Todd Parrish - US grants

DESCRIPTION (Applicant's Abstract): In this project, we propose to investigate the brain's response to acupuncture, an ancient Eastern medical method, by using a modern neuroimaging technology. Recent developments in the field of functional neuroimaging enable us to noninvasively measure blood oxygenation changes in response to a stimulus. The MR signal changes in conjunction with an appropriate statistical model can be used to detect and localize brain activation. Therefore, it is possible to measure the brain's response to acupuncture and identify the anatomic centers involved in this process. Our preliminary data of visual and auditory acupuncture point stimulation demonstrates increased activation in the associated cortices. The overall goals of this project are 1) to validate and characterize acupuncture-induced brain activation using functional magnetic resonance imaging, 2) to characterize the brain's response to different types of sham acupuncture points compared to real acupuncture points. Acupuncture is becoming more popular in Western society for medical treatment, as evidenced by the 15 million Americans who have reported treatment with acupuncture in the last year [Chicago Tribune, 2/ 11/99]. The World Health Organization reports that there are approximately 10,000 acupuncture specialists in the U.S., and an estimated 3,000 practicing acupuncturists are physicians. In 1993 the Food and Drug Administration reported that Americans were spending $500 million per year on acupuncture treatment. Nevertheless, a scientific understanding of the neuroanatomical centers involved and the method of treatment will be necessary for the widespread medical acceptance of acupuncture. In addition, since functional magnetic resonance imaging is a noninvasive, whole brain method used to visualize cortical activation, a clearer understanding of the neural substrate associated with acupuncture is likely to provide considerable insights into acupuncture treatment.

DESCRIPTION (provided by applicant): Functional magnetic resonance imaging (fMRI) relies on the coupling of blood flow changes to neuronal activity. These blood flow changes in turn alter the concentration of oxygen in the local blood pool surrounding the neurons. Because the susceptibility of oxyhemoglobin and deoxyhemoglobin are different, it is possible to make the MR signal sensitive to oxygen concentration; this is termed blood oxygenation level dependent (BOLD) signal. The BOLD signal change from the rest condition to the active condition is approximately 1-3%. Detection of the BOLD signal requires a robust paradigm, preprocessing of the signal, and statistical modeling to generate activation maps. All of this is necessary because of the weak BOLD contrast that exists in hemodynamically normal subjects. When a subject has compromised vasculature and impaired vasoreactivity from a carotid stenosis or occlusion, we have demonstrated that the BOLD signal response is absent or severely altered beyond normal detection. This is a very important issue as fMRI moves into the clinical setting and is used to study stroke recovery or aging populations. For example, in stroke recovery one may detect activation increases over time and assume they are due to neuronal recruitment or rehabilitation; however, the activations may be the result of developing collateral flow or recovery of vasoreactivity. One of the goals of this proposal is to investigate and characterize the BOLD response in the face of altered hemodynamics. A limitation to the field of functional MRI is the small difference in BOLD signal used to detect activations. We have shown that the physiologic interaction of caffeine signigicantly increases the BOLD contrast (-40% at 1,5T and 80-170% at 3T). In this proposal, the mechanisms of caffeine will be explored in normal subjects using fMRI, MR based perfusion and transcranial Doppler ultrasound to characterize the BOLD signal and the cerebral blood flow response. Individual components will be invesitgated in order to create a simple model of the interations with BOLD contrast. The improvement in BOLD contrast can be used to improve the temporal and spatial resolution or to allow the investigation of more subtle cognitive paradigms.The theme of this proposal is to investigate the impact of physiologic mechanisms (reactivity, flow, hematocrit, neuronal activity) on the generation of BOLD signal and more importantly BOLD contrast.

The ability to produce and understand the intricacies of human speech has a large impact on quality of life. Auditory communication often involves learning; for example, identifying a new friend's voice over the telephone, perceiving and producing words in a foreign language, or understanding the meaning of words used in different contexts. The traditional view of the auditory system emphasizes a feed forward pathway starting from the inner ear in the cochlea, progressing to the various brainstem nuclei, the thalamus, and finally up to the auditory cortex. As acoustical and/or functional complexities of the auditory signal increase, the more likely ?higher-level? structures are to be involved. Although the existence of this corticofugal (descending) system is acknowledged in the literature, relatively little research, especially in terms of physiology, has been conducted. The functions of individual auditory-neural structures have been studied in isolation yet researchers lack an understanding of the simultaneous contributions of these structures in performing auditory functions in humans. With support from the National Science Foundation, Dr. Patrick Wong and his research team will explore whether learning speech (particularly the lexical tones of Mandarin) and music can result in changes in lower level circuitry, which could potentially then influence processing upstream that is associated with auditory encoding. Investigations on brain anatomy and physiology will be conducted using brainstem electrophysiologic and functional magnetic resonance imaging (fMRI) procedures at various time points in a training paradigm, in which participants will learn to use pitch patterns to identify English pseudo words. This study contributes to the ongoing effort to explore the plasticity of lower and higher level structures across different stages of learning, and how these functions may differ in successful and less successful learners.

This research will lead to a more comprehensive understanding of brain plasticity as it pertains to auditory learning. On a broader level, this research is directly relevant to music and foreign language instruction. Regarding clinical applications, this research may shed light on hearing-related disorders which occur throughout the auditory pathway (e.g., peripheral hearing loss and central processing disorders). Additionally, the research focuses on a type of language (tone language) that is spoken natively in many parts of the world. The topic of second language learning is also important to global competitiveness, and this research should lead to improvements in our understanding of the process of learning Mandarin tones.

Perception of speech and music is known to rely to some extent on shared brain resources, but the extent to which those resources are shared, and how they are orchestrated is not known. With funding from the National Science Foundation, Dr. Patrick C. M. Wong and colleagues of Northwestern University, and Dr. Alice H. D. Chan of Nanyang Technological University of Singapore are investigating relationships between speech and music for perception and production. In the past, research in this area has focused on individuals with typical abilities and on individuals with extensive musical training. The current project examines the speech-music relationship from a different point of view. Namely, it considers what perceptual disorders in processing pitch reveal about speech and music. Pitch is ubiquitous in our environment. We rely on pitch to classify different types of environmental sounds, differentiate vocal sounds such as various patterns of crying, distinguish who is talking, identify the emotion of talkers, track the speech signal in adverse listening conditions, perceive musical melodies, and distinguish musical instruments. However, pitch perception can be difficult for an estimated 5% of the population in the Western world. This deficit, known as 'amusia' or 'musical tone deafness' (mTD), occurs in the absence of other known neurological or psychiatric disorders. Whether this deficit also affects other auditory domains is important to understand for its implications concerning the neural architecture of auditory perception. This project involves participants in the U.S. and in Singapore whose native language uses tone (pitch) contrasts to signal differences between words. If the brain resources required to process pitch are shared between speech and music, tone language speakers who suffer from mTD should show deficits in perceiving and producing linguistic pitch and musical pitch. The project is intended to expand our understanding about the organization of music and speech processing by our nervous system.

In addition to basic scientific information regarding speech and music, the research could lead to clinical applications. Tone deafness is not classified as a disorder by any medical or professional groups. The real-world impact of tone deafness has yet to be documented, and treatment research has not begun. This research could allow for a better understanding of the communicative consequences of tone deficits, which could ultimately lead to formal clinical recognition and treatment. Tone languages are estimated to be spoken by at least 1.66 billion people in the world, most of whom live in lower-middle income countries as classified by the World Bank. In the U.S., over 26% of non-native English or Spanish speakers (over 3.4 million total) speak a tone language, and this number is growing. Research into pitch perception and its communicative consequences has the potential to benefit large groups of individuals. The findings from this research are relevant to public and global health concerns and will be of interest to many individuals outside the English-speaking world, potentially increasing interest in international collaboration.

Recent advances in multimodal brain imaging and high throughput genotyping and sequencing techniques provide exciting new opportunities to ultimately improve our understanding of brain structure and neural dynamics, their genetic architecture, and their influences on cognition and behavior. However, data privacy and security issues have inhibited data sharing across institutes. Emerging multi-site collaborative data analysis can address these issues and facilitate data and computing resource sharing. In collaborative data analysis, the participating institutes keep their own data, which are analyzed and computed locally, and only share the computed results by communicating with a server. The server communicates with all institutes and updates the local models such that the trained machine learning models indirectly use all data and are shared with all institutes. Although some distributed/parallel computation techniques were recently proposed to address big data mining problems, most of them are synchronous models. Asynchronous distributed learning methods are much more efficient, because they allow the server to update the model with information from only one worker node without waiting for slow worker nodes in each round. However, the convergence analysis for the asynchronous distributed algorithms is much more difficult due to the inconsistent variables update across nodes. Thus, it is challenging to design efficient distributed machine learning algorithms for collaborative big data analysis. The research objective of this project is to address the computational challenges in the emerging multi-site collaborative data mining for brain big data.

This project seeks to harness the opportunities of designing new efficient asynchronous distributed machine learning algorithms with rigorous theoretical foundations for multi-site collaborative brain big data mining, creating large-scale computational strategies and effective software tools to reveal sophisticated relationships among heterogeneous brain data. This project designs the asynchronous distributed machine learning and principled big data mining models to conduct the comprehensive study of brain imaging genomics and connectomics. Specifically, the principal investigators investigate: 1) collaborative genotype and phenotype association study using new asynchronous doubly stochastic proximal gradient algorithms; 2) communication-efficient multi-site collaborative data integration models to integrate imaging genomics data for predicting outcomes of interest; 3) collaborative deep learning algorithm speedup by the asynchronous distributed algorithms with applications in temporal cognitive change prediction; and 4) new graph convolutional deep learning models for brain network mining. It is innovative to integrate new distributed machine learning and data-intensive computing with brain imaging genomics and connectomics that hold great promise for a systems biology of the brain. The developed methods and tools impact other neuroimaging, genomics, and neuroscience research, and enable investigators working on brain science to effectively test their scientific hypotheses. This project will also facilitate the development of novel educational tools.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PROJECT SUMMARY Functional activation of the cerebral cortex creates a robust increase in local temperature by increasing blood flow and metabolism because of neurovascular coupling. Changes in surface brain temperature while an awake patient performs a motor, sensory, or language task can be used to infer spatial patterns of activity to create functional maps. Awake neurosurgery is used in the management of drug-resistant epilepsy, glioma, and neurovascular malformation, in order to localize seizure and/or physiologic activity. Protection of key functional areas is imperative to avoiding postoperative neurologic deficits. Currently, direct electrical stimulation (DES) is the most commonly used method of intraoperative surgical mapping, which identifies functionally critical brain regions so they are not resected. However, DES has low spatial resolution (~1 cm), may provoke seizures, and can only test one area at a time. This project investigates a new method of intraoperative functional mapping based on infrared thermography, which has high resolution (~100 micron) and simultaneously monitors the entire exposed brain surface without risk for seizures. The Intraoperative Mapping System will be developed and tested on glioma patients, as tumors have relatively static impact on brain temperature compared to epileptogenic foci and vascular malformations. Preliminary data in motor and language mapping cases shows large (~0.5oC) positive thermal activation of contralateral motor cortex and language regions that have strong agreement with DES. Aim 1 will develop a mapping system (hardware and software) required to conduct real-time thermal-based brain mapping during awake craniotomy. We will optimize and integrate the infrared recording procedure within the surgical workflow, to maximize signal quality while minimizing treatment interference. The central piece is a mobile cart containing a powerful workstation and an articulating arm to locate the IR camera over the craniotomy. The computer will deliver stimuli, monitor and collect behavioral data (audio, video, and a wireless haptic glove), record the IR images, and display the real-time functional map. Patient tasks currently used during DES will be adapted for thermographic recording. Aim 2 will explore the temporal and spatial properties of the thermodynamic response to optimize the infrared mapping procedure. The thermal response function (TRF) is the thermal equivalent of the hemodynamic response function (HRF) that is used in fMRI. Through modeling and high resolution (spatial and temporal) IR data, we will estimate the thermal impulse response and use it to develop an efficient multi-task mapping protocol. The result will be a rapid, efficient, high resolution assessment of brain function to optimize the resection and improve patient outcomes. Aim 3 will compare the functional mapping methods (DES and infrared thermal imaging) to determine optimal synergy between them to provide the best information for the safest resection. If successful, this project will create a new method for intraoperative functional mapping during awake neurosurgery. Ultimately, we hope to improve the precision of intraoperative brain mapping while increasing the safety and efficacy of surgery for patients with drug-resistant epilepsy, glioma, and neurovascular malformations.