Default Mode Research Articles

The role of sex in the association between cannabis use disorder and resting-state functional connectivity.

While Cannabis use disorder (CUD) is twice as prevalent in males, females transition more quickly from heavy use to CUD and experience more severe withdrawal. These clinically relevant sex differences contrast the lack of knowledge about the underlying brain mechanisms. This study investigated the relationship between CUD and resting-state functional brain connectivity (RSFC), assessing potential sex differences herein. RSFC of the Salience Network (SN), Basal Ganglia Network (BGN), Executive Control Network (ECN), and Default Mode Network (DMN) was compared between 152 individuals (76 males) with CUD and 114 matched controls (47 males). Within the CUD group, relationships between RSFC and heaviness of cannabis use, age of onset, and CUD symptom severity, along with their associations with sex, were investigated. CUD and control groups showed similar RSFC across all networks, regardless of sex. In the CUD group, heavier cannabis use correlated with higher RSFC across all networks and earlier age of onset was related to higher RSFC in the anterior SN, BGN, left ECN, and dorsal DMN. These associations were similar for males and females. CUD severity was related to higher RSFC in the anterior SN, which was moderated by sex, with a positive association seen only in males. In conclusion, CUD may not necessarily be associated with altered RSFC. Individual use characteristics such age of onset and severity of use may determine the potential impact of cannabis use on RSFC in a largely similar way in males and females.

Brain Markers of Resilience to Psychosis in High-Risk Individuals: A Systematic Review and Label-Based Meta-Analysis of Multimodal MRI Studies

Background/Objectives: Most individuals who have a familial or clinical risk of developing psychosis remain free from psychopathology. Identifying neural markers of resilience in these at-risk individuals may help clarify underlying mechanisms and yield novel targets for early intervention. However, in contrast to studies on risk biomarkers, studies on neural markers of resilience to psychosis are scarce. The current study aimed to identify potential brain markers of resilience to psychosis. Methods: A systematic review of the literature yielded a total of 43 MRI studies that reported resilience-associated brain changes in individuals with an elevated risk for psychosis. Label-based meta-analysis was used to synthesize findings across MRI modalities. Results: Resilience-associated brain changes were significantly overreported in the default mode and language network, and among highly connected and central brain regions. Conclusions: These findings suggest that the DMN and language-associated areas and central brain hubs may be hotspots for resilience-associated brain changes. These neural systems are thus of key interest as targets of inquiry and, possibly, intervention in at-risk populations.

Phase-lagged tACS between executive and default mode networks modulates working memory

Transcranial alternating current stimulation (tACS) is an efficient neuromodulation technique to enhance cognitive function in a non-invasive manner. Using functional magnetic resonance imaging, we investigated whether a cross-frequency coupled tACS protocol with a phase lag (45 and 180 degrees) between the central executive and the default mode networks modulated working-memory performance. We found tACS-phase-dependent modulation of task performance reflected in hippocampal activation and task-related functional connectivity. Our observations provide a neurophysiological basis for neuromodulation and a feasible non-invasive approach to selectively stimulate a task-relevant deep brain structure. Overall, our study highlights the potential of tACS as a powerful tool for enhancing cognitive function and sheds light on the underlying mechanisms of this technique.

Changes in the functional brain networks and graph theory analysis of patients with epilepsy and comorbid migraine without aura.

This study aims to evaluate the changes in the functional brain networks and graph theory analysis of patients with epilepsy and comorbid migraine without aura (EM). We included 30 patients with EM, 20 healthy controls (HC) and 30 epilepsy controls (EC) without migraine. Resting-state functional magnetic resonance imaging was performed to obtain imaging data. The fractional amplitude of low-frequency fluctuations (fALFF) value of the left superior temporal gyrus was higher in the EM group than in the HC group. The regional homogeneity (ReHo) values of the right orbital superior frontal gyrus were higher in the EM group than in the EC group. The functional connectivity between left superior occipital gyrus and left orbital part of the superior frontal gyrus, left middle temporal gyrus, right orbital part of middle frontal gyruss, left medial superior frontal gyrus (cluster 2), right middle frontal gyrus, left middle frontal gyrus, left median cingulate and paracingulate gyri was enhanced in the EM group compared with the HC group; the functional connectivity between the left superior occipital and left medial superior frontal gyri (cluster 1) was weakened. The functional connectivity was weakened between the posterior default mode network (pDMN) and dorsal sensorimotor network (dSMN) and the left frontoparietal network (LFPN) and right frontoparietal network. The functional connectivity between pDMN and LFPN was enhanced in the EM group compared with the EC group. Graph theory analysis revealed that the area under the curve of the standardized characteristic path length of the EM group was smaller than that of the HC and EM groups. The abnormal functional brain networks associated with pain regulation, and changes in topological properties may be involved in the mechanisms underlying migraine without aura occurrence in patients with epilepsy.

Spatial patterns of individual morphological deformation in schizophrenia: Putative cortical compensatory of unaffected sibling.

Spatial patterns of individual morphological deformation in schizophrenia: Putative cortical compensatory of unaffected sibling.

Increased individual variability in functional connectivity of the default mode network and its genetic correlates in major depressive disorder

Major depressive disorder (MDD) is a highly heterogeneous psychiatric disorder characterized with considerable individual variability in clinical manifestations which may correspond to brain alterations including the default mode network (DMN). This study analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 796 MDD patients and 823 healthy controls (HC) to investigate individual variability in functional connectivity (IVFC) between the DMN and 108 non-DMN regions. We aimed to identify MDD-related IVFC abnormalities and their clinical relevance, alongside exploring gene expression correlations. The results revealed similar spatial patterns of IVFC within the DMN in both groups, yet significantly increased IVFC values in MDD patients were observed in regions such as the ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, fusiform gyrus, and occipital cortex. Notably, the mean IVFC in the DMN and fusiform gyrus was positively correlated with Hamilton Rating Scale for Depression (HAMD) scores in MDD patients. Gene expression analyses explained 47.0% of the variance in MDD-related IVFC alterations, with the most associated genes enriched in processes including membrane potential regulation, head development, synaptic transmission, and dopaminergic synapse. These findings highlight the clinical importance of IVFC variability in the DMN and suggest its potential role as a biomarker in MDD.

Brain reactivity to nicotine cues mediates the link between resting-state connectivity and cue-induced craving in individuals who smoke or vape nicotine.

Individual differences in brain intrinsic functional connectivity (FC) and reactivity to nicotine cues are linked to variability in clinical outcomes in nicotine dependence. However, the relative contributions and potential interdependencies of these brain imaging-derived phenotypes in the context of craving and nicotine dependence are unclear. Moreover, it is unknown whether these relationships differ in individuals who smoke versus vape nicotine. To investigate these questions, eighty-six individuals who use nicotine daily (n = 67 smoking, n = 19 vaping) completed either a smoking or vaping cue-reactivity task and a resting-state scan during functional magnetic resonance imaging (fMRI). Validating the efficacy of the smoking and vaping tasks, both cohorts displayed robust reactivity to nicotine versus neutral cues in the default mode network (DMN) and the anterior insula (AI), a primary node of the salience network (SN), which did not habituate over time. In the smoking and vaping groups, lower prefrontal reactivity to nicotine versus neutral cues and greater resting-state FC between nodes of the SN and DMN were associated with higher cue-induced craving. Moreover, we found that the former partially mediated the latter, suggesting a mechanism in which high resting SN-DMN connectivity increases craving susceptibility partly via a constraining effect on regulatory prefrontal reactivity to cues. These relationships were not impacted by group, suggesting that links between brain function and craving are similar regardless of smoking or vaping nicotine.

Dissociation of Structural and Functional Connectivity and Metabolism in the Neocortex of Idiopathic Generalized Epilepsy: A Simultaneous PET/MRI Multimodal Study.

Idiopathic generalized epilepsy (IGE) accounts for approximately 20% of epilepsy cases. Characterized by generalized spike-wave discharge, IGE is increasingly recognized as a network disorder with potential metabolic underpinnings. This study leverages the advantages of simultaneous PET/MRI, which enables the concurrent acquisition of MRI and PET data, to integrate structural connectivity (SC), functional connectivity (FC), and glucose metabolism into a unified framework. This study aims to elucidate the multimodal abnormalities of the neocortex in IGE, to analyze the correlations between these abnormalities and clinical presentations, and to investigate the interactions among different imaging modalities. Twenty-one patients with IGE and 34 healthy controls (HCs) were recruited. Simultaneous PET/MRI scans were performed, incorporating DTI, resting-state fMRI, and [18F]FDG-PET. DTI generated a neocortical connectivity blueprint, while resting-state fMRI provided a whole-brain connectivity matrix. [18F]FDG-PET data were processed to obtain standardized uptake value ratios (SUVRs). Multivariate distance matrix regression was used to identify abnormal neocortical regions in SC and FC. Differences in SUVRs were identified by using least absolute shrinkage and selection operator regression. Statistical analyses, including t tests, linear models, mediation analysis, and Pearson correlations, were conducted to compare values of each technique between groups and explore relationships with clinical features. SC abnormalities were primarily found in the limbic (40% of all abnormal neocortical regions) and visual networks (31%), while FC abnormalities were mostly in the default mode network (DMN, 45%). Metabolic abnormalities were predominantly in the frontoparietal (26%) and somatomotor (22%) networks. SC in the limbic was positively correlated with onset age, while seizure frequency was negative correlated with DMN FC and positively correlated with frontoparietal metabolism. Mediation analysis showed that DMN FC mediated the relationship between limbic SC and frontoparietal and somatomotor metabolism. A multimodal approach reveals distinct and interrelated abnormalities in IGE, with different modalities reflecting various aspects of the disease, thus enhancing our understanding of its complex mechanisms. This integrative analysis could inform more effective treatments.

Infraslow fluctuations of sustained attention in mood disorders.

Sustained attention is integral to goal-directed tasks in everyday life. It is a demanding and effortful process prone to failure. Deficits are particularly prevalent in mood disorders. However, conventional methods of assessment, rooted in overall measures of performance, neglect the nuanced temporal dimensions inherent in sustained attention, necessitating alternative analytical approaches. This study investigated sustained attention deficits and temporal patterns of attentional fluctuation in a large clinical cohort of patients with bipolar depression (BPd, n=33), bipolar euthymia (BPe, n=84), major depression (MDd, n=38) and controls (HC, n=138) using a continuous performance task (CPT). Longitudinal and spectral analyses were employed to examine trial-level reaction time (RT) data. Longitudinal analysis revealed a significant worsening of performance over time (vigilance decrement) in BPd, whilst spectral analysis unveiled attentional fluctuations concentrated in the frequency range of 0.077Hz (1/12.90s)-0.049Hz (1/20.24s), with BPd and MDd demonstrating greater spectral power compared to BPe and controls. Although speculative, the increased variability in this frequency range may have an association with the dysfunctional activity of the Default Mode Network, which has been shown to oscillate at a similar timescale. These findings underscore the importance of considering the temporal dimensions of sustained attention and show the potential of spectral analysis of RT in future clinical research.

The neural basis of dialectical thinking: recent advances and future prospects.

Dialectical thinking represents a cognitive style emphasizing change, contradiction, and holism. Cross-cultural studies reveal a stark contrast of dialectical thinking between East Asian and Western cultures, highlighting East Asians' superior ability to embrace contradictions and foresee transformation, fostering psychological resilience through emotional complexity and tolerance for contradictions. Despite its importance, the neural basis of dialectical thinking remains underexplored. This review synthesizes current neuroscientific findings and introduces the dialectical-integration network (DIN) hypothesis, which identifies key brain regions such as the dorsal anterior cingulate cortex (dACC), medial prefrontal cortex (mPFC), dorsal lateral prefrontal cortex (DLPFC), nucleus accumbens, basal ganglia, and amygdala. These regions, along with networks like the default mode network (DMN) and frontoparietal network (FPN), facilitate holistic reasoning, conflict resolution, and sensory-emotional integration. The psychological benefits of dialectical thinking include enhanced cognitive flexibility, reduced emotional extremes, and improved conflict resolution. This review emphasizes the need for cross-cultural and neuroscientific research to explore the principle of change, a core aspect of dialectical cognition. By bridging cultural psychology and cognitive neuroscience, this work offers theoretical and methodological insights into culturally shaped cognitive styles, with practical applications in education, mental health, and intercultural communication. The DIN model provides a framework for future research on dynamic neural interactions supporting dialectical thinking.

Multi-feature fusion RFE random forest for schizophrenia classification and treatment response prediction

Schizophrenia(SZ) classification and treatment response prediction hold substantial clinical application value. However, only a limited number of researchers have exploited the multi-feature information derived from resting-state functional magnetic resonance imaging (rs-fMRI) to achieve short-term drug-treatment SZ classification and treatment response prediction. We developed a multi-feature fusion recursive feature elimination random forest (RFE-RF) approach for SZ classification and treatment response prediction. Initially, we computed multiple features, such as regional homogeneity, fractional amplitude of low-frequency fluctuations, and functional connectivity. Subsequently, the RFE-RF method was employed to conduct SZ classification. Moreover, we utilized the rate of score reduction (RR) of the Positive and Negative Symptom Scale (PANSS) to forecast the treatment response of individual patients. Finally, we identified the neuroimaging biomarkers for SZ classification and drug-treatment response prediction. This method achieved the classification results (accuracy = 91.7%, sensitivity = 90.9%, and specificity = 92.6%), and the abnormalities in the visual and default mode networks emerged as potential neuroimaging biomarkers for differentiating SZ from healthy controls (HC). Additionally, we predicted the drug-treatment response of SZ patients in terms of their total PANSS scores, as well as negative and positive symptom scores after eight weeks of treatment. Specifically, the abnormalities in the visual network, sensorimotor network, and right superior frontal gyrus are crucial biomarkers for the short-term drug-treatment response of negative symptoms in SZ patients. Meanwhile, the abnormalities in the visual and default mode networks serve as important biomarkers of the short-term drug-treatment response of positive symptoms. There findings offer novel insights into the neural mechanisms underlying SZ following eight weeks of short-term drug treatment. With further clinical validation in the future, this research may provide potential biomarkers and intervention targets for personalized treatment of SZ.

Common neural correlates of chronic pain - A systematic review and meta-analysis of resting-state fMRI studies.

Common neural correlates of chronic pain - A systematic review and meta-analysis of resting-state fMRI studies.

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity.

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brain's intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (α-tACS) and observed that α-tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of α-tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods.Significance Statement Alpha (8-12 Hz) oscillations and the default mode network (DMN) represent two major intrinsic activities of the brain. Prominent theories and extant findings converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Combining simultaneous electroencephalogram-functional-magnetic-resonance imaging (EEG-fMRI) with alpha-frequency transcranial alternating current stimulation (α-tACS), we demonstrated tightened coupling between alpha oscillations and DMN connectivity. These results lend credence to an inherent alpha-DMN link. Given DMN dysfunctions in multiple major neuropsychiatric conditions, the findings also highlight potential utility of α-tACS in clinical interventions by regulating the DMN.

Cerebellar-Cortical Connectivity and Prediction of Antipsychotic Treatment Response in First-Episode Psychosis

Abstract Background The cerebellum has traditionally been associated with motor functions, but recent evidence highlights its critical role in cognitive and emotional regulation, contributing to the neuropathology of schizophrenia. Our previous data-driven research demonstrated that cerebellar-cortical functional connectivity can predict antipsychotic treatment outcomes in first-episode psychosis (FEP). The present study aimed to investigate specific cerebellar functional systems involved in treatment prediction. Study Design This study included 127 patients with FEP who underwent 12 weeks of antipsychotic monotherapy (either risperidone or aripiprazole). Baseline resting-state functional MRI data were collected from two 3T scanners, and functional connectivity between 10 predefined cerebellar functional systems and the whole brain was analyzed. Psychotic symptom changes were measured using the Brief Psychiatric Rating Scale-Anchored version (BPRS-A). Connectivity patterns were examined in relation to treatment outcomes. Study Results Higher baseline connectivity between the cerebellar auditory system and cortical regions, including the visual cortex, dorsolateral prefrontal cortex, and the hippocampus, predicted worse treatment outcome. In contrast, stronger connectivity between cerebellar cognitive systems (default mode and frontoparietal networks) and the anterior cingulate cortex (ACC) and medial prefrontal cortex was associated with better treatment outcome. These findings were consistently present in data acquired from both scanners and both drugs. Conclusions Our results identify specific cerebellar-cortical circuitries as prognostic biomarkers for predicting psychosis treatment outcomes, and suggest that cerebellar auditory and cognitive systems may be potential targets for future interventions aimed at improving treatment efficacy in FEP.

The Relationship of glutamate signaling to cannabis use and schizophrenia.

This review examines the literature associating cannabis with schizophrenia, glutamate dysregulation in schizophrenia, and cannabis involvement in glutamate pathways. Cannabis use is widespread among adolescents world-wide and is sold legally in many countries for recreational use in a variety of forms. Most people use it without lasting effects, but a portion of individuals have negative reactions that manifest in acute psychotic symptoms, and in some, symptoms continue even after the use of cannabis has ceased. To date, there is a huge gap in our understanding of why this occurs. Recent studies have focused on abnormalities in the glutamate pathway in schizophrenia, the effect of cannabis on the glutamate system, and the role of glutamate in the brain Default Mode Network. Given these observations, we hypothesize that perturbance of glutamate neuronal connectivity by cannabis in the brains of individuals genetically at high risk for psychosis will initiate a schizophrenia-like psychosis. Future studies may tie together these diverse observations by combining magnetic resonance spectroscopy (MRS) and functional magnetic resonance imaging (fMRI) of the default resting state network in patients with new onset schizophrenia who do and do not use cannabis compared with nonpsychotic individuals who do and do not use cannabis.

Cerebral Topographies of Perceived and Felt Emotions

Abstract Emotions modulate behavioral priorities based on exteroceptive and interoceptive inputs, and the related central and peripheral changes may be experienced subjectively. Yet, it remains unresolved whether the perceptual and subjectively felt components of the emotion processes rely on shared brain mechanisms. We applied functional magnetic resonance imaging, a rich set of emotional movies, and high-dimensional, continuous ratings of perceived and felt emotions in the movies to investigate their cerebral organization. Emotions evoked during natural movie scene perception were represented in the brain across numerous spatial scales and patterns. Perceived and felt emotions generalized both between individuals and between different stimuli depicting the same emotions. The neural affective space demonstrated an anatomical gradient from emotion-general responses in polysensory areas and default mode regions to more emotion-specific discrete processing in subcortical regions. Differences in brain activation during felt and perceived emotions suggest that temporoparietal areas and precuneus have a key role in evaluating the affective value of the sensory input, and subjective emotional state generation is associated with further and significantly stronger recruitment of the temporoparietal junction, anterior prefrontal cortices, cerebellum, and thalamus. These data reveal the similarities and differences of domain-general and emotion-specific affect networks in the brain during a wide range of perceived and felt emotions.

Amyloid pathology related to aberrant structure-function coupling of brain networks in Alzheimer's disease: insights from [18F]-florbetapir PET imaging.

Brain structure-function coupling (SFC), which reflects the degree to which anatomical structure supports neural function, is an emerging imaging marker in neurodegenerative diseases. However, its pathological underpinnings in Alzheimer's disease (AD) remain poorly understood. This study aimed to examine the association among amyloid pathology, SFC disruption and cognitive decline. We included 173 participants from the SILCODE cohort, comprising cognitively unimpaired (CU) and cognitively impaired (CI) individuals. Amyloid pathology was quantified using [18F]-florbetapir PET standardized uptake value ratios (SUVR). Structural connectivity (SC) was derived from diffusion-weighted MRI with probabilistic tractography, while functional connectivity (FC) was calculated from resting-state functional MRI. SFC was defined as the coefficient of determination from linear models predicting FC based on SC at regional level. Linear regression and mediation analyses were conducted to assess relationships between amyloid pathology, SFC, and multiple cognitive performances. Compared to CU individuals, CI participants exhibited increased regional SFC primarily within the default mode network regions (p < 0.05). In CI participants, amyloid pathology correlated with SFC across occipital lobe, precuneus and temporoparietal regions, which was specific by APOE ε4 status (p < 0.05). Mediation analyses revealed that SFC partially mediated the relationship between amyloid pathology and cognitive impairment (abMoCA-B = -0.14, 95% CI [-0.27, -0.02]). Similar findings were replicated with plasma markers. Amyloid pathology may underlie SFC disruptions, contributing to cognitive decline in AD. These findings suggest that SFC may serve as a potential biomarker for amyloid-related neurodegeneration and cognitive impairment. The SILCODE is listed on the ClinicalTrials.gov registry (SILCODE: NCT03370744).

Visual statistical learning alters low-dimensional cortical architecture.

Our brains are in a constant state of generating predictions, implicitly extracting environmental regularities to support later cognition and behavior, a process known as statistical learning (SL). While prior work investigating the neural basis of SL has focused on the activity of single brain regions in isolation, much less is known about how distributed brain areas coordinate their activity to support such learning. Using fMRI and a classic visual SL task, we investigated changes in whole-brain functional architecture as human female and male participants implicitly learned to associate pairs of images, and later, when predictions generated from learning were violated. By projecting individuals' patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was associated with changes along a single neural dimension describing covariance across the visual-parietal and perirhinal cortex (PRC). During learning, we found regions within the visual cortex expanded along this dimension, reflecting their decreased communication with other networks, whereas regions within the dorsal attention network (DAN) contracted, reflecting their increased connectivity with higher-order cortex. Notably, when SL was interrupted, we found the PRC and entorhinal cortex, which did not initially show learning-related effects, now contracted along this dimension, reflecting their increased connectivity with the default mode and DAN, and decreased covariance with visual cortex. While prior research has linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.Significance statement The current work is the first to investigate changes in whole-brain manifold architecture that underlie visual statistical learning (SL). We found that areas of the visual cortex and dorsal attention network showed significant connectivity changes during learning, reflecting their decreased, and increased covariance with other networks, respectively. Notably, when SL was later disrupted, regions within the medial temporal lobe, which had shown no evidence of initial learning, now began to increase connectivity with higher-order cortex. Together, these findings not only reveal the widespread neural interactions that underlie visual SL, but also extend prior work, suggesting separable cortical and medial temporal lobe contributions for the encoding versus violation of learned associations.

Why friendship and loneliness affect our health.

Humans, like all monkeys and apes, have an intense desire to be social. The human social world, however, is extraordinarily complex, depends on sophisticated cognitive and neural processing, and is easily destabilized, with dramatic consequences for our mental and physical health. To show why, I first summarize descriptive aspects of human friendships and what they do for us, then discuss the cognitive and neurobiological processes that underpin them. I then summarize the growing body of evidence suggesting that our mental as well as our physical health and wellbeing are best predicted by the number and quality of close friend/family relationships we have, with five being the optimal number. Finally, I review neurobiological evidence that both number of friends and loneliness itself are correlated with the volume of certain key brain regions associated with the default mode neural network and its associated gray-matter processing units.

Hubs and interaction: the brain's meta-loop.

We must reconcile the needs of the internal world and the demands of the external world to make decisions relevant to homeostasis, well-being, and flexible behavior. Engagement with the internal (eg interoceptive) world is linked to medial brain systems, whereas the extrapersonal space (eg exteroceptive) is associated with lateral brain systems. Using Human Connectome Project data, we found three association tracts connecting the action-related frontal lobe with perception-related posterior lobes. A lateral dorsal tract and a medial dorsal tract interact independently with a ventral tract at frontal and posterior hubs. The two frontal and the two posterior hubs are interconnected, forming a meta-loop that integrates lateral and medial brain systems. The four anatomical hubs correspond to the common nodes of the intrinsic cognitive brain networks such as the default mode network. These functional networks depend on the integration of both realms. Thus, the positioning of functional cognitive networks can be understood as the intersection of long anatomical association tracts. The strength of structural connectivity within lateral and medial brain systems correlates with performance on behavioral tests assessing theory of mind. The meta-loop provides an anatomical framework to associate neurological and psychiatric symptoms with functional and structural changes.