Resting-state Functional Connectivity Networks Research Articles

The effect of erenumab on brain network function in episodic migraine patients: a randomized, placebo-controlled clinical trial (RESET BRAIN)

BackgroundWe aimed to explore whether erenumab, a monoclonal antibody targeting the calcitonin gene-related peptide receptor, could exert a central effect on brain network function in migraine, and investigate the persistence of such an effect following treatment discontinuation.MethodsThis was a randomized, double-blind, placebo-controlled, multicenter trial with a crossover design performed in adult episodic migraine patients with previous treatment failure. Patients were randomized (1:1) to 12 weeks of erenumab 140 mg or placebo, followed by a 12-week crossover. Resting state (RS) functional connectivity (FC) changes of brain networks involved in migraine were investigated using a seed-based correlation approach.ResultsSixty-one patients were randomized to treatment. In each treatment sequence, 27 patients completed the visit at week 12. Forty-four enrolled patients, 22 in each treatment sequence, completed the study procedures with no major protocol violations. We observed a carry-over effect of erenumab during the placebo treatment and therefore data analysis was performed as a parallel comparison of erenumab vs placebo of the first 12 weeks of treatment. From baseline to week 12, compared to placebo, patients receiving erenumab showed RS FC changes within the cerebellar, thalamic and periaqueductal gray matter networks, significantly associated with clinical improvement. Compared to non-responders, patients achieving a 50% reduction in migraine days had distinct patterns of thalamic and visual network RS FC. Brain RS FC changes reversed when erenumab was stopped. A lower baseline RS FC of the pontine network identified patients responding to erenumab.ConclusionErenumab modulates RS FC of networks involved in migraine pathophysiology. In line with clinical response, erenumab-induced brain RS FC changes tend to reverse when treatment is stopped.

Whole-brain resting-state connectivity underlying impaired inhibitory control during early versus longer-term abstinence in cocaine addiction.

Lapses in inhibitory control have been linked to relapse in human drug addiction. Evidence suggests differences in inhibitory control depending on abstinence duration, but the underlying neural mechanisms remain unknown. We hypothesized that early abstinence (2-5 days) would be characterized by the strongest impairments of inhibitory control and most wide-spread deviations in resting-state functional connectivity of brain networks, while longer-term abstinence (>30 days) would be characterized by weaker impairments as compared to healthy controls. In this laboratory-based cross-sectional study, we compared individuals with Cocaine Use Disorder (iCUD) during early (cocaine urine-positive: N = 19, iCUD+; 32% female; mean age: 46.8 years) and longer-term abstinence (cocaine urine-negative: N = 29, iCUD-; 15% female; mean age: 46.6 years) to healthy controls (N = 33; 24% female; mean age: 40.9 years). We compared the groups on inhibitory control performance (Stop-Signal Task) and, using a whole-brain graph theory analysis (638 region parcellation) of functional magnetic resonance imaging (fMRI) data, we tested for group differences in resting-state brain function (local/global efficiency). We characterized how resting-state brain function was associated with inhibitory control performance within iCUD. Inhibitory control performance was worst in the early abstinence group, and intermediate in the longer-term abstinence group, as compared to the healthy control group (P < 0.01). More recent use of cocaine (CUD+ > CUD- > healthy controls) was characterized by decreased efficiency in fronto-temporal and subcortical networks (primarily in the salience, semantic, and basal ganglia networks) and increased efficiency in visual networks. Importantly, a similar functional connectivity pattern characterized impaired inhibitory control performance within iCUD (all brain analyses P < 0.05, FWE-corrected). Together, we demonstrated that a similar pattern of systematic and widespread deviations in resting-state brain efficiency, extending beyond the networks commonly investigated in human drug addiction, is linked to both abstinence duration and inhibitory control deficits in iCUD.

Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing

BackgroundThe recurrent nature of Major Depressive Disorder (MDD) asks for a better understanding of mechanisms underlying relapse. Previously, self-referential processing abnormalities have been linked to vulnerability for relapse. We investigated whether abnormalities in self-referential cognitions and functioning of associated brain-networks persist upon remission and predict relapse. MethodsRemitted recurrent MDD patients (n = 48) and never-depressed controls (n = 23) underwent resting-state fMRI scanning at baseline and were additionally assessed for their implicit depressed self-associations and ruminative behaviour. A template-based dual regression approach was used to investigate between-group differences in default mode, cingulo-opercular and frontoparietal network resting-state functional connectivity (RSFC). Additional prediction of relapse status at 18-month follow-up was investigated within patients using both regression analyses and machine learning classifiers. ResultsRemitted patients showed higher rumination, but no implicit depressed self-associations or RSFC abnormalities were observed between patients and controls. Nevertheless, relapse was related to i) baseline RSFC between the ventral default mode network and the precuneus, dorsomedial frontal gyrus, and inferior occipital lobe, ii) implicit self-associations, and iii) uncontrollability of ruminative thinking, when controlled for depressive symptomatology. Moreover, preliminary machine learning classifiers demonstrated that RSFC within the investigated networks predicted relapse on an individual basis. ConclusionsRemitted MDD patients seem to be commonly characterized by abnormal rumination, but not by implicit self-associations or abnormalities in relevant brain networks. Nevertheless, relapse was predicted by self-related cognitions and default mode RSFC during remission, suggesting that variations in self-relevant processing play a role in the complex dynamics associated with the vulnerability to developing recurrent depressive episodes. Clinical trial registrationNetherlands Trial Register, August 18, 2015, trial number NL53205.042.15.

Brain Functional Connectivity Networks do not Return to Resting-state During Control Trials in Block Design Experiments.

Many studies on morphology analysis show that if short inter-stimulus intervals separate tasks, the hemodynamic response amplitude will return to the resting-state baseline before the subsequent stimulation onset; hence, responses to successive tasks do not overlap. Accordingly, popular brain imaging analysis techniques assume changes in hemodynamic response amplitude subside after a short time (around 15 seconds). However, whether this assumption holds when studying brain functional connectivity has yet to be investigated. This paper assesses whether or not the functional connectivity network in control trials returns to the resting-state functional connectivity network. Traditionally, control trials in block-design experiments are used to evaluate response morphology to no stimulus. We analyzed data from an event-related experiment with audio and visual stimuli and resting state. Our results showed that functional connectivity networks during control trials were more similar to that of tasks than resting-state networks. In other words, contrary to task-related changes in the hemodynamic amplitude, where responses settle after a short time, the brain's functional connectivity networks do not return to their intrinsic resting-state network in such short intervals.

Resting-state network functional connectivity before and after bariatric surgery.

Bariatric surgery is an increasingly popular treatment for patients with severe obesity and related health issues (e.g., diabetes, cardiovascular disease). Studies have identified alterations in functional connectivity both in obesity and following surgical treatment for severe obesity. This study aimed to assess brain function via resting-state within-network connectivity in bariatric surgery patients with severe obesity. University hospital. Thirty-four bariatric surgery patients completed functional neuroimaging at baseline and postoperatively (goal, 12 weeks; actual, 16 weeks, on average). They also self-reported health information. Baseline resting-state functional connectivity (RSFC) was predicted by baseline age, body mass index (BMI), continuous positive airway pressure use, and reported history of rheumatoid arthritis and type 2 diabetes. Change in RSFC was assessed using the same predictors. This model was run with and without controlling for baseline RSFC. Higher baseline BMI predicted lower baseline RSFC in 3 networks. Lower baseline RSFC also was related to rheumatoid arthritis and type 2 diabetes. Difference between baseline and follow-up RSFC was strongly negatively associated with baseline RSFC. Controlling for baseline RSFC, type 2 diabetes negatively predicted RSFC difference. RSFC may reflect brain dysfunction in patients with obesity and related diseases. Thatless connectivity at baseline predicted greater positive change suggests that RSFC may be a biomarker of neurocognitive improvement following bariatric surgery. Diseases more prevalent in patients with obesity (e.g., rheumatoid arthritis and type 2 diabetes) along with elevated BMI negatively affect RSFC likely through inflammatory pathways.

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.

Resting-state functional network connectivity underlying conscientiousness in school-aged children

ABSTRACT Conscientiousness is a personality trait that matures from early childhood to late adolescence, yet little is known about its underlying brain mechanisms during this period. To investigate this, our study examined the resting-state functional network connectivity (rsFNC) of 69 school-aged children (mean age = 10.12 years, range = 9–12) using a whole-brain region-of-interest (ROI) based analysis, based on functional magnetic resonance imaging (fMRI). The results indicated a positive association between conscientiousness and the rsFNC between the fronto-parietal network (FPN) and two brain networks: the somatosensory motor-hand network (SMHN) and the auditory network (AN). However, conscientiousness was negatively associated with the rsFNC between FPN and two other networks: the salience network (SN); the default mode network (DMN). Moreover, our results suggest that the FPN may play a hub role in the neural performance of children’s conscientiousness. Intrinsic brain networks, particularly those involved in higher-order cognitive functions, impact children’s conscientiousness. Therefore, FPN plays an important role in the development of children’s personality, providing insight into the neural mechanisms underlying children’s personality.

Sleep mediates the effect of stressful environments on youth development of impulsivity: The moderating role of within default mode network resting-state functional connectivity

ObjectivesYouth raised in stressful environments are at increased risk for developing impulsive traits, which are a robust precursor of problem behaviors. Sleep may mediate the link between stress and problem behaviors as it is both sensitive to stress and essential for neurocognitive development underlying behavioral control during adolescence. The default mode network (DMN) is a brain network implicated in stress regulation and sleep. Yet, it is poorly understood how individual differences in resting-state DMN moderate the effect of stressful environments on impulsivity via sleep problems. MethodsThree waves of data spanning 2 years were obtained from the Adolescent Brain and Cognitive Development Study, a national longitudinal sample of 11,878 children (Mage at baseline = 10.1; 47.8% female). Structural equation modeling was used to test (a) the mediating role of sleep at T3 in the link between stressful environments at baseline and impulsivity at T5 and (b) the moderation of this indirect association by baseline levels of within-DMN resting-state functional connectivity. ResultsSleep problems, shorter sleep duration, and longer sleep latency significantly mediated the link between stressful environments and youth impulsivity. Youth with elevated within-DMN resting-state functional connectivity showed intensified associations between stressful environments and impulsivity via shorter sleep duration. ConclusionOur findings suggest that sleep health can be a target for preventive intervention and thereby mitigate the link between stressful environments and increased levels of youth impulsivity.

Resting-state network analysis of suicide attempt history in the UK Biobank.

Prior research has identified altered brain structure and function in individuals at risk for self-directed violence thoughts and behaviors. However, these studies have largely utilized healthy controls and findings have been inconsistent. Thus, this study examined differences in resting-state functional network connectivity among individuals with lifetime suicide attempt(s) v. lifetime self-directed violence thoughts alone. Using data from the UK Biobank, this study utilized a series of linear regressions to compare individuals with lifetime suicide attempt(s) (n = 566) v. lifetime self-directed violence thoughts alone (n = 3447) on within- and between- network resting-state functional connectivity subnetworks. There were no significant between-group differences for between-network, within-network, or whole-brain functional connectivity after adjusting for age, sex, ethnicity, and body mass index and performing statistical corrections for multiple comparisons. Resting-state network measures may not differentiate between individuals with lifetime suicide attempt(s) and lifetime self-directed violence thoughts alone. Null findings diverge from results reported in smaller neuroimaging studies of suicide risk, but are consistent with null findings in other large-scale studies and meta-analyses. Strengths of the study include its large sample size and stringent control group. Future research on a wider array of imaging, genetic, and psychosocial risk factors can clarify relative contributions of individual and combined variables to suicide risk and inform scientific understanding of ideation-to-action framework.

N°98 – Functional connectivity in resting-state networks associated with short-term cognitive impairments after cardiac arrest

N°98 – Functional connectivity in resting-state networks associated with short-term cognitive impairments after cardiac arrest

The analysis of brain functional connectivity of post-stroke cognitive impairment patients: an fNIRS study.

Post-stroke cognitive impairment (PSCI) is a considerable risk factor for developing dementia and reoccurrence of stroke. Understanding the neural mechanisms of cognitive impairment after stroke can facilitate early identification and intervention. Using functional near-infrared spectroscopy (fNRIS), the present study aimed to examine whether resting-state functional connectivity (FC) of brain networks differs in patients with PSCI, patients with Non-PSCI (NPSCI), and healthy controls (HCs), and whether these features could be used for clinical diagnosis of PSCI. The present study recruited 16 HCs and 32 post-stroke patients. Based on the diagnostic criteria of PSCI, post-stroke patients were divided to the PSCI or NPSCI group. All participants underwent a 6-min resting-state fNRIS test to measure the hemodynamic responses from regions of interests (ROIs) that were primarily distributed in the prefrontal, somatosensory, and motor cortices. The results showed that, when compared to the HC group, the PSCI group exhibited significantly decreased interhemispheric FC and intra-right hemispheric FC. ROI analyses showed significantly decreased FC among the regions of somatosensory cortex, dorsolateral prefrontal cortex, and medial prefrontal cortex for the PSCI group than for the HC group. However, no significant difference was found in the FC between the PSCI and the NPSCI groups. Our findings provide evidence for compromised interhemispheric and intra-right hemispheric functional connectivity in patients with PSCI, suggesting that fNIRS is a promising approach to investigate the effects of stroke on functional connectivity of brain networks.

A Multilevel Examination of Cognitive Control in Adolescents With Nonsuicidal Self-injury

BackgroundNonsuicidal self-injury (NSSI), a transdiagnostic behavior, often emerges during adolescence. This study used the Research Domain Criteria approach to examine cognitive control (CC) with a focus on response inhibition and urgency relative to NSSI severity in adolescents. MethodsOne hundred thirty-eight adolescents, assigned female sex at birth, with a continuum of NSSI severity completed negative and positive urgency measurements (self-report), an emotional Go/NoGo task within negative and positive contexts (behavioral), and structural and functional imaging during resting state and task (brain metrics). Cortical thickness, subcortical volume, resting-state functional connectivity, and task activation focused on an a priori–defined CC network. Eighty-four participants had all these main measures. Correlations and stepwise model selection followed by multiple regression were used to examine the association between NSSI severity and multiunit CC measurements. ResultsHigher NSSI severity correlated with higher negative urgency and lower accuracy during positive no-inhibition (Go). Brain NSSI severity correlates varied across modalities and valence. For right medial prefrontal cortex and right caudate, higher NSSI severity correlated with greater negative but lower positive inhibition (NoGo) activation. The opposite pattern was observed for the right dorsolateral prefrontal cortex. Higher NSSI severity correlated with lower left dorsal anterior cingulate cortex (ACC) negative inhibition activation and thicker left dorsal ACC, yet it was correlated with higher right rostral ACC positive inhibition activation and thinner right rostral ACC, as well as lower CC network resting-state functional connectivity. ConclusionsFindings revealed multifaceted signatures of NSSI severity across CC units of analysis, confirming the relevance of this domain in adolescent NSSI and illustrating how multimodal approaches can shed light on psychopathology.

Reliability and clinical utility of spatially constrained estimates of intrinsic functional networks from very short fMRI scans.

Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting-state fMRI (rsfMRI). Using a dataset containing rsfMRIscansofindividuals with schizophrenia and controls (M= 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5min) reference time course were sufficiently approximated with just 3-3.5min of data (r= 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2min (r= 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real-world (14 min) datasets, in which estimates of subject-level FNC were reliably estimated with 3-5min of data. Moreover, in the real-world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5-14 min) more reliably matched those generated from the first 5min of scan time than those generated from the last 5min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ minof scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2min of data retained 97%-98% classification accuracy relative to that of the full-length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2-5min show good clinical utility without significant loss of individual FNC information of longer scan lengths.

Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study.

Introduction: Resting-state network (RSN) connectivity is a widely used measure of the brain's functional organization in health and disease; however, little is known regarding the underlying neurophysiology of RSNs. The aim of the current study was to investigate associations between RSN connectivity and synaptic density assessed using the synaptic vesicle glycoprotein 2A radioligand 11C-UCB-J PET. Methods: Independent component analyses (ICA) were performed on resting-state fMRI and PET data from 34 healthy adult participants (16F, mean age: 46 ± 15 years) to identify a priori RSNs of interest (default-mode, right frontoparietal executive-control, salience, and sensorimotor networks) and select sources of 11C-UCB-J variability (medial prefrontal, striatal, and medial parietal). Pairwise correlations were performed to examine potential intermodal associations between the fractional amplitude of low-frequency fluctuations (fALFF) of RSNs and subject loadings of 11C-UCB-J source networks both locally and along known anatomical and functional pathways. Results: Greater medial prefrontal synaptic density was associated with greater fALFF of the anterior default-mode, posterior default-mode, and executive-control networks. Greater striatal synaptic density was associated with greater fALFF of the anterior default-mode and salience networks. Post-hoc mediation analyses exploring relationships between aging, synaptic density, and RSN activity revealed a significant indirect effect of greater age on fALFF of the anterior default-mode network mediated by the medial prefrontal 11C-UCB-J source. Discussion: RSN functional connectivity may be linked to synaptic architecture through multiple local and circuit-based associations. Findings regarding healthy aging, lower prefrontal synaptic density, and lower default-mode activity provide initial evidence of a neurophysiological link between RSN activity and local synaptic density, which may have relevance in neurodegenerative and psychiatric disorders.

Does combined training of biofeedback and neurofeedback affect smoking status, behavior, and longitudinal brain plasticity?

Introduction: Investigations of biofeedback (BF) and neurofeedback (NF) training for nicotine addiction have been long documented to lead to positive gains in smoking status, behavior and to changes in brain activity. We aimed to: (a) evaluate a multi-visit combined BF/NF intervention as an alternative smoking cessation approach, (b) validate training-induced feedback learning, and (c) document effects on resting-state functional connectivity networks (rsFCN); considering gender and degree of nicotine dependence in a longitudinal design. Methods: We analyzed clinical, behavioral, and electrophysiological data from 17 smokers who completed five BF and 20 NF sessions and three evaluation stages. Possible neuroplastic effects were explored comparing whole-brain rsFCN by phase-lag index (PLI) for different brain rhythms. PLI connections with significant change across time were investigated according to different resting-state networks (RSNs). Results: Improvements in smoking status were observed as exhaled carbon monoxide levels, Total Oxidative Stress, and Fageström scores decreased while Vitamin E levels increased across time. BF/NF promoted gains in anxiety, self-esteem, and several aspects of cognitive performance. BF learning in temperature enhancement was observed within sessions. NF learning in theta/alpha ratio increase was achieved across baselines and within sessions. PLI network connections significantly changed across time mainly between or within visual, default mode and frontoparietal networks in theta and alpha rhythms, while beta band RSNs mostly changed significantly after BF sessions. Discussion: Combined BF/NF training positively affects the clinical and behavioral status of smokers, displays benefit in smoking harm reduction, plays a neuroprotective role, leads to learning effects and to positive reorganization of RSNs across time. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02991781.

Altered whole-brain resting-state functional connectivity and brain network topology in typhoon-related post-traumatic stress disorder.

Altered resting-state functional connectivity has been found in patients with post-traumatic stress disorder (PTSD). However, the alteration of resting-state functional connectivity at whole-brain level in typhoon-traumatized individuals with PTSD remains largely unknown. To investigate changes in whole-brain resting-state functional connectivity and brain network topology in typhoon-traumatized subjects with and without PTSD. Cross-sectional study. Twenty-seven patients with typhoon-related PTSD, 33 trauma-exposed controls (TEC), and 30 healthy controls (HC) underwent resting-state functional MRI scanning. The whole brain resting-state functional connectivity network was constructed based on the automated anatomical labeling atlas. The graph theory method was used to analyze the topological properties of the large-scale resting-state functional connectivity network. Whole-brain resting-state functional connectivity and the topological network property were compared by analyzing the variance. There was no significant difference in the area under the curve of γ, λ, σ, global efficiency, and local efficiency among the three groups. The PTSD group showed increased dorsal cingulate cortex (dACC) resting-state functional connectivity with the postcentral gyrus (PoCG) and paracentral lobe and increased nodal betweenness centrality in the precuneus relative to both control groups. Compared with the PTSD and HC groups, the TEC group showed increased resting-state functional connectivity between the hippocampus and PoCG and increased connectivity strength in the putamen. In addition, compared with the HC group, both the PTSD and TEC groups showed increased connectivity strength and nodal efficiency in the insula. Aberrant resting-state functional connectivity and topology were found in all trauma-exposed individuals. These findings broaden our knowledge of the neuropathological mechanisms of PTSD.

Altered temporal connectivity and reduced meta-state dynamism in adolescents born very preterm.

Adolescents born very preterm have an increased risk for anxiety, social difficulties and inattentiveness, i.e. the 'preterm behavioural phenotype'. The extreme end of these traits comprises the core diagnostic features of attention and hyperactivity disorders and autism spectrum disorder, which have been reported to show aberrant dynamic resting-state functional network connectivity. This study aimed to compare this dynamism between adolescents born very preterm and controls. A resting-state functional magnetic resonance imaging was performed on 24 adolescents born very preterm (gestational age <32 weeks and/or birth weight ≤1500 g) and 32 controls born full term (≥37 weeks of gestation) at 13 years of age. Group-wise comparisons of dynamic connectivity between the resting-state networks were performed using both hard clustering and meta-state analysis of functional network connectivity. The very preterm group yielded a higher fraction of time spent in the least active connectivity state in hard clustering state functional network connectivity, even though no group differences in pairwise connectivity patterns were discovered. The meta-state analysis showed a decreased fluidity and dynamic range in the very preterm group compared with controls. Our results suggest that the 13-year-old adolescents born very preterm differ from controls in the temporal characteristics of functional connectivity. The findings may reflect the long-lasting effects of prematurity and the clinically acknowledged 'preterm behavioural phenotype'.

Identification of Resting-State Network Functional Connectivity and Brain Structural Signatures in Fibromyalgia Using a Machine Learning Approach.

Abnormal resting-state functional connectivity (rs-FC) and brain structure have emerged as pathological hallmarks of fibromyalgia (FM). This study investigated and compared the accuracy of network rs-FC and brain structural features in identifying FM with a machine learning (ML) approach. Twenty-six FM patients and thirty healthy controls were recruited. Clinical presentation was measured by questionnaires. After MRI acquisitions, network rs-FC z-score and network-based gray matter volume matrices were exacted and preprocessed. The performance of feature selection and classification methods was measured. Correlation analyses between predictive features in final models and clinical data were performed. The combination of the recursive feature elimination (RFE) selection method and support vector machine (rs-FC data) or logistic regression (structural data), after permutation importance feature selection, showed high performance in distinguishing FM patients from pain-free controls, in which the rs-FC ML model outperformed the structural ML model (accuracy: 0.91 vs. 0.86, AUC: 0.93 vs. 0.88). The combined rs-FC and structural ML model showed the best performance (accuracy: 0.95, AUC: 0.95). Additionally, several rs-FC features in the final ML model correlated with FM's clinical data. In conclusion, ML models based on rs-FC and brain structural MRI features could effectively differentiate FM patients from pain-free subjects.

Specific and common functional connectivity deficits in drug-free generalized anxiety disorder and panic disorder: A data-driven analysis

Evidence of comparing neural network differences between anxiety disorder subtypes is limited, while it is crucial to reveal the pathogenesis of anxiety disorders. The present study aimed to investigate specific and common resting-state functional connectivity (FC) networks in generalized anxiety disorder (GAD), panic disorder (PD), and healthy controls (HC). We employed the gRAICAR algorithm to decompose the resting-state fMRI into independent components and align the components across 61 subjects (22 GAD, 18 PD and 21 HC). The default mode network and precuneus network exhibited GAD-specific aberrance, the anterior default mode network showed atypicality specific to PD, and the right fronto-parietal network showed aberrance common to GAD and PD. Between GAD-specific networks, FC between bilateral dorsolateral prefrontal cortex (DLPFC) was positively correlated with interoceptive sensitivity. In the common network, altered FCs between DLPFC and angular gyrus, and between orbitofrontal cortex and precuneus, were positively correlated with anxiety severity and interoceptive sensitivity. The pathological mechanism of PD could closely relate to the dysfunction of prefrontal cortex, while GAD could involve more extensive brain areas, which may be related to fear generalization.

Neural correlates of spatial attention bias: Changes in functional connectivity in attention networks associated with tDCS

The prevailing theory concerning the pathophysiology of unilateral spatial neglect is that it is caused by an interhemispheric imbalance in attention networks. Previous studies have demonstrated that repetitive transcranial magnetic stimulation or transcranial direct current stimulation (tDCS) delivered over the right posterior parietal cortex can induce transitory neglect-like deficits in healthy individuals. We examined whether right cathodal and left anodal tDCS delivered over the posterior parietal cortex could produce neglect-like deficits and change the resting-state functional connectivity (rsFC) of attention networks. We found that the reaction time for targets in the left hemifield was significantly prolonged during two different types of visual search tasks, and rsFC of the attention networks was altered by tDCS. Furthermore, the change in the reaction times for the left visual target in the two different tasks significantly correlated with the change in the rsFC of either the right dorsal attention network (DAN) or right ventral attention network (VAN) based on the tasks. These results suggest that tDCS delivered to the posterior parietal cortex bilaterally induced neglect-like deficits by altering the connectivity of the attentional networks through excitability changes in the cortical area under the electrode. The results of this study are consistent with the hypothesis that the cause of neglect is the interhemispheric imbalance of attention networks. This is the first study to demonstrate that local cortical stimulation can induce changes not only in the local brain function but also in the cortical networks in healthy individuals.