Boosting interpersonal emotion regulation through facial imitation: functional neuroimaging foundations.

Empathic function, which is primarily manifested by facial imitation, is believed to play a pivotal role in interpersonal emotion regulation for mood reinstatement. To explore this association and its neural substrates, we performed a questionnaire survey (study l) to identify the relationship between empathy and interpersonal emotion regulation; and a task-mode fMRI study (study 2) to explore how facial imitation, as a fundamental component of empathic processes, promotes the interpersonal emotion regulation effect. Study 1 showed that affective empathy was positively correlated with interpersonal emotion regulation. Study 2 showed smaller negative emotions in facial imitation interpersonal emotion regulation (subjects imitated experimenter's smile while followed the interpersonal emotion regulation guidance) than in normal interpersonal emotion regulation (subjects followed the interpersonal emotion regulation guidance) and Watch conditions. Mirror neural system (e.g. inferior frontal gyrus and inferior parietal lobe) and empathy network exhibited greater activations in facial imitation interpersonal emotion regulation compared with normal interpersonal emotion regulation condition. Moreover, facial imitation interpersonal emotion regulation compared with normal interpersonal emotion regulation exhibited increased functional coupling from mirror neural system to empathic and affective networks during interpersonal emotion regulation. Furthermore, the connectivity of the right orbital inferior frontal gyrus-rolandic operculum lobe mediated the association between the accuracy of facial imitation and the interpersonal emotion regulation effect. These results show that the interpersonal emotion regulation effect can be enhanced by the target's facial imitation through increased functional coupling from mirror neural system to empathic and affective neural networks.

Distinct neurofunctional alterations during motivational and hedonic processing of natural and monetary rewards in depression - a neuroimaging meta-analysis.

Reward processing dysfunctions are considered a candidate mechanism underlying anhedonia and apathy in depression. Neuroimaging studies have documented that neurofunctional alterations in mesocorticolimbic circuits may neurally mediate these dysfunctions. However, common and distinct neurofunctional alterations during motivational and hedonic evaluation of monetary and natural rewards in depression have not been systematically examined. Here, we capitalized on pre-registered neuroimaging meta-analyses to (1) establish general reward-related neural alterations in depression, (2) determine common and distinct alterations during the receipt and anticipation of monetary v. natural rewards, and, (3) characterize the differences on the behavioral, network, and molecular level. The pre-registered meta-analysis (https://osf.io/ay3r9) included 633 depressed patients and 644 healthy controls and revealed generally decreased subgenual anterior cingulate cortex and striatal reactivity toward rewards in depression. Subsequent comparative analyses indicated that monetary rewards led to decreased hedonic reactivity in the right ventral caudate while natural rewards led to decreased reactivity in the bilateral putamen in depressed individuals. These regions exhibited distinguishable profiles on the behavioral, network, and molecular level. Further analyses demonstrated that the right thalamus and left putamen showed decreased activation during the anticipation of monetary reward. The present results indicate that distinguishable neurofunctional alterations may neurally mediate reward-processing alterations in depression, in particular, with respect to monetary and natural rewards. Given that natural rewards prevail in everyday life, our findings suggest that reward-type specific interventions are warranted and challenge the generalizability of experimental tasks employing monetary incentives to capture reward dysregulations in everyday life.

Structural and functional network analysis of twins using fMRI data.

Similarities between twins have been widely demonstrated, underscoring the remarkable influence of genetics across numerous traits. In this study, we explore the genetic underpinnings of the human brain by examining MRI data from the Queensland Twin Imaging study. Specifically, this study seeks to compare brain structure and function between twins and unrelated subjects, with an emphasis on describing the effects of genetic factors. To achieve these goals, we employed the source-based morphometry method to extract intrinsic components and elucidate recognizable patterns. Our results show that twins exhibit a higher degree of similarity in gray and white matter density compared with unrelated individuals. In addition, four distinct states of brain activity were identified using coactivation patterns analysis. Furthermore, twins demonstrated a greater degree of similarity in the temporal and spatial features of each state compared with unrelated subjects. Taken together, these results support the hypothesis that twins show greater similarity in both brain structure and dynamic functional brain activity. Further exploration of these methods may advance our understanding of the complex interplay between genes, environment, and brain networks.

Structural-functional connectivity mapping of the insular cortex: a combined data-driven and meta-analytic topic mapping.

In this study, we examined structural and functional profiles of the insular cortex and mapped associations with well-described functional networks throughout the brain using diffusion tensor imaging (DTI) and resting-state functional connectivity (RSFC) data. We used a data-driven method to independently estimate the structural-functional connectivity of the insular cortex. Data were obtained from the Human Connectome Project comprising 108 adult participants. Overall, we observed moderate to high associations between the structural and functional mapping scores of 3 different insular subregions: the posterior insula (associated with the sensorimotor network: RSFC, DTI = 50% and 72%, respectively), dorsal anterior insula (associated with ventral attention: RSFC, DTI = 83% and 83%, respectively), and ventral anterior insula (associated with the frontoparietal: RSFC, DTI = 42% and 89%, respectively). Further analyses utilized meta-analytic decoding maps to demonstrate specific cognitive and affective as well as gene expression profiles of the 3 subregions reflecting the core properties of the insular cortex. In summary, given the central role of the insular in the human brain, our results revealing correspondence between DTI and RSFC mappings provide a complementary approach and insight for clinical researchers to identify dysfunctional brain organization in various neurological disorders associated with insular pathology.

White Matter Functional Connectivity in Resting-State fMRI: Robustness, Reliability, and Relationships to Gray Matter.

A comprehensive characterization of the spatiotemporal organization in the whole brain is critical to understand both the function and dysfunction of the human brain. Resting-state functional connectivity (FC) of gray matter (GM) has helped in uncovering the inherent baseline networks of brain. However, the white matter (WM), which composes almost half of brain, has been largely ignored in this characterization despite studies indicating that FC in WM does change during task and rest functional magnetic resonance imaging (fMRI). In this study, we identify 9 white matter functional networks (WM-FNs) and 9 gray matter functional networks (GM-FNs) of resting fMRI. Intraclass correlation coefficient (ICC) was calculated on multirun fMRI data to estimate the reliability of static functional connectivity (SFC) and dynamic functional connectivity (DFC). Associations between SFC, DFC, and their respective ICCs are estimated for GM-FNs, WM-FNs, and GM-WM-FNs. SFC of GM-FNs were stronger than that of WM-FNs, but the corresponding DFC of GM-FNs was lower, indicating that WM-FNs were more dynamic. Associations between SFC, DFC, and their ICCs were similar in both GM- and WM-FNs. These findings suggest that WM fMRI signal contains rich spatiotemporal information similar to that of GM and may hold important cues to better establish the functional organization of the whole brain.

Dysregulated anterior insula reactivity as robust functional biomarker for chronic pain-Meta-analytic evidence from neuroimaging studies.

Neurobiological pain models propose that chronic pain is accompanied by neurofunctional changes that mediate pain processing dysfunctions. In contrast, meta-analyses of neuroimaging studies in chronic pain conditions have not revealed convergent evidence for robust alterations during experimental pain induction. Against this background, the present neuroimaging meta-analysis combined three different meta-analytic approaches with stringent study selection criteria for case-control functional magnetic resonance imaging experiments during acute pain processing with a focus on chronic pain disorders. Convergent neurofunctional dysregulations in chronic pain patients were observed in the left anterior insula cortex. Seed-based resting-state functional connectivity based on a large publicly available dataset combined with a meta-analytic task-based approach identified the anterior insular region as a key node of an extended bilateral insula-fronto-cingular network, resembling the salience network. Moreover, the meta-analytic decoding showed that this region presents a high probability to be specifically activated during pain-related processes, although we cannot exclude an involvement in autonomic processes. Together, the present findings indicate that dysregulated left anterior insular activity represents a robust neurofunctional maladaptation and potential treatment target in chronic pain disorders.

Shared network-level functional alterations across substance use disorders: A multi-level kernel density meta-analysis of resting-state functional connectivity studies.

An increasing number of neuroimaging studies indicate functional alterations in cortico-striatal loops in individuals with substance use disorders (SUD). Dysregulations in these circuits may contribute to drug-seeking and drug-consuming behaviour by impeding inhibitory control, habit formation, and reward processing. Despite evidence of network-level changes in SUD, a shared pattern of functional alterations within and between spatially distributed brain networks has not been systematically investigated. The present meta-analytic investigation aims at identifying common alterations in resting-state functional connectivity patterns across different SUD, including stimulant, heroin, alcohol, cannabis, and nicotine use. To this aim, seed-based whole-brain connectivity maps for different functional networks were extracted and subjected to multi-level kernel density analysis to identify dysfunctional networks in individuals with SUD compared with healthy controls. In addition, an exploratory analysis examined substance-specific effects as well as the influence of drug use status on the main findings. Our findings indicate a hypoconnectivity pattern for the limbic, salience, and frontoparietal networks in individuals with SUD as compared with healthy controls. The default mode network additionally exhibited a complex pattern of hypo- and hyperconnectivity across the studies. The observed disrupted connectivity between networks in SUD may associate with deficient inhibitory control mechanisms that are thought to contribute to excessive craving and automatic drug-related behaviour as well as failure in substance use cessation. The identified network-based alterations in SUD represent potential treatment targets for neuromodulation, for example, network-based real-time functional magnetic resonance imaging (fMRI) neurofeedback. Such interventions can evaluate the behavioural relevance of the identified neural circuits.

Common abnormality of gray matter integrity in substance use disorder and obsessive-compulsive disorder: A comparative voxel-based meta-analysis.

The objective of the current study is to determine robust transdiagnostic brain structural markers for compulsivity by capitalizing on the increasing number of case-control studies examining gray matter volume (GMV) alterations in substance use disorders (SUD) and obsessive-compulsive disorder (OCD). Voxel-based meta-analysis within the individual disorders and conjunction analysis were employed to reveal common GMV alterations between SUDs and OCD. Meta-analytic coordinates and signed brain volumetric maps determining directed (reduced/increased) GMV alterations between the disorder groups and controls served as the primary outcome. The separate meta-analysis demonstrated that SUD and OCD patients exhibited widespread GMV reductions in frontocortical regions including prefrontal, cingulate, and insular. Conjunction analysis revealed that the left inferior frontal gyrus (IFG) consistently exhibited decreased GMV across all disorders. Functional characterization suggests that the IFG represents a core hub in the cognitive control network and exhibits bidirectional (Granger) causal interactions with the striatum. Only OCD showed increased GMV in the dorsal striatum with higher changes being associated with more severe OCD symptomatology. Together the findings demonstrate robustly decreased GMV across the disorders in the left IFG, suggesting a transdiagnostic brain structural marker. The functional characterization as a key hub in the cognitive control network and casual interactions with the striatum suggest that deficits in inhibitory control mechanisms may promote compulsivity and loss of control that characterize both disorders.

Common neurofunctional dysregulations characterize obsessive-compulsive, substance use, and gaming disorders-An activation likelihood meta-analysis of functional imaging studies.

Compulsivity and loss of behavioral control represent core symptoms in obsessive-compulsive disorder (OCD), substance use disorder (SUD), and internet gaming disorder (IGD). Despite elaborated animal models suggesting that compulsivity is mediated by cortico-striatal circuits and a growing number of neuroimaging case-control studies, common neurofunctional alterations in these disorders have not been systematically examined. The present activation likelihood estimation (ALE) meta-analysis capitalized on previous functional magnetic resonance imaging (fMRI) studies to determine shared neurofunctional alterations among the three disorders. Task-based fMRI studies of individuals with SUD, OCD, or IGD were obtained. ALE was performed within each disorder. Next, contrast and conjunction meta-analyses were performed to determine differential and common alterations. Task-paradigm classes were group according to Research Domain Criteria (RDoC) domains to determine contributions of underlying behavioral domains. One hundred forty-four articles were included representing data from n = 6897 individuals (SUD = 2418, controls = 2332; IGD = 361, controls = 360; OCD = 715, controls = 711) from case-control studies. Conjunction meta-analyses revealed shared alterations in the anterior insular cortex between OCD and SUDs. SUD exhibited additionally pronounced dorsal-striatal alterations compared with both, OCD and IGD. IGD shared frontal, particularly cingulate alterations with all SUDs, while IGD demonstrated pronounced temporal alterations compared with both, SUD and OCD. No robust overlap between IGD and OCD was observed. Across the disorders, neurofunctional alterations were mainly contributed by cognitive systems and positive valence RDoC domains. The present findings indicate that neurofunctional dysregulations in prefrontal regions engaged in regulatory-control represent shared neurofunctional alterations across substance and behavioral addictions, while shared neurofunctional dysregulations in the anterior insula may mediate compulsivity in substance addiction and OCD.

Common and separable neural alterations in substance use disorders: A coordinate-based meta-analyses of functional neuroimaging studies in humans.

Delineating common and separable neural alterations in substance use disorders (SUD) is imperative to understand the neurobiological basis of the addictive process and to inform substance-specific treatment strategies. Given numerous functional MRI (fMRI) studies in different SUDs, a meta-analysis could provide an opportunity to determine robust shared and substance-specific alterations. The present study employed a coordinate-based meta-analysis covering fMRI studies in individuals with addictive cocaine, cannabis, alcohol, and nicotine use. The primary meta-analysis demonstrated common alterations in primary dorsal striatal, and frontal circuits engaged in reward/salience processing, habit formation, and executive control across different substances and task-paradigms. Subsequent sub-analyses revealed substance-specific alterations in frontal and limbic regions, with marked frontal and insula-thalamic alterations in alcohol and nicotine use disorders respectively. Examining task-specific alterations across substances revealed pronounced frontal alterations during cognitive processes yet stronger striatal alterations during reward-related processes. Finally, an exploratory meta-analysis revealed that neurofunctional alterations in striatal and frontal reward processing regions can already be determined with a high probability in studies with subjects with comparably short durations of use. Together the findings emphasize the role of dysregulations in frontostriatal circuits and dissociable contributions of these systems in the domains of reward-related and cognitive processes which may contribute to substance-specific behavioral alterations.

Altered structural and causal connectivity in frontal lobe epilepsy.

BACKGROUND: Albeit the few resting-state fMRI neuroimaging studies in frontal lobe epilepsy (FLE) patients, these studies focused on functional connectivity. The aim of this current study was to examine the effective connectivity based on voxel-based morphometry in FLE patients. METHODS: Resting-state structural and functional magnetic resonance imaging (fMRI) data were acquired from 19 FLE patients and 19 age and gender-matched healthy controls using the 3.0 Tesla magnetic resonance imaging (3.0 T MRI). The investigations were done by acquiring the structural information through voxel-based morphometry, then based on the seed obtained, Granger causality analysis was used to evaluate the causal flow of the designated seed to and from other significant voxels. RESULTS: Our results showed altered structural and effective connectivity. Compared with healthy controls, FLE patients showed reduced grey matter volume in bilateral putamen and right caudate as well as altered causality with increased, and decreased causal outflow from the right caudate (seed region) to inferior frontal gyrus-triangular, from bilateral putamen (seed regions) to right middle frontal gyrus and frontal gyrus medial-orbital representing the frontal executive areas, respectively. Also, significantly increased and decreased inflow from left calcarine to right caudate and from cerebellum_6 and vermis_6 to bilateral putamen, respectively. Moreover, we found that the causal alterations to and from the seed regions (from vermis_6 to right putamen and from left putamen to right middle frontal gyrus) negatively correlated with clinical scores (duration of epilepsy). CONCLUSIONS: The findings point to the impairment within the executive and motor-controlled system including the cerebellum, frontal, caudate and putamen regions in FLE patients. These results would therefore enhance our understanding of structural and effective mechanisms in FLE.

Altered Dynamic Functional Network Connectivity in Frontal Lobe Epilepsy.

Frontal lobe epilepsy has recently been associated with disrupted brain functional connectivity; variations among various resting-state networks (RSNs) across time remains largely unclear. This study applied dynamic functional network connectivity (dFNC) analysis to investigate functional patterns in the temporal and spatial domains of various functional systems in FLE. Resting-state fMRI data were acquired from 19 FLE patients and 18 controls. Independent component analysis was used to decompose RSNs, which were grouped into seven functional systems. Sliding windows and clustering approach were used to identify the dFNC patterns. Then, state-specific connectivity pattern and dynamic functional state interactions (dFSIs) were evaluated. Compared with healthy controls, FLE patients exhibited decreased dFNC in almost all four patterns, changes that were mostly related to the frontoparietal system, suggesting a disturbed communication of the frontoparietal system with other systems in FLE. Additionally, regarding the fundamental connectivity pattern (state 3 in this study), FLE showed decreased time spent in this state. Moreover, the duration positively correlated with seizure onset. Furthermore, significantly reduced dynamic connections in this state were observed in the frontoparietal system linked to the cerebellar and subcortical systems. These findings imply abnormal fundamental dynamic interactions and dysconnectivity associated with the subcortical and cerebellar regulation of dysfunctions in frontoparietal regions in FLE. Finally, based on the developed FSI analysis, temporal dynamic abnormalities among states were observed in FLE. Therefore, this altered dynamic FNC extended our understanding of the abnormalities in the frontoparietal system in FLE. The dynamic FNC provided novel insight into the fundamental pathophysiological mechanisms in FLE.

White Matter Connectivity Pattern Associate with Characteristics of Scalp EEG Signals.

The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI-brain computer interface.

The neurostructural consequences of glaucoma and their overlap with disorders exhibiting emotional dysregulations: A voxel-based meta-analysis and tripartite system model.

BACKGROUND: Glaucoma, a progressive neurodegenerative disorder leading to irreversible blindness, is associated with heightened rates of generalized anxiety and depression. This study aims to comprehensively investigate brain morphological changes in glaucoma patients, extending beyond visual processing areas, and explores overlaps with morphological alterations observed in anxiety and depression. METHODS: A comparative meta-analysis was conducted, using case-control studies of brain structural integrity in glaucoma patients. We aimed to identify regions with gray matter volume (GMV) changes, examine their role within distinct large-scale networks, and assess overlap with alterations in generalized anxiety disorder (GAD) and major depressive disorder (MDD). RESULTS: Glaucoma patients exhibited significant GMV reductions in visual processing regions (lingual gyrus, thalamus). Notably, volumetric reductions extended beyond visual systems, encompassing the left putamen and insula. Behavioral and functional network decoding revealed distinct large-scale networks, implicating visual, motivational, and affective domains. The insular region, linked to pain and affective processes, displayed reductions overlapping with alterations observed in GAD. LIMITATIONS: While the study identified significant morphological alterations, the number of studies from both the glaucoma and GAD cohorts remains limited due to the lack of independent studies meeting our inclusion criteria. CONCLUSION: The study proposes a tripartite brain model for glaucoma, with visual processing changes related to the lingual gyrus and additional alterations in the putamen and insular regions tied to emotional or motivational functions. These neuroanatomical changes extend beyond the visual system, implying broader implications for brain structure and potential pathological developments, providing insights into the overall neurological consequences of glaucoma.

Gender Differences in Dynamic Functional Network Connectivity in Pediatric and Adult Patients with Attention-Deficit/Hyperactivity Disorder.

Attention-deficit/hyperactivity disorder (ADHD) persistence into adulthood depends on gender, with 60% female and 35% male cases. This study sought to investigate gender differences in dynamic functional network connectivity (FNC) using resting-state functional magnetic resonance imaging data of pediatric ADHD patients (female: N = 24; 11.02 ± 2.60 years, male: N = 20;11.87 ± 2.62 years) and adult ADHD patients (female = 19; 31.11 ± 10.40 years, males: N = 20;32.05 ± 10.10 years). We identified nine and eight networks in pediatrics and adult data, respectively, using group independent component analysis (GICA). Each age group was clustered into four states using K-means. Significant gender differences in the pediatric group were only found in temporal profiles, particularly in "fraction of time" (FOT) and "mean dwell time" (MDT), but not in FNC. FOT spent by the female pediatric group in state 4 showed a negative relationship with hyperactivity severity. Compared with the adult male group, reduced connectivity was observed within the visual network (VN), between the VN and default-mode network (DMN), and frontoparietal network, as well as between the DMN and cerebellum networks in female adult ADHD patients. Significant FOT and MDT differences were observed between the two groups in state 3. Our results imply gender differences in ADHD, especially in the adult group. Furthermore, given the gender differences observed, our work provides insights into the pathophysiology of ADHD subserved by gender. Impact statement Attention-deficit/hyperactivity disorder (ADHD) symptoms differ between genders; however, little has been done to determine gender differences in pediatric and adult ADHD patients. The present work presents the first gender-specific dynamic functional network connectivity study for different age groups of ADHD patients and highlights the discrepancies between male and female ADHD patients, particularly in the adult group, which may be due to the persistence of inattentive symptoms in female ADHD patients from childhood into adulthood. Given the gender differences observed in the current study, clinicians could consider treatment strategies that target each gender in each age group. The present work provides further insight into the connectivity patterns of the resting-state network in ADHD and may also serve as a basis for future sex-specific studies in different age groups in other disorders.

Comparative analysis of group information-guided independent component analysis and independent vector analysis for assessing brain functional network characteristics in autism spectrum disorder.

Introduction: Group information-guided independent component analysis (GIG-ICA) and independent vector analysis (IVA) are two methods that improve estimation of subject-specific independent components in neuroimaging studies. These methods have shown better performance than traditional group independent component analysis (GICA) with respect to intersubject variability (ISV). Methods: In this study, we compared the patterns of community structure, spatial variance, and prediction performance of GIG-ICA and IVA-GL, respectively. The dataset was obtained from the publicly available Autism Brain Imaging Data Exchange (ABIDE) database, comprising 75 healthy controls (HC) and 102 Autism Spectrum Disorder (ASD) participants. The greedy rule was used to match components from IVA-GL and GIG-ICA in order to compare the similarities between the two methods. Results: Robust correspondence was observed between the two methods the following networks: cerebellum network (CRN; |r| = 0.7813), default mode network (DMN; |r| = 0.7263), self-reference network (SRN; |r| = 0.7818), ventral attention network (VAN; |r| = 0.7574), and visual network (VSN; |r| = 0.7503). Additionally, the Sensorimotor Network demonstrated the highest similarity between IVA-GL and GIG-ICA (SOM: |r| = 0.8125). Our findings revealed a significant difference in the number of modules identified by the two methods (HC: p < 0.001; ASD: p < 0.001). GIG-ICA identified significant differences in FNC between HC and ASD compared to IVA-GL. However, in correlation analysis, IVA-GL identified a statistically negative correlation between FNC of ASD and the social total subscore of the classic Autism Diagnostic Observation Schedule (ADOS: pi = -0.26, p = 0.0489). Moreover, both methods demonstrated similar prediction performances on age within specific networks, as indicated by GIG-ICA-CRN (R2 = 0.91, RMSE = 3.05) and IVA-VAN (R2 = 0.87, RMSE = 3.21). Conclusion: In summary, IVA-GL demonstrated lower modularity, suggesting greater sensitivity in estimating networks with higher intersubject variability. The improved age prediction of cerebellar-attention networks underscores their importance in the developmental progression of ASD. Overall, IVA-GL may be appropriate for investigating disorders with greater variability, while GIG-ICA identifies functional networks with distinct modularity patterns.

Structural differences among children, adolescents, and adults with attention-deficit/hyperactivity disorder and abnormal Granger causality of the right pallidum and whole-brain.

Attention-deficit/hyperactivity disorder (ADHD) is a childhood mental health disorder that often persists to adulthood and is characterized by inattentive, hyperactive, or impulsive behaviors. This study investigated structural and effective connectivity differences through voxel-based morphometry (VBM) and Granger causality analysis (GCA) across child, adolescent, and adult ADHD patients. Structural and functional MRI data consisting of 35 children (8.64 ± 0.81 years), 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years) was obtained from New York University Child Study Center for the ADHD-200 and UCLA dataset. Structural differences in the bilateral pallidum, bilateral thalamus, bilateral insula, superior temporal cortex, and the right cerebellum were observed among the three ADHD groups. The right pallidum was positively correlated with disease severity. The right pallidum as a seed precedes and granger causes the right middle occipital cortex, bilateral fusiform, left postcentral gyrus, left paracentral lobule, left amygdala, and right cerebellum. Also, the anterior cingulate cortex, prefrontal cortex, left cerebellum, left putamen, left caudate, bilateral superior temporal pole, middle cingulate cortex, right precentral gyrus, and the left supplementary motor area demonstrated causal effects on the seed region. In general, this study showed the structural differences and the effective connectivity of the right pallidum amongst the three ADHD age groups. Our work also highlights the evidence of the frontal-striatal-cerebellar circuits in ADHD and provides new insights into the effective connectivity of the right pallidum and the pathophysiology of ADHD. Our results further demonstrated that GCA could effectively explore the interregional causal relationship between abnormal regions in ADHD.

Structural-functional connectivity deficits of callosal-white matter-cortical circuits in schizophrenia.

Schizophrenia is increasingly recognized as a disorder with altered integration between large-scale functional networks and cortical-subcortical pathways. This spatial long-distance information communication must be associated with white matter (WM) fiber bundles. With accumulating evidence that WM functional signals reflect the intrinsic neural activities, how the deep callosal organization modulates cortical functional activities through WM remains unclear in schizophrenia. Using a data-driven method, we identified nine WM and gray matter (GM) functional networks, and then parcellated corpus callosum into distinct sub-regions. Combining functional connectivity and fiber tracking analysis, we estimated the structural and functional connectivity changes of callosal-WM-cortical circuits in schizophrenia. We observed higher structural and functional connectivity between corpus callosum, WM and GM functional networks involving visual network (visual processing), executive control network (executive controls), ventral attention network (processing of salience), and limbic network (emotion processing) in schizophrenia compared to healthy controls. We also found nine abnormal pathways of callosal-WM-cortical circuits involving the above networks and default mode network (self-related thought). These results highlight the role of connectivity deficits in callosal-WM-cortical circuits may play in understanding the delusions, hallucinations and cognitive impairment of schizophrenia.

Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging.

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligence (AI) are associated with one another and with brain structure. We sampled 90 healthy participants who provided self-reported trust in humans and AI and underwent brain structural magnetic resonance imaging assessment. We found that trust in humans, as measured by the trust facet of the personality inventory NEO-PI-R, and trust in AI products, as measured by items assessing attitudes toward AI and by a composite score based on items assessing trust toward products with in-built AI, were not significantly correlated. We also used a concomitant dimensional neuroimaging approach employing a data-driven source-based morphometry (SBM) analysis of gray-matter-density to investigate neurostructural associations with each trust domain. We found that trust in humans was negatively (and significantly) correlated with an SBM component encompassing striato-thalamic and prefrontal regions. We did not observe significant brain structural association with trust in AI. The present findings provide evidence that trust in humans and trust in AI seem to be dissociable constructs. While the personal disposition to trust in humans might be "hardwired" to the brain's neurostructural architecture (at least from an individual differences perspective), a corresponding significant link for the disposition to trust AI was not observed. These findings represent an initial step toward elucidating how different forms of trust might be processed on the behavioral and brain level.

Pathological fear, anxiety and negative affect exhibit distinct neurostructural signatures: evidence from psychiatric neuroimaging meta-analysis.

Internalizing disorders encompass anxiety, fear and depressive disorders, which exhibit overlap at both conceptual and symptom levels. Given that a neurobiological evaluation is lacking, we conducted a Seed-based D-Mapping comparative meta-analysis including coordinates as well as original statistical maps to determine common and disorder-specific gray matter volume alterations in generalized anxiety disorder (GAD), fear-related anxiety disorders (FAD, i.e., social anxiety disorder, specific phobias, panic disorder) and major depressive disorder (MDD). Results showed that GAD exhibited disorder-specific altered volumes relative to FAD including decreased volumes in left insula and lateral/medial prefrontal cortex as well as increased right putamen volume. Both GAD and MDD showed decreased prefrontal volumes compared to controls and FAD. While FAD showed less robust alterations in lingual gyrus compared to controls, this group presented intact frontal integrity. No shared structural abnormalities were found. Our study is the first to provide meta-analytic evidence for distinct neuroanatomical abnormalities underlying the pathophysiology of anxiety-, fear-related and depressive disorders. These findings may have implications for determining promising target regions for disorder-specific neuromodulation interventions (e.g. transcranial magnetic stimulation or neurofeedback).