scholarly journals EEG Biomarkers of reduced inhibition in human cortical microcircuits in depression

2021 ◽  
Author(s):  
Frank Mazza ◽  
John D Griffiths ◽  
Etay Hay
Keyword(s):  
Resting State ◽  
Computational Models ◽  
Spatial Scales ◽  
Cortical Layer ◽  
Cortical Inhibition ◽  
Eeg Signals ◽  
Physiological Mechanisms ◽  
Power Increase ◽  
Cortical Microcircuits ◽  
Depression Subtypes

Major depressive disorder (depression) is a complex condition that involves multiple physiological mechanisms, spanning a range of spatial scales. Altered cortical inhibition is associated with treatment-resistant depression, and reduced dendritic inhibition by somatostatin-expressing (SST) interneurons has been strongly implicated in this aspect of the pathology. However, whether the effects of reduced SST inhibition on microcircuit activity have signatures detectible in electroencephalography (EEG) signals remains unknown. We used detailed models of human cortical layer 2/3 microcircuits with normal or reduced SST inhibition to simulate resting-state activity together with EEG signals in health and depression. We first show that the healthy microcircuit models exhibit emergent key features of resting-state EEG. We then simulated EEG from depression microcircuits and found a significant power increase in theta, alpha and low beta frequencies (4 - 15 Hz). Following spectral decomposition, we show that the power increase involved a combination of aperiodic broadband component, and a periodic theta and low beta components. Neuronal spiking showed a spike preference for the phase preceding the EEG trough, which did not differ between conditions. Our study thus used detailed computational models to identify EEG biomarkers of reduced SST inhibition in human cortical microcircuits in depression, which may serve to improve the diagnosis and stratification of depression subtypes, and in monitoring the effects of pharmacological modulation of inhibition for treating depression.

scholarly journals Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

2021 ◽  
Vol 17 (12) ◽  
pp. e1009681
Author(s):  
Michiel W. H. Remme ◽  
Urs Bergmann ◽  
Denis Alevi ◽  
Susanne Schreiber ◽  
Henning Sprekeler ◽  
...  
Keyword(s):  
Memory Consolidation ◽  
Computational Models ◽  
Hippocampal Formation ◽  
Spatial Scales ◽  
Single Cells ◽  
Brain Regions ◽  
Quantitative Agreement ◽  
Long Term Memory ◽  
Hebbian Plasticity ◽  
Mathematical Analyses

Systems memory consolidation involves the transfer of memories across brain regions and the transformation of memory content. For example, declarative memories that transiently depend on the hippocampal formation are transformed into long-term memory traces in neocortical networks, and procedural memories are transformed within cortico-striatal networks. These consolidation processes are thought to rely on replay and repetition of recently acquired memories, but the cellular and network mechanisms that mediate the changes of memories are poorly understood. Here, we suggest that systems memory consolidation could arise from Hebbian plasticity in networks with parallel synaptic pathways—two ubiquitous features of neural circuits in the brain. We explore this hypothesis in the context of hippocampus-dependent memories. Using computational models and mathematical analyses, we illustrate how memories are transferred across circuits and discuss why their representations could change. The analyses suggest that Hebbian plasticity mediates consolidation by transferring a linear approximation of a previously acquired memory into a parallel pathway. Our modelling results are further in quantitative agreement with lesion studies in rodents. Moreover, a hierarchical iteration of the mechanism yields power-law forgetting—as observed in psychophysical studies in humans. The predicted circuit mechanism thus bridges spatial scales from single cells to cortical areas and time scales from milliseconds to years.

scholarly journals Multi-scale dynamic mean field model (MDMF) relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis

2021 ◽  
pp. 1-55
Author(s):  
Amit Naskar ◽  
Anirudh Vattikonda ◽  
Gustavo Deco ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Resting State ◽  
Computational Models ◽  
Field Model ◽  
Diffusion Tensor ◽  
Brain Activity ◽  
Mean Field ◽  
Clustering Coefficient ◽  
Brain Dynamics ◽  
Mean Field Model ◽  
Multi Scale

Abstract Previous computational models have related spontaneous resting-state brain activity with local excitatory−inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E-I balance governs resting state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions relate to functional brain activity is of critical importance. We propose a multi-scale dynamic mean field model (MDMF) – a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modelled as population of MDMF and are connected by realistic connection topologies estimated from Diffusion Tensor Imaging data. First, MDMF successfully predicts resting-state functionalconnectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from MDMF model.

P2439Reconstructing electrocardiograms from computational models of infarcted ventricles to determine the location of myocardial infarct scars based on features of the signal

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
Y R Hill ◽  
J E Fieldsend ◽  
J R Terry
Keyword(s):  
Resting State ◽  
Computational Models ◽  
Myocardial Infarct ◽  
Element Model ◽  
Electrical Activation ◽  
Electrophysiological Properties ◽  
Ecg Signals ◽  
True Location ◽  
Characteristic Features ◽  
Reentrant Circuit

Abstract Myocardial infarction can cause ventricular tachycardia as a result of reentrant electrical activation waves propagating around the infarct scar. The tachycardia can be treated by radiofrequency catheter ablation which requires the cardiologist to deliver radiofrequency, via an intracardiac catheter, to ablate a specific site within the scar, disrupting the reentrant circuit, to terminate the arrhythmia. Therefore, determining the location of the scar is an important step in the procedure. MRI and CT scans can show the region of scar but are costly and are contraindicated in many cases. Cardiologists observe ECG recordings of the patient's tachycardia, looking at the gross characteristics of the signal to determine an approximate location of the scar. However, this technique assumes a recording of the patient's tachycardia is available, and is only able to suggest a gross region of the heart. In addition, the method is based on studies which have identified characteristic features of the ECG using intracardiac mapping to determine the location of the scar, which may be unreliable. This study aims to determine features of the ECG which may be able to predict the location of an infarct scar with more accuracy and specificity than current methods allow. The use of computational models ensures that the true location of the scar is known, unlike in previous studies. Moreover, we aim to determine whether there are any characteristics of resting state ECG which may indicate the location of an infarct scar. An anatomically accurate finite element model of rabbit ventricles in a conductive bath was utilised in order to simulate electrical activation waves and generate ECG signals, by reconstructing the extracellular potentials. Scar regions comprising of non-conducting scar surrounded by tissue with altered electrophysiological properties to represent the borderzone were incorporated into the ventricular model at varying locations across the myocardium. The models were stimulated using an S1S2 protocol to produce wave block and reentry. ECGs were reconstructed and the differences between models were observed. Results suggest that differences in timing and amplitude of the R wave on the ECG could be an indication of scar location. Changes in the repolarisation phase of the ECG were also apparent, suggesting more features which could determine the location of the scar. Importantly, characteristic features of the ECG could also be determined from resting state ECG, generated from models where scar was present but no reentry occurred. Utilising computational models of rabbit ventricles with scars incorporated at a variety of locations around the myocardium, we were able to determine a set of features from the ECG which may be of use in determining the location of an infarct scar. Future validation of this study using patient data could indicate that this methodology may be of use in predicting scar location in ablation procedures. Acknowledgement/Funding YH is funded by the MRC (MR/R024995/1). JT acknowledges the financial support of the EPSRC (EP/N014391/1) and the Wellcome Trust WT105618MA.

scholarly journals Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

2015 ◽  
Vol 114 (2) ◽  
pp. 768-780 ◽  
Author(s):  
Simo Vanni ◽  
Fariba Sharifian ◽  
Hanna Heikkinen ◽  
Ricardo Vigário
Keyword(s):  
Computational Models ◽  
Real Life ◽  
Mass Action ◽  
System Level ◽  
Specific System ◽  
Physiological Mechanisms ◽  
Mathematical Functions ◽  
Neural Mass ◽  
Recent Developments ◽  
The Relationship

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals.

scholarly journals Increased Beta Activity Links to Impaired Emotional Control in ADHD Adults With High IQ

2017 ◽  
Vol 23 (7) ◽  
pp. 754-764 ◽  
Author(s):  
Hui Li ◽  
Qihua Zhao ◽  
Fang Huang ◽  
Qingjiu Cao ◽  
Qiujin Qian ◽  
...  
Keyword(s):  
Resting State ◽  
Emotional Control ◽  
Control Group ◽  
Beta Activity ◽  
Adhd Group ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Beta Power ◽  
Eeg Power ◽  
Iq Method

Objective: The present study investigated the neuropathology of everyday-life executive function (EF) deficits in adults with ADHD with high IQ. Method: Forty adults with ADHD with an IQ ≥ 120 and 40 controls were recruited. Ecological EFs were measured, and eyes-closed Electroencephalograph (EEG) signals were recorded during a resting-state condition; EEG power and correlations with impaired EFs were analyzed. Results: Compared with controls, the ADHD group showed higher scores on all clusters of EF. The ADHD group showed globally increased theta, globally decreased alpha, and increased central beta activity. In the ADHD group, central beta power was significantly related to emotional control ratings, while no such correlation was evident in the control group. Conclusion: The results suggest that resting-state beta activity might be involved in the neuropathology of emotional control in adults with ADHD with high IQ.

scholarly journals Experiments with <i>Lymantria dispar</i> larvae do not support the idea of physiological adjustment to host individuals by insect herbivores

Web Ecology ◽  
2013 ◽  
Vol 13 (1) ◽  
pp. 79-84 ◽  
Author(s):  
H. Ruhnke ◽  
D. Matthies ◽  
R. Brandl
Keyword(s):  
Growth Rate ◽  
Gypsy Moth ◽  
Lymantria Dispar ◽  
Spatial Scales ◽  
Transfer Experiment ◽  
Physiological Mechanisms ◽  
Leaf Quality ◽  
Spatial And Temporal Heterogeneity ◽  
Physiological Adjustments ◽  
Generalist Insect

Abstract. All organisms have to cope with spatial and temporal heterogeneity of the environment. At short temporal and small spatial scales, organisms may respond by behavioural or physiological mechanisms. To test for physiological adjustments to variation in host quality among tree individuals within a host species, we performed a transfer experiment in a climate chamber using larvae of the polyphagous gypsy moth (Lymantria dispar). We reared larvae for two weeks on leaves of one of three Quercus robur individuals. We found differences in the growth rate of larvae across the host individuals, which indicate that the oak individuals differed in their quality. Furthermore, families of larvae varied in their growth rate and there was variation among the families of gypsy moth larvae in response to leaves from the different oak individuals. After two weeks we offered larvae either leaves of the same or a different individual of the three oaks. We found no effect of transferring larvae to a different tree individual. The results thus do not support the idea of physiological adjustment of a generalist insect herbivore to variation in leaf quality among host individuals.

scholarly journals Intrinsic sensory disinhibition contributes to intrusive re-experiencing in combat veterans

2019 ◽  
Author(s):  
Kevin J. Clancy ◽  
Alejandro Albizu ◽  
Norman B. Schmidt ◽  
Wen Li
Keyword(s):  
Resting State ◽  
Emotional Response ◽  
Combat Veterans ◽  
Strong Association ◽  
Cortical Inhibition ◽  
Oscillatory Activity ◽  
List Type ◽  
Trauma Memory ◽  
Novel Target ◽  
The Relationship

ABSTRACTIntrusive re-experiencing of traumatic events is a hallmark symptom of posttraumatic stress disorder (PTSD). In contrast to abstract, verbal intrusions in other affective disorders, intrusive re-experiencing in PTSD is characterized by vivid sensory details as “flashbacks”. While prevailing PTSD models largely focus on dysregulated emotional processes, we hypothesize that deficient sensory inhibition in PTSD could drive overactivation of sensory representations of trauma memories, precipitating sensory-rich intrusions of trauma. In 86 combat veterans, we examined resting-state alpha (8-12 Hz) oscillatory activity (in both power and posterior→frontal connectivity), given its key role in sensory cortical inhibition, in association with intrusive re-experiencing symptoms. A subset (N = 35) of veterans further participated in an odor task (including both combat and non-combat odors) to assess olfactory trauma memory and emotional response. We observed a strong association between intrusive re-experiencing symptoms and attenuated resting-state posterior→frontal alpha connectivity, which were both correlated with olfactory trauma memory (but not emotional response). Importantly, olfactory trauma memory was further identified as a full mediator of the relationship between alpha connectivity and intrusive re-experiencing in these veterans, suggesting that deficits in intrinsic sensory inhibition can contribute to intrusive re-experiencing of trauma via heightened trauma memory. Therefore, by permitting unfiltered sensory cues to enter information processing and spontaneously activating sensory representations of trauma, impaired sensory inhibition can constitute a sensory mechanism of intrusive re-experiencing in PTSD.HIGHLIGHTSAlpha oscillations (indexing sensory inhibition) measured in 86 combat veteransRe-experiencing symptom severity was associated with attenuated alpha connectivityTrauma memory for, not emotional response to, odors mediated this relationshipTrauma memories may arise via disinhibited activation of sensory representationsSensory systems may be novel target for intrusive re-experiencing symptom treatment

scholarly journals Resting-state cortical inhibition predicts accuracy of motor inhibition

2017 ◽  
Vol 11 ◽  
Author(s):  
Jason He ◽  
Ian Fuelscher ◽  
Wei Peng Teo ◽  
Peter Enticott ◽  
Pam Barhoun ◽  
...  
Keyword(s):  
Resting State ◽  
Cortical Inhibition ◽  
Motor Inhibition
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