Task-evoked activity quenches neural correlations and variability across cortical areas

Ito, Takuya; Brincat, Scott L.; Siegel, Markus; Mill, Ravi D.; He, Biyu J.; Miller, Earl K.; Rotstein, Horacio G.; Cole, Jennifer

doi:10.1101/560730

Cited by 32 publications

(77 citation statements)

References 75 publications

(126 reference statements)

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“…Given the complex nature of subsequent analyses (i.e., network metrics) we chose this method of FC estimation for simplicity and wide-reaching comprehension. In the present study, connectivity estimates tended to decrease from rest to task, a finding that has been observed across numerous prior studies (that utilized various model species and neural recording methods) (Cohen and Maunsell, 2009;He, 2013;Cole et al, 2014;Ponce-Alvarez et al, 2015) and has well-founded neural mechanisms (Ito et al, 2019).…”

Section: Functional Connectivity Estimationsupporting

confidence: 84%

Flexible coordinator and switcher hubs for adaptive task control

Cocuzza

Ito

Schultz

et al. 2019

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AbstractFunctional 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.

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Section: Functional Connectivity Estimationsupporting

confidence: 84%

Flexible coordinator and switcher hubs for adaptive task control

Cocuzza

Ito

Schultz

et al. 2019

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“…Previous studies have shown that task-evoked coactivation might induce an inflation effect on estimates of intrinsic functional connectivity (Duff et al, 2018). As such, stimulus-evoked activity is suggested to clean up the 'background connectivity' in the task paradigm with a focus on intrinsic FC (Fair et al, 2007;Ito et al, 2019;Lurie et al, 2020;Norman-Haignere et al, 2012). Of note, our study also examined the functional connectivity using partial correlation.…”

Section: Discussionmentioning

confidence: 96%

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Cho

Korchmaros

Vogelstein

et al. 2020

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2Compelling evidence suggests the need for more data per individual to reliably map the functional 3 organization of the human connectome. As the notion that 'more data is better' emerges as a golden 4 rule for functional connectomics, researchers find themselves grappling with the challenges of how 5 to obtain the desired amounts of data per participant in a practical manner, particularly for 6 retrospective data aggregation. Increasingly, the aggregation of data across all fMRI scans 7 available for an individual is being viewed as a solution, regardless of scan condition (e.g., rest, 8 task, movie). A number of open questions exist regarding the aggregation process and the impact 9 of different decisions on the reliability of resultant aggregate data. We leveraged the availability 10 of highly sampled test-retest datasets to systematically examine the impact of data aggregation 11 strategies on the reliability of whole-brain functional connectomics. Specifically, we compared 12 functional connectivity estimates derived after concatenating from: 1) multiple scans under the 13 same state, 2) multiple scans under different states (i.e. hybrid or general functional connectivity), 14 and 3) subsets of one long scan. We also varied connectivity processing (i.e. global signal 15 regression, ICA-FIX, and task regression) and estimation procedures. When the total number of 16 time points is equal, and the scan state held constant, concatenating multiple shorter scans had a 17 clear advantage over a single long scan. However, this was not necessarily true when concatenating 18 across different fMRI states (i.e. task conditions), where the reliability from the aggregate data 19 varied across states. Concatenating fewer numbers of states that are more reliable tends to yield 20 higher reliability. Our findings provide an overview of multiple dependencies of data 21 concatenation that should be considered to optimize reliability in analysis of functional 22 connectivity data.

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“…For the pseudo-empirical simulations described above and for the empirical analysis we use fMRI resting state data from a pool of 100 random subjects from the minimally preprocessed HCP 1200 release , with additional preprocessing following Ito et al (2019) and Ciric et al (2017) . HCP data was collected in a 3T Siemens Skyra with TR = 0.72 s, 72 slices and 2.0 mm isotropic voxels.…”

Section: Empirical Fmri Analysis Methodsmentioning

confidence: 99%

“…Finally, the time series of the 360 regions were individually standardized to bring them to the same scale with mean 0 and standard deviation 1. Specific details about the nuisance regression are in Ito et al (2019) .…”

Section: Empirical Fmri Analysis Methodsmentioning

confidence: 99%

Combining multiple functional connectivity methods to improve causal inferences

Sanchez-Romero

Cole

2019

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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.

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Task-evoked activity quenches neural correlations and variability across cortical areas

Cited by 32 publications

References 75 publications

Flexible coordinator and switcher hubs for adaptive task control

Flexible coordinator and switcher hubs for adaptive task control

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Combining multiple functional connectivity methods to improve causal inferences

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