MRI Neuro-Electro-Magnetic Oscillations

Summary

Principal Investigator: Allen W Song
Duke Institute for Brain Sciences
Title: "Path Toward MRI with Direct Sensitivity to Neuro-Electro-Magnetic Oscillations"
BRAIN Category: Next Generation Human Imaging (RFA MH-14-217)

Dr. Song's group will develop a scanner technology sensitive enough to image brain activity in high resolution by directly tuning in the electromagnetic signals broadcast by neurons.

Principal Investigator: Allen W Song
Duke Institute for Brain Sciences
Title: “Path Toward MRI with Direct Sensitivity to Neuro-Electro-Magnetic Oscillations”
BRAIN Category: Next Generation Human Imaging (RFA MH-14-217)

Dr. Song’s group will develop a scanner technology sensitive enough to image brain activity in high resolution by directly tuning in the electromagnetic signals broadcast by neurons.

NIH Webpages

Spiral imaging is a fast MRI technique that is widely used in functional MRI. It is, however, vulnerable to spatial and temporal variations of the static magnetic field (B0) caused by susceptibility effects, subject motion, physiological noise, and system instabilities, resulting in blurring artifacts. To address these issues, we have developed a novel off-resonance correction method, based on k-space energy spectrum analysis (KESA), for inherent and dynamic B0 mapping and deblurring in spiral imaging. This method can generate a B0 map from the k-space data at each time point, without requiring any additional data acquisition or pulse sequence modification, and correct for the blurring caused by both spatial and temporal B0 variations, resulting in a high spatial and temporal fidelity

Spiral imaging is a fast MRI technique that is widely used in functional MRI. It is, however, vulnerable to spatial and temporal variations of the static magnetic field (B0) caused by susceptibility effects, subject motion, physiological noise, and system instabilities, resulting in blurring artifacts. To address these issues, we have developed a novel off-resonance correction method, based on k-space energy spectrum analysis (KESA), for inherent and dynamic B0 mapping and deblurring in spiral imaging. This method can generate a B0 map from the k-space data at each time point, without requiring any additional data acquisition or pulse sequence modification, and correct for the blurring caused by both spatial and temporal B0 variations, resulting in a high spatial and temporal fidelity

Project Description

In response to the NIH RFA-MH-14-217 on “Planning for Next Generation Human Brain Imaging”, we propose a comprehensive plan to organize the much needed technological resources and interdisciplinary research team for developing the next generation MRI technology that can directly detect neuroelectric activities in the human brain with a high spatial and temporal resolution, using scanners with the 3 Tesla magnetic field strength that is accessible by the majority of neuroimaging researchers. Through our encouraging preliminary investigations, we have determined that it is possible to precisely map brain activities by directly sensitizing the MRI signal to the neuro-electro-magnetic oscillations (NEMO). At the same time, we have also identified key challenges in MRI hardware, acquisition methods, and contrast mechanisms to fully enable this approach. We will address these challenges by organizing three innovative technical cores (in the form of Technical Aims) for the purpose of enabling an ultra-uniform magnetic field throughout the brain to ensure a robust detection of the NEMO signal, reaching ultra-high spatial resolutions that can resolve fine-grained cortical microstructures, and building a non-invasive human brain-machine interface (BMI) that models and classifies the functional neuroimaging signals and drives an attached robotic exoskeleton. To establish convincing evidences toward a direct and sensitive MRI detection of neuroelectric activity, and based on our solid technical foundation, we also propose four innovative pilot projects (in the form of Scientific Aims) to construct a static neural networ with causal information using high-resolution MRI, to investigate dynamic NEMO signals in vivo with enhanced sensitivity during both driven and intrinsic neuronal oscillations, and to model and classify the dynamic neuroimaging signals and to replicate and validate the human behavior in the robotic exoskeleton through our non-invasive human BMI. To help evaluate our research progress and receive critical input from researchers and leaders in the brain research community, we will organize annual workshops coordinated by our interdisciplinary research team and our scientific advisory committee. We anticipate that through these comprehensive planning and research activities, we will be able to define a clear path to reach the next generation human brain imaging technology that can precisely, non-invasively, and unambiguously map brain activities with unprecedented spatial and temporal resolution.

Public Health Relevance Statement

The emergence of hemodynamically based functional MRI (fMRI) for non-invasively imaging human brain activity has made it the preeminent method to study human brain function. Despite its explosive growth in the past two decades, however, fMRI today is still an indirect measure of functional electrical brain activity, limited by spatial dispersions and temporal delays from the hemodynamic modulation. Building on the advances in high-resolution structural and functional MRI and innovative contrast mechanisms developed in our laboratory in the past several years, we propose to build the next generation MRI methodology that can directly, and precisely image neuroelectric activities in the human brain. We expect that our comprehensive plan will carve a clear path for developing and validating this innovative neurotechnology, which will enable neuroscience researchers to investigate human brain structure and function with unprecedented spatial and temporal resolution and true neuronal sensitivity, in both healthy individuals and in those with neural and psychiatric disorders.

NIH Spending Category

Assistive Technology; Bioengineering; Brain Disorders; Clinical Research; Diagnostic Radiology; Neurosciences

Project Terms

Address; Advisory Committees; base; Behavior; Behavioral; Biological Neural Networks; Brain; brain electrical activity; Brain imaging; brain machine interface; Brain Mapping; brain research; Cereals; Communities; Detection; Educational workshop; Ensure; Etiology; Event; Foundations; Functional Magnetic Resonance Imaging; Goals; Growth; hemodynamics; Human; Image; Imaging technology; in vivo; Individual; innovation; insight; Interdisciplinary Study; Intrinsic drive; invertebrate cuticle; Investigation; Knowledge; Laboratories; Link; magnetic field; Magnetic Resonance Imaging; Magnetism; Maps; Measurement; Measures; Mental disorders; Methodology; Methods; Modeling; Monoclonal Antibody R24; Motor; neuroimaging; Neurons; Neurosciences; neurotechnology; next generation; Noise; nonhuman primate; Output; Patients; Pattern; Physiologic pulse; Pilot Projects; Process; public health relevance; relating to nervous system; Request for Applications; Research; Research Activity; Research Personnel; Residual state; Resolution; Resources; response; Robotics; sensorimotor system; Signal Transduction; Solid; Structure; Techniques; Technology; Time; tool; ultra high resolution; Validation; Visual Cortex

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