ArticleScientific reports2017
EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Effects of short- and long-term neurostimulation (tDCS) on Alzheimer's disease patients: two randomized studies.Aging clinical and experimental research · 2021Trial
- Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function.Human brain mapping · 2024Article
- Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques.Scientific reports · 2022Article
- Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- Neuroimaging Modalities in Alzheimer's Disease: Diagnosis and Clinical Features.International journal of molecular sciences · 2022Review
- Perfusion and Permeability MRI Predicts Future Cavernous Angioma Hemorrhage and Growth.Journal of magnetic resonance imaging : JMRI · 2022Article
- Toward biophysical markers of depression vulnerability.Frontiers in psychiatry · 2022Article
- Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia.Acta neurochirurgica. Supplement · 2022Review
- Perfusion and permeability as diagnostic biomarkers of cavernous angioma with symptomatic hemorrhage.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Article
- Review
- Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer's Disease and Mild Cognitive Impairment.Entropy (Basel, Switzerland) · 2021Article
- Novel and versatile artificial intelligence algorithms for investigating possible GHSR1α and DRD1 agonists for Alzheimer's disease.RSC advances · 2021Article
- Pain phenotypes classified by machine learning using electroencephalography features.NeuroImage · 2020Article
- A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.Healthcare (Basel, Switzerland) · 2020Article
- EEG microstate complexity for aiding early diagnosis of Alzheimer's disease.Scientific reports · 2020Article
- Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression.Cancers · 2020Article
- Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it?PLoS computational biology · 2020Article
- A cross-disorder connectome landscape of brain dysconnectivity.Nature reviews. Neuroscience · 2019Review
- EEG Window Length Evaluation for the Detection of Alzheimer's Disease over Different Brain Regions.Brain sciences · 2019Article
- Aberrant resting-state oscillatory brain activity in Parkinson's disease patients with visual hallucinations: An MEG source-space study.NeuroImage. Clinical · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electroencephalography (EEG) recordings to capture the brain's multi-faceted signature of disease or pharmacological intervention and use machine learning to improve classification performance. Using data from healthy subjects receiving scopolamine we developed an index of the muscarinic acetylcholine receptor antagonist (mAChR) consisting of 14 EEG biomarkers. This mAChR index yielded higher classification performance than any single EEG biomarker with cross-validated accuracy, sensitivity, specificity and precision ranging from 88-92%. The mAChR index also discriminated healthy elderly from patients with Alzheimer's disease (AD); however, an index optimized for AD pathophysiology provided a better classification. We conclude that integrating multiple EEG biomarkers can enhance the accuracy of identifying disease or drug interventions, which is essential for clinical trials.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.