scholarly journals Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

2019 ◽  
Author(s):  
Joanes Grandjean ◽  
Carola Canella ◽  
Cynthia Anckaerts ◽  
Gülebru Ayrancı ◽  
Salma Bougacha ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Brain Network ◽  
Resting State Networks ◽  
Experimental Parameters ◽  
Fmri Analysis ◽  
Animal Handling ◽  
Connectivity Parameter

AbstractPreclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories world-wide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a murine default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

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 Large-scale functional brain network changes in taxi drivers: Evidence from resting-state fMRI

2014 ◽  
Vol 36 (3) ◽  
pp. 862-871 ◽  
Author(s):  
Lubin Wang ◽  
Qiang Liu ◽  
Hui Shen ◽  
Hong Li ◽  
Dewen Hu
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Brain Network ◽  
Taxi Drivers ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals Reconfiguration of Directed Functional Connectivity Among Neurocognitive Networks with Aging: Considering the Role of Thalamo-Cortical Interactions

2020 ◽  
Author(s):  
Moumita Das ◽  
Vanshika Singh ◽  
Lucina Q Uddin ◽  
Arpan Banerjee ◽  
Dipanjan Roy
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Fmri Data ◽  
Independent Dataset ◽  
Left And Right

Abstract A complete picture of how subcortical nodes, such as the thalamus, exert directional influence on large-scale brain network interactions across age remains elusive. Using directed functional connectivity and weighted net causal outflow on resting-state fMRI data, we provide evidence of a comprehensive reorganization within and between neurocognitive networks (default mode: DMN, salience: SN, and central executive: CEN) associated with age and thalamocortical interactions. We hypothesize that thalamus subserves both modality-specific and integrative hub role in organizing causal weighted outflow among large-scale neurocognitive networks. To this end, we observe that within-network directed functional connectivity is driven by thalamus and progressively weakens with age. Secondly, we find that age-associated increase in between CEN- and DMN-directed functional connectivity is driven by both the SN and the thalamus. Furthermore, left and right thalami act as a causal integrative hub exhibiting substantial interactions with neurocognitive networks with aging and play a crucial role in reconfiguring network outflow. Notably, these results were largely replicated on an independent dataset of matched young and old individuals. Our findings strengthen the hypothesis that the thalamus is a key causal hub balancing both within- and between-network connectivity associated with age and maintenance of cognitive functioning with aging.

scholarly journals Resting State Brain Network Function in Elderly: The Formation of Social Ties

2020 ◽  
Vol 13 (2) ◽  
pp. 59-67
Author(s):  
Seolah Oh ◽  
Aran Kim ◽  
Eunji Kang ◽  
Sungwon Choi
Keyword(s):  
Resting State ◽  
Successful Aging ◽  
Social Ties ◽  
Resting State Fmri ◽  
Brain Network ◽  
Basic Unit ◽  
Network Function ◽  
The Difference ◽  
Fmri Analysis ◽  
Questionnaire Surveys

Background: The purpose of this study is to determine the relevance of the relationship between brain network and the social ties management.Methods: Participants are based on 52 Korean seniors aged 65 and older who live in Ganghwa-gun, Incheon. We used a closed-triad index (CTI), which is the most basic unit of analysis in the study of group phenomena. This index is a social networking variable that has been shown to have a different implication depending on the subject’s condition and role. After two questionnaire surveys were conducted at three years intervals, participants were classified into an increased group and a decreased group according to the change of CTI. Resting-state fMRI analysis were followed to investigate the difference of brain networks between groups. Results: According to the analysis of the study, the whole participants who had increased in number of CTI has higher local efficiency than the group of the participants who had no effect or decreased in CTI. Conclusions: Our study suggests that social relationship, which is substantially related to brain network, is a major factor in successful aging. Lastly, since there is a restriction that the study cannot explain the causal aspect of the brain network and the triad-relationship, there is a need for further investigation.

Large-Scale Granger Causal Brain Network based on Resting-State fMRI data

Neuroscience ◽  
2020 ◽  
Vol 425 ◽  
pp. 169-180 ◽  
Author(s):  
Xuewei Wang ◽  
Ru Wang ◽  
Fei Li ◽  
Qiang Lin ◽  
Xiaohu Zhao ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Brain Network ◽  
Fmri Data

scholarly journals Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

NeuroImage ◽  
2020 ◽  
Vol 205 ◽  
pp. 116278 ◽  
Author(s):  
Joanes Grandjean ◽  
Carola Canella ◽  
Cynthia Anckaerts ◽  
Gülebru Ayrancı ◽  
Salma Bougacha ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Resting State ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Fmri Analysis

scholarly journals Neurophysiology (fMRI)

2015 ◽  
Vol 42 (S1) ◽  
pp. S38-S38
Author(s):  
ST Lang ◽  
B Goodyear ◽  
J Kelly ◽  
P Federico
Keyword(s):  
Cognitive Functioning ◽  
Default Mode Network ◽  
Resting State ◽  
Attentional Control ◽  
Brain Tumours ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Resting State Networks ◽  
Default Mode ◽  
Eloquent Cortex

Background: Resting state functional MRI (rs-fMRI) provides many advantages to task-based fMRI in neurosurgical populations, foremost of which is the lack of the need to perform a task. Many networks can be identified by rs-fMRI in a single period of scanning. Despite the advantages, there is a paucity of literature on rs-fMRI in neurosurgical populations. Methods: Eight patients with tumours near areas traditionally considered as eloquent cortex participated in a five minute rs-fMRI scan. Resting-state fMRI data underwent Independent Component Analysis (ICA) using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) toolbox in FSL. Resting state networks (RSNs) were identified on a visual basis. Results: Several RSNs, including language (N=7), sensorimotor (N=7), visual (N=7), default mode network (N=8) and frontoparietal attentional control (n=7) networks were readily identifiable using ICA of rs-fMRI data. Conclusion: These pilot data suggest that ICA applied to rs-fMRI data can be used to identify motor and language networks in patients with brain tumours. We have also shown that RSNs associated with cognitive functioning, including the default mode network and the frontoparietal attentional control network can be identified in individual subjects with brain tumours. While preliminary, this suggests that rs-fMRI may be used pre-operatively to localize areas of cortex important for higher order cognitive functioning.

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.

scholarly journals Local GABA concentration is related to network-level resting functional connectivity

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Charlotte J Stagg ◽  
Velicia Bachtiar ◽  
Ugwechi Amadi ◽  
Christel A Gudberg ◽  
Andrei S Ilie ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Empirical Studies ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Resonance Spectroscopy ◽  
Resting State Networks ◽  
Inhibitory Neurotransmitter ◽  
Motor Network

Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.

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