scholarly journals Ongoing brain rhythms shape I-wave properties in a computational model

2017 ◽  
Author(s):  
Natalie Schaworonkow ◽  
Jochen Triesch
Keyword(s):  
Computational Model ◽  
Phase Modulation ◽  
Closed Loop ◽  
Brain Activity ◽  
Cortical Layer ◽  
Oscillatory Activity ◽  
Cortical Circuits ◽  
Brain Rhythms ◽  
Sensorimotor Rhythms ◽  
Wave Properties

AbstractBackgroundResponses to transcranial magnetic stimulation (TMS) are notoriously variable. Previous studies have observed a dependence of TMS-induced responses on ongoing brain activity, for instance sensorimotor rhythms. This suggests an opportunity for the development of more effective stimulation protocols through closed-loop TMS-EEG. However, it is not yet clear how features of ongoing activity affect the responses of cortical circuits to TMS.Objective/HypothesisHere we investigate the dependence of TMS-responses on power and phase of ongoing oscillatory activity in a computational model of TMS-induced I-waves.MethodsThe model comprises populations of cortical layer 2/3 (L2/3) neurons and a population of cortical layer 5 (L5) neurons and generates I-waves in response to TMS. Oscillatory input to the L2/3 neurons induces rhythmic fluctuations in activity of L5 neurons. TMS pulses are simulated at different phases and amplitudes of the ongoing rhythm.ResultsThe model shows a robust dependence of I-wave properties on phase and power of ongoing rhythms, with the strongest response occurring for TMS at maximal L5 depolarization. The amount of phase-modulation depends on stimulation intensity, with stronger modulation for lower intensity.ConclusionThe model predicts that responses to TMS are highly variable for low stimulation intensities if ongoing brain rhythms are not taken into account. Closed-loop TMS-EEG holds promise for obtaining more reliable TMS effects.

scholarly journals Selection of stimulus parameters for enhancing slow wave sleep events with a Neural-field theory thalamocortical computational model

2021 ◽  
Author(s):  
Felipe A. Torres ◽  
Patricio Orio ◽  
María-José Escobar
Keyword(s):  
Computational Model ◽  
Slow Wave ◽  
Memory Consolidation ◽  
Closed Loop ◽  
Slow Wave Sleep ◽  
Brain Activity ◽  
Sensory Stimulation ◽  
Sleep Spindles ◽  
Slow Oscillations ◽  
Closed Loop Stimulation

AbstractSlow-wave sleep cortical brain activity, conformed by slow-oscillations and sleep spindles, plays a key role in memory consolidation. The increase of the power of the slow-wave events, obtained by auditory sensory stimulation, positively correlates to memory consolidation performance. However, little is known about the experimental protocol maximizing this effect, which could be induced by the power of slow-oscillation, the number of sleep spindles, or the timing of both events’ co-occurrence. Using a mean-field model of thalamocortical activity, we studied the effect of several stimulation protocols, varying the pulse shape, duration, amplitude, and frequency, as well as a target-phase using a closed-loop approach. We evaluated the effect of these parameters on slow-oscillations (SO) and sleep-spindles (SP), considering: (i) the power at the frequency bands of interest, (ii) the number of SO and SP, (iii) co-occurrences between SO and SP, and (iv) synchronization of SP with the up-peak of the SO. The first three targets are maximized using a decreasing ramp pulse with a pulse duration of 50 ms. Also, we observed a reduction in the number of SO when increasing the stimulus energy by rising its amplitude. To assess the target-phase parameter, we applied closed-loop stimulation at 0º, 45º, and 90º of the phase of the narrow-band filtered ongoing activity, at 0.85 Hz as central frequency. The 0º stimulation produces better results in the power and number of SO and SP than the rhythmic or aleatory stimulation. On the other hand, stimulating at 45º or 90º change the timing distribution of spindles centers but with fewer co-occurrences than rhythmic and 0º phase. Finally, we propose the application of closed-loop stimulation at the rising zero-cross point using pulses with a decreasing ramp shape and 50 ms of duration for future experimental work.Author summaryDuring the non-REM (NREM) phase of sleep, events that are known as slow oscillations (SO) and spindles (SP) can be detected by EEG. These events have been associated with the consolidation of declarative memories and learning. Thus, there is an ongoing interest in promoting them during sleep by non-invasive manipulations such as sensory stimulation. In this paper, we used a computational model of brain activity that generates SO and SP, to investigate which type of sensory stimulus –shape, amplitude, duration, periodicity– would be optimal for increasing the events’ frequency and their co-occurrence. We found that a decreasing ramp of 50 ms duration is the most effective. The effectiveness increases when the stimulus pulse is delivered in a closed-loop configuration triggering the pulse at a target phase of the ongoing SO activity. A desirable secondary effect is to promote SPs at the rising phase of the SO oscillation.

scholarly journals Ongoing brain rhythms shape I-wave properties in a computational model

2018 ◽  
Vol 11 (4) ◽  
pp. 828-838 ◽  
Author(s):  
Natalie Schaworonkow ◽  
Jochen Triesch
Keyword(s):  
Computational Model ◽  
Brain Rhythms ◽  
Wave Properties

scholarly journals Closed-loop Neuroscience of brain rhythms: optimizing real-time quantification of narrow-band signals to expedite feedback delivery

2019 ◽  
Author(s):  
Nikolai Smetanin ◽  
Anastasia Belinskaya ◽  
Mikhail Lebedev ◽  
Alexei Ossadtchi
Keyword(s):  
Closed Loop ◽  
Filter Design ◽  
Human Subjects ◽  
Brain Activity ◽  
Broad Band ◽  
Neural Oscillations ◽  
Direct Stimulation ◽  
Brain Rhythms ◽  
Closed Loops ◽  
The Brain

AbstractClosed-loop Neuroscience is based on the experimental approach where the ongoing brain activity is recorded, processed, and passed back to the brain as sensory feedback or direct stimulation of neural circuits. The artificial closed loops constructed with this approach expand the traditional stimulus-response experimentation. As such, closed-loop Neuroscience provides insights on the function of loops existing in the brain and the ways the flow of neural information could be modified to treat neurological conditions.Neural oscillations, or brain rhythms, are a class of neural activities that have been extensively studied and also utilized in brain rhythm-contingent (BRC) paradigms that incorporate closed loops. In these implementations, instantaneous power and phase of neural oscillations form the signal that is fed back to the brain.Here we addressed the problem of feedback delay in BRC paradigms. In many BRC systems, it is critical to keep the delay short. Long delays could render the intended modification of neural activity impossible because the stimulus is delivered after the targeted neural pattern has already completed. Yet, the processing time needed to extract oscillatory components from the broad-band neural signals can significantly exceed the period of oscillations, which puts a demand for algorithms that could minimize the delay.We used EEG data collected in human subjects to systematically investigate the performance of a range of signal processing methods in the context of minimizing delay in BRC systems. We proposed a family of techniques based on the least-squares filter design – a transparent and simple approach, as it required a single parameter to adjust the accuracy versus latency trade-off. Our algorithm performed on par or better than the state-of the art techniques currently used for the estimation of rhythm envelope and phase in closed-loop EEG paradigms.

Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson’s disease

2020 ◽  
Vol 132 (4) ◽  
pp. 1234-1242 ◽  
Author(s):  
Paolo Belardinelli ◽  
Ramin Azodi-Avval ◽  
Erick Ortiz ◽  
Georgios Naros ◽  
Florian Grimm ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Effects ◽  
Brain Activity ◽  
Dynamic Imaging ◽  
Oscillatory Activity ◽  
Network Information ◽  
Novel Approach ◽  
Electrophysiological Recordings ◽  
Intraoperative Localization

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for symptomatic Parkinson’s disease (PD); the clinical benefit may not only mirror modulation of local STN activity but also reflect consecutive network effects on cortical oscillatory activity. Moreover, STN-DBS selectively suppresses spatially and spectrally distinct patterns of synchronous oscillatory activity within cortical-subcortical loops. These STN-cortical circuits have been described in PD patients using magnetoencephalography after surgery. This network information, however, is currently not available during surgery to inform the implantation strategy.The authors recorded spontaneous brain activity in 3 awake patients with PD (mean age 67 ± 14 years; mean disease duration 13 ± 7 years) during implantation of DBS electrodes into the STN after overnight withdrawal of dopaminergic medication. Intraoperative propofol was discontinued at least 30 minutes prior to the electrophysiological recordings. The authors used a novel approach for performing simultaneous recordings of STN local field potentials (LFPs) and multichannel electroencephalography (EEG) at rest. Coherent oscillations between LFP and EEG sensors were computed, and subsequent dynamic imaging of coherent sources was performed.The authors identified coherent activity in the upper beta range (21–35 Hz) between the STN and the ipsilateral mesial (pre)motor area. Coherence in the theta range (4–6 Hz) was detected in the ipsilateral prefrontal area.These findings demonstrate the feasibility of detecting frequency-specific and spatially distinct synchronization between the STN and cortex during DBS surgery. Mapping the STN with this technique may disentangle different functional loops relevant for refined targeting during DBS implantation.

scholarly journals An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

2021 ◽  
Vol 11 (3) ◽  
pp. 330
Author(s):  
Dalton J. Edwards ◽  
Logan T. Trujillo
Keyword(s):  
External Validity ◽  
Spectral Power ◽  
Brain Activity ◽  
Outdoor Environment ◽  
Oscillatory Activity ◽  
Brain State ◽  
Beta Band ◽  
Eeg Spectral Power ◽  
Eeg Power ◽  
Human Participants

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4–7 Hz, alpha: 8–13 Hz, low beta: 14–20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

scholarly journals Visual predictions, neural oscillations and naïve physics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Blake W. Saurels ◽  
Wiremu Hohaia ◽  
Kielan Yarrow ◽  
Alan Johnston ◽  
Derek H. Arnold
Keyword(s):  
Brain Activity ◽  
Dynamic Environment ◽  
Predictive Performance ◽  
Brain Regions ◽  
Internal Models ◽  
Oscillatory Activity ◽  
Alpha Band ◽  
Core Property ◽  
Ball Tracking ◽  
Band Activity

AbstractPrediction is a core function of the human visual system. Contemporary research suggests the brain builds predictive internal models of the world to facilitate interactions with our dynamic environment. Here, we wanted to examine the behavioural and neurological consequences of disrupting a core property of peoples’ internal models, using naturalistic stimuli. We had people view videos of basketball and asked them to track the moving ball and predict jump shot outcomes, all while we recorded eye movements and brain activity. To disrupt people’s predictive internal models, we inverted footage on half the trials, so dynamics were inconsistent with how movements should be shaped by gravity. When viewing upright videos people were better at predicting shot outcomes, at tracking the ball position, and they had enhanced alpha-band oscillatory activity in occipital brain regions. The advantage for predicting upright shot outcomes scaled with improvements in ball tracking and occipital alpha-band activity. Occipital alpha-band activity has been linked to selective attention and spatially-mapped inhibitions of visual brain activity. We propose that when people have a more accurate predictive model of the environment, they can more easily parse what is relevant, allowing them to better target irrelevant positions for suppression—resulting in both better predictive performance and in neural markers of inhibited information processing.

scholarly journals Stimulus-driven brain rhythms within the alpha band: The attentional-modulation conundrum

2018 ◽  
Author(s):  
Christian Keitel ◽  
Anne Keitel ◽  
Christopher SY Benwell ◽  
Christoph Daube ◽  
Gregor Thut ◽  
...  
Keyword(s):  
Spatial Attention ◽  
Visual Processing ◽  
Visual Stimulation ◽  
Brain Activity ◽  
Gain Control ◽  
Sensory Stimulation ◽  
Alpha Band ◽  
Visual Evoked Response ◽  
Brain Rhythms ◽  
Inhibitory Function

Two largely independent research lines use rhythmic sensory stimulation to study visual processing. Despite the use of strikingly similar experimental paradigms, they differ crucially in their notion of the stimulus-driven periodic brain responses: One regards them mostly as synchronised (entrained) intrinsic brain rhythms; the other assumes they are predominantly evoked responses (classically termed steady-state responses, or SSRs) that add to the ongoing brain activity. This conceptual difference can produce contradictory predictions about, and interpretations of, experimental outcomes. The effect of spatial attention on brain rhythms in the alpha-band (8-13 Hz) is one such instance: alpha-range SSRs have typically been found to increase in power when participants focus their spatial attention on laterally presented stimuli, in line with a gain control of the visual evoked response. In nearly identical experiments, retinotopic decreases in entrained alpha-band power have been reported, in line with the inhibitory function of intrinsic alpha. Here we reconcile these contradictory findings by showing that they result from a small but far-reaching difference between two common approaches to EEG spectral decomposition. In a new analysis of previously published EEG data, recorded during bilateral rhythmic visual stimulation, we find the typical SSR gain effect when emphasising stimulus-locked neural activity and the typical retinotopic alpha suppression when focusing on ongoing rhythms. These opposite but parallel effects suggest that spatial attention may bias the neural processing of dynamic visual stimulation via two complementary neural mechanisms.

scholarly journals Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults

2018 ◽  
Author(s):  
Luis F. Ciria ◽  
Pandelis Perakakis ◽  
Antonio Luque-Casado ◽  
Daniel Sanabria
Keyword(s):  
Physical Exercise ◽  
Inhibitory Control ◽  
Frequency Band ◽  
Brain Function ◽  
Acute Exercise ◽  
Brain Activity ◽  
Oscillatory Activity ◽  
Power Increase ◽  
Oscillatory Brain Activity ◽  
Exercise Induced

AbstractExtant evidence suggests that acute exercise triggers a tonic power increase in the alpha frequency band at frontal locations, which has been linked to benefits in cognitive function. However, recent literature has questioned such a selective effect on a particular frequency band, indicating a rather overall power increase across the entire frequency spectrum. Moreover, the nature of task-evoked oscillatory brain activity associated to inhibitory control after exercising, and the duration of the exercise effect, are not yet clear. Here, we investigate for the first time steady state oscillatory brain activity during and following an acute bout of aerobic exercise at two different exercise intensities (moderate-to-high and light), by means of a data-driven cluster-based approach to describe the spatio-temporal distribution of exercise-induced effects on brain function without prior assumptions on any frequency range or site of interest. We also assess the transient oscillatory brain activity elicited by stimulus presentation, as well as behavioural performance, in two inhibitory control (flanker) tasks, one performed after a short delay following the physical exercise and another completed after a rest period of 15’ post-exercise to explore the time course of exercise-induced changes on brain function and cognitive performance. The results show that oscillatory brain activity increases during exercise compared to the resting state, and that this increase is higher during the moderate-to-high intensity exercise with respect to the light intensity exercise. In addition, our results show that the global pattern of increased oscillatory brain activity is not specific to any concrete surface localization in slow frequencies, while in faster frequencies this effect is located in parieto-occipital sites. Notably, the exercise-induced increase in oscillatory brain activity disappears immediately after the end of the exercise bout. Neither transient (event-related) oscillatory activity, nor behavioral performance during the flanker tasks following exercise showed significant between-intensity differences. The present findings help elucidate the effect of physical exercise on oscillatory brain activity and challenge previous research suggesting improved inhibitory control following moderate-to-high acute exercise.

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