scholarly journals Rapid encoding of temporal sequences discovered in brain dynamics

2020 ◽  
Author(s):  
Leonardo Bonetti ◽  
Elvira Brattico ◽  
Francesco Carlomagno ◽  
Giulia Donati ◽  
Joana Cabral ◽  
...  
Keyword(s):  
Brain Activity ◽  
Primary Auditory Cortex ◽  
Brain Network ◽  
Brain Dynamics ◽  
Information Encoding ◽  
Temporal Pole ◽  
Rapid Transition ◽  
Advanced Analysis ◽  
Pattern Encoding ◽  
Temporal Sequences

Information encoding has received a wide neuroscientific attention, but the underlying rapid spatiotemporal brain dynamics remain largely unknown. Here, we investigated the rapid brain mechanisms for encoding and prediction of sounds forming a complex temporal sequence. Specifically, we used magnetoencephalography (MEG) to record the brain activity of 68 participants while they listened to a highly structured musical prelude. Advanced analysis of the phase synchronisation and graph theoretical measures showed the rapid transition of brain activity from primary auditory cortex to higher order association areas including insula and superior temporal pole within a whole-brain network, occurring during the first 220 ms of the encoding process. We discovered individual differences, revealing the rapid unfolding of brain network dynamics responsible for the processing of the current sounds and the prediction of the forthcoming events of the sequence. This provides a first glimpse of the general mechanisms underlying pattern encoding in the human brain.

scholarly journals Spatiotemporal brain dynamics during recognition of the music of Johann Sebastian Bach

2020 ◽  
Author(s):  
L. Bonetti ◽  
E. Brattico ◽  
F. Carlomagno ◽  
J. Cabral ◽  
A. Stevner ◽  
...  
Keyword(s):  
Phase Synchronization ◽  
Brain Activity ◽  
Primary Auditory Cortex ◽  
Superior Temporal Gyrus ◽  
Brain Network ◽  
Brain Dynamics ◽  
High Temporal Resolution ◽  
Original Musical ◽  
Frontal Operculum ◽  
Musical Patterns

ABSTRACTMusic is a universal non-verbal human language, built on logical structures and articulated in balanced hierarchies between sounds, offering excellent opportunities to explore how the brain creates meaning for complex spatiotemporal auditory patterns. Using the high temporal resolution of magnetoencephalography in 70 participants, we investigated their unfolding brain dynamics during the recognition of Johann Sebastian Bach’s original musical patterns compared to new variations thereof. Remarkably, the recognition of Bach’s original music ignited a widespread music processing brain network comprising primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia and hippocampus. Furthermore, both activity and connectivity increased over time, following the evolution and unfolding of Bach’s original patterns. This study shed new light on brain activity and connectivity dynamics underlying auditory patterns recognition, highlighting the crucial role of fast neural phase synchronization underlying meaningful, complex cognitive processes.

scholarly journals Sniffing the Human Body-Volatile Hexadecanal Blocks Aggression in Men but Triggers Aggression in Women

2020 ◽  
Author(s):  
Eva Mishor ◽  
Daniel Amir ◽  
Tali Weiss ◽  
Danielle Honigstein ◽  
Aharon Weissbrod ◽  
...  
Keyword(s):  
Human Body ◽  
Brain Activity ◽  
Brain Network ◽  
Functional Brain Imaging ◽  
Angular Gyrus ◽  
Dependent Manner ◽  
Terrestrial Mammals ◽  
Temporal Pole ◽  
Conspecific Aggression ◽  
Left Angular Gyrus

AbstractBody-volatiles can effectively trigger or block conspecific aggression in terrestrial mammals. Here we tested whether hexadecanal (HEX), a human body-volatile implicated as a mammalian-wide social cue, impacts human aggression. Using validated behavioural paradigms, we observed a remarkable dissociation: sniffing HEX blocked aggression in men, but triggered aggression in women. Next, using functional brain imaging, we uncovered a pattern of brain activity mirroring behaviour: In both men and women, HEX increased activity in the left angular gyrus, an area implicated in perception of social cues. Hex then modulated functional connectivity between the angular gyrus and a brain network implicated in social appraisal (temporal pole) and aggressive execution (amygdala and orbitofrontal cortex) in a sex-dependent manner consistent with behaviour: increasing connectivity in men, but decreasing connectivity in women. These findings implicate sex-specific social chemosignaling at the mechanistic heart of human aggressive behaviour.

scholarly journals Modeling brain dynamics after tumor resection using The Virtual Brain

2019 ◽  
Author(s):  
Hannelore Aerts ◽  
Michael Schirner ◽  
Thijs Dhollander ◽  
Ben Jeurissen ◽  
Eric Achten ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Network Model ◽  
Brain Activity ◽  
Tumor Resection ◽  
Network Models ◽  
Brain Network ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Tumor Patients ◽  
Healthy Control

AbstractBrain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, computational modeling of brain activity using so-called brain network models has been introduced as a promising tool for this purpose. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first “virtual neurosurgery” analyses to evaluate the potential of brain network modeling in predicting brain dynamics after tumor resection.We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. In addition, we identify several robust associations between individually optimized model parameters, structural network topology and cognitive performance from pre-to post-operative assessment. Concerning the virtual neurosurgery analyses, we obtain promising results in some patients, whereas the predictive accuracy of the currently applied model is poor in others. These findings reveal interesting avenues for future research, as well as important limitations that warrant further investigation.

scholarly journals Cognitive chimera states in human brain networks

2019 ◽  
Vol 5 (4) ◽  
pp. eaau8535 ◽  
Author(s):  
Kanika Bansal ◽  
Javier O. Garcia ◽  
Steven H. Tompson ◽  
Timothy Verstynen ◽  
Jean M. Vettel ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Brain Dynamics ◽  
Cognitive Systems ◽  
Regional Variability ◽  
Chimera States ◽  
Regional Brain ◽  
The Impact

The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of regional brain activity remains an open question. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the spatial patterning of these states plays a fundamental role in the cognitive organization of the brain and present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We analyze these emergent patterns within our framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.

Brain reactivity to humorous films is affected by insomnia

SLEEP ◽  
2021 ◽  
Author(s):  
Ernesto Sanz-Arigita ◽  
Yannick Daviaux ◽  
Marc Joliot ◽  
Bixente Dilharreguy ◽  
Jean-Arthur Micoulaud-Franchi ◽  
...  
Keyword(s):  
Emotional Reactivity ◽  
Brain Activity ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Neural Representation ◽  
Cingulate Gyrus ◽  
Anterior Cingulate Gyrus ◽  
Left Caudate ◽  
Neural Reactivity ◽  
The Right

Abstract Study objectives Emotional reactivity to negative stimuli has been investigated in insomnia, but little is known about emotional reactivity to positive stimuli and its neural representation. Methods We used 3T fMRI to determine neural reactivity during the presentation of standardized short, 10-40-s, humorous films in insomnia patients (n=20, 18 females, aged 27.7 +/- 8.6 years) and age-matched individuals without insomnia (n=20, 19 females, aged 26.7 +/- 7.0 years), and assessed humour ratings through a visual analogue scale (VAS). Seed-based functional connectivity was analysed for left and right amygdala networks: group-level mixed-effects analysis (FLAME; FSL) was used to compare amygdala connectivity maps between groups. Results fMRI seed-based analysis of the amygdala revealed stronger neural reactivity in insomnia patients than in controls in several brain network clusters within the reward brain network, without humour rating differences between groups (p = 0.6). For left amygdala connectivity, cluster maxima were in the left caudate (Z=3.88), left putamen (Z=3.79) and left anterior cingulate gyrus (Z=4.11), while for right amygdala connectivity, cluster maxima were in the left caudate (Z=4.05), right insula (Z=3.83) and left anterior cingulate gyrus (Z=4.29). Cluster maxima of the right amygdala network were correlated with hyperarousal scores in insomnia patients only. Conclusions Presentation of humorous films leads to increased brain activity in the neural reward network for insomnia patients compared to controls, related to hyperarousal features in insomnia patients, in the absence of humor rating group differences. These novel findings may benefit insomnia treatment interventions.

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Increased connectivity among sensory and motor regions during visual and audiovisual speech perception

2020 ◽  
Author(s):  
Jonathan E Peelle ◽  
Brent Spehar ◽  
Michael S Jones ◽  
Sarah McConkey ◽  
Joel Myerson ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Speech Perception ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Temporal Cortex ◽  
Primary Auditory Cortex ◽  
Visual Speech ◽  
Auditory Signal ◽  
Audiovisual Speech ◽  
Audiovisual Speech Perception

In everyday conversation, we usually process the talker's face as well as the sound of their voice. Access to visual speech information is particularly useful when the auditory signal is degraded. Here we used fMRI to monitor brain activity while adults (n = 60) were presented with visual-only, auditory-only, and audiovisual words. As expected, audiovisual speech perception recruited both auditory and visual cortex, with a trend towards increased recruitment of premotor cortex in more difficult conditions (for example, in substantial background noise). We then investigated neural connectivity using psychophysiological interaction (PPI) analysis with seed regions in both primary auditory cortex and primary visual cortex. Connectivity between auditory and visual cortices was stronger in audiovisual conditions than in unimodal conditions, including a wide network of regions in posterior temporal cortex and prefrontal cortex. Taken together, our results suggest a prominent role for cross-region synchronization in understanding both visual-only and audiovisual speech.

scholarly journals Sweet anticipation: Predictability of familiar music in autism

2020 ◽  
Author(s):  
Patricia Alves Da Mota ◽  
Eloise A Stark ◽  
Henrique M Fernandes ◽  
Christine Ahrends ◽  
Joana Cabral ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Group Analysis ◽  
Brain Network ◽  
Parahippocampal Gyrus ◽  
Dynamics Analysis ◽  
Typically Developing ◽  
Cognitive Profiles ◽  
Musical Excerpts ◽  
Temporal Pole ◽  
Music Track

AbstractAutism has been characterised by different behavioural and cognitive profiles compared to typically developing (TD) individuals, and increasingly these differences have been associated with differences in structural and functional brain connectivity. It is currently unknown as to whether autistic and TD listeners process music in the same way: emotionally, mnemonically, and perceptually. The present study explores the brain’s dynamical landscape linked to music familiarity in an fMRI dataset from autistic and TD individuals. Group analysis using leading eigenvector dynamics analysis (LEiDA) revealed significantly higher probability of occurrence of a brain network in TD compared to autistic individuals during listening to familiar music. This network includes limbic and paralimbic areas (amygdala, hippocampus, parahippocampal gyrus, and temporal pole). No significant differences were found between autistic and TD individuals while listening to a scrambled, i.e. unfamiliar and more unpredictable, version of the same music track. These findings provide novel neuroimaging insights into how autistic prediction monitoring may shape brain networks during listening to familiar musical excerpts.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

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