Intrinsic Brain Networks Research Articles

How Can Graph Theory Inform the Dual-stream Model of Speech Processing? A Resting-state Functional Magnetic Resonance Imaging Study of Stroke and Aphasia Symptomology.

The dual-stream model of speech processing describes a cortical network involved in speech processing. However, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream model's regions are related to speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI data sets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors collected at another site. We successfully identified an intrinsic functional network among the dual-stream model's regions in the control group using functional connectivity. We then used both standard functional connectivity analyses and graph theory approaches to determine how this connectivity may predict performance on clinical aphasia assessments. Our findings provide evidence that the dual-stream model of speech processing is an intrinsic network as measured via resting-state MRI and that functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. In addition, the functional connectivity of the hub nodes predicted linguistic impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemisphere's homologues of the left dorsal stream hubs to the left dorsal hubs, versus to the right ventral stream hubs, is a particularly strong predictor of poststroke aphasia severity and symptomology.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Resting-state functional connectivity in anxiety disorders: a multicenter fMRI study.

Anxiety disorders (AD) are associated with altered connectivity in large-scale intrinsic brain networks. It remains uncertain how much these signatures overlap across different phenotypes due to a lack of well-powered cross-disorder comparisons. We used resting-state functional magnetic resonance imaging (rsfMRI) to investigate differences in functional connectivity (FC) in a cross-disorder sample of AD patients and healthy controls (HC). Before treatment, 439 patients from two German multicenter clinical trials at eight different sites fulfilling a primary diagnosis of panic disorder and/or agoraphobia (PD/AG, N = 154), social anxiety disorder (SAD, N = 95), or specific phobia (SP, N = 190) and 105 HC underwent an 8 min rsfMRI assessment. We performed categorical and dimensional regions of interest (ROI)-to-ROI analyses focusing on connectivity between regions of the defensive system and prefrontal regulation areas. AD patients showed increased connectivity between the insula and the thalamus compared to controls. This was mainly driven by PD/AG patients who showed increased (insula/hippocampus/amygdala-thalamus) and decreased (dorsomedial prefrontal cortex/periaqueductal gray-anterior cingulate cortex) positive connectivity between subcortical and cortical areas. In contrast, SAD patients showed decreased negative connectivity exclusively in cortical areas (insula-orbitofrontal cortex), whereas no differences were found in SP patients. State anxiety associated with the scanner environment did not explain the FC between these regions. Only PD/AG patients showed pronounced connectivity changes along a widespread subcortical-cortical network, including the midbrain. Dimensional analyses yielded no significant results. The results highlighting categorical differences between ADs at a systems neuroscience level are discussed within the context of personalized neuroscience-informed treatments. PROTECT-AD's registration at NIMH Protocol Registration System: 01EE1402A and German Register of Clinical Studies: DRKS00008743. SpiderVR's registration at ClinicalTrials.gov: NCT03208400.

Prenatal environment is associated with the pace of cortical network development over the first three years of life

Environmental influences on brain structure and function during early development have been well-characterized, but whether early environments are associated with the pace of brain development is not clear. In pre-registered analyses, we use flexible non-linear models to test the theory that prenatal disadvantage is associated with differences in trajectories of intrinsic brain network development from birth to three years (n = 261). Prenatal disadvantage was assessed using a latent factor of socioeconomic disadvantage that included measures of mother’s income-to-needs ratio, educational attainment, area deprivation index, insurance status, and nutrition. We find that prenatal disadvantage is associated with developmental increases in cortical network segregation, with neonates and toddlers with greater exposure to prenatal disadvantage showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between prenatal disadvantage and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. Disadvantage-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results shed light on associations between the early environment and trajectories of cortical development.

Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal–external attention switching

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

The dissociative role of bursting and non-bursting neural activity in the oscillatory nature of functional brain networks

Abstract The oscillatory nature of intrinsic brain networks is largely taken for granted in the systems neuroscience community. However, the hypothesis that brain rhythms—and by extension transient bursting oscillations—underlie functional networks has not been demonstrated per se. Electrophysiological measures of functional connectivity are indeed affected by the power bias, which may lead to artefactual observations of spectrally specific network couplings not genuinely driven by neural oscillations, bursting or not. We investigate this crucial question by introducing a unique combination of a rigorous mathematical analysis of the power bias in frequency-dependent amplitude connectivity with a neurobiologically informed model of cerebral background noise based on hidden Markov modeling of resting-state magnetoencephalography (MEG). We demonstrate that the power bias may be corrected by a suitable renormalization depending nonlinearly on the signal-to-noise ratio, with noise identified as non-bursting oscillations. Applying this correction preserves the spectral content of amplitude connectivity, definitely proving the importance of brain rhythms in intrinsic functional networks. Our demonstration highlights a dichotomy between spontaneous oscillatory bursts underlying network couplings and non-bursting oscillations acting as background noise but whose function remains unsettled.

Default mode network electrophysiological dynamics and causal role in creative thinking.

The default mode network (DMN) is a widely distributed, intrinsic brain network thought to play a crucial role in internally directed cognition. The present study employs stereo-EEG in 13 human patients, obtaining high resolution neural recordings across multiple canonical DMN regions during two processes that have been associated with creative thinking: spontaneous and divergent thought. We probe these two DMN-associated higher cognitive functions through mind wandering and alternate uses tasks, respectively. Our results reveal DMN recruitment during both tasks, as well as a task-specific dissociation in spatiotemporal response dynamics. When compared to the fronto-parietal network, DMN activity was characterized by a stronger increase in gamma band power (30-70 Hz) coupled with lower theta band power (4-8 Hz). The difference in activity between the two networks was especially strong during the mind wandering task. Within the DMN, we found that the tasks showed different dynamics, with the alternate uses task engaging the DMN more during the initial stage of the task, and mind wandering in the later stage. Gamma power changes were mainly driven by lateral DMN sites, while theta power displayed task-specific effects. During alternate uses task, theta changes did not show spatial differences within the DMN, while mind wandering was associated to an early lateral and late dorsomedial DMN engagement. Furthermore, causal manipulations of DMN regions using direct cortical stimulation preferentially decreased the originality of responses in the alternative uses task, without affecting fluency or mind wandering. Our results suggest that DMN activity is flexibly modulated as a function of specific cognitive processes and supports its causal role in divergent thinking. These findings shed light on the neural constructs supporting different forms of cognition and provide causal evidence for the role of DMN in the generation of original connections among concepts.

Investigating the relationship between resting-state EEG microstates and executive functions: A null finding

Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = −.18, 95% CI [−.33, −.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics − as measured by resting-state EEG microstate metrics − are a reliable signature of executive functioning remains unsupported.

Unveiling convergent and divergent intrinsic brain network alternations in depressed adolescents engaged in non-suicidal self-injurious behaviors with and without suicide attempts.

Limited understanding exists regarding the neurobiological mechanisms underlying non-suicidal self-injury (NSSI) and suicide attempts (SA) in depressed adolescents. The maturation of brain network is crucial during adolescence, yet the abnormal alternations in depressed adolescents with NSSI or NSSI+SA remain poorly understood. Resting-state functional magnetic resonance imaging data were collected from 114 depressed adolescents, classified into three groups: clinical control (non-self-harm), NSSI only, and NSSI+SA based on self-harm history. The alternations of resting-state functional connectivity (RSFC) were identified through support vector machine-based classification. Convergent alterations in NSSI and NSSI+SA predominantly centered on the inter-network RSFC between the Limbic network and the three core neurocognitive networks (SalVAttn, Control, and Default networks). Divergent alterations in the NSSI+SA group primarily focused on the Visual, Limbic, and Subcortical networks. Additionally, the severity of depressive symptoms only showed a significant correlation with altered RSFCs between Limbic and DorsAttn or Visual networks, strengthening the fact that increased depression severity alone does not fully explain observed FC alternations in the NSSI+SA group. Convergent alterations suggest a shared neurobiological mechanism along the self-destructiveness continuum. Divergent alterations may indicate biomarkers differentiating risk for SA, informing neurobiologically guided interventions.

Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia.

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Connectome-based predictive modeling of Internet addiction symptomatology.

Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.

Intrinsic network abnormalities in children with autism spectrum disorder: an independent component analysis.

Aberrant intrinsic brain networks are consistently observed in individuals with autism spectrum disorder. However, studies examining the strength of functional connectivity across brain regions have yielded conflicting results. Therefore, this study aimed to investigate the functional connectivity of the resting brain in children with low-functioning autism, including during the early developmental stages. We explored the functional connectivity of 43 children with autism spectrum disorder and 54 children with typical development aged 2 to 12 years using resting-state functional magnetic resonance imaging data. We used independent component analysis to classify the brain regions into six intrinsic networks and analyzed the functional connectivity within each network. Moreover, we analyzed the relationship between functional connectivity and clinical scores. In children with autism, the under-connectivities were observed within several brain networks, including the cognitive control, default mode, visual, and somatomotor networks. In contrast, we found over-connectivities between the subcortical, visual, and somatomotor networks in children with autism compared with children with typical development. Moderate effect sizes were observed in entire networks (Cohen's d = 0.43-0.77). These network alterations were significantly correlated with clinical scores such as the communication sub-score (r = - 0.442, p = 0.045) and the calibrated severity score (r = - 0.435, p = 0.049) of the Autism Diagnostic Observation Schedule. These opposing results observed based on the brain areas suggest that aberrant neurodevelopment proceeds in various ways depending on the functional brain regions in individuals with autism spectrum disorder.

Higher-Order Intrinsic Brain Network Trajectories After Antipsychotic Treatment in Medication-Naïve Patients With First-Episode Psychosis

BackgroundIntrinsic brain network connectivity is already altered in first-episode psychosis (FEP), but the longitudinal trajectories of network connectivity, especially in response to antipsychotic treatment, remain poorly understood. The goal of this study was to investigate how antipsychotic medications affect higher-order intrinsic brain network connectivity in FEP. MethodsData from 87 antipsychotic medication-naïve patients with FEP and 87 healthy control participants were used. Medication-naïve patients received antipsychotic treatment for 16 weeks. Resting-state functional connectivity (FC) of the default mode, salience, dorsal attention, and executive control networks were assessed prior to treatment and at 6 and 16 weeks after treatment. We evaluated baseline and FC changes using linear mixed models to test group × time interactions within each network. Associations between FC changes after 16 weeks and response to treatment were also evaluated. ResultsPrior to treatment, significant group differences in all networks were found. However, significant trajectory changes in FC were found only in the default mode and executive control networks. Changes in FC in these networks were associated with treatment response. Several sensitivity analyses showed a consistent normalization of executive control network FC in response to antipsychotic treatment. ConclusionsHere, we found that alterations in intrinsic brain network FC were not only alleviated with antipsychotic treatment, but the extent of this normalization was also associated with the degree of reduction in symptom severity. Taken together, our data suggest modulation of intrinsic brain network connectivity (mainly frontoparietal connectivity) as a mechanism underlying antipsychotic treatment response in FEP.

Interventional clinical trial findings from targeted multiday repetitive transcranial magnetic stimulation to treat memory loss in amnestic mild cognitive impairment

AbstractBackgroundMemory loss is the hallmark cognitive symptom of Alzheimer’s disease (AD), and it affects independence, autonomy, and identity. AD‐related memory deficits are associated with pathological changes in brain structure and function, and current treatments offer limited relief. Targeted repetitive transcranial magnetic stimulation (rTMS) of AD‐vulnerable intrinsic brain networks can enhance memory abilities in healthy adults (Wang et al., 2014). To test rTMS approaches as a potential treatment for clinical memory loss, we administered a local clinical trial (ClinicalTrials.gov #alz078533) that enrolled patients with amnestic mild cognitive impairment (aMCI), a frequent precursor to AD (target enrollment of 16). Here, we report cognitive and brain findings from our trial.MethodsPatients with aMCI completed two weeklong interventions consisting of pre‐stimulation brain and cognitive measurement, rTMS for five consecutive days, and post‐stimulation brain and cognitive measurement. Brain measures included structural and resting‐state functional MRI data; cognitive assessments included measures of memory function. Interventions were identically formatted except for rTMS intensity (treatment/sham). Stimulation used β‐frequency pulse sequences which alternated 2‐sec. 20 Hz stimulation with 28‐sec. rest for 20 min (1600 pulses); stimulation location was a left lateral parietal cortex location coactive with a hippocampus‐centered functional network. Stimulation intensity varied with each participant’s resting motor threshold (treatment, 100%; sham, 10%). Condition order was counterbalanced.ResultsWe identified hippocampus‐centered networks in each participant, then localized parietal rTMS targets. Critically, we observed changes in brain and cognitive (memory) measures in the treatment‐intensity condition that were not observed in the sham‐intensity condition. Furthermore, we observed changes in intrinsic network connectivity within the stimulated hippocampus‐centered network.ConclusionsOur trial of patients with aMCI measured changes in brain and cognitive variables after rTMS of a brain network supporting memory processes. We found evidence consistent with neural plasticity, and we frequently observed memory improvement in patients with aMCI after treatment‐intensity rTMS. Our findings suggest that rTMS can improve memory in clinical populations. This is important because, counterintuitively, new disease modifying therapies for AD may increase need for symptomatic treatment of memory loss due to prolongation of prodromal/mild stages of AD. Thus, adjuvant therapies such as rTMS for memory loss could improve AD treatment outcomes.

Interventional clinical trial findings from targeted multiday repetitive transcranial magnetic stimulation to treat memory loss in amnestic mild cognitive impairment

AbstractBackgroundMemory loss is the hallmark cognitive symptom of Alzheimer’s disease (AD), and it affects independence, autonomy, and identity. AD‐related memory deficits are associated with pathological changes in brain structure and function, and current treatments offer limited relief. Targeted repetitive transcranial magnetic stimulation (rTMS) of AD‐vulnerable intrinsic brain networks can enhance memory abilities in healthy adults (Wang et al., 2014). To test rTMS approaches as a potential treatment for clinical memory loss, we administered a local clinical trial (ClinicalTrials.gov #alz081816) that enrolled patients with amnestic mild cognitive impairment (aMCI), a frequent precursor to AD (target enrollment of 16). Here, we report cognitive and brain findings from our trial.MethodPatients with aMCI completed two weeklong interventions consisting of pre‐stimulation brain and cognitive measurement, rTMS for five consecutive days, and post‐stimulation brain and cognitive measurement. Brain measures included structural and resting‐state functional MRI data; cognitive assessments included measures of memory function. Interventions were identically formatted except for rTMS intensity (treatment/sham). Stimulation used ß‐frequency pulse sequences which alternated 2‐sec. 20 Hz stimulation with 28‐sec. rest for 20 min (1600 pulses); stimulation location was a left lateral parietal cortex location coactive with a hippocampus‐centered functional network. Stimulation intensity varied with each participant’s resting motor threshold (treatment, 100%; sham, 10%). Condition order was counterbalanced.ResultWe identified hippocampus‐centered networks in each participant, then localized parietal rTMS targets. Critically, we observed changes in brain and cognitive (memory) measures in the treatment‐intensity condition that were not observed in the sham‐intensity condition. Furthermore, we observed changes in intrinsic network connectivity within the stimulated hippocampus‐centered network.ConclusionOur trial of patients with aMCI measured changes in brain and cognitive variables after rTMS of a brain network supporting memory processes. We found evidence consistent with neural plasticity, and we frequently observed memory improvement in patients with aMCI after treatment‐intensity rTMS. Our findings suggest that rTMS can improve memory in clinical populations. This is important because, counterintuitively, new disease modifying therapies for AD may increase need for symptomatic treatment of memory loss due to prolongation of prodromal/mild stages of AD. Thus, adjuvant therapies such as rTMS for memory loss could improve AD treatment outcomes.

Alterations via inter-regional connective relationships in Alzheimer's disease.

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.

Disrupted brain state dynamics in opioid and alcohol use disorder: attenuation by nicotine use.

Substance use disorder (SUD) is a chronic relapsing disorder with long-lasting changes in brain intrinsic networks. While most research to date has focused on static functional connectivity, less is known about the effect of chronic drug use on dynamics of brain networks. Here we investigated brain state dynamics in individuals with opioid use (OUD) and alcohol use disorder (AUD) and assessed how concomitant nicotine use, which is frequent among individuals with OUD and AUD, affects brain dynamics. Resting-state functional magnetic resonance imaging data of 27 OUD, 107 AUD, and 137 healthy participants were included in the analyses. To identify recurrent brain states and their dynamics, we applied a data-driven clustering approach that determines brain states at a single time frame. We found that OUD and AUD non-smokers displayed similar changes in brain state dynamics including decreased fractional occupancy or dwell time in default mode network (DMN)-dominated brain states and increased appearance rate in visual network (VIS)-dominated brain states, which were also reflected in transition probabilities of related brain states. Interestingly, co-use of nicotine affected brain states in an opposite manner by lowering VIS-dominated and enhancing DMN-dominated brain states in both OUD and AUD participants. Our finding revealed a similar pattern of brain state dynamics in OUD and AUD participants that differed from controls, with an opposite effect for nicotine use suggesting distinct effects of various drugs on brain state dynamics. Different strategies for treating SUD may need to be implemented based on patterns of co-morbid drug use.

Opponent intrinsic brain network connectivity profiles associated with posttraumatic fear and dysphoria symptoms in trauma-exposed refugees.

Resting-state functional magnetic resonance imaging (rsfMRI) studies report functional alterations in the connectivity between intrinsic brain networks in posttraumatic stress disorder (PTSD), but PTSD heterogeneity is rarely considered. Evidence points to fear (e.g., reexperiencing) and dysphoria (e.g., withdrawal) symptom factors as important in PTSD presentations, including relating to variable emotion dysregulation patterns. This study, therefore, tested how fear and dysphoria posttraumatic symptoms were differentially associated with core network connectivity and emotion dysregulation behaviors in a large group of trauma-exposed refugees. A final sample of 77 trauma-exposed participants completed a rsfMRI scan. Independent component analysis identified active networks and functional network connectivity (FNC) between networks was assessed. Fear and dysphoria posttraumatic symptoms were partially correlated with FNCs, and linear regression models examined relationships with self-reported difficulties in emotion regulation. Twenty-three active networks were identified, eight being in the networks of interest (p < .05 false discovery rate-corrected). Fear and dysphoria symptoms were specifically related to connectivity patterns between two subnetworks of the default mode network (DMN). Fear symptoms were negatively associated with anterior dorsomedial DMN (admDMN) and temporoparietal DMN (tpDMN) connectivity; whereas dysphoria symptoms were positively associated with admDMN-tpDMN connectivity. Additionally, admDMN-tpDMN connectivity was positively predicted by goal-directed emotion dysregulation but negatively predicted by poor emotional clarity. Fear and dysphoria posttraumatic symptoms showed opponent associations with admDMN and tpDMN connectivity, potentially reflecting patterns of under- and overemotion dysregulation associated with these symptom profiles respectively. Findings highlight the importance of considering posttraumatic heterogeneity when constructing neural models of PTSD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Supplemental Material for Opponent Intrinsic Brain Network Connectivity Profiles Associated with Posttraumatic Fear and Dysphoria Symptoms in Trauma-Exposed Refugees

Supplemental Material for Opponent Intrinsic Brain Network Connectivity Profiles Associated with Posttraumatic Fear and Dysphoria Symptoms in Trauma-Exposed Refugees