Characterizing cognitive control networks using a precision neuroscience approach
Poster No:
745
Submission Type:
Abstract Submission
Authors:
Patrick Bissett1, Sunjae Shim2, Logan Bennett1, Jaime Ali Rios3, Henry Jones4, McKenzie Hagen5, Kriti Achyutuni1, Michael Demidenko6, Jeanette Mumford7, James Shine8, Russell Poldrack1
Institutions:
1Stanford University, Stanford, CA, 2University of California, Berkeley, Berkeley, CA, 3Duke University, Durham, NC, 4The University of Chicago, Chicago, IL, 5University of Washington, Seattle, WA, 6Stanford University, Portland, OR, 7Stanford, Stanford, CA, 8The University of Sydney, Sydney, NSW
First Author:
Co-Author(s):
Introduction:
The standard approach in cognitive neuroscience has been to acquire small amounts of data (e.g., 1-hour) on dozens of subjects. Recent work has highlighted a new approach – dense sampling – that seeks to precisely capture brain network architecture in individuals by collecting substantially more data per subject, supporting individual-subject level conclusions. However, most existing dense sampling studies focus primarily on resting-state fMRI or a small number of tasks, which limits the ability to link neuroimaging results to a variety of cognitive functions.
In the present work, we acquire a dense sampling dataset that broadly samples the cognitive control constructs of working memory, attention, set shifting, response inhibition, and performance monitoring. This density supports our aim to characterize the precise, reproducible network structure of cognitive control in individual subjects.
In the present work, we acquire a dense sampling dataset that broadly samples the cognitive control constructs of working memory, attention, set shifting, response inhibition, and performance monitoring. This density supports our aim to characterize the precise, reproducible network structure of cognitive control in individual subjects.
Methods:
46 participants were each scanned in 12, 1-hour 3T MRI sessions across 6 months. Each subject completed five repetitions of the following tasks: N-back, directed forgetting, flanker, shape matching, stop-signal, go/no-go, and both cued and spatial task switching. They also completed 2 sessions of novel dual tasks: stop + flanker, stop + directed-forgetting, and a bespoke pair of tasks that was chosen for them based upon which pair of single tasks had maximally overlapping brain maps. The fMRI data were acquired with a multi-band (3), multi-echo (3) sequence with TR = 1.49s and 2.8mm isometric voxels. The data were quality-assured using MRIQC (Esteban et al., 2017), pre-processed using fMRIPrep (Esteban et al., 2019), and echoes were optimally combined with Tedana (DuPre et al., 2021).
First-level models were built that coded for key aspects of each trial including stimuli, reaction time, and parametric regressors which coded for different conditions across each task. The five repetitions of each task were combined in a fixed-effect analysis to create individual-subject, across session contrast maps to base our analysis of precision network structure.
After completion of the scanning, subjects completed an additional ~12-hour out-of-scanner acquisition in which they completed each of the 8 single tasks and all 28 possible combinations of those 8 single tasks.
First-level models were built that coded for key aspects of each trial including stimuli, reaction time, and parametric regressors which coded for different conditions across each task. The five repetitions of each task were combined in a fixed-effect analysis to create individual-subject, across session contrast maps to base our analysis of precision network structure.
After completion of the scanning, subjects completed an additional ~12-hour out-of-scanner acquisition in which they completed each of the 8 single tasks and all 28 possible combinations of those 8 single tasks.
Results:
In order to find the structure of cognitive control networks in each subject, we normalized each subject into MNI space and then parcellated each fixed-effects map with the DiFuMo atlas (Dadi et al., 2020) into 1024 dimensions. We then ran exploratory factor analysis (EFA) to evaluate the extent to which the activity in each of the 1024 parcels covaried across 28 contrasts chosen from the 8 tasks. In Figure 1, we show the factor loadings in a representative individual. The first factor has strong loadings from task-baseline contrasts, the second factor reflects mostly reaction time contrasts, and subsequent factors included more specific loadings on specific constructs, including inhibition (factor 3), and task switching (factor 4 and 5). The factor loadings can be projected back into the brain with a Bartlett transformation (Bartlett, 1937) to evaluate which parcels were most strongly related to a given factor (see Figure 2).
Conclusions:
We announce a novel MRI dataset that contains multiple structural MRIs, diffusion MRI, 40 minutes of RSFC, and approximately 10-hours of single- and dual-task fMRI in each of 46 subjects. In this abstract, we focus on an EFA analysis evaluating the structure of cognitive control networks in individual subjects. We found both task-general factors reflecting a task-positive system (factor 1) and the speed of responses (factor 2, Mumford et al., 2024), and construct-specific factors that are consistent with more specific functions like inhibition and task switching. This work is a first step towards an individualized, data-driven neural ontology of cognitive control. In the near future, we will be openly sharing this dataset as a resource for the community.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Learning and Memory:
Working Memory
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI
Keywords:
Cognition
FUNCTIONAL MRI
STRUCTURAL MRI
Other - Dense sampling; precision neuroscience; cognitive control; executive functions; response inhibition; working memory; task switching
1|2Indicates the priority used for review
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Dadi, K., Varoquaux, G., Machlouzarides-Shalit, A., Gorgolewski, K. J., Wassermann, D., Thirion, B., & Mensch, A. (2020). Fine-grain atlases of functional modes for fMRI analysis. NeuroImage, 221, 117126.
Du Pre, E., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L., Caballero-Gauden, C., …., & Handwerker, D. A. (2021). TE-dependent analysis of multi-echo fMRI with tedana. The Journal of Open Source Software, 6(66), 3669.
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PloS one, 12, e0184661.
Esteban O., Markiewicz C. J., Blair R. W., Moodie C. A., Isik A. I., Erramuzpe A., … Gorgolewski K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods, 16, 111-116.
Mumford, J. A., Bissett, P. G., Jones, H. M., Shim, S., Rios, J. A. H., & Poldrack, R. A. (2024). The response time paradox in functional magnetic resonance imaging analyses. Nature Human Behaviour, 8, 349-360.