scholarly journals Networked information interactions of epileptic EEG based on symbolic transfer entropy

2019 ◽  
Author(s):  
Wenpo Yao ◽  
Jun Wang
Keyword(s):  
Healthy Subjects ◽  
Information Transfer ◽  
Probability Distributions ◽  
Brain Area ◽  
Brain Regions ◽  
Transfer Entropy ◽  
Symbolic Transfer Entropy ◽  
Electrical Activities ◽  
The Brain

AbstractIdentifying networked information exchanges among brain regions is important for understanding the brain structure. We employ symbolic transfer entropy to facilitate the construction of networked information interactions for EEGs of 22 epileptics and 22 healthy subjects. The epileptic patients during seizure-free interval have lower information transfer in each individual and whole brain regions than the healthy subjects. Among all of the brain regions, the information flows out of and into the brain area of O1 of the epileptic EEGs are significantly lower than those of the healthy (p<0.0005), and the information flow from F7 to F8 (p<0.00001) is particularly promising to discriminate the two groups of EEGs. Moreover, Shannon entropy of probability distributions of information exchanges suggests that the healthy EEGs have higher complexity and irregularity than the epileptic brain electrical activities. By characterizing the brain networked information interactions, our findings highlight the long-term reduced information exchanges, degree of brain interactivities and informational complexity of the epileptic EEG.

scholarly journals Functional Connectivity within Brain Networks of Long Term and Short term Meditators

2019 ◽  
Vol 9 (2S) ◽  
pp. 29-34
Keyword(s):  
Functional Connectivity ◽  
Temporal Correlation ◽  
Brain Regions ◽  
Invasive Technique ◽  
Long Term Effects ◽  
Short Term ◽  
Statistical Concept ◽  
The Brain ◽  
Meditative Practices

Meditation refers to a state of mind of relaxation and concentration, where generally the mind and body is at rest. The process of meditation reflects the state of the brain which is distinct from sleep or typical wakeful states of consciousness. Meditative practices usually involve regulation of emotions and monitoring of attention. Over the past decade there has been a tremendous increase in an interest to study the neural mechanisms involved in meditative practices. It could also be beneficial to explore if the effect of meditation is altered by the number of years of meditation practice. Functional Magnetic Resonance Imaging (fMRI) is a very useful imaging technique which can be used to perform this analysis due to its inherent benefits, mainly it being a non-invasive technique. Functional activation and connectivity analysis can be performed on the fMRI data to find the active regions and the connectivity in the brain regions. Functional connectivity is defined as a simple temporal correlation between anatomically separate, active neural regions. Functional connectivity gives the statistical dependencies between regional time series. It is a statistical concept and is quantified using metrics like Correlation. In this study, a comparison is made between functional connectivity in the brain regions of long term meditation practitioners (LTP) and short-term meditation practitioners (STP) to see the differences and similarities in the connectivity patterns. From the analysis, it is evident that in fact there is a difference in connectivity between long term and short term practitioners and hence continuous practice of meditation can have long term effects.

scholarly journals The Efficacy of Iron Chelators for Removing Iron from Specific Brain Regions and the Pituitary—Ironing out the Brain

2019 ◽  
Vol 12 (3) ◽  
pp. 138 ◽  
Author(s):  
Robert R. Crichton ◽  
Roberta J. Ward ◽  
Robert C. Hider
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Iron Chelation ◽  
Brain Regions ◽  
Beta Thalassemia ◽  
Excess Iron ◽  
New Class ◽  
Clinical Conditions ◽  
The Brain

Iron chelation therapy, either subcutaneous or orally administered, has been used successfully in various clinical conditions. The removal of excess iron from various tissues, e.g., the liver spleen, heart, and the pituitary, in beta thalassemia patients, has become an essential therapy to prolong life. More recently, the use of deferiprone to chelate iron from various brain regions in Parkinson’s Disease and Friederich’s Ataxia has yielded encouraging results, although the side effects, in <2% of Parkinson’s Disease(PD) patients, have limited its long-term use. A new class of hydroxpyridinones has recently been synthesised, which showed no adverse effects in preliminary trials. A vital question remaining is whether inflammation may influence chelation efficacy, with a recent study suggesting that high levels of inflammation may diminish the ability of the chelator to bind the excess iron.

scholarly journals Synaptic Plasticity and its Modulation by Alcohol

2020 ◽  
Vol 6 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Yosef Avchalumov ◽  
Chitra D. Mandyam
Keyword(s):  
Synaptic Plasticity ◽  
Learning And Memory ◽  
Dorsal Striatum ◽  
Long Term Potentiation ◽  
Brain Regions ◽  
Public Health Issue ◽  
Cellular Mechanisms ◽  
Brain Disorder ◽  
The Brain

Alcohol is one of the oldest pharmacological agents used for its sedative/hypnotic effects, and alcohol abuse and alcohol use disorder (AUD) continues to be major public health issue. AUD is strongly indicated to be a brain disorder, and the molecular and cellular mechanism/s by which alcohol produces its effects in the brain are only now beginning to be understood. In the brain, synaptic plasticity or strengthening or weakening of synapses, can be enhanced or reduced by a variety of stimulation paradigms. Synaptic plasticity is thought to be responsible for important processes involved in the cellular mechanisms of learning and memory. Long-term potentiation (LTP) is a form of synaptic plasticity, and occurs via N-methyl-D-aspartate type glutamate receptor (NMDAR or GluN) dependent and independent mechanisms. In particular, NMDARs are a major target of alcohol, and are implicated in different types of learning and memory. Therefore, understanding the effect of alcohol on synaptic plasticity and transmission mediated by glutamatergic signaling is becoming important, and this will help us understand the significant contribution of the glutamatergic system in AUD. In the first part of this review, we will briefly discuss the mechanisms underlying long term synaptic plasticity in the dorsal striatum, neocortex and the hippocampus. In the second part we will discuss how alcohol (ethanol, EtOH) can modulate long term synaptic plasticity in these three brain regions, mainly from neurophysiological and electrophysiological studies. Taken together, understanding the mechanism(s) underlying alcohol induced changes in brain function may lead to the development of more effective therapeutic agents to reduce AUDs.

scholarly journals Statistical Method to Extract Evoked Potentials from Noise

2020 ◽  
Vol 170 ◽  
pp. 02005
Author(s):  
Besma Benchabane ◽  
Moncef Benkherrat ◽  
Salah Djelel
Keyword(s):  
Wavelet Transform ◽  
Evoked Potentials ◽  
A Priori ◽  
Brain Regions ◽  
Statistical Test ◽  
A Priori Information ◽  
Time Frequency ◽  
Priori Information ◽  
Electrical Activities ◽  
The Brain

Evoked Potentials are induced by visual or auditory stimulation. The Evoked Potentials represent transient electrical activities of some limited brain regions. The signal-noise ratio (SNR) of the EPs is typically around -10 dB. In order to study brain activities related to information processing in the brain, one has to “extract” the single EPs from the noise. We propose a method does not require a priori information concerning the characteristics (time, frequency) of the signal and does not use a template. The method proposed in this work use the wavelet transform associated with a statistical test.

scholarly journals Geometric renormalization unravels self-similarity of the multiscale human connectome

2020 ◽  
Vol 117 (33) ◽  
pp. 20244-20253 ◽  
Author(s):  
Muhua Zheng ◽  
Antoine Allard ◽  
Patric Hagmann ◽  
Yasser Alemán-Gómez ◽  
M. Ángeles Serrano
Keyword(s):  
Healthy Subjects ◽  
Multiple Scales ◽  
Structural Connectivity ◽  
Coarse Graining ◽  
Brain Regions ◽  
Self Similarity ◽  
Digital Reconstruction ◽  
Self Similar ◽  
The Brain ◽  
Organizing Principles

Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organized over multiple scales linked to one another. We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions. Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the resolution by coarse-graining and averaging over short similarity distances. Our results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.

scholarly journals Modelling a Multiplex Brain Network by Local Transfer Entropy

2020 ◽  
Author(s):  
Fabrizio Parente ◽  
Colosimo Alfredo
Keyword(s):  
Information Transfer ◽  
Brain Regions ◽  
Brain Network ◽  
Transfer Entropy ◽  
Clustering Methods ◽  
Time Step ◽  
Cognitive States ◽  
Functional Brain Network ◽  
Hierarchical Clustering Methods ◽  
Transfer Mechanisms

Abstract In this work we report on a systematic study of the causal relations in information transfer mechanisms between brain regions under resting condition. The 1000 Functional Connectomes Beijing Zang dataset was used, which includes brain functional images of 180 healthy individuals. We first characterize the information transfer mechanisms by means of Transfer Entropy concepts and, on this basis, propose a set of indexes concerning the whole functional brain network in the frame of a multilayer description. By exploring the influence of a set of states in two given regions at time t (At; Bt.) over the state of one of them at a following time step (Bt+1), a series of time-dependent events can be observed pointing to four kinds of significant interactions, namely:- (de)activation in the same state (ActS); - (de)activation in the oppostive state (ActO);- turn off in the same state (TfS); - turn off in the opposite state (TfO).This leads to four specific rules and to a directional multilayer network based upon four interaction matrices, one for each rule. By hierarchical clustering methods the four rules can be reduced to two sharing some similarities with positive and negative functional connectivity. The global architecture of the four interactions and the features of single nodes were initially explored under stationary conditions. The information transfer mechanisms on the ensuing functional network were studied by specific indexes describing in a multilayer frame the effects of the network structure in several dynamical processes. The healthy subjects database was used to carefully calibrate and validate the proposed approach, whose final aim remains the detection of clinical differences among individuals, as well as among different cognitive states.

scholarly journals Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

2019 ◽  
Author(s):  
Mike Li ◽  
Yinuo Han ◽  
Matthew J. Aburn ◽  
Michael Breakspear ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Information Processing ◽  
Information Transfer ◽  
Large Scale ◽  
Functional Integration ◽  
Brain Network ◽  
Information Storage ◽  
Transfer Entropy ◽  
Arousal System ◽  
Neural Gain ◽  
The Brain

AbstractA key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system. In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain led to a ‘critical’ transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain.Author summaryHigher brain function relies on a dynamic balance between functional integration and segregation. Previous work has shown that this balance is mediated in part by alterations in neural gain, which are thought to relate to projections from ascending neuromodulatory nuclei, such as the locus coeruleus. Here, we extend this work by demonstrating that the modulation of neural gain alters the information processing dynamics of the neural components of a biophysical neural model. Specifically, we find that low levels of neural gain are characterized by high Active Information Storage, whereas higher levels of neural gain are associated with an increase in inter-regional Transfer Entropy. Our results suggest that the modulation of neural gain via the ascending arousal system may fundamentally alter the information processing mode of the brain, which in turn has important implications for understanding the biophysical basis of cognition.

The somatic error hypothesis of anxiety

2018 ◽  
Author(s):  
Sahib S. Khalsa ◽  
Justin S. Feinstein
Keyword(s):  
Central Nervous System ◽  
Physiological State ◽  
Brain Regions ◽  
Novel Therapies ◽  
Body State ◽  
Long Term Changes ◽  
The Central Nervous System ◽  
And Behavior ◽  
The Brain

A regulatory battle for control ensues in the central nervous system following a mismatch between the current physiological state of an organism as mapped in viscerosensory brain regions and the predicted body state as computed in visceromotor control regions. The discrepancy between the predicted and current body state (i.e. the “somatic error”) signals a need for corrective action, motivating changes in both cognition and behavior. This chapter argues that anxiety disorders are fundamentally driven by somatic errors that fail to be adaptively regulated, leaving the organism in a state of dissonance where the predicted body state is perpetually out of line with the current body state. Repeated failures to quell somatic error can result in long-term changes to interoceptive circuitry within the brain. This chapter explores the neuropsychiatric sequelae that can emerge following chronic allostatic dysregulation of somatic errors and discusses novel therapies that might help to correct this dysregulation.

scholarly journals Neural computations underlying strategic social decision-making in groups

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Seongmin A. Park ◽  
Mariateresa Sestito ◽  
Erie D. Boorman ◽  
Jean-Claude Dreher
Keyword(s):  
Brain Regions ◽  
Anterior Cingulate ◽  
Temporoparietal Junction ◽  
Effective Strategies ◽  
Frontopolar Cortex ◽  
Collective Decisions ◽  
Neural Computations ◽  
Social Decision Making ◽  
The Brain

AbstractWhen making decisions in groups, the outcome of one’s decision often depends on the decisions of others, and there is a tradeoff between short-term incentives for an individual and long-term incentives for the groups. Yet, little is known about the neurocomputational mechanisms at play when weighing different utilities during repeated social interactions. Here, using model-based fMRI and Public-good-games, we find that the ventromedial prefrontal cortex encodes immediate expected rewards as individual utility while the lateral frontopolar cortex encodes group utility (i.e., pending rewards of alternative strategies beneficial for the group). When it is required to change one’s strategy, these brain regions exhibited changes in functional interactions with brain regions engaged in switching strategies. Moreover, the anterior cingulate cortex and the temporoparietal junction updated beliefs about the decision of others during interactions. Together, our findings provide a neurocomputational account of how the brain dynamically computes effective strategies to make adaptive collective decisions.

scholarly journals An fMRI Study Using a Combined Task of Interval Discrimination and Oddball Could Reveal Common Brain Circuits of Cognitive Change

2021 ◽  
Vol 12 ◽  
Author(s):  
María Sol Garcés ◽  
Irene Alústiza ◽  
Anton Albajes-Eizagirre ◽  
Javier Goena ◽  
Patricio Molero ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Healthy Subjects ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Brain Structures ◽  
Cognitive Change ◽  
Brain Regions ◽  
Task Type ◽  
Meta Analyses ◽  
The Brain

Recent functional neuroimaging studies suggest that the brain networks responsible for time processing are involved during other cognitive processes, leading to a hypothesis that time-related processing is needed to perform a range of tasks across various cognitive functions. To examine this hypothesis, we analyze whether, in healthy subjects, the brain structures activated or deactivated during performance of timing and oddball-detection type tasks coincide. To this end, we conducted two independent signed differential mapping (SDM) meta-analyses of functional magnetic resonance imaging (fMRI) studies assessing the cerebral generators of the responses elicited by tasks based on timing and oddball-detection paradigms. Finally, we undertook a multimodal meta-analysis to detect brain regions common to the findings of the two previous meta-analyses. We found that healthy subjects showed significant activation in cortical areas related to timing and salience networks. The patterns of activation and deactivation corresponding to each task type partially coincided. We hypothesize that there exists a time and change-detection network that serves as a common underlying resource used in a broad range of cognitive processes.

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