Functional Connectivity Networks Research Articles

Frequent absent mindedness and the neural mechanism trapped by mobile phone addiction

IntroductionWith the increased availability and sophistication of digital devices in the last decade, young people have become mainstream mobile phone users. Heavy mobile phone dependence causes affective problems (depression, anxiety) and loss of attention on current activities, leading to more cluttered thoughts. Problematic mobile phone use has been found to increase the occurrence of mind wandering, but the neural mechanism underlying this relationship remains unclear. MethodThe current study aimed to investigate the neural mechanism between mobile phone use and mind wandering. 459 university students (averaged age, 19.26 years) from datasets (ongoing research project named Gene-Brain-Behavior project, GBB) completed psychological assessments of mobile phone addiction and mind wandering and underwent resting-state functional connectivity (FC) scanning. FC matrix was constructed to further conduct correlation and mediation analyses. ResultsStudents with high mobile phone addiction scores were more likely to have high mind wandering scores. Functional connectivity among the default mode, motor, frontoparietal, basal ganglia, limbic, medial frontal, visual association, and cerebellar networks formed the neural basis of mind wandering. Functional connectivity between the frontoparietal and motor networks, between the default mode network and cerebellar network, and within the cerebellar network mediated the relationship between mobile phone addiction and mind wandering. ConclusionThe findings of this study confirm that mobile phone addiction is a risk factor for increased mind wandering and reveal that FC in several brain networks underlies this relationship. They contribute to research on behavioral addiction, education, and mental health among young adults.

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.

Neural correlates of pain acceptance and the role of the cerebellum: Functional connectivity and anatomical differences in individuals with headaches versus matched controls.

Despite functional connectivity network dysfunction among individuals with headaches, no studies have examined functional connectivity neural correlates and anatomical differences in coping with headaches. This study investigated inter-individual variability in whole-brain functional connectivity and anatomical differences among 37 individuals with primary headaches and 24 age- and gender-matched controls, and neural correlates of psychological flexibility (PF) that was previously found to contribute to headache adjustment. Participants (84% women; M headache severity = 4/10; M age = 43 years) underwent functional magnetic resonance imaging scans and completed questionnaires to examine global and subnetwork brain areas, and their relations with PF components, controlling for age, gender, education, and head-motion. Seed and voxel-based contrast analyses between groups showed atypical functional connectivity of regions involved in pain matrix and core resting-state networks. Pain acceptance was the sole PF component that correlated with the cerebellum (x, y, z: 28, -72, -34, p-false discovery rate <0.001), where individuals with headaches showed higher grey matter density compared to controls. The cerebellum, recently implicated in modulating emotional and cognitive processes, was indicated to process information resembling what individuals do when practicing pain acceptance. Our findings establish for the first time this connection of the cerebellum and its role in pain acceptance. We propose that pain acceptance might be a behavioural biomarker target that could modulate problematic headache perceptions and brain networks abnormalities. This study highlights the potential use of emerging behavioural biomarkers in headache management, such as pain acceptance, and their role in modifying the headache experience. Notably, grey matter reorganization in the cerebellum and other known brain pain networks, could indicate brain networks that can be modified from targeted behavioural interventions to help decode the nociplastic mechanisms that predominates in headaches.

Network-level enrichment provides a framework for biological interpretation of machine learning results.

Machine learning algorithms are increasingly being utilized to identify brain connectivity biomarkers linked to behavioral and clinical outcomes. However, research often prioritizes prediction accuracy at the expense of biological interpretability, and inconsistent implementation of ML methods may hinder model accuracy. To address this, our paper introduces a network-level enrichment approach, which integrates brain system organization in the context of connectome-wide statistical analysis to reveal network-level links between brain connectivity and behavior. To demonstrate the efficacy of this approach, we used linear support vector regression (LSVR) models to examine the relationship between resting-state functional connectivity networks and chronological age. We compared network-level associations based on raw LSVR weights to those produced from the forward and inverse models. Results indicated that not accounting for shared family variance inflated prediction performance, the k-best feature selection via Pearson correlation reduced accuracy and reliability, and raw LSVR model weights produced network-level associations that deviated from the significant brain systems identified by forward and inverse models. Our findings offer crucial insights for applying machine learning to neuroimaging data, emphasizing the value of network enrichment for biological interpretation.

Clinical Features and Electroencephalogram Analysis ofBrain Network Functional Connectivityin Anti-Leucine-RichGlioma-Inactivated1Antibody Encephalitis.

To summarize the clinical manifestations, laboratory findings, and magnetic resonance imaging (MRI) characteristics of anti-leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis (anti-LGI1 antibody encephalitis) and explore the electroencephalogram (EEG) features. We retrospectively analyzed the medical history of 16 patients diagnosed with anti-LGI1 antibody encephalitis at the First Hospital of Hebei Medical University from 2021 to 2023. EEGs of patients with anti-LGI1 antibody encephalitis and healthy individuals were analyzed. Based on Video-EEG signal analysis of EEG δ, θ, α, β frequency bands, weighted phase lag index values were calculated to form brain network matrices, studying differences in coherence between brain regions of patients with anti-LGI1 antibody encephalitis and healthy individuals. Patients with anti-LGI1 antibody encephalitis often presented with subacute onset seizures and cognitive decline. Routine test of cerebrospinal fluid was mostly normal. Serum testing revealed hyponatremia in 62.50% of patients, along with positive serum antinuclear antibodies, decreased vitamin B12, and abnormal cytokines such as interleukin-6. Head MRI revealed abnormal lesions related to the disease in seven cases (43.75%), mainly located in the unilateral or bilateral frontal and temporal lobes of the hippocampus. The EEG mainly showed generalized and focal slow waves, sometimes with focal discharges. Brain network functional connectivity analysis found a significant weakening of functional connections in the frontal-temporal lobe in the δ and β frequency bands. Intravenous pulse corticosteroids and intravenous immunoglobulin are first-line immunotherapies for anti-LGI1 antibody-related encephalitis. The disease recovery and cognitive decline improved in most patients. Anti-LGI1 antibody encephalitis is characterized by seizures and cognitive dysfunction. Serum may show abnormalities in immune indicators such as cytokines. Head MRI mainly reveals abnormal signals in the frontal-temporal lobes and the hippocampus. EEG brain network connectivity analysis reveals characteristic weakening of functional connections in the frontal-temporal lobe in the δ and β frequency bands.

Metabolic syndrome is associated with reduced default mode network functional connectivity in young post-9/11 Veterans.

Metabolic syndrome is a collection of health factors that increases risk for cardiovascular disease. A condition of aging, metabolic syndrome is associated with reduced brain network integrity, including functional connectivity alterations among the default mode, regions vulnerable to neurodegeneration. Prevalence of metabolic syndrome is elevated in younger populations including post-9/11 Veterans and individuals with posttraumatic stress disorder, but it is unclear whether metabolic syndrome affects brain function in earlier adulthood. Identifying early effects of metabolic syndrome on brain network integrity is critical, as these impacts could contribute to increased risk for cognitive disorders later in life for Veterans. The current study examined whether metabolic syndrome and its individual components were associated with default mode functional connectivity. We also explored the contribution of posttraumatic stress disorder and traumatic brain injury on these metabolic syndrome-brain relationships. Post-9/11 Veterans with combat deployment history (95 with and 325 without metabolic syndrome) underwent functional magnetic resonance imaging to capture seed-based resting-state functional connectivity within the default mode. The metabolic syndrome group demonstrated reduced positive functional connectivity between the posterior cingulate cortex seed and the bilateral superior frontal gyrus. Data-driven analyses demonstrated that metabolic syndrome components, particularly cholesterol and central adiposity, were associated with widespread reductions in default mode network connectivity. Functional connectivity was also reduced in participants with metabolic syndrome but without current posttraumatic stress disorder diagnosis and with traumatic brain injury history. These results suggest that metabolic syndrome disrupts resting-state functional connectivity decades earlier than prior work has shown.

Abnormal thalamocortical network dynamics in patients with migraine and its relationship with electroacupuncture treatment response.

Acupuncture is an effective and safe alternative treatment to prevent and treat migraine, but its central analgesic mechanism remains poorly understood. It is believed that the dysfunction of the thalamocortical connectivity network is an important contributor to migraine pathophysiology. This study aimed to investigate the abnormal thalamocortical network dynamics in patients with migraine without aura (MWoA) before and after an 8-week electroacupuncture treatment. A total of 143 patients with MWoA and 100 healthy controls (HC) were included, and resting-state functional magnetic resonance imaging (fMRI) data were acquired. Dynamic functional network connectivity (dFNC) was calculated for each subject. The modulation effect of electroacupuncture on clinical outcomes of migraine, dFNC, and their association were investigated. In our results, dFNC matrices were classified into two clusters (brain states). As compared with the HC, patients with MWoA had a higher proportion of brain states with a strong thalamocortical between-network connection, implying an abnormal balance of the network organization across dFNC brain states. Correlation analysis showed that this abnormality was associated with summarized clinical measurements of migraine. A total of 60 patients were willing to receive an 8-week electroacupuncture treatment, and 24 responders had 50% changes in headache frequency. In electroacupuncture responders, electroacupuncture could change the abnormal thalamocortical connectivities towards a pattern more similar to that of HC. Our findings suggested that electroacupuncture could relieve the symptoms of migraine and has the potential capacity to regulate the abnormal function of the thalamocortical circuits.

Imaging genetics of language network functional connectivity reveals links with language-related abilities, dyslexia and handedness

Language is supported by a distributed network of brain regions with a particular contribution from the left hemisphere. A multi-level understanding of this network requires studying its genetic architecture. We used resting-state imaging data from 29,681 participants (UK Biobank) to measure connectivity between 18 left-hemisphere regions involved in multimodal sentence-level processing, as well as their right-hemisphere homotopes, and interhemispheric connections. Multivariate genome-wide association analysis of this total network, based on genetic variants with population frequencies >1%, identified 14 genomic loci, of which three were also associated with asymmetry of intrahemispheric connectivity. Polygenic dispositions to lower language-related abilities, dyslexia and left-handedness were associated with generally reduced leftward asymmetry of functional connectivity. Exome-wide association analysis based on rare, protein-altering variants (frequencies <1%) suggested 7 additional genes. These findings shed new light on genetic contributions to language network organization and related behavioural traits.

Structural-functional connectomics in major depressive disorder following aiTBS treatment

Major depressive disorder (MDD) has been associated with changes in the structural (SC) and functional connectivity (FC) of the brain. This study investigated the effects of accelerated intermittent theta burst stimulation (aiTBS) on SC-FC coupling and graph theory measures, focusing on the association between baseline SC-FC coupling of the dorsolateral prefrontal cortex (dlPFC) and clinical improvement. In a randomized, sham-controlled, quadruple-blind, crossover study, aiTBS was delivered to the left dlPFC of depressed patients with MDD, and diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rsfMRI) data were acquired. In 77 MDD patients, significantly increased whole-brain SC-FC coupling was observed, primarily driven by default mode network (DMN) SC-FC coupling, along with increased somatomotor network FC, and decreased FC between the DMN hubs and limbic regions after active aiTBS. Furthermore, significant increases were observed in structural global and local efficiency measures that were not specific to the stimulation condition (active/sham aiTBS). However, these changes did not significantly correlate with clinical improvement. Notably, baseline SC-FC coupling of the left dlPFC was a significant predictor of clinical improvement. Our findings highlight the potential of left dlPFC SC-FC coupling as a predictor of aiTBS treatment outcomes, as well as the effect of aiTBS in enhancing SC-FC coupling.

Characteristics of locus coeruleus functional connectivity network in patients with comorbid migraine and insomnia

BackgroundMigraine and insomnia are prevalent conditions that often co-occur, each exacerbating the other and substantially impacting the quality of life. The locus coeruleus (LC), a brainstem region responsible for norepinephrine synthesis, participates in pain modulation, sleep/wake cycles, and emotional regulation, rendering it a potential nexus in the comorbidity of migraine and insomnia. Disruptions in the LC-noradrenergic system have been hypothesized to contribute to the comorbidities of migraine and insomnia, although neuroimaging evidence in humans remains scarce. In this study, we aimed to investigate the intrinsic functional connectivity (FC) network of the LC in patients with comorbid migraine and subjective chronic insomnia and patients with migraine with no insomnia (MnI) using resting-state functional magnetic resonance imaging (rs-fMRI) and seed-based FC analyses.MethodsIn this cross-sectional study, 30 patients with comorbid migraine and chronic insomnia (MI), 30 patients with MnI, and 30 healthy controls (HCs) were enrolled. Participants underwent neuropsychological testing and rs-fMRI. The LC-FC network was constructed using seed-based voxel-wise FC analysis. To identify group differences in LC-FC networks, voxel-wise covariance analysis was conducted with sex and age as covariates. Subsequently, a partial correlation analysis was conducted to probe the clinical relevance of aberrant LC-FC in patients with MI and MnI.ResultsExcept for the insomnia score, no other significant difference was detected in demographic characteristics and behavioral performance between the MI and MnI groups. Compared with HCs, patients with MI exhibited altered LC-FC in several brain regions, including the dorsomedial prefrontal cortex (DMPFC), anterior cerebellum, dorsolateral prefrontal cortex (DLPFC), thalamus, and parahippocampal gyrus (PHG). Lower FC between the LC and DLPFC was associated with greater insomnia severity, whereas higher FC between the LC and DMPFC was linked to longer migraine attack duration in the MI group.ConclusionOur findings reveal the presence of aberrant LC-FC networks in patients with MI, providing neuroimaging evidence of the interplay between these conditions. The identified LC-FC alterations may serve as potential targets for therapeutic interventions and highlight the importance of considering the LC-noradrenergic system in the management of MI.

Task-based modulation of functional connectivity of dorsal attention network in adult-ADHD

Recent studies have prompted a shift in the understanding of attention deficit hyperactivity disorder (ADHD) from models positing dysfunction of individual brain areas to those that assume alterations in large-scale brain networks. Despite this shift, the underlying neural mechanism of ADHD in the adult population remains uncertain. With functional magnetic resonance imaging (fMRI), this study examined brain connectivity of dorsal and ventral attention networks. Adults with and without ADHD completed a Go/No-Go task inside the scanner and the functional connectivity of attention networks was analysed. The generalized psychophysiological interaction analysis indicated differences involving the dorsal attention network. For the ADHD group, an interaction effect revealed altered dorsal attention-default mode network connectivity modulation, particularly between the right frontal eye field and posterior cingulate gyrus. We conclude that dorsal attention network dysfunction may be involved in sustained attention deficits in adult-ADHD. This study sheds light into network-level alterations contributing to the understanding of adult-ADHD, which may be a potential avenue for future research and clinical interventions.

Sleep state-dependent development of resting state functional connectivity during the preterm period.

The brains of preterm infants exhibit altered functional connectivity (FC) networks, but the potential variation in sleep states and the impact of breathing patterns on FC networks are unclear. This study explores the evolution of resting-state FC from preterm to term, focusing on breathing patterns and distinguishing between active sleep and quiet sleep. We recruited 63 preterm infants and 44 healthy-term infants and performed simultaneous electroencephalography and functional near-infrared spectroscopy. FC was calculated using oxy- and deoxyhemoglobin signals across eight channels. First, FC was compared between periodic breathing (PB) and non-PB segments. Then sleep state-dependent FC development was explored. FC was compared between active sleep and quiet sleep segments and between preterm infants at term and term-born infants in each sleep state. Finally, associations between FC at term, clinical characteristics, and neurodevelopmental outcomes in late infancy were assessed in preterm infants. In total, 148 records from preterm infants and 44 from term-born infants were analyzed. PB inflated FC values. After excluding PB segments, FC was found to be elevated during active sleep compared to quiet sleep, particularly in connections involving occipital regions. Preterm infants had significantly higher FC in both sleep states compared to term-born infants. Furthermore, stronger FC in specific connections during active sleep at term was associated with unfavorable neurodevelopment in preterm infants. Sleep states play a critical role in FC development and preterm infants show observable changes in FC.

Functional activity, functional connectivity and complex network biomarkers of progressive hyposmia Parkinson's disease with no cognitive impairment: evidences from resting-state fMRI study.

Olfactory dysfunction stands as one of the most prevalent non-motor symptoms in the initial stage of Parkinson's disease (PD). Nevertheless, the intricate mechanisms underlying olfactory deficits in Parkinson's disease still remain elusive. This study collected rs-fMRI data from 30 PD patients [15 with severe hyposmia (PD-SH) and 15 with no/mild hyposmia (PD-N/MH)] and 15 healthy controls (HC). To investigate functional segregation, the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) were utilized. Functional connectivity (FC) analysis was performed to explore the functional integration across diverse brain regions. Additionally, the graph theory-based network analysis was employed to assess functional networks in PD patients. Furthermore, Pearson correlation analysis was conducted to delve deeper into the relationship between the severity of olfactory dysfunction and various functional metrics. We discovered pronounced variations in ALFF, ReHo, FC, and topological brain network attributes across the three groups, with several of these disparities exhibiting a correlation with olfactory scores. Using fMRI, our study analyzed brain function in PD-SH, PD-N/MH, and HC groups, revealing impaired segregation and integration in PD-SH and PD-N/MH. We hypothesize that changes in temporal, frontal, occipital, and cerebellar activities, along with aberrant cerebellum-insula connectivity and node degree and betweenness disparities, may be linked to olfactory dysfunction in PD patients.

Online functional connectivity analysis of large all-to-all networks in MNE Scan

Abstract The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity

A comparative study of interhemispheric functional connectivity in patients with basal ganglia ischemic stroke.

Voxel-mirrored homotopic connectivity (VMHC) is utilized to assess the functional connectivity of neural networks by quantifying the similarity between corresponding regions in the bilateral hemispheres of the brain. The exploration of VMHC abnormalities in basal ganglia ischemic stroke (BGIS) patients across different cerebral hemispheres has been limited. This study seeks to establish a foundation for understanding the functional connectivity status of both brain hemispheres in BGIS patients through the utilization of VMHC analysis utilizing resting-state functional magnetic resonance imaging (rs-fMRI). This study examined a total of 38 patients with left basal ganglia ischemic stroke (LBGIS), 44 patients with right basal ganglia ischemic stroke (RBGIS), and 41 individuals in a healthy control (HC) group. Rs-fMRI studies were performed on these patients, and the pre-processed rs-fMRI data were analyzed using VMHC method. Subsequently, the VMHC values were compared between three groups using a one-way ANOVA and post hoc analysis. Correlation analysis with clinical scales was also conducted. The results indicated that compared to the HC group, significant differences were detected in postcentral gyrus, extending to precentral gyrus in both BGIS groups. Post hoc analysis showed that in the pairwise ROI-based comparison, individuals with LBGIS and RBGIS exhibited reduced VMHC values compared to HC groups. There was no significant difference between the LBGIS and RBGIS groups. In the LBGIS group, the VMHC value showed a negative correlation with NIHSS and a positive correlation with BI. The analysis of VMHC in rs-fMRI revealed a pattern of brain functional remodeling in patients with unilateral BGIS, marked by reduced synchronization and coordination between hemispheres. This may contribute to the understanding of the neurological mechanisms underlying motor dysfunction in these patients.

Exploring Nonlinear Dynamics In Brain Functionality Through Phase Portraits And Fuzzy Recurrence Plots.

Much of the complexity and diversity found in nature is driven by nonlinear phenomena, and this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in explaining brain functions from a biophysics standpoint, and the field of statistical physics continues to make substantial progress in understanding brain connectivity and function. This study delves into complex brain functional connectivity using biophysical nonlinear dynamics approaches. We aim to uncover hidden information in high-dimensional and nonlinear neural signals, with the hope of providing a useful tool for analyzing information transitions in functionally complex networks. By utilizing phase portraits and fuzzy recurrence plots, we investigated the latent information in the functional connectivity of complex brain networks. Our numerical experiments, which include synthetic linear dynamics neural time series and a biophysically realistic neural mass model, showed that phase portraits and fuzzy recurrence plots are highly sensitive to changes in neural dynamics and can also be used to predict functional connectivity based on structural connectivity. Furthermore, the results showed that phase trajectories of neuronal activity encode low-dimensional dynamics, and the geometric properties of the limit-cycle attractor formed by the phase portraits can be used to explain the neurodynamics. Additionally, our results showed that the phase portrait and fuzzy recurrence plots can be used as functional connectivity descriptors, and both metrics were able to capture and explain nonlinear dynamics behavior during specific cognitive tasks. In conclusion, our findings suggest that phase portraits and fuzzy recurrence plots could be highly effective as functional connectivity descriptors, providing valuable insights into nonlinear dynamics in the brain.

Pilot trial testing the effects of exercise on chemotherapy-induced peripheral neurotoxicity (CIPN) and the interoceptive brain system.

Chemotherapy-induced peripheral neurotoxicity (CIPN) is a prevalent, dose-limiting, tough-to-treat toxicity involving numbness, tingling, and pain in the extremities with enigmatic pathophysiology. This randomized controlled pilot study explored the feasibility and preliminary efficacy of exercise during chemotherapy on CIPN and the role of the interoceptive brain system, which processes bodily sensations. Nineteen patients (65 ± 11 years old, 52% women; cancer type: breast, gastrointestinal, multiple myeloma) starting neurotoxic chemotherapy were randomized to 12 weeks of exercise (home-based, individually tailored, moderate intensity, progressive walking, and resistance training) or active control (nutrition education). At pre-, mid-, and post-intervention, we assessed CIPN symptoms (primary clinical outcome: CIPN-20), CIPN signs (tactile sensitivity using monofilaments), and physical function (leg strength). At pre- and post-intervention, we used task-free ("resting") fMRI to assess functional connectivity in the interoceptive brain system, involving the salience and default mode networks. The study was feasible (74-89% complete data across measures) and acceptable (95% retention). We observed moderate/large beneficial effects of exercise on CIPN symptoms (CIPN-20, 0-100 scale: - 7.9 ± 5.7, effect size [ES] = - 0.9 at mid-intervention; - 4.8 ± 7.3, ES = - 0.5 at post-intervention), CIPN signs (ES = - 1.0 and - 0.1), and physical function (ES = 0.4 and 0.3). Patients with worse CIPN after neurotoxic chemotherapy had lower functional connectivity within the default mode network (R2 = 40-60%) and higher functional connectivity within the salience network (R2 = 20-40%). Exercise tended to increase hypoconnectivity and decrease hyperconnectivity seen in CIPN (R2 = 12%). Exercise during neurotoxic chemotherapy is feasible and may attenuate CIPN symptoms and signs, perhaps via changes in interoceptive brain circuitry. Future work should test for replication with larger samples. Registered Jan 2017 on ClinicalTrials.gov as NCT03021174.

Altered functional connectivity of resting-state networks and the correlation with clinical characteristics in intermittent exotropia adult patients: a resting-state magnetic resonance imaging study

BackgroundThe pathogenesis of intermittent exotropia (IXT) remains unclear. The study aims to investigate alterations of resting-state networks (RSNs) in IXT adult patients using resting-state functional magnetic resonance imaging (rs-fMRI) data to explore the potential neural mechanisms.MethodsTwenty-six IXT adult patients and 22 age-, sex-, handedness-, and education-matched healthy controls (HCs) underwent fMRI scanning and ophthalmological examinations. Brain areas with significant functional connectivity (FC) differences between the IXT and HC groups were selected as regions of interest (ROI) and mean z-scores were calculated to control for individual differences.ResultsCompared with HCs, IXT patients exhibited altered FC in various brain regions within RSNs involved in binocular fusion, stereopsis, ocular movement, emotional processes and social cognition, including the default mode network (DMN), the dorsal attention network (DAN), the visual network (VN), the sensorimotor network (SMN), the executive control network (ECN), the frontoparietal network (FPN) and the auditory network (AN). The degree of exodeviation was positively correlated with FC value of left middle occipital gyrus (MOG) within the VN. Correspondingly, we found a negative correlation between the degree of exodeviation and the FC value of left angular gyrus (AG) within FPN (P < 0.05). The FNC analysis between different RSNs also provides evidence on visual-motor cortical plasticity.ConclusionsIXT patients showed widespread changes of brain activity within RSNs related to binocular fusion, stereopsis, oculomotor control, emotional processes, and social cognition. These findings extend our current understanding of the neuropathological mechanisms of IXT.Trial registrationBeginning date of the trial: 2021-09-01. Date of registration:2021-07-18. Trial registration number: ChiCTR 2,100,048,852. Trial registration site: http://www.chictr.org.cn/index.aspx.

Prediction of individual performance and verbal intelligence scores from resting-state fMRI in children and adolescents.

The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome-based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting-state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting-state features from whole-brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10-fold cross-validation framework, our findings revealed that prediction models based on whole-brain functional connectivity effectively predicted performance IQ scores( ) but not verbal IQ scores( ). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto-parietal task control network (FTPN) for performance IQ scores ( ). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity.

Biological markers of brain network connectivity and pain sensitivity distinguish low coping from high coping Veterans with persistent post-traumatic headache.

Headache is the most common type of pain following mild traumatic brain injury. Roughly half of those with persistent post-traumatic headache (PPTH) also report neck pain which is associated with greater severity and functional impact of headache. This observational cohort study aimed to identify biological phenotypes to help inform mechanism-based approaches in the management of PPTH with and without concomitant neck pain. Thirty-three military Veterans (mean (SD) = 37±16 years, 29 males) with PPTH completed a clinical assessment, quantitative sensory testing, and magnetic resonance imaging of the brain and cervical spine. Multidimensional phenotyping was performed using a Random Forest analysis and Partitioning Around Medoids (PAM) clustering of input features from three biologic domains: 1) resting state functional connectivity (rsFC) of the periaqueductal gray (PAG), 2) quality and size of cervical muscles, and 3) mechanical pain sensitivity and central modulation of pain. Two subgroups were distinguished by biological features that included forehead pressure pain threshold and rsFC between the PAG and selected nodes within the default mode, salience, and sensorimotor networks. Compared to the High Pain Coping group, the Low Pain Coping group exhibited higher pain-related anxiety (p=0.009), higher pain catastrophizing (p=0.004), lower pain self-efficacy (p=0.010), and greater headache-related disability (p=0.012). Findings suggest that greater functional connectivity of pain modulation networks involving the PAG combined with impairments in craniofacial pain sensitivity, but not cervical muscle health, distinguish a clinically important subgroup of individuals with PPTH who are less able to cope with pain and more severely impacted by headache.