Biology subjects

Cole, M. W.

Publications and source records attributed to Cole, M. W..

30 records · Page 2Linked to original sources

bioRxiv2020

The functional relevance of task-state functional connectivity

Resting-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 brains 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 brains functional network organization is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the brains 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.

neuroscience↗

bioRxiv2019

Combining multiple functional connectivity methods to improve causal inferences

Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet approaches that characterize relationships among neural time series--functional connectivity (FC) methods--are dominated by methods that assess bivariate statistical associations rather than causal interactions. Such bivariate approaches result in substantial false positives since they do not account for confounders (common causes) among neural populations. A major reason for the dominance of methods such as bivariate Pearson correlation (with functional MRI) and coherence (with electrophysiological methods) may be their simplicity. Thus, we sought to identify an FC method that was both simple and improved causal inferences relative to the most popular methods. We started with partial correlation, showing with neural network simulations that this substantially improves causal inferences relative to bivariate correlation. However, the presence of colliders (common effects) in a network resulted in false positives with partial correlation, though this was not a problem for bivariate correlations. This led us to propose a new combined functional connectivity method (combinedFC) that incorporates simple bivariate and partial correlation FC measures to make more valid causal inferences than either alone. We release a toolbox for implementing this new combinedFC method to facilitate improvement of FC-based causal inferences. CombinedFC is a general method for functional connectivity and can be applied equally to resting-state and task-based paradigms.

neuroscience↗

bioRxiv2019

Flexible coordinator and switcher hubs for adaptive task control

Functional connectivity studies have identified at least two large-scale neural systems that constitute cognitive control networks - the frontoparietal network (FPN) and cingulo-opercular network (CON). Control networks are thought to support goal-directed cognition and behavior. It was previously shown that the FPN flexibly shifts its global connectivity pattern according to task goal, consistent with a "flexible hub" mechanism for cognitive control. Our aim was to build on this finding to develop a functional cartography (a multi-metric profile) of control networks in terms of dynamic network properties. We quantified network properties in (male and female) humans using a high-control-demand cognitive paradigm involving switching among 64 task sets. We hypothesized that cognitive control is enacted by the FPN and CON via distinct but complementary roles reflected in network dynamics. Consistent with a flexible "coordinator" mechanism, FPN connections were varied across tasks, while maintaining within-network connectivity to aid cross-region coordination. Consistent with a flexible "switcher" mechanism, CON regions switched to other networks in a task-dependent manner, driven primarily by reduced within-network connections to other CON regions. This pattern of results suggests FPN acts as a dynamic, global coordinator of goal-relevant information, while CON transiently disbands to lend processing resources to other goal-relevant networks. This cartography of network dynamics reveals a dissociation between two prominent cognitive control networks, suggesting complementary mechanisms underlying goal-directed cognition. Significance StatementCognitive control supports a variety of behaviors requiring flexible cognition, such as rapidly switching between tasks. Furthermore, cognitive control is negatively impacted in a variety of mental illnesses. We used tools from network science to characterize the implementation of cognitive control by large-scale brain systems. This revealed that two systems - the frontoparietal (FPN) and cingulo-opercular (CON) networks - have distinct but complementary roles in controlling global network reconfigurations. The FPN exhibited properties of a flexible coordinator (orchestrating task changes), while CON acted as a flexible switcher (switching specific regions to other systems to lend processing resources). These findings reveal an underlying distinction in cognitive processes that may be applicable to clinical, educational, and machine learning work targeting cognitive flexibility.

neuroscience↗

bioRxiv2019

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

Alzheimers 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 Alzheimers 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 Alzheimers-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.\n\nSignificance StatementDeveloping analytic approaches that can reliably predict features of Alzheimers 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 Alzheimers 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.

neuroscience↗

bioRxiv2019

Transcranial alternating current stimulation reduces BOLD adaptation and increases functional connectivity

Transcranial alternating current stimulation (tACS) is used as a non-invasive tool for cognitive enhancement and clinical applications. The physiological effects of tACS, however, are complex and poorly understood (Liu et al. 2018). Most studies of tACS focus on its ability to entrain brain oscillations (Herrmann et al. 2013), but our behavioral results in humans (Kar and Krekelberg 2014a) and extracellular recordings in nonhuman primates (Kar et al. 2017) support the view that tACS at 10 Hz additionally affects brain function by reducing sensory adaptation. Our primary goal here was to test this hypothesis using BOLD imaging in human subjects. Using a motion adaptation paradigm developed to quantify BOLD adaptation (Huk et al. 2001) and concurrent fMRI and tACS, we found that tACS significantly attenuated adaptation in the human motion area (hMT+). In addition, an exploratory analysis showed that tACS increased functional connectivity between the stimulated hMT+ and the rest of the brain, in particular the dorsal attention network. We conclude that weak 10 Hz currents applied to the scalp affect both local and global measures of brain activity.\n\nNew and NoteworthyConcurrent transcranial alternating current stimulation (tACS) and fMRI show that tACS affects the human brain by attenuating adaptation and increasing functional connectivity. This work is important for our basic understanding of what tACS does, but also for therapeutic applications, which need insight into the full range of ways in which tACS affects the brain.

neuroscience↗

bioRxiv2019

Task-evoked activity quenches neural correlations and variability in large-scale brain systems

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

neuroscience↗

bioRxiv2018

A whole-brain and cross-diagnostic perspective on functional brain network dysfunction

A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations have seemed to support various theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in broad, whole-brain perspective. Using a graph distance measure - connectome-wide correlation - we found that whole-brain resting-state functional network organization in humans is highly similar across a variety of mental diseases and healthy controls. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease those differences are informative. Such small network alterations may reflect the fact that most psychiatric patients maintain overall cognitive abilities similar to those of healthy individuals (relative to, e.g., the most severe schizophrenia cases), such that whole-brain functional network organization is expected to differ only subtly even for mental diseases with devastating effects on everyday life. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases.

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bioRxiv2018

Task activations produce spurious but systematic inflation of task functional connectivity estimates

Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of taskevoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference \"likely active or interacting during the task\", rather than the intended inference \"likely interacting during the task\". Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the models predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.\n\nHighlightsO_LIComputational model shows task inflation of functional connectivity estimates\nC_LIO_LIHemodynamic responses cause task activations to further inflate estimates\nC_LIO_LIStandard approaches to remove task activations leave many false positives\nC_LIO_LIMethods that flexibly fit hemodynamic response shape effectively correct inflation\nC_LIO_LICorrection of functional connectivity inflation verified with empirical fMRI data\nC_LI

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bioRxiv2018

Network dimensionality underlies flexible representation of cognitive information

Many studies have identified the role of localized and distributed cognitive functionality by mapping either local task-related activity or distributed functional connectivity (FC). However, few studies have directly explored the relationship between a brain regions localized task activity and its distributed task FC. Here we systematically evaluated the differential contributions of task-related activity and FC changes to identify a relationship between localized and distributed processes across the cortical hierarchy. We found that across multiple tasks, the magnitude of regional task-evoked activity was high in unimodal areas, but low in transmodal areas. In contrast, we found that task-state FC was significantly reduced in unimodal areas relative to transmodal areas. This revealed a strong negative relationship between localized task activity and distributed FC across cortical regions that was associated with the previously reported principal gradient of macroscale organization. Moreover, this dissociation corresponded to hierarchical cortical differences in the intrinsic timescale estimated from resting-state fMRI and region myelin content estimated from structural MRI. Together, our results contribute to a growing literature illustrating the differential contributions of a hierarchical cortical gradient representing localized and distributed cognitive processes. HighlightsO_LITask activations and functional connectivity changes are negatively correlated across cortex C_LIO_LITask activation and connectivity dissociations reflect differences in localized and distributed processes in cortex C_LIO_LIDifferences in localized and distributed processes are associated with differences in intrinsic timescale organization C_LIO_LIDifferences in localized and distributed processes are associated with differences in cortical myelin content C_LIO_LICortical heterogeneity in localized and distributed processes revealed by activity flow mapping prediction error C_LI

neuroscience↗

bioRxiv2017

Mapping the human brain’s cortical-subcortical functional network organization

Highlights O_LILarge-scale functional network map of the entire human brain\nC_LIO_LICortical networks based on multiband fMRI, recently-identified regions\nC_LIO_LISubcortical extension of networks covering all subcortical structures\nC_LIO_LIMultiple quality assessments demonstrate robustness of functional networks\nC_LIO_LINetwork atlas released as public resource, providing framework for future studies\nC_LI\n\nABSTRACTUnderstanding complex systems such as the human brain requires characterization of the systems architecture across multiple levels of organization - from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brains large-scale network organization, as it provides an overall framework for the organization of all other levels. We developed a highly principled approach to identify cortical network communities at the level of functional systems, calibrating our community detection algorithm using extremely well-established sensory and motor systems as guides. Building on previous network partitions, we replicated and expanded upon well-known and recently identified networks, including several higher-order cognitive networks such as a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 358 highly organized subcortical parcels that take part in forming whole-brain functional networks. Notably, the identified subcortical parcels are similar in number to a recent estimate of the number of cortical parcels (360). This whole-brain network atlas - released as an open resource for the neuroscience community - places all brain structures across both cortex and subcortex into a single large-scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease.

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bioRxiv2017

Global connectivity of the frontoparietal cognitive control network is related to depression symptoms in undiagnosed individuals

We all vary in our mental health, even among healthy (undiagnosed) individuals. Understanding this variability may reveal factors driving the onset of mental illness, as well as factors driving sub-clinical mental health problems that can still influence quality of life. To better understand the large-scale brain network mechanisms underlying this variability in mental health we examined the relationship between mental health symptoms and resting-state functional connectivity patterns in cognitive control systems. The frontoparietal cognitive control network (FPN) consists of flexible hubs that can regulate distributed systems depending on current goals, and dysfunction in the FPN has been identified in a variety of psychiatric disorders. Alterations in FPN connectivity may influence mental health by disrupting the ability to regulate symptoms in a goal-directed manner. This suggests that the FPN may play an important role in the promotion and maintenance of mental health generally. Here we test the hypothesis that disruptions in FPN connectivity are related to mental health (depression) symptoms even among healthy individuals. This hypothesis is consistent with a general role of FPN in the regulation of mental health symptoms. We found that depression symptoms were negatively correlated with between-network global connectivity (BGC) of the FPN as well as the default mode network (DMN). This suggests that decreased connectivity between the FPN (and, separately, DMN) and the rest of the brain is related to increased depression symptoms among undiagnosed individuals. These findings complement previous clinical studies to support the hypothesis that global FPN connectivity contributes to the regulation of mental health symptoms across both mentally healthy and unhealthy individuals.

neuroscience↗

bioRxiv2017

Cognitive task information is transferred between brain regions via resting-state network topology

Resting-state network connectivity has been associated with a variety of cognitive abilities, yet it remains unclear how these connectivity properties might contribute to the neurocognitive computations underlying these abilities. We developed a new approach - information transfer mapping - to test the hypothesis that resting-state functional network topology describes the computational mappings between brain regions that carry cognitive task information. Here we report that the transfer of diverse, task-rule information in distributed brain regions can be predicted based on estimated activity flow through resting-state network connections. Further, we find that these task-rule information transfers are coordinated by global hub regions within cognitive control networks. Activity flow over resting-state connections thus provides a large-scale network mechanism for cognitive task information transfer and global information coordination in the human brain, demonstrating the cognitive relevance of resting-state network topology.

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