Stephen M. LaConte, Ph.D. - US grants

DESCRIPTION (provided by applicant): This research plan envisages a methodological advance in functional MRI (fMRI) to allow for adaptive stimulus presentation derived directly from the acquired image data. Adaptation of stimuli will be accomplished by modeling fMRI to classify brain states during the image reconstruction process, and subsequently modulating a visual display. This emphasis on image-based prediction constitutes a fundamental shift from the conventional approach of using temporal changes in images to detect spatial "hot spots". This research will generate significant insights and development of capabilities for adaptive fMRI experiments using prediction of brain states. This has several significant applications. Primary among these is the potential contribution to designing much more flexible experiments to enhance our basic understanding of brain function. Also relevant are biofeedback rehabilitation, therapeutic meditation, learning studies, sports therapy or other virtual reality-based training, and lie-detection. Moreover this approach will provide spatially resolved data that complements ongoing EEG-based brain computer interface (BCI) research. The experimental plan incorporates a constructive progression that first develops offtine predictive algorithms to a range, of fMRI experminents, secondly treats the case of measurable human learning characterized by offline analysis, and ifinally utilizes these initial studies to characterize system comprising a real-time machine learning algorithm coupled with a responsive human volunteer. Long-term goal: Initiate a research program that will enhance current spatial mapping studies by allowing for temporal classification of brain states based on image data and biofeedback capabilities for adaptive fMRI experiments. Specific Aims: 1) Characterize the relationship between choice of fMRI task and choice of predictive technique to examine the importance of the particular predictive model, the connection between task difficulty and modeling accuracy, and the amount of training data required to build accurate predictive models. 2) Analyze fMRi data from a motor-learning task to study how behaviorally demonstrated learning by a subject corresponds with changes in the image data, and if this effect is directly observable using predictive models. 3) Develop capabilities to perform real-time feedback of stimulus based on interaction between a predictive algorithm and subject adaptation.

DESCRIPTION (provided by applicant): This research plan outlines crucial software enhancements to a program called 3dsvm, which is a command line program and graphical user interface (gui) plugin for AFNI (Cox, 1996). 3dsvm performs support vector machine (SVM) analysis on fMRI data, which constitutes one important approach to performing multivariate supervised learning of neuroimaging data. 3dsvm originally provided the ability to analyze fMRI data as described in (LaConte et al., 2005). Since its first distribution as a part of AFNI, it has been steadily extended to provide new functionality including regression and non-linear kernels, as well as multiclass classification capabilities. In addition to its integration into AFNI, features that make 3dsvm particularly well suited for fMRI analysis are that it is easy to spatially mask voxels (to include/exclude them in the SVM analysis) as well as to flexibly select subsets of a dataset to use as training or testing samples. It has been used to generate results for our own work and for collaborative efforts and has been cited as a resource by others (Mur et al. 2009;Hanke et al. 2009). Despite many positive aspects of 3dsvm, the priorities of PAR-07-417 address a genuine need that this software project has - the ability to focus on improvements that will increase its dissemination and interoperability. A major motivation for PAR-07-417 is to facilitate the improved interface, characterization, and documentation to enhance the extent of sharing and to provide the groundwork for future extensions. Our aims are well aligned with this program announcement. Further, there is a growing need in the neuroimaging community for tools such as 3dsvm. Since 3dsvm is not a new project, is tightly integrated into the software environment of AFNI, and can be further integrated to enable better functionality to support needs as diverse as NIfTI format capabilities to rtFMRI, this proposed project will help to further the NIH Blueprint for Neuroscience Research by supporting its need for wide-spread adoption of high-quality neuroimaging tools. PUBLIC HEALTH RELEVANCE: This proposal focuses on improving, characterizing, and documenting an existing neuroinformatics software tool. The project described will help to further the NIH Blueprint for Neuroscience Research by supporting its need for wide-spread adoption of high-quality neuroimaging tools.

DESCRIPTION (provided by applicant): Alcohol-dependent individuals frequently have deficits in self-control, as measured by their excessive discounting of delayed rewards. The dual neurobehavioral decision systems model suggests that these deficits may result from a disruption in the regulatory balance between two interacting neurobiological systems. These systems are the executive system (prefrontal cortex and parietal cortex) which is responsible for valuing delayed rewards (i.e., long-term goals), and the impulsive system (limbic and paralimbic areas) which is associated with immediate rewards (i.e., instant gratification). Our recent research has demonstrated that working memory (WM) training repairs self-control, putatively by restoring regulatory balance between these systems. There is, however, still much to learn about repairing self-control. In Aim 1, we will further explore the effects WM training on a range of WM, self-control, and clinically relevant (e.g., craving) measures. Additionally, the neural mechanisms of WM training will be explored though functional neuroimaging techniques. We will examine the dose-effect function of WM training by systematically varying the number of WM training sessions across several groups of alcohol-dependent individuals. The duration of these improvements in self-control will be tested by conducting follow-up assessments one month, three months, and six months after training. In Aim 2 we plan to capitalize on the neurobiological knowledge gained in Aim 1 to test the effects of two variants of real time fMRI neuro-feedback on our suite of WM, self-control, and clinically significant measures. This novel exploration of neuro-feedback effects on neurocognitive and clinically significant measures will include a direct comparison of feedback techniques based on a specific brain region and across a distributed neural network. Long-term changes in neural function and/or our neurocognitive measures will be explored during a one-month follow-up visit. Successfully achieving our aims could allow us to both refine our current techniques (i.e., WM training), and possibly begin the process of developing novel techniques (i.e., neuro-feedback) for the repair of self-control in alcohol-dependent individuals. This application will contribute to personalized medicine approaches in alcohol dependence, where treatment is defined by documented self-control deficits. Furthermore, the functional neuroimaging data collected across both aims should provide unique insights into both patterns of neural disruption seen in alcohol dependence and any treatment associated changes in neural function.

PROJECT SUMMARY Developing a new generation of interventions for alcohol use disorder (AUD) constitutes an important scientific gap and, if addressed, will open innovation opportunities. To address this gap, we propose to examine an emerging novel framework for addiction, reinforcer pathology. Reinforcer pathology specifies that reinforcers are integrated over a temporal window, and the length of that window determines the relative value of different reinforcers. When the temporal window is short, reinforcers such as alcohol, which are brief, intense, and reliable, will have greater value. Conversely, as the temporal window lengthens, other more temporally extended reinforcers begin to have greater influence and alcohol valuation will decrease. The concept of reinforcer pathology identifies the temporal window, measured with delay discounting (i.e., the decline in the value of a reinforcer as a function of its delay), as a therapeutic target for AUD, and it permits target engagement via innovative interventions (e.g., episodic future thinking; EFT) to provide novel insights into alcohol valuation. This project uses multiple analytical levels (e.g., the behavioral laboratory, an outpatient field study, neuroimaging, and computational modeling) to quantify, predict, and modulate alcohol valuation among individuals with AUD. In Aim 1, we will examine manipulations that increase and decrease the temporal window to mechanistically test the reinforcer pathology framework. In Aim 1a, we will examine the effects of an intervention that increases the temporal window (EFT) on concomitant changes in alcohol valuation (self-administration, craving, and behavioral economic alcohol demand). In addition, participants in Aim 1a will participate in a proof-of-concept field study, where remote implementation of EFT will be used to impact alcohol drinking (measured by remote monitoring of breath alcohol) in the natural environment. In Aim1b, we will examine the effects of a manipulation that decreases the temporal window (simulation of economic scarcity) on concomitant changes in alcohol valuation. Throughout Aim 1, neural activity associated with changes in the temporal window will also be examined. In Aim 2, we will use multi-voxel analyses of fMRI data to explore two independent sub-aims related to reinforcer pathology in AUD. First, in Aim 2a, we will build multivariate group regression models of fMRI delay discounting data in a subset of participants with AUD to predict discounting in an independent subset of participants. Second, in Aim 2b, we will use real-time fMRI neurofeedback to enhance participants' ability to control their temporal window, and hence their ability to modulate delay discounting and alcohol valuation. In Aim 3, we will model the temporal window to extend the existing literature by computationally quantifying results from Aims 1 and 2 (Aim 3a), and connecting subjective value to brain regions of interest using computational neuroscience (Aim 3b). Together, the findings from this rigorous and innovative research project will improve our understanding of AUD and highlight potential novel and efficacious intervention strategies.

PROJECT SUMMARY This proposal develops the next generation magnetoencephalography (MEG) for human social neuroscience by combining the latest available technology in optically-pumped magnetometers (OPMs) and magnetically shielded rooms (MSRs). Successful completion of the proposed research and development will enhance MEG's ease of use to enable the first ever 2-person face-to-face MEG recordings of social interactions. Motivated by a pressing need to improve the relevance of human neuroimaging - which includes upright, social movement - scalp-based sensors represent the most promising set of technologies that are both available now and are also expected to enjoy major improvements over the next several decades. While electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are currently capable of such recordings, both suffer from limitations that would be complimented or ameliorated by ?untethered? MEG. Within this context, the research objectives of this proposal are threefold. First, Aim 1 integrates the latest generation of OPM sensors and MSR technology to deliver a next generation platform for OPM-MEG experiments. These data, then provide a positive control to use this system for multi-person, movement tolerant neuroimaging in Aim 2. Finally, in Aim 3 we will evaluate sensor mounting strategies and source reconstructions strategies that will avoid obscuring parts of the face and could reduce cost and improve experimental ease. The work proposed will conducted by an assembled team of the world's leading academic and industry experts in OPM-MEG, magnetic shielding, social neuroimaging, and neuroimaging data analysis.