ABSTRACT
INTRODUCTION: The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHODS: From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined. RESULTS: Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer's disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70-0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve. CONCLUSION: We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.
Subject(s)
Alzheimer Disease , Cognitive Dysfunction/diagnosis , Dementia , Electroencephalography/methods , Aged , Alzheimer Disease/diagnosis , Alzheimer Disease/psychology , Dementia/diagnosis , Dementia/psychology , Diagnostic Self Evaluation , Disease Progression , Female , Humans , Male , Predictive Value of Tests , PrognosisABSTRACT
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.
Subject(s)
Acetylcholine/metabolism , Alzheimer Disease/metabolism , Dementia/metabolism , Electroencephalography/methods , Aged , Aged, 80 and over , Aging/physiology , Alzheimer Disease/psychology , Brain/metabolism , Dementia/psychology , Female , Humans , Male , Middle Aged , Muscarinic Antagonists , Pattern Recognition, Automated , ScopolamineABSTRACT
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.
