High-beta/low-gamma frequency activity reflects top-down predictive coding during a spatial working memory test.

Abstract

Numerous mental health disorders are characterized by cognitive impairments that result in poor vocational and social outcomes. Among the cognitive domains commonly affected, working memory deficits have been noted in patients with attention-deficit/hyperactivity disorder (Martinussen et al. in J Am Acad Child Adolesc Psychiatry 44:377-384, 2005), post-traumatic stress disorder (Honzel et al. in Cogn Affect Behav Neurosci 14:792-804, 2014), and consistently with schizophrenia patients (Callicott et al. in Cereb Cortex 10:1078-1092, 2000; Lewis et al. in Front Hum Neurosci 10:85, 2005; Amann et al. in Brain Res Bull 83:147-161, 2010; Limongi et al. in Schizophr Res 197:386-391, 2018). Oscillations in neural activity from electroencephalogram (EEG) recordings are decomposed by frequency, and band-specific decreases in gamma power (> 30Hz) have been correlated with working memory ability. This study examined within-subject changes in power of frequency-specific bands during sample versus choice trials during a spatial working memory paradigm (T-maze). EEG was recorded using a relatively novel wireless EEG telemetry system fully implanted within the mouse, enabling uninhibited movement during behavioral tasks. No significant differences were found between sample and correct choice phases in the alpha, theta or gamma frequency ranges. Evoked power was significantly higher during the choice phase than the sample phase in the high-beta/low-gamma frequency range. This frequency range has been implicated in the propagation of cortical predictions to lower levels of stimuli encoding in a top-down hierarchical manner. Results suggest there is an increase in brain activity during correct trials when the mouse enters the opposite arm during the choice phase compared to the sample phase, likely due to prediction error resulting from a discrepancy between present and prior experience. Future studies should identify specific cortical networks involved and investigate neural activity at the neuronal level.