EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease
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 capt...
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Nature Portfolio
2017
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oai:doaj.org-article:9ab39da007ab4afbb95dbc8502f9fe862021-12-02T12:32:08ZEEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease10.1038/s41598-017-06165-42045-2322https://doaj.org/article/9ab39da007ab4afbb95dbc8502f9fe862017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06165-4https://doaj.org/toc/2045-2322Abstract 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.Sonja SimpragaRicardo Alvarez-JimenezHuibert D. MansvelderJoop M. A. van GervenGeert Jan GroeneveldSimon-Shlomo PoilKlaus Linkenkaer-HansenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017) |
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| topic |
Medicine R Science Q |
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Medicine R Science Q Sonja Simpraga Ricardo Alvarez-Jimenez Huibert D. Mansvelder Joop M. A. van Gerven Geert Jan Groeneveld Simon-Shlomo Poil Klaus Linkenkaer-Hansen EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease |
| description |
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. |
| format |
article |
| author |
Sonja Simpraga Ricardo Alvarez-Jimenez Huibert D. Mansvelder Joop M. A. van Gerven Geert Jan Groeneveld Simon-Shlomo Poil Klaus Linkenkaer-Hansen |
| author_facet |
Sonja Simpraga Ricardo Alvarez-Jimenez Huibert D. Mansvelder Joop M. A. van Gerven Geert Jan Groeneveld Simon-Shlomo Poil Klaus Linkenkaer-Hansen |
| author_sort |
Sonja Simpraga |
| title |
EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease |
| title_short |
EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease |
| title_full |
EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease |
| title_fullStr |
EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease |
| title_full_unstemmed |
EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease |
| title_sort |
eeg machine learning for accurate detection of cholinergic intervention and alzheimer’s disease |
| publisher |
Nature Portfolio |
| publishDate |
2017 |
| url |
https://doaj.org/article/9ab39da007ab4afbb95dbc8502f9fe86 |
| work_keys_str_mv |
AT sonjasimpraga eegmachinelearningforaccuratedetectionofcholinergicinterventionandalzheimersdisease AT ricardoalvarezjimenez eegmachinelearningforaccuratedetectionofcholinergicinterventionandalzheimersdisease AT huibertdmansvelder eegmachinelearningforaccuratedetectionofcholinergicinterventionandalzheimersdisease AT joopmavangerven eegmachinelearningforaccuratedetectionofcholinergicinterventionandalzheimersdisease AT geertjangroeneveld eegmachinelearningforaccuratedetectionofcholinergicinterventionandalzheimersdisease AT simonshlomopoil eegmachinelearningforaccuratedetectionofcholinergicinterventionandalzheimersdisease AT klauslinkenkaerhansen eegmachinelearningforaccuratedetectionofcholinergicinterventionandalzheimersdisease |
| _version_ |
1718394174207688704 |