The efficiency of the brain is a measure of the degree to which the neural, metabolic, and vascular systems work together collectively to perform cerebral function. The coordination of physiological events between these systems, which collectively comprise a functional unit of the brain, is believed to be an important marker of brain fitness. Concurrent multimodal hemodynamic and electrophysiological measurements offer the unique ability to quantify these neurovascular relationships and thereby investigate the properties of the cerebral functional unit. In this project, we propose to develop novel multimodal experimental and model-based analysis tools to characterize the properties of the cerebral functional unit. We hypothesize that multimodal characterization of the relationships between neural, metabolic, and vascular changes will provide more robust and intrinsic assessments of the brain in comparison to autonomous (single- modality) measurements alone. We will develop an analysis framework based on a bottom-up model of the cerebral functional unit that will allow us to better utilize the unique attributes of concurrent multimodal measurements. Our model will be applied to simultaneous non-invasive, near-infrared optical imaging (NIRS) and magnetoencephalography (MEG) measurements in order to develop, test, and refine our methods based on the application of our model to a set of somatosensory experiments.
The specific aims of this project are:
Aim 1. Integrate optical and MEG imaging systems to allow for concurrent neurovascular measurements. We will improve existing instrumentation, hardware, and analysis framework, which will allow for collection and coregistration of concurrent near-infrared optical (NIRS) and MEG signals.
Aim 2. Quantify the relationships between neural and hemodynamic evoked signals. Using a combination of visual and somatosensory stimulation paradigms with parametric inputs, we will experimentally investigate the canonical relationships between neural and vascular evoked responses.
Aim 3. Develop the cerebral functional unit model. We will develop and characterize an integrated multimodal model of the cerebral functional unit to incorporate information from concurrent neural and vascular measurements.
Within a healthy brain, the neural, metabolic, and vascular systems are highly coupled to balance the use of energy by neural and synaptic processes and the supply of substrates and removal of waste products by the vascular system. While it is generally accepted that such coupling is important to the health of the brain, analysis and interpretation methods to investigate these effects have not been adequately developed to allow detailed characterization of these relationships. In particular, the utility of multimodal neuroimaging experiments can be improved by developing new analysis methodologies that are specific to the unique characteristics of concurrent multimodal measurements. We propose to develop a state-space model of the neural, metabolic, and vascular units of the brain that will allow us to statistically combine concurrent measurements from differing neuroimaging techniques, specifically near-infrared spectroscopy (NIRS) and magnetoencephalography (MEG), into a unified estimate of brain function. This model will provide a new tool to investigate and characterize the underlying relationships between neural, metabolic, and vascular physiology and will offer a novel framework for fusion of experimental multimodal information.
|Huppert, Theodore; Barker, Jeff; Schmidt, Benjamin et al. (2017) Comparison of group-level, source localized activity for simultaneous functional near-infrared spectroscopy-magnetoencephalography and simultaneous fNIRS-fMRI during parametric median nerve stimulation. Neurophotonics 4:015001|
|Schmidt, Benjamin T; Ghuman, Avniel S; Huppert, Theodore J (2014) Whole brain functional connectivity using phase locking measures of resting state magnetoencephalography. Front Neurosci 8:141|
|Abdelnour, Farras; Genovese, Christopher; Huppert, Theodore (2010) Hierarchical Bayesian regularization of reconstructions for diffuse optical tomography using multiple priors. Biomed Opt Express 1:1084-1103|
|Abdelnour, Farras; Huppert, Theodore (2010) A random-effects model for group-level analysis of diffuse optical brain imaging. Biomed Opt Express 2:1-25|
|Abdelnour, F; Schmidt, B; Huppert, T J (2009) Topographic localization of brain activation in diffuse optical imaging using spherical wavelets. Phys Med Biol 54:6383-413|
|Abdelnour, A Farras; Huppert, Theodore (2009) Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model. Neuroimage 46:133-43|