scholarly journals Natural Music Evokes Correlated EEG Responses Reflecting Temporal Structure and Beat

2019 ◽  
Author(s):  
Blair Kaneshiro ◽  
Duc T. Nguyen ◽  
Anthony M. Norcia ◽  
Jacek. P. Dmochowski ◽  
Jonathan Berger
Keyword(s):  
Brain Activity ◽  
Temporal Structure ◽  
Brain State ◽  
Music Listening ◽  
Neural Responses ◽  
Stimulus Response ◽  
Neural Correlation ◽  
Temporal Correlations ◽  
Musical Engagement ◽  
The Brain

AbstractThe brain activity of multiple subjects has been shown to synchronize during salient moments of natural stimuli, suggesting that correlation of neural responses indexes a brain state operationally termed ‘engagement’. While past electroencephalography (EEG) studies have considered both auditory and visual stimuli, the extent to which these results generalize to music—a temporally structured stimulus for which the brain has evolved specialized circuitry—is less understood. Here we investigated neural correlation during natural music listening by recording dense-array EEG responses from N = 48 adult listeners as they heard real-world musical works, some of which were temporally disrupted through shuffling of short-term segments (measures), reversal, or randomization of phase spectra. We measured neural correlation across responses (inter-subject correlation) and between responses and stimulus envelope fluctuations (stimulus-response correlation) in the time and frequency domains. Stimuli retaining basic musical features evoked significantly correlated neural responses in all analyses. However, while unedited songs were self-reported as most pleasant, time-domain correlations were highest during measure-shuffled versions. Frequency-domain measures of correlation (coherence) peaked at frequencies related to the musical beat, although the magnitudes of these spectral peaks did not explain the observed temporal correlations. Our findings show that natural music evokes significant inter-subject and stimulus-response correlations, and suggest that the neural correlates of musical engagement may be distinct from those of enjoyment.

scholarly journals Inter-Subject EEG Correlation Reflects Time-Varying Engagement with Natural Music

2021 ◽  
Author(s):  
Blair Kaneshiro ◽  
Duc T. Nguyen ◽  
Anthony M. Norcia ◽  
Jacek P. Dmochowski ◽  
Jonathan Berger
Keyword(s):  
Temporal Dynamics ◽  
Temporal Structure ◽  
Low Frequency ◽  
Music Listening ◽  
Time Varying ◽  
Musical Work ◽  
Neural Correlation ◽  
Cello Concerto ◽  
Musical Engagement ◽  
Shed Light

AbstractMusical engagement can be conceptualized through various activities, modes of listening, and listener states—among these a state of focused engagement. Recent research has reported that this state can be indexed by the inter-subject correlation (ISC) of EEG responses to a shared naturalistic stimulus. While statistically significant ISC has been reported during music listening, these reports have considered only correlations computed across entire excerpts and do not provide insights into time-varying engagement. Here we present the first EEG-ISC investigation of time-varying engagement within a musical work. From a sample of 23 adult musicians who listened to a cello concerto movement, we find varying levels of ISC throughout the excerpt. In particular, significant ISC is observed during periods of musical tension that build to climactic highpoints, but not at the highpoints themselves. In addition, we find that a control stimulus retaining low-frequency envelope characteristics of the intact music, but little other temporal structure, also elicits significant neural correlation, though to a lesser extent than the original. In all, our findings shed light on temporal dynamics of listener engagement during music listening, establish connections between salient musical events and EEG-ISC, and clarify specific listener states that are indexed by this measure.

scholarly journals Macroscopic quantities of collective brain activity during wakefulness and anesthesia

2021 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Lynn Uhrig ◽  
Nikolas Deco ◽  
Camilo M. Signorelli ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Loss Of Consciousness ◽  
Information Theoretic ◽  
Maximum Entropy Models ◽  
Information Theoretic Measures ◽  
Brain States ◽  
Parietal Cortices ◽  
The Brain

AbstractThe study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

scholarly journals Weight Loss Maintenance: Have We Missed the Brain?

2018 ◽  
Vol 8 (9) ◽  
pp. 174 ◽  
Author(s):  
Dimitrios Poulimeneas ◽  
Mary Yannakoulia ◽  
Costas Anastasiou ◽  
Nikolaos Scarmeas
Keyword(s):  
Weight Loss ◽  
Brain Activity ◽  
Weight Loss Maintenance ◽  
Regions Of Interest ◽  
Neural Processing ◽  
Obesity Management ◽  
Neural Responses ◽  
Successful Weight Loss ◽  
The Brain

Even though obese individuals often succeed with weight loss, long-term weight loss maintenance remains elusive. Dietary, lifestyle and psychosocial correlates of weight loss maintenance have been researched, yet the nature of maintenance is still poorly understood. Studying the neural processing of weight loss maintainers may provide a much-needed insight towards sustained obesity management. In this narrative review, we evaluate and critically discuss available evidence regarding the food-related neural responses of weight loss maintainers, as opposed to those of obese or lean persons. While research is still ongoing, available data indicate that following weight loss, maintainers exhibit persistent reward related feeling over food, similar to that of obese persons. However, unlike in obese persons, in maintainers, reward-related brain activity appears to be counteracted by subsequently heightened inhibition. These findings suggest that post-dieting, maintainers acquire a certain level of cognitive control which possibly protects them from weight regaining. The prefrontal cortex, as well as the limbic system, encompass key regions of interest for weight loss maintenance, and their contributions to long term successful weight loss should be further explored. Future possibilities and supportive theories are discussed.

scholarly journals Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

2017 ◽  
Author(s):  
Giri P. Krishnan ◽  
Oscar C. González ◽  
Maxim Bazhenov
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Ion Concentration ◽  
Brain State ◽  
The Brain

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

scholarly journals Exploring electroencephalography with a model inspired by quantum mechanics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas J. M. Popiel ◽  
Colin Metrow ◽  
Geoffrey Laforge ◽  
Adrian M. Owen ◽  
Bobby Stojanoski ◽  
...  
Keyword(s):  
Quantum Mechanics ◽  
Brain Activity ◽  
Brain State ◽  
The Novel ◽  
Heisenberg Uncertainty Principle ◽  
Eeg Data ◽  
Organizational Principle ◽  
Heisenberg Uncertainty ◽  
Task Conditions ◽  
The Brain

AbstractAn outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during “rest”, and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant KBrain, which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object.

scholarly journals Lempel-Ziv complexity of cortical activity during sleep and waking in rats

2015 ◽  
Vol 113 (7) ◽  
pp. 2742-2752 ◽  
Author(s):  
Daniel Abásolo ◽  
Samantha Simons ◽  
Rita Morgado da Silva ◽  
Giulio Tononi ◽  
Vladyslav V. Vyazovskiy
Keyword(s):  
Sleep Deprivation ◽  
Eye Movement ◽  
Brain Activity ◽  
Temporal Structure ◽  
Nrem Sleep ◽  
Brain State ◽  
Rapid Eye Movement ◽  
Sleep Regulation ◽  
Spatio Temporal ◽  
Underlying Mechanisms

Understanding the dynamics of brain activity manifested in the EEG, local field potentials (LFP), and neuronal spiking is essential for explaining their underlying mechanisms and physiological significance. Much has been learned about sleep regulation using conventional EEG power spectrum, coherence, and period-amplitude analyses, which focus primarily on frequency and amplitude characteristics of the signals and on their spatio-temporal synchronicity. However, little is known about the effects of ongoing brain state or preceding sleep-wake history on the nonlinear dynamics of brain activity. Recent advances in developing novel mathematical approaches for investigating temporal structure of brain activity based on such measures, as Lempel-Ziv complexity (LZC) can provide insights that go beyond those obtained with conventional techniques of signal analysis. Here, we used extensive data sets obtained in spontaneously awake and sleeping adult male laboratory rats, as well as during and after sleep deprivation, to perform a detailed analysis of cortical LFP and neuronal activity with LZC approach. We found that activated brain states—waking and rapid eye movement (REM) sleep are characterized by higher LZC compared with non-rapid eye movement (NREM) sleep. Notably, LZC values derived from the LFP were especially low during early NREM sleep after sleep deprivation and toward the middle of individual NREM sleep episodes. We conclude that LZC is an important and yet largely unexplored measure with a high potential for investigating neurophysiological mechanisms of brain activity in health and disease.

How Does the Brain Discriminate Familiar and Unfamiliar Faces?: A PET Study of Face Categorical Perception

2001 ◽  
Vol 13 (7) ◽  
pp. 1019-1034 ◽  
Author(s):  
Bruno Rossion ◽  
Christine Schiltz ◽  
Laurence Robaye ◽  
David Pirenne ◽  
Marc Crommelinck
Keyword(s):  
Categorical Perception ◽  
Brain Activity ◽  
Principal Component ◽  
Neural Responses ◽  
Lexical Information ◽  
Ventral Pathway ◽  
Middle Occipital Gyrus ◽  
The Right ◽  
The Brain ◽  
Unfamiliar Faces

Where and how does the brain discriminate familiar and unfamiliar faces? This question has not been answered yet by neuroimaging studies partly because different tasks were performed on familiar and unfamiliar faces, or because familiar faces were associated with semantic and lexical information. Here eight subjects were trained during 3 days with a set of 30 faces. The familiarized faces were morphed with unfamiliar faces. Presented with continua of unfamiliar and familiar faces in a pilot experiment, a group of eight subjects presented a categorical perception of face familiarity: there was a sharp boundary in percentage of familiarity decisions between 40% and 60% faces. In the main experiment, subjects were scanned (PET) on the fourth day (after 3 days of training) in six conditions, all requiring a sex classification task. Completely novel faces (0%) were presented in Condition 1 and familiar faces (100%) in Condition 6, while faces of steps of 20% in the continuum of familiarity were presented in Conditions 2 to 5 (20% to 80%). A principal component analysis (PCA) indicated that most variations in neural responses were related to the dissociation between faces perceived as familiar (60% to 100%) and faces perceived as unfamiliar (0 to 40%). Subtraction analyses did not disclose any increase of activation for faces perceived as familiar while there were large relative increases for faces perceived as unfamiliar in several regions of the right occipito-temporal visual pathway. These changes were all categorical and were observed mainly in the right middle occipital gyrus, the right posterior fusiform gyrus, and the right inferotemporal cortex. These results show that (1) the discrimination between familiar and unfamiliar faces is related to relative increases in the right ventral pathway to unfamiliar/novel faces; (2) familiar and unfamiliar faces are discriminated in an all-or-none fashion rather than proportionally to their resemblance to stored representations; and (3) categorical perception of faces is associated with abrupt changes of brain activity in the regions that discriminate the two extremes of the multidimensional continuum.

scholarly journals LSD flattens the brain′s energy landscape: evidence from receptor-informed network control theory

2021 ◽  
Author(s):  
S. Parker Singleton ◽  
Andrea I Luppi ◽  
Robin L. Carhart-Harris ◽  
Josephine Cruzat ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Control Theory ◽  
Subjective Experience ◽  
Brain Activity ◽  
Energy Landscape ◽  
Network Control ◽  
State Transitions ◽  
Brain State ◽  
Control Analysis ◽  
Brain States ◽  
The Brain

Psychedelics like lysergic acid diethylamide (LSD) offer a powerful window into the function of the human brain and mind, by temporarily altering subjective experience through their neurochemical effects. The RElaxed Beliefs Under Psychedelics (REBUS) model postulates that 5-HT2a receptor agonism allows the brain to explore its dynamic landscape more readily, as suggested by more diverse (entropic) brain activity. Formally, this effect is theorized to correspond to a reduction in the energy required to transition between different brain-states, i.e. a ″flattening of the energy landscape.″ However, this hypothesis remains thus far untested. Here, we leverage network control theory to map the brain′s energy landscape, by quantifying the energy required to transition between recurrent brain states. In accordance with the REBUS model, we show that LSD reduces the energy required for brain-state transitions, and, furthermore, that this reduction in energy correlates with more frequent state transitions and increased entropy of brain-state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors, we demonstrate the specific role of this receptor in flattening the brain′s energy landscape. Also, in accordance with REBUS, we show that the occupancy of bottom-up states is increased by LSD. In addition to validating fundamental predictions of the REBUS model of psychedelic action, this work highlights the potential of receptor-informed network control theory to provide mechanistic insights into pharmacological modulation of brain dynamics.

scholarly journals Neural Correlation of Faradarmani Consciousness Field Mind Mediation: A Comparative Functional Connectivity and Graph Analysis

2021 ◽  
Author(s):  
Mohammad Ali Taheri ◽  
Fatemeh Modarresi-Asem ◽  
Noushin Nabavi ◽  
Parisa Maftoun ◽  
Farid Semsarha
Keyword(s):  
Graph Theory ◽  
Functional Connectivity ◽  
Brain Activity ◽  
Effective Connectivity ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Temporal Lobes ◽  
Neural Correlation ◽  
Graph Theory Analysis ◽  
The Brain

The study of the brain networks using analysis of electroencephalography (EEG) data based on statistical dependencies (functional connectivity) and mathematical graph theory concepts is common in neuroscience and cognitive sciences for examinations of patient and healthy individuals. The Consciousness Fields according to Taheri theory and applications in the optimization of system under study have been investigated in various studies. In this study, we examine the results of working with Faradarmani Consciousness Field (FCF) in the brain of Faradarmangars. Faradarmangars are one of the necessary components in mind mediation of the function of Faradarmani Consciousness Fields according to Taheri. For this purpose, the functional and effective connectivity and the corresponding brain graphs of EEG from the brain of Faradarmangars is compared with that of non Faradarmangar groups during FCF connection. According to the results of the present study, the brain of the Faradarmangars shows significant decreased activity in delta (BA8), beta2 (BA4/6/8/9/10/11/32/44/47) and beta3 (in 34 of 52 BA) frequency bands mainly in frontal lobe and after that in parietal and temporal lobes in the comparison with the non Faradarmangars. Moreover, the functional and effective connectivity analysis in the frontal network shows dominant multiple decreased connectivity mainly in the case of beta3 frequency band in all parts of the frontal network. On the other hand, the graph theory analysis of the Faradarmangar brain shows an increase in the activity of the O2-T5-F4-F3-FP2-F8 areas and significant decrease in the characteristic path length and increases in global efficiency, clustering coefficient and transitivity. In conclusion, the unique higher graph function efficiency and the reduction in the brain activity and connectivity during the Faradarmani Consciousness Field mind mediation, shown the passive and detector like function of the human brain in this task.

scholarly journals Personality modulates brain responses to emotion in music: Comparing whole-brain and regions-of-variance approaches

2019 ◽  
Author(s):  
Kendra Oudyk ◽  
Iballa Burunat ◽  
Elvira Brattico ◽  
Petri Toiviainen
Keyword(s):  
Parietal Lobe ◽  
Brain Activity ◽  
Action Observation ◽  
Music Listening ◽  
Neural Responses ◽  
Whole Brain ◽  
Action Observation Network ◽  
Brain Responses ◽  
Observation Network ◽  
The Individual

AbstractWhether and how personality traits explain the individual variance in neural responses to emotion in music remains unclear. The sparse studies on this topic report inconsistent findings. The present study extends previous work using regions of variance (ROVs) as regions of interest, compared with whole-brain analysis. Fifty-five subjects listened to happy, sad, and fearful music during functional Magnetic Resonance Imaging. Personality was measured with the Big Five Questionnaire. Results confirmed previous observations of Neuroticism being positively related to activation during sad music, in the left inferior parietal lobe. In an exploratory analysis, Openness was positively related to activation during Happy music in an extended cluster in auditory areas, primarily including portions of the left Heschl’s gyrus, superior and middle temporal gyri, supramarginal gyrus, and Rolandic operculum. In the whole-brain analysis, similar results were found for Neuroticism but not for Openness. In turn, we did not replicate previous findings of Extraversion associated to activity during happy music, nor Neuroticism during fearful music. These results support a trait-congruent link between personality and emotion-elicited brain activity, and further our understanding of the action-observation network during emotional music listening. This study also indicates the usefulness of the ROV method in individual-differences research.

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