scholarly journals Binaural Beats through the auditory pathway: from brainstem to connectivity patterns

2019 ◽  
Author(s):  
Hector D Orozco Perez ◽  
Guillaume Dumas ◽  
Alexandre Lehmann
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Beat Frequency ◽  
Auditory Pathway ◽  
Experimental Manipulation ◽  
Binaural Beats ◽  
Frequency Following Response ◽  
Sham Condition ◽  
Connectivity Patterns ◽  
Cortical Entrainment

AbstractBinaural beating is a perceptual auditory illusion occurring when presenting two neighboring frequencies to each ear separately. Binaural beats have been attributed to several controversial claims regarding their ability to modulate brain activity and mood, in both the scientific literature and the marketing realm. Here, we sought to address those questions in a robust fashion using a single-blind, sham-controlled protocol. To do so, we characterized responses to theta and gamma binaural beats and “sham” stimulation (monaural beats) across four distinct levels: subcortical and cortical entrainment, scalp-level Functional Connectivity and self-reports. Both stimuli elicited standard subcortical responses at the pure tone frequencies of the stimulus (i.e., Frequency Following Response), and entrained the cortex at the beat frequency (i.e., Auditory Steady State Response). Furthermore, Functional Connectivity patterns were modulated differentially by both kinds of stimuli, with binaural beats being the only one eliciting cross-frequency activity. Despite this, we did not find any mood modulation related to our experimental manipulation. Our results provide evidence that binaural beats elicit cross frequency connectivity patterns, but weakly entrain the cortex when compared to a sham stimulus. Whether these patterns have an impact in cognitive performance or other mood measurements remains to be seen.Significance StatementBinaural beats have been a source of speculation and debate in the scientific community. Our study addresses pseudo-scientific marketing claims and approaches them using proper experimental control and state-of-the-art signal processing techniques. Here we show that binaural beats can both entrain the cortex and elicit specific connectivity patterns. Regardless of this, our sham condition was able to entrain the cortex more strongly, and both binaural beats and the sham condition failed to regulate mood. All in all, though binaural beats weakly entrain cortical activity and elicit complex patterns of connectivity, the functional significance (if any) of these patterns remains an open question.

scholarly journals A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ali Yener Mutlu ◽  
Edward Bernat ◽  
Selin Aviyente
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Dynamic Network ◽  
Principal Component ◽  
Brain Network ◽  
Event Related Potential ◽  
Time Varying ◽  
Error Related Negativity ◽  
Connectivity Patterns

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

scholarly journals Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Andreas A. Ioannides ◽  
Stavros I. Dimitriadis ◽  
George A. Saridis ◽  
Marotesa Voultsidou ◽  
Vahe Poghosyan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Clinical Applications ◽  
Brain Areas ◽  
Connectivity Patterns ◽  
The Brain ◽  
Typical Subject ◽  
Different Parts ◽  
Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

scholarly journals Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability

2016 ◽  
Vol 37 (2) ◽  
pp. 471-484 ◽  
Author(s):  
Jonathan R Bumstead ◽  
Adam Q Bauer ◽  
Patrick W Wright ◽  
Joseph P Culver
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Functional Connectivity ◽  
Large Variability ◽  
Mayer Waves ◽  
Intrinsic Signal ◽  
Optical Intrinsic Signal ◽  
Connectivity Patterns

Resting-state functional connectivity is a growing neuroimaging approach that analyses the spatiotemporal structure of spontaneous brain activity, often using low-frequency (<0.08 Hz) hemodynamics. In addition to these fluctuations, there are two other low-frequency hemodynamic oscillations in a nearby spectral region (0.1–0.4 Hz) that have been reported in the brain: vasomotion and Mayer waves. Despite how close in frequency these phenomena exist, there is little research on how vasomotion and Mayer waves are related to or affect resting-state functional connectivity. In this study, we analyze spontaneous hemodynamic fluctuations over the mouse cortex using optical intrinsic signal imaging. We found spontaneous occurrence of oscillatory hemodynamics ∼0.2 Hz consistent with the properties of Mayer waves reported in the literature. Across a group of mice (n = 19), there was a large variability in the magnitude of Mayer waves. However, regardless of the magnitude of Mayer waves, functional connectivity patterns could be recovered from hemodynamic signals when filtered to the lower frequency band, 0.01–0.08 Hz. Our results demonstrate that both Mayer waves and resting-state functional connectivity patterns can co-exist simultaneously, and that they can be separated by applying bandpass filters.

Short-Term Effects of Binaural Beats on EEG Power, Functional Connectivity, Cognition, Gait and Anxiety in Parkinson’s Disease

2018 ◽  
Vol 28 (05) ◽  
pp. 1750055 ◽  
Author(s):  
Gerardo Gálvez ◽  
Manuel Recuero ◽  
Leonides Canuet ◽  
Francisco Del-Pozo
Keyword(s):  
Parkinson’S Disease ◽  
Working Memory ◽  
Parkinson's Disease ◽  
Functional Connectivity ◽  
Repeated Measures ◽  
Brain Activity ◽  
Controlled Study ◽  
Binaural Beats ◽  
Repeated Measures Design ◽  
Eeg Power

We applied rhythmic binaural sound to Parkinson’s Disease (PD) patients to investigate its influence on several symptoms of this disease and on Electrophysiology (Electrocardiography and Electroencephalography (EEG)). We conducted a double-blind, randomized controlled study in which rhythmic binaural beats and control were administered over two randomized and counterbalanced sessions (within-subjects repeated-measures design). Patients ([Formula: see text], age [Formula: see text], stage I–III Hoehn & Yahr scale) participated in two sessions of sound stimulation for 10[Formula: see text]min separated by a minimum of 7 days. Data were collected immediately before and after both stimulations with the following results: (1) a decrease in theta activity, (2) a general decrease in Functional Connectivity (FC), and (3) an improvement in working memory performance. However, no significant changes were identified in the gait performance, heart rate or anxiety level of the patients. With regard to the control stimulation, we did not identify significant changes in the variables analyzed. The use of binaural-rhythm stimulation for PD, as designed in this study, seems to be an effective, portable, inexpensive and noninvasive method to modulate brain activity. This influence on brain activity did not induce changes in anxiety or gait parameters; however, it resulted in a normalization of EEG power (altered in PD), normalization of brain FC (also altered in PD) and working memory improvement (a normalizing effect). In summary, we consider that sound, particularly binaural-rhythmic sound, may be a co-assistant tool in the treatment of PD, however more research is needed to consider the use of this type of stimulation as an effective therapy.

scholarly journals Binaural Beats through the Auditory Pathway: From Brainstem to Connectivity Patterns

eNeuro ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. ENEURO.0232-19.2020
Author(s):  
Hector D. Orozco Perez ◽  
Guillaume Dumas ◽  
Alexandre Lehmann
Keyword(s):  
Auditory Pathway ◽  
Binaural Beats ◽  
Connectivity Patterns

scholarly journals Classification of emotions based on functional connectivity patterns of the human brain

2020 ◽  
Author(s):  
Heini Saarimäki ◽  
Enrico Glerean ◽  
Dmitry Smirnov ◽  
Henri Mynttinen ◽  
Iiro P. Jääskeläinen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Pattern Recognition ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Whole Brain ◽  
Connectivity Patterns

AbstractNeurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity signatures differ across emotions. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks, and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We conclude that functional connectivity patterns consistently differ across different emotions particularly within the default mode system.

scholarly journals Brain Activity and Functional Connectivity Patterns Associated With Fast and Slow Motor Sequence Learning in Late Middle Adulthood

2022 ◽  
Vol 13 ◽  
Author(s):  
Maite Aznárez-Sanado ◽  
Luis Eudave ◽  
Martín Martínez ◽  
Elkin O. Luis ◽  
Federico Villagra ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sequence Learning ◽  
Motor Performance ◽  
Brain Activity ◽  
Connectivity Pattern ◽  
Elderly Subjects ◽  
Middle Adulthood ◽  
Motor Sequence Learning ◽  
Motor Sequence ◽  
Connectivity Patterns

The human brain undergoes structural and functional changes across the lifespan. The study of motor sequence learning in elderly subjects is of particularly interest since previous findings in young adults might not replicate during later stages of adulthood. The present functional magnetic resonance imaging (fMRI) study assessed the performance, brain activity and functional connectivity patterns associated with motor sequence learning in late middle adulthood. For this purpose, a total of 25 subjects were evaluated during early stages of learning [i.e., fast learning (FL)]. A subset of these subjects (n = 11) was evaluated after extensive practice of a motor sequence [i.e., slow learning (SL) phase]. As expected, late middle adults improved motor performance from FL to SL. Learning-related brain activity patterns replicated most of the findings reported previously in young subjects except for the lack of hippocampal activity during FL and the involvement of cerebellum during SL. Regarding functional connectivity, precuneus and sensorimotor lobule VI of the cerebellum showed a central role during improvement of novel motor performance. In the sample of subjects evaluated, connectivity between the posterior putamen and parietal and frontal regions was significantly decreased with aging during SL. This age-related connectivity pattern may reflect losses in network efficiency when approaching late adulthood. Altogether, these results may have important applications, for instance, in motor rehabilitation programs.

scholarly journals Signature of consciousness in the dynamics of resting-state brain activity

2015 ◽  
Vol 112 (3) ◽  
pp. 887-892 ◽  
Author(s):  
Pablo Barttfeld ◽  
Lynn Uhrig ◽  
Jacobo D. Sitt ◽  
Mariano Sigman ◽  
Béchir Jarraya ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Small World ◽  
Brain Regions ◽  
Connectivity Matrix ◽  
Anatomical Connectivity ◽  
Functional Dynamics ◽  
Connectivity Patterns ◽  
Continuous Stream

At rest, the brain is traversed by spontaneous functional connectivity patterns. Two hypotheses have been proposed for their origins: they may reflect a continuous stream of ongoing cognitive processes as well as random fluctuations shaped by a fixed anatomical connectivity matrix. Here we show that both sources contribute to the shaping of resting-state networks, yet with distinct contributions during consciousness and anesthesia. We measured dynamical functional connectivity with functional MRI during the resting state in awake and anesthetized monkeys. Under anesthesia, the more frequent functional connectivity patterns inherit the structure of anatomical connectivity, exhibit fewer small-world properties, and lack negative correlations. Conversely, wakefulness is characterized by the sequential exploration of a richer repertoire of functional configurations, often dissimilar to anatomical structure, and comprising positive and negative correlations among brain regions. These results reconcile theories of consciousness with observations of long-range correlation in the anesthetized brain and show that a rich functional dynamics might constitute a signature of consciousness, with potential clinical implications for the detection of awareness in anesthesia and brain-lesioned patients.

scholarly journals The Default Mode Network and the Problem of Determining Intrinsic Mental Contents

2015 ◽  
Vol 40 (1) ◽  
pp. 145-160 ◽  
Author(s):  
Marek Havlík ◽  
Tomáš Marvan
Keyword(s):  
Functional Connectivity ◽  
Default Mode Network ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Default Mode ◽  
Connectivity Patterns ◽  
Mental Contents

Abstract We provide a brief overview of the shift toward the intrinsic view of brain activity, describing in particular the structural and functional connectivity patterns of the “Default mode network” (part I). We then consider the Default mode network in a specifically cognitive setting and ask what changes the focus on the Default mode network and other sorts of intrinsic activity require from models put forward by cognitive neuroscientists (part II).

scholarly journals A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

2017 ◽  
Author(s):  
Jeremy R. Manning ◽  
Xia Zhu ◽  
Theodore L. Willke ◽  
Rajesh Ranganath ◽  
Kimberly Stachenfeld ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Probabilistic Approach ◽  
Covariance Structure ◽  
Fmri Data ◽  
Cognitive State ◽  
Bayesian Hierarchical ◽  
Connectivity Patterns ◽  
Hierarchical Matrix

AbstractRecent research shows that the covariance structure of functional magnetic resonance imaging (fMRI) data - commonly described as functional connectivity - can change as a function of the participant’s cognitive state (for review see [35]). Here we present a Bayesian hierarchical matrix factorization model, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets. HTFA approximates each subject’s network by first re-representing each brain image in terms of the activities of a set of localized nodes, and then computing the covariance of the activity time series of these nodes. The number of nodes, along with their locations, sizes, and activities (over time) are learned from the data. Because the number of nodes is typically substantially smaller than the number of fMRI voxels, HTFA can be orders of magnitude more efficient than traditional voxel-based functional connectivity approaches. In one case study, we show that HTFA recovers the known connectivity patterns underlying a collection of synthetic datasets. In a second case study, we illustrate how HTFA may be used to discover dynamic full-brain activity and connectivity patterns in real fMRI data, collected as participants listened to a story. In a third case study, we carried out a similar series of analyses on fMRI data collected as participants viewed an episode of a television show. In these latter case studies, we found that the HTFA-derived activity and connectivity patterns can be used to reliably decode which moments in the story or show the participants were experiencing. Further, we found that these two classes of patterns contained partially non-overlapping information, such that decoders trained on combinations of activity-based and dynamic connectivity-based features performed better than decoders trained on activity or connectivity patterns alone. We replicated this latter result with two additional (previously developed) methods for efficiently characterizing full-brain activity and connectivity patterns.

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