scholarly journals Segregation between the parietal memory network and the default mode network: Effects of spatial smoothing and model order in ICA

2016 ◽  
Author(s):  
Yang Hu ◽  
Jijun Wang ◽  
Chunbo Li ◽  
Yin-shan Wang ◽  
Zhi Yang ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Network Effects ◽  
Posterior Cingulate Cortex ◽  
Brain Network ◽  
Parameter Determination ◽  
Spatial Smoothing ◽  
Model Order ◽  
Default Mode ◽  
Memory Network ◽  
Group Ica

AbstractA brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network (DMN), its function has been associated with memory processing, and it has been referred to as the parietal memory network (PMN). Independent component analysis (ICA) is the most common data-driven method of extracting PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determination in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high-order model across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.

scholarly journals Disrupting The Resting State: Meta-Analytic Evidence That Mindfulness Training Alters Default Mode Network Connectivity

2022 ◽  
Author(s):  
Hadley Rahrig ◽  
David R. Vago ◽  
Matthew Passarelli ◽  
Allison Auten ◽  
Nicholas A. Lynn ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Resting State ◽  
Meta Analysis ◽  
Network Connectivity ◽  
Posterior Cingulate Cortex ◽  
Brain Network ◽  
Mindfulness Training ◽  
Resting State Functional Connectivity ◽  
Default Mode ◽  
Brain Network Connectivity

Abstract This meta-analysis sought to expand upon neurobiological models of mindfulness through investigation of inherent brain network connectivity outcomes, indexed via resting state functional connectivity (rsFC). We conducted a systematic review and meta-analysis of rsFC as an outcome of mindfulness training (MT) relative to structurally-equivalent programs, with the hypothesis that that MT would increase cross-network connectivity between nodes of the Default Mode Network (DMN), Salience Network (SN), and Frontoparietal Control Network (FPCN) as a mechanism of internally-oriented attentional control. Texts were identified from the databases: MEDLINE/PubMed, ERIC, PSYCINFO, ProQuest, Scopus, and Web of Sciences; and were screened for inclusion based on experimental/quasi-experimental trial design and use of standardized mindfulness-based interventions. RsFC effects were extracted from twelve studies (mindfulness n = 226; control n = 204). Voxel-based meta-analysis revealed significantly greater rsFC (MT > control) between the left middle cingulate (Hedge’s g = .234, p = 0288, I2 = 15.87), located within the SN, and the posterior cingulate cortex, a focal hub of the DMN. Egger’s test for publication bias was nonsignificant, bias = 2.17, p = .162. In support of our hypothesis, results suggest that MT targets internetwork (SN-DMN) connectivity implicated in the flexible control of internally-oriented attention.

scholarly journals Parietal memory network and default mode network in first‐episode drug‐naïve schizophrenia: Associations with auditory hallucination

2020 ◽  
Vol 41 (8) ◽  
pp. 1973-1984
Author(s):  
Qian Guo ◽  
Yang Hu ◽  
Botao Zeng ◽  
Yingying Tang ◽  
Guanjun Li ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Auditory Hallucination ◽  
First Episode ◽  
Default Mode ◽  
Drug Naïve ◽  
Memory Network

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

2017 ◽  
Vol 9 (4) ◽  
pp. 46-66 ◽  
Author(s):  
Bhuvaneshwari Bhaskaran ◽  
Kavitha Anandan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Disorder ◽  
Default Mode ◽  
Graph Metrics ◽  
Functional Topology ◽  
The Brain

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.

scholarly journals Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

2019 ◽  
Vol 40 (7) ◽  
pp. 2212-2228 ◽  
Author(s):  
Akhil Kottaram ◽  
Leigh A. Johnston ◽  
Luca Cocchi ◽  
Eleni P. Ganella ◽  
Ian Everall ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Network Dynamics ◽  
Brain Network ◽  
Default Mode

scholarly journals Coactivation of the Default Mode Network regions and Working Memory Network regions during task preparation

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Hideya Koshino ◽  
Takehiro Minamoto ◽  
Ken Yaoi ◽  
Mariko Osaka ◽  
Naoyuki Osaka
Keyword(s):  
Working Memory ◽  
Default Mode Network ◽  
Default Mode ◽  
Task Preparation ◽  
Memory Network

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 Symptoms of fatigue and depression is reflected in altered default mode network connectivity in multiple sclerosis

2018 ◽  
Author(s):  
Einar August Høgestøl ◽  
Gro Owren Nygaard ◽  
Dag Alnæs ◽  
Mona K. Beyer ◽  
Lars T. Westlye ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Default Mode Network ◽  
Symptom Burden ◽  
Network Connectivity ◽  
Principal Component ◽  
Brain Network ◽  
Common Symptom ◽  
Linear Regression Models ◽  
Default Mode ◽  
Network Function

Background: Fatigue and depression are frequent and often co-occurring symptoms in multiple sclerosis (MS). Resting-state functional magnetic resonance imaging (rs-fMRI) represents a promising tool for disentangling differential associations between depression and fatigue and brain network function and connectivity. In this study we tested for associations between symptoms of fatigue and depression and DMN connectivity in patients with MS. Materials and methods: Seventy-four MS patients were included on average 14 months after diagnosis. They underwent MRI scanning of the brain including rs-fMRI, and symptoms of fatigue and depression were assessed with Fatigue Severity Scale (FSS) and Beck Depression Inventory II (BDI). A principal component analysis (PCA) on FSS and BDI scores was performed, and the component scores were analysed using linear regression models to test for associations with default mode network (DMN) connectivity. Results: We observed higher DMN connectivity with higher scores on the primary principal component reflecting common symptom burden for fatigue and depression (Cohen's f2=0.075, t=2.17, p=0.03). The secondary principal component reflecting a pattern of low fatigue scores with high scores of depression was associated with lower DMN connectivity (Cohen's f2=0.067, t=-2.1, p=0.04). Using continuous mean scores of FSS we also observed higher DMN connectivity with higher symptom burden (t=3.1, p=0.003), but no significant associations between continuous sum scores of BDI and DMN connectivity (t=0.8, p=0.4). Conclusion: Multivariate decomposition of FSS and BDI data supported both overlapping and unique manifestation of fatigue and depression in MS patients. Rs-fMRI analyses showed that symptoms of fatigue and depression was reflected in altered DMN connectivity, and that higher DMN activity was seen in MS patients with fatigue even with low depression scores.

scholarly journals Large-scale brain networks and Freudian ego

2019 ◽  
Vol 12 (2) ◽  
pp. 162-175 ◽  
Author(s):  
Petar Radoev Dimkov
Keyword(s):  
Sigmund Freud ◽  
Default Mode Network ◽  
Large Scale ◽  
Structural Model ◽  
Brain Network ◽  
Default Mode ◽  
Current Thinking ◽  
Theoretical Hypothesis ◽  
Brain Correlates ◽  
The Brain

Sigmund Freud, the founder of psychoanalysis, is predominantly known for his conception of the id, ego and super-ego, representing a part of his meta-psychology of the psychic apparatus. Nowadays, with the advancements in technology and science, his meta-psychological structural model of the psyche might be either confirmed or denied by comparing the account of the psychic apparatus of the classical psychoanalysis to the newest findings in neuropsychology and cognitive neuroscience. Indeed, the founded interdisciplinary project of neuro-psychoanalysis strives to answer such questions. In this article, the current thinking on the discussions around Freudian ego and its possible brain correlates is presented. In 2010, Robin Carhart-Harris and Karl Friston introduced a neuro-psychoanalytic account of the psychic apparatus, where the ego correlated with a large-scale brain network called the default-mode network. In the end of this paper, an original theoretical hypothesis is offered, supplemented with review of the literature, namely that the central-executive network and the salience network are viewed as the true representatives of Freudian ego. The offered hypothesis criticizes Carhart-Harris and Friston’s postulating of the default-mode network as being the brain representative of Freudian ego.

Sign in / Sign up

Export Citation Format

Share Document