Intrinsic functional connectivity (FC) refers to the spatiotemporal coherence of spontaneous, low-frequency (<0.10 Hz) fluctuations in the blood-oxygen level dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI). Previous research suggest that separable networks of intrinsic functional connectivity can be reliably identified even in the resting state, that is, in the absence of an assigned cognitive or behavioral task. Converging evidence from diffusion tensor imaging (DTI) suggests further that intrinsic functional connectivity is constrained by anatomical connectivity, as reflected in the integrity of white matter pathways associated with individual intrinsic connectivity network (ICN). ICN mapping is promising as a neural biomarker that will be valuable in translational contexts for understanding both healthy development and disease progression. A critical barrier to progress in research on ICN mapping, however, is the limited resolution of current methods for measuring functional connectivity. In addition, little is known regarding the relation of ICN to phenotypic signatures of neurological diseases. Thus, the goals in this project are twofold. The first goal is to design novel high-resolution and high-throughput MRI techniques. This will allow a more reliable detection of critical nodes (e.g., brainstem nuclei and sub-regions of hippocampus, among others) of ICNs, which cannot be reliably measured with conventional low-resolution and artifact-prone fMRI. The second goal is to develop novel data analysis algorithms, so that the ICNs that are commonly or dissociably correlated with different phenotypic signatures of neurological impairment, such Parkinson's disease (e.g., motor function decline, cognitive decline and depression) can be characterized. This new research direction: high-resolution mapping of phenotype-specific ICN vulnerability, makes it possible to investigate the mechanistic connection, at the level of neuronal network, among multiple phenotypes of neurological diseases. To achieve these goals, this research has three specific aims: 1) Development of high-resolution and high-throughput ICN mapping techniques, by integrating a novel time-domain phase-regularized parallel (T-PREP) imaging and state-of-the-art scan acceleration strategies, specifically the simultaneous multi-band parallel imaging;2) Development of effective and inherent artifact removal techniques for ICN mapping, so that the susceptibility related distortions and intra-scan pulsation artifacts can be eliminated with an improved k-space energy spectrum analysis and a novel multi-band imaging scheme with location-dependent temporal-resolution, respectively;3) Development of phenotype-based connectivity analysis (PBCA) to characterize the associations among high-resolution ICN patterns, major phenotypic signatures, and the progression from one to multiple disease symptoms in Parkinson's disease.

Public Health Relevance

The proposed methods enable reliable measurements of the intrinsic neuronal connectivity in patients with neurological diseases (including the Parkinson's disease). From the proposed studies we will gain in-depth knowledge on alternations of the neuronal architectures during the progression of neurological diseases.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Medical Imaging Study Section (MEDI)
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Ludwig, Kip A
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Duke University
Internal Medicine/Medicine
Schools of Medicine
United States
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Chen, Nan-Kuei; Chang, Hing-Chiu; Bilgin, Ali et al. (2018) A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI. PLoS One 13:e0195952
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