Longitudinal Data in Two Groups Longitudinal Data in Two Groups with interaction of covariate by group 42 Adapting the CAT12 workflow for unusual populations Customized Tissue Probability Maps 46 Customized DARTEL-template Other variants of computational morphometry Deformation-based morphometry (DBM) Surface-based morphometry (SBM) Region of interest (ROI) analysis Additional information on native, normalized and modulated volumes Naming convention of output files Calling CAT from the UNIX command line Technical information CAT12 Citation References 1 Note to filter sizes for Gaussian smoothing Due to the high accuracy of the spatial registration approaches used in CAT12, you can also try to use smaller filter sizes. However, for very small filter sizes or even no filtering, you have to apply a non-parametric permutation test such as the TFCE-statistics. Please also note that for the analysis of cortical folding measures such as gyrification or cortical complexity the filter sizes have to be larger (i.e. in the range of 15-25mm). This is due to the underlying nature of this measure that reflects contributions from both sulci as well as gyri. Therefore, the filter size should exceed the distance between a gyral crown and a sulcal fundus.

Previous research has observed that the speed of alpha band oscillations (8–12 Hz range) recorded during resting electroencephalography is slowed in chronic pain patients. While this slowing may reflect pathological changes that occur during the chronification of pain, an alternative explanation is that healthy individuals with slower alpha oscillations are more sensitive to prolonged pain, and by extension, more susceptible to developing chronic pain. To test this hypothesis, we examined the relationship between the pain-free, resting alpha oscillation speed of healthy individuals and their sensitivity to two models of prolonged pain, Phasic Heat Pain and Capsaicin Heat Pain, at two visits separated by 8 weeks on average (n = 61 Visit 1, n = 46 Visit 2). We observed that the speed of an individual’s pain-free alpha oscillations was negatively correlated with sensitivity to both models and that this relationship was reliable across short (minutes) and long (weeks) timescales. Furthermore, the speed of pain-free alpha oscillations can successfully identify the most pain sensitive individuals, which we validated on data from a separate, independent study. These results suggest that alpha oscillation speed is a reliable biomarker of prolonged pain sensitivity with potential for prospectively identifying pain sensitivity in the clinic.

Study Objectives: Nonrapid eye movement sleep boosts hippocampus-dependent, long-term memory formation more so than wake. Studies have pointed to several electrophysiological events that likely play a role in this process, including thalamocortical sleep spindles (12–15 Hz). However, interventional studies that directly probe the causal role of spindles in consolidation are scarce. Previous studies have used zolpidem, a GABA-A agonist, to increase sleep spindles during a daytime nap and promote hippocampal-dependent episodic memory. The current study investigated the effect of zolpidem on nighttime sleep and overnight improvement of episodic memories. Methods: We used a double-blind, placebo-controlled within-subject design to test the a priori hypothesis that zolpidem would lead to increased memory performance on a word-paired associates task by boosting spindle activity. We also explored the impact of zolpidem across a range of other spectral sleep features, including slow oscillations (0–1 Hz), delta (1–4 Hz), theta (4–8 Hz), sigma (12–15 Hz), as well as spindle–SO coupling. Results: We showed greater memory improvement after a night of sleep with zolpidem, compared to placebo, replicating a prior nap study. Additionally, zolpidem increased sigma power, decreased theta and delta power, and altered the phase angle of spindle–SO coupling, compared to placebo. Spindle density, theta power, and spindle–SO coupling were associated with next-day memory performance. Conclusions: These results are consistent with the hypothesis that sleep, specifically the timing and amount of sleep spindles, plays a causal role in the long-term formation of episodic memories. Furthermore, our results emphasize the role of nonrapid eye movement theta activity in human memory consolidation.

The stream of human consciousness persists during sleep, albeit in altered form. Disconnected from external input, the mind and brain remain active, at times creating the bizarre sequences of thought and imagery that comprise “dreaming.” Yet despite substantial effort toward understanding this unique state of consciousness, no reliable neurophysiological indicator of dreaming has been discovered. Here, we identified electroencephalographic (EEG) correlates of dreaming using a within‐subjects design to characterize the EEG preceding awakenings from sleep onset, REM (rapid eye movement) sleep, and N2 (NREM Stage 2) sleep from which participants were asked to report their mental experience. During the transition into sleep, compared to periods during which participants reported thinking, emergence of dream imagery was associated with increased absolute power below 7 Hz. During later N2, dreaming conversely occurred during periods of decreased relative power below 1 Hz, accompanied by an increase in relative power above 4 Hz. No EEG predictors of dreaming were identified during REM. These observations suggest an inverted‐U relationship between dreaming and the prevalence of low‐frequency EEG rhythms, such that dreaming first emerges in concert with EEG slowing during the sleep‐wake transition, but then disappears as high‐amplitude slow oscillations come to dominate the recording during later N2 sleep.

The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. In this article we examine the evidence for the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. We specifically focus on the prefrontal–subcortical structures supporting working memory and mechanisms underlying the formation of hippocampal-dependent episodic memory. Our Slow Oscillation Switch Model identifies separate and competing underlying mechanisms supporting the two memory domains at the synaptic, systems, and behavioral levels. We propose that sleep is a competitive arena in which both memory domains vie for limited resources, experimentally demonstrated when boosting one system leads to a functional trade-off in electrophysiological and behavioral outcomes. As these findings inevitably lead to further questions, we suggest areas of future research to better understand how the brain and body interact to support a wide range of cognitive domains during a single sleep episode.