β- And γ-band EEG power predicts illusory auditory continuity perception

2012 ◽  
Vol 108 (10) ◽  
pp. 2717-2724 ◽  
Author(s):  
Ekaterina Vinnik ◽  
Pavel M. Itskov ◽  
Evan Balaban
Keyword(s):  
Brain Activity ◽  
Brain State ◽  
High Temporal Resolution ◽  
Gap Detection ◽  
Noisy Environments ◽  
Baseline Activity ◽  
Masking Noise ◽  
Auditory Systems ◽  
Sound Source Segregation ◽  
Physically Identical

Because acoustic landscapes are complex and rapidly changing, auditory systems have evolved mechanisms that permit rapid detection of novel sounds, sound source segregation, and perceptual restoration of sounds obscured by noise. Perceptual restoration is particularly important in noisy environments because it allows organisms to track sounds over time even when they are masked. The continuity illusion is a striking example of perceptual restoration with sounds perceived as intact even when parts of them have been replaced by gaps and rendered inaudible by being masked by an extraneous sound. The mechanisms of auditory filling-in are complex and are currently not well-understood. The present study used the high temporal resolution of EEG to examine brain activity related to continuity illusion perception. Masking noise loudness was adjusted individually for each subject so that physically identical sounds on some trials elicited a continuity illusion (failure to detect a gap in a sound) and on other trials resulted in correct gap detection. This design ensured that any measurable differences in brain activity would be due to perceptual differences rather than physical differences among stimuli. We found that baseline activity recorded immediately before presentation of the stimulus significantly predicted the occurrence of the continuity illusion in 10 out of 14 participants based on power differences in γ-band EEG (34–80 Hz). Across all participants, power in the β and γ (12- to 80-Hz range) was informative about the subsequent perceptual decision. These data suggest that a subject's baseline brain state influences the strength of continuity illusions.

scholarly journals States and traits of neural irregularity in the age-varying human brain

2017 ◽  
Author(s):  
Leonhard Waschke ◽  
Malte Wöestmann ◽  
Jonas Obleser
Keyword(s):  
Brain Activity ◽  
Physical World ◽  
Permutation Entropy ◽  
Brain State ◽  
Information Theoretic ◽  
Age Related ◽  
Brain States ◽  
Sensory Encoding ◽  
Physically Identical ◽  
Human Electroencephalogram

AbstractSensory representations of the physical world and thus human percepts are susceptible to fluctuations in brain state or “neural irregularity”. Furthermore, aging brains display altered levels of irregularity. We here show that a single, within-trial information-theoretic measure (weighted permutation entropy) captures neural irregularity in the human electroencephalogram as a proxy for both, trait-like differences between individuals of varying age, and state-like fluctuations that bias perceptual decisions. First, the overall level of neural irregularity increased with participants‘ age, paralleled by a decrease in variability over time, likely indexing age-related disintegration on structural and functional levels of brain activity. Second, states of higher neural irregularity were associated with optimized sensory encoding and a subsequently increased probability of choosing the first of two physically identical stimuli. In sum, neural irregularity not only characterizes behaviorally relevant brain states, but also can identify trait-like changes that come with age.

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 Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Laura Cornelissen ◽  
Seong-Eun Kim ◽  
Patrick L Purdon ◽  
Emery N Brown ◽  
Charles B Berde
Keyword(s):  
General Anesthesia ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Body Movement ◽  
Video Recording ◽  
Brain State ◽  
Alpha Power ◽  
State Monitoring ◽  
Spatio Temporal ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) approaches may provide important information about developmental changes in brain-state dynamics during general anesthesia. We used multi-electrode EEG, analyzed with multitaper spectral methods and video recording of body movement to characterize the spatio-temporal dynamics of brain activity in 36 infants 0–6 months old when awake, and during maintenance of and emergence from sevoflurane general anesthesia. During maintenance: (1) slow-delta oscillations were present in all ages; (2) theta and alpha oscillations emerged around 4 months; (3) unlike adults, all infants lacked frontal alpha predominance and coherence. Alpha power was greatest during maintenance, compared to awake and emergence in infants at 4–6 months. During emergence, theta and alpha power decreased with decreasing sevoflurane concentration in infants at 4–6 months. These EEG dynamic differences are likely due to developmental factors including regional differences in synaptogenesis, glucose metabolism, and myelination across the cortex. We demonstrate the need to apply age-adjusted analytic approaches to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.

Applying Independent Component Analysis to the Artifact Detection Problem in Magnetoencephalogram Background Recordings

2008 ◽  
pp. 84-92
Author(s):  
Javier Escudero ◽  
Roberto Hornero ◽  
Daniel Abásolo ◽  
Jesús Poza ◽  
Alberto Fernández
Keyword(s):  
Magnetic Fields ◽  
Quantum Interference ◽  
Brain Activity ◽  
External Noise ◽  
Low Noise ◽  
High Temporal Resolution ◽  
Artifact Detection ◽  
Mental Diseases ◽  
Electroencephalogram Eeg ◽  
The Brain

The analysis of the electromagnetic brain activity can provide important information to help in the diagnosis of several mental diseases. Both electroencephalogram (EEG) and magnetoencephalogram (MEG) record the neural activity with high temporal resolution (Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993). Nevertheless, MEG offers some advantages over EEG. For example, in contrast to EEG, MEG does not depend on any reference point. Moreover, the magnetic fields are less distorted than the electric ones by the skull and the scalp (Hämäläinen et al., 1993). Despite these advantages, the use of MEG data involves some problems. One of the most important difficulties is that MEG recordings may be severely contaminated by additive external noise due to the intrinsic weakness of the brain magnetic fields. Hence, MEG must be recorded in magnetically shielded rooms with low-noise SQUID (Superconducting QUantum Interference Devices) gradiometers (Hämäläinen et al., 1993).

The proper place of subjectivity, meaning, and folk psychology in psychiatry

2020 ◽  
pp. 290-303
Author(s):  
Doug McConnell
Keyword(s):  
Mental Disorders ◽  
Effective Treatment ◽  
Folk Psychology ◽  
Brain Activity ◽  
Mental Content ◽  
Brain State ◽  
Conceptual Content ◽  
Proper Place ◽  
Complex Interactions ◽  
Discursive Interaction

‘The proper place of subjectivity, meaning, and folk psychology in psychiatry’ argues that Steven Hyman’s vision for psychiatry is excessively bioreductive. Hyman wrongly assumes that conceptual mental content is reducible to brain state descriptions and mistakes the neural vehicle of content for the content itself. Once we see that conceptual content, including the referents of folk psychology, shape brain activity, it becomes clear that content itself (or a lack of it) can be pathological. Therefore, treatment will sometimes be effective, even curative, by addressing that content through discursive interaction with the patient qua person. Diagnosis and effective treatment of mental disorders cannot just focus on neurobiology, as Hyman claims, both processes must also consider conceptual content and the complex interactions between content and the neurobiology instantiating it.

scholarly journals A functional MRI study of presurgical cognitive deficits in glioma patients

2020 ◽  
Author(s):  
Irena T Schouwenaars ◽  
Miek J de Dreu ◽  
Geert-Jan M Rutten ◽  
Nick F Ramsey ◽  
Johan M Jansma
Keyword(s):  
Functional Mri ◽  
Cognitive Deficits ◽  
Brain Activity ◽  
Neuropsychological Testing ◽  
Region Of Interest ◽  
High Grade Glioma ◽  
Brain Regions ◽  
Brain State ◽  
Low Grade ◽  
Activity Levels

Abstract Background The main goal of this functional MRI (fMRI) study was to examine whether cognitive deficits in glioma patients prior to treatment are associated with abnormal brain activity in either the central executive network (CEN) or default mode network (DMN). Methods Forty-six glioma patients, and 23 group-matched healthy controls (HCs) participated in this fMRI experiment, performing an N-back task. Additionally, cognitive profiles of patients were evaluated outside the scanner. A region of interest–based analysis was used to compare brain activity in CEN and DMN between groups. Post hoc analyses were performed to evaluate differences between low-grade glioma (LGG) and high-grade glioma (HGG) patients. Results In-scanner performance was lower in glioma patients compared to HCs. Neuropsychological testing indicated cognitive impairment in LGG as well as HGG patients. fMRI results revealed normal CEN activation in glioma patients, whereas patients showed reduced DMN deactivation compared to HCs. Brain activity levels did not differ between LGG and HGG patients. Conclusions Our study suggests that cognitive deficits in glioma patients prior to treatment are associated with reduced responsiveness of the DMN, but not with abnormal CEN activation. These results suggest that cognitive deficits in glioma patients reflect a reduced capacity to achieve a brain state necessary for normal cognitive performance, rather than abnormal functioning of executive brain regions. Solely focusing on increases in brain activity may well be insufficient if we want to understand the underlying brain mechanism of cognitive impairments in patients, as our results indicate the importance of assessing deactivation.

scholarly journals Distinct Montages of Slow Oscillatory Transcranial Direct Current Stimulation (so-tDCS) Constitute Different Mechanisms during Quiet Wakefulness

2019 ◽  
Vol 9 (11) ◽  
pp. 324
Author(s):  
Ping Koo-Poeggel ◽  
Verena Böttger ◽  
Lisa Marshall
Keyword(s):  
Slow Wave Sleep ◽  
Brain Activity ◽  
Electrode Placement ◽  
Brain State ◽  
Electrode Pair ◽  
Alpha Power ◽  
Applied Current ◽  
Large Power ◽  
Quiet Wakefulness ◽  
Eyes Closed

Slow oscillatory- (so-) tDCS has been applied in many sleep studies aimed to modulate brain rhythms of slow wave sleep and memory consolidation. Yet, so-tDCS may also modify coupled oscillatory networks. Efficacy of weak electric brain stimulation is however variable and dependent upon the brain state at the time of stimulation (subject and/or task-related) as well as on stimulation parameters (e.g., electrode placement and applied current. Anodal so-tDCS was applied during wakefulness with eyes-closed to examine efficacy when deviating from the dominant brain rhythm. Additionally, montages of different electrodes size and applied current strength were used. During a period of quiet wakefulness bilateral frontolateral stimulation (F3, F4; return electrodes at ipsilateral mastoids) was applied to two groups: ‘Group small’ (n = 16, f:8; small electrodes: 0.50 cm2; maximal current per electrode pair: 0.26 mA) and ‘Group Large’ (n = 16, f:8; 35 cm2; 0.35 mA). Anodal so-tDCS (0.75 Hz) was applied in five blocks of 5 min epochs with 1 min stimulation-free epochs between the blocks. A finger sequence tapping task (FSTT) was used to induce comparable cortical activity across sessions and subject groups. So-tDCS resulted in a suppression of alpha power over the parietal cortex. Interestingly, in Group Small alpha suppression occurred over the standard band (8–12 Hz), whereas for Group Large power of individual alpha frequency was suppressed. Group Small also revealed a decrease in FSTT performance at retest after stimulation. It is essential to include concordant measures of behavioral and brain activity to help understand variability and poor reproducibility in oscillatory-tDCS studies.

scholarly journals Emerging imaging methods to study whole-brain function in rodent models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marija Markicevic ◽  
Iurii Savvateev ◽  
Christina Grimm ◽  
Valerio Zerbi
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Imaging Techniques ◽  
Brain Activity ◽  
Research Question ◽  
Careful Consideration ◽  
Mammalian Brain ◽  
High Temporal Resolution ◽  
Imaging Methods ◽  
Evoked Activity

AbstractIn the past decade, the idea that single populations of neurons support cognition and behavior has gradually given way to the realization that connectivity matters and that complex behavior results from interactions between remote yet anatomically connected areas that form specialized networks. In parallel, innovation in brain imaging techniques has led to the availability of a broad set of imaging tools to characterize the functional organization of complex networks. However, each of these tools poses significant technical challenges and faces limitations, which require careful consideration of their underlying anatomical, physiological, and physical specificity. In this review, we focus on emerging methods for measuring spontaneous or evoked activity in the brain. We discuss methods that can measure large-scale brain activity (directly or indirectly) with a relatively high temporal resolution, from milliseconds to seconds. We further focus on methods designed for studying the mammalian brain in preclinical models, specifically in mice and rats. This field has seen a great deal of innovation in recent years, facilitated by concomitant innovation in gene-editing techniques and the possibility of more invasive recordings. This review aims to give an overview of currently available preclinical imaging methods and an outlook on future developments. This information is suitable for educational purposes and for assisting scientists in choosing the appropriate method for their own research question.

scholarly journals Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness

2021 ◽  
Vol 17 (7) ◽  
pp. e1009139
Author(s):  
Yonatan Sanz Perl ◽  
Carla Pallavicini ◽  
Ignacio Pérez Ipiña ◽  
Athena Demertzi ◽  
Vincent Bonhomme ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Relevant Information ◽  
Complete Characterization ◽  
Brain State ◽  
Whole Brain ◽  
Brain Injured ◽  
Level Of Consciousness ◽  
The Stability ◽  
Injured Patients

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

scholarly journals Getting the gist faster: Blurry images enhance the early temporal similarity between neural signals and convolutional neural networks

2021 ◽  
Author(s):  
David A. Tovar ◽  
Tijl Grootswagers ◽  
James Jun ◽  
Oakyoon Cha ◽  
Randolph Blake ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Brain Activity ◽  
Visual Image ◽  
High Temporal Resolution ◽  
Neural Signals ◽  
Time Frequency ◽  
Brain Signals ◽  
Spatial Frequencies ◽  
Full Picture

Humans are able to recognize objects under a variety of noisy conditions, so models of the human visual system must account for how this feat is accomplished. In this study, we investigated how image perturbations, specifically reducing images to their low spatial frequency (LSF) components, affected correspondence between convolutional neural networks (CNNs) and brain signals recorded using magnetoencephalography (MEG). Using the high temporal resolution of MEG, we found that CNN-Brain correspondence for deeper and more complex layers across CNN architectures emerged earlier for LSF images than for their unfiltered broadband counterparts. The early emergence of LSF components is consistent with the coarse-to-fine theoretical framework for visual image processing, but surprisingly shows that LSF signals from images are more prominent when high spatial frequencies are removed. In addition, we decomposed MEG signals into oscillatory components and found correspondence varied based on frequency bands, painting a full picture of how CNN-Brain correspondence varies with time, frequency, and MEG sensor locations. Finally, we varied image properties of CNN training sets, and found marked changes in CNN processing dynamics and correspondence to brain activity. In sum, we show that image perturbations affect CNN-Brain correspondence in unexpected ways, as well as provide a rich methodological framework for assessing CNN-Brain correspondence across space, time, and frequency.

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