Marvin M. Chun - US grants

Many of our behaviors depend on the ability to rapidly recognize objects in the real world. Yet, as effortless as visual object perception seems to be, even for young infants rapidly learning the names of surrounding objects, this capability eludes the most sophisticated computers and devices. In fact, many details of this process remain unknown despite decades of research progress in neuroscience and cognitive psychology. Understanding how the human brain, which is a physical device that performs computations, recognizes objects is therefore a useful and important endeavor. Recent advances in brain imaging technology, especially functional magnetic resonance imaging (fMRI), have now made it possible to safely examine the brain mechanisms in everyday adult human observers. One basic question concerns how neurons represent complex visual objects that typically consist of distinct parts arrayed in a particular configuration. For example, a bicycle has wheels, a frame, and handlebars arranged in a certain way that enables people, such as car drivers, to quickly recognize one on the road. With support from the National Science Foundation, Dr. Yaoda Xu and Dr. Marvin Chun are using fMRI to probe detailed brain activity while observers perform visual recognition tasks in the MR scanner. In particular, this project focuses on how specific object parts and part configurations are represented and distinguished from others. This knowledge will advance our understanding of how the human brain recognizes visual objects.

The work should have significant implications for theories of visual object perception and especially for understanding the impact of brain damage on visual recognition abilities (agnosias). Furthermore, an understanding of how the brain recognizes objects should facilitate the development of sophisticated computer systems that can recognize and learn visual objects in our environment. During the course of this project, student collaborators will gain training in advanced functional brain imaging technologies and experimental methods to study human behavior.

PROJECT SUMMARY 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.