Functional brain network efficiency predicts intelligence

Neuromodulatory effects of parietal high-definition transcranial direct-current stimulation on network-level activity serving fluid intelligence

Fluid intelligence (Gf) involves rational thinking skills and requires the integration of information from different cortical regions to resolve novel complex problems. The effects of non-invasive brain stimulation on Gf have been studied in attempts to improve Gf, but such studies are rare and the few existing have reached conflicting conclusions. The parieto-frontal integration theory of intelligence (P-FIT) postulates that the parietal and frontal lobes play a critical role in Gf. To investigate the suggested role of parietal cortices, we applied high-definition transcranial direct current stimulation (HD-tDCS) to the left and right parietal cortices of 39 healthy adults (age 19-33 years) for 20 min in three separate sessions (left active, right active and sham). After completing the stimulation session, the participants completed a logical reasoning task based on Raven's Progressive Matrices during magnetoencephalography. Significant neural responses at the sensor level across all stimulation conditions were imaged using a beamformer. Whole-brain, spectrally constrained functional connectivity was then computed to examine the network-level activity. Behaviourally, we found that participants were significantly more accurate following left compared to right parietal stimulation. Regarding neural findings, we found significant HD-tDCS montage-related effects in brain networks thought to be critical for P-FIT, including parieto-occipital, fronto-occipital, fronto-parietal and occipito-cerebellar connectivity during task performance. In conclusion, our findings showed that left parietal stimulation improved abstract reasoning abilities relative to right parietal stimulation and support both P-FIT and the neural efficiency hypothesis. KEY POINTS: Abstract reasoning is a critical component of fluid intelligence and is known to be served by multispectral oscillatory activity in the fronto-parietal cortices. Recent studies have aimed to improve abstract reasoning abilities and fluid intelligence overall through behavioural training, but the results have been mixed. High-definition transcranial direct-current stimulation (HD-tDCS) applied to the parietal cortices modulated task performance and neural oscillations during abstract reasoning. Left parietal stimulation resulted in increased accuracy and decreased functional connectivity between occipital regions and frontal, parietal, and cerebellar regions. Future studies should investigate whether HD-tDCS alters abstract reasoning abilities in those who exhibit declines in performance, such as healthy ageing populations.

From gifted to high potential and twice exceptional: A state-of-the-art meta-review

Despite the abundant literature on intelligence and high potential individuals, there is still a lack of international consensus on the terminology and clinical characteristics associated to this population. It has been argued that unstandardized use of diagnosis tools and research methods make comparisons and interpretations of scientific and epidemiological evidence difficult in this field. If multiple cognitive and psychological models have attempted to explain the mechanisms underlying high potentiality, there is a need to confront new scientific evidence with the old, to uproot a global understanding of what constitutes the neurocognitive profile of high-potential in gifted individuals. Another particularly relevant aspect of applied research on high potentiality concerns the challenges faced by individuals referred to as "twice exceptional" in the field of education and in their socio-affective life. Some individuals have demonstrated high forms of intelligence together with learning, affective or neurodevelopmental disorders posing the question as to whether compensating or exacerbating psycho-cognitive mechanisms might underlie their observed behavior. Elucidating same will prove relevant to questions concerning the possible need for differential diagnosis tools, specialized educational and clinical support. A meta-review of the latest findings from neuroscience to developmental psychology, might help in the conception and reviewing of intervention strategies.

Healthy aging alters the oscillatory dynamics and fronto-parietal connectivity serving fluid intelligence

Fluid intelligence (Gf) involves logical reasoning and novel problem-solving abilities. Often, abstract reasoning tasks like Raven's progressive matrices are used to assess Gf. Prior work has shown an age-related decline in fluid intelligence capabilities, and although many studies have sought to identify the underlying mechanisms, our understanding of the critical brain regions and dynamics remains largely incomplete. In this study, we utilized magnetoencephalography (MEG) to investigate 78 individuals, ages 20-65 years, as they completed an abstract reasoning task. MEG data was co-registered with structural MRI data, transformed into the time-frequency domain, and the resulting neural oscillations were imaged using a beamformer. We found worsening behavioral performance with age, including prolonged reaction times and reduced accuracy. MEG analyses indicated robust oscillations in the theta, alpha/beta, and gamma range during the task. Whole brain correlation analyses with age revealed relationships in the theta and alpha/beta frequency bands, such that theta oscillations became stronger with increasing age in a right prefrontal region and alpha/beta oscillations became stronger with increasing age in parietal and right motor cortices. Follow-up connectivity analyses revealed increasing parieto-frontal connectivity with increasing age in the alpha/beta frequency range. Importantly, our findings are consistent with the parieto-frontal integration theory of intelligence (P-FIT). These results further suggest that as people age, there may be alterations in neural responses that are spectrally specific, such that older people exhibit stronger alpha/beta oscillations across the parieto-frontal network during abstract reasoning tasks.

Impact of brain parcellation on prediction performance in models of cognition and demographics

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.

Brain connectome correlates of short-term motor learning in healthy older subjects

The motor learning process entails plastic changes in the brain, especially in brain network reconfigurations. In the current study, we sought to characterize motor learning by determining changes in the coupling behaviour between the brain functional and structural connectomes on a short timescale. 39 older subjects (age: mean (SD) = 69.7 (4.7) years, men:women = 15:24) were trained on a visually guided sequential hand grip learning task. The brain structural and functional connectomes were constructed from diffusion-weighted MRI and resting-state functional MRI, respectively. The association of motor learning ability with changes in network topology of the brain functional connectome and changes in the correspondence between the brain structural and functional connectomes were assessed. Motor learning ability was related to decreased efficiency and increased modularity in the visual, somatomotor, and frontoparietal networks of the brain functional connectome. Between the brain structural and functional connectomes, reduced correspondence in the visual, ventral attention, and frontoparietal networks as well as the whole-brain network was related to motor learning ability. In addition, structure-function correspondence in the dorsal attention, ventral attention, and frontoparietal networks before motor learning was predictive of motor learning ability. These findings indicate that, in the view of brain connectome changes, short-term motor learning is represented by a detachment of the brain functional from the brain structural connectome. The structure-function uncoupling accompanied by the enhanced segregation into modular structures over the core functional networks involved in the learning process may suggest that facilitation of functional flexibility is associated with successful motor learning.

Dysfunctions of multiscale dynamic brain functional networks in subjective cognitive decline

Subjective cognitive decline is potentially the earliest symptom of Alzheimer's disease, whose objective neurological basis remains elusive. To explore the potential biomarkers for subjective cognitive decline, we developed a novel deep learning method based on multiscale dynamical brain functional networks to identify subjective cognitive declines. We retrospectively constructed an internal data set (with 112 subjective cognitive decline and 64 healthy control subjects) to develop and internally validate the deep learning model. Conventional deep learning methods based on static and dynamic brain functional networks are compared. After the model is established, we prospectively collect an external data set (26 subjective cognitive decline and 12 healthy control subjects) for testing. Meanwhile, our method provides monitoring of the transitions between normal and abnormal (subjective cognitive decline-related) dynamical functional network states. The features of abnormal dynamical functional network states are quantified by network and variability metrics and associated with individual cognitions. Our method achieves an area under the receiver operating characteristic curve of 0.807 ± 0.046 in the internal validation data set and of 0.707 (P = 0.007) in the external testing data set, which shows improvements compared to conventional methods. The method further suggests that, at the local level, the abnormal dynamical functional network states are characterized by decreased connectivity strength and increased connectivity variability at different spatial scales. At the network level, the abnormal states are featured by scale-specifically altered modularity and all-scale decreased efficiency. Low tendencies to stay in abnormal states and high state transition variabilities are significantly associated with high general, language and executive functions. Overall, our work supports the deficits in multiscale brain dynamical functional networks detected by the deep learning method as reliable and meaningful neural alternation underpinning subjective cognitive decline.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Elevated CRP and TNF-α levels are associated with blunted neural oscillations serving fluid intelligence

INTRODUCTION

Inflammatory processes help protect the body from potential threats such as bacterial or viral invasions. However, when such inflammatory processes become chronically engaged, synaptic impairments and neuronal cell death may occur. In particular, persistently high levels of C-reactive protein (CRP) and tumor necrosis factor-alpha (TNF-α) have been linked to deficits in cognition and several psychiatric disorders. Higher-order cognitive processes such as fluid intelligence (Gf) are thought to be particularly vulnerable to persistent inflammation. Herein, we investigated the relationship between elevated CRP and TNF-α and the neural oscillatory dynamics serving Gf.

METHODS

Seventy adults between the ages of 20-66 years (Mean = 45.17 years, SD = 16.29, 21.4% female) completed an abstract reasoning task that probes Gf during magnetoencephalography (MEG) and provided a blood sample for inflammatory marker analysis. MEG data were imaged in the time-frequency domain, and whole-brain regressions were conducted using each individual's plasma CRP and TNF-α concentrations per oscillatory response, controlling for age, BMI, and education.

RESULTS

CRP and TNF-α levels were significantly associated with region-specific neural oscillatory responses. In particular, elevated CRP concentrations were associated with altered gamma activity in the right inferior frontal gyrus and right cerebellum. In contrast, elevated TNF-α levels scaled with alpha/beta oscillations in the left anterior cingulate and left middle temporal, and gamma activity in the left intraparietal sulcus.

DISCUSSION

Elevated inflammatory markers such as CRP and TNF-α were associated with aberrant neural oscillations in regions important for Gf. Linking inflammatory markers with regional neural oscillations may hold promise in identifying mechanisms of cognitive and psychiatric disorders.

The influence of very preterm birth on adolescent EEG connectivity, network organization and long-term outcome

OBJECTIVE

The aim of this study was to explore differences in functional connectivity and network organization between very preterm born adolescents and term born controls and to investigate if these differences might explain the relation between preterm birth and adverse long-term outcome.

METHODS

Forty-seven very preterm born adolescents (53% males) and 54 controls (54% males) with matching age, sex and parental educational levels underwent high-density electroencephalography (EEG) at 13 years of age. Long-term outcome was assessed by Intelligence Quotient (IQ), motor, attentional functioning and academic performance. Two minutes of EEG data were analysed within delta, theta, lower alpha, upper alpha and beta frequency bands. Within each frequency band, connectivity was assessed using the Phase Lag Index (PLI) and Amplitude Envelope Correlation, corrected for volume conduction (AEC-c). Brain networks were constructed using the minimum spanning tree method.

RESULTS

Very preterm born adolescents had stronger beta PLI connectivity and less differentiated network organization. Beta AEC-c and differentiation of AEC-c based networks were negatively associated with long-term outcomes. EEG measures did not mediate the relation between preterm birth and outcomes.

CONCLUSIONS

This study shows that very preterm born adolescents may have altered functional connectivity and brain network organization in the beta frequency band. Alterations in measures of functional connectivity and network topologies, especially its differentiating characteristics, were associated with neurodevelopmental functioning.

SIGNIFICANCE

The findings indicate that EEG connectivity and network analysis is a promising tool for investigating underlying mechanisms of impaired functioning.

Antisocial behavior is associated with reduced frontoparietal network efficiency in youth

Youth antisocial behavior (AB) is associated with deficits in socioemotional processing, reward and threat processing and executive functioning. These deficits are thought to emerge from differences in neural structure, functioning and connectivity, particularly within the default, salience and frontoparietal networks. However, the relationship between AB and the organization of these networks remains unclear. To address this gap, the current study applied unweighted, undirected graph analyses to resting-state functional magnetic resonance imaging data in a cohort of 161 adolescents (95 female) enriched for exposure to poverty, a risk factor for AB. As prior work indicates that callous-unemotional (CU) traits may moderate the neurocognitive profile of youth AB, we examined CU traits as a moderator. Using multi-informant latent factors, AB was found to be associated with less efficient frontoparietal network topology, a network associated with executive functioning. However, this effect was limited to youth at low or mean levels of CU traits, indicating that these neural differences were specific to those high on AB but not CU traits. Neither AB, CU traits nor their interaction was significantly related to default or salience network topologies. Results suggest that AB, specifically, may be linked with shifts in the architecture of the frontoparietal network.

Brain working memory network indices as landmarks of intelligence

Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22-35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant association of intelligence with hallmark properties of brain networks during working memory.

Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach

Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.

Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?

OBJECTIVE

Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities.

METHODS

EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested.

RESULTS

The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated.

CONCLUSIONS

Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data.

SIGNIFICANCE

Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.

Decomposing age effects in EEG alpha power

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.

Multimodal Brain Signal Complexity Predicts Human Intelligence

Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.

The establishment of the general microexpression recognition ability and its relevant brain activity

Microexpressions are very transitory expressions lasting about 1/25∼1/2 s, which can reveal people's true emotions they try to hide or suppress. The PREMERT (pseudorandom ecological microexpression recognition test) could test the individual's microexpression recognition ability with six microexpression Ms (the mean of accuracy rates of a microexpression type under six expression backgrounds), and six microexpression SDs (the standard deviation of accuracy rates of this microexpression type under six expression backgrounds), but it and other studies did not explore the general microexpression recognition ability (the GMERA) or could not test the GMERA effectively. Therefore, the current study put forward and established the GMERA with the behavioral data of the PREMERT. The spontaneous brain activity in the resting state is a stable index to measure individual cognitive characteristics. Therefore, the current study explored the relevant resting-state brain activity of the GMERA indicators to prove that GMERA is an individual cognitive characteristic from brain mechanisms with the neuroimaging data of the PREMERT. The results showed that (1) there was a three-layer hierarchical structure in human microexpression recognition ability: The GMERA (the highest layer); recognition of a type of microexpression under different expression backgrounds (the second layer); and recognition of a certain microexpression under a certain expression background (the third layer). A common factor GMERA was extracted from the six microexpression types recognition in PREMERT. Four indicators of the GMERA were calculated from six microexpression Ms and six microexpression SDs, such as GMERAL (level of GMERA), GMERAF (fluctuation of GMERA), GMERAB (background effect of GMERA), and GMERABF (fluctuation of GMERAB), which had good parallel-forms reliability, calibration validity, and ecological validity. The GMERA provided a concise and comprehensive overview of the individual's microexpression recognition ability. The PREMERT was proved as a good test to measure the GMERA. (2) ALFFs (the amplitude of low-frequency fluctuations) in both eyes-closed and eyes-opened resting-states and ALFFs-difference could predict the four indicators of the GMERA. The relevant resting-state brain areas were some areas of the expression recognition network, the microexpression consciousness and attention network, and the motor network for the change from expression backgrounds to microexpression. (3) The relevant brain areas of the GMERA and different types of microexpression recognition belonged to the three cognitive processes, but the relevant brain areas of the GMERA were the "higher-order" areas to be more concise and critical than those of different types of microexpression recognition.

Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process

Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the spatial dependence and the sparsity of the latent source signals. To address these major limitations, we propose a Bayesian spatial blind source separation (BSP-BSS) approach for neuroimaging data analysis. We assume the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, for which we construct a new class of Bayesian nonparametric prior models by thresholding Gaussian processes. We assign the vMF priors to mixing coefficients in the model. Under some regularity conditions, we show that the proposed method has several desirable theoretical properties including the large support for the priors, the consistency of joint posterior distribution of the latent source intensity functions and the mixing coefficients, and the selection consistency on the number of latent sources. We use extensive simulation studies and an analysis of the resting-state fMRI data in the Autism Brain Imaging Data Exchange (ABIDE) study to demonstrate that BSP-BSS outperforms the existing method for separating latent brain networks and detecting activated brain activation in the latent sources.

Improving fluid intelligence of children through working memory training: The role of inhibition control

Intelligence is strongly associated with working memory. Working memory training can improve fluid intelligence, but the underlying mechanism requires further investigation. Because inhibition control may play a key role in working memory training, this study investigated this process from an electrophysiological perspective. In total, 40 children aged 9 to 11 years were enrolled and randomly divided into a training group (n = 20) and a control group (n = 20). The training group received 20 days of working memory training, whereas the control group did not receive any training. Before and after the training period, all participants were tested using Raven's Standard Progressive Matrices (RSPM), and electrophysiological indicators were recorded while they performed go/no-go and Stroop tasks. The results revealed that relative to the control group, the training group had significantly improved RSPM scores in the test conducted after their training. For the go/no-go tasks, the training group exhibited a significant decrease in N2 amplitude, a significant increase in P3 amplitude, a significant decrease in theta band energy, and an improvement in response inhibition ability. No significant change was observed for the Stroop task. Correlation analysis revealed that an improvement in individual response inhibition can positively predict an improvement in fluid intelligence. These results suggest that working memory training enhances the fluid intelligence of children by enhancing their response inhibition ability.

Maximizing brain networks engagement via individualized connectome-wide target search

BACKGROUND

In recent years, the possibility to noninvasively interact with the human brain has led to unprecedented diagnostic and therapeutic opportunities. However, the vast majority of approved interventions and approaches still rely on anatomical landmarks and rarely on the individual structure of networks in the brain, drastically reducing the potential efficacy of neuromodulation.

OBJECTIVE

Here we implemented a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we aimed to identify the optimal stimulation target(s)- at the individual brain level- capable of reaching maximal engagement of the stimulated networks' nodes.

RESULTS

At the model level, in silico predictions suggest that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, demonstrating NCT to be a useful tool in guiding brain stimulation. Indeed, NCT allows us to computationally model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain states.

CONCLUSIONS

The use of NCT to computationally predict TMS pulse propagation suggests that individualized targeting is crucial for more successful network engagement. Future studies will be needed to verify such prediction in real stimulation scenarios.

Conduct disorder symptomatology is associated with an altered functional connectome in a large national youth sample

Conduct disorder (CD), characterized by youth antisocial behavior, is associated with a variety of neurocognitive impairments. However, questions remain regarding the neural underpinnings of these impairments. To investigate novel neural mechanisms that may support these neurocognitive abnormalities, the present study applied a graph analysis to resting-state functional magnetic resonance imaging (fMRI) data collected from a national sample of 4,781 youth, ages 9-10, who participated in the baseline session of the Adolescent Brain Cognitive Development^SM Study (ABCD Study®). Analyses were then conducted to examine the relationships among levels of CD symptomatology, metrics of global topology, node-level metrics for subcortical structures, and performance on neurocognitive assessments. Youth higher on CD displayed higher global clustering (β = .039, 95% CI_corrected [.0027 .0771]), but lower Degree_subcortical (β = -.052, 95% CI_corrected [-.0916 -.0152]). Youth higher on CD had worse performance on a general neurocognitive assessment (β = -.104, 95% CI [-.1328 -.0763]) and an emotion recognition memory assessment (β = -.061, 95% CI [-.0919 -.0290]). Finally, global clustering mediated the relationship between CD and general neurocognitive functioning (indirect β = -.002, 95% CI [-.0044 -.0002]), and Degree_subcortical mediated the relationship between CD and emotion recognition memory performance (indirect β = -.002, 95% CI [-.0046 -.0005]). CD appears associated with neuro-topological abnormalities and these abnormalities may represent neural mechanisms supporting CD-related neurocognitive disruptions.

Decomposing the role of alpha oscillations during brain maturation

Childhood and adolescence are critical stages of the human lifespan, in which fundamental neural reorganizational processes take place. A substantial body of literature investigated accompanying neurophysiological changes, focusing on the most dominant feature of the human EEG signal: the alpha oscillation. Recent developments in EEG signal-processing show that conventional measures of alpha power are confounded by various factors and need to be decomposed into periodic and aperiodic components, which represent distinct underlying brain mechanisms. It is therefore unclear how each part of the signal changes during brain maturation. Using multivariate Bayesian generalized linear models, we examined aperiodic and periodic parameters of alpha activity in the largest openly available pediatric dataset (N=2529, age 5-22 years) and replicated these findings in a preregistered analysis of an independent validation sample (N=369, age 6-22 years). First, the welldocumented age-related decrease in total alpha power was replicated. However, when controlling for the aperiodic signal component, our findings provided strong evidence for an age-related increase in the aperiodic-adjusted alpha power. As reported in previous studies, also relative alpha power revealed a maturational increase, yet indicating an underestimation of the underlying relationship between periodic alpha power and brain maturation. The aperiodic intercept and slope decreased with increasing age and were highly correlated with total alpha power. Consequently, earlier interpretations on age-related changes of total alpha power need to be reconsidered, as elimination of active synapses rather links to decreases in the aperiodic intercept. Instead, analyses of diffusion tensor imaging data indicate that the maturational increase in aperiodic-adjusted alpha power is related to increased thalamocortical connectivity. Functionally, our results suggest that increased thalamic control of cortical alpha power is linked to improved attentional performance during brain maturation.

Intelligence Correlates with the Temporal Variability of Brain Networks

Intelligence is the ability to recognize and understand objective things, and use knowledge and experience to solve problems. Highly intelligent people show the ability to switch between different thought patterns and shift their mental focus. This suggests a link between intelligence and the dynamic interaction of brain networks. Thus, we investigated the relationships between resting-state dynamic brain network remodeling (temporal variability) and scores on the Wechsler Adult Intelligent Scale using a large dataset comprising 606 individuals. We found that performance intelligence was associated with greater temporal variability in the functional connectivity patterns of the dorsal attention network. High variability in these areas indicates flexible connectivity patterns, which may contribute to cognitive processes such as attention selection. In addition, performance intelligence was related to greater temporal variability in the functional connectivity patterns of the salience network. Thus, this study revealed a close relationship between performance intelligence and high variability in brain networks involved in attentional choice, spatial orientation, and cognitive control.

Resting state electroencephalography (EEG) correlates with children's language skills: Evidence from sentence repetition

Spontaneous neural oscillatory activity reflects the brain's functional architecture and has previously been shown to correlate with perceptual, motor and executive skills. The current study used resting state electroencephalography to examine the relationship between spontaneous neural oscillatory activity and children's language skills. Participants in the study were 52 English-speaking children aged around 10-years. Language was assessed using a sentence repetition task. The main analysis revealed resting state theta power negatively correlated with this task. No significant correlations were found in the other studied frequency bands (delta, alpha, beta, gamma). As part of typical brain development, spontaneous theta power declines across childhood and adolescence. The negative correlation observed in this study may therefore be indicating children's language skills are related to the maturation of theta oscillations. More generally, the study provides further evidence that oscillatory activity in the developing brain, even at rest, is reliably associated with children's language skills.

Structural connectivity and intelligence in brain-injured children

In children, higher general intelligence corresponds with better processing speed ability. However, the relationship between structural brain connectivity and processing speed in the context of intelligence is unclear. Furthermore, the impact of brain injury on this relationship is also unknown. Structural networks were constructed for 36 brain tumor patients (mean age: 13.45 ± 2.73, 58% males) and 35 typically developing children (13.30 ± 2.86, 51% males). Processing speed and general intelligence scores were acquired using standard batteries. The relationship between network properties, processing speed, and intelligence was assessed using a partial least squares analysis. Results indicated that structural networks in brain-injured children were less integrated (β = -.38, p = 0.001) and more segregated (β = 0.4, p = 0.0005) compared to typically developing children. There was an indirect effect of network segregation on general intelligence via processing speed, where greater network segregation predicted slower processing speed which in turn predicted worse general intelligence (GoF = 0.37). These findings provide the first evidence of relations between structural connectivity, processing speed, and intelligence in children. Injury-related disruption to the structural network may result in worse intelligence through impacts on information processing. Our findings are discussed in the context of a network approach to understanding brain-behavior relationships.

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

Self- and Situation-Focused Reappraisal are not homogeneous: Evidence from behavioral and brain networks

Reappraisal is an effective emotion regulation strategy which can be divided into self- and situation-focused subtypes. Previous studies have produced inconsistent findings on the moderating effects and neural mechanisms of reappraisal; thus, further research is necessary to clarify these inconsistencies. In this study, a total of 44 participants were recruited and randomly assigned to two groups. 23 participants were assigned to the self-focused group, while 21 participants were assigned to the situation-focused group. The participants' resting EEG data were collected for 6 minutes before the experiment began, followed by an emotional regulation task. During this task, participants were asked to view emotion-provoking images under four emotion regulation conditions (View, Watch, Increase, and Decrease). Late positive potential (LPP) was obtained when these emotional images were observed. LPP is an effective physiological indicator of emotion regulation, enabling this study to explore emotion regulation under different reappraisal strategies, as well as the functional connectivity and node efficiency within the brain. It was found that, in terms of the effect on emotion regulation, situation-focused reappraisal was significantly better than self-focused reappraisal at enhancing the valence of negative emotion, while self-focused reappraisal was significantly better than situation-focused reappraisal at increasing the arousal of negative emotion. In terms of neural mechanisms, multiple brain regions such as the anterior cingulate cortex, the frontal lobe, the parahippocampal gyrus, parts of the temporal lobe, and parts of the parietal lobe were involved in both reappraisal processes. In addition, there were some differences in brain regions associated with different forms of cognitive reappraisal. Self-focused reappraisal was associated with the posterior cingulate gyrus, fusiform gyrus, and lingual gyrus, and situation-focused reappraisal was associated with the parietal lobule, anterior central gyrus, and angular gyrus. In conclusion, this research demonstrates that self- and situation-focused reappraisal are not homogenous in terms of their effects and neural mechanisms and clarifies the uncertainties over their regulatory effects. Different types of reappraisal activate different brain regions when used, and the functional connectivity or node efficiency of these brain regions seems to be a suitable indicator for assessing the effects of different types of reappraisal.

Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study

Brain network analysis represents a powerful technique to gain insights into the connectivity profile characterizing individuals with different levels of fluid intelligence (Gf). Several studies have used diffusion tensor imaging (DTI) and slow-oscillatory resting-state fMRI (rs-fMRI) to examine the anatomical and functional aspects of human brain networks that support intelligence. In this study, we expand this line of research by investigating fast-oscillatory functional networks. We performed graph theory analyses on resting-state magnetoencephalographic (MEG) signal, in addition to structural brain networks from DTI data, comparing degree, modularity and segregation coefficient across the brain of individuals with high versus average Gf scores. Our results show that high Gf individuals have stronger degree and lower segregation coefficient than average Gf participants in a significantly higher number of brain areas with regards to structural connectivity and to the slower frequency bands of functional connectivity. The opposite result was observed for higher-frequency (gamma) functional networks, with higher Gf individuals showing lower degree and higher segregation across the brain. We suggest that gamma oscillations in more intelligent individuals might support higher local processing in segregated subnetworks, while slower frequency bands would allow a more effective information transfer between brain subnetworks, and stronger information integration.

Individual-specific networks for prediction modelling – A scoping review of methods

Background

Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.

Methods

We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase.

Results

Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.

Conclusion

The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01544-6.

High-definition transcranial direct current stimulation modulates performance and alpha/beta parieto-frontal connectivity serving fluid intelligence

Fluid intelligence (Gƒ) includes logical reasoning abilities and is an essential component of normative cognition. Despite the broad consensus that parieto-prefrontal connectivity is critical for Gƒ (e.g. the parieto-frontal integration theory of intelligence, P-FIT), the dynamics of such functional connectivity during logical reasoning remains poorly understood. Further, given the known importance of these brain regions for Gƒ, numerous studies have targeted one or both of these areas with non-invasive stimulation with the goal of improving Gƒ, but to date there remains little consensus on the overall stimulation-related effects. To examine this, we applied high-definition direct current anodal stimulation to the left and right dorsolateral prefrontal cortex (DLPFC) of 24 healthy adults for 20 min in three separate sessions (sham, left, and right active). Following stimulation, participants completed a logical reasoning task during magnetoencephalography (MEG). Significant neural responses at the sensor-level were imaged using a beamformer, and peak task-induced activity was subjected to dynamic functional connectivity analyses to evaluate the impact of distinct stimulation montages on network activity. We found that participants responded faster following right DLPFC stimulation vs. sham. Moreover, our neural findings followed a similar trajectory of effects such that left parieto-frontal connectivity decreased following right and left DLPFC stimulation compared to sham, with connectivity following right stimulation being significantly correlated with the faster reaction times. Importantly, our findings are consistent with P-FIT, as well as the neural efficiency hypothesis (NEH) of intelligence. In sum, this study provides evidence for beneficial effects of right DLPFC stimulation on logical reasoning. KEY POINTS: Logical reasoning is an indispensable component of fluid intelligence and involves multispectral oscillatory activity in parietal and frontal regions. Parieto-frontal integration is well characterized in logical reasoning; however, its direct neural quantification and neuromodulation by brain stimulation remain poorly understood. High-definition transcranial direct current stimulation of dorsolateral prefrontal cortex (DLPFC) had modulatory effects on task performance and neural interactions serving logical reasoning, with right stimulation showing beneficial effects. Right DLPFC stimulation led to a decrease in the response time (i.e. better task performance) and left parieto-frontal connectivity with a marginal positive association between behavioural and neural metrics. Other modes of targeted stimulation of DLPFC (e.g. frequency-specific) can be employed in future studies.

Personality network neuroscience: Promises and challenges on the way toward a unifying framework of individual variability

We propose that the application of network theory to established psychological personality conceptions has great potential to advance a biologically plausible model of human personality. Stable behavioral tendencies are conceived as personality “traits.” Such traits demonstrate considerable variability between individuals, and extreme expressions represent risk factors for psychological disorders. Although the psychometric assessment of personality has more than hundred years tradition, it is not yet clear whether traits indeed represent “biophysical entities” with specific and dissociable neural substrates. For instance, it is an open question whether there exists a correspondence between the multilayer structure of psychometrically derived personality factors and the organizational properties of traitlike brain systems. After a short introduction into fundamental personality conceptions, this article will point out how network neuroscience can enhance our understanding about human personality. We will examine the importance of intrinsic (task-independent) brain connectivity networks and show means to link brain features to stable behavioral tendencies. Questions and challenges arising from each discipline itself and their combination are discussed and potential solutions are developed. We close by outlining future trends and by discussing how further developments of network neuroscience can be applied to personality research.

INTELLIGENCE LEVEL MIGHT BE PREDICTED BY THE CHARACTERISTICS OF EEG SIGNALS AT SPECIFIC FREQUENCIES AND BRAIN REGIONS

The total energy the brain consumed and the intensities of information flows across different brain regions in an intellectual activity may help to explain an individual’s intelligence level. To verify this assumption, 43 students aged 18–25 were recruited as the research subjects. Their intelligence quotients (IQ) were scored by using Wechsler Adult Intelligence Scale (WAIS), while their electroencephalogram (EEG) signals were recorded simultaneously by using Neuroscan system. The total energy and distribution patterns of EEG signals were acquired in Curry 8.0. The intensities of information flow across different brain regions were measured by Phase Slope Index (PSI). 20 channels and 190 channel combinations were selected for data analysis. The results show that the IQ score negatively correlates to the EEG energy and positively correlates to the intensities of information flows at specific frequency bands in specific channel pairs, especially in some long distance (18–24[Formula: see text]cm) channel pairs.

Directed Connectivity Analysis of the Brain Network in Mathematically Gifted Adolescents

The neurocognitive characteristics of mathematically gifted adolescents are characterized by highly developed functional interactions between the right hemisphere and excellent cognitive control of the prefrontal cortex, enhanced frontoparietal cortex, and posterior parietal cortex. However, it is still unclear when and how these cortical interactions occur. In this paper, we used directional coherence analysis based on Granger causality to study the interactions between the frontal brain area and the posterior brain area in the mathematical frontoparietal network system during deductive reasoning tasks. Specifically, the scalp electroencephalography (EEG) signal was first converted into a cortical dipole source signal to construct a Granger causality network over the θ-band and γ-band ranges. We constructed the binary Granger causality network at the 40 pairs of cortical nodes in the frontal lobe and parietal lobe across the θ-band and the γ-band, which were selected as regions of interest (ROI). We then used graph theory to analyze the network differences. It was found that, in the process of reasoning tasks, the frontoparietal regions of the mathematically gifted show stronger working memory information processing at the θ-band. Additionally, in the middle and late stages of the conclusion period, the mathematically talented individuals have less information flow in the anterior and posterior parietal regions of the brain than the normal subjects. We draw the conclusion that the mathematically gifted brain frontoparietal network appears to have more “automated” information processing during reasoning tasks.

Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis

OBJECTIVE

To understand the association between brain networks and behaviors of an individual, most studies build predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brain networks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets.

METHODS

We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data.

RESULTS

After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competing data fusion methods.

CONCLUSION

Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance.

SIGNIFICANCE

To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse different types of fMRI data for predicting fluid intelligence (gF).

The organization of functional neurocognitive networks in focal epilepsy correlates with domain-specific cognitive performance

Understanding and diagnosing cognitive impairment in epilepsy remains a prominent challenge. New etiological models suggest that cognitive difficulties might not be directly linked to seizure activity, but are rather a manifestation of a broader brain pathology. Consequently, treating seizures is not sufficient to alleviate cognitive symptoms, highlighting the need for novel diagnostic tools. Here, we investigated whether the organization of three intrinsic, resting-state functional connectivity networks was correlated with domain-specific cognitive test performance. Using individualized EEG source reconstruction and graph theory, we examined the association between network small worldness and cognitive test performance in 23 patients with focal epilepsy and 17 healthy controls, who underwent a series of standardized pencil-and-paper and digital cognitive tests. We observed that the specific networks robustly correlated with test performance in distinct cognitive domains. Specifically, correlations were evident between the default mode network and memory in patients, the central-executive network and executive functioning in controls, and the salience network and social cognition in both groups. Interestingly, the correlations were evident in both groups, but in different domains, suggesting an alteration in these functional neurocognitive networks in focal epilepsy. The present findings highlight the potential clinical relevance of functional brain network dysfunction in cognitive impairment.

Association between physical fitness components and fluid intelligence according to body mass index in schoolchildren

Physical fitness is considered a protective factor for children's general health and has been related to enhanced cognitive functioning. However, it appears that cognition could be affected in children with overweight or obesity. The present study aimed to determine the relationship between physical fitness components and fluid intelligence in normal-weight and overweight/obese children. In this cross-sectional study, a total of 317 schoolchildren participated (165 boys, 52.05%), aged between six and 11 years old (1st to 5th grade), belonging to a public school in the south of Brazil. Psychologists evaluated fluid intelligence through the Raven's Colored Progressive Matrix Test. The physical fitness evaluation followed the procedures of the "Brazil Sports Project". Weight and height were measured to determine body mass index and generalized linear regression analyses were used with a 95% confidence interval. Our results showed that agility was inversely associated with fluid intelligence only in the overweight/obese group (β = -1.506; p = 0.01). Cardiorespiratory and muscular fitness were not associated with fluid intelligence. In conclusion, agility was the only physical fitness component related to fluid intelligence, and this relationship was found exclusively in overweight/obese schoolchildren.

Multi-Modal Analysis of Resting-State fMRI Data in mTBI Patients and Association With Neuropsychological Outcomes

Various cognitive disorders have been reported for mild traumatic brain injury (mTBI) patients during the acute stage. This acute stage provides an opportunity for clinicians to optimize treatment protocols, which are based on the evaluation of brain structural connectivity. So far, most brain functional magnetic resonance imaging studies are focused on moderate to severe traumatic brain injuries (TBIs). In this study, we prospectively collected resting state data on 50 mTBI within 3 days of injury and 50 healthy volunteers and analyzed them using Amplitude of low-frequency fluctuation (ALFF), Regional Homogeneity (ReHo), graph theory methods and behavior measure, to explore the dysfunctional brain regions in acute mTBI. In our study, a total of 50 patients suffering <3 days mTBI and 50 healthy subjects were tested in rs-fMRI, as well as under neuropsychological examinations including the Wechsler Intelligence Scale and Stroop Color and Word Test. The correlation analysis was conducted between graph theoretic parameters and neuropsychological results. For the mTBI group, the ReHo of the inferior temporal gyrus and the cerebellum superior are significantly lower than in the control group, and the ALFF of the left insula, the cerebellum inferior, and the middle occipital gyrus were significantly higher than in the control group, which implies the dysfunctionality usually observed in Parkinson's disease. Executive function disorder was significantly correlated with the global efficiencies of the dorsolateral superior frontal gyrus and the anterior cingulate cortex, which is consistent with the literature: the acute mTBI patients demonstrate abnormality in terms of motor speed, association, information processing speed, attention, and short-term memory function. Correlation analysis between the neuropsychological outcomes and the network efficiency for the mTBI group indicates that executive dysfunction might be caused by local brain changes. Our data support the idea that the cerebral internal network has compensatory reactions in response to sudden pathological and neurophysiological changes. In the future, multimode rs-fMRI analysis could be a valuable tool for evaluating dysfunctional brain regions after mTBI.

Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy

OBJECTIVE

The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life.

METHODS

We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands.

RESULTS

We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation.

CONCLUSIONS

Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy.

SIGNIFICANCE

Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms.

Overlapping and dissociable brain activations for fluid intelligence and executive functions

Cognitive enhancement interventions aimed at boosting human fluid intelligence (gf) have targeted executive functions (EFs), such as updating, inhibition, and switching, in the context of transfer-inducing cognitive training. However, even though the link between EFs and gf has been demonstrated at the psychometric level, their neurofunctional overlap has not been quantitatively investigated. Identifying whether and how EFs and gf might share neural activation patterns could provide important insights into the overall hierarchical organization of human higher-order cognition, as well as suggest specific targets for interventions aimed at maximizing cognitive transfer. We present the results of a quantitative meta-analysis of the available fMRI and PET literature on EFs and gf in humans, showing the similarity between gf and (i) the overall global EF network, as well as (ii) specific maps for updating, switching, and inhibition. Results highlight a higher degree of similarity between gf and updating (80% overlap) compared with gf and inhibition (34%), and gf and switching (17%). Moreover, three brain regions activated for both gf and each of the three EFs also were identified, located in the left middle frontal gyrus, left inferior parietal lobule, and anterior cingulate cortex. Finally, resting-state functional connectivity analysis on two independent fMRI datasets showed the preferential behavioural correlation and anatomical overlap between updating and gf. These findings confirm a close link between gf and EFs, with implications for brain stimulation and cognitive training interventions.

Altered brain network organization in romantic love as measured with resting-state fMRI and graph theory

Romantic love is a complex state that has been seen as similar to addiction. Previous task-based functional magnetic resonance imaging (fMRI) studies have shown that being in love is closely associated with functional brain changes in the reward and motivation system. However, romantic love-related functional connectivity network organization in resting-state fMRI has yet to be elucidated. To that end, here we used resting-state fMRI and graph theory to compare whole-brain functional network topology between an "in-love" group (n = 34, 16 females, currently in love and in a romantic relationship) and a "single" group (n = 32, 14 females, never in love and not in a romantic relationship). Compared to the single group, we found lower network segregation in the love group (i.e., lower small-worldness, mean clustering coefficient, and modularity), and these metrics were negatively associated with scores on the Passionate Love Scale (PLS) (an index of intense passionate/romantic love). Additionally, the love group displayed altered connectivity degree (reflecting the importance of a node): decreased degree in left angular gyrus and left medial orbitofrontal cortex, but increased degree in left fusiform gyrus. Furthermore, local efficiency or degree of these regions was significantly correlated to PLS scores. Taken together, results showed decreased overall brain functional segregation but enhanced emotional-social processing in romantic lovers. These findings provide the first evidence of love-related brain network organization changes and suggest similar but different brain network alterations between romantic love and addiction, providing new insights on the neural systems underlying romantic love.

Using multivariate endophenotypes to identify psychophysiological mechanisms associated with polygenic scores for substance use, schizophrenia, and education attainment

BACKGROUND

To better characterize brain-based mechanisms of polygenic liability for psychopathology and psychological traits, we extended our previous report (Liu et al. Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci. Psychological Medicine, 2017), focused solely on schizophrenia, to test the association between multivariate psychophysiological candidate endophenotypes (including novel measures of θ/δ oscillatory activity) and a range of polygenic scores (PGSs), namely alcohol/cannabis/nicotine use, an updated schizophrenia PGS (containing 52 more genome-wide significant loci than the PGS used in our previous report) and educational attainment.

METHOD

A large community-based twin/family sample (N = 4893) was genome-wide genotyped and imputed. PGSs were constructed for alcohol use, regular smoking initiation, lifetime cannabis use, schizophrenia, and educational attainment. Eleven endophenotypes were assessed: visual oddball task event-related electroencephalogram (EEG) measures (target-related parietal P3 amplitude, frontal θ, and parietal δ energy/inter-trial phase clustering), band-limited resting-state EEG power, antisaccade error rate. Principal component analysis exploited covariation among endophenotypes to extract a smaller number of meaningful dimensions/components for statistical analysis.

RESULTS

Endophenotypes were heritable. PGSs showed expected intercorrelations (e.g. schizophrenia PGS correlated positively with alcohol/nicotine/cannabis PGSs). Schizophrenia PGS was negatively associated with an event-related P3/δ component [β = -0.032, nonparametric bootstrap 95% confidence interval (CI) -0.059 to -0.003]. A prefrontal control component (event-related θ/antisaccade errors) was negatively associated with alcohol (β = -0.034, 95% CI -0.063 to -0.006) and regular smoking PGSs (β = -0.032, 95% CI -0.061 to -0.005) and positively associated with educational attainment PGS (β = 0.031, 95% CI 0.003-0.058).

CONCLUSIONS

Evidence suggests that multivariate endophenotypes of decision-making (P3/δ) and cognitive/attentional control (θ/antisaccade error) relate to alcohol/nicotine, schizophrenia, and educational attainment PGSs and represent promising targets for future research.

QEEG as a Useful Tool for the Evaluation of Early Cognitive Changes in Dementia and Traumatic Brain Injury

Quantitative electroencephalography (QEEG)-electrical neuroimaging has been underutilized in general neurology practice. Recent advances in computer technology have made this electrophysiological testing relatively inexpensive as well as precise in identifying brain areas with electrical dysfunction related to either traumatic injury or neurodegenerative process. In this article, the author presents 2 cases that can be frequently encountered in every general neurological practice: case of early dementia and traumatic brain injury. The clinical usefulness of QEEG is demonstrated by showing evidence of electrical abnormalities and networks dysfunctions (including an elevation of frontal/temporal delta and theta powers as well as abnormalities in functional connectivity). In addition, the correlation of QEEG and findings from structural imaging technique-magnetic resonance imaging diffusion tensor imaging and another functional imaging-positron emission tomography is presented.

Neurocognition applied to psychotherapy: A brief theoretical proposal based on the complex neural network perspective

Impairments on executive functions, attention, memory, and self-perception had been systematically associated and document across several psychological disorders. Individuals with anxiety, depression, and schizophrenia spectrum disorders tend to manifest difficulties in response modulation/inhibition, cognitive flexibility, selective attention, updating autobiographical memory patterns, and maintenance in the sense of self and boundaries of others. Difficulties in cognitive, emotional, behavioral, and interpersonal functions in intrapsychic and interpsychic mental domains may be theoretically related to the maladaptive functioning of several neural networks. Frontal-Parietal Executive Network (FPEN), Salience Network (SN), Amygdaloid-Hippocampal Memory Network (AHMN), and Default Mode-Network (DMN) are four major complex neural pathways associated with these neurocognitive processes, sharing some neuroanatomical elements. These shared elements may support a latent factor that accounts for the common neurocognitive symptomatology across several psychopathological conditions. Based on these preliminary observations a new theoretical neurocognitive syndrome is hypothesized, potentially a productive target for clinical case conceptualization. Several articulations bettween neurocognition and psychotherapy are discussed and a new assessment measure is proposed.

Large-Scale Morphological Network Efficiency of Human Brain: Cognitive Intelligence and Emotional Intelligence

Network efficiency characterizes how information flows within a network, and it has been used to study the neural basis of cognitive intelligence in adolescence, young adults, and elderly adults, in terms of the white matter in the human brain and functional connectivity networks. However, there were few studies investigating whether the human brain at different ages exhibited different underpins of cognitive and emotional intelligence (EI) from young adults to the middle-aged group, especially in terms of the morphological similarity networks in the human brain. In this study, we used 65 datasets (aging 18–64), including sMRI and behavioral measurements, to study the associations of network efficiency with cognitive intelligence and EI in young adults and the middle-aged group. We proposed a new method of defining the human brain morphological networks using the morphological distribution similarity (including cortical volume, surface area, and thickness). Our results showed inverted age × network efficiency interactions in the relationship of surface-area network efficiency with cognitive intelligence and EI: a negative age × global efficiency (nodal efficiency) interaction in cognitive intelligence, while a positive age × global efficiency (nodal efficiency) interaction in EI. In summary, this study not only proposed a new method of morphological similarity network but also emphasized the developmental effects on the brain mechanisms of intelligence from young adult to middle-aged groups and may promote mental health study on the middle-aged group in the future.

Impact of Brain Functional Network Properties on Intelligence in Children and Adolescents with Focal Epilepsy: A Resting-state MRI Study

RATIONALE AND OBJECTIVE

Epilepsy is a common pediatric disease that often leads to cognitive and intellectual impairments. Here, we explore the reorganized functional networks in children and adolescents with focal epilepsy (CAFE) and analyze the relationship between network reorganization and intellectual deficits to reveal the underlying link between them.

MATERIALS AND METHODS

Fifty-four CAFE (6-16 years old; right-handed) and 42 well-matched healthy controls were recruited. Subjects underwent resting-state functional magnetic resonance imaging, and functional networks were analyzed by graph analysis. Intelligence testing (Wechsler Intelligence Scale for Children-Chinese revision) included measures for verbal IQ (VIQ), performance IQ, and full-scale IQ.

RESULTS

(1) In the CAFE compared with the healthy controls, (a) the local efficiency, clustering coefficient and standardized clustering coefficient were significantly decreased (p < 0.05); (b) the degree centrality and nodal efficiency of the left precentral gyrus (LPG) were significantly increased (p < 0.05, Bonferroni correction), and the nodal shortest path length was significantly decreased (p < 0.05, Bonferroni correction); and (c) functional connectivity of the LPG with the bilateral inferior frontal ventral gyrus, right lateral superior occipital gyrus, left middle occipital gyrus, bilateral superior parietal lobule, right anterior prefrontal cortex, and bilateral cerebellum was enhanced (p < 0.05,GRF correction), while functional connectivity with the bilateral superior temporal gyrus was decreased (p < 0.05, GRF correction). (2) The nodal shortest path length of the LPG in CAFE was associated with full-scale IQ, performance IQ, and VIQ, and local efficiency was associated with VIQ.

CONCLUSION

Our results showed that the middle LPG in CAFE undergoes network reorganization that positively influences intelligence. Differences in local efficiency of functional networks in children and early adolescents have a significant effect on intelligence.

Distance-based functional criticality in the human brain: intelligence and emotional intelligence

Background

Anatomical distance has been identified as a key factor in the organizational principles of the human brain. On the other hand, criticality was proposed to accommodate the multiscale properties of human brain dynamics, and functional criticality based on resting-state functional magnetic resonance imaging (rfMRI) is a sensitive neuroimaging marker for human brain dynamics. Hence, to explore the effects of anatomical distance of the human brain on behaviors in terms of functional criticality, we proposed a revised algorithm of functional criticality called the distance-based vertex-wise index of functional criticality, and assessed this algorithm compared with the original neighborhood-based functional criticality.

Results

We recruited two groups of healthy participants, including young adults and middle-aged participants, for a total of 60 datasets including rfMRI and intelligence as well as emotional intelligence to study how human brain functional criticalities at different spatial scales contribute to individual behaviors. Furthermore, we defined the average distance between the particular behavioral map and vertices with significant functional connectivity as connectivity distance. Our results demonstrated that intelligence and emotional intelligence mapped to different brain regions at different ages. Additionally, intelligence was related to a wider distance range compared to emotional intelligence.

Conclusions

For different age groups, our findings not only provided a linkage between intelligence/emotional intelligence and functional criticality but also quantitatively characterized individual behaviors in terms of anatomical distance.