Neurodegenerative Disease Alzheimer’s Disease
A relatively well-known feature of the ERP wave is the P300 peak. In ERP terminology, P represents a positive valence of the EEG waveform, occurring about 300ms after the external stimulus. In fact, P300 amplitude and latency have been demonstrated to change systematically with a variety of neurological conditons, including AD.11 Unfortunately, P300 alone is not a sufficiently accurate marker of AD, as there is too much individual variation.
Advances in Event-related Potential Technology ERP technology has advanced greatly in recent years. Some modern ERP systems feature a wearable electrode cap, precluding the necessity of individual EEG electrodes with gel or scalp abrasion. Some ERP studies have demonstrated the importace of combining ERP responses to different stimuli and collected from different cortical areas to achieve higher diagnostic accuracy. Some of these systems use advanced pattern-recognition software to automatically evaluate ERP test data. These developments greatly enhance the applicability of ERP in a physician’s office.
Polikar et al. reported on an ERP study where signals from several EEG electrodes were recorded and features in addition to the P300 were automatically combined using artificial neural networks decision fusion algorithms to achieve very high diagnostic accuracy between Alzheimer's patients and healthy, age-matched controls. In their approach, multiple features from several electrodes were merged using a special algorithm developed specifically for this purpose. They reported that this method could equal the diagnostic accuracy of many highly trained clinical specialists and could exceed the diagnostic accuracy of most community physicians.13
A sufficient database now
exists to allow for discrimination of AD versus normal controls early in the course of illness.14
The COGNISION™ System
Neuronetrix has developed a new system utilizing an approach of this type. The COGNISION™ system is a handheld, wireless device that can be used in an office environment. It includes an ergonomic headset with
1. DSM-IV TR. American Psychiatric Association, Washington, DC; 2000.
2. Frierson R, Dementia, delirium, and other cognitive disorders. In: Tasman A, Kay J, Lieberman JA, et al. (eds), Psychiatry, 3rd edn, West Sussex: Wiley, 2008;897–930.
3. 4.
Sabri O, Gertz H, Dresel S et al., Multicentre phase 2 trial on florbetaben for Beta-amyloid brain PET in Alzheimer disease, J Nuclear Med, 2010;51:S2:384.
Klunk WE, Engler H, Nordberg A, et al., Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B, Ann Neurol, 2004;55(3):306–19.
5. Whitehouse P, George D, The Myth of Alzheimer’s: What You Aren’t Being Told About Today’s Most Dreaded Diagnosis, New York: St. Martin’s Press, 2008.
David A Casey, MD, is an Associate Professor in the Department of Psychiatry and Behavioral Sciences at the University of Louisville School of Medicine. He is also Director of Geriatric Psychiatry as well as Senior Vice-Chair and Head of Clinical Services in Psychiatry at the same institution. He is certified in general and geriatric psychiatry. Dr Casey graduated from the University of Louisville School of Medicine and trained in Psychiatry at the University of Washington in Seattle.
high-performance active electrodes and integrated earphones. The system performs a selection of standardized auditory ERP tests that have been developed to target specific cognitive domains. Various classification studies may be automatically performed using advanced neural network pattern-recognition methods. The test data and classification results are stored in an online electronic patient record system. This record system may be useful in office-based diagnosis, as well as in research and drug development. Changes in the ERP signal over time in AD may allow clinicians to monitor progression of the disease or follow response to therapies.
The COGNISION system is currently undergoing clinical trials. The portability, non-invasiveness, and inexpensive nature of the test make it a promising tool in a general physician’s office.
Conclusion
A new and evolving definition of AD has been created that relies on biomarkers along with progressive cognitive decline, which will hopefully aid earlier diagnosis and treatment of the disease. It is hoped that the increased number of patients diagnosed with AD will facilitate a unified research program expanding the knowledge and application of biomarkers and eventual treatment options for AD.
AD has been demonstrated to have a recognizable ERP signature, which makes ERPs good AD biomarker candidates. Recent advances in ERP technology may make the process of measuring such a biomarker painless, non-invasive and portable. These advantages suggest that ERP should be further considered as a potential AD biomarker. n
6. Available at:
www.alz.org/research/diagnostic_criteria/ (accessed November 16, 2010).
7.
Schuff N, Woerner N, Boreta L, et al., MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers, Brain, 2009;132(4):1067–77.
8. 9.
Zetterberg H, Blennow K, Biological CSF markers of Alzheimer’s disease, Handbook of Clinical Neurology, 2008;89:261–8.
Trojanowski JQ, Vandeerstichele H, Korecka M et al., Update on the biomarker core of the Alzheimer’s Disease Neuroimaging Initiative subjects, Alzheimers Dement, 2010:6(3):230–8.
10. O’Bryant SE, Xiao G, Barber R et al., A Serum 11. 12. 13. 14.
Protein-Based Algorithm for the Detection of Alzheimer Disease, Archives of Neurology, 2010;67(9):1077–81.
Polich J, Corey-Bloom J, Alzheimer’s Disease and P300: Review and Evaluation of Task and Modality, Current Alzheimer Research, 2005;2:515–25.
Polich J, Herbst KL, P300 as a clinical assay: rationale, evaluation, and findings, Int J Psychophysiol, 2003;38:3–19.
Polikar R, Topalis A, Parikh D, An ensemble based data fusion approach for early diagnosis of Alzheimer’s disease, Information Fusion, 2008;9(1):83–95.
Golob EJ, Ringman JM, Irimajiri R, et al, Cortical event-related potentials in preclinical familial Alzheimer disease, Neurology, 2009;73:1649–55.
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