Exploring the neurological basis of sensory processing sensitivity and emotional reactivity: Insights from large-scale brain networks.

Objective: Although the experience of pain is multidimentional, and general psychological factors such as anxiety are found to be associated with acute pain, it is necessary to investigate individual emotional variables such as emotional reactivity (ER). The aim of this study was to determine if increased ER is associated with pain during intrauterine device (IUD) insertion even without existing pre-procedure anxiety. Study Design: Prospective cohort study. Methods: We measured the anxiety level in 237 women using the Beck Anxiety Inventory and the ER level using the Emotional Reactivity Scale (ERS) at Zonguldak Maternity Hospital between November 2018 and March 2019. The women rated their pain during IUD insertion. To evaluate the role of ER in the prediction of pain during IUD insertion, we divided the women into subgroups based on the presence of anxiety and level of pain during IUD insertion. Results: ER was higher in women who had anxiety and moderate-severe pain (p = 0.001). In women who were not anxious, ER was higher in those who had moderate-severe anticipated pain or IUD insertion pain than in those who had mild anticipated pain or IUD insertion pain (p = 0.001). In anxious women, the ERS cut-off value was ˃ 34 for predicting the level of pain whereas in non-anxious women, it was ˃ 25. Conclusion: ER is a psychological factor strongly associated with pain at IUD insertion and the ERS appears to be a beneficial tool for detecting ER for this purpose. Health professionals should be aware that psychological factors could contribute to perceived pain more than physiological factors do. Implications: ER is a psychological factor associated with pain at IUD insertion and increased ER is a predictive factor for pain during IUD insertion even without existing pre-procedure anxiety. ERS seems to be a beneficial tool for detecting ER.

Regardless of whether it is conceptualized as a behavioral addiction or an impulse-control disorder, internet gaming disorder (IGD) has been speculated to be associated with impaired cognitive control. Efficient cognitive behavior involves the coordinated activity of large-scale brain networks, however, whether the interactions among these networks during resting state modulated cognitive control behavior in IGD adolescents remain unclear. Twenty-eight IGD adolescents and twenty-five age-, gender-, and education-matched healthy controls participated in our study. Stroop color-word task was conducted to evaluate the cognitive control deficits in IGD adolescents. Functional connectivity and Granger Causal Analysis were employed to investigate the functional and effective connections within and between the salience, central executive, and default mode networks. Meanwhile, diffusion tensor imaging was used to assess the structural integrity of abnormal network connections. The abnormal functional connectivity within central executive networks and effective connectivity within salience network in IGD adolescents were detected. Moreover, the inefficient interactions between these two brain networks were observed. In addition, we identified reduced fractional anisotropy in salience network, right central executive network tracts, and between-network (the anterior cingulate cortex-right dorsolateral prefrontal cortex tracts) pathways in IGD individuals. Notably, we observed a significant correlation between the effective and structural connection from salience network to central executive network and the number of errors during incongruent condition in Stroop task in both IGD and control subjects. Our results suggested that impaired cognitive control in IGD adolescents is likely to be mediated through the abnormal interactions and structural connection between intrinsic large-scale brain networks.

IntroductionHeart failure (HF) is frequently accompanied by cognitive and affective impairments, yet the neural mechanisms underlying these comorbidities remain insufficiently understood. This study aimed to investigate alterations in static and dynamic functional connectivity (FC) within large-scale brain networks in patients with reduced (HFrEF) and mid-range (HFmrEF) ejection fraction.MethodsIndependent component analysis (ICA) was used to identify resting-state networks (RSNs) and FC disparities between HF patients and healthy controls (HCs) within the RSNs. The ICA, sliding window approach, and k-means clustering analysis were used to compute dynamic functional network connectivity (dFNC) matrices and estimate different dynamic connection states. The temporal characteristics of the two groups were analyzed in each state. The correlations among significantly diverse temporal aspects and clinical measures were finally determined.ResultsCompared to HCs, HF patients showed reduced FC in the right inferior parietal lobule (IPL) within the dorsal attention and frontoparietal networks, alongside increased FC in the salience network. dFNC analysis revealed five recurrent connectivity states. Notably, HF patients exhibited shorter dwell time in a sensory–cognitive segregation state (State 5), and dwell time in this state correlated positively with both left ventricular ejection fraction (LVEF) and Mini-Mental State Examination (MMSE) scores.ConclusionThe disrupted static and dynamic connectivity in HF patients—marked by alterations in frontoparietal, attention, and salience networks and reduced stability of a sensory–cognitive segregation state—may underlie cognitive and affective vulnerability, providing potential imaging markers for early risk monitoring and management in HF.

BackgroundMenstrually-related migraine (MRM) is a subtype of migraine associated with the ovarian cycle that imposes a significant burden on female patients. Although MRM and non-menstrual migraine (NMM) differ in clinical presentation and treatment response, their distinct neural mechanisms remain unclear. Emerging evidence suggests that alterations in intrinsic functional connectivity (FC) within and between large-scale brain networks may underlie the phenotypic heterogeneity of migraine subtypes. This study investigated FC alterations between patients with MRM and NMM, explored their correlations with clinical characteristics, and assessed the preliminary utility of FC in subtype differentiation.MethodsResting-state functional magnetic resonance imaging (MRI) with independent component analysis was used to examine whole-brain FC in 50 patients with MRM, 50 with NMM and 50 age-balanced healthy controls (HC). We analyzed within- and between-network connectivity across major resting-state networks, including the frontoparietal, default mode, salience and dorsal attention networks, and applied logistic regression to test whether FC values could classify migraine subtypes. Correlation analyses were further performed between FC measures and clinical indices, including disease duration, headache frequency, visual analog scale scores and Headache Impact Test (HIT-6) scores.ResultsBoth MRM and NMM groups showed weaker within-network connectivity compared to HCs, primarily in the right frontoparietal, default mode and salience networks. Compared with NMM, the MRM group exhibited significantly stronger connectivity in the left frontoparietal network and weaker between-network connectivity between the dorsal attention and default mode networks. In the women with migraine, FC within the dorsal attention network (DAN) was negatively correlated with disease duration (r = -0.200, p = 0.046) and HIT-6 score (r = -0.183, p = 0.049). Furthermore, FC between the DAN and auditory network was inversely associated with disease duration (r = -0.225, p = 0.025). The logistic regression model achieved an area under the receiver operating characteristic curve of 0.73 (sensitivity = 0.70; specificity = 0.64) in distinguishing MRM from NMM.ConclusionsOur findings reveal both shared and distinct alterations in large-scale brain networks in MRM and NMM, potentially explaining differences in clinical presentation and treatment response. This enhanced understanding of migraine pathophysiology supports the development of subtype-specific diagnostic tools and targeted therapies and underscores the value of resting-state fMRI as a non-invasive tool for migraine phenotyping and personalized care.Registration NumberChiCTR2200065586.

Prenatal famine exposure and late-life functional brain network connectivity: A longitudinal study.

Frontoparietal areas link impairments of large-scale intrinsic brain networks with aberrant fronto-striatal interactions in OCD: a meta-analysis of resting-state functional connectivity

Several studies have demonstrated that psychogenic erectile dysfunction (pED) patients potentially suffer from cognitive dysfunction. Despite that previous neuroimaging studies have reported abnormal functional connections of brain areas associated with cognitive function in pED, the underlying mechanisms of cognitive dysfunction in pED remain elusive. This study aims to investigate the underlying mechanisms of cognitive dysfunction by analyzing large-scale brain networks. A total of 30 patients with pED and 30 matched healthy controls (HCs) were recruited in this study and scanned by resting-state functional magnetic resonance imaging. The Dosenbach Atlas was used to define large-scale networks across the brain. The resting-state functional connectivity (FC) within and between large-scale brain networks was calculated to compare pED patients with HCs. The relationship among cognitive performances and altered FC of large-scale brain networks was further explored in pED patients. Our results showed that the decreased FC within visual network, and between visual network and default mode network, visual network and frontoparietal network, and ventral attention and default mode network were found in pED patients. Furthermore, there was a positive correlation between immediate memory score and FC within visual network. The visuospatial score was negatively correlated with decreased FC between ventral attention network and default mode network. Taken together, our findings revealed the relationship between cognitive impairments and altered FC between large-scale brain networks in pED patients, providing the new evidence about the neural mechanisms of cognitive dysfunction in pED patients.

Multi-modal analysis of functional connectivity and cerebral blood flow reveals shared and unique effects of propofol in large-scale brain networks

SummaryFunctional dissociations in the brain observed during non-rapid eye movement (NREM) sleep have been associated with reduced information integration and impaired consciousness that accompany increasing sleep depth. Here, we explored the dynamical properties of large-scale functional brain networks derived from transient brain activity using functional magnetic resonance imaging. Spatial brain maps generally display significant modifications in terms of their tendency to occur across wakefulness and NREM sleep. Unexpectedly, almost all networks predominated in activity during NREM stage 2 before an abrupt loss of activity is observed in NREM stage 3. Yet, functional connectivity and mutual dependencies between these networks progressively broke down with increasing sleep depth. Thus, the efficiency of information transfer during NREM stage 2 is low despite the high attempt to communicate. Critically, our approach provides relevant data for evaluating functional brain network integrity and our findings robustly support a significant advance in our neural models of human sleep and consciousness.

Coupling and segregation of large-scale brain networks predict individual differences in delay discounting

Objective:Nonpathological aging has been linked to decline in both verbal and visuospatial memory abilities in older adults. Disruptions in resting-state functional connectivity within well-characterized, higherorder cognitive brain networks have also been coupled with poorer memory functioning in healthy older adults and in older adults with dementia. However, there is a paucity of research on the association between higherorder functional connectivity and verbal and visuospatial memory performance in the older adult population. The current study examines the association between resting-state functional connectivity within the cingulo-opercular network (CON), frontoparietal control network (FPCN), and default mode network (DMN) and verbal and visuospatial learning and memory in a large sample of healthy older adults. We hypothesized that greater within-network CON and FPCN functional connectivity would be associated with better immediate verbal and visuospatial memory recall. Additionally, we predicted that within-network DMN functional connectivity would be associated with improvements in delayed verbal and visuospatial memory recall. This study helps to glean insight into whether within-network CON, FPCN, or DMN functional connectivity is associated with verbal and visuospatial memory abilities in later life.Participants and Methods:330 healthy older adults between 65 and 89 years old (mean age = 71.6 ± 5.2) were recruited at the University of Florida (n = 222) and the University of Arizona (n = 108). Participants underwent resting-state fMRI and completed verbal memory (Hopkins Verbal Learning Test - Revised [HVLT-R]) and visuospatial memory (Brief Visuospatial Memory Test - Revised [BVMT-R]) measures. Immediate (total) and delayed recall scores on the HVLT-R and BVMT-R were calculated using each test manual’s scoring criteria. Learning ratios on the HVLT-R and BVMT-R were quantified by dividing the number of stimuli (verbal or visuospatial) learned between the first and third trials by the number of stimuli not recalled after the first learning trial. CONN Toolbox was used to extract average within-network connectivity values for CON, FPCN, and DMN. Hierarchical regressions were conducted, controlling for sex, race, ethnicity, years of education, number of invalid scans, and scanner site.Results:Greater CON connectivity was significantly associated with better HVLT-R immediate (total) recall (ß = 0.16, p = 0.01), HVLT-R learning ratio (ß = 0.16, p = 0.01), BVMT-R immediate (total) recall (ß = 0.14, p = 0.02), and BVMT-R delayed recall performance (ß = 0.15, p = 0.01). Greater FPCN connectivity was associated with better BVMT-R learning ratio (ß = 0.13, p = 0.04). HVLT-R delayed recall performance was not associated with connectivity in any network, and DMN connectivity was not significantly related to any measure.Conclusions:Connectivity within CON demonstrated a robust relationship with different components of memory function as well across verbal and visuospatial domains. In contrast, FPCN only evidenced a relationship with visuospatial learning, and DMN was not significantly associated with memory measures. These data suggest that CON may be a valuable target in longitudinal studies of age-related memory changes, but also a possible target in future non-invasive interventions to attenuate memory decline in older adults.

BackgroundThe thickness of cerebral cortex varies across individuals as well as across different regions within an individual. Shared trophic or plastic influences such as repeated task-related recruitment of extant brain regions results in morphological covariance within large-scale brain networks. Pathological processes disrupting functional co-activation can result in higher than expected degree of variability within large-scale networks in an individual level, resulting in spatial incoherence. We studied spatial incoherence of cortical thickness in 17 cortical networks identified on the basis of well-known patterns of intrinsic connectivity, to identify the spatially incoherent networks and relate them to differences in severity of thought disorder among patients with schizophrenia.MethodsUltra-high field 7T anatomical MRI scans (MPRAGE) were obtained from 20 subjects in a clinically stable, medicated early stage of schizophrenia, and 19 sex, parental socioeconomic-status and age matched healthy controls. Cortical thickness was estimated using Freesurfer v5.0, across 17 networks based on the parcellation scheme of Yeo et al. We computed within-network coefficient of variation in thickness (CVT) across vertices that constitute each network. Higher CVT of a network in a subject indicates higher spatial incoherence within the network for that individual. Independent 2-tailed t-tests were used to compare CVT of 17 networks between the 2 groups with FDR-corrected p=0.05 considered as statistically significant. We related CVT of affected networks to the scores of positive and negative Formal Thought Disorder measured using Thought and Language Index in patients.ResultsSalience Network (aka Ventral Attention Network as per Yeo atlas), Default Mode Network and Central Executive Network (aka dorsal Attention Network in Yeo atlas) showed most significant reduction in MRI-derived cortical thickness (networks #8, #12, #15 as well as #16 of Yeo atlas). Only the Salience and Executive Networks (network #8 and #12) showed higher coefficient of variation in patients compared to controls, indicating either a failure of coordinated maturation or co-ordinated function. Higher spatial incoherence of Salience Network related to reduced mean thickness of Central Executive Network in patients with schizophrenia; this relationship was not seen in healthy controls (Fisher’s z test, p=0.02). Both higher coefficient of variation in Salience Network and lower mean thickness in Central Executive Network predicted the severity of positive but not negative thought disorder scores.DiscussionOur results indicate that (1) large-scale cortical networks involved in information processing (Salience and Executive Networks) show spatial incoherence in schizophrenia (2) the degree of spatial incoherence relates to the severity of disorganisation of thoughts and language in patients. Spatial incoherence may be the result of a dysmaturational or functional dysplastic effect reflecting inefficient cortical recruitment in schizophrenia. Within-subject morphological variability carries useful information that can potentially explain the elusive neural basis of complex symptoms such as formal thought disorder.

Civil aviation is a distinctive career. Pilots need to monitor the entire system in real time. However, the psychophysiological mechanism of flying is largely unknown. The human brain is a large-scale interconnected organization, and many stable intrinsic large-scale brain networks have been identified. Among them are three core neurocognitive networks: default mode network (DMN), central executive network (CEN), and salience network (SN). These three networks play a critical role in human cognition. This study aims to examine the dynamic properties of the three large-scale brain networks in civil aviation pilots. We collected resting-state functional magnetic resonance imaging data from pilots. Independent component analysis, which is a data-driven approach, was combined with sliding window dynamic functional connectivity analysis to detect the dynamic properties of large-scale brain networks. Our results revealed that pilots exhibit an increased interaction of the CEN with the DMN and the SN along with a decreased interaction within the CEN. In addition, the temporal properties of functional dynamics (number of transitions) increased in pilots compared to healthy controls. In general, pilots exhibited increased between-network functional connectivity, decreased within-network functional connectivity, and a higher number of transitions. These findings suggest that pilots might have better functional dynamics and cognitive flexibility.

Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear. The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state. The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics. For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores. Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.

Altered default mode, fronto-parietal and salience networks in adolescents with Internet addiction