Altered degree centrality in childhood absence epilepsy: A resting-state fMRI study

An rs-fMRI based neuroimaging marker for adult absence epilepsy

OBJECTIVE

Approximately 20-30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE.

METHODS

This research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing.

RESULTS

Compared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %.

SIGNIFICANCE

According to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.

Disrupted default mode network connectivity in bipolar disorder: a resting-state fMRI study

BACKGROUND

Theoretical and empirical evidence indicates the critical role of the default mode network (DMN) in the pathophysiology of the bipolar disorder (BD). This study aims to identify the specific brain regions of the DMN that is impaired in patients with BD.

METHODS

A total of 56 patients with BD and 71 healthy controls (HC) underwent resting-state functional magnetic resonance imaging. Three commonly used functional indices, i.e., fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree centrality (DC), were utilized to identify the brain region showing abnormal spontaneous brain activity in patients with BD. Then, this region served as the seed region for resting-state functional connectivity (rsFC) analysis.

RESULTS

Compared to the HC group, the BD group showed reduced fALFF, ReHo, and DC values in the left precuneus. Moreover, patients exhibited decreased rsFCs within the left precuneus and between the left precuneus and the medial prefrontal cortex. Additionally, there was diminished negative connectivity between the left precuneus and the left putamen, extending to the left insula (putamen/insula). The abnormalities in DMN functional connectivity were confirmed through various analysis strategies.

CONCLUSIONS

Our findings provide convergent evidence for the abnormalities in the DMN, particularly located in the left precuneus. Decreased functional connectivity within the DMN and the reduced anticorrelation between the DMN and the salience network are found in patients with BD. These findings suggest that the DMN is a key aspect for understanding the neural basis of BD, and the altered functional patterns of DMN may be a potential candidate biomarker for diagnosis of BD.

Spatio-Temporal Graph Hubness Propagation Model for Dynamic Brain Network Classification

Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.

Transcranial electrical stimulation during functional magnetic resonance imaging in patients with genetic generalized epilepsy: a pilot and feasibility study

Objective

A third of patients with epilepsy continue to have seizures despite receiving adequate antiseizure medication. Transcranial direct current stimulation (tDCS) might be a viable adjunct treatment option, having been shown to reduce epileptic seizures in patients with focal epilepsy. Evidence for the use of tDCS in genetic generalized epilepsy (GGE) is scarce. We aimed to establish the feasibility of applying tDCS during fMRI in patients with GGE to study the acute neuromodulatory effects of tDCS, particularly on sensorimotor network activity.

Methods

Seven healthy controls and three patients with GGE received tDCS with simultaneous fMRI acquisition while watching a movie. Three tDCS conditions were applied: anodal, cathodal and sham. Periods of 60 s without stimulation were applied between each stimulation condition. Changes in sensorimotor cortex connectivity were evaluated by calculating the mean degree centrality across eight nodes of the sensorimotor cortex defined by the Automated Anatomical Labeling atlas (primary motor cortex (precentral left and right), supplementary motor area (left and right), mid-cingulum (left and right), postcentral gyrus (left and right)), across each of the conditions, for each participant.

Results

Simultaneous tDCS-fMRI was well tolerated in both healthy controls and patients without adverse effects. Anodal and cathodal stimulation reduced mean degree centrality of the sensorimotor network (Friedman's ANOVA with Dunn's multiple comparisons test; adjusted p = 0.02 and p = 0.03 respectively). Mean degree connectivity of the sensorimotor network during the sham condition was not different to the rest condition (adjusted p = 0.94).

Conclusion

Applying tDCS during fMRI was shown to be feasible and safe in a small group of patients with GGE. Anodal and cathodal stimulation caused a significant reduction in network connectivity of the sensorimotor cortex across participants. This initial research supports the feasibility of using fMRI to guide and understand network modulation by tDCS that might facilitate its clinical application in GGE in the future.

Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study

BACKGROUND

Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy.

METHODS

Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN.

RESULTS

Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables.

CONCLUSIONS

Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.

Relapse risk revealed by degree centrality and cluster analysis in heroin addicts undergoing methadone maintenance treatment

BACKGROUND

Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.

METHODS

Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.

RESULTS

Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical-striatal-thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.

CONCLUSION

MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.

Abnormal metabolic connectivity in default mode network of right temporal lobe epilepsy

Aims

Temporal lobe epilepsy (TLE) is a common neurological disorder associated with the dysfunction of the default mode network (DMN). Metabolic connectivity measured by 18F-fluorodeoxyglucose Positron Emission Computed Tomography (18F-FDG PET) has been widely used to assess cumulative energy consumption and provide valuable insights into the pathophysiology of TLE. However, the metabolic connectivity mechanism of DMN in TLE is far from fully elucidated. The present study investigated the metabolic connectivity mechanism of DMN in TLE using 18F-FDG PET.

Method

Participants included 40 TLE patients and 41 health controls (HC) who were age- and gender-matched. A weighted undirected metabolic network of each group was constructed based on 14 primary volumes of interest (VOIs) in the DMN, in which Pearson's correlation coefficients between each pair-wise of the VOIs were calculated in an inter-subject manner. Graph theoretic analysis was then performed to analyze both global (global efficiency and the characteristic path length) and regional (nodal efficiency and degree centrality) network properties.

Results

Metabolic connectivity in DMN showed that regionally networks changed in the TLE group, including bilateral posterior cingulate gyrus, right inferior parietal gyrus, right angular gyrus, and left precuneus. Besides, significantly decreased (P < 0.05, FDR corrected) metabolic connections of DMN in the TLE group were revealed, containing bilateral hippocampus, bilateral posterior cingulate gyrus, bilateral angular gyrus, right medial of superior frontal gyrus, and left inferior parietal gyrus.

Conclusion

Taken together, the present study demonstrated the abnormal metabolic connectivity in DMN of TLE, which might provide further insights into the understanding the dysfunction mechanism and promote the treatment for TLE patients.

The brain network underlying attentional blink predicts symptoms of attention deficit hyperactivity disorder in children

Attention deficit hyperactivity disorder (ADHD) is a chronic neuropsychiatric disease that can markedly impair educational, social, and occupational function throughout life. Behavioral deficits may provide clues to the underlying neurological impairments. Children with ADHD exhibit a larger attentional blink (AB) deficit in rapid serial visual presentation (RSVP) tasks than typically developing children, so we examined whether brain connectivity in the neural network associated with AB can predict ADHD symptoms and thus serve as potential biomarkers of the underlying neuropathology. We first employed a connectome-based predictive model analysis of adult resting-state functional magnetic resonance imaging data to identify a distributed brain network for AB. The summed functional connectivity (FC) strength within the AB network reliably predicted individual differences in AB magnitude measured by a classical dual-target RSVP task. Furthermore, the summed FC strength within the AB network predicted individual differences in ADHD Rating Scale scores from an independent dataset of pediatric patients. Our findings suggest that the individual AB network could serve as an applicable neuroimaging-based biomarker of AB deficit and ADHD symptoms.

Higher betweenness and degree centrality in the frontal and cerebellum cortex of Meige's syndrome patients than hemifacial spasm patients

Meige's syndrome and hemifacial spasm (HFS) are two different forms of dystonic movement disorder, but their difference in terms of resting state functional connectivity (rsFC) remains unclear. The present study applied resting state fMRI on the patients and quantified their functional connectivity with graph theoretical measures, including the degree centrality and the betweenness centrality. Fifteen Meige's syndrome patients and 19 HFS patients matched in age and gender were recruited and their MRI data were collected. To analyze the rsFC, we adopted the Anatomical Automatic Labeling (AAL) template, a brain atlas system including 90 regions of interest (ROIs) covering all the brain regions of cerebral cortex. For each participant, the time-course of each ROI was extracted, and the corresponding degree centrality and betweenness centrality of each ROI were computed. These measures were then compared between the Meige's syndrome patients and the HFS patients. Meige's syndrome patients showed higher betweenness centrality and degree centrality of bilateral superior medial frontal cortex, the left cerebellum cortex, etc. than the HFS patients. Our results suggest that the rsFC pattern in Meige's syndrome patients might become more centralized toward the prefrontal and vestibular cerebellar systems, indicating less flexibility in their functional connections. These results preliminarily revealed the characteristic abnormality in the functional connection of Meige's patients and may help to explore better treatment.

Study of brain network alternations in non-lesional epilepsy patients by BOLD-fMRI

Objective

To investigate the changes of brain network in epilepsy patients without intracranial lesions under resting conditions.

Methods

Twenty-six non-lesional epileptic patients and 42 normal controls were enrolled for BOLD-fMRI examination. The differences in brain network topological characteristics and functional network connectivity between the epilepsy group and the healthy controls were compared using graph theory analysis and independent component analysis.

Results

The area under the curve for local efficiency was significantly lower in the epilepsy patients compared with healthy controls, while there were no differences in global indicators. Patients with epilepsy had higher functional connectivity in 4 connected components than healthy controls (orbital superior frontal gyrus and medial superior frontal gyrus, medial superior frontal gyrus and angular gyrus, superior parietal gyrus and paracentral lobule, lingual gyrus, and thalamus). In addition, functional connectivity was enhanced in the default mode network, frontoparietal network, dorsal attention network, sensorimotor network, and auditory network in the epilepsy group.

Conclusion

The topological characteristics and functional connectivity of brain networks are changed in in non-lesional epilepsy patients. Abnormal functional connectivity may suggest reduced brain efficiency in epilepsy patients and also may be a compensatory response to brain function early at earlier stages of the disease.

Resting-State fMRI Can Detect Alterations in Seizure Onset and Spread Regions in Patients with Non-Lesional Epilepsy: A Pilot Study

Introduction

Epilepsy is defined as non-lesional (NLE) when a lesion cannot be localized via standard neuroimaging. NLE is known to have a poor response to surgery. Stereotactic electroencephalography (sEEG) can detect functional connectivity (FC) between zones of seizure onset (OZ) and early (ESZ) and late (LSZ) spread. We examined whether resting-state fMRI (rsfMRI) can detect FC alterations in NLE to see whether noninvasive imaging techniques can localize areas of seizure propagation to potentially target for intervention.

Methods

This is a retrospective study of 8 patients with refractory NLE who underwent sEEG electrode implantation and 10 controls. The OZ, ESZ, and LSZ were identified by generating regions around sEEG contacts that recorded seizure activity. Amplitude synchronization analysis was used to detect the correlation of the OZ to the ESZ. This was also done using the OZ and ESZ of each NLE patient for each control. Patients with NLE were compared to controls individually using Wilcoxon tests and as a group using Mann-Whitney tests. Amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DoC), and voxel-mirrored homotopic connectivity (VMHC) were calculated as the difference between NLE and controls and compared between the OZ and ESZ and to zero. A general linear model was used with age as a covariate with Bonferroni correction for multiple comparisons.

Results

Five out of 8 patients with NLE showed decreased correlations from the OZ to the ESZ. Group analysis showed patients with NLE had lower connectivity with the ESZ. Patients with NLE showed higher fALFF and ReHo in the OZ but not the ESZ, and higher DoC in the OZ and ESZ. Our results indicate that patients with NLE show high levels of activity but dysfunctional connections in seizure-related areas.

Discussion

rsfMRI analysis showed decreased connectivity directly between seizure-related areas, while FC metric analysis revealed increases in local and global connectivity in seizure-related areas. FC analysis of rsfMRI can detect functional disruption that may expose the pathophysiology underlying NLE.

Abnormal Spontaneous Blood Oxygenation Level Dependent Fluctuations in Children with Focal Cortical Dysplasias: Initial Findings in Surgically Confirmed Cases

BACKGROUND

 Focal cortical dysplasias (FCD) are a frequent cause of drug-resistant epilepsy in children but are often undetected on structural magnetic resonance imaging (MRI). We aimed to measure and validate the variation of resting state functional MRI (rs-fMRI) blood oxygenation level dependent (BOLD) metrics in surgically proven FCDs in children, to assess the potential yield for detecting and understanding these lesions.

METHODS

 We prospectively included pediatric patients with surgically proven FCD with inconclusive structural MRI and healthy controls, who underwent a ten-minute rs-fMRI acquired at 3T. Rs-fMRI data was pre-processed and maps of values of regional homogeneity (ReHo), degree centrality (DC), amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) were calculated. The variations of BOLD metrics within the to-be-resected areas were analyzed visually, and quantitatively using lateralization indices. BOLD metrics variations were also analyzed in fluorodeoxyglucose-positron emission tomography (FDG-PET) hypometabolic areas.

RESULTS

 We included 7 patients (range: 3-15 years) and 6 aged-matched controls (range: 6-17 years). ReHo lateralization indices were positive in the to-be-resected areas in 4/7 patients, and in 6/7 patients in the additional PET hypometabolic areas. These indices were significantly higher compared to controls in 3/7 and 4/7 patients, respectively. Visual analysis revealed a good spatial correlation between high ReHo areas and MRI structural abnormalities (when present) or PET hypometabolic areas. No consistent variation was seen using DC, ALFF, or fALFF.

CONCLUSION

 Resting-state fMRI metrics, noticeably increase in ReHo, may have potential to help detect MRI-negative FCDs in combination with other morphological and functional techniques, used in clinical practice and epilepsy-surgery screening.

Altered neurovascular coupling in children with idiopathic generalized epilepsy

AIMS

Alterations in neuronal activity and cerebral hemodynamics have been reported in idiopathic generalized epilepsy (IGE) patients, possibly resulting in neurovascular decoupling; however, no neuroimaging evidence confirmed this disruption. This study aimed to investigate the possible presence of neurovascular decoupling and its clinical implications in childhood IGE using resting-state fMRI and arterial spin labeling imaging.

METHODS

IGE patients and healthy participants underwent resting-state fMRI and arterial spin labeling imaging to calculate degree centrality (DC) and cerebral blood flow (CBF), respectively. Across-voxel CBF-DC correlations were analyzed to evaluate the neurovascular coupling within the whole gray matter, and the regional coupling of brain region was assessed with the CBF/DC ratio.

RESULTS

The study included 26 children with IGE and 35 sex- and age-matched healthy controls (HCs). Compared with the HCs, the IGE group presented lower across-voxel CBF-DC correlations, higher CBF/DC ratio in the right posterior cingulate cortex/precuneus, middle frontal gyrus, and medial frontal gyrus (MFG), and lower ratio in the left inferior frontal gyrus. The increased CBF/DC ratio in the right MFG was correlated with lower performance intelligence quotient scores in the IGE group.

CONCLUSION

Children with IGE present altered neurovascular coupling, associated with lower performance intelligence quotient scores. The study shed a new insight into the pathophysiology of epilepsy and provided potential imaging biomarkers of cognitive performances in children with IGE.

Separation of memory span and learning rate: Evidence from behavior and spontaneous brain activity in older adults

It is unclear how the ability to initially acquire information in a first learning trial relates to learning rate in subsequent repeated trials. The separation of memory span and learning rate is an important psychological dilemma that remains unaddressed. Given the potential effects of aging on memory and learning, this study investigated the separation of memory span and learning rate from behavior and spontaneous brain activity in older adults. We enrolled a total of 758 participants, including 707 healthy older adults and 51 mild cognitive impairment (MCI) patients. Sixty-five participants out of 707 completed resting-state functional magnetic resonance imaging (fMRI) scanning. Behaviorally, memory span and learning rate were not correlated with each other in the paired-associative learning test (PALT) but were negatively correlated in the auditory verbal learning test (AVLT). This indicated that the relationship between memory span and learning rate for item memory might be differentially affected by aging. Interaction analysis confirmed that these two capacities were differentially affected by test type (associative memory vs. item memory). Additionally, at three progressive brain activity indexes (ALFF, ReHo, and DC), the right brain regions (right inferior temporal gyrus and right middle frontal gyrus) were more negatively correlated with memory span, whereas, the left precuneus was more positively correlated with learning rate. Regarding pathological aging, none of the correlations between memory span and learning rate were significant in either PALT or AVLT in MCI. This study provides novel evidence for the dissociation of memory span and learning rate at behavioral and brain activity levels, which may have useful applications in detecting cognitive deficits or conducting cognitive interventions.

A connectome-based neuromarker of nonverbal number acuity and arithmetic skills

The approximate number system (ANS) is vital for survival and reproduction in animals and is crucial for constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, a neuromarker to characterize an individual's ANS acuity is lacking, especially one based on whole-brain functional connectivity (FC). Here, based on the resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from a large sample, we identified a distributed brain network (i.e. a numerosity network) using a connectome-based predictive modeling (CPM) analysis. The summed FC strength within the numerosity network reliably predicted individual differences in ANS acuity regarding behavior, as measured using a nonsymbolic number-comparison task. Furthermore, in an independent dataset of the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network also specifically predicted individual differences in arithmetic skills, but not domain-general cognitive abilities. Therefore, our findings revealed that the identified numerosity network could serve as an applicable neuroimaging-based biomarker of nonverbal number acuity and arithmetic skills.

A hands-on tutorial on network and topological neuroscience

The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

Topological Characteristics Associated with Intraoperative Stimulation Related Epilepsy of Glioma Patients: A DTI Network Study

Background: Awake craniotomy with intraoperative stimulation has been utilized in glioma surgical resection to preserve the quality of life. Epilepsy may occur in 5–20% of cases, leading to severe consequences. This study aimed to discuss the mechanism of intraoperative stimulation-related epilepsy (ISE) using DTI-based graph theoretical analysis. Methods: Twenty patients with motor-area glioma were enrolled and divided into two groups (Ep and nEp) according to the presence of ISE. Additionally, a group of 10 healthy participants matched by age, sex, and years of education was also included. All participants underwent T1, T2, and DTI examinations. Graph theoretical analysis was applied to reveal the topological characteristics of white matter networks. Results: Three connections were found to be significantly lower in at least one weighting in the Ep group. These connections were between A1/2/3truL and A4ulL, A1/2/3truR and A4tR, and A6mL and A6mR. Global efficiency was significantly decreased, while the shortest path length increased in the Ep group in at least one weighting. Ten nodes exhibited significant differences in nodal efficiency and degree centrality analyses. The nodes A6mL and A6mR showed a marked decrease in total four weightings in the Ep group. Conclusions: The hub nodes A6mL and A6mR are disconnected in patients with ISE, causing subsequent lower efficiency of global and regional networks. These findings provide a basis for presurgical assessment of ISE, for which caution should be taken when it involves hub nodes during intraoperative electrical stimulation.

Abnormal Fractional Amplitude of Low-Frequency Fluctuation as a Potential Imaging Biomarker for First-Episode Major Depressive Disorder: A Resting-State fMRI Study and Support Vector Machine Analysis

Objective: Major depressive disorder (MDD) is a psychiatric disorder with serious negative health outcomes; however, there is no reliable method of diagnosis. This study explored the clinical diagnostic value of the fractional amplitude of low-frequency fluctuation (fALFF) based on the support vector machine (SVM) method for the diagnosis of MDD.

Methods: A total of 198 first-episode MDD patients and 234 healthy controls were involved in this study, and all participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Imaging data were analyzed with the fALFF and SVM methods.

Results: Compared with the healthy controls, the first-episode MDD patients showed higher fALFF in the left mid cingulum, right precuneus, and left superior frontal gyrus (SFG). The increased fALFF in these three brain regions was positively correlated with the executive control reaction time (ECRT), and the increased fALFF in the left mid cingulum and left SFG was positively correlated with the 17-item Hamilton Rating Scale for Depression (HRSD-17) scores. The SVM results showed that increased fALFF in the left mid cingulum, right precuneus, and left SFG exhibited high diagnostic accuracy of 72.92% (315/432), 71.76% (310/432), and 73.84% (319/432), respectively. The highest diagnostic accuracy of 76.39% (330/432) was demonstrated for the combination of increased fALFF in the right precuneus and left SFG, along with a sensitivity of 84.34% (167/198), and a specificity of 70.51% (165/234).

Conclusion: Increased fALFF in the left mid cingulum, right precuneus, and left SFG may serve as a neuroimaging marker for first-episode MDD. The use of the increased fALFF in the right precuneus and left SFG in combination showed the best diagnostic value.

The brain mechanism of awakening dysfunction in children with primary nocturnal enuresis based on PVT-NAc neural pathway: a resting-state fMRI study

Primary nocturnal enuresis (PNE) affects children's physical and mental health with a high rate. However, its neural mechanism is still unclear. Studies have found that the paraventricular thalamus (PVT) is among the key brain regions implicated with awakening regulation and its control of the transition between sleep and wakening is dependent on signaling through the PVT-nucleus accumbens (NAc) pathway. So this study analyzed the function of brain regions and their connectivity of PVT and NAc. A total of twenty-six PNE and typically developing (TD) children were involved in the study and the methods of amplitude of low frequency fluctuation (ALFF), degree centrality (DC) and functional connectivity (FC) based on resting-state functional magnetic resonance imaging (rs-fMRI) were used to analyze the brain functions. Results showed that there was no statistical significant difference in ALFF and DC between PNE and TD children in bilateral PVT and NAc. And there was statistical significant difference of the comparison of the FC of left PVT (lPVT) and left NAc (lNAc) between PNE and TD children. Meanwhile, there was negative correlation between awakening score and the FC of rPVT and lNAc, and no obvious correlation between awakening score and the FC of lPVT and lNAc in PNE children. Meanwhile, there was both negative correlation between awakening score and the FC of lPVT, rPTV and lNAc in TD children. Therefore, the FC between rPVT and lNAc was more reliable in assessing the degree of awakening ability in PNE children. This finding could help establish the evaluation index of PNE.

Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy

BACKGROUND AND PURPOSE

The purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI).

METHODS

RS-fMRI data from 35 CAE patients were collected and compared with findings from 35 age and gender matched healthy controls (HCs). The patients were treated with AEDs for 46.03 weeks before undergoing a second RS-fMRI scan.

RESULTS

CAE children at baseline showed increased DC in thalamus, postcentral and precentral and reduced DC in medial frontal cortex, superior frontal cortex, middle temporal cortex, angular and precuneus. However, those abnormalities showed a clear renormalization after AEDs treatments. We then explored the viability of graph-theory-based degree centrality to accurately classify effectiveness to AEDs. Support Vector Machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.22% [sensitivity 78.76%, specificity 89.65%, and area under the receiver operating characteristic curve (AUC) 0.96] for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the right thalamus, bilateral middle temporal gyrus, right medial frontal gyrus, right inferior frontal gyrus, left precuneus, bilateral angular right precentral and left postcentral. Furthermore, the DC change within the bilateral angular are positively correlated with the symptom improvements after AEDs treatment.

CONCLUSION

These findings suggest that graph-theory-based measures, such as DC, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of AEDs.

Topological Organization Alterations of Whole-Brain Functional Networks in Patients with Childhood Absence Epilepsy: Associations with Treatment Effects

PURPOSE

The purpose of the current study is to detect changes of topological organization of whole-brain functional networks and their relationship with the clinical treatment effects of antiepileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI). Patients and Methods. RS-fMRI data from 30 CAE patients were collected and compared with findings from 30 age- and gender-matched healthy controls (HCs). The patients were treated with first-line AEDs for 46.03 months before undergoing a second RS-fMRI scan.

RESULTS

CAE children at baseline showed a reduced clustering coefficient (Cp) and local efficiency (El) than the HC group, implying the reduction of functional segregation. CAE children at baseline also showed smaller characteristic path length (Lp) and higher global efficiency (Eg) compared with the HC group, implying the impairment of functional segregation. However, those metrics showed no significant differences between CAE children at follow-up and the HC group which indicated a clear renormalization of topological organization after AED treatments. CAE at follow-up also showed significantly decreased connectivity between several network regions, with which the thalamus is mainly involved. Furthermore, the reduced connectivity change between the left superior parietal gyrus and the left thalamus is positively correlated with the symptom improvements after AED treatment.

CONCLUSION

We highlighted the convergence and divergence of brain functional network dysfunctions in CAE patients and provided crucial insights into pathophysiological mechanisms and the AED effects.

Altered spontaneous brain activity in patients with childhood absence epilepsy: associations with treatment effects

The study aims to detect resting-state functional MRI (RS-fMRI) changes and their relationships with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using the fractional amplitude of low-frequency fluctuation (fALFF). RS-fMRI data from 30 CAE patients were collected and compared with findings from 30 healthy controls (HCs) with matched sex and age. Patients were treated with first-line AEDs for 46.2 months before undergoing a second RS-fMRI scan. fALFF data were processed using DPABI and SPM12 software. Compared with the HCs, CAE patients at baseline showed increased fALFF in anterior cingulate cortex, inferior parietal lobule, inferior frontal lobule, supplementary motor area and reduced fALFF in putamen and thalamus. At follow-up, the fALFF showed a clear rebound which indicated a normalization of spontaneous brain activities in these regions. In addition, the fALFF changes within thalamus showed significant positive correlation with the seizure frequency improvements. Our results suggest that specific cortical and subcortical regions are involved in seizure generation and the neurological impairments found in CAE children and might shed new light about the AEDs effects on CAE patients.

The interictal activities load and cognitive performance of children with typical absence epilepsy

Background

The description of childhood absence epilepsy (CAE) a benign self-limited generalized epilepsy has become a matter of debate. The objectives of this work were to evaluate the existence of psychiatric and cognitive impairments among patients with typical CAE and to correlate their possible relation to seizure frequency, duration of epilepsy, IISL, and valproate therapy.

Methods

The study was conducted on 19 typical CAE patients receiving valproate therapy, 11 newly diagnosed CAE patients not receiving AEDs, and 30 healthy control subjects (HCS). Participants were subjected to medical history taking, EEG monitoring, child behavior checklist (CBCL), Stanford Binet Intelligence Scale 5th edition, and computerized psychometric tests that assess cognitive domains and executive functions.

Results

The study revealed a high rate of cognitive and psychiatric dysfunctions in CAE patients. 53.3% of patients had psychiatric problems versus 16.6% in HCS. Attention deficit hyperactive disorder (ADHD) (26.6%), anxiety (16.6%), and depression (6.6%) were the most common psychiatric disorders in the patient group. Withdrawn/depressed symptoms, thought problems, social problems, and attention problems in CAE patients were significantly increased compared to HCS. At the same time, CAE patients perform worse in cognitive scales than HCS with comparable intelligent quotient (IQ) scores.

Conclusion

Cognitive and psychiatric impairments in typical CAE patients appear multifactorial in origin with epilepsy-related factors including the duration of epilepsy and interictal spike load (IISL).

Reorganization of auditory-visual network interactions in long-term unilateral postlingual hearing loss

Long-term unilateral hearing loss could reorganize the functional network association between the bilateral auditory cortices, while alterations of other functional networks need to be further explored. We attempted to investigate the pattern of the reorganization of functional network associations between the auditory and visual cortex caused by long-term postlingual unilateral hearing loss (UHI) and its relationship with clinical characteristics. Therefore, 48 patients with hearing loss caused by unilateral acoustic tumors and 52 matched healthy controls were enrolled, and their high-resolution structural MRI and resting-state functional MRI data were also collected to depict the brain network. Degree centrality (DC) was employed to evaluate the functional network association of the auditory-visual network interaction. Group comparisons were performed to investigate the network reorganization, and its correlations with clinical data were calculated. Compared with the healthy control group, patients with UHI showed significantly increased DC between the auditory network (superior temporal gyrus and the medial geniculate body) and the visual network. Meanwhile, this difference was positively correlated with the extent of hearing impairment, and the correlation was more significant with the ipsilateral superior temporal gyrus in cases of acoustic neuroma. These results suggest that long-term unilateral hearing impairment may lead to enhancement of the visual-auditory network interactions and that the degree of reorganization is positively correlated with the pure tone average (PTA) and is more significant for the ipsilateral superior temporal gyrus, which provides clinical evidence regarding cross-modal plasticity in the UHI and its lateralization.

Altered brain intrinsic functional hubs and connectivity associated with relapse risk in heroin dependents undergoing methadone maintenance treatment: A resting-state fMRI study

BACKGROUND

The neural substrates underlying the relapse behavior of heroin dependents (HD) who received long-term methadone maintenance treatment (MMT) have yet to be thoroughly expounded. This study investigated the relapse-related intrinsic functional hubs of HD and their functional integration feature at whole brain network level.

METHODS

57 male HD receiving MMT and 49 matched healthy controls (HC) were enrolled. All of the subjects received resting-state functional magnetic resonance imaging scan. And the 57 patients were assigned a 26-month follow-up for collecting illegal drug use information. Of them, 11 were non-relapsers and 46 relapsers. We analyzed the voxel-based degree centrality (DC) to reveal the differences in nodule centrality between HD and HC, conducted Pearson partial-correlation analysis to confirm the relationship between relapse frequency and DC value of the nodes with significant intergroup differences, and finally compared the functional connectivity (FC) of the relapse-related hubs between non-relapsers and relapsers.

RESULTS

We found the DC values of right insula and left nucleus accumbens (NAc) were negatively correlated with relapse frequency. Compared with the non-relapsers, the relapsers had a significant decreased FC between left NAc and inhibitory control circuitry, including left dorsolateral prefrontal cortex, left inferior frontal gyrus and motor regions.

CONCLUSIONS

These findings suggest that the neural substrates of relapse vulnerability in HD undergoing MMT are the intrinsic functional hubs of introceptive and reward systems and the latter modulates relapse behavior via interaction with inhibitory control circuit.

Regional abnormality of functional connectivity is associated with clinical manifestations in individuals with intractable focal epilepsy

We explored regional functional connectivity alterations in intractable focal epilepsy brains using resting-state functional MRI. Distributions of the network parameters (corresponding to degree and eigenvector centrality) measured at each brain region for all 25 patients were significantly different from age- and sex-matched control data that were estimated by a healthy control dataset (n = 582, 18-84 years old). The number of abnormal regions whose parameters exceeded the mean + 2 SD of age- and sex-matched data for each patient were associated with various clinical parameters such as the duration of illness and seizure severity. Furthermore, abnormal regions for each patient tended to have functional connections with each other (mean ± SD = 58.6 ± 20.2%), the magnitude of which was negatively related to the quality of life. The abnormal regions distributed within the default mode network with significantly higher probability (p < 0.05) in 7 of 25 patients. We consider that the detection of abnormal regions by functional connectivity analysis using a large number of control datasets is useful for the numerical assessment of each patient's clinical conditions, although further study is necessary to elucidate etiology-specific abnormalities.

Intermittent Theta-Burst Stimulation Over the Suprahyoid Muscles Motor Cortex Facilitates Increased Degree Centrality in Healthy Subjects

Theta-burst stimulation (TBS), a variant of repetitive transcranial magnetic stimulation (rTMS), can potentially benefit the treatment of swallowing disorders. However, the after-effects of TBS on the swallowing motor cortex remain uncertain. The newly developed graph-based analysis of the centrality approach has been increasingly used to explore brain networks. The purpose of this study was to identify degree centrality (DC) alterations in the brain network after different TBS protocols were performed over the suprahyoid muscles motor cortex in healthy subjects. A total of 40 right-handed healthy subjects (mean age: 23.73 ± 2.57 years, range: 21–30, 20 females) were included in this study and randomly assigned to two groups, including the continuous TBS (cTBS) group and the intermittent TBS (iTBS) group. All of the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning before and after TBS implementation. Compared to the baseline, cTBS resulted in increased DC values in the left inferior frontal gyrus (P < 0.01). In the iTBS group, decreased DC was observed in the left cerebellum and left medial frontal gyrus; However, increased DC was observed in several brain areas including the right superior temporal gyrus, right superior frontal gyrus, right postcentral gyri and left paracentral lobule (P < 0.01). These results indicated that cTBS mainly results in increasing DC in the ipsilateral. However, iTBS is capable of facilitating the excitability of the swallowing motor cortex and increasing the connectivity of multiple brain regions, including the bilateral sensorimotor network, and might have therapeutic potential in the treatment of swallowing disorders.

Interictal structural and functional connectivity in idiopathic generalized epilepsy: A systematic review of graph theoretical studies

The evaluation of the role of anomalous neuronal networks in epilepsy using a graph theoretical approach is of growing research interest. There is currently no consensus on optimal methods for performing network analysis, and it is possible that variations in study methodology account for diverging findings. This review focuses on global functional and structural interictal network characteristics in people with idiopathic generalized epilepsy (IGE) with the aim of appraising the methodological approaches used and assessing for meaningful consensus. Thirteen studies were included in the review. Data were heterogenous and not suitable for meta-analysis. Overall, there is a suggestion that the cerebral neuronal networks of people with IGE have different global structural and functional characteristics to people without epilepsy. However, the nature of the aberrations is inconsistent with some studies demonstrating a more regular network configuration in IGE, and some, a more random topology. There is greater consistency when different data modalities and connectivity subtypes are compared separately, with a tendency towards increased small-worldness of networks in functional electroencephalography/magnetoencephalography (EEG/MEG) studies and decreased small-worldness of networks in structural studies. Prominent variation in study design at all stages is likely to have contributed to differences in study outcomes. Despite increasing literature surrounding neuronal network analysis, systematic methodological studies are lacking. Absence of consensus in this area significantly limits comparison of results from different studies, and the ability to draw firm conclusions about network characteristics in IGE.

Functional brain network characteristics are associated with epilepsy severity in childhood absence epilepsy

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The functional network of children with childhood absence epilepsy is less efficiently organized in terms of clustering and small-worldness.

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Longer path lengths (i.e. less efficient organization) of the functional network relate to a longer duration of childhood absence epilepsy.

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Longer path lengths of the functional network relate to a higher seizure frequency in childhood absence epilepsy.

While cognitive impairments are not generally considered to be part of the childhood absence epilepsy (CAE) syndrome, some recent studies report cognitive, mainly attentional, deficits. Here we set out to investigate the whole brain functional network of children with CAE and controls. Furthermore, the possible relation of the functional network abnormalities with epilepsy and neurocognitive characteristics is studied.

Seventeen children with childhood CAE (aged 9.2 ± 2.1 years) and 15 controls (aged 9.8 ± 1.8 years) were included. Resting state functional MRI was acquired to study the functional network. Using graph theoretical analysis, three global metrics of the functional network were investigated: the characteristic path length, the clustering coefficient, and the small-worldness. A multivariable linear regression model including age, sex, and subject motion as covariates was used to investigate group differences in the graph metrics. Subsequently, relations of the graph metrics with epilepsy and neurocognitive characteristics were assessed.

Longer path lengths, weaker clustering and a lower small-world network topology were observed in children with CAE compared to controls. Moreover, longer path lengths were related to a longer duration of CAE and a higher number of absence seizure per hour. Clustering and small-worldness were not significantly related to epilepsy or neurocognitive characteristics.

The organization of the functional network of children with CAE is less efficient compared to controls, and is related to disease duration. These preliminary findings suggest that CAE is associated with alterations in the functional network.

Single-subject manual independent component analysis and resting state fMRI connectivity outcomes in patients with juvenile absence epilepsy

The quality of fMRI data impacts functional connectivity measures and consequently, the decisions that clinicians and researchers make regarding functional connectivity interpretation. The present study used resting state fMRI to investigate resting state network connectivity in a sample of patients with Juvenile Absence Epilepsy. Single-subject manual independent component analysis was used in two levels, whereby all noise components were removed, and cerebrospinal fluid pulsation components only were isolated and removed. Improved temporal signal to noise ratios and functional connectivity metrics were observed in each of the cleaning levels for both epilepsy and control cohorts. Results showed full, single-subject manual independent component analysis reduced the number of functional connectivity correlations and increased the strength of these correlations. Similar effects were also observed for the cerebrospinal fluid pulsation only cleaned data relative to the uncleaned, and fully cleaned data. Single-subject manual independent component analysis coupled with short TR multiband acquisition can significantly improve the validity of findings derived from fMRI data sets.

The epileptic network and cognition: What functional connectivity is teaching us about the childhood epilepsies

Our objective was to summarize and evaluate the rapidly expanding body of literature studying functional connectivity in childhood epilepsy. In the self-limited childhood epilepsies, awareness of cognitive comorbidities has been steadily increasing, and recent advances in our understanding of the network effects of these disorders promise insights into the underlying neurobiology. We reviewed publications addressing functional connectivity in children with epilepsy with an emphasis on studies of children with self-limited childhood epilepsies. The majority of studies have been published in the past 10 years and predominantly examine childhood epilepsy with centrotemporal spikes and childhood absence epilepsy. Cognitive network alterations are commonly observed across the childhood epilepsies. Some of these effects appear to be nonspecific to epilepsy syndrome or even to category of neurological disorder. Other patterns, such as changes in the connectivity of cortical language areas in childhood epilepsy with centrotemporal spikes, provide clues to the underlying cognitive deficits seen in affected children. The literature to date is dominated by general observations of connectivity patterns without a priori hypotheses. These data-driven studies build an important foundation for hypothesis generation and are already providing useful insights into the neuropathology of the childhood epilepsies. Future work should emphasize hypothesis-driven approaches and rigorous clinical correlations to better understand how the knowledge of network alterations can be applied to guidance and treatment for the children in our clinics.

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.

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.

The Cerebellum Posterior Lobe Associates with the Exophthalmos of Primary Hyperthyroidism: A Resting-State fMRI Study

BACKGROUND

Exophthalmos occurs in patients with primary hyperthyroidism. There were few studies about the changes of brain functional networks of patients with exophthalmos of primary hyperthyroidism (EOPH). However, differences in spontaneous brain activity in patients with EOPH remain unclear.

OBJECTIVE

This study explored alterations in the brain functional networks of patients with EOPH using a voxel-wise degree centrality (DC) method.

METHODS

A total of 20 patients with EOPH (8 men and 12 women) were enrolled. In addition, 20 patients with primary hyperthyroidism without exophthalmos, matched in age, sex, and education status, were enrolled as a control group. The Hospital Anxiety and Depression Scale was used to assess the anxiety and depression status of participants. All participants were examined using resting-state functional MRI. Changes in spontaneous brain activity were investigated using the DC method. To distinguish between the DC values of the patients with EOPH and those of the control group, we analyzed the receiver operating characteristic (ROC) curve. The interrelationships between the DC values and clinical variables in the patients with EOPH were evaluated using Pearson's correlation coefficient.

RESULTS

Patients with EOPH exhibited notably lower DC values in the cerebellum posterior lobe than the control group. In addition, there were negative correlations between the anxiety scores (AS) and the depression scores (DS) and DC values of the cerebellum posterior lobe. The ROC curve analysis of the cerebellum posterior lobe demonstrated that the area under the curve method had a high diagnostic accuracy.

CONCLUSIONS

Our study was the first, to our knowledge, to explore changes in the brains of patients with EOPH using the DC method. The DC value was significantly different in the cerebellum posterior lobe in patients with EOPH, indicating that the cerebellum posterior lobe is associated with EOPH.

Juvenile myoclonic epilepsy has hyper dynamic functional connectivity in the dorsolateral frontal cortex

PURPOSE

Characterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach.

METHODS

Whole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density.

RESULTS

The static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results.

CONCLUSION

Incorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME.

A Study of the Brain Functional Network of Post-Stroke Depression in Three Different Lesion Locations

Research on the mechanism of post stroke depression (PSD) is the key way to improve the treatment of PSD. However, the functional brain network of PSD has not been entirely supported by the results of functional magnetic resonance imaging (fMRI) studies. The aims of this study are to investigate the brain response of PSD in three different lesions. The brain responses of the three PSD subgroups were similar. However, each subgroup had its own characteristics of the brain network. In the temporal lobe subgroup, the right thalamus had increased degree centrality (DC) values which were different from the other two subgroups. In the frontal lobe subgroup, the left dorsolateral prefrontal cortex, caudate, and postcentral gyrus had increased DC values which were different from the other two subgroups. The hemodynamic response of PSD indicates that PSD has activities of similar emotional networks, of which the negative network realizes its function through the limbic system and default mode network. The brain network has unique characteristics for different lesion locations. The neurological function of the lesion location, the compensatory mechanism of the brain, and the mechanism of integrity and locality of the brain are the important factors in the individual emotional network.