Significance: When we are awake, purposeful thinking and behavior require the synchronization of brain cells involved in different aspects of the same task. Cerebral cortex electrical oscillations in the gamma (30–80 Hz) range are particularly important in such synchronization. In this report we identify a particular subcortical cell type which has increased activity during waking and is involved in activating the cerebral cortex and generating gamma oscillations, enabling active cortical processing. Abnormalities of the brain mechanisms controlling gamma oscillations are involved in the disordered thinking typical of neuropsychiatric disorders such as schizophrenia. Thus, these findings may pave the way for targeted therapies to treat schizophrenia and other disorders involving abnormal cortical gamma oscillations.

Purpose of review We review our current understanding of abnormal γ band oscillations in schizophrenia, their association with symptoms and the underlying cortical circuit abnormality, with a particular focus on the role of fast-spiking parvalbumin gamma-aminobutyric acid (GABA) neurons in the disease state. Recent findings Clinical electrophysiological studies of schizophrenia patients and pharmacological models of the disorder show an increase in spontaneous γ band activity (not stimulus-evoked) measures. These findings provide a crucial link between preclinical and clinical work examining the role of γ band activity in schizophrenia. MRI-based experiments measuring cortical GABA provides evidence supporting impaired GABAergic neurotransmission in schizophrenia patients, which is correlated with γ band activity level. Several studies suggest that stimulation of the cortical circuitry, directly or via subcortical structures, has the potential to modulate cortical γ activity, and improve cognitive function. Summary Abnormal γ band activity is observed in patients with schizophrenia and disease models in animals, and is suggested to underlie the psychosis and cognitive/perceptual deficits. Convergent evidence from both clinical and preclinical studies suggest the central factor in γ band abnormalities is impaired GABAergic neurotransmission, particularly in a subclass of neurons which express parvalbumin. Rescue of γ band abnormalities presents an intriguing option for therapeutic intervention.

The thalamic reticular nucleus (TRN) is implicated in schizophrenia pathology. However, it remains unclear whether alterations of TRN activity can account for abnormal electroencephalographic activity observed in patients, namely reduced spindles (10–15 Hz) during sleep and increased delta (0.5–4 Hz) and gamma-band activity (30–80 Hz) during wakefulness. Here, we utilized optogenetic and reverse-microdialysis approaches to modulate activity of the major subpopulation of TRN GABAergic neurons, which express the calcium-binding protein parvalbumin (PV), and are implicated in schizophrenia dysfunction. An automated algorithm with enhanced efficiency and reproducibility compared to manual detection was used for sleep spindle assessment. A novel, low power, waxing-and-waning optogenetic stimulation paradigm preferentially induced spindles that were indistinguishable from spontaneously occurring sleep spindles without altering the behavioral state, when compared to a single pulse laser stimulation used by us and others. Direct optogenetic inhibition of TRN-PV neurons was ineffective in blocking spindles but increased both wakefulness and cortical delta/gamma activity, as well as impaired the 40 Hz auditory steady-state response. For the first time we demonstrate that spindle density is markedly reduced by (i) optogenetic stimulation of a major GABA/PV inhibitory input to TRN arising from basal forebrain parvalbumin neurons (BF-PV) and; (ii) localized pharmacological inhibition of low-threshold calcium channels, implicated as a genetic risk factor for schizophrenia. Together with clinical findings, our results support impaired TRN-PV neuron activity as a potential cause of schizophrenia-linked abnormalities in cortical delta, gamma, and spindle activity. Modulation of the BF-PV input to TRN may improve these neural abnormalities.

Study Objectives: 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. Methods: 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. Results: 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). Conclusions: 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.