Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Psychological resilience-related functional connectomes predict creative personality.

Both psychological resilience and creativity are complex concepts that have positive effects on individual adaptation. Previous studies have shown overlaps between the key brain regions or brain functional networks related to psychological resilience and creativity. However, no direct experimental evidence has been provided to support the assumption that psychological resilience and creativity share a common brain basis. Therefore, the present study investigated the relationship between psychological resilience and creativity using neural imaging method with a machine learning approach. At the behavioral level, we found that psychological resilience was positively related to creative personality. Predictive analysis based on static functional connectivity (FC) and dynamic FC demonstrated that FCs related to psychological resilience could effectively predict an individual's creative personality score. Both the static FC and dynamic FC were mainly located in the default mode network. These results prove that psychological resilience and creativity share a common brain functional basis. These findings also provide insights into the possibility of promoting individual positive adaptation from negative events or situations in a creative way.

Reduced hippocampal-cortical connectivity during memory suppression predicts the ability to forget unwanted memories.

The ability to suppress unwelcome memories is important for productivity and well-being. Successful memory suppression is associated with hippocampal deactivations and a concomitant disruption of this region's functionality. Much of the previous neuroimaging literature exploring such suppression-related hippocampal modulations has focused on the region's negative coupling with the prefrontal cortex. Task-based changes in functional connectivity between the hippocampus and other brain regions still need further exploration. In the present study, we utilize psychophysiological interactions and seed connectome-based predictive modeling to investigate the relationship between the hippocampus and the rest of the brain as 134 participants attempted to suppress unwanted memories during the Think/No-Think task. The results show that during retrieval suppression, the right hippocampus exhibited decreased functional connectivity with visual cortical areas (lingual and cuneus gyrus), left nucleus accumbens and the brain-stem that predicted superior forgetting of unwanted memories on later memory tests. Validation tests verified that prediction performance was not an artifact of head motion or prediction method and that the negative features remained consistent across different brain parcellations. These findings suggest that systemic memory suppression involves more than the modulation of hippocampal activity-it alters functional connectivity patterns between the hippocampus and visual cortex, leading to successful forgetting.

Gene transcriptional expression of cortical thinning during childhood and adolescence.

The cognitive and behavioral development of children and adolescents is closely related to the maturation of brain morphology. Although the trajectory of brain development has been depicted in detail, the underlying biological mechanism of normal cortical morphological development in childhood and adolescence remains unclear. By combining the Allen Human Brain Atlas dataset with two single-site magnetic resonance imaging data including 427 and 733 subjects from China and the United States, respectively, we performed partial least squares regression and enrichment analysis to explore the relationship between the gene transcriptional expression and the development of cortical thickness in childhood and adolescence. We found that the spatial model of normal cortical thinning during childhood and adolescence is associated with genes expressed predominantly in astrocytes, microglia, excitatory and inhibitory neurons. Top cortical development-related genes are enriched for energy-related and DNA-related terms and are associated with psychological and cognitive disorders. Interestingly, there is a great deal of similarity between the findings derived from the two single-site datasets. This fills the gap between early cortical development and transcriptomes, which promotes an integrative understanding of the potential biological neural mechanisms.

Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes.

Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen's d = 0.16-0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18-0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

The bright side and dark side of daydreaming predict creativity together through brain functional connectivity.

Daydreaming and creativity have similar cognitive processes and neural basis. However, few empirical studies have examined the relationship between daydreaming and creativity using cognitive neuroscience methods. The present study explored the relationship between different types of daydreaming and creativity and their common neural basis. The behavioral results revealed that positive constructive daydreaming is positively related to creativity, while poor attentional control is negatively related to it. Machine learning framework was adopted to examine the predictive effect of daydreaming-related brain functional connectivity (FC) on creativity. The results demonstrated that task FCs related to positive constructive daydreaming and task FCs related to poor attentional control both predicted an individual's creativity score successfully. In addition, task FCs combining the positive constructive daydreaming and poor attentional control also had significant predictive effect on creativity score. Furthermore, predictive analysis based on resting-state FCs showed similar patterns. Both of the subscale-related FCs and combined FCs had significant predictive effect on creativity score. Further analysis showed the task and the resting-state FCs both mainly located in the default mode network, central executive network, salience network, and attention network. These results showed that daydreaming was closely related to creativity, as they shared common FC basis.

The trait and state negative affect can be separately predicted by stable and variable resting-state functional connectivity.

BACKGROUND: Many emotional experiences such as anxiety and depression are influenced by negative affect (NA). NA has both trait and state features, which play different roles in physiological and mental health. Attending to NA common to various emotional experiences and their trait-state features might help deepen the understanding of the shared foundation of related emotional disorders. METHODS: The principal component of five measures was calculated to indicate individuals' NA level. Applying the connectivity-based correlation analysis, we first identified resting-state functional connectives (FCs) relating to NA in sample 1 (n = 367), which were validated through an independent sample (n = 232; sample 2). Next, based on the variability of FCs across large timescale, we further divided the NA-related FCs into high- and low-variability groups. Finally, FCs in different variability groups were separately applied to predict individuals' neuroticism level (which is assumed to be the core trait-related factor underlying NA), and the change of NA level (which represents the state-related fluctuation of NA). RESULTS: The low-variability FCs were primarily within the default mode network (DMN) and between the DMN and dorsal attention network/sensory system and significantly predicted trait rather than state NA. The high-variability FCs were primarily between the DMN and ventral attention network, the fronto-parietal network and DMN/sensory system, and significantly predicted the change of NA level. CONCLUSIONS: The trait and state NA can be separately predicted by stable and variable spontaneous FCs with different attentional processes and emotion regulatory mechanisms, which could deepen our understanding of NA.

Memory Suppression Ability can be Robustly Predicted by the Internetwork Communication of Frontoparietal Control Network.

Memory suppression (MS) is essential for mental well-being. However, no studies have explored how intrinsic resting-state functional connectivity (rs-FC) predicts this ability. Here, we adopted the connectome-based predictive modeling (CPM) based on the resting-state fMRI data to investigate whether and how rs-FC profiles in predefined brain networks (the frontoparietal control networks or FPCN) can predict MS in healthy individuals with 497 participants. The MS ability was assessed by MS-induced forgetting during the think/no-think paradigm. The results showed that FPCN network was especially informative for generating the prediction model for MS. Some regions of FPCN, such as middle frontal gyrus, superior frontal gyrus and inferior parietal lobe were critical in predicting MS. Moreover, functional interplay between FPCN and multiple networks, such as dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), the limbic system and subcortical regions, enabled prediction of MS. Crucially, the predictive FPCN networks were stable and specific to MS. These results indicated that FPCN flexibility interacts with other networks to underpin the ability of MS. These would also be beneficial for understanding how compromises in these functional networks may have led to the intrusive thoughts and memories characterized in some mental disorders.

Connectome-based evidence for creative thinking as an emergent property of ordinary cognitive operations.

Creative thinking is a hallmark of human cognition, which enables us to generate novel and useful ideas. Nevertheless, its emergence within the macro-scale neurocognitive circuitry remains largely unknown. Using resting-state fMRI data from two large population samples (SWU: n = 931; HCP: n = 1001) and a novel "travelling pattern prediction analysis", here we identified the modularized functional connectivity patterns linked to creative thinking ability, which concurrently explained individual variability across ordinary cognitive abilities such as episodic memory, working memory and relational processing. Further interrogation of this neural pattern with graph theoretical tools revealed both hub-like brain structures and globally-efficient information transfer paths that together may facilitate higher creative thinking ability through the convergence of distinct cognitive operations. Collectively, our results provide reliable evidence for the hypothesized emergence of creative thinking from core cognitive components through neural integration, and thus allude to a significant theoretical advancement in the study of creativity.

Morphometry of the Hippocampus Across the Adult Life-Span in Patients with Depressive Disorders: Association with Neuroticism.

Neuroticism is one of the main endophenotypes of major depressive disorder (MDD) and is closely related to the negative effect systems of Research Domain Criteria (RDoC) domains. The relationship between neuroticism and aging is dynamic and complex. Moreover, reduced hippocampal volumes are probably the most frequently reported structural neuroimaging finding associated with MDD. However, it remains unclear to what extent hippocampal abnormalities are linked with age and neuroticism changes in people with depression through the adult life span. This study aimed to examine the interplay between aging and neuroticism on hippocampal morphometric across the adult life-span in a relative large sample of patients with depressive disorders (114 patients, 73 females, age range: 18-74 years) and healthy control (HC) subjects (112 healthy controls, 72 females, age range: 19-72 years). MDD patients showed reduced bilateral hippocampal volumes. The effect of aging on the left hippocampal showed linear and the right hippocampal volume non-linear trajectories throughout the adult life span in healthy groups and MDD groups respectively. The hippocampal atrophy was dynamically impacted by depression at the early stages of adult life. Furthermore, we observed that right hippocampal volume reduction was associated with higher neuroticism in depressive patients younger than 30.65 years old. Our results suggest that the age-related atrophy in the right hippocampal volume was more affected by individual differences in neuroticism among younger depressive patients. Hippocampal volume reduction as a vulnerability factor for early-onset and major geriatric depression may have a distinct endophenotype.

Brain Entropy is Associated with Divergent Thinking.

Creativity is the ability to generate original and useful products, and it is considered central to the progression of human civilization. As a noninherited emerging process, creativity may stem from temporally dynamic brain activity, which, however, has not been well studied. The purpose of this study was to measure brain dynamics using entropy and to examine the associations between brain entropy (BEN) and divergent thinking in a large healthy sample. The results showed that divergent thinking was consistently positively correlated with regional BEN in the left dorsal anterior cingulate cortex/pre-supplementary motor area and left dorsolateral prefrontal cortex, suggesting that creativity is closely related to the functional dynamics of the control networks involved in cognitive flexibility and inhibitory control. Importantly, our main results were cross-validated in two independent cohorts from two different cultures. Additionally, three dimensions of divergent thinking (fluency, flexibility, and originality) were positively correlated with regional BEN in the left inferior frontal gyrus and left middle temporal gyrus, suggesting that more highly creative individuals possess more flexible semantic associative networks. Taken together, our findings provide the first evidence of the associations of regional BEN with individual variations in divergent thinking and show that BEN is sensitive to detecting variations in important cognitive abilities in healthy subjects.

Cortical thickness distinguishes between major depression and schizophrenia in adolescents.

BACKGROUND: Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. METHODS: We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. RESULTS: We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. CONCLUSIONS: Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

The reductions in the subcallosal region cortical volume and surface area in major depressive disorder across the adult life span.

BACKGROUND: Imaging studies have shown that the subcallosal region (SCR) volume was decreased in patients with major depressive disorder (MDD). However, whether the volumetric reductions in the SCR are due to thinning of the cortex or a loss of surface area (SA) remains unclear. In addition, the relationship between cortical measurements of the SCR and age through the adult life span in MDD remains unclear. METHODS: We used a cross-sectional design from 114 individuals with MDD and 112 matched healthy control (HC) individuals across the adult life span (range: 18-74 years). The mean cortical volume (CV), SA and cortical thickness (CT) of the SCR were computed using cortical parcellation based on FreeSurfer software. Multivariate analyses of covariance models were performed to compare differences between the MDD and HC groups on cortical measurements of the SCR. Multiple linear regression models were used to test age-by-group interaction effects on these cortical measurements of the SCR. RESULTS: The MDD had significant reductions in the CV and SA of the left SCR compared with HC individuals after controlling of other variables. The left SCR CV and SA reductions compared with matched controls were observed only in early adulthood patients. We also found a significant age-related CT reduction in the SCR both in the MDD and HC participants. CONCLUSIONS: The SCR volume reduction was mainly driven by SA in MDD. The different trajectories between the CT and SA of the SCR with age may provide valuable information to distinguish pathological processes and normal ageing in MDD.

The role of frontal-subcortical connectivity in the relation between coping styles and reactivity and downregulation of negative emotion.

Coping styles (CS) reflect individuals' habitual use of strategies for coping with negative events in daily life. Although research into coping has not reached consistent agreement about classifying coping strategies as either inherently adaptive or maladaptive, the influence of maladaptive CS on mental health is noticeable. CS might also be related to emotion regulation and associated brain systems. Participants (N = 165) completed measurements of CS, trait emotions including trait anxiety, depressive symptoms and happiness and then performed an emotion regulation task, in conjunction with functional MRI. Individual differences in maladaptive CS use were associated with higher trait negative emotionality and higher state reactivity of negative emotion. Concurrent bilateral amygdala-right middle frontal gyrus (MFG) connectivity during passive negative stimulus processing mediated the relation between maladaptive CS and negative emotion ratings. Psychophysiological interaction analyses showed that maladaptive and adaptive CS were linked to patterns of frontal-subcortical connectivity during state emotion regulation. These results suggest that maladaptive CS might be related to negative emotion processing and weaker spontaneous regulation and indicate that maladaptive CS is a risk factor in individual mental health.

Verbal Creativity Correlates with the Temporal Variability of Brain Networks During the Resting State.

Creativity is the ability to see the world in new ways. Creative individuals exhibit the ability to switch between different modes of thinking and shift their mental focus. This suggests a connection between creativity and dynamic interactions of brain networks. We report here the first investigation into the relationship between the reconfiguration of dynamic brain networks during the resting state and verbal creativity using two fMRI datasets involving 574 subjects. We find that verbal creativity correlates with temporal variability of the functional-connectivity (FC) patterns of the lateral prefrontal cortex, the precuneus, and the parahippocampal gyrus. High variability of these regions indicates flexible connectivity patterns which may facilitate executive functions. Furthermore, verbal creativity correlates with the temporal variability of FC patterns within the default mode network (DMN), between the DMN and attention/sensorimotor network, and between control and sensory networks. High variability of FCs between the DMN and attention networks characterizes frequent adjustments of attention. Finally, dynamic interaction between the cerebellum and task control network also contributes to verbal creativity, suggesting a relationship between the cerebellum and creativity. This study reveals a close relationship between verbal creativity and high variability of cortical networks involved in spontaneous thought, attention and cognitive control.

Functional Connectivity of the Anterior Cingulate Cortex in Depression and in Health.

The first voxel-level resting-state functional connectivity (FC) neuroimaging analysis of depression of the anterior cingulate cortex (ACC) showed in 282 patients with major depressive disorder compared with 254 controls, some higher, and some lower FCs. However, in 125 unmedicated patients, primarily increases of FC were found: of the subcallosal anterior cingulate with the lateral orbitofrontal cortex, of the pregenual/supracallosal anterior cingulate with the medial orbitofrontal cortex, and of parts of the anterior cingulate with the inferior frontal gyrus, superior parietal lobule, and with early cortical visual areas. In the 157 medicated patients, these and other FCs were lower than in the unmedicated group. Parcellation was performed based on the FC of individual ACC voxels in healthy controls. A pregenual subdivision had high FC with medial orbitofrontal cortex areas, and a supracallosal subdivision had high FC with lateral orbitofrontal cortex and inferior frontal gyrus. The high FC in depression between the lateral orbitofrontal cortex and the subcallosal parts of the ACC provides a mechanism for more non-reward information transmission to the ACC, contributing to depression. The high FC between the medial orbitofrontal cortex and supracallosal ACC in depression may also contribute to depressive symptoms.

The spatiotemporal pattern of pure tone processing: A single-trial EEG-fMRI study.

Although considerable research has been published on pure tone processing, its spatiotemporal pattern is not well understood. Specifically, the link between neural activity in the auditory pathway measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) markers of pure tone processing in the P1, N1, P2, and N4 components is not well established. In this study, we used single-trial EEG-fMRI as a multi-modal fusion approach to integrate concurrently acquired EEG and fMRI data, in order to understand the spatial and temporal aspects of the pure tone processing pathway. Data were recorded from 33 subjects who were presented with stochastically alternating pure tone sequences with two different frequencies: 200 and 6400 Hz. Brain network correlated with trial-to-trial variability of the task-discriminating EEG amplitude was identified. We found that neural responses responding to pure tone perception are spatially along the auditory pathway and temporally divided into three stages: (1) the early stage (P1), wherein activation occurs in the midbrain, which constitutes a part of the low level auditory pathway; (2) the middle stage (N1, P2), wherein correlates were found in areas associated with the posterodorsal auditory pathway, including the primary auditory cortex and the motor cortex; (3) the late stage (N4), wherein correlation was found in the motor cortex. This indicates that trial-by-trial variation in neural activity in the P1, N1, P2, and N4 components reflects the sequential engagement of low- and high-level parts of the auditory pathway for pure tone processing. Our results demonstrate that during simple pure tone listening tasks, regions associated with the auditory pathway transiently correlate with trial-to-trial variability of the EEG amplitude, and they do so on a millisecond timescale with a distinct temporal ordering.

Distinct neuronal entrainment to beat and meter: Revealed by simultaneous EEG-fMRI.

Rhythm perception refers to the mental interpretation of rhythm by a listener. Musical rhythm perception typically involves two steps: beat extraction and metrical structure assignment (meter perception). The entrainment theories propose that different neuronal oscillations entrain to different levels of metrical structure in the rhythm (e.g., beat and meter) and thereby form a representation of the rhythm in the mind. Thus, neuronal populations that entrain to beat and meter should theoretically be different. However, although entrainment theories have been supported by many studies, the neuronal populations that entrain to beat and meter remain largely unknown. In this study, we used a paradigm to induce neuronal entrainment to beat and meter and obtained images of the neuronal populations with an electroencephalogram functional magnetic resonance imaging (EEG-fMRI) fusion method. We observed that some neuronal populations, including the bilateral putamen, bilateral caudate, left thalamus, and supplementary motor area (SMA), entrain to both beat and meter. We also observed that the bilateral putamen entrains more to meter and the SMA entrains more to beat. Our results suggest that the bilateral putamen plays an important role in meter perception.

Brain connection pattern under interoceptive attention state predict interoceptive intensity and subjective anxiety feeling.

Interoception involves the processing of a variety of different types of information ascending from the body. Accumulating evidence has indicated that interoception plays a fundamental role in cognitive and emotional processes, such as anxiety, but how different functional connectivity patterns contribute to emotions and visceral feelings during an interoceptive attention state is still unclear. In the present study, an interoceptive attention task was performed during functional magnetic resonance imaging of healthy subjects, and the participants' subjective ratings of the intensity of interoception and feelings of anxiety were recorded. Several network nodes were selected, based on previous studies, to construct task-dependent functional connectivity patterns, which were processed by support vector regression to predict the corresponding feeling scores. The results showed that for interoception, the cingulo-opercular task control network provided the greatest contribution, whereas the most important feature for anxiety was the connections between the sensorimotor area (SSM) and the salience network (SN). There existed four overlapping connections between the two predictions: two negative connections between the default mode network (DMN) and the SSM, one negative connection between the DMN and the SN, and one positive connection between the ventral attention network and the SN; this overlap might suggest common bodily attention processing that is involved in both interoception and anxiety. This study remediates the lack of network-level biomarkers of interoception and provides a reference at the level of the brain for further understanding anxiety from an interoceptive perspective.