Evaluation of node-inhomogeneity effects on the functional brain network properties using an anatomy-constrained hierarchical brain parcellation

Inside information: Systematic within-node functional connectivity changes observed across tasks or groups

Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .

The integrated understanding of structural and functional connectomes in depression: A multimodal meta-analysis of graph metrics

BACKGROUND

From the perspective of information processing, an integrated understanding of the structural and functional connectomes in depression patients is important, a multimodal meta-analysis is required to detect the robust alterations in graph metrics across studies.

METHODS

Following a systematic search, 952 depression patients and 1447 controls in nine diffusion magnetic resonance imaging (dMRI) and twelve rest state functional MRI (rs-fMRI) studies with high methodological quality met the inclusion criteria and were included in the meta-analysis.

RESULTS

Regarding the dMRI results, no significant differences of meta-analytic metrics were found; regarding the rs-fMRI results, the modularity and local efficiency were found to be significantly lower in the depression group than in the controls (Hedge's g = -0.330 and -0.349, respectively).

CONCLUSION

Our findings suggested a lower modularity and network efficiency in the rs-fMRI network in depression patients, indicating that the pathological imbalances in brain connectomes needs further exploration.

LIMITATIONS

Included number of trials was low and heterogeneity should be noted.

Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain

Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nodes reconfigure with brain state. To date, all graph theory studies of functional connectivity in the brain have used fixed nodes. Here, using fixed-, group-, state-specific, and individualized- parcellations for defining nodes, we demonstrate that functional connectivity changes within the nodes significantly influence the findings at the network level. In some cases, state- or group-dependent changes of the sort typically reported do not persist, while in others, changes are only observed when node reconfigurations are considered. The findings suggest that graph theory investigations into connectivity contrasts between brain states and/or groups should consider the influence of voxel-level changes that lead to node reconfigurations; the fundamental building block of a graph.

Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics

The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.

Impact of early adverse life events and sex on functional brain networks in patients with urological chronic pelvic pain syndrome (UCPPS): A MAPP Research Network study

Pain is a highly complex and individualized experience with biopsychosocial components. Neuroimaging research has shown evidence of the involvement of the central nervous system in the development and maintenance of chronic pain conditions, including urological chronic pelvic pain syndrome (UCPPS). Furthermore, a history of early adverse life events (EALs) has been shown to adversely impact symptoms throughout childhood and into adulthood. However, to date, the role of EAL's in the central processes of chronic pain have not been adequately investigated. We studied 85 patients (56 females) with UCPPS along with 86 healthy controls (HCs) who had resting-state magnetic resonance imaging scans (59 females), and data on EALs as a part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network Study. We used graph theory methods in order to investigate the impact of EALs on measures of centrality, which characterize information flow, communication, influence, and integration in a priori selected regions of interest. Patients with UCPPS exhibited lower centrality in the right anterior insula compared to HCs, a key node in the salience network. Males with UCPPS exhibited lower centrality in the right anterior insula compared the HC males. Females with UCPPS exhibited greater centrality in the right caudate nucleus and left angular gyrus compared to HC females. Males with UCPPS exhibited lower centrality in the left posterior cingulate, angular gyrus, middle temporal gyrus, and superior temporal sulcus, but greater centrality in the precuneus and anterior mid-cingulate cortex (aMCC) compared to females with UCPPS. Higher reports of EALs was associated with greater centrality in the left precuneus and left aMCC in females with UCPPS. This study provides evidence for disease and sex-related alterations in the default mode, salience, and basal ganglia networks in patients with UCPPS, which are moderated by EALs, and associated with clinical symptoms and quality of life (QoL).

Neurofeedback learning for mental practice rather than repetitive practice improves neural pattern consistency and functional network efficiency in the subsequent mental motor execution

During brain modulation, repeated mental practice may not always result in efficient learning. Particularly, the effectiveness of mental motor practice depends on how well one induces neural activity in a desired state consistently across mental trials, which calls for feedbacks to adjust one's performance. We hypothesized that even a brief experience of neurofeedback learning enhances trial-by-trial neural pattern consistency during subsequent mental motor execution and that this experience would change recruitment of functional connectivity in the motor imagery and default mode networks. To test this hypothesis, we conducted an experiment with two sessions of mental motor practice before and after a neurofeedback training session, in which participants conducted four types of first-person mental motor execution tasks (walking forward, turning left, turning right, and touching a tree). During the neurofeedback training session, in which participants conducted a virtual navigation game, 10 experimental participants received real-time fMRI neuro-feedbacks, while 10 control participants simply repeated the same mental task according to given cues without feedbacks. The experimental group showed significantly higher effects of neuro-feedback training on trial-by-trial consistencies and classification accuracies of activated neural patterns than the control group. Task-performing global node strength and network efficiency were increased in the motor imagery network but decreased in the default mode network only in the experimental group. These results demonstrate that even a brief experience of feedback learning is more effective than simple practice repetitions without evaluation, which was reflected in increased neural pattern consistency and task-dependent functional connectivity during a mental motor execution task.

T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions-an MRI evaluation study

PURPOSE

Development of a warp-based automated brain segmentation approach of 3D fluid-attenuated inversion recovery (FLAIR) images and comparison to 3D T1-based segmentation.

METHODS

3D FLAIR and 3D T1-weighted sequences of 30 healthy subjects (mean age 29.9 ± 8.3 years, 8 female) were acquired on the same 3T MR scanner. Warp-based segmentation was applied for volumetry of total gray matter (GM), white matter (WM), and 116 atlas regions. Segmentation results of both sequences were compared using Pearson correlation (r).

RESULTS

Correlation of GM segmentation results based on FLAIR and T1 was overall good for cortical structures (mean r across all cortical structures = 0.76). Comparatively weaker results were found in the occipital lobe (r = 0.77), central region (mean r = 0.58), basal ganglia (mean r = 0.59), thalamus (r = 0.30), and cerebellum (r = 0.73). FLAIR segmentation underestimated volume of the central region compared to T1, but showed a better anatomic concordance with the occipital lobe on visual review and subcortical structures, when also compared to manual segmentation. Visual analysis of FLAIR-based WM segmentation revealed frequent misclassification of regions of high signal intensity as GM.

CONCLUSION

Warp-based FLAIR segmentation yields comparable results to T1 segmentation for most cortical GM structures and may provide anatomically more congruent segmentation of subcortical GM structures. Selected cortical regions, especially the central region and total WM, seem to be underestimated on FLAIR segmentation.

Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders

Background

Brain connectome research based on graph theoretical analysis shows that small-world topological properties play an important role in the structural and functional alterations observed in patients with psychiatric disorders. However, the reported global topological alterations in small-world properties are controversial, are not consistently conceptualized according to agreed-upon criteria, and are not critically examined for consistent alterations in patients with each major psychiatric disorder.

Methods

Based on a comprehensive PubMed search, we systematically reviewed studies using noninvasive neuroimaging data and graph theoretical approaches for 6 major psychiatric disorders: schizophrenia, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), obsessive–compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Here, we describe the main patterns of altered small-world properties and then systematically review the evidence for these alterations in the structural and functional connectome in patients with these disorders.

Results

We selected 40 studies of schizophrenia, 33 studies of MDD, 5 studies of ADHD, 5 studies of BD, 7 studies of OCD and 5 studies of PTSD. The following 4 patterns of altered small-world properties are defined from theperspectives of segregation and integration: "regularization," "randomization," "stronger small-worldization" and "weaker small-worldization." Although more differences than similarities are noted in patients with these disorders, a prominent trend is the structural regularization versus functional randomization in patients with schizophrenia.

Limitations

Differences in demographic and clinical characteristics, preprocessing steps and analytical methods can produce contradictory results, increasing the difficulty of integrating results across different studies.

Conclusion

Four psychoradiological patterns of altered small-world properties are proposed. The analysis of altered smallworld properties may provide novel insights into the pathophysiological mechanisms underlying psychiatric disorders from a connectomic perspective. In future connectome studies, the global network measures of both segregation and integration should be calculated to fully evaluate altered small-world properties in patients with a particular disease.

Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics

Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.

Structural Brain Connectivity Constrains within-a-Day Variability of Direct Functional Connectivity

The idea that structural white matter connectivity constrains functional connectivity (interactions among brain regions) has widely been explored in studies of brain networks; studies have mostly focused on the “average” strength of functional connectivity. The question of how structural connectivity constrains the “variability” of functional connectivity remains unresolved. In this study, we investigated the variability of resting state functional connectivity that was acquired every 3 h within a single day from 12 participants (eight time sessions within a 24-h period, 165 scans per session). Three different types of functional connectivity (functional connectivity based on Pearson correlation, direct functional connectivity based on partial correlation, and the pseudo functional connectivity produced by their difference) were estimated from resting state functional magnetic resonance imaging data along with structural connectivity defined using fiber tractography of diffusion tensor imaging. Those types of functional connectivity were evaluated with regard to properties of structural connectivity (fiber streamline counts and lengths) and types of structural connectivity such as intra-/inter-hemispheric edges and topological edge types in the rich club organization. We observed that the structural connectivity constrained the variability of direct functional connectivity more than pseudo-functional connectivity and that the constraints depended strongly on structural connectivity types. The structural constraints were greater for intra-hemispheric and heterologous inter-hemispheric edges than homologous inter-hemispheric edges, and feeder and local edges than rich club edges in the rich club architecture. While each edge was highly variable, the multivariate patterns of edge involvement, especially the direct functional connectivity patterns among the rich club brain regions, showed low variability over time. This study suggests that structural connectivity not only constrains the strength of functional connectivity, but also the within-a-day variability of functional connectivity and connectivity patterns, particularly the direct functional connectivity among brain regions.

Analysis of structure-function network decoupling in the brain systems of spastic diplegic cerebral palsy

Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals, Inc.

State-dependent modulation of functional connectivity in early blind individuals

Resting-state functional connectivity (RSFC) studies have provided strong evidences that visual deprivation influences the brain's functional architecture. In particular, reduced RSFC coupling between occipital (visual) and temporal (auditory) regions has been reliably observed in early blind individuals (EB) at rest. In contrast, task-dependent activation studies have repeatedly demonstrated enhanced co-activation and connectivity of occipital and temporal regions during auditory processing in EB. To investigate this apparent discrepancy, the functional coupling between temporal and occipital networks at rest was directly compared to that of an auditory task in both EB and sighted controls (SC). Functional brain clusters shared across groups and cognitive states (rest and auditory task) were defined. In EBs, we observed higher occipito-temporal correlations in activity during the task than at rest. The reverse pattern was observed in SC. We also observed higher temporal variability of occipito-temporal RSFC in EB suggesting that occipital regions in this population may play the role of a multiple demand system. Our study reveals how the connectivity profile of sighted and early blind people is differentially influenced by their cognitive state, bridging the gap between previous task-dependent and RSFC studies. Our results also highlight how inferring group-differences in functional brain architecture solely based on resting-state acquisition has to be considered with caution.

Immediate and Longitudinal Alterations of Functional Networks after Thalamotomy in Essential Tremor

Thalamotomy at the ventralis intermedius nucleus has been an effective treatment method for essential tremor, but how the brain network changes immediately responding to this deliberate lesion and then reorganizes afterwards are not clear. Taking advantage of a non-cranium-opening MRI-guided focused ultrasound ablation technique, we investigated functional network changes due to a focal lesion. To classify the diverse time courses of those network changes with respect to symptom-related long-lasting treatment effects and symptom-unrelated transient effects, we applied graph-theoretic analyses to longitudinal resting-state functional magnetic resonance imaging data before and 1 day, 7 days, and 3 months after thalamotomy with essential tremor. We found reduced average connections among the motor-related areas, reduced connectivity between substantia nigra and external globus pallidum and reduced total connection in the thalamus after thalamotomy, which are all associated with clinical rating scales. The average connectivity among whole brain regions and inter-hemispheric network asymmetry show symptom-unrelated transient increases, indicating temporary reconfiguration of the whole brain network. In summary, thalamotomy regulates interactions over the motor network via symptom-related connectivity changes but accompanies transient, symptom-unrelated diaschisis in the global brain network. This study suggests the significance of longitudinal network analysis, combined with minimal-invasive treatment techniques, in understanding time-dependent diaschisis in the brain network due to a focal lesion.

Sex and Age Effects of Functional Connectivity in Early Adulthood

Functional connectivity (FC) in resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to find coactivating regions in the human brain. Despite its widespread use, the effects of sex and age on resting FC are not well characterized, especially during early adulthood. Here we apply regression and graph theoretical analyses to explore the effects of sex and age on FC between the 116 AAL atlas parcellations (a total of 6670 FC measures). rs-fMRI data of 494 healthy subjects (203 males and 291 females; age range: 22–36 years) from the Human Connectome Project were analyzed. We report the following findings. (1) Males exhibited greater FC than females in 1352 FC measures (1025 survived Bonferroni correction; \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$p < 7.49{ \rm{E}} - 6$$ \end{document}). In 641 FC measures, females exhibited greater FC than males but none survived Bonferroni correction. Significant FC differences were mainly present in frontal, parietal, and temporal lobes. Although the average FC values for males and females were significantly different, FC values of males and females exhibited large overlap. (2) Age effects were present only in 29 FC measures and all significant age effects showed higher FC in younger subjects. Age and sex differences of FC remained significant after controlling for cognitive measures. (3) Although sex \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $$\times$$ \end{document} age interaction did not survive multiple comparison correction, FC in females exhibited a faster cross-sectional decline with age. (4) Male brains were more locally clustered in all lobes but the cerebellum; female brains had a higher clustering coefficient at the whole-brain level. Our results indicate that although both male and female brains show small-world network characteristics, male brains were more segregated and female brains were more integrated. Findings of this study further our understanding of FC in early adulthood and provide evidence to support that age and sex should be controlled for in FC studies of young adults.

Altered functional network architecture in orbitofronto-striato-thalamic circuit of unmedicated patients with obsessive-compulsive disorder

Dysfunction of corticostriatal loops has been proposed to underlie certain cognitive and behavioral problems associated with various neuropsychiatric disorders, such as obsessive-compulsive disorder (OCD) characterized by repetitive, unwanted thoughts, and behaviors. Although functional abnormalities in the loops involving the orbitofronto-striato-thalamic (OFST) circuitry in patients with OCD have been reported, our understanding of a link between disruptions in the architecture of the intrinsic functional network of the OFST circuit and their symptoms remain incomplete. Using resting-state functional MRI in conjunction with unsupervised clustering and multilevel functional connectivity (FC) techniques, FC of the OFST network and its topological organization in 61 OCD patients versus 61 matched controls were characterized. Patients exhibited disruptions in small-world properties of the OFST circuit, which indicates an imbalance between functional integration and segregation. Patients also showed decreased FC between the central orbitofrontal cortex and dorsomedial striatum but increased FC between the medial thalamus and striatal areas. Using one of the largest samples of unmedicated OCD patients to date, our findings provide evidence supporting the OFST dysconnection hypothesis in OCD as a basic pathophysiological mechanism underlying the disorder, showing the disruption of FC between specific cortical, striatal, and thalamic clusters and aberrant topological patterns of the OFST circuit. Hum Brain Mapp 38:109-119, 2017. © 2016 Wiley Periodicals, Inc.

Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation

Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on “distance correlation”; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data.

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We introduce distance correlation as a new measure of ROI-based connectivity.

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It can be used for functional connectivity and structural covariance analyses.

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Simulations show that distance correlation copes with inhomogeneous ROIs.

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In real data, distance correlation is more reliable and robust than Pearson correlation.

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Distance correlation improves correspondence between different ROI definitions.

Graph independent component analysis reveals repertoires of intrinsic network components in the human brain

Does each cognitive task elicit a new cognitive network each time in the brain? Recent data suggest that pre-existing repertoires of a much smaller number of canonical network components are selectively and dynamically used to compute new cognitive tasks. To this end, we propose a novel method (graph-ICA) that seeks to extract these canonical network components from a limited number of resting state spontaneous networks. Graph-ICA decomposes a weighted mixture of source edge-sharing subnetworks with different weighted edges by applying an independent component analysis on cross-sectional brain networks represented as graphs. We evaluated the plausibility in our simulation study and identified 49 intrinsic subnetworks by applying it in the resting state fMRI data. Using the derived subnetwork repertories, we decomposed brain networks during specific tasks including motor activity, working memory exercises, and verb generation, and identified subnetworks associated with performance on these tasks. We also analyzed sex differences in utilization of subnetworks, which was useful in characterizing group networks. These results suggest that this method can effectively be utilized to identify task-specific as well as sex-specific functional subnetworks. Moreover, graph-ICA can provide more direct information on the edge weights among brain regions working together as a network, which cannot be directly obtained through voxel-level spatial ICA.