scholarly journals Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

2019 ◽  
Author(s):  
João F. Guassi Moreira ◽  
Katie A. McLaughlin ◽  
Jennifer A. Silvers
Keyword(s):  
Emotion Regulation ◽  
Skill Acquisition ◽  
Network Architecture ◽  
Evolutionary Biology ◽  
Brain Activity ◽  
Brain Networks ◽  
Adult Life ◽  
Brain Network ◽  
Network Activity ◽  
Whole Brain

AbstractThe ability to regulate emotions is key to goal attainment and wellbeing. Although much has been discovered about how the human brain develops to support the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework in conjunction with machine learning to: (i) parse the neural underpinnings of emotion regulation skill acquisition while accounting for age, and (ii) build a working taxonomy of brain network activity supporting emotion regulation in a sample of youth (N = 70, 34 female). We were able to predict emotion regulation ability, but not age, using network activity metrics from whole-brain networks during an emotion regulation task. Further, by leveraging analytic techniques traditionally used in evolutionary biology (e.g., cophenetic correlations), we were able to demonstrate that brain networks evince reliable taxonomic organization to meet emotion regulation demands in youth. This work shows that meaningful information about emotion regulation development is encoded in whole-brain network activity, suggesting that brain activity during emotion regulation encodes unique information about regulatory skill acquisition in youth but not domain-general maturation.Significance StatementThe acquisition of emotion regulation is critical for healthy functioning in later adult life. To date, little is known about how brain networks support the developmental acquisition of emotion regulation skills. This is noteworthy because brain networks have been increasingly shown to provide highly useful information about neural activity. Here we show that brain activity during an emotion regulation task encodes information about regulatory abilities over and above age. These results suggest emotion regulation skills are dependent on neural specialization of domain-specific systems, whereas age is encoded via domain-general systems.

scholarly journals Focused attention meditation changes the boundary and configuration of functional networks in the brain

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shogo Kajimura ◽  
Naoki Masuda ◽  
Johnny King L. Lau ◽  
Kou Murayama
Keyword(s):  
Network Architecture ◽  
Single Case ◽  
Brain Networks ◽  
Brain Network ◽  
Focused Attention ◽  
Community Size ◽  
Whole Brain ◽  
Motor Network ◽  
Functional Brain Networks ◽  
The Brain

Abstract Research has shown that focused attention meditation not only improves our cognitive and motivational functioning (e.g., attention, mental health), it influences the way our brain networks [e.g., default mode network (DMN), fronto-parietal network (FPN), and sensory-motor network (SMN)] function and operate. However, surprisingly little attention has been paid to the possibility that meditation alters the architecture (composition) of these functional brain networks. Here, using a single-case experimental design with intensive longitudinal data, we examined the effect of mediation practice on intra-individual changes in the composition of whole-brain networks. The results showed that meditation (1) changed the community size (with a number of regions in the FPN being merged into the DMN after meditation) and (2) led to instability in the community allegiance of the regions in the FPN. These results suggest that, in addition to altering specific functional connectivity, meditation leads to reconfiguration of whole-brain network architecture. The reconfiguration of community architecture in the brain provides fruitful information about the neural mechanisms of meditation.

scholarly journals Investigating the interaction between white matter and brain state on tDCS-induced changes in brain network activity

2020 ◽  
Author(s):  
Danielle L. Kurtin ◽  
Ines R. Violante ◽  
Karl Zimmerman ◽  
Robert Leech ◽  
Adam Hampshire ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Brain Networks ◽  
Brain Network ◽  
Network Activity ◽  
Brain State ◽  
State Dependent ◽  
Cathodal Tdcs ◽  
The Right

AbstractBackgroundTranscranial direct current stimulation (tDCS) is a form of noninvasive brain stimulation whose potential as a cognitive therapy is hindered by our limited understanding of how participant and experimental factors influence its effects. Using functional MRI to study brain networks, we have previously shown in healthy controls that the physiological effects of tDCS are strongly influenced by brain state. We have additionally shown, in both healthy and traumatic brain injury (TBI) populations, that the behavioral effects of tDCS are positively correlated with white matter (WM) structure.ObjectivesIn this study we investigate how these two factors, WM structure and brain state, interact to shape the effect of tDCS on brain network activity.MethodsWe applied anodal, cathodal and sham tDCS to the right inferior frontal gyrus (rIFG) of healthy (n=22) and TBI participants (n=34). We used the Choice Reaction Task (CRT) performance to manipulate brain state during tDCS. We acquired simultaneous fMRI to assess activity of cognitive brain networks and used Fractional Anisotropy (FA) as a measure of WM structure.ResultsWe find that the effects of tDCS on brain network activity in TBI participants are highly dependent on brain state, replicating findings from our previous healthy control study in a separate, patient cohort. We then show that WM structure further modulates the brain-state dependent effects of tDCS on brain network activity. These effects are not unidirectional – in the absence of task with anodal and cathodal tDCS, FA is positively correlated with brain activity in several regions of the default mode network. Conversely, with cathodal tDCS during CRT performance, FA is negatively correlated with brain activity in a salience network region.ConclusionsOur results show that experimental and participant factors interact to have unexpected effects on brain network activity, and that these effects are not fully predictable by studying the factors in isolation.

scholarly journals Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

2019 ◽  
Author(s):  
Yongjie Zhu ◽  
Chi Zhang ◽  
Petri Toiviainen ◽  
Minna Huotilainen ◽  
Klaus Mathiak ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Brain Networks ◽  
Brain Network ◽  
Network Activity ◽  
Sensor Data ◽  
Frequency Dependent ◽  
Feature Processing ◽  
Ongoing Eeg ◽  
Musical Feature

AbstractRecently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

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.

Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

2020 ◽  
Vol 30 (10) ◽  
pp. 2050051
Author(s):  
Feng Fang ◽  
Thomas Potter ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Emotion Regulation ◽  
Large Scale ◽  
Brain Networks ◽  
Cognitive Reappraisal ◽  
Brain Network ◽  
Affective Processing ◽  
Functional Brain ◽  
Affective Stimuli ◽  
Functional Brain Network ◽  
Functional Brain Networks

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

Altered brain network centrality in patients with trigeminal neuralgia: a resting-state fMRI study

2019 ◽  
Vol 61 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Pei-Wen Zhu ◽  
You Chen ◽  
Ying-Xin Gong ◽  
Nan Jiang ◽  
Wen-Feng Liu ◽  
...  
Keyword(s):  
Trigeminal Neuralgia ◽  
Brain Activity ◽  
Characteristic Curve ◽  
Brain Regions ◽  
Brain Network ◽  
Network Activity ◽  
Degree Centrality ◽  
Healthy Controls ◽  
Postcentral Gyrus ◽  
The Right

Background Neuroimaging studies revealed that trigeminal neuralgia was related to alternations in brain anatomical function and regional function. However, the functional characteristics of network organization in the whole brain is unknown. Purpose The aim of the present study was to analyze potential functional network brain-activity changes and their relationships with clinical features in patients with trigeminal neuralgia via the voxel-wise degree centrality method. Material and Methods This study involved a total of 28 trigeminal neuralgia patients (12 men, 16 women) and 28 healthy controls matched in sex, age, and education. Spontaneous brain activity was evaluated by degree centrality. Correlation analysis was used to examine the correlations between behavioral performance and average degree centrality values in several brain regions. Results Compared with healthy controls, trigeminal neuralgia patients had significantly higher degree centrality values in the right lingual gyrus, right postcentral gyrus, left paracentral lobule, and bilateral inferior cerebellum. Receiver operative characteristic curve analysis of each brain region confirmed excellent accuracy of the areas under the curve. There was a positive correlation between the mean degree centrality value of the right postcentral gyrus and VAS score (r = 0.885, P < 0.001). Conclusions Trigeminal neuralgia causes abnormal brain network activity in multiple brain regions, which may be related to underlying disease mechanisms.

scholarly journals The neural basis of attentional alterations in prenatally protein malnourished rats

2020 ◽  
Vol 31 (1) ◽  
pp. 497-512
Author(s):  
R J Rushmore ◽  
J A McGaughy ◽  
A C Amaral ◽  
D J Mokler ◽  
P J Morgane ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Brain Development ◽  
Brain Networks ◽  
Brain Network ◽  
Protein Malnutrition ◽  
Network Activity ◽  
Neural Basis ◽  
Cortical Function ◽  
Cognitive Changes ◽  
Attention Task

Abstract Protein malnutrition during gestation alters brain development and produces specific behavioral and cognitive changes that persist into adulthood and increase the risks of neuropsychiatric disorders. Given evidence for the role of the prefrontal cortex in such diseases, it is significant that studies in humans and animal models have shown that prenatal protein malnutrition specifically affects functions associated with prefrontal cortex. However, the neural basis underlying these changes is unclear. In the current study, prenatally malnourished and control rats performed a sustained attention task with an unpredictable distractor, a task that depends on intact prefrontal cortical function. Radiolabeled 2-deoxyglucose was used to measure neural and brain network activity during the task. Results confirmed that adult prenatally malnourished rats were more distractible than controls and exhibited lower functional activity in prefrontal cortices. Thus, prefrontal activity was a predictor of task performance in controls but not prenatally malnourished animals. Instead, prenatally malnourished animals relied on different brain networks involving limbic structures such as the hippocampus. These results provide evidence that protein reduction during brain development has more wide-reaching effects on brain networks than previously appreciated, resulting in the formation of brain networks that may reflect compensatory responses in prenatally malnourished brains.

scholarly journals The dynamic connectome of speech control

2021 ◽  
Vol 376 (1836) ◽  
Author(s):  
Davide Valeriani ◽  
Kristina Simonyan
Keyword(s):  
Neural Network ◽  
Effective Connectivity ◽  
Vocal Learning ◽  
Brain Regions ◽  
Brain Network ◽  
Motor Output ◽  
Network Activity ◽  
Imaging Data ◽  
Whole Brain ◽  
Cost Efficient

Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during the production of meaningful English sentences and meaningless syllables. We identified that the two dynamic connectomes have distinct architectures that are dependent on the complexity of task production. The speech was regulated by a dynamic neural network, the most influential nodes of which were centred around superior and inferior parietal areas and influenced the whole-brain network activity via long-ranging coupling with primary sensorimotor, prefrontal, temporal and insular regions. By contrast, syllable production was controlled by a more compressed, cost-efficient network structure, involving sensorimotor cortico-subcortical integration via superior parietal and cerebellar network hubs. These data demonstrate the mechanisms by which the neural network reorganizes the connectivity of its influential regions, from supporting the fundamental aspects of simple syllabic vocal motor output to multimodal information processing of speech motor output. This article is part of the theme issue ‘Vocal learning in animals and humans’.

scholarly journals Identification of neural oscillations and epileptiform changes in human brain organoids

2019 ◽  
Author(s):  
Ranmal A. Samarasinghe ◽  
Osvaldo A. Miranda ◽  
Simon Mitchell ◽  
Isabella Ferando ◽  
Momoko Watanabe ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Network Architecture ◽  
Network Formation ◽  
Neurological Diseases ◽  
Extracellular Recording ◽  
Brain Network ◽  
Network Activity ◽  
Intact Brain ◽  
Oscillatory Network

ABSTRACTHuman brain organoids represent a powerful tool for the study of human neurological diseases particularly those that impact brain growth and structure. However, many neurological diseases lack obvious anatomical abnormalities, yet significantly impact neural network functions, raising the question of whether organoids possess sufficient neural network architecture and complexity to model these conditions. Here, we explore the network level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex oscillatory network behaviors reminiscent of intact brain preparations. We further demonstrate strikingly abnormal epileptiform network activity in organoids derived from a Rett Syndrome patient despite only modest anatomical differences from isogenically matched controls, and rescue with an unconventional neuromodulatory drug Pifithrin-α. Together, these findings provide an essential foundation for the utilization of human brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.

scholarly journals A DEEP LEARNING APPROACH TO ESTIMATING INITIAL CONDITIONS OF BRAIN NETWORK MODELS IN REFERENCE TO MEASURED FMRI DATA

2021 ◽  
Author(s):  
Amrit Kashyap ◽  
Sergey Plis ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Shella Keilholz
Keyword(s):  
Deep Learning ◽  
Initial Conditions ◽  
Brain Activity ◽  
Network Models ◽  
Brain Network ◽  
Measured Data ◽  
Fmri Data ◽  
Short Term ◽  
Whole Brain ◽  
Neural Processes

Brain Network Models (BNMs) are a family of dynamical systems that simulate whole brain activity using neural mass models to represent local activity in different brain regions that influence each other via a global structural network. Research has been interested in using these network models to explain measured whole brain activity measured via resting state functional magnetic resonance imaging (rs-fMRI). Properties computed over longer periods of simulated and measured data such as average functional connectivity (FC), have shown to be comparable with similar properties estimated from measured rs-fMRI data. While this shows that these network models have similar properties over the dynamical landscape, it is unclear how well simulated trajectories compare with empirical trajectories on a timepoint-by-timepoint basis. Previous studies have shown that BNMs are able to produce relevant features at shorter timescales, but analysis of short-term trajectories or transient dynamics as defined by synchronized predictions from BNM made at the same timescale as the collected data has not yet been conducted. Relevant neural processes exist in the time frame of measurements and are often used in task fMRI studies to understand neural responses to behavioral cues. Therefore, it is important to investigate how much of these dynamics are captured by our current brain simulations. To test the nature of BNMs short term trajectories against observed data, we utilize a deep learning technique known as Neural ODE that based on an observed sequence of fMRI measurements, estimates the initial conditions such that the BNMs simulation is synchronized to produce the closest trajectory relative to the observed data. We test to see if the parameterization of a specific well studied BNM, the Firing Rate Model, calculated by maximizing its accuracy in reproducing observed short term trajectories matches with the parameterized model that produces the best average long-term metrics. Our results show that such an agreement between parameterization using long and short simulation analysis exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity. Therefore, we conclude that there is evidence that by solving for initial conditions, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.

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