scholarly journals The functional relevance of task-state functional connectivity

2020 ◽  
Author(s):  
Michael W. Cole ◽  
Takuya Ito ◽  
Carrisa Cocuzza ◽  
Ruben Sanchez-Romero
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Cognitive Task ◽  
Brain Network ◽  
Network Organization ◽  
Functional Importance ◽  
Resting State Functional Connectivity ◽  
Connectivity Patterns ◽  
Functional Relevance ◽  
Insight Into

AbstractResting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the human brain’s ability to adaptively alter its functionality via rapid changes in inter-regional relationships. We utilized activity flow mapping – an approach for building empirically-derived network models – to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.Significance StatementHuman cognition is highly dynamic, yet the human brain’s functional network organization is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the brain’s intrinsic (resting-state) network organization strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

scholarly journals Predicting dysfunctional age-related task activations from resting-state network alterations

2019 ◽  
Author(s):  
Ravi D. Mill ◽  
Brian A. Gordon ◽  
David A. Balota ◽  
Jeffrey M. Zacks ◽  
Michael W. Cole
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Functional Connectivity ◽  
Resting State ◽  
Cognitive Task ◽  
Low Risk ◽  
Flow Modeling ◽  
Resting State Functional Connectivity ◽  
Age Related

AbstractAlzheimer’s disease (AD) is linked to changes in fMRI task activations and fMRI resting-state functional connectivity (restFC), which can emerge early in the timecourse of illness. Study of these fMRI correlates of unhealthy aging has been conducted in largely separate subfields. Taking inspiration from neural network simulations, we propose a unifying mechanism wherein restFC network alterations associated with Alzheimer’s disease disrupt the ability for activations to flow between brain regions, leading to aberrant task activations. We apply this activity flow modeling framework in a large sample of clinically unimpaired older adults, which was segregated into healthy (low-risk) and at-risk subgroups based on established imaging (positron emission tomography amyloid) and genetic (apolipoprotein) risk factors for AD. We identified healthy task activations in individuals at low risk for AD, and then by estimating activity flow using at-risk AD restFC data we were able to predict the altered at-risk AD task activations. Thus, modeling the flow of healthy activations over at-risk AD connectivity effectively transformed the healthy aged activations into unhealthy aged activations. These results provide evidence that activity flow over altered intrinsic functional connections may act as a mechanism underlying Alzheimer’s-related dysfunction, even in very early stages of the illness. Beyond these mechanistic insights linking restFC with cognitive task activations, this approach has potential clinical utility as it enables prediction of task activations and associated cognitive dysfunction in individuals without requiring them to perform in-scanner cognitive tasks.Significance StatementDeveloping analytic approaches that can reliably predict features of Alzheimer’s disease is a major goal for cognitive and clinical neuroscience, with particular emphasis on identifying such diagnostic features early in the timeline of disease. We demonstrate the utility of an activity flow modeling approach, which predicts fMRI cognitive task activations in subjects identified as at-risk for Alzheimer’s disease. The approach makes activation predictions by transforming a healthy aged activation template via the at-risk subjects’ individual pattern of fMRI resting-state functional connectivity (restFC). The observed prediction accuracy supports activity flow as a mechanism linking age-related alterations in restFC and task activations, thereby providing a theoretical basis for incorporating restFC into imaging biomarker and personalized medicine interventions.

scholarly journals Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Joseph J. Taylor ◽  
Hatice Guncu Kurt ◽  
Amit Anand
Keyword(s):  
Functional Connectivity ◽  
Treatment Response ◽  
Mood Disorders ◽  
Resting State ◽  
Network Models ◽  
Evidence Base ◽  
Brain Network ◽  
Treatment Modalities ◽  
Resting State Functional Connectivity ◽  
Graph Theory Analysis

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

scholarly journals Advanced Meditation Alters Resting-State Brain Network Connectivity Correlating With Improved Mindfulness

2021 ◽  
Vol 12 ◽  
Author(s):  
Ramana V. Vishnubhotla ◽  
Rupa Radhakrishnan ◽  
Kestas Kveraga ◽  
Rachael Deardorff ◽  
Chithra Ram ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Network Connectivity ◽  
Blood Oxygen Level Dependent ◽  
Brain Network ◽  
Resting State Functional Connectivity ◽  
Blood Oxygen Level ◽  
Default Mode ◽  
Before And After ◽  
Brain Network Connectivity

Purpose: The purpose of this study was to investigate the effect of an intensive 8-day Samyama meditation program on the brain functional connectivity using resting-state functional MRI (rs-fMRI).Methods: Thirteen Samyama program participants (meditators) and 4 controls underwent fMRI brain scans before and after the 8-day residential meditation program. Subjects underwent fMRI with a blood oxygen level dependent (BOLD) contrast at rest and during focused breathing. Changes in network connectivity before and after Samyama program were evaluated. In addition, validated psychological metrics were correlated with changes in functional connectivity.Results: Meditators showed significantly increased network connectivity between the salience network (SN) and default mode network (DMN) after the Samyama program (p < 0.01). Increased connectivity within the SN correlated with an improvement in self-reported mindfulness scores (p < 0.01).Conclusion: Samyama, an intensive silent meditation program, favorably increased the resting-state functional connectivity between the salience and default mode networks. During focused breath watching, meditators had lower intra-network connectivity in specific networks. Furthermore, increased intra-network connectivity correlated with improved self-reported mindfulness after Samyama.Clinical Trials Registration: [https://clinicaltrials.gov], Identifier: [NCT04366544]. Registered on 4/17/2020.

scholarly journals Increased resting-state functional connectivity within theta and alpha frequency bands in dysphoria: Towards a novel measure of depression risk

2020 ◽  
Author(s):  
Carola Dell'Acqua ◽  
Shadi Ghiasi ◽  
Simone Messerotti ◽  
Alberto Greco ◽  
Claudio Gentili ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Power Spectra ◽  
Female Gender ◽  
Phase Coherence ◽  
Resting State Functional Connectivity ◽  
Alpha Asymmetry ◽  
Connectivity Patterns ◽  
Eeg Power ◽  
Eeg Functional Connectivity

Background: The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown. Methods: 32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence. Results: While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally. Limitations: All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas. Conclusions: An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression.

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