Increased fronto-temporal connectivity by modified melody in real music

AbstractIn real music, the original melody may appear intact, with little elaboration only, or significantly modified. Since a melody is most easily perceived in music, hearing significantly modified melody may change a brain connectivity. Mozart KV 265 is comprised of an original melody of “Twinkle Twinkle Little Star” with its significant variations. We studied whether effective connectivity changes with significantly modified melody, between bilateral inferior frontal gyri (IFGs) and Heschl’s gyri (HGs) using magnetoencephalography (MEG). Among the 12 connectivities, the connectivity from the left IFG to the right HG was consistently increased with significantly modified melody compared to the original melody in 2 separate sets of the same rhythmic pattern with different melody (p = 0.005 and 0.034, Bonferroni corrected). Our findings show that the modification of an original melody in a real music changes the brain connectivity.Significant statementsOur data show how a regional connectivity changes when the original melody is intact or significantly modified, consistent in two different sets of variations with the same rhythmic patterns but with the different melody pattern. The present study employed real music of Mozart’s Variation KV 265 as musical stimuli, dissected musical elements in each variation, and devised the two comparable sets of variation, which have the same rhythmic pattern but different melody. We exploited naturalistic conditions in real music instead of devising artificial conditions, and successfully demonstrated how variations of melody in real music change a regional connectivity in the brain.

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Brain circuits signaling the absence of emotion in body language

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2007141117 ◽

2020 ◽

Vol 117 (34) ◽

pp. 20868-20873 ◽

Cited By ~ 1

Author(s):

Arseny A. Sokolov ◽

Peter Zeidman ◽

Michael Erb ◽

Frank E. Pollick ◽

Andreas J. Fallgatter ◽

...

Keyword(s):

Brain Connectivity ◽

Effective Connectivity ◽

Well Being ◽

Body Language ◽

Emotional Expressions ◽

Social Signals ◽

Adaptive Social Behavior ◽

The Right ◽

Mental Well Being ◽

The Brain

Adaptive social behavior and mental well-being depend on not only recognizing emotional expressions but also, inferring the absence of emotion. While the neurobiology underwriting the perception of emotions is well studied, the mechanisms for detecting a lack of emotional content in social signals remain largely unknown. Here, using cutting-edge analyses of effective brain connectivity, we uncover the brain networks differentiating neutral and emotional body language. The data indicate greater activation of the right amygdala and midline cerebellar vermis to nonemotional as opposed to emotional body language. Most important, the effective connectivity between the amygdala and insula predicts people’s ability to recognize the absence of emotion. These conclusions extend substantially current concepts of emotion perception by suggesting engagement of limbic effective connectivity in recognizing the lack of emotion in body language reading. Furthermore, the outcome may advance the understanding of overly emotional interpretation of social signals in depression or schizophrenia by providing the missing link between body language reading and limbic pathways. The study thus opens an avenue for multidisciplinary research on social cognition and the underlying cerebrocerebellar networks, ranging from animal models to patients with neuropsychiatric conditions.

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Direct Causal Networks for the Study of Transcranial Magnetic Stimulation Effects on Focal Epileptiform Discharges

International Journal of Neural Systems ◽

10.1142/s0129065715500069 ◽

2015 ◽

Vol 25 (05) ◽

pp. 1550006 ◽

Cited By ~ 23

Author(s):

Dimitris Kugiumtzis ◽

Vasilios K. Kimiskidis

Keyword(s):

Transcranial Magnetic Stimulation ◽

Network Structure ◽

Magnetic Stimulation ◽

Brain Connectivity ◽

Effective Connectivity ◽

Epileptic Focus ◽

Sham Stimulation ◽

Focal Seizures ◽

Epileptiform Discharges ◽

The Brain

Background: Transcranial magnetic stimulation (TMS) can have inhibitory effects on epileptiform discharges (EDs) of patients with focal seizures. However, the brain connectivity before, during and after EDs, with or without the administration of TMS, has not been extensively explored. Objective: To investigate the brain network of effective connectivity during ED with and without TMS in patients with focal seizures. Methods: For the effective connectivity a direct causality measure is applied termed partial mutual information from mixed embedding (PMIME). TMS-EEG data from two patients with focal seizures were analyzed. Each EEG record contained a number of EDs in the majority of which TMS was administered over the epileptic focus. As a control condition, sham stimulation over the epileptogenic zone or real TMS at a distance from the epileptic focus was also performed. The change in brain connectivity structure was investigated from the causal networks formed at each sliding window. Conclusion: The PMIME could detect distinct changes in the network structure before, within, and after ED. The administration of real TMS over the epileptic focus, in contrast to sham stimulation, terminated the ED prematurely in a node-specific manner and regained the network structure as if it would have terminated spontaneously.

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Determination of Dynamic Brain Connectivity via Spectral Analysis

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.655576 ◽

2021 ◽

Vol 15 ◽

Author(s):

Peter A. Robinson ◽

James A. Henderson ◽

Natasha C. Gabay ◽

Kevin M. Aquino ◽

Tara Babaie-Janvier ◽

...

Keyword(s):

Spectral Analysis ◽

Brain Structure ◽

Brain Connectivity ◽

Spatial Scales ◽

Effective Connectivity ◽

Physical Nature ◽

Building Blocks ◽

Brain Dynamics ◽

Compact Representation ◽

The Brain

Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.

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Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

Neural Plasticity ◽

10.1155/2018/5340717 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Anna Lardone ◽

Marianna Liparoti ◽

Pierpaolo Sorrentino ◽

Rosaria Rucco ◽

Francesca Jacini ◽

...

Keyword(s):

Resting State ◽

Mindfulness Meditation ◽

Brain Connectivity ◽

Brain Networks ◽

Brain Network ◽

Long Term Effects ◽

Age Related ◽

Functional Topology ◽

The Right ◽

The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

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Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0062 ◽

2020 ◽

Vol 65 (1) ◽

pp. 23-32

Author(s):

Mehdi Rajabioun ◽

Ali Motie Nasrabadi ◽

Mohammad Bagher Shamsollahi ◽

Robert Coben

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Effective Connectivity ◽

Estimation Error ◽

Brain Regions ◽

Estimation Methods ◽

Estimation Errors ◽

Normal Children ◽

Brain Functions ◽

The Brain

AbstractBrain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities between active brain regions of autistic and normal children in the resting state are estimated and compared. In this simulation, the brain is divided into eight regions and the connectivity between regions and within them is calculated. It can be concluded from the results that in the resting state condition the effective connectivity of active regions is decreased between regions and is increased within each region in autistic children. In another result, by averaging the connectivity between the extracted active sources of each region, the connectivity between the left and right of the central part is more than that in other regions and the connectivity in the occipital part is less than that in others.

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Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network

Frontiers in Neuroscience ◽

10.3389/fnins.2020.596109 ◽

2021 ◽

Vol 14 ◽

Author(s):

Dongya Wu ◽

Xin Li ◽

Jun Feng

Keyword(s):

Neural Network ◽

Functional Connectivity ◽

Brain Connectivity ◽

Face Area ◽

The Face ◽

The Individual ◽

The Right ◽

And Function ◽

The Relationship ◽

The Brain

Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.

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Structurally constrained effective brain connectivity

10.31219/osf.io/4erqd ◽

2021 ◽

Author(s):

Alessandro Crimi

Keyword(s):

Community Detection ◽

Autoregressive Model ◽

Brain Connectivity ◽

Effective Connectivity ◽

Structural Connectivity ◽

Ground Truth ◽

Autism Spectrum ◽

Case Control ◽

Functional Dependencies ◽

The Brain

The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of effective connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal.The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task.

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Dynamics of Epileptiform Discharges Induced by Transcranial Magnetic Stimulation in Genetic Generalized Epilepsy

International Journal of Neural Systems ◽

10.1142/s012906571750037x ◽

2017 ◽

Vol 27 (07) ◽

pp. 1750037 ◽

Cited By ~ 9

Author(s):

Dimitris Kugiumtzis ◽

Christos Koutlis ◽

Alkiviadis Tsimpiris ◽

Vasilios K. Kimiskidis

Keyword(s):

Transcranial Magnetic Stimulation ◽

Magnetic Stimulation ◽

Brain Connectivity ◽

Effective Connectivity ◽

Brain Network ◽

Brain State ◽

Epileptiform Discharges ◽

Eeg Recordings ◽

Generalized Epilepsy ◽

The Brain

Objective: In patients with Genetic Generalized Epilepsy (GGE), transcranial magnetic stimulation (TMS) can induce epileptiform discharges (EDs) of varying duration. We hypothesized that (a) the ED duration is determined by the dynamic states of critical network nodes (brain areas) at the early post-TMS period, and (b) brain connectivity changes before, during and after the ED, as well as within the ED. Methods: EEG recordings from two GGE patients were analyzed. For hypothesis (a), the characteristics of the brain dynamics at the early ED stage are measured with univariate and multivariate EEG measures and the dependence of the ED duration on these measures is evaluated. For hypothesis (b), effective connectivity measures are combined with network indices so as to quantify the brain network characteristics and identify changes in brain connectivity. Results: A number of measures combined with specific channels computed on the first EEG segment post-TMS correlate with the ED duration. In addition, brain connectivity is altered from pre-ED to ED and post-ED and statistically significant changes were also detected across stages within the ED. Conclusion: ED duration is not purely stochastic, but depends on the dynamics of the post-TMS brain state. The brain network dynamics is significantly altered in the course of EDs.

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Effective connectivity during faces processing in major depression: Distinguishing markers of pathology, risk, and resilience

10.1101/2021.04.12.21255310 ◽

2021 ◽

Author(s):

Seda Sacu ◽

Carolin Wackerhagen ◽

Susanne Erk ◽

Nina Romanczuk-Seiferth ◽

Kristina Schwarz ◽

...

Keyword(s):

Prefrontal Cortex ◽

Major Depression ◽

Orbitofrontal Cortex ◽

Dorsolateral Prefrontal Cortex ◽

Brain Connectivity ◽

Effective Connectivity ◽

Fusiform Gyrus ◽

Risk And Resilience ◽

Dorsolateral Prefrontal ◽

The Right

Abstract Background: Aberrant brain connectivity during emotional processing, especially within the fronto-limbic pathway, is one of the hallmarks of major depressive disorder (MDD). However, a lack of systematic approaches in previous studies made it difficult to determine whether a specific alteration in brain connectivity reflects a cause, correlate, or effect of the disorder. The current study aimed to investigate neural mechanisms that correspond to disease, risk and resilience in major depression during implicit processing of emotion cues. Methods: Forty-eight patients with MDD, 49 first-degree relatives of patients with MDD and 103 healthy controls performed a face-matching task during functional magnetic resonance imaging. We used dynamic causal modelling to estimate task-dependent effective connectivity at the subject level. Parametric empirical Bayes was then performed to quantify group differences in effective connectivity. Results: Depressive pathology was associated with decreased effective connectivity from the left amygdala and left dorsolateral prefrontal cortex to the right fusiform gyrus, whereas familial risk for depression corresponded to decreased connectivity from the right orbitofrontal cortex to the left insula and from the left orbitofrontal cortex to the right fusiform gyrus. Resilience for depression was related to increased connectivity from the anterior cingulate cortex to the left dorsolateral prefrontal cortex. Conclusions: Our results suggest that the depressive state alters top-down control of higher visual regions during the processing of emotional faces, whereas increased connectivity within the cognitive control network promotes resilience to depression.

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Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex

Nature Communications ◽

10.1038/s41467-021-27926-w ◽

2022 ◽

Vol 13 (1) ◽

Author(s):

Dace Apšvalka ◽

Catarina S. Ferreira ◽

Taylor W. Schmitz ◽

James B. Rowe ◽

Michael C. Anderson

Keyword(s):

Prefrontal Cortex ◽

Inhibitory Control ◽

Specific Activity ◽

Effective Connectivity ◽

General System ◽

Connectivity Analysis ◽

Action Execution ◽

Domain Specific ◽

The Right ◽

The Brain

AbstractOver the last two decades, inhibitory control has featured prominently in accounts of how humans and other organisms regulate their behaviour and thought. Previous work on how the brain stops actions and thoughts, however, has emphasised distinct prefrontal regions supporting these functions, suggesting domain-specific mechanisms. Here we show that stopping actions and thoughts recruits common regions in the right dorsolateral and ventrolateral prefrontal cortex to suppress diverse content, via dynamic targeting. Within each region, classifiers trained to distinguish action-stopping from action-execution also identify when people are suppressing their thoughts (and vice versa). Effective connectivity analysis reveals that both prefrontal regions contribute to action and thought stopping by targeting the motor cortex or the hippocampus, depending on the goal, to suppress their task-specific activity. These findings support the existence of a domain-general system that underlies inhibitory control and establish Dynamic Targeting as a mechanism enabling this ability.

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