scholarly journals No Alteration Between Intrinsic Connectivity Networks by a Pilot Study on Localized Exposure to the Fourth-Generation Wireless Communication Signals

2022 ◽  
Vol 9 ◽  
Author(s):  
Lei Yang ◽  
Qingmeng Liu ◽  
Yu Zhou ◽  
Xing Wang ◽  
Tongning Wu ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Network Connectivity ◽  
Fourth Generation ◽  
Imaging Data ◽  
Communication Signals ◽  
Exposure Effect ◽  
Brain Functions ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
The Impact

Neurophysiological effect of human exposure to radiofrequency signals has attracted considerable attention, which was claimed to have an association with a series of clinical symptoms. A few investigations have been conducted on alteration of brain functions, yet no known research focused on intrinsic connectivity networks, an attribute that may relate to some behavioral functions. To investigate the exposure effect on functional connectivity between intrinsic connectivity networks, we conducted experiments with seventeen participants experiencing localized head exposure to real and sham time-division long-term evolution signal for 30 min. The resting-state functional magnetic resonance imaging data were collected before and after exposure, respectively. Group-level independent component analysis was used to decompose networks of interest. Three states were clustered, which can reflect different cognitive conditions. Dynamic connectivity as well as conventional connectivity between networks per state were computed and followed by paired sample t-tests. Results showed that there was no statistical difference in static or dynamic functional network connectivity in both real and sham exposure conditions, and pointed out that the impact of short-term electromagnetic exposure was undetected at the ICNs level. The specific brain parcellations and metrics used in the study may lead to different results on brain modulation.

scholarly journals Disambiguating the role of blood flow and global signal with Partial Information Decomposition

2019 ◽  
Author(s):  
Nigel Colenbier ◽  
Frederik Van de Steen ◽  
Lucina Q. Uddin ◽  
Russell A. Poldrack ◽  
Vince D. Calhoun ◽  
...  
Keyword(s):  
Blood Flow ◽  
Functional Connectivity ◽  
Arrival Time ◽  
Partial Information ◽  
Physiological Noise ◽  
Global Signal ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
The Impact

AbstractIn resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It effectively removes non-neuronal artifacts, but at the same time it alters correlational patterns in unpredicted ways. Furthermore the global signal includes neural BOLD signal by construction, and is consequently related to neural and behavioral function. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proved to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improve denoising methods. Using GSR but not correcting for blood flow might selectively introduce physiological artifacts across intrinsic connectivity networks that distort the functional connectivity estimates.

scholarly journals The impact of a closed-loop thalamocortical model on the spatiotemporal dynamics of cortical and thalamic traveling waves

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sayak Bhattacharya ◽  
Matthieu B. L. Cauchois ◽  
Pablo A. Iglesias ◽  
Zhe Sage Chen
Keyword(s):  
Traveling Waves ◽  
Closed Loop ◽  
Network Connectivity ◽  
Spatiotemporal Patterns ◽  
Oscillatory Activity ◽  
Brain Functions ◽  
Wave Patterns ◽  
Cortical Excitation ◽  
Thalamocortical Network ◽  
The Impact

AbstractPropagation of activity in spatially structured neuronal networks has been observed in awake, anesthetized, and sleeping brains. How these wave patterns emerge and organize across brain structures, and how network connectivity affects spatiotemporal neural activity remains unclear. Here, we develop a computational model of a two-dimensional thalamocortical network, which gives rise to emergent traveling waves similar to those observed experimentally. We illustrate how spontaneous and evoked oscillatory activity in space and time emerge using a closed-loop thalamocortical architecture, sustaining smooth waves in the cortex and staggered waves in the thalamus. We further show that intracortical and thalamocortical network connectivity, cortical excitation/inhibition balance, and thalamocortical or corticothalamic delay can independently or jointly change the spatiotemporal patterns (radial, planar and rotating waves) and characteristics (speed, direction, and frequency) of cortical and thalamic traveling waves. Computer simulations predict that increased thalamic inhibition induces slower cortical frequencies and that enhanced cortical excitation increases traveling wave speed and frequency. Overall, our results provide insight into the genesis and sustainability of thalamocortical spatiotemporal patterns, showing how simple synaptic alterations cause varied spontaneous and evoked wave patterns. Our model and simulations highlight the need for spatially spread neural recordings to uncover critical circuit mechanisms for brain functions.

scholarly journals Oscillatory default mode network coupling in concussion

2017 ◽  
Author(s):  
B.T Dunkley ◽  
K. Urban ◽  
L. Da Costa ◽  
S. Wong ◽  
E.W. Pang ◽  
...  
Keyword(s):  
Large Scale ◽  
Critical Role ◽  
Network Connectivity ◽  
Permutation Testing ◽  
Default Mode ◽  
Spectrum Density ◽  
Intrinsic Connectivity Networks ◽  
Time Courses ◽  
Band Limited ◽  
The Impact

AbstractBackgroundConcussion is a common form of mild traumatic brain injury (mTBI). Despite the descriptor ‘mild’, a single injury can leave long-lasting and sustained alterations to brain function, including changes to localised activity and large-scale interregional communication. Cognitive complaints are thought to arise from such functional deficits. We investigated the impact of injury on neurophysiological and functionally-specialised resting networks, known as intrinsic connectivity networks (ICNs), using MEG.MethodsWe assessed neurophysiological connectivity in 40 males, 20 with concussion, 20 without, using MEG. Regions-of-interest that comprise nodes of ICNs were defined, and their time courses derived using a beamformer approach. Pairwise fluctuations and covariations in band-limited amplitude envelopes were computed reflecting measures of functional connectivity. Intra-network connectivity was compared between groups using permutation testing, and correlated with symptoms.ResultsWe observed increased resting spectral connectivity in the default mode and motor networks in our concussion group when compared with controls, across alpha through gamma ranges. Moreover, these differences were not explained by power spectrum density (absolute changes in the spectral profiles within the ICNs). Furthermore, this increased coupling was significantly associated with symptoms in the DMN and MOT networks – but once accounting for comorbid symptoms (including, depression, anxiety, and ADHD) only the DMN continued to be associated with symptoms.ConclusionThe DMN network plays a critical role in shifting between cognitive tasks. These data suggest even a single concussion can perturb the intrinsic coupling of functionally-specialised networks in the brain and may explain persistent and wide-ranging symptomatology.

scholarly journals Rapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation

2019 ◽  
Author(s):  
Valerio Zerbi ◽  
Amalia Floriou-Servou ◽  
Marija Markicevic ◽  
Yannick Vermeiren ◽  
Oliver Sturman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Locus Coeruleus ◽  
Large Scale ◽  
Network Connectivity ◽  
Brain Regions ◽  
Causal Link ◽  
Receptor Expression ◽  
Brain Functions ◽  
Neuropsychiatric Diseases ◽  
The Impact

AbstractThe locus coeruleus (LC) supplies norepinephrine (NE) to the entire forebrain, regulates many fundamental brain functions, and is implicated in several neuropsychiatric diseases. Although selective manipulation of the LC is not possible in humans, studies have suggested that strong LC activation might shift network connectivity to favor salience processing. To test this hypothesis, we use a mouse model to study the impact of LC stimulation on large-scale functional connectivity by combining chemogenetic activation of the LC with resting-state fMRI, an approach we term “chemo-connectomics”. LC activation rapidly interrupts ongoing behavior and strongly increases brain-wide connectivity, with the most profound effects in the salience and amygdala networks. We reveal a direct correlation between functional connectivity changes and transcript levels of alpha-1, alpha-2, and beta-1 adrenoceptors across the brain, and a positive correlation between NE turnover and functional connectivity within select brain regions. These results represent the first brain-wide functional connectivity mapping in response to LC activation, and demonstrate a causal link between receptor expression, brain states and functionally connected large-scale networks at rest. We propose that these changes in large-scale network connectivity are critical for optimizing neural processing in the context of increased vigilance and threat detection.

scholarly journals An Approach to Automatically Label & Order Brain Activity/Component Maps

2020 ◽  
Author(s):  
Mustafa S. Salman ◽  
Tor D. Wager ◽  
Eswar Damaraju ◽  
Vince D. Calhoun
Keyword(s):  
Brain Imaging ◽  
Brain Activity ◽  
Region Of Interest ◽  
Network Connectivity ◽  
Data Driven ◽  
Reproducible Research ◽  
Validation Dataset ◽  
Imaging Data ◽  
Intrinsic Connectivity Networks ◽  
Using Data

AbstractFunctional magnetic resonance imaging (fMRI) is a brain imaging technique which provides detailed in-sights into brain function and its disruption in various brain disorders. fMRI data can be analyzed using data-driven or region-of-interest based methods. The data-driven analysis of brain activity maps involves several steps, the first of which is identifying whether the maps capture what might be interpreted as intrinsic connectivity networks (ICNs) or artifacts. This is followed by linking the ICNs to known anatomical and/or functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary for systematic interpretation. Here we present a toolbox that automates all these processes under minimal or no supervision with high accuracy. We provide a pretrained cross-validated elastic-net regularized general linear model for the noisecloud toolbox to separate the ICNs from artifacts. We include several well-known anatomical and functional parcellations from which researchers can choose to label the activity maps. Finally, we integrate a method for maximizing the within-domain modularity to generate a more systematically structured FNC matrix. We improve upon and integrate existing techniques and new methods to design this toolbox which can take care of all the above needs. Specifically, we show that our pretrained model achieves 89% accuracy and 100% precision at classifying ICNs from artifacts in a validation dataset. Researchers are generating brain imaging data and analyzing brain activity at an ever-increasing rate. The Autolabeller toolbox can help automate such analyses for faster and reproducible research.

The Impact of Mobile Gadget in EFL Learning: Perceptions of EFL Undergraduates

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Imam - Fauzi
Keyword(s):  
Self Report ◽  
Fourth Generation ◽  
Learning Activity ◽  
Advanced Country ◽  
Innovative Methods ◽  
Phone Calls ◽  
Teachers And Students ◽  
Efl Students ◽  
The Impact ◽  
Efl Learning

AbstractMost of young people are enthusiasticin having the most recent mobile gadgets just to boast among their peers. They likely utilize them to make phone calls, take pictures, listen to songs, watch videos, or surf the internet access for learning or just entertainment. In a technologically advanced country like Indonesia, the third and fourth generation (3G, 4G) mobile devices are available at affordable prices, and people of all streams find it necessary to own a mobile gadget for connecting and communicating.  Moreover, it has become a common trend among undergraduates to carry a mobile gadget to the classroom as well.In this paper, the researcher emphasize the potential of mobile gadgets as a learning tool for students and have incorporated them into the learning environment.The present study examines the application of mobile gadgetin EFL learning and investigates the perceptions of EFL students about mobile gadget in learning activity.  A field study was conducted on thirty undergraduatestudents majoring in accounting study Serang Raya University.  The methodology of data collection included a self-report for students and teachers’ and students’ questionnaire. Findings of the research are significant for EFL teachers and researchers for introducing innovative methods and helpful materials for the English classroom.Keywords: Mobile gadget, students’ perception, teachers’ perception..

scholarly journals Psychological Distress and Post-Traumatic Symptomatology among Dental Healthcare Workers in Russia: Results of a Pilot Study

2021 ◽  
Vol 18 (2) ◽  
pp. 708 ◽  
Author(s):  
Maria Sarapultseva ◽  
Alena Zolotareva ◽  
Igor Kritsky ◽  
Natal’ya Nasretdinova ◽  
Alexey Sarapultsev
Keyword(s):  
Psychological Distress ◽  
Healthcare Workers ◽  
Mental Health Problems ◽  
Clinical Symptoms ◽  
Stress Disorders ◽  
Self Report ◽  
Ptsd Symptoms ◽  
Event Scale ◽  
Post Traumatic ◽  
The Impact

The spread of SARS-CoV-2 infection has increased the risk of mental health problems, including post-traumatic stress disorders (PTSD), and healthcare workers (HCWs) are at greater risk than other occupational groups. This observational cross-sectional study aimed to explore the symptoms of depression, anxiety, and PTSD among dental HCWs in Russia during the coronavirus disease 2019 (COVID-19) pandemic. The survey was carried out among 128 dental HCWs from three dental clinics of Ekaterinburg, Russia. The mean age of the sample was 38.6 years. Depression, anxiety, and stress were assessed using the Depression Anxiety and Stress Scale-21 (DASS-21); PTSD was assessed using the PTSD Symptom Scale-Self-Report (PSS-SR); subjective distress was assessed using the Impact of Event Scale-Revised (IES-R). The results indicated that 20.3–24.2% HCWs had mild to extremely severe symptoms of psychological distress, and 7.1–29.7% had clinical symptoms of PTSD. No differences between females and males were revealed. HCWs working directly with patients had significantly higher levels of PTSD symptoms and the risk of PTSD development compared to those working indirectly, whereas older HCWs had significantly higher levels of both psychological distress and PTSD symptoms compared to younger HCWs. Thus, dental HCWs are at high risk for psychological distress and PTSD symptoms during the COVID-19 pandemic.

scholarly journals Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia from 18F-FDG PET brain persistent homology network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tun-Wei Hsu ◽  
Jong-Ling Fuh ◽  
Da-Wei Wang ◽  
Li-Fen Chen ◽  
Chia-Jung Chang ◽  
...  
Keyword(s):  
Algebraic Topology ◽  
Fdg Pet ◽  
Persistent Homology ◽  
Network Connectivity ◽  
Brain Regions ◽  
Imaging Data ◽  
Cholinergic Pathways ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
Neurochemical Changes

AbstractDementia is related to the cellular accumulation of β-amyloid plaques, tau aggregates, or α-synuclein aggregates, or to neurotransmitter deficiencies in the dopaminergic and cholinergic pathways. Cellular and neurochemical changes are both involved in dementia pathology. However, the role of dopaminergic and cholinergic networks in metabolic connectivity at different stages of dementia remains unclear. The altered network organisation of the human brain characteristic of many neuropsychiatric and neurodegenerative disorders can be detected using persistent homology network (PHN) analysis and algebraic topology. We used 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging data to construct dopaminergic and cholinergic metabolism networks, and used PHN analysis to track the evolution of these networks in patients with different stages of dementia. The sums of the network distances revealed significant differences between the network connectivity evident in the Alzheimer’s disease and mild cognitive impairment cohorts. A larger distance between brain regions can indicate poorer efficiency in the integration of information. PHN analysis revealed the structural properties of and changes in the dopaminergic and cholinergic metabolism networks in patients with different stages of dementia at a range of thresholds. This method was thus able to identify dysregulation of dopaminergic and cholinergic networks in the pathology of dementia.

scholarly journals Relative Frequency and Risk Factors of COVID-19 Related Headache in a Sample of Egyptian Population: A Hospital-Based Study

Pain Medicine ◽  
2021 ◽  
Author(s):  
Mona Hussein ◽  
Wael Fathy ◽  
Ragaey A Eid ◽  
Hoda M Abdel-Hamid ◽  
Ahmed Yehia ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Platelet Count ◽  
Relative Frequency ◽  
Clinical Laboratory ◽  
Primary Headache ◽  
Female Gender ◽  
Imaging Data ◽  
Laboratory Findings ◽  
Neutrophil Lymphocyte Ratio ◽  
The Impact

Abstract Objectives Headache is considered one of the most frequent neurological manifestations of coronavirus disease 2019 (COVID-19). This work aimed to identify the relative frequency of COVID-19-related headache and to clarify the impact of clinical, laboratory findings of COVID-19 infection on headache occurrence and its response to analgesics. Design Cross-sectional study. Setting Recovered COVID-19 patients. Subjects In total, 782 patients with a confirmed diagnosis of COVID-19 infection. Methods Clinical, laboratory, and imaging data were obtained from the hospital medical records. Regarding patients who developed COVID-19 related headache, a trained neurologist performed an analysis of headache and its response to analgesics. Results The relative frequency of COVID-19 related headache among our sample was 55.1% with 95% confidence interval (CI) (.516–.586) for the estimated population prevalence. Female gender, malignancy, primary headache, fever, dehydration, lower levels of hemoglobin and platelets and higher levels of neutrophil/lymphocyte ratio (NLR) and CRP were significantly associated with COVID-19 related headache. Multivariate analysis revealed that female gender, fever, dehydration, primary headache, high NLR, and decreased platelet count were independent predictors of headache occurrence. By evaluating headache response to analgesics, old age, diabetes, hypertension, primary headache, severe COVID-19, steroid intake, higher CRP and ferritin and lower hemoglobin levels were associated with poor response to analgesics. Multivariate analysis revealed that primary headache, steroids intake, moderate and severe COVID-19 were independent predictors of non-response to analgesics. Discussion Headache occurs in 55.1% of patients with COVID-19. Female gender, fever, dehydration, primary headache, high NLR, and decreased platelet count are considered independent predictors of COVID-19 related headache.

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