Network-level permutation entropy of resting-state MEG recordings:A novel biomarker for early-stage Alzheimer’s disease?
| العنوان: | Network-level permutation entropy of resting-state MEG recordings:A novel biomarker for early-stage Alzheimer’s disease? |
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| المؤلفون: | Elliz P. Scheijbeler, Anne M. van Nifterick, Cornelis J. Stam, Arjan Hillebrand, Alida A. Gouw, Willem de Haan |
| المساهمون: | Neurology, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Systems & Network Neuroscience |
| المصدر: | Scheijbeler, E P, van Nifterick, A M, Stam, C J, Hillebrand, A, Gouw, A A & Haan, W D 2022, ' Network-level permutation entropy of resting-state MEG recordings : A novel biomarker for early-stage Alzheimer’s disease? ', Network Neuroscience, vol. 6, no. 2, pp. 382-400 . https://doi.org/10.1162/netn_a_00224, https://doi.org/10.1162/netn_a_00224 Network Neuroscience, 6(2), 382-400 Network neuroscience (Cambridge, Mass.), 6(2), 382-400 |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Artificial Intelligence, Applied Mathematics, General Neuroscience, Computer Science Applications |
| الوصف: | Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer’s disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5–93.3%]) slightly outperformed PE (76.9% [60.3–93.4%]) and relative theta power–based models (76.9% [60.4–93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies. |
| اللغة: | English |
| تدمد: | 2472-1751 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8584fab41fd4117aec7c20a0fd2504c https://research.vumc.nl/en/publications/eb6cb95b-4a68-4412-80b3-ab1b163fb7d4 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....b8584fab41fd4117aec7c20a0fd2504c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 24721751 |
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