New Analysis Tool Identified for Predicting the Efficacy of AChE Inhibitors in Treating Alzheimer’s Disease
In a recent study published in the journal Computational and Mathematical Methods in Medicine, a team of researchers found that the use of multiscale entropy analysis (MSE), of EEG signals, especially Slope 2, provides a potential tool for predicting the efficacy of AChE inhibitors prior to therapy in patients with Alzheimer’s Disease (AD).
Alzheimer’s disease (AD) is a dementia condition that causes a progressive decline in cognitive functioning. AD neurodegeneration is thought to be caused by deposition of amyloid beta-peptide in plaques or formation of neurofibrillary tangle in brain tissue. Symptoms of AD are related to a cholinergic deficit in the cerebral cortex and other areas of the brain.
Acetylcholinesterase (AChE) inhibitors inhibit the acetylcholinesterase enzyme from breaking down acetylcholine and cause an increase in the level and duration of neurotransmitter acetylcholine activity. AChEs have been found to be an effective therapy for patients with AD.
Pharmacoeconomic studies have shown that therapies can postpone dementia from progressing to more severe stages, however, clinicians still debate that AChE inhibitors have an effect on only 25–50% of patients with AD, which cannot be identified before the start of the treatment. Moreover, the time scale for assessing the effect of AChE inhibitors can last between several months to several years.
Tracing the activation of the neurotransmitter ACh, which is released from the presynaptic vesicles that are triggered by action potential (electrical impulse), diffused in the synapse, and conjugated to the postsynaptic receptor (5–50 msec), these activations are seen at different time scales, each may be calculated using multiscale entropy analysis (MSE), a technique that can disclose the embedded information in different time scales, in electroencephalography (EEG).
In their study titled “A Novel Application of Multiscale Entropy in Electroencephalography to Predict the Efficacy of Acetylcholinesterase Inhibitor in Alzheimer’s Disease,” Ping-Huang Tsai from the Neurology Department, National Yang-Ming University Hospital, Yi-Lan in Taiwan and colleagues hypothesized that the slope of MSE analysis of EEG data could be related to the efficacy of AChE inhibitors in patients with AD. This belief was based on the ability of MSE to demonstrate different mechanisms with multiple temporal or spatial scales.
To assess the predicted efficacy of AChE inhibitors, the researchers evaluated using the minimental state examination (MMSE) a total of 17 patients with a recent diagnosis of AD. Following 12 months of treatment with AChE inhibitor, the results showed that 7 AD patients were responsive and 10 AD patients were nonresponsive.
The results also showed that the major difference between the two groups was in Slope 2 (MSE6 to 20). The area below the receiver operating characteristic (ROC) curve of Slope 2 was 0.871. The sensitivity was of 85.7% and the specificity was of 60%, whereas the cut-off value of Slope 2 was of −0.024.
Based on the results, the researchers concluded that MSE analysis of EEG recordings could show characteristics both at short and long time scales and provide a potential tool for predicting the efficacy of AChE inhibitors in AD. According to the researchers, this nonlineal method improved EMD-based sampling entropy, which was introduced as an optimum method for evaluating embedded information in EEG and as an objective, noninvasive, and cost-effective tool for evaluating and monitoring AD patients, but not for providing enough information about the possible responder to the AChE inhibitor in AD.