The acetylcholine index: an electroencephalographic marker of cholinergic activity in the living human brain applied to Alzheimer's disease and other dementias.

BACKGROUND: The cholinergic hypothesis is well established and has led to the development of pharmacological treatments for Alzheimer's disease (AD). However, there has previously been no physiological means of monitoring cholinergic activity in vivo. METHODS: An electroencephalography (EEG)-based acetylcholine (Ach) index reflecting the cholinergic activity in the brain was developed using data from a scopolamine challenge study. The applicability of the Ach index was examined in an elderly population of healthy controls and patients suffering from various causes of cognitive decline. RESULTS: The Ach index showed a strong reduction in the severe stages of AD dementia. A high correlation was demonstrated between the Ach index and cognitive function. The index was reduced in patients with mild cognitive impairment and prodromal AD, indicating a decreased cholinergic activity. When considering the distribution of the Ach index in a population of healthy elderly subjects, an age-related threshold was revealed, beyond which there is a general decline in cholinergic activity. CONCLUSIONS: The EEG-based Ach index provides, for the first time, a physiological means of monitoring the cholinergic activity in the human brain in vivo. This has great potential for aiding diagnosis and patient stratification as well as for monitoring disease progression and treatment response.

Diagnostic accuracy of statistical pattern recognition of electroencephalogram registration in evaluation of cognitive impairment and dementia.

BACKGROUND: There is still a need for simple, noninvasive, and inexpensive methods to diagnose the causes of cognitive impairment and dementia. In this study, contemporary statistical methods were used to classify the clinical cases of cognitive impairment based on electroencephalograms (EEG). METHODS: An EEG database was established from seven different groups of subjects with cognitive impairment and dementia as well as healthy controls. A classifier was created for each possible pair of groups using statistical pattern recognition (SPR). RESULTS: A good-to-excellent separation was found when differentiating cases of degenerative disorders from controls, vascular disorders, and depression but this was less so when the likelihood of comorbidity was high. CONCLUSIONS: Using EEG with SPR seems to be a reliable method for diagnosing the causes of cognitive impairment and dementia, but comorbidity must be taken into account.