Differences In Network Organization Research Articles

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging

BackgroundGraph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying “lesion filling” by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling.MethodsThe study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling.ResultsArtefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients.ConclusionLesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads.

Cingulo-Opercular and Frontoparietal Network Control of Effort and Fatigue in Mild Traumatic Brain Injury.

Neural substrates of fatigue in traumatic brain injury (TBI) are not well understood despite the considerable burden of fatigue on return to productivity. Fatigue is associated with diminishing performance under conditions of high cognitive demand, sense of effort, or need for motivation, all of which are associated with cognitive control brain network integrity. We hypothesize that the pathophysiology of TBI results in damage to diffuse cognitive control networks, disrupting coordination of moment-to-moment monitoring, prediction, and regulation of behavior. We investigate the cingulo-opercular (CO) and frontoparietal (FP) networks, which are engaged to sustain attention for task and maintain performance. A total of 61 individuals with mild TBI and 42 orthopedic control subjects participated in functional MRI during performance of a constant effort task requiring altering the amount of effort (25, 50, or 75% of maximum effort) utilized to manually squeeze a pneumostatic bulb across six 30-s trials. Network-based statistics assessed within-network organization and fluctuation with task manipulations by group. Results demonstrate small group differences in network organization, but considerable group differences in the evolution of task-related modulation of connectivity. The mild TBI group demonstrated elevated CO connectivity throughout the task with little variation in effort level or time on task (TOT), while CO connectivity diminished over time in controls. Several interregional CO connections were predictive of fatigue in the TBI group. In contrast, FP connectivity fluctuated with task manipulations and predicted fatigue in the controls, but connectivity fluctuations were delayed in the mild traumatic brain injury (mTBI) group and did not relate to fatigue. Thus, the mTBI group’s hyper-connectivity of the CO irrespective of task demands, along with hypo-connectivity and delayed peak connectivity of the FP, may allow for attainment of task goals, but also contributes to fatigue. Findings are discussed in relation to performance monitoring of prediction error that relies on internal cues from sensorimotor feedback during task performance. Delay or inability to detect and respond to prediction errors in TBI, particularly evident in bilateral insula-temporal CO connectivity, corresponds to day-to-day fatigue and fatigue during task performance.

Hypermetabolic Cerebellar Connectome in Alzheimer's Disease.

Introduction: Regional hypermetabolism in Alzheimer's disease (AD), especially in the cerebellum, has been consistently observed but often neglected as an artefact produced by the commonly used proportional scaling procedure in the statistical parametric mapping. We hypothesize that the hypermetabolic regions are also important in disease pathology in AD. Methods: Using fluorodeoxyglucose (FDG)-positron emission tomography (PET) images from 88 AD subjects and 88 age-sex matched normal controls (NL) from the publicly available Alzheimer's Disease Neuroimaging Initiative database, we developed a general linear model-based classifier that differentiated AD patients from normal individuals (sensitivity = 87.50%, specificity = 82.95%). We constructed region-region group-wise correlation matrices and evaluated differences in network organization by using the graph theory analysis between AD and control subjects. Results: We confirmed that hypermetabolism found in AD is not an artefact by replicating it using white matter as the reference region. The role of the hypermetabolic regions has been further investigated by using the graph theory. The differences in betweenness centrality (BC) between AD and NL network were correlated with region weights of FDG PET-based AD classifier. In particular, the hypermetabolism in cerebellum was accompanied with higher BC. The brain regions with higher BC in AD network showed a progressive increase in FDG uptake over 2 years in prodromal AD patients (n = 39). Discussion: This study suggests that hypermetabolism found in AD may play an important role in forming the AD-related metabolic network. In particular, hypermetabolic cerebellar regions represent a good candidate for further investigation in altered network organization in AD.

Subject identification using edge-centric functional connectivity

Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed ‘fingerprinting’ analyses on functional connectivity to identify subjects’ idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects’ identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

Combining Electrostimulation With Fiber Tracking to Stratify the Inferior Fronto-Occipital Fasciculus.

The inferior fronto-occipital fasciculus (IFOF) is one of the longest association fiber tracts of the brain. According to the most recent anatomical studies, it may be formed by several layers, suggesting a role in multiple cognitive functions. However, to date, no attempt has been made to dissociate the functional contribution of the IFOF subpathways. In this study, real-time, cortico-subcortical mapping with direct electrostimulation was performed in 111 patients operated on in wide-awake surgery for a right low-grade glioma. Patients performed two behavioral tasks during stimulation, tapping, respectively, mentalizing and visual semantic cognition—two functions supposed to be partly mediated by the IFOF. Responsive white matter sites were first subjected to a clustering analysis to assess potential topological differences in network organization. Then they were used as seeds to generate streamline tractograms based on the HC1021 diffusion dataset (template-based approach). The tractograms obtained for each function were overlapped and contrasted to determine whether some fiber pathways were more frequently involved in one or the other function. The obtained results not only provided strong evidence for a role of the right IFOF in both functions, but also revealed that the tract is dissociable into two functional strata according to a ventral (semantic) and dorsal (mentalizing) compartmentalization. Besides, they showed a high degree of anatomo-functionnal variability across patients in the functional implication of the IFOF, possibly related to symmetrical/hemispheric differences in network organization. Collectively, these findings support the view that the right IFOF is a functionally multi-layered structure, with nevertheless interindividual variations.

Probabilistic mapping of human functional brain networks identifies regions of high group consensus

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show “core” (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.

Subcutaneous and intramuscular fat transcriptomes show large differences in network organization and associations with adipose traits in pigs.

Subcutaneous fat (SCF) and intramuscular fat (IMF) deposition is relevant to health in humans, as well as meat production and quality in pigs. In this study, we generated RNA sequence data for 122 SCF, 120 IMF, and 87 longissimus dorsi muscle (LDM) samples using 155 F6 pigs from a specially designed heterogeneous population generated by intercrossing four highly selected European commercial breeds and four indigenous Chinese pig breeds. The phenotypes including waist back fat thickness and intramuscular fat content were also measured in the 155 F6 pigs. We found that the genes in SCF and IMF differed largely in both expression levels and network connectivity, and highlighted network modules that exhibited strongest gain of connectivity in SCF and IMF, containing genes that were associated with the immune process and DNA double-strand repair, respectively. We identified 215 SCF genes related to kinase inhibitor activity, mitochondrial fission, and angiogenesis, and 90 IMF genes related to lipolysis and fat cell differentiation, displayed a tissue-specific association with back fat thickness and IMF content, respectively. We found that cis-expression QTL for trait-associated genes in the two adipose tissues tended to have tissue-dependent predictability for the two adipose traits. Alternative splicing of genes was also found to be associated with SCF or IMF deposition, but the association was much less extensive than that based on expression levels. This study provides a better understanding of SCF and IMF gene transcription and network organization and identified critical genes and network modules that displayed tissue-specific associations with subcutaneous and intramuscular fat deposition. These features are helpful for designing breeding programs to genetically improve the two adipose traits in a balanced way.

Resting-state EEG Dynamics Reveals Differences in Network Organization and its Fluctuation between Frequency Bands

Functional connectivity in EEG resting-state is not stable but fluctuates considerably. The aim of this study was to investigate how efficient information flows through a network, i.e. how resting-state EEG networks are organized and whether this organization it also subject to fluctuations. Differences of the network organization (small-worldness), degree of clustered connectivity, and path length as an indicator of how information is integrated into the network across time was compared between theta, alpha and beta bands. We show robust differences in network organization (small-worldness) between frequency bands. Fluctuations in network organization were larger in the theta, compared to the alpha and beta frequency. Variation in network organization and not the frequency of fluctuations differs between frequency bands. Furthermore, the degree of clustered connectivity and its modulation across time is the same across frequency bands, but the path length revealed the same modulatory pattern as the small-world metric. It is therefore the interplay of local processing efficiency and global information processing efficiency in the brain that fluctuates in a frequency-specific way. Properties of how information can be integrated is subject to fluctuations in a frequency-specific way in the resting-state. The possible relevance of these resting-state EEG properties is discussed including its clinical relevance.

Altered network architecture of functional brain communities in chronic nociplastic pain

Neuroimaging has enhanced our understanding of the neural correlates of pain. Yet, how neural circuits interact and contribute to persistent pain remain largely unknown. Here, we investigate the mesoscale organization of the brain through intrinsic functional communities generated from resting state functional MRI data from two independent datasets, a discovery cohort of 43 Fibromyalgia (FM) patients and 20 healthy controls (HC) as well as a replication sample of 34 FM patients and 21 HC. Using normalized mutual information, we found that the global network architecture in chronic pain patients is less stable (more variable). Subsequent analyses of node community assignment revealed the composition of the communities differed between FM and HC. Furthermore, differences in network organization were associated with the changes in the composition of communities between patients with varying levels of clinical pain. Together, this work demonstrates that intrinsic network communities differ substantially between patients with FM and controls. These differences may represent a novel aspect of the pathophysiology of chronic nociplastic pain.

A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction.

A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach-connectome-wide similarity-we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.

Alterations of neural network organization during REM sleep in women: implication for sex differences in vulnerability to mood disorders

BackgroundSleep plays an important role in vulnerability to mood disorders. However, despite the existence of sex differences in vulnerability to mood disorders, no study has yet investigated the sex effect on sleep network organization and its potential involvement in vulnerability to mood disorders. The aim of our study was to empirically investigate the sex effect on network organization during REM and slow-wave sleep using the effective connectivity measured by Granger causality.MethodsPolysomnographic data from 44 healthy individuals (28 men and 16 women) recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realized by importing these connectivity matrices into EEGNET Toolbox.ResultsIn men and women, all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep. However, compared to men, women present greater small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage, which indicates the presence of a small-world network organization less marked during REM sleep as well as for all EEG bands during this sleep stage in women. In addition, in women, these small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage are positively correlated with the presence of subclinical symptoms of depression.ConclusionsThus, the highlighting of these sex differences in network organization during REM sleep indicates the presence of differences in the global and local processing of information during sleep between women and men. In addition, this small-world network organization less marked during REM sleep appears to be a marker of vulnerability to mood disorders specific to women, which opens up new perspectives in understanding sex differences in the occurrence of mood disorders.

A complex-valued functional brain connectivity descriptor amenable to Riemannian geometry

Objective. We introduce a novel, phase-based, functional connectivity descriptor that encapsulates not only the synchronization strength between distinct brain regions, but also the time-lag between the involved neural oscillations. The new estimator employs complex-valued measurements and results in a brain network sketch that lives on the smooth manifold of Hermitian Positive Definite (HPD) matrices. Approach: Leveraging the HPD property of the proposed descriptor, we adapt a recently introduced dimensionality reduction methodology that is based on Riemannian Geometry and discriminatively detects the recording sites which best reflect the differences in network organization between contrasting recording conditions in order to overcome the problem of high-dimensionality, usually encountered in the connectivity patterns derived from multisite encephalographic recordings. Main results. The proposed framework is validated using an EEG dataset that refers to the challenging problem of differentiating between attentive and passive visual responses. We provide evidence that the reduced connectivity representation facilitates high classification performance and caters for neuroscientific explorations. Significance. Our paper is the very first that introduces an advanced connectivity descriptor that can take advantage of Riemannian geometry tools. The proposed descriptor, that inherently and simultaneously captures both the strength and the corresponding time-lag of the phase synchronization, is the first phase-based descriptor tailored to leverage the benefits of Riemannian geometry.

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks.

As the field of dynamic brain networks continues to expand, new methods are needed to allow for optimal handling and understanding of this explosion in data. We propose here a novel approach that embeds dynamic brain networks onto a two-dimensional (2D) manifold based on similarities and differences in network organization. Each brain network is represented as a single point on the low dimensional manifold with networks of similar topology being located in close proximity. The rich spatio-temporal information has great potential for visualization, analysis, and interpretation of dynamic brain networks. The fact that each network is represented by a single point makes it possible to switch between the low-dimensional space and the full connectivity of any given brain network. Thus, networks in a specific region of the low-dimensional space can be examined to identify network features, such as the location of brain network hubs or the interconnectivity between brain circuits. In this proof-of-concept manuscript, we show that these low dimensional manifolds contain meaningful information, as they were able to successfully discriminate between cognitive tasks and study populations. This work provides evidence that embedding dynamic brain networks onto low dimensional manifolds has the potential to help us better visualize and understand dynamic brain networks with the hope of gaining a deeper understanding of normal and abnormal brain dynamics.

Large-scale network interactions supporting item-context memory formation.

Episodic memory is thought to involve functional interactions of large-scale brain networks that dynamically reconfigure depending on task demands. Although the hippocampus and closely related structures have been implicated, little is known regarding how large-scale and distributed networks support different memory formation demands. We investigated patterns of interactions among distributed networks while human individuals formed item-context memories for two stimulus categories. Subjects studied object-scene and object-location associations in different fMRI sessions. Stimulus-responsive brain regions were organized based on their fMRI interconnectivity into networks and modules using probabilistic module-detection algorithms to maximize measurement of individual differences in modular structure. Although there was a great deal of consistency in the modular structure between object-scene and object-location memory formation, there were also significant differences. Interactions among functional modules predicted later memory accuracy, explaining substantial portions of variability in memory formation success. Increased interactivity of modules associated with internal thought and anti-correlation of these modules with those related to stimulus-evoked processing robustly predicted object-scene memory, whereas decreased interactivity of stimulus-evoked processing modules predicted object-location memory. Assessment of individual differences in network organization therefore allowed identification of distinct patterns of functional interactions that robustly predicted memory formation. This highlights large-scale brain network interactions for memory formation and indicates that although networks are largely robust to task demands, reconfiguration nonetheless occurs to support distinct memory formation demands.

Collective sparse symmetric non-negative matrix factorization for identifying overlapping communities in resting-state brain functional networks

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable tool to study spontaneous brain activity. Using rs-fMRI, researchers have extensively studied the organization of the brain functional network and found several consistent communities consisting of functionally connected but spatially separated brain regions across subjects. However, increasing evidence in many disciplines has shown that most realistic complex networks have overlapping community structure. Only recently has the overlapping community structure drawn increasing interest in the domain of brain network studies. Another issue is that the inter-subject variability is often not directly reflected in the process of community detection at the group level. In this paper, we propose a novel method called collective sparse symmetric non-negative matrix factorization (cssNMF) to address these issues. The cssNMF approach identifies the group-level overlapping communities across subjects and in the meantime preserves the information of individual variation in brain functional network organization. To comprehensively validate cssNMF, a simulated fMRI dataset with ground-truth, a real rs-fMRI dataset with two repeated sessions and another different real rs-fMRI dataset have been used for performance comparison in the experiment. Experimental results show that the proposed cssNMF method accurately and stably identifies group-level overlapping communities across subjects as well as individual differences in network organization with neurophysiologically meaningful interpretations. This research extends our understanding of the common underlying community structures and individual differences in community strengths in brain functional network organization.

Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations

Graph theoretical analysis has become an important tool in the examination of brain dysconnectivity in neurological and psychiatric brain disorders. A common analysis step in the construction of the functional graph or network involves “thresholding” of the connectivity matrix, selecting the set of edges that together form the graph on which network organization is evaluated. To avoid systematic differences in absolute number of edges, studies have argued against the use of an “absolute threshold” in case-control studies and have proposed the use of “proportional thresholding” instead, in which a pre-defined number of strongest connections are selected as network edges, ensuring equal network density across datasets. Here, we systematically studied the effect of proportional thresholding on the construction of functional matrices and subsequent graph analysis in patient-control functional connectome studies. In a few simple experiments we show that differences in overall strength of functional connectivity (FC) – as often observed between patients and controls – can have predictable consequences for between-group differences in network organization. In individual networks with lower overall FC the proportional thresholding algorithm has to select more edges based on lower correlations, which have (on average) a higher probability of being spurious, and thus introduces a higher degree of randomness in the resulting network. We show across both empirical and artificial patient-control datasets that lower levels of overall FC in either the patient or control group will most often lead to differences in network efficiency and clustering, suggesting that differences in FC across subjects will be artificially inflated or translated into differences in network organization. Based on the presented case-control findings we inform about the caveats of proportional thresholding in patient-control studies in which groups show a between-group difference in overall FC. We make recommendations on how to examine, report and to take into account overall FC effects in future patient-control functional connectome studies.

Asymmetric Interhemispheric Transfer in the Auditory Network: Evidence from TMS, Resting-State fMRI, and Diffusion Imaging.

Hemispheric asymmetries in human auditory cortical function and structure are still highly debated. Brain stimulation approaches can complement correlational techniques by uncovering causal influences. Previous studies have shown asymmetrical effects of transcranial magnetic stimulation (TMS) on task performance, but it is unclear whether these effects are task-specific or reflect intrinsic network properties. To test how modulation of auditory cortex (AC) influences functional networks and whether this influence is asymmetrical, the present study measured resting-state fMRI connectivity networks in 17 healthy volunteers before and immediately after TMS (continuous theta burst stimulation) to the left or right AC, and the vertex as a control. We also examined the relationship between TMS-induced interhemispheric signal propagation and anatomical properties of callosal auditory fibers as measured with diffusion-weighted MRI. We found that TMS to the right AC, but not the left, resulted in widespread connectivity decreases in auditory- and motor-related networks in the resting state. Individual differences in the degree of change in functional connectivity between auditory cortices after TMS applied over the right AC were negatively related to the volume of callosal auditory fibers. The findings show that TMS-induced network modulation occurs, even in the absence of an explicit task, and that the magnitude of the effect differs across individuals as a function of callosal structure, supporting a role for the corpus callosum in mediating functional asymmetry. The findings support theoretical models emphasizing hemispheric differences in network organization and are of practical significance in showing that brain stimulation studies need to take network-level effects into account.

Impulsivity and the Modular Organization of Resting-State Neural Networks

Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation ("modularity") of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors.

Microvascular architecture in rat soleus and extensor digitorum longus muscles

Microvascular architecture was investigated in the slow-twitch soleus (SOL) and fast-twitch extensor digitorum longus (EDL) muscles. Rats ( n = 5) were anesthetized and papaverine was infused into a carotid artery cannula to induce vasodilation. Microfil casting compound was then infused at an inflation pressure (caudal artery) of 100 mm Hg. Bilateral SOL and EDL muscles were excised 24–72 hr postcasting, dehydrated in ethanol, and cleared in methyl salicylate. Branch frequencies (BR) and segment lengths (SL) of intramuscular arterioles and venules were quantified along primary (1°), secondary (2°), and tertiary (3°) order microvessels using microscopy. In both muscles, BR decreased with increasing vessel order. Regional differences in network organization were observed within the EDL muscle. SL of 1° arterioles was 47% shorter in the SOL muscle indicating more compact microvascular networks compared to the EDL muscle. These findings provide a structural basis for reported differences in blood flow between the SOL and EDL muscles at rest and during exercise.