Resting-state functional connectivity mapping using Magnetic Particle Imaging (MPI)

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PROJECT SUMMARY/ABSTRACT: Since the development of functional MRI (fMRI)-based neuroimaging studies thirty years ago, extensive work has been undertaken to describe the intrinsic connectivity of the human brain, both in response to external stimulus and in resting-state. There have been only modest improvements in the contrast-to-noise ratio (CNR) of fMRI, and resultingly, there have been limited clinical applications of fMRI, as the CNR of fMRI limits its ability for individual-based functional neuroimaging. Magnetic particle imaging (MPI) is a novel imaging modality that capitalizes on the large magnetic moment of injected super-paramagnetic iron oxide nanoparticles (SPIONs) for an improved CNR. By harnessing the increased CNR of MPI, this proposal aims to validate the ability of fMPI to detect resting-state CBV fluctuations and use these signals to map functional connectivity networks in analogy to resting-state fMRI (rs- fMRI) correlation-based connectivity mapping. Recent work in our group has demonstrated the ability to detect MPI-based physiological noise signals in a rodent model that scale with the dose of the injected magnetic SPION. Building off this work and an on-going effort to construct a human MPI scanner, this proposal will characterize the signal fluctuations in resting-state MPI tomographic time-series data and test the ability to generate functional connectivity mappings from these signals. My central hypothesis is that armed with these prototype scanners and an increased focus on signal sensitivity, the ratio of the physiological component of the signal leveraged in correlational mapping to other, non-neuronally-generated fluctuations will be higher in MPI than in conventional blood-oxygen-level-dependent (BOLD) fMRI and more robust network maps will result. I plan to generate the first MPI-based resting-state functional brain connectivity maps, compare to rs-fMRI, and potentially demonstrate improved sensitivity to uncover subtle network alterations, thus potentially advancing our ability to understand and phenotype individual patients with neuropsychiatric disease. In this proposal, I will first demonstrate the ability to characterize cortical association networks in rodents using a small-bore MPI scanner, after improving existing image reconstruction methods. The second aim of the proposal will focus first on updating the hardware of the human MPI scanner to harness the sensitivity of SPIONs necessary to perform resting-state fMPI experiments and then on validating a stable and sensitive phantom image time-series to detect hemodynamic fluctuations that dominate the physiologic “noise”. I will then validate the ability to perform fMPI cortical association network characterization in a macaque model where a wide choice of SPION tracer is available. The third aim is focused on performing rs-fMPI characterization studies in humans. The successful completion of the proposal will determine the potential of MPI as a novel and highly sensitive functional connectivity mapping modality advancing our ability to uncover altered brain networks in individual patients with mental illness.