Universality in the brain while listening to music

The Impact of Listening to, Reciting, or Memorizing the Quran on Physical and Mental Health of Muslims: Evidence From Systematic Review

Objectives: Listening to or memorizing the Quran has been suggested to affect the psychosocial health and well-being of Muslims. Muslims who memorized Quran have a higher quality of life (QoL) and lower anxiety and stress. Hence, this systematic review was conducted to evaluate the studies that assessed the effect of listening to, reciting, or memorizing the Quran on physical and mental health.

Methods: This review was performed on articles published from the inception and April 2021. Databases including ProQuest, PubMed and Web of Science were searched on 19 April 2021. Keywords such as “Quran”, “al- Quran,” “al- Kareem,” “Holy Quran,” “memori,” “Tahfiz,” “Huffaz,” “listening” and “reciting” were used for databases searching. The risk of bias was assessed using Cochrane risk of bias tool and Joanna Briggs Institute (JBI). Only 20 articles were included in data synthesis out of a total of 230.

Results: The findings revealed that listening to, reciting or memorizing the Quran had a favorable effect on depression, anxiety, physiologic parameters, quality of life, quality of sleep and intelligence quotient.

Conclusion: The current evidence suggests that, listening to, reciting or memorizing the Quran may be useful as an intervention to improve physical and mental health.

Revisiting Functional Connectivity for Infraslow Scale-Free Brain Dynamics Using Complex Wavelets

The analysis of human brain functional networks is achieved by computing functional connectivity indices reflecting phase coupling and interactions between remote brain regions. In magneto- and electroencephalography, the most frequently used functional connectivity indices are constructed based on Fourier-based cross-spectral estimation applied to specific fast and band-limited oscillatory regimes. Recently, infraslow arrhythmic fluctuations (below the 1 Hz) were recognized as playing a leading role in spontaneous brain activity. The present work aims to propose to assess functional connectivity from fractal dynamics, thus extending the assessment of functional connectivity to the infraslow arrhythmic or scale-free temporal dynamics of M/EEG-quantified brain activity. Instead of being based on Fourier analysis, new Imaginary Coherence and weighted Phase Lag indices are constructed from complex-wavelet representations. Their performances are first assessed on synthetic data by means of Monte-Carlo simulations, and they are then compared favorably against the classical Fourier-based indices. These new assessments of functional connectivity indices are also applied to MEG data collected on 36 individuals both at rest and during the learning of a visual motion discrimination task. They demonstrate a higher statistical sensitivity, compared to their Fourier counterparts, in capturing significant and relevant functional interactions in the infraslow regime and modulations from rest to task. Notably, the consistent overall increase in functional connectivity assessed from fractal dynamics from rest to task correlated with a change in temporal dynamics as well as with improved performance in task completion, which suggests that the complex-wavelet weighted Phase Lag index is the sole index is able to capture brain plasticity in the infraslow scale-free regime.

Modifications in the Topological Structure of EEG Functional Connectivity Networks during Listening Tonal and Atonal Concert Music in Musicians and Non-Musicians

The present work aims to demonstrate the hypothesis that atonal music modifies the topological structure of electroencephalographic (EEG) connectivity networks in relation to tonal music. To this, EEG monopolar records were taken in musicians and non-musicians while listening to tonal, atonal, and pink noise sound excerpts. EEG functional connectivities (FC) among channels assessed by a phase synchronization index previously thresholded using surrogate data test were computed. Sound effects, on the topological structure of graph-based networks assembled with the EEG-FCs at different frequency-bands, were analyzed throughout graph metric and network-based statistic (NBS). Local and global efficiency normalized (vs. random-network) measurements (NLE|NGE) assessing network information exchanges were able to discriminate both music styles irrespective of groups and frequency-bands. During tonal audition, NLE and NGE values in the beta-band network get close to that of a small-world network, while during atonal and even more during noise its structure moved away from small-world. These effects were attributed to the different timbre characteristics (sounds spectral centroid and entropy) and different musical structure. Results from networks topographic maps for strength and NLE of the nodes, and for FC subnets obtained from the NBS, allowed discriminating the musical styles and verifying the different strength, NLE, and FC of musicians compared to non-musicians.

Functional Brain Connectivity Analysis in Intellectual Developmental Disorder During Music Perception

Intellectual Developmental Disorder (IDD) is a neurodevelopmental disorder involving impairment of general cognitive abilities. This disorder impacts the conceptual, social, and practical skills adversely. There is a growing interest in exploring the neurological behavior associated with these disorders. Assessment of functional brain connectivity and graph theory measures have emerged as powerful tools to aid these research goals. The current research contributes by comparing brain connectivity patterns of IDD individuals to those typical controls. Considering the intellectual deficits linked to the IDD population, we hypothesized an atypical connectivity pattern in the IDD group. Brain signals were recorded by a dry-electrode Electroencephalography (EEG) system during the rest and music states observed by the subjects. We studied a group of seven IDD subjects and seven healthy controls to understand the connectivity within the human brain during the resting-state vis-à-vis while listening to music. Findings of this research emphasize (1) hyper-connected functional brain networks and increased modularity as potential characteristics of the IDD group, (2) the ability of soothing music to reduce the resting state hyper-connected pattern in the IDD group, and (3) the effect of soothing music in the lower frequency bands of the control group compared to the higher frequency bands of the IDD group.

Scaling behaviour in music and cortical dynamics interplay to mediate music listening pleasure

The pleasure of music listening regulates daily behaviour and promotes rehabilitation in healthcare. Human behaviour emerges from the modulation of spontaneous timely coordinated neuronal networks. Too little is known about the physical properties and neurophysiological underpinnings of music to understand its perception, its health benefit and to deploy personalized or standardized music-therapy. Prior studies revealed how macroscopic neuronal and music patterns scale with frequency according to a 1/f^α relationship, where a is the scaling exponent. Here, we examine how this hallmark in music and neuronal dynamics relate to pleasure. Using electroencephalography, electrocardiography and behavioural data in healthy subjects, we show that music listening decreases the scaling exponent of neuronal activity and-in temporal areas-this change is linked to pleasure. Default-state scaling exponents of the most pleased individuals were higher and approached those found in music loudness fluctuations. Furthermore, the scaling in selective regions and timescales and the average heart rate were largely proportional to the scaling of the melody. The scaling behaviour of heartbeat and neuronal fluctuations were associated during music listening. Our results point to a 1/f resonance between brain and music and a temporal rescaling of neuronal activity in the temporal cortex as mechanisms underlying music appreciation.

The Impact of Frequency Scale on the Response Sensitivity and Reliability of Cortical Neurons to 1/fβ Input Signals

What type of principle features intrinsic inside of the fluctuated input signals could drive neurons with the maximal excitations is one of the crucial neural coding issues. In this article, we examined both experimentally and theoretically the cortical neuronal responsivity (including firing rate and spike timing reliability) to input signals with different intrinsic correlational statistics (e.g., white-type noise, showed 1/f0 power spectrum, pink noise 1/f, and brown noises 1/f2) and different frequency ranges. Our results revealed that the response sensitivity and reliability of cortical neurons is much higher in response to 1/f noise stimuli with long-term correlations than 1/f0 with short-term correlations for a broad frequency range, and also higher than 1/f2 for all frequency ranges. In addition, we found that neuronal sensitivity diverges to opposite directions for 1/f noise comparing with 1/f0 white noise as a function of cutoff frequency of input signal. As the cutoff frequency is progressively increased from 50 to 1,000 Hz, the neuronal responsiveness increased gradually for 1/f noise, while decreased exponentially for white noise. Computational simulations of a general cortical model revealed that, neuronal sensitivity and reliability to input signal statistics was majorly dominated by fast sodium inactivation, potassium activation, and membrane time constants.

The neural underpinnings of music listening under different attention conditions

Most studies examining the neural underpinnings of music listening have no specific instruction on how to process the presented musical pieces. In this study, we explicitly manipulated the participants' focus of attention while they listened to the musical pieces. We used an ecologically valid experimental setting by presenting the musical stimuli simultaneously with naturalistic film sequences. In one condition, the participants were instructed to focus their attention on the musical piece (attentive listening), whereas in the second condition, the participants directed their attention to the film sequence (passive listening). We used two instrumental musical pieces: an electronic pop song, which was a major hit at the time of testing, and a classical musical piece. During music presentation, we measured electroencephalographic oscillations and responses from the autonomic nervous system (heart rate and high-frequency heart rate variability). During passive listening to the pop song, we found strong event-related synchronizations in all analyzed frequency bands (theta, lower alpha, upper alpha, lower beta, and upper beta). The neurophysiological responses during attentive listening to the pop song were similar to those of the classical musical piece during both listening conditions. Thus, the focus of attention had a strong influence on the neurophysiological responses to the pop song, but not on the responses to the classical musical piece. The electroencephalographic responses during passive listening to the pop song are interpreted as a neurophysiological and psychological state typically observed when the participants are 'drawn into the music'.

Task Context Influences Brain Activation during Music Listening

In this paper, we examined brain activation in subjects during two music listening conditions: listening while simultaneously rating the musical piece being played [Listening and Rating (LR)] and listening to the musical pieces unconstrained [Listening (L)]. Using these two conditions, we tested whether the sequence in which the two conditions were fulfilled influenced the brain activation observable during the L condition (LR → L or L → LR). We recorded high-density EEG during the playing of four well-known positively experienced soundtracks in two subject groups. One group started with the L condition and continued with the LR condition (L → LR); the second group performed this experiment in reversed order (LR → L). We computed from the recorded EEG the power for different frequency bands (theta, lower alpha, upper alpha, lower beta, and upper beta). Statistical analysis revealed that the power in all examined frequency bands increased during the L condition but only when the subjects had not had previous experience with the LR condition (i.e., L → LR). For the subjects who began with the LR condition, there were no power increases during the L condition. Thus, the previous experience with the LR condition prevented subjects from developing the particular mental state associated with the typical power increase in all frequency bands. The subjects without previous experience of the LR condition listened to the musical pieces in an unconstrained and undisturbed manner and showed a general power increase in all frequency bands. We interpret the fact that unconstrained music listening was associated with increased power in all examined frequency bands as a neural indicator of a mental state that can best be described as a mind-wandering state during which the subjects are “drawn into” the music.

Three Effective Ways to Nurture Our Brain

Abstract. A growing body of research suggests that physical activity, healthy eating, and music can, either directly or indirectly, have positive effects on our brain and cognition. More specifically, exercising and eating seem to enhance cognitive abilities, such as memory, creativity, and perception. They also improve academic performance and play a protective role from many degenerative diseases, including Alzheimer’s disease. Concerning music, research has shown that there exists a general positive relation between music aptitude and cognitive functioning. Furthermore, the presence of music seems to create a positive mood and a higher arousal, which translates into better performance in many cognitive tasks. This literature review provides an overview of the major empirical findings in this domain. Studies on both healthy and clinical individuals are reviewed and discussed. We conclude with suggestions for educators, policymakers, people in helping professions, and any others interested in making informed decisions about possible ways to nurture their own brain or the brain of the people they are trying to help. We also provide suggestions for additional research on this important topic.

Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic Approach

A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing four prior-labeled emotion-inducing video clips from a publically available, validated database. Besides emotion category labels, the video clips also came with arousal scores. Our subjects were also asked to provide their own emotion labels. In total 30 subjects with age range 19–70 years participated in our study. Rather than relying on predefined frequency bands, we estimate multivariate sample entropy over multiple data-driven scales using the multivariate empirical mode decomposition (MEMD) technique and show that in this way we can discriminate between five self-reported emotions ([Formula: see text]). These results could not be obtained by analyzing the relation between arousal scores and video clips, signal complexity and arousal scores, and self-reported emotions and traditional power spectral densities and their hemispheric asymmetries in the theta, alpha, beta, and gamma frequency bands. This shows that multivariate, multiscale sample entropy is a promising technique to discriminate multiple emotional states from EEG recordings.

How different are the local field potentials and spiking activities? Insights from multi-electrodes arrays

Simultaneous recording of multiple neurons, or neuron groups, offers new promise for investigating fundamental questions about the neural code. We used arrays of 16 electrodes in the tonotopic, primary, auditory cortex of guinea pigs and we extracted LFP- and spike-based spectro-temporal receptive fields (STRFs). We confirm here that LFP signals provide broadly tuned activity which lacks frequency resolution compared to multiunit signals and, therefore, lead to large redundancy in neural responses even between recording sites far apart. Thanks to the use of multi-electrode arrays which allows simultaneous recordings, we also focused on functional relationships between neuronal discharges (through cross-correlations) and between LFPs (through coherence). Since the LFP is composed of distinct brain rhythms, the LFP results were split into three frequency bands from the slowest to the fastest components of LFPs. For driven as well as spontaneous activity, we show that components >70 Hz in LFPs are much less coherent between recording sites than slower components. In general, coherence between LFPs from two recordings sites is positively correlated with the degree of frequency overlap between the two corresponding STRFs, similar to cross-correlation between multiunit activities. However, coherence is only weakly correlated with cross-correlation in all frequency ranges. Altogether, these results suggest that LFPs reflect global functional connectivity in the thalamocortical auditory system whereas spiking activities reflect more independent local processing.

Fractals in the nervous system: conceptual implications for theoretical neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power-law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review.

Macroscopic models of local field potentials and the apparent 1/f noise in brain activity

The power spectrum of local field potentials (LFPs) has been reported to scale as the inverse of the frequency, but the origin of this 1/f noise is at present unclear. Macroscopic measurements in cortical tissue demonstrated that electric conductivity (as well as permittivity) is frequency-dependent, while other measurements failed to evidence any dependence on frequency. In this article, we propose a model of the genesis of LFPs that accounts for the above data and contradictions. Starting from first principles (Maxwell equations), we introduce a macroscopic formalism in which macroscopic measurements are naturally incorporated, and also examine different physical causes for the frequency dependence. We suggest that ionic diffusion primes over electric field effects, and is responsible for the frequency dependence. This explains the contradictory observations, and also reproduces the 1/f power spectral structure of LFPs, as well as more complex frequency scaling. Finally, we suggest a measurement method to reveal the frequency dependence of current propagation in biological tissue, and which could be used to directly test the predictions of this formalism.

Increase of universality in human brain during mental imagery from visual perception

Background

Different complex systems behave in a similar way near their critical points of phase transitions which leads to an emergence of a universal scaling behaviour. Universality indirectly implies a long-range correlation between constituent subsystems. As the distributed correlated processing is a hallmark of higher complex cognition, I investigated a measure of universality in human brain during perception and mental imagery of complex real-life visual object like visual art.

Methodology/Principal Findings

A new method was presented to estimate the strength of hidden universal structure in a multivariate data set. In this study, I investigated this method in the electrical activities (electroencephalogram signals) of human brain during complex cognition. Two broad groups - artists and non-artists - were studied during the encoding (perception) and retrieval (mental imagery) phases of actual paintings. Universal structure was found to be stronger in visual imagery than in visual perception, and this difference was stronger in artists than in non-artists. Further, this effect was found to be largest in the theta band oscillations and over the prefrontal regions bilaterally.

Conclusions/Significance

Phase transition like dynamics was observed in the electrical activities of human brain during complex cognitive processing, and closeness to phase transition was higher in mental imagery than in real perception. Further, the effect of long-term training on the universal scaling was also demonstrated.

Statistical geometric affinity in human brain electric activity

The representation of the human electroencephalogram (EEG) records by neurophysiologists demands standardized time-amplitude scales for their correct conventional interpretation. In a suite of graphical experiments involving scaling affine transformations we have been able to convert electroencephalogram samples corresponding to any particular sleep phase and relaxed wakefulness into each other. We propound a statistical explanation for that finding in terms of data collapse. As a sequel, we determine characteristic time and amplitude scales and outline a possible physical interpretation. An analysis for characteristic times based on lacunarity is also carried out as well as a study of the synchrony between left and right EEG channels.

Feedback modulates the temporal scale-free dynamics of brain electrical activity in a hypothesis testing task

We used the electroencephalogram (EEG) to investigate whether positive and negative performance feedbacks exert different long-lasting modulations of electrical activity in a reasoning task. Nine college students serially tested hypotheses concerning a hidden rule by judging its presence or absence in triplets of digits, and revised them on the basis of an exogenous performance feedback. The scaling properties of the transition period between feedback and triplet presentation were investigated with detrended fluctuation analysis (DFA). DFA showed temporal scale-free dynamics of EEG activity in both feedback conditions for time scales larger than 150 ms. Furthermore, DFA revealed that negative feedback elicits significantly higher scaling exponents than positive feedback. This effect covers a wide network comprising parieto-occipital and left frontal regions. We thus showed that specific task demands can modify the temporal scale-free dynamics of the ongoing brain activity. Putative neural correlates of these long-lasting feedback-specific modulations are proposed.

Discovering EEG Signals Response to Musical Signal Stimuli by Time-frequency analysis and Independent Component Analysis

In recent years, a lot of research has focus on the physiological effect of music. The electroencephalographic (EEG) is often used to verify the influence of music on human brain activity. In this study, we used frequency distribution analysis and the independent component analysis (ICA) to analyze to discover the EEG responses of subjects with different musical signal stimuli. It is expected that some features on EEG can be demonstrated to reflect the different musical signal stimuli. The EEG of six healthy volunteers listening different music was recorded. We used International 10-20 System to get 19 channels of EEG signal. Musical signal stimuli are metal music, sonata music and the favorite music selected by subjects. Spectra analyses based on Fourier transform were applied to obtain the α,β,γ and θ band power of EEG signal under different music stimuli. We used the power at each band of each channel as the features of EEG. The correlation of the features between different situations and subjects was used to show which channel display the difference of EEG signals. Besides, ICA was applied to assist us in the process of isolating noise components and to provide cues to explain the functions of different brain areas in point of neurology. The result showed that some independent components obtained from ICA can demonstrate more significant difference for different music. The features composed of spectral power of each band are very similar in listening metal music, but showed less similarity in listening sonata music.

The influence of Mozart's sonata K. 448 on brain activity during the performance of spatial rotation and numerical tasks

The study investigated the influence of Mozart's music on respondents' brain activity while solving spatial rotation and numerical tasks. The method of induced event-related desynchronization/synchronization (ERD/ERS) and coherence (ERCoh) was used. The music condition had a beneficial influence on respondents' performance of spatial rotation tasks, and a slightly negative influence on the performance of numerical tasks as compared with the silence condition. On the psychophysiological level a general effect of Mozart's music on brain activity in the induced gamma band was observed, accompanied by a more specific effect in theinduced lower-2 alpha band whichwas only present while respondents solved the numerical tasks. It is suggested that listening to Mozart's music increases the activity of specific brain areas and in that way facilitates the selection and "binding" together of pertinent aspects of sensory stimulus into a perceived whole.

Drawing on mind's canvas: differences in cortical integration patterns between artists and non-artists

Our primary question was to learn whether mentally composing drawings of their own choice produce different brain electric features in artists and laymen. To this purpose, we studied multichannel electroencephalograph (EEG) signals from two broad groups (all participants were females): artists (professionally trained in visual arts) and non-artists (without any training in art). To assess the underlying synchronization, which is assumed to be the platform for general cognitive integration between different cortical regions, three measures inspired by nonlinear dynamical system theory were applied as follows: (1) index based on generalized synchronization; (2) index based on mean phase coherence; and (3) index of phase synchrony based on entropy. Results consistent over all three measures were as follows: comparing the tasks to rest, the artists showed significantly stronger short- and long-range delta band synchronization, whereas the non-artists showed enhancement in short-range beta and gamma band synchronization primarily in frontal regions; comparing the two groups during the tasks, the artists showed significantly stronger delta band synchronization and alpha band desynchronization than did the non-artists. Strong right hemispheric dominance in terms of synchronization was found in the artists. In artists, the higher synchrony in the low-frequency band is possibly due to the involvement of a more advanced long-term visual art memory and to extensive top-down processing. The results demonstrate that in artists, patterns of functional cooperation between cortical regions during mental creation of drawings were significantly different from those in non-artists.

The acute effect of music on interictal epileptiform discharges

This study was a prospective, randomized, single-blinded, crossover, placebo-controlled, pilot clinical trial investigating the effect of Mozart's Sonata for Two Pianos (K448) on the frequency of interictal epileptiform discharges (IEDs) from the EEGs of children with benign childhood epilepsy with centrotemporal spikes, or "rolandic" epilepsy. The goal was to demonstrate decreased frequency of IEDs with exposure to K448. Four subjects were recruited and 4-hour awake EEG recordings performed. IED frequency per minute was averaged over each of three epochs per hour. Mean IED count per epoch, standard deviations, and variance were calculated. Only complete waking epochs were analyzed. Two subjects demonstrated sufficient waking IEDs for statistical analysis, consisting of three epochs of K448-related effects. Significant decreases in IEDs per minute (33.7, 50.6, and 33.9%) were demonstrated comparing baseline with exposure to K448, but not to control music (Beethoven's Für Elise).

The influence of auditory background stimulation (Mozart's sonata K. 448) on visual brain activity

Twenty individuals solved a visual oddball task in two response conditions: while listening to the Mozart's sonata K. 448, and while listening to nothing. The recorded event-related potentials (ERP) were analyzed in the time and frequency domains. In the music response condition the ERP peak latencies on the left hemisphere increased, whereas on the right hemisphere a decrease of peak latencies as compared with the silence response condition was observed. In the theta, lower-1 alpha and gamma band increases in induced event-related coherences were observed while respondents solved the oddball task and listened to music, whereas a decoupling of brain areas in the gamma band was observed in the silence response condition. It is suggested that auditory background stimulation can influence visual brain activity, even if both stimuli are unrelated.

Substance identification by quantitative characterization of oscillatory activity in murine spinal cord networks on microelectrode arrays

This paper presents a novel and comprehensive method to identify substances on the basis of electrical activity and is a substantial improvement for drug screening. The spontaneous activity of primary neuronal networks is influenced by neurotransmitters, ligands, and other substances in a similar fashion as known from in vivo pharmacology. However, quantitative methods for the identification of substances through their characteristic effects on network activity states have not yet been reported. We approached this problem by creating a database including native activity and five drug-induced oscillatory activity states from extracellular multisite recordings from microelectrode arrays. The response profiles consisted of 30 activity features derived from the temporal distribution of action potentials, integrated burst properties, calculated coefficients of variation, and features of Gabor fits to autocorrelograms. The different oscillatory states were induced by blocking neurotransmitter receptors for: (i) GABA(A); (ii) glycine; (iii) GABA(A) and glycine; (iv) all major synaptic types except AMPA, and (v) all major synapses except NMDA. To test the identification capability of the six substance-specific response profiles, five blind experiments were performed. The response features from the unknown substances were compared to the database using proximity measures using the normalized Euclidian distance to each activity state. This process created six identification coefficients where the smallest correctly identified the unknown substances. Such activity profiles are expected to become substance-specific 'finger prints' that classify unique responses to known and unknown substances. It is anticipated that this kind of approach will help to quantify pharmacological responses of networks used as biosensors.