GRETNA: a graph theoretical network analysis toolbox for imaging connectomics

Associations of the Disrupted Functional Brain Network and Cognitive Function in End-Stage Renal Disease Patients on Maintenance Hemodialysis: A Graph Theory-Based Study of Resting-State Functional Magnetic Resonance Imaging

Objective: Cognitive impairment (CI) is a common neurological complication in patients with end-stage renal disease undergoing maintenance hemodialysis (MHD). Brain network analysis based on graph theory is a promising tool for studying CI. Therefore, the purpose of this study was to analyze the changes of functional brain networks in patients on MHD with and without CI by using graph theory and further explore the underlying neuropathological mechanism of CI in these patients. Methods: A total of 39 patients on MHD (19 cases with CI and 20 without) and 25 healthy controls (HCs) matched for age, sex, and years of education were enrolled in the study. Resting-state functional magnetic resonance imaging (rs-fMRI) and T1-weighted high-resolution anatomical data were obtained, and functional brain networks for each subject were constructed. The brain network parameters at the global and regional levels were calculated, and a one-way analysis of covariance was used to compare the differences across the three groups. The associations between the changed graph-theory parameters and cognitive function scores in patients on MHD were evaluated using Spearman correlation analysis. Results: Compared with HCs, the global parameters [sigma, gamma, and local efficiency (Eloc)] in both patient groups decreased significantly (p < 0.05, Bonferroni corrected). The clustering coefficient (Cp) in patients with CI was significantly lower than that in the other two groups (p < 0.05, Bonferroni corrected). The regional parameters were significantly lower in the right superior frontal gyrus, dorsolateral (SFGdor) and gyrus rectus (REC) of patients with CI than those of patients without CI; however the nodal local efficiency in the left amygdala was significantly increased (all p < 0.05, Bonferroni corrected). The global Cp and regional parameters in the three brain regions (right SFGdor, REC, and left amygdala) were significantly correlated with the cognitive function scores (all FDR q < 0.05). Conclusion: This study confirmed that the topology of the functional brain network was disrupted in patients on MHD with and without CI and the disruption of brain network was more severe in patients with CI. The abnormal brain network parameters are closely related to cognitive function in patients on MHD.

Altered Default Mode Network Is Associated With Cognitive Impairment in CADASIL as Revealed by Multimodal Neuoimaging

Background and Purpose: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy caused by mutations in the NOTCH3 gene is a hereditary cerebral small vessel disease, manifesting with stroke, cognitive impairment, and mood disturbances. Functional or structural changes in the default mode network (DMN), which plays important role in cognitive and mental maintenance, have been found in several neurological and mental diseases. However, it remains unclear whether DMN is altered in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Methods: Multimodal imaging methods, including MRI and positron emission tomography (PET), were applied to evaluate the functional, structural, and metabolic characteristics of DMN in 25 patients with CADASIL and 42 healthy controls. Results: Compared with controls, patients with CADASIL had decreased nodal efficiency and degree centrality of the dorsal medial pre-frontal cortex and hippocampal formation within DMN. Structural MRI and diffusion tensor imaging (DTI) showed decreased gray matter volume and fiber tracks presented in the bilateral hippocampal formation. Meanwhile, PET imaging showed decreased metabolism within the whole DMN in CADASIL. Furthermore, correlation analyses showed that these nodal characteristics, gray matter volume, and metabolic signals of DMN were related to cognitive scores in CADASIL. Conclusions: Our results suggested that altered network characteristics of DMN might play important roles in cognitive deficits of CADASIL.

Disrupted Functional Connectivity Within and Between Resting-State Networks in the Subacute Stage of Post-stroke Aphasia

Background: Post-stroke aphasia (PSA) results from brain network disorders caused by focal stroke lesions. However, it still remains largely unclear whether the impairment is present in intra- and internetwork functional connectivity (FC) within each resting-state network (RSN) and between RSNs in the subacute stage of PSA.

Objectives: This study aimed to investigate the resting-state FC within and between RSNs in patients with PSA and observe the relationships between FC alterations and Western Aphasia Battery (WAB) measures.

Methods: A total of 20 individuals with subacute PSA and 20 healthy controls (HCs) were recruited for functional MRI (fMRI) scanning, and only patients with PSA underwent WAB assessment. Independent component analysis was carried out to identify RSNs. Two-sample t-tests were used to calculate intra- and internetwork FC differences between patients with PSA and HCs. The results were corrected for multiple comparisons using the false discovery rate (FDR correction, p < 0.05). Partial correlation analysis was performed to observe the relationship between FC and WAB scores with age, gender, mean framewise displacement, and lesion volume as covariates (p < 0.05).

Results: Compared to HCs, patients with PSA showed a significant increase in intranetwork FC in the salience network (SN). For internetwork FC analysis, patients showed a significantly increased coupling between left frontoparietal network (lFPN) and SN and decreased coupling between lFPN and right frontoparietal network (rFPN) as well as between lFPN and posterior default mode network (pDMN) (FDR correction, p < 0.05). Finally, a significant positive correlation was found between the intergroup difference of FC (lFPN-rFPN) and auditory-verbal comprehension (p < 0.05).

Conclusion: Altered FC was revealed within and between multiple RSNs in patients with PSA at the subacute stage. Reduced FC between lFPN and rFPN was the key element participating in language destruction. These findings proved that PSA is a brain network disorder caused by focal lesions; besides, it may improve our understanding of the pathophysiological mechanisms of patients with PSA at the subacute stage.

Effect of APOEε4 on Functional Brain Network in Patients with Subjective Cognitive Decline: A Resting State Functional MRI Study

PURPOSE

Subjective cognitive decline (SCD) is the earliest symptom stage of Alzheimer's disease (AD), and the APOEε4 allele is the strongest genetic risk factor for sporadic AD. Based on graph theory, the resting state functional connectivity (rsFC) in SCD patients with APOEε4 was studied to explore the effect of APOEε4 on the rsFC network properties of SCD patients.

PATIENTS AND METHODS

This cross-sectional study included MRI image data from 19 SCD patients with APOEε4 (SCD+), 29 SCD patients without APOEε4 (SCD-), and 30 normal control (NC-) individuals without APOEε4. We generated a binary matrix based on anatomical automatic labeling (AAL) 90 atlas to construct the functional network. We then calculated the whole brain network characteristics and intracerebral node characteristics by graph theory.

RESULTS

For the whole brain network characteristics, all three groups showed small-worldness. The SCD+ group had increased compensatory information transfer speed and enhanced integration capability. This group also had high heterogeneity for intracerebral node characteristics, mainly in the default mode network, left superior occipital gyrus, and bilateral putamen.

CONCLUSION

APOEε4 effects the functional brain network in patients with SCD and may be a potential indicator for the identification of SCD.

Differences in Brain Functional Networks of Executive Function Between Cantonese-Mandarin Bilinguals and Mandarin Monolinguals

It remains controversial whether long-term logographic-logographic bilingual experience shapes the special brain functional subnetworks underlying different components of executive function (EF). To address this question, this study explored the differences in the functional connections underlying EF between the Cantonese-Mandarin bilinguals and Mandarin monolinguals. 31 Cantonese-Mandarin bilinguals and 31 Mandarin monolinguals were scanned in a 3-T magnetic resonance scanner at rest. 4 kinds of behavioral tasks of EF were tested. Network-based statistics (NBS) was performed to compare the connectomes of fronto-parietal (FP) and cingulo-opercular (CO) network between groups. The results showed that the bilinguals had stronger connectivity than monolinguals in a subnetwork located in the CO network rather than the FP network. The identified differential subnetwork referred to as the CO subnetwork contained 9 nodes and 10 edges, in which the center node was the left mid-insula with a degree centrality of 5. The functional connectivity of the CO subnetwork was significantly negatively correlated with interference effect in bilinguals. The results suggested that long-term Cantonese-Mandarin bilingual experience was associated with stronger functional connectivity underlying inhibitory control in the CO subnetwork.

Alternation in Effective Connectivity With Cognitive Aging: A Longitudinal Study of Elderly Populations

In this research, we investigated the alterations in the directionality and strength of regional interactions within functionally changed brain networks and their relationship to cognitive decline during the aging process in normal elderly individuals. Thirty-seven cognitively normal elderly people received resting-state fMRI scans and cognitive assessments at baseline (age = 78.65 ± 3.56 years) and at 4-year follow-up. Functional connectivity analyses were used to identify networks containing brain regions whose functions changed with age as regions of interest. The spectral dynamic causal modeling (spDCM) method was used to estimate the causal interactions within networks in subjects at different time points and in subjects with different cognitive levels to explore the alterations with cognitive aging. The results showed that, at both time points, all the networks, except the frontal-parietal network (FPN) at baseline, had mutual interactions between each pair of nodes. Furthermore, when the subjects were divided with global cognition level, lost connections were only found in the subgroup with better performance. These indicated that elderly people appeared to need more interaction pathways between brain areas with cognitive decline. We also observed that the strength of the flow of information from the left angular gyrus to the precuneus, which is associated with activation of memory retrieval and the functional hub involved in various cognitive domains, was predictive of declines in executive function with the aging process, making it a potential predictor of such situation.

Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging

Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains. Graphic abstract: Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm.

The brain's structural differences between postherpetic neuralgia and lower back pain

The purpose is to explore the brain's structural difference in local morphology and between-region networks between two types of peripheral neuropathic pain (PNP): postherpetic neuralgia (PHN) and lower back pain (LBP). A total of 54 participants including 38 LBP and 16 PHN patients were enrolled. The average pain scores were 7.6 and 7.5 for LBP and PHN. High-resolution structural T1 weighted images were obtained. Both grey matter volume (GMV) and morphological connectivity (MC) were extracted. An independent two-sample t-test with false discovery rate (FDR) correction was used to identify the brain regions where LBP and PHN patients showed significant GMV difference. Next, we explored the differences of MC network between LBP and PHN patients and detected the group differences in network properties by using the two-sample t-test and FDR correction. Compared with PHN, LBP patients had significantly larger GMV in temporal gyrus, insula and fusiform gyrus (p < 0.05). The LBP cohort had significantly stronger MC in the connection between right precuneus and left opercular part of inferior frontal gyrus (p < 0.05). LBP patients had significantly stronger degree in left anterior cingulate gyrus and left rectus gyrus (p < 0.05) while had significantly weaker degree than PHN patients in left orbital part of middle frontal gyrus, left supplementary motor area and left superior parietal lobule (p < 0.05). LBP and PHN patients had significant differences in the brain's GMV, MC, and network properties, which implies that different PNPs have different neural mechanisms concerning pain modulation.

Prediction of Deep Brain Stimulation Outcome in Parkinson's Disease With Connectome Based on Hemispheric Asymmetry

Parkinson’s disease (PD) is a neurodegenerative disease that is associated with motor and non-motor symptoms and caused by lack of dopamine in the substantia nigra of the brain. Subthalamic nucleus deep brain stimulation (STN-DBS) is a widely accepted therapy of PD that mainly inserts electrodes into both sides of the brain. The effect of STN-DBS was mainly for motor function, so this study focused on the recovery of motor function for PD after DBS. Hemispherical asymmetry in the brain network is considered to be a potential indicator for diagnosing PD patients. This study investigated the value of hemispheric brain connection asymmetry in predicting the DBS surgery outcome in PD patients. Four types of brain connections, including left intra-hemispheric (LH) connection, right intra-hemispheric (RH) connection, inter-hemispheric homotopic (Ho) connection, and inter-hemispheric heterotopic (He) connection, were constructed based on the resting state functional magnetic resonance imaging (rs-fMRI) performed before the DBS surgery. We used random forest for selecting features and the Ridge model for predicting surgical outcome (i.e., improvement rate of motor function). The functional connectivity analysis showed that the brain has a right laterality: the RH networks has the best correlation (r = 0.37, p = 5.68E-03) between the predicted value and the true value among the above four connections. Moreover, the region-of-interest (ROI) analysis indicated that the medioventral occipital cortex (MVOcC)–superior temporal gyrus (STG) and thalamus (Tha)–precentral gyrus (PrG) contributed most to the outcome prediction model for DBS without medication. This result provides more support for PD patients to evaluate DBS before surgery.

Altered structural brain networks in linguistic variants of frontotemporal dementia

Semantic (svPPA) and nonfluent (nfvPPA) variants of primary progressive aphasia (PPA) have recently been associated with distinct patterns of white matter and functional network alterations in left frontoinsular and anterior temporal regions, respectively. Little information exists, however, about the topological characteristics of gray matter covariance networks in these two PPA variants. In the present study, we used a graph theory approach to describe the structural covariance network organization in 34 patients with svPPA, 34 patients with nfvPPA and 110 healthy controls. All participants underwent a 3 T structural MRI. Next, we used cortical thickness values and subcortical volumes to define subject-specific connectivity networks. Patients with svPPA and nfvPPA were characterized by higher values of normalized characteristic path length compared with controls. Moreover, svPPA patients had lower values of normalized clustering coefficient relative to healthy controls. At a regional level, patients with svPPA showed a reduced connectivity and impaired information processing in temporal and limbic brain areas relative to controls and nfvPPA patients. By contrast, local network changes in patients with nfvPPA were focused on frontal brain regions such as the pars opercularis and the middle frontal cortex. Of note, a predominance of local metric changes was observed in the left hemisphere in both nfvPPA and svPPA brain networks. Taken together, these findings provide new evidences of a suboptimal topological organization of the structural covariance networks in svPPA and nfvPPA patients. Moreover, we further confirm that distinct patterns of structural network alterations are related to neurodegenerative mechanisms underlying each PPA variant.

Disrupted Brain Connectivity Networks in Aphasia Revealed by Resting-State fMRI

Aphasia is characterized by the disability of spontaneous conversation, listening, understanding, retelling, naming, reading, or writing. However, the neural mechanisms of language damage after stroke are still under discussion. This study aimed to investigate the global and nodal characterization of the functional networks in patients with aphasic stroke based on resting-state functional MRI (fMRI). Twenty-four right-handed patients with aphasia after stroke and 19 healthy controls (HC) underwent a 3-TfMRI scan. A whole-brain large-scale functional connectivity network was then constructed based on Power's atlas of 264 functional regions of interest, and the global and nodal topological properties of these networks were analyzed using graph theory approaches. The results showed that patients with aphasia had decreased in small-worldness (sigma), normalized clustering coefficient (gamma), and local efficiency (E_loc) values. Furthermore, E_loc was positively correlated with language ability, retelling, naming, and listening comprehension in patients with aphasia. Patients with aphasia also had decreased nodal degree and decreased nodal efficiency in the left postcentral gyrus, central opercular cortex, and insular cortex. Our results suggest that the global and local topology attributes were altered by injury in patients with aphasic stroke. We argue that the local efficiency of brain networks might be used as a potential indicator of basic speech function in patients with aphasia.

Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD. Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4-8 Hz), alpha (8-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels. Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05). Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure

Need for closure (NFC) reflects stable individual differences in the desire for a quick, definite, and stable answer to a question. A large body of research has documented the association between NFC and various cognitive, emotional and social processes. Despite considerable interest in psychology, little effort has been made to uncover the neural substrates of individual variations in NFC. Herein, we took a data-driven approach to predict NFC trait combining machine learning framework and the whole-brain grey matter volume (GMV) features, which represent a reliable brain imaging measure and have been commonly employed to explore neural basis underlying individual differences of cognition and behaviors. Brain regions contributing to the prediction were then subjected to functional connectivity and decoding analyses for a quantitative inference on their psychophysiological functions. Our results indicated that multivariate patterns of GMV derived from multiple regions across distributed brain systems predicted NFC at individual level. The contributing regions are distributed across the emotional processing network (e.g., striatum), cognitive control network (e.g., lateral prefrontal cortex), social cognition network (e.g., temporoparietal junction) and perceptual processing network (e.g., occipital cortex). The current study provided the first evidence that dispositional NFC is embodied in multiple large-scale brain networks, helping to delineate a more complete picture about the neuropsychological processes that support individual differences in NFC. Beyond these findings, the current interdisciplinary approach to constructing and interpreting neuroimaging-based prediction model of personality traits would be informative to a wide range of future studies on personality.

Functional magnetic resonance imaging research in China

Functional magnetic resonance imaging (fMRI) non-invasively measures the activity of the human brain and provides a unique technological tool for investigating aspects of the human brain including cognition, development, and disorders. As one of the main funding agencies for basic research in China, the National Natural Scientific Foundation of China (NSFC) has initiated various research programs during the last two decades that are related to fMRI research. In this review, we collected and analyzed the metadata of the projects and published studies in research fields using fMRI that were funded by the NSFC. We observed a trend of increasing funding amounts from the NSFC for fMRI research, typically from the General Program and Key Program. Leading research institutes from economically developed municipalities and provinces received the most support and formed close collaboration relationships. Finally, we reviewed several representative achievements from research institutions in china, involving data analysis methods, brain connectomes, and computational platforms in addition to their applications in brain disorders.

Altered Degree Centrality of Brain Networks in Parkinson's Disease With Freezing of Gait: A Resting-State Functional MRI Study

Freezing of gait (FOG) in Parkinson's disease (PD) leads to devastating consequences; however, little is known about its functional brain network. We explored the differences in degree centrality (DC) of functional networks among PD with FOG (PD FOG+), PD without FOG (PD FOG–), and healthy control (HC) groups. In all, 24 PD FOG+, 37 PD FOG–, and 22 HCs were recruited and their resting-state functional magnetic imaging images were acquired. The whole brain network was analyzed using graph theory analysis. DC was compared among groups using the two-sample t-test. The DC values of disrupted brain regions were correlated with the FOG Questionnaire (FOGQ) scores. Receiver operating characteristic curve analysis was performed. We found significant differences in DC among groups. Compared with HCs, PD FOG+ patients showed decreased DC in the middle frontal gyrus (MFG), superior temporal gyrus (STG), parahippocampal gyrus (PhG), inferior temporal gyrus (ITG), and middle temporal gyrus (MTG). Compared with HC, PD FOG– presented with decreased DC in the MFG, STG, PhG, and ITG. Compared with PD FOG–, PD FOG+ showed decreased DC in the MFG and ITG. A negative correlation existed between the DC of ITG and FOGQ scores; the DC in ITG could distinguish PD FOG+ from PD FOG– and HC. The calculated AUCs were 81.3, 89.5, and 77.7% for PD FOG+ vs. HC, PD FOG– vs. HC, and PD FOG+ vs. PD FOG–, respectively. In conclusion, decreased DC of ITG in PD FOG+ patients compared to PD FOG– patients and HCs may be a unique feature for PD FOG+ and can likely distinguish PD FOG+ from PD FOG– and HC groups.

Analysis of Correlation Between White Matter Changes and Functional Responses in Post-stroke Depression

Objective: Post-stroke depression (PSD) is one of the most common neuropsychiatric symptoms with high prevalence, however, the mechanism of the brain network in PSD and the relationship between the structural and functional network remain unclear. This research applies graph theory to structural networks and explores the relationship between structural and functional networks.

Methods: Forty-five patients with acute ischemic stroke were divided into the PSD group and post-stroke without depression (non-PSD) group respectively and underwent the magnetic resonance imaging scans. Network construction and Module analysis were used to explore the structural connectivity-functional connectivity (SC-FC) coupling of multi-scale brain networks in patients with PSD.

Results: Compared with non-PSD, the structural network in PSD was related to the reduction of clustering and the increase of path length, but the degree of modularity was lower.

Conclusions: The SC-FC coupling may serve as a biomarker for PSD. The similarity in SC and FC is associated with cognitive dysfunction, retardation, and desperation. Our findings highlighted the distinction in brain structural-functional networks in PSD.

Clinical Trial Registration: https://www.clinicaltrials.gov/ct2/show/NCT03256305, NCT03256305.

Disrupted fronto-temporal function in panic disorder: a resting-state connectome study

Recent neuroimaging studies have identified alterations in activity and connectivity among many brain regions as potential biomarkers for panic disorder. However, the functional connectome of panic disorder is not well understood. Therefore, a graph-theoretical approach was applied in this study to construct functional networks of patients and healthy controls in order to discover topological changes in panic disorder. 31 patients and 33 age and sex matched healthy controls underwent resting-state functional magnetic resonance imaging. Brain networks for each participant were structured using nodes from the Anatomical Automatic Labeling template and edges from connectivity matrices. Then, topological organizations of networks were calculated. Network-based statistical analysis was conducted, and global and nodal properties were compared between patients and controls. Unlike controls, patients with panic disorder displayed a small-world network. Patients also revealed decreased nodal efficiency in right superior frontal gyrus (SFG), middle frontal gyrus (MFG), right superior temporal gyrus (STG), and left middle temporal gyrus (MTG). Decreased functional connectivity was found in panic disorder between right MTG and extensive temporal regions. Among these disrupted regions, the decreased nodal efficiency of SFG showed a positive correlation with clinical symptoms while nodal betweenness centrality in angular gyrus showed a negative correlation. Our results indicated decreased function of global and regional information transmission in panic disorder and emphasized the role of temporal regions in its pathology.

Resting-State MEG Source Space Network Metrics Associated with the Duration of Temporal Lobe Epilepsy

To evaluate the relationship between the network metrics of 68 brain regions and duration of temporal lobe epilepsy (TLE). Magnetoencephalography (MEG) data from 53 patients with TLE (28 left TLE, 25 right TLE) were recorded between seizures at resting state and analyzed in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), lower alpha (8-10 Hz), upper alpha (10-13 Hz), beta (13-30 Hz), and lower gamma (30-48 Hz). Three local network metrics, betweenness centrality, nodal degree, and nodal efficiency, were chosen to analyze the functional brain network. In Left, Right, and All (Left + Right) TLE groups, different metrics provide significant positive or negative correlations with the duration of TLE, in different frequency bands, and in different brain regions. In the Left TLE group, significant correlation between TLE duration and metric exists in the delta, beta, or lower gamma band, with network betweenness centrality, nodal degree, or nodal efficiency, in left caudal middle frontal, left middle temporal, or left supramarginal. In the Right TLE group, significant correlation exists in lower gamma or delta band, with nodal degree, or nodal efficiency, in left precuneus or right temporal pole. In the All TLE group, the significant correlation exists in delta, theta, beta, or lower gamma band, with nodal degree, or betweenness centrality, in either left or right hemisphere. Network metrics for some specific brain regions changed in patients with TLE as the duration of their TLE increased. Further researching these changes may be important for studying the pathogenesis, presurgical evaluation, and clinical treatment of long-term TLE.

Topologically convergent and divergent morphological gray matter networks in early-stage Parkinson's disease with and without mild cognitive impairment

Patients with Parkinson's disease with mild cognitive impairment (PD-M) progress to dementia more frequently than those with normal cognition (PD-N), but the underlying neurobiology remains unclear. This study aimed to define the specific morphological brain network alterations in PD-M, and explore their potential diagnostic value. Twenty-four PD-M patients, 17 PD-N patients, and 29 healthy controls (HC) underwent a structural MRI scan. Similarity between interregional gray matter volume distributions was used to construct individual morphological brain networks. These were analyzed using graph theory and network-based statistics (NBS), and their relationship to neuropsychological tests was assessed. Support vector machine (SVM) was used to perform individual classification. Globally, compared with HC, PD-M showed increased local efficiency (p = .001) in their morphological networks, while PD-N showed decreased normalized path length (p = .008). Locally, similar nodal deficits were found in the rectus and lingual gyrus, and cerebellum of both PD groups relative to HC; additionally in PD-M nodal deficits involved several frontal and parietal regions, correlated with cognitive scores. NBS found that similar connections were involved in the default mode and cerebellar networks of both PD groups (to a greater extent in PD-M), while PD-M, but not PD-N, showed altered connections involving the frontoparietal network. Using connections identified by NBS, SVM allowed discrimination with high accuracy between PD-N and HC (90%), PD-M and HC (85%), and between the two PD groups (65%). These results suggest that default mode and cerebellar disruption characterizes PD, more so in PD-M, whereas frontoparietal disruption has diagnostic potential.

Modular-level alterations of single-subject gray matter networks in schizophrenia

Schizophrenia is often regarded as a psychiatric disorder caused by disrupted connections in the brain. Evidence suggests that the gray matter of schizophrenia patients is damaged in a modular pattern. Recently, abnormal topological organization was observed in the gray matter networks of patients with schizophrenia. However, the modular-level alteration of gray matter networks in schizophrenia remains unclear. In this study, single-subject gray matter networks were constructed for a total of 217 subjects (116 patients with schizophrenia and 101 controls). We analyzed the topological characteristics of the brain network and the strengths of connections between and within modules. Compared with the outcomes in the control group, the global efficiency and participation coefficient values of the single-subject gray matter networks in schizophrenic patients were significantly reduced. The nodal participation coefficient of the regions involving the frontoparietal attention network, default mode network and subcortical network were significantly decreased in subjects with schizophrenia. The intermodule connections between the frontoparietal attention network and visual network and between the default mode network and subcortical network, in the frontoparietal attention network were significantly reduced in the patient group. In the frontoparietal attention network, the intramodule nodal connection strength of the left orbital inferior frontal gyrus and right inferior parietal gyrus was significantly decreased in schizophrenia patients. Reduced intermodule nodal connection strength between the frontoparietal attention network and visual network was associated with the severity of schizophrenia symptoms. These findings suggest that abnormal intramodule and intermodule connections in the structural brain network may a biomarker of schizophrenia symptoms.

Altered Functional Topological Organization in Type-2 Diabetes Mellitus With and Without Microvascular Complications

Microvascular complications can accelerate cognitive impairment in patients with type 2 diabetes mellitus (T2DM) and have a high impact on their quality of life; however, the underlying mechanism is still unclear. The complex network in the human brain is the physiological basis for information processing and cognitive expression. Therefore, this study explored the relationship between the functional network topological properties and cognitive function in T2DM patients with and without microvascular complications (T2DM-C and T2DM-NC, respectively). Sixty-seven T2DM patients and 41 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological assessment. Then, graph theoretical network analysis was performed to explore the global and nodal topological alterations in the functional whole brain networks of T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. The T2DM-C group exhibited significantly higher local efficiency (Eloc), normalized cluster coefficient (γ), and small-world characteristics (σ) than the HCs. Patients with T2DM at different clinical stages (T2DM-C and T2DM-NC) showed varying degrees of abnormalities in node properties. In addition, compared with T2DM-NC patients, T2DM-C patients showed nodal properties disorders in the occipital visual network, cerebellum and middle temporal gyrus. The Eloc metrics were positively correlated with HbA1c level (P = 0.001, r = 0.515) and the NE values in the right paracentral lobule were negatively related with serum creatinine values (P = 0.001, r = −0.517) in T2DM-C patients. This study found that T2DM-C patients displayed more extensive changes at different network topology scales. The visual network and cerebellar may be the central vulnerable regions of T2DM-C patients.

Brain network topology and future development of freezing of gait in Parkinson's disease: a longitudinal study

BACKGROUND

Freezing of gait (FOG) is a common disabling gait disturbance in Parkinson's disease (PD). The objectives of this study were to explore alterations in the topological organization of whole-brain functional networks in patients with PD who will develop FOG.

METHODS

We recruited 20 patients with PD who developed FOG (PD-FOGt) during a 5-year follow-up period, 20 patients with PD who did not developed FOG (PD-FOGn) within the follow-up period, and 20 healthy control subjects. Using graph theory approaches, we performed a comparative analysis of the topological organization of whole-brain functional networks among the groups, and further explored their potential relationships with latency to develop FOG.

RESULTS

At baseline, the global topological properties of functional brain networks in PD-FOGt and PD-FOGn showed no abnormalities. Additionally, regarding regional topological properties, compared with PD-FOGn patients, PD-FOGt patients exhibited decreased nodal centrality in the left middle frontal gyrus (MFG). Although there were no significant differences compared with PD-FOGn patients, the PD-FOGt group exhibited the lowest nodal centrality values in the frontal cortex (left gyrus rectus), and visual cortex (bilateral inferior occipital gyrus and left fusiform gyrus), and the highest nodal centrality values in the cerebellum (vermis_6) among the three groups. However, no relationship was found between the nodal centrality in above brain regions and latency to develop FOG.

CONCLUSION

This study demonstrates the disrupted regional topological organization might contribute to the future development of FOG in PD patients, especially associated with damage to the left MFG.

Dynamic Network Connectivity Reveals Markers of Response to Deep Brain Stimulation in Parkinson's Disease

Background: Neuronal loss in Parkinson's Disease (PD) leads to widespread neural network dysfunction. While graph theory allows for analysis of whole brain networks, patterns of functional connectivity (FC) associated with motor response to deep brain stimulation of the subthalamic nucleus (STN-DBS) have yet to be explored. Objective/Hypothesis: To investigate the distributed network properties associated with STN-DBS in patients with advanced PD. Methods: Eighteen patients underwent 3-Tesla resting state functional MRI (rs-fMRI) prior to STN-DBS. Improvement in UPDRS-III scores following STN-DBS were assessed 1 year after implantation. Independent component analysis (ICA) was applied to extract spatially independent components (ICs) from the rs-fMRI. FC between ICs was calculated across the entire time series and for dynamic brain states. Graph theory analysis was performed to investigate whole brain network topography in static and dynamic states. Results: Dynamic analysis identified two unique brain states: a relative hypoconnected state and a relative hyperconnected state. Time spent in a state, dwell time, and number of transitions were not correlated with DBS response. There were no significant FC findings, but graph theory analysis demonstrated significant relationships with STN-DBS response only during the hypoconnected state - STN-DBS was negatively correlated with network assortativity. Conclusion: Given the widespread effects of dopamine depletion in PD, analysis of whole brain networks is critical to our understanding of the pathophysiology of this disease. Only by leveraging graph theoretical analysis of dynamic FC were we able to isolate a hypoconnected brain state that contained distinct network properties associated with the clinical effects of STN-DBS.

Regional brain network and behavioral alterations in EGR3 gene transfected rat model of schizophrenia

Schizophrenia is a severe psychiatric disease while its etiology and effective treatment are not completely clear. A rat model of schizophrenia was previously established by transfecting EGR3 gene into the hippocampus of rats. This study aimed to investigate the behavioral and cerebral alterations of the schizophrenic model rats and the risperidone effects. Twenty-six rats were divided into 3 groups: schizophrenia model group (E group), risperidone treatment group (T group), and healthy control group (H group). Morris water maze and open field test were used as behavioral tests, resting-state functional magnetic resonance imaging (fMRI) was performed after EGR3 gene transfection and risperidone therapy. Graph analyses were used for examining cerebral alterations of the rats. Behavioral tests demonstrated reduced spatial working memory and exploring unfamiliar space ability in schizophrenic model rats. Graph analyses revealed reduced regional architectures in the olfactory bulb, nucleus accumbens, and pineal gland in group E compared to group H (p < 0.05), while group T showed increased regional architecture in pineal gland compared to group E (p < 0.05). Besides, the regional architectures in the olfactory bulb, nucleus accumbens were lower in group T than group H, while the hippocampus showed increased regional architecture in group T compared to group H (p < 0.05). Schizophrenia induced several regional alterations in the cerebrum while risperidone can reverse part of these alterations. This study lends support for future research on the pathology of schizophrenia and provides new insights on the role of risperidone in schizophrenia.

Aberrant brain network topology in the frontoparietal-limbic circuit in bipolar disorder: a graph-theory study

Characterizing the properties of brain networks across mood states seen in bipolar disorder (BP) can provide a deeper insight into the mechanisms involved in this type of affective disorder. In this study, graph theoretical methods were used to examine global, modular and nodal brain network topology in the resting state using functional magnetic resonance imaging data acquired from 95 participants, including those with bipolar depression (BPD; n = 30) and bipolar mania (BPM; n = 39) and healthy control (HC) subjects (n = 26). The threshold value of the individual subjects' connectivity matrix varied from 0.15 to 0.30 with steps of 0.01. We found that: (1) at the global level, BP patients showed a significantly increased global efficiency and synchronization and a decreased path length; (2) at the nodal level, BP patients showed impaired nodal parameters, predominantly within the frontoparietal and limbic sub-network; (3) at the module level, BP patients were characterized by denser FCs (edges) between Module III (the front-parietal system) and Module V (limbic/paralimbic systems); (4) at the nodal level, the BPD and BPM groups showed state-specific differences in the orbital part of the left superior-frontal gyrus, right putamen, right parahippocampal gyrus and left fusiform gyrus. These results revealed abnormalities in topological organization in the whole brain, especially in the frontoparietal-limbic circuit in both BPD and BPM. These deficits may reflect the pathophysiological processes occurring in BP. In addition, state-specific regional nodal alterations in BP could potentially provide biomarkers of conversion across different mood states.

Reduced functional network connectivity is associated with upper limb dysfunction in acute ischemic brainstem stroke

This study aimed to detect alterations in intra- and inter-network functional connectivity (FC) of multiple networks in acute brainstem ischemic stroke patients, and the relationship between FC and movement assessment scores to assess their ability to predict upper extremity motor impairment. Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from acute brainstem ischemic stroke patients (n = 50) and healthy controls (HCs) (n = 45). Resting-state networks (RSNs) were established based on independent component analysis (ICA) and the functional network connectivity (FNC) analysis was performed. Subsequently, correlation analysis was subsequently used to explore the relationship between FNC abnormalities and upper extremity motor impairment. Altered FC within default mode network (DMN), executive control network (ECN), the salience network (SN), auditory network (AN), and cerebellum network (CN) were found in the acute brainstem ischemic stroke group relative to HCs. Moreover, different patterns of altered network interactions were found between the patients and HCs, including the SN-CN, SN-AN, and ECN-DMN connections. Correlations between functional disconnection and upper limb dysfunction measurements in acute brainstem ischemic stroke patients were also found. This study intimated that widespread FNC impairment and altered integration existed in brainstem ischemic stroke at acute stage, suggesting that FNC disruption may be applied for early diagnosis and prediction of upper limb dysfunction in acute brainstem ischemic stroke.

Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

OBJECTIVE

There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate.

METHODS

Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms.

RESULTS

Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient Cp (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small-worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%.

CONCLUSION

Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

Investigation of functional brain network reconfiguration during exposure to naturalistic stimuli using graph-theoretical analysis

Objective.One of the most significant features of the human brain is that it can dynamically reconfigure itself to adapt to a changing environment. However, dynamic interaction characteristics of the brain networks in naturalistic scenes remain unclear.Approach.We used open-source functional magnetic resonance imaging (fMRI) data from 15 participants who underwent fMRI scans while watching an audio-visual movie 'Forrest Gump'. The community detection algorithm based on inter-subject functional correlation was used to study the time-varying functional networks only induced by the movie stimuli. The whole brain reconfiguration patterns were quantified by the temporal co-occurrence matrix that describes the probability of two brain regions engage in the same community (or putative functional module) across time and the time-varying brain modularity. Four graph metrics of integration, recruitment, spatio-temporal diversity and within-community normalised centrality were further calculated to summarise the brain network dynamic roles and hub features in their spatio-temporal topology.Main results.Our results suggest that the networks that were involved in attention and audio-visual information processing, such as the visual network, auditory network, and dorsal attention network, were considered to play a role of 'stable loners'. By contrast, 'unstable loner' networks such as the default mode network (DMN) and fronto-parietal network tended to interact more flexibly with the other networks. In addition, global brain network showed significant fluctuations in modularity. The 'stable loner' networks always maintained high functional connectivity (FC) strength while 'unstable loner' networks, especially the DMN, exhibited high intra- and inter-network FC only during a low modularity period. Finally, changes in brain modularity were significantly associated with variations in emotions induced by the movie.Significance.Our findings provide new insight for understanding the dynamic interaction characteristics of functional brain networks during naturalistic stimuli.

The Corticospinal Excitability Can Be Predicted by Spontaneous Electroencephalography Oscillations

Transcranial magnetic stimulation (TMS) has a wide range of clinical applications, and there is growing interest in neural oscillations and corticospinal excitability determined by TMS. Previous studies have shown that corticospinal excitability is influenced by fluctuations of brain oscillations in the sensorimotor region, but it is unclear whether brain network activity modulates corticospinal excitability. Here, we addressed this question by recording electroencephalography (EEG) and TMS measurements in 32 healthy individuals. The resting motor threshold (RMT) and active motor threshold (AMT) were determined as markers of corticospinal excitability. The least absolute shrinkage and selection operator (LASSO) was used to identify significant EEG metrics and then correlation analysis was performed. The analysis revealed that alpha2 power in the sensorimotor region was inversely correlated with RMT and AMT. Innovatively, graph theory was used to construct a brain network, and the relationship between the brain network and corticospinal excitability was explored. It was found that the global efficiency in the theta band was positively correlated with RMT. Additionally, the global efficiency in the alpha2 band was negatively correlated with RMT and AMT. These findings indicated that corticospinal excitability can be modulated by the power spectrum in sensorimotor regions and the global efficiency of functional networks. EEG network analysis can provide a useful supplement for studying the association between EEG oscillations and corticospinal excitability.

Acupuncture With deqi Modulates the Hemodynamic Response and Functional Connectivity of the Prefrontal-Motor Cortical Network

As a world intangible cultural heritage, acupuncture is considered an essential modality of complementary and alternative therapy to Western medicine. Despite acupuncture’s long history and public acceptance, how the cortical network is modulated by acupuncture remains largely unclear. Moreover, as the basic acupuncture unit for regulating the central nervous system, how the cortical network is modulated during acupuncture at the Hegu acupoint is mostly unclear. Here, multi-channel functional near-infrared spectroscopy (fNIRS) data were recorded from twenty healthy subjects for acupuncture manipulation, pre- and post-manipulation tactile controls, and pre- and post-acupuncture rest controls. Results showed that: (1) acupuncture manipulation caused significantly increased acupuncture behavioral deqi performance compared with tactile controls. (2) The bilateral prefrontal cortex (PFC) and motor cortex were significantly inhibited during acupuncture manipulation than controls, which was evidenced by the decreased power of oxygenated hemoglobin (HbO) concentration. (3) The bilateral PFC’s hemodynamic responses showed a positive correlation trend with acupuncture behavioral performance. (4) The network connections with bilateral PFC as nodes showed significantly increased functional connectivity during acupuncture manipulation compared with controls. (5) Meanwhile, the network’s efficiency was improved by acupuncture manipulation, evidenced by the increased global efficiency and decreased shortest path length. Taken together, these results reveal that a cooperative PFC-Motor functional network could be modulated by acupuncture manipulation at the Hegu acupoint. This study provides neuroimaging evidence that explains acupuncture’s neuromodulation effects on the cortical network.

Emotion-Related Network Reorganization Following Fish Oil Supplementation in Depressed Bipolar Offspring: An fMRI Graph-Based Connectome Analysis

INTRODUCTION

Mood disorders are associated with fronto-limbic structural and functional abnormalities and deficits in omega-3 polyunsaturated fatty acids including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Emerging evidence also suggests that n-3 PUFA, which are enriched in fish oil, promote cortical plasticity and connectivity. The present study performed a graph-based connectome analysis to investigate the role of n-3 PUFA in emotion-related network organization in medication-free depressed adolescent bipolar offspring.

METHODS

At baseline patients (n = 53) were compared with healthy controls (n = 53), and patients were then randomized to 12-week double-blind treatment with placebo or fish oil. At baseline and endpoint, erythrocyte EPA+DHA levels were measured and fMRI scans (4 Tesla) were obtained while performing a continuous performance task with emotional and neutral distractors (CPT-END). Graph-based analysis was used to characterize topological properties of large-scale brain network organization.

RESULTS

Compared with healthy controls, patients exhibited lower erythrocyte EPA+DHA levels (p = 0.0001), lower network clustering coefficients (p = 0.029), global efficiency (p = 0.042), and lower node centrality and connectivity strengths in frontal-limbic regions (p<0.05). Compared with placebo, 12-week fish oil supplementation increased erythrocyte EPA+DHA levels (p<0.001), network clustering coefficient (p = 0.005), global (p = 0.047) and local (p = 0.023) efficiency, and node centralities mainly in temporal regions (p<0.05).

LIMITATIONS

The duration of fish oil supplementation was relatively short and the sample size was relatively small.

CONCLUSIONS

These findings provide preliminary evidence that abnormalities in emotion-related network organization observed in depressed high-risk youth may be amenable to modification through fish oil supplementation.

Disrupted brain gray matter networks in drug-naïve participants with essential tremor

PURPOSE

To use structural magnetic resonance imaging and graph theory approaches to investigate the topological organization of the brain morphological network based on gray matter in essential tremor, and its potential relation to disease severity.

METHODS

In this prospective study conducted from November 2018 to November 2019, 36 participants with essential tremor and 37 matched healthy controls underwent magnetic resonance imaging. Brain networks based on the morphological similarity of gray matter across regions were analyzed using graph theory. Nonparametric permutation testing was used to assess group differences in topological metrics. Support vector machine was applied to the gray matter morphological matrices to classify participants with essential tremor vs. healthy controls.

RESULTS

Compared with healthy controls, participants with essential tremor showed increased global efficiency (p < 0.01) and decreased path length (p < 0.01); abnormal nodal properties in frontal, parietal, and cerebellar lobes; and disconnectivity in cerebello-thalamo-cortical network. The abnormal brain nodal centralities (left superior cerebellum gyrus; right caudate nucleus) correlated with clinical measures, both motor (Fahn-Tolosa-Marìn tremor rating, p = 0.017, r = - 0.41) and nonmotor (Hamilton depression scale, p = 0.040, r = - 0.36; Hamilton anxiety scale, p = 0.008, r = - 0.436). Gray matter morphological matrices classified individuals with high accuracy of 80.0%.

CONCLUSION

Participants with essential tremor showed randomization in global properties and dysconnectivity in the cerebello-thalamo-cortical network. Participants with essential tremor could be distinguished from healthy controls by gray matter morphological matrices.

Aberrant Static and Dynamic Functional Network Connectivity in Acute Mild Traumatic Brain Injury with Cognitive Impairment

PURPOSE

This study aimed to investigate differences in static and dynamic functional network connectivity (FNC) and explore their association with neurocognitive performance in acute mild traumatic brain injury (mTBI).

METHODS

A total of 76 patients with acute mTBI and 70 age-matched and sex-matched healthy controls were enrolled (age 43.79 ± 10.22 years vs. 45.63 ± 9.49 years; male/female: 34/42 vs. 38/32; all p > 0.05) and underwent resting-state functional magnetic resonance imaging (fMRI) scan (repetition time/echo time = 2000/30 ms, 230 volumes). Independent component analysis was conducted to evaluate static and dynamic FNC patterns on the basis of nine resting-state networks, namely, auditory network (AUDN), dorsal attention network (dAN), ventral attention network (vAN), default mode network (DMN), left frontoparietal network (LFPN), right frontoparietal network (RFPN), somatomotor network (SMN), visual network (VN), and salience network (SN). Spearman's correlation among aberrances in FNC values, and Montreal cognitive assessment (MoCA) scores was further measured in mTBI.

RESULTS

Compared with controls, patients with mTBI showed wide aberrances of static FNC, such as reduced FNC in DMN-vAN and VN-vAN pairs. The mTBI patients exhibited aberrant dynamic FNC in state 2, involving reduced FNC aberrance in the vAN with AUDN, VN with DMN and dAN, and SN with SMN and vAN. Reduced dFNC in the SN-vAN pair was negatively correlated with the MoCA score.

CONCLUSION

Our findings suggest that aberrant static and dynamic FNC at the acute stage may contribute to cognitive symptoms, which not only may expand knowledge regarding FNC cognition relations from the static perspective but also from the dynamic perspective.

Altered resting-state functional networks in patients with premenstrual syndrome: a graph-theoretical based study

Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Previous studies have indicated alterations of brain functional connectivity in PMS patients. However, little is known about the overall organization of brain network in PMS patients. Functional magnetic resonance imaging data deriving from 20 PMS patients and 21 healthy controls (HCs). Pearson correlation between mean time-series was used to estimate connectivity matrix between each paired regions of interest, and the connectivity matrix for each participant was then binarized. Graph theory analysis was applied to assess each participant's global and local topological properties of brain functional network. Correlation analysis was performed to evaluate relationships between the daily rating of severity of problems (DRSP) and abnormal network properties. PMS patients had lower small-worldness values than HCs. PMS-related alterations of nodal properties were mainly found in the posterior cingulate cortex, precuneus and angular gyrus. The PMS-related abnormal connectivity components were mainly associated with the thalamus, putamen and middle cingulate cortex. In the PMS group, the DRSP score were negatively correlated with the area under the curves of nodal local efficiency in the posterior cingulate cortex. Our study suggests that the graph-theory method may be one potential tool to detect disruptions of brain connections and may provide important evidence for understanding the PMS from the disrupted network organization perspective.

Altered Brain Functional Network Topology in Lung Cancer Patients After Chemotherapy

Purpose: This study aimed to explore the topological features of brain functional network in lung cancer patients before and after chemotherapy using graph theory.

Methods: Resting-state functional magnetic resonance imaging scans were obtained from 44 post-chemotherapy and 46 non-chemotherapy patients as well as 49 healthy controls (HCs). All groups were age- and gender-matched. Then, the topological features of brain functional network were assessed using graph theory analysis.

Results: At the global level, compared with the HCs, both the non-chemotherapy group and the post-chemotherapy group showed significantly increased values in sigma (p < 0.05), gamma (p < 0.05), and local efficiency, E_loc (p < 0.05). The post-chemotherapy group and the non-chemotherapy group did not differ significantly in the above-mentioned parameters. At the nodal level, when non-chemotherapy or post-chemotherapy patients were compared with the HCs, abnormal nodal centralities were mainly observed in widespread brain regions. However, when the post-chemotherapy group was compared with the non-chemotherapy group, significantly decreased nodal centralities were observed primarily in the prefrontal–subcortical regions.

Conclusions: These results indicate that lung cancer and chemotherapy can disrupt the topological features of functional networks, and chemotherapy may cause a pattern of prefrontal–subcortical brain network abnormality. As far as we know, this is the first study to report that altered functional brain networks are related to lung cancer and chemotherapy.

Different effects of the DRD4 genotype on intrinsic brain network connectivity strength in drug-naïve children with ADHD and healthy controls

The dopamine D4 receptor gene (DRD4) has been consistently reported to be associated with attention-deficit/hyperactivity disorder (ADHD). Recent studies have linked DRD4 to functional connectivity among specific brain regions. The current study aimed to compare the effects of the DRD4 genotype on functional integrity in drug-naïve ADHD children and healthy children. Resting-state functional MRI images were acquired from 49 children with ADHD and 37 healthy controls (HCs). We investigated the effects of the 2-repeat allele of DRD4 on brain network connectivity in both groups using a parameter called the degree of centrality (DC), which indexes local functional relationships across the entire brain connectome. A voxel-wise two-way ANCOVA was performed to examine the diagnosis-by-genotype interactions on DC maps. Significant diagnosis-by-genotype interactions with DC were found in the temporal lobe, including the left inferior temporal gyrus (ITG) and bilateral middle temporal gyrus (MTG) (GRF corrected at voxel level p < 0.001 and cluster level p < 0.05, two-tailed). With the further subdivision of the DC network according to anatomical distance, additional brain regions with significant interactions were found in the long-range DC network, including the left superior parietal gyrus (SPG) and right middle frontal gyrus (MFG). The post-hoc pairwise analysis found that altered network centrality related to DRD4 differed according to diagnostic status (p < 0.05). This genetic imaging study suggests that the DRD4 genotype regulates the functional integration of brain networks in children with ADHD and HCs differently. This may have important implications for our understanding of the role of DRD4 in altering functional connectivity in ADHD subjects.

Multivariate Machine Learning Analyses in Identification of Major Depressive Disorder Using Resting-State Functional Connectivity: A Multicentral Study

Diagnosis of major depressive disorder (MDD) using resting-state functional connectivity (rs-FC) data faces many challenges, such as the high dimensionality, small samples, and individual difference. To assess the clinical value of rs-FC in MDD and identify the potential rs-FC machine learning (ML) model for the individualized diagnosis of MDD, based on the rs-FC data, a progressive three-step ML analysis was performed, including six different ML algorithms and two dimension reduction methods, to investigate the classification performance of ML model in a multicentral, large sample dataset [1021 MDD patients and 1100 normal controls (NCs)]. Furthermore, the linear least-squares fitted regression model was used to assess the relationships between rs-FC features and the severity of clinical symptoms in MDD patients. Among used ML methods, the rs-FC model constructed by the eXtreme Gradient Boosting (XGBoost) method showed the optimal classification performance for distinguishing MDD patients from NCs at the individual level (accuracy = 0.728, sensitivity = 0.720, specificity = 0.739, area under the curve = 0.831). Meanwhile, identified rs-FCs by the XGBoost model were primarily distributed within and between the default mode network, limbic network, and visual network. More importantly, the 17 item individual Hamilton Depression Scale scores of MDD patients can be accurately predicted using rs-FC features identified by the XGBoost model (adjusted R² = 0.180, root mean squared error = 0.946). The XGBoost model using rs-FCs showed the optimal classification performance between MDD patients and HCs, with the good generalization and neuroscientifical interpretability.

Structural and functional brain connectivity in moderate-late preterm infants with low-grade intraventricular hemorrhage

PURPOSE

Brain functional connectivity (FC) changes and microstructural abnormalities are reported in infants born moderate and late preterm (MLPT). We evaluated the effect of low-grade (grades I, II) intraventricular hemorrhage (IVH) in MLPT babies on brain structural connectivity (SC) and FC.

METHODS

Babies born MLPT between January 2014 and May 2017 underwent brain ultrasound (US) at 72 h and 7 days after birth, and MRI at around term equivalent. The MRI protocol comprised T1- and T2-weighted sequences, diffusion tensor imaging (DTI), and resting-state functional MRI (fMRI). SC and FC were assessed using graph analysis.

RESULTS

Of 350 MLPT neonates, 15 showed low-grade IVH on US at 72 h, for which brain MRI was available in 10. These 10 infants, with mean gestational age (GA) 34.0 ± 0.8 weeks, comprised the study group, and 10 MLPT infants of mean GA 33.9 ± 1.1 weeks, with no abnormalities on brain US and MRI, were control subjects. All study subjects presented modularity, small world topology, and rich club organization for both SC and FC. The patients with low-grade IVH had lower FC rich club coefficient and lower SC betweenness centrality in the left frontoparietal operculum, and lower SC rich club coefficient in the right superior orbitofrontal cortex than the control subjects.

CONCLUSIONS

Topological and functional properties of mature brain connectivity are present in MLPT infants. IVH in these infants was associated with structural and functional abnormalities in the left frontoparietal operculum and right orbitofrontal cortex, regions related to language and cognition.

The disrupted topological properties of structural networks showed recovery in ischemic stroke patients: a longitudinal design study

INTRODUCTION

Stroke is one of the leading causes of substantial disability worldwide. Previous studies have shown brain functional and structural alterations in adults with stroke. However, few studies have examined the longitudinal reorganization in whole-brain structural networks in stroke.

METHODS

Here, we applied graph theoretical analysis to investigate the longitudinal topological organization of white matter networks in 20 ischemic stroke patients with a one-month interval between two timepoints. Two sets of clinical scores, Fugl-Meyer motor assessment (FMA) and neurological deficit scores (NDS), were assessed for all patients on the day the image data were collected.

RESULTS

The stroke patients exhibited significant increases in FMA scores and significant reductions in DNS between the two timepoints. All groups exhibited small-world organization (σ  >  1) in the brain structural network, including a high clustering coefficient (γ  >  1) and a low normalized characteristic path length (λ ≈ 1). However, compared to healthy controls, stroke patients showed significant decrease in nodal characteristics at the first timepoint, primarily in the right supplementary motor area, right middle temporal gyrus, right inferior parietal lobe, right postcentral gyrus and left posterior cingulate gyrus. Longitudinal results demonstrated that altered nodal characteristics were partially restored one month later. Additionally, significant correlations between the nodal characteristics of the right supplementary motor area and the clinical scale scores (FMA and NDS) were observed in stroke patients. Similar behavioral-neuroimaging correlations were found in the right inferior parietal lobe.

CONCLUSION

Altered topological properties may be an effect of stroke, which can be modulated during recovery. The longitudinal results and the neuroimaging-behavioral relationship may provide information for understanding brain recovery from stroke. Future studies should detect whether observed changes in structural topological properties can predict the recovery of daily cognitive function in stroke.

Disruption of human brain connectivity networks in patients with cervical spondylotic myelopathy

Background

Brain functional plasticity and reorganization in patients with cervical spondylotic myelopathy (CSM) is increasingly being explored and validated. However, specific topological alterations in functional networks and their role in CSM brain functional reorganization remain unclear. This study investigates the topological architecture of intrinsic brain functional networks in CSM patients using graph theory.

Methods

Functional MRI was conducted on 67 CSM patients and 60 healthy controls (HCs). The topological organization of the whole-brain functional network was then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Nonparametric permutation tests were used to compare network measures between the two groups.

Results

Small-world architecture in functional brain networks were identified in both CSM patients and HCs. Compared with HCs, CSM patients showed a decreased area under the curve (AUC) of the characteristic path length (FDR q=0.040), clustering coefficient (FDR q=0.037), and normalized characteristic path length (FDR q=0.038) of the network. In contrast, there was an increased AUC of normalized clustering coefficient (FDR q=0.014), small-worldness (FDR q=0.009), and global network efficiency (FDR q=0.027) of the network. In local brain regions, nodal topological properties revealed group differences which were predominantly in the default-mode network (DMN), left postcentral gyrus, bilateral putamen, lingual gyrus, and posterior cingulate gyrus.

Conclusions

This study reported altered functional topological organization in CSM patients. Decreased nodal centralities in the visual cortex and sensory-motor regions may indicate sensory-motor dysfunction and blurred vision. Furthermore, increased nodal centralities in the cerebellum may be compensatory for sensory-motor dysfunction in CSM, while the increased DMN may indicate increased psychological processing in CSM patients.

Abnormal white matter within brain structural networks is associated with high-impulse behaviour in codeine-containing cough syrup dependent users

Codeine-containing cough syrup (CCS) is considered as one of the most popular drug of dependence among adolescents because of its inexpensiveness and easy availability. However, its relationship with neurobiological effects remains sparsely explored. Herein, we examined how high-impulse behaviours relate to changes in the brain structural networks. Forty codeine-containing cough syrup dependent (CCSD) users and age-, gender-, and number of cigarettes smoked per day -matched forty healthy control (HC) subjects underwent structural brain imaging via MRI. High-impulse behaviour was assessed using the 30-item self-rated Barratt Impulsiveness Scale (BIS-11), and structural networks were constructed using diffusion tensor imaging and AAL-90 template. Between-group topological metrics were compared using nonparametric permutations. Benjamin-Hochberg false discovery rate correction was used to correct for multiple comparisons (P < 0.05). The relationships between abnormal network metrics and clinical characteristics of CCS dependent (BIS-11 total score, CCS- dependent duration and mean dose) were examined by Spearman's correlation. Structural networks of the CCSD group demonstrated lower small-world properties than those of the HC group. Abnormal changes in nodal properties among CCSD users were located mainly in the frontal gyrus, inferior parietal lobe and olfactory cortex. NBS analysis further indicated disrupted structural connections between the frontal gyrus and multiple brain regions. There were significant correlations between abnormal nodal properties of the frontal gyrus and clinical characteristics (BIS-11 total score, CCS dependent duration and mean dose) in the CCSD group. These findings suggest that the high-impulse behavioural expression in CCS addiction is associated with widespread brain regions, particularly within those in the frontal cortex. Aberrant brain regions and disrupted connectivity of structural network may be the bases of neuropathology for underlying symptoms of high-impulse behaviours in CCSD users, which may provide a novel sight to better treat and prevent codeine dependency in adolescents.

Prediction of pain intensity with uterine morphological features and brain microstructural and functional properties in women with primary dysmenorrhea

Primary dysmenorrhea (PDM), defined as painful menstrual cramps of uterine origin, could cause brain structural and functional changes after long-term menstrual pain. Here, we aimed to investigate the predictive value of uterine morphological features and microstructural/functional properties of the brain extracted from periovulatory phases for the intensity of menstrual pain as rated by women with PDM during their subsequent menstrual period. Forty-five women with PDM were recruited and classified into the high and mild pain intensity groups. Pelvic MRI was employed to extract the uterine texture features. White matter diffusion properties, grey matter and functional connectivity features were extracted as brain features. Multivariate logistic regression models with iteration optimization were built for classifying different pain intensity groups. Texture features from myometrium and uterine junction zone had outstanding prediction performance with an area under the receiver operating characteristic (AUC) of 0.96 (P < 0.05, permutation test), and diffusion properties along the thalamic fiber bundles were the most discriminative features with AUC of 0.95. Applying features from uterus and brain together, we could gain better prediction performance. Our results indicated that accumulated differences in menstrual pain were associated not only with uterine structure but also diffusion properties of thalamic-related fiber tracts, suggesting that treatment options of PDM patients may be expanded from only being able to manage pain in the uterus focusing on the functional/structural modifications of the pain processing system.

Differences in topological properties of functional brain networks between menstrually-related and non-menstrual migraine without aura

Menstrually-related migraine without aura refers to a specific type of migraine that is associated with the female ovarian cycle. Compared with non-menstrual migraine without aura, in menstrually-related migraine without aura, there are additional attacks of migraine outside of the menstrual period. Menstrually-related migraine without aura tends to be less responsive to acute treatment and more prone to relapse than non-menstrual migraine without aura. Currently menstrually-related migraine without aura is treated no differently from any other migraine but, the differences in the central mechanisms underlying menstrually-related migraine without aura and non-menstrual migraine without aura remain poorly understood. Here, using resting-state functional magnetic resonance imaging and graph theory approaches, we aimed to explore the differences in topological properties of functional networks in 51 menstrually-related migraine without aura patients and 47 non-menstrual migraine without aura patients. The major finding of our study was that significant differences in topological properties between the two groups were mainly evident in the nodal centrality of the inferior frontal gyrus and the thalamus. Nodal centrality in inferior frontal gyrus was negatively correlated with Headache Impact Test questionnaire scores in the menstrually-related migraine without aura patients. Partial least squares correlation analysis revealed enhanced correlations of inferior frontal gyrus to pain-related behavior in the non-menstrual migraine without aura group, while within the menstrually-related migraine without aura group these effects were non-significant. These results indicate that the regulatory mechanisms in the central nervous system may differ between the two subtypes of migraine. The results provide novel insights into the pathophysiology of different subtypes of migraine, and could help us to enhance their clinical diagnosis and treatment.

Multi-Dimensional Diffusion Tensor Imaging Biomarkers for Cognitive Decline From the Preclinical Stage: A Study of Post-stroke Small Vessel Disease

Objectives: We aim to investigate whether multi-dimensional diffusion tensor imaging (DTI) measures can sensitively identify different cognitive status of cerebral small vessel disease (CSVD) and to explore the underlying pattern of white matter disruption in CSVD.

Methods: Two hundred and two participants were recruited, composed of 99 CSVD patients with mild cognitive impairment (VaMCI) and 60 with no cognitive impairment (NCI) and 43 healthy subjects as normal controls (NC). Full domain neuropsychological tests and diffusion-weighted imaging were performed on each subject. DTI metrics such as fractional anisotropy (FA), mean diffusivity (MD), the skeletonized mean diffusivity (PSMD), and structural brain network measures including network strength, global efficiency (E_Global), and local efficiency (E_Local) were calculated. Region of interest (ROI) analysis of 42 white matter tracts was performed to examine the regional anatomical white matter disruption for each group.

Results: Significant differences of multiple cognitive test scores across all cognitive domains especially processing and executive function existed among the three groups. DTI measures (FA, MD, and PSMD) showed significant group difference with the cognitive status changing. FA and E_Global showed significant correlation with processing speed, executive function, and memory. ROI analysis found that white matter integrity impairment occurred from the preclinical stage of vascular cognitive impairment (VCI) due to CSVD. These lesions in the NCI group mainly involved some longitudinal fibers such as right superior longitudinal fasciculus (SLF-R), right superior fronto-occipital fasciculus (SFO-R), and right uncinate fasciculus (UNC-R), which might be more vulnerable to the cerebrovascular aging and disease process.

Conclusions: DTI measures are sensitive neuroimaging markers in detecting the early cognitive impairment and able to differentiate the different cognitive status due to CSVD. Subtle changes of some vulnerable white matter tracts may be observed from the preclinical stage of VCI and have a local to general spreading pattern during the disease progression.

Disrupted Topological Organization in White Matter Networks in Unilateral Sudden Sensorineural Hearing Loss

Sudden sensorineural hearing loss (SSNHL) is a sudden-onset hearing impairment that rapidly develops within 72 h and is mostly unilateral. Only a few patients can be identified with a defined cause by routine clinical examinations. Recently, some studies have shown that unilateral SSNHL is associated with alterations in the central nervous system. However, little is known about the topological organization of white matter (WM) networks in unilateral SSNHL patients in the acute phase. In this study, 145 patients with SSNHL and 91 age-, gender-, and education-matched healthy controls were evaluated using diffusion tensor imaging (DTI) and graph theoretical approaches. The topological properties of WM networks, including global and nodal parameters, were investigated. At the global level, SSNHL patients displayed decreased clustering coefficient, local efficiency, global efficiency, normalized clustering coefficient, normalized characteristic path length, and small-worldness and increased characteristic path length (p < 0.05) compared with healthy controls. At the nodal level, altered nodal centralities in brain regions involved the auditory network, visual network, attention network, default mode network (DMN), sensorimotor network, and subcortical network (p < 0.05, Bonferroni corrected). These findings indicate a shift of the WM network topology in SSNHL patients toward randomization, which is characterized by decreased global network integration and segregation and is reflected by decreased global connectivity and altered nodal centralities. This study could help us understand the potential pathophysiology of unilateral SSNHL.

Poor speech recognition, sound localization and reorganization of brain activity in children with unilateral microtia-atresia

Microtia-atresia is a congenital malformation of the external ear, often affecting one side and being associated with severe-to-profound unilateral conductive hearing loss (UCHL). Although the impact of unilateral hearing loss (UHL) on speech recognition, sound localization and brain plasticity has been intensively investigated, less is known about the subjects with unilateral microtia-atresia (UMA). Considering these UMA subjects have hearing loss from birth, we hypothesize it has a great effect on brain organization. A questionnaire on speech recognition and spatial listening ability was administered to 40 subjects with UMA and 40 age- and sex-matched controls. UMA subjects showed poorer speech recognition in laboratory and poorer spatial listening ability. However, cognitive scores determined by the Montreal Cognitive Assessment (MoCA) and Wechsler Intelligence Scale for Children (WISC-IV) did not differ significantly in these two groups. The impact of hearing loss in UMA on brain functional organization was examined by comparing resting-state fMRIs (rs-fMRI) in 27 subjects with right-sided UMA and 27 matched controls. UMA subjects had increased nodal betweenness in visual networks and DMN but decreases in auditory and attention networks. These results indicate that UCHL in UMA causes significant abnormalities in brain organization. The impact of UCHL on cognition should be further examined with a battery of tests that are more challenging and better focused on the cognitive networks identified.

White matter degeneration in remote brain areas of stroke patients with motor impairment due to basal ganglia lesions

Diffusion tensor imaging (DTI) studies have revealed distinct white matter (WM) characteristics of the brain following diseases. Beyond the lesion‐symptom maps, stroke is characterized by extensive structural and functional alterations of brain areas remote to local lesions. Here, we further investigated the structural changes over a global level by using DTI data of 10 ischemic stroke patients showing motor impairment due to basal ganglia lesions and 11 healthy controls. DTI data were processed to obtain fractional anisotropy (FA) maps, and multivariate pattern analysis was used to explore brain regions that play an important role in classification based on FA maps. The WM structural network was constructed by the deterministic fiber‐tracking approach. In comparison with the controls, the stroke patients showed FA reductions in the perilesional basal ganglia, brainstem, and bilateral frontal lobes. Using network‐based statistics, we found a significant reduction in the WM subnetwork in stroke patients. We identified the patterns of WM degeneration affecting brain areas remote to the lesions, revealing the abnormal organization of the structural network in stroke patients, which may be helpful in understanding of the neural mechanisms underlying hemiplegia.

Brain network analyses of diffusion tensor imaging for brain aging

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

Associations between hemispheric asymmetry and schizophrenia-related risk genes in people with schizophrenia and people at a genetic high risk of schizophrenia

BACKGROUND

Schizophrenia is considered a polygenic disorder. People with schizophrenia and those with genetic high risk of schizophrenia (GHR) have presented with similar neurodevelopmental deficits in hemispheric asymmetry. The potential associations between neurodevelopmental abnormalities and schizophrenia-related risk genes in both schizophrenia and those with GHR remains unclear.

AIMS

To investigate the shared and specific alternations to the structural network in people with schizophrenia and those with GHR. And to identify an association between vulnerable structural network alternation and schizophrenia-related risk genes.

METHOD

A total of 97 participants with schizophrenia, 79 participants with GHR and 192 healthy controls, underwent diffusion tensor imaging (DTI) scans at a single site. We used graph theory to characterise hemispheric and whole-brain structural network topological metrics. For 26 people in the schizophrenia group and 48 in the GHR group with DTI scans we also calculated their schizophrenia-related polygenic risk scores (SZ-PRSs). The correlations between alterations to the structural network and SZ-PRSs were calculated. Based on the identified genetic-neural association, bioinformatics enrichment was explored.

RESULTS

There were significant hemispheric asymmetric deficits of nodal efficiency, global and local efficiency in the schizophrenia and GHR groups. Hemispheric asymmetric deficit of local efficiency was significantly positively correlated with SZ-PRSs in the schizophrenia and GHR groups. Bioinformatics enrichment analysis showed that these risk genes may be linked to signal transduction, neural development and neuron structure. The schizophrenia group showed a significant decrease in the whole-brain structural network.

CONCLUSIONS

The shared asymmetric deficits in people with schizophrenia and those with GHR, and the association between anomalous asymmetry and SZ-PRSs suggested a vulnerability imaging marker regulated by schizophrenia-related risk genes. Our findings provide new insights into asymmetry regulated by risk genes and provides a better understanding of the genetic-neural pathological underpinnings of schizophrenia.

Increased segregation of structural brain networks underpins enhanced broad cognitive abilities of cognitive training

A major challenge in the cognitive training field is inducing broad, far-transfer training effects. Thus far, little is known about the neural mechanisms underlying broad training effects. Here, we tested a set of competitive hypotheses regarding the role of brain integration versus segregation underlying the broad training effect. We retrospectively analyzed data from a randomized controlled trial comparing neurocognitive effects of vision-based speed of processing training (VSOP) and an active control consisting of mental leisure activities (MLA) in older adults with MCI. We classified a subset of participants in the VSOP as learners, who showed improvement in executive function and episodic memory. The other participants in the VSOP (i.e., VSOP non-learners) and a subset of participants in the MLA (i.e., MLA non-learners) served as controls. Structural brain networks were constructed from diffusion tensor imaging. Clustering coefficients (CCs) and characteristic path lengths were computed as measures of segregation and integration, respectively. Learners showed significantly greater global CCs after intervention than controls. Nodal CCs were selectively enhanced in cingulate cortex, parietal regions, striatum, and thalamus. Among VSOP learners, those with more severe baseline neurodegeneration had greater improvement in segregation after training. Our findings suggest broad training effects are related to enhanced segregation in selective brain networks, providing insight into cognitive training related neuroplasticity.