded through a high-density sensor net. The event-related spectral changes were extracted using the discrete wavelet transform in the delta, theta, alpha, and beta bands. The support vector machine (SVM) machine learning method was employed to classify participants, and classification performance was assessed using the Area Under the Curve (AUC) metric. The relative power of the beta and delta bands outperformed other frequency bands with higher AUC values. The 2-back task obtained higher AUC values than the 0 and 1-back tasks. The highest AUC values were from the 2-back task beta band (AUC = 0.86) and delta bands (AUC = 0.85) nontarget data. This study demonstrates the promise of using machine learning on EEG event-related oscillations from working memory tasks to detect preclinical AD.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer's disease 95%
- Brain networks and cognitive impairment in Parkinson's disease 94%
- Dementia risk factors modify hubs but leave other connectivity measures unchanged in asymptomatic individuals: a graph theoretical analysis. 93%
Similar papers in this journal
- Greek High Phenolic Early Harvest Extra Virgin Olive Oil Reduces the Over-Excitation of Information Flow based on Dominant Coupling Model in patients with Mild Cognitive Impairment: An EEG Resting-State Validation Approach 95%
- Effect of cognitive reserve on physiological measures of cognitive workload in older adults with cognitive impairments 94%
- Topographical overlapping of the Aβ and Tau pathologies in the Default mode networks predicts Alzheimer’s Disease with higher specificity 94%
Similar papers in this journal
- Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI methods & outcome measures 93%
- A Novel Ensemble-Based Machine Learning Algorithm To Predict The Conversion From Mild Cognitive Impairment To Alzheimer's Disease Using Socio-demographic Characteristics, Clinical Information And Neuropsychological Measures 92%
- Neurophysiological biomarkers of post-concussion syndrome: a scoping review 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.