Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

• Published in: Journal of neuroscience methods Vol. 212; no. 2; pp. 344 - 354
• Main Authors: Laskaris, N.A., Tarnanas, I., Tsolaki, M.N., Vlaikidis, N., Karlovasitou, A.K.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30.01.2013
• Subjects: Aged; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - physiopathology; Early Diagnosis; Electroencephalography; Event-related potentials; Evoked Potentials - physiology; Evoked Potentials, Auditory - physiology; Female; Humans; Male; Mild cognitive impairment; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Single-trial analysis; Vector quantization; Event-related potentials; Vector quantization; Mild cognitive impairment; Single-trial analysis
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2012.10.014

► Single trial ERPs reveal cognitive impairments better than the averaged response. ► Using a semantically defined codebook, response dynamics can be described and compared in an intelligible way. ► This codebook representation contributes significantly to the reliable detection of cognitive decline. Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.