Separable amygdala activation patterns in the evaluations of robots.

Given the increasing presence of robots in everyday environments and the significant challenge posed by social interactions with robots, it is crucial to gain a deeper understanding into the social evaluations of robots. One potentially effective approach to comprehend the fundamental processes underlying controlled and automatic evaluations of robots is to probe brain response to different perception levels of robot-related stimuli. Here, we investigate controlled and automatic evaluations of robots based on brain responses during viewing of suprathreshold (duration: 200 ms) and subthreshold (duration: 17 ms) humanoid robot stimuli. Our behavioral analysis revealed that despite participants' self-reported positive attitudes, they held negative implicit attitudes toward humanoid robots. Neuroimaging analysis indicated that subthreshold presentation of humanoid robot stimuli elicited significant activation in the left amygdala, which was associated with negative implicit attitudes. Conversely, no significant left amygdala activation was observed during suprathreshold presentation. Following successful attenuation of negative attitudes, the left amygdala response to subthreshold presentation of humanoid robot stimuli decreased, and this decrease correlated positively with the reduction in negative attitudes. These findings provide evidence for separable patterns of amygdala activation between controlled and automatic processing of robots, suggesting that controlled evaluations may influence automatic evaluations of robots.

Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder.

Background: Internet gaming disorder (IGD) is a mental health issue that affects individuals worldwide. However, the lack of knowledge about the biomarkers associated with the development of IGD has restricted the diagnosis and treatment of this disorder. Aims: We aimed to reveal the biomarkers associated with the development of IGD through resting-state brain network analysis and provide clues for the diagnosis and treatment of IGD. Methods: Twenty-six patients with IGD, 23 excessive internet game users (EIUs) who recurrently played internet games but were not diagnosed with IGD and 29 healthy controls (HCs) performed delay discounting task (DDT) and Iowa gambling task (IGT). Resting-state functional magnetic resonance imaging (fMRI) data were also collected. Results: Patients with IGD exhibited significantly lower hubness in the right medial orbital part of the superior frontal gyrus (ORBsupmed) than both the EIU and the HC groups. Additionally, the hubness of the right ORBsupmed was found to be positively correlated with the highest excessive internet gaming degree during the past year in the EIU group but not the IGD group; this might be the protective mechanism that prevents EIUs from becoming addicted to internet games. Moreover, the hubness of the right ORBsupmed was found to be related to the treatment outcome of patients with IGD, with higher hubness of this region indicating better recovery when undergoing forced abstinence. Further modelling analysis of the DDT and IGT showed that patients with IGD displayed higher impulsivity during the decision-making process, and impulsivity-related parameters were negatively correlated with the hubness of right ORBsupmed. Conclusions: Our findings revealed that the impulsivity-related right ORBsupmed hubness could serve as a potential biomarker of IGD and provide clues for the diagnosis and treatment of IGD.

Implicit Perception of Differences between NLP-Produced and Human-Produced Language in the Mentalizing Network.

Natural language processing (NLP) is central to the communication with machines and among ourselves, and NLP research field has long sought to produce human-quality language. Identification of informative criteria for measuring NLP-produced language quality will support development of ever-better NLP tools. The authors hypothesize that mentalizing network neural activity may be used to distinguish NLP-produced language from human-produced language, even for cases where human judges cannot subjectively distinguish the language source. Using the social chatbots Google Meena in English and Microsoft XiaoIce in Chinese to generate NLP-produced language, behavioral tests which reveal that variance of personality perceived from chatbot chats is larger than for human chats are conducted, suggesting that chatbot language usage patterns are not stable. Using an identity rating task with functional magnetic resonance imaging, neuroimaging analyses which reveal distinct patterns of brain activity in the mentalizing network including the DMPFC and rTPJ in response to chatbot versus human chats that cannot be distinguished subjectively are conducted. This study illustrates a promising empirical basis for measuring the quality of NLP-produced language: adding a judge's implicit perception as an additional criterion.

Internet gaming disorder and tobacco use disorder share neural connectivity patterns between the subcortical and the motor network.

Internet gaming disorder (IGD) and tobacco use disorder (TUD) are globally common, non-substance-related disorders and substance-related disorders worldwide, respectively. Recognizing the commonalities between IGD and TUD will deepen understanding of the underlying mechanisms of addictive behavior and excessive online gaming. Using node strength, 141 resting-state data were collected in this study to compute network homogeneity. The participants included participants with IGD (PIGD: n = 34, male = 29, age: 15-25 years), participants with TUD (PTUD: n = 33, male = 33, age: 19-42 years), and matched healthy controls (control-for-IGD: n = 41, male = 38, age: 17-32 years; control-for-TUD: n = 33, age: 21-27 years). PIGD and PTUD exhibited common enhanced node strength between the subcortical and motor networks. Additionally, a common enhanced resting-state functional connectivity (RSFC) was found between the right thalamus and right postcentral gyrus in PIGD and PTUD. Node strength and RSFC were used to distinguish PIGD and PTUD from their respective healthy controls. Interestingly, models trained on PIGD versus controls could classify PTUD versus controls and vice versa, suggesting that these disorders share common neurological patterns. Enhanced connectivity may indicate a greater association between rewards and behaviors, inducing addiction behaviors without flexible and complex regulation. This study discovered that the connectivity between the subcortical and motor networks is a potential biological target for developing addiction treatment in the future.

Altered white matter functional network in nicotine addiction.

Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.

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.

How far is brain-inspired artificial intelligence away from brain?

Fueled by the development of neuroscience and artificial intelligence (AI), recent advances in the brain-inspired AI have manifested a tipping-point in the collaboration of the two fields. AI began with the inspiration of neuroscience, but has evolved to achieve a remarkable performance with little dependence upon neuroscience. However, in a recent collaboration, research into neurobiological explainability of AI models found that these highly accurate models may resemble the neurobiological representation of the same computational processes in the brain, although these models have been developed in the absence of such neuroscientific references. In this perspective, we review the cooperation and separation between neuroscience and AI, and emphasize on the current advance, that is, a new cooperation, the neurobiological explainability of AI. Under the intertwined development of the two fields, we propose a practical framework to evaluate the brain-likeness of AI models, paving the way for their further improvements.

Effects of retrieval-extinction training on internet gaming disorder.

Background and aims: Internet gaming disorder (IGD) leads to serious impairments in cognitive functions, and lacks of effective treatments. Cue-induced craving is a hallmark feature of this disease and is associated with addictive memory elements. Memory retrieval-extinction manipulations could interfere with addictive memories and attenuate addictive syndromes, which might be a promising intervention for IGD. The aims of this study were to explore the effect of a memory retrieval-extinction manipulation on gaming cue-induced craving and reward processing in individuals with IGD. Methods: A total of 49 individuals (mean age: 20.52 ± 1.58) with IGD underwent a memory retrieval-extinction training (RET) with a 10-min interval (R-10min-E, n = 24) or a RET with a 6-h interval (R-6h-E, n = 25) for two consecutive days. We assessed cue-induced craving pre- and post-RET, and at the 1- and 3-month follow-ups. The neural activities during reward processing were also assessed pre- and post-RET. Results: Compared with the R-6h-E group, gaming cravings in individuals with IGD were significantly reduced after R-10min-E training at the 3-month follow-up (P < 0.05). Moreover, neural activities in the individuals with IGD were also altered after R-10min-E training, which was corroborated by enhanced reward processing, such as faster responses (P < 0.05) and stronger frontoparietal functional connectivity to monetary reward cues, while the R-6h-E training had no effects. Discussion and Conclusions: The two-day R-10min-E training reduced addicts' craving for Internet games, restored monetary reward processing in IGD individuals, and maintained long-term efficacy.

No smoking signs with strong smoking symbols induce weak cravings: an fMRI and EEG study.

No smoking signs (NSSs) that combine smoking symbols (SSs) and prohibition symbols (PSs) represent common examples of reward and prohibition competition. To evaluate how SSs within NSSs influence their effectiveness in guiding reward vs. prohibition, we studied 93 male smokers. We collected self-reported craving ratings (N=30), cue reactivity under fMRI/EEG (N=33), and smoking-behavior anticipation for paired NSSs and SSs (N=30). We found that NSS-induced cravings were negatively correlated with SS-induced cravings and PS-induced inhibition. fMRI indicated that both correlations were mediated by activation of the inferior frontal gyrus and precuneus, suggesting that the effects of SSs and PSs interact with each other. EEG revealed that the prohibition response occurs after the cigarette response, indicating that the cigarette response might be precluded by the prohibition, supporting the effect of SSs in discouraging smoking. Moreover, stronger SSs induced stronger slow positive waves and late positive potentials, and the stronger the late positive potentials, the stronger the late positive potentials. Both the amplitudes of late positive potentials and slow positive waves were positively correlated with the amplitude of N2, which was positively correlated with the attention grabbed score by the NSS. In addition, the weaker the NSS-induced craving, the greater the smoking behavior anticipation reduction, indicating the capability of NSSs to decrease smoking behavior. Our study provides empirical evidence for selecting the most effective NSSs: those combining strong SS and PS, offering insights about competition between cigarette reward and prohibition and providing neural evidence on how cigarette reward and prohibition interact.

A behavioral protocol to assess the relationship between three cognitive biases and future depression severity.

According to the cognitive model of depression, memory bias, interpretation bias, and attention bias are associated with the development and maintenance of depression. Here, we present a protocol for investigating whether and how the novel coronavirus disease 2019 (COVID-19) pandemic may affect the relationship between current cognitive biases and future depression severity in a population with non-clinical depression. This protocol can also be used in other contexts, including cognitive bias-related studies and depression-related functional magnetic resonance imaging (fMRI) studies. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021).

A role of the lateral prefrontal cortex in the congruency sequence effect revealed by transcranial direct current stimulation.

Congruency effect is the increase in response time when relevant and irrelevant cues indicate incongruent rather than congruent responses. The congruency effect is smaller in the trial after an incongruent trial than after a congruent trial: this difference is known as the congruency sequence effect (CSE). Psychophysical and neural studies have suggested that the lateral prefrontal cortex (LPFC) and the medial prefrontal cortex are associated with the CSE. In the present study, we applied anodal and cathodal transcranial direct current stimulation, which is thought to result in excitation and inhibition, respectively, on the LPFC, while human participants were performing a flanker task. We found that the CSE was increased under cathodal stimulation (inhibition) of the LPFC. Moreover, the LPFC stimulation modulated the congruency effect after a congruent trial. Further analyses suggested that the results cannot be explained by any of the currently prevailing hypotheses of the CSE, including the conflict monitoring hypothesis, feature integration hypothesis, and temporal learning account. Based on our findings, we propose that a new distinct mechanism might be involved in the CSE. Specifically, the LPFC might contribute to the CSE by maintaining the attention to the task-relevant information, which is an endogenous goal-oriented function and reduces the carry-over of the task-irrelevant information after a congruent trial.

Outbreak of COVID-19 altered the relationship between memory bias and depressive degree in nonclinical depression.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has increased concern about people's mental health under such serious stressful situation, especially depressive symptoms. Cognitive biases have been related to depression degree in previous studies. Here, we used behavioral and brain imaging analysis, to determine if and how the COVID-19 pandemic affects the relationship between current cognitive biases and future depression degree and the underlying neural basis in a nonclinical depressed population. An out-expectation result showed that a more negative memory bias was associated with a greater decrease in future depressive indices in nonclinical depressed participants during the COVID-19 pandemic, which might be due to decreased social stress. These data enhance our understanding of how the depressive degree of nonclinical depressed populations will change during the COVID-19 pandemic and also provide support for social distancing policies from a psychological perspective.

Quantum reinforcement learning during human decision-making.

Classical reinforcement learning (CRL) has been widely applied in neuroscience and psychology; however, quantum reinforcement learning (QRL), which shows superior performance in computer simulations, has never been empirically tested on human decision-making. Moreover, all current successful quantum models for human cognition lack connections to neuroscience. Here we studied whether QRL can properly explain value-based decision-making. We compared 2 QRL and 12 CRL models by using behavioural and functional magnetic resonance imaging data from healthy and cigarette-smoking subjects performing the Iowa Gambling Task. In all groups, the QRL models performed well when compared with the best CRL models and further revealed the representation of quantum-like internal-state-related variables in the medial frontal gyrus in both healthy subjects and smokers, suggesting that value-based decision-making can be illustrated by QRL at both the behavioural and neural levels.

Repetitive Transcranial Magnetic Stimulation Induced Hypoconnectivity Within the Default Mode Network Yields Cognitive Improvements in Amnestic Mild Cognitive Impairment: A Randomized Controlled Study.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is thought to be effective in alleviating cognitive symptoms in patients with amnestic mild cognitive impairment (aMCI), but the mechanisms related to network modification are poorly understood. OBJECTIVE: Here we tested rTMS efficacy and explored the effect of rTMS-induced changes in the default mode network (DMN) and their predictive value for treatment response. METHODS: Twenty-one subjects clinically diagnosed with aMCI were recruited to complete a 10-session randomized and sham-controlled rTMS treatment targeting the right dorsolateral prefrontal cortex. Resting-state functional magnetic resonance imaging in tandem with neuropsychological assessments were administered before and after the intervention. Changes in functional connectivity of the DMN and relevant brain regions, as well as the correlations between baseline functional connectivity and clinical rating scales were calculated in order to elucidate the mechanism of treatment response to rTMS therapy. RESULTS: Compared to the sham group, the rTMS group achieved improvement of neuropsychological performance and significant functional connectivity changes within the DMN. Group×Time interactions were found between posterior cingulate gyrus and right fusiform gyrus (F (1,19) â€Š= 17.154, p = 0.001), and also left anterior cingulate gyrus (F (1,19) â€Š= 3.908, p = 0.063), showing an rTMS-induced deactivation of functional connectivity within the DMN. Baseline functional connectivity analysis of seeds within the DMN in the rTMS group revealed negative correlation with AVLT-Recognition score changes. CONCLUSION: rTMS-induced hypoconnectivity within DMN is associated with clinical cognitive improvements in patients with aMCI. Further, pre-rTMS baseline activity of the DMN at rest may be a predictor for favorable rTMS treatment response.

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.

Chronic nicotine exposure impairs uncertainty modulation on reinforcement learning in anterior cingulate cortex and serotonin system.

Deficits in the computational processes of reinforcement learning have been suggested to underlie addiction. Additionally, environmental uncertainty, which is encoded in the anterior cingulate cortex (ACC), modulates reward prediction errors (RPEs) during reinforcement learning and exacerbates addiction. The present study tested whether and how the ACC would have an essential role in drug addiction by failing to use uncertainty to modulate the RPEs during reinforcement learning. In Experiment I, we found that the ACC/medial prefrontal cortex (MPFC) did not modulate RPE learning according to uncertainty in smokers. The effect of uncertainty × RPE in the ACC/MPFC was correlated with the learning rate of RPEs and the duration of nicotine use. Experiment II demonstrated that serotonin, but not dopamine, receptor mRNA expression significantly decreased in the ACC of the nicotine exposed compared to the control rats. Furthermore, there was a positive correlation between learning rate and serotonin receptor mRNA expression in the ACC. Therefore, all present results suggest that impairments in uncertainty modulation in the ACC disrupt reinforcement learning processes in chronic nicotine users and contribute to maladaptive decision-making. These findings support interventions for pathological decision-making in drug addiction that strongly focus on the serotonin system in ACC.

Received View of Addiction, Relapse and Treatment.

It is important to highlight that attempts at understanding and explaining addiction have been made for centuries. It is, however, just five decades ago, with the growth of science and technology that more interest has been observed in this field. This chapter examines different views and theories that have been posited to understand and explain addiction. More attention will be given to prominent views that seem to draw consensus among researchers and medical practitioners. The first section of the chapter introduces the addiction debate, the different theories that have been provided to explain it from different perspectives and disciplines such as neurosciences, philosophy and psychology. Then, the chapter discusses different views on the role of relapse and what it entails in understanding addiction. The second section discusses different proposed and used forms of treating addiction. Thus, the chapter discusses the received view of addiction, the understanding of relapse as a critical element in addiction and treatments.

Similarities and Differences in Diagnostic Criterion.

In this chapter, the main content is to discuss the similarities and differences in diagnostic criteria between substance and non-substance addictions. Firstly, diagnostic criteria of substance addiction were introduced, mainly focused on Diagnostic and Statistical Manual for the Mental Disorders, fifth edition (DSM-5). Then, we described the diagnostic criteria of several non-substance addictions, including gambling disorder, internet addiction, food addiction and hypersexual disorder. Depending on the proof, substance and non-substance addictions have many similarities in symptoms. Though the proposed diagnostic criteria of many non-substance addictions are currently most useful as survey instruments to access the prevalence of the problem, there is little or no validating proof for these diagnostic criteria. Finally, animal model is useful tool for addiction research. But, present animal models for gambling studying do not meet enough diagnostic criteria and could not be regarded as gambling disorder. By introducing the animal models evolved to resemble the diagnostic criteria of substance addiction and two classical paradigms for substance addiction, self-administration and conditioned place preference, we hope it is helpful to improve the validation of animal model of gambling disorder.

Summary and Prospect.

In this chapter, the main content is to summarize the similarities and differences between substance and non-substance addictions in several aspects, involving definition, mechanisms, diagnosis and treatment. We try to display the complete picture of addictions in a brief but comprehensive way. Mechanism includes molecule and neural circuit, genetics, neuroimaging and cognitive psychology; diagnosis includes diagnostic criterion, diagnostic scales, biochemical diagnosis and new diagnostic techniques; treatment includes drug therapy, physical therapy, traditional Chinese medical therapy, nutrition support therapy, psychotherapy and cognitive-behavioral therapy. This chapter also covers some prospect which will induct future studies on addiction. We aim at providing the researchers and graduate students with better understanding of substance and non-substance addictions.

Neural substrates of updating the prediction through prediction error during decision making.

Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e.g., in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making.