Distinct Neural Patterns Research Articles

Neural bases of social facilitation and inhibition: how peer presence affects elementary eye movements.

Social Facilitation/Inhibition (SFI) refers to how others' presence influences task performance positively or negatively. Our previous study revealed that peer presence modulated saccadic eye movements, a fundamental sensorimotor activity. Pro- and anti-saccades were either facilitated or inhibited depending on trial block complexity (Tricoche et al., 2020). In the present fMRI study, we adapted our paradigm to investigate the neural basis of SFI on saccades. Considering inter- and intra-individual variabilities, we evaluated the shared and distinct neural patterns between social facilitation and inhibition. We predicted an involvement of the saccade-related and attention networks, alongside the Theory-of-Mind (ToM) network, with opposite activity changes between facilitation and inhibition. Results confirmed peer presence modulation in fronto-parietal areas related to saccades and attention, in opposite directions for facilitation and inhibition. Additionally, the ventral attention network was modulated during inhibition. Default mode regions, including ToM areas, were also modulated. Finally, pupil size, often linked to arousal, increased with peers and correlated with dorsal attention regions and anterior insula activities. These results suggest that SFI engages task-specific and domain-general networks, modulated differently based on observed social effect. Attention network seemed to play a central role at both basic (linked to arousal or vigilance) and cognitive control levels.

Multiscale neural dynamics in sleep transition volatility across age scales: a multimodal EEG-EMG-EOG analysis of temazepam effects.

Recent advances in computational modeling techniques have facilitated a more nuanced understanding of sleep neural dynamics across the lifespan. In this study, we tensorize multiscale multimodal electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals and apply Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to quantify interactions between age scales and the use of pharmacological sleep aids on sleep stage transitions. Our cohort consists of 22 subjects in a crossover design study, where each subject received both a sleep aid and a placebo in different sessions. To understand these effects across the lifespan, three evenly distributed age groups were formed: 18-29, 30-49, and 50-66years. The methodological framework implemented here employs tensor-based machine learning techniques to compute continuous wavelet transform time-frequency features and utilizes a GARCH model to quantify sleep signal volatility across age scales. Support Vector Machines are used for feature ranking, and our analysis captures interactions between signal entropy, age, and sleep aid status across frequency bands, sleep transitions, and sleep stages. GARCH model results reveal statistically significant volatility clustering in EEG, EMG, and EOG signals, particularly during transitions between REM and non-REM sleep. Notably, volatility was higher in the 50-66 age group compared to the 18-29 age group, with marked fluctuations during transitions from deep sleep to REM sleep (standard deviation of 0.35 in the older group vs. 0.30 in the 18-29 age group, p < 0.05). Statistical comparisons of volatility across frequency bands, age scales, and sleep stages highlight significant differences attributable to sleep aid use. Mean conditional volatility parameterization of the GARCH model reveals directional influences, with a causality index of 0.75 from frontal to occipital regions during REM sleep transition periods. Our methodological framework identifies distinct neural behavior patterns across age groups associated with each sleep stage and transition, offering insights into the development of targeted interventions for sleep regularity across the lifespan.

Effect of neural firing pattern on NbOx/Al2O3 memristor-based reservoir computing system

The implementation of reservoir computing using resistive random-access memory as a physical reservoir has attracted attention due to its low training cost and high energy efficiency during parallel data processing. In this work, a NbOx/Al2O3-based memristor device was fabricated through a sputter and atomic layer deposition process to realize reservoir computing. The proposed device exhibits favorable resistive switching properties (&amp;gt;103 cycle endurance) and demonstrates short-term memory characteristics with current decay. Utilizing the controllability of the resistance state and its variability during cycle repetition, electrical pulses are applied to investigate the synapse-emulating properties of the device. The results showcase the functions of potentiation and depression, the coexistence of short-term and long-term plasticity, excitatory post-synaptic current, and spike-rate dependent plasticity. Building upon the functionalities of an artificial synapse, pulse spikes are categorized into three distinct neural firing patterns (normal, adapt, and boost) to implement 4-bit reservoir computing, enabling a significant distinction between “0” and “1.”

Long-term findings on working memory neural dynamics in healthcare workers after mild COVID-19

ObjectiveUnderstanding the long-term impact of Coronavirus Disease 2019 (COVID-19) on cognitive function, even in mild cases, is critical to the well-being of individuals, especially for healthcare workers who are at increased risk of exposure to the virus. To the best of our knowledge, the electrophysiological activity underlying cognitive functioning has not yet been explored. MethodsSeventy-seven healthcare workers took part in the study (43 with mild infection about one year before the study and 34 uninfected). To assess cognitive status, event-related potentials (ERPs) and behavioural responses were recorded while participants performed a working memory task. ResultsCOVID-19 participants exhibited a distinct neural pattern with lower parieto-occipital N1 amplitudes and higher frontal P2 amplitudes as compared to non-infected healthcare workers. We found no behavioural differences (reaction times and error rates) in working memory functioning between groups. ConclusionsThis neural pattern suggests the presence of a decrement of processing resources linked to the encoding of sensory information (N1), followed by the enhanced of the P2 response which could be interpreted as the activation of compensation mechanism in COVID-19 participants. SignificanceThe current findings point out that ERPs could serve as valuable neural indices for detecting distinctive patterns in working memory functioning of COVID-19 participants, even in mild cases. However, further research is required to precisely ascertain the long-term cognitive effects of COVID-19 beyond one-year post-infection.

A Nexus of Neural Mechanisms of Cooperation and Social Conformity: toward a Neuro-Cultural Interdependence Model

Increasing evidence links cultural influences on brain activity to various cognitive and affective processes, necessitating an integrative framework to account for the dynamic interplay between culture, behavior, and neural function. The mechanisms of cooperation and social conformity within culture exhibit variable interdependence across contexts, manifested by distinct neural patterns. To address the isolated examination of these mechanisms, we propose a NeuroCultural Interdependence Model delineating four modes (competitive interdependence, conditional interdependence, selective interdependence, communal interdependence) characterized by specific neural signatures and behavioral tendencies within cultural contexts. These modes represent varying degrees of interdependence, elucidating how individuals interact and rely upon others in their cultural milieu. The framework posits a direct linkage between cultural orientations and neural activity, offering a novel perspective on culture’s internalization and manifestation at the individual level, underscoring its embeddedness within neural processes influencing cognitive, affective, and behavioral inclinations in relation to others. Crucially, our proposed model highlights an isomorphic relationship between cultural orientations and their corresponding neural structures. It demonstrates that the mechanisms of cooperation and social conformity function concurrently at both the cultural and neural levels, revealing a direct parallel in how these elements operate. We conclude by providing recommendations for future elaboration and empirical validation of our model to be conducted using advanced neuroimaging techniques.

Working memory performance is tied to stimulus complexity

Working memory is the cognitive capability to maintain and process information over short periods. Behavioral and computational studies have shown that visual information is associated with working memory performance. However, the underlying neural correlates remain unknown. To identify how visual information affects working memory performance, we conducted behavioral experiments in pigeons (Columba livia) and single unit recordings in the avian prefrontal analog, the nidopallium caudolaterale (NCL). Complex pictures featuring luminance, spatial and color information, were associated with higher working memory performance compared to uniform gray pictures in conjunction with distinct neural coding patterns. For complex pictures, we found a multiplexed neuronal code displaying visual and value-related features that switched to a representation of the upcoming choice during a delay period. When processing gray stimuli, NCL neurons did not multiplex and exclusively represented the choice already during stimulus presentation and throughout the delay period. The prolonged representation possibly resulted in a decay of the memory trace ultimately leading to a decrease in performance. In conclusion, we found that high stimulus complexity is associated with neuronal multiplexing of the working memory representation possibly allowing a facilitated read-out of the neural code resulting in enhancement of working memory performance.

Atypical Associations between Functional Connectivity during Pragmatic and Semantic Language Processing and Cognitive Abilities in Children with Autism.

Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain-behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks.

Not all bad decisions are alike: approach and avoidant bad decisions are associated with distinct network organization.

Decisions under ambiguity occurs daily for everyone. Subsequently, we all deliberate upon options to initiate an action most appropriate for current goal demands. Researchers has attempted to identify factors which contribute to risk taking, alongside the neurocircuitry underpinning it. Empirically, uncertain decision making is frequently assessed using the Iowa Gambling Task (IGT). Research have reliably identified varying regions implicating two broader circuits known as the reward and salience networks. However, considerable work has focused on contrasting "good" versus "bad" decisions. The present investigation attempted a unique approach to analyzing the modified IGT acquired during fMRI (n = 24) and focused on active and passive bad decisions to identify potential internetwork connectivity, dissociable connectivity patterns between approach and avoidant bad decisions, and their relationship with personality traits, which can be linked with behavioral approach styles. Network cluster analyses revealed general internetwork connectivity when passing (avoiding) good decks; however, the OFC was functionally disconnected from the rest of the selected brain regions when playing (approaching) bad decks. Decreased reward responsiveness was linked to increased functional connectivity between the lateral OFC and aSMG, while drive was associated with increased functional connectivity between dACC and aINS. We report evidence that approach and avoidant bad decisions are associated with distinct neural communication patterns. Avoidant decisions were marked by substantial network integration and coherence, contrasted with the general scarcity of internetwork communication observed for approach decisions. Furthermore, the present investigation observed preliminary evidence of personality traits linked with neural communication between salience and reward evaluative networks.

Decoding neural patterns for the processing of fearful faces under different visual awareness conditions: A multivariate pattern analysis.

Previous studies have provided mixed findings regarding the nonconscious processing of fearful faces. Here, we used multivariate pattern analysis on electroencephalography data from three backward masking experiments to examine the processing of fearful faces under different visual awareness conditions. Three groups of participants were shown pairs of face images presented very briefly (for 16 ms) or for sufficiently long (for 266 ms), and completed tasks where the faces were either relevant to the experimental task (Experiment 1) or not (Experiments 2 and 3). Three main decoding analyses were performed. First, in the visual awareness decoding, the visibility of the faces, and hence participants' awareness of them, was maximally decodable in three time windows: 158-168 ms, 235-260 ms and 400-600 ms where the earlier neural patterns were generalized to the later stage activity. Second, we found that the spatial location of a fearful face in the face pairs was decodable, however only when the faces were consciously seen and task-relevant. Finally, we successfully decoded distinct neural patterns associated with the fearful-face-present conditions, compared to the fearful-face-absent conditions, and these patterns were decodable during both short and long presentations of the faces. Together, our results suggest that, while the processing of the spatial location of fearful faces requires awareness and task-relevancy, the mere presence of fearful faces can be processed even when visual awareness is highly restricted.

Convergent and distinct neural structural and functional patterns of mild cognitive impairment: a multimodal meta-analysis.

Mild cognitive impairment (MCI) is regarded as a transitional stage between normal aging and Alzheimer's disease. Numerous voxel-based morphometry (VBM) and resting-state fMRI (rs-fMRI) studies have provided strong evidence of abnormalities in the structure and intrinsic function of brain regions in MCI. Studies have recently begun to explore their association but have not employed systematic information in this pursuit. Herein, a multimodal meta-analysis was performed, which included 43 VBM datasets (1,247 patients and 1,352 controls) of gray matter volume (GMV) and 42 rs-fMRI datasets (1,468 patients and 1,605 controls) that combined 3 metrics: amplitude of low-frequency fluctuation, the fractional amplitude of low-frequency fluctuation, and regional homogeneity. Compared to controls, patients with MCI displayed convergent reduced regional GMV and altered intrinsic activity, mainly in the default mode network and salience network. Decreased GMV alone in ventral medial prefrontal cortex and altered intrinsic function alone in bilateral dorsal anterior cingulate/paracingulate gyri, right lingual gyrus, and cerebellum were identified, respectively. This meta-analysis investigated complex patterns of convergent and distinct brain alterations impacting different neural networks in MCI patients, which contributes to a further understanding of the pathophysiology of MCI.

Neural Correlates of Formal Thought Disorder Dimensions in Psychosis.

Formal thought disorder (FTD) is a core symptom of psychosis, but its neural correlates remain poorly understood. This study tested whether four FTD dimensions differ in their association with brain perfusion and brain structure. This cross-sectional study investigated 110 patients with schizophrenia spectrum disorders using 3T magnetic resonance imaging (MRI). The Thought and Language Disorder scale (TALD) was utilized, which comprises four subscales: Objective Positive (OP), Objective Negative (ON), Subjective Positive (SP), and Subjective Negative (SN). Resting-state cerebral blood flow (rsCBF), cortical thickness (CortTh), gray matter volume (GMV), and diffusion MRI tractography were tested for associations with TALD subscales controlling for age, medication, total intracranial volume, and for variance of the 3 other TALD subscales. Following Bonferroni correction, the FTD dimensions presented distinct neural correlates. OP scores were associated with increased rsCBF and increased GMV in the right cerebellum lingual gyrus. Higher SP scores were linked to increased GMV in bilateral prefrontal cortex. In contrast, ON was associated with increased GMV in the right premotor cortex. At more liberal statistical thresholds, higher SP was associated with increased CortTh in the right inferior frontal gyrus, whereas SN scores were linked to decreased GMV in the right prefrontal lobe, the left inferior temporal gyrus, and the left supplementary motor area. Unadjusted analyses mostly corroborated these findings. These findings stress the heterogeneity in FTD, suggesting distinct neural patterns for specific FTD experiences. In sum, FTD in psychosis may require distinct treatment strategies and further mechanistic investigations on single-item levels.

Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis

BackgroundAttention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) have partially overlapping symptom profiles and are highly comorbid in adults. However, whether the behavioral similarities correspond to shared neurobiological substrates is not known. MethodsAn overlapping meta-analysis of 58 ADHD and 66 BPD whole-brain articles incorporating observations from 3401 adult patients and 3238 healthy participants was performed by seed-based d mapping. Brain maps were subjected to meta-analytic connectivity modeling and data-driven functional decoding analyses to identify associated neural circuit alterations and relations to behavioral dimensions. ResultsBoth groups exhibited hypoactivated abnormalities in the left inferior parietal lobule, and altered clusters of the bilateral superior temporal gyrus were disjunctive in ADHD and BPD. No overlapping structural abnormalities were found. Multimodal alterations of ADHD were located in the right putamen and of BPD in the left orbitofrontal cortex. ConclusionsThe transdiagnostic neural bases of ADHD and BPD in temporoparietal circuitry may underlie overlapping problems of behavioral control, while disorder-specific substrates in frontostriatal circuitry may account for their distinguishing features in motor and emotion domains, respectively.

Neural oscillations while remembering traumatic memories in post-traumatic stress disorder

ObjectiveThe current study investigated the oscillatory brain activity of PTSD patients during directed and imaginal exposure to the traumatic memory using magnetoencephalography (MEG), in a paradigm resembling exposure therapy. MethodsBrain activity of healthy trauma-exposed controls and PTSD participants was measured with MEG as they listened to individualized trauma narratives as well as to a neutral narrative and as they imagined the narrative in detail. Source localization analysis by frequency bands was conducted in order to map neural generators of oscillatory activity. ResultsElicitation of traumatic memories resulted in a distinct neural pattern in PTSD patients compared to healthy trauma-exposed individuals. In response to trauma scripts PTSD patients showed increases in high-gamma band power in visual areas, increased frontal and temporal theta as well as prefrontal alpha and medial temporal beta power relative to neutral scripts. ConclusionsResults suggest that when recollecting and imagining traumatic memories PTSD patients attempt to engage control or inhibition mechanisms. However, these are either not successfully recruited or inefficient leading to heightened responses and recollection. SignificanceInvestigating the oscillatory neural dynamics of PTSD patients can help us better understand the processes underlying trauma re-experiencing.

Nx4 attenuated stress-induced activity of the anterior cingulate cortex-A post-hoc analysis of a randomized placebo-controlled crossover trial.

Stress-related symptoms are associated with significant health and economic burden. Several studies suggest Nx4 for the pharmacological management of the stress response and investigated the underlying neural processes. Here we hypothesized that Nx4 can directly affect the stress response in a predefined stress network, including the anterior cingulate cortex (ACC), which is linked to various stress-related symptoms in patients. In a randomized, placebo-controlled, double-blind, crossover trial, 39 healthy males took a single dose of placebo or Nx4. Psychosocial stress was induced by the ScanSTRESS paradigm inside an MRI scanner, and stress network activation was analyzed in brain regions defined a priori. Using the placebo data only, we could validate the activation of a distinct neural stress pattern by the ScanSTRESS paradigm. For Nx4, we provide evidence of an attenuating effect on this stress response. A statistically significant reduction in differential stress-induced activation in the right supracallosal ACC was observed for the rotation stress task of the ScanSTRESS paradigm. The results add to previously published results of Nx4 effects on emotion regulation. Our results strengthen the hypothesis that Nx4 modulates the stress response by reducing the activation in parts of the neural stress network, particularly in the ACC. NCT02602275; ClinicalTrials.gov.

Sex Differences in Aggression Are Paralleled by Differential Activation of the Brain Social Decision-Making Network in Zebrafish

Although aggression is more prevalent in males, females also express aggressive behaviors and in specific ecological contexts females can be more aggressive than males. The aim of this work is to assess sex differences in aggression and to characterize the patterns of neuronal activation of the social-decision making network (SDMN) in response to intra-sexual aggression in both male and female zebrafish. Adult fish were exposed to social interaction with a same-sex opponent and all behavioral displays, latency, and time of resolution were quantified. After conflict resolution, brains were sampled and sex differences on functional connectivity throughout the SDMN were assessed by immunofluorescence of the neuronal activation marker pS6. Results suggest that both sexes share a similar level of motivation for aggression, but female encounters show shorter conflict resolution and a preferential use of antiparallel displays instead of overt aggression, showing a reduction of putative maladaptive effects. Although there are no sex differences in the neuronal activation in any individual brain area from the SDMN, agonistic interactions increased neuronal activity in most brain areas in both sexes. Functional connectivity was assessed using bootstrapped adjacency matrices that capture the co-activation of the SDMN nodes. Male winners increased the overall excitation and showed no changes in inhibition across the SDMN, whereas female winners and both male and female losers showed a decrease in both excitation and inhibition of the SDMN in comparison to non-interacting control fish. Moreover, network centrality analysis revealed both shared hubs, as well as sex-specific hubs, between the sexes for each social condition in the SDMN. In summary, a distinct neural activation pattern associated with social experience during fights was found for each sex, suggesting a sex-specific differential activation of the social brain as a consequence of social experience. Overall, our study adds insights into sex differences in agonistic behavior and on the neuronal architecture of intrasexual aggression in zebrafish.

The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots.

The increasing presence of robots in society necessitates a deeper understanding into what attitudes people have toward robots. People may treat robots as mechanistic artifacts or may consider them to be intentional agents. This might result in explaining robots' behavior as stemming from operations of the mind (intentional interpretation) or as a result of mechanistic design (mechanistic interpretation). Here, we examined whether individual attitudes toward robots can be differentiated on the basis of default neural activity pattern during resting state, measured with electroencephalogram (EEG). Participants observed scenarios in which a humanoid robot was depicted performing various actions embedded in daily contexts. Before they were introduced to the task, we measured their resting state EEG activity. We found that resting state EEG beta activity differentiated people who were later inclined toward interpreting robot behaviors as either mechanistic or intentional. This pattern is similar to the pattern of activity in the default mode network, which was previously demonstrated to have a social role. In addition, gamma activity observed when participants were making decisions about a robot's behavior indicates a relationship between theory of mind and said attitudes. Thus, we provide evidence that individual biases toward treating robots as either intentional agents or mechanistic artifacts can be detected at the neural level, already in a resting state EEG signal.

Covariant Cluster Transfer for Kernel Reinforcement Learning in Brain-Machine Interface.

Brain-Machine Interface (BMI) provides a promising way to help disabled people restore their motor functions. The patients are able to control the external devices directly from their neural signals by the decoder. Due to various reasons such as mental fatigue and distraction, the distribution of the neural signals might change, which might lead to poor performance for the decoder. In this case, we need to calibrate the parameters before each session, which needs the professionals to label the data and is not convenient for the patient's usage at home. In this paper, we propose a covariant cluster transfer mechanism for the kernel reinforcement learning (RL) algorithm to speed up the adaptation across sessions. The parameters of the decoder will adaptively change according to a reward signal, which could be easily set by the patient. More importantly, we cluster the neural patterns in previous sessions. The cluster represents the conditional distribution from neural patterns to actions. When a distinct neural pattern appears in the new session, the nearest cluster will be transferred. In this way, the knowledge from the old session could be utilized to accelerate the learning in the new session. Our proposed algorithm is tested on the simulated neural data where the neural signal's distribution differs across sessions. Compared with the training from random initialization and a weight transfer policy, our proposed cluster transfer mechanism maintains a significantly higher success rate and a faster adaptation when the conditional distribution from neural signals to actions remains similar.

Spatial Information of Somatosensory Stimuli in the Brain: Multivariate Pattern Analysis of Functional Magnetic Resonance Imaging Data.

Background Multivoxel pattern analysis has provided new evidence on somatotopic representation in the human brain. However, the effects of stimulus modality (e.g., penetrating needle versus non-penetrating touch) and level of classification (e.g., multiclass versus binary classification) on patterns of brain activity encoding spatial information of body parts have not yet been studied. We hypothesized that performance of brain-based prediction models may vary across the types of stimuli, and neural patterns of voxels in the SI and parietal cortex would significantly contribute to the prediction of stimulated locations. Objective We aimed to (1) test whether brain responses to tactile stimuli could distinguish among stimulated locations on the body surface, (2) investigate whether the stimulus modality and number of classes affect classification performance, and (3) localize brain regions encoding the spatial information of somatosensory stimuli. Methods Fifteen healthy participants completed two functional magnetic resonance imaging (MRI) scans and were stimulated via the insertion of acupuncture needles or by non-invasive touch stimuli (5.46-sized von Frey filament). Participants received the stimuli at four different locations on the upper and lower limbs (two sites each) for 5 min while blood-oxygen-level-dependent activity (BOLD) was measured using 3-Tesla MRI. We performed multivariate pattern analysis (MVPA) using parameter estimate images of each trial for each participant and the support vector classifier (SVC) function, and the prediction accuracy and other MVPA outcomes were evaluated using stratified five-fold cross validation. We estimated the significance of the classification accuracy using a permutation test with randomly labeled training data (n = 10,000). Searchlight analysis was conducted to identify brain regions associated with significantly higher accuracy compared to predictions based on chance as obtained from a random classifier. Results For the four-class classification (classifying four stimulated points on the body), SVC analysis of whole-brain beta values in response to acupuncture stimulation was able to discriminate among stimulated locations (mean accuracy, 0.31; q < 0.01). The searchlight analysis found that values related to the right primary somatosensory cortex (SI) and intraparietal sulcus were significantly more accurate than those due to chance (p < 0.01). On the other hand, the same classifier did not predict stimulated locations accurately for touch stimulation (mean accuracy, 0.25; q = 0.66). For binary classification (discriminating between two stimulated body parts, i.e., the arm or leg), the SVC algorithm successfully predicted the stimulated body parts for both acupuncture (mean accuracy, 0.63; q < 0.001) and touch stimulation (mean accuracy, 0.60; q < 0.01). Searchlight analysis revealed that predictions based on the right SI, primary motor cortex (MI), paracentral gyrus, and superior frontal gyrus were significantly more accurate compared to predictions based on chance (p < 0.05). Conclusion Our findings suggest that the SI, as well as the MI, intraparietal sulcus, paracentral gyrus, and superior frontal gyrus, is responsible for the somatotopic representation of body parts stimulated by tactile stimuli. The MVPA approach for identifying neural patterns encoding spatial information of somatosensory stimuli may be affected by the stimulus type (penetrating needle versus non-invasive touch) and the number of classes (classification of four small points on the body versus two large body parts). Future studies with larger samples will identify stimulus-specific neural patterns representing stimulated locations, independent of subjective tactile perception and emotional responses. Identification of distinct neural patterns of body surfaces will help in improving neural biomarkers for pain and other sensory percepts in the future.

Using the force: STEM knowledge and experience construct shared neural representations of engineering concepts

How does STEM knowledge learned in school change students’ brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their “novice” peers not pursuing engineering degrees. A data-driven MVPA and machine-learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices’ when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world.

Appetite Changes in Depression are Associated With Salience Neurocircuitry

Increasing evidence suggests that neurovegetative features of Major Depressive Disorder (MDD), specifically appetite changes, are associated with specific immunometabolic and neurobiological profiles. Although MDD in general has been associated with aberrant resting-state functional connectivity, it remains to be elucidated whether neurovegetative symptoms are associated with distinct neural connectivity patterns.