Brain Functional Connectivity Research Articles

Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age prediction

Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typical brain development and neuropsychiatric disorders in mammals. Various biological phenotypes have been successfully applied to predict BA of human using chronological age (CA) as label. However, whether the BA of macaque, one of the most important animal models, can also be reliably predicted is largely unknown. To address this question, we propose a novel deep learning model called Multi-Branch Vision Transformer (MB-ViT) to fuse multi-scale (i.e., from coarse-grained to fine-grained) brain functional connectivity (FC) patterns derived from resting state functional magnetic resonance imaging (rs-fMRI) data to predict BA of macaques. The discriminative functional connections and the related brain regions contributing to the prediction are further identified based on Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our proposed model successfully predicts BA of 450 normal rhesus macaques from the publicly available PRIMatE Data Exchange (PRIME-DE) dataset with lower mean absolute error (MAE) and mean square error (MSE) as well as higher Pearson's correlation coefficient (PCC) and coefficient of determination (R2) compared to other baseline models. The correlation between the predicted BA and CA reaches as high as 0.82 of our proposed method. Furthermore, our analysis reveals that the functional connections predominantly contributing to the prediction results are situated in the primary motor cortex (M1), visual cortex, area v23 in the posterior cingulate cortex, and dysgranular temporal pole. In summary, our proposed deep learning model provides an effective tool to accurately predict BA of primates (macaque in this study), and lays a solid foundation for future studies of age-related brain diseases in those animal models.

Can physical activity counteract the negative effects of sedentary behavior on the physical and mental health of children and adolescents? A narrative review.

The increase in sedentary behavior (SB) in children and adolescents is one of the major threats to global public health, and the relationship between physical activity (PA) and SB has always been a key topic. The literature search was conducted through PubMed, Web of Science, CNKI, Wanfang, and Scopus, and 121 pieces of literature were included in this study after screening and evaluation. (1) SB caused by screen time such as mobile phones and TVs has varying degrees of negative impact on obesity, cardiovascular metabolism, skeletal muscle development, and cognitive, and psychological disorders in children and adolescents. (2) Regular physical activity could effectively prevent, offset, or improve the harm of SB to the physical and mental health of children and adolescents, mainly by reducing the incidence of obesity, and cardiovascular and metabolic risks, promoting skeletal muscle development, and improving cognitive function and mental health. (3) The mechanism of physical activity to prevent or ameliorate the harm of SB was relatively complex, mainly involving the inhibition or activation of neurobiomolecules, the improvement of blood and cell metabolic factors, and the enhancement of brain functional connectivity. Children and adolescents should avoid excessive SB, and through a variety of moderate to vigorous physical activity (MVPA) to replace or intermittent SB, which could effectively prevent or improve the harm of SB to physical and mental health.

Research hotspots and global trends in transcranial magnetic stimulation for stroke neurorestoration: A 30-year bibliometric analysis

BackgroundStroke patients often experience various functional impairments. Transcranial magnetic stimulation (TMS) has emerged as a novel non-invasive brain stimulation technique, and has gained widespread attention over the past 30 years for its application in stroke. The present study used bibliometric and content analysis methods to explore the research hotspots and future trends of TMS in stroke, with an aim to provide benefits for subsequent research. MethodsPublications and reviews related to TMS in stroke from 1992 to 2022 were retrieved from the Web of Science Core Collection database. Data visualization and analysis were performed using CiteSpace, VOSviewer, and Scimago Graphica. ResultsWe included 1666 papers in the analysis and noted an increasing trend in annual publication volume. Collaboration network analysis revealed close cooperation among scholars from these major countries and institutions. Emerging keywords included TMS, stroke, recovery, rehabilitation, rTMS, plasticity, cortex, excitability, upper limb, and tDCS. Keyword citation bursts appeared to be shifting toward clinical applications, including functional connectivity, systematic reviews, double-blind studies, connectivity, injury, and dysphagia. ConclusionOur study indicates substantial growth in TMS publications and citations in stroke rehabilitation since 2006, suggesting a maturing field that is advancing toward more sophisticated treatment protocols. The increasing relevance of TMS in neurological and rehabilitative applications highlights its potential for promoting post-stroke recovery. Emerging keywords such as brain functional connectivity, systematic reviews, and double-blind studies highlight a shift toward clinical applications. The future of TMS in stroke rehabilitation promises continued growth, interdisciplinary approaches, and improved patient outcomes.

Exploration of resting-state brain functional connectivity as preclinical markers for arousal prediction in prolonged disorders of consciousness: A pilot study based on functional near-infrared spectroscopy.

There is no diagnostic assessment procedure with moderate or strong evidence of use, and evidence for current means of treating prolonged disorders of consciousness (pDOC) is sparse. This may be related to the fact that the mechanisms of pDOC have not been studied deeply enough and are not clear enough. Therefore, the aim of this study was to explore the mechanism of pDOC using functional near-infrared spectroscopy (fNIRS) to provide a basis for the treatment of pDOC, as well as to explore preclinical markers for determining the arousal of pDOC patients. Five minutes resting-state data were collected from 10 pDOC patients and 13healthy adults using fNIRS. Based on the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the time series, the resting-state cortical brain functional connectivity strengths of the two groups were calculated, and the functional connectivity strengths of homologous and heterologous brain networks were compared at the sensorimotor network (SEN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), frontoparietal network (FPN), and visual network (VIS) levels. Univariate binary logistic regression analyses were performed on brain networks with statistically significant differences to identify brain networks associated with arousal in pDOC patients. The receiver operating characteristic (ROC) curves were further analyzed to determine the cut-off value of the relevant brain networks to provide clinical biomarkers for the prediction of arousal in pDOC patients. The results showed that the functional connectivity strengths of oxyhemoglobin (HbO)-based SEN∼SEN, VIS∼VIS, DAN∼DAN, DMN∼DMN, SEN∼VIS, SEN∼FPN, SEN∼DAN, SEN∼DMN, VIS∼FPN, VIS∼DAN, VIS∼DMN, HbR-based SEN∼SEN, and SEN∼DAN were significantly reduced in the pDOC group and were factors that could reflect the participants' state of consciousness. The cut-off value of resting-state functional connectivity strength calculated by ROC curve analysis can be used as a potential preclinical marker for predicting the arousal state of subjects. Resting-state functional connectivity strength of cortical networks is significantly reduced in pDOC patients. The cut-off values of resting-state functional connectivity strength are potential preclinical markers for predicting arousal in pDOC patients.

Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for brain disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such brain modularity prior. In this paper, we propose a brain modularity-constrained dynamic representation learning framework for interpretable fMRI analysis, consisting of dynamic graph construction, dynamic graph learning via a novel modularity-constrained graph neural network (MGNN), and prediction and biomarker detection. The designed MGNN is constrained by three core neurocognitive modules (i.e., salience network, central executive network, and default mode network), encouraging ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we encourage the MGNN to preserve network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.

Non-invasive TMS attenuates neuropathic pain after spinal cord injury associated with enhancing brain functional connectivity and HPA axis activity

Patients with spinal cord injury (SCI) often suffer from varying degrees of neuropathic pain.Non-invasive repetitive transcranial magnetic stimulation (TMS) has been shown to improve neuropathic pain, while the appropriate intervention strategies of TMS treatment and how TMS affects brain function after SCI were not entirely clear. To investigate the effects and mechanisms of TMS on neuropathic pain after SCI, high-frequency TMS on primary motor cortex (M1) of mice was performed after SCI and pain response was evaluated through an electronic Von-Frey device and cold/hot plates. Functional magnetic resonance imaging (fMRI), bulk RNA sequencing, immunofluorescence and molecular experiments were used to evaluate brain and spinal cord function changes and mechanisms. TMS significantly improved SCI induced mechanical allodynia, cold and thermal hyperalgesia with a durative effect, and TMS intervention at 1 week after SCI had pain relief advantages than at 2 weeks. TMS intervention not only affected the functional connections between the primary motor cortex and the thalamus, but also increased the close connection of multiple brain regions. Importantly, TMS treatment activated the hypothalamic pituitary adrenal (HPA) axis and increased the transcript levels of genes encode hormone proteins, accompanied with the attenuation of inflammatory microenvironment in spinal cord associated with pain relief. Totally, these results elucidate that early intervention with TMS could improve neuropathic pain after SCI associated with enhancing brain functional connectivity and HPA axis activity which should be harnessed to modulate neuropathic pain after SCI.

Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

Interference of default mode on attention networks in adults with attention-deficit/hyperactivity disorder and its association with genetic variants and treatment outcomes.

Altered brain functional connectivity has been proposed as the neurobiological underpinnings of attention-deficit/hyperactivity disorder (ADHD), and the default mode interference hypothesis is one of the most popular neuropsychological models. Here, we explored whether this hypothesis is supported in adults with ADHD and the association with high-risk genetic variants and treatment outcomes. Voxel-based whole-brain connectome analysis was conducted on resting-state functional MRI data from 84 adults with ADHD and 89 healthy controls to identify functional connectivity substrates corresponding to ADHD-related alterations. The candidate genetic variants and 12-week cognitive behavioral therapy data were leveraged from the same population to assess these associations. We detected breakdowns of functional connectivity in the precuneus and left middle temporal gyrus in adults with ADHD, with exact contributions from decreased connectivity within the default mode, dorsal and ventral attention networks, as well as increased connectivity among them with the middle temporal gyrus serving as a crucial 'bridge'. Additionally, significant associations between the altered functional connectivity and genetic variants in both MAOA and MAOB were detected. Treatment restored brain function, with the amelioration of connectivity of the middle temporal gyrus, accompanied by improvements in ADHD core symptoms. These findings support the interference of default mode on attention in adults with ADHD and its association with genetic risk variants and clinical management, providing insights into the underlying pathogenesis of ADHD and potential biomarkers for treatment evaluation.

Bidirectional brain image translation using transfer learning from generic pre-trained models

Brain imaging plays a crucial role in the diagnosis and treatment of various neurological disorders, providing valuable insights into the structure and function of the brain. Techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) enable non-invasive visualization of the brain, aiding in the understanding of brain anatomy, abnormalities, and functional connectivity. However, cost and radiation dose may limit the acquisition of specific image modalities, so medical image synthesis can be used to generate required medical images without actual addition. CycleGAN and other GANs are valuable tools for generating synthetic images across various fields. In the medical domain, where obtaining labeled medical images is labor-intensive and expensive, addressing data scarcity is a major challenge. Recent studies propose using transfer learning to overcome this issue. This involves adapting pre-trained CycleGAN models, initially trained on non-medical data, to generate realistic medical images. In this work, transfer learning was applied to the task of MR-CT image translation and vice versa using 18 pre-trained non-medical models, and the models were fine-tuned to have the best result. The models’ performance was evaluated using four widely used image quality metrics: Peak-signal-to-noise-ratio, Structural Similarity Index, Universal Quality Index, and Visual Information Fidelity. Quantitative evaluation and qualitative perceptual analysis by radiologists demonstrate the potential of transfer learning in medical imaging and the effectiveness of the generic pre-trained model. The results provide compelling evidence of the model’s exceptional performance, which can be attributed to the high quality and similarity of the training images to actual human brain images. These results underscore the significance of carefully selecting appropriate and representative training images to optimize performance in brain image analysis tasks.

Differences Between Resting State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults.

Resting state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and mean based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches. 34 healthy participants were separated into a younger adult group (age 25-35, n=17) and an older adult group (age 65+, n=17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network. Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity. The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.

Impact of trauma type on neural mechanisms of threat conditioning and its extinction

Trauma type moderates the impact of trauma exposure on clinical symptomatology; however, the impact of trauma type on the neural correlates of emotion regulation is not as well understood. This study examines how violent and nonviolent trauma differentially influence the neural correlates of conditioned fear and extinction. We aggregated psychophysiological and fMRI data from three studies; we categorized reported trauma as violent or nonviolent, and subdivided violent trauma as sexual or nonsexual. We examined skin conductance responses (SCR) during a fear conditioning and extinction paradigm. For fMRI data analyses, we conducted region-specific and whole-brain analyses. We examined associations between beta weights from specific brain regions and CAPS scores. The group exposed to violent trauma showed significantly higher SCR during extinction recall. Those exposed to nonviolent trauma showed significantly higher functional activation during late extinction learning. The group exposed to violent trauma showed higher functional connectivity within the default mode network (DMN) and between the DMN and frontoparietal control network. For secondary analyses of sexual vs nonsexual trauma, we did not observe any between-group differences in SCR. During late extinction learning, the group exposed to sexual trauma showed significantly higher activation in the prefrontal cortex and precuneus. During extinction recall, the group exposed to nonsexual trauma showed significantly higher activation in the insular cortex. Violent trauma significantly impacts functional brain activations and connectivity in brain areas important for perception and attention with no significant impact on brain areas that modulate emotion regulation. Sexual trauma impacts brain areas important for internal perception.

A mediation approach in resting-state connectivity between the medial prefrontal cortex and anterior cingulate in mild cognitive impairment

Mild cognitive impairment (MCI) is recognized as the prodromal phase of dementia, a condition that can be either maintained or reversed through timely medical interventions to prevent cognitive decline. Considerable studies using functional magnetic resonance imaging (fMRI) have indicated that altered activity in the medial prefrontal cortex (mPFC) serves as an indicator of various cognitive stages of aging. However, the impacts of intrinsic functional connectivity in the mPFC as a mediator on cognitive performance in individuals with and without MCI have not been fully understood. In this study, we recruited 42 MCI patients and 57 healthy controls, assessing their cognitive abilities and functional brain connectivity patterns through neuropsychological evaluations and resting-state fMRI, respectively. The MCI patients exhibited poorer performance on multiple neuropsychological tests compared to the healthy controls. At the neural level, functional connectivity between the mPFC and the anterior cingulate cortex (ACC) was significantly weaker in the MCI group and correlated with multiple neuropsychological test scores. The result of the mediation analysis further demonstrated that functional connectivity between the mPFC and ACC notably mediated the relationship between the MCI and semantic fluency performance. These findings suggest that altered mPFC-ACC connectivity may have a plausible causal influence on cognitive decline and provide implications for early identifications of neurodegenerative diseases and precise monitoring of disease progression.

Age dictates brain functional connectivity and axonal integrity following repetitive mild traumatic brain injuries in mice

Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific symptoms and risk factors. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following TBIs. Repetitive mild TBIs (rmTBI) compound these issues, resulting in cumulative and long-term brain damage in the brain. In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI by employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI). Our hypothesis is that the effects of rmTBI are worsened in aged animals, with this group showing more pronounced alterations in brain connectivity and white matter structure. Utilizing the closed-head impact model of engineered rotational acceleration (CHIMERA) model, we conducted rmTBIs or sham (control) procedures on young (2.5–3-months-old) and aged (22-months-old) male and female mice to model high-risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposhigh-risking effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean diffusivity, radial diffusivity, axial diffusivity, and fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Neuroinflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in injured aged animals compared to sham aged. These findings offer insight into the interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBI.

FMRI signals in white matter rewire gray matter community organization

Human brain gray matter (GM) has usually been clustered into multiple functional networks. The white matter (WM) fiber bundles are known to interconnect these networks simultaneously, engaging in numerous cognitive functions. However, the exact interconnections between GM and WM are still unclear, whether functional signals in WM rewires GM community organization remains to be explored. In this study, we divided brain functional connections into three types by using edge-centric method, including intra-GM, intra-WM and GM-WM connections, and calculated the edge community evaluation indexes for quantifying GM community engagement. The results showed that the involvement of WM significantly enhanced community entropy in the heteromodal system, while the sensory-attention system remained barely changed. In addition, delta community entropy showed a significant correlation with clinical cognitive scale. Our results suggested that WM rewired GM community organization, enhancing the community engagement of brain regions in the heteromodal system. This involvement was observed to be disrupted in disease groups. Our study revealed that considering the functional signals of GM and WM simultaneously could better understand the brain's functional organization.

Contrastive voxel clustering for multiscale modeling of brain network

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

Functional and structural brain connectivity in disorders of consciousness.

Brain connectivity, allowing information to be shared between distinct cortical areas and thus to be processed in an integrated way, has long been considered critical for consciousness. However, the relationship between functional intercortical interactions and the structural connections thought to underlie them is poorly understood. In the present work, we explore both functional (with an EEG-based metric: the median weighted symbolic mutual information in the theta band) and structural (with a brain MRI-based metric: fractional anisotropy) connectivities in a cohort of 78 patients with disorders of consciousness. Both metrics could distinguish patients in a vegetative state from patients in minimally conscious state. Crucially, we discovered a significant positive correlation between functional and structural connectivities. Furthermore, we showed that this structure-function relationship is more specifically observed when considering structural connectivity within the intra- and inter-hemispheric long-distance cortico-cortical bundles involved in the Global Neuronal Workspace (GNW) theory of consciousness, thus supporting predictions of this model. Altogether, these results support the interest of multimodal assessments of brain connectivity in refining the diagnostic evaluation of patients with disorders of consciousness.

Resting-State Network Analysis Reveals Altered Functional Brain Connectivity in Essential Tremor.

Introduction: Essential tremor (ET) comprises motor and non-motor-related features, whereas the current neuro-pathogenetic basis is still insufficient to explain the etiologies of ET. Although cerebellum-associated circuits have been discovered, the large-scale cerebral network connectivity in ET remains unclear. This study aimed to characterize the ET in terms of functional connectivity as well as network. We hypothesized that the resting-state network (RSN) within cerebrum could be altered in patients with ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) was used to evaluate the inter- and intra-network connectivity as well as the functional activity in ET and normal control. Correlation analysis was performed to explore the relationship between RSN metrics and tremor features. Results: Comparison of inter-network connectivity indicated a decreased connectivity between default mode network and ventral attention network in the ET group (p < 0.05). Differences in functional activity (assessed by amplitude of low-frequency fluctuation, ALFF) were found in several brain regions participating in various RSNs (p < 0.05). The ET group generally has higher degree centrality over normal control. Correlation analysis has revealed that tremor features are associated with inter-network connectivity (|r| = 0.135-0.506), ALFF (|r| = 0.313-0.766), and degree centrality (|r| = 0.523-0.710). Conclusion: Alterations in the cerebral network of ET were detected by using resting-state fMRI, demonstrating a potentially useful approach to explore the cerebral alterations in ET.

Contrasting topologies of synchronous and asynchronous functional brain networks.

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy (oCSE) and compared aFN topology to the correlation-based synchronous functional networks (sFNs) which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the Default Mode Network (DMN) in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity which the other type of network does not.

Association of plasma metals with resting-state functional connectivity in ischemic stroke

BackgroundMetal exposure has long been considered a significant risk factor for ischemic stroke. However, existing data on the effects of metal exposure on brain function in ischemic stroke are limited. Therefore, this study aimed to explore the correlation between exposure to various metals and changes in resting-state functional connectivity (rs-FC) in ischemic stroke patients. MethodsThis study included 28 acute ischemic stroke patients with hemiplegia and 28 matched healthy controls (HCs). All participants underwent T1-weighted MRI and 3.0 T resting-state functional magnetic resonance imaging (fMRI). After MRI acquisition, the rs-FC between 137 cortical and subcortical regions was extracted and preprocessed. Plasma levels of 19 metals were measured using inductively coupled plasma mass spectrometry (ICP-MS). The Bayesian kernel machine regression (BKMR) model and the weighted quantile sum regression (WQS) model were used to assess the overall effect of metal mixture exposure. The severity of neurological deficits in each acute ischemic stroke patient was evaluated using the National Institutes of Health Stroke Scale (NIHSS). Additionally, the associations between exposure to various metals and modifications in brain functional connectivity were determined using Pearson or Spearman correlation analysis. ResultsBilateral brain connectivity was significantly decreased compared to controls and was associated with neurological impairment in ischemic stroke. In patients with ischemic stroke, the plasma concentrations of Cr (p < 0.001), Cu (p = 0.004), As (p = 0.010), Cs (p = 0.046), Rb (p = 0.041), and Sb (p = 0.001) were significantly higher than those in the HCs, whereas the plasma Tl concentrations (p = 0.022) were significantly lower. The results of the BKMR and WQS models showed that combined exposure to metal mixtures was linked to a higher risk of ischemic stroke. Cr was positively correlated with the rs-FC between the left Rolandic_Oper and the left Supp_Motor_Area (r = 0.414, p = 0.029), while negatively correlated with the rs-FC between the right Parietal_Inf and the left supramarginal (r = −0.398, p = 0.037). Cu was negatively correlated with the rs-FC between the left paracentral lobule and the left thalamus (r = −0.409, p = 0.031). Tl was positively correlated with the rs-FC between the right Parietal_Inf and the left supramarginal cortex (r = 0.590, p = 0.001). A negative correlation was observed between Cs and rs-FC between the right Cingulate_Mid and left Occipital_Sup (r = −0.429, p = 0.024). Sb was negatively correlated with the rs-FC between the left Parietal_Inf and the right SupraMarginal (r = −0.384, p = 0.044), the right Parietal_Inf and the left SupraMarginal (r = −0.583, p = 0.001), and the left SupraMarginal and the right SupraMarginal (r = −0.377, p = 0.048). ConclusionPlasma levels of Cr, Cu, Tl, Cs, and Sb were associated with altered rs-FC in brain regions related to motor control, sensory integration, executive function, language processing, and emotional regulation in ischemic stroke patients with basal ganglia infarction.

Within-person biological mechanisms of mood variability in childhood and adolescence.

Mood variability, the day-to-day fluctuation in mood, differs between individuals and develops during adolescence. Because adolescents show higher mood variability and average mood than children and adults, puberty might be a potential biological mechanism underlying this increase. The goal of this preregistered developmental study was to examine the neural and hormonal underpinnings of adolescent-specific within-person changes in mood variability, with a specific focus on testosterone, cortisol, pubertal status, and resting-state functional brain connectivity. Data from two longitudinal cohorts were used: the L-CID twin study (aged 7-13, N at the first timepoint = 258) and the accelerated Leiden Self-Concept study (SC; aged 11-21, N at the first timepoint = 138). In both studies resting-state functional magnetic resonance imaging (rs-fMRI) data was collected, as well as daily mood. Additionally, in the SC study self-reported puberty testosterone and cortisol were collected. Random intercept cross-lagged panel models (RI-CLPM) were used to study the within-person relations between these biological measures and mood variability and average mood. Mood variability and average mood peaked in adolescence and testosterone levels and self-reported puberty also showed an increase. Connectivity between prefrontal cortex (dlPFC and vmPFC) and subcortical regions (caudate, amygdala) decreased across development. Moreover, higher testosterone predicted average negative mood at the next time point, but not vice versa. Further, stronger vmPFC-amygdala functional connectivity predicted decreases in mood variability. Here, we show that brain connectivity during development is an important within-person biological mechanism of the development of mood in adolescents. PRACTITIONER POINTS: Mood variability peaks in adolescence. Within-person changes in testosterone predict within-person changes in mood. Within-person changes in vmPFC-amygdala connectivity predict within-person changes in mood variability.