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.

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 Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

2017 ◽  
Author(s):  
Giri P. Krishnan ◽  
Oscar C. González ◽  
Maxim Bazhenov
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Ion Concentration ◽  
Brain State ◽  
The Brain

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

scholarly journals Altered brain network centrality in patients with adult comitant exotropia strabismus: A resting-state fMRI study

2017 ◽  
Vol 46 (1) ◽  
pp. 392-402 ◽  
Author(s):  
Gang Tan ◽  
Zeng-Renqing Dan ◽  
Ying Zhang ◽  
Xin Huang ◽  
Yu-Lin Zhong ◽  
...  
Keyword(s):  
Brain Activity ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Brain Network ◽  
Network Activity ◽  
Ocular Motility ◽  
Behavioral Performance ◽  
Parietal Lobule ◽  
The Right ◽  
The Relationship

Objective To investigate the underlying functional network brain-activity changes in patients with adult comitant exotropia strabismus (CES) and the relationship with clinical features using the voxel-wise degree centrality (DC) method. Methods A total of 30 patients with CES (17 men, 13 women), and 30 healthy controls (HCs; 17 men, 13 women) matched in age, sex, and education level participated in the study. DC was used to evaluate spontaneous brain activity. Receiver operating characteristic (ROC) curve analysis was conducted to distinguish CESs from HCs. The relationship between mean DC values in various brain regions and behavioral performance was examined with correlation analysis. Results Compared with HCs, CES patients exhibited decreased DC values in the right cerebellum posterior lobe, right inferior frontal gyrus, right middle frontal gyrus and right superior parietal lobule/primary somatosensory cortex (S1), and increased DC values in the right superior temporal gyrus, bilateral anterior cingulate, right superior temporal gyrus, and left inferior parietal lobule. However, there was no correlation between mean DC values and behavioral performance in any brain regions. Conclusions Adult comitant exotropia strabismus is associated with abnormal brain network activity in various brain regions, possibly reflecting the pathological mechanisms of ocular motility disorders in CES.

scholarly journals Local Perturbations of Cortical Excitability Propagate Differentially Through Large-Scale Functional Networks

2020 ◽  
Vol 30 (5) ◽  
pp. 3352-3369 ◽  
Author(s):  
Zachary P Rosenthal ◽  
Ryan V Raut ◽  
Ping Yan ◽  
Deima Koko ◽  
Andrew W Kraft ◽  
...  
Keyword(s):  
Large Scale ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Field Potential ◽  
Brain Network ◽  
Network Activity ◽  
Wide Field ◽  
Activity Levels ◽  
Electrophysiological Recordings ◽  
Parvalbumin Interneuron

Abstract Electrophysiological recordings have established that GABAergic interneurons regulate excitability, plasticity, and computational function within local neural circuits. Importantly, GABAergic inhibition is focally disrupted around sites of brain injury. However, it remains unclear whether focal imbalances in inhibition/excitation lead to widespread changes in brain activity. Here, we test the hypothesis that focal perturbations in excitability disrupt large-scale brain network dynamics. We used viral chemogenetics in mice to reversibly manipulate parvalbumin interneuron (PV-IN) activity levels in whisker barrel somatosensory cortex. We then assessed how this imbalance affects cortical network activity in awake mice using wide-field optical neuroimaging of pyramidal neuron GCaMP dynamics as well as local field potential recordings. We report 1) that local changes in excitability can cause remote, network-wide effects, 2) that these effects propagate differentially through intra- and interhemispheric connections, and 3) that chemogenetic constructs can induce plasticity in cortical excitability and functional connectivity. These findings may help to explain how focal activity changes following injury lead to widespread network dysfunction.

Large Scale Neurocognitive Networks Underlying Episodic Memory

2000 ◽  
Vol 12 (1) ◽  
pp. 163-173 ◽  
Author(s):  
Lars Nyberg ◽  
Jonas Persson ◽  
Reza Habib ◽  
Endel Tulving ◽  
Anthony R. McIntosh ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Target Density ◽  
Functional Connections ◽  
Positron Emission ◽  
Episodic Encoding

Large-scale networks of brain regions are believed to mediate cognitive processes, including episodic memory. Analyses of regional differences in brain activity, measured by functional neuroimaging, have begun to identify putative components of these networks. To more fully characterize neurocognitive networks, however, it is necessary to use analytical methods that quantify neural network interactions. Here, we used positron emission tomography (PET) to measure brain activity during initial encoding and subsequent recognition of sentences and pictures. For each type of material, three recognition conditions were included which varied with respect to target density (0%, 50%, 100%). Analysis of large-scale activity patterns identified a collection of foci whose activity distinguished the processing of sentences vs. pictures. A second pattern, which showed strong prefrontal cortex involvement, distinguished the type of cognitive process (encoding or retrieval). For both pictures and sentences, the manipulation of target density was associated with minor activation changes. Instead, it was found to relate to systematic changes of functional connections between material-specific regions and several other brain regions, including medial temporal, right prefrontal and parietal regions. These findings provide evidence for large-scale neural interactions between material-specific and process-specific neural substrates of episodic encoding and retrieval.

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 Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

Brain Network Dysfunction in Poststroke Delirium and Spatial Neglect: An fMRI Study

Stroke ◽  
2021 ◽  
Author(s):  
Olga Boukrina ◽  
Mateusz Kowalczyk ◽  
Yury Koush ◽  
Yekyung Kong ◽  
A.M. Barrett
Keyword(s):  
Right Hemisphere ◽  
Functional Neuroimaging ◽  
Brain Network ◽  
Spatial Neglect ◽  
Stroke Recovery ◽  
Network Activity ◽  
Brain Disorder ◽  
Fmri Study ◽  
The Right ◽  
Right Hemisphere Stroke

Background and Purpose: Delirium, an acute reduction in cognitive functioning, hinders stroke recovery and contributes to cognitive decline. Right-hemisphere stroke is linked with higher delirium incidence, likely, due to the prevalence of spatial neglect (SN), a right-brain disorder of spatial processing. This study tested if symptoms of delirium and SN after right-hemisphere stroke are associated with abnormal function of the right-dominant neural networks specialized for maintaining attention, orientation, and arousal. Methods: Twenty-nine participants with right-hemisphere ischemic stroke undergoing acute rehabilitation completed delirium and SN assessments and functional neuroimaging scans. Whole-brain functional connectivity of 4 right-hemisphere seed regions in the cortical-subcortical arousal and attention networks was assessed for its relationship to validated SN and delirium severity measures. Results: Of 29 patients, 6 (21%) met the diagnostic criteria for delirium and 16 (55%) for SN. Decreased connectivity of the right basal forebrain to brain stem and basal ganglia predicted more severe SN. Increased connectivity of the arousal and attention network regions with the parietal, frontal, and temporal structures in the unaffected hemisphere was also found in more severe delirium and SN. Conclusions: Delirium and SN are associated with decreased arousal network activity and an imbalance of cortico-subcortical hemispheric connectivity. Better understanding of neural correlates of poststroke delirium and SN will lead to improved neuroscience-based treatment development for these disorders.

scholarly journals Social and asocial learning in zebrafish are encoded by a shared brain network that is differentially modulated by local activation

2021 ◽  
Author(s):  
Julia Pinho ◽  
Vincent T. Cunliffe ◽  
Giovanni Petri ◽  
Rui Oliveira
Keyword(s):  
Social Learning ◽  
Brain Activity ◽  
Group Living ◽  
Brain Regions ◽  
Brain Network ◽  
Early Gene ◽  
Learning Performance ◽  
Social Stimulus ◽  
Visual Response ◽  
Learning Module

Group living animals can use social and asocial cues to predict the presence of a reward or a punishment in the environment through associative learning. The degree to which social and asocial learning share the same mechanisms is still a matter of debate, and, so far, studies investigating the neuronal basis of these two types of learning are scarce and have been restricted to primates, including humans, and rodents. Here we have used a Pavlovian fear conditioning paradigm in which a social (fish image) or an asocial (circle image) conditioned stimulus (CS) have been paired with an unconditioned stimulus (US=food), and we have used the expression of the immediate early gene c-fos to map the neural circuits associated with social and asocial learning. Our results show that the learning performance is similar with social (fish image) and asocial (circle image) CSs. However, the brain regions involved in each learning type are distinct. Social learning is associated with an increased expression of c-fos in olfactory bulbs, ventral zone of ventral telencephalic area, ventral habenula and ventromedial thalamus, whereas asocial learning is associated with a decreased expression of c-fos in dorsal habenula and anterior tubercular nucleus. Using egonetworks, we further show that each learning type has an associated pattern of functional connectivity across brain regions. Moreover, a community analysis of the network data reveals four segregated functional submodules, which seem to be associated with different cognitive functions involved in the learning tasks: a generalized attention module, a visual response module, a social stimulus integration module and a learning module. Together, these results suggest that, although there are localized differences in brain activity between social and asocial learning, the two learning types share a common learning module and social learning also recruits a specific social stimulus integration module. Therefore, our results support the occurrence of a common general-purpose learning module, that is differentially modulated by localized activation in social and asocial learning.

scholarly journals Methodological Considerations in Conducting an Olfactory fMRI Study

2013 ◽  
Vol 27 (3) ◽  
pp. 267-276 ◽  
Author(s):  
Faezeh Vedaei ◽  
Mohammad Fakhri ◽  
Mohammad Hossein Harirchian ◽  
Kavous Firouznia ◽  
Yones Lotfi ◽  
...  
Keyword(s):  
Functional Neuroimaging ◽  
Neurological Diseases ◽  
Brain Regions ◽  
Brain Network ◽  
Functional Brain Imaging ◽  
Behavioral Studies ◽  
Fmri Study ◽  
Methodological Considerations ◽  
Technical Principles ◽  
Review Current

The sense of smell is a complex chemosensory processing in human and animals that allows them to connect with the environment as one of their chief sensory systems. In the field of functional brain imaging, many studies have focused on locating brain regions that are involved during olfactory processing. Despite wealth of literature about brain network in different olfactory tasks, there is a paucity of data regarding task design. Moreover, considering importance of olfactory tasks for patients with variety of neurological diseases, special contemplations should be addressed for patients. In this article, we review current olfaction tasks for behavioral studies and functional neuroimaging assessments, as well as technical principles regarding utilization of these tasks in functional magnetic resonance imaging studies.

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