Nitin Tandon - US grants

DESCRIPTION (provided by applicant): The goal of this project is to determine whether low frequency oscillations serve as a mechanism for coordinating cortical areas underlying human episodic memory. To address this issue, we will first employ innovative multilobular electrocortigraphic (ECOG) recordings in patients to determine the sets of interconnected brain areas underlying episodic memory, which previous research and our preliminary work strongly suggest to include the medial temporal lobes, prefrontal cortex, and parietal cortex. We will then perturb areas of high connectivity (hubs) in the same patients using chronometric stimulation, which involves simultaneous recording and stimulation from two different brain regions. Chronometric stimulation is advantageous because it involves stimulation that mimics the frequency and amplitude of on-going recorded activity in another brain region, potentially mitigating unwanted spread of stimulation to other brain areas and at the same time providing insight into how neural communication might actually occur. We will employ two different stimulation methods with this approach, either in phase or out of phase stimulation with the on-going recorded oscillations in connecting hubs. This will allow us to determine whether 1) areas with high degrees of connectivity (hubs) are necessary for episodic memory 2) whether in- phase, coherent oscillatory can enhance episodic memory retrieval 3) whether out-of- phase oscillations result in decrements in memory performance. Our approach here combines innovative tools, such as electrocorticography and chronometric stimulation in humans and analysis techniques involving graph theory. These in turn will allow us to advance our understanding of how and in what manner networks of brain regions interact as part of their role in episodic memory. This work is relevant to clinical research because it can provide insight into the extent to which other brain regions can compensate for lost function following stroke-related lesions to the medial temporal lobes, a known hub in episodic memory. It will also advance our understanding of potential ways to design and implement deep brain stimulators to treat cognitive impairments accompanying neural disease. For example, if the experiments outlined here are successful, they would imply that devices that time stimulation to be in-phase with distant recorded oscillatory activity could restore or even enhance impaired memory function in patients suffering from neural disease.

Understanding the relationship between brain activity and human behavior is not only one of the most important scientific challenges of our generation but also one of the most important challenges in medicine and public health. This project develops new technology that can address the minute size of the neurons, and the vast amount of data generated by neural activity. This project leverages the collaborative environment between Rice and Texas Medical Center to develop novel electrical stimulation approaches to modulate the seizure network, adaptively and selectively. If successful, the end result would be a reparative therapy that leverages inherent brain plasticity mechanisms and may one day be independent of chronically implanted electronics.

This project develops algorithms that capture the dynamic, frequency dependent connectivity of the brain from real-time monitoring of the brain using ECoG (Electrocorticography) and then identifying the "optimal" parameters of the LFS (low-frequency electrical stimulation) to modulate the connectivity of the epilepsy network with temporal and spatial precision. The complexity of modeling such connectivity in real-time is managed by first segmenting neural activity into different epochs and spectral bands and then deriving the sparse connectivity in each of the segments. Effective connectivity in each spectral-temporal segment is estimated using Granger causality. LFS is applied after detecting interictal epileptiform discharges (IEDs) at spatial locations identified from the model. These critical steps lead to the development of a prototype system of real-time stimulation with a natural trade-off of complexity versus accuracy prompting a compromise between battery life and efficacy. The efficacy of spatially-optimized, activity-triggered LFS is evaluated by measuring the irritability of the seizure network and comparing the rate of IEDs detected during pre- and post-treatment periods. These experiments would point the way to treatment of pharmacologically refractory epilepsy without surgical resection of brain tissue and lead to reparative therapies leveraging inherent brain plasticity. The proposed methodology presents the first of its kind reparative, real-time, and selective network modulation to treat a debilitating disease.

? DESCRIPTION (provided by applicant): Speech production relies upon a distributed network that is disrupted by a variety of neurological diseases including trauma, stroke, neuro-degeneration and neoplasms. Despite the central role of language in human behavior, our understanding of linguistic disorders and the development of appropriate treatment strategies is impeded by the absence of biologically instantiated models to explain one of the most basic of language processes - the production of words. We propose to use intracranial electroencephalographic (icEEG) recordings to generate a network-based description of the within region (Broca's area, insula and temporal pole) as well as inter-regional dynamics and use this data to critically evaluate current neuro-linguistic models and a model proposed by our group. We will cross-validate these findings using a novel direct cortical stimulation approach. We will collect high spatio-temporal resolution datasets during naming of objects, people and places from a large cohort of neurosurgical patients (n=70) and generate a network representation of information flow between visual, frontal, insular and temporal pole regions during word production. We hypothesize that word production relies on local cortical networks, coupled with long-range interactions within more distributed language network. This hypothesis will be tested with multiple analytic pipelines (using amplitude envelope correlations, phase locking values, phase-amplitude coupling and directed transfer functions). We will implement chronometrically controlled closed loop cortical stimulation - timed to the onset of focal high gamma band activity - to induce transient dysfunction of critical word production nodes enabling assessment of the causality of the icEEG data. We will leverage our expertise with measuring induced current spreads in the brain, by studying the effect of ongoing network processes on the amplitude and latency of cortico-cortical evoked potentials and high gamma activity to further assess network representations. In summary, the proposed work aims to investigate the neuro-dynamics of naming at multiple spatial levels (regional and global) using a variety of network analysis methods. The proposed work will provide unique information on the neurobiology of word production with broad implications for the understanding of normal language and for the clinical management of patients with anomia.

Humans produce language, which is a defining characteristic of our species and our civilization. We can select words precisely out of a large lexicon with remarkably low error rates. It is perhaps not surprising that this complex speech production system is easily affected by disease. Brain damage induced language disorders affect millions of Americans, and there is little hope of remediation. Research on the anatomical, physiological, and computational bases of speech production has made important strides in recent years but this has been limited by a glaring lack of information on the dynamics of the process. This limitation results from the low spatio-temporal resolution of the available tools to collect data and the effectiveness of the current tools for analysis. Our driving vision in this project is to develop an unparalleled understanding of cortical connectivity in the human language system at small spatio-temporal scales. We possess much expertise in signal decoding of the processes of cued word production with intracranial recording techniques, as well as using cortical stimulation to modulate the system. FDA-approved arrays will be used to perform closed-loop decoding of sensorimotor processes during speech production and transient neuromodulation of the language system in patients with epilepsy undergoing intracranial electrode placement for the localization of seizures. Ultimately though, the fine-grained understanding and representation of sensorimotor loops in the language system necessitates the development of ultra-small energy efficient detectors that will enable the knowledge gained in this exploratory project to be eventually applied in patients who have sustained neurological injuries that have resulted in pervasive language impairments. This integrative project brings innovative microelectronics technologies together with state of the art large data analysis techniques to begin to develop a first of its kind system to remediate language disorders.

The engineering objective is to develop biocompatible microchips to vastly enhance our insight into language and other cognitive processes and learning. Miniaturized microchips in silicon technology will be developed that can record neural signals, digitize them, and transmit the signals to an in vitro receiver wirelessly. The three-fold thrust of the project will be integrated when the PIs develop closed-loop real time decoding and transient neuromodulation system based on a population of miniaturized detectors and neuromodulators. The system has the potential to provide an unprecedented detailed understanding of the human language system and provide the framework and hardware for neural prosthetics in patients with aphasia and other language disorders. The project embodies multiple high-risk goals that have the potential to shift neuroengineering paradigm from recording and modulating in ?only? a few regions of the brain to deploying a population of ultra-small and energy efficient detectors-modulators.

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.

The purpose of this R25 Research Education Program is to provide mentorship and guidance, in addition to protected time and resources, to diverse and outstanding Neurosurgery and Neurology residents. These mentees will have access to faculty across three departments at The University of Texas Health Science Center at Houston ? the Department of Neurosurgery, the Department of Neurology and the Department of Anatomy and Neurobiology. The unique elements of our program include (1) novel entrance criteria designed to attract residents with a strong track record of basic science research, (2) mandatory participation in UTHealth's 2-year Masters of Clinical Research Curriculum, (3) financial support for a full-time technician for 2 years after the resident completes their first R25 year to ensure that their project is followed through to completion, and (4) an additional year of protected, non-clinical research. The location of this proposed grant, at UTHealth, is also a major benefit as this institution is located in the heart of The Texas Medical Center in Houston, one of the most diverse city in the USA and one whose population is growing rapidly. The mentoring of a greater number of clinician-scientists at our institution will transform the research landscape as it combines our major strengths in basic research with a proven track record of clinical excellence.