# Mechanisms of attentional control: Structure and dynamics from simultaneous EEG-fMRI and machine learning

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2020 · $530,307

## Abstract

PROJECT SUMMARY/ABSTRACT
Selective attention is an essential cognitive ability that permits us to effectively process and act upon relevant
information while ignoring distracting events. A network involving frontal and parietal cortex for top-down
attentional control, referred to as the Dorsal Attention Network (DAN), is active during both spatial and non-
spatial (feature-based) attention. However, we know very little about the fine structure of attentional control
activity in the DAN, how this structure changes to represent different to-be-attended stimulus features, how the
connectivity within the DAN, and between the DAN and sensory cortex shifts when attending different features,
or how these top-down processes and their influence in sensory cortex unfold over time. This gap in our
knowledge is a critical problem for our models and theories of attention, and because attentional deficits are
involved in a wide variety of neuropsychiatric disorders including autism, attention deficit disorder, dementia,
and schizophrenia.
The working model guiding this research is that top-down attentional control, based on different to-be-attended
stimulus attributes, is guided by a smaller-scale neural fine structure within the DAN and prefrontal cortex that
makes specific connections with specialized areas of visual cortex coding the attended attributes. Moreover,
the time course of activity within the DAN in relation to that in sensory cortex follows a top-down cascading
model, being earliest in frontal, then parietal cortex, and finally sensory cortex for preparatory, voluntary,
attentional control.
To identify the functional networks for attentional control for different forms of attention, and to define their time
courses, this project uses innovative simultaneous recording of electroencephalographic (EEG) and functional
magnetic resonance imaging (fMRI) data. Advanced signal processing and modeling, including multivariate
pattern analysis (MVPA), graph theoretic connectivity analysis, and Granger causality analysis will be used to
reveal the fine functional anatomy and time course of attentional control and selection. The project includes
three experiments that vary the to-be-attended stimulus attributes from spatial location to stimulus features
(color and motion), and pursues three aims. Aim 1 is to reveal the fine structure of top-down preparatory
attentional control for different to-be-attended stimulus features. Aim 2 is to elucidate the specific connectivity
between fine structures for preparatory attentional control in the DAN and their target sensory structures in
sensory cortex. Aim 3 is to reveal the time course of top-down attentional control for different to-be-attended
stimulus attributes.

## Key facts

- **NIH application ID:** 9878145
- **Project number:** 5R01MH117991-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** MINGZHOU DING
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $530,307
- **Award type:** 5
- **Project period:** 2018-06-08 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9878145

## Citation

> US National Institutes of Health, RePORTER application 9878145, Mechanisms of attentional control: Structure and dynamics from simultaneous EEG-fMRI and machine learning (5R01MH117991-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9878145. Licensed CC0.

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