Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks

Blurred streamlines: A novel representation to reduce redundancy in tractography

Tractography is a powerful tool to study brain connectivity in vivo, but it is well known to suffer from an intrinsic trade-off between sensitivity and specificity. A critical - but usually underrated - parameter to choose that can heavily impact the quality of the estimates is the number of streamlines to be reconstructed for a given data set. In fact, sensitivity can be improved by generating more and more streamlines, as all real anatomical connections are likely reconstructed, but lots of false positives are inevitably introduced, too. Consequently, so-called tractography filtering techniques have become increasingly popular to get rid of these false positives and improve specificity. However, increasing number of streamlines introduces redundancy in tractography reconstructions, which may negatively impact the performance of filtering algorithms, especially those based on linear formulations. To address this problem, we introduce a novel streamlines representation, called "blurred streamlines", which drastically reduces the redundancy among streamlines by (i) clustering similar trajectories and (ii) spatially blurring the corresponding signal contributions. We tested the effectiveness of the blurred streamlines both on synthetic and in vivo data. Our results clearly show that this new representation is as accurate as state-of-the-art methods despite using only 5% of the input streamlines, thus significantly decreasing the computational complexity of filtering algorithms as well as storage requirements of the resulting reconstructions.

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Abnormal wiring of the structural connectome in adults with ADHD

Current knowledge of white matter changes in large-scale brain networks in adult attention-deficit/hyperactivity disorder (ADHD) is scarce. We collected diffusion-weighted magnetic resonance imaging data in 40 adults with ADHD and 36 neurotypical controls and used constrained spherical deconvolution-based tractography to reconstruct whole-brain structural connectivity networks. We used network-based statistic (NBS) and graph theoretical analysis to investigate differences in these networks between the ADHD and control groups, as well as associations between structural connectivity and ADHD symptoms assessed with the Adult ADHD Self-Report Scale or performance in the Conners Continuous Performance Test 2 (CPT-2). NBS revealed decreased connectivity in the ADHD group compared to the neurotypical controls in widespread unilateral networks, which included subcortical and corticocortical structures and encompassed dorsal and ventral attention networks and visual and somatomotor systems. Furthermore, hypoconnectivity in a predominantly left-frontal network was associated with higher amount of commission errors in CPT-2. Graph theoretical analysis did not reveal topological differences between the groups or associations between topological properties and ADHD symptoms or task performance. Our results suggest that abnormal structural wiring of the brain in adult ADHD is manifested as widespread intrahemispheric hypoconnectivity in networks previously associated with ADHD in functional neuroimaging studies.

Structural Network Topology Reveals Higher Brain Resilience in Individuals with Preclinical Alzheimer's Disease

Introduction: The diagnosis of Alzheimer's disease (AD) requires the presence of amyloid and tau pathology, but it remains unclear how they affect the structural network in the pre-clinical stage. We aimed to assess differences in topological properties in cognitively normal (CN) individuals with varying levels of amyloid and tau pathology, as well as their association with AD pathology burden. Methods: A total of 68 CN individuals were included and stratified by normal/abnormal (-/+) amyloid (A) and tau (T) status based on positron emission tomography results, yielding three groups: A-T- (n = 19), A+T- (n = 28), and A+T+ (n = 21). Topological properties were measured from structural connectivity. Group differences and correlations with A and T were evaluated. Results: Compared with the A-T- group, the A+T+ group exhibited changes in the structural network topology. At the global level, higher assortativity was shown in the A+T+ group and was correlated with greater tau burden (r = 0.29, p = 0.02), while no difference in global efficiency was found across the three groups. At the local level, the A+T+ group showed disrupted topological properties in the left hippocampus compared with the A-T- group, characterized by lower local efficiency (p < 0.01) and a lower clustering coefficient (p = 0.014). Conclusions: The increased linkage in the higher level architecture of the white matter network reflected by assortativity may indicate increased brain resilience in the early pathological state. Our results encourage further investigation of the topological properties of the structural network in pre-clinical AD.

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network

PURPOSE

The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers.

METHODS

Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability.

RESULTS

The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware.

CONCLUSIONS

The results show low variability of connectivity topological metrics across sites running a harmonised protocol.

PPA: Principal parcellation analysis for brain connectomes and multiple traits

Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.

Large-Scale Exploration of Whole-Brain Structural Connectivity in Anorexia Nervosa: Alterations in the Connectivity of Frontal and Subcortical Networks

BACKGROUND

Anorexia nervosa (AN) is characterized by disturbances in cognition and behavior surrounding eating and weight. The severity of AN combined with the absence of localized brain abnormalities suggests distributed, systemic underpinnings that may be identified using diffusion-weighted magnetic resonance imaging and tractography to reconstruct white matter pathways.

METHODS

Diffusion-weighted magnetic resonance imaging data acquired from female patients with AN (n= 147) and female healthy control (HC) participants (n = 119), ages 12 to 40 years, were combined across 5 studies. Probabilistic tractography was completed, and full-cortex connectomes describing streamline counts between 84 brain regions were generated and harmonized. Graph theory methods were used to describe alterations in network organization in AN. The network-based statistic tested between-group differences in brain subnetwork connectivity. The metrics strength and efficiency indexed the connectivity of brain regions (network nodes) and were compared between groups using multiple linear regression.

RESULTS

Individuals with AN, relative to HC peers, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < .05, familywise error corrected). Node-based analyses indicated reduced connectivity of the left hippocampus in patients relative to HC peers (p < .05, permutation corrected). Severity of illness, assessed by body mass index, was associated with subcortical connectivity (p < .05, uncorrected).

CONCLUSIONS

Analyses identified reduced structural connectivity of subcortical networks and regions, and stronger cortical network connectivity, among individuals with AN relative to HC peers. These findings are consistent with alterations in feeding, emotion, and executive control circuits in AN, and may direct hypothesis-driven research into mechanisms of persistent restrictive eating behavior.

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

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

Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the high angular resolution dMRI acquisitions capable of fitting the microstructural models needed for tractography are often time-consuming and not routinely collected clinically, restricting the scope of tractography analyses. To address this limitation, we build on recent advances in deep learning which have demonstrated that streamline propagation can be learned from dMRI directly without traditional model fitting. Specifically, we propose learning the streamline propagator from T1w MRI to facilitate arbitrary tractography analyses when dMRI is unavailable. To do so, we present a novel convolutional-recurrent neural network (CoRNN) trained in a teacher-student framework that leverages T1w MRI, associated anatomical context, and streamline memory from data acquired for the Human Connectome Project. We characterize our approach under two common tractography paradigms, WM bundle analysis and structural connectomics, and find approximately a 5-15% difference between measures computed from streamlines generated with our approach and those generated using traditional dMRI tractography. When placed in the literature, these results suggest that the accuracy of WM measures computed from T1w MRI with our method is on the level of scan-rescan dMRI variability and raise an important question: is tractography truly a microstructural phenomenon, or has dMRI merely facilitated its discovery and implementation?

The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics

The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.

Topological Structural Brain Connectivity Alterations in Aspartylglucosaminuria: A Case-Control Study

BACKGROUND AND PURPOSE

We investigated global and local properties of the structural brain connectivity networks in aspartylglucosaminuria, an autosomal recessive and progressive neurodegenerative lysosomal storage disease. Brain connectivity in aspartylglucosaminuria has not been investigated before, but previous structural MR imaging studies have shown brain atrophy, delayed myelination, and decreased thalamic and increased periventricular WM T2 signal intensity.

MATERIALS AND METHODS

We acquired diffusion MR imaging and T1-weighted data from 12 patients with aspartylglucosaminuria (mean age, 23 [SD, 8] years; 5 men), and 30 healthy controls (mean age, 25 [SD, 10] years; 13 men). We performed whole-brain constrained spherical deconvolution tractography, which enables the reconstruction of neural tracts through regions with complex fiber configurations, and used graph-theoretical analysis to investigate the structural brain connectivity networks.

RESULTS

The integration of the networks was decreased, as demonstrated by a decreased normalized global efficiency and an increased normalized characteristic path length. In addition, the average strength of the networks was decreased. In the local analyses, we found decreased strength in 11 nodes, including, for example, the right thalamus, right putamen, and, bilaterally, several occipital and temporal regions.

CONCLUSIONS

We found global and local structural connectivity alterations in aspartylglucosaminuria. Biomarkers related to the treatment efficacy are needed, and brain network properties may provide the means for long term follow-up.

The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults

Introduction

We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimer's disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children.

Methods

Diffusion MRI data were used to perform whole-brain tractography and generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participant's genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and (a) the graph theoretical metrics of the structural networks and (b) the rich-club, feeder and local connectivity of the whole-brain networks.

Results

In the visual subnetwork, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.19, p = 1.4 × 10-3), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r = 0.16, p = 5.5 × 10-3), and the mean clustering coefficient was negatively correlated with the tau-protein binding PRS (r = -0.16, p = 0.016). In the default mode network, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.14, p = 0.044). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r = -0.16, p = 0.035; r = -0.15, p = 0.036).

Discussion

We identified small reductions in brain connectivity in young adults at risk of developing Alzheimer's disease in later life.

Accuracy and reliability of diffusion imaging models

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity

Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).

Impact of thresholding on the consistency and sensitivity of diffusion MRI-based brain networks in patients with cerebral small vessel disease

INTRODUCTION

Thresholding of low-weight connections of diffusion MRI-based brain networks has been proposed to remove false-positive connections. It has been previously established that this yields more reproducible scan-rescan network architecture in healthy subjects. In patients with brain disease, network measures are applied to assess inter-individual variation and changes over time. Our aim was to investigate whether thresholding also achieves improved consistency in network architecture in patients, while maintaining sensitivity to disease effects for these applications.

METHODS

We applied fixed-density and absolute thresholding on brain networks in patients with cerebral small vessel disease (SVD, n = 86; ≈24 months follow-up), as a clinically relevant exemplar condition. In parallel, we applied the same methods in healthy young subjects (n = 44; scan-rescan interval ≈4 months) as a frame of reference. Consistency of network architecture was assessed with dice similarity of edges and intraclass correlation coefficient (ICC) of edge-weights and hub-scores. Sensitivity to disease effects in patients was assessed by evaluating interindividual variation, changes over time, and differences between those with high and low white matter hyperintensity burden, using correlation analyses and mixed ANOVA.

RESULTS

Compared to unthresholded networks, both thresholding methods generated more consistent architecture over time in patients (unthresholded: dice = .70; ICC: .70-.78; thresholded: dice = .77; ICC: .73-.83). However, absolute thresholding created fragmented nodes. Similar observations were made in the reference group. Regarding sensitivity to disease effects in patients, fixed-density thresholds that were optimal in terms of consistency (densities: .10-.30) preserved interindividual variation in global efficiency and node strength as well as the sensitivity to detect effects of time and group. Absolute thresholding produced larger fluctuations of interindividual variation.

CONCLUSIONS

Our results indicate that thresholding of low-weight connections, particularly when using fixed-density thresholding, results in more consistent network architecture in patients with longer rescan intervals, while preserving sensitivity to disease effects.

Network analysis shows decreased ipsilesional structural connectivity in glioma patients

Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.

Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients.

Structural brain connectivity correlates with outcome in mild traumatic brain injury

We investigated the topology of structural brain connectivity networks and its association to outcome following mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent MRI twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. However, we found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared to control subjects. Furthermore, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage, and therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.

Brain and cognitive ageing: The present, and some predictions (…about the future)

Experiencing decline in one's cognitive abilities is among the most feared aspects of growing old [53]. Age-related cognitive decline carries a huge personal, societal, and financial cost both in pathological ageing (such as dementias) and also within the non-clinical majority of the population. A projected 152 million people worldwide will suffer from dementia by 2050 [3]. The early stages of cognitive decline are much more prevalent than dementia, and can still impose serious limitations of performance on everyday activities, independence, and quality of life in older age [5], [60], [80]. Cognitive decline also predicts poorer health, adherence to medical regimens, and financial decision-making, and can herald dementia, illness, and death [6], [40]. Of course, when seeking to understand why some people experience more severe cognitive ageing than others, researchers have turned to the organ of thinking for clues about the nature, possible mechanisms, and determinants that might underpin more and less successful cognitive agers. However, that organ is relatively inaccessible, a limitation partly alleviated by advances in neuroimaging. Here we discuss lessons for cognitive and brain ageing that have come from neuroimaging research (especially structural brain imaging), what neuroimaging still has left to teach us, and our views on possible ways forward in this multidisciplinary field.

Structural Brain Network Reproducibility: Influence of Different Diffusion Acquisition and Tractography Reconstruction Schemes on Graph Metrics

Background: Graph metrics of structural brain networks demonstrate to be a powerful tool for investigating brain topology at a large scale. However, the variability of the results related to applying different magnetic resonance acquisition schemes and tractography reconstruction techniques is not fully characterized. Materials and Methods: The present work aims to evaluate the influence of different combinations of diffusion acquisition schemes (single and multishell), diffusion models (tensor and spherical deconvolution), and tractography reconstruction approaches (deterministic and probabilistic) on the reproducibility of graph metrics derived from structural connectome on test/retest (TRT) data released by the Human Connectome Project. From each implemented experimental setup, both global and local graph metrics were evaluated and their reproducibility was estimated by the intraclass correlation coefficient (ICC). Moreover, the percentage relative standard deviation (pRSD) from the ICC values of local graph metrics was calculated to quantify how much the reproducibility varied across nodes within each experimental setup. Results: The presented results show that different combinations of diffusion acquisition schemes, diffusion models, and tractography algorithms can strongly affect the reproducibility of global and local graph metrics. The combination of constrained spherical deconvolution (CSD) and deterministic tractography gave generally high reproducibility (ICCs >0.75) and lowest pRSD for the considered graph metrics, meanwhile probabilistic CSD with a high b-value returned the highest reproducibility. Notably, the introduction of streamline selection filters on CSD can substantially affect the reproducibility. Discussion: This work demonstrates that the TRT reproducibility of graph metrics is generally high but can vary substantially with different combinations of acquisition and reconstruction schemes. Impact statement This work demonstrates the influence of different diffusion acquisition schemes, diffusion models, and tractography reconstruction approaches on the reproducibility of graph metrics derived from structural connectome. The presented findings impact on the choice of both acquisition protocol and processing pipeline for topological analyses to produce reproducible measurements for brain network studies.

MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI

PURPOSE

Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge.

METHODS

To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length.

RESULTS

We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability.

CONCLUSIONS

This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.

Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique

Effective amygdalar functionality depends on the concerted activity of a complex network of regions. Thus, the role of the amygdala cannot be fully understood without identifying the set of brain structures that allow the processes performed by the amygdala to emerge. However, this identification has yet to occur, hampering our ability to understand both normative and pathological processes that rely on the amygdala. We developed and applied novel graph theory methods to diffusion-based anatomical networks in a large sample (n = 1,052, 54.28% female, mean age=28.75) to identify nodes that critically support amygdalar interactions with the larger brain network. We examined three graph properties, each indexing a different emergent aspect of amygdalar network communication: current-flow betweenness centrality (amygdalar influence on information flowing between other pairs of nodes), node communicability (clarity of communication between the amygdala and other nodes), and subgraph centrality (amygdalar influence over local network processing). Findings demonstrate that each of these aspects of amygdalar communication is associated with separable sets of regions and, in some cases, these sets map onto previously identified sub-circuits. For example, betweenness and communicability were each associated with different sub-circuits that have been identified in previous work as supporting distinct aspects of memory-guided behavior. Other regions identified span basic (e.g., visual cortex) to higher-order (e.g., insula) sensory processing and executive functions (e.g., dorsolateral prefrontal cortex). Present findings expand our current understanding of amygdalar function by showing that there is no single 'amygdala network', but rather multiple networks, each supporting different modes of amygdalar interaction with the larger brain network. Additionally, our novel method allowed for the identification of how such regions support the amygdala, which has not been previously explored.

Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models.

The structural and functional connectivities of the brain, which reflect the anatomical connections of axonal bundles and the amount of coactivation between brain regions, respectively, only weakly correlate with each other. In order to enhance and investigate this relationship, large-scale whole-brain dynamical models were involved in this branch of research. However, how the definitions of the brain regions parcellated according to a so-called brain atlas influence these models has so far not been systematically assessed. In this article, we show that this influence can be large, and link group-averaged, atlas-induced deviations to network properties extracted from both types of connectivity. Additionally, we demonstrate that the same association does not apply to subject-specific variations. These results may contribute to the further personalization of the whole-brain models.

Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition

Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and there are ongoing movements toward data sharing and multi-center studies. To extract meaningful information from multi-center data, methods to correct for the bias caused by scanner differences, that is, harmonization, are urgently needed. In this work, we report the cross-scanner differences in structural network analyses using data from nine traveling subjects (four males and five females, 21-49 years-old) who underwent scanning using four 3T scanners (public database available from the Brain/MINDS Beyond Human Brain MRI project (http://mriportal.umin.jp/)). The reliability and reproducibility were compared to those of data from another set of four subjects (all males, 29-42 years-old) who underwent scan-rescan (interval, 105-147 days) with the same scanner as well as scan-rescan data from the Human Connectome Project database. The results demonstrated that the reliability of the edge weights and graph theory metrics was lower for data including different scanners, compared to the scan-rescan with the same scanner. Besides, systematic differences between scanners were observed, indicating the risk of bias in comparing networks obtained from different scanners directly. We further demonstrate that it is feasible to reduce inter-scanner variabilities while preserving the inter-subject differences among healthy individuals by modeling the scanner effects at the level of network matrices, when traveling-subject data are available for calibration between scanners. The present data and results are expected to serve as a basis for developing and evaluating novel harmonization methods.

Structural connectivity changes in the cerebral pain matrix in burning mouth syndrome: a multi-shell, multi-tissue-constrained spherical deconvolution model analysis

PURPOSE

Burning mouth syndrome (BMS) is a chronic intraoral pain syndrome. Previous studies have attempted to determine the brain connectivity features in BMS using functional and structural magnetic resonance imaging. However, no study has investigated the structural connectivity using multi-shell, multi-tissue-constrained spherical deconvolution (MSMT-CSD), anatomically constrained tractography (ACT), and spherical deconvolution informed filtering of tractograms (SIFT). Therefore, this study aimed to assess the differences in brain structural connectivity of patients with BMS and healthy controls using probabilistic tractography with these methods, and graph analysis.

METHODS

Fourteen patients with BMS and 11 age- and sex-matched healthy volunteers underwent 3-T magnetic resonance imaging. MSMT-CSD-based probabilistic structural connectivity was computed using the second-order integration over fiber orientation distributions algorithm based on nodes set in 84 anatomical cortical regions with ACT and SIFT. A t-test was performed for comparisons between the BMS and healthy control brain networks.

RESULTS

The betweenness centrality was significantly higher in the left insula, right amygdala, and right lateral orbitofrontal cortex and significantly lower in the right inferotemporal cortex in the BMS group than that in healthy controls. However, no significant difference was found in the clustering coefficient, node degree, and small-worldness between the two groups.

CONCLUSION

Graph analysis of brain probabilistic structural connectivity, based on diffusion imaging using an MSMT-CSD model with ACT and SIFT, revealed alterations in the regions comprising the pain matrix and medial pain ascending pathway. These results highlight the emotional-affective profile of BMS, which is a type of chronic pain syndrome.

Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities

Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1.

Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

Dynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models informed by SC. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. The main objective of this study is to explore how the quality of the model validation can vary across the considered simulation conditions. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying WBT density such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.

Constrained spherical deconvolution of nonspherically sampled diffusion MRI data

Constrained spherical deconvolution (CSD) of diffusion-weighted MRI (DW-MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW-MRI signal on the sphere can be represented as the spherical convolution of a single-fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW-MRI data is sampled shell-wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b-values. On shell-wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell-wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics.

Diffusion imaging in Huntington's disease: comprehensive review

Huntington's disease (HD) is a monogenic disorder with 100% penetrance. With the advent of genetic testing in adults, disease-related, structural brain changes can be investigated from the earliest, premorbid stages of HD. While examining macrostructural change characterises global neuronal damage, investigating microstructural alterations provides information regarding brain organisation and its underlying biological properties. Diffusion MRI can be used to track the progression of microstructural anomalies in HD decades prior to clinical disease onset, providing a greater understanding of neurodegeneration. Multiple approaches, including voxelwise, region of interest and tractography, have been used in HD cohorts, showing a centrifugal pattern of white matter (WM) degeneration starting from deep brain areas, which is consistent with neuropathological studies. The corpus callosum, longer WM tracts and areas that are more densely connected, in particular the sensorimotor network, also tend to be affected early during premanifest stages. Recent evidence supports the routine inclusion of diffusion analyses within clinical trials principally as an additional measure to improve understanding of treatment effects, while the advent of novel techniques such as multitissue compartment models and connectomics can help characterise the underpinnings of progressive functional decline in HD.

Test-retest reliability and long-term stability of three-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data

PURPOSE

Several recent studies have used a three-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics have not yet been evaluated.

METHODS

This study examined estimates of whole-brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters.

RESULTS

The CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and gray matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3-month interval cohort to both compartments having ICC > 0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort.

CONCLUSION

The three-tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue-microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three-tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

MRI Predictors of Cognitive Training Outcomes

The prospect of improving or maintaining cognitive functioning has provoked a steadily increasing number of cognitive training interventions over the last years, especially for clinical and elderly populations. However, there are discrepancies between the findings of the studies. One of the reasons behind these heterogeneous findings is that there are vast inter-individual differences in how people benefit from the training and in the extent that training-related gains are transferred to other untrained tasks and domains. In this paper, we address the value of incorporating neural measures to cognitive training studies in order to fully understand the mechanisms leading to inter-individual differences in training gains and their generalizability to other tasks. Our perspective is that it is necessary to collect multimodal neural measures in the pre- and post-training phase, which can enable us to understand the factors contributing to successful training outcomes. More importantly, this understanding can enable us to predict who will benefit from different types of interventions, thereby allowing the development of individually tailored intervention programs.

Effect of Fixed-Density Thresholding on Structural Brain Networks: A Demonstration in Cerebral Small Vessel Disease

A popular solution to control for edge density variability in structural brain network analysis is to threshold the networks to a fixed density across all subjects. However, it remains unclear how this type of thresholding affects the basic network architecture in terms of edge weights, hub location, and hub connectivity and, especially, how it affects the sensitivity to detect disease-related abnormalities. We investigated these two questions in a cohort of patients with cerebral small vessel disease and age-matched controls. Brain networks were reconstructed from diffusion magnetic resonance imaging data using deterministic fiber tractography. Networks were thresholded to a fixed density by removing edges with the lowest number of streamlines. We compared edge length (mm), fractional anisotropy (FA), proportion of hub connections, and hub location between the unthresholded and the thresholded networks of each subject. Moreover, we compared weighted graph measures of global and local connectivity obtained from the (un)thresholded networks between patients and controls. We performed these analyses over a range of densities (2-20%). Results indicate that fixed-density thresholding disproportionally removes edges composed of long streamlines, but is independent of FA. The edges removed were not preferentially connected to hub or nonhub nodes. Over half of the original hubs were reproducible when networks were thresholded to a density ≥10%. Furthermore, the between-group differences in graph measures observed in the unthresholded network remained present after thresholding, irrespective of the chosen density. We therefore conclude that moderate fixed-density thresholds can successfully be applied to control for the effects of density in structural brain network analysis.

Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. Here, we sought to identify multidimensional associations between cognitive performance and structural network topology in TLE.

METHODS

We studied 34 drug-resistant adult TLE patients and 24 age- and sex-matched healthy controls. Participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based hierarchical clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies.

RESULTS

Both methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized cognitive performance better than morphometric measures did.

CONCLUSIONS

Our multivariate approach emphasized a close coupling of cognitive dysfunction and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy.

How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning

Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it is run, potentially leading to microstructural measurements or anatomical delineations that are not reproducible. Generating a sufficiently large number of streamlines is required to avoid this scenario, but what constitutes "sufficient" is difficult to assess and so streamline counts are typically chosen in an arbitrary or qualitative manner. This work explores several factors influencing tractography reliability and details two methods for estimating this reliability. The first method automatically estimates the number of streamlines required to achieve reliable microstructural measurements, whilst the second estimates the number of streamlines required to achieve a reliable binarised trackmap than can be used clinically. Using these methods, we calculated the number of streamlines required to achieve a range of quantitative reproducibility criteria for three anatomical tracts in 40 Human Connectome Project datasets. Actual reproducibility was checked by repeatedly generating the tractograms with the calculated numbers of streamlines. We found that the required number of streamlines varied strongly by anatomical tract, image resolution, number of diffusion directions, the degree of reliability desired, the microstructural measurement of interest, and/or the specifics on how the tractogram was converted to a binary volume. The proposed methods consistently predicted streamline counts that achieved the target reproducibility. Implementations are made available to enable the scientific community to more-easily achieve reproducible tractography.

Challenges and Opportunities in Connectome Construction and Quantification in the Developing Human Fetal Brain

The white matter structure of the human brain undergoes critical developmental milestones in utero, which we can observe noninvasively using diffusion-weighted magnetic resonance imaging. In order to understand this fascinating developmental process, we must establish the variability inherent in such a challenging imaging environment and how measurable quantities can be transformed into meaningful connectomes. We review techniques for reconstructing and studying the brain connectome and explore promising opportunities for in utero studies that could lead to more accurate measurement of structural properties and allow for more refined and insightful analyses of the fetal brain. Opportunities for more sophisticated analyses of the properties of the brain and its dynamic changes have emerged in recent years, based on the development of iterative techniques to reconstruct motion-corrupted diffusion-weighted data. Although reconstruction quality is greatly improved, the treatment of fundamental quantities like edge strength requires careful treatment because of the specific challenges of imaging in utero. There are intriguing challenges to overcome, from those in analysis due to both imaging limitations and the significant changes in structural connectivity, to further image processing to address the specific properties of the target anatomy and quantification into a developmental connectome.

Topological Alterations of the Structural Brain Connectivity Network in Children with Juvenile Neuronal Ceroid Lipofuscinosis

BACKGROUND AND PURPOSE

We used diffusion MR imaging to investigate the structural brain connectivity networks in juvenile neuronal ceroid lipofuscinosis, a neurodegenerative lysosomal storage disease of childhood. Although changes in conventional MR imaging are typically not visually apparent in children aged <10 years, we previously found significant microstructural abnormalities by using diffusion MR imaging. Therefore, we hypothesized that the structural connectivity networks would also be affected in the disease.

MATERIALS AND METHODS

We acquired diffusion MR imaging data from 14 children with juvenile neuronal ceroid lipofuscinosis (mean ± SD age, 9.6 ± 3.4 years; 10 boys) and 14 control subjects (mean ± SD age, 11.2 ± 2.3 years; 7 boys). A follow-up MR imaging was performed for 12 of the patients (mean ± SD age, 11.4 ± 3.2 years; 8 boys). We used graph theoretical analysis to investigate the global and local properties of the structural brain connectivity networks reconstructed with constrained spherical deconvolution-based whole-brain probabilistic tractography.

RESULTS

We found significantly increased characteristic path length (P = .003) and decreased degree (P = .003), which indicated decreased network integration and centrality in children with juvenile neuronal ceroid lipofuscinosis. The findings were similar for the follow-up MR imaging, and there were no significant differences between the two acquisitions of the patients. In addition, we found that the disease severity correlated negatively (P < .007) with integration, segregation, centrality, and small-worldness of the networks. Moreover, we found significantly (P < .0003) decreased local efficiency in the left supramarginal gyrus and temporal plane, and decreased strength in the right lingual gyrus.

CONCLUSIONS

We found significant global and local network alterations in juvenile neuronal ceroid lipofuscinosis that correlated with the disease severity and in areas related to the symptomatology.

Brain structural connectivity network alterations in insomnia disorder reveal a central role of the right angular gyrus

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People with insomnia show widespread brain structural hyperconnectivity.

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The right angular gyrus is central to the structural connectivity alterations.

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Connectivity of this angular gyrus subnetwork correlates with reactive hyperarousal.

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Brain structural hyperconnectivity may mark vulnerability to insomnia.

Insomnia Disorder (ID) is a prevalent and persistent condition, yet its neural substrate is not well understood. The cognitive, emotional, and behavioral characteristics of ID suggest that vulnerability involves distributed brain networks rather than a single brain area or connection. The present study utilized probabilistic diffusion tractography to compare the whole-brain structural connectivity networks of people with ID and those of matched controls without sleep complaints.

Diffusion-weighted images and T1-weighed images were acquired in 51 people diagnosed with ID (21–69 years of age, 37 female) and 48 matched controls without sleep complaints (22–70 years of age, 31 female). Probabilistic tractography was performed to construct the whole-brain structural connectivity network of each participant. Case–control differences in connectivity strength and network efficiency were evaluated with permutation tests.

People with ID showed structural hyperconnectivity within a subnetwork that spread over frontal, parietal, temporal, and subcortical regions and was anchored at the right angular gyrus. The result was robust across different edge-weighting strategies. Moreover, converging support was given by the finding of heightened right angular gyrus nodal efficiency (harmonic centrality) across varying graph density in people with ID. Follow-up correlation analyses revealed that subnetwork connectivity was associated with self-reported reactive hyperarousal.

The findings demonstrate that the right angular gyrus is a hub of enhanced structural connectivity in ID. Hyperconnectivity within the identified subnetwork may contribute to increased reactivity to stimuli and may signify vulnerability to ID.

Optimization of graph construction can significantly increase the power of structural brain network studies

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm²). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.