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Background: This study explores the compensatory neural mechanisms associated with congenital deafness through an examination of tactile discrimination abilities using high-resolution functional magnetic resonance imaging (fMRI). Objective: To analyze the neural substrates underlying tactile processing in congenitally deaf individuals and compare them with hearing controls. Methods: Our participant pool included thirty-five congenitally deaf individuals and thirty-five hearing controls. All participants engaged in tactile discrimination tasks involving the identification of common objects by touch. We utilized an analytical suite comprising voxel-based statistics, functional connectivity multivariate/voxel pattern analysis (fc-MVPA), and seed-based connectivity analysis to examine neural activity. Results: Our findings revealed pronounced neural activity in congenitally deaf participants within regions typically associated with auditory processing, including the bilateral superior temporal gyrus, right middle temporal gyrus, and right rolandic operculum. Additionally, unique activation and connectivity patterns were observed in the right insula and bilateral supramarginal gyrus, indicating a strategic reorganization of neural pathways for tactile information processing. Behaviorally, both groups demonstrated high accuracy in the tactile tasks, exceeding 90%. However, the deaf participants outperformed their hearing counterparts in reaction times, showcasing significantly enhanced efficiency in tactile information processing. Conclusion: These insights into the brain's adaptability to sensory loss through compensatory neural reorganization highlight the intricate mechanisms by which tactile discrimination is enhanced in the absence of auditory input. Understanding these adaptations can help develop strategies to harness the brain's plasticity to improve sensory processing in individuals with sensory impairments, ultimately enhancing their quality of life through improved tactile perception and sensory integration. [ABSTRACT FROM AUTHOR]

Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has identified abnormal brain function in PACG patients, the relationship between PACG and alterations in brain functional networks has yet to be explored. This study seeks to examine the influence of PACG on brain networks, aiming to advance knowledge of its neurobiological processes for better diagnostic and therapeutic approaches utilizing graph theory analysis. A cohort of 44 primary angle-closure glaucoma (PACG) patients and 44 healthy controls participated in this study. Functional brain networks were constructed using fMRI data and the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was employed to evaluate global metrics, nodal metrics, modular organization, and network-based statistics (NBS), enabling a comparative analysis between PACG patients and the control group. The analysis of global metrics, including small-worldness and network efficiency, did not exhibit significant differences between the two groups. However, PACG patients displayed elevated nodal metrics, such as centrality and efficiency, in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, along with reduced values in the right temporal superior gyrus region compared to healthy controls. Furthermore, Module 5 showed notable disparities in intra-module connectivity, while Module 1 demonstrated substantial differences in inter-module connectivity with both Module 7 and Module 8. Noteworthy, the NBS analysis unveiled a significantly altered network when comparing the PACG and healthy control groups. The study proposes that PACG patients demonstrate variations in nodal metrics and modularity within functional brain networks, particularly affecting the prefrontal, occipital, and temporal lobes, along with cerebellar regions. However, an analysis of global metrics suggests that the overall connectivity patterns of the entire brain network remain unaltered in PACG patients. These results have the potential to serve as early diagnostic and differential markers for PACG, and interventions focusing on brain regions with high degree centrality and nodal efficiency could aid in optimizing therapeutic approaches. [ABSTRACT FROM AUTHOR]

Background: Individuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis. Methods: We recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity. Results: The MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN. Conclusion: MUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group. [ABSTRACT FROM AUTHOR]

PurposeMaterials and methodsResultsConclusionsWe conducted a proof-of-concept study to evaluate the effects of galantamine treatment versus placebo combined to cognitive rehabilitation (CR) after stroke.In this 12-week, double blinded, randomized, controlled trial, patients were assigned to either combined approach of galantamine and CR (G-CR) or placebo and CR (P-CR). Primary outcome was the proportion of patients who crossed over from vascular cognitive disorder (VCD) to no-VCD at 12 weeks. Secondary outcomes included changes in cognition, mood, quality of life and the N-back fMRI paradigm, assessed at baseline, 6 and 12 weeks and after an 8-week washout period.Ten patients were allocated to G-RC group, 12 to the P-RC group. After 12 weeks, 40.1% of all patients converted to no-VCD with similar proportions between groups. Both groups showed improvements in episodic and working memory, executive and quality of life after 6 weeks of CR. Decreased depression and anxiety were noted, and all benefits persisted after the washout period. An interaction effect was observed in the right parietal lobule during the N-back task.Interventions lead to improved cognition and distinct cortical reorganization without being able to establish correlation between neural changes and behavioral measures. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end‐to‐end frequency‐specific dual‐attention‐based adversarial network (FDAA‐Net) to extend the time series of existing blood oxygen level‐dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency‐dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial–temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA‐Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA‐Net effectively overcame linear frequency‐specific challenges and outperformed other popular prediction models. Test–retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA‐Net using short‐term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans. [ABSTRACT FROM AUTHOR]

The present study aims to investigate whether begging calls elicit specific auditory responses in non-parenting birds, whether these responses are influenced by the hormonal status of the bird, and whether they reflect biparental care for offspring in the European starling (Sturnus vulgaris). An fMRI experiment was conducted to expose non-parenting male and female European starlings to recordings of conspecific nestling begging calls during both artificially induced breeding and non-breeding seasons. This response was compared with their reaction to conspecific individual warbling song motifs and artificial pure tones, serving as social species-specific and artificial control stimuli, respectively. Our findings reveal that begging calls evoke a response in non-parenting male and female starlings, with significantly higher responsiveness observed in the right Field L and the Caudomedial Nidopallium (NCM), regardless of season or sex. Moreover, a significant seasonal variation in auditory brain responses was elicited in both sexes exclusively by begging calls, not by the applied control stimuli, within a ventral midsagittal region of NCM. This heightened response to begging calls, even in nonparenting birds, in the right primary auditory system (Field L), and the photoperiod induced hormonal neuromodulation of auditory responses to offspring's begging calls in the secondary auditory system (NCM), bears resemblance to mammalian responses to hunger calls. This suggests a convergent evolution aimed at facilitating swift adult responses to such calls crucial for offspring survival. [ABSTRACT FROM AUTHOR]

Abstract Background Aggressive weight management in patients with type 2 diabetes mellitus has demonstrated numerous metabolic advantages, however, existing therapies for weight control have not reached satisfactory results. This study aimed to evaluate the efficacy and safety of acupuncture in the weight management of type 2 diabetes mellitus (T2DM) patients by using a randomized, sham-controlled clinical trial design. Methods In this single-blind randomized clinical trial, 102 overweight adult T2DM patients were randomized into two groups. The control group receives diet, exercise, and sham acupuncture intervention, whereas the acupuncture group receives diet and exercise and acupuncture intervention, both for 1 month. Body weight and other anthropometric and laboratory indices were assessed at baseline and endpoint, meanwhile, the body fat content and spontaneous brain activity were measured by functional magnetic resonance imaging(fMRI)as the exploratory outcomes. Results No significant difference was observed between the studied parameters at the baseline. The body weight and BMI were significantly reduced both in the control and acupuncture groups after intervention, without statistical difference between the two groups. What’s interesting is that compared to the control, the acupuncture group displayed a greater improvement in central fat tissue. It notes that the acupuncture group achieved significant liver fat content reduction than the sham acupuncture group. At the same time, the spontaneous brain activity in the occipital lobe and parietal lobe significantly increased in the acupuncture group. Conclusion One month of acupuncture treatment preferentially improved ectopic fat deposition and was accompanied by changes in brain activity compared with the control group, even before significant changes in total body weight had occurred. further studies of longer duration are necessary for validation. Trial registration : The protocol of this clinical trial is registered at the Acupuncture-Moxibustion Clinical Trial Registry (AMCTR, http://www.acmctr.org/ , No. AMCTR-IOR-20000341).

Abstract Background Individuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis. Methods We recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity. Results The MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN. Conclusion MUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.

BACKGROUND: Individuals with psychosis spectrum disorders (PSD) have difficulty developing social relationships. This difficulty may reflect reduced response to social feedback involving functional alterations in brain regions that support the social motivation system: ventral striatum, orbital frontal cortex, insula, dorsal anterior cingulate cortex, and amygdala. Whether these alterations span PSD is unknown. METHODS: 71 individuals with PSD, 27 unaffected siblings, and 37 control participants completed a team-based fMRI task. After each trial, participants received performance feedback paired with the expressive face of a teammate or opponent. A 2 × 2 (win versus loss outcome x teammate versus opponent) repeated measures ANOVA by group was performed on activation in the five key regions of interest during receipt of feedback. RESULTS: Across groups, three social motivation regions, ventral striatum, orbital frontal cortex, and amygdala, showed sensitivity to feedback (significant main effect of outcome), with greater activation during win versus loss trials, regardless of whether the feedback was from a teammate or opponent. In PSD, ventral striatum and orbital frontal cortex activation to win feedback was negatively correlated with social anhedonia scores. CONCLUSIONS: Patterns of neural activation during social feedback were similar in PSD, their unaffected siblings, and healthy controls. Across the psychosis spectrum, activity in key social motivation regions during social feedback was associated with individual differences in social anhedonia.

Background: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges. Methods: The study employed a three-stage model to gain a better understanding of the neurobiology underlying schizophrenia and techniques that could be applied for diagnosis. At first, we constructed four-level hierarchical sets from each fMRI volume of 34 schizophrenia patients (SZ) and healthy controls (HC) individually in terms of hemisphere, gyrus, lobes, and Brodmann areas. Second, we employed statistical methods, namely, t-tests and Pearson's correlation, to assess the group differences in cortical activation. Finally, we assessed the predictive power of the brain regions for machine learning algorithms using K-nearest Neighbor (KNN), Naive Bayes, Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Learning Machine (ELM). Results: Our investigation depicts promising results, obtaining an accuracy of up to 84% when applying Pearson's correlation-selected features at lobes and Brodmann region level (81% for Gyrus), as well as Hemispheres involving different stages. Thus, the results of our study were consistent with previous studies that have revealed some functional abnormalities in several brain regions. We also discovered the involvement of other brain regions which were never sufficiently studied in previous literature, such as the posterior lobe (posterior cerebellum), Pyramis, and Brodmann Area 34. Conclusions: We present a unique and comprehensive approach to investigating the neurological basis of schizophrenia in this study. By bridging the gap between neuroimaging and computable analysis, we aim to improve diagnostic accuracy in patients with schizophrenia and identify potential prognostic markers for disease progression. [ABSTRACT FROM AUTHOR]

This study explores the subjective evaluation of supplementary motor area (SMA) regulation performance in a real-time functional magnetic resonance imaging neurofeedback (fMRI-NF) task. In fMRI-NF, people learn how to self-regulate their brain activity by performing mental actions to achieve a certain target level (TL) of blood-oxygen-level-dependent (BOLD) activation. Here, we studied two types of self-evaluation: performance predictions and perceived confidence in the prediction judgement. Participants completed three sessions of SMA regulation in a 7 T fMRI scanner, performing a mental drawing task. During each trial, they modulated their imagery strategy to achieve one of two different levels of SMA activation and reported a performance prediction and their confidence in the prediction before receiving delayed BOLD-activation feedback. Results show that participants' performance predictions improved with learning throughout the three sessions, and that these improvements were not driven exclusively by their knowledge of previous performance. Confidence reports on the other hand showed no change throughout training and did not correlate with better and worse predictions. In addition to shedding light on mechanisms of internal self-evaluation during neurofeedback training, these results also point to a dissociation between predictions of performance and confidence reports in the presence of feedback. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'. [ABSTRACT FROM AUTHOR]

Abstract Background Insomnia disorder (ID) is one of the most common sleep problems, usually accompanied by anxiety and depression symptoms. Functional magnetic resonance imaging (fMRI) study suggests that both poor sleep quality and negative emotion are linked to the dysregulation of brain network related to emotion processing in ID patients. Acupuncture therapy has been proven effective in improving sleep quality and mood of ID patients, but the involved neurobiological mechanism remains unclear. We aimed to investigate the modulation effect of acupuncture on resting-state functional connectivity (rsFC) of the emotional network (EN) in patients experiencing insomnia. Methods A total of 30 healthy controls (HCs) and 60 ID patients were enrolled in this study. Sixty ID patients were randomly assigned to real and sham acupuncture groups and attended resting-state fMRI scans before and after 4 weeks of acupuncture treatment. HCs completed an MRI/fMRI scan at baseline. The rsFC values within EN were calculated, and Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy data were collected for clinical efficacy evaluation. Results Resting-state FC analysis showed abnormalities in rsFC centered on the thalamus and dorsolateral prefrontal cortex within EN of ID patients compared to HCs. After real acupuncture treatment, rsFC of the anterior cingulate cortex, hippocampus, and amygdala were increased compared with the sham acupuncture group (p

Background: Insomnia disorder (ID) is one of the most common sleep problems, usually accompanied by anxiety and depression symptoms. Functional magnetic resonance imaging (fMRI) study suggests that both poor sleep quality and negative emotion are linked to the dysregulation of brain network related to emotion processing in ID patients. Acupuncture therapy has been proven effective in improving sleep quality and mood of ID patients, but the involved neurobiological mechanism remains unclear. We aimed to investigate the modulation effect of acupuncture on resting-state functional connectivity (rsFC) of the emotional network (EN) in patients experiencing insomnia. Methods: A total of 30 healthy controls (HCs) and 60 ID patients were enrolled in this study. Sixty ID patients were randomly assigned to real and sham acupuncture groups and attended resting-state fMRI scans before and after 4 weeks of acupuncture treatment. HCs completed an MRI/fMRI scan at baseline. The rsFC values within EN were calculated, and Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy data were collected for clinical efficacy evaluation. Results: Resting-state FC analysis showed abnormalities in rsFC centered on the thalamus and dorsolateral prefrontal cortex within EN of ID patients compared to HCs. After real acupuncture treatment, rsFC of the anterior cingulate cortex, hippocampus, and amygdala were increased compared with the sham acupuncture group (p < 0.05, FDR corrected). In real acupuncture group, the rsFC value was decreased between left amygdala and left thalamus after 4 weeks of treatment compared with baseline. A trend of correlation was found that the increased rsFC value between the right amygdala and left hippocampus was positively correlated with the decreased HAMA scores across all ID patients, and the decreased left amygdala rsFC value with the left thalamus was negatively correlated with the increased sleep efficiency in the real acupuncture group. Conclusion: Our findings showed that real acupuncture could produce a positive effect on modulating rsFC within network related to emotion processing in ID patients, which may illustrate the central mechanism underlying acupuncture for insomnia in improving sleep quality and emotion regulation. Trial registration: http://www.chictr.org.cn., ChiCTR1800015282, 20/03/2018. [ABSTRACT FROM AUTHOR]

Brain networks based on functional magnetic resonance imaging (fMRI) provide a crucial perspective for diagnosing brain diseases. Representation learning has recently attracted tremendous attention due to its strong representation capability, which can be naturally applied to brain disease analysis. However, traditional representation learning only considers direct and local node interactions in original brain networks, posing challenges in constructing higher-order brain networks to represent indirect and extensive node interactions. To address this problem, we propose the Continuous Dictionary of Nodes model and Bilinear-Diffusion (CDON-BD) network for brain disease analysis. The CDON model is innovatively used to learn the original brain network, with its encoder weights directly regarded as latent features. To fully integrate latent features, we further utilize Bilinear Pooling to construct higher-order brain networks. The Diffusion Module is designed to capture extensive node interactions in higher-order brain networks. Compared to state-of-the-art methods, CDON-BD demonstrates competitive classification performance on two real datasets. Moreover, the higher-order representations learned by our method reveal brain regions relevant to the diseases, contributing to a better understanding of the pathology of brain diseases. [ABSTRACT FROM AUTHOR]

A growing body of research has provided evidence for the foreign language effect on thinking, notably decision-making. Our prior work found reduction of recency effect following positive feedback in a foreign language as compared to the native tongue during even-probability gambling. However, the fundamental mechanisms underlying this effect remain unclear. The present study, therefore, aims to probe into this by engaging Chinese-English bilinguals in a functional magnetic resonance imaging version of our gambling task, which required participants to make decisions between playing and leaving equal-odds bets whilst manipulating language and valence of feedback. Results showed fewer 'play' choices following positive feedback presented in English relative to Chinese while no cross-language differences were observed after negative feedback. This valence-dependent language effect on risk-taking behaviour was supported by a language-emotion-decision neural circuit involving interplay between the right lingual gyrus, left fusiform gyrus, and right inferior frontal gyrus. Overall, our findings suggest valence-differential mechanisms of the foreign language effect in the risky decision. That is, the use of a foreign language in feedback presentation attenuates emotional reaction to positive feedback and thus diminishes subsequent risk-taking behaviour. Differently, negative counterparts seem to trigger detachment from negative emotion, leading to dissociation between feedback-encoding and decision-making. [ABSTRACT FROM AUTHOR]

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Human brain vision is mysterious and complex, and it interprets the world through the connection between the brain and the eyes. In recent years, several methods have relied on fMRI to successfully reconstruct visual images from human brain activity. However, these reconstruction methods focus more on the semantics of the reconstruction image and lack attention to the image structure and foreground targets. To alleviate this problem, we propose a diffusion model-based image reconstruction architecture (Mind-Bridge) that utilizes fMRI to reconstruct visual images from human brain activity. Specifically, we first develop a novel Depth Structure Variational AutoEncoder (DSVAE) to capture image structural information at the initial stage. To obtain more foreground target information, we further introduce Edge estimation through the edge detection operator. In addition, we utilize Contrastive Language Image Pre-training (CLIP) text and image encoders as image and text prompt conditions for visual reconstruction. Finally, our proposed Mind-Bridge utilizes the Versatile Diffusion (VD) to fuse different stages of image information for visual images reconstruction. Qualitative and quantitative analysis results on the challenging Natural Scene Dataset (NSD) show that our proposed Mind-Bridge is effective. [ABSTRACT FROM AUTHOR]

Purpose: The utilization of functional magnetic resonance imaging (fMRI) in studying the mechanisms and treatment of chronic pain has gained significant popularity. However, there is currently a dearth of literature conducting bibliometric analysis on fMRI studies focused on chronic pain. Methods: All the literature included in this study was obtained from the Science Citation Index Expanded of Web of Science Core Collection. We used CiteSpace and VOSviewer to analyze publications, authors, countries or regions, institutions, journals, references and keywords. Additionally, we evaluated the timeline and burst analysis of keywords, as well as the timeline and burst analysis of references. The search was conducted from 2004 to 2023 and completed within a single day on October 4th, 2023. Results: A total of 1,327 articles were retrieved. The annual publication shows an overall increasing trend. The United States has the highest number of publications and the main contributing institution is Harvard University. The journal PAIN produces the most articles. In recent years, resting-state fMRI, the prefrontal cortex, nucleus accumbens, thalamus, and migraines have been researched hotspots of fMRI studies on chronic pain. Conclusions: This study provides an in-depth perspective on fMRI for chronic pain research, revealing key points, research hotspots and research trends, which offers valuable ideas for future research activities. It concludes with a summary of advances in clinical practice in this area, pointing out the need for critical evaluation of these findings in the light of guidelines and expert recommendations. It is anticipated that further high-quality research outputs will be generated in the future, which will facilitate the utilization of fMRI in clinical decision-making for chronic pain. [ABSTRACT FROM AUTHOR]

Belief, defined by William James as the mental state or function of cognizing reality, is a core psychological function with strong influence on emotion and behavior. Furthermore, strong and aberrant beliefs about the world and oneself play important roles in mental disorders. The underlying processes of belief have been the matter of a long debate in philosophy and psychology, and modern neuroimaging techniques can provide insight into the underlying neural processes. Here, we conducted a functional magnetic resonance imaging study with N = 30 healthy participants in which we presented statements about facts, politics, religion, conspiracy theories, and superstition. Participants judged whether they considered them as true (belief) or not (disbelief) and reported their certainty in the decision. We found belief‐associated activations in bilateral dorsolateral prefrontal cortex, left superior parietal cortex, and left lateral frontopolar cortex. Disbelief‐associated activations were found in an anterior temporal cluster extending into the amygdala. We found a larger deactivation for disbelief than belief in the ventromedial prefrontal cortex that was most pronounced during decisions, suggesting a role of the vmPFC in belief‐related decision‐making. As a category‐specific effect, we found disbelief‐associated activation in retrosplenial cortex and parahippocampal gyrus for conspiracy theory statements. Exploratory analyses identified networks centered at anterior cingulate cortex for certainty, and dorsomedial prefrontal cortex for uncertainty. The uncertainty effect identifies a neural substrate for Alexander Bain's notion from 1859 of uncertainty as the real opposite of belief. Taken together, our results suggest a two‐factor neural process model of belief with falsehood/veracity and uncertainty/certainty factors. Belief is a core psychological function with strong influence on emotion and behavior. We provide neuroimaging evidence of a two‐factor neural process model with falsehood/veracity and uncertainty/certainty factors. Specifically, the identified uncertainty processes can be understood as a neural marker of the subjective epistemic risk of statements. [ABSTRACT FROM AUTHOR]

Background: Deep‐learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain‐specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real‐world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders. Objectives and Methods: In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network. Results: In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline. Conclusions: We are confident that this study can help more researchers enter the field and create accessible fMRI and deep‐learning diagnostic models for neurological disorders. [ABSTRACT FROM AUTHOR]

Pain is a highly subjective and multidimensional experience, significantly influenced by various psychological factors. Placebo analgesia and nocebo hyperalgesia exemplify this influence, where inert treatments result in pain relief or exacerbation, respectively. While extensive research has elucidated the psychological and neural mechanisms behind these effects, most studies have focused on transient pain stimuli. To explore these mechanisms in the context of tonic pain, we conducted a study using a 15-minute tonic muscle pain induction procedure, where hypertonic saline was infused into the left masseter of healthy participants. We collected real-time Visual Analogue Scale (VAS) scores and functional magnetic resonance imaging (fMRI) data during the induction of placebo analgesia and nocebo hyperalgesia via conditioned learning. Our findings revealed that placebo analgesia was more pronounced and lasted longer than nocebo hyperalgesia. Real-time pain ratings correlated significantly with neural activity in several brain regions. Notably, the putamen was implicated in both effects, while the caudate and other regions were differentially involved in placebo and nocebo effects. These findings confirm that the tonic muscle pain paradigm can be used to investigate the mechanisms of placebo and nocebo effects and indicate that placebo analgesia and nocebo hyperalgesia may have more distinct than common neural bases.

The present study aims to investigate whether begging calls elicit specific auditory responses in non-parenting birds, whether these responses are influenced by the hormonal status of the bird, and whether they reflect biparental care for offspring in the European starling (Sturnus vulgaris). An fMRI experiment was conducted to expose non-parenting male and female European starlings to recordings of conspecific nestling begging calls during both artificially induced breeding and non-breeding seasons. This response was compared with their reaction to conspecific individual warbling song motifs and artificial pure tones, serving as social species-specific and artificial control stimuli, respectively. Our findings reveal that begging calls evoke a response in non-parenting male and female starlings, with significantly higher responsiveness observed in the right Field L and the Caudomedial Nidopallium (NCM), regardless of season or sex. Moreover, a significant seasonal variation in auditory brain responses was elicited in both sexes exclusively by begging calls, not by the applied control stimuli, within a ventral midsagittal region of NCM. This heightened response to begging calls, even in non-parenting birds, in the right primary auditory system (Field L), and the photoperiod induced hormonal neuromodulation of auditory responses to offspring’s begging calls in the secondary auditory system (NCM), bears resemblance to mammalian responses to hunger calls. This suggests a convergent evolution aimed at facilitating swift adult responses to such calls crucial for offspring survival.

INTRODUCTION: We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). METHODS: For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. RESULTS: We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. DISCUSSION: Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. HIGHLIGHTS: We investigated brain network predictors of dementia symptom onset Frontotemporal dementia results in characteristic dynamic network patterns Alterations in network dynamics are associated with neuropsychological impairment Network dynamic changes predict symptomatic conversion in presymptomatic carriers Network dynamic changes are associated with longitudinal cognitive decline.

Nasal cycle (NC) is a rhythmic change of lateralised nasal airflow mediated by the autonomous nervous system. Previous studies reported the dependence of NC dominance or more patent side on handedness and hemispheric cerebral activity. We aimed to investigate firstly the possible lateralised effect of NC on olfactory bulb volume and secondly the association of NC with the lateralised cerebral dominance in terms of olfactory processing. Thirty‐five subjects (22 women and 13 men, mean age 26 ± 3 years) participated in the study. NC was ascertained using a portable rhino‐flowmeter. Structural and functional brain measurements were assessed using a 3T MR scanner. Vanillin odorant was presented during functional scans using a computer‐controlled olfactometer. NC was found to be independent of the olfactory bulb volumes. Also, cerebral activations were found independent of the NC during odorant perception. NC potency is not associated with lateralised structural or functional differences in the cerebral olfactory system. [ABSTRACT FROM AUTHOR]

Introduction: Cue-induced craving is central to addictive disorders. Most cue-reactivity functional magnetic resonance imaging studies are analyzed statically and report averaged signals, disregarding the dynamic nature of craving and task fatigue. Accordingly, this study investigates temporal dynamics of the neural response to drug cues as a functional magnetic resonance imaging study among methamphetamine users. Methods: A total of 32 early abstinent methamphetamine users underwent functional magnetic resonance imaging while viewing visual methamphetamine cues. A craving > neutral contrast was obtained in regions of interest. To explore the changes over time, the pre-processed signal was divided into three intervals. Contrast estimates were calculated within each interval, and were compared using the analysis of variance followed by the post hoc t-tests. The results were compared with those from a static analysis across all blocks. Results: A priori expected activations in the prefrontal cortex, insula, and striatum not detected by static analysis were discovered by the dynamic analysis. Post hoc tests revealed distinct temporal activation patterns in several regions. Most patterns showed rapid activation (including both ventral/dorsal striata and most regions in the prefrontal, insular, and cingulate cortices), whereas some had delayed activation (the right anterior insula, left middle frontal gyrus, and left dorsal anterior cingulate cortex). Conclusion: This study provided preliminary insights into the temporal dynamicity of cuereactivity, and the potential of a conventional blocked-design task to consider it as a simple dynamic analysis. We highlight regional activations that were only uncovered by dynamic analysis and discuss the interesting and theoretically expected early versus late regional activation patterns. Rapidly activated regions are mostly those involved in the earlier stages of cue reactivity, while regions with later activation participate in cognitive functions relevant later, such as reappraisal, interoception, and executive control. [ABSTRACT FROM AUTHOR]

Rationale: The ability to monitor the consequences of our actions for others is imperative for flexible and adaptive behavior, and allows us to act in a (pro)social manner. Yet, little is known about the neurochemical mechanisms underlying alterations in (pro)social performance monitoring. Objective: The aim of this functional magnetic resonance imaging (fMRI) study was to improve our understanding of the role of dopamine and oxytocin and their potential overlap in the neural mechanisms underlying performance monitoring for own versus others' outcomes. Method: Using a double-blind placebo-controlled cross-over design, 30 healthy male volunteers were administered oxytocin (24 international units), the dopamine precursor L-DOPA (100 mg + 25 mg carbidopa), or placebo in three sessions. Participants performed a computerized cannon shooting game in two recipient conditions where mistakes resulted in negative monetary consequences for (1) oneself or (2) an anonymous other participant. Results: Results indicated reduced error-correct differentiation in the ventral striatum after L-DOPA compared to placebo, independent of recipient. Hence, pharmacological manipulation of dopamine via L-DOPA modulated performance-monitoring activity in a brain region associated with reward prediction and processing in a domain-general manner. In contrast, oxytocin modulated the BOLD response in a recipient-specific manner, such that it specifically enhanced activity for errors that affected the other in the pregenual anterior cingulate cortex (pgACC), a region previously implicated in the processing of social rewards and prediction errors. Behaviorally, we also found reduced target sizes—indicative of better performance—after oxytocin, regardless of recipient. Moreover, after oxytocin lower target sizes specifically predicted higher pgACC activity when performing for others. Conclusions: These different behavioral and neural patterns after oxytocin compared to L-DOPA administration highlight a divergent role of each neurochemical in modulating the neural mechanisms underlying social performance monitoring. [ABSTRACT FROM AUTHOR]

Despite broad recognition of the central role of avoidance in anxiety, a lack of specificity in its operationalization has hindered progress in understanding this clinically significant construct. The current study uses a multimodal approach to investigate how specific measures of avoidance relate to neural reactivity to threat in youth with anxiety disorders. Children with anxiety disorders (ages 6–12 years; n = 65 for primary analyses) completed laboratory task- and clinician-based measures of avoidance, as well as a functional magnetic resonance imaging task probing neural reactivity to threat. Primary analyses examined the ventral anterior insula (vAI), amygdala, and ventromedial prefrontal cortex (vmPFC). Significant but distinct patterns of association with task- versus clinician-based measures of avoidance emerged. Clinician-rated avoidance was negatively associated with right and left vAI reactivity to threat, whereas laboratory-based avoidance was positively associated with right vAI reactivity to threat. Moreover, left vAI-right amygdala and bilateral vmPFC-right amygdala functional connectivity were negatively associated with clinician-rated avoidance but not laboratory-based avoidance. These results should be considered in the context of the restricted range of our treatment-seeking sample, which limits the ability to draw conclusions about these associations across children with a broader range of symptomatology. In addition, the limited racial and ethnic diversity of our sample may limit the generalizability of findings. These findings mark an important step towards bridging neural findings and behavioral patterns using a multimodal approach. Advancing understanding of behavioral avoidance in pediatric anxiety may guide future treatment optimization by identifying individual-specific targets for treatment. • We bridge neural and behavioral findings using a multimodal approach. • Results differed for laboratory- versus clinician-based avoidance measures. • Laboratory-based avoidance was related to increased insula reactivity to threat. • Clinician-rated avoidance was related to reduced insula reactivity to threat. • Clinician-rated avoidance was also related to connectivity between key regions. [ABSTRACT FROM AUTHOR]

Prior studies that have examined consumer behavior have focused more on purchasing intention than selling intention. Sellers hold certain personal values or economic views when selling their second-hand products on auction sites, and so do barter site exchangers, but the latter have values that differ from the former. In this study, we investigate how second-hand owners (sellers or exchangers) determine whether to place an item on an auction or barter site. Based on the Engel-Kollat-Blackwell model, we used a validated questionnaire to explore several internal and external factors. We found that four factors--perceived product condition, reference group, online word of mouth, and reference price--supported both the intention to use auction sites and their actual use, while we found two factors--personal value and reference group--supported barter site use. We used functional magnetic resonance imaging to further observe how utilitarian and hedonic personal values have different effects on sellers' brain activities and decision-making behaviors regarding site choice. [ABSTRACT FROM AUTHOR]

Introduction: Declarative memories are initially dependent on the hippocampus and become stabilized through the neural reorganization of connections between the medial temporal lobe and neocortex. The exact time-course of these neural changes is not well established, although time-dependent changes in retrieval-related brain function can be detected across relatively short time periods in humans (e.g., hours to months). Methods: In a study involving older adults with normal cognition (N = 24), we investigated changes in brain activity and functional connectivity associated with the long-term memory consolidation of verbal material over one month. Participants studied fact-like, three-word sentences at 1-month, 1-week, 1- day, and 1-hour intervals before a recognition memory test inside an MRI scanner. Old/new recognition with confidence ratings and response times were recorded. We examined whole-brain changes in retrieval-related brain activity, as well as functional connectivity of the hippocampus and ventromedial prefrontal cortex (vmPFC), as memories aged from 1 hour to 1 month. Secondary analyses minimized the effect of confounding factors affected by memory age (i.e., changes in confidence and response time or re-encoding of targets). Results: Memory accuracy, confidence ratings, and response times changed with memory age. A memory age network was identified where retrieval-related brain activity in cortical regions increased or decreased as a function of memory age. Hippocampal brain activity in an anatomical region of interest decreased with memory age. Importantly, these changes in retrieval-related activity were not confounded with changes in activity related to concomitant changes in behavior or encoding. Exploratory analyses of vmPFC functional connectivity as a function of memory age revealed increased connectivity with the posterior parietal cortex, as well as with the vmPFC itself. In contrast, hippocampal functional connectivity with the vmPFC and orbitofrontal cortex decreased with memory age. Discussion: The observed changes in retrieval-related brain activity and functional connectivity align with the predictions of standard systems consolidation theory. These results suggest that processes consistent with longterm memory consolidation can be identified over short time periods using fMRI, particularly for verbal material. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the changes in the Blood Oxygen Level Dependent signal of Primary somatosensory area (S1) and Brodmann area 3 (BA3) per finger and phalanx in comparison to the activation voxel when 250 Hz vibratory stimulation with high sensitivity for the Pacinian corpuscle was given to the four fingers and three phalanges. The result of analyzing the activation voxel showed a significant difference for S1 per finger and phalanx, but for BA3, no significant difference was observed despite a similar trend to S1. In contrast, the activation intensity (BOLD) displayed a significant difference for S1 per finger and phalanx and for BA3, where the activation voxel had no significant variation. In addition, while the result of S1 did not indicate whether the index or the little fingers had the highest sensitivity based on the BOLD signal per finger, the result of BA3 marked the strongest BOLD signal for the little finger as a response to 250 Hz vibratory stimulation. The activation intensity per phalanx was the highest for the intermediate phalanx for S1 and BA3, which was in line with a previous study comparing the activation voxel. The method based on the intensity of the nerve activation is presumed to have high sensitivity as the signal intensity is monitored within a specific, defined area. Thus, for the extraction of brain activation patterns of micro-domains, such as BA3, monitoring the BOLD signal that reflects the nerve activation intensity more sensitively is likely to be advantageous. [ABSTRACT FROM AUTHOR]

Abstract Background Deep‐learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain‐specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real‐world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders. Objectives and Methods In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network. Results In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline. Conclusions We are confident that this study can help more researchers enter the field and create accessible fMRI and deep‐learning diagnostic models for neurological disorders.

Abstract Background Unfamiliarity with academic research may contribute to higher levels of anticipatory state anxiety about affective neuroimaging tasks. Children with high trait anxiety display differences in brain response to fearful facial affect compared to non-anxious youth, but little is known about the influence of state anxiety on this association. Because reduced engagement in scientific research and greater mistrust among minoritized groups may lead to systematic differences in pre-scan state anxiety, it is crucial to understand the neural correlates of state anxiety during emotion processing so as to disambiguate sources of individual differences. Methods The present study probed the interactive effects of pre-scan state anxiety, trait anxiety, and emotional valence (fearful vs. happy faces) on neural activation during implicit emotion processing in a community sample of 46 preadolescent Latina girls (8–13 years). Results Among girls with mean and high levels of trait anxiety, pre-scan state anxiety was associated with greater right amygdala-hippocampal and left inferior parietal lobe response to fearful faces relative to happy faces. Conclusions Anticipatory state anxiety in the scanning context may cause children with moderate and high trait anxiety to be hypervigilant to threats, further compounding the effects of trait anxiety. Neuroimaging researchers should control for state anxiety so that systematic differences in brain activation resulting from MRI apprehension are not misleadingly attributed to demographic or environmental characteristics.

Research suggests that structural and functional changes within the brain are associated with chronic low back pain, and these cortical alterations might contribute to impaired sensorimotor control of the trunk and hips in this population. However, linking sensorimotor brain changes with altered movement of the trunk and hips during task-based neuroimaging presents significant challenges. An MRI-safe pressure measurement system was developed to ensure proper task completion during neuroimaging by capturing movement patterns of the trunk (sensors under the lower back) and hips (sensors embedded in the foam roll under the knees). Pressure changes were measured outside of the scanner by digital differential pressure sensors to capture time-series data and analog pressure gauges for real-time determination of task performance occurring within an MRI bore during brain imaging. This study examined the concurrent validity of air pressure changes between the digital and analog sensors. The digital and analog data were compared in 23 participants during the performance of modified bilateral and unilateral right and left hip bridges. Spearman’s correlations were calculated for each sensor during the three bridging tasks and showed high positive correlations, indicating that over 87% of pressure change from the analog gauge can be explained by the pressure from the digital sensor. Bland-Altman plots showed no bias and mean differences were under three mmHg. This pressure system improves the rigor of future studies by validating the digital data from the system and increasing the capabilities of capturing lumbopelvic task performance occurring inside the scanner bore.

Abstract Background Insomnia disorder (ID) seriously affects people’s daily life. Difficulty falling asleep is the most commonly reported complaint in patients with ID. However, the mechanism of prolonged sleep latency (SL) is still obscure. The aim of our present study was to investigate the relationship between prolonged SL and alterations in spontaneous neural activity and brain functional connectivity (FC) in ID patients using functional magnetic resonance imaging (fMRI). Methods A total of 52 insomniacs with difficulty falling asleep and 30 matched healthy controls (HCs) underwent resting-state fMRI. The amplitude of low-frequency fluctuation (ALFF) was measured and group differences were compared. The peak areas with significantly different ALFF values were identified as the seed regions to calculate FC to the whole brain. SL was assessed by a wrist actigraphy device in ID patients. The Pittsburgh Sleep Quality Index (PSQI), Hamilton Anxiety Rating Scale (HAMA), and Hyperarousal Scale (HAS) were evaluated in both ID patients and HCs. Finally, correlation analyses were performed between the clinical features and FC/ALFF values. Results ID patients showed higher PSQI, HAMA, HAS scores than HCs. The functional MRI results indicated increased ALFF value in the left insula and right amygdala and decreased ALFF value in the right superior parietal lobe (SPL) in ID patients. The seed-based FC analysis demonstrated increased FC between the left insula and the bilateral precentral gyrus and FC between the right amygdala and the left posterior cingulate cortex (PCC) in patients with ID. Correlation analysis indicated that the increased FC value of the right amygdala-left PCC was positively correlated with SL measured by actigraphy. Conclusion This study revealed abnormal regional spontaneous fluctuations in the right amygdala, left insula, and right SPL, as well as increased FC in the left insula-precentral and right amygdala-left PCC. Moreover, the prolonged SL was positively correlated with the abnormal FC in the right amygdala-left PCC in ID patients. The current study showed the correlation between prolonged SL and the abnormal function of emotion-related brain regions in ID patients, which may contribute to a better understanding of the neural mechanisms underlying difficulty falling asleep in patients with ID. Clinical Trial Registration http://www.chictr.org.cn ., ChiCTR1800015282. Registered on 20th March 2018.

Background: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges. Methods: The study employed a three-stage model to gain a better understanding of the neurobiology underlying schizophrenia and techniques that could be applied for diagnosis. At first, we constructed four-level hierarchical sets from each fMRI volume of 34 schizophrenia patients (SZ) and healthy controls (HC) individually in terms of hemisphere, gyrus, lobes, and Brodmann areas. Second, we employed statistical methods, namely, t-tests and Pearson's correlation, to assess the group differences in cortical activation. Finally, we assessed the predictive power of the brain regions for machine learning algorithms using K-nearest Neighbor (KNN), Naive Bayes, Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and Extreme Learning Machine (ELM). Results: Our investigation depicts promising results, obtaining an accuracy of up to 84% when applying Pearson's correlation-selected features at lobes and Brodmann region level (81% for Gyrus), as well as Hemispheres involving different stages. Thus, the results of our study were consistent with previous studies that have revealed some functional abnormalities in several brain regions. We also discovered the involvement of other brain regions which were never sufficiently studied in previous literature, such as the posterior lobe (posterior cerebellum), Pyramis, and Brodmann Area 34. Conclusions: We present a unique and comprehensive approach to investigating the neurological basis of schizophrenia in this study. By bridging the gap between neuroimaging and computable analysis, we aim to improve diagnostic accuracy in patients with schizophrenia and identify potential prognostic markers for disease progression.

Individuals who have lost a loved one to suicide demonstrate an attentional bias to deceased-related stimuli during early grief. Regulating attention toward reminders of the deceased during acute bereavement may be linked to grief trajectory and pathological grief development. Despite the potential prognostic importance, little is known about underlying neural circuitry correlates of deceased-related grief processing. The current study examines neural substrates of deceased-related attentional processing during acute grief in individuals bereaved by suicide. Thirty-seven participants grieving the loss of a first-degree relative or partner to suicide in the prior six months, underwent functional magnetic resonance imaging (fMRI) while performing an emotional Stroop task using words related to the deceased and a living attachment figure, in order to examine neural correlates of deceased-specific attentional processing. Clinical interviews were conducted at baseline. Deceased-related attentional bias was associated with blood oxygen level-dependent (BOLD) activation in a brain network, including dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), orbitofrontal cortex (OFC), and insula. Greater activation of a bilateral prefrontal cluster during deceased-specific attention was negatively correlated with self-reported grief avoidance behaviors. Lack of non-suicide grief control and small sample size. These data, if confirmed, indicate a neural network specific to deceased-related attention, and that cognitive control regions within this network appear to be related to grief avoidance behaviors during acute bereavement. • Suicide bereaved show deceased-related attentional biases during acute grief • Neural correlates of deceased-related processing are unknown • Results suggest a unique neural network specific to deceased-related attention • Cognitive control regions in this network were related to loss avoidance [ABSTRACT FROM AUTHOR]

Inattention is a key characteristic of attention deficit hyperactivity disorder (ADHD). Specific brain abnormalities associated with this symptom form a discernible pattern related with ADHD in children (i.e., ADHD related pattern) in our earlier research. The developmental processes of segregation and integration may be crucial to ADHD. However, how brains reconfigure these processes of the ADHD related pattern in different subtypes of ADHD and across sexes remain unclear. Nested-spectral partition method was applied to identify effects of subtype and sex on segregation and integration of the ADHD related pattern, using 145 ADHD patients and 135 typically developing controls (TDC) aged 7–14. Relationships between the measures and inattention symptoms were also investigated. Children with ADHD exhibited lower segregation of the ADHD related pattern (p = 1.17 × 10−8) than TDCs. Only the main effect of subtype was significant (p = 1.14 × 10−5). Both ADHD-C (p = 2.16 × 10−6) and ADHD-I (p = 2.87 × 10−6) patients had lower segregation components relative to the TDC. Moreover, segregation components were negatively correlated with inattention scores. This study identified impaired segregation in the ADHD related pattern of children with ADHD and found shared neural bases among different subtypes and sexes. [ABSTRACT FROM AUTHOR]

Background: Unfamiliarity with academic research may contribute to higher levels of anticipatory state anxiety about affective neuroimaging tasks. Children with high trait anxiety display differences in brain response to fearful facial affect compared to non-anxious youth, but little is known about the influence of state anxiety on this association. Because reduced engagement in scientific research and greater mistrust among minoritized groups may lead to systematic differences in pre-scan state anxiety, it is crucial to understand the neural correlates of state anxiety during emotion processing so as to disambiguate sources of individual differences. Methods: The present study probed the interactive effects of pre-scan state anxiety, trait anxiety, and emotional valence (fearful vs. happy faces) on neural activation during implicit emotion processing in a community sample of 46 preadolescent Latina girls (8–13 years). Results: Among girls with mean and high levels of trait anxiety, pre-scan state anxiety was associated with greater right amygdala-hippocampal and left inferior parietal lobe response to fearful faces relative to happy faces. Conclusions: Anticipatory state anxiety in the scanning context may cause children with moderate and high trait anxiety to be hypervigilant to threats, further compounding the effects of trait anxiety. Neuroimaging researchers should control for state anxiety so that systematic differences in brain activation resulting from MRI apprehension are not misleadingly attributed to demographic or environmental characteristics. [ABSTRACT FROM AUTHOR]

This paper provides a critical review of studies using neurophysiological measures in tourism and hospitality. Among 145 articles covering 20 years of research, 16 studies applied either electroencephalography (EEG), functional magnetic resonance imaging (fMRI), or skin conductance (SC) measures in tourism and hospitality settings. Results show that, in general, (1) EEG studies investigated the relationships between EEG components and attention/emotion induced by destination advertisements; (2) fMRI studies examined the correlation between brain area activation and behavior (e.g., visit intention); and (3) SC studies focused on emotional responses to tourism stimuli. Neurophysiological techniques are theoretically and practically useful in tourism and hospitality: these tools uncover subjects' objective, unbiased, and real-time responses to provide academic insight and guide industry practitioners' decisions. Directions for future research are proposed along with solutions to address the current limitations of neuroscience measures in tourism and hospitality applications. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) poses as a multifaceted neurodevelopmental condition, significantly impacting children's social, behavioral, and communicative capacities. Despite extensive research, the precise etiological origins of ASD remain elusive, with observable connections to brain activity. In this study, we propose a novel framework for ASD detection, extracting the characteristics of functional magnetic resonance imaging (fMRI) data and phenotypic data, respectively. Specifically, we employ recursive feature elimination (RFE) for feature selection of fMRI data and subsequently apply graph neural networks (GNN) to extract informative features from the chosen data. Moreover, we devise a phenotypic feature extractor (PFE) to extract phenotypic features effectively. We then, synergistically fuse the features and validate them on the ABIDE dataset, achieving 78.7% and 80.6% accuracy, respectively, thereby showcasing competitive performance compared to state-of-the-art methods. The proposed framework provides a promising direction for the development of effective diagnostic tools for ASD. [ABSTRACT FROM AUTHOR]

Background: Insomnia disorder (ID) seriously affects people's daily life. Difficulty falling asleep is the most commonly reported complaint in patients with ID. However, the mechanism of prolonged sleep latency (SL) is still obscure. The aim of our present study was to investigate the relationship between prolonged SL and alterations in spontaneous neural activity and brain functional connectivity (FC) in ID patients using functional magnetic resonance imaging (fMRI). Methods: A total of 52 insomniacs with difficulty falling asleep and 30 matched healthy controls (HCs) underwent resting-state fMRI. The amplitude of low-frequency fluctuation (ALFF) was measured and group differences were compared. The peak areas with significantly different ALFF values were identified as the seed regions to calculate FC to the whole brain. SL was assessed by a wrist actigraphy device in ID patients. The Pittsburgh Sleep Quality Index (PSQI), Hamilton Anxiety Rating Scale (HAMA), and Hyperarousal Scale (HAS) were evaluated in both ID patients and HCs. Finally, correlation analyses were performed between the clinical features and FC/ALFF values. Results: ID patients showed higher PSQI, HAMA, HAS scores than HCs. The functional MRI results indicated increased ALFF value in the left insula and right amygdala and decreased ALFF value in the right superior parietal lobe (SPL) in ID patients. The seed-based FC analysis demonstrated increased FC between the left insula and the bilateral precentral gyrus and FC between the right amygdala and the left posterior cingulate cortex (PCC) in patients with ID. Correlation analysis indicated that the increased FC value of the right amygdala-left PCC was positively correlated with SL measured by actigraphy. Conclusion: This study revealed abnormal regional spontaneous fluctuations in the right amygdala, left insula, and right SPL, as well as increased FC in the left insula-precentral and right amygdala-left PCC. Moreover, the prolonged SL was positively correlated with the abnormal FC in the right amygdala-left PCC in ID patients. The current study showed the correlation between prolonged SL and the abnormal function of emotion-related brain regions in ID patients, which may contribute to a better understanding of the neural mechanisms underlying difficulty falling asleep in patients with ID. Clinical Trial Registration: http://www.chictr.org.cn., ChiCTR1800015282. Registered on 20th March 2018. [ABSTRACT FROM AUTHOR]

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

Neurovascular coupling serves as an essential neurophysiological mechanism in functional neuroimaging, which is generally presumed to be robust and invariant across different physiological states, encompassing both task engagement and resting state. Nevertheless, emerging evidence suggests that neurovascular coupling may exhibit state dependency, even in normal human participants. To investigate this premise, we analyzed the cross-frequency spectral correspondence between concurrently recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, utilizing them as proxies for neurovascular coupling during the two conditions: an eye-open-eye-close (EOEC) task and a resting state. We hypothesized that given the state dependency of neurovascular coupling, EEG-fMRI spectral correspondences would change between the two conditions in the visual system. During the EOEC task, we observed a negative phase-amplitude-coupling (PAC) between EEG alpha-band and fMRI visual activity. Conversely, in the resting state, a pronounced amplitude-amplitude-coupling (AAC) emerged between EEG and fMRI signals, as evidenced by the spectral correspondence between the EEG gamma-band of the midline occipital channel (Oz) and the high-frequency fMRI signals (0.15–0.25 Hz) in the visual network. This study reveals distinct scenarios of EEG-fMRI spectral correspondence in healthy participants, corroborating the state-dependent nature of neurovascular coupling.

While it is well established that grammar learning success varies with age, the cause of this developmental change is largely unknown. This study examined functional MRI activation across a broad developmental sample of 165 Dutch-speaking individuals (8–25 years) as they were implicitly learning a new grammatical system. This approach allowed us to assess the direct effects of age on grammar learning ability while exploring its neural correlates. In contrast to the alleged advantage of children language learners over adults, we found that adults outperformed children. Moreover, our behavioral data showed a sharp discontinuity in the relationship between age and grammar learning performance: there was a strong positive linear correlation between 8 and 15.4 years of age, after which age had no further effect. Neurally, our data indicate two important findings: (i) during grammar learning, adults and children activate similar brain regions, suggesting continuity in the neural networks that support initial grammar learning; and (ii) activation level is age-dependent, with children showing less activation than older participants. We suggest that these age-dependent processes may constrain developmental effects in grammar learning. The present study provides new insights into the neural basis of age-related differences in grammar learning in second language acquisition.

Abstract concepts like mental state concepts lack a physical referent, which can be directly perceived. Classical theories therefore claim that abstract concepts require amodal representations detached from experiential brain systems. However, grounded cognition approaches suggest an involvement of modal experiential brain regions in the processing of abstract concepts. In the present functional magnetic resonance imaging study, we investigated the relation of the processing of abstract mental state concepts to modal experiential brain systems in a fine-grained fashion. Participants performed lexical decisions on abstract mental state as well as on verbal association concepts as control category. Experiential brain systems related to the processing of mental states, generating verbal associations, automatic speech as well as hand and lip movements were determined by corresponding localizer tasks. Processing of abstract mental state concepts neuroanatomically overlapped with activity patterns associated with processing of mental states, generating verbal associations, automatic speech and lip movements. Hence, mental state concepts activate the mentalizing brain network, complemented by perceptual-motor brain regions involved in simulation of visual or action features associated with social interactions, linguistic brain regions as well as face-motor brain regions recruited for articulation. The present results provide compelling evidence for the rich grounding of abstract mental state concepts in experiential brain systems related to mentalizing, verbal communication and mouth action.

Brain networks based on functional magnetic resonance imaging (fMRI) provide a crucial perspective for diagnosing brain diseases. Representation learning has recently attracted tremendous attention due to its strong representation capability, which can be naturally applied to brain disease analysis. However, traditional representation learning only considers direct and local node interactions in original brain networks, posing challenges in constructing higher-order brain networks to represent indirect and extensive node interactions. To address this problem, we propose the Continuous Dictionary of Nodes model and Bilinear-Diffusion (CDON-BD) network for brain disease analysis. The CDON model is innovatively used to learn the original brain network, with its encoder weights directly regarded as latent features. To fully integrate latent features, we further utilize Bilinear Pooling to construct higher-order brain networks. The Diffusion Module is designed to capture extensive node interactions in higher-order brain networks. Compared to state-of-the-art methods, CDON-BD demonstrates competitive classification performance on two real datasets. Moreover, the higher-order representations learned by our method reveal brain regions relevant to the diseases, contributing to a better understanding of the pathology of brain diseases.

Abstract Background Substance use disorders (SUDs) affect ~ 35 million people globally and are associated with strong cravings, stress, and brain alterations. Mindfulness-based interventions (MBIs) can mitigate the adverse psychosocial outcomes of SUDs, but the underlying neurobiology is unclear. Emerging findings were systematically synthesised from fMRI studies about MBI-associated changes in brain function in SUDs and their associations with mindfulness, drug quantity, and craving. Methods PsycINFO, Medline, CINAHL, PubMed, Scopus, and Web of Science were searched. Seven studies met inclusion criteria. Results Group by time effects indicated that MBIs in SUDs (6 tobacco and 1 opioid) were associated with changes in the function of brain pathways implicated in mindfulness and addiction (e.g., anterior cingulate cortex and striatum), which correlated with greater mindfulness, lower craving and drug quantity. Conclusions The evidence for fMRI-related changes with MBI in SUD is currently limited. More fMRI studies are required to identify how MBIs mitigate and facilitate recovery from aberrant brain functioning in SUDs.

IntroductionDeclarative memories are initially dependent on the hippocampus and become stabilized through the neural reorganization of connections between the medial temporal lobe and neocortex. The exact time-course of these neural changes is not well established, although time-dependent changes in retrieval-related brain function can be detected across relatively short time periods in humans (e.g., hours to months).MethodsIn a study involving older adults with normal cognition (N = 24), we investigated changes in brain activity and functional connectivity associated with the long-term memory consolidation of verbal material over one month. Participants studied fact-like, three-word sentences at 1-month, 1-week, 1-day, and 1-hour intervals before a recognition memory test inside an MRI scanner. Old/new recognition with confidence ratings and response times were recorded. We examined whole-brain changes in retrieval-related brain activity, as well as functional connectivity of the hippocampus and ventromedial prefrontal cortex (vmPFC), as memories aged from 1 hour to 1 month. Secondary analyses minimized the effect of confounding factors affected by memory age (i.e., changes in confidence and response time or re-encoding of targets).ResultsMemory accuracy, confidence ratings, and response times changed with memory age. A memory age network was identified where retrieval-related brain activity in cortical regions increased or decreased as a function of memory age. Hippocampal brain activity in an anatomical region of interest decreased with memory age. Importantly, these changes in retrieval-related activity were not confounded with changes in activity related to concomitant changes in behavior or encoding. Exploratory analyses of vmPFC functional connectivity as a function of memory age revealed increased connectivity with the posterior parietal cortex, as well as with the vmPFC itself. In contrast, hippocampal functional connectivity with the vmPFC and orbitofrontal cortex decreased with memory age.DiscussionThe observed changes in retrieval-related brain activity and functional connectivity align with the predictions of standard systems consolidation theory. These results suggest that processes consistent with long-term memory consolidation can be identified over short time periods using fMRI, particularly for verbal material.