Neuropsychobiology of fear-induced bradycardia in humans: progress and pitfalls

In the last century, the paradigm of fear conditioning has greatly evolved in a variety of scientific fields. The techniques, protocols, and analysis methods now most used have undergone a progressive development, theoretical and technological, improving the quality of scientific productions. Fear-induced bradycardia is among these techniques and represents the temporary deceleration of heart beats in response to negative outcomes. However, it has often been used as a secondary measure to assess defensive responding to threat, along other more popular techniques. In this review, we aim at paving the road for its employment as an additional tool in fear conditioning experiments in humans. After an overview of the studies carried out throughout the last century, we describe more recent evidence up to the most contemporary research insights. Lastly, we provide some guidelines concerning the best practices to adopt in human fear conditioning studies which aim to investigate fear-induced bradycardia.

Changes in brain rhythms and connectivity tracking fear acquisition and reversal

Fear conditioning is used to investigate the neural bases of threat and anxiety, and to understand their flexible modifications when the environment changes. This study aims to examine the temporal evolution of brain rhythms using electroencephalographic signals recorded in healthy volunteers during a protocol of Pavlovian fear conditioning and reversal. Power changes and Granger connectivity in theta, alpha, and gamma bands are investigated from neuroelectrical activity reconstructed on the cortex. Results show a significant increase in theta power in the left (contralateral to electrical shock) portion of the midcingulate cortex during fear acquisition, and a significant decrease in alpha power in a broad network over the left posterior-frontal and parietal cortex. These changes occur since the initial trials for theta power, but require more trials (3/4) to develop for alpha, and are also present during reversal, despite being less pronounced. In both bands, relevant changes in connectivity are mainly evident in the last block of reversal, just when power differences attenuate. No significant changes in the gamma band were detected. We conclude that the increased theta rhythm in the cingulate cortex subserves fear acquisition and is transmitted to other cortical regions via increased functional connectivity allowing a fast theta synchronization, whereas the decrease in alpha power can represent a partial activation of motor and somatosensory areas contralateral to the shock side in the presence of a dangerous stimulus. In addition, connectivity changes at the end of reversal may reflect long-term alterations in synapses necessary to reverse the previously acquired contingencies.

The underlying neural bases of the reversal error while solving algebraic word problems

Problem solving is a core element in mathematical learning. The reversal error in problem solving occurs when students are able to recognize the information in the statement of comparison word problems, but they reverse the relationship between two variables when building the equations. Functional magnetic resonance images were acquired to identify for the first time the neural bases associated with the reversal error. The neuronal bases linked to this error have been used as inputs in 13 classifiers to discriminate between reversal error and non-reversal error groups. We found brain activation in bilateral fronto-parietal areas in the participants who committed reversal errors, and only left fronto-parietal activation in those who did not, suggesting that the reversal error group needed a greater cognitive demand. Instead, the non-reversal error group seems to show that they have developed solid algebraic knowledge. Additionally, the results showed brain activation in the right middle temporal gyrus when comparing the reversal error vs non-reversal error groups. This activation would be associated with the semantic processing which is required to understand the statement and build the equation. Finally, the classifier results show that the brain areas activated could be considered good biomarkers to help us identify competent solvers.

Characterizing cardiac autonomic dynamics of fear learning in humans

Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency-domain analysis of heart rate was performed using a short-time Fourier transform, and instantaneous spectral estimates extracted from a point-process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness.

Pavlovian threat learning shapes the kinematics of action

Prompt response to environmental threats is critical to survival. Previous research has revealed mechanisms underlying threat-conditioned physiological responses, but little is known about how threats shape action. Here we tested if threat learning shapes the kinematics of reaching in human adults. In two different experiments conducted on independent samples of participants, after Pavlovian threat learning, in which a stimulus anticipated the delivery of an aversive shock, whereas another did not, the peak velocity and acceleration of reaching increased for the shocked-paired stimulus, relative to the unpaired one. These kinematic changes appeared as a direct consequence of learning, emerging even in absence of an actual threat to body integrity, as no shock occurred during reaching. Additionally, they correlated with the strength of sympathetic response during threat learning, establishing a direct relationship between previous learning and subsequent changes in action. The increase in velocity and acceleration of action following threat learning may be adaptive to facilitate the implementation of defensive responses. Enhanced action invigoration may be maladaptive, however, when defensive responses are inappropriately enacted in safe contexts, as exemplified in a number of anxiety-related disorders.

Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches

Vision plays a peculiar role in intelligence. Visual information, forming a large part of the sensory information, is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents. Recent advances have led to the development of brain-inspired algorithms and models for machine vision. One of the key components of these methods is the utilization of the computational principles underlying biological neurons. Additionally, advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information. Thus, there is a high demand for mapping out functional models for reading out visual information from neural signals. Here, we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals, from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography (EEG) and functional magnetic resonance imaging recordings of brain signals.

Prefrontal Cortical Control of Activity in Nucleus Accumbens Core Is Weakened by High-Fat Diet and Prevented by Co-Treatment with N-Acetylcysteine: Implications for the Development of Obesity

A loss of neuroplastic control on nucleus accumbens (NAc) neuronal activity exerted by the medial prefrontal cortex (mPFC) through long-term depression (LTD) is involved in triggering drug-seeking behavior and relapse on several substances of abuse due to impaired glutamate homeostasis in tripartite synapses of the nucleus accumbens (NAc) core. To test whether this maladaptive neuroplastic mechanism underlies the addiction-like behavior induced in young mice by a high-fat diet (HFD), we utilized 28-days-old male mice fed HFD ad-libitum over 2 weeks, followed by 5 days of HFD abstinence. Control groups were fed a regular diet. HFD fed mice showed increased ΔFosB levels in the NAc core region, whereas LTD triggered from the mPFC became suppressed. Interestingly, LTD suppression was prevented by an i.p. injection of 100 mg/kg N-acetylcysteine 2.5 h before inducing LTD from the mPFC. In addition, excessive weight gain due to HFD feeding was diminished by adding 2mg/mL N-acetylcysteine in drinking water. Those results show a loss of neuroplastic mPFC control over NAc core activity induced by HFD consumption in young subjects. In conclusion, ad libitum consumption of HFD can lead to neuroplastic changes an addiction-like behavior that can be prevented by N-acetylcysteine, helping to decrease the rate of excessive weight gain.

Mitochondrial Impairment: A Common Motif in Neuropsychiatric Presentation? The Link to the Tryptophan-Kynurenine Metabolic System

Nearly half a century has passed since the discovery of cytoplasmic inheritance of human chloramphenicol resistance. The inheritance was then revealed to take place maternally by mitochondrial DNA (mtDNA). Later, a number of mutations in mtDNA were identified as a cause of severe inheritable metabolic diseases with neurological manifestation, and the impairment of mitochondrial functions has been probed in the pathogenesis of a wide range of illnesses including neurodegenerative diseases. Recently, a growing number of preclinical studies have revealed that animal behaviors are influenced by the impairment of mitochondrial functions and possibly by the loss of mitochondrial stress resilience. Indeed, as high as 54% of patients with one of the most common primary mitochondrial diseases, mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) syndrome, present psychiatric symptoms including cognitive impairment, mood disorder, anxiety, and psychosis. Mitochondria are multifunctional organelles which produce cellular energy and play a major role in other cellular functions including homeostasis, cellular signaling, and gene expression, among others. Mitochondrial functions are observed to be compromised and to become less resilient under continuous stress. Meanwhile, stress and inflammation have been linked to the activation of the tryptophan (Trp)-kynurenine (KYN) metabolic system, which observably contributes to the development of pathological conditions including neurological and psychiatric disorders. This review discusses the functions of mitochondria and the Trp-KYN system, the interaction of the Trp-KYN system with mitochondria, and the current understanding of the involvement of mitochondria and the Trp-KYN system in preclinical and clinical studies of major neurological and psychiatric diseases.

Relationship between Attention Deficit Hyperactivity Disorder and epilepsy: a literature review

Background

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurological disorder. ADHD has been linked to epilepsy.

Main body

ADHD was found to be present in 30–40% of epileptic children. Researchers have developed numerous theories to explain how and why ADHD and epilepsy coexist. Whether ADHD and epilepsy symptoms are caused by co-occurring psychiatric disorders or by the temporary effects of epileptic discharges or by antiepileptic medicines is critical to consider. Diagnosis and treatment of individuals with ADHD and epilepsy are complicated and challenging from the clinical base.

Conclusions

Comorbidity between ADHD and epilepsy is still challenging to understand. The two diseases have a bidirectional link, so the association may not be coincidental. A disputable point is whether co-occurring ADHD and epilepsy symptoms represent a comorbid psychiatric disorder or are the epileptic discharges’ temporary effects, and are they related to antiepileptic drugs (AEDs). It is recommended to follow up with children with epilepsy or ADHD as they may develop comorbidity after a while.

Memory Enhancement with Kynurenic Acid and Its Mechanisms in Neurotransmission

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite known to possess neuroprotective property. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases or psychiatric disorders such as depression and autism spectrum disorders, whereas a higher level of KYNA is associated with the pathogenesis of schizophrenia. Little is known about the optimal concentration for neuroprotection and the threshold for neurotoxicity. In this study the effects of KYNA on memory functions were investigated by passive avoidance test in mice. Six different doses of KYNA were administered intracerebroventricularly to previously trained CFLP mice and they were observed for 24 h. High doses of KYNA (i.e., 20–40 μg/2 μL) significantly decreased the avoidance latency, whereas a low dose of KYNA (0.5 μg/2 μL) significantly elevated it compared with controls, suggesting that the low dose of KYNA enhanced memory function. Furthermore, six different receptor blockers were applied to reveal the mechanisms underlying the memory enhancement induced by KYNA. The series of tests revealed the possible involvement of the serotonergic, dopaminergic, α and β adrenergic, and opiate systems in the nootropic effect. This study confirmed that a low dose of KYNA improved a memory component of cognitive domain, which was mediated by, at least in part, four systems of neurotransmission in an animal model of learning and memory.

A Possible Pathogenic PSEN2 Gly56Ser Mutation in a Korean Patient with Early-Onset Alzheimer's Disease

Early-onset Alzheimer’s disease (EOAD) is characterized by the presence of neurological symptoms in patients with Alzheimer’s disease (AD) before 65 years of age. Mutations in pathological genes, including amyloid protein precursor (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2), were associated with EOAD. Seventy-six mutations in PSEN2 have been found around the world, which could affect the activity of γ-secretase in amyloid beta processing. Here, a heterozygous PSEN2 point mutation from G to A nucleotide change at position 166 (codon 56; c.166G>A, Gly56Ser) was identified in a 64-year-old Korean female with AD with progressive cognitive memory impairment for the 4 years prior to the hospital visit. Hippocampal atrophy was observed from magnetic resonance imaging-based neuroimaging analyses. Temporal and parietal cortex hypometabolisms were identified using fluorodeoxyglucose positron emission tomography. This mutation was at the N-terminal portion of the presenilin 2 protein on the cytosolic side. Therefore, the serine substitution may have promoted AD pathogenesis by perturbing to the mutation region through altered phosphorylation of presenilin. In silico analysis revealed that the mutation altered protein bulkiness with increased hydrophilicity and reduced flexibility of the mutated region of the protein. Structural changes were likely caused by intramolecular interactions between serine and other residues, which may have affected APP processing. The functional study will clarify the pathogenicity of the mutation in the future.

Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects

Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model’s ability to learn the face-inversion effect is demonstrated once more.

Fragile X Mental Retardation Protein and Cerebral Expression of Metabotropic Glutamate Receptor Subtype 5 in Men with Fragile X Syndrome: A Pilot Study

Multiple lines of evidence suggest that a deficiency of Fragile X Mental Retardation Protein (FMRP) mediates dysfunction of the metabotropic glutamate receptor subtype 5 (mGluR5) in the pathogenesis of fragile X syndrome (FXS), the most commonly known single-gene cause of inherited intellectual disability (ID) and autism spectrum disorder (ASD). Nevertheless, animal and human studies regarding the link between FMRP and mGluR5 expression provide inconsistent or conflicting findings about the nature of those relationships. Since multiple clinical trials of glutamatergic agents in humans with FXS did not demonstrate the amelioration of the behavioral phenotype observed in animal models of FXS, we sought measure if mGluR5 expression is increased in men with FXS to form the basis for improved clinical trials. Unexpectedly marked reductions in mGluR5 expression were observed in cortical and subcortical regions in men with FXS. Reduced mGluR5 expression throughout the living brains of men with FXS provides a clue to examine FMRP and mGluR5 expression in FXS. In order to develop the findings of our previous study and to strengthen the objective tools for future clinical trials of glutamatergic agents in FXS, we sought to assess the possible value of measuring both FMRP levels and mGluR5 expression in men with FXS. We aimed to show the value of measurement of FMRP levels and mGluR5 expression for the diagnosis and treatment of individuals with FXS and related conditions. We administered 3-[18F]fluoro-5-(2-pyridinylethynyl)benzonitrile ([18F]FPEB), a specific mGluR5 radioligand for quantitative measurements of the density and the distribution of mGluR5s, to six men with the full mutation (FM) of FXS and to one man with allele size mosaicism for FXS (FXS-M). Utilizing the seven cortical and subcortical regions affected in neurodegenerative disorders as indicator variables, adjusted linear regression of mGluR5 expression and FMRP showed that mGluR5 expression was significantly reduced in the occipital cortex and the thalamus relative to baseline (anterior cingulate cortex) if FMRP levels are held constant (F(7,47) = 6.84, p < 0.001).These findings indicate the usefulness of cerebral mGluR5 expression measured by PET with [18F]FPEB and FMRP values in men with FXS and related conditions for assessments in community facilities within a hundred-mile radius of a production center with a cyclotron. These initial results of this pilot study advance our previous study regarding the measurement of mGluR5 expression by combining both FMRP levels and mGluR5 expression as tools for meaningful clinical trials of glutamatergic agents for men with FXS. We confirm the feasibility of this protocol as a valuable tool to measure FMRP levels and mGluR5 expression in clinical trials of individuals with FXS and related conditions and to provide the foundations to apply precision medicine to tailor treatment plans to the specific needs of individuals with FXS and related conditions.

The Functional Interactions between Cortical Regions through Theta-Gamma Coupling during Resting-State and a Visual Working Memory Task

Theta phase-gamma amplitude coupling (TGC) plays an important role in several different cognitive processes. Although spontaneous brain activity at the resting state is crucial in preparing for cognitive performance, the functional role of resting-state TGC remains unclear. To investigate the role of resting-state TGC, electroencephalogram recordings were obtained for 56 healthy volunteers while they were in the resting state, with their eyes closed, and then when they were engaged in a retention interval period in the visual memory task. The TGCs of the two different conditions were calculated and compared. The results indicated that the modulation index of TGC during the retention interval of the visual working memory (VWM) task was not higher than that during the resting state; however, the topographical distribution of TGC during the resting state was negatively correlated with TGC during VWM task at the local level. The topographical distribution of TGC during the resting state was negatively correlated with TGC coordinates’ engagement of brain areas in local and large-scale networks and during task performance at the local level. These findings support the view that TGC reflects information-processing and signal interaction across distant brain areas. These results demonstrate that TGC could explain the efficiency of competing brain networks.

Coarse-Grained Neural Network Model of the Basal Ganglia to Simulate Reinforcement Learning Tasks

Computational models of the basal ganglia (BG) provide a mechanistic account of different phenomena observed during reinforcement learning tasks performed by healthy individuals, as well as by patients with various nervous or mental disorders. The aim of the present work was to develop a BG model that could represent a good compromise between simplicity and completeness. Based on more complex (fine-grained neural network, FGNN) models, we developed a new (coarse-grained neural network, CGNN) model by replacing layers of neurons with single nodes that represent the collective behavior of a given layer while preserving the fundamental anatomical structures of BG. We then compared the functionality of both the FGNN and CGNN models with respect to several reinforcement learning tasks that are based on BG circuitry, such as the Probabilistic Selection Task, Probabilistic Reversal Learning Task and Instructed Probabilistic Selection Task. We showed that CGNN still has a functionality that mirrors the behavior of the most often used reinforcement learning tasks in human studies. The simplification of the CGNN model reduces its flexibility but improves the readability of the signal flow in comparison to more detailed FGNN models and, thus, can help to a greater extent in the translation between clinical neuroscience and computational modeling.

Repetitive Transcranial Magnetic Stimulation for Comorbid Major Depressive Disorder and Alcohol Use Disorder

Major depressive disorder (MDD) and alcohol use disorder (AUD) are leading causes of disability, and patients are frequently affected by both conditions. This comorbidity is known to confer worse outcomes and greater illness severity. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation method that has demonstrated antidepressant effects. However, the study of rTMS for patients with MDD and commonly associated comorbidities, such as AUD, has been largely overlooked, despite significant overlap in clinical presentation and neurobiological mechanisms. This narrative review aims to highlight the interrelated aspects of the literature on rTMS for MDD and rTMS for AUD. First, we summarize the available evidence on the effectiveness of rTMS for each condition, both most studied through stimulation of the dorsolateral prefrontal cortex (DLPFC). Second, we describe common symptom constructs that can be modulated by rTMS, such as executive dysfunction, that are transdiagnostic across these disorders. Lastly, we describe promising approaches in the personalization and optimization of rTMS that may be applicable to both AUD and MDD. By bridging the gap between research efforts in MDD and AUD, rTMS is well positioned to be developed as a treatment for the many patients who have both conditions concurrently.

An Exploratory Study on Resting-State Functional Connectivity in Individuals with Disorganized Attachment: Evidence for Key Regions in Amygdala and Hippocampus

Studies comparing organized (O) and unresolved/disorganized (UD) attachment have consistently shown structural and functional brain abnormalities, although whether and how attachment patterns may affect resting state functional connectivity (RSFC) is still little characterized. Here, we investigated RSFC of temporal and limbic regions of interest for UD attachment. Participants’ attachment was classified via the Adult Attachment Interview, and all participants underwent clinical assessment. Functional magnetic resonance imaging data were collected from 11 UD individuals and seven matched O participants during rest. A seed-to-voxel analysis was performed, including the anterior and the posterior cingulate cortex, the bilateral insula, amygdala and hippocampus as seed regions. No group differences in the clinical scales emerged. Compared to O, the UD group showed lower RSFC between the left amygdala and the left cerebellum (lobules VIII), and lower functional coupling between the right hippocampus and the posterior portion of the right middle temporal gyrus. Moreover, UD participants showed higher RSFC between the right amygdala and the anterior cingulate cortex. Our findings suggest RSFC alterations in regions associated with encoding of salient events, emotion processing, memories retrieval and self-referential processing in UD participants, highlighting the potential role of attachment experiences in shaping brain abnormalities also in non-clinical UD individuals.

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

The Role of Expectation and Beliefs on the Effects of Non-Invasive Brain Stimulation

Non-invasive brain stimulation (NIBS) techniques are used in clinical and cognitive neuroscience to induce a mild magnetic or electric field in the brain to modulate behavior and cortical activation. Despite the great body of literature demonstrating promising results, unexpected or even paradoxical outcomes are sometimes observed. This might be due either to technical and methodological issues (e.g., stimulation parameters, stimulated brain area), or to participants’ expectations and beliefs before and during the stimulation sessions. In this narrative review, we present some studies showing that placebo and nocebo effects, associated with positive and negative expectations, respectively, could be present in NIBS trials, both in experimental and in clinical settings. The lack of systematic evaluation of subjective expectations and beliefs before and after stimulation could represent a caveat that overshadows the potential contribution of placebo and nocebo effects in the outcome of NIBS trials.

Personality Traits Modulate the Effect of tDCS on Reading Speed of Social Sentences

In this case, 62 university students participated in the study, in which a between-subjects design was adopted. Participants were also given the behavioral approach system (BAS) and behavioral inhibition system (BIS) scales. Participants had to read a list of 60 sentences with interpersonal and neutral content: 20 approach (“Pedro accepted Rosa in Whatsapp”), 20 avoidance (“Pedro Blocked Rosa in Whatsapp”) and 20 neutral (“Marta thought about the causes of the problem”). After reading them, they were subjected to 20 min of transcranial direct current stimulation (tDCS) in one of the two conditions: anodal (31) or sham (31). After tDCS, they had to read other list of 60 sentences matched in approach, avoidance and neutral contents with the former list. We found significant improvement in reading speed after anodal stimulation for social and neutral sentences. Regarding affective traits, we found that anodal stimulation benefitted reading speed in low-BIS and low-BAS participants and had no effect in either high BAS or high BIS participants. In addition, tDCS improvement in reading speed was significantly lower in avoidance sentences in low-BIS (avoidance) participants. We discuss these results at the light of previous research and highlight the importance of approach and avoidance traits as moderators of tDCS effects.

TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals

Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms of recognition. Electroencephalography (EEG) signals have significant potential for use in the measurement of learning style. This study uses EEG signals to design a deep-learning-based model of recognition to recognize people's learning styles with EEG features by using a non-overlapping sliding window, one-dimensional spatio-temporal convolutions, multi-scale feature extraction, global average pooling, and the group voting mechanism; this model is named the TSMG model (Temporal-Spatial-Multiscale-Global model). It solves the problem of processing EEG data of variable length, and improves the accuracy of recognition of the learning style by nearly 5% compared with prevalent methods, while reducing the cost of calculation by 41.93%. The proposed TSMG model can also recognize variable-length data in other fields. The authors also formulated a dataset of EEG signals (called the LSEEG dataset) containing features of the learning style processing dimension that can be used to test and compare models of recognition. This dataset is also conducive to the application and further development of EEG technology to recognize people's learning styles.

Memories are not written in stone: Re-writing fear memories by means of non-invasive brain stimulation and optogenetic manipulations

The acquisition of fear associative memory requires brain processes of coordinated neural activity within the amygdala, prefrontal cortex (PFC), hippocampus, thalamus and brainstem. After fear consolidation, a suppression of fear memory in the absence of danger is crucial to permit adaptive coping behavior. Acquisition and maintenance of fear extinction critically depend on amygdala-PFC projections. The robust correspondence between the brain networks encompassed cortical and subcortical hubs involved into fear processing in humans and in other species underscores the potential utility of comparing the modulation of brain circuitry in humans and animals, as a crucial step to inform the comprehension of fear mechanisms and the development of treatments for fear-related disorders. The present review is aimed at providing a comprehensive description of the literature on recent clinical and experimental researches regarding the noninvasive brain stimulation and optogenetics. These innovative manipulations applied over specific hubs of fear matrix during fear acquisition, consolidation, reconsolidation and extinction allow an accurate characterization of specific brain circuits and their peculiar interaction within the specific fear processing.

Fear-related signals are prioritised in visual, somatosensory and spatial systems

The human brain has evolved a multifaceted fear system, allowing threat detection to enable rapid adaptive responses crucial for survival. Although many cortical and subcortical brain areas are believed to be involved in the survival circuits detecting and responding to threat, the amygdala has reportedly a crucial role in the fear system. Here, we review evidence demonstrating that fearful faces, a specific category of salient stimuli indicating the presence of threat in the surrounding, are preferentially processed in the fear system and in the connected sensory cortices, even when they are presented outside of awareness or are irrelevant to the task. In the visual domain, we discuss evidence showing in hemianopic patients that fearful faces, via a subcortical colliculo-pulvinar-amygdala pathway, have a privileged visual processing even in the absence of awareness and facilitate responses towards visual stimuli in the intact visual field. Moreover, evidence showing that somatosensory cortices prioritise fearful-related signals, to the extent that tactile processing is enhanced in the presence of fearful faces, will be also reported. Finally, we will review evidence revealing that fearful faces have a pivotal role in modulating responses in peripersonal space, in line with the defensive functional definition of PPS.

Subliminal determinants of cue-guided choice

By anticipating potential rewards, external cues can guide behavior to achieve a goal. Whether the conscious elaboration of these cues is necessary to elicit cue-guided choices is still unknown. The goal of the present study is to test whether the subliminal presentation of a visual cue previously paired with a reward is sufficient to bias responses that can lead to the same or a similar reward. To this aim, three experiments compared the subliminal and supraliminal presentation of reward-associated cues during a Pavlovian-to-Instrumental Transfer task. In line with previous evidence, results showed that the supraliminal presentation of reward-associated Pavlovian cues biased participant’s choice towards motivationally similar rewards (general transfer) as well as towards rewards sharing the precise sensory-specific properties of the cue (outcome-specific transfer). In striking contrast, subliminal cues biased choice only towards motivationally similar rewards (general transfer). Taken together, these findings suggest that cue-guided choices are modulated by the level of perceptual threshold (i.e., subliminal vs supraliminal) of reward-associated cues. Although conscious elaboration of the cue is necessary to guide choice towards a specific reward, subliminal processing is still sufficient to push towards choices sharing the motivational properties of the cue. Implications for everyday life, clinical conditions, and theoretical accounts of cue-guided choices are discussed.

Aberrant reward prediction error during Pavlovian appetitive learning in alexithymia

Extensive literature shows that alexithymia, a subclinical trait defined by difficulties in identifying and describing feelings, is characterized by multifaceted impairments in processing emotional stimuli. Nevertheless, its underlying mechanisms remain elusive. Here, we hypothesize that alexithymia may be characterized by an alteration in learning the emotional value of encountered stimuli and test this by assessing differences between individuals with low (LA) and high (HA) levels of alexithymia in the computation of reward prediction errors (RPEs) during Pavlovian appetitive conditioning. As a marker of RPE, the amplitude of the feedback-related negativity (FRN) event-related potential was assessed while participants were presented with two conditioned stimuli (CS) associated with expected or unexpected feedback, indicating delivery of reward or no-reward. No-reward (vs reward) feedback elicited the FRN both in LA and HA. However, unexpected (vs expected) feedback enhanced the FRN in LA but not in HA, indicating impaired computation of RPE in HA. Thus, although HA show preserved sensitivity to rewards, they cannot use this response to update the value of CS that predict them. This impairment may hinder the construction of internal representations of emotional stimuli, leaving individuals with alexithymia unable to effectively recognize, respond and regulate their response to emotional stimuli.

Error, rather than its probability, elicits specific electrocortical signatures: a combined EEG-immersive virtual reality study of action observation

Detecting errors in one’s own actions, and in the actions of others, is a crucial ability for adaptable and flexible behavior. Studies show that specific EEG signatures underpin the monitoring of observed erroneous actions (error-related negativity, error positivity, mid-frontal theta oscillations). However, the majority of studies on action observation used sequences of trials where erroneous actions were less frequent than correct actions. Therefore, it was not possible to disentangle whether the activation of the performance monitoring system was due to an error, as a violation of the intended goal, or to a surprise/novelty effect, associated with a rare and unexpected event. Combining EEG and immersive virtual reality (IVR-CAVE system), we recorded the neural signal of 25 young adults who observed, in first-person perspective, simple reach-to-grasp actions performed by an avatar aiming for a glass. Importantly, the proportion of erroneous actions was higher than correct actions. Results showed that the observation of erroneous actions elicits the typical electrocortical signatures of error monitoring, and therefore the violation of the action goal is still perceived as a salient event. The observation of correct actions elicited stronger alpha suppression. This confirmed the role of the alpha-frequency band in the general orienting response to novel and infrequent stimuli. Our data provide novel evidence that an observed goal error (the action slip) triggers the activity of the performance-monitoring system even when erroneous actions, which are, typically, relevant events, occur more often than correct actions and thus are not salient because of their rarity.

NEW & NOTEWORTHY Activation of the performance-monitoring system (PMS) is typically investigated when errors in a sequence are comparatively rare. However, whether the PMS is activated by errors per se or by their infrequency is not known. Combining EEG-virtual reality techniques, we found that observing frequent (70%) action errors performed by avatars elicits electrocortical error signatures suggesting that deviation from the prediction of how learned actions should correctly deploy, rather than its frequency, is coded in the PMS.