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.

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 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.

scholarly journals Brain-behavior patterns define a dimensional biotype in medication-naïve adults with attention-deficit hyperactivity disorder

2017 ◽  
Author(s):  
Hsiang-Yuan Lin ◽  
Luca Cocchi ◽  
Andrew Zalesky ◽  
Jinglei Lv ◽  
Alistair Perry ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Functional Connectivity ◽  
Attention Deficit ◽  
Resting State ◽  
Adult Adhd ◽  
Healthy Controls ◽  
Clustering Methods ◽  
Childhood Onset ◽  
Default Mode ◽  
Hyperactivity Disorder

AbstractBackgroundChildhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD.MethodsWe first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naïve adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the individual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype.ResultsAdults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain-symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD.ConclusionsOverall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain-symptom mediators.

scholarly journals Disrupted controlling mechanism of salience network on default-mode network and central-executive network in schizophrenia

2021 ◽  
Author(s):  
Ganesh B. Chand ◽  
Deepa S. Thakuri ◽  
Bhavin Soni
Keyword(s):  
Neural Networks ◽  
Default Mode Network ◽  
Central Executive ◽  
Causal Modeling ◽  
Salience Network ◽  
Healthy Controls ◽  
Interaction Patterns ◽  
Default Mode ◽  
Controlling Mechanism ◽  
Central Executive Network

AbstractNeuroimaging studies suggest that the human brain consists of intrinsically organized large-scale neural networks. Among those networks, the interplay among default-mode network (DMN), salience network (SN), and central-executive network (CEN)has been widely employed to understand the functional interaction patterns in health and diseases. This triple network model suggests that SN causally controls DMN and CEN in healthy individuals. This interaction is often referred to as the dynamic controlling mechanism of SN. However, such interactions are not well understood in individuals with schizophrenia. In this study, we leveraged resting state functional magnetic resonance imaging (fMRI) data of schizophrenia (n = 67) and healthy controls (n = 81) to evaluate the functional interactions among DMN, SN, and CEN using dynamical causal modeling. In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10−8). In schizophrenia, however, our analyses revealed the disrupted SN-based controlling mechanism on DMN and CEN (Mann-Whitney U test; p < 10−16). These results indicate that the disrupted controlling mechanism of SN on two other neural networks may be a candidate neuroimaging phenotype in schizophrenia.

Examining resting-state functional connectivity in key hubs of the default mode network in chronic low back pain

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sin Ki Ng ◽  
Donna M. Urquhart ◽  
Paul B. Fitzgerald ◽  
Flavia M. Cicuttini ◽  
Melissa Kirkovski ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Chronic Low Back Pain ◽  
Resting State ◽  
Angular Gyrus ◽  
Healthy Controls ◽  
Low Back ◽  
Default Mode

Abstract Objectives Changes in brain connectivity have been observed within the default mode network (DMN) in chronic low back pain (CLBP), however the extent of these disruptions and how they may be related to CLBP requires further examination. While studies using seed-based analysis have found disrupted functional connectivity in the medial prefrontal cortex (mPFC), a major hub of the DMN, limited studies have investigated other equally important hubs, such as the posterior cingulate cortex (PCC) in CLBP. Methods This preliminary study comprised 12 individuals with CLBP and 12 healthy controls who completed a resting-state functional magnetic resonance imaging (fMRI) scan. The mPFC and PCC were used as seeds to assess functional connectivity. Results Both groups displayed similar patterns of DMN connectivity, however group comparisons showed that CLBP group had reduced connectivity between the PCC and angular gyrus compared to healthy controls. An exploratory analysis examined whether the alterations observed in mPFC and PCC connectivity were related to pain catastrophizing in CLBP, but no significant associations were observed. Conclusions These results may suggest alterations in the PCC are apparent in CLBP, however, the impact and functional role of these disruptions require further investigation.

Abstract 19: Default Mode Network Alterations in Cerebral Amyloid Angiopathy: A Resting State FMRI Study

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Mitchell J Horn ◽  
Elif Gokcal ◽  
Aina Frau-Pascual ◽  
Kristin M Schwab ◽  
Anand Viswanathan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cerebral Amyloid Angiopathy ◽  
Default Mode Network ◽  
Resting State ◽  
Resting State Fmri ◽  
Amyloid Angiopathy ◽  
Healthy Controls ◽  
Default Mode ◽  
Posterior Cingulate ◽  
Cerebral Amyloid

Introduction: Cerebral amyloid angiopathy (CAA) is an established cause of intracerebral hemorrhage and vascular dysfunction leading to ischemia. Functional connectivity analysis using MRI is becoming an important tool to analyze the brain activity during resting state, the default mode network (DMN) representing the prototypical set of connections. As CAA pathology has a posterior predominance, we sought to characterize the functional connectivity of the posterior DMN at resting state in patients with CAA. Methods: Patients with probable CAA diagnosed using Boston Criteria and healthy controls (HC) were prospectively enrolled and received high resolution 3T MRI scans including dedicated resting-state fMRI sequences. Functional seed-to-seed analyses were done using the default processing pipeline in the CONN Toolbox. Correlation maps between the established DMN and specific regions of the posterior DMN, the precuneus and posterior cingulate, were averaged within groups and compared in an ANCOVA model. Results: Study participants consisted of 60 patients with probable CAA and 20 healthy controls [aged 69 ± 7.5 vs 72.3 ± 8 years, P = 0.108]. Seed-to-seed analysis revealed a significantly lower strength of DMN connectivity in CAA when compared to controls in the precuneus [ P = 0.009] and posterior cingulate [ P = 0.003] adjusted for age and sex (Fig 1). Conclusion: Patients with CAA exhibited significant loss of connectivity in the posterior regions of the DMN when compared to controls. The precuneus and posterior cingulate are core regions of the DMN with reportedly high metabolic rates at rest. Disruption of these posterior DMN regions might occur due to vascular amyloid pathology that shows a predominantly posterior distribution.

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