scholarly journals Short-term effects on brain functional network caused by focused-attention meditation revealed by Tucker3 clustering on graph theoretical metrics

2019 ◽  
Author(s):  
Takuma Miyoshi ◽  
Kensuke Tanioka ◽  
Shoko Yamamoto ◽  
Hiroshi Yadohisa ◽  
Tomoyuki Hiroyasu ◽  
...  
Keyword(s):  
Low Frequency ◽  
Brain Regions ◽  
Brain State ◽  
Focused Attention ◽  
Functional Magnetic Resonance ◽  
Short Term ◽  
Functional Brain ◽  
Brain States ◽  
The Given ◽  
Short Term Effects

AbstractThis study examines the short-term effects of focused-attention meditation on functional brain state in novice meditators. There are a number of feature metrics for functional brain states, such as functional connectivity, graph theoretical metrics, and amplitude of low frequency fluctuation (ALFF). It is necessary to choose appropriate metrics and also to specify the region of interests (ROIs) from a number of brain regions. Here, we use a Tucker3 clustering method, which simultaneously selects the feature vectors (graph theoretical metrics and fractional ALFF) and the ROIs that can discriminate between resting and meditative states based on the characteristics of the given data. In this study, breath-counting meditation, one of the most popular forms of focused-attention meditation, was used and brain activities during resting and meditation states were measured by functional magnetic resonance imaging. The results indicated that the clustering coefficients of eight brain regions tended to increase through the meditation. Our results reveal that short-term effects of breath-counting meditation can be explained by network density changes in these eight brain regions.

scholarly journals Increased sensitivity to strong perturbations in a whole-brain model of LSD

2021 ◽  
Author(s):  
Beatrice M. Jobst ◽  
Selen Atasoy ◽  
Adrián Ponce-Alvarez ◽  
Ana Sanjuán ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
External Perturbation ◽  
Brain Regions ◽  
Lysergic Acid ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Whole Brain ◽  
Response Diversity ◽  
Brain States ◽  
The Brain

AbstractLysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain’s global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.HighlightsNovel offline perturbational method applied on functional magnetic resonance imaging (fMRI) data under the effect of lysergic acid diethylamide (LSD)Shift of brain’s global working point to more complex dynamics after LSD intakeConsistently longer recovery time after model perturbation under LSD influenceStrongest effects in resting state networks relevant for psychedelic experienceHigher response diversity across brain regions under LSD influence after an external in silico perturbation

scholarly journals Theta activity paradoxically boosts gamma and ripple frequency sensitivity in prefrontal interneurons

2021 ◽  
Vol 118 (51) ◽  
pp. e2114549118
Author(s):  
Ricardo Martins Merino ◽  
Carolina Leon-Pinzon ◽  
Walter Stühmer ◽  
Martin Möck ◽  
Jochen F. Staiger ◽  
...  
Keyword(s):  
Low Frequency ◽  
Gamma Oscillations ◽  
Brain State ◽  
Gabaergic Interneurons ◽  
Time Resolved ◽  
Cortical Rhythms ◽  
Brain States ◽  
Fast Spiking ◽  
Fast Oscillations ◽  
Cross Frequency Coupling

Fast oscillations in cortical circuits critically depend on GABAergic interneurons. Which interneuron types and populations can drive different cortical rhythms, however, remains unresolved and may depend on brain state. Here, we measured the sensitivity of different GABAergic interneurons in prefrontal cortex under conditions mimicking distinct brain states. While fast-spiking neurons always exhibited a wide bandwidth of around 400 Hz, the response properties of spike-frequency adapting interneurons switched with the background input’s statistics. Slowly fluctuating background activity, as typical for sleep or quiet wakefulness, dramatically boosted the neurons’ sensitivity to gamma and ripple frequencies. We developed a time-resolved dynamic gain analysis and revealed rapid sensitivity modulations that enable neurons to periodically boost gamma oscillations and ripples during specific phases of ongoing low-frequency oscillations. This mechanism predicts these prefrontal interneurons to be exquisitely sensitive to high-frequency ripples, especially during brain states characterized by slow rhythms, and to contribute substantially to theta-gamma cross-frequency coupling.

Roles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI

2013 ◽  
Vol 109 (5) ◽  
pp. 1250-1258 ◽  
Author(s):  
Oliver Hinds ◽  
Todd W. Thompson ◽  
Satrajit Ghosh ◽  
Julie J. Yoo ◽  
Susan Whitfield-Gabrieli ◽  
...  
Keyword(s):  
Real Time ◽  
Default Mode Network ◽  
Human Performance ◽  
Supplementary Motor Area ◽  
Brain Regions ◽  
Motor Area ◽  
Neural Systems ◽  
Brain State ◽  
Default Mode ◽  
Brain States

We used real-time functional magnetic resonance imaging (fMRI) to determine which regions of the human brain have a role in vigilance as measured by reaction time (RT) to variably timed stimuli. We first identified brain regions where activation before stimulus presentation predicted RT. Slower RT was preceded by greater activation in the default-mode network, including lateral parietal, precuneus, and medial prefrontal cortices; faster RT was preceded by greater activation in the supplementary motor area (SMA). We examined the roles of these brain regions in vigilance by triggering trials based on brain states defined by blood oxygenation level-dependent activation measured using real-time fMRI. When activation of relevant neural systems indicated either a good brain state (increased activation of SMA) or a bad brain state (increased activation of lateral parietal cortex and precuneus) for performance, a target was presented and RT was measured. RTs on trials triggered by a good brain state were significantly faster than RTs on trials triggered by a bad brain state. Thus human performance was controlled by monitoring brain states that indicated high or low vigilance. These findings identify neural systems that have a role in vigilance and provide direct evidence that the default-mode network has a role in human performance. The ability to control and enhance human behavior based on brain state may have broad implications.

scholarly journals Macroscopic quantities of collective brain activity during wakefulness and anesthesia

2021 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Lynn Uhrig ◽  
Nikolas Deco ◽  
Camilo M. Signorelli ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Loss Of Consciousness ◽  
Information Theoretic ◽  
Maximum Entropy Models ◽  
Information Theoretic Measures ◽  
Brain States ◽  
Parietal Cortices ◽  
The Brain

AbstractThe study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

SHORT TERM EFFECTS OF LOW-FREQUENCY AND LOW INTENSITY ELECTROSTATIC FIELD IN PATIENTS WITH KNEE JOINT OSTEOARTHRITIS

2018 ◽  
Vol 28 (2) ◽  
pp. 547-551
Author(s):  
Galina Мratskova ◽  
Damyan Petrov ◽  
Nedko Dimitrov
Keyword(s):  
Knee Joint ◽  
Range Of Motion ◽  
Electrostatic Field ◽  
Clinical Symptoms ◽  
Low Frequency ◽  
Short Term ◽  
Low Intensity ◽  
Therapeutic Exercises ◽  
Reduction Of Pain ◽  
Short Term Effects

Introduction: Osteoarthritis (OA) is a widespread disease among adult population and is one of the major public health problems. OA is leading cause of disability the joints of lower limbs: knee and hip. As global life expectancy increases, it predicted that OA will be the leading cause of damage resulting in permanent disability. In cases of OA a reduction in cartilage tissue is observed, which is radiographically demonstrated by narrowing of the joint space and bone changes, osteophytes and subchondral bone sclerosis. However, a significant proportion of patients with radiological evidence of gonarthritis do not report joint pain. It is important to evaluate the changes occurring in the surrounding tissues. Muscle weakness of m. quadriceps femoris may occur before pain and impaired joint function. The development and application of new non-pharmacological methods in the rehabilitation of degenerative joint diseases is particularly important.Purpose: To establish the short-term therapeutic effects of treatment with Low-frequency and Low-intensive electrostatic field, applied through Deep Oscillation® method and complex of therapeutic exercises in rehabilitation of patients with osteoarthritis of the knee.Materials and methods: We conducted a one-year observational study involving 23 patients with clinical symptoms and radiologically proven II and III stage according Kellgren-Lawrence gonarthritis, aged between 42 and 78 years, were observed. 15 of them were women average age 61.73±12.9 years vs 8 - males average age 61.75±9.6 years (p=0.997). The duration of the current pain-episode was 1.7±0.7 months. The treatment was conducted in 10 sessions and included: Low-frequency and Low-intensity electrostatic field and complex therapeutic exercises.Results: The results were evaluated before and after completion of therapeutic course by assessing pain (VAS) at rest, when walking, climbing and descending on stairs, Manual Muscle Testing, Measurment of the knee joint circumference, Test Range of Motion and WOMAC Osteoarthritis Index, V.LK 3.1. were tracked. For processing statistical data SPSS v.13 was used. There was a statistically significant reduction of pain syndrome at rest (p<0.001), walking (p<0.001), descending stairs (p<0.001), climbing (p<0.001). Reduction of knee joint circumference (p<0.001). Increasing the range of flexion before Ме (Range) from 105º (90º-120º) versus 120º (100º-125º) after therapy. Reduced deficiency at an extension from 3.48 ± 4.38 before therapy to recovery of the extension. Improved total WOMAC Index (p<0.001), Stiffness (p<0.001) and Function (p<0.001).Conclusion: The short-term effects of the application of Low-frequency and Low-intensive electrostatic field in complex with therapeutic exercises show reduction of clinical symptoms and improvement of daily functional activity in patients with knee joint osteoarthritis. Reduction of pain of rest and physical activity (walking, descending and climbing stairs) is observed, oedema is reduced, joint range of motion increases, immediately after completion of the therapeutic course. Because of the small number of patients included in the study for better objectifying of the effects of the low-frequency and low-intensity electrostatic field, the studies should continue.

Short-Term Effects of Extremely Low Frequency Pulsed Electromagnetic Field on Corneas with Alkaline Burns in Rabbits

2012 ◽  
Vol 53 (12) ◽  
pp. 7881 ◽  
Author(s):  
Mozhgan Rezaei Kanavi ◽  
Farzin Sahebjam ◽  
Faraj Tabeie ◽  
Paniz Davari ◽  
Aminpasha Samadian ◽  
...  
Keyword(s):  
Electromagnetic Field ◽  
Low Frequency ◽  
Pulsed Electromagnetic Field ◽  
Short Term ◽  
Extremely Low Frequency ◽  
Pulsed Electromagnetic ◽  
Short Term Effects

scholarly journals Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks

2020 ◽  
Author(s):  
A. Grigis ◽  
J. Tasserie ◽  
V. Frouin ◽  
B. Jarraya ◽  
L. Uhrig
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Supervised Machine Learning ◽  
Brain State ◽  
Level Of Consciousness ◽  
Clinical Consequences ◽  
Brain States ◽  
The Brain

AbstractDecoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called “brain states” corresponding to “functional configurations” of the brain. Here, we propose to use a supervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. Because it is key to consider the topology of brain regions used to build the dynamical functional connectivity matrices describing the brain state at a given time, we applied BrainNetCNN, a graph-convolutional neural network (CNN), to predict the brain states in awake and anesthetized non-human primate rsfMRI data. BrainNetCNN achieved a high prediction accuracy that lies in [0.674, 0.765] depending on the experimental settings. We propose to derive the set of connections found to be important for predicting a brain state, reflecting the level of consciousness. The results demonstrate that deep learning methods can be used not only to predict brain states but also to provide additional insight on cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.

scholarly journals Different Modes of Low-Frequency Focused Ultrasound-Mediated Attenuation of Epilepsy Based on the Topological Theory

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1001
Author(s):  
Minjian Zhang ◽  
Bo Li ◽  
Yafei Liu ◽  
Rongyu Tang ◽  
Yiran Lang ◽  
...  
Keyword(s):  
Focused Ultrasound ◽  
Continuous Wave ◽  
Low Frequency ◽  
Brain Regions ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Significant Difference ◽  
The Brain ◽  
Brain Connections

Epilepsy is common brain dysfunction, where abnormal synchronized activities can be observed across multiple brain regions. Low-frequency focused pulsed ultrasound has been proven to modulate the epileptic brain network. In this study, we used two modes of low-intensity focused ultrasound (pulsed-wave and continuous-wave) to sonicate the brains of KA-induced epileptic rats, analyzed the EEG functional brain connections to explore their respective effect on the epileptic brain network, and discuss the mechanism of ultrasound neuromodulation. By comparing the brain network characteristics before and after sonication, we found that two modes of ultrasound both significantly affected the functional brain network, especially in the low-frequency band below 12 Hz. After two modes of sonication, the power spectral density of the EEG signals and the connection strength of the brain network were significantly reduced, but there was no significant difference between the two modes. Our results indicated that the ultrasound neuromodulation could effectively regulate the epileptic brain connections. The ultrasound-mediated attenuation of epilepsy was independent of modes of ultrasound.

scholarly journals Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI

2021 ◽  
Author(s):  
Anant Mittal ◽  
Priya Aggarwal ◽  
Luiz Pessoa ◽  
Anubha Gupta
Keyword(s):  
Short Term Memory ◽  
Fmri Data ◽  
Brain State ◽  
Short Term ◽  
Term Memory ◽  
Feature Representations ◽  
Human Connectome Project ◽  
Brain States ◽  
Long Short Term Memory ◽  
The Brain

Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information avail- able in the fMRI data. Long short term memory (LSTM), a class of machine learning model possessing a "memory" component, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bidirectional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18 percent better than it's unidirectional counterpart in terms of accuracy in predicting the brain states.

Sign in / Sign up

Export Citation Format

Share Document