Large-scale Brain Networks Research Articles

Fast signaling by glucocorticoids shapes neural representations of behaviors

Glucocorticoids are stress hormones that play central roles in the immediate and slower adaptive responses of the brain and body to new behavioral experience. The exact mechanisms by which the rapid and slow processes underlying glucocorticoid mnemonic effects unfold are under intensive scrutiny. It is possible that glucocorticoids rapidly modify memory representations in the brain by interfering with synaptic functions between inhibitory and excitatory neurons in a timing and context dependent manner. In particular, activity-dependent trans-synaptic messengers appear to have all the necessary attributes to engage in the rapid signaling by glucocorticoids and regulate the brain and behaviors. Novel frameworks for the treatment of stress-related disorders could emerge from a better characterization of the dynamic interplay between the rapid and slow signaling components by glucocorticoids on large-scale brain networks. Here I present some of the exact factors that could help reach this objective.

Network models to enhance the translational impact of cross-species studies.

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Preliminary evidence for changes in frontoparietal network connectivity in the early abstinence period in alcohol use disorder: a longitudinal resting-state functional magnetic resonance imaging study.

The early abstinence period is a crucial phase in alcohol use disorder (AUD) in which patients have to find a new equilibrium and may start recovery, or conversely, relapse. However, the changes in brain functions during this key period are still largely unknown. We set out to study longitudinal changes in large-scale brain networks during the early abstinence period using resting-state scans. We scanned AUD patients twice in a well-controlled inpatient setting, with the first scan taking place shortly after admission and the second scan 4 weeks (±9 days) later near the end of the treatment period. We studied 37 AUD patients (22 males) and 27 healthy controls (16 males). We focused on three networks that are affected in AUD and underly core symptom dimensions in this disorder: the frontoparietal networks (left and right FPN) and default mode network (DMN). Both the whole brain and within network connectivity of these networks were studied using dual regression. Finally, we explored correlations between these brain networks and various neuropsychological and behavioral measures. In contrast to the controls (Z = -1.081, p = 0.280), the AUD patients showed a decrease in within left FPN connectivity (Z = -2.029, p = 0.042). However, these results did not survive a strict Bonferroni correction. The decrease in left FPN connectivity during the early abstinence period in AUD may reflect an initially upregulated FPN, which recovers to a lower resting-state connectivity level during subsequent weeks of abstinence. The AUD patients showed a trend for a positive association between the change in left FPN connectivity and trait anxiety (rs = 0.303, p = 0.068), and a trend for a negative association between the change in left FPN connectivity and delay discounting (rs = -0.283, p = 0.089) (uncorrected for multiple comparisons). This suggests that the FPN might be involved in top-down control of impulsivity and anxiety, which are important risk factors for relapse. Although there were no statistically significant results (after multiple comparison correction), our preliminary findings encourage further research into the dynamic neuroadaptations during the clinically crucial early abstinence period and could inform future study designs.

Functional and structural networks decoupling in generalized tonic-clonic seizures and its reorganization by drugs.

To investigate potential functional and structural large-scale network disturbances in untreated patients with generalized tonic-clonic seizures (GTCS) and the effects of antiseizure drugs. In this study, 41 patients with GTCS, comprising 21 untreated patients and 20 patients who received antiseizure medications (ASMs), and 29 healthy controls were recruited to construct large-scale brain networks based on resting-state functional magnetic resonance imaging and diffusion tensor imaging. Structural and functional connectivity and network-level weighted correlation probability (NWCP) were further investigated to identify network features that corresponded to response to ASMs. Untreated patients showed more extensive enhancement of functional and structural connections than controls. Specifically, we observed abnormally enhanced connections between the default mode network (DMN) and the frontal-parietal network. In addition, treated patients showed similar functional connection strength to that of the control group. However, all patients exhibited similar structural network alterations. Moreover, the NWCP value was lower for connections within the DMN and between the DMN and other networks in the untreated patients; receiving ASMs could reverse this pattern. Our study identified alterations in structural and functional connectivity in patients with GTCS. The influence of ASMs may be more noticeable within the functional network; moreover, abnormalities in both the functional and structural coupling state may be improved by ASM treatment. Therefore, the coupling state of structural and functional connectivity may be used as an indicator of the efficacy of ASMs.

Age differences in the neural processing of semantics, within and beyond the core semantic network

Aging is associated with functional activation changes in domain-specific regions and large-scale brain networks. This preregistered Functional magnetic resonance imaging (fMRI) study investigated these effects within the domain of semantic cognition. Participants completed 1 nonsemantic and 2 semantic tasks. We found no age differences in semantic activation in core semantic regions. However, the right inferior frontal gyrus showed difficulty-related increases in both age groups. This suggests that age-related upregulation of this area may be a compensatory response to increased processing demands. At a network level, older people showed more engagement in the default mode network and less in the executive multiple-demand network, aligning with older people’s greater knowledge reserves and executive declines. In contrast, activation was age-invariant in semantic control regions. Finally, older adults showed reduced demand-related modulation of multiple-demand network activation in the nonsemantic task but not the semantic tasks. These findings provide a new perspective on the neural basis of semantic cognition in aging, suggesting that preserved function in specialized semantic networks may help to maintain semantic cognition in later life.

Connectomic Networks and Their Impact on Clinical Outcomes in Glioma Treatment: A Review

AbstractThe emerging field of connectomics has provided an improved understanding of the structural and functional organization of the human brain into large-scale brain networks. Recent studies have helped define the canonical neurological networks and outline how considering their presence may aid in surgical decision-making in brain tumor patients. Gliomas represent one of the most common types of brain tumor and often involve displacement and/or infiltration of neurological pathways, suggesting an opportunity to use connectomic maps to improve patient morbidity and mortality based on oncofunctional goals. This review aims to provide a working knowledge of important neurological networks, examine the use of networks in surgical planning, and describe the current literature discussing the impact of these networks on clinical outcomes in glioma resection.

How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method.

Many studies have shown that changes in the functional connectivity are diverse along with aging. However, few studies have addressed how aging affects connectivity among large-scale brain networks, and it is challenging to examine gradual aging trajectories from middle adulthood to old age. In this work, based on large-sample fMRI data from 6300 subjects aged between 49 to 73 years, we apply an independent component analysis-based method called NeuroMark to extract brain functional networks and their connectivity (i.e., functional network connectivity (FNC)), and then propose a two-level statistical analysis method to explore robust aging-related changes in functional network connectivity. We found that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network and cerebellum. Our results emphasize the diversity of brain aging and provide new evidence for non-pathological aging of the whole brain.Clinical Relevance-This provides new evidence for non-pathological aging of functional network connectivity in the whole brain.

Dynamic attentional mechanisms of creative cognition

In popular imagination creativity requires us to surrender control. Yet, attention is at the heart of control, and many studies show attention to play a key role in the creative process. This is partly due to the selective nature of attention—creative cognition consists of two phases, idea generation and idea evaluation, and selective processes are essential for both phases. Here, we investigate attentional (i.e., selective) mechanisms underlying each phase, using the framework of two major attention taxonomies: top-down/bottom-up and internal/external attention. We argue that creative cognition is supported by a dynamic interplay between the typically opposing sides of each taxonomy. Further, we argue that this dynamic relationship is reflected in interactions across three large-scale brain networks: the default mode (DMN), frontoparietal control (FPN), and salience (SN) networks. Our review of the evidence suggests that creative cognition is best achieved through the flexible use of multiple forms of attention, rather than through reduced attention. We thus propose a two-dimensional space, including one dimension for top-down/bottom-up attention and another for internal/external attention, which can sufficiently capture the flexibility and diversity of attentional mechanisms underlying different stages and components of creative cognition.

Functional network segregation is associated with higher functional connectivity in endurance runners

Neuroimaging studies have identified significant differences in brain structure, function, and connectivity between endurance runners and healthy controls. However, the topological organization of large-scale functional brain networks remains unexplored in endurance runners. Using resting-state functional magnetic resonance imaging data, this study examined the differences in the topological organization of functional networks between endurance runners (n = 22) and healthy controls (n = 20). Endurance runners had significantly higher clustering coefficients in the whole-brain functional network than healthy controls, but the two did not differ regarding the shortest path length or small-world index. Using network-based statistics, we identified one subnetwork in endurance runners with higher functional connectivity than healthy controls, and the mean functional connectivity of the subnetwork significantly correlated with the three aforementioned small-world parameters. In this subnetwork, the mean clustering coefficient of nodes associated with short-range connections was higher in endurance runners than in healthy controls, but the mean clustering coefficient of nodes associated with long-range connections did not differ between the two groups. In conclusion, using graph theoretical approaches, we revealed significant differences in the topological organization of the whole-brain functional network and functional connectivity between endurance runners and healthy controls. The relationship between these two features suggests that a more segregated network may arise from the optimization of the identified subnetwork in endurance runners. These findings are possibly the neural basis underlying the good performance of endurance runners in endurance running.

Astrocytic clearance and fragmentation of toxic proteins in Alzheimer’s disease on large-scale brain networks

The human brain is the most complicated biological structure on the planet. A major challenge of brain network modelling lies in its multi-scale spatio-temporal nature, covering scales from synapses to the whole brain. The coupled multiphysics and biochemical activities which spread through such a complex system shape brain capacity inside a structure-function relationship that requires a particular mathematical framework. Next-generation coupled-based mathematical modelling approaches to brain networks and the analysis of data-driven dynamical systems are needed to advance state-of-the-art therapeutic strategies for treating neurodegenerative diseases (NDDs) that affect millions of people worldwide, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Importantly, AD is marked by the presence of amyloid-beta (Aβ) plaques and tau (τ) proteins. Some disease-specific misfolded proteins can interact with healthy proteins to form long chains and aggregates of different sizes that have different transport properties and toxicity. An improved large-scale brain network model is proposed here to understand the pathogenesis of AD, especially the role of astrocytes in the presence of misfolded proteins (Aβ and τ). The idea involves astrocytic clearance, which assists in eliminating toxic Aβ via fragmentation. We use the general Smoluchowski theory of nucleation, aggregation, and fragmentation to predict the development and propagation of aggregates of misfolded proteins in the brain. It has been shown that the developed model leads to different size distributions and propagation along the network. We predicted that astrocytic clearance varies with the aggregate size, which is key to slowing down AD progression. The clearance and fragmentation of toxic proteins span several spatial and temporal scales, and this research will potentially yield new insight into the associated processes and brain networks in health and disease. Detailed multi-scale brain modelling provides a promising approach for consolidating, organizing, and bridging the data sets of data-driven brain network models.

Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities.

In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.

Impaired salience network switching in psychopathy

Growing evidence suggests that psychopathy is related to altered connectivity within and between three large-scale brain networks that support core cognitive functions, including allocation of attention. In healthy individuals, default mode network (DMN) is involved in internally-focused attention and cognition such as self-reference. Frontoparietal network (FPN) is anticorrelated with DMN and is involved in externally-focused attention to cognitively demanding tasks. A third network, salience network (SN), is involved in detecting salient cues and, crucially, appears to play a role in switching between the two anticorrelated networks, DMN and FPN, to efficiently allocate attentional resources. Psychopathy has been related to reduced anticorrelation between DMN and FPN, suggesting SN’s role in switching between these two networks may be diminished in the disorder. To test this hypothesis, we used independent component analysis to derive DMN, FPN, and SN activity in resting-state fMRI data in a sample of incarcerated men (N = 148). We entered the activity of the three networks into dynamic causal modeling to test SN’s switching role. The previously established switching effect of SN among young, healthy adults was replicated in a group of low psychopathy participants (posterior model probability = 0.38). As predicted, SN’s switching role was significantly diminished in high psychopathy participants (t(145) = 26.39, p < .001). These findings corroborate a novel theory of brain function in psychopathy. Future studies may use this model to test whether disrupted SN switching is related to high psychopathy individuals’ abnormal allocation of attention.

Study protocol for a randomised controlled trial investigating the effects of Mindfulness Based Stress Reduction on stress regulation and associated neurocognitive mechanisms in stressed university students: the MindRest study

BackgroundStress-related disorders are a growing public health concern. While stress is a natural and adaptive process, chronic exposure to stressors can lead to dysregulation and take a cumulative toll on physical and mental well-being. One approach to coping with stress and building resilience is through Mindfulness-Based Stress Reduction (MBSR). By understanding the neural mechanisms of MBSR, we can gain insight into how it reduces stress and what drives individual differences in treatment outcomes. This study aims to establish the clinical effects of MBSR on stress regulation in a population that is susceptible to develop stress-related disorders (i.e., university students with mild to high self-reported stress), to assess the role of large-scale brain networks in stress regulation changes induced by MBSR, and to identify who may benefit most from MBSR.MethodsThis study is a longitudinal two-arm randomised, wait-list controlled trial to investigate the effects of MBSR on a preselected, Dutch university student population with elevated stress levels. Clinical symptoms are measured at baseline, post-treatment, and three months after training. Our primary clinical symptom is perceived stress, with additional measures of depressive and anxiety symptoms, alcohol use, stress resilience, positive mental health, and stress reactivity in daily life. We investigate the effects of MBSR on stress regulation in terms of behaviour, self-report measures, physiology, and brain activity. Repetitive negative thinking, cognitive reactivity, emotional allowance, mindfulness skills, and self-compassion will be tested as potential mediating factors for the clinical effects of MBSR. Childhood trauma, personality traits and baseline brain activity patterns will be tested as potential moderators of the clinical outcomes.DiscussionThis study aims to provide valuable insights into the effectiveness of MBSR in reducing stress-related symptoms in a susceptible student population and crucially, to investigate its effects on stress regulation, and to identify who may benefit most from the intervention.Trial registrationRegistered on September 15, 2022, at clinicaltrials.gov, NCT05541263.

Altered large-scale individual-based morphological brain network in spinocerebellar ataxia type 3.

Accumulating evidences indicate regional gray matter (GM) morphology atrophy in spinocerebellar ataxia type 3 (SCA3); however, whether large-scale morphological brain networks (MBNs) undergo widespread reorganization in these patients remains unclear. To investigate the topological organization of large-scale individual-based MBNs in SCA3 patients. The individual-based MBNs were constructed based on the inter-regional morphological similarity of GM regions. Graph theoretical analysis was taken to assess GM structural connectivity in 76 symptomatic SCA3, 24 pre-symptomatic SCA3, and 54 healthy normal controls (NCs). Topological parameters of the resulting graphs and network-based statistics analysis were compared among symptomatic SCA3, pre-symptomatic SCA3, and NCs groups. The inner association between network properties and clinical variables was further analyzed. Compared to NCs and pre-symptomatic SCA3 patients, symptomatic SCA3 indicated significantly decreased integration and segregation, a shift to "weaker small-worldness", characterized by decreased Cp , lower Eloc, and Eglob (all p < 0.005). Regarding nodal properties, symptomatic SCA3 exhibited significantly decreased nodal profiles in the central executive network (CEN)-related left inferior frontal gyrus, limbic regions involving the bilateral amygdala, left hippocampus, and bilateral pallidum, thalamus; and increased nodal degree, efficiency in bilateral caudate (all pFDR <0.05). Meanwhile, clinical variables were correlated with altered nodal profiles (pFDR ≤0.029). SCA3-related subnetwork was closely interrelated with dorsolateral cortico-striatal circuitry extending to orbitofrontal-striatal circuits and dorsal visual systems (lingual gyrus-striatal). Symptomatic SCA3 patients undergo an extensive and significant reorganization in large-scale individual-based MBNs, probably due to disrupted prefrontal cortico-striato-thalamo-cortical loops, limbic-striatum circuitry, and enhanced connectivity in the neostriatum. This study highlights the crucial role of abnormal morphological connectivity alterations beyond the pattern of brain atrophy, which might pave the way for therapeutic development in the future.

Frequency-dependent connectivity in large-scale resting-state brain networks during sleep.

Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.

Connectivity gradients in spontaneous brain activity at multiple frequency bands.

The intrinsic organizational structure of the brain is reflected in spontaneous brain oscillations. Its functional integration and segregation hierarchy have been discovered in space by leveraging gradient approaches to low-frequency functional connectivity. This hierarchy of brain oscillations has not yet been fully understood, since previous studies have mainly concentrated on the brain oscillations from a single limited frequency range (~ 0.01-0.1Hz). In this work, we extended the frequency range and performed gradient analysis across multiple frequency bands of fast resting-state fMRI signals from the Human Connectome Project and condensed a frequency-rank cortical map of the highest gradient. We found that the coarse skeletons of the functional organization hierarchy are generalizable across the multiple frequency bands. Beyond that, the highest integration levels of connectivity vary in the frequency domain across different large-scale brain networks. These findings are replicated in another independent dataset and demonstrated that different brain networks can integrate information at varying rates, indicating the significance of examining the intrinsic architecture of spontaneous brain activity from the perspective of multiple frequency bands.

Multi-modal and multi-model interrogation of large-scale functional brain networks

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome.To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data.We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears).Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

Cognitive, behavioral, and brain functional connectivity correlates of fatigue in amyotrophic lateral sclerosis.

Fatigue is defined as a symptom of exhaustion unexplained by drug effects or psychiatric disorders and comprises two main components (i.e., central or "mental" and peripheral or "physical" components), both influencing global disability in amyotrophic lateral sclerosis (ALS). We aim at investigating the clinical correlations between "physical" and "mental" components of fatigue, measured by the Multidimensional Fatigue Inventory scale, and motor and cognitive/behavioral disability in a large sample of patients with ALS. We also investigated the correlations between these measures of fatigue and resting-state functional connectivity of brain functional magnetic resonance imaging (RS-fMRI) large-scale networks in a subset of patients. One hundred and thirty ALS patients were assessed for motor disability, cognitive and behavioral dysfunctions, fatigue, anxiety, apathy, and daytime sleepiness. Moreover, the collected clinical parameters were correlated with RS-fMRI functional connectivity changes in the large-scale brain networks of 30 ALS patients who underwent MRI. Multivariate correlation analysis revealed that "physical" fatigue was related to anxiety and respiratory dysfunction, while "mental" fatigue was related to memory impairment and apathy. Moreover, the mental fatigue score was directly related to functional connectivity in the right and left insula (within the salience network), and inversely related to functional connectivity in the left middle temporal gyrus (within the default mode network). Although the "physical" component of fatigue may be influenced by the disease itself, in ALS the "mental" component of fatigue correlates with cognitive and behavioral impairment, as well as with alterations of functional connectivity in extra-motor networks.

Deconvoluting human Brodmann area 8 based on its unique structural and functional connectivity.

Brodmann area 8 (BA8) is traditionally defined as the prefrontal region of the human cerebrum just anterior to the premotor cortices and enveloping most of the superior frontal gyrus. Early studies have suggested the frontal eye fields are situated at its most caudal aspect, causing many to consider BA8 as primarily an ocular center which controls contralateral gaze and attention. However, years of refinement in cytoarchitectural studies have challenged this traditional anatomical definition, providing a refined definition of its boundaries with neighboring cortical areas and the presence of meaningful subdivisions. Furthermore, functional imaging studies have suggested its involvement in a diverse number of higher-order functions, such as motor, cognition, and language. Thus, our traditional working definition of BA8 has likely been insufficient to truly understand the complex structural and functional significance of this area. Recently, large-scale multi-modal neuroimaging approaches have allowed for improved mapping of the neural connectivity of the human brain. Insight into the structural and functional connectivity of the brain connectome, comprised of large-scale brain networks, has allowed for greater understanding of complex neurological functioning and pathophysiological diseases states. Simultaneously, the structural and functional connectivity of BA8 has recently been highlighted in various neuroimaging studies and detailed anatomic dissections. However, while Brodmann's nomenclature is still widely used today, such as for clinical discussions and the communication of research findings, the importance of the underlying connectivity of BA8 requires further review.

Altered functional connectivity between cortical networks associated with inhibitory control in trauma-exposed females

Post-traumatic stress disorder (PTSD) is associated with impaired inhibitory control and alterations in large-scale brain network connectivity. However, few studies to date have examined the construct of inhibitory control as it relates to resting-state functional connectivity (rsFC) in a population with PTSD or trauma-exposure. The present study investigated the relationship between impaired inhibitory control and rsFC within the default mode network (DMN), central executive network (CEN), and salience network (SN) in a sample of females exposed to interpersonal trauma with and without PTSD (n = 67). Participants completed a classic Color-Word Stroop task as a measure of inhibitory control and two resting-state fMRI scans. We conducted voxelwise rsFC analyses with seed regions in the DMN, CEN, and SN and voxelwise linear regression analyses to examine the relationship between inhibitory control and rsFC of these networks across the sample. Better Stroop performance was negatively associated with total self-reported PTSD symptoms. An analysis of PTSD symptom clusters indicated that better Stroop performance was also associated with re-experiencing and hyperarousal symptoms, but not avoidance PTSD symptoms. Decreased coupling between the CEN and the DMN was associated with better inhibitory control in this sample of trauma-exposed females. These findings lend support to the hypothesis that efficient switching between these networks may contribute to better performance on cognitive and attentional tasks in trauma-exposed individuals.