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The motivation to socially connect with peers increases during adolescence in parallel with changes in neurodevelopment. These changes in social motivation create opportunities for experiences that can impact risk for psychopathology, but the specific motivational presentations that confer greater psychopathology risk are not fully understood. To address this issue, we used a latent profile analysis to identify the multidimensional presentations of self-reported social goals in a sample of 220 girls (9-15 years old, M = 11.81, SD = 1.81) that was enriched for internalizing symptoms, and tested the association between social goal profiles and psychopathology. Associations between social goals and brain network connectivity were also examined in a subsample of 138 youth. Preregistered analyses revealed four unique profiles of social goal presentations in these girls. Greater psychopathology was associated with heightened social goals such that higher clinical symptoms were related to a greater desire to attain social competence, avoid negative feedback and gain positive feedback from peers. The profiles endorsing these excessive social goals were characterized by denser connections among social-affective and cognitive control brain regions. These findings thus provide preliminary support for adolescent-onset changes in motivating factors supporting social engagement that may contribute to risk for psychopathology in vulnerable girls.

Elucidating how adaptive and maladaptive changes to the structural connectivity of brain networks influences neural synchrony, and how this structure–function coupling impacts cognition is an important question in human neuroscience. This study assesses these links in the default mode and executive control networks during resting state, a visual-motor task, and through computational modeling in the developing brain and in acquired brain injuries. Pediatric brain tumor survivors were used as an injury model as they are known to exhibit cognitive deficits, structural connectivity compromise, and perturbations in neural communication. Focusing on information processing speed to assess cognitive performance, we demonstrate that during the presence and absence of specific task demands, structural connectivity of these critical brain networks directly influences neural communication and information processing speed, and white matter compromise has an indirect adverse impact on reaction time via perturbed neural synchrony. Further, when our experimentally acquired structural connectomes simulated neural activity, the resulting functional simulations aligned with our empirical results and accurately predicted cognitive group differences. Overall, our synergistic findings further our understanding of the neural underpinnings of cognition and when it is perturbed. Further establishing alterations in structural–functional coupling as biomarkers of cognitive impairments could facilitate early intervention and monitoring of these deficits.

Postural control is a complex feedback system that relies on vast array of sensory inputs in order to maintain a stable upright stance. The brain cortex plays a crucial role in the processing of this information and in the elaboration of a successful adaptive strategy to external stimulation preventing loss of balance and falls. In the present work, the participants postural control system was challenged by disrupting the upright stance via a mechanical skeletal muscle vibration applied to the calves. The EEG source connectivity method was used to investigate the cortical response to the external stimulation and highlight the brain network primarily involved in high-level coordination of the postural control system. The cortical network reconfiguration was assessed during two experimental conditions of eyes open and eyes closed and the network flexibility (i.e. its dynamic reconfiguration over time) was correlated with the sample entropy of the stabilogram sway. The results highlight two different cortical strategies in the alpha band: the predominance of frontal lobe connections during open eyes and the strengthening of temporal-parietal network connections in the absence of visual cues. Furthermore, a high correlation emerges between the flexibility in the regions surrounding the right temporo-parietal junction and the sample entropy of the CoP sway, suggesting their centrality in the postural control system. These results open the possibility to employ network-based flexibility metrics as markers of a healthy postural control system, with implications in the diagnosis and treatment of postural impairing diseases.

Postural control is a complex feedback system that relies on vast array of sensory inputs in order to maintain a stable upright stance. The brain cortex plays a crucial role in the processing of this information and in the elaboration of a successful adaptive strategy to external stimulation preventing loss of balance and falls. In the present work, the participants postural control system was challenged by disrupting the upright stance via a mechanical skeletal muscle vibration applied to the calves. The EEG source connectivity method was used to investigate the cortical response to the external stimulation and highlight the brain network primarily involved in high-level coordination of the postural control system. The cortical network reconfiguration was assessed during two experimental conditions of eyes open and eyes closed and the network flexibility (i.e. its dynamic reconfiguration over time) was correlated with the sample entropy of the stabilogram sway. The results highlight two different cortical strategies in the alpha band: the predominance of frontal lobe connections during open eyes and the strengthening of temporal-parietal network connections in the absence of visual cues. Furthermore, a high correlation emerges between the flexibility in the regions surrounding the right temporo-parietal junction and the sample entropy of the CoP sway, suggesting their centrality in the postural control system. These results open the possibility to employ network-based flexibility metrics as markers of a healthy postural control system, with implications in the diagnosis and treatment of postural impairing diseases. [ABSTRACT FROM AUTHOR]

Motor recovery after stroke relies on functional reorganization of the motor network, which is commonly assessed via functional magnetic resonance imaging (fMRI)‐based resting‐state functional connectivity (rsFC) or task‐related effective connectivity (trEC). Measures of either connectivity mode have been shown to successfully explain motor impairment post‐stroke, posing the question whether motor impairment is more closely reflected by rsFC or trEC. Moreover, highly similar changes in ipsilesional and interhemispheric motor network connectivity have been reported for both rsFC and trEC after stroke, suggesting that altered rsFC and trEC may capture similar aspects of information integration in the motor network reflecting principle, state‐independent mechanisms of network reorganization rather than state‐specific compensation strategies. To address this question, we conducted the first direct comparison of rsFC and trEC in a sample of early subacute stroke patients (n = 26, included on average 7.3 days post‐stroke). We found that both rsFC and trEC explained motor impairment across patients, stressing the clinical potential of fMRI‐based connectivity. Importantly, intrahemispheric connectivity between ipsilesional M1 and premotor areas depended on the activation state, whereas interhemispheric connectivity between homologs was state‐independent. From a mechanistic perspective, our results may thus arise from two distinct aspects of motor network plasticity: task‐specific compensation within the ipsilesional hemisphere and a more fundamental form of reorganization between hemispheres. [ABSTRACT FROM AUTHOR]

Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.

Introduction: Ambient air pollution is a neurotoxicant with hypothesized immune-related mechanisms. Adolescent brain structural and functional connectivity may be especially vulnerable to ambient pollution due to the refinement of large-scale brain networks during this period, which vary by sex and have important implications for cognitive, behavioral, and emotional functioning. In the current study we explored associations between air pollutants, immune markers, and structural and functional connectivity in early adolescence by leveraging cross-sectional sex-stratified data from the Adolescent Brain Cognitive Development℠ Study®., Methods: Pollutant concentrations of fine particulate matter, nitrogen dioxide, and ozone were assigned to each child's primary residential address during the prenatal period and childhood (9-10 years-old) using an ensemble-based modeling approach. Data collected at 11-13 years-old included resting-state functional connectivity of the default mode, frontoparietal, and salience networks and limbic regions of interest, intracellular directional and isotropic diffusion of available white matter tracts, and markers of cellular immune activation. Using partial least squares correlation, a multivariate data-driven method that identifies important variables within latent dimensions, we investigated associations between 1) pollutants and structural and functional connectivity, 2) pollutants and immune markers, and 3) immune markers and structural and functional connectivity, in each sex separately., Results: Air pollution exposure was related to white matter intracellular directional and isotropic diffusion at ages 11-13 years, but the direction of associations varied by sex. There were no associations between pollutants and resting-state functional connectivity at ages 11-13 years. Childhood exposure to nitrogen dioxide was negatively correlated with white blood cell count in males. Immune biomarkers were positively correlated with white matter intracellular directional diffusion in females and both white matter intracellular directional and isotropic diffusion in males. Lastly, there was a reliable negative correlation between lymphocyte-to-monocyte ratio and default mode network resting-state functional connectivity in females, as well as a compromised immune marker profile associated with lower resting-state functional connectivity between the salience network and the left hippocampus in males. In post-hoc exploratory analyses, we found that the PLSC-identified white matter tracts and resting-state networks related to processing speed and cognitive control performance from the NIH Toolbox., Conclusions: We identified novel links between childhood nitrogen dioxide and cellular immune activation in males, and brain network connectivity and immune markers in both sexes. Future research should explore the potentially mediating role of immune activity in how pollutants affect neurological outcomes as well as the potential consequences of immune-related patterns of brain connectivity in service of improved brain health for all., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Megan Herting, PhD reports financial support was provided by 10.13039/100000066National Institute of Environmental Health Sciences. Dr. Joel D. Schwartz, PhD reports financial support was provided by 10.13039/100000139United States Environmental Protection Agency., (© 2024 The Authors.)

A cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes.We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n = 44) and matched controls (n = 100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tracking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load.MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes.

Although vagus nerve stimulation (VNS) is a common procedure, seizure outcomes are heterogeneous, with few available means to preoperatively identify the ideal surgical candidate. Here, we perform a scoping review of the literature to identify biomarkers of VNS response in patients with drug‐resistant epilepsy. Several databases (Ovid MEDLINE, Ovid Embase, BIOSIS Previews, and Web of Science) were searched for all relevant articles that reported at least one biomarker of VNS response following implantation for intractable epilepsy. Patient demographics, seizure data, and details related to biomarkers were abstracted from all studies. From the 288 records screened, 28 articles reporting on 16 putative biomarkers were identified. These were grouped into four categories: network/connectomic‐based biomarkers, electrophysiological signatures, structural findings on neuroimaging, and systemic assays. Differences in brain network organization, connectivity, and electrophysiological synchronicity demonstrated the most robust ability to identify VNS responders. Structural findings on neuroimaging yielded inconsistent associations with VNS responsiveness. With regard to systemic biomarkers, heart rate variability was shown to be an independent marker of VNS response, whereas inflammatory markers were not useful. There is an unmet need to preoperatively identify candidates who are likely to benefit from VNS. Several biomarkers demonstrate promise in predicting seizure responsiveness to VNS, particularly measures of brain network connectivity. Further efforts are required to validate existing biomarkers to inform clinical decision‐making. [ABSTRACT FROM AUTHOR]

In cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity—modularity and flexibility—which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role in predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.

In cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity--modularity and flexibility--which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role in predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes. [ABSTRACT FROM AUTHOR]

Background: Brain network connectivity modeling is a crucial method for studying the brain's cognitive functions. Meta-analyses can unearth reliable results from individual studies. Meta-analytic connectivity modeling is a connectivity analysis method based on regions of interest (ROIs) which showed that meta-analyses could be used to discover brain network connectivity.Results: In this paper, we propose a new meta-analysis method that can be used to find network connectivity models based on the Apriori algorithm, which has the potential to derive brain network connectivity models from activation information in the literature, without requiring ROIs. This method first extracts activation information from experimental studies that use cognitive tasks of the same category, and then maps the activation information to corresponding brain areas by using the automatic anatomical label atlas, after which the activation rate of these brain areas is calculated. Finally, using these brain areas, a potential brain network connectivity model is calculated based on the Apriori algorithm. The present study used this method to conduct a mining analysis on the citations in a language review article by Price (Neuroimage 62(2):816-847, 2012). The results showed that the obtained network connectivity model was consistent with that reported by Price.Conclusions: The proposed method is helpful to find brain network connectivity by mining the co-activation relationships among brain regions. Furthermore, results of the co-activation relationship analysis can be used as a priori knowledge for the corresponding dynamic causal modeling analysis, possibly achieving a significant dimension-reducing effect, thus increasing the efficiency of the dynamic causal modeling analysis. [ABSTRACT FROM AUTHOR]

Unlabelled: Sickle cell disease (SCD) is a hemoglobinopathy that affects more than 100,000 individuals in the United States. The disease is characterized by the presence of sickle hemoglobin and recurrent episodes of pain. Some individuals with SCD experience frequent hospitalizations and a high burden of pain. The role of central mechanisms in SCD pain has not been explored. Twenty-five adolescents and young adults with SCD underwent functional magnetic resonance imaging. Participants were stratified into groups with high pain or low pain based on the number of hospitalizations for pain in the preceding 12 months. Resting state functional connectivity was analyzed using seed-based and dual regression independent component analysis. Intrinsic brain connectivity was compared between the high pain and low pain groups, and association with fetal hemoglobin, a known modifier of SCD, was explored. Patients in the high pain group displayed an excess of pronociceptive connectivity such as between anterior cingulate and default mode network structures, such as the precuneus, whereas patients in the low pain group showed more connectivity to antinociceptive structures such as the perigenual and subgenual cingulate. Although a similar proportion of patients in both groups reported that they were on hydroxyurea, the fetal hemoglobin levels were significantly higher in the low pain group and were associated with greater connectivity to antinociceptive structures. These findings support the role of central mechanisms in SCD pain. Intrinsic brain connectivity should be explored as a complementary and objective outcome measure in SCD pain research.Perspective: Altered connectivity patterns associated with high pain experience in patients with sickle cell disease suggest a possible role of central mechanisms in sickle cell pain. Resting state brain connectivity studies should be explored as an effective methodology to investigate pain in SCD. [ABSTRACT FROM AUTHOR]

A correction to the article "Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance" that was published in 2017 issue of the periodical, is presented.

The emergence of real-time functional magnetic resonance imaging (rt-fMRI) has provided foundations for neurofeedback based on brain hemodynamics and has given the new opportunity and challenge to cognitive neuroscience research. Along with the study of advanced brain neural mechanisms, the regulation goal of rt-fMRI neurofeedback develops from the early specific brain region activity to the brain network connectivity more accordant with the brain functional activities, and the study of the latter may be a trend in the area. Firstly, this paper introduces basic principle and development of rt-fMRI neurofeedback. Then, it specifically discusses the current research status of brain connectivity neurofeedback technology, including research approaches, experimental methods, conclusions, and so on. Finally, it discusses the problems in this field in the future development.