Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2.

The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2  years of age. Our findings highlight network-level neural substrates underlying early cognitive development.

Verbal memory network mapping in individual patients predicts postoperative functional impairments.

Verbal memory decline is a significant concern following temporal lobe surgeries in patients with epilepsy, emphasizing the need for precision presurgical verbal memory mapping to optimize functional outcomes. However, the inter-individual variability in functional networks and brain function-structural dissociations pose challenges when relying solely on group-level atlases or anatomical landmarks for surgical guidance. Here, we aimed to develop and validate a personalized functional mapping technique for verbal memory using precision resting-state functional MRI (rs-fMRI) and neurosurgery. A total of 38 patients with refractory epilepsy scheduled for surgical interventions were enrolled and 28 patients were analyzed in the study. Baseline 30-min rs-fMRI scanning, verbal memory and language assessments were collected for each patient before surgery. Personalized verbal memory networks (PVMN) were delineated based on preoperative rs-fMRI data for each patient. The accuracy of PVMN was assessed by comparing post-operative functional impairments and the overlapping extent between PVMN and surgical lesions. A total of 14 out of 28 patients experienced clinically meaningful declines in verbal memory after surgery. The personalized network and the group-level atlas exhibited 100% and 75.0% accuracy in predicting postoperative verbal memory declines, respectively. Moreover, six patients with extra-temporal lesions that overlapped with PVMN showed selective impairments in verbal memory. Furthermore, the lesioned ratio of the personalized network rather than the group-level atlas was significantly correlated with postoperative declines in verbal memory (personalized networks: r = -0.39, p = .038; group-level atlas: r = -0.19, p = .332). In conclusion, our personalized functional mapping technique, using precision rs-fMRI, offers valuable insights into individual variability in the verbal memory network and holds promise in precision verbal memory network mapping in individuals.

Auditory change detection and visual selective attention: association between MMN and N2pc.

While the auditory and visual systems each provide distinct information to our brain, they also work together to process and prioritize input to address ever-changing conditions. Previous studies highlighted the trade-off between auditory change detection and visual selective attention; however, the relationship between them is still unclear. Here, we recorded electroencephalography signals from 106 healthy adults in three experiments. Our findings revealed a positive correlation at the population level between the amplitudes of event-related potential indices associated with auditory change detection (mismatch negativity) and visual selective attention (posterior contralateral N2) when elicited in separate tasks. This correlation persisted even when participants performed a visual task while disregarding simultaneous auditory stimuli. Interestingly, as visual attention demand increased, participants whose posterior contralateral N2 amplitude increased the most exhibited the largest reduction in mismatch negativity, suggesting a within-subject trade-off between the two processes. Taken together, our results suggest an intimate relationship and potential shared mechanism between auditory change detection and visual selective attention. We liken this to a total capacity limit that varies between individuals, which could drive correlated individual differences in auditory change detection and visual selective attention, and also within-subject competition between the two, with task-based modulation of visual attention causing within-participant decrease in auditory change detection sensitivity.

Self-esteem mediates the relationship between the parahippocampal gyrus and decisional procrastination at resting state.

When faced with a conflict or dilemma, we tend to postpone or even avoid making a decision. This phenomenon is known as decisional procrastination. Here, we investigated the neural correlates of this phenomenon, in particular the parahippocampal gyrus (PHG) that has previously been identified in procrastination studies. In this study, we applied an individual difference approach to evaluate participants' spontaneous neural activity in the PHG and their decisional procrastination levels, assessed outside the fMRI scanner. We discovered that the fractional amplitude of low-frequency fluctuations (fALFF) in the caudal PHG (cPHG) could predict participants' level of decisional procrastination, as measured by the avoidant decision-making style. Importantly, participants' self-esteem mediated the relationship between the cPHG and decisional procrastination, suggesting that individuals with higher levels of spontaneous activity in the cPHG are likely to have higher levels of self-esteem and thus be more likely to make decisions on time. In short, our study broadens the PHG's known role in procrastination by demonstrating its link with decisional procrastination and the mediating influence of self-esteem, underscoring the need for further exploration of this mediation mechanism.

Individual differences of white matter characteristic along the anterior insula-based fiber tract circuit for pain empathy in healthy women and women with primary dysmenorrhea.

Pain empathy, defined as the ability of one person to understand another person's pain, shows large individual variations. The anterior insula is the core region of the pain empathy network. However, the relationship between white matter (WM) properties of the fiber tracts connecting the anterior insula with other cortical regions and an individual's ability to modulate pain empathy remains largely unclear. In this study, we outline an automatic seed-based fiber streamline (sFS) analysis method and multivariate pattern analysis (MVPA) to predict the levels of pain empathy in healthy women and women with primary dysmenorrhoea (PDM). Using the sFS method, the anterior insula-based fiber tract network was divided into five fiber cluster groups. In healthy women, interindividual differences in pain empathy were predicted only by the WM properties of the five fiber cluster groups, suggesting that interindividual differences in pain empathy may rely on the connectivity of the anterior insula-based fiber tract network. In women with PDM, pain empathy could be predicted by a single cluster group. The mean WM properties along the anterior insular-rostroventral area of the inferior parietal lobule further mediated the effect of pain on empathy in patients with PDM. Our results suggest that chronic periodic pain may lead to maladaptive plastic changes, which could further impair empathy by making women with PDM feel more pain when they see other people experiencing pain. Our study also addresses an important gap in the analysis of the microstructural characteristics of seed-based fiber tract network.

Onscreen presence of instructors in video lectures affects learners' neural synchrony and visual attention during multimedia learning.

COVID-19 forced students to rely on online learning using multimedia tools, and multimedia learning continues to impact education beyond the pandemic. In this study, we combined behavioral, eye-tracking, and neuroimaging paradigms to identify multimedia learning processes and outcomes. College students viewed four video lectures including slides with either an onscreen human instructor, an animated instructor, or no onscreen instructor. Brain activity was recorded via fMRI, visual attention was recorded via eye-tracking, and learning outcome was assessed via post-tests. Onscreen presence of instructor, compared with no instructor presence, resulted in superior post-test performance, less visual attention on the slide, more synchronized eye movements during learning, and higher neural synchronization in cortical networks associated with socio-emotional processing and working memory. Individual variation in cognitive and socio-emotional abilities and intersubject neural synchronization revealed different levels of cognitive and socio-emotional processing in different learning conditions. The instructor-present condition evoked increased synchronization, likely reflecting extra processing demands in attentional control, working memory engagement, and socio-emotional processing. Although human instructors and animated instructors led to comparable learning outcomes, the effects were due to the dynamic interplay of information processing vs. attentional distraction. These findings reflect a benefit-cost trade-off where multimedia learning outcome is enhanced only when the cognitive benefits motivated by the social presence of onscreen instructor outweigh the cognitive costs brought about by concurrent attentional distraction unrelated to learning.

Personalized motor imagery prediction model based on individual difference of ERP.

Objective. Motor imagery-based brain-computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference.Approach.A novel paradigm named action observation-based multi-delayed matching posture task evokes ERP during a delayed matching posture task phase by retrieving picture stimuli and videos, and generates MI electroencephalogram through action observation and autonomous imagery in an action observation-based motor imagery phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual's suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods.Main results.The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual's ERP amplitude and event-related desynchronization (ERD) intensity are the largest, which helps to improve the accuracy by 14.25%.Significance.The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual's MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.

Association between visuo-spatial working memory and gait motor imagery.

Although motor imagery and working memory (WM) appear to be closely linked, no previous studies have demonstrated direct evidence for the relationship between motor imagery and WM abilities. This study investigated the association between WM and gait motor imagery and focused on the individual differences in young adults. This study included 33 participants (mean age: 22.2 ± 0.9 years). We used two methods to measure the ability of different WM domains: verbal and visuo-spatial WM. Gait motor imagery accuracy was assessed via the mental chronometry paradigm. We measured the times participants took to complete an actual and imagined walk along a 5 m walkway, with three different path widths. The linear mixed effects model analysis revealed that visuo-spatial WM ability was a significant predictor of the accuracy of gait motor imagery, but not of verbal WM ability. Specifically, individuals with lower visuo-spatial WM ability demonstrated more inaccuracies in the difficult path-width conditions. However, gait motor imagery was not as accurate as actual walking even in the easiest path width or in participants with high visuo-spatial WM ability. Further, visuo-spatial WM ability was significantly correlated with mental walking but not with actual walking. These results suggest that visuo-spatial WM is related to motor imagery rather than actual movement.

Long-term abacus training gains in children are predicted by medial temporal lobe anatomy and circuitry.

Abacus-based mental calculation (AMC) is a widely used educational tool for enhancing math learning, offering an accessible and cost-effective method for classroom implementation. Despite its universal appeal, the neurocognitive mechanisms that drive the efficacy of AMC training remain poorly understood. Notably, although abacus training relies heavily on the rapid recall of number positions and sequences, the role of memory systems in driving long-term AMC learning remains unknown. Here, we sought to address this gap by investigating the role of the medial temporal lobe (MTL) memory system in predicting long-term AMC training gains in second-grade children, who were longitudinally assessed up to fifth grade. Leveraging multimodal neuroimaging data, we tested the hypothesis that MTL systems, known for their involvement in associative memory, are instrumental in facilitating AMC-induced improvements in math skills. We found that gray matter volume in bilateral MTL, along with functional connectivity between the MTL and frontal and ventral temporal-occipital cortices, significantly predicted learning gains. Intriguingly, greater gray matter volume but weaker connectivity of the posterior parietal cortex predicted better learning outcomes, offering a more nuanced view of brain systems at play in AMC training. Our findings not only underscore the critical role of the MTL memory system in AMC training but also illuminate the neurobiological factors contributing to individual differences in cognitive skill acquisition. A video abstract of this article can be viewed at https://youtu.be/StVooNRc7T8. RESEARCH HIGHLIGHTS: We investigated the role of medial temporal lobe (MTL) memory system in driving children's math learning following abacus-based mental calculation (AMC) training. AMC training improved math skills in elementary school children across their second and fifth grade. MTL structural integrity and functional connectivity with prefrontal and ventral temporal-occipital cortices predicted long-term AMC training-related gains.

Wearable functional near-infrared spectroscopy for measuring dissociable activation dynamics of prefrontal cortex subregions during working memory.

The prefrontal cortex (PFC) has been extensively studied in relation to various cognitive abilities, including executive function, attention, and memory. Nevertheless, there is a gap in our scientific knowledge regarding the functionally dissociable neural dynamics across the PFC during a cognitive task and their individual differences in performance. Here, we explored this possibility using a delayed match-to-sample (DMTS) working memory (WM) task using NIRSIT, a high-density, wireless, wearable functional near-infrared spectroscopy (fNIRS) system. First, upon presentation of the sample stimulus, we observed an immediate signal increase in the ventral (orbitofrontal) region of the anterior PFC, followed by activity in the dorsolateral PFC. After the DMTS test stimulus appeared, the orbitofrontal cortex activated once again, while the rest of the PFC showed overall disengagement. Individuals with higher accuracy showed earlier and sustained activation of the PFC across the trial. Furthermore, higher network efficiency and functional connectivity in the PFC were correlated with individual WM performance. Our study sheds new light on the dynamics of PFC subregional activity during a cognitive task and its potential applicability in explaining individual differences in experimental, educational, or clinical populations. PRACTITIONER POINTS: Wearable functional near-infrared spectroscopy (fNIRS) captured dissociable temporal dynamics across prefrontal subregions during a delayed match-to-sample task. Anterior regions of the orbitofrontal cortex (OFC) activated first during the delay period, followed by the dorsolateral prefrontal cortex (PFC). PFC disengaged overall after the delay, but the OFC reactivated to the test stimulus. Earlier and sustained activation of PFC was associated with better accuracy. Functional connectivity and network efficiency also varied with task performance.

Inter-individual, hemispheric and sex variability of brain activations during numerosity processing.

Numerosity perception is a fundamental and innate cognitive function shared by both humans and many animal species. Previous research has primarily focused on exploring the spatial and functional consistency of neural activations that were associated with the processing of numerosity information. However, the inter-individual variability of brain activations of numerosity perception remains unclear. In the present study, with a large-sample functional magnetic resonance imaging (fMRI) dataset (n = 460), we aimed to localize the functional regions related to numerosity perceptions and explore the inter-individual, hemispheric, and sex differences within these brain regions. Fifteen subject-specific activated regions, including the anterior intraparietal sulcus (aIPS), posterior intraparietal sulcus (pIPS), insula, inferior frontal gyrus (IFG), inferior temporal gyrus (ITG), premotor area (PM), middle occipital gyrus (MOG) and anterior cingulate cortex (ACC), were delineated in each individual and then used to create a functional probabilistic atlas to quantify individual variability in brain activations of numerosity processing. Though the activation percentages of most regions were higher than 60%, the intersections of most regions across individuals were considerably lower, falling below 50%, indicating substantial variations in brain activations related to numerosity processing among individuals. Furthermore, significant hemispheric and sex differences in activation location, extent, and magnitude were also found in these regions. Most activated regions in the right hemisphere had larger activation volumes and activation magnitudes, and were located more lateral and anterior than their counterparts in the left hemisphere. In addition, in most of these regions, males displayed stronger activations than females. Our findings demonstrate large inter-individual, hemispheric, and sex differences in brain activations related to numerosity processing, and our probabilistic atlas can serve as a robust functional and spatial reference for mapping the numerosity-related neural networks.

Evaluation of Acarbose Bioequivalence in Healthy Chinese Populations Using Novel Pharmacodynamic End Points.

Acarbose is a widely used α-glucosidase inhibitor for the management of postprandial hyperglycemia in patients with type 2 diabetes mellitus. Recent pilot studies on acarbose bioequivalence (BE) have successfully identified additional pharmacodynamic (PD) parameters as valid end points. Nevertheless, there was a scarcity of published pivotal studies using novel PD parameters. The purpose of the study is to investigate the acarbose BE using the new PD parameters. The study was conducted with an open, randomized, 2-period crossover design. A total of 64 healthy Chinese volunteers received either the reference (R) or test (T) acarbose at a dose of 2×50 mg orally, followed by a 1-week washout period. After sucrose treatment (baseline) and sucrose/acarbose co-administration, serum glucose, and insulin concentrations were assessed. The rectifying approach yielded geometric mean ratios of 102.9% for maximum serum glucose concentration with deduction of glucose concentration at 0 hour and 105.3% for the area under the serum glucose concentration-time curve profile 0-2 hours after coadministration of sucrose and acarbose with deduction of baseline (AUC0-2 h,r ). The 90% confidence intervals of maximum serum glucose concentration with deduction of glucose concentration at 0 hour and the area under the serum glucose concentration-time curve profile 0-2 hours after coadministration of sucrose and acarbose with deduction of baseline all fell within the acceptance limits. The incidence of adverse events after the T or R drug was comparable, and healthy subjects were well tolerated. The findings of our investigation clearly show that the PD parameters of the rectifying method exhibit enhanced suitability and sensitivity when assessing acarbose BE in healthy participants. The T and R drugs were bioequivalent using the novel PD parameters, and both drugs demonstrated good safety and tolerability.

A predisposed motor bias shapes individuality in vocal learning.

The development of individuality during learned behavior is a common trait observed across animal species; however, the underlying biological mechanisms remain understood. Similar to human speech, songbirds develop individually unique songs with species-specific traits through vocal learning. In this study, we investigate the developmental and molecular mechanisms underlying individuality in vocal learning by utilizing F1 hybrid songbirds (Taeniopygia guttata cross with Taeniopygia bichenovii), taking an integrating approach combining experimentally controlled systematic song tutoring, unbiased discriminant analysis of song features, and single-cell transcriptomics. When tutoring with songs from both parental species, F1 hybrid individuals exhibit evident diversity in their acquired songs. Approximately 30% of F1 hybrids selectively learn either song of the two parental species, while others develop merged songs that combine traits from both species. Vocal acoustic biases during vocal babbling initially appear as individual differences in songs among F1 juveniles and are maintained through the sensitive period of song vocal learning. These vocal acoustic biases emerge independently of the initial auditory experience of hearing the biological father's and passive tutored songs. We identify individual differences in transcriptional signatures in a subset of cell types, including the glutamatergic neurons projecting from the cortical vocal output nucleus to the hypoglossal nuclei, which are associated with variations of vocal acoustic features. These findings suggest that a genetically predisposed vocal motor bias serves as the initial origin of individual variation in vocal learning, influencing learning constraints and preferences.

Functional connectivity via the dorsolateral prefrontal cortex in the late phase of rest periods predicts offline learning.

The relationship between offline learning gains and functional connectivity (FC) has been investigated in several studies. They have focused on average motor task performance and resting-state FC across subjects. Generally, individual differences are seen in both offline learning gain and neurophysiological profiles in resting-state FC. However, few studies have focused on the relationship between individual differences in offline learning gain and temporal characteristics of resting-state FC. The present study aimed to clarify this relationship between the two profiles. Thirty-four healthy right-handed participants performed a force-controlled motor task. Electroencephalography was performed during the 15-minute wakeful rest period between tasks. The results revealed a significant correlation between offline learning gain and FC between the contralateral dorsolateral prefrontal cortex (DLPFC) and contralateral primary motor cortex (M1), and ipsilateral primary somatosensory cortex (S1) during late phase of the rest interval. These results are consistent with the findings of previous studies showing the FC between M1, which is necessary for awake offline learning, and DLPFC, which is related to motor control. Additionally, sensory feedback related to force control may be caused by the interaction between contralateral DLPFC and ipsilateral S1. Our study shed light on the temporal profiles of resting-state FC associated with individual differences in offline learning.

Uncovering individual variations in bystander intervention of injustice through intrinsic brain connectivity patterns.

When confronted with injustice, individuals often intervene as third parties to restore justice by either punishing the perpetrator or helping the victim, even at their own expense. However, little is known about how individual differences in third-party intervention propensity are related to inter-individual variability in intrinsic brain connectivity patterns and how these associations vary between help and punishment intervention. To address these questions, we employed a novel behavioral paradigm in combination with resting-state fMRI and inter-subject representational similarity analysis (IS-RSA). Participants acted as third-party bystanders and needed to decide whether to maintain the status quo or intervene by either helping the disadvantaged recipient (Help condition) or punishing the proposer (Punish condition) at a specific cost. Our analyses focused on three brain networks proposed in the third-party punishment (TPP) model: the salience (e.g., dorsal anterior cingulate cortex, dACC), central executive (e.g., dorsolateral prefrontal cortex, dlPFC), and default mode (e.g., dorsomedial prefrontal cortex, dmPFC; temporoparietal junction, TPJ) networks. IS-RSA showed that individual differences in resting-state functional connectivity (rs-FC) patterns within these networks were associated with the general third-party intervention propensity. Moreover, rs-FC patterns of the right dlPFC and right TPJ were more strongly associated with individual differences in the helping propensity rather than the punishment propensity, whereas the opposite pattern was observed for the dmPFC. Post-hoc predictive modeling confirmed the predictive power of rs-FC in these regions for intervention propensity across individuals. Collectively, these findings shed light on the shared and distinct roles of key regions in TPP brain networks at rest in accounting for individual variations in justice-restoring intervention behaviors.

Field study on pregnant women's thermal preference in different trimesters in winter.

This study investigated the differences in the thermal preferences of pregnant women during various trimesters and the factors influencing these preferences. The survey was conducted in a hospital waiting room, encompassing the testing of thermal environmental parameters, and the distribution of questionnaires to pregnant women. These questionnaires encompassed various aspects, including basic information, thermal responses, pregnancy diseases, and more. In total, 1388 questionnaires were collected, distributed across the first trimester (225 participants), second trimester (498 participants), and third trimester (665 participants). The findings revealed a notable shift in the thermal preferences of pregnant women as their pregnancies progressed, transitioning from a preference for warmer conditions to a preference for cooler environments. Specifically, the mean thermal preference scores for the first, second, and third trimesters were 0.82, -0.27, and -1.76, respectively. These shifting preferences were associated with various factors, including pregnancy diseases, pre-pregnancy body mass index (PBMI), and exercise habits. Notably, hyperthyroidism, a higher PBMI, and regular exercise were correlated with a preference for cooler conditions, whereas hypothyroidism, anemia, a lower PBMI, and rare exercise were associated with a preference for warmer environments. Furthermore, it was observed that the actual neutral temperatures for pregnant women in the first, second, and third trimesters were 20.3 °C, 19.5 °C, and 19 °C, respectively. By contrast, the predicted neutral temperatures were 23.5 °C for the first and third trimesters and 23.4 °C for the second trimester. This indicated that the Predicted Mean Vote (PMV) model tended to underestimate the acceptability that pregnant women experienced in colder environments. Given the unique thermal preferences of pregnant women, further research is essential to refine thermal comfort parameters and the PMV model tailored specifically to this demographic.

Individual differences in the language task-evoked and resting-state functional networks.

The resting state functional network is highly variable across individuals. However, inter-individual differences in functional networks evoked by language tasks and their comparison with resting state are still unclear. To address these two questions, we used T1 anatomical data and functional brain imaging data of resting state and a story comprehension task from the Human Connectome Project (HCP) to characterize functional network variability and investigate the uniqueness of the functional network in both task and resting states. We first demonstrated that intrinsic and task-induced functional networks exhibited remarkable differences across individuals, and language tasks can constrain inter-individual variability in the functional brain network. Furthermore, we found that the inter-individual variability of functional networks in two states was broadly consistent and spatially heterogeneous, with high-level association areas manifesting more significant variability than primary visual processing areas. Our results suggested that the functional network underlying language comprehension is unique at the individual level, and the inter-individual variability architecture of the functional network is broadly consistent in language task and resting state.

Time course of effective connectivity associated with perspective taking in utterance comprehension.

This study discusses the effective connectivity in the brain and its time course in realizing perspective taking in verbal communication through electroencephalogram (EEG) associated with the understanding of Japanese utterances. We manipulated perspective taking in a sentence with the Japanese subsidiary verbs -ageru and -kureru, which mean "to give". We measured the EEG during the auditory presentation of the sentences with a multichannel electroencephalograph, and the partial directed coherence and its temporal variations were analyzed using the source localization method to examine causal interactions between nineteen regions of interest in the brain. Three different processing stages were recognized on the basis of the connectivity hubs, direction of information flow, increase or decrease in flow, and temporal variation. We suggest that perspective taking in speech comprehension is realized by interactions between the mentalizing network, mirror neuron network, and executive control network. Furthermore, we found that individual differences in the sociality of typically developing adult speakers were systematically related to effective connectivity. In particular, attention switching was deeply concerned with perspective taking in real time, and the precuneus played a crucial role in implementing individual differences.

Fusing the spatial structure of electroencephalogram channels can increase the individualization of the functional connectivity network.

An electroencephalogram (EEG) functional connectivity (FC) network is individualized and plays a significant role in EEG-based person identification. Traditional FC networks are constructed by statistical dependence and correlation between EEG channels, without considering the spatial relationships between the channels. The individual identification algorithm based on traditional FC networks is sensitive to the integrity of channels and crucially relies on signal preprocessing; therefore, finding a new presentation for FC networks may help increase the performance of the identification algorithms. EEG signals are smooth across space owing to the volume conduction effect. Considering such spatial relationships among channels can provide a more accurate representation of FC networks. In this study, we propose an EEG FC network with virtual nodes that combines the spatial relationships and functional connectivity of channels. The comparison results for individual identification show that the novel EEG network is more individualized and achieves an accuracy of 98.64% for data without preprocessing. Furthermore, our algorithm is more robust in reducing the number of channels and can perform well even when a large area of channels is removed.

"Surrounded, detached": the relationship between defensive peripersonal space and personality.

Introduction: Personality shapes the cognitive, affective, and behavioral interactions between individuals and the environment. Defensive peripersonal space (DPPS) is the projected interface between the body and the world with a protective function for the body. Previous studies suggest that DPPS displays inter-individual variability that is associated with psychiatric symptoms, such as anxiety. However, DPPS may share a link with personality traits. Methods: Fifty-five healthy participants were assessed with the Personality Inventory for DSM-5 (PID-5)-Adult to evaluate personality dimensions. Subjects underwent the Hand Blink Reflex (HBR) task that estimates the DPPS limits by assessing the modulation of blink intensity in response to the median nerve stimulation. Data of the HBR was analyzed with Bayesian multilevel models, while the relationship between DPPS and personality traits was explored using network analysis. Results: HBR was best modeled using a piecewise linear regression model, with two distinct slope parameters for electromyographic data. Network analyzes showed a positive correlation between the proximal slope and detachment personality trait, suggesting that individuals with higher scores in the detachment trait had an increased modulation of HBR, resulting in a larger extension of the DPPS. Discussion: Features of the detachment personality trait include avoidance of interpersonal experiences, restricted affectivity, and suspiciousness, which affect interpersonal functioning. We suggest that DPPS may represent a characteristic feature of maladaptive personality traits, thus constitute a biomarker or a target for rehabilitative interventions.