scholarly journals Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing

2021 ◽  
Vol 11 ◽  
Author(s):  
Fa-Hsuan Lin ◽  
Hsin-Ju Lee ◽  
Wen-Jui Kuo ◽  
Iiro P. Jääskeläinen
Keyword(s):  
Large Scale ◽  
Brain Structures ◽  
Brain Network ◽  
Network Activity ◽  
Joint Analysis ◽  
Behavioral Measures ◽  
Experimental Conditions ◽  
Free Viewing ◽  
Fmri Data Analysis ◽  
Temporal Models

While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.

Identifying neuroanatomical signatures in insomnia and migraine comorbidity

SLEEP ◽  
2020 ◽  
Author(s):  
Kun-Hsien Chou ◽  
Pei-Lin Lee ◽  
Chih-Sung Liang ◽  
Jiunn-Tay Lee ◽  
Hung-Wen Kao ◽  
...  
Keyword(s):  
Large Scale ◽  
Gray Matter Volume ◽  
Brain Structures ◽  
Brain Network ◽  
Structural Covariance ◽  
Matter Volume ◽  
The Right ◽  
Global Brain ◽  
The Brain

Abstract Study Objectives While insomnia and migraine are often comorbid, the shared and distinct neuroanatomical substrates underlying these disorders and the brain structures associated with the comorbidity are unknown. We aimed to identify patterns of neuroanatomical substrate alterations associated with migraine and insomnia comorbidity. Methods High-resolution T1-weighted images were acquired from subjects with insomnia, migraine, and comorbid migraine and insomnia, respectively, and healthy controls (HC). Direct group comparisons with HC followed by conjunction analyses identified shared regional gray matter volume (GMV) alterations between the disorders. To further examine large-scale anatomical network changes, a seed-based structural covariance network (SCN) analysis was applied. Conjunction analyses also identified common SCN alterations in two disease groups, and we further evaluated these shared regional and global neuroanatomical signatures in the comorbid group. Results Compared with controls, patients with migraine and insomnia showed GMV changes in the cerebellum and the lingual, precentral, and postcentral gyri (PCG). The bilateral PCG were common GMV alteration sites in both groups, with decreased structural covariance integrity observed in the cerebellum. In patients with comorbid migraine and insomnia, shared regional GMV and global SCN changes were consistently observed. The GMV of the right PCG also correlated with sleep quality in these patients. Conclusion These findings highlight the specific role of the PCG in the shared pathophysiology of insomnia and migraine from a regional and global brain network perspective. These multilevel neuroanatomical changes could be used as potential image markers to decipher the comorbidity of the two disorders.

scholarly journals Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

2020 ◽  
Vol 4 (2) ◽  
pp. 448-466
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Resting State ◽  
Large Scale ◽  
Brain Network ◽  
Generative Models ◽  
Network Activity ◽  
Fmri Data ◽  
State Transitions ◽  
Whole Brain

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007 ). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco, 2017 ; Kashyap & Keilholz, 2019 ). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012 ; Majeed et al., 2011 ). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.

A gene coexpression network for bovine skeletal muscle inferred from microarray data

2006 ◽  
Vol 28 (1) ◽  
pp. 76-83 ◽  
Author(s):  
Antonio Reverter ◽  
Nicholas J. Hudson ◽  
Yonghong Wang ◽  
Siok-Hwee Tan ◽  
Wes Barris ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Large Scale ◽  
Mixed Model ◽  
Structural Proteins ◽  
Joint Analysis ◽  
Experimental Conditions ◽  
Model Equations ◽  
Highly Correlated ◽  
Bovine Skeletal Muscle ◽  
Fast Twitch

We present the application of large-scale multivariate mixed-model equations to the joint analysis of nine gene expression experiments in beef cattle muscle and fat tissues with a total of 147 hybridizations, and we explore 47 experimental conditions or treatments. Using a correlation-based method, we constructed a gene network for 822 genes. Modules of muscle structural proteins and enzymes, extracellular matrix, fat metabolism, and protein synthesis were clearly evident. Detailed analysis of the network identified groupings of proteins on the basis of physical association. For example, expression of three components of the z-disk, MYOZ1, TCAP, and PDLIM3, was significantly correlated. In contrast, expression of these z-disk proteins was not highly correlated with the expression of a cluster of thick (myosins) and thin (actin and tropomyosins) filament proteins or of titin, the third major filament system. However, expression of titin was itself not significantly correlated with the cluster of thick and thin filament proteins and enzymes. Correlation in expression of many fast-twitch muscle structural proteins and enzymes was observed, but slow-twitch-specific proteins were not correlated with the fast-twitch proteins or with each other. In addition, a number of significant associations between genes and transcription factors were also identified. Our results not only recapitulate the known biology of muscle but have also started to reveal some of the underlying associations between and within the structural components of skeletal muscle.

scholarly journals Altered EEG resting-state large-scale brain network dynamics in euthymic bipolar disorder patients

2019 ◽  
Author(s):  
Alena Damborská ◽  
Camille Piguet ◽  
Jean-Michel Aubry ◽  
Alexandre G. Dayer ◽  
Christoph M. Michel ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Resting State ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Network Activity ◽  
Anxiety State ◽  
Temporal Characteristics ◽  
Functional Brain Network ◽  
And Gender

AbstractBackgroundNeuroimaging studies provided evidence for disrupted resting-state functional brain network activity in bipolar disorder (BD). Electroencephalographic (EEG) studies found altered temporal characteristics of functional EEG microstates during depressive episode within different affective disorders. Here we investigated whether euthymic patients with BD show deviant resting-state large-scale brain network dynamics as reflected by altered temporal characteristics of EEG microstates.MethodsWe used high-density EEG to explore between-group differences in duration, coverage and occurrence of the resting-state functional EEG microstates in 17 euthymic adults with BD in on-medication state and 17 age- and gender-matched healthy controls. Two types of anxiety, state and trait, were assessed separately with scores ranging from 20 to 80.ResultsMicrostate analysis revealed five microstates (A-E) in global clustering across all subjects. In patients compared to controls, we found increased occurrence and coverage of microstate A that did not significantly correlate with anxiety scores.ConclusionOur results provide neurophysiological evidence for altered large-scale brain network dynamics in BD patients and suggest the increased presence of A microstate to be an electrophysiological trait characteristic of BD.

scholarly journals Causal coupling between neural activity, metabolism, and behavior across the Drosophila brain

2020 ◽  
Author(s):  
Kevin Mann ◽  
Stephane Deny ◽  
Surya Ganguli ◽  
Thomas R. Clandinin
Keyword(s):  
Energy Metabolism ◽  
Neural Activity ◽  
Large Scale ◽  
Metabolic Flux ◽  
Fruit Fly ◽  
Brain Network ◽  
Network Activity ◽  
Tight Coupling ◽  
Behavioral States ◽  
The Brain

Coordinated activity across networks of neurons is a hallmark of both resting and active behavioral states in many species, including worms, flies, fish, mice and humans1–5. These global patterns alter energy metabolism in the brain over seconds to hours, making oxygen consumption and glucose uptake widely used proxies of neural activity6,7. However, whether changes in neural activity are causally related to changes in metabolic flux in intact circuits on the sub-second timescales associated with behavior, is unknown. Moreover, it is unclear whether transitions between rest and action are associated with spatiotemporally structured changes in neuronal energy metabolism. Here, we combine two-photon microscopy of the entire fruit fly brain with sensors that allow simultaneous measurements of neural activity and metabolic flux, across both resting and active behavioral states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across large-scale brain networks. Further, these studies reveal that the initiation of even minimal behavioral movements causes large-scale changes in the pattern of neural activity and energy metabolism, revealing unexpected structure in the functional architecture of the brain. The relationship between neural activity and energy metabolism is likely evolutionarily ancient. Thus, these studies provide a critical foundation for using metabolic proxies to capture changes in neural activity and reveal that even minimal behavioral movements are associated with changes in large-scale brain network activity.

scholarly journals Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms

2019 ◽  
Author(s):  
Jessica S. Flannery ◽  
Michael C. Riedel ◽  
Katherine L. Bottenhorn ◽  
Ranjita Poudel ◽  
Taylor Salo ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Reward Processing ◽  
Network Activity ◽  
Reward Learning ◽  
Learning Theories ◽  
Behavior Profiles

ABSTRACTReward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we utilized emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution which represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 & MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

The Functional Role of Large-scale Brain Network Coordination in Placebo-induced Anxiolysis

2018 ◽  
Vol 29 (8) ◽  
pp. 3201-3210 ◽  
Author(s):  
Benjamin Meyer ◽  
Kenneth S L Yuen ◽  
Victor Saase ◽  
Raffael Kalisch
Keyword(s):  
Functional Role ◽  
Large Scale ◽  
Activity Patterns ◽  
Clinical Importance ◽  
Mental States ◽  
Brain Network ◽  
Anxiety Reduction ◽  
Anterior Cingulate ◽  
Network Activity

Abstract Anxiety reduction through mere expectation of anxiolytic treatment effects (placebo anxiolysis) has enormous clinical importance. Recent behavioral and electrophysiological data suggest that placebo anxiolysis involves reduced vigilance and enhanced internalization of attention; however, the underlying neurobiological mechanisms are not yet clear. Given the fundamental function of intrinsic connectivity networks (ICNs) in basic cognitive processes, we investigated ICN activity patterns associated with externally and internally directed mental states under the influence of an anxiolytic placebo medication. Based on recent findings, we specifically analyzed the functional role of the rostral anterior cingulate cortex (rACC) in coordinating placebo-dependent cue-related (phasic) and cue-unrelated (sustained) network activity. Under placebo, we observed a down-regulation of the entire salience network (SN), particularly in response to threatening cues. The rACC exhibited enhanced cue-unrelated functional connectivity (FC) with the SN, which correlated with reductions in tonic arousal and anxiety. Hence, apart from the frequently reported modulation of aversive cue responses, the rACC appears to be crucially involved in exerting a tonically dampening control over salience-responsive structures. In line with a more internally directed mental state, we also found enhanced FC within the default mode network (DMN), again predicting reductions in anxiety under placebo.

scholarly journals Rifle Shooting Performance Correlates with Electroencephalogram Beta Rhythm Network Activity during Aiming

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Anmin Gong ◽  
Jianping Liu ◽  
Changhao Jiang ◽  
Yunfa Fu
Keyword(s):  
Network Topology ◽  
Information Integration ◽  
Correlation Coefficients ◽  
Significant Negative Correlation ◽  
Brain Network ◽  
Network Activity ◽  
Functional Coupling ◽  
Experimental Conditions ◽  
Functional Brain Network ◽  
The Right

To study the relationship between brain network and shooting performance during shooting aiming, we collected electroencephalogram (EEG) signals from 40 skilled shooters during rifle shooting and calculated the EEG functional coupling, functional brain network topology, and correlation coefficients between these EEG characteristics and shooting performance. Our result shows a significant negative correlation between shooting performance and functional coupling between the prefrontal, frontal, and temporal regions of the right brain in the Beta1 and Beta2 frequency bands. Global and local brain network topology characteristics were also significantly correlated with shooting performance. These findings indicate that under these experimental conditions, shooters with higher shooting performances exhibit lower functional coupling, higher global, and lower local information integration efficiency during shooting. These conclusions may provide a theoretical basis of the EEG brain network for studying the mental status of shooters while shooting.

Identifying Emotional Specificity in Complex Large-Scale Brain Networks

2018 ◽  
Vol 10 (3) ◽  
pp. 217-218 ◽  
Author(s):  
Stefan Koelsch
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Structures ◽  
Brain Network ◽  
Nonlinear Interactions ◽  
Brain Science ◽  
Human Emotion ◽  
Target Article ◽  
Neural Processes ◽  
Network Properties

The target article is well in accordance with recent theoretical advances considering the complex large-scale brain network organization underlying emotions. Given current limitations of the methods in brain science, however, research is faced with the difficult question as to how it will be possible to elucidate the complex nonlinear interactions, the neurotransmitters involved, and the excitatory or inhibitory nature of neural processes underlying human emotion in such networks. Moreover, while investigating the network properties of neural processes underlying emotions, it is also important to keep in mind that specific brain structures, or specific brain networks, generate specific emotions. Thus, while aiming at elucidating complex large-scale brain networks of emotion, it is important to identify emotional specificity of, or within, these networks.

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