Jack L. Lancaster - US grants

The award will support the Human BrainMap Database: Workshop I". The support for this workshop spans several Federal Agencies (Office of Naval Research, National Institute of Mental Health). The workshop will be held in San Antonio, Texas, from November 29 through December 2, 1992. The workshop will focus on issues of functional brain mapping (identifying the locations and computational properties of the neural populations that orchestrate human behavior) via positron emission tomography. In particular, issues of database design, standards, interfaces with computer software as well as data sharing and intellectual property will be discussed. This activity follows recommendations set out by the Institute of Medicine report: Mapping the Brain. It is anticipated that this will be the first of two workshops, with an intervening period of testing of specific software, BrainMap.

With National Science Foundation support, Dr. Xiong will develop imaging and modeling strategies to study mechanisms underlying adaptive changes of the human brain. The focus of this proposal is to explore the mechanisms underlying motor learning. Learning-induced neural plasticity and functional reorganization are well-established and well-documented, but not well-understood. Current neuroimaging studies investigate neural mechanisms underlying learning by exploring the changes in regional neural activity and inter-regional activity of task-performance. Little effort has been given to studying the more fundamental changes of neural connections and synaptic weighting. On the technical front, human functional imaging research sorely needs more rigorous approaches, as can be provided by mathematical modeling. A modeling framework - Structural Equation Modeling - is now accepted as appropriate for human imaging data. Structural equation modeling however, is currently performed with anatomical constraints based on neuroanatomical studies in non-human species. The performance of structural equation modeling might be greatly enhanced if anatomical constraints are individually optimized using the same subject's task-independent anatomical connectivity data. To date, this strategy has not been reported by any laboratory. The present proposal seeks to develop system-level modeling strategies for neuroimaging and to apply these novel strategies to mechanisms of action of motor learning. The overall goal of this proposal will be accomplished through the following four goals. First, developing and optimizing imaging strategies for detecting anatomical connectivity for each individual subject. Second, developing a structural equation modeling strategy by incorporating individual anatomical constraints to enhance those models' performance. Third, investigating changes in regional neural activity and inter-regional activity of task-performance induced by motor learning using the enhanced modeling strategy. Fourth, investigating synaptic plasticity by applying the enhanced modeling and demonstrating that synaptic plasticity is an underlying mechanism of action of motor learning. When completed, this research project will increase the understanding of mechanisms of adaptive learning and has the potential of defining a new strategy by which functional imaging can be applied to study mechanisms of action and disease pathophysiology.

[unreadable] DESCRIPTION (provided by applicant): Interest in transcranial magnetic stimulation (IMS) has grown rapidly since its introduction in 1985, with clinicians and researchers worldwide now using IMS. Promising clinical applications include pre-surgical language mapping, and treatment of depression, Parkinson's disease, and Schizophrenia. Promising research applications include reversible-lesion mapping and connectivity analysis. An image-guided, robotically positioned TMS (irTMS) system that provides accurate and precise IMS for both research and clinical applications was developed at the UTHSCSA Research Imaging Center. This is the first system with fully integrated robotic control of IMS. This system additionally provides a full set of features for treatment planning, similar to modern radiation therapy treatment planning. A new irTMS system and robot intended for broader clinical use is now being evaluated. The initial target market for this irTMS system is for treatment of depression, and our specific aims therefore focus on clinical features important for such treatments, which are also unique to TMS/robotics. The aims of this Phase I proposal are to: 1) replicate prior validation studies using the new robot, 2) develop and evaluate single- session multi-site treatments, 3) develop and evaluate within-session head motion correction, and 4) develop and evaluate optimal TMS coil positioning using feedback from EMG. Concurrent with the performance of these aims we will be developing additional safety features and collecting data regarding safe use of the new robot. In Phase II, the irTMS system will be further refined for its first clinical use, TMS treatment of depression. Testing in this Phase I proposal is with a head phantom to simulate the patient, and testing in Phase II will include research in human subjects. This application is in response to the NIHM Topic B "Instrumentation for Basic and Clinical Neurosciences Research" in the omnibus solicitation. [unreadable] [unreadable]

DESCRIPTION (provided by applicant): The overall goal of this proposal is to expand the functionality and accessibility of Mango, a widely used, freely distributed, multi-platform, software application created by the PI and colleagues as a viewing- analysis tool for the neuroimaging community. Mango's primary design is to help researchers interpret results of brain imaging studies. It supports volumetric (3-D) structural (e.g., anatomical MRI and CT) as well as 3-D/4- D functional (e.g., fMRI, PET and SPECT) images in several standard formats, including DICOM, NIfTI, and Analyze. Mango supports multi-subject processing levels including per-subject images, statistical parametric images (SPIs), resting state network images (RSNs), as well as group-wise versions of SPIs, RSNs and meta- analytic synthetic images. Mango's functionality not only emphasizes visualization (e.g., function-structure overlays, surface rending, 3-D viewing, and flexible reslicing, with anatomical atlas overlays) but also provides important analytic tools (e.g., ROI statistics, histograms, and image calculators). Interpretative descriptions, keyed by coordinates, are derived from the meta-data fields of the BrainMap database, an NIH-funded projected developed by the PIs. We propose to significantly expand Mango's interpretive functionality, accessibility, and built-in features. Aim 1 provides extensions to Mango's regional brain 'Behavior Analysis' tool by adding 'Paradigm Analysis', interpretation for a neighborhood about an x-y-z coordinate, and synthesis of Behavioral and Paradigm 'Similarity Networks' for co-active brain regions. Aim 2 supports open source access via 'github' to Mango's plugins and the proposed JavaScript version of webMango. The JavaScript version will provide enhanced web-based image viewing for two popular open-source neuroimaging packages (XNAT & FSL) as well as the NIH sponsored web resource, NITRC. Aim 3 adds important new features including automated script building, 3-D visualization enhancements (overlays and cine), and a collection of features recommended by users. The proposed continuing software development project will provide broad access to Mango's excellent visualization capabilities and literature-informed interpretations for human brain images.