Attention has a ubiquitous role in perception and cognition, and attention deficits are common in mental illness and as symptomatic of brain damage. Yet, despite its central importance, researchers lack a straightforward way to measure a person's overall attentional functioning. The goal of this project is to develop an attention profile index that can 1) quantify a person's attentional abilities along several dimensions, 2) predict behavior, 3) facilitate comparison across individual differences and within individuals over time, and 4) be measurable in a standardized and practical way across sites. The attention profile measure proposed here uses functional magnetic resonance imaging (fMRI) data to predict individual differences in behavioral performance, based on resting state data, collected while participants are scanned without an explicit task. The hypothesis is that attention can be better predicted in terms of intrinsic whole brain functional connectivity networks (individual connectomes) than by specific task activation of localized brain areas.
Aim 1 is to develop a battery of whole brain functional connectivity network models that can predict individual differences for different components of attentional performance. We will start with measures of sustained attention, alerting, orienting, executive control, working memory, and tracking.
Aim 2 is to apply this battery of functional connectivity models to resting state data, producing an individual attention profile measure, which predicts that individual's behavioral performance for the different attention components. Because our models successfully apply to novel individuals or independent groups, our approach goes beyond a descriptive analysis towards a predictive measure.
Aim 3 is to cross- validate the attention models and to characterize their underlying functional neuroanatomy. For example, our sustained attention models can predict attention deficit and hyperactivity disorder symptoms in an independent sample. We can analyze, compare, and even computationally ?lesion? the network nodes and connections that are vital to performance across models and tasks, versus those that are specific to particular tasks or cognitive operations. A whole-brain attention profile neuromarker can have transformative utility for both clinical and research applications. An attention profile can help quantify symptoms of attentional deficits in other clinical conditions such as dementia, schizophrenia, and brain trauma. An attention profile would also be useful to measure and compare attentional performance longitudinally across the lifespan. These applications are facilitated by the use of widely collected resting state connectome data.
Attention is a complex process central to perception and cognition, and when compromised, a common symptom of mental illness or brain damage. Despite its importance, a standardized way to quantify an individual's attentional function is lacking, so this project aims to develop and validate new measures of attention based on whole brain functional connectivity, as measured with functional magnetic resonance imaging during rest. A whole brain attention measure can have transformative utility for both research and clinical applications such as attention deficit and hyperactivity disorder.
| O'Connell, Thomas P; Chun, Marvin M (2018) Predicting eye movement patterns from fMRI responses to natural scenes. Nat Commun 9:5159 |
| Lin, Qi; Rosenberg, Monica D; Yoo, Kwangsun et al. (2018) Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease. Front Aging Neurosci 10:94 |
| Yoo, Kwangsun; Rosenberg, Monica D; Hsu, Wei-Ting et al. (2018) Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets. Neuroimage 167:11-22 |
| Rosenberg, M D; Finn, E S; Scheinost, D et al. (2017) Characterizing Attention with Predictive Network Models. Trends Cogn Sci 21:290-302 |
