The discrepancy in timing between synchronous signals and visual stimulation should not be underestimated.

Response latency is a critical parameter in studying human behavior, representing the time interval between the onset of stimulus and the response. However, different time between devices can introduce errors. Serial port synchronization signal can mitigate this, but limited information is available regarding their accuracy. Optical signals offer another option, but the difference in the positioning of optical signals and visual stimuli can introduce errors, and there have been limited reports of error reduction. This study aims to investigate methods for reducing the time errors. We used the Psychtoolbox to generate visual stimuli and serial port synchronization signals to explore their accuracy. Subsequently, we proposed a calibration formula to minimize the error between optical signals and visual stimuli. The findings are as follows: Firstly, the serial port synchronization signal presenting precedes visual stimulation, with a smaller lead time observed at higher refresh rates. Secondly, the lead time increases as the stimulus position deviates to the right and downwards. In Linux and IOPort(), serial port synchronization signals exhibited greater accuracy. Considering the poor accuracy and the multiple influencing factors associated with serial port synchronization signals, it is recommended to use optical signals to complete time synchronization. The results indicate that under the darkening process, the time error is - 0.23 ~ 0.08 ms (mean). This calibration formula can help measure the response latency accurately. This study provides valuable insights for optimizing experimental design and improving the accuracy of response latency. Although it only involves visual stimuli, the methods and results of this study can still serve as a reference.

A novel approach to modulating response inhibition: Multi-channel beta transcranial alternating current stimulation.

BACKGROUND: Deficits in response inhibition are associated with numerous psychiatric disorders. Previous studies have revealed the crucial role of the right inferior frontal gyrus (rIFG), pre-supplementary motor area (preSMA), and beta activity in these brain regions in response inhibition. Multi-channel transcranial alternating current stimulation (tACS) has garnered significant attention for its ability to modulate neural oscillations in brain networks. In this study, we employed multi-channel tACS targeting rIFG-preSMA network to investigate its impact on response inhibition in healthy adults. METHODS: In Experiment 1, 70 healthy participants were randomly assigned to receive 20 Hz in-phase, anti-phase, or sham stimulation over rIFG-preSMA network. Response inhibition was assessed using the stop-signal task during and after stimulation, and impulsiveness was measured via the Barratt Impulsiveness Scale. Additionally, 25 participants received stimulation at the left supraorbital area to account for potential effects of the "return" electrode. Experiment 2, consisting of 25 participants, was conducted to validate the primary findings of Experiment 1, including both in-phase and sham stimulation conditions, based on prior estimations derived from the results of Experiment 1. RESULTS: In Experiment 1, we found that in-phase stimulation significantly improved response inhibition compared with sham stimulation, whereas anti-phase stimulation did not. These findings were consistently replicated in Experiment 2. We also conducted an exploratory analysis of the multi-channel tACS impact, revealing that its effects primarily emerged during the post-stimulation phase. Furthermore, individuals with higher baseline attentional impulsiveness showed greater improvements in the in-phase stimulation group. CONCLUSIONS: These results demonstrate that in-phase beta-tACS over rIFG-preSMA network can effectively improve response inhibition in healthy adults and provides a new potential treatment for patients with deficits in response inhibition.

A voltage-controlled current source for temporal interference stimulation: Analysis, design, and study.

The temporal interference stimulation is a new technique to modulate brain activity by applying multiple channels of voltage-controlled current source (VCCS) simultaneously to the receptor surface. In this paper, the envelopes of the overlapped current waves at several areas of the receptor were analyzed and discussed with different circuit structures of VCCS. A complementary differential current source (CDCS) was designed to fit the best circuit topology based on the analysis of the enhanced Howland current source structure. Experiments were conducted by injecting current to a swine tissue using the CDCS and conventional VCCS and acquiring the voltage waveform data from different parts of the tissue. The waveforms were compared and analyzed, revealing that the conventional VCCS may generate an interference envelope in unexpected regions, while the CDCS did not. The CDCS design approach in this paper provides a new solution for temporal interference stimulation VCCS.

Dual-site beta tACS over rIFG and M1 enhances response inhibition: A parallel multiple control and replication study

Response inhibition is a core component of cognitive control. Past electrophysiology and neuroimaging studies have identified beta oscillations and inhibitory control cortical regions correlated with response inhibition, including the right inferior frontal gyrus (rIFG) and primary motor cortex (M1). Hence, increasing beta activity in multiple brain regions is a potential way to enhance response inhibition. Here, a novel dual-site transcranial alternating current stimulation (tACS) method was used to modulate beta activity over the rIFG-M1 network in a sample of 115 (excluding 2 participants) with multiple control groups and a replicated experimental design. In Experiment 1, 70 healthy participants were randomly assigned to three dual-site beta-tACS groups, including in-phase, anti-phase or sham stimulation. During and after stimulation, participants were required to complete the stop-signal task, and electroencephalography (EEG) was collected before and after stimulation. The Barratt Impulsiveness Scale was completed before the experiment to evaluate participants' impulsiveness. In addition, we conducted an active control experiment with a sample size of 20 to exclude the potential effects of the dual-site tACS “return” electrode. To validate the behavioural findings of Experiment 1, 25 healthy participants took part in Experiment 2 and were randomized into two groups, including in-phase and sham stimulation groups. We found that compared to the sham group, in-phase but not anti-phase beta-tACS significantly improved both response inhibition performance and beta synchronization of the inhibitory control network in Experiment 1. Furthermore, the increased beta synchronization was correlated with enhanced response inhibition. In an independent sample of Experiment 2, the enhanced response inhibition performance observed in the in-phase group was replicated. (AU)

Resting-state electroencephalography theta predicts neurofeedback treatment 4-month follow-up response in nicotine addiction.

Background: The high rate of long-term relapse is a major cause of smoking cessation failure. Recently, neurofeedback training has been widely used in the treatment of nicotine addiction; however, approximately 30% of subjects fail to benefit from this intervention. Our previous randomised clinical trial (RCT) examined cognition-guided neurofeedback and demonstrated a significant decrease in daily cigarette consumption at the 4-month follow-up. However, significant individual differences were observed in the 4-month follow-up effects of decreased cigarette consumption. Therefore, it is critical to identify who will benefit from pre-neurofeedback. Aims: We examined whether the resting-state electroencephalography (EEG) characteristics from pre-neurofeedback predicted the 4-month follow-up effects and explored the possible mechanisms. Methods: This was a double-blind RCT. A total of 60 participants with nicotine dependence were randomly assigned to either the real-feedback or yoked-feedback group. They underwent 6 min closed-eye resting EEG recordings both before and after two neurofeedback sessions. A follow-up assessment was conducted after 4 months. Results: The frontal resting-state theta power spectral density (PSD) was significantly altered in the real-feedback group after two neurofeedback visits. Higher theta PSD in the real-feedback group before neurofeedback was the only predictor of decreased cigarette consumption at the 4-month follow-up. Further reliability analysis revealed a significant positive correlation between theta PSD pre-neurofeedback and post-neurofeedback. A leave-one-out cross-validated linear regression of the theta PSD pre-neurofeedback demonstrated a significant correlation between the predicted and observed reductions in cigarette consumption at the 4-month follow-up. Finally, source analysis revealed that the brain mechanisms of the theta PSD predictor were located in the orbital frontal cortex. Conclusions: Our study demonstrated changes in the resting-state theta PSD following neurofeedback training. Moreover, the resting-state theta PSD may serve as a prognostic marker of neurofeedback effects. A higher resting-state theta PSD predicts a better long-term response to neurofeedback treatment, which may facilitate the selection of individualised interventions. Trial registration number: ChiCTR-IPR-17011710.

Dual-site beta tACS over rIFG and M1 enhances response inhibition: A parallel multiple control and replication study.

Response inhibition is a core component of cognitive control. Past electrophysiology and neuroimaging studies have identified beta oscillations and inhibitory control cortical regions correlated with response inhibition, including the right inferior frontal gyrus (rIFG) and primary motor cortex (M1). Hence, increasing beta activity in multiple brain regions is a potential way to enhance response inhibition. Here, a novel dual-site transcranial alternating current stimulation (tACS) method was used to modulate beta activity over the rIFG-M1 network in a sample of 115 (excluding 2 participants) with multiple control groups and a replicated experimental design. In Experiment 1, 70 healthy participants were randomly assigned to three dual-site beta-tACS groups, including in-phase, anti-phase or sham stimulation. During and after stimulation, participants were required to complete the stop-signal task, and electroencephalography (EEG) was collected before and after stimulation. The Barratt Impulsiveness Scale was completed before the experiment to evaluate participants' impulsiveness. In addition, we conducted an active control experiment with a sample size of 20 to exclude the potential effects of the dual-site tACS "return" electrode. To validate the behavioural findings of Experiment 1, 25 healthy participants took part in Experiment 2 and were randomized into two groups, including in-phase and sham stimulation groups. We found that compared to the sham group, in-phase but not anti-phase beta-tACS significantly improved both response inhibition performance and beta synchronization of the inhibitory control network in Experiment 1. Furthermore, the increased beta synchronization was correlated with enhanced response inhibition. In an independent sample of Experiment 2, the enhanced response inhibition performance observed in the in-phase group was replicated. After combining the data from the above two experiments, the time dynamics analysis revealed that the in-phase beta-tACS effect occurred in the post-stimulation period but not the stimulation period. The state-dependence analysis showed that individuals with poorer baseline response inhibition or higher attentional impulsiveness had greater improvement in response inhibition for the in-phase group. These findings strongly support that response inhibition in healthy adults can be improved by in-phase dual-site beta-tACS of the rIFG-M1 network, and provide a new potential treatment targets of synchronized cortical network activity for patients with clinically deficient response inhibition.

Alpha oscillatory activity is causally linked to working memory retention.

Although previous studies have reported correlations between alpha oscillations and the "retention" subprocess of working memory (WM), causal evidence has been limited in human neuroscience due to the lack of delicate modulation of human brain oscillations. Conventional transcranial alternating current stimulation (tACS) is not suitable for demonstrating the causal evidence for parietal alpha oscillations in WM retention because of its inability to modulate brain oscillations within a short period (i.e., the retention subprocess). Here, we developed an online phase-corrected tACS system capable of precisely correcting for the phase differences between tACS and concurrent endogenous oscillations. This system permits the modulation of brain oscillations at the target stimulation frequency within a short stimulation period and is here applied to empirically demonstrate that parietal alpha oscillations causally relate to WM retention. Our experimental design included both in-phase and anti-phase alpha-tACS applied to participants during the retention subprocess of a modified Sternberg paradigm. Compared to in-phase alpha-tACS, anti-phase alpha-tACS decreased both WM performance and alpha activity. These findings strongly support a causal link between alpha oscillations and WM retention and illustrate the broad application prospects of phase-corrected tACS.

Correlation between abnormal brain network activity and electroencephalogram microstates on exposure to smoking-related cues.

BACKGROUND: Research into neural mechanisms underlying cue-induced cigarette craving has attracted considerable attention for its significant role in treatments. However, there is little understanding about the effects of exposure to smoking-related cues on electroencephalogram (EEG) microstates of smokers, which can reflect abnormal brain network activity in several psychiatric disorders. AIMS: To explore whether abnormal brain network activity in smokers on exposure to smoking-related cues would be captured by EEG microstates. METHOD: Forty smokers were exposed to smoking and neutral imagery conditions (cues) during EEG recording. Behavioural data and parameters for microstate topographies associated with the auditory (A), visual (B), salience and memory (C) and dorsal attention networks (D) were compared between conditions. Correlations between microstate parameters and cigarette craving as well as nicotine addiction characteristics were also analysed. RESULTS: The smoking condition elicited a significant increase in the duration of microstate classes B and C and in the duration and contribution of class D compared with the neutral condition. A significant positive correlation between the increased duration of class C (smoking minus neutral) and increased craving ratings was observed, which was fully mediated by increased posterior alpha power. The increased duration and contribution of class D were both positively correlated with years of smoking. CONCLUSIONS: Our results indicate that smokers showed abnormal EEG microstates when exposed to smoking-related cues compared with neutral cues. Importantly, microstate class C (duration) might be a biomarker of cue-induced cigarette craving, and class D (duration and contribution) might reflect the relationship between cue-elicited activation of the dorsal attention network and years of smoking.

Using multisession tDCS stimulation as an early intervention on memory bias processing in subthreshold depression.

Transcranial direct current stimulation (tDCS) as an intervention tool has gained promising results in major depression disorder. However, studies related to subthreshold depression's (SD) cognitive deficits and neuromodulation approaches for the treatment of SD are still rare. We adopted Beck's cognitive model of depression and tested the tDCS stimulation effects on attentional and memory deficits on SD. First, this was a single-blinded, randomized, sham-controlled clinical trial to determine a 13-day tDCS modulation effect on 49 SD (27: Stimulation; 22: Sham) and 17 healthy controls. Second, the intervention effects of the consecutive and single-session tDCS were compared. Furthermore, the attentional and memory biases were explored in SD. Anodal tDCS was administrated over left dorsolateral prefrontal cortex for 13 consecutive days. Attentional and memory bias were assessed through a modified Sternberg task and a dot-probe task on the 1st, 2nd, and 15th day while their EEG was being recorded. After the 13-day tDCS stimulation (not after single-session stimulation), we found reduced memory bias (Stimulation vs. Sham, p = .02, r2  = .09) and decreased mid-frontal alpha power (p < .01, r2  = .13). In contrast, tDCS did not affect any attentional related behavioral or neural indexes (all ps > .15). Finally, reduced depressive symptoms (e.g., BDI score) were found for both groups. The criteria of SD varied across studies; the efficacy of this protocol should be tested in elderly patients. Our study suggests memory bias of SD can be modulated by the multisession tDCS and alpha power could serve as a neural index for intervention.

School Psychological Environment and Learning Burnout in Medical Students: Mediating Roles of School Identity and Collective Self-Esteem.

Learning burnout is an important indicator that reflects an individual's learning state. Understanding the influencing factors and mechanism of learning burnout of medical students has practical significance for improving their mental health. This study aimed to explore the mediating roles of school identity and collective self-esteem between school psychological environment and learning burnout in medical students. A total of 2,031 medical students (942 men and 1,089 women, age range: 17-23 years) were surveyed using the School Psychological Environment Questionnaire (SPEQ), School Identity Questionnaire (SIQ), Collective Self-esteem Scale (CSES), and Learning Burnout Scale (LBS). The results showed the following: (1) school psychological environment had a negative effect on learning burnout among medical students (ß = -0.19, p < 0.001), and (2) school identity and collective self-esteem played significant mediating roles between school psychological environment and learning burnout [95% CI = (-0.43, -0.31)]. Specifically, there were three paths that school psychological environment and learning burnout: first, through the independent mediating role of school identity; second, through the independent mediating role of collective self-esteem; and third, through the chain mediating roles of school identity and collective self-esteem. The findings reveal that school psychological environment not only directly influences the learning burnout of medical students but also indirectly influences it through school identity and collective self-esteem. Thus, this study has some important implications for prevention and intervention of learning burnout among medical students.

Novel Non-invasive Transcranial Electrical Stimulation for Parkinson's Disease.

Conventional transcranial electrical stimulation (tES) is a non-invasive method to modulate brain activity and has been extensively used in the treatment of Parkinson's disease (PD). Despite promising prospects, the efficacy of conventional tES in PD treatment is highly variable across different studies. Therefore, many have tried to optimize tES for an improved therapeutic efficacy by developing novel tES intervention strategies. Until now, these novel clinical interventions have not been discussed or reviewed in the context of PD therapy. In this review, we focused on the efficacy of these novel strategies in PD mitigation, classified them into three categories based on their distinct technical approach to circumvent conventional tES problems. The first category has novel stimulation modes to target different modulating mechanisms, expanding the rang of stimulation choices hence enabling the ability to modulate complex brain circuit or functional networks. The second category applies tES as a supplementary intervention for PD hence amplifies neurological or behavioral improvements. Lastly, the closed loop tES stimulation can provide self-adaptive individualized stimulation, which enables a more specialized intervention. In summary, these novel tES have validated potential in both alleviating PD symptoms and improving understanding of the pathophysiological mechanisms of PD. However, to assure wide clinical used of tES therapy for PD patients, further large-scale trials are required.

Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Background: Due to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: The preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analyzed retrospectively. All included cases were divided into MVI-negative (n = 33; no MVI) and MVI-positive (n = 49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical and CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI. Results: We found that tumor diameter and pathological grade were effective clinical predictors in clinical model and 12 radiomics features were effective for MVI prediction of each CT phase. The AUCs of the clinical, plain, artery, venous, and delay models were 0.77 (95% CI: 0.67-0.88), 0.75 (95% CI: 0.64-0.87), 0.79 (95% CI: 0.69-0.89), 0.73 (95% CI: 0.61-0.85), and 0.80 (95% CI: 0.70-0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74-0.93). Conclusions: Models derived from triphasic CT can preoperatively predict MVI in patients with HCC. Of the models tested here, the novel combined model was most predictive and could become a useful tool to guide subsequent personalized treatment of HCC.

A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction.

Compared with the traditional neurofeedback paradigm, the cognition-guided neurofeedback brain-computer interface (BCI) is a novel paradigm with significant effect on nicotine addiction. However, the cognition-guided neurofeedback BCI dataset is extremely lacking at present. This paper provides a BCI dataset based on a novel cognition-guided neurofeedback on nicotine addiction. Twenty-eight participants are recruited and involved in two visits of neurofeedback training. This cognition-guided neurofeedback includes two phases: an offline classifier construction and a real-time neurofeedback training. The original electroencephalogram (EEG) raw data of two phases are provided and evaluated in this paper. The event-related potential (ERP) amplitude and channel waveform suggest that our BCI dataset is of good quality and consistency. During neurofeedback training, the participants' smoking cue reactivity patterns have a significant reduction. The mean accuracy of the multivariate pattern analysis (MVPA) classifier can reach approximately 70%. This novel cognition-guided neurofeedback BCI dataset can be used to develop comparisons with other neurofeedback systems and provide a reference for the development of other BCI algorithms and neurofeedback paradigms on addiction.

The neural mechanisms of immediate and follow-up of the treatment effect of hypnosis on smoking craving.

Hypnosis has a therapeutic effect on substance dependence. However, its neural basis remains unclear, which impedes its further clinical applications. This study investigated the mechanisms of smoking treatment based on hypnosis from two perspectives: immediate and follow-up effects. Twenty-four smokers screened from 132 volunteers underwent hypnosis suggestion and performed a smoking-related cue task twice during functional magnetic resonance imaging (fMRI) scanning (in normal and hypnotic states). The number of cigarettes smoked per day was recorded at follow-up visits. The smokers reported decreased craving after hypnosis. The activations in the right dorsal lateral prefrontal cortex (rDLPFC), the left insula and the right middle frontal gyrus (rMFG), and the functional connectivity between the rDLPFC and the left insula were increased in the hypnotic state. The reduced craving was related to the DLPFC-insula network, which reflected the immediate mechanism of hypnosis on smoking. The number of cigarette use at the 1-week and 1 month follow-up was correlated with the rMFG activation which reflecting hypnotic depth, suggesting the follow-up effect of hypnosis on smoking depended on the trait of smokers. We identified two different mechanisms of hypnosis effect on smoking, which have important implications for design and optimization of hypnotic treatments on mental disorders.

Effect of deactivation of activity patterns related to smoking cue reactivity on nicotine addiction.

With approximately 75% of smokers resuming cigarette smoking after using the Gold Standard Programme for smoking cessation, investigation into novel therapeutic approaches is warranted. Typically, smoking cue reactivity is crucial for smoking behaviour. Here we developed a novel closed-loop, smoking cue reactivity patterns EEG-based neurofeedback protocol and evaluated its therapeutic efficacy on nicotine addiction. During an evoked smoking cue reactivity task participants' brain activity patterns corresponding to smoking cues were obtained with multivariate pattern analysis of all EEG channels data, then during neurofeedback the EEG activity patterns of smoking cue reactivity were continuously deactivated with adaptive closed-loop training. In a double-blind, placebo-controlled, randomized clinical trial, 60 nicotine-dependent participants were assigned to receive two neurofeedback training sessions (∼1 h/session) either from their own brain (n = 30, real-feedback group) or from the brain activity pattern of a matched participant (n = 30, yoked-feedback group). Cigarette craving and craving-related P300 were assessed at pre-neurofeedback and post-neurofeedback. The number of cigarettes smoked per day was assessed at baseline, 1 week, 1 month, and 4 months following the final neurofeedback visit. In the real-feedback group, participants successfully deactivated EEG activity patterns of smoking cue reactivity. The real-feedback group showed significant decrease in cigarette craving and craving-related P300 amplitudes compared with the yoked-feedback group. The rates of cigarettes smoked per day at 1 week, 1 month and 4 months follow-up decreased 30.6%, 38.2%, and 27.4% relative to baseline in the real-feedback group, compared to decreases of 14.0%, 13.7%, and 5.9% in the yoked-feedback group. The neurofeedback effects on craving change and smoking amount at the 4-month follow-up were further predicted by neural markers at pre-neurofeedback. This novel neurofeedback training approach produced significant short-term and long-term effects on cigarette craving and smoking behaviour, suggesting the neurofeedback protocol described herein is a promising brain-based tool for treating addiction.

Low-Theta Electroencephalography Coherence Predicts Cigarette Craving in Nicotine Addiction.

Addicts are often vulnerable to drug use in the presence of drug cues, which elicit significant drug cue reactivity. Mounting neuroimaging evidence suggests an association between functional magnetic resonance imaging connectivity networks and smoking cue reactivity; however, there is still little understanding of the electroencephalography (EEG) coherence basis of smoking cue reactivity. We therefore designed two independent experiments wherein nicotine-dependent smokers performed a smoking cue reactivity task during EEG recording. Experiment I showed that a low-theta EEG coherence network occurring 400-600 ms after onset during long-range (mainly between frontal and parieto-occipital) scalp regions, which was involved in smoking cue reactivity. Moreover, the average coherence of this network was significantly correlated with participants' level of cigarette craving. In experiment II, we tested an independent group of smokers and demonstrated that the low-theta coherence network significantly predicted changes in individuals' cigarette craving. Thus, the low-theta EEG coherence in smokers' brains might be a biomarker of smoking cue reactivity and can predict addiction behavior.

The top-down regulation from the prefrontal cortex to insula via hypnotic aversion suggestions reduces smoking craving.

Hypnosis has been shown to have treatment effects on nicotine addiction. However, the neural basis of these effects is poorly understood. This preliminary study investigated the neural mechanisms of hypnosis-based treatment on cigarette smoking, specifically, whether the hypnosis involves a top-down or bottom-up mechanism. Two groups of 45 smokers underwent a smoking aversion suggestion and viewed smoking-related pictures and neutral pictures. One group underwent functional magnetic resonance imaging scanning twice (control and hypnotic states), whereas the other group underwent two electroencephalograph sessions. Our study found that self-reported smoking craving decreased in both groups following hypnosis. Smoking cue-elicited activations in the right dorsal lateral prefrontal cortex (rDLPFC) and left insula (lI) and the functional connectivity between the rDLPFC and lI were increased in the hypnotic state compared with the control state. The delta band source waveforms indicated the activation from 390 to 862 ms at the rDLPFC and from 490 to 900 ms at the lI was significantly different between the smoking and neutral conditions in the hypnotic state, suggesting the activation in the rDLPFC preceded that in the lI. These results suggest that the decreased smoking craving via hypnotic aversion suggestions may arise from the top-down regulation of the rDLPFC to the lI. Our findings provide novel neurobiological evidence for understanding the therapeutic effects of hypnosis on nicotine addiction, and the prefrontal-insula circuit may serve as an imaging biomarker to monitor the treatment efficacy noninvasively.

Transforming brain signals related to value evaluation and self-control into behavioral choices.

The processes involved in value evaluation and self-control are critical when making behavioral choices. However, the evidence linking these two types of processes to behavioral choices in intertemporal decision-making remains elusive. As the ventromedial prefrontal cortex (vmPFC), striatum, and dorsolateral prefrontal cortex (dlPFC) have been associated with these two processes, we focused on these three regions. We employed functional magnetic resonance imaging during a delayed discounting task (DDT) using a relatively large sample size, three independent samples. We evaluated how much information about a specific choice could be decoded from local patterns in each brain area using multivoxel pattern analysis (MVPA). To investigate the relationship between the dlPFC and vmPFC/striatum regions, we performed a psychophysiological interaction (PPI) analysis. In Experiment I, we found that the vmPFC and dlPFC, but not the striatum, could determine choices in healthy participants. Furthermore, we found that the dlPFC showed significant functional connectivity with the vmPFC, but not the striatum, when making decisions. These results could be replicated in Experiment II with an independent sample of healthy participants. In Experiment III, the choice-decoding accuracy in the vmPFC and dlPFC was lower in patients with addiction (smokers and participants with Internet gaming disorder) than in healthy participants, and decoding accuracy in the dlPFC was related to impulsivity in addicts. Taken together, our findings may provide neural evidence supporting the hypothesis that value evaluation and self-control processes both guide the intertemporal choices, and might provide potential neural targets for the diagnosis and treatment of impulsivity-related brain disorders.

Neuroimaging Studies Reveal the Subtle Difference Among Social Network Size Measurements and Shed Light on New Directions.

Social network size is a key feature when we explore the constructions of human social networks. Despite the disparate understanding of individuals' social networks, researchers have reached a consensus that human's social networks are hierarchically organized with different layers, which represent emotional bonds and interaction frequency. Social brain hypothesis emphasizes the significance of complex and demanding social interaction environments and assumes that the cognitive constraints may have an impact on the social network size. This paper reviews neuroimaging studies on social networks that explored the connection between individuals' social network size and neural mechanisms and finds that Social Network Index (SNI) and Social Network Questionnaires (SNQs) are the mostly-adopted measurements of one's social network size. The two assessments have subtle difference in essence as they measure the different sublayers of one's social network. The former measures the relatively outer sub-layer of one's stable social relationship, similar to the sympathy group, while the latter assesses the innermost layer-the core of one's social network, often referred to as support clique. This subtle difference is also corroborated by neuroimaging studies, as SNI-measured social network size is largely correlated with the amygdala, while SNQ-assessed social network size is closely related to both the amygdala and the orbitofrontal cortex. The two brain regions respond to disparate degrees of social closeness, respectively. Finally, it proposes a careful choice among the measurements for specific purposes and some new approaches to assess individuals' social network size.