scholarly journals Learning differentially reorganizes brain activity and connectivity

2020 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
Azeez Adebimpe ◽  
Ankit N. Khambhati ◽  
Marcelo G. Mattar ◽  
Daniel Romer ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Learning Performance ◽  
Functional Architecture ◽  
Time Space ◽  
Novel Objects ◽  
Value Learning ◽  
The Brain

Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activity or functional connectivity across subjects during learning reflects reorganization toward a common functional architecture. We assessed the presence of reorganization in activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic reorganization of brain connectivity and activity—as a function of time, space, and performance—that occurs while subjects learn. Spatially localized brain activity reorganizes across the brain to a common functional architecture early in learning, and this reorganization tracks early learning performance. In contrast, spatially distributed connectivity reorganizes across the brain to a common functional architecture as training progresses, and this reorganization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of reorganization in activity and connectivity at different phases of value learning, which inform the ongoing study of learning processes more generally.

scholarly journals Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study

2020 ◽  
pp. 1-21
Author(s):  
Alexandra Anagnostopoulou ◽  
Charis Styliadis ◽  
Panagiotis Kartsidis ◽  
Evangelia Romanopoulou ◽  
Vasiliki Zilidou ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Functional Connectivity ◽  
Cognitive Training ◽  
Resting State ◽  
Network Architecture ◽  
Right Hemisphere ◽  
Random Network ◽  
Brain Activity ◽  
Hierarchical Organization ◽  
The Brain

Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Resting-state connectivity studies as a marker of the acute and delayed effects of subanaesthetic ketamine administration in healthy and depressed individuals: A systematic review

2021 ◽  
Vol 5 ◽  
pp. 239821282110554
Author(s):  
Vasileia Kotoula ◽  
Toby Webster ◽  
James Stone ◽  
Mitul A Mehta
Keyword(s):  
Resting State ◽  
Emotional Regulation ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Reward Processing ◽  
Brain Areas ◽  
Delayed Effects ◽  
Treatment Naïve ◽  
Resting State Connectivity ◽  
The Brain

Acute ketamine administration has been widely used in neuroimaging research to mimic psychosis-like symptoms. Within the last two decades, ketamine has also emerged as a potent, fast-acting antidepressant. The delayed effects of the drug, observed 2–48 h after a single infusion, are associated with marked improvements in depressive symptoms. At the systems’ level, several studies have investigated the acute ketamine effects on brain activity and connectivity; however, several questions remain unanswered around the brain changes that accompany the drug’s antidepressant effects and how these changes relate to the brain areas that appear with altered function and connectivity in depression. This review aims to address some of these questions by focusing on resting-state brain connectivity. We summarise the studies that have examined connectivity changes in treatment-naïve, depressed individuals and those studies that have looked at the acute and delayed effects of ketamine in healthy and depressed volunteers. We conclude that brain areas that are important for emotional regulation and reward processing appear with altered connectivity in depression whereas the default mode network presents with increased connectivity in depressed individuals compared to healthy controls. This finding, however, is not as prominent as the literature often assumes. Acute ketamine administration causes an increase in brain connectivity in healthy volunteers. The delayed effects of ketamine on brain connectivity vary in direction and appear to be consistent with the drug normalising the changes observed in depression. The limited number of studies however, as well as the different approaches for resting-state connectivity analysis make it very difficult to draw firm conclusions and highlight the importance of data sharing and larger future studies.

scholarly journals Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Frigyes Samuel Racz ◽  
Orestis Stylianou ◽  
Peter Mukli ◽  
Andras Eke
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Brain Activity ◽  
Surrogate Data ◽  
Brain Regions ◽  
Local Dynamic ◽  
Dynamic Functional Connectivity ◽  
Multifractal Scaling ◽  
The Brain

Abstract Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

scholarly journals Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Andreas A. Ioannides ◽  
Stavros I. Dimitriadis ◽  
George A. Saridis ◽  
Marotesa Voultsidou ◽  
Vahe Poghosyan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Clinical Applications ◽  
Brain Areas ◽  
Connectivity Patterns ◽  
The Brain ◽  
Typical Subject ◽  
Different Parts ◽  
Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

scholarly journals Brain activity fluctuations propagate as waves traversing the cortical hierarchy

2020 ◽  
Author(s):  
Yameng Gu ◽  
Lucas E. Sainburg ◽  
Sizhe Kuang ◽  
Feng Han ◽  
Jack W. Williams ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Hierarchical Organization ◽  
Subcortical Structures ◽  
Cortical Hierarchy ◽  
Neural Origin ◽  
The Brain

AbstractThe brain exhibits highly organized patterns of spontaneous activity as measured by resting-state fMRI fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, though this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

scholarly journals A Study Over Brain Connectivity Network of Parkinson's Patients, Using Nonparametric Bayesian Model

2021 ◽  
pp. 1-18
Author(s):  
Fatemeh Pourmotahari ◽  
◽  
Seyyed Mohammad Tabatabaei ◽  
Nasrin Borumandnia ◽  
Naghmeh Khadembashi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Functional Connectivity ◽  
Correlation Coefficient ◽  
Resting State ◽  
Bayesian Model ◽  
Functional Relationship ◽  
Brain Connectivity ◽  
Healthy Individuals ◽  
Nonparametric Bayesian ◽  
The Brain

Introduction: Parkinson’s disease is a neurodegenerative disease that disrupts functional brain networks. Many neurodegenerative disorders are associated with changes in brain communication patterns. Resting-state functional connectivity studies can distinguish the topological structure of Parkinson's patients from healthy individuals by analyzing patterns between different regions of the brain. Accordingly, the present study aimed to determine the brain topological features and functional connectivity in patients with Parkinson's disease, using a Bayesian approach. Method: The data of this study were downloaded from the open neuro site. These data include "Resting-State Functional MRI" (rs-fMRI) of 11 healthy individuals and 11 Parkinson’s patients with mean ages of 64.36 and 63.73, respectively. An advanced nonparametric Bayesian model was used to evaluate topological characteristics, including clustering of brain regions and correlation coefficient of the clusters. The significance of functional relationships based on each edge between the two groups was examined through false discovery rate (FDR) and network-based statistics (NBS) methods. Results: Brain connectivity results showed a major difference in terms of the number of regions in each cluster and the correlation coefficient between the patient and healthy groups. The largest clusters in the patient and control groups were 26 and 53 regions, respectively, with clustering correlation values of 0.36 and 0.26. Although there are 15 common areas across the two clusters, the intensity of the functional relationship between these areas was different in the two groups. Moreover, using NBS and FDR methods, no significant difference was observed for each edge between the patient and healthy groups (p-value>0.05). Conclusion: The results of this study show a different topological configuration of the brain network between the patient and healthy groups, indicating changes in the functional relationship between a set of areas of the brain.

scholarly journals Brain Activity Fluctuations Propagate as Waves Traversing the Cortical Hierarchy

2021 ◽  
Author(s):  
Yameng Gu ◽  
Lucas E Sainburg ◽  
Sizhe Kuang ◽  
Feng Han ◽  
Jack W Williams ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Hierarchical Organization ◽  
Subcortical Structures ◽  
Cortical Hierarchy ◽  
Neural Origin ◽  
The Brain

Abstract The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state functional magnetic resonance imaging (fMRI) fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, although this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here, we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

scholarly journals The Effect of Grain and Grass Fed Beef and Chicken Breast Consumption on the Functional Connectivity in the Brain Using Resting State Functional Magnetic Resonance Imaging

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
E. S. Beyer ◽  
M. F. Miller ◽  
T. H. Davis ◽  
J. F. Legako
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Chemical Components ◽  
Volatile Components ◽  
Functional Brain ◽  
Dimethyl Sulfone ◽  
True Value ◽  
Significant Difference ◽  
The Brain

ObjectivesUnderstanding functional connectivity after consuming meat can be essential to fully understanding consumer’s preferences and the connection to certain flavor compounds. The objective of this study was to determine differences in the functional brain connectivity of consumers after consuming grass-fed beef, grain-fed beef and chicken while determining the different chemical and volatile components that differentiate the treatments.Materials and MethodsGrass-fed strip steaks, Grain-fed strip steaks and chicken breasts were collected, aged 21 d and cut into 1×1-inch consumer steaks. Each steak was vacuum sealed with a random identification number and frozen at –20°C. 23 volunteered consumers evaluated each treatment randomly followed by a Blood Oxygen Level-Dependent (BOLD) fMRI scan. Each consumer received a resting state scan and three scans following each sample. The beef was cooked to a medium degree of doneness (71°C) and the chicken was cooked to a well-done degree of doneness (75°C), followed by a 1-min resting period. The consumers were asked to complete a sensory ballot for each sample to quantify tenderness, juiciness, flavor, overall liking and quality. Each attribute was evaluated on a 100mm line scale. The sensory ballot, volatile and fatty acid data were analyzed by ANOVA and multiple means comparison using SAS while the fMRI data were analyzed using FSL’s FEAT software.ResultsThe results indicated all treatments were equal for tenderness and flavor, but the chicken was the least juicy (P < 0.05) and the grain-fed steak was ranked higher for overall liking (P < 0.05) in comparison to chicken. Furthermore, based on an independent component analysis, there was a significant difference in the functional connectivity (P < 0.05) from the resting state scan to all three treatments within the insular, medial prefrontal cortex, and amygdala regions. Additionally, there were significant differences in connectivity (P < 0.05) between the insula and orbitofrontal cortex in grass-fed compared to grain-fed beef. These areas are involved in processing sensory characteristics related to smell and taste and tend to track differences in preferences and stimulus value. Also, the samples were evaluated for volatile compounds with GC–MS and fatty acids using the FAMES method. Chicken and grass-fed beef was found to have a higher concentration (P < 0.05) of dimethyl sulfone in comparison to grain-fed beef, while the grass-fed steaks possessed a higher concentration (P < 0.05) of toluene in comparison to grain-fed steaks, but not differing from chicken. Dimethyl sulfone and toluene have been tied to grass-fed beef and chicken flavor profiles (Tansawat et al., 2013).ConclusionThe results from the functional brain connectivity in the reward pathways and the chemical components of the different treatments indicated a trend for grain-fed beef to be the most different from grass-fed beef and chicken. Moreover, tying brain activity to the flavor and chemical components in meat can be vital in understanding consumer’s preferences not observed in behavior alone. Therefore, these results can provide a basis to determine the ability to track reactions within the functional connectivity in the brain and the chemical aspects of different steaks to determine and understand consumer’s preferences and the true value of beef and chicken.

scholarly journals Spatiotemporal Trajectories in Resting-state FMRI Revealed by Convolutional Variational Autoencoder

2021 ◽  
Author(s):  
Xiaodi Zhang ◽  
Eric Maltbie ◽  
Shella Keilholz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Spatiotemporal Patterns ◽  
Network Activity ◽  
Fmri Data ◽  
Variational Autoencoder ◽  
Spatiotemporal Trajectories ◽  
The Brain

AbstractRecent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.

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