Brain anatomical network and intelligence

Intrinsic brain mapping of cognitive abilities: A multiple-dataset study on intelligence and its components

This study investigates how functional brain network features contribute to general intelligence and its cognitive components by analyzing three independent cohorts of healthy participants. Cognitive scores were derived from 1) the Wechsler Adult Intelligence Scale (WAIS-IV), 2) the Raven Standard Progressive Matrices (RPM), and 3) the NIH and Penn cognitive batteries from the Human Connectome Project. Factor analysis on the NIH and Penn cognitive batteries yielded latent variables that closely resembled the content of the WAIS-IV indices and RPM. We employed graph theory and a multi-resolution network analysis by varying the modularity parameter (γ) to investigate hierarchical brain-behavior relationships across different scales of brain organization. Brain-behavior associations were quantified using multi-level robust regression analyses to accommodate variability and confounds at the subject-level, node-level, and resolution-level. Our findings reveal consistent brain-behavior relationships across the datasets. Nodal efficiency in fronto-parietal sensorimotor regions consistently played a pivotal role in fluid reasoning, whereas efficiency in visual networks was linked to executive functions and memory. A broad, low-resolution 'task-positive' network emerged as predictive of full-scale IQ scores, indicating a hierarchical brain-behavior coding. Conversely, increased cross-network connections involving default mode and subcortical-limbic networks were associated with reductions in both general and specific cognitive performance. These outcomes highlight the relevance of network efficiency and integration, as well as of the hierarchical organization in supporting specific aspects of intelligence, while recognizing the inherent complexity of these relationships. Our multi-resolution network approach offers new insights into the interplay between multilayer network properties and the structure of cognitive abilities, advancing the understanding of the neural substrates of the intelligence construct.

Exploring Graph Theory Mechanisms of Fluid Intelligence in the DLPFC: Insights From Resting-State fNIRS Across Various Time Windows

BACKGROUND

Brain imaging technologies can measure fluid intelligence (gF) levels more directly, objectively, and dynamically, compared to traditional questionnaire scales. To clarify the temporal mechanisms of graph theory in measuring gF, this study investigated the relationship between graph theoretical indicators in the dorsolateral prefrontal cortex (DLPFC) and gF levels under various time windows.

METHODS

Using 30-min resting-state fNIRS (rs-fNIRS) data and Raven's Advanced Progressive Matrices from 116 healthy participants, the relationship between individual gF levels and DLPFC brain signals was analyzed using average degree (AD) and global efficiency (Eglob).

RESULTS

AD and Eglob in the resting-state DLPFC were significantly negatively correlated with the RAPM score. Considering the effectiveness and efficiency of gF measurement, a 2-min data collection might suffice, while for Eglob, more than 15-min collection was more effective.

CONCLUSION

These findings help clarify brain indicators and demonstrate the effectiveness of rs-fNIRS in intelligence measurement, providing a theoretical and practical basis for portable and objective gF assessment .

Exploring the transmission of cognitive task information through optimal brain pathways

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

White matter correlates of cognition: A diffusion magnetic resonance imaging study

BACKGROUND

Our comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition.

METHODS

125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05.

RESULTS

We identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005).

CONCLUSION

Our results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.

Age-related changes of node degree in the multiple-demand network predict fluid intelligence

Fluid intelligence is an individual's innate ability to cope with complex situations and is gradually reduced across adults aging. The realization of fluid intelligence requires the simultaneous activity of multiple brain regions and depends on the structural connection of distributed brain regions. Uncovering the structural features of brain connections associated with fluid intelligence decline will provide reference for the development of intervention and treatment programs for cognitive decline. Using structural magnetic resonance imaging data of 454 healthy participants (18-87 years) from the Cam-CAN dataset, we constructed structural similarity network for each participant and calculated the node degree. Spearman correlation analysis showed that age was positively correlated with degree centrality in the cingulate cortex, left insula and subcortical regions, while negatively correlated with that in the orbito-frontal cortex, right middle temporal and precentral regions. Partial least squares (PLS) regression showed that the first PLS components explained 32 % (second PLS component: 20 %, p perm < 0.001) of the variance in fluid intelligence. Additionally, the degree centralities of anterior insula, supplementary motor area, prefrontal, orbito-frontal and anterior cingulate cortices, which are critical nodes of the multiple-demand network (MDN), were linked to fluid intelligence. Increased degree centrality in anterior cingulate cortex and left insula partially mediated age-related decline in fluid intelligence. Collectively, these findings suggest that the structural stability of MDN might contribute to the maintenance of fluid intelligence.

A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-Based Graph Contrastive Learning

Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing brain network construction tools have some limitations, including dependency on empirical users, weak consistency in repeated experiments and time-consuming processes. In this work, a diffusion-based brain network pipeline, DGCL is designed for end-to-end construction of brain networks. Initially, the brain region-aware module (BRAM) precisely determines the spatial locations of brain regions by the diffusion process, avoiding subjective parameter selection. Subsequently, DGCL employs graph contrastive learning to optimize brain connections by eliminating individual differences in redundant connections unrelated to diseases, thereby enhancing the consistency of brain networks within the same group. Finally, the node-graph contrastive loss and classification loss jointly constrain the learning process of the model to obtain the reconstructed brain network, which is then used to analyze important brain connections. Validation on two datasets, ADNI and ABIDE, demonstrates that DGCL surpasses traditional methods and other deep learning models in predicting disease development stages. Significantly, the proposed model improves the efficiency and generalization of brain network construction. In summary, the proposed DGCL can be served as a universal brain network construction scheme, which can effectively identify important brain connections through generative paradigms and has the potential to provide disease interpretability support for neuroscience research.

Controlling Alzheimer's disease by deep brain stimulation based on a data-driven cortical network model

This work aims to explore the control effect of DBS on Alzheimer's disease (AD) from a neurocomputational perspective. Firstly, a data-driven cortical network model is constructed using the Diffusion Tensor Imaging data. Then, a typical electrophysiological feature of EEG slowing in AD is reproduced by reducing the synaptic connectivity parameters. The corresponding changes in kinetic behavior mainly include an oscillation decrease in the amplitude and frequency of the pyramidal neuron population. Subsequently, DBS current with specific parameters is introduced into three potential targets of the hippocampus, the nucleus accumbens and the olfactory tubercle, respectively. The results indicate that applying DBS to simulated mild AD patients induces an increase in relative alpha power, a decrease in relative theta power, and a significant rightward shift of the dominant frequency. This is consistent with the EEG reversal in pharmacological treatments for AD. Further, the optimal stimulation strategy of DBS is investigated through spectral and statistical analyses. Specifically, the pathological symptoms of AD could be alleviated by adjusting the critical parameters of DBS, and the control effect of DBS on various targets is that the hippocampus is superior to the olfactory tubercle and nucleus accumbens. Finally, using correlation analysis between the power increments and the nodal degrees, it is concluded that the control effect of DBS is related to the importance of the nodes in the brain network. This study provides a theoretical guidance for determining DBS targets and parameters, which may have a substantial impact on the development of DBS treatment for AD.

Plastic reorganization of the topological asymmetry of hemispheric white matter networks induced by congenital visual experience deprivation

Congenital blindness offers a unique opportunity to investigate human brain plasticity. The influence of congenital visual loss on the asymmetry of the structural network remains poorly understood. To address this question, we recruited 21 participants with congenital blindness (CB) and 21 age-matched sighted controls (SCs). Employing diffusion and structural magnetic resonance imaging, we constructed hemispheric white matter (WM) networks using deterministic fiber tractography and applied graph theory methodologies to assess topological efficiency (i.e., network global efficiency, network local efficiency, and nodal local efficiency) within these networks. Statistical analyses revealed a consistent leftward asymmetry in global efficiency across both groups. However, a different pattern emerged in network local efficiency, with the CB group exhibiting a symmetric state, while the SC group showed a leftward asymmetry. Specifically, compared to the SC group, the CB group exhibited a decrease in local efficiency in the left hemisphere, which was caused by a reduction in the nodal properties of some key regions mainly distributed in the left occipital lobe. Furthermore, interhemispheric tracts connecting these key regions exhibited significant structural changes primarily in the splenium of the corpus callosum. This result confirms the initial observation that the reorganization in asymmetry of the WM network following congenital visual loss is associated with structural changes in the corpus callosum. These findings provide novel insights into the neuroplasticity and adaptability of the brain, particularly at the network level.

Frontoparietal network topology as a neural marker of musical perceptual abilities

Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.

Dynamic adaptation to novelty in the brain is related to arousal and intelligence

How does the human brain respond to novelty? Here, we address this question using fMRI data wherein human participants watch the same movie scene four times. On the first viewing, this movie scene is novel, and on later viewings it is not. We find that brain activity is lower-dimensional in response to novelty. At a finer scale, we find that this reduction in the dimensionality of brain activity is the result of increased coupling in specific brain systems, most specifically within and between the control and dorsal attention systems. Additionally, we found that novelty induced an increase in between-subject synchronization of brain activity in the same brain systems. We also find evidence that adaptation to novelty, herein operationalized as the difference between baseline coupling and novelty-response coupling, is related to fluid intelligence. Finally, using separately collected out-of-sample data, we find that the above results may be linked to psychological arousal.

Discernible interindividual patterns of global efficiency decline during theoretical brain surgery

The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural connectivity graphs were created from diffusion tractography obtained from the brains of 80 healthy adults. These graphs were then used to simulate parcellation resection in every gross anatomical region of the cerebrum by identifying every possible combination of adjacent nodes in a graph and then measuring the drop in GE following nodal deletion. Progressive removal of brain parcellations led to patterns of GE decline that were reasonably predictable but had inter-subject differences. Additionally, as expected, there were deletion of some nodes that were worse than others. However, in each lobe examined in every subject, some deletion combinations were worse for GE than removing a greater number of nodes in a different region of the brain. Among certain patients, patterns of common nodes which exhibited worst GE upon removal were identified as "connectotypes". Given some evidence in the literature linking GE to certain aspects of neuro-cognitive abilities, investigating these connectotypes could potentially mitigate the impact of brain surgery on cognition.

Brain network topology and its cognitive impact in adult glioma survivors

Structural brain network topology can be altered in case of a brain tumor, due to both the tumor itself and its treatment. In this study, we explored the role of structural whole-brain and nodal network metrics and their association with cognitive functioning. Fifty WHO grade 2-3 adult glioma survivors (> 1-year post-therapy) and 50 matched healthy controls underwent a cognitive assessment, covering six cognitive domains. Raw cognitive assessment scores were transformed into w-scores, corrected for age and education. Furthermore, based on multi-shell diffusion-weighted MRI, whole-brain tractography was performed to create weighted graphs and to estimate whole-brain and nodal graph metrics. Hubs were defined based on nodal strength, betweenness centrality, clustering coefficient and shortest path length in healthy controls. Significant differences in these metrics between patients and controls were tested for the hub nodes (i.e. n = 12) and non-hub nodes (i.e. n = 30) in two mixed-design ANOVAs. Group differences in whole-brain graph measures were explored using Mann-Whitney U tests. Graph metrics that significantly differed were ultimately correlated with the cognitive domain-specific w-scores. Bonferroni correction was applied to correct for multiple testing. In survivors, the bilateral putamen were significantly less frequently observed as a hub (pbonf < 0.001). These nodes' assortativity values were positively correlated with attention (r(90) > 0.573, pbonf < 0.001), and proxy IQ (r(90) > 0.794, pbonf < 0.001). Attention and proxy IQ were significantly more often correlated with assortativity of hubs compared to non-hubs (pbonf < 0.001). Finally, the whole-brain graph measures of clustering coefficient (r = 0.685), global (r = 0.570) and local efficiency (r = 0.500) only correlated with proxy IQ (pbonf < 0.001). This study demonstrated potential reorganization of hubs in glioma survivors. Assortativity of these hubs was specifically associated with cognitive functioning, which could be important to consider in future modeling of cognitive outcomes and risk classification in glioma survivors.

Predictability of intelligence and age from structural connectomes

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

Distinct brain network organizations between club players and novices under different difficulty levels

SIGNIFICANT

Chunk memory is one of the essential cognitive functions for high-expertise (HE) player to make efficient decisions. However, it remains unknown how the neural mechanisms of chunk memory processes mediate or alter chess players' performance when facing different opponents.

AIM

This study aimed at inspecting the significant brain networks associated with chunk memory, which would vary between club players and novices.

APPROACH

Functional networks and topological features of 20 club players (HE) and 20 novice players (LE) were compared at different levels of difficulty by means of functional near-infrared spectroscopy.

RESULTS

Behavioral performance indicated that the club player group was unaffected by differences in difficulty. Furthermore, the club player group demonstrated functional connectivity among the dorsolateral prefrontal cortex, the frontopolar cortex, the supramarginal gyrus, and the subcentral gyrus, as well as higher clustering coefficients and lower path lengths in the high-difficulty task.

CONCLUSIONS

The club player group illustrated significant frontal-parietal functional connectivity patterns and topological characteristics, suggesting enhanced chunking processes for improved chess performance.

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.

Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study

The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

Multiscale network neuroscience in neuro-oncology: How tumors, brain networks, and behavior connect across scales

Network neuroscience refers to the investigation of brain networks across different spatial and temporal scales, and has become a leading framework to understand the biology and functioning of the brain. In neuro-oncology, the study of brain networks has revealed many insights into the structure and function of cells, circuits, and the entire brain, and their association with both functional status (e.g., cognition) and survival. This review connects network findings from different scales of investigation, with the combined aim of informing neuro-oncological healthcare professionals on this exciting new field and also delineating the promising avenues for future translational and clinical research that may allow for application of network methods in neuro-oncological care.

Disrupted white matter structural networks in individuals with alcohol dependence

Previous diffusion tensor imaging (DTI) studies have demonstrated widespread white matter microstructure damage in individuals with alcoholism. However, very little is known about the alterations in the topological architecture of white matter structural networks in alcohol dependence (AD). This study included 67 AD patients and 69 controls. The graph theoretical analysis method was applied to examine the topological organization of the white matter structural networks, and network-based statistics (NBS) were employed to detect structural connectivity alterations. Compared to controls, AD patients exhibited abnormal global network properties characterized by increased small-worldness, normalized clustering coefficient, clustering coefficient, and shortest path length; and decreased global efficiency and local efficiency. Further analyses revealed decreased nodal efficiency and degree centrality in AD patients mainly located in the default mode network (DMN), including the precuneus, anterior cingulate and paracingulate gyrus, median cingulate and paracingulate gyrus, posterior cingulate gyrus, and medial part of the superior frontal gyrus. Furthermore, based on NBS approaches, patients displayed weaker subnetwork connectivity mainly located in the region of the DMN. Additionally, altered network metrics were correlated with intelligence quotient (IQ) scores and global assessment function (GAF) scores. Our results may reveal the disruption of whole-brain white matter structural networks in AD individuals, which may contribute to our comprehension of the underlying pathophysiological mechanisms of alcohol addiction at the level of white matter structural networks.

Assessment of the corpus callosum size in male individuals with high intelligence quotient (members of Mensa International)

OBJECTIVES

The aim of this study was to assess the size of the corpus callosum in members of Mensa International, which is the world's largest and oldest high-intelligence quotient (IQ) society.

METHODS

We performed T2-weighted magnetic resonance imaging (Repetition Time, TR = 3200 ms, Time of Echo, TE = 409 ms) to examine the brain of members of Mensa International (Polish national group) in order to assess the size of the corpus callosum. Results from 113 male MENSA members and 96 controls in the age range of 21-40 years were analyzed.

RESULTS

The comparative analysis showed that the mean length of the corpus callosum and the thickness of the isthmus were significantly greater in the Mensa members compared to the control groups. A statistically significant difference was also identified in the largest linear dimension of the brain from the frontal lobe to the occipital lobe. The mean corpus callosum cross-sectional area and its ratio to the brain area were significantly greater in the Mensa members.

CONCLUSIONS

The results show that the dimensions (linear measures and midsagittal cross-sectional surface area) of the corpus callosum were significantly greater in the group of Mensa members than in the controls.

Early Childhood Development of Node Centrality in the White Matter Connectome and Its Relationship to IQ at Age 6 Years

BACKGROUND

The white matter (WM) connectome is important for cognitive development and intelligence and is altered in neuropsychiatric illnesses. Little is known about how the WM connectome develops or its relationship to IQ in early childhood.

METHODS

The development of node centrality in the WM connectome was studied in a longitudinal cohort of 226 (123 female) children from the University of North Carolina Early Brain Development Study. Structural and diffusion-weighted images were acquired after birth and at 1, 2, 4, and 6 years, and IQ was assessed at 6 years. Eigenvector centrality, betweenness centrality, and the global graph metrics of global efficiency, small worldness, and modularity were determined at each age.

RESULTS

The greatest developmental change in eigenvector centrality and betweenness centrality occurred during the first year of life, with relative stability between ages 1 and 6 years. Most of the high-centrality hubs at age 6 were also high-centrality hubs at 1 year, and many were already high-centrality hubs at birth. There were generally small but significant changes in global efficiency and modularity from birth to 6 years, while small worldness increased between 2 and 4 years. Individual node centrality was not significantly correlated with IQ at 6 years.

CONCLUSIONS

Node centrality in the WM connectome is established very early in childhood and is relatively stable from age 1 to 6 years. Many high-centrality hubs are established before birth, and most are present by age 1.

Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.

Trade-offs among cost, integration, and segregation in the human connectome

The human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis.

Structural architecture and brain network efficiency link polygenic scores to intelligence

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

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.

Comparison of brain network between schizophrenia and bipolar disorder: A multimodal MRI analysis of comparative studies

OBJECTIVE

Cognitive impairment is a shared symptom of Schizophrenia (SCZ) and bipolar disorder (BP), but the underlying neural mechanisms for both remain unclear. We aimed to identify abnormalities in the structural and functional brain network of patients with SCZ and BP.

METHODS

The study included 69 patients with SCZ, 40 with BP, and 63 healthy controls (HC). After neurocognitive function assessment, resting-state functional magnetic resonance imaging and diffusion tensor imaging were acquired respectively. We compared the network of structural connectivity (SC) and functional connectivity (FC) among the three groups and performed graph theoretical analyses. The SC-FC coupling was calculated, and the correlations between the cognitive function scores and network properties were ascertained.

RESULTS

The BP group showed significantly higher indicators in subnetworks and graph theory analysis than SCZ and HC. Several brain regions, such as the inferior parietal lobe, exhibited differences among all pairwise comparisons and showed significant correlations with cognitive scores in both SCZ and BP. SC-FC coupling did not significantly differ between the three groups but showed close associations with clinical performance. Interestingly, the direction of correlations between the network properties and cognition tends to present the opposite between SCZ and BP, especially regarding the working memory, attention, and language sections.

CONCLUSIONS

The FC and SC network of the SCZ group appeared more inefficient and disconnected than BP. The network demonstrated to be closely but differently associated with cognitive function at both local and global levels, indicating the potentially separated pathologies of cognition deficits in SCZ and BP.

White matter, cognition and psychotic-like experiences in UK Biobank

BACKGROUND

Psychotic-like experiences (PLEs) are risk factors for the development of psychiatric conditions like schizophrenia, particularly if associated with distress. As PLEs have been related to alterations in both white matter and cognition, we investigated whether cognition (g-factor and processing speed) mediates the relationship between white matter and PLEs.

METHODS

We investigated two independent samples (6170 and 19 891) from the UK Biobank, through path analysis. For both samples, measures of whole-brain fractional anisotropy (gFA) and mean diffusivity (gMD), as indications of white matter microstructure, were derived from probabilistic tractography. For the smaller sample, variables whole-brain white matter network efficiency and microstructure were also derived from structural connectome data.

RESULTS

The mediation of cognition on the relationships between white matter properties and PLEs was non-significant. However, lower gFA was associated with having PLEs in combination with distress in the full available sample (standardized β = -0.053, p = 0.011). Additionally, lower gFA/higher gMD was associated with lower g-factor (standardized β = 0.049, p < 0.001; standardized β = -0.027, p = 0.003), and partially mediated by processing speed with a proportion mediated of 7% (p = < 0.001) for gFA and 11% (p < 0.001) for gMD.

CONCLUSIONS

We show that lower global white matter microstructure is associated with having PLEs in combination with distress, which suggests a direction of future research that could help clarify how and why individuals progress from subclinical to clinical psychotic symptoms. Furthermore, we replicated that processing speed mediates the relationship between white matter microstructure and g-factor.

Effective connectivity analysis of brain networks of mathematically gifted adolescents using transfer entropy

Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing.

The association between subjective cognitive decline and trajectories of objective cognitive decline: Do social relationships matter?

OBJECTIVES

We examine the association between subjective cognitive decline (SCD) and the trajectories of objective cognitive decline (OCD); and the extent to which this association is moderated by social relationships.

METHODS

Data come from waves 10 (2010) through 14 (2018) of the Health and Retirement Study, a nationally representative panel survey of individuals aged 50 and above in the United States. OCD is measured using episodic memory, and overall cognition. SCD is assessed using a baseline measure of self-rated memory. Social relationships are measured by social network size and perceived positive and negative social support. Growth curve models estimate the longitudinal link between SCD and subsequent OCD trajectories and the interactions between SCD and social relationship variables on OCD.

RESULTS

SCD is associated with subsequent OCD. A wider social network and lower perceived negative support are linked to slower decline in memory, and overall cognition. None of the social relationship variables, however, moderate the link between SCD and future OCD.

CONCLUSION

Knowing that SCD is linked to subsequent OCD is useful because at SCD stage, deficits are more manageable relative to those at subsequent stages of OCD. Future work on SCD and OCD should consider additional dimensions of social relationships.

Reorganization of brain networks in patients with temporal lobe epilepsy and comorbid headache

OBJECTIVE

The white matter structural network changes remain poorly understood in patients with temporal lobe epilepsy and comorbid headache (PWH). This study aimed at exploring topological changes in the structural network.

METHODS

Twenty-five PWH, 32 patients with temporal lobe epilepsy without headache, and 22 healthy controls were recruited in this study. High-resolution structural MRI and diffusion tensor imaging data were acquired from these participants. A graph theory-based approach was employed to characterize the topological properties of the structural network. A network-based statistical analysis was employed to explore abnormal connectivity alterations in PWH.

RESULTS

Compared with healthy controls, PWH exhibited significantly decreased small-world index, shortest path length, increased clustering coefficient, global efficiency, and local efficiency. Patients with temporal lobe epilepsy and comorbid headache displayed a significantly reduced small-world index, shortest path length, and increased global efficiency when compared with patients with temporal lobe epilepsy without headache. In addition, PWH exhibited abnormal local network parameters, mainly located in the prefrontal, temporal, occipital, and parietal regions. Furthermore, network-based statistical analysis revealed that PWH had abnormal structural connections between the temporoparietal lobe, occipital lobe, insula, cingulate gyrus, and thalamus.

CONCLUSION

This study reveals the abnormal white matter structural network alterations in PWH, allowing a better insight into the neuroanatomical mechanisms that predispose epileptic patients to comorbid headaches from the network levels.

Computational Models of Typical and Atypical Brain Network Development

Over the last decade, the organization of brain networks at both micro- and macroscales has become a key focus of neuroscientific inquiry. This has revealed fundamental features of brain network organization-small-worldness, modularity, heavy-tailed degree distributions-and has highlighted how these structural features support brain function. However, the driving forces that shape brain networks over the course of development have begun to be explored only recently. Here, we review recent efforts to gain insights into the mechanisms of brain development through generative modeling of both macroscale human brain networks and microscale cellular connectomes in Caenorhabditis elegans and other organisms. We show how these mathematical models can begin to shed light on the biological processes that drive and constrain the development of brain networks. Finally, we show how generative network models can translate genetic and environmental differences into variability in developmental trajectories, leading to diverse cognitive and mental health outcomes in children and young people.

Brain plasticity of structural connectivity networks and topological properties in baseball players with different levels of expertise

Brain plasticity in structural connectivity networks along the development of expertise has remained largely unknown. To address this, we recruited individuals with three different levels of baseball-playing experience: skilled batters (SB), intermediate batters (IB), and healthy controls (HC). We constructed their structural connectivity networks using diffusion tractography and compared their region-to-region structural connections and the topological characteristics of the constructed networks using graph-theoretical analysis. The group differences were detected in 35 connections predominantly involving sensorimotor and visual systems; the intergroup changes could be depicted either in a stepwise (HC < / = IB < / = SB) or a U-/inverted U-shaped manner as experience increased (IB < SB and/or HC, or opposite). All groups showed small-world topology in their constructed networks, but SB had increased global and local network efficiency than IB and/or HC. Furthermore, although the number and cortical regions identified as hubs of the networks in the three groups were highly similar, SB exhibited higher nodal global efficiency in both the dorsolateral and medial parts of the bilateral superior frontal gyri than IB. Our findings add new evidence of topological reorganization in brain networks associated with sensorimotor experience in sports. Interestingly, these changes do not necessarily increase as a function of experience as previously suggested in literature.

Modeling functional difference between gyri and sulci within intrinsic connectivity networks

Recently, the functional roles of the human cortical folding patterns have attracted increasing interest in the neuroimaging community. However, most existing studies have focused on the gyro-sulcal functional relationship on a whole-brain scale but possibly overlooked the localized and subtle functional differences of brain networks. Actually, accumulating evidences suggest that functional brain networks are the basic unit to realize the brain function; thus, the functional relationships between gyri and sulci still need to be further explored within different functional brain networks. Inspired by these evidences, we proposed a novel intrinsic connectivity network (ICN)-guided pooling-trimmed convolutional neural network (I-ptFCN) to revisit the functional difference between gyri and sulci. By testing the proposed model on the task functional magnetic resonance imaging (fMRI) datasets of the Human Connectome Project, we found that the classification accuracy of gyral and sulcal fMRI signals varied significantly for different ICNs, indicating functional heterogeneity of cortical folding patterns in different brain networks. The heterogeneity may be contributed by sulci, as only sulcal signals show heterogeneous frequency features across different ICNs, whereas the frequency features of gyri are homogeneous. These results offer novel insights into the functional difference between gyri and sulci and enlighten the functional roles of cortical folding patterns.

Brain Network Organization and Aging

Despite recent substantial progress in neuroscience, the mechanisms and principles of the complex structure, functions, and the relationship between the brain and cognitive functions have not been fully understood. The modeling method of brain network can provide a new perspective for neuroscience research, and it is possible to provide new solutions to the related research problems. On this basis, the researchers define the concept of human brain connectome to highlight and emphasize the importance of network modeling methods in neuroscience. For example, using diffusion-weighted magnetic resonance imaging (dMRI) technology and fiber tractography methods, a white matter connection network of the whole brain can be constructed. From the perspective of brain function, functional magnetic resonance imaging (fMRI) data can build the brain functional connection network. A structural covariation modeling method is used to obtain a brain structure covariation network, and it appears to reflect developmental coordination or synchronized maturation between areas of the brain. In addition, network modeling and analysis methods can also be applied to other types of image data, such as positron emission tomography (PET), electroencephalogram (EEG), and magnetoencephalography (MEG). This chapter mainly reviews the research progress of researchers on brain structure, function, and other aspects at the network level in recent years.

Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data

Variable screening is a powerful and efficient tool for dimension reduction under ultrahigh dimensional settings. However, most existing methods overlook useful prior knowledge in specific applications. In this work, from a Bayesian modeling perspective, we develop a unified variable screening procedure for the linear regression model. We discuss different constructions of posterior mean screening (PMS) statistics to incorporate different types of prior knowledge according to specific applications. With non-informative prior specifications, PMS is equivalent to high-dimensional ordinary least-square projections (HOLP). We establish the screening consistency property for PMS with different types of prior knowledge. We show that PMS is robust to prior misspecifications; and when the prior knowledge provides correct information on summarizing the true parameter settings, PMS can substantially improve the selection accuracy compared to HOLP and other existing methods. We illustrate our method with extensive simulation studies and an analysis of neuroimaging data.

Structural-Functional Connectivity Bandwidth of the Human Brain

BACKGROUND

The human brain is a complex network that seamlessly manifests behaviour and cognition. This network comprises neurons that directly, or indirectly mediate communication between brain regions. Here, we show how multilayer/multiplex network analysis provides a suitable framework to uncover the throughput of structural connectivity (SC) to mediate information transfer-giving rise to functional connectivity (FC).

METHOD

We implemented a novel method to reconcile SC and FC using diffusion and resting-state functional MRI connectivity data from 484 subjects (272 females, 212 males; age = 29.15 ± 3.47) from the Human Connectome Project. First, we counted the number of direct and indirect structural paths that mediate FC. FC nodes with indirect SC paths were then weighted according to their least restrictive SC path. We refer to this as SC-FC Bandwidth. We then mapped paths with the highest SC-FC Bandwidth across 7 canonical resting-state networks.

FINDINGS

We found that most pairs of FC nodes were connected by SC paths of length two and three (SC paths of length >5 were virtually non-existent). Direct SC-FC connections accounted for only 10% of all SC-FC connections. The majority of FC nodes without a direct SC path were mediated by a proportion of two (44%) or three SC path lengths (39%). Only a small proportion of FC nodes were mediated by SC path lengths of four (5%). We found high-bandwidth direct SC-FC connections show dense intra- and sparse inter-network connectivity, with a bilateral, anteroposterior distribution. High bandwidth SC-FC triangles have a right superomedial distribution within the somatomotor network. High-bandwidth SC-FC quads have a superoposterior distribution within the default mode network.

CONCLUSION

Our method allows the measurement of indirect SC-FC using undirected, weighted graphs derived from multimodal MRI data in order to map the location and throughput of SC to mediate FC. An extension of this work may be to explore how SC-FC Bandwidth changes over time, relates to cognition/behavior, and if this measure reflects a marker of neurological injury or psychiatric disorders.

Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability

Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo.

Diffusion spectrum imaging of patients with middle cerebral artery stenosis

OBJECTIVE

We aimed to detect microstructural changes in the brains of patients with unilateral middle cerebral artery (MCA) stenosis and to assess the integrity of the fiber structure and the small-world networks using diffusion spectrum imaging (DSI).

METHODS

A total of 21 healthy controls and 48 patients with unilateral MCA stenosis underwent 3.0 T MRI examination using DSI technique. Differential tractography, diffusion connectometry, and structural networks were performed by using DSI software. The correlation between the stenosis and quantitative anisotropy (QA) were analyzed using multiple regression models in the correlation tractography.

RESULTS

Differential tractography analysis showed that the left or right MCA stenosis group had decreased fiber connectivity in the brain network compared with the control group. The correlation tractography analysis of the patients with MCA stenosis showed that QA was negatively correlated with stenosis in the bilateral arcuate fasciculus, bilateral corticostriatal and corticothalamic pathway, bilateral corticopontine and corticospinal tract, right superior longitudinal fasciculus, right cingulum, corpus callosum, and left frontal aslant tract. Statistically significant differences were shown between the MCA stenosis groups and control group in graph density, global efficiency, network path length, and rich club coefficient.

CONCLUSION

DSI revealed that stroke-free patients with unilateral MCA stenosis have a disrupted structural network and damaged white matter fibers. Furthermore, the fiber connection disruption is more severe in the ipsilateral hemisphere and less prominent in the contralateral hemisphere in patients with unilateral MCA stenosis. Therefore, microstructural impairment has happened to patients with unilateral MCA stenosis even at a subclinical stage.

State and Trait Anxiety Share Common Network Topological Mechanisms of Human Brain

Anxiety is a future-oriented unpleasant and negative mental state induced by distant and potential threats. It could be subdivided into momentary state anxiety and stable trait anxiety, which play a complex and combined role in our mental and physical health. However, no studies have systematically investigated whether these two different dimensions of anxiety share a common or distinct topological mechanism of human brain network. In this study, we used macroscale human brain morphological similarity network and functional connectivity network as well as their spatial and temporal variations to explore the topological properties of state and trait anxiety. Our results showed that state and trait anxiety were both negatively correlated with the coefficient of variation of nodal efficiency in the left frontal eyes field of volume network; state and trait anxiety were both positively correlated with the median and mode of pagerank centrality distribution in the right insula for both static and dynamic functional networks. In summary, our study confirmed that state and trait anxiety shared common human brain network topological mechanisms in the insula and the frontal eyes field, which were involved in preliminary cognitive processing stage of anxiety. Our study also demonstrated that the common brain network topological mechanisms had high spatiotemporal robustness and would enhance our understanding of human brain temporal and spatial organization.

Coupling analysis between functional and structural brain networks in Alzheimer's disease

The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (p < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (p < 0.05), positive correlations with CDR-SB and FAQ scores (p < 0.05), and no correlation with MoCA score (p > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.

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.

Reorganization of the Brain Structural Covariance Network in Ischemic Moyamoya Disease Revealed by Graph Theoretical Analysis

Objective

Ischemic moyamoya (MMD) disease could alter the cerebral structure, but little is known about the topological organization of the structural covariance network (SCN). This study employed structural magnetic resonance imaging and graph theory to evaluate SCN reorganization in ischemic MMD patients.

Method

Forty-nine stroke-free ischemic MMD patients and 49 well-matched healthy controls (HCs) were examined by T1-MPRAGE imaging. Structural images were pre-processed using the Computational Anatomy Toolbox 12 (CAT 12) based on the diffeomorphic anatomical registration through exponentiated lie (DARTEL) algorithm and both the global and regional SCN parameters were calculated and compared using the Graph Analysis Toolbox (GAT).

Results

Most of the important metrics of global network organization, including characteristic path length (Lp), clustering coefficient (Cp), assortativity, local efficiency, and transitivity, were significantly reduced in MMD patients compared with HCs. In addition, the regional betweenness centrality (BC) values of the bilateral medial orbitofrontal cortices were significantly lower in MMD patients than in HCs after false discovery rate (FDR) correction for multiple comparisons. The BC was also reduced in the left medial superior frontal gyrus and hippocampus, and increased in the bilateral middle cingulate gyri of patients, but these differences were not significant after FDR correlation. No differences in network resilience were detected by targeted attack analysis or random failure analysis.

Conclusions

Both global and regional properties of the SCN are altered in MMD, even in the absence of major stroke or hemorrhagic damage. Patients exhibit a less optimal and more randomized SCN than HCs, and the nodal BC of the bilateral medial orbitofrontal cortices is severely reduced. These changes may account for the cognitive impairments in MMD patients.

Individual Differences in Hemispheric Emotional Valence by Computerized Test Correlate with Lateralized Differences in Nucleus Accumbens, Hippocampal and Amygdala Volumes

Purpose

Conventional theories of hemispheric emotional valence (HEV) postulate fixed hemispheric differences in emotional processing. Schiffer’s dual brain psychology proposes that there are prominent individual differences with a substantial subset showing a reversed laterality pattern. He further proposed that hemispheric differences were more akin to differences in personality than in emotional processing. This theory is supported by findings that unilateral treatments, such as transcranial magnetic stimulation, are effective if they accurately target individual differences in laterality. The aim of this paper was to assess if a computer test of hemispheric emotional valence (CTHEV) could effectively identify individual differences in HEV and to ascertain if these individual differences were associated with underlying differences in brain structure and connectivity.

Patients and Methods

The CTHEV was administered to 50 (18 male/32 female) right-handed participants, aged 18–19 years, enrolled in a study assessing the neurobiological effects of childhood maltreatment. Based on a literature review, we determined whether CTHEV correlated with lateralized volumes of the nucleus accumbens, amygdala, hippocampus, and subgenual anterior cingulate as well as volume of the corpus callosum.

Results

CTHEV scores correlated with laterality indices of the nucleus accumbens (p = 0.00016), amygdala (p = 0.0138) and hippocampus (p = 0.031). A positive left hemispheric valence was associated with a larger left-sided nucleus accumbens and hippocampus and a smaller left amygdala. We identified four eigenvector network centrality DTI measures that predict CTHEV, most notably the left amygdala, and found that CTHEV results correlated with total and segment-specific corpus callosal volumes.

Conclusion

Individual differences in HEV can be readily assessed by computer test and correlate with differences in brain structure and connectivity that could provide a mechanistic understanding. These findings provide further support for a revised understanding of HEV and provide a tool that could be used to guide lateralized brain treatments.

Different Approximation Methods for Calculation of Integrated Information Coefficient in the Brain during Instrumental Learning

The amount of integrated information, Φ, proposed in an integrated information theory (IIT) is useful to describe the degree of brain adaptation to the environment. However, its computation cannot be precisely performed for a reasonable time for time-series spike data collected from a large count of neurons.. Therefore, Φ was only used to describe averaged activity of a big group of neurons, and the behavior of small non-brain systems. In this study, we reported on ways for fast and precise Φ calculation using different approximation methods for Φ calculation in neural spike data, and checked the capability of Φ to describe a degree of adaptation in brain neural networks. We show that during instrumental learning sessions, all applied approximation methods reflect temporal trends of Φ in the rat hippocampus. The value of Φ is positively correlated with the number of successful acts performed by a rat. We also show that only one subgroup of neurons modulates their Φ during learning. The obtained results pave the way for application of Φ to investigate plasticity in the brain during the acquisition of new tasks.

Using resting thalamic connectivity to identify the relationship between Eysenck personality traits and intelligence in healthy adults

Personality refers to a set of relatively stable psychological characteristics of individuals and has been associated with intelligence. It is well known that the thalamus plays an important role in cognitive processes and personality traits, but the relationship between personality traits, thalamic function, and intelligence has rarely been directly explored. Hence, we investigated the relationship between Eysenck personality traits, resting-state functional connectivity (rsFC) of the thalamus, and intelligence in a large sample of healthy adults (N = 176). We found that the trait of psychoticism was negatively associated with intelligence. The high intelligence group showed significantly lower psychoticism and demonstrated enhanced thalamic connectivity to the amygdala, inferior parietal lobules, pallidum, medial superior/middle frontal gyrus, and precuneus. Furthermore, a mediation analysis indicated that the FC between the left thalamus and left amygdala significantly mediated the correlation between psychoticism and full IQ (FIQ). These findings suggest that intelligent people may be less prone to psychoticism. Meanwhile, thalamic rsFC may reflect individual differences in intelligence and play a key role in the relationship between personality traits and intellectual abilities.

Physics Clues on the Mind Substrate and Attributes

The last decade has witnessed a remarkable progress in our understanding of the brain. This has mainly been based on the scrutiny and modeling of the transmission of activity among neurons across lively synapses. A main conclusion, thus far, is that essential features of the mind rely on collective phenomena that emerge from a willful interaction of many neurons that, mediating other cells, form a complex network whose details keep constantly adapting to their activity and surroundings. In parallel, theoretical and computational studies developed to understand many natural and artificial complex systems, which have truthfully explained their amazing emergent features and precise the role of the interaction dynamics and other conditions behind the different collective phenomena they happen to display. Focusing on promising ideas that arise when comparing these neurobiology and physics studies, the present perspective article shortly reviews such fascinating scenarios looking for clues about how high-level cognitive processes such as consciousness, intelligence, and identity can emerge. We, thus, show that basic concepts of physics, such as dynamical phases and non-equilibrium phase transitions, become quite relevant to the brain activity while determined by factors at the subcellular, cellular, and network levels. We also show how these transitions depend on details of the processing mechanism of stimuli in a noisy background and, most important, that one may detect them in familiar electroencephalogram (EEG) recordings. Thus, we associate the existence of such phases, which reveal a brain operating at (non-equilibrium) criticality, with the emergence of most interesting phenomena during memory tasks.

Brain Network Topology and Structural–Functional Connectivity Coupling Mediate the Association Between Gut Microbiota and Cognition

Increasing evidence indicates that gut microbiota can influence cognition via the gut–brain axis, and brain networks play a critical role during the process. However, little is known about how brain network topology and structural–functional connectivity (SC–FC) coupling contribute to gut microbiota-related cognition. Fecal samples were collected from 157 healthy young adults, and 16S amplicon sequencing was used to assess gut diversity and enterotypes. Topological properties of brain structural and functional networks were acquired by diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI data), and SC–FC coupling was further calculated. 3-Back, digit span, and Go/No-Go tasks were employed to assess cognition. Then, we tested for potential associations between gut microbiota, complex brain networks, and cognition. The results showed that gut microbiota could affect the global and regional topological properties of structural networks as well as node properties of functional networks. It is worthy of note that causal mediation analysis further validated that gut microbial diversity and enterotypes indirectly influence cognitive performance by mediating the small-worldness (Gamma and Sigma) of structural networks and some nodal metrics of functional networks (mainly distributed in the cingulate gyri and temporal lobe). Moreover, gut microbes could affect the degree of SC–FC coupling in the inferior occipital gyrus, fusiform gyrus, and medial superior frontal gyrus, which in turn influence cognition. Our findings revealed novel insights, which are essential to provide the foundation for previously unexplored network mechanisms in understanding cognitive impairment, particularly with respect to how brain connectivity participates in the complex crosstalk between gut microbiota and cognition.

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.