Modular organization of brain resting state networks in chronic back pain patients

Graph theory analysis reveals an assortative pain network vulnerable to attacks

The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we employ graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Within this network, 63% of brain areas share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes that have similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain that processes nociceptive information. Furthermore, the vulnerability of this network to attack presents the possibility of alleviating pain by targeting the most connected brain areas in the network.

Functional disruptions of the brain network in low back pain: a graph-theoretical study

PURPOSE

The aim of this study was to investigate alterations in the topological organization of whole-brain functional networks in patients with chronic low back pain (CLBP) and characterize the relationship of these alterations with pain characteristics.

METHODS

Thirty-three CLBP patients and 34 matched healthy controls (HCs) underwent fMRI scans. A graph-theoretical approach was applied to identify brain network changes in patients suffering from chronic low back pain given its nonspecific etiology and complexity. Graph theory-based analysis was used to construct functional connectivity matrices and extract the features of small-world networks of the brain in both groups. Then, the whole-brain functional connectivity differences were characterized by network-based statistics (NBS) analysis, and the relationship between the altered brain features and clinical measures was explored.

RESULTS

At the global level, patients with CLBP showed significantly decreased gamma, sigma, global efficiency, and local efficiency and increased lambda and shortest path length compared with HCs. At the regional level, there were deficits in nodal efficiency within the default mode network and salience network. NBS analysis demonstrated that decreased functional connectivity was present in the CLBP patients, mainly in the frontolimbic circuit and temporal regions. Furthermore, aspects of topological dysfunctions in CLBP were correlated with pain severity.

CONCLUSION

This study highlighted the aberrant topological organization of functional brain networks in CLBP, which may shed light on the pathophysiology of CLBP and support the development of pain management approaches.

Altered resting-state functional connectivity within corticostriatal and subcortical-striatal circuits in chronic pain

Brain corticostriatal circuits are important for understanding chronic pain and highly relevant to motivation and cognitive processes. It has been demonstrated that in patients with chronic back pain, altered nucleus accumbens (NAcc)-medial prefrontal cortex (MPFC) circuit fMRI-based activity is predictive of patient outcome. We evaluated the NAcc-MPFC circuit in patients with another chronic pain condition, fibromyalgia, to extend these important findings. First, we compared fMRI-based NAcc-MPFC resting-state functional connectivity in patients with fibromyalgia (N = 32) vs. healthy controls (N = 37). Compared to controls, the NAcc-MPFC circuit's connectivity was significantly reduced in fibromyalgia. In addition, within the fibromyalgia group, NAcc-MPFC connectivity was significantly correlated with trait anxiety. Our expanded connectivity analysis of the NAcc to subcortical brain regions showed reduced connectivity of the right NAcc with mesolimbic circuit regions (putamen, thalamus, and ventral pallidum) in fibromyalgia. Lastly, in an exploratory analysis comparing our fibromyalgia and healthy control cohorts to a separate publicly available dataset from patients with chronic back pain, we identified reduced NAcc-MPFC connectivity across both the patient groups with unique alterations in NAcc-mesolimbic connectivity. Together, expanding upon prior observed alterations in brain corticostriatal circuits, our results provide novel evidence of altered corticostriatal and mesolimbic circuits in chronic pain.

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain

Chronic back pain (CBP) is a maladaptive health problem affecting the brain function and behavior of the patient. Accumulating evidence has shown that CBP may alter the organization of functional brain networks; however, whether the severity of CBP is associated with changes in dynamics of functional network topology remains unclear. Here, we generated dynamic functional networks based on resting-state functional magnetic resonance imaging (rs-fMRI) of 34 patients with CBP and 34 age-matched healthy controls (HC) in the OpenPain database via a sliding window approach, and extracted nodal degree, clustering coefficient (CC), and participation coefficient (PC) of all windows as features to characterize changes of network topology at temporal scale. A novel feature, named temporal grading index (TGI), was proposed to quantify the temporal deviation of each network property of a patient with CBP to the normal oscillation of the HCs. The TGI of the three features achieved outstanding performance in predicting pain intensity on three commonly used regression models (i.e., SVR, Lasso, and elastic net) through a 5-fold cross-validation strategy, with the minimum mean square error of 0.25 ± 0.05; and the TGI was not related to depression symptoms of the patients. Furthermore, compared to the HCs, brain regions that contributed most to prediction showed significantly higher CC and lower PC across time windows in the CBP cohort. These results highlighted spatiotemporal changes in functional network topology in patients with CBP, which might serve as a valuable biomarker for assessing the sensation of pain in the brain and may facilitate the development of CBP management/therapy approaches.

Individually unique dynamics of cortical connectivity reflect the ongoing intensity of chronic pain

ABSTRACT

Chronic pain diseases are characterised by an ongoing and fluctuating endogenous pain, yet it remains to be elucidated how this is reflected by the dynamics of ongoing functional cortical connections.Here, we investigated the cortical encoding of 20 chronic back pain patients and 20 chronic migraineurs in four repeated fMRI sessions. A brain parcellation approach subdivided the whole brain into 408 regions. Linear mixed effects models were fitted for each pair of brain regions to explore the relationship between the dynamic cortical connectivity and the observed trajectory of the patients' ratings of fluctuating endogenous pain.Overall, we found that periods of high and increasing pain were predominantly related to low cortical connectivity. The change of pain intensity in chronic back pain was subserved by connections in left parietal opercular regions, right insular regions, as well as large parts of the parietal, cingular and motor cortices. The change of pain intensity direction in chronic migraine was reflected by decreasing connectivity between the anterior insular cortex and orbitofrontal areas, as well as between the PCC and frontal and ACC regions.Interestingly, the group results were not mirrored by the individual patterns of pain-related connectivity, which is suggested to deny the idea of a common neuronal core problem for chronic pain diseases. The diversity of the individual cortical signatures of chronic pain encoding results adds to the understanding of chronic pain as a complex and multifaceted disease. The present findings support recent developments for more personalised medicine.

Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability

Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1% (p < 0.004) and AUC of 0.937 (p < 0.002), respectively. Similarly, we achieved a sensitivity and specificity of 87.0 and 79.7%. The classification results from this study suggest that graph matrices derived from rsFC can be used as biomarkers of LBP. In addition, our findings suggest that the proposed feature selection method, Enet-subset, might act as a better technique to remove redundant variables and improve the performance of the machine learning classifier.

Sex differences in brain modular organization in chronic pain

ABSTRACT

Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.

A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles

The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.

Altered Functional Connectivity Within and Between Salience and Sensorimotor Networks in Patients With Functional Constipation

Functional constipation (FCon) is a common functional gastrointestinal disorder. A considerable portion of patients with FCon is associated with anxiety/depressive status (FCAD). Previous neuroimaging studies mainly focused on patients with FCon without distinguishing FCAD from FCon patients without anxiety/depressive status (FCNAD). Differences in brain functions between these two subtypes remain unclear. Thus, we employed resting-state functional magnetic resonance imaging (RS-fMRI) and graph theory method to investigate differences in brain network connectivity and topology in 41 FCAD, 42 FCNAD, and 43 age- and gender-matched healthy controls (HCs). FCAD/FCNAD showed significantly lower normalized clustering coefficient and small-world-ness. Both groups showed altered nodal degree/efficiency mainly in the rostral anterior cingulate cortex (rACC), precentral gyrus (PreCen), supplementary motor area (SMA), and thalamus. In the FCAD group, nodal degree in the SMA was negatively correlated with difficulty of defecation, and abdominal pain was positively correlated with nodal degree/efficiency in the rACC, which had a lower within-module nodal degree. The salience network (SN) exhibited higher functional connectivity (FC) with the sensorimotor network (SMN) in FCAD/FCNAD, and FC between these two networks was negatively correlated with anxiety ratings in FCAD group. Additionally, FC of anterior insula (aINS)-rACC was only correlated with constipation symptom (i.e., abdominal pain) in the FCNAD group. In the FCAD group, FCs of dorsomedial prefrontal cortex-rACC, PreCen-aINS showed correlations with both constipation symptom (i.e., difficulty of defecation) and depressive status. These findings indicate the differences in FC of the SN-SMN between FCAD and FCNAD and provide neuroimaging evidence based on brain function, which portrays important clues for improving new treatment strategies.

Multi-modal biomarkers of low back pain: A machine learning approach

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Widespread differences in cortical thickness (CT) were observed in patients with low back pain.

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Changes in CT correlated with self-reported clinical scores of pain and emotion.

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Changes in resting state fMRI metrics of functional networks.

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Support vector machines separated low back pain patients from controls with a high performance.

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Multi-modal biomarkers can be useful when identifying personalized treatments for low back pain.

Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66–0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy.

Iterative consensus spectral clustering improves detection of subject and group level brain functional modules

Specialized processing in the brain is performed by multiple groups of brain regions organized as functional modules. Although, in vivo studies of brain functional modules involve multiple functional Magnetic Resonance Imaging (fMRI) scans, the methods used to derive functional modules from functional networks of the brain ignore individual differences in the functional architecture and use incomplete functional connectivity information. To correct this, we propose an Iterative Consensus Spectral Clustering (ICSC) algorithm that detects the most representative modules from individual dense weighted connectivity matrices derived from multiple scans. The ICSC algorithm derives group-level modules from modules of multiple individuals by iteratively minimizing the consensus-cost between the two. We demonstrate that the ICSC algorithm can be used to derive biologically plausible group-level (for multiple subjects) and subject-level (for multiple subject scans) brain modules, using resting-state fMRI scans of 589 subjects from the Human Connectome Project. We employed a multipronged strategy to show the validity of the modularizations obtained from the ICSC algorithm. We show a heterogeneous variability in the modular structure across subjects where modules involved in visual and motor processing were highly stable across subjects. Conversely, we found a lower variability across scans of the same subject. The performance of our algorithm was compared with existing functional brain modularization methods and we show that our method detects group-level modules that are more representative of the modules of multiple individuals. Finally, the experiments on synthetic images quantitatively demonstrate that the ICSC algorithm detects group-level and subject-level modules accurately under varied conditions. Therefore, besides identifying functional modules for a population of subjects, the proposed method can be used for applications in personalized neuroscience. The ICSC implementation is available at https://github.com/SCSE-Biomedical-Computing-Group/ICSC.

Hub disruption in patients with chronic neck pain: a graph analytical approach

Chronic pain is known to alter the brain's network dynamics. These dynamics are often demonstrated by identifying alterations in the brain network topology. A common approach used for this purpose is graph theory. To date, little is known on how these potentially altered networks in chronic pain relate to the symptoms reported by these patients. Here, we applied a graph theoretical approach to identify network changes in patients suffering from chronic neck pain, a group that is often neglected in chronic pain research. Participants with chronic traumatic and nontraumatic neck pain were compared to healthy pain-free controls. They showed higher levels of self-reported symptoms of sensitization, higher levels of disability, and impaired sensorimotor control. Furthermore, the brain suffering from chronic neck pain showed altered network properties in the posterior cingulate cortex, amygdala, and pallidum compared with the healthy pain-free brain. These regions have been identified as brain hubs (ie, regions that are responsible for orchestrating communication between other brain regions) and are therefore known to be more vulnerable in brain disorders including chronic pain. We were furthermore able to uncover associations between these altered brain network properties and the symptoms reported by patients. Our findings indicate that chronic neck pain patients reflect brain network alterations and that targeting the brain in patients might be of utmost importance.

Modular organization of brain resting state networks in patients with classical trigeminal neuralgia

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Sensorimotor network and default mode network activities were lower in trigeminal neuralgia patients and increased after surgery.

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Higher communication between the default mode network module and other modules before surgery was associated with better treatment response.

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Subcortical modules was associated with pain duration.

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A lower connection between the default mode network and subcortical modules was associated with a better treatment response and the thalamus and midcingulate cortex were the major connectors within the subcortical module.

Background

The modular organization of brain networks in trigeminal neuralgia patients has remained largely unknown. We aimed to analyze the brain modules and intermodule connectivity in patients with trigeminal neuralgia before and after percutaneous radiofrequency rhizotomy treatment to identify specific modules that may be associated with the development and brain plasticity of trigeminal neuralgia and to test the ability of modularity analysis to be a predictive imaging biomarker for the treatment effect in patients with trigeminal neuralgia.

Methods

A total of 25 patients with right trigeminal neuralgia and 20 matched healthy subjects were included. Blood-oxygen-level dependent resting state fMRI was used to analyze the brain modular organization.

Results

Whole brain modularity analysis identified seven modules. The metric of intermodule connectivity, participation coefficient, of the sensorimotor network and default mode network modules were significantly lower in patients and increased after surgery. The participation coefficient of the subcortical modules was associated with the pain duration. Higher communication between the default mode network module and other modules before surgery was associated with a better treatment response. Furthermore, the subcortical module was a significant contributor to the participation coefficient relationship of the default mode network module with the treatment response, and the bilateral midcingulate cortex and thalamus were major connectors in the subcortical module.

Conclusions

These findings have important implications regarding the global brain modular responses to chronic neuropathic pain and it may be feasible to use the modularity analysis as part of a risk stratification to predict the treatment response.

Experience-dependent neuroplasticity in trained musicians modulates the effects of chronic pain on insula-based networks - A resting-state fMRI study

Recent resting-state fMRI studies associated extensive musical training with increased insula-based connectivity in large-scale networks involved in salience, emotion, and higher-order cognitive processes. Similar changes have also been found in chronic pain patients, suggesting that both types of experiences can have comparable effects on insula circuitries. Based on these observations, the current study asked the question whether, and if so in what way, different forms of experience-dependent neuroplasticity may interact. Here we assessed insula-based connectivity during fMRI resting-state between musicians and non-musicians both with and without chronic pain, and correlated the results with clinical pain duration and intensity. As expected, insula connectivity was increased in chronic pain non-musicians relative to healthy non-musicians (with cingulate cortex and supplementary motor area), yet no differences were found between chronic pain non-musicians and healthy musicians. In contrast, musicians with chronic pain showed decreased insula connectivity relative to both healthy musicians (with sensorimotor and memory regions) and chronic pain non-musicians (with the hippocampus, inferior temporal gyrus, and orbitofrontal cortex), as well as lower pain-related inferences with daily activities. Pain duration correlated positively with insula connectivity only in non-musicians, whereas pain intensity exhibited distinct relationships across groups. We conclude that although music-related sensorimotor training and chronic pain, taken in isolation, can lead to increased insula-based connectivity, their combination may lead to higher-order plasticity (metaplasticity) in chronic pain musicians, engaging brain mechanisms that can modulate the consequences of maladaptive experience-dependent neural reorganization (i.e., pain chronification).

Aberrant resting-state functional connectivity of the dorsolateral prefrontal cortex to the anterior insula and its association with fear avoidance belief in chronic neck pain patients

Chronic neck pain (CNP), a global health problem, involves a large amount of psychological and socioeconomic burdens. Not only physical causes but also behavioral disorders such as a fear-avoidance belief (FAB) can associate with the chronicity of neck pain. However, functional brain mechanisms underlying CNP and its related behavioral disorders remain unknown. The aim of the current resting-state functional magnetic resonance imaging (fMRI) study was to explore how the functional brain networks differed between CNP patients and age- and sex-matched healthy, pain-free controls (HCs). We also investigated whether these possible brain network changes in CNP patients were associated with fear avoidance belief (FAB) and the intensity of pain. We analyzed the resting-state fMRI data of 20 CNP patients and 20 HCs. FAB and the intensity of pain were assessed by Tampa Scale for Kinesiophobia (TSK) and Visual Analog Scale (VAS) of pain. The whole brain analysis showed that CNP patients had significant different functional connectivity (FC) compared with HCs, and the right dorsolateral prefrontal cortex (DLPFC) was a core hub of these altered functional networks. Furthermore, general linear model analyses showed that, in CNP patients, the increased FC between the right DLPFC and the right anterior insular cortex (aIC) significantly associated with increased TSK (p = 0.01, statistical significance after Bonferroni correction: p<0.025), and the FC between the right DLPFC and dorsal posterior cingulate cortex had a trend of inverse association with VAS (p = 0.04). Our findings suggest that aberrant FCs between the right DLPFC and aIC associated with CNP and its related FAB.

Functional and neurochemical disruptions of brain hub topology in chronic pain

Supplemental Digital Content is Available in the Text.

Hub topology is altered in fibromyalgia, and disruption of the excitatory tone within the insula influences hub status and is associated with clinical pain intensity.

A critical component of brain network architecture is a robust hub structure, wherein hub regions facilitate efficient information integration by occupying highly connected and functionally central roles in the network. Across a wide range of neurological disorders, hub brain regions seem to be disrupted, and the character of this disruption can yield insights into the pathophysiology of these disorders. We applied a brain network–based approach to examine hub topology in fibromyalgia, a chronic pain condition with prominent central nervous system involvement. Resting state functional magnetic resonance imaging data from 40 fibromyalgia patients and 46 healthy volunteers, and a small validation cohort of 11 fibromyalgia patients, were analyzed using graph theoretical techniques to model connections between 264 brain regions. In fibromyalgia, the anterior insulae functioned as hubs and were members of the rich club, a highly interconnected nexus of hubs. In fibromyalgia, rich-club membership varied with the intensity of clinical pain: the posterior insula, primary somatosensory, and motor cortices belonged to the rich club only in patients with the highest pain intensity. Furthermore, the eigenvector centrality (a measure of how connected a region is to other highly connected regions) of the posterior insula positively correlated with clinical pain and mediated the relationship between glutamate + glutamine (assessed by proton magnetic resonance spectroscopy) within this structure and the patient's clinical pain report. Together, these findings reveal altered hub topology in fibromyalgia and demonstrate, for the first time to our knowledge, a neurochemical basis for altered hub strength and its relationship to the perception of pain.

Altered segregation between task-positive and task-negative regions in mild traumatic brain injury

Changes in large-scale brain networks that accompany mild traumatic brain injury (mTBI) were investigated using functional magnetic resonance imaging (fMRI) during the N-back working memory task at two cognitive loads (1-back and 2-back). Thirty mTBI patients were examined during the chronic stage of injury and compared to 28 control participants. Demographics and behavioral performance were matched across groups. Due to the diffuse nature of injury, we hypothesized that there would be an imbalance in the communication between task-positive and Default Mode Network (DMN) regions in the context of effortful task execution. Specifically, a graph-theoretic measure of modularity was used to quantify the extent to which groups of brain regions tended to segregate into task-positive and DMN sub-networks. Relative to controls, mTBI patients showed reduced segregation between the DMN and task-positive networks, but increased functional connectivity within the DMN regions during the more cognitively demanding 2-back task. Together, our findings reveal that patients exhibit alterations in the communication between and within neural networks during a cognitively demanding task. These findings reveal altered processes that persist through the chronic stage of injury, highlighting the need for longitudinal research to map the neural recovery of mTBI patients.

When pain gets stuck: the evolution of pain chronification and treatment resistance

It is well-recognized that, despite similar pain characteristics, some people with chronic pain recover, whereas others do not. In this review, we discuss possible contributions and interactions of biological, social, and psychological perturbations that underlie the evolution of treatment-resistant chronic pain. Behavior and brain are intimately implicated in the production and maintenance of perception. Our understandings of potential mechanisms that produce or exacerbate persistent pain remain relatively unclear. We provide an overview of these interactions and how differences in relative contribution of dimensions such as stress, age, genetics, environment, and immune responsivity may produce different risk profiles for disease development, pain severity, and chronicity. We propose the concept of "stickiness" as a soubriquet for capturing the multiple influences on the persistence of pain and pain behavior, and their stubborn resistance to therapeutic intervention. We then focus on the neurobiology of reward and aversion to address how alterations in synaptic complexity, neural networks, and systems (eg, opioidergic and dopaminergic) may contribute to pain stickiness. Finally, we propose an integration of the neurobiological with what is known about environmental and social demands on pain behavior and explore treatment approaches based on the nature of the individual's vulnerability to or protection from allostatic load.

Graph theory analysis reveals how sickle cell disease impacts neural networks of patients with more severe disease

Sickle cell disease (SCD) is a hereditary blood disorder associated with many life-threatening comorbidities including cerebral stroke and chronic pain. The long-term effects of this disease may therefore affect the global brain network which is not clearly understood. We performed graph theory analysis of functional networks using non-invasive fMRI and high resolution EEG on thirty-one SCD patients and sixteen healthy controls. Resting state data were analyzed to determine differences between controls and patients with less severe and more severe sickle cell related pain. fMRI results showed that patients with higher pain severity had lower clustering coefficients and local efficiency. The neural network of the more severe patient group behaved like a random network when performing a targeted attack network analysis. EEG results showed the beta1 band had similar results to fMRI resting state data. Our data show that SCD affects the brain on a global level and that graph theory analysis can differentiate between patients with different levels of pain severity.

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Graph theory used to study global impact of long term sickle cell disease on brain.

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EEG and fMRI results compared to study spatial and temporal impact of disease.

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More severe patients have less clustering and efficiency.

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Small world values correlated with past hospitalizations, linking results to pain.

Unaltered intrinsic functional brain architecture in young women with primary dysmenorrhea

Primary dysmenorrhea (PDM), painful menstruation without organic causes, is the most prevalent gynecological problem in women of reproductive age. Dysmenorrhea later in life often co-occurs with many chronic functional pain disorders, and chronic functional pain disorders exhibit altered large-scale connectedness between distributed brain regions. It is unknown whether the young PDM females exhibit alterations in the global and local connectivity properties of brain functional networks. Fifty-seven otherwise healthy young PDM females and 62 age- and education-matched control females participated in the present resting-state functional magnetic resonance imaging study. We used graph theoretical network analysis to investigate the global and regional network metrics and modular structure of the resting-state brain functional networks in young PDM females. The functional network was constructed by the interregional functional connectivity among parcellated brain regions. The global and regional network metrics and modular structure of the resting-state brain functional networks were not altered in young PDM females at our detection threshold (medium to large effect size differences [Cohen's d ≥ 0.52]). It is plausible that the absence of significant changes in the intrinsic functional brain architecture allows young PDM females to maintain normal psychosocial outcomes during the pain-free follicular phase.

Disrupted functional connectome in antisocial personality disorder

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD.

Abnormal Spontaneous Brain Activity in Acute Low-Back Pain Revealed by Resting-State Functional MRI

OBJECTIVE

Neuroimaging studies have revealed that low-back pain (LBP) alters spatiotemporal dynamics of the blood oxygen level-dependent signal in response to persistent noxious stimulus. This study aimed to investigate changes in spontaneous neural activity of various brain regions in acute LBP using resting-state functional magnetic resonance imaging and amplitude of low-frequency fluctuation (ALFF).

DESIGN

Twelve healthy subjects underwent two separate resting-state functional magnetic resonance imaging scans at health status as baseline and after intramuscular injection of hypertonic saline (0.5 mL, 5%) into the back muscles to induce acute LBP.

RESULTS

Compared with baseline, acute LBP showed decreased ALFF in the right posterior cingulate cortex/precuneus and left primary somatosensory cortex (S1) but increased ALFF in the right medial prefrontal cortex, right middle temporal gyrus, bilateral inferior temporal gyrus, bilateral insula, right anterior cingulate cortex, and left cerebellum. In addition, significant negative correlations were observed between visual analog scale scores and ALFF of the bilateral medial prefrontal cortex, left inferior frontal gyrus, left S1, right anterior cingulate cortex, and left middle temporal gyrus.

CONCLUSIONS

These findings suggest that abnormally spontaneous neural activity involving some brain regions are responsible for sensory, affective, and cognitive functions, which may be implicated in the underlying pathophysiology of acute LBP.

Community detection in weighted brain connectivity networks beyond the resolution limit

Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or "communities", that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules (Nicolini and Bifone, 2016), in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymptotical Surprise with leading community detection methods currently in use and show its superior sensitivity in the detection of small modules even in the presence of noise and intersubject variability such as those observed in fMRI data. We apply our novel approach to functional connectivity networks from resting state fMRI experiments, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales. Finally, we discuss the implications of these findings for the identification of connector hubs, the brain regions responsible for the integration of the different network elements, showing that the improved resolution afforded by Asymptotical Surprise leads to a different classification compared to current methods.

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Methods to study modularity of brain connectivity networks have a resolution limit.

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Asymptotical Surprise, a nearly resolution-limit-free method for weighted graphs, is proposed.

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Improved sensitivity and specificity are demonstrated in model networks.

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Resting state functional connectivity networks consist of heterogeneous modules.

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Classification of hubs in function connectivity networks should be revised.

Early changes of brain connectivity in primary open angle glaucoma

Our aim was to assess in primary open angle glaucoma (POAG), a major cause of irreversible blindness worldwide, whether diffuse brain changes recently shown in advanced stage can be detected since the early stage. We used multimodal magnetic resonance imaging (MRI) in 57 patients with the three POAG stages and in 29 age-matched normal controls (NC). Voxelwise statistics was performed with nonparametric permutation testing. Compared with NC, disrupted anatomical connectivity (AC) was found in the whole POAG group along the visual pathway and in nonvisual white matter tracts (P < 0.001). Moreover, POAG patients showed decreased functional connectivity (FC) in the visual (P = 0.004) and working memory (P < 0.001) networks whereas an increase occurred in the default mode (P = 0.002) and subcortical (P < 0.001) networks. Altered AC and FC were already present in early POAG (n = 14) in both visual and nonvisual systems (P ≤ 0.01). Only severe POAG (n = 30) showed gray matter atrophy and this mapped on visual cortex (P < 0.001) and hippocampus (P < 0.001). Increasing POAG stage was associated with worsening AC in both visual and nonvisual pathway (P < 0.001), progressive atrophy in the hippocampus and frontal cortex (P < 0.003). Most of the structural and functional alterations within and outside the visual system showed correlation (P < 0.001 to 0.02) with computerized visual field and retinal nerve fiber layer thickness. In conclusion, the complex pathogenesis of POAG includes widespread damage of AC and altered FC within and beyond the visual system since the early disease stage. The association of brain MRI changes with measures of visual severity emphasizes the clinical relevance of our findings. Hum Brain Mapp 37:4581-4596, 2016. © 2016 Wiley Periodicals, Inc.

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity.

Partial recovery of abnormal insula and dorsolateral prefrontal connectivity to cognitive networks in chronic low back pain after treatment

We previously reported that effective treatment of chronic low back pain (CLBP) reversed abnormal brain structure and functional MRI (fMRI) activity during cognitive task performance, particularly in the left dorsolateral prefrontal cortex (DLPFC). Here, we used resting-state fMRI to examine how chronic pain affects connectivity of brain networks supporting cognitive functioning and the effect of treatment in 14 CLBP patients and 16 healthy, pain-free controls (scans were acquired at baseline for all subjects and at 6-months post-treatment for patients and a matched time-point for 10 controls). The main networks activated during cognitive task performance, task-positive network (TPN) and task-negative network (TNN) (aka default mode) network, were identified in subjects' task fMRI data and used to define matching networks in resting-state data. The connectivity of these cognitive resting-state networks was compared between groups, and before and after treatment. Our findings converged on the bilateral insula (INS) as the region of aberrant cognitive resting-state connectivity in patients pretreatment versus controls. These findings were complemented by an independent, data-driven approach showing altered global connectivity of the INS. Detailed investigation of the INS confirmed reduced connectivity to widespread TPN and TNN areas, which was partially restored post-treatment. Furthermore, analysis of diffusion-tensor imaging (DTI) data revealed structural changes in white matter supporting these findings. The left DLPFC also showed aberrant connectivity that was restored post-treatment. Altogether, our findings implicate the bilateral INS and left DLPFC as key nodes of disrupted cognition-related intrinsic connectivity in CLBP, and the resulting imbalance between TPN and TNN function is partially restored with treatment.

Resting-state connectivity in the default mode network and insula during experimental low back pain

Functional magnetic resonance imaging studies have shown that the insular cortex has a significant role in pain identification and information integration, while the default mode network is associated with cognitive and memory-related aspects of pain perception. However, changes in the functional connectivity between the default mode network and insula during pain remain unclear. This study used 3.0 T functional magnetic resonance imaging scans in 12 healthy subjects aged 24.8 ± 3.3 years to compare the differences in the functional activity and connectivity of the insula and default mode network between the baseline and pain condition induced by intramuscular injection of hypertonic saline. Compared with the baseline, the insula was more functionally connected with the medial prefrontal and lateral temporal cortices, whereas there was lower connectivity with the posterior cingulate cortex, precuneus and inferior parietal lobule in the pain condition. In addition, compared with baseline, the anterior cingulate cortex exhibited greater connectivity with the posterior insula, but lower connectivity with the anterior insula, during the pain condition. These data indicate that experimental low back pain led to dysfunction in the connectivity between the insula and default mode network resulting from an impairment of the regions of the brain related to cognition and emotion, suggesting the importance of the interaction between these regions in pain processing.

Intrinsic brain networks normalize with treatment in pediatric complex regional pain syndrome

Pediatric complex regional pain syndrome (P-CRPS) offers a unique model of chronic neuropathic pain as it either resolves spontaneously or through therapeutic interventions in most patients. Here we evaluated brain changes in well-characterized children and adolescents with P-CRPS by measuring resting state networks before and following a brief (median = 3 weeks) but intensive physical and psychological treatment program, and compared them to matched healthy controls. Differences in intrinsic brain networks were observed in P-CRPS compared to controls before treatment (disease state) with the most prominent differences in the fronto-parietal, salience, default mode, central executive, and sensorimotor networks. Following treatment, behavioral measures demonstrated a reduction of symptoms and improvement of physical state (pain levels and motor functioning). Correlation of network connectivities with spontaneous pain measures pre- and post-treatment indicated concomitant reductions in connectivity in salience, central executive, default mode and sensorimotor networks (treatment effects). These results suggest a rapid alteration in global brain networks with treatment and provide a venue to assess brain changes in CRPS pre- and post-treatment, and to evaluate therapeutic effects.

Altered modular organization of functional connectivity networks in cirrhotic patients without overt hepatic encephalopathy

Minimal hepatic encephalopathy (MHE) is associated with changes in functional connectivity. To investigate the patterns of modular changes of the functional connectivity in the progression of MHE, resting-state functional magnetic resonance imaging was acquired in 24 MHE patients, 31 cirrhotic patients without minimal hepatic encephalopathy (non-HE), and 38 healthy controls. Newman's metric, the modularity Q value, was maximized and compared in three groups. Topological roles with the progression of MHE were illustrated by intra- and intermodular connectivity changes. Results showed that the Q value of MHE patients was significantly lower than that of controls (P < 0.01) rather than that of non-HE patients (P > 0.05), which was correlated with neuropsychological test scores rather than the ammonia level and Child-Pugh score. Less intrasubcortical connections and more isolated subcortical modules were found with the progression of MHE. The non-HE patients had the same numbers of connect nodes as controls and had more hubs compared with MHE patients and healthy controls. Our findings supported that both intra- and intermodular connectivity, especially those related to subcortical regions, were continuously impaired in cirrhotic patients. The adjustments of hubs and connector nodes in non-HE patients could be a compensation for the decreased modularity in their functional connectivity networks.

Gray matter alterations in chronic pain: A network-oriented meta-analytic approach

Several studies have attempted to characterize morphological brain changes due to chronic pain. Although it has repeatedly been suggested that longstanding pain induces gray matter modifications, there is still some controversy surrounding the direction of the change (increase or decrease in gray matter) and the role of psychological and psychiatric comorbidities. In this study, we propose a novel, network-oriented, meta-analytic approach to characterize morphological changes in chronic pain. We used network decomposition to investigate whether different kinds of chronic pain are associated with a common or specific set of altered networks. Representational similarity techniques, network decomposition and model-based clustering were employed: i) to verify the presence of a core set of brain areas commonly modified by chronic pain; ii) to investigate the involvement of these areas in a large-scale network perspective; iii) to study the relationship between altered networks and; iv) to find out whether chronic pain targets clusters of areas. Our results showed that chronic pain causes both core and pathology-specific gray matter alterations in large-scale networks. Common alterations were observed in the prefrontal regions, in the anterior insula, cingulate cortex, basal ganglia, thalamus, periaqueductal gray, post- and pre-central gyri and inferior parietal lobule. We observed that the salience and attentional networks were targeted in a very similar way by different chronic pain pathologies. Conversely, alterations in the sensorimotor and attention circuits were differentially targeted by chronic pain pathologies. Moreover, model-based clustering revealed that chronic pain, in line with some neurodegenerative diseases, selectively targets some large-scale brain networks. Altogether these findings indicate that chronic pain can be better conceived and studied in a network perspective.

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Chronic pain causes both core and pathology-specific.

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GM alterations in brain networks.

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Model-based clustering revealed that chronic pain selectively targets brain networks.

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Chronic pain can be better conceived and studied in a network perspective.

Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks

Multiple sclerosis (MS) is an autoimmune inflammatory demyelinating and neurodegenerative disorder of the central nervous system characterized by multifocal white matter brain lesions leading to alterations in connectivity at the subcortical and cortical level. Graph theory, in combination with neuroimaging techniques, has been recently developed into a powerful tool to assess the large-scale structure of brain functional connectivity. Considering the structural damage present in the brain of MS patients, we hypothesized that the topological properties of resting-state functional networks of early MS patients would be re-arranged in order to limit the impact of disease expression. A standardized dual task (Paced Auditory Serial Addition Task simultaneously performed with a paper and pencil task) was administered to study the interactions between behavioral performance and functional network re-organization. We studied a group of 16 early MS patients (35.3±8.3 years, 11 females) and 20 healthy controls (29.9±7.0 years, 10 females) and found that brain resting-state networks of the MS patients displayed increased network modularity, i.e. diminished functional integration between separate functional modules. Modularity correlated negatively with dual task performance in the MS patients. Our results shed light on how localized anatomical connectivity damage can globally impact brain functional connectivity and how these alterations can impair behavioral performance. Finally, given the early stage of the MS patients included in this study, network modularity could be considered a promising biomarker for detection of earliest-stage brain network reorganization, and possibly of disease progression.

Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients

Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer's Disease (AD). However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI) method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN) group. Our main finding is that the largest homotopic module (defined as the insula module) in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole) of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC) was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A) and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups). Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.

Default mode network functional connectivity altered in failed back surgery syndrome

The purpose of this study was to identify alterations in the default mode network of failed back surgery syndrome patients as compared to healthy subjects. Resting state functional magnetic resonance imaging was conducted at 3 Tesla and data were analyzed with an independent component analysis. Results indicate an overall reduced functional connectivity of the default mode network and recruitment of additional pain modulation brain regions, including dorsolateral prefrontal cortex, insula, and additional sensory motor integration brain regions, including precentral and postcentral gyri, for failed back surgery syndrome patients.

PERSPECTIVE

This article presents alterations in the default mode network of chronic low back pain patients with failed back surgery syndrome as compared to healthy participants.

Large-scale brain functional modularity is reflected in slow electroencephalographic rhythms across the human non-rapid eye movement sleep cycle

Large-scale brain functional networks (measured with functional magnetic resonance imaging, fMRI) are organized into separated but interacting modules, an architecture supporting the integration of distinct dynamical processes. In this work we study how the aforementioned modular architecture changes with the progressive loss of vigilance occurring in the descent to deep sleep and we examine the relationship between the ensuing slow electroencephalographic rhythms and large-scale network modularity as measured with fMRI. Graph theoretical methods are used to analyze functional connectivity graphs obtained from fifty-five participants at wakefulness, light and deep sleep. Network modularity (a measure of functional segregation) was found to increase during deeper sleep stages but not in light sleep. By endowing functional networks with dynamical properties, we found a direct link between increased electroencephalographic (EEG) delta power (1-4 Hz) and a breakdown of inter-modular connectivity. Both EEG slowing and increased network modularity were found to quickly decrease during awakenings from deep sleep to wakefulness, in a highly coordinated fashion. Studying the modular structure itself by means of a permutation test, we revealed different module memberships when deep sleep was compared to wakefulness. Analysis of node roles in the modular structure revealed an increase in the number of locally well-connected nodes and a decrease in the number of globally well-connected hubs, which hinders interactions between separated functional modules. Our results reveal a well-defined sequence of changes in brain modular organization occurring during the descent to sleep and establish a close parallel between modularity alterations in large-scale functional networks (accessible through whole brain fMRI recordings) and the slowing of scalp oscillations (visible on EEG). The observed re-arrangement of connectivity might play an important role in the processes underlying loss of vigilance and sensory awareness during deep sleep.

Functional network connectivity of pain-related resting state networks in somatoform pain disorder: an exploratory fMRI study

BACKGROUND

Without stimulation, the human brain spontaneously produces highly organized, low-frequency fluctuations of neural activity in intrinsic connectivity networks (ICNs). Furthermore, without adequate explanatory nociceptive input, patients with somatoform pain disorder experience pain symptoms, thus implicating a central dysregulation of pain homeostasis. The present study aimed to test whether interactions among pain-related ICNs, such as the default mode network (DMN), cingular-insular network (CIN) and sensorimotor network (SMN), are altered in somatoform pain during resting conditions.

METHODS

Patients with somatoform pain disorder and healthy controls underwent resting functional magnetic resonance imaging that lasted 370 seconds. Using a data-driven approach, the ICNs were isolated, and the functional network connectivity (FNC) was computed.

RESULTS

Twenty-one patients and 19 controls enrolled in the study. Significant FNC (p < 0.05, corrected for false discovery rate) was detected between the CIN and SMN/anterior DMN, the anterior DMN and posterior DMN/SMN, and the posterior DMN and SMN. Interestingly, no group differences in FNC were detected.

LIMITATIONS

The most important limitation of this study was the relatively short resting state paradigm.

CONCLUSION

To our knowledge, our results demonstrated for the first time the resting FNC among pain-related ICNs. However, our results suggest that FNC signatures alone are not able to characterize the putative central dysfunction underpinning somatoform pain disorder.

The young brain and concussion: imaging as a biomarker for diagnosis and prognosis

Concussion (mild traumatic brain injury (mTBI)) is a significant pediatric public health concern. Despite increased awareness, a comprehensive understanding of the acute and chronic effects of concussion on central nervous system structure and function remains incomplete. Here we review the definition, epidemiology, and sequelae of concussion within the developing brain, during childhood and adolescence, with current data derived from studies of pathophysiology and neuroimaging. These findings may contribute to a better understanding of the neurological consequences of traumatic brain injuries, which in turn, may lead to the development of brain biomarkers to improve identification, management and prognosis of pediatric patients suffering from concussion.

Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State

Neuroimaging studies have shown that functional brain networks composed from select regions of interest have a modular community structure. However, the organization of functional network connectivity (FNC), comprising a purely data-driven network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state functional magnetic resonance imaging data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain networks in this mental illness.

Disrupted functional connectivity of the pain network in fibromyalgia

OBJECTIVE

To investigate the impact of chronic pain on brain dynamics at rest.

METHODS

Functional connectivity was examined in patients with fibromyalgia (FM) (n = 9) and healthy controls (n = 11) by calculating partial correlations between low-frequency blood oxygen level-dependent fluctuations extracted from 15 brain regions.

RESULTS

Patients with FM had more positive and negative correlations within the pain network than healthy controls. Patients with FM displayed enhanced functional connectivity of the anterior cingulate cortex (ACC) with the insula (INS) and basal ganglia (p values between .01 and .05), the secondary somatosensory area with the caudate (CAU) (p = .012), the primary motor cortex with the supplementary motor area (p = .007), the globus pallidus with the amygdala and superior temporal sulcus (both p values < .05), and the medial prefrontal cortex with the posterior cingulate cortex (PCC) and CAU (both p values < .05). Functional connectivity of the ACC with the amygdala and periaqueductal gray (PAG) matter (p values between .001 and .05), the thalamus with the INS and PAG (both p values < .01), the INS with the putamen (p = .038), the PAG with the CAU (p = .038), the secondary somatosensory area with the motor cortex and PCC (both p values < .05), and the PCC with the superior temporal sulcus (p = .002) was also reduced in FM. In addition, significant negative correlations were observed between depression and PAG connectivity strength with the thalamus (r = -0.64, p = .003) and ACC (r = -0.60, p = .004).

CONCLUSIONS

These findings demonstrate that patients with FM display a substantial imbalance of the connectivity within the pain network during rest, suggesting that chronic pain may also lead to changes in brain activity during internally generated thought processes such as occur at rest.

Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty

Characterizing interactions between multiple brain regions is important for understanding brain function. Functional connectivity measures based on partial correlation provide an estimate of the linear conditional dependence between brain regions after removing the linear influence of other regions. Estimation of partial correlations is, however, difficult when the number of regions is large, as is now increasingly the case with a growing number of large-scale brain connectivity studies. To address this problem, we develop novel methods for estimating sparse partial correlations between multiple regions in fMRI data using elastic net penalty (SPC-EN), which combines L1- and L2-norm regularization We show that L1-norm regularization in SPC-EN provides sparse interpretable solutions while L2-norm regularization improves the sensitivity of the method when the number of possible connections between regions is larger than the number of time points, and when pair-wise correlations between brain regions are high. An issue with regularization-based methods is choosing the regularization parameters which in turn determine the selection of connections between brain regions. To address this problem, we deploy novel stability selection methods to infer significant connections between brain regions. We also compare the performance of SPC-EN with existing methods which use only L1-norm regularization (SPC-L1) on simulated and experimental datasets. Detailed simulations show that the performance of SPC-EN, measured in terms of sensitivity and accuracy is superior to SPC-L1, especially at higher rates of feature prevalence. Application of our methods to resting-state fMRI data obtained from 22 healthy adults shows that SPC-EN reveals a modular architecture characterized by strong inter-hemispheric links, distinct ventral and dorsal stream pathways, and a major hub in the posterior medial cortex - features that were missed by conventional methods. Taken together, our findings suggest that SPC-EN provides a powerful tool for characterizing connectivity involving a large number of correlated regions that span the entire brain.

Neurological diseases and pain

Chronic pain is a frequent component of many neurological disorders, affecting 20-40% of patients for many primary neurological diseases. These diseases result from a wide range of pathophysiologies including traumatic injury to the central nervous system, neurodegeneration and neuroinflammation, and exploring the aetiology of pain in these disorders is an opportunity to achieve new insight into pain processing. Whether pain originates in the central or peripheral nervous system, it frequently becomes centralized through maladaptive responses within the central nervous system that can profoundly alter brain systems and thereby behaviour (e.g. depression). Chronic pain should thus be considered a brain disease in which alterations in neural networks affect multiple aspects of brain function, structure and chemistry. The study and treatment of this disease is greatly complicated by the lack of objective measures for either the symptoms or the underlying mechanisms of chronic pain. In pain associated with neurological disease, it is sometimes difficult to obtain even a subjective evaluation of pain, as is the case for patients in a vegetative state or end-stage Alzheimer's disease. It is critical that neurologists become more involved in chronic pain treatment and research (already significant in the fields of migraine and peripheral neuropathies). To achieve this goal, greater efforts are needed to enhance training for neurologists in pain treatment and promote greater interest in the field. This review describes examples of pain in different neurological diseases including primary neurological pain conditions, discusses the therapeutic potential of brain-targeted therapies and highlights the need for objective measures of pain.

The brain as a complex system: using network science as a tool for understanding the brain

Although graph theory has been around since the 18th century, the field of network science is more recent and continues to gain popularity, particularly in the field of neuroimaging. The field was propelled forward when Watts and Strogatz introduced their small-world network model, which described a network that provided regional specialization with efficient global information transfer. This model is appealing to the study of brain connectivity, as the brain can be viewed as a system with various interacting regions that produce complex behaviors. In practice, graph metrics such as clustering coefficient, path length, and efficiency measures are often used to characterize system properties. Centrality metrics such as degree, betweenness, closeness, and eigenvector centrality determine critical areas within the network. Community structure is also essential for understanding network organization and topology. Network science has led to a paradigm shift in the neuroscientific community, but it should be viewed as more than a simple "tool du jour." To fully appreciate the utility of network science, a greater understanding of how network models apply to the brain is needed. An integrated appraisal of multiple network analyses should be performed to better understand network structure rather than focusing on univariate comparisons to find significant group differences; indeed, such comparisons, popular with traditional functional magnetic resonance imaging analyses, are arguably no longer relevant with graph-theory based approaches. These methods necessitate a philosophical shift toward complexity science. In this context, when correctly applied and interpreted, network scientific methods have a chance to revolutionize the understanding of brain function.