Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy

Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures.

Impact of prenatal marijuana exposure on adolescent brain structural and functional connectivity and behavioural outcomes

There has been an increase in the number of women using marijuana whilst pregnant. Previous studies have shown that children with prenatal marijuana exposure have developmental deficits in memory and decreased attentiveness. In this study, we assess whether prenatal marijuana exposure is associated with alterations in brain regional morphometry and functional and structural connectivity in adolescents. We downloaded behavioural scores and subject image files from the Adolescent Brain Cognitive DevelopmentSM Study. A total of 178 anatomical and diffusion magnetic resonance imaging files (88 prenatal marijuana exposure and 90 age- and gender-matched controls) and 152 resting-state functional magnetic resonance imaging files (76 prenatal marijuana exposure and 76 controls) were obtained. Behavioural metrics based on the parent-reported child behavioural checklist were also obtained for each subject. The associations of prenatal marijuana exposure with 17 subscales of the child behavioural checklist were calculated. We assessed differences in brain morphometry based on voxel-based and surface-based morphometry in adolescents with prenatal marijuana exposure versus controls. We also evaluated group differences in structural and functional connectivity in adolescents for region-to-region connectivity and graph theoretical metrics. Interactions of prenatal marijuana exposure and graph networks were assessed for impact on behavioural scores. Multiple comparison correction was performed as appropriate. Adolescents with prenatal marijuana exposure had greater abnormal or borderline child behavioural checklist scores in 9 out of 17 subscales. There were no significant differences in voxel- or surface-based morphometry, structural connectivity or functional connectivity between prenatal marijuana exposure and controls. However, there were significant differences in prenatal marijuana exposure-graph network interactions with respect to behavioural scores. There were three structural prenatal marijuana exposure-graph network interactions and seven functional prenatal marijuana exposure-graph network interactions that were significantly associated with behavioural scores. Whilst this study was not able to confirm anatomical or functional differences between prenatal marijuana exposure and unexposed pre-adolescent children, there were prenatal marijuana exposure-brain structural and functional graph network interactions that were significantly associated with behavioural scores. This suggests that altered brain networks may underlie behavioural outcomes in adolescents with prenatal marijuana exposure. More work needs to be conducted to better understand the prognostic value of brain structural and functional network measures in prenatal marijuana exposure.

Decreased functional concordance in male children with autism spectrum disorder

Autism spectrum disorder (ASD) is an early-onset neurodevelopmental condition with altered function of the brain. At present, a variety of functional metrics from neuroimaging techniques have been used to explore ASD neurological mechanisms. However, the concordance of these functional metrics in ASD is still unclear. This study used resting-state functional magnetic resonance imaging data, which were obtained from the open-access Autism Brain Imaging Data Exchange database, including 105 children with ASD and 102 demographically matched typically developing (TD) children. Both voxel-wise and volume-wise functional concordance were calculated by combining the dynamic amplitude of low-frequency fluctuations, dynamic regional homogeneity, and dynamic global signal correlation. Furthermore, a two-sample t-test was performed to compare the functional concordance between ASD and TD groups. Finally, the relationship between voxel-wise functional concordance and Autism Diagnostic Observation Schedule subscores was analyzed using the multivariate support vector regression in the ASD group. Compared with the TD group, we found that ASD showed decreased voxel-wise functional concordance in the left superior temporal pole (STGp), right amygdala, and left opercular part of the inferior frontal gyrus (IFGoper). Moreover, decreased functional concordance was associated with restricted and repetitive behaviors in ASD. Our results found altered brain function in the left STGp, right amygdala, and left IFGoper in ASD by functional concordance, indicating that functional concordance may provide new insights into the neurological mechanisms of ASD.

Application and underlying mechanism of acupuncture for the nerve repair after peripheral nerve injury: remodeling of nerve system

Peripheral nerve injury (PNI) is a structural event with harmful consequences worldwide. Due to the limited intrinsic regenerative capacity of the peripheral nerve in adults, neural restoration after PNI is difficult. Neurological remodeling has a crucial effect on the repair of the form and function during the regeneration of the peripheral nerve after the peripheral nerve is injured. Several studies have demonstrated that acupuncture is effective for PNI-induced neurologic deficits, and the potential mechanisms responsible for its effects involve the nervous system remodeling in the process of nerve repair. Moreover, acupuncture promotes neural regeneration and axon sprouting by activating related neurotrophins retrograde transport, such as nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), glial cell-derived neurotrophic factor (GDNF), N-cadherin, and MicroRNAs. Peripheral nerve injury enhances the perceptual response of the central nervous system to pain, causing central sensitization and accelerating neuronal cell apoptosis. Together with this, the remodeling of synaptic transmission function would worsen pain discomfort. Neuroimaging studies have shown remodeling changes in both gray and white matter after peripheral nerve injury. Acupuncture not only reverses the poor remodeling of the nervous system but also stimulates the release of neurotrophic substances such as nerve growth factors in the nervous system to ameliorate pain and promote the regeneration and repair of nerve fibers. In conclusion, the neurological remodeling at the peripheral and central levels in the process of acupuncture treatment accelerates nerve regeneration and repair. These findings provide novel insights enabling the clinical application of acupuncture in the treatment of PNI.

Human amygdala involvement in Alzheimer's disease revealed by stereological and dia-PASEF analysis

Alzheimer's disease (AD) is characterized by the accumulation of pathological amyloid-β (Aβ) and Tau proteins. According to the prion-like hypothesis, both proteins can seed and disseminate through brain regions through neural connections and glial cells. The amygdaloid complex (AC) is involved early in the disease, and its widespread connections with other brain regions indicate that it is a hub for propagating pathology. To characterize changes in the AC as well as the involvement of neuronal and glial cells in AD, a combined stereological and proteomic analysis was performed in non-Alzheimer's disease and AD human samples. The synaptic alterations identified by proteomic data analysis could be related to the volume reduction observed in AD by the Cavalieri probe without neuronal loss. The pathological markers appeared in a gradient pattern with the medial region (cortical nucleus, Co) being more affected than lateral regions, suggesting the relevance of connections in the distribution of the pathology among different brain regions. Generalized astrogliosis was observed in every AC nucleus, likely related to deposits of pathological proteins. Astrocytes might mediate phagocytic microglial activation, whereas microglia might play a dual role since protective and toxic phenotypes have been described. These results highlight the potential participation of the amygdala in the disease spreading from/to olfactory areas, the temporal lobe and beyond. Proteomic data are available via ProteomeXchange with identifier PXD038322.

EEG alpha band functional connectivity reveals distinct cortical dynamics for overt and covert emotional face processing

Current knowledge regarding how the focus of our attention during face processing influences neural responses largely comes from neuroimaging studies reporting on regional brain activations. The present study was designed to add novel insights to this research by studying how attention can differentially impact the way cortical regions interact during emotional face processing. High-density electroencephalogram was recorded in a sample of fifty-two healthy participants during an emotional face processing task. The task required participants to either attend to the expressions (i.e., overt processing) or attend to a perceptual distractor, which rendered the expressions task-irrelevant (i.e., covert processing). Functional connectivity in the alpha band was estimated in source space and modeled using graph theory to quantify whole-brain integration and segregation. Results revealed that overt processing of facial expressions is linked to reduced cortical segregation and increased cortical integration, this latter specifically for negative expressions of fear and sadness. Furthermore, we observed increased communication efficiency during overt processing of negative expressions between the core and the extended face processing systems. Overall, these findings reveal that attention makes the interaction among the nodes involved in face processing more efficient, also uncovering a connectivity signature of the prioritized processing mechanism of negative expressions, that is an increased cross-communication within the nodes of the face processing network.

Functional and structural alterations as diagnostic imaging markers for depression in de novo Parkinson's disease

Background

Depression in Parkinson's disease (PD) is identified and diagnosed with behavioral observations and neuropsychological measurements. Due to the large overlaps of depression and PD symptoms in clinical manifestations, it is challenging for neurologists to distinguish and diagnose depression in PD (DPD) in the early clinical stage of PD. The advancement in magnetic resonance imaging (MRI) technology provides potential clinical utility in the diagnosis of DPD. This study aimed to explore the alterations of functional and structural MRI in DPD to produce neuroimaging markers in discriminating DPD from non-depressed PD (NDPD) and healthy controls (HC).

Methods

We recruited 20 DPD, 37 NDPD, and 41 HC matched in age, gender, and education years. The patients' diagnosis with PD was de novo. The differences in regional homogeneity (ReHo), voxel-wise degree centrality (DC), cortical thickness, cortical gray matter (GM) volumes, and subcortical GM volumes among these groups were detected, and the relationship between altered indicators and depression was analyzed. Moreover, the receiver operating characteristic (ROC) analysis was performed to assess the diagnostic efficacy of altered indicators for DPD.

Results

Compared to NDPD and HC, DPD showed significantly increased ReHo in left dorsolateral superior frontal gyrus (DSFG) and DC in left inferior temporal gyrus (ITG), and decreased GM volumes in left temporal lobe and right Amygdala. Among these altered indicators, ReHo value in left DSFG and DC values in left ITG and left DSFG were significantly correlated with the severity of depression in PD patients. Comparing DPD and NDPD, the ROC analysis revealed a better area under the curve value for the combination of ReHo value in left DSFG and DC value in left ITG, followed by each independent indicator. However, the difference is not statistically significant.

Conclusion

This study demonstrates that both functional and structural impairments are present in DPD. Among them, ReHo value of left DSFG and DC value of left ITG are equally well suited for the diagnosis and differential diagnosis of DPD, with a combination of them being slightly preferable. The multimodal MRI technique represents a promising approach for the classification of subjects with PD.

Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment

Objective

Accumulating evidence shows that cognitive impairment (CI) in chronic heart failure (CHF) patients is related to brain network dysfunction. This study investigated brain network structure and rich-club organization in chronic heart failure patients with cognitive impairment based on graph analysis of diffusion tensor imaging data.

Methods

The brain structure networks of 30 CHF patients without CI and 30 CHF patients with CI were constructed. Using graph theory analysis and rich-club analysis, changes in global and local characteristics of the subjects' brain network and rich-club organization were quantitatively calculated, and the correlation with cognitive function was analyzed.

Results

Compared to the CHF patients in the group without CI group, the CHF patients in the group with CI group had lower global efficiency, local efficiency, clustering coefficient, the small-world attribute, and increased shortest path length. The CHF patients with CI group showed lower nodal degree centrality in the fusiform gyrus on the right (FFG.R) and nodal efficiency in the orbital superior frontal gyrus on the left (ORB sup. L), the orbital inferior frontal gyrus on the left (ORB inf. L), and the posterior cingulate gyrus on the right (PCG.R) compared with CHF patients without CI group. The CHF patients with CI group showed a smaller fiber number of edges in specific regions. In CHF patients with CI, global efficiency, local efficiency and the connected edge of the orbital superior frontal gyrus on the right (ORB sup. R) to the orbital middle frontal gyrus on the right (ORB mid. R) were positively correlated with Visuospatial/Executive function. The connected edge of the orbital superior frontal gyrus on the right to the orbital inferior frontal gyrus on the right (ORB inf. R) is positively correlated to attention/calculation. Compared with the CHF patients without CI group, the connection strength of feeder connection and local connection in CHF patients with CI group was significantly reduced, although the strength of rich-club connection in CHF patients complicated with CI group was decreased compared with the control, there was no statistical difference. In addition, the rich-club connection strength was related to the orientation (direction force) of the Montreal cognitive assessment (MoCA) scale, and the feeder and local connection strength was related to Visuospatial/Executive function of MoCA scale in the CHF patients with CI.

Conclusion

Chronic heart failure patients with CI exhibited lower global and local brain network properties, reduced white matter fiber connectivity, as well as a decreased strength in local and feeder connections in key brain regions. The disrupted brain network characteristics and connectivity was associated with cognitive impairment in CHF patients. Our findings suggest that impaired brain network properties and decreased connectivity, a feature of progressive disruption of brain networks, predict the development of cognitive impairment in patients with chronic heart failure.

Brain diffusion tensor imaging reveals altered connections and networks in epilepsy patients

Introduction

Accumulating evidence shows that epilepsy is a disease caused by brain network dysfunction. This study explored changes in brain network structure in epilepsy patients based on graph analysis of diffusion tensor imaging data.

Methods

The brain structure networks of 42 healthy control individuals and 26 epilepsy patients were constructed. Using graph theory analysis, global and local network topology parameters of the brain structure network were calculated, and changes in global and local characteristics of the brain network in epilepsy patients were quantitatively analyzed.

Results

Compared with the healthy control group, the epilepsy patient group showed lower global efficiency, local efficiency, clustering coefficient, and a longer shortest path length. Both healthy control individuals and epilepsy patients showed small-world attributes, with no significant difference between groups. The epilepsy patient group showed lower nodal local efficiency and nodal clustering coefficient in the right olfactory cortex and right rectus and lower nodal degree centrality in the right olfactory cortex and the left paracentral lobular compared with the healthy control group. In addition, the epilepsy patient group showed a smaller fiber number of edges in specific regions of the frontal lobe, temporal lobe, and default mode network, indicating reduced connection strength.

Discussion

Epilepsy patients exhibited lower global and local brain network properties as well as reduced white matter fiber connectivity in key brain regions. These findings further support the idea that epilepsy is a brain network disorder.

Potential biomarkers for distinguishing primary from acquired premature ejaculation: A diffusion tensor imaging based network study

Introduction

Premature ejaculation (PE) is classified as primary and acquired and may be facilitated by different pathophysiology. Brain plays an important role in PE, however, differences in the central neuropathological mechanisms among subtypes of PE are unknown.

Materials and methods

We acquired diffusion tensor imaging (DTI) data from 44 healthy controls (HC) and 47 PE patients (24 primary PE and 23 acquired PE). Then, the whole-brain white matter (WM) structural networks were constructed and between-group differences of nodal segregative parameters were identified by the method of graph theoretical analysis. Moreover, receiver operating characteristic (ROC) curves were performed to determine the suitability of the altered parameters as potential neuroimaging biomarkers for distinguishing primary PE from acquired PE.

Results

PE patients showed significantly increased clustering coefficient C(i) in the left inferior frontal gyrus (triangular part) (IFGtriang.L) and increased local efficiency E_loc(i) in the left precental gyrus (PreCG.L) and IFGtriang.L when compared with HC. Compared to HC, primary PE patients had increased C(i) and E_loc(i) in IFGtriang.L and the left amygdala (AMYG.L) while acquired PE patients had increased C(i) and E_loc(i) in IFGtriang.L, and decreased C(i) and E_loc(i) in AMYG.L. Compared to acquired PE, primary PE patients had increased C(i) and E_loc(i) in AMYG.L. Moreover, ROC analysis revealed that PreCG.L, IFGtriang.L and AMYG.L might be helpful for distinguishing different subtypes of PE from HC (PE from HC: sensitivity, 61.70–78.72%; specificity, 56.82–77.27%; primary PE from HC: sensitivity, 66.67–87.50%; specificity, 52.27–77.27%; acquired PE from HC: sensitivity, 34.78–86.96%; specificity, 54.55–100%) while AMYG.L might be helpful for distinguishing primary PE from acquired PE (sensitivity, 83.33–91.70%; specificity, 69.57–73.90%).

Conclusion

These findings improved our understanding of the pathophysiological processes that occurred in patients with ejaculatory dysfunction and suggested that the abnormal segregation of left amygdala might serve as a useful marker to help clinicians distinguish patients with primary PE from those with acquired PE.

Migraine: interactions between brain's trait and state

It is well established that migraine is a multifactorial disorder. A deep understanding of migraine should be based upon both the underlying traits and the current states affected by different physiological, psychological, and environmental factors. At this point, there is no framework fully meeting these criteria. Here, we describe a broader view of the migraine disorder defined as a dysfunctional brain state and trait interaction. In this model, we consider events that may enhance or diminish migraine responsivity based on an individual's trait and state. This could provide an expanded view for considering how migraine attacks are sometimes precipitated by "triggers" and sometimes not, how these factors only lead to migraine attacks in migraine patients, or how individuals with an increased risk for migraine do not show any symptoms at all. Summarizing recent studies and evidence that support the concept of migraine as a brain state-trait interaction can also contribute to improving patient care by highlighting the importance of precision medicine and applying measures that are able to capture how different traits and states work together to determine migraine.

Brain structural connectome in neonates with prenatal opioid exposure

Introduction

Infants with prenatal opioid exposure (POE) are shown to be at risk for poor long-term neurobehavioral and cognitive outcomes. Early detection of brain developmental alterations on neuroimaging could help in understanding the effect of opioids on the developing brain. Recent studies have shown altered brain functional network connectivity through the application of graph theoretical modeling, in infants with POE. In this study, we assess global brain structural connectivity through diffusion tensor imaging (DTI) metrics and apply graph theoretical modeling to brain structural connectivity in infants with POE.

Methods

In this prospective observational study in infants with POE and control infants, brain MRI including DTI was performed before completion of 3 months corrected postmenstrual age. Tractography was performed on the whole brain using a deterministic fiber tracking algorithm. Pairwise connectivity and network measure were calculated based on fiber count and fractional anisotropy (FA) values. Graph theoretical metrics were also derived.

Results

There were 11 POE and 18 unexposed infants included in the analysis. Pairwise connectivity based on fiber count showed alterations in 32 connections. Pairwise connectivity based on FA values showed alterations in 24 connections. Connections between the right superior frontal gyrus and right paracentral lobule and between the right superior occipital gyrus and right fusiform gyrus were significantly different after adjusting for multiple comparisons between POE infants and unexposed controls. Additionally, alterations in graph theoretical network metrics were identified with fiber count and FA value derived tracts.

Conclusion

Comparisons show significant differences in fiber count in two structural connections. The long-term clinical outcomes related to these findings may be assessed in longitudinal follow-up studies.

Extracting default mode network based on graph neural network for resting state fMRI study

Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant information to explain a wide range of pathological conditions and behavioral characteristics. In this paper, we propose the use of a graph neural network, a deep learning technique called graphSAGE, to investigate resting state fMRI (rs-fMRI) and extract the default mode network (DMN). Comparing typical methods such as seed-based correlation, independent component analysis, and dictionary learning, real data experiment results showed that the graphSAGE is more robust, reliable, and defines a clearer region of interests. In addition, graphSAGE requires fewer and more relaxed assumptions, and considers the single subject analysis and group subjects analysis simultaneously.

Mapping intrinsic functional network topological architecture in major depression disorder after electroconvulsive therapy

Disrupted topological organization of functional brain networks has been well documented in major depressive disorder (MDD). However, there is no report about how electroconvulsive therapy (ECT), a rapid way for depression remission, affects whole-brain functional network topological architecture to improve clinical symptoms in individuals with MDD. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) data were collected for twenty-four MDD patients before and after receiving ECT and 25 gender-, age- and education-matched healthy controls (HC). The functional brain network for each subject was mapped using Brainnetome Atlas and graph-theory was applied to measure topological properties for both binary and weighted network. The results showed that ECT can significantly increase shortest path length and decrease global efficiency in MDD patients. In addition, significant alterations in nodal degree, nodal efficiency as well as between nodal functional connectivity strength were found in MDD patients after ECT. The network nodes showing changed degree, efficiency and connectivity were primarily distributed in default mode network (DMN), fronto-parietal network (FPN), and limbic system. Our findings demonstrates that ECT improves depressive symptoms by reorganizing disrupted network topological architecture in MDD patients and highlights the important role of functional reorganization of DMN, FPN, and limbic network contributing to depression remission.

The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia

Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.

Dynamical Complexity Fingerprints of Occupation-Dependent Brain Functional Networks in Professional Seafarers

The complexity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data has been applied for exploring cognitive states and occupational neuroplasticity. However, there is little information about the influence of occupational factors on dynamic complexity and topological properties of the connectivity networks. In this paper, we proposed a novel dynamical brain complexity analysis (DBCA) framework to explore the changes in dynamical complexity of brain activity at the voxel level and complexity topology for professional seafarers caused by long-term working experience. The proposed DBCA is made up of dynamical brain entropy mapping analysis and complex network analysis based on brain entropy sequences, which generate the dynamical complexity of local brain areas and the topological complexity across brain areas, respectively. First, the transient complexity of voxel-wise brain map was calculated; compared with non-seafarers, seafarers showed decreased dynamic entropy values in the cerebellum and increased values in the left fusiform gyrus (BA20). Further, the complex network analysis based on brain entropy sequences revealed small-worldness in terms of topological complexity in both seafarers and non-seafarers, indicating that it is an inherent attribute of human the brain. In addition, seafarers showed a higher average path length and lower average clustering coefficient than non-seafarers, suggesting that the information processing ability is reduced in seafarers. Moreover, the reduction in efficiency of seafarers suggests that they have a less efficient processing network. To sum up, the proposed DBCA is effective for exploring the dynamic complexity changes in voxel-wise activity and region-wise connectivity, showing that occupational experience can reshape seafarers’ dynamic brain complexity fingerprints.

A New Multi-Resolution Approach to EEG Brain Modeling Using Local-Global Graphs and Stochastic Petri-Nets

Recent modeling of brain activities encompasses the fusion of different modalities. However, fusing brain modalities requires not only the efficient and compatible representation of the signals but also the benefits associated with it. For instance, the combination of the functional characteristics of EEGs with the structural features of functional magnetic resonance imaging contributes to a better interpretation localization of brain activities. In this paper, we consider the EEG signals as parallel 2D string images from which we extract their visual abstract representations of EEG features. This representation can benefit not only the EEG modeling of the signals but also a future fusion with another modality, like fMRI. In particular, the new methodology, called Bar-LG, provides a reduced discretization of the EEG signals into selected minima/maxima in order to be used in a form of tokens for EEG brain activities of interest. A formal context-free language is used to express and represent the extracted tokens for the selected active brain regions. Then, a Generalized Stochastic Petri-Nets (GSPN) model is used for expressing the functional associations and interactions of these EEG signals as 2D image regions. An illustrative EEG example of epileptic seizure is presented to show the Bar-LG methodology’s abstract capabilities.

Disruption of regional homogeneity in the brains of chronic methamphetamine users

Previous studies have reported evidence supporting structural and functional alterations in the brains of methamphetamine (MA) users. The aim of the present study was to extend current knowledge regarding brain function(s) in MA users by examining regional homogeneity (ReHo). Chronic MA users (51 male, 46 female), who were undergoing supervised abstinence for 12 to 621 days, and 79 healthy controls (43 male, 36 female) underwent resting-state functional brain magnetic resonance imaging. Voxel-wise whole-brain scale group differences in ReHo were examined. The mean ReHo values of significant clusters were extracted, and linear regression was used to identify factors that contributed to these mean ReHo values. MA users exhibited lower ReHo values in the left orbital part of the inferior frontal gyrus extending to the left insula and left temporal pole, left amygdala, and left fusiform gyrus. MA users also exhibited greater ReHo values in the bilateral pre- and postcentral gyri and right cerebellum. Characteristics of MA use, including duration, duration of abstinence from MA, and age at onset of MA use, demonstrated no reliable contribution to ReHo of the significant clusters. Findings of the present study demonstrated that chronic MA use was associated with regional specific disruption of ReHo, which is relatively independent of structural and functional alterations and, apparently, does not recover after relatively long-term abstinence. This disruption may underlie overall neurocognitive deficits in MA users, which is difficult to recover.

Aberrant Topological Patterns of Structural Covariance Networks in Cognitively Normal Elderly Adults With Mild Behavioral Impairment

Mild behavioral impairment (MBI), characterized by the late-life onset of sustained and meaningful neuropsychiatric symptoms, is increasingly recognized as a prodromal stage of dementia. However, the underlying neural mechanisms of MBI remain unclear. Here, we examined alterations in the topological organization of the structural covariance networks of patients with MBI (N = 32) compared with normal controls (N = 38). We found that the gray matter structural covariance networks of both the patients with MBI and controls exhibited a small-world topology evidenced by sigma value larger than one. The patients with MBI had significantly decreased clustering coefficients at several network densities and local efficiency at densities ranging from 0.05 to 0.26, indicating decreased local segregation. No significant differences in the characteristic path length, gamma value, sigma value, or global efficiency were detected. Locally, the patients with MBI showed significantly decreased nodal betweenness centrality in the left middle frontal gyrus, right inferior frontal gyrus (opercular part), and left Heschl gyrus and increased betweenness centrality in the left gyrus rectus, right insula, bilateral precuneus, and left thalamus. Moreover, the difference in the bilateral precuneus survived after correcting for multiple comparisons. In addition, a different number and distribution of hubs was identified in patients with MBI, showing more paralimbic hubs than observed in the normal controls. In conclusion, we revealed abnormal topological patterns of the structural covariance networks in patients with MBI and offer new insights into the network dysfunctional mechanisms of MBI.

Amygdaloid complex anatomopathological findings in animal models of status epilepticus

Temporal lobe epileptic seizures are one of the most common and well-characterized types of epilepsies. The current knowledge on the pathology of temporal lobe epilepsy relies strongly on studies of epileptogenesis caused by experimentally induced status epilepticus (SE). Although several temporal lobe structures have been implicated in the epileptogenic process, the hippocampal formation is the temporal lobe structure studied in the greatest amount and detail. However, studies in human patients and animal models of temporal lobe epilepsy indicate that the amygdaloid complex can be also an important seizure generator, and several pathological processes have been shown in the amygdala during epileptogenesis. Therefore, in the present review, we systematically selected, organized, described, and analyzed the current knowledge on anatomopathological data associated with the amygdaloid complex during SE-induced epileptogenesis. Amygdaloid complex participation in the epileptogenic process is evidenced, among others, by alterations in energy metabolism, circulatory, and fluid regulation, neurotransmission, immediate early genes expression, tissue damage, cell suffering, inflammation, and neuroprotection. We conclude that major efforts should be made in order to include the amygdaloid complex as an important target area for evaluation in future research on SE-induced epileptogenesis. This article is part of the Special Issue "NEWroscience 2018".

Nonperiodic stimulation for the treatment of refractory epilepsy: Applications, mechanisms, and novel insights

Electrical stimulation of the central nervous system is a promising alternative for the treatment of pharmacoresistant epilepsy. Successful clinical and experimental stimulation is most usually carried out as continuous trains of current or voltage pulses fired at rates of 100 Hz or above, since lower frequencies yield controversial results. On the other hand, stimulation frequency should be as low as possible, in order to maximize implant safety and battery efficiency. Moreover, the development of stimulation approaches has been largely empirical in general, while they should be engineered with the neurobiology of epilepsy in mind if a more robust, efficient, efficacious, and safe application is intended. In an attempt to reconcile evidence of therapeutic effect with the understanding of the underpinnings of epilepsy, our group has developed a nonstandard form of low-frequency stimulation with randomized interpulse intervals termed nonperiodic stimulation (NPS). The rationale was that an irregular temporal pattern would impair neural hypersynchronization, which is a hallmark of epilepsy. In this review, we start by briefly revisiting the literature on the molecular, cellular, and network level mechanisms of epileptic phenomena in order to highlight this often-overlooked emergent property of cardinal importance in the pathophysiology of the disease. We then review our own studies on the efficacy of NPS against acute and chronic experimental seizures and also on the anatomical and physiological mechanism of the method, paying special attention to the hypothesis that the lack of temporal regularity induces desynchronization. We also put forward a novel insight regarding the temporal structure of NPS that may better encompass the set of findings published by the group: the fact that intervals between stimulation pulses have a distribution that follows a power law and thus may induce natural-like activity that would compete with epileptiform discharge for the recruitment of networks. We end our discussion by mentioning ongoing research and future projects of our lab.

Abnormal Rich Club Organization in Hemispheric White Matter Networks of ADHD

Objective: Brain network studies have revealed abnormal topology asymmetry of white matter (WM) in ADHD. Recently, rich club organization was proposed to be a key feature of brain network topology. However, abnormalities in the rich club organization of hemispheric WM networks in ADHD remain unclear. Method: Forty ADHD patients and 51 normal controls participated in this study. Structural networks were reconstructed based on diffusion tensor imaging (DTI) and analyzed with graph theory. Results: The two groups exhibited different patterns of asymmetry in connectivity measures of rich club connections. ADHD patients showed more feeder connections than normal controls. Reduced rightward asymmetry was observed in connectivity measures of local connections involving several peripheral regions of the ADHD patients. In addition, abnormal regional asymmetry scores were associated with ADHD symptoms. Conclusion: The topological changes in rich club organization provide a novel insight into the alteration of WM connections in ADHD.

Resting-state functional hypoconnectivity of amygdala in clinical high risk state and first-episode schizophrenia

Resting-state functional hypoconnectivity of the amygdala with several brain regions has been identified in patients with schizophrenia. However, little is known about it in individuals at clinical high risk state. Treatment-seeking, drug-naive young adults were recruited for the study. The participants included 33 adults at Clinical High Risk (CHRs), 31 adults with first-episode schizophrenia (FSZs), and 37 age-, gender-, and education-matched healthy controls. All the participants were subjected to resting-state functional magnetic resonance imaging scans. Seed-based voxel-wise amygdala/whole-brain functional connectivity (FC) was calculated and compared. In the CHR group, the right amygdala showed decreased FC with clusters located in the left orbital, right temporal, insular, and bilateral frontal and cingulate areas. In the FSZ group, the right amygdala showed decreased FC with clusters located in the right temporal, insular, cingulate, and frontal areas. Exactly 30% of the voxels showing decreased FC in the FSZ group coincided with those in the CHR group. No difference in FC was identified between the CHR and FSZ groups. Voxel-wise FC values with the left or right amygdala in the bilateral occipital cortex were negatively correlated with the PANSS total score in the FSZ group. Resting-state functional hypoconnectivity of the amygdala is a valuable risk phenotype of schizophrenia, and its distribution, rather than degree, distinguishes CHR state from schizophrenia. This particular hypoconnectivity in CHRs and FSZs is relatively independent of the symptomatology and may reflect a dysfunctional dopamine system.

Topological changes in white matter connectivity network in patients with Parkinson's disease and depression

Depression is the most common non-motor symptom accompanying Parkinson's disease (PD) with high prevalence but unclear pathophysiological mechanism. Relatively little is known about the topological patterns of white matter structural networks in depressed patients with PD. In this study, we used diffusion-tensor imaging (DTI) and graph theory approaches to explore the brain structural connectome in non-depressed patients with PD (n = 47), depressed patients with PD (n = 20) and healthy controls (n = 46). All three groups exhibited small-world topology. Compared with healthy controls, non-depressed patients with PD and depressed patients with PD showed a significant reduction of network efficiency in the cortico-subcortical circuits. Moreover, depressed patients with PD exhibited higher network efficiency in fronto-limbic system, compared to non-depressed patients with PD. To sum up, our data indicated a disrupted integrity in the large-scale brain systems in depressed patients with PD patients. The structural connectome provided a basis for functional alterations in depressed patients with PD that may advance our current understanding of pathophysiological mechanism underlying Parkinson's disease.

NMDA receptor-independent LTP in mammalian nervous system

Long-term potentiation (LTP) of synaptic transmission is a form of activity-dependent synaptic plasticity that exists at most synapses in the nervous system. In the central nervous system (CNS), LTP has been recorded at numerous synapses and is a prime candidate mechanism associating activity-dependent plasticity with learning and memory. LTP involves long-lasting increase in synaptic strength with various underlying mechanisms. In the CNS, the predominant type of LTP is believed to be dependent on activation of the ionotropic glutamate N-methyl-D-aspartate receptor (NMDAR), which is highly calcium-permeable. However, various forms of NMDAR-independent LTP have been identified in diverse areas of the nervous system. The NMDAR-independent LTP may require activation of glutamate metabotropic receptors (mGluR) or ionotropic receptors other than NMDAR such as nicotinic acetylcholine receptor (α7-nAChR), serotonin 5-HT3 receptor or calcium-permeable AMPA receptor (CP-AMPAR). In this review, NMDAR-independent LTP of various areas of the central and peripheral nervous systems are discussed.

Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy

Functional near-infrared spectroscopy (fNIRS) has been widely employed in the objective diagnosis of patients with schizophrenia during a verbal fluency task (VFT). Most of the available methods depended on the time-domain features extracted from the data of single or multiple channels. The present study proposed an alternative method based on the functional connectivity strength (FCS) derived from an individual channel. The data measured 100 patients with schizophrenia and 100 healthy controls, who were used to train the classifiers and to evaluate their performance. Different classifiers were evaluated, and support machine vector achieved the best performance. In order to reduce the dimensional complexity of the feature domain, principal component analysis (PCA) was applied. The classification results by using an individual channel, a combination of several channels, and 52 ensemble channels with and without the dimensional reduced technique were compared. It provided a new approach to identify schizophrenia, improving the objective diagnosis of this mental disorder. FCS from three channels on the medial prefrontal and left ventrolateral prefrontal cortices rendered accuracy as high as 84.67%, sensitivity at 92.00%, and specificity at 70%. The neurophysiological significance of the change at these regions was consistence with the major syndromes of schizophrenia.

Multidimensional processing in the amygdala

Brain-wide circuits that coordinate affective and social behaviours intersect in the amygdala. Consequently, amygdala lesions cause a heterogeneous array of social and non-social deficits. Social behaviours are not localized to subdivisions of the amygdala even though the inputs and outputs that carry social signals are anatomically restricted to distinct subnuclear regions. This observation may be explained by the multidimensional response properties of the component neurons. Indeed, the multitudes of circuits that converge in the amygdala enlist the same subset of neurons into different ensembles that combine social and non-social elements into high-dimensional representations. These representations may enable flexible, context-dependent social decisions. As such, multidimensional processing may operate in parallel with subcircuits of genetically identical neurons that serve specialized and functionally dissociable functions. When combined, the activity of specialized circuits may grant specificity to social behaviours, whereas multidimensional processing facilitates the flexibility and nuance needed for complex social behaviour.

Improved and residual functional abnormalities in major depressive disorder after electroconvulsive therapy

Electroconvulsive therapy (ECT) can induce fast remission of depression but still retain the residual functional impairments in major depressive disorder (MDD) patients. To delineate the different functional circuits of effective antidepressant treatment and residual functional impairments is able to better guide clinical therapy for depression. Herein, voxel-level whole brain functional connectivity homogeneity (FcHo), functional connectivity, multivariate pattern classification approaches were applied to reveal the specific circuits for treatment response and residual impairments in MDD patients after ECT. Increased FcHo values in right dorsomedial prefrontal cortex (dmPFC) and left angular gyrus (AG) and their corresponding functional connectivities between dmPFC and right AG, dorsolateral prefrontal cortex (dlPFC), superior frontal gyrus, precuneus (Pcu) and between left AG with dlPFC, bilateral AG, and left ventrolateral prefrontal cortex in MDD patients after ECT. Moreover, we found decreased FcHo values in left middle occipital gyrus (MOG) and lingual gyrus (LG) and decreased functional connectivities between MOG and dorsal postcentral gyrus (PCG) and between LG and middle PCG/anterior superior parietal lobule in MDD patients before and after ECT compared to healthy controls (HCs). The increased or normalized FcHo and functional connections may be related to effective antidepressant therapy, and the decreased FcHo and functional connectivities may account for the residual functional impairments in MDD patients after ECT. The different change patterns in MDD after ECT indicated a specific brain circuit supporting fast remission of depression, which was supported by the following multivariate pattern classification analyses. Finally, we found that the changed FcHo in dmPFC was correlated with changed depression scores. These results revealed a specific functional circuit supporting antidepressant effects of ECT and neuroanatomical basis for residual functional impairments. Our findings also highlighted the key role of dmPFC in antidepressant and will provide an important reference for depression treatment.

The human olfactory system in two proteinopathies: Alzheimer's and Parkinson's diseases

Alzheimer's and Parkinson's diseases are the most prevalent neurodegenerative disorders. Their etiologies are idiopathic, and treatments are symptomatic and orientated towards cognitive or motor deficits. Neuropathologically, both are proteinopathies with pathological aggregates (plaques of amyloid-β peptide and neurofibrillary tangles of tau protein in Alzheimer's disease, and Lewy bodies mostly composed of α-synuclein in Parkinson's disease). These deposits appear in the nervous system in a predictable and accumulative sequence with six neuropathological stages. Both disorders present a long prodromal period, characterized by preclinical signs including hyposmia. Interestingly, the olfactory system, particularly the anterior olfactory nucleus, is initially and preferentially affected by the pathology. Cerebral atrophy revealed by magnetic resonance imaging must be complemented by histological analyses to ascertain whether neuronal and/or glial loss or neuropil remodeling are responsible for volumetric changes. It has been proposed that these proteinopathies could act in a prion-like manner in which a misfolded protein would be able to force native proteins into pathogenic folding (seeding), which then propagates through neurons and glia (spreading). Existing data have been examined to establish why some neuronal populations are vulnerable while others are resistant to pathology and to what extent glia prevent and/or facilitate proteinopathy spreading. Connectomic approaches reveal a number of hubs in the olfactory system (anterior olfactory nucleus, olfactory entorhinal cortex and cortical amygdala) that are key interconnectors with the main hubs (the entorhinal-hippocampal-cortical and amygdala-dorsal motor vagal nucleus) of network dysfunction in Alzheimer's and Parkinson's diseases.

Analysis of brain connectivity during nitrous oxide sedation using graph theory

Nitrous oxide, the least potent inhalation anesthetic, is widely used for conscious sedation. Recently, it has been reported that the occurrence of anesthetic-induced loss of consciousness decreases the interconnection between brain regions, resulting in brain network changes. However, few studies have investigated these changes in conscious sedation using nitrous oxide. Therefore, the present study aimed to use graph theory to analyze changes in brain networks during nitrous oxide sedation. Participants were 20 healthy volunteers (10 men and 10 women, 20–40 years old) with no history of systemic disease. We acquired electroencephalogram (EEG) recordings of 32 channels during baseline, nitrous oxide inhalation sedation, and recovery. EEG epochs from the baseline and the sedation state (50% nitrous oxide) were extracted and analyzed with the network connection parameters of graph theory. Analysis of 1/f dynamics, revealed a steeper slope while in the sedation state than during the baseline. Network connectivity parameters showed significant differences between the baseline and sedation state, in delta, alpha1, alpha2, and beta2 frequency bands. The most pronounced differences in functional distance during nitrous oxide sedation were observed in the alpha1 and alpha2 frequency bands. Change in 1/f dynamics indicates that changes in brain network systems occur during nitrous oxide administration. Changes in network parameters imply that nitrous oxide interferes with the efficiency of information integration in the frequency bands important for cognitive processes and attention tasks. Alteration of brain network during nitrous oxide administration may be associated to the sedative mechanism of nitrous oxide.

Finding Community Modules of Brain Networks Based on PSO with Uniform Design

The brain has the most complex structures and functions in living organisms, and brain networks can provide us an effective way for brain function analysis and brain disease detection. In brain networks, there exist some important neural unit modules, which contain many meaningful biological insights. It is appealing to find the neural unit modules and obtain their affiliations. In this study, we present a novel method by integrating the uniform design into the particle swarm optimization to find community modules of brain networks, abbreviated as UPSO. The difference between UPSO and the existing ones lies in that UPSO is presented first for detecting community modules. Several brain networks generated from functional MRI for studying autism are used to verify the proposed algorithm. Experimental results obtained on these brain networks demonstrate that UPSO can find community modules efficiently and outperforms the other competing methods in terms of modularity and conductance. Additionally, the comparison of UPSO and PSO also shows that the uniform design plays an important role in improving the performance of UPSO.

Altered Granger causality connectivity within motor-related regions of patients with Parkinson's disease: a resting-state fMRI study

PURPOSE

Although numerous clinical neuroimaging studies have demonstrated that there are functional abnormalities of motor-related regions in patients with Parkinson's disease (PD) by resting-state functional magnetic resonance imaging (fMRI), little studies have explored the causal interactions within these motor-related regions. The present study aimed to examine Granger causality connectivity patterns within motor-related regions in PD patients.

METHODS

Resting-state fMRI was conducted to investigate the causal connectivity differences within motor-related regions between 17 PD patients and 17 matched healthy controls. Subsequently, the relationship between the Unified Parkinson's Disease Rating Scale scores and causal connectivity values within motor-related regions was examined in PD patients.

RESULTS

An increased causal connectivity from the left premotor cortex (PMC) to right primary motor cortex (M1) was found in PD patients compared with that of healthy controls. Also, increased causal flow from the PMC to M1 was negatively correlated with motor scores.

CONCLUSION

PD patients have abnormal causal connectivity in specific motor-related regions, which may reflect a compensatory role of motor deficits in PD patients.

Increased Global Interaction Across Functional Brain Modules During Cognitive Emotion Regulation

Cognitive emotion regulation (CER) enables humans to flexibly modulate their emotions. While local theories of CER neurobiology suggest interactions between specialized local brain circuits underlying CER, e.g., in subparts of amygdala and medial prefrontal cortices (mPFC), global theories hypothesize global interaction increases among larger functional brain modules comprising local circuits. We tested the global CER hypothesis using graph-based whole-brain network analysis of functional MRI data during aversive emotional processing with and without CER. During CER, global between-module interaction across stable functional network modules increased. Global interaction increase was particularly driven by subregions of amygdala and cuneus-nodes of highest nodal participation-that overlapped with CER-specific local activations, and by mPFC and posterior cingulate as relevant connector hubs. Results provide evidence for the global nature of human CER, complementing functional specialization of embedded local brain circuits during successful CER.

Altered whole brain functional connectivity pattern homogeneity in medication-free major depressive disorder

BACKGROUND

Many previous studies have revealed abnormal functional connectivity patterns between brain areas underlying the onset of major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to exactly characterize the voxel-wise whole brain functional connectivity pattern changes in MDD remains unclear, which will provide more convincing evidence for localizing the exactly functional connectivity abnormality in MDD.

METHODS

In this study, we employed our newly developed whole brain functional connectivity homogeneity (FcHo) method to identify the voxel-wise changes of functional connectivity patterns in 27 medication-free MDD patients and 34 gender-, age-, and education level-matched healthy controls (HC). Furthermore, seed-based functional connectivity analysis was then used to identify the alteration of corresponding functional connectivity.

RESULTS

Significantly decreased FcHo values in right ventral anterior insula (INS) and medial prefrontal cortex (MPFC) were identified in MDD patients. The ensuing functional connectivity analyses identified decreased functional connectivity between MPFC and left angular gyrus (AG) in MDD patients. Moreover, both decreased FcHo values in INS, MPFC and functional connectivity between MPFC and left AG showed significant negative correlations with Hamilton depression rating scale (HDRS) scores. The FcHo values in INS were also negatively correlated with disease duration. Finally, meta-analysis based functional characterization found that these brain areas are mainly involved in emotion, theory of mind and reward processing.

CONCLUSIONS

Our findings revealed abnormal whole brain FcHo in INS and MPFC and functional interactions between MPFC and AG in MDD and suggested that dysfunctions of INS and MPFC play an important role in the neuropathology of MDD.

The negative correlation between energy consumption and communication efficiency in motor network

OBJECTIVE

Motor network plays an important role in people's daily lives. However, until now, the energy consumption mechanism of motor network remains unclear. In this study, we aimed to investigate the energy consumption of motor network.

MATERIALS AND METHODS

Fluorine-18-fluorodeoxyglucose PET ([F]FDG PET) data of 81 healthy male Sprague-Dawley rats were included in this study. Metabolic motor network was constructed on the basis of group independent component analysis. Properties of motor network such as degree and nodal efficiency were investigated using graph theory-based analysis. Furthermore, the relationships between [F]FDG standardized uptake value ratio and these properties of each node were investigated.

RESULTS

A motor network comprising of the following 11 regions were found: left primary motor cortex, right primary motor cortex, left secondary motor cortex, right secondary motor cortex, left primary somatosensory cortex, right primary somatosensory cortex, left secondary somatosensory cortex, right secondary somatosensory cortex, left insular cortex, right insular cortex, and left orbital cortex. Graph theory-based analysis indicated that right primary somatosensory cortex and left secondary somatosensory cortex were the hubs of motor network, and the nodal efficiency and nodal degree share the same order. Further investigation found a significantly negative correlation between nodal efficiency and [F]FDG standardized uptake value ratios.

CONCLUSION

This study investigated the energy consumption of motor network and found a relationship between energy consumption and communication efficiency. These results may provide insights into the understanding of energy consumption mechanism underlying motor network.Video abstract: http://links.lww.com/NMC/A142.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Abnormal coupling among spontaneous brain activity metrics and cognitive deficits in major depressive disorder

BACKGROUND

A variety of functional metrics derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been employed to explore spontaneous brain activity changes in major depressive disorder (MDD) and have enjoyed significant success in unraveling the neurobiological mechanisms underlying this disorder. However, it is unclear whether spatial and temporal coupling relationships among these rs-fMRI metrics are altered in MDD.

METHODS

50 patients with MDD and 36 well-matched healthy controls underwent rs-fMRI scans. A dynamic analysis was applied to compute multiple frequently used metrics including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity. Kendall's W was used to calculate volume-wise (across voxels) and voxel-wise (across time windows) concordance among these metrics. Inter-group differences in the concordance and their associations with clinical and cognitive variables were tested.

RESULTS

Compared to healthy controls, patients with MDD showed decreased whole gray matter volume-wise concordance. Despite similar spatial distributions, quantitative comparison analysis revealed that MDD patients exhibited reduced voxel-wise concordance in multiple cortical and subcortical regions. Moreover, the lower concordance was associated with worse performances in prospective memory and sustained attention in the MDD group.

LIMITATIONS

The study design of fairly modest sample size did not allow us to perform a full analysis of the potential effects of medication and illness duration.

CONCLUSIONS

Our findings suggest that spatial and temporal decoupling of multiple resting-state brain activity metrics may help elucidate the neural mechanisms of cognitive deficits in depression.

Music enhances activity in the hypothalamus, brainstem, and anterior cerebellum during script-driven imagery of affective scenes

Music is frequently used to establish atmosphere and to enhance/alter emotion in dramas and films. During music listening, visual imagery is a common mechanism underlying emotion induction. The present functional magnetic resonance imaging (fMRI) study examined the neural substrates of the emotional processing of music and imagined scene. A factorial design was used with factors emotion valence (positive; negative) and music (withoutMUSIC: script-driven imagery of emotional scenes; withMUSIC: script-driven imagery of emotional scenes and simultaneously listening to affectively congruent music). The baseline condition was imagery of neutral scenes in the absence of music. Eleven females and five males participated in this fMRI study. Behavioural data revealed that during scene imagery, participants' subjective emotions were significantly intensified by music. The contrasts of positive and negative withoutMUSIC conditions minus the baseline (imagery of neutral scenes) showed no significant activation. When comparing the withMUSIC to withoutMUSIC conditions, activity in a number of emotion-related regions was observed, including the temporal pole (TP), amygdala, hippocampus, hypothalamus, anterior ventral tegmental area (VTA), locus coeruleus, and anterior cerebellum. We hypothesized that the TP may integrate music and the imagined scene to extract socioemotional significance, initiating the subcortical structures to generate subjective feelings and bodily responses. For the withMUSIC conditions, negative emotions were associated with enhanced activation in the posterior VTA compared to positive emotions. Our findings replicated and extended previous research which suggests that different subregions of the VTA are sensitive to rewarding and aversive stimuli. Taken together, this study suggests that emotional music embedded in an imagined scenario is a salient social signal that prompts preparation of approach/avoidance behaviours and emotional responses in listeners.

Localization of Epileptogenic Zone Based on Cortico-Cortical Evoked Potential (CCEP): A Feature Extraction and Graph Theory Approach

Objective

Epilepsy is a chronic brain disease, which is prone to relapse and affects individuals of all ages worldwide, particularly the very young and elderly. Up to one-third of these patients are medically intractable and require resection surgery. However, the outcomes of epilepsy surgery rely upon the clear identification of epileptogenic zone (EZ). The combination of cortico-cortical evoked potential (CCEP) and electrocorticography (ECoG) provides an opportunity to observe the connectivity of human brain network and more comprehensive information that may help the clinicians localize the epileptogenic focus more precisely. However, there is no standard analysis method in the clinical application of CCEPs, especially for the quantitative analysis of abnormal connectivity of epileptic networks. The aim of this paper was to present an approach on the batch processing of CCEPs and provide information relating to the localization of EZ for clinical study.

Methods

Eight medically intractable epilepsy patients were included in this study. Each patient was implanted with subdural grid electrodes and electrical stimulations were applied directly to their cortex to induce CCEPs. After signal preprocessing, we constructed three effective brain networks at different spatial scales for each patient, regarding the amplitudes of CCEPs as the connection weights. Graph theory was then applied to analyze the brain network topology of epileptic patients, and the topological metrics of EZ and non-EZ (NEZ) were compared.

Results

The effective connectivity network reconstructed from CCEPs was asymmetric, both the number and the amplitudes of effective CCEPs decreased with increasing distance between stimulating and recording sites. Besides, the distribution of CCEP responses was associated with the locations of EZ which tended to have higher degree centrality (DC) and nodal shortest path length (NLP) than NEZ.

Conclusion

Our results indicated that the brain networks of epileptics were asymmetric and mainly composed of short-distance connections. The DC and NLP were highly consistent to the distribution of the EZ, and these topological parameters have great potential to be readily applied to the clinical localization of the EZ.

Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia

Brain functional disruption and cognitive shortfalls as consequences of neurodegeneration are among the most investigated aspects in current clinical research. Traditionally, specific anatomical and behavioral traits have been associated with neurodegeneration, thus directly translatable in clinical terms. However, these qualitative traits, do not account for the extensive information flow breakdown within the functional brain network that deeply affect cognitive skills. Behavioural variant Frontotemporal Dementia (bvFTD) is a neurodegenerative disorder characterized by behavioral and executive functions disturbances. Deviations from the physiological cognitive functioning can be accurately inferred and modeled from functional connectivity alterations. Although the need for unbiased metrics is still an open issue in imaging studies, the graph-theory approach applied to neuroimaging techniques is becoming popular in the study of brain dysfunction. In this work, we assessed the global connectivity and topological alterations among brain regions in bvFTD patients using a minimum spanning tree (MST) based analysis of resting state functional MRI (rs-fMRI) data. Whilst several graph theoretical methods require arbitrary criteria (including the choice of network construction thresholds and weight normalization methods), MST is an unambiguous modeling solution, ensuring accuracy, robustness, and reproducibility. MST networks of 116 regions of interest (ROIs) were built on wavelet correlation matrices, extracted from 41 bvFTD patients and 39 healthy controls (HC). We observed a global fragmentation of the functional network backbone with severe disruption of information-flow highways. Frontotemporal areas were less compact, more isolated, and concentrated in less integrated structures, respect to healthy subjects. Our results reflected such complex breakdown of the frontal and temporal areas at both intra-regional and long-range connections. Our findings highlighted that MST, in conjunction with rs-fMRI data, was an effective method for quantifying and detecting functional brain network impairments, leading to characteristic bvFTD cognitive, social, and executive functions disorders.

Changed hub and functional connectivity patterns of the posterior fusiform gyrus in chess experts

The hubs of the brain network play a key role in integrating and transferring information between different functional modules. However, the effects of long-term practice on functional network hubs in chess experts are largely undefined. Here, we investigated whether alterations of hubs can be detected in chess experts using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory methods. We first mapped the whole-brain voxel-wise functional connectivity and calculated the functional connectivity strength (FCS) map in each of the 28 chess players and 27 gender- and age-matched healthy novice players. Whole-brain resting-state functional connectivity analyses for the changed hub areas were conducted to further elucidate the corresponding changes of functional connectivity patterns in chess players. The hub analysis revealed increased FCS in the right posterior fusiform gyrus of the chess players, which was supported by analyses of this area's regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and fractional amplitude of low frequency fluctuations (fALFF). The following functional connectivity analyses revealed increased functional connectivities between the right posterior fusiform gyrus and the visuospatial attention and motor networks in chess players. These findings demonstrate that cognitive expertise has a positive influence on the functions of the brain regions associated with the chess expertise and that increased functional connections might in turn facilitate within and between networks communication for expert behavior to get superior performance.

Pivotal Role of Subcortical Structures as a Network Hub in Focal Epilepsy: Evidence from Graph Theoretical Analysis Based on Diffusion-Tensor Imaging

Background and Purpose

There is accumulating evidence that epilepsy is caused by network dysfunction. We evaluated the hub reorganization of subcortical structures in patients with focal epilepsy using graph theoretical analysis based on diffusion-tensor imaging (DTI). In addition, we investigated differences in the values of diffusion tensors and scalars, fractional anisotropy (FA), and mean diffusivity (MD) of subcortical structures between patients with focal epilepsy and healthy subjects.

Methods

One hundred patients with focal epilepsy and normal magnetic resonance imaging (MRI) findings and 80 age- and sex-matched healthy subjects were recruited prospectively. All subjects underwent DTI to obtain data suitable for graph theoretical analysis. We investigated the differences in the node strength, cluster coefficient, eigenvector centrality, page-rank centrality measures, FA, and MD of subcortical structures between patients with epilepsy and healthy subjects.

Results

After performing multiple corrections, the cluster coefficient and the eigenvector centrality of the globus pallidus were higher in patients with epilepsy than in healthy subjects (p=0.006 and p=0.008, respectively). In addition, the strength and the page-rank centrality of the globus pallidus tended to be higher in patients with epilepsy than in healthy subjects (p=0.092 and p=0.032, respectively). The cluster coefficient of the putamen was lower in patients with epilepsy than in healthy subjects (p=0.004). The FA values of the caudate nucleus and thalamus were significantly lower in patients with epilepsy than in healthy subjects (p=0.009 and p=0.007, respectively), whereas the MD value of the thalamus was higher than that in healthy subjects (p=0.005).

Conclusions

We discovered the presence of hub reorganization of subcortical structures in focal epilepsy patients with normal MRI findings, suggesting that subcortical structures play a pivotal role as a hub in the epilepsy network. These findings further reinforce the idea that epilepsy is a network disease.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Emotion-related brain organization and behavioral responses to socioemotional stimuli in pediatric cancer survivors with posttraumatic stress symptoms

BACKGROUND

Pediatric cancer is a life-changing, stressful experience for children and their families. Although most children adjust well, psychologically, a significant subset report posttraumatic stress symptoms (PTSS), with nearly 75% reexperiencing traumatic parts of cancer and/or its treatment. However, little research has examined the effects of pediatric cancer and related PTSS on emotional processing, and on functional properties of key emotional centers in the brain (e.g., amygdala).

PROCEDURE

We examined cancer-related PTSS, behavioral responses during an emotion-processing task, and resting-state functional connectivity of the amygdala in 17 pediatric cancer survivors (ages 6-11) and 17 age- and sex-matched controls.

RESULTS

Cancer survivors, relative to controls, were more likely to rate ambiguous (i.e., neutral) faces as negative (i.e., "negativity bias"). Higher reexperiencing PTSS was associated with faster responses to neutral faces. Although there were no group differences in amygdala centrality, within survivors, both higher reexperiencing PTSS and faster reaction times were associated with increased centrality of the amygdala-a functional property associated with hubs of information processing in the brain. In an exploratory mediation analysis, we found that amygdala centrality mediated the link between reaction time and PTSS, suggesting that changes in the brain may be a proximal marker of the expression of emotion-related symptomology.

CONCLUSIONS

Negativity bias in cancer survivors may reflect their stressful experiences with cancer and/or its treatment. This negativity bias may represent a susceptibility to changes in emotion-related brain functioning, which may, in turn, lead to PTSS.

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.

Connective Tissue Growth Factor Is a Novel Prodepressant

BACKGROUND

While downregulation of several growth factors in major depressive disorder is well established, less attention has been paid to the upregulation of other growth factors. Yet, upregulated growth factors may offer better therapeutic targets. We show that connective tissue growth factor (CTGF) represents a target based on its upregulation in major depressive disorder and studies in animal models implicating it in negative affect.

METHODS

CTGF gene expression was first evaluated in the postmortem human amygdala. The findings were followed up in outbred rats and in two rat lines that were selectively bred for differences in novelty-seeking and anxiety behavior (bred low responders and bred high responders). We studied the impact of social defeat and early-life treatment with fibroblast growth factor 2 on CTGF expression. Finally, we assessed the ability of an anti-CTGF antibody (FG-3019) to alter CTGF expression and emotionality.

RESULTS

In the human amygdala, CTGF expression was significantly increased in major depressive disorder compared with control subjects. CTGF expression was also significantly increased in the dentate gyrus of adult bred low responders compared with bred high responders. Social defeat stress in bred low responders significantly increased CTGF expression in the dentate gyrus. Early-life fibroblast growth factor 2, a treatment that reduces anxiety-like behavior throughout life, decreased CTGF expression in the adult dentate gyrus. In outbred rats, CTGF administration increased depression-like behavior. Chronic treatment with FG-3019 decreased CTGF expression, and acute and chronic treatment was antidepressant.

CONCLUSIONS

This study is the first to implicate CTGF as a prodepressant molecule that could serve as a target for the development of novel therapeutics.

Abnormal Global Functional Connectivity Patterns in Medication-Free Major Depressive Disorder

Mounting studies have applied resting-state functional magnetic resonance imaging (rs-fMRI) to study major depressive disorder (MDD) and have identified abnormal functional activities. However, how the global functional connectivity patterns change in MDD is still unknown. Using rs-fMRI, we investigated the alterations of global resting-state functional connectivity (RSFC) patterns in MDD using weighted global brain connectivity (wGBC) method. First, a whole brain voxel-wise wGBC map was calculated for 23 MDD patients and 34 healthy controls. Two-sample t-tests were applied to compare the wGBC and RSFC maps and the significant level was set at p < 0.05, cluster-level correction with voxel-level p < 0.001. MDD patients showed significantly decreased wGBC in left temporal pole (TP) and increased wGBC in right parahippocampus (PHC). Subsequent RSFC analyses showed decreased functional interaction between TP and right posterior superior temporal cortex and increased functional interaction between PHC and right inferior frontal gyrus in MDD patients. These results revealed the abnormal global FC patterns and its corresponding disrupted functional connectivity in MDD. Our findings present new evidence for the functional interruption in MDD.