Topological alterations in white matter anatomical networks in cervical dystonia.

BACKGROUND: Accumulating neuroimaging evidence indicates that patients with cervical dystonia (CD) have changes in the cortico-subcortical white matter (WM) bundle. However, whether these patients' WM structural networks undergo reorganization remains largely unclear. We aimed to investigate topological changes in large-scale WM structural networks in patients with CD compared to healthy controls (HCs), and explore the network changes associated with clinical manifestations. METHODS: Diffusion tensor imaging (DTI) was conducted in 30 patients with CD and 30 HCs, and WM network construction was based on the BNA-246 atlas and deterministic tractography. Based on the graph theoretical analysis, global and local topological properties were calculated and compared between patients with CD and HCs. Then, the AAL-90 atlas was used for the reproducibility analyses. In addition, the relationship between abnormal topological properties and clinical characteristics was analyzed. RESULTS: Compared with HCs, patients with CD showed changes in network segregation and resilience, characterized by increased local efficiency and assortativity, respectively. In addition, a significant decrease of network strength was also found in patients with CD relative to HCs. Validation analyses using the AAL-90 atlas similarly showed increased assortativity and network strength in patients with CD. No significant correlations were found between altered network properties and clinical characteristics in patients with CD. CONCLUSION: Our findings show that reorganization of the large-scale WM structural network exists in patients with CD. However, this reorganization is attributed to dystonia-specific abnormalities or hyperkinetic movements that need further identification.

Graph-based, dynamics-preserving reduction of (bio)chemical systems.

Complex dynamical systems are often governed by equations containing many unknown parameters whose precise values may or may not be important for the system's dynamics. In particular, for chemical and biochemical systems, there may be some reactions or subsystems that are inessential to understanding the bifurcation structure and consequent behavior of a model, such as oscillations, multistationarity and patterning. Due to the size, complexity and parametric uncertainties of many (bio)chemical models, a dynamics-preserving reduction scheme that is able to isolate the necessary contributors to particular dynamical behaviors would be useful. In this contribution, we describe model reduction methods for mass-action (bio)chemical models based on the preservation of instability-generating subnetworks known as critical fragments. These methods focus on structural conditions for instabilities and so are parameter-independent. We apply these results to an existing model for the control of the synthesis of the NO-detoxifying enzyme Hmp in Escherichia coli that displays bistability.

Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes.

BACKGROUND: While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is a lack of community consensus regarding the number of streamlines needed to generate connectomes. PURPOSE: The purpose was to define the relationship between streamline count and graph-measure value, reproducibility, and repeatability. STUDY TYPE: Retrospective analysis of previously prospective study. POPULATION: Ten healthy subjects, 70% female, aged 25.3 ± 5.9 years. FIELD STRENGTH/SEQUENCE: A 3-T, T1-weighted sequences and diffusion-weighted imaging (DWI) with two gradient strengths (b-values = 1200 and 3000 sec/mm2 , echo time [TE] = 68 msec, repetition time [TR] = 5.4 seconds, 120 slices, field of view = 188 mm2 ). ASSESSMENT: A total of 13 graph-theory measures were derived for each subject by generating probabilistic whole-brain tractography from DWI and mapping the structural connectivity to connectomes. The streamline count invariance from changes in mean, repeatability, and reproducibility were derived. STATISTICAL TESTS: Paired t-test with P value <0.05 was used to compare graph-measure means with a reference, intraclass correlation coefficient (ICC) to measure repeatability, and concordance correlation coefficient (CCC) to measure reproducibility. RESULTS: Modularity and global efficiency converged to their reference mean with ICC > 0.90 and CCC > 0.99. Edge count, small-worldness, randomness, and average betweenness centrality converged to the reference mean, with ICC > 0.90 and CCC > 0.95. Assortativity and average participation coefficient converged with ICC > 0.75 and CCC > 0.90. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient did not converge, though had ICC > 0.90 and CCC > 0.99. For these measures, alternate definitions that converge a reference mean are provided. DATA CONCLUSION: Modularity and global efficiency are streamline count invariant for greater than 6 million and 100,000 streamlines, respectively. Density, average node strength, average node degree, characteristic path length, average local efficiency, and average clustering coefficient were strongly dependent on streamline count. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 1.

Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting-State Functional MRI After Working Memory Training in People With HIV.

BACKGROUND: Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown. PURPOSE: To test whether WMT affects PWH brain functional connectivity in resting-state fMRI (rsfMRI). STUDY TYPE: Prospective. POPULATION: A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53 HIV-seronegative controls (SN, ages 49.5 ± 1.6 years, six women). FIELD STRENGTH/SEQUENCE: Axial single-shot gradient-echo echo-planar imaging at 3.0 T was performed at baseline (TL1), at 1-month (TL2), and at 6-months (TL3), after WMT. ASSESSMENT: All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training, n = 58: 28 PWH, 30 SN; nonadaptive training, n = 48: 25 PWH, 23 SN), 25 sessions over 5-8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2. STATISTICAL TESTS: Two-way analyses of variance (ANOVA) on GT metrics and two-sample t-tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set at P < 0.05 after false discovery rate correction. RESULTS: The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = -0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%). DATA CONCLUSION: ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: 1.

Disrupted topological organization of functional brain networks is associated with cognitive impairment in hypertension patients: a resting-state fMRI study.

PURPOSE: To investigate the alterations of topological organization of the whole brain functional networks in hypertension patients with cognitive impairment (HTN-CI) and characterize its relationship with cognitive scores. METHODS: Fifty-seven hypertension patients with cognitive impairment and 59 hypertension patients with normal cognition (HTN-NC), and 49 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Graph theoretical analysis was used to investigate the altered topological organization of the functional brain networks. The global topological properties and nodal metrics were compared among the three groups. Network-based statistic (NBS) analysis was used to determine the connected subnetwork. The relationships between network metrics and cognitive scores were also characterized. RESULTS: HTN-CI patients exhibited significantly decreased global efficiency, lambda, and increased shortest path length when compared with HCs. In addition, both HTN-CI and HTN-NC groups exhibited altered nodal degree centrality and nodal efficiency in the right precentral gyrus. The disruptions of global network metrics (lambda, Lp) and the nodal metrics (degree centrality and nodal efficiency) in the right precentral gyrus were positively correlated with the MoCA scores in HTN-CI. NBS analysis demonstrated that decreased subnetwork connectivity was present both in the HTN-CI and HTN-NC groups, which were mainly involved in the default mode network, frontoparietal network, and cingulo-opercular network. CONCLUSION: This study demonstrated the alterations of topographical organization and subnetwork connectivity of functional brain networks in HTN-CI. In addition, the global and nodal network properties were correlated with cognitive scores, which may provide useful insights for the understanding of neuropsychological mechanisms underlying HTN-CI.

Epilepsy-related white matter network changes in patients with frontal lobe glioma.

PURPOSE: Epilepsy is a common symptom in patients with frontal lobe glioma. Tumor-related epilepsy was recently considered a type of network disease. Glioma can severely influence the integrity of the white matter network. The association between white matter network changes and presurgical epilepsy remains unclear in glioma patients. This study aims to identify alterations to the subcortical brain networks caused by glioma and glioma-related epilepsy. METHODS: Sixty-one patients with frontal lobe gliomas were enrolled and stratified into the epileptic and non-epileptic groups. Additionally, 14 healthy participants were enrolled after matching for age, sex, and education level. All participants underwent diffusion tensor imaging. Graph theoretical analysis was applied to reveal topological changes in their white matter networks. Regions affected by tumors were excluded from the analysis. RESULTS: Global efficiency was significantly decreased (p = 0.008), while the shortest path length increased (p = 0.02) in the left and right non-epileptic groups compared to the controls. A total of five edges exhibited decreased fiber count in the non-epileptic group (p < 0.05, false discovery rate-corrected). The topological properties and connectional edges showed no significant differences when comparing the epileptic groups and the controls. Additionally, the degree centrality of several nodes connected to the alternated edges was also diminished. CONCLUSIONS: Compared to the controls, the epilepsy groups showed raletively intact WM networks, while the non-epileptsy groups had damaged network with lower efficiency and longer path length. These findings indicated that the occurrence of glioma related epilepsy have association with white matter network intergrity.

Impaired topological properties of cortical morphological brain networks correlate with motor symptoms in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is characterized by loss of selectively vulnerable neurons within the basal ganglia circuit and progressive atrophy in subcortical and cortical regions. However, the impact of neurodegenerative pathology on the topological organization of cortical morphological networks has not been explored. The aims of this study were to investigate altered network patterns of covariance in cortical thickness and complexity, and to evaluate how morphological network integrity in PD is related to motor impairment. METHODS: Individual morphological networks were constructed for 50 PD patients and 46 healthy controls (HCs) by estimating interregional similarity distributions in surface-based indices. We performed graph theoretical analysis and network-based statistics to detect PD-related alterations and further examined the correlation of network metrics with clinical scores. Furthermore, support vector regression based on topological characteristics was applied to predict the severity of motor impairment in PD. RESULTS: Compared with HCs, PD patients showed lower local efficiency (p = 0.004), normalized characteristic path length (p = 0.022), and clustering coefficient (p = 0.005) for gyrification index-based morphological brain networks. Nodal topological abnormalities were mainly in the frontal, parietal and temporal regions, and impaired morphological connectivity was involved in the sensorimotor and default mode networks. The support vector regression model using network-based features allowed prediction of motor symptom severity with a correlation coefficient of 0.606. CONCLUSIONS: This study identified a disrupted topological organization of cortical morphological networks that could substantially advance our understanding of the network degeneration mechanism of PD and might offer indicators for monitoring disease progression.

Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease.

BACKGROUND: White matter hyperintensity (WMH) is one of the typical neuroimaging manifestations of cerebral small vessel disease (CSVD), and the WMH correlates closely to cognitive impairment (CI). CSVD patients with WMH own altered topological properties of brain functional network, which is a possible mechanism that leads to CI. This study aims to identify differences in the characteristics of some brain functional network among patients with different grades of WMH and estimates the correlations between these different brain functional network characteristics and cognitive assessment scores. METHODS: 110 CSVD patients underwent 3.0 T Magnetic resonance imaging scans and neuropsychological cognitive assessments. WMH of each participant was graded on the basis of Fazekas grade scale and was divided into two groups: (A) WMH score of 1-2 points (n = 64), (B) WMH score of 3-6 points (n = 46). Topological indexes of brain functional network were analyzed using graph-theoretical method. T-test and Mann-Whitney U test was used to compare the differences in topological properties of brain functional network between groups. Partial correlation analysis was applied to explore the relationship between different topological properties of brain functional networks and overall cognitive function. RESULTS: Patients with high WMH scores exhibited decreased clustering coefficient values, global and local network efficiency along with increased shortest path length on whole brain level as well as decreased nodal efficiency in some brain regions on nodal level (p < 0.05). Nodal efficiency in the left lingual gyrus was significantly positively correlated with patients' total Montreal Cognitive Assessment (MoCA) scores (p < 0.05). No significant difference was found between two groups on the aspect of total MoCA and Mini-mental State Examination (MMSE) scores (p > 0.05). CONCLUSION: Therefore, we come to conclusions that patients with high WMH scores showed less optimized small-world networks compared to patients with low WMH scores. Global and local network efficiency on the whole-brain level, as well as nodal efficiency in certain brain regions on the nodal level, can be viewed as markers to reflect the course of WMH.

Alteration in topological organization characteristics of gray matter covariance networks in patients with prediabetes. / ç³–å°¿ç— å‰æœŸæ‚£è€ ç°è´¨åå˜ç½‘ç»œæ‹“æ‰‘ç»„ç»‡ç‰¹æ€§çš„æ”¹å˜.

OBJECTIVES: Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes. METHODS: A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters. RESULTS: The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001). CONCLUSIONS: The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.

Graph theory analysis reveals premature ejaculation is a brain disorder with altered structural connectivity and depressive symptom: A DTI-based connectome study.

Recent research has shown that premature ejaculation (PE) is associated with negative psychological effects (e.g., depression) and the decline of control over ejaculation is accompanied by structural and functional abnormalities in specific brain areas and connections. However, little is known about the alterations of topological organization in the brain network of patients with PE and its relationship with depressive symptom. We acquired diffusion tensor images, sexual function and depression assessment in 16 lifelong PE patients with depressive symptom, 16 lifelong PE patients without depression and 32 age- and education-matched healthy controls (HC). The differences in nodal centrality and different hub regions among the three groups were compared. Correlation analyses were conducted between the nodal centrality of brain regions displaying significant group differences and the clinical parameters of PE patients. PE patients with depression had increased nodal degree in the right middle frontal gyrus (orbital part) (ORBmid.R) (survived FDR-correction) compared with HC and PE without depression. PE patients with depression also had increased nodal degree in the left and right posterior cingulate gyrus (PCG.L; PCG.R) compared with HC. In addition, PE with depression had increased nodal betweenness in ORBmid.R compared with HC and PE without depression. Moreover PE with depression had decreased nodal participation in the right rolandic operculum (ROL.R), postcentral gyrus (PoCG.R) and supramarginal gyrus (SMG.R) compared with HC, and had decreased nodal participation in ROL.R and the right inferior parietal gyrus (IPL.R) compared with PE without depression, while PE without depression had increased nodal participation in the left precuneus (PCUN.L) compared with HC. The degree and betweenness of ORBmid.R were positively correlated with the total scores of Beck depression inventory (BDI) while the participation of IPL.R had a negative association with the total scores of BDI. Different hubs were found among PE patients with and without depression and HC based on nodal degree, betweenness and participation; however, no significant group differences were found in the frequency distribution of high-degree hubs, high-betweenness hubs, provincial hubs and connector hubs. These findings demonstrated that PE was a brain disorder with altered structural connectivity pattern of brain network and depressive symptom, which suggested that altered structural connectivities of the fronto-cingulate-parietal control network were core neurobiological features associated with PE and depression. Together, these alterations could prove helpful for understanding the pathophysiological mechanisms of PE in depression.

Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder.

A number of studies have indicated alterations of brain morphology in individuals with autism spectrum disorder (ASD); however, how ASD influences the topological organization of the brain cortex at different developmental stages is not yet well characterized. In this study, we used structural images of 492 high-functioning participants in the Autism Brain Imaging Data Exchange database acquired from 17 international imaging centers, including 75 autistic children (age 7-11 years), 91 adolescents with ASD (age 12-17 years), and 80 young adults with ASD (age 18-29 years), and 246 typically developing controls (TDCs) that were age, gender, handedness, and full-scale IQ matched. Cortical thickness (CT) and surface area (SA) were extracted and the covariance between cortical regions across participants were treated as a network to examine developmental patterns of the cortical topological organization at different stages. A center-paired resampling strategy was developed to control the center bias during the permutation test. Compared with the TDCs, network of SA (but not CT) of individuals with ASD showed reduced small-worldness in childhood, and the network hubs were reorganized in the adulthood such that hubs inclined to connect with nonhub nodes and demonstrated more dispersed spatial distribution. Furthermore, the SA network of the ASD cohort exhibited increased segregation of the inferior parietal lobule and prefrontal cortex, and insular-opercular cortex in all three age groups, resulting in the emergence of two unique modules in the autistic brain. Our findings suggested that individuals with ASD may undergo remarkable remodeling of the cortical topology from childhood to adulthood, which may be associated with the altered social and cognitive functions in ASD.

Whole-brain computational modeling reveals disruption of microscale brain dynamics in HIV infected individuals.

MRI-based neuroimaging techniques have been used to investigate brain injury associated with HIV-infection. Whole-brain cortical mean-field dynamic modeling provides a way to integrate structural and functional imaging outcomes, allowing investigation of microscale brain dynamics. In this study, we adopted the relaxed mean-field dynamic modeling to investigate structural and functional connectivity in 42 HIV-infected subjects before and after 12-week of combination antiretroviral therapy (cART) and compared them with 46 age-matched healthy subjects. Microscale brain dynamics were modeled by a set of parameters including two region-specific microscale brain properties, recurrent connection strengths, and subcortical inputs. We also analyzed the relationship between the model parameters (i.e., the recurrent connection and subcortical inputs) and functional network topological characterizations, including smallworldness, clustering coefficient, and network efficiency. The results show that untreated HIV-infected individuals have disrupted local brain dynamics that in part correlate with network topological measurements. Notably, after 12 weeks of cART, both the microscale brain dynamics and the network topological measurements improved and were closer to those in the healthy brain. This was also associated with improved cognitive performance, suggesting that improvement in local brain dynamics translates into clinical improvement.

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.

Topological Alterations in White Matter Structural Networks in Blepharospasm.

BACKGROUND: Accumulating evidence indicates regional structural changes in the white matter (WM) of brains in patients with blepharospasm (BSP); however, whether large-scale WM structural networks undergo widespread reorganization in these patients remains unclear. OBJECTIVE: We investigated topology changes and global and local features of large-scale WM structural networks in BSP patients compared with hemifacial spasm (HFS) patients or healthy controls (HCs). METHODS: This cross-sectional study applied graph theoretical analysis to assess deterministic diffusion tensor tractography findings in 41 BSP patients, 41 HFS patients, and 41 HCs. WM structural connectivity in 246 cortical and subcortical regions was assessed, and topological parameters of the resulting graphs were calculated. Networks were compared among BSP, HFS, and HCs groups. RESULTS: Compared to HCs, both BSP and HFS patients showed alterations in network integration and segregation characterized by increased global efficiency and modularity and reduced shortest path length. Moreover, increased nodal efficiency in multiple cortical and subcortical regions was found in BSP and HFS patients compared with HCs. However, these differences were not found between BSP and HFS patients. Whereas all participants showed highly similar hub distribution patterns, BSP patients had additional hub regions not present in either HFS patients or HCs, which were located in the primary head and face motor cortex and basal ganglia. CONCLUSIONS: Our findings suggest that the large-scale WM structural network undergoes an extensive reorganization in BSP, probably due to both dystonia-specific abnormalities and facial hyperkinetic movements. © 2021 International Parkinson and Movement Disorder Society.

Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities.

Diffusion MRI-based tractography is the most commonly-used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory-a powerful mathematical approach for modeling complex network systems-for analyzing tractography-based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1.

Alterations of brain network topology and structural connectivity-functional connectivity coupling in capsular versus pontine stroke.

BACKGROUND AND PURPOSE: This study was conducted to investigate whether capsular stroke (CS) and pontine stroke (PS) have different topological alterations of structural connectivity (SC) and functional connectivity (FC), as well as correlations of SC-FC coupling with movement assessment scores. METHODS: Resting-state functional magnetic resonance imaging and diffusion tensor imaging were prospectively acquired in 46 patients with CS, 36 with PS, and 29 healthy controls (HCs). Graph theoretical network analyses of SC and FC were performed. Patients with left and right lesions were analyzed separately. RESULTS: With regard to FC, the PS and CS groups both showed higher local efficiency than the HCs, and the CS group also had a higher clustering coefficient (Cp) than the HCs in the right lesion analysis. With regard to SC, the PS and CS groups both showed different normalized clustering coefficient (γ), small-worldness (σ), and characteristic path length (Lp) compared with the HC group. Additionally, the CS group showed higher normalized characteristic path length (λ) and a lower Cp than the HCs and the PS group showed higher λ and lower global efficiency than the HCs in the right-lesion analysis. However, γ, σ, Cp and Lp were only significantly different in the PS and CS groups compared with the HC group in the right-lesion analysis. Importantly, the CS group was found to have a weaker SC-FC coupling than the PS group and the HC group in the right-lesion analysis. In addition, both patient groups had weaker structural-functional connectome correlation than the HCs. CONCLUSIONS: The CS and PS groups both showed FC and SC disruption and the CS group had a weaker SC-FC coupling than the PS group in the right lesion analysis. This may provide useful information for individualized rehabilitative strategies.

Resting-State Functional Connectivity and Brain Network Abnormalities in Depressive Patients with Suicidal Ideation.

Our study aimed to investigate whether changes in brain function measured with functional magnetic resonance imaging (fMRI) can be detected among individuals with depressive disorders and suicidal ideation. The association between depression severity and brain images is also discussed. Our study recruited 111 participants in three groups: 35 depressive patients with suicidal ideation (SI), 32 depressive patients without suicidal ideation (NS), and 44 healthy controls (HCs). All participants were scanned using 3T MRI to obtain resting-state functional images, and functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and graph theoretical analysis (GTA) were performed. We found functional activity differences, such as the hippocampus and thalamus, in the SI group compared with the NS group. We also concluded lower activity in the thalamus and cuneus regions were related to suicidal ideation. We also found several functional connectivity of the brain areas, such as hippocampus, cuneus, and frontal regions, in the SI group correlated with Hamilton Depression Rating Scale (HAM-D) and Hospital Anxiety and Depression Scale (HADS). A graph theoretical analysis (GTA) and network-based statistical (NBS) analysis revealed different topological organization and slightly better local segregation of the brain network in healthy participants compared with those in depressive patients with suicidal ideation. We suggest that brain functional connectivity may be affected in depressive patients with suicidal ideation.

Disrupted white matter connectivity and organization of brain structural connectomes in tuberous sclerosis complex patients with neuropsychiatric disorders using diffusion tensor imaging.

OBJECTIVE: Tuberous sclerosis complex (TSC) is a genetic neurocutaneous syndrome with variable and unpredictable neurological comorbidity that includes epilepsy, intellectual disability (ID), autism spectrum disorder, and neurobehavioral abnormalities. The degree of white matter involvement is believed to be associated with the severity of neurological impairment. The goal of the present study was to evaluate diffusion characteristics of tubers, white matter lesions, and brain structural network alterations in TSC patients using diffusion tensor imaging (DTI), graph theoretical analysis (GTA), and network-based statistical (NBS) analysis. MATERIALS AND METHODS: Forty-two patients with a definitive diagnosis of TSC were recruited for this study. All patients underwent brain DTI examination using a 3 T magnetic resonance imaging system. Mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) values, and fractional anisotropy (FA) mapping in 52 tubers and white matter lesions were measured and compared with those of contralateral normal regions. GTA was performed on the inter-regional connectivity matrix, and NBS analysis was used to identify the significance of any connected subnetworks evident in the set of altered connections. For neurological severity subgrouping, a neurological severity score was assigned to TSC patients including those with ID, seizure, autism, and other neuropsychiatric disorders (NPDs). RESULTS: Significantly higher MD, AD, and RD, and lower FA values, were found in TSC lesions compared with those measured in contralateral normal regions for tubers (P < 0.05). GTA and NBS analysis provided better local segregation but worse global integration of the structural network (regular-like network) in TSC patients with ID, seizure, and higher Neurological Severity Score. Disrupted subnetworks in TSC patients with severe status included connections from the frontal lobe to the parietal lobe, temporal lobe to the caudate, and temporal lobe to the insula. DISCUSSION: DTI has the potential to provide valuable information about cytoarchitectural changes in TSC lesions beyond morphological MRI findings alone. Using GTA and NBS, current results provide the information of disrupted white matter connectivity and organization in TSC patients with different neuropsychological impairments.

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting-state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low-frequency fluctuation, regional homogeneity and two connectome-wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10-fold cross-validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.

Disrupted brain functional networks in patients with end-stage renal disease undergoing hemodialysis.

Patterns of change in whole-brain functional networks remain poorly understood in patients with end-stage renal disease (ESRD) undergoing hemodialysis (HD). We conducted a prospective research to investigate the topological properties of whole-brain functional networks in those patients using a graph-based network analysis. Resting-state functional magnetic resonance imaging was performed on 51 ESRD patients (25 HD and 26 nondialysis patients) and 36 healthy controls (HCs). We compared the topological properties of brain functional networks among the three groups, and analyzed the relationships between those significant parameters and clinical variables in ESRD patients. Progressively disrupted global topological organizations were observed from nondialysis patients to HD patients compared with HCs (all p < 0.05 after Bonferroni correction). HD patients, relative to HCs, showed significantly decreased nodal centralities in the left temporal pole: superior temporal gyrus, bilateral median cingulate and paracingulate gyri, bilateral hippocampus, bilateral parahippocampal gyrus, and bilateral amygdala, and showed increased nodal centralities in the orbital part of the bilateral middle frontal gyrus, left cuneus, and left superior occipital gyrus (all p < 0.05 after Bonferroni correction). Furthermore, nodal centralities in the bilateral hippocampus were significantly decreased in HD patients compared with nondialysis patients (p < 0.05 after Bonferroni correction). Dialysis duration negatively correlated with global efficiency in ESRD patients undergoing HD (r = -0.676, FDR q = 0.004). This study indicates that ESRD patients exhibit disruptions in brain functional networks, which are more severe in HD patients, and these alterations are correlated with cognitive performance and clinical markers.