Network-Level Structure-Function Relationships in Human Neocortex

Structural Disconnections Explain Brain Network Dysfunction after Stroke

Stroke causes focal brain lesions that disrupt functional connectivity (FC), a measure of activity synchronization, throughout distributed brain networks. It is often assumed that FC disruptions reflect damage to specific cortical regions. However, an alternative explanation is that they reflect the structural disconnection (SDC) of white matter pathways. Here, we compare these explanations using data from 114 stroke patients. Across multiple analyses, we find that SDC measures outperform focal damage measures, including damage to putative critical cortical regions, for explaining FC disruptions associated with stroke. We also identify a core mode of structure-function covariation that links the severity of interhemispheric SDCs to widespread FC disruptions across patients and that correlates with deficits in multiple behavioral domains. We conclude that a lesion's impact on the structural connectome is what determines its impact on FC and that interhemispheric SDCs may play a particularly important role in mediating FC disruptions after stroke.

Structural Brain Architectures Match Intrinsic Functional Networks and Vary across Domains: A Study from 15 000+ Individuals

Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across nine functional domains, in both a discovery cohort (n = 6005) and a replication cohort (UK Biobank, n = 9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g., somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of changes compared with unimodal cortex, which may lead to higher interindividual variability and lower S-F coherence.

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain's underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region's spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

A unified framework for multimodal structure-function mapping based on eigenmodes

Characterizing the connection between brain structure and brain function is essential for understanding how behaviour emerges from the underlying anatomy. A number of studies have shown that the network structure of the white matter shapes functional connectivity. Therefore, it should be possible to predict, at least partially, functional connectivity given the structural network. Many structure-function mappings have been proposed in the literature, including several direct mappings between the structural and functional connectivity matrices. However, the current literature is fragmented and does not provide a uniform treatment of current methods based on eigendecompositions. In particular, existing methods have never been compared to each other and their relationship explicitly derived in the context of brain structure-function mapping. In this work, we propose a unified computational framework that generalizes recently proposed structure-function mappings based on eigenmodes. Using this unified framework, we highlight the link between existing models and show how they can be obtained by specific choices of the parameters of our framework. By applying our framework to 50 subjects of the Human Connectome Project, we reproduce 6 recently published results, devise two new models and provide a direct comparison between all mappings. Finally, we show that a glass ceiling on the performance of mappings based on eigenmodes seems to be reached and conclude with possible approaches to break this performance limit.

A Large-Scale High-Density Weighted Structural Connectome of the Macaque Brain Acquired by Predicting Missing Links

As a substrate for function, large-scale brain structural networks are crucial for fundamental and systems-level understanding of primate brains. However, it is challenging to acquire a complete primate whole-brain structural connectome using track tracing techniques. Here, we acquired a weighted brain structural network across 91 cortical regions of a whole macaque brain hemisphere with a connectivity density of 59% by predicting missing links from the CoCoMac-based binary network with a low density of 26.3%. The prediction model combines three factors, including spatial proximity, topological similarity, and cytoarchitectural similarity-to predict missing links and assign connection weights. The model was tested on a recently obtained high connectivity density yet partial-coverage experimental weighted network connecting 91 sources to 29 target regions; the model showed a prediction sensitivity of 74.1% in the predicted network. This predicted macaque hemisphere-wide weighted network has module segregation closely matching functional domains. Interestingly, the areas that act as integrators linking the segregated modules are mainly distributed in the frontoparietal network and correspond to the regions with large wiring costs in the predicted weighted network. This predicted weighted network provides a high-density structural dataset for further exploration of relationships between structure, function, and metabolism in the primate brain.

Alteration of the Intra- and Inter-Lobe Connectivity of the Brain Structural Network in Normal Aging

The morphological changes in cortical parcellated regions during aging and whether these atrophies may cause brain structural network intra- and inter-lobe connectivity alterations are subjects that have been minimally explored. In this study, a novel fractal dimension-based structural network was proposed to measure atrophy of 68 parcellated cortical regions. Alterations of structural network parameters, including intra- and inter-lobe connectivity, were detected in a middle-aged group (30–45 years old) and an elderly group (50–65 years old). The elderly group exhibited significant lateralized atrophy in the left hemisphere, and most of these fractal dimension atrophied regions were included in the regions of the “last-in, first-out” model. Globally, the elderly group had lower modularity values, smaller component size modules, and fewer bilateral association fibers. They had lower intra-lobe connectivity in the frontal and parietal lobes, but higher intra-lobe connectivity in the temporal and occipital lobes. Both groups exhibited similar inter-lobe connecting pattern. The elderly group revealed separations, sparser long association fibers, commissural fibers, and lateral inter-lobe connectivity lost effect, mainly in the right hemisphere. New wiring and reconfiguring modules may have occurred within the brain structural network to compensate for connectivity, decreasing and preventing functional loss in cerebral intra- and inter-lobe connectivity.

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FC_EEG to second range of FC_fMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

Functional connectivity is governed by a whole-brain organization measurable over multiple timescales by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The relationship across the whole-brain organization captured at the different timescales of EEG and fMRI is largely unknown. Using concurrent EEG-fMRI, we identified spatially independent components consisting of brain connectivity patterns that co-occur in EEG and fMRI over subjects. We observed a component with similar connectivity organization across EEG and fMRI as well as a component with divergent connectivity. The former component governed all EEG frequencies while the latter was modulated by frequency. These findings show that part of functional connectivity organizes in a common spatial layout over several timescales, while a spatially independent part is modulated by frequency-specific information.

Rich-club in the brain's macrostructure: Insights from graph theoretical analysis

The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.

Mapping brain-behavior networks using functional and structural connectome fingerprinting in the HCP dataset

INTRODUCTION

Connectome analysis of the human brain's structural and functional architecture provides a unique opportunity to understand the organization of the brain's functional architecture. In previous studies, connectome fingerprinting using brain functional connectivity profiles as an individualized trait was able to predict an individual's neurocognitive performance from the Human Connectome Project (HCP) neurocognitive datasets.

MATERIALS AND METHODS

In the present study, we extend connectome fingerprinting from functional connectivity (FC) to structural connectivity (SC), identifying multiple relationships between behavioral traits and brain connectivity. Higher-order neurocognitive tasks were found to have a weaker association with structural connectivity than its functional connectivity counterparts.

RESULTS

Neurocognitive tasks with a higher sensory footprint were, however, found to have a stronger association with structural connectivity than their functional connectivity counterparts. Language behavioral measurements had a particularly stronger correlation, especially between performance on the picture language test (Pic Vocab) and both FC (r = .28, p < .003) and SC (r = 0.27, p < .00077).

CONCLUSIONS

At the neural level, we found that the pattern of structural brain connectivity related to high-level language performance is consistent with the language white matter regions identified in presurgical mapping. We illustrate how this approach can be used to generalize the connectome fingerprinting framework to structural connectivity and how this can help understand the connections between cognitive behavior and the white matter connectome of the brain.

Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review

Many biological and neural systems can be seen as networks of interacting periodic processes. Importantly, their functionality, i.e., whether these networks can perform their function or not, depends on the emerging collective dynamics of the network. Synchrony of oscillations is one of the most prominent examples of such collective behavior and has been associated both with function and dysfunction. Understanding how network structure and interactions, as well as the microscopic properties of individual units, shape the emerging collective dynamics is critical to find factors that lead to malfunction. However, many biological systems such as the brain consist of a large number of dynamical units. Hence, their analysis has either relied on simplified heuristic models on a coarse scale, or the analysis comes at a huge computational cost. Here we review recently introduced approaches, known as the Ott-Antonsen and Watanabe-Strogatz reductions, allowing one to simplify the analysis by bridging small and large scales. Thus, reduced model equations are obtained that exactly describe the collective dynamics for each subpopulation in the oscillator network via few collective variables only. The resulting equations are next-generation models: Rather than being heuristic, they exactly link microscopic and macroscopic descriptions and therefore accurately capture microscopic properties of the underlying system. At the same time, they are sufficiently simple to analyze without great computational effort. In the last decade, these reduction methods have become instrumental in understanding how network structure and interactions shape the collective dynamics and the emergence of synchrony. We review this progress based on concrete examples and outline possible limitations. Finally, we discuss how linking the reduced models with experimental data can guide the way towards the development of new treatment approaches, for example, for neurological disease.

Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. ​Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks.

Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists

The 21st century marks the emergence of "big data" with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such "big data" repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data, such as in recently available big biomedical datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.

Connections, Tracts, Fractals, and the Rest: A Working Guide to Network and Connectivity Studies in Neurosurgery

Brain mapping and connectomics can probe networks that span the entire brain, producing a diverse range of outputs for probing specific clinically relevant questions. The potential for understanding the effect of focal lesions on brain function, cognition, and plasticity abounds, any one of which would likely yield more effective and safer neurosurgical strategies. However, the possibilities of advanced magnetic resonance imaging and connectomics have been somewhat underused in neurosurgery, arising from actual or perceived difficulties in either application or analysis. The present review builds on previous work describing the theoretical attractions of connectomics to deliberate on the practical details of performing high-quality connectomics studies in neurosurgery. First, the data and methods involved in deriving connectomics models will be considered, specifically for the purpose of determining the nature of inferences that can be made subsequently. Next, a selection of key analysis methods will be explored using practical examples that illustrate their effective implementation and the insights that can be gleaned. The principles of study design will be introduced, including analysis tips and methods for making efficient use of available resources. Finally, a review of the best research practices for neuroimaging studies will be discussed, including principles of open access data sharing, study preregistration, and methods for improving replicability. Ultimately, we hope readers will be better placed to appraise the current connectomics studies in neurosurgery and empowered to develop their own high-quality studies, both of which are key steps in realizing the true potential of connectomics and advanced neuroimaging analyses in general.

Hierarchy of Connectivity-Function Relationship of the Human Cortex Revealed through Predicting Activity across Functional Domains

Many studies showed that anatomical connectivity supports both anatomical and functional hierarchies that span across the primary and association cortices in the cerebral cortex. Even though a structure-function relationship has been indicated to uncouple in the association cortex, it is still unknown whether anatomical connectivity can predict functional activations to the same degree throughout the cortex, and it remains unclear whether a hierarchy of this connectivity-function relationship (CFR) exists across the human cortex. We first addressed whether anatomical connectivity could be used to predict functional activations across different functional domains using multilinear regression models. Then, we characterized the CFR by predicting activity from anatomical connectivity throughout the cortex. We found that there is a hierarchy of CFR between sensory-motor and association cortices. Moreover, this CFR hierarchy was correlated to the functional and anatomical hierarchies, respectively, reflected in functional flexibility and the myelin map. Our results suggest a shared hierarchical mechanism in the cortex, a finding which provides important insights into the anatomical and functional organizations of the human brain.

Induction and propagation of transient synchronous activity in neural networks endowed with short-term plasticity

Transient, task related synchronous activity within neural populations has been recognized as the substrate of temporal coding in the brain. The mechanisms underlying inducing and propagation of transient synchronous activity are still unknown, and we propose that short-term plasticity (STP) of neural circuits may serve as a supplemental mechanism therein. By computational modeling, we showed that short-term facilitation greatly increases the reactivation rate of population spikes and decreases the latency of response to reactivation stimuli in local recurrent neural networks. Meanwhile, the timing of population spike reactivation is controlled by the memory effect of STP, and it is mediated primarily by the facilitation time constant. Furthermore, we demonstrated that synaptic facilitation dramatically enhances synchrony propagation in feedforward neural networks and that response timing mediated by synaptic facilitation offers a scheme for information routing. In addition, we verified that synaptic strengthening of intralayer or interlayer coupling enhances synchrony propagation, and we verified that other factors such as the delay of synaptic transmission and the mode of synaptic connectivity are also involved in regulating synchronous activity propagation. Overall, our results highlight the functional role of STP in regulating the inducing and propagation of transient synchronous activity, and they may inspire testable hypotheses for future experimental studies.

Linking Structure and Function in Macroscale Brain Networks

Structure-function relationships are a fundamental principle of many naturally occurring systems. However, network neuroscience research suggests that there is an imperfect link between structural connectivity and functional connectivity in the brain. Here, we synthesize the current state of knowledge linking structure and function in macroscale brain networks and discuss the different types of models used to assess this relationship. We argue that current models do not include the requisite biological detail to completely predict function. Structural network reconstructions enriched with local molecular and cellular metadata, in concert with more nuanced representations of functions and properties, hold great potential for a truly multiscale understanding of the structure-function relationship.

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke

Focal brain lesions disrupt resting-state functional connectivity, but the underlying structural mechanisms are unclear. Here, we examined the direct and indirect effects of structural disconnections on resting-state functional connectivity in a large sample of sub-acute stroke patients with heterogeneous brain lesions. We estimated the impact of each patient's lesion on the structural connectome by embedding the lesion in a diffusion MRI streamline tractography atlas constructed using data from healthy individuals. We defined direct disconnections as the loss of direct structural connections between two regions, and indirect disconnections as increases in the shortest structural path length between two regions that lack direct structural connections. We then tested the hypothesis that functional connectivity disruptions would be more severe for disconnected regions than for regions with spared connections. On average, nearly 20% of all region pairs were estimated to be either directly or indirectly disconnected by the lesions in our sample, and extensive disconnections were associated primarily with damage to deep white matter locations. Importantly, both directly and indirectly disconnected region pairs showed more severe functional connectivity disruptions than region pairs with spared direct and indirect connections, respectively, although functional connectivity disruptions tended to be most severe between region pairs that sustained direct structural disconnections. Together, these results emphasize the widespread impacts of focal brain lesions on the structural connectome and show that these impacts are reflected by disruptions of the functional connectome. Further, they indicate that in addition to direct structural disconnections, lesion-induced increases in the structural shortest path lengths between indirectly structurally connected region pairs provide information about the remote functional disruptions caused by focal brain lesions.

Functional and structural connectivity substrates of cognitive performance in relapsing remitting multiple sclerosis with mild disability

Multiple Sclerosis (MS) is the most common chronic inflammatory and neurodegenerative disease of the central nervous system (CNS), which can lead to severe cognitive impairment over time. Magnetic resonance imaging (MRI) is currently the best available biomarker to track MS pathophysiology in vivo and examine the link to clinical disability. However, conventional MRI metrics have limited sensitivity and specificity to detect direct associations between symptoms and their underlying CNS substrates. In this study, we aimed to investigate structural and resting state functional connectomes and subnetworks associated with neuropsychological (NP) performance using a graph theoretical approach. A comprehensive NP test battery was administered in a sample of patients with relapsing remitting MS (RRMS) and mild disability [n = 33, F/M = 20/13, age = 40.9 ± 9.7, median [Expanded Disability Status Scale] (EDSS) = 2, range =0-4] and compared to healthy controls (HC) [n = 29, F/M = 19/10, age = 41.0 ± 8.5] closely matched for age, sex, and level of education. The NP battery comprised the most relevant domains of cognitive dysfunction in MS including attention, processing speed, verbal and spatial learning and memory, and executive function. While standard MRI metrics showed good correlations with TAP Alertness test, disease duration and neurological exams, structural networks showed closer associations with 9-hole peg test and cognitive performances. Decreased graph strength was associated with two out of the 5 NP tests in the spatial learning and memory domain specified by BVMT [Sum 1-3] and BVMT [Recall], and with also SDMT which is one out of the 9 NP tests in the attention/processing speed domain, while no correlation was found between these scores and functional connectivity. Nodal strength was decreased in all subnetworks based on Yeo atlas in patients compared to HC; however, no difference was observed in nodal level of functional connectivity between the groups. The difference in structural and functional nodal connectivity between the groups was also observed in the relationship between structural and functional connectivity within the groups; the relationship between nodal degree and nodal strength was reversed in patients but positive in controls. On a nodal level, structural and functional networks (mainly the default mode network) were correlated with more than one cognitive domain rather than one specific network for each domain within patients. Interestingly, poorer cognitive performance was mostly correlated with increased functional connectivity but decreased structural connectivity in patients. Increased functional connectivity in the default mode network had both positive as well as negative associations with verbal and spatial learning and memory, possibly indicating adaptive and maladaptive mechanisms. In conclusion, our results suggest that cognitive performance, even in patients with RRMS and very mild disability, may reflect a loss of structural connectivity. In contrast, widespread increases in functional connectivity may be the result of maladaptive processes.

Development of structure-function coupling in human brain networks during youth

The human brain is organized into a hierarchy of functional systems that evolve in childhood and adolescence to support the dynamic control of attention and behavior. However, it remains unknown how developing white-matter architecture supports coordinated fluctuations in neural activity underlying cognition. We document marked remodeling of structure–function coupling in youth, which aligns with cortical hierarchies of functional specialization and evolutionary expansion. Further, we demonstrate that structure–function coupling in rostrolateral prefrontal cortex supports age-related improvements in executive ability. These findings have broad relevance for accounts of experience-dependent plasticity in healthy development and abnormal development associated with neuropsychiatric illness.

The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as executive function. However, it remains unclear how white-matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure–function coupling using diffusion-weighted imaging and n-back functional MRI data in a sample of 727 individuals (ages 8 to 23 y). We found that spatial variability in structure–function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure–function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n = 294). Moreover, structure–function coupling in rostrolateral prefrontal cortex was associated with executive performance and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.

Interindividual Covariations of Brain Functional and Structural Connectivities Are Decomposed Blindly to Subnetworks: A Fusion-Based Approach

BACKGROUND

Studying brain interindividual variations has recently gained interest to understand different human behaviors. It is particularly important to investigate how a variety of functional differences can be associated with a few differences in brain structure. It would be more meaningful if such an investigation is performed jointly at the network level to connect structural building blocks to functional variations modules.

PURPOSE

To decompose the interindividual variations of brain in the form of mutual functional and structural subnetworks based on a data-driven approach.

STUDY TYPE

Retrospective.

POPULATION

In all, 92 healthy subjects.

FIELD STRENGTH/SEQUENCE

3T Siemens/MPRAGE, diffusion spectrum imaging (DSI) acquisition protocol, gradient echo sequence.

ASSESSMENT

The proposed approach was quantitatively assessed by examining the consistency of the networks against the number of subjects. Distribution of the obtained components across brain regions was studied and their relevance was qualitatively evaluated by comparison to variations that had been independently reported previously.

STATISTICAL TESTS

Permutation test, two-sample t-test, Pearson correlation coefficient.

RESULTS

Ten pairs of components including functional and structural subnetworks were obtained. Assessing the reproducibility of the proposed method with respect to the sample size indicated reliable detection of connections (above 70%) for all components by reducing the number of subjects to 70. Specifically, one of the functional subnetworks can be used to distinguish left-handed from right-handed people (P = 2.6 × 10^-8 ) as the basic interindividual variation. This functional subnetwork has a main overlap (40.18%) with the somatomotor system and the Broca part was captured in its corresponding structural subnetwork.

DATA CONCLUSION

These results show that the proposed method can reveal intersubject variations systematically through a mathematical algorithm of joint independent component analysis. They confirm that intersubject variations can be expressed in the form of building blocks. In contrast to the functional subnetworks that were discoverable independently, their structural counterparts were found and interpreted only in conjunction with the functional subnetworks.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1779-1788.

Network-Based Asymmetry of the Human Auditory System

Converging evidence from activation, connectivity, and stimulation studies suggests that auditory brain networks are lateralized. Here we show that these findings can be at least partly explained by the asymmetric network embedding of the primary auditory cortices. Using diffusion-weighted imaging in 3 independent datasets, we investigate the propensity for left and right auditory cortex to communicate with other brain areas by quantifying the centrality of the auditory network across a spectrum of communication mechanisms, from shortest path communication to diffusive spreading. Across all datasets, we find that the right auditory cortex is better integrated in the connectome, facilitating more efficient communication with other areas, with much of the asymmetry driven by differences in communication pathways to the opposite hemisphere. Critically, the primacy of the right auditory cortex emerges only when communication is conceptualized as a diffusive process, taking advantage of more than just the topologically shortest paths in the network. Altogether, these results highlight how the network configuration and embedding of a particular region may contribute to its functional lateralization.

Gradients of structure-function tethering across neocortex

The white matter architecture of brain networks promotes synchrony among neuronal populations, giving rise to richly patterned functional networks. Relating structure and function is a fundamental question for systems neuroscience, but the nature of the relationship is unknown. Here we examine the possibility that structure–function relationships are not uniform in the brain. We find that structure and function are closely aligned in unimodal cortex (primary sensory and motor regions), but diverge in transmodal cortex (default mode and salience networks). The divergence between structure and function closely follows representational and cytoarchitectonic hierarchies, reflecting a macroscale gradient. Our findings suggest structure and function are not uniformly related, but gradually decouple in parallel to this macroscale gradient.

The white matter architecture of the brain imparts a distinct signature on neuronal coactivation patterns. Interregional projections promote synchrony among distant neuronal populations, giving rise to richly patterned functional networks. A variety of statistical, communication, and biophysical models have been proposed to study the relationship between brain structure and function, but the link is not yet known. In the present report we seek to relate the structural and functional connection profiles of individual brain areas. We apply a simple multilinear model that incorporates information about spatial proximity, routing, and diffusion between brain regions to predict their functional connectivity. We find that structure–function relationships vary markedly across the neocortex. Structure and function correspond closely in unimodal, primary sensory, and motor regions, but diverge in transmodal cortex, particularly the default mode and salience networks. The divergence between structure and function systematically follows functional and cytoarchitectonic hierarchies. Altogether, the present results demonstrate that structural and functional networks do not align uniformly across the brain, but gradually uncouple in higher-order polysensory areas.

Inferring neural signalling directionality from undirected structural connectomes

Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and outgoing communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.

Synchronization lag in post stroke: relation to motor function and structural connectivity

Stroke is characterized by delays in the resting-state hemodynamic response, resulting in synchronization lag in neural activity between brain regions. However, the structural basis of this lag remains unclear. In this study, we used resting-state functional MRI (rs-fMRI) to characterize synchronization lag profiles between homotopic regions in 15 individuals (14 males, 1 female) with brain lesions consequent to stroke as well as a group of healthy comparison individuals. We tested whether the network communication efficiency of each individual's structural brain network (connectome) could explain interindividual and interregional variation in synchronization lag profiles. To this end, connectomes were mapped using diffusion MRI data, and communication measures were evaluated under two schemes: shortest paths and navigation. We found that interindividual variation in synchronization lags was inversely associated with communication efficiency under both schemes. Interregional variation in lag was related to navigation efficiency and navigation distance, reflecting its dependence on both distance and structural constraints. Moreover, severity of motor deficits significantly correlated with average synchronization lag in stroke. Our results provide a structural basis for the delay of information transfer between homotopic regions inferred from rs-fMRI and provide insight into the clinical significance of structural-functional relationships in stroke individuals.

Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction

OBJECTIVE

To explain individual differences in development, behavior, and cognition, most previous studies focused on projecting resting-state functional MRI (fMRI) based functional connectivity (FC) data into a low-dimensional space via linear dimensionality reduction techniques, followed by executing analysis operations. However, linear dimensionality analysis techniques may fail to capture the nonlinearity of brain neuroactivity. Moreover, besides resting-state FC, the FC based on task fMRI can be expected to provide complementary information. Motivated by these considerations, we nonlinearly fuse resting-state and task-based FC networks (FCNs) to seek a better representation in this paper.

METHODS

We propose a framework based on alternating diffusion map (ADM), which extracts geometry-preserving low-dimensional embeddings that successfully parameterize the intrinsic variables driving the phenomenon of interest. Specifically, we first separately build resting-state and task-based FCNs by symmetric positive definite matrices using sparse inverse covariance estimation for each subject, and then utilize the ADM to fuse them in order to extract significant low-dimensional embeddings, which are used as fingerprints to identify individuals.

RESULTS

The proposed framework is validated on the Philadelphia Neurodevelopmental Cohort data, where we conduct extensive experimental study on resting-state and fractal n-back task fMRI for the classification of intelligence quotient (IQ). The fusion of resting-state and n-back task fMRI by the proposed framework achieves better classification accuracy than any single fMRI, and the proposed framework is shown to outperform several other data fusion methods.

CONCLUSION AND SIGNIFICANCE

To our knowledge, this paper is the first to demonstrate a successful extension of the ADM to fuse resting-state and task-based fMRI data for accurate prediction of IQ.

Hierarchical Connectome Modes and Critical State Jointly Maximize Human Brain Functional Diversity

The brain requires diverse segregated and integrated processing to perform normal functions in terms of anatomical structure and self-organized dynamics with critical features, but the fundamental relationships between the complex structural connectome, critical state, and functional diversity remain unknown. Herein, we extend the eigenmode analysis to investigate the joint contribution of hierarchical modular structural organization and critical state to brain functional diversity. We show that the structural modes inherent to the hierarchical modular structural connectome allow a nested functional segregation and integration across multiple spatiotemporal scales. The real brain hierarchical modular organization provides large structural capacity for diverse functional interactions, which are generated by sequentially activating and recruiting the hierarchical connectome modes, and the critical state can best explore the capacity to maximize the functional diversity. Our results reveal structural and dynamical mechanisms that jointly support a balanced segregated and integrated brain processing with diverse functional interactions, and they also shed light on dysfunctional segregation and integration in neurodegenerative diseases and neuropsychiatric disorders.

Brain structural alterations are distributed following functional, anatomic and genetic connectivity

The pathological brain is characterized by distributed morphological or structural alterations in the grey matter, which tend to follow identifiable network-like patterns. We analysed the patterns formed by these alterations (increased and decreased grey matter values detected with the voxel-based morphometry technique) conducting an extensive transdiagnostic search of voxel-based morphometry studies in a large variety of brain disorders. We devised an innovative method to construct the networks formed by the structurally co-altered brain areas, which can be considered as pathological structural co-alteration patterns, and to compare these patterns with three associated types of connectivity profiles (functional, anatomical, and genetic). Our study provides transdiagnostical evidence that structural co-alterations are influenced by connectivity constraints rather than being randomly distributed. Analyses show that although all the three types of connectivity taken together can account for and predict with good statistical accuracy, the shape and temporal development of the co-alteration patterns, functional connectivity offers the better account of the structural co-alteration, followed by anatomic and genetic connectivity. These results shed new light on the possible mechanisms at the root of neuropathological processes and open exciting prospects in the quest for a better understanding of brain disorders.

Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function

Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.

Tracking mood fluctuations with functional network patterns

Subjective mood is a psychophysiological property that depends on complex interactions among the central and peripheral nervous systems. How network interactions in the brain drive temporal fluctuations in mood is unknown. Here we investigate how functional network configuration relates to mood profiles in a single individual over the course of 1 year. Using data from the 'MyConnectome Project', we construct a comprehensive mapping between resting-state functional connectivity (FC) patterns and subjective mood scales using an associative multivariate technique (partial least squares). We report three principal findings. First, FC patterns reliably tracked daily fluctuations in mood. Second, positive mood was marked by an integrated architecture, with prominent interactions between canonical resting-state networks. Finally, one of the top-ranked nodes in mood-related network reconfiguration was the subgenual anterior cingulate cortex, an area commonly associated with mood regulation and dysregulation. Altogether, these results showcase the utility of highly sampled individual-focused data sets for affective neuroscience.

Functional control of electrophysiological network architecture using direct neurostimulation in humans

Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.

Brain stimulation devices capable of perturbing the physiological state of neural systems are rapidly gaining popularity for their potential to treat neurological and psychiatric disease. A root problem is that underlying dysfunction spans a large-scale network of brain regions, requiring the ability to control the complex interactions between multiple brain areas. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. We demonstrate the ability to predictably reconfigure patterns of interactions between functional brain areas by modulating the strength and location of stimulation. Our findings have high significance for designing stimulation protocols capable of modulating distributed neural circuits in the human brain.

Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI

Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated MDD (uMDD) patients (n = 46), medicated MDD (mMDD) patients (n = 38), and healthy controls (n = 50), which yielded a spectrum of multiscale community partitions. we selected an optimal resolution level by identifying the most stable community partition for each group. uMDD and mMDD groups exhibited a similar reconfiguration of the community structure of the visual association and the default mode systems but showed different reconfiguration profiles in the frontoparietal control (FPC) subsystems. Furthermore, the central system (somatomotor/salience) and 3 frontoparietal subsystems showed strengthened connectivity with other communities in uMDD but, with the exception of 1 frontoparietal subsystem, returned to control levels in mMDD. These findings provide evidence for reconfiguration of specific cortical functional systems associated with MDD, as well as potential effects of medication in restoring disease-related network alterations, especially those of the FPC system.

Changes in the Functional and Structural Default Mode Network across the Adult Lifespan Based on Partial Least Squares

The default mode network (DMN) has been extensively investigated in the literature. However, previous studies have mainly focused on age-related changes in the DMN between old and young participants. Age-dependent changes in specific regions within the DMN have not been adequately explored across the entire adult lifespan. Thus, in the present study, we performed a seed partial least squares (PLS) analysis to investigate lifespan-wide changes in the regions of the functional and structural DMNs using resting-state functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (MRI) data from healthy subjects aged 16-85 years. The posterior cingulate area was selected as the seed region based on prior fMRI studies. The single-group functional connectivity analysis showed a stable connection between the seed and the posterior cingulate cortex (PCC), middle temporal gyrus (MTG) and inferior temporal gyrus (ITG); a decreased connection between the seed and the medial prefrontal cortex (MPFC), anterior cingulate cortex (ACC) and superior frontal gyrus (SFG); and an increased connection between the seed and the precuneus (PreC), inferior parietal lobule (IPL) and middle frontal gyrus (MFG) across the entire lifespan. In contrast, in the single-group structural covariance analysis, the covariance connections of the seed to the DMN regions demonstrated a stable covariance trend to the PCC, MTG, superior temporal gyrus (STG) and ITG; an inverted U-shaped covariance trend to the MPFC, ACC, SFG, MFG and inferior frontal gyrus (IFG); and a U-shaped covariance trend to the PreC with age. Full-group analyses found significant linear decreases in functional and structural DMN integrity. Our findings provide crucial information regarding the influence of age on the function and structure of the DMN and may contribute to the understanding of the underlying mechanism of age-related changes in the DMN over the lifespan.

System-level matching of structural and functional connectomes in the human brain

The brain can be considered as an information processing network, where complex behavior manifests as a result of communication between large-scale functional systems such as visual and default mode networks. As the communication between brain regions occurs through underlying anatomical pathways, it is important to define a "traffic pattern" that properly describes how the regions exchange information. Empirically, the choice of the traffic pattern can be made based on how well the functional connectivity between regions matches the structural pathways equipped with that traffic pattern. In this paper, we present a multimodal connectomics paradigm utilizing graph matching to measure similarity between structural and functional connectomes (derived from dMRI and fMRI data) at node, system, and connectome level. Through an investigation of the brain's structure-function relationship over a large cohort of 641 healthy developmental participants aged 8-22 years, we demonstrate that communicability as the traffic pattern describes the functional connectivity of the brain best, with large-scale systems having significant agreement between their structural and functional connectivity patterns. Notably, matching between structural and functional connectivity for the functionally specialized modular systems such as visual and motor networks are higher as compared to other more integrated systems. Additionally, we show that the negative functional connectivity between the default mode network (DMN) and motor, frontoparietal, attention, and visual networks is significantly associated with its underlying structural connectivity, highlighting the counterbalance between functional activation patterns of DMN and other systems. Finally, we investigated sex difference and developmental changes in brain and observed that similarity between structure and function changes with development.

Structural and functional abnormalities of the insular cortex in trigeminal neuralgia: a multimodal magnetic resonance imaging analysis

Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder characterized by intense, lancinating attacks of facial pain. Increasing evidence suggests that TN is accompanied by abnormalities in brain morphology, white matter microstructure, and function. However, whether these abnormalities are linked or reflect independent etiologies remains unknown. Using multimodal magnetic resonance imaging data of 20 patients with TN and 21 healthy controls, we investigated cortical gyrification abnormalities, their relationships with abnormalities of the underlying white matter microstructure and gray matter morphology, as well as their functional significance in TN. Compared with controls, patients with TN showed significant local gyrification index (LGI) reductions predominantly in the left insular cortex, which were negatively correlated with pain intensity. In this cluster, patients with TN had concurrent cortical thickness reductions but unaltered cortical surface area. Meanwhile, LGI of this cluster was not correlated with overlying cortical thickness or surface area but was positively correlated with the fractional anisotropy of 2 nearby white matter clusters, suggesting that insular LGI reductions may be primarily driven by microstructural abnormalities of the underlying white matter tracts, rather than by abnormalities in cortical thickness and surface area. In addition, patients with TN exhibited increased insula functional connectivity to the left posterior cingulate cortex and thalamus, which was positively correlated with disease duration. These findings provide new evidence for the involvement of insular abnormalities in the pathophysiology of TN.

Discordant attributes of structural and functional brain connectivity in a two-layer multiplex network

Several studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine node strength assortativity within and between the SC and FC layer. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may provide clues to understand laterality of some neurological dysfunctions and recovery.

Centralized and distributed cognitive task processing in the human connectome

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straightforward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated with different functional brain networks, and use the proposed measure to infer changes in the information-processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well-grounded mathematical quantification of connectivity changes associated with cognitive processing in large-scale brain networks.

Network organization of co-opetitive genetic influences on morphologies of the human cerebral cortex

OBJECTIVE

The brain can be represented as a network, where anatomical regions are nodes and relations between regions are edges. Within a network, the co-existence of co-operative and competitive relationships between different nodes is called co-opetition. Inter-regional genetic influences on morphological phenotypes (thickness, surface area) of the cerebral cortex display such co-opetitive relationships. However, whether these co-operative and competitive genetic influences are organized similarly has remained elusive. How the collective organization of the co-operative and competitive genetic influences is related to the inter-individual variations of cortical morphological phenotypes has also remained unexplored.

APPROACH

We constructed inter-regional genetic influence networks underlying the morphologies (thickness, surface area) of the human cerebral cortex combining the T1 weighted MRI of genetically confirmed 593 siblings and twin-study design. Graph theory was used to characterize the genetic influence networks and the collective organizations of genetic influences were characterized using the theory of structural balance. Principal component (PC) analysis was used to estimate the principal modes of morphological phenotype variations.

MAIN RESULTS

The inter-regional co-operative genetic influences are assortative, while competitive influences are disassortative. Co-operative genetic influences are more cohesive and less diverse than the competitive influences. The collective organization of co-opetitive genetic influences partially explains the fifth principal modes of inter-individual variation of cortical morphological phenotypes. Other principal modes were not significantly associated with collective genetic influences.

SIGNIFICANCE

Our study furnishes fundamental insight regarding the organization of co-opetitive genetic influences underlying the morphologies of the human cerebral cortex. In future studies, investigation of the alterations of co-opetitive genetic network properties in brain disorders may furnish disorder-specific insight that may be associated with the disease state or lead to vulnerability to those conditions.

The thalamus in drug addiction: from rodents to humans

Impairments in response inhibition and salience attribution (iRISA) have been proposed to underlie the clinical symptoms of drug addiction as mediated by cortico-striatal-thalamo-cortical networks. The bulk of evidence supporting the iRISA model comes from neuroimaging research that has focused on cortical and striatal influences with less emphasis on the role of the thalamus. Here, we highlight the importance of the thalamus in drug addiction, focusing on animal literature findings on thalamic nuclei in the context of drug-seeking, structural and functional changes of the thalamus as measured by imaging studies in human drug addiction, particularly during drug cue and non-drug reward processing, and response inhibition tasks. Findings from the animal literature suggest that the paraventricular nucleus of the thalamus, the lateral habenula and the mediodorsal nucleus may be involved in the reinstatement, extinction and expression of drug-seeking behaviours. In support of the iRISA model, the human addiction imaging literature demonstrates enhanced thalamus activation when reacting to drug cues and reduced thalamus activation during response inhibition. This pattern of response was further associated with the severity of, and relapse in, drug addiction. Future animal studies could widen their field of focus by investigating the specific role(s) of different thalamic nuclei in different phases of the addiction cycle. Similarly, future human imaging studies should aim to specifically delineate the structure and function of different thalamic nuclei, for example, through the application of advanced imaging protocols at higher magnetic fields (7 Tesla).This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'.

Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks

The notion that specific cognitive and emotional processes arise from functionally distinct brain regions has lately shifted toward a connectivity-based approach that emphasizes the role of network-mediated integration across regions. The clinical neurosciences have likewise shifted from a predominantly lesion-based approach to a connectomic paradigm-framing disorders as diverse as stroke, schizophrenia (SCZ), and dementia as "dysconnection syndromes". Here we position bipolar disorder (BD) within this paradigm. We first summarise the disruptions in structural, functional and effective connectivity that have been documented in BD. Not surprisingly, these disturbances show a preferential impact on circuits that support emotional processes, cognitive control and executive functions. Those at high risk (HR) for BD also show patterns of connectivity that differ from both matched control populations and those with BD, and which may thus speak to neurobiological markers of both risk and resilience. We highlight research fields that aim to link brain network disturbances to the phenotype of BD, including the study of large-scale brain dynamics, the principles of network stability and control, and the study of interoception (the perception of physiological states). Together, these findings suggest that the affective dysregulation of BD arises from dynamic instabilities in interoceptive circuits which subsequently impact on fear circuitry and cognitive control systems. We describe the resulting disturbance as a "psychosis of interoception".

Mesolimbic connectivity signatures of impulsivity and BMI in early adolescence

Across age groups, differences in connectivity of the mesolimbic and the prefrontal cortex co-vary with trait impulsivity and sensation-seeking. Impulsivity and sensation-seeking are also known to increase during early adolescence as maturation of subcortical structures outpaces that of the prefrontal cortex. While an imbalance between the striatum and prefrontal cortex is considered a normal developmental process, higher levels of adolescent impulsivity and sensation-seeking are associated with an increased risk for diverse problems, including obesity. To determine how the relationship between sensation-seeking, impulsivity and body mass index (BMI) is related to shared neural correlates we measured their relationships with the connectivity of nuclei in the striatum and dopaminergic midbrain in young adolescents. Data were collected from 116 children between the ages of 12 and 14, and included resting state functional magnetic resonance imaging, personality measures from the Substance Use Risk Profile Scale, and BMI Z-score for age. The shared variance for the connectivity of regions of interest in the substantia nigra, ventral tegmental area, ventral striatum and sub-thalamic nucleus, personality measures and BMI Z-score for age, were analyzed using partial least squares correlation. This analysis identified a single significant striato-limbic network that was connected with the substantia nigra, ventral tegmental area and sub-thalamic nuclei (p = 0.002). Connectivity within this network which included the hippocampi, amygdalae, parahippocampal gyri and the regions of interest, correlated positively with impulsivity and BMI Z-score for age and negatively with sensation-seeking. Together, these findings emphasize that, in addition to the well-established role that frontostriatal circuits play in the development of adolescent personality traits, connectivity of limbic regions with the striatum and midbrain also impact impulsivity, sensation-seeking and BMI Z-score in adolescents.

Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment

According to the network-based neurodegeneration hypothesis, neurodegenerative diseases target specific large-scale neural networks, such as the default mode network, and may propagate along the structural and functional connections within and between these brain networks. Cognitive impairment no dementia (CIND) represents an early prodromal stage but few studies have examined brain topological changes within and between brain structural and functional networks. To this end, we studied the structural networks [diffusion magnetic resonance imaging (MRI)] and functional networks (task-free functional MRI) in CIND (61 mild, 56 moderate) and healthy older adults (97 controls). Structurally, compared with controls, moderate CIND had lower global efficiency, and lower nodal centrality and nodal efficiency in the thalamus, somatomotor network, and higher-order cognitive networks. Mild CIND only had higher nodal degree centrality in dorsal parietal regions. Functional differences were more subtle, with both CIND groups showing lower nodal centrality and efficiency in temporal and somatomotor regions. Importantly, CIND generally had higher structural-functional connectome correlation than controls. The higher structural-functional topological similarity was undesirable as higher correlation was associated with poorer verbal memory, executive function, and visuoconstruction. Our findings highlighted the distinct and progressive changes in brain structural-functional networks at the prodromal stage of neurodegenerative diseases.

Increased cognitive complexity reveals abnormal brain network activity in individuals with corpus callosum dysgenesis

Cognitive reasoning is thought to require functional interactions between whole-brain networks. Such networks rely on both cerebral hemispheres, with the corpus callosum providing cross-hemispheric communication. Here we used high-field functional magnetic resonance imaging (7 T fMRI), a well validated cognitive task, and brain network analyses to investigate the functional networks underlying cognitive reasoning in individuals with corpus callosum dysgenesis (CCD), an anatomical abnormality that affects the corpus callosum. Participants with CCD were asked to solve cognitive reasoning problems while their brain activity was measured using fMRI. The complexity of these problems was parametrically varied by changing the complexity of relations that needed to be established between shapes within each problem matrix. Behaviorally, participants showed a typical reduction in task performance as problem complexity increased. Task-evoked neural activity was observed in brain regions known to constitute two key cognitive control systems: the fronto-parietal and cingulo-opercular networks. Under low complexity demands, network topology and the patterns of local neural activity in the CCD group closely resembled those observed in neurotypical controls. By contrast, when asked to solve more complex problems, participants with CCD showed a reduction in neural activity and connectivity within the fronto-parietal network. These complexity-induced, as opposed to resting-state, differences in functional network activity help resolve the apparent paradox between preserved network architecture found at rest in CCD individuals, and the heterogeneous deficits they display in response to cognitive task demands [preprint: https://doi.org/10.1101/312629].

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Individuals with corpus callosum dysgenesis fail to develop a normal corpus callosum.

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Resting-state functional brain networks in callosal dysgenesis appear relatively normal.

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Cognitive complexity revealed a deficit in fronto-parietal network activity.

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Differences in brain activity might only be revealed when under cognitive load.

Cortico-thalamic hypo- and hyperconnectivity extend consistently to basal ganglia in schizophrenia

Schizophrenia is characterized by hypoconnectivity or decreased intrinsic functional connectivity (iFC) between prefrontal-limbic cortices and thalamic nuclei, as well as hyperconnectivity or increased iFC between primary-sensorimotor cortices and thalamic nuclei. However, cortico-thalamic iFC overlaps with larger, structurally defined cortico-striato-pallido-thalamo-cortical (CSPTC) circuits. If such an overlap is relevant for intrinsic hypo-/hyperconnectivity, it suggests (i) that patterns of cortico-subcortical hypo-/hyperconnectivity extend consistently from thalamus to basal ganglia nuclei; and (ii) such consistent hypo-/hyperconnectivity might link distinctively but consonant with different symptom dimensions, namely cognitive and psychotic impairments. To test this hypothesis, 57 patients with schizophrenia and 61 healthy controls were assessed by resting-state functional magnetic resonance imaging (fMRI) and clinical-behavioral testing. IFC from intrinsic cortical networks into thalamus, striatum, and pallidum was estimated by partial correlations between fMRI time courses. In patients, the salience network covering prefrontal-limbic cortices was hypoconnected with the mediodorsal thalamus and ventral parts of striatum and pallidum; these iFC-hypoconnectivity patterns were correlated both among each other and specifically with patients' impaired cognition. In contrast, the auditory-sensorimotor network covering primary-sensorimotor cortices was hyperconnected with the anterior ventral nucleus of the thalamus and dorsal parts of striatum and pallidum; these iFC-hyperconnectivity patterns were likewise correlated among each other and specifically with patients' psychotic symptoms. The results demonstrate that prefrontal-limbic hypoconnectivity and primary-sensorimotor hyperconnectivity extend consistently across subcortical nuclei and specifically across distinct symptom dimensions. Data support the model of consistent cortico-subcortical hypo-/hyperconnectivity within CSPTC circuits in schizophrenia.

Subject specificity of the correlation between large-scale structural and functional connectivity

Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual's SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.

Unique Mapping of Structural and Functional Connectivity on Cognition

The unique mapping of structural brain connectivity (SC) and functional brain connectivity (FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the spatial distributions of SC and FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects (males, 269; females, 340) from the Human Connectome Project. We identified several sets of connections that each uniquely map onto cognitive function. We found a small number of distributed SCs and a larger set of corticocortical and corticosubcortical FCs that express this association. Importantly, the SC and FC each show unique and distinct patterns of variance across subjects as they relate to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.SIGNIFICANCE STATEMENT Structural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal coactivations between the activity of the brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavior patterns capture the correlations between SC and FC with a wide range of cognitive tasks, respectively. These brain-behavior patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

Mapping hybrid functional-structural connectivity traits in the human connectome

One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework, to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common "hybrid" connectivity patterns that represent the connectivity fingerprint of a subject. We test this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracted two main task-sensitive hybrid traits. The first, encompassing the within and between connections of dorsal attentional and visual areas, as well as fronto-parietal circuits. The second, mainly encompassing the connectivity between visual, attentional, DMN and subcortical networks. Overall, these findings confirms the potential of the hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.