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Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters ( = 2, = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD.
This article was published in the following journal.
Name: Frontiers in neuroscience
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The analysis of a critical number of sensory stimuli or facts (the pattern) by physiological processes such as vision (PATTERN RECOGNITION, VISUAL), touch, or hearing.
A progressive form of dementia characterized by the global loss of language abilities and initial preservation of other cognitive functions. Fluent and nonfluent subtypes have been described. Eventually a pattern of global cognitive dysfunction, similar to ALZHEIMER DISEASE, emerges. Pathologically, there are no Alzheimer or PICK DISEASE like changes, however, spongiform changes of cortical layers II and III are present in the TEMPORAL LOBE and FRONTAL LOBE. (From Brain 1998 Jan;121(Pt 1):115-26)
A degenerative disease of the BRAIN characterized by the insidious onset of DEMENTIA. Impairment of MEMORY, judgment, attention span, and problem solving skills are followed by severe APRAXIAS and a global loss of cognitive abilities. The condition primarily occurs after age 60, and is marked pathologically by severe cortical atrophy and the triad of SENILE PLAQUES; NEUROFIBRILLARY TANGLES; and NEUROPIL THREADS. (From Adams et al., Principles of Neurology, 6th ed, pp1049-57)
Mental process to visually perceive a critical number of facts (the pattern), such as characters, shapes, displays, or designs.
A chromosome disorder associated either with an extra chromosome 21 or an effective trisomy for chromosome 21. Clinical manifestations include hypotonia, short stature, brachycephaly, upslanting palpebral fissures, epicanthus, Brushfield spots on the iris, protruding tongue, small ears, short, broad hands, fifth finger clinodactyly, Simian crease, and moderate to severe MENTAL RETARDATION. Cardiac and gastrointestinal malformations, a marked increase in the incidence of LEUKEMIA, and the early onset of ALZHEIMER DISEASE are also associated with this condition. Pathologic features include the development of NEUROFIBRILLARY TANGLES in neurons and the deposition of AMYLOID BETA-PROTEIN, similar to the pathology of ALZHEIMER DISEASE. (Menkes, Textbook of Child Neurology, 5th ed, p213)
Neurology - Central Nervous System (CNS)
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