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Magnetoencephalography (MEG) is a non-invasive functional imaging technique for pre-surgical mapping. However, movement-related MEG functional mapping of primary motor cortex (M1) has been challenging in presurgical patients with brain lesions and sensorimotor dysfunction due to the large numbers of trials needed to obtain adequate signal to noise. Moreover, it is not fully understood how effective the brain communication is with the muscles at frequencies above the movement frequency and its harmonics. We developed a novel Electromyography (EMG)-projected MEG source imaging technique for localizing early-stage (-100 to 0 ms) M1 activity during ~l min recordings of left and right self-paced finger movements (~1 Hz). High-resolution MEG source images were obtained by projecting M1 activity towards the skin EMG signal without trial averaging. We studied delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), gamma (30-90 Hz), and upper-gamma (60-90 Hz) bands in 13 healthy participants (26 datasets) and three presurgical patients with sensorimotor dysfunction. In healthy participants, EMG-projected MEG accurately localized M1 with high accuracy in delta (100.0%), theta (100.0%), and beta (76.9%) bands, but not alpha (34.6%) or gamma/upper-gamma (0.0%) bands. Except for delta, all other frequency bands were above the movement frequency and its harmonics. In three presurgical patients, M1 activity in the affected hemisphere was also accurately localized, despite highly irregular EMG movement patterns in one patient. Altogether, our EMG-projected MEG imaging approach is highly accurate and feasible for M1 mapping in presurgical patients. The results also provide insight into movement-related brain-muscle coupling above the movement frequency and its harmonics.

Calorie restriction (CR) is a common approach to inducing negative energy balance. Recently, time-restricted feeding (TRF), which involves consuming food within specific time windows during a 24-h day, has become popular owing to its relative ease of practice and potential to aid in achieving and maintaining a negative energy balance. TRF can be implemented intentionally with CR, or TRF might induce CR simply because of the time restriction. This review focuses on summarizing our current knowledge on how TRF and continuous CR affect gut peptides that influence satiety. Based on peer-reviewed studies, in response to CR there is an increase in the orexigenic hormone ghrelin and a reduction in fasting leptin and insulin. There is likely a reduction in glucagon-like peptide-1 (GLP-1), peptide YY (PYY), and cholecystokinin (CCK), albeit the evidence for this is weak. After TRF, unlike CR, fasting ghrelin decreased in some TRF studies, whereas it showed no change in several others. Further, a reduction in fasting leptin, insulin, and GLP-1 has been observed. In conclusion, when other determinants of food intake are held equal, the peripheral satiety systems appear to be somewhat similarly affected by CR and TRF with regard to leptin, insulin, and GLP-1. But unlike CR, TRF did not appear to robustly increase ghrelin, suggesting different influences on appetite with a potential decrease of hunger after TRF when compared with CR. However, there are several established and novel gut peptides that have not been measured within the context of CR and TRF, and studies that have evaluated effects of TRF are often short-term, with nonuniform study designs and highly varying temporal eating patterns. More evidence and studies addressing these aspects are needed to draw definitive conclusions.

This review focuses on summarizing current knowledge on how time-restricted feeding (TRF) and continuous caloric restriction (CR) affect central neuroendocrine systems involved in regulating satiety. Several interconnected regions of the hypothalamus, brainstem, and cortical areas of the brain are involved in the regulation of satiety. Following CR and TRF, the increase in hunger and reduction in satiety signals of the melanocortin system [neuropeptide Y (NPY), proopiomelanocortin (POMC), and agouti-related peptide (AgRP)] appear similar between CR and TRF protocols, as do the dopaminergic responses in the mesocorticolimbic circuit. However, ghrelin and leptin signaling via the melanocortin system appears to improve energy balance signals and reduce hyperphagia following TRF, which has not been reported in CR. In addition to satiety systems, CR and TRF also influence circadian rhythms. CR influences the suprachiasmatic nucleus (SCN) or the primary circadian clock as seen by increased clock gene expression. In contrast, TRF appears to affect both the SCN and the peripheral clocks, as seen by phasic changes in the non-SCN (potentially the elusive food entrainable oscillator) and metabolic clocks. The peripheral clocks are influenced by the primary circadian clock but are also entrained by food timing, sleep timing, and other lifestyle parameters, which can supersede the metabolic processes that are regulated by the primary circadian clock. Taken together, TRF influences hunger/satiety, energy balance systems, and circadian rhythms, suggesting a role for adherence to CR in the long run if implemented using the TRF approach. However, these suggestions are based on only a few studies, and future investigations that use standardized protocols for the evaluation of the effect of these diet patterns (time, duration, meal composition, sufficiently powered) are necessary to verify these preliminary observations.

Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an important role in incremental learning by replaying recent and old conflicting memory traces. Here we tested the hypothesis that implementing a sleep-like phase in artificial neural networks can protect old memories during new training and alleviate catastrophic forgetting. Sleep was implemented as off-line training with local unsupervised Hebbian plasticity rules and noisy input. In an incremental learning framework, sleep was able to recover old tasks that were otherwise forgotten. Previously learned memories were replayed spontaneously during sleep, forming unique representations for each class of inputs. Representational sparseness and neuronal activity corresponding to the old tasks increased while new task related activity decreased. The study suggests that spontaneous replay simulating sleep-like dynamics can alleviate catastrophic forgetting in artificial neural networks.

Central and autonomic nervous system activities are coupled during sleep. Cortical slow oscillations (SOs;

The biophysical mechanisms underlying epileptogenesis and the generation of seizures remain to be better understood. Among many factors triggering epileptogenesis are traumatic brain injury breaking normal synaptic homeostasis and genetic mutations disrupting ionic concentration homeostasis. Impairments in these mechanisms, as seen in various brain diseases, may push the brain network to a pathological state characterized by increased susceptibility to unprovoked seizures. Here, we review recent computational studies exploring the roles of ionic concentration dynamics in the generation, maintenance, and termination of seizures. We further discuss how ionic and synaptic homeostatic mechanisms may give rise to conditions which prime brain networks to exhibit recurrent spontaneous seizures and epilepsy.

While anatomical pathways between forebrain cognitive and brainstem autonomic nervous centers are well-defined, autonomic-central interactions during sleep and their contribution to waking performance are not understood. Here, we analyzed simultaneous central activity via electroencephalography (EEG) and autonomic heart beat-to-beat intervals (RR intervals) from electrocardiography (ECG) during wake and daytime sleep. We identified bursts of ECG activity that lasted 4-5 s and predominated in non-rapid-eye-movement sleep (NREM). Using event-based analysis of NREM sleep, we found an increase in delta (0.5-4 Hz) and sigma (12-15 Hz) power and an elevated density of slow oscillations (0.5-1 Hz) about 5 s prior to peak of the heart rate burst, as well as a surge in vagal activity, assessed by high-frequency (HF) component of RR intervals. Using regression framework, we show that these Autonomic/Central Events (ACE) positively predicted post-nap improvement in a declarative memory task after controlling for the effects of spindles and slow oscillations from sleep periods without ACE. No such relation was found between memory performance and a control nap. Additionally, NREM ACE negatively correlated with REM sleep and learning in a non-declarative memory task. These results provide the first evidence that coordinated autonomic and central events play a significant role in declarative memory consolidation.

Objective: Complete surgical resection is the standard of care for treatment of oral cancer although the positive margin rate remains 15-30%. Tissue sampling from the resected specimen and from the wound bed for frozen section analysis (FSA) remains the mainstay for intraoperative margin assessment but is subject to sampling error and can require the processing of multiple samples. We sought to understand if an ex vivo imaging strategy using a tumor-targeted fluorescently labeled antibody could accurately identify the closest peripheral margin on the mucosal surface of resected tumor specimen, so that this "sentinel margin" could be used to guide pathological sampling. Materials and Methods: Twenty-nine patients with oral squamous cell carcinoma scheduled for surgical resection were consented for the study and received systemic administration of a tumor-targeted fluorescently labeled antibody (Panitumumab IRDye800CW). After surgical resection, the tumor specimen was imaged using a closed-field fluorescent imaging device. Relevant pathological data was available for five patients on retrospective review. For each of these five patients, two regions of highest fluorescence intensity at the peripheral margin and one region of lowest fluorescence intensity were identified, and results were correlated with histology to determine if the region of highest fluorescence intensity along the mucosal margin (i.e., the sentinel margin) was truly the closest margin. Results: Imaging acquisition of the mucosal surface of the specimen immediately after surgery took 30 s. In all of the specimens, the region of highest fluorescence at the specimen edge had a significantly smaller margin distance than other sampled regions. The average margin distance at the closest, "sentinel," margin was 3.2 mm compared to a margin distance of 8.0 mm at other regions (p < 0.0001). Conclusions: This proof-of-concept study suggests that, when combined with routine FSA, ex vivo fluorescent specimen imaging can be used to identify the closest surgical margin on the specimen. This approach may reduce sampling error of intraoperative evaluation, which should ultimately improve the ability of the surgeon to identify the sentinel margin. This rapid sentinel margin identification improves the surgeon's orientation to areas most likely to be positive in the surgical wound bed and may expedite pathology workflow.

In patients with obstructive sleep apnea (OSA), the pharyngeal muscles become relaxed during sleep, which leads to a partial or complete closure of upper airway. Experimental studies suggest that withdrawal of noradrenergic and serotonergic drives importantly contributes to depression of hypoglossal motoneurons and, therefore, may contribute to OSA pathophysiology; however, specific cellular and synaptic mechanisms remain unknown. In this new study, we developed a biophysical network model to test the hypothesis that, to explain experimental observations, the neuronal network for monoaminergic control of excitability of hypoglossal motoneurons needs to include excitatory and inhibitory perihypoglossal interneurons that mediate noradrenergic and serotonergic drives to hypoglossal motoneurons. In the model, the state-dependent activation of the hypoglossal motoneurons was in qualitative agreement with in vivo data during simulated rapid eye movement (REM) and non-REM sleep. The model was applied to test the mechanisms of action of noradrenergic and serotonergic drugs during REM sleep as observed in vivo. We conclude that the proposed minimal neuronal circuit is sufficient to explain in vivo data and supports the hypothesis that perihypoglossal interneurons may mediate state-dependent monoaminergic drive to hypoglossal motoneurons. The population of the hypothesized perihypoglossal interneurons may serve as novel targets for pharmacological treatment of OSA. NEW & NOTEWORTHY In vivo studies suggest that during rapid eye movement sleep, withdrawal of noradrenergic and serotonergic drives critically contributes to depression of hypoglossal motoneurons (HMs), which innervate the tongue muscles. By means of a biophysical model, which is consistent with a broad range of empirical data, we demonstrate that the neuronal network controlling the excitability of HMs needs to include excitatory and inhibitory interneurons that mediate noradrenergic and serotonergic drives to HMs.

Resting- or baseline-state low-frequency (0.01-0.2 Hz) brain activity is observed in fMRI, EEG, and local field potential recordings. These fluctuations were found to be correlated across brain regions and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow resting-state fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (

A balance between excitation and inhibition is necessary to maintain stable brain network dynamics. Traditionally, seizure activity is believed to arise from the breakdown of this delicate balance in favor of excitation with loss of inhibition. Surprisingly, recent experimental evidence suggests that this conventional view may be limited, and that inhibition plays a prominent role in the development of epileptiform synchronization. Here, we explored the role of the KCC2 co-transporter in the onset of inhibitory network-induced seizures. Our experiments in acute mouse brain slices, of either sex, revealed that optogenetic stimulation of either parvalbumin- or somatostatin-expressing interneurons induced ictal discharges in rodent entorhinal cortex during 4-aminopyridine application. These data point to a proconvulsive role of GABAA receptor signaling that is independent of the inhibitory input location (i.e., dendritic vs. somatic). We developed a biophysically realistic network model implementing dynamics of ion concentrations to explore the mechanisms leading to inhibitory network-induced seizures. In agreement with experimental results, we found that stimulation of the inhibitory interneurons induced seizure-like activity in a network with reduced potassium A-current. Our model predicts that interneuron stimulation triggered an increase of interneuron firing, which was accompanied by an increase in the intracellular chloride concentration and a subsequent KCC2-dependent gradual accumulation of the extracellular potassium promoting epileptiform ictal activity. When the KCC2 activity was reduced, stimulation of the interneurons was no longer able to induce ictal events. Overall, our study provides evidence for a proconvulsive role of GABAA receptor signaling that depends on the involvement of the KCC2 co-transporter.

Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease. Author summary: Slow Wave Sleep (SWS) is a primary brain state found during Non-Rapid Eye Movement (NREM) sleep. While previously thought of as homogeneous waves of activity that sweep across the entire brain, recent research has suggested a more nuanced pattern of activity that can vary between local and global slow waves. However, understanding how these states emerge from small-scale neuronal dynamics and network connectivity remains unclear. We developed a biophysically realistic model of the human brain capable of generating SWS-like behavior and investigated the role of connectivity in the spatio-temporal dynamics of the sleep slow waves. We found that the overall strength and relative balance between long and short-range synaptic connections determined the network behavior—specifically, models with relatively weaker long-range connectivity resulted in mixed states of global and local slow waves. Comparing these results to human data, we found that models producing mixed states best matched the network behavior and functional connectivity observed in human subjects. These findings shed light on how the spatio-temporal properties of SWS emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease. [ABSTRACT FROM AUTHOR]

The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic mechanisms, associated with the putative action of acetylcholine (ACh), GABA and monoamines, which lead to transitions between primary brain vigilance states (waking, non-rapid eye movement sleep [NREM] and REM sleep) within an ultradian cycle. Using ECoG recordings from humans and LFP recordings from cats and mice, we found that during NREM sleep the power of spindle and delta oscillations is negatively correlated in humans and positively correlated in animal recordings. We explained this discrepancy by the differences in the relative level of ACh. Overall, our study revealed the critical intrinsic and synaptic mechanisms through which different neuromodulators acting in combination result in characteristic brain EEG rhythms and transitions between sleep stages.

Sleep, specifically non-rapid eye movement (NREM) sleep, is thought to play a critical role in the consolidation of recent memories. Two main oscillatory activities observed during NREM, cortical slow oscillations (SO, 0.5-1.0 Hz) and thalamic spindles (12-15 Hz), have been shown to independently correlate with memory improvement. Yet, it is not known how these thalamocortical events interact, or the significance of this interaction, during the consolidation process. Here, we found that systemic administration of the GABAergic drug (zolpidem) increased both the phase-amplitude coupling between SO and spindles, and verbal memory improvement in humans. These results suggest that thalamic spindles that occur during transitions to the cortical SO Up state are optimal for memory consolidation. Our study predicts that the timely interactions between cortical and thalamic events during consolidation, contribute to memory improvement and is mediated by the level of inhibitory neurotransmission.