scholarly journals Frontoparietal Activity Interacts With Task-Evoked Changes in Functional Connectivity

2018 ◽  
Vol 29 (2) ◽  
pp. 802-813 ◽  
Author(s):  
Kai Hwang ◽  
James M Shine ◽  
Mark D’Esposito
Keyword(s):  
Functional Connectivity ◽  
Activity Patterns ◽  
Relevant Information ◽  
Brain Regions ◽  
Task Demands ◽  
Neural Systems ◽  
Time Varying ◽  
Fmri Study ◽  
Tracking Time ◽  
Candidate Regions

Abstract Flexible interactions between brain regions enable neural systems to adaptively transfer and process information. However, the neural substrates that regulate adaptive communications between brain regions are understudied. In this human fMRI study, we investigated this issue by tracking time-varying, task-evoked changes in functional connectivity between localized occipitotemporal regions while participants performed different tasks on the same visually presented stimuli. We found that functional connectivity between ventral temporal and the primary visual regions selectively increased during the processing of task-relevant information. Further, additional task demands selectively strengthen these targeted connectivity patterns. To identify candidate regions that contribute to this increase in inter-regional coupling, we regressed the task-specific time-varying connectivity strength between primary visual and occipitotemporal regions against voxel-wise activity patterns elsewhere in the brain. This allowed us to identify a set of frontal and parietal regions whose activity increased as a function of task-evoked functional connectivity. These results suggest that frontoparietal regions may provide top-down biasing signals to influence task-specific interactions between brain regions.

scholarly journals Fronto-Parietal Interactions with Task-Evoked Functional Connectivity During Cognitive Control

2017 ◽  
Author(s):  
Kai Hwang ◽  
James M. Shine ◽  
Mark D’Esposito
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Activity Patterns ◽  
Relevant Information ◽  
Brain Regions ◽  
Temporal Changes ◽  
Stimulus Category ◽  
Top Down ◽  
Theoretical Predictions ◽  
Connectivity Strength

AbstractFlexible interaction between brain regions enables neural systems to transfer and process information adaptively for goal-directed behaviors. In the current study, we investigated neural substrates that interact with task-evoked functional connectivity during cognitive control. We conducted a human fMRI study where participants selectively attended to a category of visual stimuli in the presence of competing distractors from another stimulus category. To study flexible interactions between brain regions, we performed a dynamic functional connectivity analysis to estimate temporal changes in connectivity strength between brain regions under different levels of cognitive control. Consistent with theoretical predictions, we found that cognitive control selectively enhances functional connectivity for prioritizing the processing of task-relevant information. By regressing temporal changes in connectivity strength against activity patterns elsewhere in the brain, we localized frontal and parietal regions that potentially provide top-down biasing signals for influencing, or reading information out from, task-evoked functional connectivity. Our results suggest that in addition to modulating local activity, fronto-parietal regions could also exert top-down biasing signals to influence functional connectivity between distributed brain regions.

scholarly journals Comparing Task-Relevant Information Across Different Methods of Extracting Functional Connectivity

2018 ◽  
Author(s):  
Sophie Benitez Stulz ◽  
Andrea Insabato ◽  
Gustavo Deco ◽  
Matthieu Gilson ◽  
Mario Senden
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Cross Validation ◽  
Activity Patterns ◽  
Relevant Information ◽  
Brain Regions ◽  
Classification Performance ◽  
Phase Coherence ◽  
Brain State ◽  
Brain States

AbstractThe concept of brain states, functionally relevant large-scale activity patterns, has become popular in neuroimaging. Not all components of such patterns are equally characteristic for each brain state, but machine learning provides a possibility for extracting and comparing the structure of brain states from functional data. However, their characterization in terms of functional connectivity measures varies widely, from cross-correlation to phase coherence, and the idea that different measures provide similar or coherent information is a common assumption made in neuroimaging. Here, we compare the brain state signatures extracted from of phase coherence, pairwise covariance, correlation, regularized covariance and regularized precision for a dataset of subjects performing five different cognitive tasks. In addition, we compare the classification performance in identifying the tasks for each connectivity measure. The measures are evaluated in their ability to discriminate the five tasks with two types of cross-validation: within-subject cross-validation, which reflects the stability of the signature over time; and between-subject cross-validation, which aims at extracting signatures that generalize across subjects. Secondly, we compare the informative features (connections or links between brain regions/areas) across measures to test the assumption that similar information is obtained about brain state signatures from different connectivity measures. In our results, the different types of cross-validation give different classification performance and emphasize that functional connectivity measures on fMRI require observation windows of sufficient duration. Furthermore, we find that informative links for the classification, meaning changes between tasks that are consistent across subjects, are entirely uncorrelated between BOLD correlations and covariances. These results indicate that the corresponding FC signature can strongly differ across FC methods used and that interpretation is subject to caution in terms of subnetworks related to a task.

scholarly journals Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits

2020 ◽  
Author(s):  
Jacob Billings ◽  
Manish Saggar ◽  
Shella Keilholz ◽  
Giovanni Petri
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Persistent Homology ◽  
Brain Regions ◽  
Topological Data Analysis ◽  
Time Varying ◽  
Imaging Data ◽  
Experimental Conditions ◽  
Common Time ◽  
Brain Imaging Data

Functional connectivity (FC) and its time-varying analogue (TVFC) leverage brain imaging data to interpret brain function as patterns of coordinating activity among brain regions. While many questions remain regarding the organizing principles through which brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights into the brain’s spontaneous self-organization. One tool from TDA, “persistent homology”, observes the occurrence and the persistence of n-dimensional holes presented in the metric space over a dataset. The occurrence of n-dimensional holes within the TVFC point cloud may denote conserved and preferred routes of information flow among brain regions. In the present study, we compare the use of persistence homology versus more traditional TVFC metrics at the task of segmenting brain states that differ across a common time-series of experimental conditions. We find that the structures identified by persistence homology more accurately segment the stimuli, more accurately segment volunteer performance during experimentally defined tasks, and generalize better across volunteers. Finally, we present empirical and theoretical observations that interpret brain function as a topological space defined by cyclic and interlinked motifs among distributed brain regions, especially, the attention networks.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Unmasking Anxiety in Autism: Explicit and Implicit Threat Dissociate Amygdala-centered Functional Connectivity

2020 ◽  
Author(s):  
Yu-Chun Chen ◽  
Chenyi Chen ◽  
Chia-Chien Liu ◽  
Róger Marcelo Martínez ◽  
Yang-Tang Fan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Relevant Information ◽  
Autism Spectrum ◽  
Autism Spectrum Quotient ◽  
Neural Pathways ◽  
Backward Masking ◽  
Threat Processing ◽  
Fmri Study ◽  
Socially Relevant ◽  
Amygdala Reactivity

Abstract Background Anxiety is the most prevalent comorbidity in individuals diagnosed with autism spectrum disorder (ASD). Amygdala reactivity to explicit and implicit threat processing offers a platform to assess anxiety. The neural mechanisms underlying the link between anxiety and ASD remains elusive.Methods In this fMRI study, we recruited young adults with ASD (N = 31) and matched them with controls, then proceeded to assess their autistic and anxiety traits by the use of the Autism-Spectrum Quotient (AQ) and the State-Trait Anxiety Inventory (STAI-S), respectively; and scanned their amygdala reactivity in response to explicit and implicit (backward masking) perception of threatening faces.Results As compared to controls, the amygdala reactivity in ASD subjects was significantly reduced to explicit threat, but comparable to implicit threat. The correlations of the amygdala reactivity with the AQ and STAI-S were dissociated depending on threat processing (explicit or implicit). Furthermore, the amygdala in ASD relative to controls had a more negative functional connectivity with the superior parietal cortex, fusiform gyrus, and hippocampus for explicit threat, whereas a more positive connectivity with the medial prefrontal cortex, temporal pole, and hippocampus for implicit threat.Conclusion In ASD, the transmission of socially relevant information along dorsal and ventral neural pathways centered on the amygdala is dissociated depending on explicit and implicit threat processing. This dissociation, ascribed to their failure to compromise pre-existing hyperarousal, might contribute to anxiety in ASD.

scholarly journals Altered functional connectivity of brain regions based on a meta‐analysis in patients with T2DM: A resting‐state fMRI study

2020 ◽  
Vol 10 (8) ◽  
Author(s):  
Dongsheng Zhang ◽  
Jie Gao ◽  
Xuejiao Yan ◽  
Min Tang ◽  
Xia Zhe ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fmri Study

scholarly journals Behavioural relevance of spontaneous, transient brain network interactions in fMRI

2019 ◽  
Author(s):  
D. Vidaurre ◽  
A. Llera ◽  
S.M. Smith ◽  
M.W. Woolrich
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Brain Networks ◽  
Structural Data ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Human Connectome Project ◽  
Brain Data ◽  
Structural Brain

AbstractHow spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

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