Intrinsic brain network abnormalities in codeine-containing cough syrup-dependent male individuals revealed in resting-state fMRI

2016 ◽  
Vol 45 (1) ◽  
pp. 177-186 ◽  
Author(s):  
Ying-wei Qiu ◽  
Huan-Huan Su ◽  
Xiao-fei Lv ◽  
Xiao-fen Ma ◽  
Gui-hua Jiang ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cough Syrup

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

Altered brain network organization in romantic love as measured with resting-state fMRI and graph theory

2020 ◽  
Vol 14 (6) ◽  
pp. 2771-2784 ◽  
Author(s):  
Chuan Wang ◽  
Sensen Song ◽  
Federico d’Oleire Uquillas ◽  
Anna Zilverstand ◽  
Hongwen Song ◽  
...  
Keyword(s):  
Graph Theory ◽  
Resting State ◽  
Romantic Love ◽  
Resting State Fmri ◽  
Brain Network ◽  
Network Organization

scholarly journals Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

2019 ◽  
Vol 33 ◽  
pp. 2556-2563 ◽  
Author(s):  
Abhay M S Aradhya ◽  
Aditya Joglekar ◽  
Sundaram Suresh ◽  
M. Pratama
Keyword(s):  
Resting State ◽  
Cognitive Disorders ◽  
Feature Space ◽  
Resting State Fmri ◽  
Transformation Method ◽  
Brain Network ◽  
Fmri Data ◽  
Functional Activities ◽  
Temporal Correlations ◽  
Spatio Temporal

Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.

scholarly journals Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting-state fMRI study

2015 ◽  
Vol 36 (9) ◽  
pp. 3677-3686 ◽  
Author(s):  
Xueling Suo ◽  
Du Lei ◽  
Kaiming Li ◽  
Fuqin Chen ◽  
Fei Li ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Network Topology ◽  
Resting State ◽  
Stress Disorder ◽  
Resting State Fmri ◽  
Brain Network ◽  
Fmri Study

scholarly journals Impact of anaesthesia on static and dynamic functional connectivity in mice

2021 ◽  
Author(s):  
Tomokazu Tsurugizawa ◽  
Daisuke Yoshimaru
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Resting State ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Brain Network ◽  
List Type ◽  
Thalamic Nuclei ◽  
Dynamic Functional Connectivity ◽  
Subcortical Regions

AbstractA few studies have compared the static functional connectivity between awake and anaesthetized states in rodents by resting-state fMRI. However, impact of anaesthesia on static and dynamic fluctuations in functional connectivity has not been fully understood. Here, we developed a resting-state fMRI protocol to perform awake and anaesthetized functional MRI in the same mice. Static functional connectivity showed a widespread decrease under anaesthesia, such as when under isoflurane or a mixture of isoflurane and medetomidine. Several interhemispheric connections were key connections for anaesthetized condition from awake. Dynamic functional connectivity demonstrates the shift from frequent broad connections across the cortex, the hypothalamus, and the auditory-visual cortex to frequent local connections within the cortex only. Fractional amplitude of low frequency fluctuation in the thalamic nuclei decreased under both anaesthesia. These results indicate that typical anaesthetics for functional MRI alters the spatiotemporal profile of the dynamic brain network in subcortical regions, including the thalamic nuclei and limbic system.HighlightsResting-state fMRI was compared between awake and anaesthetized in the same mice.Anaesthesia induced a widespread decrease of static functional connectivity.Anaesthesia strengthened local connections within the cortex.fALFF in the thalamus was decreased by anaesthesia.

scholarly journals A distributed brain network predicts general intelligence from resting-state human neuroimaging data

2018 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Resting State ◽  
Brain Volume ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Reliable Estimate ◽  
General Intelligence ◽  
Total Brain Volume ◽  
General Ability ◽  
Human Connectome Project

AbstractIndividual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of longterm life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.

scholarly journals Individual Variation in Brain Network Topology Predicts Emotional Intelligence

2018 ◽  
Author(s):  
Ling George ◽  
Lee Ivy ◽  
Guimond Synthia ◽  
Lutz Olivia ◽  
Tandon Neeraj ◽  
...  
Keyword(s):  
Emotional Intelligence ◽  
Social Cognition ◽  
Network Topology ◽  
Resting State ◽  
Large Scale ◽  
Psychotic Disorders ◽  
Brain Connectivity ◽  
Distance Matrix ◽  
Resting State Fmri ◽  
Brain Network

AbstractBackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.ObjectiveUsing ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.MethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).ResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.ConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

scholarly journals A distributed brain network predicts general intelligence from resting-state human neuroimaging data

2018 ◽  
Vol 373 (1756) ◽  
pp. 20170284 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Brain Volume ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Reliable Estimate ◽  
General Intelligence ◽  
Total Brain Volume ◽  
Human Connectome Project

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N= 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.

scholarly journals Brain network coupling associated with cognitive performance varies as a function of a child’s environment in the ABCD study

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Monica E. Ellwood-Lowe ◽  
Susan Whitfield-Gabrieli ◽  
Silvia A. Bunge
Keyword(s):  
Resting State ◽  
Test Performance ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cognitive Test ◽  
Functional Coupling ◽  
School Type ◽  
Frontoparietal Network ◽  
Cognitive Test Performance ◽  
Representative Samples

AbstractPrior research indicates that lower resting-state functional coupling between two brain networks, lateral frontoparietal network (LFPN) and default mode network (DMN), relates to cognitive test performance, for children and adults. However, most of the research that led to this conclusion has been conducted with non-representative samples of individuals from higher-income backgrounds, and so further studies including participants from a broader range of socioeconomic backgrounds are required. Here, in a pre-registered study, we analyzed resting-state fMRI from 6839 children ages 9–10 years from the ABCD dataset. For children from households defined as being above poverty (family of 4 with income > $25,000, or family of 5+ with income > $35,000), we replicated prior findings; that is, we found that better performance on cognitive tests correlated with weaker LFPN-DMN coupling. For children from households defined as being in poverty, the direction of association was reversed, on average: better performance was instead directionally related to stronger LFPN-DMN connectivity, though there was considerable variability. Among children in households below poverty, the direction of this association was predicted in part by features of their environments, such as school type and parent-reported neighborhood safety. These results highlight the importance of including representative samples in studies of child cognitive development.

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