Resting-state Networks Research Articles

Resting-state functional network segregation of the default mode network predicts valence bias across the lifespan

Abstract The brain is organized into intrinsically connected functional networks that can be reliably identified during resting-state functional magnetic resonance imaging (fMRI). Healthy aging is marked by decreased network segregation, which is linked to worse cognitive functioning, but aging-related changes in emotion are less well characterized. Valence bias, which represents the tendency to interpret emotionally ambiguous information as positive or negative, is more positive in older than younger adults and is associated with differences in task-based fMRI activation in the amygdala, prefrontal cortex, and a cingulo-opercular (CO) network. Here, we examined valence bias, age, and resting-state network segregation of 12 brain networks in a sample of 221 healthy individuals from 6 to 80 years old. Resting-state network segregation decreased linearly with increasing age, extending prior reports of de-differentiation across the lifespan. Critically, a more positive valence bias was related to lower segregation of the default mode network (DMN), due to stronger functional connectivity of the DMN with CO and, to a lesser extent, the ventral attention network (VAN) in all participants. In contrast to this overall segregation effect, in participants over 39 years old (who tend to show a positive valence bias), bias was also related to weaker connectivity between the DMN and Reward networks. The present findings indicate that specific interactions between the DMN, a task control network (CO), an emotion processing network (Reward), and, to a weaker extent, an attention network (VAN), support a more positive valence bias, perhaps through regulatory control of self-referential processing and reduced emotional reactivity in aging. The current work offers further insight into the functional brain network alterations that may contribute to affective well-being and dysfunction across the lifespan.

Alterations of the resting-state brain network connectivity and gray matter volume in patients with fibromyalgia in comparison to ankylosing spondylitis

Fibromyalgia (FM) and ankylosing spondylitis (AS) are both rheumatic diseases characterized by significant musculoskeletal pain. In this study, we investigated the differences of the resting-state network (RSN) connectivity and gray matter volume (GMV) between FM, AS and healthy controls (HCs). We recruited 38 FM patients, 82 AS patients and 61 HCs in this study. All the participants underwent resting-state functional MRI (rs-fMRI) scans in a GE 3.0T MR system. Independent component analysis (ICA) was conducted on the rs-fMRI data, and group differences of the rsFC between different resting-state networks were calculated using dual regression. We also conducted voxel-based morphometry (VBM) analysis to investigate the differences of the GMV in FM, AS and HCs. The rsFC between the dorsal default mode network (DDMN) and the body of left caudate nucleus was significantly decreased in FM patients in comparison to AS patients (87 voxels, p = 0.025). VBM analysis showed that the GMV of the left posterior lobe of cerebellum was significantly increased in FM patients compared with AS patients (88 voxels, p = 0.036). Neither ICA nor VBM analysis revealed significant differences of RSN connectivity or GMV between FM patients and HCs. The altered rsFC between DMN and the caudate nucleus suggested an aberrant cortico-striato-thalamo-cortical circuit in FM patients, indicating aberrant reward processing, with potential association with mood, motivation and cognitive functions. The increased GMV in the left posterior lobe of cerebellum indicated the participation of cerebellum in the abnormal pain processing in FM patients.

Aberrant intra-network resting-state functional connectivity in chronic insomnia with or without cognitive impairment

Chronic insomnia (CI) is a common sleep disorder in middle-aged and elderly individuals. Long-term sleep deprivation can lead to physical, mental, and cognitive damage. Resting-state networks (RSNs) in the brain are closely linked to cognition and behavior. Therefore, we investigated changes in RSNs to explore behavioral and cognitive abnormalities in middle-aged and elderly CI patients. Resting state functional magnetic resonance imaging (rs-fMRI) and independent component analysis were used to study the intrinsic functional connectivity (FC) of the RSNs in 36 CI patients (20 CI with cognitive impairment (CI-I) patients and 16 CI without cognitive impairment (CI-N) patients) and 20 healthy controls (HC). Two-sample t-tests were used to compare RSNs differences between CI and HC groups and the RSNs differences between CI-I and CI-N groups. Partial correlation analysis was used to explore the relationship between the significant abnormal brain regions in RSN and clinical scales. Compared with HCs, CI patients showed significant differences in multiple RSNs, and FC values in two brain regions within RSNs were correlated with clinical scales. Furthermore, compared with CI-N group, CI-I group also showed significantly altered FC in multiple RSNs. Moreover, FC values in the right middle frontal gyrus within right frontal parietal network of CI-I patients were negatively correlated with the Mini-Mental State Examination scores. These results may explain hyperarousal, decreased attention and motor function impairments in CI patients. Furthermore, the aberrant alterations of RSNs in CI-I patients may play a crucial role in the onset and progression of cognitive impairment in CI patients.

Characterization of changes in the resting-state intrinsic network in patients with diabetic peripheral neuropathy

Diabetic peripheral neuropathy (DPN) is the most common complication of type 2 diabetes mellitus (T2DM) and is often accompanied by a variety of cognitive and emotional deficits, but the neurologic mechanisms underlying these deficits have not been fully elucidated. Therefore, this study aimed to use independent component analysis to explore the changes in the characteristics within the intrinsic network and to reveal patterns of interactions between networks in patients with DPN. Forty-one patients with T2DM who showed DPN, 37 patients with T2DM who did not show DPN (NDPN group), and 43 healthy controls (HC) underwent a neuropsychological assessment and resting-state functional magnetic resonance imaging examinations to examine the patterns of intra- and inter-network variations in the patients with T2DM at different clinical stages (with and without DPN). The relationships of intra- and inter-network functional connectivity (FC) with clinical/cognitive variables were also examined. In comparison with the NDPN group and HC, patients with DPN showed decreased FC within the visual network and sensorimotor network (SMN). Moreover, in comparison with the HC group, patients with DPN showed decreased FC within the anterior default mode network and increased FC within the basal ganglia network. Inter-network analysis showed decreased FC between the SMN and salience network in patients with DPN relative to the NDPN and HC groups. The decreased FC within the bilateral paracentral lobule (BA 6) of SMN was associated with Color Trails Test part 1 scores (r = -0.302, P = 0.007) and disease duration (r = -0.328, P = 0.003) in all patients with T2DM. In conclusion, the results revealed that patients with DPN have abnormal FC in multiple resting-state intrinsic networks in addition to the SMN, and that decreased FC between the SMN and salience network may be involved in the neural basis of abnormal sensorimotor function in patients with DPN.

Functional and vascular neuroimaging in maritime pilots with long-term sleep disruption.

The mechanism underlying the possible causal association between long-term sleep disruption and Alzheimer's disease remains unclear Musiek et al. 2015. A hypothesised pathway through increased brain amyloid load was not confirmed in previous work in our cohort of maritime pilots with long-term work-related sleep disruption Thomas et al. Alzheimer's Res Ther 2020;12:101. Here, using functional MRI, T2-FLAIR, and arterial spin labeling MRI scans, we explored alternative neuroimaging biomarkers related to both sleep disruption and AD: resting-state network co-activation and between-network connectivity of the default mode network (DMN), salience network (SAL) and frontoparietal network (FPN), vascular damage and cerebral blood flow (CBF). We acquired data of 16 maritime pilots (56 ± 2.3years old) with work-related long-term sleep disruption (23 ± 4.8 working years) and 16 healthy controls (59 ± 3.3years old), with normal sleep patterns (Pittsburgh Sleep Quality Index ≤ 5). Maritime pilots did not show altered co-activation in either the DMN, FPN, or SAL and no differences in between-network connectivity. We did not detect increased markers of vascular damage in maritime pilots, and additionally, maritime pilots did not show altered CBF-patterns compared to healthy controls. In summary, maritime pilots with long-term sleep disruption did not show neuroimaging markers indicative of preclinical AD compared to healthy controls. These findings do not resemble those of short-term sleep deprivation studies. This could be due to resiliency to sleep disruption or selection bias, as participants have already been exposed to and were able to deal with sleep disruption for multiple years, or to compensatory mechanisms Mentink et al. PLoS ONE. 2021;15(12):e0237622. This suggests the relationship between sleep disruption and AD is not as strong as previously implied in studies on short-term sleep deprivation, which would be beneficial for all shift workers suffering from work-related sleep disruptions.

CNSC-16. ASSOCIATION BETWEEN RESTING-STATE-NETWORK FUNCTIONAL CONNECTIVITY AND SURVIVAL IN RECURRENT GLIOMAS DIAGNOSED USING FET PET

Abstract BACKGROUND Functional connectivity between glioma-infiltrated brain tissue and remote cortical regions has been linked to overall survival (OS) in patients with newly diagnosed glioblastoma. We studied the association of functional connectivity between tumor and resting-state networks and OS in patients with recurrent gliomas. PATIENTS AND METHODS The study included 82 patients (26-81 years; median ECOG performance score, 0) with recurrent gliomas (grade 4 glioblastoma, n=57; grade 3 and 4 astrocytoma, n=12; grade 2 and 3 oligodendroglioma, n=13) who had undergone first-line therapy. Diagnosis of recurrence was confirmed by amino acid PET using the tracer O-(2-[18F]-fluoroethyl)-L-tyrosine (FET). FET PET and resting-state functional MRI were simultaneously acquired on a 3T hybrid PET/MR scanner. We performed a seed-based dual regression analysis to determine the functional connectivity between metabolically active tumors and seven canonical resting state networks. RESULTS Average functional connectivity was highest in oligodendrogliomas (z-score, 6.9±1.9) and was inversely related to the tumor grade (p=0.031). Univariate Cox regression analysis revealed a significant (p<0.05) positive association between OS and tumor connectivity with the visual, somatomotor, and dorsal attention networks. Subsequent stepwise multivariate Cox regression analysis refined this association to the dorsal attention network alone (HR, 0.88; 95% CI, 0.80-0.97; p=0.007). Patients with the highest functional connectivity (z-score>8.3, n=21) had a median OS of 31.4 months, while those with the lowest functional connectivity (z-score<4.3, n=21) had 10.6 months. Functional connectivity between tumors and the dorsal attention network showed an independent association with OS (HR, 0.90; 95% CI, 0.81-0.99; p=0.033) in a multivariate model, including various prognostic factors. CONCLUSION Recurrent gliomas exhibit significant functional connectivity to most of the major known resting-state networks, highest in oligodendrogliomas. The data suggest that high functional connectivity between the tumor and the dorsal attention network is an independent prognostic factor for improved OS.

NIMG-57. EMBEDDED GRAPH DERIVED FROM PSEUDO-RESTING-STATE FUNCTIONAL MRI CAN PREDICT COGNITIVE IMPAIRMENT IN GLIOMA

Abstract Resting-state brain network analyses are of great interest in studying the neurocognition and measuring the functional connectivity (FC) patterns of glioma patients. However, resting-state functional MRI (rs-fMRI) is not assessed in clinical routine due to the scan time and cost. The current study used pseudo-resting-state functional MRI (pseudo-rs-fMRI) derived from dynamic susceptibility contrast (DSC) perfusion MRI to predict cognitive impairment in glioma. 37 glioma patients were enrolled consecutively in the current study with DSC perfusion MRI acquired and neurocognition assessed, where 24 glioma patients also got rs-fMRI collected. The pseudo-rs-fMRI was created by voxel-wise subtracting the Gamma-variate modeled signal of contrast agent bolus from the original DSC perfusion signal. Following the pre-processing of pseudo-rs-fMRI and full rs-fMRI, the functional connectivity network (FCN) was established for each patient. Graph embedding was implemented to learn and extract features of each node in the binarized FCN, and the decision tree classification algorithm was applied to distinguish cognitive impairment. FCNs of 24 patients with DSC perfusion and rs-fMRI acquired were used to train the model, and FCNs of 13 patients with only DSC perfusion MRI acquired were used to test the model. The AUC (area under the receiver operating characteristic curve) was used to evaluate the model’s performance. The similarity and mean-square-difference between average FCNs extracted from rs-fMRI and pseudo-rs-fMRI were 0.6769 and 0.0263, respectively. Classifier trained using 24 FCNs of pseudo-rs-fMRI has an AUC of 0.750 in identifying cognitively impaired patients in the testing cohort. Combining FCNs of both rs-fMRI and pseudo-rs-fMRI to train the classifier resulted in a higher AUC of 0.833 in the testing cohort. To summary, DSC perfusion MRI-derived pseudo-rs-fMRI can be used to predict cognitive impairment in patients with gliomas. External validation would be required.

Aberrant dynamic functional network connectivity in patients with diffuse axonal injury

Diffuse axonal injury (DAI) results in aberrant functional connectivity and is significantly linked to cognitive impairment. Nevertheless, the network mechanisms influencing neurocognitive function following DAI remain unclear. This study aimed to examine the characteristics of static and dynamic functional network connectivity (FNC) in patients with DAI. Resting-state functional magnetic resonance imaging data were collected from 26 patients with DAI and 27 healthy controls. Resting-state networks were extracted using independent component analysis. We evaluated the connectivity strength through spatial maps and static FNC, and then further dynamic properties were identified using a sliding time-window approach and k-means clustering, and investigated their associations with clinical variables. Patients with DAI showed stronger intra-network spatial maps in the default mode network and subcortical network than healthy controls, but static inter-network functional connectivity remained stable. Furthermore, three recurring states for dynamic connectivity were identified in all participants, and state 1 occurred most frequently in patients with DAI and exhibited higher fractional time, and as well as longer mean dwell time, which was positively associated with MMSE scores. Meanwhile, patients with DAI exhibited mostly increased functional connectivity strength of dynamic FNC in all states, particularly within the default mode network and visual network. These findings suggest that patients with DAI are characterized by altered dynamic FNC and temporal properties, which provide distinct complementary information different from static functional connectivity, and new insights into the neural pathophysiology of DAI associated with cognitive impairment.

Investigating the impact of the regularization parameter on EEG resting-state source reconstruction and functional connectivity using real and simulated data

Accurate EEG source localization is crucial for mapping resting-state network dynamics and it plays a key role in estimating source-level functional connectivity. However, EEG source estimation techniques encounter numerous methodological challenges, with a key one being the selection of the regularization parameter in minimum norm estimation. This choice is particularly intricate because the optimal amount of regularization for EEG source estimation may not align with the requirements of EEG connectivityanalysis, highlighting a nuanced trade-off. In this study, we employed a methodological approach to determine the optimal regularization coefficient that yields the most effective reconstruction outcomes across all simulations involving varying signal-to-noise ratios for synthetic EEG signals. To this aim, we considered three resting state networks: the Motor Network, the Visual Network, and the Dorsal Attention Network. The performance was assessed using three metrics, at different regularization parameters: the Region Localization Error, source extension, and source fragmentation. The results were validated using real functional connectivity data. We show that the best estimate of functional connectivity is obtained using 10−2, while 10−1 has to be preferred when source localization only is at target.

Brain connectomes in youth at risk for serious mental illness: a longitudinal perspective.

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one of 5 groups: healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9). Constrained spherical deconvolution was used to generate whole brain tractography maps, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional magnetic resonance imaging (fMRI) signal between pairs of brain regions. Linear mixed models revealed structural and functional abnormalities in global metrics of small world lambda, and resting state networks involving the fronto-parietal, default mode, and deep grey matter networks, along with the visual and dorsal attention networks. Machine learning analysis additionally identified changes in nodal metrics of betweenness centrality in the angular gyrus and bilateral temporal gyri as potential features which can discriminate between the groups. Our findings further support the view that abnormalities in large scale networks (particularly those involving fronto-parietal, temporal, default mode,and deep grey matter networks) may underlie transdiagnostic risk for SMIs.

EEG-Derived Markers to Improve Prognostic Evaluation of Disorders of Consciousness.

Disorders of consciousness (DoC) are characterized by alteration in arousal and/or awareness commonly caused by severe brain injury. There exists a consensus on adopting advanced neuroimaging and electrophysiological procedures to improve diagnosis/prognosis of DoC patients. Currently, these procedures are prevalently applied in a research-oriented context and their translation into clinical practice is yet to come. The aim of the study consisted in the identification of measures derived from routinary electroencephalography (EEG) able to support clinicians in the prediction of DoC patients' outcome. In the present study, a routine EEG was recorded during rest from a sample of 58 DoC patients clinically diagnosed as Unresponsive Wakefulness State (UWS) and Minimally Conscious State (MCS) and followed-up for 3 months. EEG-based features characterizing brain activity in terms of spectral content and resting state networks organization were used in a predictive machine learning model to i) identify which were the most promising features in predicting patients' exit from the DoC, regardless of the clinical diagnosis and ii) verify whether such features would have been the same best discriminating UWS from MCS or specific of the outcome prediction. A predictive machine learning model was built on EEG features related to spectral content and resting state networks which returned up to 85% of performance accuracy in outcome prediction and 76% in DoC state recognition (UWS vs MCS). We provided preliminary evidence for the exploitation of a routine EEG to improve the clinical management of non-communicative patients to be confirmed in a larger DoC population.

Resting-state functional MRI of the nose as a novel investigational window into the nervous system

Besides being responsible for olfaction and air intake, the nose contains abundant vasculature and autonomic nervous system innervations, and it is a cerebrospinal fluid clearance site. Therefore, the nose is an attractive target for functional MRI (fMRI). Yet, nose fMRI has not been possible so far due to signal losses originating from nasal air-tissue interfaces. Here, we demonstrated feasibility of nose fMRI by using novel ultrashort/zero echo time (TE) MRI. Results obtained in the resting-state from 13 healthy participants at 7T and in 5 awake mice at 9.4T revealed a highly reproducible resting-state nose functional network that likely reflects autonomic nervous system activity. Another network observed in humans involves the nose, major brain vessels and CSF spaces, presenting a temporal dynamic that correlates with heart rate and breathing rate. These resting-state nose functional signals should help elucidate peripheral and central nervous system integrations.

Neurophysiological correlates of trait and behavioral impulsivity across methamphetamine and gambling Addiction

Investigating neurophysiological markers linked to impulsivity in individuals with gambling and methamphetamine addiction using resting-state EEG data offers valuable insights into the underlying neurophysiological mechanisms associated with impulsivity in individuals with addiction. This study aims to use resting-state EEG to explore the connection between various types of addiction and different aspects of impulsivity. Participants from the methamphetamine, gambling, and healthy control groups (abbreviation: MA, GB, HC) underwent EEG recordings and completed measures of impulsivity. Group differences in trait scores and behavioral tendencies were analyzed. Abnormal connections with node linkage and importance changes were analyzed through the resting-state EEG power spectral and network analyses. Further, relationships between impulsivity scores and connectivity differences in groups were explored through correlation analysis. Finally, these abnormal connections related to impulsivity were tested for their effect of distinguishing individuals with addiction from healthy controls through the ROC analysis. Results revealed that GB displayed the highest trait impulsivity on the overall score, while MA exhibited greater attentional impulsivity. Variations in behavioral impulsivity were reflected in response times. Resting-state EEG analysis showed higher beta power in GB. Specific channel pairs demonstrated abnormal connections and altered connectivity patterns in the beta band, with MA displaying a less efficient network compared to GB. Correlation analyses uncovered associations between impulsivity scores and connectivity, which were influenced by group differences. Furthermore, resting-state EEG connections effectively differentiated individuals with addiction from healthy controls. Overall, this study contributes valuable insights into the neural mechanisms of addiction-related impulsivity, emphasizing the potential of resting-state EEG connections as an important neurophysiological correlate.

Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis.

The demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant changes, not only in neuroanatomical traits but also in neurophysiological components. One of the most impactful factors we deal with during this time is our environment, especially when encountering external factors such as social behaviors or substance consumption. Binge drinking (BD) has emerged as an extended pattern of alcohol consumption in teenagers, not only affecting their future lifestyle but also changing their neurodevelopment. Recent studies have changed their scope into finding predisposition factors that may lead adolescents into this kind of patterns of consumption. In this article, using unsupervised machine learning (UML) algorithms, we analyze the relationship between electrophysiological activity of healthy teenagers and the levels of consumption they had 2 years later. We used hierarchical agglomerative UML techniques based on Ward's minimum variance criterion to clusterize relations between power spectrum and functional connectivity and alcohol consumption, based on similarity in their correlations, in frequency bands from theta to gamma. We found that all frequency bands studied had a pattern of clusterization based on anatomical regions of interest related to neurodevelopment and cognitive and behavioral aspects of addiction, highlighting the dorsolateral and medial prefrontal, the sensorimotor, the medial posterior, and the occipital cortices. All these patterns, of great cohesion and coherence, showed an abnormal electrophysiological activity, representing a dysregulation in the development of core resting-state networks. The clusters found maintained not only plausibility in nature but also robustness, making this a great example of the usage of UML in the analysis of electrophysiological activity-a new perspective into analysis that, while contributing to classical statistics, can clarify new characteristics of the variables of interest.

Exploring causal association between functional/structural connectivity and major depression disorder: A bidirectional Mendelian randomization study

ObjectivesPrior observational studies have suggested a correlation between major depressive disorder (MDD) and communication imbalances within the resting-state brain network (RSN), but the causal relationship remains unclear. This research uses Mendelian randomization (MR) analysis to explore the potential causal effects between functional connectivity (FC), structural connectivity (SC) and MDD. MethodsTwo-sample bidirectional MR analysis was employed in this study. Inverse variance weighted (IVW) was used to explore the causal relationship between the FC/SC and MDD, with various methods such as MR-Egger to conduct sensitivity analyses. ResultsThe IVW analysis results showed that higher genetic predicted dorsal attention network FC, limbic network SC, and dorsal attention network SC were associated with an increased risk of MDD (β: 15.08, 95%CI: 5.89–24.27, p = 0.001; β: 3.79, 95%CI: −0.22-7.8, p = 0.034; β: 9.89, 95%CI: 0.88–18.90, p = 0.031). Reverse MR analysis demonstrated that a genetically predicted elevated risk of MDD was associated with reduced frontal parietal network FC (β: −0.00046, p = 0.041). ConclusionsThe study suggests a causal relationship between the FC and SC within specific RSNs and the risk of MDD. Abnormalities in the dorsal attention network FC/SC and the limbic network SC were risk factors for MDD. The FC abnormality of the frontal parietal network may be the downstream influence following the MDD onset. Further investigation is needed to determine the potential utility of these neuroimaging markers in the prevention of MDD or the evaluation of treatment efficacy.

Resting-state brain activation patterns and network topology distinguish human sign and goal trackers

The “Sign-tracker/Goal-tracker” (ST/GT) is an animal model of individual differences in learning and motivational processes attributable to distinctive conditioned responses to environmental cues. While GT rats value the reward-predictive cue as a mere predictor, ST rats attribute it with incentive salience, engaging in aberrant reward-seeking behaviors that mirror those of impulse control disorders. Given its potential clinical value, the present study aimed to map such model onto humans and investigated resting state functional magnetic resonance imaging correlates of individuals categorized as more disposed to sign-tracking or goal-tracking behavior. To do so, eye-tracking was used during a translationally informed Pavlovian paradigm to classify humans as STs (n = 36) GTs (n = 35) or as Intermediates (n = 33), depending on their eye-gaze towards the reward-predictive cue or the reward location. Using connectivity and network-based approach, measures of resting state functional connectivity and centrality (role of a node as a hub) replicated preclinical findings, suggesting a major involvement of subcortical areas in STs, and dominant cortical involvement in GTs. Overall, the study strengthens the translational value of the ST/GT model, with important implications for the early identification of vulnerable phenotypes for psychopathological conditions such as substance use disorder.

Characteristics of Functional Connections and Topographical Properties Distinguished the Healthy State and Obsessive-Compulsive Disorder

Background: Obsessive-compulsive disorder (OCD) is characterized by alterations in brain connectivity, particularly within the default mode network (DMN) and the salience network (SN). Investigating these connectivity differences can provide a deeper understanding of the neural mechanisms underlying OCD. Methods: This cross-sectional study involved 58 patients diagnosed with OCD and 38 healthy control subjects, totaling 96 participants. Resting-state functional MRI (fMRI) data were acquired and analyzed using the CONN toolbox to examine functional connectivity within intrinsic resting-state networks. Graph theory metrics were applied to evaluate node connections and the overall network topology. Clinical symptoms were assessed using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), and their correlations with connectivity patterns and graph-theory parameters were analyzed. Results: The OCD patients and healthy controls were matched in terms of age, gender, marital status, socioeconomic status, and handedness. However, OCD patients had significantly worse general health, quality of life, and higher levels of depression and anxiety. Network analyses revealed altered whole-brain connectivity in OCD patients, particularly within the DMN and the frontoparietal network. The most significant between-group connectivity differences were observed between the posterior parietal cortex (PPC) and the precuneus. Disruptions in the DMN, specifically altered connectivity between the medial prefrontal cortex and posterior cingulate cortex, and changes in the SN involving the anterior insula and anterior cingulate cortex, were significantly correlated with the severity of OCD symptoms. Conclusions: The findings suggest that OCD is associated with distinct alterations in DMN connectivity, which may play a critical role in the disorder's pathophysiology. These disruptions offer potential targets for therapeutic intervention. Further research is needed to explore these connectivity changes in larger cohorts and at various stages of OCD to better understand their clinical significance.

Resting state network changes induced by experimental inaudible infrasound exposure and associations with self-reported noise sensitivity and annoyance.

The effects of prolonged infrasound (IS) exposure on brain function and behavior are largely unknown, with only one prior study investigating functional connectivity (FC) changes. In a long-term randomized-controlled trial, 38 participants were exposed to inaudible airborne IS (6Hz, 80-90 dB) or sham devices for four weeks (8h/night). We assessed FC changes in resting-state networks (auditory, default mode (DMN), sensorimotor (SMN), and executive control (ECN)), and explored IS 'sensitivity' as a predictor of identified significant FC changes. We also examined correlations between somatic symptoms and FC. IS exposure led to decreased FC in the right precuneus (DMN) and increased FC in the Vermis IV and V (SMN). In the ECN, we observed increased FC in the right frontal middle gyrus (BA8) and the right inferior parietal lobe, and decreased FC in another region of the right frontal middle gyrus. Changes in the ECN (right inferior parietal lobe) were negatively associated with self-reported annoyance from IS/low-frequency noise. A significant negative association was found between FC changes in the DMN (right precuneus) and somatic symptoms. Our study is the first to investigate prolonged IS exposure effects on brain FC, revealing changes in the vDMN, SMN, and ECN, but not in the auditory network. Future studies should assess annoyance and sensitivity markers, fine-grained measures of somatic symptoms, and stratify samples by sensitivity to uncover individual differences in response to IS.

Cardiorespiratory Fitness and Sleep, but not Physical Activity, are Associated with Functional Connectivity in Older Adults.

Aging results in changes in resting state functional connectivity within key networks associated with cognition. Cardiovascular function, physical activity, sleep, and body composition may influence these age-related changes in the brain. Better understanding these associations may help clarify mechanisms related to brain aging and guide interventional strategies to reduce these changes. In a large (n = 398) sample of healthy community dwelling older adults that were part of a larger interventional trial, we conducted cross sectional analyses of baseline data to examine the relationships between several modifiable behaviors and resting state functional connectivity within networks associated with cognition and emotional regulation. Additionally, maximal aerobic capacity, physical activity, quality of sleep, and body composition were assessed. Associations were explored both through correlation and best vs. worst group comparisons. Greater cardiovascular fitness, but not larger quantity of daily physical activity, was associated with greater functional connectivity within the Default Mode (p = 0.008 r = 0.142) and Salience Networks (p = 0.005, r = 0.152). Better sleep (greater efficiency and fewer nighttime awakenings) was also associated with greater functional connectivity within multiple networks including the Default Mode, Executive Control, and Salience Networks. When the population was split into quartiles, the highest body fat group displayed higher functional connectivity in the Dorsal Attentional Network compared to the lowest body fat percentage (p = 0.011; 95% CI - 0.0172 to - 0.0023). These findings confirm and expand on previous work indicating that, in older adults, higher levels of cardiovascular fitness and better sleep quality, but not greater quantity of physical activity, total sleep time, or lower body fat percentage are associated with increased functional connectivity within key resting state networks.

EEG Oscillatory Activity and Resting-State Networks Associated with Neurocognitive Function in Mild Traumatic Brain Injury.

This study aimed to investigate the characteristics of resting-state electroencephalography (EEG) activity and brain networks in patients with mild traumatic brain injury (mTBI) and their association with neurocognitive function (NCF). We analyzed 26 patients with subacute mTBI and 21 healthy controls. The subacute mTBI group (9 females, 17 males) had a mean age of 29.9 ± 9.9 years, and the healthy controls (11 females, 10 males) had a mean age of 29.7 ± 11.5 years. Current source density, lagged phase synchronization, and resting-state network activity were analyzed using exact low-resolution electromagnetic tomography (eLORETA) with 60 s resting-state EEG data. In addition, a correlation analysis was performed between these EEG parameters and NCF in patients with mTBI. We used the statistical nonparametric mapping method in eLORETA to correct for multiple comparisons. There were no significant differences in EEG parameters between the patients with mTBI and healthy controls. However, in patients with mTBI, correlation analysis revealed negative correlations between theta activity in the anterior cingulate cortex and verbal short-term memory and between activity in the memory perception network and verbal memory. Our findings suggest that resting-state EEG may be clinically useful in investigating the mechanism of NCF decline in patients with mTBI.