Most current approaches to understanding the neural basis of cognitive processes are severely limited in two respects. First, most commonly used methods do not have the temporal (e.g., fMRI) or spatial (e.g., MEG/ EEG) resolution to capture the relevant dynamics. Second, even methods with high spatio-temporal resolution (intracranial EEG - icEEG) typically approach target cognitive processes in a fragmentary, un- integrated way. For instance, language is typically studied as a conglomeration of separate subsystems: perception, pattern recognition, categorization, semantically/syntactically appropriate response selection, cross-modal integration, motor control and sensorimotor integration. The present proposal aims to remedy both limitations by using icEEG to study a model system, reading/speech/language, from an integrative and unified perspective. We focus on reading, a complex task that involves visual pattern recognition, visual- auditory and visuo-motor integration, semantic, syntactic and phonological access, and (in reading aloud) - response selection and motor sequencing. Reading allows for easy, yet ecologically valid manipulations of cognitive load in the language system. The neuro-computational framework we propose to test is that computation is achieved not by information passing through a sequence of discrete processing stages in individual modules but via state transitions of a distributed network. We will recruit a large cohort of 80 patients in whom we will quantify both local as well as inter-regional cortical dynamics during word reading - from early primary visual perception, through selection, to word output. We will leverage our established techniques for precise co-localization and analysis of grouped icEEG data, circumventing the sparse sampling problem inherent to human icEEG experiments. The combined use of sub-dural grid electrodes and stereo-electroencephalographic depth electrodes will enable the study of not only classic peri-sylvian regions, but also of deep sulci (and regions such as the planum temporale). We will then characterize dynamic network interactions using linear and non linear measures of amplitude covariance in high frequencies, following analyses we have developed previously. Critical nodes and critical transitions in network states will then be perturbed using closed-loop activity-triggered direct cortical stimulation. To achieve these goals we have set up a collaboration between the Texas Comprehensive Epilepsy Program and Johns Hopkins Medical Center - both centers have a proven record of studying language with icEEG. Our team has expertise in all aspects language, reading, icEEG signal analysis, population level network modeling from intracranial recordings; and neural networks. This work will dramatically improve our understanding of language systems and test and develop a new way to model neural computation generally.

Public Health Relevance

?A unified cognitive network model of language? In this proposal we will study human language function using an innovative approach of obtaining direct recordings from the human brain with electrodes placed to localize epilepsy. We will identify the specific roles of particular sub-components of the language system play in reading and speaking. Using advanced methods for intracranial EEG data co-registration and analysis, we will generate a model of the network behavior that enables us to read letters, words and derive meaning from sentences. Lastly, we will deliver carefully timed electrical pulses to perturb the network and test our hypotheses about the networks of language in the human brain- all of which will yield novel insights into language organization - applicable to normal language capacity and language dysfunction in a wide variety of patients with neurologic and psychiatric illnesses.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01NS098981-02
Application #
9355244
Study Section
Special Emphasis Panel (ZNS1)
Program Officer
Gnadt, James W
Project Start
2016-09-25
Project End
2019-05-31
Budget Start
2017-06-01
Budget End
2018-05-31
Support Year
2
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77030