scholarly journals Hyperconnectivity of the default mode network in multiorgan dysfunction syndrome

2018 ◽  
Author(s):  
Antonio Jimenez-Marin ◽  
Diego Rivera ◽  
Victoria Boado ◽  
Ibai Diez ◽  
Fermin Labayen ◽  
...  
Keyword(s):  
Executive Control ◽  
Default Mode Network ◽  
Structural Damage ◽  
Brain Network ◽  
Multiple Organ ◽  
Specific Situation ◽  
Physiological Disorder ◽  
Default Mode ◽  
Icu Discharge

AbstractMultiple Organ Dysfunction Syndrome (MODS) is a systemic physiological disorder affecting two or more body organs triggered after an insult complication. Beyond the systemic failure, patients who survive MODS present cognitive and neurological impairments that remain stable even several years after Intensive Care Unit (ICU) discharge. Here, we focus on the specific situation of MODS patients with no apparent brain damage (NABD), where the mechanisms driving cognitive impairment at long term are not well-understood. We recruit N1 = 13 MODS patients with NABD at 6 months after ICU discharge, together with N2 = 13 healthy controls (matched by age, sex and years of education), and acquire functional magnetic resonance imaging at rest to find that, as compared to control, MODS patients with NABD present an overall increase of the functional connectivity (FC) at rest. In particular, we find that the default mode network (DMN) hyperconnects (increasing the node strength of the FC matrix) to three classes of networks: primary sensory (such as auditory, sensory-motor and visual), multimodal integration (such as dorsal attention and salience) and higher order cognition networks (such as fronto-parietal, language and executive control). Therefore, although these patients do not have an apparent structural damage after MODS, at the functional level, we found brain network alterations coexisting with hyperconnectivity of the DMN, that similar to what happens at the onset of other pathologies, might indicate a possible mechanism for brain compensation occurring after MODS.

scholarly journals Long-term stress and trait anxiety alter brain network balance in dynamic decisions during working memory

2021 ◽  
Author(s):  
Liangying Liu ◽  
Jianhu Wu ◽  
Haiyang Geng ◽  
Chao Liu ◽  
Yuejia Luo ◽  
...  
Keyword(s):  
Working Memory ◽  
Trait Anxiety ◽  
Default Mode Network ◽  
Brain Network ◽  
Decision Threshold ◽  
Default Mode ◽  
Evidence Accumulation ◽  
Functional Brain Network ◽  
Dynamic Decisions

Long-term stress has a profound impact on the human brain and cognition, and trait anxiety influences stress-induced adaptive and maladaptive effects. However, the neurocognitive mechanisms underlying long-term stress and trait anxiety interactions remain elusive. Here we investigated how long-term stress and trait anxiety interact to affect dynamic decisions during working-memory (WM) by altering functional brain network balance. In comparison to controls, male participants under long-term stress experienced higher psychological distress and exhibited faster evidence accumulation but had a lower decision-threshold during WM. This corresponded with hyper-activation in the anterior insula, less WM-related deactivation in the default-mode network, and stronger default-mode network decoupling with the frontoparietal network. Critically, high trait anxiety under long-term stress led to slower evidence accumulation through higher WM-related frontoparietal activity, and increased decoupling between the default-mode and frontoparietal networks. Our findings provide neurocognitive evidence for long-term stress and trait anxiety interactions on executive functions with (mal)adaptive changes.

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 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 Brains striving for coherence: Long-term cumulative plot formation in the default mode network

NeuroImage ◽  
2015 ◽  
Vol 121 ◽  
pp. 106-114 ◽  
Author(s):  
K. Tylén ◽  
P. Christensen ◽  
A. Roepstorff ◽  
T. Lund ◽  
S. Østergaard ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Default Mode

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 Measuring Learning in the Blink of an Eye: Adolescents' Neurophysiological Reactions Predict Long-Term Memory for Stories

2021 ◽  
Vol 5 ◽  
Author(s):  
Rebecca J. M. Gotlieb ◽  
Xiao-Fei Yang ◽  
Mary Helen Immordino-Yang
Keyword(s):  
Neural Activity ◽  
Default Mode Network ◽  
Cued Recall ◽  
Network Activity ◽  
Long Term Memory ◽  
Blink Rate ◽  
Additive Variance ◽  
Term Memory ◽  
Default Mode

Anticipating what adolescents will remember is a common goal in education research, but what tools allow us to predict adolescents' memory without interrupting the learning process as it naturally occurs? To attempt to identify neurophysiological markers of deep processing that may predict long-term retention, here we conducted an exploratory study by adding a cued recall probe to the last wave of data collection in a longitudinal psychosocial and neuroimaging study of 65 urban adolescents. Five years prior, and again 3 years prior, participants had reacted to the same emotionally evocative true stories during a videotaped interview that allowed us to measure eye-blink rate (EBR), and again during fMRI scanning. We analyzed EBR and neural data from the initial story exposure. We found that memory for a story was predicted by both EBR (a proxy for striatal dopamine) and default mode network neural activity to that story (involved in integrative memory and processing of emotional feelings). EBR and default mode network activity were uncorrelated and explained additive variance. Though more work is needed, our study contributes preliminary supportive evidence linking EBR and neural activity trial-by-trial to long-term memory in a naturalistic task. The analyses suggest that including EBR, a non-invasive, portable, and inexpensive measure that can be coded from high-quality video recording, could be useful in future studies of adolescents' learning.

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.

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