title: Classical conditioning of the electrically elicited blink reflex in humans: a new method of data analysis.
abstract: The eyeblink conditioning paradigm is a well-established model to study learning processes in humans and animals. Especially results from animal studies have supplied new insight into physiological pathways and brain structures involved in associative motor learning and memory. An important role of the cerebellum and its afferent fiber systems could be shown. Recent studies in humans have given evidence that results of animal experiments can be applied directly to the human condition. A high variation of baseline EMG activity and/or spontaneous blinks may influence the analysis of classical conditioning of the electrically elicited blink reflex in humans. To optimize differentiation between real conditioned responses and stimulus-independent EMG activity, we developed an algorithm which is fully automated and independent of a possible bias of an examiner. In a first step the algorithm decides whether a subject fulfills the criteria of a successful learning process or not. The second step quantifies the learning process. For quantification of the learning process, the following parameters were calculated: number of conditioned responses, onset of conditioning, time and amount of maximal conditioning, speed of conditioning and speed of habituation. According to our criteria, 80% of the healthy volunteers acquired conditioned responses. There is an age-related decline in eyeblink classical conditioning. Analysis of patient groups with different types of lesions will further improve our knowledge and understanding of pathways involved in learning processes in humans. The proposed new algorithm of data analysis takes less than 10 s on a standard computer, is more sensitive and more specific in detecting conditioned responses and, therefore, may further improve the value and reliability of the eyeblink conditioning paradigm in clinical research.
authors.affiliation: Department of Neurology and Clinical Neurophysiology, University of Freiburg, Germany. glocker@nzll.ukl.uni-freiburg.de
authors.forename: F X
authors.initials: FX
authors.lastname: Glocker
authors.forename: M
authors.initials: M
authors.lastname: Lauk
authors.forename: D
authors.initials: D
authors.lastname: Föll
authors.forename: B
authors.initials: B
authors.lastname: Köster
authors.forename: B
authors.initials: B
authors.lastname: Guschlbauer
authors.forename: J
authors.initials: J
authors.lastname: Timmer
authors.forename: G
authors.initials: G
authors.lastname: Deuschl
authors.forename: C H
authors.initials: CH
authors.lastname: Lücking
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001767:Blinking / Q000502:physiology*; D002531:Cerebellum / Q000503:physiopathology; D003214:Conditioning, Classical / Q000502:physiology*; D004558:Electric Stimulation; D004576:Electromyography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D013223:Statistics as Topic; D013997:Time Factors