scholarly journals Structural disconnections explain brain network dysfunction after stroke

2019 ◽  
Author(s):  
Joseph C. Griffis ◽  
Nicholas V. Metcalf ◽  
Maurizio Corbetta ◽  
Gordon L. Shulman
Keyword(s):  
System Integration ◽  
Brain Network ◽  
Structural Basis ◽  
Multivariate Models ◽  
Stroke Patients ◽  
Behavioral Deficits ◽  
Damage Models ◽  
Human Connectome Project ◽  
Low Dimensional ◽  
Critical Regions

SummaryFunctional connectivity (FC) studies have identified physiological signatures of stroke that correlate with behavior. Using structural and functional MRI data from 114 stroke patients, 24 matched controls, and the Human Connectome Project, we tested the hypothesis that structural disconnection, not damage to critical regions, underlies FC disruptions. Disconnection severity outperformed damage to putative FC connector nodes for explaining reductions in system modularity, and multivariate models based on disconnection outperformed damage models for explaining FC disruptions within and between systems. Across patients, disconnection and FC patterns exhibited a low-dimensional covariance dominated by a single axis linking interhemispheric disconnections to reductions in FC measures of interhemispheric system integration, ipsilesional system segregation, and system modularity, and that correlated with multiple behavioral deficits. These findings clarify the structural basis of FC disruptions in stroke patients and demonstrate a low-dimensional link between perturbations of the structural connectome, disruptions of the functional connectome, and behavioral deficits.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals Manifold Learning of Dynamic Functional Connectivity Reliably Identifies Functionally Consistent Coupling Patterns in Human Brains

2019 ◽  
Vol 9 (11) ◽  
pp. 309
Author(s):  
Yuyuan Yang ◽  
Lubin Wang ◽  
Yu Lei ◽  
Yuyang Zhu ◽  
Hui Shen
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Manifold Learning ◽  
Temporal Dynamics ◽  
Functional Coupling ◽  
Dimensional Manifold ◽  
Dynamic Functional Connectivity ◽  
Local Linear Embedding ◽  
Human Connectome Project ◽  
Low Dimensional

Most previous work on dynamic functional connectivity (dFC) has focused on analyzing temporal traits of functional connectivity (similar coupling patterns at different timepoints), dividing them into functional connectivity states and detecting their between-group differences. However, the coherent functional connectivity of brain activity among the temporal dynamics of functional connectivity remains unknown. In the study, we applied manifold learning of local linear embedding to explore the consistent coupling patterns (CCPs) that reflect functionally homogeneous regions underlying dFC throughout the entire scanning period. By embedding the whole-brain functional connectivity in a low-dimensional manifold space based on the Human Connectome Project (HCP) resting-state data, we identified ten stable patterns of functional coupling across regions that underpin the temporal evolution of dFC. Moreover, some of these CCPs exhibited significant neurophysiological meaning. Furthermore, we apply this method to HCP rsfMR and tfMRI data as well as sleep-deprivation data and found that the topological organization of these low-dimensional structures has high potential for predicting sleep-deprivation states (classification accuracy of 92.3%) and task types (100% identification for all seven tasks).In summary, this work provides a methodology for distilling coherent low-dimensional functional connectivity structures in complex brain dynamics that play an important role in performing tasks or characterizing specific states of the brain.

scholarly journals Are global and specific interindividual differences in cortical thickness associated with facets of cognitive abilities, including face cognition?

2019 ◽  
Vol 6 (7) ◽  
pp. 180857 ◽  
Author(s):  
Kristina Meyer ◽  
Benjamín Garzón ◽  
Martin Lövdén ◽  
Andrea Hildebrandt
Keyword(s):  
Cortical Thickness ◽  
Cognitive Abilities ◽  
Structural Equation ◽  
Brain Network ◽  
Morphological Properties ◽  
Interindividual Differences ◽  
Measurement Models ◽  
The Core ◽  
Human Connectome Project ◽  
Small Effect Size

Face cognition (FC) is a specific ability that cannot be fully explained by general cognitive functions. Cortical thickness (CT) is a neural correlate of performance and learning. In this registered report, we used data from the Human Connectome Project (HCP) to investigate the relationship between CT in the core brain network of FC and performance on a psychometric task battery, including tasks with facial content. Using structural equation modelling (SEM), we tested the existence of face-specific interindividual differences at behavioural and neural levels. The measurement models include general and face-specific factors of performance and CT. There was no face-specificity in CT in functionally localized areas. In post hoc analyses, we compared the preregistered, small regions of interest (ROIs) to larger, non-individualized ROIs and identified a face-specific CT factor when large ROIs were considered. We show that this was probably due to low reliability of CT in the functional localization (intra-class correlation coefficients (ICC) between 0.72 and 0.85). Furthermore, general cognitive ability, but not face-specific performance, could be predicted by latent factors of CT with a small effect size. In conclusion, for the core brain network of FC, we provide exploratory evidence (in need of cross-validation) that areas of the cortex sharing a functional purpose did also share morphological properties as measured by CT.

scholarly journals Structural Basis for Distinctions between Substrate and Inhibitor Specificities for Feline Immunodeficiency Virus and Human Immunodeficiency Virus Proteases

2003 ◽  
Vol 77 (12) ◽  
pp. 6589-6600 ◽  
Author(s):  
Ying-Chuan Lin ◽  
Zachary Beck ◽  
Garrett M. Morris ◽  
Arthur J. Olson ◽  
John H. Elder
Keyword(s):  
Human Immunodeficiency Virus ◽  
Active Site ◽  
Feline Immunodeficiency Virus ◽  
Structural Basis ◽  
Inhibitor Specificity ◽  
Loop Region ◽  
Immunodeficiency Virus ◽  
Substrate Inhibitor ◽  
Critical Regions ◽  
Hiv 1

ABSTRACT We used feline immunodeficiency virus (FIV) protease (PR) as a mutational framework to define determinants for the observed substrate and inhibitor specificity distinctions between FIV and human immunodeficiency virus (HIV) PRs. Multiple-substitution mutants were constructed by replacing the residues in and around the active site of FIV PR with the structurally equivalent residues of HIV-1 PR. Mutants included combinations of three critical regions (FIV numbering, with equivalent HIV numbering in superscript): I3732V in the active core region; N5546M, M5647I, and V5950I in the flap region; and L9780T, I9881P, Q9982V, P10083N, and L10184I in the 90s loop region. Significant alterations in specificity were observed, consistent with the involvement of these residues in determining the substrate-inhibitor specificity distinctions between FIV and HIV PRs. Two previously identified residues, I35 and I57 of FIV PR, were intolerant to substitution and yielded inactive PRs. Therefore, we attempted to recover the activity by introducing secondary mutations. The addition of G6253F and K6354I, located at the top of the flap and outside the active site, compensated for the activity lost in the I5748G substitution mutants. An additional two substitutions, D10588N and N8874T, facilitated recovery of activity in mutants that included the I3530D substitution. Determination of Ki values of potent HIV-1 PR inhibitors against these mutants showed that inhibitor specificity paralleled that of HIV-1 PR. The findings indicate that maintenance of both substrate and inhibitor specificity is a function of interactions between residues both inside and outside the active site. Thus, mutations apparently peripheral to the active site can have a dramatic influence on inhibitor efficacy.

scholarly journals Local connectome phenotypes predict social, health, and cognitive factors

2018 ◽  
Vol 2 (1) ◽  
pp. 86-105 ◽  
Author(s):  
Michael A. Powell ◽  
Javier O. Garcia ◽  
Fang-Cheng Yeh ◽  
Jean M. Vettel ◽  
Timothy Verstynen
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Social Health ◽  
Cognitive Factors ◽  
High Dimensional ◽  
Cognitive Measures ◽  
Human Connectome Project ◽  
Low Dimensional ◽  
The Brain ◽  
Over Time

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.

scholarly journals Longitudinal Brain Functional Connectivity Changes of the Cortical Motor-Related Network in Subcortical Stroke Patients with Acupuncture Treatment

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yongxin Li ◽  
Ya Wang ◽  
Chenxi Liao ◽  
Wenhua Huang ◽  
Ping Wu
Keyword(s):  
Functional Connectivity ◽  
Western Medicine ◽  
Primary Motor Cortex ◽  
Brain Connectivity ◽  
Effective Means ◽  
Brain Network ◽  
Stroke Patients ◽  
Rehabilitation Therapy ◽  
Cortical Motor ◽  
Before And After

In clinical practice, the effectiveness of the rehabilitation therapy such as acupuncture combining conventional Western medicine (AG) on stroke people’s motor-related brain network and their behaviors has not been systematically studied. In the present study, seventeen adult ischemic patients were collected and divided into two groups: the conventional Western medicine treatment group (CG) and the AG. The neurological deficit scores (NDS) and resting-state functional MRI data were collected before and after treatment. Compared with the CG patients, AG patients exhibited a significant enhancement of the percent changes of NDS from pre- to posttreatment intervention. All patients showed significant changes of functional connectivity (FC) between the pair of cortical motor-related regions. After treatment, both patient groups showed a recovery of brain connectivity to the nearly normal level compared with the controls in these pairs. Moreover, a significant correlation between the percent changes of NDS and the pretreatment FC values of bilateral primary motor cortex (M1) in all patients was found. In conclusion, our results showed that AG therapy can be an effective means for ischemic stroke patients to recover their motor function ability. The FC strengths between bilateral M1 of stroke patients can predict stroke patients’ treatment outcome after rehabilitation therapy.

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