EEG detection of early Alzheimer's disease using psychophysical tasks

Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

Cognitive decline in Parkinson's disease is associated with reduced complexity of EEG at baseline

Parkinson’s disease is a neurodegenerative disorder requiring motor signs for diagnosis, but showing more widespread pathological alterations from its beginning. Compared to age-matched healthy individuals, patients with Parkinson’s disease bear a 6-fold lifetime risk of dementia. For individualized counselling and treatment, prognostic biomarkers for assessing future cognitive deterioration in early stages of Parkinson’s disease are needed. In a case–control study, 42 cognitively normal patients with Parkinson’s disease were compared with 24 healthy control participants matched for age, sex and education. Tsallis entropy and band power of the δ, θ, α, β and γ-band were evaluated in baseline EEG at eyes-open and eyes-closed condition. As the θ-band showed the most pronounced differences between Parkinson’s disease and healthy control groups, further analysis focussed on this band. Tsallis entropy was then compared across groups with 16 psychological test scores at baseline and follow-ups at 6 months and 3 years. In group comparison, patients with Parkinson’s disease showed lower Tsallis entropy than healthy control participants. Cognitive deterioration at 3 years was correlated with Tsallis entropy in the eyes-open condition (P < 0.00079), whereas correlation at 6 months was not yet significant. Tsallis entropy measured in the eyes-closed condition did not correlate with cognitive outcome. In conclusion, the lower the EEG entropy levels at baseline in the eyes-open condition, the higher the probability of cognitive decline over 3 years. This makes Tsallis entropy a candidate prognostic biomarker for dementia in Parkinson’s disease. The ability of the cortex to execute complex functions underlies cognitive health, whereas cognitive decline might clinically appear when compensatory capacity is exhausted.

Discrimination of mild cognitive impairment and Alzheimer's disease using transfer entropy measures of scalp EEG

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.

Scalp EEG-based discrimination of cognitive deficits after traumatic brain injury using event-related Tsallis entropy analysis

Traumatic brain injury (TBI) is the leading cause of death and disability in children and adolescents in the U.S. This is a pilot study, which explores the discrimination of chronic TBI from normal controls using scalp EEG during a memory task. Tsallis entropies are computed for responses during an old-new memory recognition task. A support vector machine model is constructed to discriminate between normal and moderate/severe TBI individuals using Tsallis entropies as features. Numerical analyses of 30 records (15 normal and 15 TBI) show a maximum discrimination accuracy of 93% (p-value = 7.8557E-5) using four features. These results suggest the potential of scalp EEG as an efficacious method for noninvasive diagnosis of TBI.

Changes of EEG spectra and coherence following performance in a cognitive task in Alzheimer's disease

Electroencephalographic measures combined with cognitive tasks are widely used for the assessment of cognitive and pathophysiological changes in Alzheimer's disease (AD). Instead of the analysis of EEG data obtained during the performance of the task, in this study data recorded in the immediate after-task period were analyzed. It was expected that this period would correspond to the electrophysiological consequences of the cognitive effort. Data of 14 patients with AD (MMS score: 16-24) were compared to that of 10 healthy control subjects. Reverse counting of a fix duration was used as a cognitive task. Changes of relative frequency spectra, and those of inter-and intrahemispheric coherence were analyzed. Relative theta power was significantly higher in AD patients compared to the controls both before and after the task. The performance of the task resulted in an increase of the relative alpha2 band in the AD group, whereas it slightly decreased in the control group. The most prominent coherence differences between AD and controls were found in the alpha1 band, especially for long-range coherence values. Coherence in this frequency band increased in the control group following the task, not seen in the AD group. We conclude that EEG parameters calculated from epochs following the completion of a cognitive task clearly differentiates patients with AD from normal controls. The electrophysiological changes found in AD may correspond to the decrease of functional connectivity of cortical areas and to the malfunctioning of the networks engaged in the cognitive task investigated.

EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease

Identification of preclinical markers is required for early diagnosis of Alzheimer's disease (AD) and cognitive dysfunction in advancing age. Quantitative EEG was examined in 145 individuals with AD, their unaffected relatives and unrelated individuals. The AD patients and their relatives were stratified by ApoE genotype. The resting EEG parameters were severely changed in AD patients, and in patients carrying the ApoE epsilon4 allele the decrease in alpha power was higher than in epsilon4 non-carriers. The resting EEG parameters were indistinguishable in AD relatives with different ApoE genotypes and similar to EEG pattern in common population. Under hyperventilation the presence of the epsilon4 allele in AD relatives was associated with the manifestation of synchronous high-voltage delta-, theta-activity and sharp-waves, pronounced decrease in alpha and increase in delta and theta relative powers. The data suggest that neurophysiological endophenotype of non-demented individuals at genetic risk for AD, characterized by increased excitability and dysfunction of deep brain and alpha rhythm-generating structures, may be revealed decades before the first clinical symptoms of presumable dementia.

QEEG monitoring of Alzheimer's disease treatment: a preliminary report of three case studies

Previous research has shown that quantitative electroencephalography (qEEG) can monitor treatment of Alzheimer's Disease (AD). This study investigated the ability of a qEEG measure based on EEG variance, combined with a delayed recognition memory task, to measure treatment effects on patients with AD. Three AD patients with very mild AD (CDR=0.5, FAST stage 3) were monitored with task specific EEG at multiple time points before and after medication treatment. Patients had their EEG recorded while performing a recognition memory task. A measure of (normalized) variance was applied to the EEG data. To the extent possible, the subjects received this treatment monitoring multiple times. These patients were monitored a total of 14 times, which yielded 11 measurements of qEEG change during the course of treatment. The direction of change in patients' qEEG values agreed with patients' medication treatment on 10 out of 11 occasions, p < 0.006 (binomial test) and was more accurate than monitoring with the relative theta power, p < 0.05. The results of this monitoring also showed that the qEEG measure accurately reflected treatment in a dose dependent manner. These results were independent of the specific medication monitored; Galantamine, Memantine, Nicotine, and Rivastigmine. In conclusion, this qEEG method may be useful for measuring AD treatment responses.