Structural brain network fingerprints of focal dystonia

Background: Focal dystonias are severe and disabling movement disorders of a still unclear origin. The structural brain networks associated with focal dystonia have not been well characterized. Here, we investigated structural brain network fingerprints in patients with blepharospasm (BSP) compared with those with hemifacial spasm (HFS), and healthy controls (HC). The patients were also examined following treatment with botulinum neurotoxin (BoNT). Methods: This study included matched groups of 13 BSP patients, 13 HFS patients, and 13 HC. We measured patients using structural-magnetic resonance imaging (MRI) at baseline and after one month BoNT treatment, at time points of maximal and minimal clinical symptom representation, and HC at baseline. Group regional cross-correlation matrices calculated based on grey matter volume were included in graph-based network analysis. We used these to quantify global network measures of segregation and integration, and also looked at local connectivity properties of different brain regions. Results: The networks in patients with BSP were more segregated than in patients with HFS and HC ( p < 0.001). BSP patients had increased connectivity in frontal and temporal cortices, including sensorimotor cortex, and reduced connectivity in the cerebellum, relative to both HFS patients and HC ( p < 0.05). Compared with HC, HFS patients showed increased connectivity in temporal and parietal cortices and a decreased connectivity in the frontal cortex ( p < 0.05). In BSP patients, the connectivity of the frontal cortex diminished after BoNT treatment ( p < 0.05). In contrast, HFS patients showed increased connectivity in the temporal cortex and reduced connectivity in cerebellum after BoNT treatment ( p < 0.05). Conclusions: Our results show that BSP patients display alterations in both segregation and integration in the brain at the network level. The regional differences identified in the sensorimotor cortex and cerebellum of these patients may play a role in the pathophysiology of focal dystonia. Moreover, symptomatic reduction of hyperkinesia by BoNT treatment was associated with different brain network fingerprints in both BSP and HFS patients.

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Neurological Soft Signs and Brain Network Abnormalities in Schizophrenia

Schizophrenia Bulletin ◽

10.1093/schbul/sbz118 ◽

2019 ◽

Vol 46 (3) ◽

pp. 562-571 ◽

Cited By ~ 3

Author(s):

Li Kong ◽

Christina J Herold ◽

Eric F C Cheung ◽

Raymond C K Chan ◽

Johannes Schröder

Keyword(s):

Network Analysis ◽

Frontal Cortex ◽

Temporal Cortex ◽

Brain Network ◽

Global Network ◽

Orbital Frontal Cortex ◽

Neural Basis ◽

Neurological Soft Signs ◽

Middle Temporal ◽

Significant Difference

Abstract Neurological soft signs (NSS) are often found in patients with schizophrenia. A wealth of neuroimaging studies have reported that NSS are related to disturbed cortical-subcortical-cerebellar circuitry in schizophrenia. However, the association between NSS and brain network abnormalities in patients with schizophrenia remains unclear. In this study, the graph theoretical approach was used to analyze brain network characteristics based on structural magnetic resonance imaging (MRI) data. NSS were assessed using the Heidelberg scale. We found that there was no significant difference in global network properties between individuals with high and low levels of NSS. Regional network analysis showed that NSS were associated with betweenness centrality involving the inferior orbital frontal cortex, the middle temporal cortex, the hippocampus, the supramarginal cortex, the amygdala, and the cerebellum. Global network analysis also demonstrated that NSS were associated with the distribution of network hubs involving the superior medial frontal cortex, the superior and middle temporal cortices, the postcentral cortex, the amygdala, and the cerebellum. Our findings suggest that NSS are associated with alterations in topological attributes of brain networks corresponding to the cortical-subcortical-cerebellum circuit in patients with schizophrenia, which may provide a new perspective for elucidating the neural basis of NSS in schizophrenia.

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Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

International Journal of Image and Graphics ◽

10.1142/s0219467822500292 ◽

2021 ◽

pp. 2250029

Author(s):

Geng Zhang ◽

Qi Zhu ◽

Jing Yang ◽

Ruting Xu ◽

Zhiqiang Zhang ◽

...

Keyword(s):

Brain Connectivity ◽

Medical Image Analysis ◽

Brain Regions ◽

Brain Network ◽

Brain Diseases ◽

Functional Brain ◽

Functional Brain Network ◽

Structural Brain Network ◽

The Brain ◽

Structural Brain

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

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STRUCTURAL BRAIN NETWORK ABNORMALITIES IN SUBJECTS WITH INTERNET ADDICTION

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519417400310 ◽

2017 ◽

Vol 17 (07) ◽

pp. 1740031 ◽

Cited By ~ 4

Author(s):

MIN-HEE LEE ◽

AREUM MIN ◽

YOON HO HWANG ◽

DONG YOUN KIM ◽

BONG SOO HAN ◽

...

Keyword(s):

Internet Addiction ◽

Diffusion Tensor ◽

Brain Connectivity ◽

Brain Regions ◽

Brain Network ◽

Imaging Data ◽

Structural Brain Network ◽

Global And Local ◽

The Impact ◽

Structural Brain

Although problematic overuse of internet has increased, psychopathological characteristics and neurobiological mechanisms for internet addiction (IA) remain poorly understood. Therefore, it is necessary to investigate the impact of IA on the brain. The present study included 17 subjects with IA and 20 healthy subjects. We constructed the structural brain network from diffusion tensor imaging data and investigated alteration of structural connections in subjects with IA using the network analysis on the global and local levels. The subjects with IA showed increase of regional efficiency (RE) in bilateral orbitofrontal cortex (OFC) and decrease in right middle cingulate and middle temporal gyri ([Formula: see text]), whereas the global properties did not show significant changes. Young’s internet addiction test (IAT) scores and RE in left OFC showed positive correlation, and average time spent on internet per day was positively correlated with the RE in right OFC. This is the first study examining alterations of the structural brain connectivity in IA. We found that subjects with IA showed alterations of RE in some brain regions and RE was positively associated with the severity of IA and average time spent on internet per day. Therefore, RE may be a good property for IA assessment.

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Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

10.1101/2020.05.26.116475 ◽

2020 ◽

Author(s):

Lu Wang ◽

Feng Vankee Lin ◽

Martin Cole ◽

Zhengwu Zhang

Keyword(s):

Oral Reading ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Learning Differences ◽

Structural Brain Network ◽

Levels Of Functioning ◽

The Brain ◽

Variable Selection Problem ◽

Structural Brain

AbstractStructural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.

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Review for "Structural brain network development in children following prenatal methamphetamine exposure"

10.1002/cne.24858/v2/review1 ◽

2020 ◽

Keyword(s):

Brain Network ◽

Network Development ◽

Structural Brain Network ◽

Structural Brain

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Review for "Structural brain network development in children following prenatal methamphetamine exposure"

10.1002/cne.24858/v1/review2 ◽

2019 ◽

Keyword(s):

Brain Network ◽

Network Development ◽

Structural Brain Network ◽

Structural Brain

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Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

Scientific Reports ◽

10.1038/s41598-020-78899-7 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Rieke Fruengel ◽

Timo Bröhl ◽

Thorsten Rings ◽

Klaus Lehnertz

Keyword(s):

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Brain Network ◽

Theoretical Concept ◽

Global Network ◽

Time Resolved ◽

Functional Connections ◽

Node Centrality ◽

Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

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Structural brain network topology underpinning ADHD and response to methylphenidate treatment

Translational Psychiatry ◽

10.1038/s41398-021-01278-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kristi R. Griffiths ◽

Taylor A. Braund ◽

Michael R. Kohn ◽

Simon Clarke ◽

Leanne M. Williams ◽

...

Keyword(s):

Brain Network ◽

Global Network ◽

Support Vector ◽

Structural Topology ◽

Local Efficiency ◽

Structural Brain Network ◽

Structural Architecture ◽

The Right ◽

Tractography Data ◽

Treatment Prognosis

AbstractBehavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.

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