Structural Connectivity Research Articles

Predicting an individual’s functional connectivity from their structural connectome: Evaluation of evidence, recommendations, and future prospects

Abstract Several recent studies have optimized deep neural networks to learn high-dimensional relationships linking structural and functional connectivity across the human connectome. However, the extent to which these models recapitulate individual-specific characteristics of resting-state functional brain networks remains unclear. A core concern relates to whether current individual predictions outperform simple benchmarks such as group averages and null conditions. Here, we consider two measures to statistically evaluate whether functional connectivity predictions capture individual effects. We revisit our previously published functional connectivity predictions for 1,000 healthy adults and provide multiple lines of evidence supporting that our predictions successfully capture subtle individual-specific variation in connectivity. While predicted individual effects are statistically significant and outperform several benchmarks, we find that effect sizes are small (i.e., 8%–11% improvement relative to group-average benchmarks). As such, initial expectations about individual prediction performance expressed by us and others may require moderation. We conclude that individual predictions can significantly outperform appropriate benchmark conditions and we provide several recommendations for future studies in this area. Future studies should statistically assess the individual prediction performance of their models using one of the measures and benchmarks provided here.

Structure connectivity and substructure connectivity of Möbius cubes

Abstract The connectivity is an important measurement for the fault-tolerance of networks. To provide more accurate measures for the fault-tolerance of networks than the connectivity, some generalizations of connectivity have been introduced. Substructure connectivity and structure connectivity are two extended concepts of classical connectivity. As a variant of the popular network hypercube, the Möbius cubes is also a famous interconnection network in parallel and distributed systems. In this article, we calculate $H$-substructure connectivity and $H$-structure connectivity of Möbius cubes when $H$ is isomorphic to $P_{m},C_{m}$ and $K_{1,m}$.

Multilayer network analysis of idiosyncratic volatility connectedness: Evidence from China

This paper proposes multilayer networks, including lagged, contemporaneous, and long-run networks, to examine the idiosyncratic connectedness among Chinese financial institutions. We explore the topology of multilayer networks through static and dynamic analysis to capture the information transmission mechanism of idiosyncratic risks. Moreover, we investigate the drivers influencing idiosyncratic connectedness across financial institutions. We find that idiosyncratic risk contagion among financial institutions is stronger in the long-run network. At the same time, we note that the three networks contain different connection structures, which means that the information transmission mechanism of idiosyncratic risks is heterogeneous in multilayer networks. In addition, when the financial system is under stress, we observe that the contemporaneous effect of idiosyncratic connectedness rises rapidly. Institutional level analysis shows that diversified financial institutions play active roles in the lagged network, securities institutions are transmitters of contemporaneous information spillovers, and banking institutions are the main drivers of the long-run network. Finally, our study shows that size is the main factor driving idiosyncratic risk spillovers among financial institutions.

Similarity and characterization of structural and functional neural connections within species under isoflurane anesthesia in the common marmoset

The common marmoset is an essential model for understanding social cognition and neurodegenerative diseases. This study explored the structural and functional brain connectivity in a marmoset under isoflurane anesthesia, aiming to statistically overcome the effects of high inter-individual variability and noise-related confounds such as physiological noise, ensuring robust and reliable data. Similarities and differences in individual subject data, including assessments of functional and structural brain connectivities derived from resting-state functional MRI and diffusion tensor imaging were meticulously captured. The findings highlighted the high consistency of structural neural connections within the species, indicating a stable neural architecture, while functional connectivity under anesthesia displayed considerable variability. Through independent component and dual regression analyses, several distinct brain connectivities were identified, elucidating their characteristics under anesthesia. Insights into the structural and functional features of the marmoset brain from this study affirm its value as a neuroscience research model, promising advancements in the field through fundamental and translational studies.

Structural connectivity modifications following deep brain stimulation of the subcallosal cingulate and nucleus accumbens in severe anorexia nervosa.

Anorexia nervosa (AN) is a mental health disorder characterized by significant weight loss and associated medical and psychological comorbidities. Conventional treatments for severe AN have shown limited effectiveness, leading to the exploration of novel interventional strategies, including deep brain stimulation (DBS). However, the neural mechanisms driving DBS interventions, particularly in psychiatric conditions, remain uncertain. This study aims to address this knowledge gap by examining changes in structural connectivity in patients with severe AN before and after DBS. Sixteen participants, including eight patients with AN and eight controls, underwent baseline T1-weigthed and diffusion tensor imaging (DTI) acquisitions. Patients received DBS targeting either the subcallosal cingulate (DBS-SCC, N = 4) or the nucleus accumbens (DBS-NAcc, N = 4) based on psychiatric comorbidities and AN subtype. Post-DBS neuroimaging evaluation was conducted in four patients. Data analyses were performed to compare structural connectivity between patients and controls and to assess connectivity changes after DBS intervention. Baseline findings revealed that structural connectivity is significantly reduced in patients with AN compared to controls, mainly regarding callosal and subcallosal white matter (WM) tracts. Furthermore, pre- vs. post-DBS analyses in AN identified a specific increase after the intervention in two WM tracts: the anterior thalamic radiation and the superior longitudinal fasciculus-parietal bundle. This study supports that structural connectivity is highly compromised in severe AN. Moreover, this investigation preliminarily reveals that after DBS of the SCC and NAcc in severe AN, there are WM modifications. These microstructural plasticity adaptations may signify a mechanistic underpinning of DBS in this psychiatric disorder.

Optimization study of strengthened T-shaped concrete-filled steel tubular (CFST) column-steel beam joint

In this paper, a strengthened T-shaped concrete-filled steel tubular (CFST) column-steel beam joint is proposed. The joint is strengthened in the core region of the column wall as well as the beam, to avoid buckling of the column wall and outward displacement of the plastic hinge at the beam end. To study the damage modes and seismic performance of the joint, low-cycle loading experiments on two full-scale specimens of the joint were carried out. The results show that each specimen was damaged by plastic hinging of the steel beam flanges, the column walls did not buckle, and all had a good seismic performance. In addition, Abaqus finite element software was utilized to optimize the joints by analyzing the effects of changing the dimensions of the strengthened column and modifying the structure of the beam-column connection. Simulation results show that increasing the thickness and height of the strengthened column wall enhances the joint's load capacity, stiffness, and energy dissipation. it is recommended that the width-to-thickness ratio of the strengthened columns should be at most 0.08 and the beam-to-column height ratio should be kept at least 1.3. Additionally, two optimization methods, reducing the wall thickness of the steel corbel and weakening the steel beam flange, can effectively reduce weld tearing at the beam-to-column connections, while guaranteeing a good seismic performance. It is suggested that the slope of the steel corbel is not less than 1:6.25, and the weakening ratio of the steel beam flange is not greater than 0.267.

Research of a 0.14 THz Dual-Cavity Parallel Structure Extended Interaction Oscillator.

This paper presents a method to enhance extended interaction oscillator (EIO) output power based on a dual-cavity parallel structure (DCPS). This stucture consists of two conventional ladder-line structures in parallel through a connecting structure, which improves the coupling efficiency between the cavities. The dual output power fusion structure employs an H-T type combiner as the output coupler, which can effectively combine the two input waves in phase to further increase the output power. The dispersion characteristics, coupling impedance, and field distribution of the DCPS are investigated through numerical and simulation calculations, and the optimal operating parameters and output structure are obtained by PIC simulation. At an operating voltage of 12.6 kV, current density of 200 A/cm2, and longitudinal magnetic field of 0.5 T, the DCPS EIO exhibits an output power exceeding 600 W at a frequency of 140.6 GHz. This represents a nearly three-fold enhancement compared with the 195 W output of the conventional ladder-line EIO structure. These findings demonstrate the significant improvement in output power and interaction efficiency achieved by the DCPS for the EIO device.

Harmonization of Structural Brain Connectivity through Distribution Matching.

The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power for investigating brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods exist for dMRI data harmonization, few specifically address structural brain connectivity. We introduce a new distribution-matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from two distinct datasets of OASIS-3 and ADNI-2, comparing its performance to the widely used ComBat method. Our approach is meant to align the statistical properties of connectivity data from these two datasets. We examine the impact of harmonization on the correlation of brain connectivity with the Mini-Mental State Examination score and age. Our results demonstrate that our distribution-matching technique more effectively harmonizes structural brain connectivity, often producing stronger and more significant correlations compared to ComBat. Qualitative assessments illustrate the desired distributional alignment of ADNI-2 with OASIS-3, while quantitative evaluations confirm robust performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research.

The primacy model and the structure of olfactory space.

Understanding sensory processing relies on the establishment of a consistent relationship between the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy model for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.

A Virtual In Vivo Dissection and Analysis of Socioaffective Symptoms Related to Cerebellum-Midbrain Reward Circuitry in Humans.

Emerging research in nonhuman animals implicates cerebellar projections to the ventral tegmental area (VTA) in appetitive behaviors, but these circuits have not been characterized in humans. Here, we mapped cerebello-VTA white matter connectivity in a cohort of men and women using probabilistic tractography on diffusion imaging data from the Human Connectome Project. We uncovered the topographical organization of these connections by separately tracking from parcels of cerebellar lobule VI, crus I/II, vermis, paravermis, and cerebrocerebellum. Results revealed that connections between the cerebellum and VTA predominantly originate in the right cerebellar hemisphere, interposed nucleus, and paravermal cortex and terminate mostly ipsilaterally. Paravermal crus I sends the most connections to the VTA compared with other lobules. We discovered a mediolateral gradient of connectivity, such that the medial cerebellum has the highest connectivity with the VTA. Individual differences in microstructure were associated with measures of negative affect and social functioning. By splitting the tracts into quarters, we found that the socioaffective effects were driven by the third quarter of the tract, corresponding to the point at which the fibers leave the deep nuclei. Taken together, we produced detailed maps of cerebello-VTA structural connectivity for the first time in humans and established their relevance for trait differences in socioaffective regulation.

Bonding Behavior and Quality of Pressureless Ag Sintering on (111)-Oriented Nanotwinned Cu Substrate in Ambient Air.

(111)-oriented nanotwinned Cu ((111)nt-Cu) has shown its high surface diffusion rate and better oxidation resistance over common polycrystalline Cu (C-Cu). The application of (111)nt-Cu as an interface metallization layer in Ag-sintered technology under the role of oxygen was investigated in this work, and its connecting behavior was further clarified by comparing it with C-Cu. As the sintering temperature decreasing from 300 to 200 °C, the shear strength on the (111)nt-Cu substrate was still greater than 55 MPa after sintering for 10 min. The fracture surface correspondingly changed from the interface of Ag/die to mixed fracture mode, involving the interface of the Ag/Cu substrate and Ag/die. The existence of copper oxide provided a tight connection between Ag and the (111)nt-Cu substrate at all of the studied temperatures. Although lots of small dispersed voids were seen at the interface between copper oxide and (111)nt-Cu at 300 °C, these impurity-induced voids would not necessarily be a failure position and could be improved by adjusting the sintering temperature and time; for example, 200 °C/10 min or heating to 300 °C, and then start cooling at the same time. The microstructure of Ag-Cu joint on (111)nt-Cu behaved better than that on C-Cu. The thinner copper oxide layer and the higher connection ratio of the interface between copper oxide and Ag were still found on the (111)nt-Cu connection's structure. The poor connection between copper oxide and Ag on C-Cu easily became the failure interface. By controlling the thickness of copper oxide and the content of impurity-induced voids, the use of (111)nt-Cu in advanced-packaging could be improved to a new level.

The Causes of Waterlogging in Chinese Southern Hilly Cities Based on Hydrological Processes

The various hydrological processes that cause waterlogging exhibit regional differences. Studies on the causes of waterlogging in Chinese southern hilly cities from the perspective of urban regional hydrological processes are needed. This article examines Changsha Central City to study the hydrological processes of Chinese southern hilly cities based on waterlogging point data from 2015 to 2017, analyzing the relationships between the degree of waterlogging and changes in the material elements of runoff sources, runoff convergence terminals, and runoff convergence processes using correlation analysis, principal component analysis and comparative analysis. These results show that the urban waterlogging in Chinese southern hilly cities is caused by the hardening of convergence spaces, concentrated water distribution, a decrease in the connectivity of rainwater corridors, complex topographic slopes, and a lag in the construction of drainage facilities. The expansion of impervious surfaces, particularly in areas intended for convergence terminals, has significantly reduced the number of these critical structures. Additionally, disordered changes in topographic slopes, the division caused by roads, and the hardening of underlying surfaces in rainwater corridors have collectively diminished the structural and ecological connectivity of the rainwater corridor system. This obstruction of surface runoff into concentrated water bodies has rendered the runoff regulation function of these water bodies ineffective, making their size a leading cause of urban waterlogging. To mitigate the risk of waterlogging, Chinese southern hilly cities should prevent urban development from encroaching on natural runoff areas. They should also implement dispersed water body layouts, enhance both the structural and ecological connectivity of rainwater corridors and their underlying surfaces, and improve the design standards of drainage facilities.

CloudSwinNet: A hybrid CNN-transformer framework for ground-based cloud images fine-grained segmentation

Solar irradiance is the main factor affecting the output of a photovoltaic (PV) power station. The dominant role of ground-based clouds on the variation of direct solar radiation in the whole sky. Ground-based cloud segmentation plays a crucial role in photovoltaic power generation prediction. Despite advancements, current cloud segmentation methods fail to meet the requirements of this application. While convolutional neural networks demonstrate impressive segmentation capabilities in ground-based cloud segmentation tasks, their inherent limitations hinder further performance enhancement. Hence, this paper introduces CloudSwinNet, a hybrid CNN-Transformer framework for ground-based cloud images fine-grained segmentation. CloudSwinNet leverages concepts from convolutional neural networks, including stepwise downsampling, local convolution, and skip connections. To further explore the fine-grained features in the ground-based cloud images, we incorporates the Fine-grained Feature Fusion Module (Fg-FFM) in encoder structure, while the jump connection structure integrates the GloRe spatial graph inference module. These additions enable comprehensive learning of multi-scale and long-range dependencies. We conducted extensive experiments on the fine-grained ground-based cloud dataset, demonstrating that CloudSwinNet outperforms six semantic segmentation networks in both qualitative and quantitative evaluations. The results of ablation experiments prove the effectiveness of the module introduced in this paper.

High fault-tolerant performance of the divide-and-swap cube network

Two crucial metrics used to evaluate the fault tolerance of interconnection networks are connectivity and diagnosability. By improving the connectivity and diagnosability of the interconnection network, its fault tolerance can be enhanced. In this paper, we focus on determining the g-extra connectivity (0≤g≤10) of the divide-and-swap cube DSCn, as well as its diagnosability based on the pessimistic diagnosis strategy and g-extra precise diagnosis strategy, under the PMC and MM* models. The research analysis suggests that compared with some other connectivity and diagnosability of DSCn, such as classical connectivity, structure connectivity, super connectivity, and classical diagnosability, the extra connectivity/diagnosability and pessimistic diagnosability of DSCn enable it to have a higher fault tolerance. Moreover, we propose two O(Nlog2⁡N) effective diagnosis algorithms of DSCn: the g-extra diagnosis algorithm (EX-DiagnosisDSCn) and the pessimistic diagnosis algorithm (PE-DiagnosisDSCn), where the EX-DiagnosisDSCn algorithm can accurately diagnose the state of all processors in DSCn.

Structural connectivity and bioactivity in sol–gel silicate glass design

Bioactive glasses (BGs) bond with bone by forming hydroxy carbonate apatite (HCA) upon reaction in physiological fluid, a phenomenon known as bioactivity. BGs structural network connectivity determines their bioactivity. Sol–gel BGs are synthesized through the hydrolysis and condensation of metal alkoxide precursors in the presence of a catalyst, in aqueous environments. Several sol–gel synthesis parameters directly impact BG network connectivity: pH (i.e. acid or basic catalysis), water to alkoxide ratio (Rw), alkoxide type and presence of dopant ions. However, the relationship between bioactivity and these parameters remains surprisingly unexplored.This study highlights the relationship between synthesis pH, Rw, network connectivity and bioactivity in silica-based sol-gel BGs and BGs doped with titanium (Ti) ions (TiBGs), the latter selected for their known ability to enhance network connectivity. BGs and TiBGs are synthesized with various Rw values under acidic and basic conditions, and their bioactivity is assessed in simulated body fluid for 7 days.Increasing Rw decreases network connectivity and increases bioactivity of BGs with high network connectivity, as observed for base-catalyzed BGs and for both acid and base catalyzed TiBGs, but not in BGs with lower connectivity as evidenced in acid-catalyzed BGs. Basic catalysis of TiBGs prevents crystalline TiO2 domain formation, which was instead consistently observed in TiBGs synthesized under acidic catalysis.These findings help the design of BGs for applications where ion release needs to be enhanced even in the presence of dopants that slow down HCA formation, and of BGs with specific properties, e.g. TiO2-containing BGs with potential bactericidal activity. Statement of significanceBioactive glasses (BGs) bond with bone by dissolving and forming hydroxycarbonate apatite (HCA) on their surface, offering applications in medicine and dentistry. BG's network connectivity influences its dissolution rate, and hence HCA formation. While solution-gelation (sol-gel) is commonly used for BG production, the effect of sol gel synthesis parameters on HCA formation remains unexplored. We studied the relationship between synthesis parameters (water-to-alkoxide ratio (Rw), catalyst, and dopant ions, particularly titanium), BG network connectivity, and HCA formation. We find that increasing Rw with any catalyst enhances HCA formation, particularly in glasses with high network connectivity. This understanding allows tailoring BG synthesis for different applications, e.g. those requiring doping with ions that increase network connectivity and fills a crucial gap in BG literature.

Effect of Dialysis on Structural Brain Connectivity in Patients with End-Stage Renal Disease

Introduction: Patients with end-stage renal disease (ESRD) are known to have reduced structural and functional brain connectivity in the brain regions associated with cognitive function. However, the effect of dialysis on brain connectivity remains unclear. This study aimed to evaluate the effects of dialysis on structural brain connectivity in patients with ESRD. Methods: This prospective study included 20 patients with ESRD in the pre-dialysis stage and 35 healthy controls. The patients underwent T2-weighted and three-dimensional T1-weighted magnetic resonance imaging before and 3 months after dialysis initiation. Moreover, the cortical thickness was calculated. We applied graph theoretical analysis to calculate the structural covariance network based on cortical thickness. We compared the cortical thickness and structural covariance network of patients with ESRD in the pre-dialysis stage with those of healthy controls and with those of patients with ESRD in the post-dialysis stage. Results: The mean cortical thickness in both hemispheres was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (2.296 vs. 2.354, p = 0.030; 2.282 vs. 2.362, p = 0.004, respectively) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (2.333 vs. 2.296, p = 0.001; 2.322 vs. 2.282, p = 0.002, respectively). Analysis of the structural covariance network revealed that the assortative coefficient was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (−0.062 vs. −0.031, p = 0.029) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (−0.002 vs. −0.062, p = 0.042). Conclusion: We observed differences in the cortical thickness and structural covariance networks before and after dialysis in patients with ESRD. This indicates that dialysis affects structural brain connectivity, contributing to the understanding of the pathophysiological mechanism of cognitive function alterations resulting from dialysis in patients with ESRD.

Enhanced diversity on connector hubs following sleep deprivation: Evidence from diffusion and functional magnetic resonance imaging

Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.

The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond.

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.

Structure-function coupling in highly sampled individual brains.

Structural connectivity (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to SC may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78min of diffusion-weighted MRI for SC and 360min of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas.

Multiplex measures for higher-order networks

A wide variety of complex systems are characterized by interactions of different types involving varying numbers of units. Multiplex hypergraphs serve as a tool to describe such structures, capturing distinct types of higher-order interactions among a collection of units. In this work, we introduce a comprehensive set of measures to describe structural connectivity patterns in multiplex hypergraphs, considering scales from node and hyperedge levels to the system’s mesoscale. We validate our measures with three real-world datasets: scientific co-authorship in physics, movie collaborations, and high school interactions. This validation reveals new collaboration patterns, identifies trends within and across movie subfields, and provides insights into daily interaction dynamics. Our framework aims to offer a more nuanced characterization of real-world systems marked by both multiplex and higher-order interactions.