Cerebral cortex communications最新文献

The waking brain efficiently detects emotional signals to promote survival. However, emotion detection during sleep is poorly understood and may be influenced by individual sleep characteristics or neural reactivity. Notably, dream recall frequency has been associated with stimulus reactivity during sleep, with enhanced stimulus-driven responses in high vs. low recallers. Using electroencephalography (EEG), we characterized the neural responses of healthy individuals to emotional, neutral voices, and control stimuli, both during wakefulness and NREM sleep. Then, we tested how these responses varied with individual dream recall frequency. Event-related potentials (ERPs) differed for emotional vs. neutral voices, both in wakefulness and NREM. Likewise, EEG arousals (sleep perturbations) increased selectively after the emotional voices, indicating emotion reactivity. Interestingly, sleep ERP amplitude and arousals after emotional voices increased linearly with participants' dream recall frequency. Similar correlations with dream recall were observed for beta and sigma responses, but not for theta. In contrast, dream recall correlations were absent for neutral or control stimuli. Our results reveal that brain reactivity to affective salience is preserved during NREM and is selectively associated to individual memory for dreams. Our findings also suggest that emotion-specific reactivity during sleep, and not generalized alertness, may contribute to the encoding/retrieval of dreams.

Performance impairment as an effect of prolonged engagement in a specific task is commonly observed. Although this is a well-known effect in everyday life, little is known about how this affects central cognitive functions such as working memory (WM) processes. In the current study, we ask how time-on-task affects WM gating processes and thus processes regulating WM maintenance and updating. To this end, we combined electroencephalography methods and recordings of the pupil diameter as an indirect of the norepinephrine (NE) system activity. Our results showed that only WM gate opening but not closing processes showed time-on-task effects. On the neurophysiological level, this was associated with modulation of dorsolateral prefrontal theta band synchronization processes, which vanished with time-on-task during WM gate opening. Interestingly, also the modulatory pattern of the NE system, as inferred using pupil diameter data, changed. At the beginning, a strong correlation of pupil diameter data and theta band synchronization processes during WM gate opening is observed. This modulatory effect vanished at the end of the experiment. The results show that time-on-task has very specific effects on WM gate opening and closing processes and suggests an important role of NE system in the time-on-task effect on WM gate opening process.

To adapt one's behavior, in a timely manner, to an environment that changes in many different aspects, one must be sensitive to uncertainty about each aspect of the environment. Although the medial prefrontal cortex has been implicated in the representation and reduction of a variety of uncertainties, it is unknown whether different types of uncertainty are distinguished by distinct neuronal populations. To investigate how the prefrontal cortex distinguishes between different types of uncertainty, we recorded neuronal activities from the medial and lateral prefrontal cortices of monkeys performing a visual feedback-based action-learning task in which uncertainty of coming feedback and that of context change varied asynchronously. We found that the activities of two groups of prefrontal cells represented the two different types of uncertainty. These results suggest that different types of uncertainty are represented by distinct neural populations in the prefrontal cortex.

Transcranial alternating current stimulation (tACS) modulates oscillations in a frequency- and location-specific manner and affects cognitive and motor functions. This effect appears during stimulation as well as "offline," following stimulation, presumably reflecting neuroplasticity. Whether tACS produces long-lasting aftereffects that are physiologically meaningful, is still of current debate. Thus, for tACS to serve as a reliable method for modulating activity within neural networks, it is important to first establish whether "offline" aftereffects are robust and reliable. In this study, we employed a novel machine-learning approach to detect signatures of neuroplasticity following 10-Hz tACS to two critical nodes of the motor network: left motor cortex (lMC) and right cerebellum (rCB). To this end, we trained a classifier to distinguish between signals following lMC-tACS, rCB-tACS, and sham. Our results demonstrate better classification of electroencephalography (EEG) signals in both theta (θ, 4-8 Hz) and alpha (α, 8-13 Hz) frequency bands to lMC-tACS compared with rCB-tACS/sham, at lMC-tACS stimulation location. Source reconstruction allocated these effects to premotor cortex. Stronger correlation between classification accuracies in θ and α in lMC-tACS suggested an association between θ and α efffects. Together these results suggest that EEG signals over premotor cortex contains unique signatures of neuroplasticity following 10-Hz motor cortex tACS.

Visual stimulus-induced gamma oscillations in electroencephalogram (EEG) recordings have been recently shown to be compromised in subjects with preclinical Alzheimer's Disease (AD), suggesting that gamma could be an inexpensive biomarker for AD diagnosis provided its characteristics remain consistent across multiple recordings. Previous magnetoencephalography studies in young subjects have reported consistent gamma power over recordings separated by a few weeks to months. Here, we assessed the consistency of stimulus-induced slow (20-35 Hz) and fast gamma (36-66 Hz) oscillations in subjects (n = 40) (age: 50-88 years) in EEG recordings separated by a year, and tested the consistency in the magnitude of gamma power, its temporal evolution and spectral profile. Gamma had distinct spectral/temporal characteristics across subjects, which remained consistent across recordings (average intraclass correlation of ~0.7). Alpha (8-12 Hz) and steady-state-visually evoked-potentials were also reliable. We further tested how EEG features can be used to identify 2 recordings as belonging to the same versus different subjects and found high classifier performance (AUC of ~0.89), with temporal evolution of slow gamma and spectral profile being most informative. These results suggest that EEG gamma oscillations are reliable across sessions separated over long durations and can also be a potential tool for subject identification.

A major goal of neuroscience is to reveal mechanisms supporting collaborative actions of neurons in local and larger-scale networks. However, no clear overall principle of operation has emerged despite decades-long experimental efforts. Here, we used an unbiased method to extract and identify the dynamics of local postsynaptic network states contained in the cortical field potential. Field potentials were recorded by depth electrodes targeting a wide selection of cortical regions during spontaneous activities, and sensory, motor, and cognitive experimental tasks. Despite different architectures and different activities, all local cortical networks generated the same type of dynamic confined to one region only of state space. Surprisingly, within this region, state trajectories expanded and contracted continuously during all brain activities and generated a single expansion followed by a contraction in a single trial. This behavior deviates from known attractors and attractor networks. The state-space contractions of particular subsets of brain regions cross-correlated during perceptive, motor, and cognitive tasks. Our results imply that the cortex does not need to change its dynamic to shift between different activities, making task-switching inherent in the dynamic of collective cortical operations. Our results provide a mathematically described general explanation of local and larger scale cortical dynamic.

Introduction: Muscle sympathetic nerve activity (MSNA) controls the diameter of arterioles in skeletalmuscle, contributing importantly to the beat-to-beat regulation of blood pressure (BP). Although brain imaging studies have shown that bursts of MSNA originate in the rostral ventrolateral medulla, other subcortical and cortical structures-including the dorsolateral prefrontal cortex (dlPFC)-contribute.

Hypothesis: We tested the hypothesis that MSNA and BP could be modulated by stimulating the dlPFC.

Method: dlPFC. In 22 individuals MSNA was recorded via microelectrodes inserted into the common peroneal nerve, together with continuous BP, electrocardiographic, and respiration.Stimulation of the right (n=22) or left dlPFC (n=10) was achieved using transcranial alternating current (tcACS; +2 to -2mA, 0.08 Hz,100 cycles), applied between the nasion and electrodes over the F3 or F4 EEG sites on the scalp.

Results: Sinusoidal stimulation of either dlPFC caused cyclicmodulation of MSNA, BP and heart rate, and a significant increase in BP.

Conclusion: We have shown, for the first time, that tcACS of the dlPFC in awake humans causes partial entrainment of MSNA, heart rate and BP, arguing for an important role of this higher-level cortical area in the control of cardiovascular function.

Detailed knowledge of the BOLD hemodynamic response function (HRF) is crucial for accurate analyses and interpretation of functional MRI data. Considerable efforts have been made to characterize the HRF in gray matter (GM), but much less attention has been paid to BOLD effects in white matter (WM). However, several recent reports have demonstrated reliable detection and analyses of WM BOLD signals both after stimulation and in a resting state. WM and GM differ in composition, energy requirements, and blood flow, so their neurovascular couplings also may well be different. We aimed to derive a comprehensive characterization of the HRF in WM across a population, including accurate measurements of its shape and its variation along and between WM pathways, using resting-state fMRI acquisitions. Our results show that the HRF is significantly different between WM and GM. Features of the HRF, such as a prominent initial dip, show strong relationships with features of the tissue microstructure derived from diffusion imaging, and these relationships differ between WM and GM, consistent with BOLD signal fluctuations reflecting different energy demands and neurovascular couplings in tissues of different composition and function. We also show that the HRF varies in shape significantly along WM pathways and is different between different WM pathways, suggesting the temporal evolution of BOLD signals after an event vary in different parts of the WM. These features of the HRF in WM are especially relevant for interpretation of the biophysical basis of BOLD effects in WM.