Validation of an automated sleep spindle detection method for mouse electroencephalography.

Sleep spindles are abnormal in several neuropsychiatric conditions and have been implicated in associated cognitive symptoms. Accordingly, there is growing interest in elucidating the pathophysiology behind spindle abnormalities using rodent models of such disorders. However, whether sleep spindles can reliably be detected in mouse electroencephalography (EEG) is controversial necessitating careful validation of spindle detection and analysis techniques. Manual spindle detection procedures were developed and optimized to generate an algorithm for automated detection of events from mouse cortical EEG. Accuracy and external validity of this algorithm were then assayed via comparison to sigma band (10-15 Hz) power analysis, a proxy for sleep spindles, and pharmacological manipulations. We found manual spindle identification in raw mouse EEG unreliable, leading to low agreement between human scorers as determined by F1-score (0.26 ± 0.07). Thus, we concluded it is not possible to reliably score mouse spindles manually using unprocessed EEG data. Manual scoring from processed EEG data (filtered, cubed root-mean-squared), enabled reliable detection between human scorers, and between human scorers and algorithm (F1-score > 0.95). Algorithmically detected spindles correlated with changes in sigma-power and were altered by the following conditions: sleep-wake state changes, transitions between NREM and REM sleep, and application of the hypnotic drug zolpidem (10 mg/kg, intraperitoneal). Here we describe and validate an automated paradigm for rapid and reliable detection of spindles from mouse EEG recordings. This technique provides a powerful tool to facilitate investigations of the mechanisms of spindle generation, as well as spindle alterations evident in mouse models of neuropsychiatric disorders.