Adverse interaction effects of chronic and acute sleep deficits on spatial working memory but not on verbal working memory or declarative memory.

The accumulation of chronic sleep deficits combined with acute sleep loss is common in shift workers and increases the risk of errors and accidents. We investigated single and combined effects of chronic and acute sleep loss and recovery sleep on working memory performance (N-back task) and on overnight declarative memory recall (paired-associate lists) in 36 healthy participants. After baseline measurements, the chronic sleep restriction group (n = 21; mean [SD] age 26 [4] years) underwent 5 nights of sleep restriction (5-hr time in bed [TIB]), whereas the control group (n = 15; mean [SD] age 28 [6] years) had 8-hr TIB during those nights. Afterwards, both groups spent 1 night with 8-hr TIB prior to acute sleep deprivation for 38 hr, and a final recovery night (10-hr TIB). Chronic sleep restriction decreased spatial N-back performance compared to baseline (omissions: p = .001; sensitivity: p = .012), but not letter N-back performance or word-pair recall. Acute sleep deprivation impaired spatial N-back performance more in the chronic sleep restriction group than in the control group (interaction between group and time awake: p ≤ .02). No group differences during acute sleep loss appeared in letter N-back performance or word recall. It is concluded that chronic sleep loss, even when followed by a night of recovery sleep, increases the vulnerability to impairments in spatial working memory during subsequent acute sleep loss. Verbal working memory and declarative memory were not affected by restricted sleep.

FreiBox: A Versatile Open-Source Behavioral Setup for Investigating the Neuronal Correlates of Behavioral Flexibility via 1-Photon Imaging in Freely Moving Mice.

To survive in a complex and changing environment, animals must adapt their behavior. This ability is called behavioral flexibility and is classically evaluated by a reversal learning paradigm. During such a paradigm, the animals adapt their behavior according to a change of the reward contingencies. To study these complex cognitive functions (from outcome evaluation to motor adaptation), we developed a versatile, low-cost, open-source platform, allowing us to investigate the neuronal correlates of behavioral flexibility with 1-photon calcium imaging. This platform consists of FreiBox, a novel low-cost Arduino behavioral setup, as well as further open-source tools, which we developed and integrated into our framework. FreiBox is controlled by a custom Python interface and integrates a new licking sensor (strain gauge lickometer) for controlling spatial licking behavioral tasks. In addition to allowing both discriminative and serial reversal learning, the Arduino can track mouse licking behavior in real time to control task events in a submillisecond timescale. To complete our setup, we also developed and validated an affordable commutator, which is crucial for recording calcium imaging with the Miniscope V4 in freely moving mice. Further, we demonstrated that FreiBox can be associated with 1-photon imaging and other open-source initiatives (e.g., Open Ephys) to form a versatile platform for exploring the neuronal substrates of licking-based behavioral flexibility in mice. The combination of the FreiBox behavioral setup and our low-cost commutator represents a highly competitive and complementary addition to the recently emerging battery of open-source initiatives.

3D pose estimation enables virtual head fixation in freely moving rats.

The impact of spontaneous movements on neuronal activity has created the need to quantify behavior. We present a versatile framework to directly capture the 3D motion of freely definable body points in a marker-free manner with high precision and reliability. Combining the tracking with neural recordings revealed multiplexing of information in the motor cortex neurons of freely moving rats. By integrating multiple behavioral variables into a model of the neural response, we derived a virtual head fixation for which the influence of specific body movements was removed. This strategy enabled us to analyze the behavior of interest (e.g., front paw movements). Thus, we unveiled an unexpectedly large fraction of neurons in the motor cortex with tuning to the paw movements, which was previously masked by body posture tuning. Once established, our framework can be efficiently applied to large datasets while minimizing the experimental workload caused by animal training and manual labeling.

Real-time detection of neural oscillation bursts allows behaviourally relevant neurofeedback.

Neural oscillations as important information carrier in the brain, are increasingly interpreted as transient bursts rather than as sustained oscillations. Short (<150 ms) bursts of beta-waves (15-30 Hz) have been documented in humans, monkeys and mice. These events were correlated with memory, movement and perception, and were even suggested as the primary ingredient of all beta-band activity. However, a method to measure these short-lived events in real-time and to investigate their impact on behaviour is missing. Here we present a real-time data analysis system, capable to detect short narrowband bursts, and demonstrate its usefulness to increase the beta-band burst-rate in rats. This neurofeedback training induced changes in overall oscillatory power, and bursts could be decoded from the movement of the rats, thus enabling future investigation of the role of oscillatory bursts.