To avoid post-operative language impairments after surgery for drug-resistant epilepsy, clinicians rely primarily on electrocortical stimulation mapping (ESM), but this can trigger afterdischarges, clinical seizures, or cause uncomfortable sensations, all of which can prevent mapping in some areas. Moreover, ESM can be very time- consuming and the resulting impairment is usually all-or-none, complicating its interpretation. These practical limitations have long motivated passive electrocorticography (ECoG) as an alternative, or complementary, functional mapping technique that can map function at all sites simultaneously, resulting in significant time savings without adverse side-effects. Recent technical developments also permit ECoG functional mapping to be performed online during testing, but the correspondence between these results and ESM has not been as good for language mapping as it has been for motor mapping. This may be, in part, because even simple language tasks such as object naming or word reading require the recruitment and interaction of widely distributed and potentially redundant cortical areas responsible for different stages of cognitive processing, and because there is no a priori threshold for a magnitude of activation critically important for task performance.. Our overall hypothesis is that the functional importance of a cortical site depends on its role in task-related network dynamics, i.e. the propagation of activation between distributed cortical regions performing the distinct cognitive operations necessary for successful task performance. In this project we will study the task-related network dynamics of spoken word production in order to improve the accuracy of ECoG language maps and to better understand the relationship between ECoG and ESM language maps. First, we will use ECoG to capture the fine temporal dynamics of network propagation during a series of simple word production tasks. We will decompose these network dynamics into temporally cascaded subnets corresponding to functional-anatomical modules responsible for each task's constitutive cognitive operations. In addition, we will identify high centrality noes that serve as hubs facilitating propagation across subnets. Second, we will test these ECoG models of task-specific network dynamics by temporarily deactivating the hubs of high efficiency pathways linking subnets. We will test the effect of this deactivation on verbal latency and accuracy using much briefer (200- 400 ms) stimulation trains than those used during routine clinical ESM (2-5 seconds). This will also test the feasibility of performing ESM with a lower risk of afterdischarges and seizures, and with greater functional specificity. Third, we will compare both ECoG network mapping and ESM to ground-truth post-operative language outcomes in order to comparatively assess their predictive abilities. Although the immediate goal of these studies is to gain deeper insights into the cortical network dynamics of spoken word production and how they are affected by ESM, these studies will exert their most profound and lasting impact by improving the clinical utility of ECoG for both extraoperative and intraoperative functional mapping prior to respective surgery.

Public Health Relevance

Epilepsy surgery can bring relief from drug-resistant seizures, but to avoid post-operative speech impairments, the brain regions responsible for language must be identified and then preserved. To map language function, passive recordings of the brain's electrical activity have important potential advantages over traditional electrical stimulation. This project is designed to improve on the accuracy of passive brain recordings for language mapping by taking into account how neural activity normally spreads through the brain during language tasks.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS091139-04
Application #
9393355
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Gnadt, James W
Project Start
2015-02-01
Project End
2019-11-30
Budget Start
2017-12-01
Budget End
2018-11-30
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Neurology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
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