# Characterizing cognitive control networks using a precision neuroscience approach

> **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $456,399

## Abstract

Impairments in cognitive control are central to many mental health disorders (McTeague et al., 2017). In
parallel, there is mounting evidence from a range of neuroimaging studies implicating impairments of network
computations in disorders of mental health (Fornito et al., 2015). A crucial ‘missing piece’ bridging these two
aspects of brain function is a relatively poor understanding of the way in which the network-level computations
of the brain relate to cognitive control processes, and the precise ways in which these relationships fluctuate
and unfold over weeks and months in each individual.
Before we can understand fluctuations in the trajectories of mental illnesses, we need to first understand the
temporal variability of healthy individuals over time. “Recent ‘dense-scanning’ datasets that acquire
substantially more data per subject provide a potential solution to this challenge, but these studies have lacked
width (they include few subjects, e.g., 4-10) and breadth (they focus on individual tasks/states, often the
‘resting state’). We will overcome these shortcoming with a dataset scanning 55 subjects each for a total 12
hours over the course of 6 months on 8 unique tasks that span multiple constructs of cognitive control
(working memory, attention, set shifting, inhibition, and performance monitoring). The resultant dataset will
be wide (i.e. multiple subjects per task), broad (e.g. multiple tasks per construct) and deep (e.g. multiple
repetitions of each task over time). This precision neuroscience approach allows us to identify global and local
changes in neural networks that are necessary both (a) in preparation for fast, effective controlled performance,
and (b) to support flexible post-error and post-conflict control adjustments to improve subsequent
performance. Once we have identified these behavioral and neural network signatures of cognitive control that
are reproducible across task, construct, session, we will leverage this information in a novel ‘targeted network
attack’ procedure to engineer breakdowns in the network architecture by precision challenges to the cognitive
system. Tailored combinations of tasks that rely on overlapping network architectures will be combined to
identify specific network features that are ripe for failure in healthy subjects, and as such, represent likely
nodes for subsequent failure in disease.
Together, this work will uncover novel links between cognitive control and functional brain network
architecture across tasks, constructs, and sessions (Aim 1) that are essential for effective and flexible behavior
(Aim 2) and are likely to fail across diverse disease states (Aim 3). Our precision neuroscience approach relates
closely to the precision medicine initiative at the NIH, as our deep-scanning procedure allows us to identify
subject-level network features necessary for effective cognitive control. In addition, by making the data openly
accessible to other researchers, we expect these data sets wi...

## Key facts

- **NIH application ID:** 9906911
- **Project number:** 5R01MH117772-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Russell A Poldrack
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $456,399
- **Award type:** 5
- **Project period:** 2018-07-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9906911

## Citation

> US National Institutes of Health, RePORTER application 9906911, Characterizing cognitive control networks using a precision neuroscience approach (5R01MH117772-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9906911. Licensed CC0.

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