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The human cortex exhibits intrinsic neural timescales that shape a temporal hierarchy. Whether this temporal hierarchy follows the spatial hierarchy of its topography, namely the core-periphery organization, remains an open issue. Using magnetoencephalography data, we investigate intrinsic neural timescales during rest and task states; we measure the autocorrelation window in short (ACW-50) and, introducing a novel variant, long (ACW-0) windows. We demonstrate longer ACW-50 and ACW-0 in networks located at the core compared to those at the periphery with rest and task states showing a high ACW correlation. Calculating rest-task differences, i.e., subtracting the shared core-periphery organization, reveals task-specific ACW changes in distinct networks. Finally, employing kernel density estimation, machine learning, and simulation, we demonstrate that ACW-0 exhibits better prediction in classifying a region’s time window as core or periphery. Overall, our findings provide fundamental insight into how the human cortex’s temporal hierarchy converges with its spatial core-periphery hierarchy.Golesorkhi et al. use a combination of magnetoencephalography data, machine learning and simulation to investigate intrinsic neural timescales during resting and task states. They provide insight into how the temporal hierarchy of the human cortex converges with its spatial core-periphery hierarchy.

We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs’ stochastics with the ongoing temporal statistics of the brain’s neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.Golesorkhi et al. discuss recent literature on intrinsic neural timescales, their potential role in input processing including computational mechanism, and how they relate to mental features, psychiatric disorders and artificial intelligence.

Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.

Transcranial direct current stimulation (tDCS) has been shown to modulate subjective craving ratings in drug dependents by modification of cortical excitability in dorsolateral prefrontal cortex (DLPFC). Given the mechanism of craving in methamphetamine (meth) users, we aimed to test whether tDCS of DLPFC could also alter self-reported craving in abstinent meth users while being exposed to meth cues. In this double-blinded, crossover, sham-controlled study, thirty two right-handed abstinent male meth users were recruited. We applied 20 min ‘anodal’ tDCS (2 mA) or ‘sham’ tDCS over right DLPFC in a random sequence while subjects performed a computerized cue-induced craving task (CICT) starting after 10 min of stimulation. Immediate craving was assessed before the stimulation, after 10 min of tDCS, and after tDCS termination by visual analog scale (VAS) of 0 to 100. Anodal tDCS of rDLPFC altered craving ratings significantly. We found a significant reduction of craving at rest in real tDCS relative to the sham condition (p = 0.016) after 10 min of stimulation. On the other hand, cue-induced VAS craving was rated significantly higher in the real condition in comparison with sham stimulation (p = 0.012). Our findings showed a state dependent effect of tDCS: while active prefrontal tDCS acutely reduced craving at rest in the abstinent meth users, it increased craving during meth-related cue exposure. These findings reflect the important role of the prefrontal cortex in both cue saliency evaluation and urge to meth consumption.

Introduction:Methamphetamine is a powerful psychostimulant that causes significant neurological impairments with long-lasting effects and has provoked serious international concerns about public health. Denial of drug abuse and drug craving are two important factors that make the diagnosis and treatment extremely challenging. Here, we present a novel and rapid noninvasive method with potential application for differentiation and monitoring methamphetamine abuse.Methods: Visual stimuli comprised a series of images with neutral and methamphetamine-related content. A total of 10 methamphetamine abusers and 10 age-gender matched controls participated in the experiments. Event-related potentials (ERPs) were recorded and compared using a time window analysis method. The ERPs were divided into 19 time windows of 100 ms with 50 ms overlaps. The area of positive sections below each window was calculated to measure the differences between the two groups.Results: Significant differences between two groups were observed from 250 to 500 ms (P300) in response to methamphetamine-related visual stimuli and 600 to 800 ms in response to neutral stimuli.Conclusion: This study presented a novel and noninvasive method based on neural correlates to discriminate healthy individuals from methamphetamine drug abusers. This method can be employed in treatment and monitoring of the methamphetamine abuse.

Transcranial current stimulation (TCS) is a neuromodulation method in which the patient is exposed to a mild electric current (direct or alternating) at 1-2 mA, resulting in an increase or a decrease in the brain excitability. This modi.cation in neural activities can be used as a method for functional human brain mapping with causal inferences. This method might also facilitate the treatments of many neuropsychiatric disorders based on its inexpensive, simple, safe, noninvasive, painless, semi-focal excitatory and inhibitory effects. Given this, a comparison amongst different brain stimulation modalities has been made to determine the potential advantages of the TCS method. In addition, considerable methodological details on using TCS in basic and clinical neuroscience studies in human subjects have been introduced. Technical characteristics of TCS devices and their related accessories with regard to safety concerns have also been well articulated. Finally, some TCS application opportunities have been emphasized, including its potential use in the near future

Abstract The human cortex exhibits intrinsic neural timescales that shape a temporal hierarchy. Whether this temporal hierarchy follows the spatial hierarchy of its topography namely the core-periphery organization remains an open issue. Using Magnetoencephalography data, we investigate intrinsic neural timescales during rest and task states; we measure the autocorrelation window in short (ACW-50) and, introducing a novel variant, long (ACW-0) windows. We demonstrate longer ACW-50 and ACW-0 in networks located at the core compared to those at the periphery with rest and task states showing a high ACW correlation. Calculating rest-task differences, i.e., subtracting the shared core-periphery organization, reveals task-specific ACW changes in distinct networks. Finally, employing kernel density estimation, machine learning, and simulation, we demonstrate that ACW-0 exhibits better prediction in classifying a region’s time window as core or periphery. Overall, our findings provide fundamental insight into how the human cortex’s temporal hierarchy converges with its spatial core-periphery hierarchy. Competing Interest Statement The authors have declared no competing interest.

Abstract Unlike the brain’s faster frequencies, the exact role of its more powerful infraslow frequencies (ISF, 0.01 – 0.1Hz) in information processing remains poorly understood. Do and how ISF process information? We investigate information processing and related temporal dynamics of ISF in resting and task state fMRI. To quantify information, we apply the Lempel-Ziv complexity (LZC), a measure of signal compression indexing information. The LZC is combined with direct measurement of the dynamics of ISF themselves, namely their power spectral density by median frequency (MF). We demonstrate the following: (I) topographical differences in resting state between higher- and lower-order networks, showing statistically lower LZC in the former; (II) task-related changes in LZC; (III) modulation of LZC associated with MF changes, with low and high MF resting-state values correlated with different degrees of LZC change. In sum, we provide evidence that ISF carry and process information as mediated through their temporal dynamics. Competing Interest Statement The authors have declared no competing interest.

A. Abstract The standard approach in neuroscience research infers from the external stimulus (outside) to the brain (inside) through stimulus-evoked activity. Recently challenged by Buzsáki, he advocates the reverse; an inside-out approach inferring from the brain’s activity to the neural effects of the stimulus. If so, stimulus-evoked activity should be a hybrid of internal and external components. Providing direct evidence for this hybrid nature, we measured human intracranial stereo-electroencephalography (sEEG) to investigate how prestimulus variability, i.e., standard deviation, shapes poststimulus activity through trial-to-trial variability. We first observed greater poststimulus variability quenching in trials exhibiting high prestimulus variability. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. These results were extended by our Deep Learning LSTM network models at the single trial level. The accuracy to classify single trials (prestimulus low/high) increased greatly when the models were trained and tested with real trials compared to trials that exclude the effects of the prestimulus-related ongoing dynamics (corrected trials). Lastly, we replicated our findings showing that trials with high prestimulus variability in theta and alpha bands exhibits faster reaction times. Together, our results support the inside-out approach by demonstrating that stimulus-related activity is a hybrid of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period, with the second, i.e., the inside, dwarfing the influence of the first, i.e., the outside. Significance Statement Our findings signify a significant conceptual advance in the relationship between pre- and poststimulus dynamics in humans. These findings are important as they show that we miss an essential component - the impact of the ongoing dynamics - when restricting our analyses to the effects of the external stimulus alone. Consequently, these findings may be crucial to fully understand higher cognitive functions and their impairments, as can be seen in psychiatric illnesses. In addition, our Deep Learning LSTM models show a second conceptual advance: high classification accuracy of a single trial to its prestimulus state. Finally, our replicated results in an independent dataset and task showed that this relationship between pre- and poststimulus dynamics exists across tasks and is behaviorally relevant. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵‡ Lead Contact: awolf037{at}uottawa.ca.