scholarly journals Altered structural brain networks in linguistic variants of frontotemporal dementia

2021 ◽  
Author(s):  
Salvatore Nigro ◽  
Benedetta Tafuri ◽  
Daniele Urso ◽  
Roberto De Blasi ◽  
Alessia Cedola ◽  
...  
Keyword(s):  
Brain Networks ◽  
Primary Progressive Aphasia ◽  
Structural Mri ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Local Network ◽  
Theory Approach ◽  
Healthy Controls ◽  
Characteristic Path Length ◽  
Structural Covariance

AbstractSemantic (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.

scholarly journals A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment

2021 ◽  
Vol 13 ◽  
Author(s):  
Cuibai Wei ◽  
Shuting Gong ◽  
Qi Zou ◽  
Wei Zhang ◽  
Xuechun Kang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Path Length ◽  
Brain Networks ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Glucose Intake ◽  
Item Scores ◽  
Structural Brain

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

scholarly journals Aberrant Brain Network Integration and Segregation in Diabetic Peripheral Neuropathy Revealed by Structural Connectomics

2020 ◽  
Vol 14 ◽  
Author(s):  
Fangxue Yang ◽  
Minli Qu ◽  
Youming Zhang ◽  
Linmei Zhao ◽  
Wu Xing ◽  
...  
Keyword(s):  
Peripheral Neuropathy ◽  
Diabetic Peripheral Neuropathy ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Global Integration ◽  
Characteristic Path Length ◽  
Structural Covariance ◽  
The Central Nervous System

Diabetic peripheral neuropathy (DPN) is one of the most common forms of peripheral neuropathy, and its incidence has been increasing. Mounting evidence has shown that patients with DPN have been associated with widespread alterations in the structure, function and connectivity of the brain, suggesting possible alterations in large-scale brain networks. Using structural covariance networks as well as advanced graph-theory-based computational approaches, we investigated the topological abnormalities of large-scale brain networks for a relatively large sample of patients with DPN (N = 67) compared to matched healthy controls (HCs; N = 88). Compared with HCs, the structural covariance networks of patients with DPN showed an increased characteristic path length, clustering coefficient, sigma, transitivity, and modularity, suggestive of inefficient global integration and increased local segregation. These findings may improve our understanding of the pathophysiological mechanisms underlying alterations in the central nervous system of patients with DPN from the perspective of large-scale structural brain networks.

scholarly journals Altered topology of large-scale structural brain networks in chronic stroke

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Bastian Cheng ◽  
Eckhard Schlemm ◽  
Robert Schulz ◽  
Marlene Boenstrup ◽  
Arnaud Messé ◽  
...  
Keyword(s):  
Information Transfer ◽  
Large Scale ◽  
Neuronal Degeneration ◽  
Brain Networks ◽  
Brain Regions ◽  
Local Network ◽  
Structural Connectome ◽  
The Impact ◽  
Structural Brain ◽  
Topological Changes

Abstract Beyond disruption of neuronal pathways, focal stroke lesions induce structural disintegration of distant, yet connected brain regions via retrograde neuronal degeneration. Stroke lesions alter functional brain connectivity and topology in large-scale brain networks. These changes are associated with the degree of clinical impairment and recovery. In contrast, changes of large scale, structural brain networks after stroke are less well reported. We therefore aimed to analyse the impact of focal lesions on the structural connectome after stroke based on data from diffusion-weighted imaging and probabilistic fibre tracking. In total, 17 patients (mean age 64.5 ± 8.4 years) with upper limb motor deficits in the chronic stage after stroke and 21 healthy participants (mean age 64.9 ± 10.3 years) were included. Clinical deficits were evaluated by grip strength and the upper extremity Fugl-Meyer assessment. We calculated global and local graph theoretical measures to characterize topological changes in the structural connectome. Results from our analysis demonstrated significant alterations of network topology in both ipsi- and contralesional, primarily unaffected, hemispheres after stroke. Global efficiency was significantly lower in stroke connectomes as an indicator of overall reduced capacity for information transfer between distant brain areas. Furthermore, topology of structural connectomes was shifted toward a higher degree of segregation as indicated by significantly higher values of global clustering and modularity. On a level of local network parameters, these effects were most pronounced in a subnetwork of cortico-subcortical brain regions involved in motor control. Structural changes were not significantly associated with clinical measures. We propose that the observed network changes in our patients are best explained by the disruption of inter- and intrahemispheric, long white matter fibre tracts connecting distant brain regions. Our results add novel insights on topological changes of structural large-scale brain networks in the ipsi- and contralesional hemisphere after stroke.

Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

2020 ◽  
Vol 30 (09) ◽  
pp. 2050047
Author(s):  
Lubin Wang ◽  
Xianbin Li ◽  
Yuyang Zhu ◽  
Bei Lin ◽  
Qijing Bo ◽  
...  
Keyword(s):  
High Risk ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Support Vector ◽  
Healthy Controls ◽  
Intrinsic Functional Connectivity ◽  
Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

scholarly journals Altered Topological Properties of Grey Matter Structural Covariance Networks in Complete Thoracic Spinal Cord Injury Patients: A Graph Theoretical Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wen-Li Wang ◽  
Yu-Lin Li ◽  
Mou-Xiong Zheng ◽  
Xu-Yun Hua ◽  
Jia-Jia Wu ◽  
...  
Keyword(s):  
Grey Matter ◽  
Brain Regions ◽  
Healthy Controls ◽  
Age And Gender ◽  
Structural Covariance ◽  
Clinical Variables ◽  
Cord Injury ◽  
Network Properties ◽  
And Gender ◽  
The Right

Purpose. This study is aimed at investigating brain structural changes and structural network properties in complete spinal cord injury (SCI) patients, as well as their relationship with clinical variables. Materials and Methods. Structural MRI of brain was acquired in 24 complete thoracic SCI patients ( 38.50 ± 11.19 years, 22 males) within the first postinjury year, while 26 age- and gender-matched healthy participants ( 38.38 ± 10.63 years, 24 males) were enrolled as control. The voxel-based morphometry (VBM) approach and graph theoretical network analysis based on cross-subject grey matter volume- (GMV-) based structural covariance networks (SCNs) were conducted to investigate the impact of SCI on brain structure. Partial correlation analysis was performed to explore the relationship between the GMV of structurally changed brain regions and SCI patients’ clinical variables, including injury duration, injury level, Visual Analog Scale (VAS), American Spinal Injury Association Impairment Scale (AIS), International Classification of Functioning, Disability and Health (ICF) scale, Self-rating Depression Scale (SDS), and Self-rating Anxiety Scale (SAS), after removing the effects of age and gender. Results. Compared with healthy controls, SCI patients showed higher SDS score ( t = 4.392 and p < 0.001 ). In the VBM analysis, significant GMV reduction was found in the left middle frontal cortex, right superior orbital frontal cortex (OFC), and left inferior OFC. No significant difference was found in global network properties between SCI patients and healthy controls. In the regional network properties, significantly higher betweenness centrality (BC) was noted in the right anterior cingulum cortex (ACC) and left inferior OFC and higher nodal degree and efficiency in bilateral middle OFCs, while decreased BC was noted in the right putamen in SCI patients. Only negative correlation was found between GMV of right middle OFC and SDS score in SCI patients ( r = − 0.503 and p = 0.017 ), while no significant correlation between other abnormal brain regions and any of the clinical variables (all p > 0.05 ). Conclusions. SCI patients would experience depressive and/or anxious feelings at the early stage. Their GMV reduction mainly involved psychology-cognition related rather than sensorimotor brain regions. The efficiency of regional information transmission in psychology-cognition regions increased. Greater GMV reduction in psychology region was related with more severe depressive feelings. Therefore, early neuropsychological intervention is suggested to prevent psychological and cognitive dysfunction as well as irreversible brain structure damage.

scholarly journals Hemispheric Asymmetry of Functional Brain Networks under Different Emotions Using EEG Data

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 939
Author(s):  
Rui Cao ◽  
Huiyu Shi ◽  
Xin Wang ◽  
Shoujun Huo ◽  
Yan Hao ◽  
...  
Keyword(s):  
Hemispheric Asymmetry ◽  
Right Hemisphere ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Phase Lag ◽  
Characteristic Path Length ◽  
High Arousal ◽  
Eeg Data ◽  
The Right

Despite many studies reporting hemispheric asymmetry in the representation and processing of emotions, the essence of the asymmetry remains controversial. Brain network analysis based on electroencephalography (EEG) is a useful biological method to study brain function. Here, EEG data were recorded while participants watched different emotional videos. According to the videos’ emotional categories, the data were divided into four categories: high arousal high valence (HAHV), low arousal high valence (LAHV), low arousal low valence (LALV) and high arousal low valence (HALV). The phase lag index as a connectivity index was calculated in theta (4–7 Hz), alpha (8–13 Hz), beta (14–30 Hz) and gamma (31–45 Hz) bands. Hemispheric networks were constructed for each trial, and graph theory was applied to quantify the hemispheric networks’ topological properties. Statistical analyses showed significant topological differences in the gamma band. The left hemispheric network showed significantly higher clustering coefficient (Cp), global efficiency (Eg) and local efficiency (Eloc) and lower characteristic path length (Lp) under HAHV emotion. The right hemispheric network showed significantly higher Cp and Eloc and lower Lp under HALV emotion. The results showed that the left hemisphere was dominant for HAHV emotion, while the right hemisphere was dominant for HALV emotion. The research revealed the relationship between emotion and hemispheric asymmetry from the perspective of brain networks.

Abnormal cerebellar functional MRI connectivity in patients with paediatric multiple sclerosis

2015 ◽  
Vol 22 (3) ◽  
pp. 292-301 ◽  
Author(s):  
Sara Cirillo ◽  
Maria A Rocca ◽  
Angelo Ghezzi ◽  
Paola Valsasina ◽  
Lucia Moiola ◽  
...  
Keyword(s):  
Disease Duration ◽  
Structural Mri ◽  
Brain Regions ◽  
Lesion Volume ◽  
Seed Region ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Focal Lesions ◽  
Caudate Nuclei ◽  
The Right

Objectives: We investigated resting state functional connectivity (RSFC) of the cerebellar dentate nuclei in paediatric MS patients and its correlations with clinical, neuropsychological and structural MRI measures. Methods: RSFC analysis was performed using a seed-region correlation approach and SPM8 from 48 paediatric MS patients and 27 matched healthy controls. Results: In both groups, dentate nuclei RSFC was significantly correlated with RSFC of several cerebellar and extra-cerebellar brain regions. Compared with healthy controls, paediatric MS patients had reduced RSFC between the right dentate nuclei and the bilateral caudate nuclei and left thalamus as well as increased RSFC between the right dentate nuclei and the left precentral and postcentral gyri. Cognitively impaired patients showed a reduced RSFC between the dentate nuclei and bilateral regions located in the parietal, frontal and temporal lobes. Decreased RSFC was correlated with longer disease duration and higher T2 lesion volumes, whereas increased RSFC correlated with shorter disease duration, lower T2 lesion volume and a better motor performance. Conclusions: Modifications of cerebellar RSFC occur in paediatric MS and are influenced by the duration of the disease and brain focal lesions. Decreased RSFC may reflect early maladaptive plasticity contributing to cognitive impairment.

scholarly journals Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia

2021 ◽  
Vol 11 (2) ◽  
pp. 192
Author(s):  
Salvatore Nigro ◽  
Benedetta Tafuri ◽  
Daniele Urso ◽  
Roberto De Blasi ◽  
Maria Elisa Frisullo ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Cortical Thickness ◽  
Local Level ◽  
Small World ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Single Subject ◽  
Theory Approach ◽  
Healthy Controls ◽  
Graph Properties

Recent research on behavioral variant frontotemporal dementia (bvFTD) has shown that personality changes and executive dysfunctions are accompanied by a disease-specific anatomical pattern of cortical and subcortical atrophy. We investigated the structural topological network changes in patients with bvFTD in comparison to healthy controls. In particular, 25 bvFTD patients and 20 healthy controls underwent structural 3T MRI. Next, bilaterally averaged values of 34 cortical surface areas, 34 cortical thickness values, and six subcortical volumes were used to capture single-subject anatomical connectivity and investigate network organization using a graph theory approach. Relative to controls, bvFTD patients showed altered small-world properties and decreased global efficiency, suggesting a reduced ability to combine specialized information from distributed brain regions. At a local level, patients with bvFTD displayed lower values of local efficiency in the cortical thickness of the caudal and rostral middle frontal gyrus, rostral anterior cingulate, and precuneus, cuneus, and transverse temporal gyrus. A significant correlation was also found between the efficiency of caudal anterior cingulate thickness and Mini-Mental State Examination (MMSE) scores in bvFTD patients. Taken together, these findings confirm the selective disruption in structural brain networks of bvFTD patients, providing new insights on the association between cognitive decline and graph properties.

scholarly journals Brain Structural Covariance Network in Asperger’s Syndrome Differs From Other Types of Autism and Healthy Controls

2021 ◽  
Author(s):  
Farnaz Faridi ◽  
◽  
Afrooz Seyedebrahimi ◽  
Reza Khosrowabadi ◽  
◽  
...  
Keyword(s):  
Asperger's Syndrome ◽  
Asperger’S Syndrome ◽  
Neurodevelopmental Disorder ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Healthy Controls ◽  
Structural Differentiation ◽  
Structural Covariance ◽  
Behavioral Impairments ◽  
The Brain

Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD). In this study, we compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age= 14-50, IQ= 92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144). Results of one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is decreased in ASD. This attenuated structural differentiation could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.

scholarly journals Widespread Cortical Thickness Reductions Following Non-medical Use of Ketamine: a Structural MRI Study of Individuals with Ketamine Dependence

2021 ◽  
Author(s):  
Jinsong Tang ◽  
Qiuxia Wu ◽  
Chang Qi ◽  
An Xie ◽  
Jianbin Liu ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Structural Mri ◽  
Brain Regions ◽  
Analysis Of Covariance ◽  
P Value ◽  
Healthy Controls ◽  
Brain Images ◽  
Dorsolateral Prefrontal ◽  
The Right ◽  
Mri Study

AbstractBackgroundA version of ketamine, called Esketamine has been approved for treatment-resistant depression (TRD). Ketamine (“K powder”), a “dissociative” anesthetic agent, however, has been used non-medically alone or with other illicit substances. Our previous studies showed a link between non-medical ketamine use and brain structural and functional alterations. We found dorsal prefrontal gray matter reduction in chronic ketamine users. It is unknown, however, whether these observations might parallel findings of cortical thickness alterations. This study aimed at exploring cortical thickness abnormalities following non-medical, long-term use of ketamine.MethodsStructural brain images were acquired for 95 patients with ketamine dependence, and 169 drug-free healthy controls. FreeSurfer software was used to measure cortical thickness for 68 brain regions. Cortical thickness was compared between the two groups using analysis of covariance (ANCOVA) with covariates of age, gender, educational level, smoking, drinking, and whole brain mean cortical thickness. Results were considered significant if the Bonferroni corrected P-value < 0.01.ResultsCompared to healthy controls, patients with ketamine dependence have widespread decreased cortical thickness, with the most extensive reductions in the frontal (including the dorsolateral prefrontal cortex, DLPFC) and parietal (including the precuneus) lobes. Increased cortical thickness was not observed in ketamine users relative to comparison subjects. Estimated total lifetime ketamine consumption is correlated with the right inferior parietal and the right rostral middle frontal cortical thickness reductions.ConclusionsThis study provides first evidence that, compared with healthy controls, chronic ketamine users had cortical thickness reductions.

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