Aberrant Functional Connectivity Within and Across the Default Mode, Central-executive, and Salience Network in Patients with Schizophrenia: a Resting-state FMRIi Study

2015 ◽  
Vol 30 ◽  
pp. 253 ◽  
Author(s):  
H.L. Wang ◽  
H. Huang ◽  
C. Chen ◽  
P.F. Li ◽  
Y. Zhou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Central Executive ◽  
Salience Network ◽  
Default Mode

scholarly journals Altered Functional Connectivity of the Salience Network in Problematic Smartphone Users

2021 ◽  
Vol 12 ◽  
Author(s):  
Jaeun Ahn ◽  
Deokjong Lee ◽  
Kee Namkoong ◽  
Young-Chul Jung
Keyword(s):  
Functional Connectivity ◽  
Adverse Effects ◽  
Everyday Life ◽  
Resting State ◽  
Central Executive ◽  
Salience Network ◽  
Healthy Controls ◽  
Problematic Smartphone Use ◽  
Default Mode ◽  
Smartphone Use

Smartphones provide convenience in everyday life. Smartphones, however, can elicit adverse effects when used excessively. The purpose of this study was to examine the underlying neurobiological alterations that arise from problematic smartphone use. We performed resting state seed-based functional connectivity (FC) analysis of 44 problematic smartphone users and 54 healthy controls. This analysis assessed the salience, central executive, default mode, and affective networks. Compared to controls, problematic smartphone users showed enhanced FC within the salience network and between the salience and default mode network. Moreover, we observed decreased FC between the salience and central executive network in problematic smartphone users, compared to controls. These results imply that problematic smartphone use is associated with aberrant FC in key brain networks. Our results suggest that changes in FC of key networks centered around the salience network might be associated with problematic smartphone use.

scholarly journals Macroscopic cortical dynamics: Spatially uncorrelated but temporally coherent rich-club organisations in source-space resting-state EEG

2020 ◽  
Author(s):  
Steve Mehrkanoon
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Complex Dynamics ◽  
Cortical Activity ◽  
Computational Technique ◽  
Salience Network ◽  
Resting State Functional Connectivity ◽  
Default Mode ◽  
Rich Club ◽  
Topological Features

AbstractSynchronous oscillations of neuronal populations support resting-state cortical activity. Recent studies indicate that resting-state functional connectivity is not static, but exhibits complex dynamics. The mechanisms underlying the complex dynamics of cortical activity have not been well characterised. Here, we directly apply singular value decomposition (SVD) in source-reconstructed electroencephalography (EEG) in order to characterise the dynamics of spatiotemporal patterns of resting-state functional connectivity. We found that changes in resting-state functional connectivity were associated with distinct complex topological features, “Rich-Club organisation”, of the default mode network, salience network, and motor network. Rich-club topology of the salience network revealed greater functional connectivity between ventrolateral prefrontal cortex and anterior insula, whereas Rich-club topologies of the default mode networks revealed bilateral functional connectivity between fronto-parietal and posterior cortices. Spectral analysis of the dynamics underlying Rich-club organisations of these source-space network patterns revealed that resting-state cortical activity exhibit distinct dynamical regimes whose intrinsic expressions contain fast oscillations in the alpha-beta band and with the envelope-signal in the timescale of < 0.1 Hz. Our findings thus demonstrated that multivariate eigen-decomposition of source-reconstructed EEG is a reliable computational technique to explore how dynamics of spatiotemporal features of the resting-state cortical activity occur that oscillate at distinct frequencies.

scholarly journals Frequency Specific Effects of ApoE ε4 Allele on Resting-State Networks in Nondemented Elders

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Ying Liang ◽  
Zhenzhen Li ◽  
Jing Wei ◽  
Chunlin Li ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Frequency Band ◽  
Default Mode Network ◽  
Resting State ◽  
Brain Network ◽  
Salience Network ◽  
Default Mode ◽  
Specific Effects ◽  
Network Time ◽  
Time Courses

We applied resting-state functional magnetic resonance imaging (fMRI) to examine the Apolipoprotein E (ApoE) ε4 allele effects on functional connectivity of the default mode network (DMN) and the salience network (SN). Considering the frequency specific effects of functional connectivity, we decomposed the brain network time courses into two bands: 0.01–0.027 Hz and 0.027–0.08 Hz. All scans were acquired by the Alzheimer’s Disease Neuroscience Initiative (ADNI). Thirty-two nondemented subjects were divided into two groups based on the presence (n=16) or absence (n=16) of the ApoE ε4 allele. We explored the frequency specific effects of ApoE ε4 allele on the default mode network (DMN) and the salience network (SN) functional connectivity. Compared to ε4 noncarriers, the DMN functional connectivity of ε4 carriers was significantly decreased while the SN functional connectivity of ε4 carriers was significantly increased. Many functional connectivities showed significant differences at the lower frequency band of 0.01–0.027 Hz or the higher frequency band of 0.027–0.08 Hz instead of the typical range of 0.01–0.08 Hz. The results indicated a frequency dependent effect of resting-state signals when investigating RSNs functional connectivity.

Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study

2016 ◽  
Vol 46 (13) ◽  
pp. 2695-2704 ◽  
Author(s):  
P. Mikolas ◽  
T. Melicher ◽  
A. Skoch ◽  
M. Matejka ◽  
A. Slovakova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Resting State ◽  
Classification Accuracy ◽  
Anterior Insula ◽  
First Episode ◽  
Salience Network ◽  
Healthy Controls ◽  
Default Mode ◽  
Schizophrenia Spectrum

BackgroundEarly diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differences have a limited diagnostic utility. Novel methods of magnetic resonance imaging (MRI) analyses, such as machine learning (ML), may help bring neuroimaging from the bench to the bedside. Here, we used ML to differentiate participants with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls based on resting-state functional connectivity (rsFC).MethodWe acquired resting-state functional MRI data from 63 patients with FES who were individually matched by age and sex to 63 healthy controls. We applied linear kernel support vector machines (SVM) to rsFC within the default mode network, the salience network and the central executive network.ResultsThe SVM applied to the rsFC within the salience network distinguished the FES from the control participants with an accuracy of 73.0% (p = 0.001), specificity of 71.4% and sensitivity of 74.6%. The classification accuracy was not significantly affected by medication dose, or by the presence of psychotic symptoms. The functional connectivity within the default mode or the central executive networks did not yield classification accuracies above chance level.ConclusionsSeed-based functional connectivity maps can be utilized for diagnostic classification, even early in the course of schizophrenia. The classification was probably based on trait rather than state markers, as symptoms or medications were not significantly associated with classification accuracy. Our results support the role of the anterior insula/salience network in the pathophysiology of FES.

scholarly journals Re-Establishing Brain Networks in Patients with ESRD after Successful Kidney Transplantation

2017 ◽  
Vol 13 (1) ◽  
pp. 109-117 ◽  
Author(s):  
Hui Juan Chen ◽  
Jiqiu Wen ◽  
Rongfeng Qi ◽  
Jianhui Zhong ◽  
U. Joseph Schoepf ◽  
...  
Keyword(s):  
Kidney Transplantation ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Resting State ◽  
Central Executive ◽  
Attention Network ◽  
Default Mode ◽  
Sensorimotor Network ◽  
Dorsal Attention Network ◽  
Visual Network

Background and objectivesCognition in ESRD may be improved by kidney transplantation, but mechanisms are unclear. We explored patterns of resting-state networks with resting-state functional magnetic resonance imaging among patients with ESRD before and after kidney transplantation.Design, setting, participants, & measurementsThirty-seven patients with ESRD scheduled for kidney transplantation and 22 age-, sex-, and education-matched healthy subjects underwent resting-state functional magnetic resonance imaging. Patients were imaged before and 1 and 6 months after kidney transplantation. Functional connectivity of seven resting-state subnetworks was evaluated: default mode network, dorsal attention network, central executive network, self-referential network, sensorimotor network, visual network, and auditory network. Mixed effects models tested associations of ESRD, kidney transplantation, and neuropsychological measurements with functional connectivity.ResultsCompared with controls, pretransplant patients showed abnormal functional connectivity in six subnetworks. Compared with pretransplant patients, increased functional connectivity was observed in the default mode network, the dorsal attention network, the central executive network, the sensorimotor network, the auditory network, and the visual network 1 and 6 months after kidney transplantation (P=0.01). Six months after kidney transplantation, no significant difference in functional connectivity was observed for the dorsal attention network, the central executive network, the auditory network, or the visual network between patients and controls. Default mode network and sensorimotor network remained significantly different from those in controls when assessed 6 months after kidney transplantation. A relationship between functional connectivity and neuropsychological measurements was found in specific brain regions of some brain networks.ConclusionsThe recovery patterns of resting-state subnetworks vary after kidney transplantation. The dorsal attention network, the central executive network, the auditory network, and the visual network recovered to normal levels, whereas the default mode network and the sensorimotor network did not recover completely 6 months after kidney transplantation. Neural resting-state functional connectivity was lower among patients with ESRD compared with control subjects, but it significantly improved with kidney transplantation. Resting-state subnetworks exhibited variable recovery, in some cases to levels that were no longer significantly different from those of normal controls.

scholarly journals Modulatory Interactions between the Default Mode Network and Task Positive Networks in Resting-State

2013 ◽  
Author(s):  
Xin Di ◽  
Bharat B. Biswal
Keyword(s):  
Basal Ganglia ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Resting State ◽  
Brain Networks ◽  
Brain Regions ◽  
Salience Network ◽  
Imaging Data ◽  
Default Mode ◽  
Brain Functions

Communications between different brain systems are critical to support complex brain functions. Unlike generally high functional connectivity between brain regions from same system, functional connectivity between regions from different systems are more variable. In the present study, we examined whether the connectivity between different brain networks were modulated by other regions by using physiophysiological interaction (PPI) on resting-state functional magnetic resonance imaging data. Spatial independent component analysis was first conducted to identify the default mode network (DMN) and several task positive networks, including the salience, dorsal attention, left and right executive networks. PPI analysis was conducted between pairs of these networks to identify networks or regions that showed modulatory interactions with the two networks. Network-wise analysis revealed reciprocal modulatory interactions between the DMN, salience, and executive networks. Together with the anatomical properties of the salience network regions, the results suggest that the salience network may modulate the relationship between the DMN and executive networks. In addition, voxel-wise analysis demonstrated that the basal ganglia and thalamus positively interacted with the salience network and the dorsal attention network, and negatively interacted with the salience network and the DMN. The results demonstrated complex relationships among brain networks in resting-state, and suggested that between network communications of these networks may be modulated by some critical brain structures such as the salience network, basal ganglia, and thalamus.

scholarly journals Recreational use of GHB is associated with alterations of resting state functional connectivity of the central executive and default mode networks

2019 ◽  
Vol 40 (8) ◽  
pp. 2413-2421 ◽  
Author(s):  
Filipa Raposo Pereira ◽  
Paul Zhutovsky ◽  
Minni T.B. Mcmaster ◽  
Nikki Polderman ◽  
Yvon D.A.T. Vries ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Central Executive ◽  
Resting State Functional Connectivity ◽  
Default Mode ◽  
Recreational Use

Decreased Functional Connectivity by Aging Is Associated with Cognitive Decline

2012 ◽  
Vol 24 (11) ◽  
pp. 2186-2198 ◽  
Author(s):  
Keiichi Onoda ◽  
Masaki Ishihara ◽  
Shuhei Yamaguchi
Keyword(s):  
Functional Connectivity ◽  
Independent Component Analysis ◽  
Cognitive Decline ◽  
Elderly People ◽  
Default Mode Network ◽  
Resting State ◽  
Cognitive Abilities ◽  
Salience Network ◽  
Resting State Networks ◽  
Default Mode

Aging is related to cognitive decline, and it has been reported that aging disrupts some resting state brain networks. However, most studies have focused on the default mode network and ignored other resting state networks. In this study, we measured resting state activity using fMRI and explored whether cognitive decline with aging is related to disrupted resting state networks. Independent component analysis was used to evaluate functional connectivity. Notably, the connectivity within the salience network that consisted of the bilateral insula and the anterior cingulated cortex decreased with aging; the impairment of functional connectivity was correlated with measured decreases in individual cognitive abilities. Furthermore, certain internetwork connectivities (salience to auditory, default mode to visual, etc.) also decreased with aging. These results suggest that (1) aging affects not only the default mode network but also other networks, specifically the salience network; (2) aging affects internetwork connectivity; and (3) disruption of the salience network is related to cognitive decline in elderly people.

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