scholarly journals Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Orestis Stylianou ◽  
Frigyes Samuel Racz ◽  
Andras Eke ◽  
Peter Mukli
Keyword(s):  
Resting State ◽  
Functional Coupling ◽  
Dependent Manner ◽  
Coupled Dynamics ◽  
Scale Free ◽  
Neural Processes ◽  
Eeg Recordings ◽  
Cortical Regions ◽  
The Brain

While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

scholarly journals Pre-stimulus phase and amplitude regulation of phase-locked responses are maximized in the critical state

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Arthur-Ervin Avramiea ◽  
Richard Hardstone ◽  
Jan-Matthis Lueckmann ◽  
Jan Bím ◽  
Huibert D Mansvelder ◽  
...  
Keyword(s):  
Computational Model ◽  
Critical State ◽  
Global Dynamics ◽  
Critical Dynamics ◽  
Neuronal Responses ◽  
Scale Free ◽  
Stimulus Response ◽  
Functional States ◽  
The Brain

Understanding why identical stimuli give differing neuronal responses and percepts is a central challenge in research on attention and consciousness. Ongoing oscillations reflect functional states that bias processing of incoming signals through amplitude and phase. It is not known, however, whether the effect of phase or amplitude on stimulus processing depends on the long-term global dynamics of the networks generating the oscillations. Here, we show, using a computational model, that the ability of networks to regulate stimulus response based on pre-stimulus activity requires near-critical dynamics—a dynamical state that emerges from networks with balanced excitation and inhibition, and that is characterized by scale-free fluctuations. We also find that networks exhibiting critical oscillations produce differing responses to the largest range of stimulus intensities. Thus, the brain may bring its dynamics close to the critical state whenever such network versatility is required.

scholarly journals Resting-State Electroencephalography for Prognosis in Disorders of Consciousness Following Traumatic Brain Injury

2020 ◽  
Vol 11 ◽  
Author(s):  
Ruth Pauli ◽  
Alice O'Donnell ◽  
Damian Cruse
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Resting State ◽  
Disorders Of Consciousness ◽  
Alpha Power ◽  
Disorder Of Consciousness ◽  
Term Care ◽  
Prognostic Utility ◽  
Eeg Recordings

Although the majority of patients recover consciousness after a traumatic brain injury (TBI), a minority develop a prolonged disorder of consciousness, which may never fully resolve. For these patients, accurate prognostication is essential to treatment decisions and long-term care planning. In this review, we evaluate the use of resting-state electroencephalography (EEG) as a prognostic measure in disorders of consciousness following TBI. We highlight that routine clinical EEG recordings have prognostic utility in the short to medium term. In particular, measures of alpha power and variability are indicative of relatively better functional outcomes within the first year post-TBI. This is hypothesized to reflect intact thalamocortical loops, and thus the potential for recovery of consciousness even in the apparent absence of current consciousness. However, there is a lack of research into the use of resting-state EEG for predicting longer-term recovery following TBI. We conclude that, given the potential for patients to demonstrate improvements in consciousness and functional capacity even years after TBI, a research focus on EEG-augmented prognostication in very long-term disorders of consciousness is now required.

Functional Stability of Dorsolateral Prefrontal Neurons

2004 ◽  
Vol 92 (2) ◽  
pp. 1042-1055 ◽  
Author(s):  
Paul A. Greenberg ◽  
Fraser A. W. Wilson
Keyword(s):  
Dorsolateral Prefrontal Cortex ◽  
Response Strength ◽  
Functional Stability ◽  
Firing Rates ◽  
Different Types ◽  
Dorsolateral Prefrontal ◽  
Cortical Regions ◽  
Response Timing ◽  
The Brain

Stable multiday recordings from the dorsolateral prefrontal cortex of 2 monkeys performing 2 Go/NoGo visual-discrimination tasks (one requiring well-learned responses, the other requiring learning) demonstrate that the majority of prefrontal neurons were “functionally stable”. Recordings were made using a series of removable microdrives, each implanted for 3–6 mo, housing independently mobile electrodes. Action potential waveforms of 94 neurons were stable over 2–9 days; 66/94 (70%) of these cells responded each day, 22/94 (23%) never responded significantly, and 6/94 (6%) responded one day but not the next. Of 66 responsive neurons, 55 were selective for either Go or NoGo trials, individual stimuli, or eye movements. This selectivity was functionally stable (i.e., maintained) for 46/55 neurons across all recording days. Functional stability was also noted in terms of response strength (baseline firing rates compared with poststimulation firing rates) and event-related response timing. Two neurons with consistent responses in familiar testing conditions responded flexibly when the monkeys learned to make correct responses to novel stimuli. We conclude that the majority of prefrontal neurons were functionally stable during the performance of well-learned tasks. Such stability may be a general property of prefrontal neurons, given that neurons with 4 different types of task selectivity were found to be functionally stable. Conceptually similar studies based on long-term recordings in other cortical regions reached similar conclusions, suggesting that neurons throughout the brain are functionally stable.

Remitted major depression is related to increased functional coupling between ventral striatum and cortical regions in resting state fMRI

2011 ◽  
Vol 26 (S2) ◽  
pp. 948-948 ◽  
Author(s):  
G. Pail ◽  
C. Scharinger ◽  
K. Kalcher ◽  
W. Huf ◽  
R. Boubela ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Orbitofrontal Cortex ◽  
Resting State ◽  
Ventral Striatum ◽  
Resting State Fmri ◽  
Anterior Cingulate ◽  
Functional Coupling ◽  
Healthy Controls ◽  
Full Remission ◽  
Cortical Regions

IntroductionDysfunction in the basal ganglia has been related to impaired reward processing and anhedonia, a core symptom of Major Depressive Disorder (MDD). In particular, the ventral striatum including the nucleus accumbens is increasingly implicated in the pathophysiology of MDD, but evidence for a specific role during episodes of full remission is lacking so far.ObjectivesTo investigate functional connectivity patterns of resting-state activity in patients in the remitted phase of MDD (rMDD).AimsTo determine whether rMDD is related to disruptions of functional coupling between the ventral striatum and cortical regions.MethodsForty-three remitted depressed patients and thirty-five healthy controls were recruited at Medical University of Vienna, Vienna, Austria, and performed a six minute resting-state fMRI scan. Seed time series were extracted from the preprocessed data using individual masks for ventral striatum and correlated with all nodes in a surface based analysis using FreeSurfer, AFNI and SUMA. The resulting correlation coefficients were then Fishertransformed, group results were determined by comparing group mean smoothed z-scores with a two-sample ttest.ResultsIncreased resting-state functional connectivity was revealed between ventral striatum (seed region) and anterior cingulate cortex as well as orbitofrontal cortex in the rMDD group compared to healthy controls.ConclusionsOur preliminary data is in accordance with the idea that increased functional coupling between the ventral striatum and two major emotion processing regions, the anterior cingulate cortex and the orbitofrontal cortex, may represent a neural mechanism contributing to the maintenance of full remission of MDD.

scholarly journals Caffeine intake increases plasma ketones: an acute metabolic study in humans

2017 ◽  
Vol 95 (4) ◽  
pp. 455-458 ◽  
Author(s):  
Camille Vandenberghe ◽  
Valérie St-Pierre ◽  
Alexandre Courchesne-Loyer ◽  
Marie Hennebelle ◽  
Christian-Alexandre Castellano ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Caffeine Intake ◽  
Dependent Manner ◽  
Medium Chain Triglycerides ◽  
Metabolic Study ◽  
Brain Glucose ◽  
Glucose Reduction ◽  
Dose Dependent ◽  
The Brain

Brain glucose uptake declines during aging and is significantly impaired in Alzheimer’s disease. Ketones are the main alternative brain fuel to glucose so they represent a potential approach to compensate for the brain glucose reduction. Caffeine is of interest as a potential ketogenic agent owing to its actions on lipolysis and lipid oxidation but whether it is ketogenic in humans is unknown. This study aimed to evaluate the acute ketogenic effect of 2 doses of caffeine (2.5; 5.0 mg/kg) in 10 healthy adults. Caffeine given at breakfast significantly stimulated ketone production in a dose-dependent manner (+88%; +116%) and also raised plasma free fatty acids. Whether caffeine has long-term ketogenic effects or could enhance the ketogenic effect of medium chain triglycerides remains to be determined.

scholarly journals CRISPR/Cas9 mediated intersectional knockout of GSK3β in D2 receptor expressing mPFC neurons reveals contributions to emotional regulation

2019 ◽  
Author(s):  
Jivan Khlghatyan ◽  
Jean-Martin Beaulieu
Keyword(s):  
Emotional Regulation ◽  
Large Scale ◽  
Dopamine D2 Receptor ◽  
Glycogen Synthase ◽  
D2 Receptor ◽  
Dependent Manner ◽  
Targeted Deletion ◽  
Conventional Animal ◽  
Cortical Regions ◽  
The Brain

AbstractBackgroundGlycogen synthase kinase 3β (GSK3β) regulates neurodevelopment, synaptic plasticity as well as mood, cognition, social interaction, and depressive-like behaviors. Inhibition of GSK3β is a shared consequence of treatment by lithium, SSRIs, ketamine and antipsychotics. GSK3β activity is regulated by dopamine D2 receptor signaling and can be inhibited by psychoactive drugs in a D2 receptor dependent manner. Functions of GSK3β in striatal D2 neurons has been studied extensively. However, GSK3β is ubiquitously expressed in the brain and D2 receptor expressing cells are distributed as a mosaic in multiple cortical regions. This complicates the interrogation of GSK3β functions in cortical D2 cells in a circuit defined manner using conventional animal models.MethodsWe have used a CRISPR/Cas9 mediated intersectional approach to achieve targeted deletion of GSK3β in D2 expressing neurons of the adult medial prefrontal cortex (mPFC).ResultsIsolation and analysis of ribosome associated RNA specifically from mPFC D2 neurons lacking GSK3β demonstrated large scale translatome alterations. Deletion of GSK3β in mPFC D2 neurons revealed its contribution to anxiety-related, cognitive, and social behaviors.ConclusionsOur results underscore the viability of intersectional knockout approach to study functions of a ubiquitous gene in a network defined fashion while uncovering a contribution of GSK3β expressed in mPFC D2 neurons in the regulation of behavioral dimensions related to mood and emotions. This advances our understanding of GSK3β action at a brain circuit level and can potentially lead to the development of circuit selective therapeutics.

scholarly journals Efficient small-world and scale-free functional brain networks at rest using k-nearest neighbors thresholding

2019 ◽  
Author(s):  
Caroline Garcia Forlim ◽  
Siavash Haghiri ◽  
Sandra Düzel ◽  
Simone Kühn
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Nearest Neighbors ◽  
Resting State Fmri ◽  
Brain Network ◽  
K Nearest Neighbors ◽  
Scale Free ◽  
The Brain ◽  
Density Threshold

AbstractIn recent years, there has been a massive effort to analyze the topological properties of brain networks. Yet, one of the challenging questions in the field is how to construct brain networks based on the connectivity values derived from neuroimaging methods. From a theoretical point of view, it is plausible that the brain would have evolved to minimize energetic costs of information processing, and therefore, maximizes efficiency as well as to redirect its function in an adaptive fashion, that is, resilience. A brain network with such features, when characterized using graph analysis, would present small-world and scale-free properties.In this paper, we focused on how the brain network is constructed by introducing and testing an alternative method: k-nearest neighbor (kNN). In addition, we compared the kNN method with one of the most common methods in neuroscience: namely the density threshold. We performed our analyses on functional connectivity matrices derived from resting state fMRI of a big imaging cohort (N=434) of young and older healthy participants. The topology of networks was characterized by the graph measures degree, characteristic path length, clustering coefficient and small world. In addition, we verified whether kNN produces scale-free networks. We showed that networks built by kNN presented advantages over traditional thresholding methods, namely greater values for small-world (linked to efficiency of networks) than those derived by means of density thresholds and moreover, it presented also scale-free properties (linked to the resilience of networks), where density threshold did not. A brain network with such properties would have advantages in terms of efficiency, rapid adaptive reconfiguration and resilience, features of brain networks that are relevant for plasticity and cognition as well as neurological diseases as stroke and dementia.HighlightsA novel thresholding method for brain networks based on k-nearest neighbors (kNN)kNN applied on resting state fMRI from a big cohort of healthy subjects BASE-IIkNN built networks present greater small world properties than density thresholdkNN built networks present scale-free properties whereas density threshold did not

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