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 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 Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks

2020 ◽  
Author(s):  
A. Grigis ◽  
J. Tasserie ◽  
V. Frouin ◽  
B. Jarraya ◽  
L. Uhrig
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Supervised Machine Learning ◽  
Brain State ◽  
Level Of Consciousness ◽  
Clinical Consequences ◽  
Brain States ◽  
The Brain

AbstractDecoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called “brain states” corresponding to “functional configurations” of the brain. Here, we propose to use a supervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. Because it is key to consider the topology of brain regions used to build the dynamical functional connectivity matrices describing the brain state at a given time, we applied BrainNetCNN, a graph-convolutional neural network (CNN), to predict the brain states in awake and anesthetized non-human primate rsfMRI data. BrainNetCNN achieved a high prediction accuracy that lies in [0.674, 0.765] depending on the experimental settings. We propose to derive the set of connections found to be important for predicting a brain state, reflecting the level of consciousness. The results demonstrate that deep learning methods can be used not only to predict brain states but also to provide additional insight on cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.

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 Arousal impacts distributed hubs modulating the integration of brain functional connectivity

2021 ◽  
Author(s):  
Kangjoo Lee ◽  
Corey Horien ◽  
David O'Connor ◽  
Bronwen Garand-Sheridan ◽  
Fuyuze Tokoglu ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Network Integration ◽  
State Dependent ◽  
Show Evidence ◽  
Brain States ◽  
High Level ◽  
The Impact

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications.

scholarly journals Fundamental Dynamical Modes Underlying Human Brain Synchronization

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Catalina Alvarado-Rojas ◽  
Michel Le Van Quyen
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Electrode Placement ◽  
High Dimensional ◽  
Sleep Cycle ◽  
Epileptic Patients ◽  
Brain States ◽  
Intracranial Recordings ◽  
Characteristic Dynamic

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations

2018 ◽  
Vol 30 (9) ◽  
pp. 1209-1228 ◽  
Author(s):  
David Rothlein ◽  
Joseph DeGutis ◽  
Michael Esterman
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Networks ◽  
Relevant Information ◽  
Mental States ◽  
Attention Task ◽  
Default Mode ◽  
Neural Representations ◽  
Similarity Structure ◽  
Similarity Matrices

Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not “all or none,” but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants ( n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars ( n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.

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 Reduced Brain Electric Activity and Functional Connectivity in Bipolar Euthymia: An sLORETA Source Localization Study

2019 ◽  
Vol 51 (3) ◽  
pp. 155-166
Author(s):  
Annamaria Painold ◽  
Pascal L. Faber ◽  
Eva Z. Reininghaus ◽  
Sabrina Mörkl ◽  
Anna K. Holl ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Time Course ◽  
Temporal Cortex ◽  
Electric Activity ◽  
Brain Regions ◽  
Frequency Bands ◽  
Head Surface ◽  
Density Values ◽  
Surface Eeg

Bipolar disorder (BD) is a chronic illness with a relapsing and remitting time course. Relapses are manic or depressive in nature and intermitted by euthymic states. During euthymic states, patients lack the criteria for a manic or depressive diagnosis, but still suffer from impaired cognitive functioning as indicated by difficulties in executive and language-related processing. The present study investigated whether these deficits are reflected by altered intracortical activity in or functional connectivity between brain regions involved in these processes such as the prefrontal and the temporal cortices. Vigilance-controlled resting state EEG of 13 euthymic BD patients and 13 healthy age- and sex-matched controls was analyzed. Head-surface EEG was recomputed into intracortical current density values in 8 frequency bands using standardized low-resolution electromagnetic tomography. Intracortical current densities were averaged in 19 evenly distributed regions of interest (ROIs). Lagged coherences were computed between each pair of ROIs. Source activity and coherence measures between patients and controls were compared (paired t tests). Reductions in temporal cortex activity and in large-scale functional connectivity in patients compared to controls were observed. Activity reductions affected all 8 EEG frequency bands. Functional connectivity reductions affected the delta, theta, alpha-2, beta-2, and gamma band and involved but were not limited to prefrontal and temporal ROIs. The findings show reduced activation of the temporal cortex and reduced coordination between many brain regions in BD euthymia. These activation and connectivity changes may disturb the continuous frontotemporal information flow required for executive and language-related processing, which is impaired in euthymic BD patients.

Neurocognitive Aging and Functional Connectivity Using Functional Magnetic Resonance Imaging

2018 ◽  
Author(s):  
Hana Burianová
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processes ◽  
Large Scale ◽  
Healthy Aging ◽  
Brain Activity ◽  
Brain Regions ◽  
Effective Strategies ◽  
Cognitive Improvement ◽  
Age Related ◽  
Neurocognitive Aging

Determining the mechanisms that underlie neurocognitive aging, such as compensation or dedifferentiation, and facilitating the development of effective strategies for cognitive improvement is essential due to the steadily rising aging population. One approach to study the characteristics of healthy aging comprises the assessment of functional connectivity, delineating markers of age-related neurocognitive plasticity. Functional connectivity paradigms characterize complex one-to-many (or many-to-many) structure–function relations, as higher-level cognitive processes are mediated by the interaction among a number of functionally related neural areas rather than localized to discrete brain regions. Task-related or resting-state interregional correlations of brain activity have been used as reliable indices of functional connectivity, delineating age-related alterations in a number of large-scale brain networks, which subserve attention, working memory, episodic retrieval, and task-switching. Together with behavioral and regional activation studies, connectivity studies and modeling approaches have contributed to our understanding of the mechanisms of age-related reorganization of distributed functional networks; specifically, reduced neural specificity (dedifferentiation) and associated impairment in inhibitory control and compensatory neural recruitment.

Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

2020 ◽  
Vol 30 (09) ◽  
pp. 2050047
Author(s):  
Lubin Wang ◽  
Xianbin Li ◽  
Yuyang Zhu ◽  
Bei Lin ◽  
Qijing Bo ◽  
...  
Keyword(s):  
High Risk ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Support Vector ◽  
Healthy Controls ◽  
Intrinsic Functional Connectivity ◽  
Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

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