Nan-kuei Chen - US grants

DESCRIPTION (provided by applicant): 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.

DESCRIPTION (provided by applicant): Here we propose a plan to develop and integrate novel strategies to effectively eliminate motion-related artifact, which is the major bottleneck in achieving high-quality clinical MRI for challenging patient populations such as children, tremor-dominant Parkinson's patients and seriously ill patients among others. The impact of our project is expected to be immediate and significant across multiple clinical areas. For example, 1) for abdominal MR imaging, the proposed methods enable free-breathing data acquisition of high-quality and high- resolution MRI even when the respiratory frequency changes significantly over time; 2) for neuro MRI, our strategies make it possible to achieve high-quality imaging even when subjects have continual intra-scan head tremor (e.g., Parkinson's patients). The proposed motion-immune MRI strategies are novel, and superior to existing approaches in multiple ways. First, our motion artifact correction technique can more effectively address nonlinear and local motions (e.g., in free-breathing abdominal MRI), which have been limitations for existing methods that rely on either low-resolution navigator echo signals or external non-MRI based motion measurement (e.g., with infra-red sensors). Second, our method can address motion-related artifacts of different time scales (e.g., ranging from ~ 3 Hz head tremor to infrequent intra-scan movement), which may not always be corrected by methods that rely on real-time system updating (e.g., dynamically changing the slice location after 1 or 2 TRs). Third, our approach can be applied to both Cartesian and non-Cartesian imaging pulse sequences, and thus can be translated to various clinical applications more easily and quickly than methods only applicable to non-Cartesian imaging (e.g., radial-sampling; PROPELLER imaging). Fourth, our integrated technique can simultaneously and effectively address multiple forms of motion-related artifacts, ranging from phase errors in multi-shot diffusion-weighted imaging to signal loss due to large-scale position changes. Our motion-immune MRI is achieved through uniquely integrating and optimizing two novel approaches: 1) aliasing-artifact removal with multiplexed sensitivity encoding (MUSE), and 2) motion-immune structural MRI based on repeated k-t-subsampling and artifact-minimization (REKAM). We plan to build motion-immune MRI methods, and assess the developed methods in healthy volunteers and two challenging populations: un-sedated children and tremor-dominant Parkinson's patients.

Iron concentration in deep gray matter (DGM) regions is associated with neurodegenerative disease, but DGM iron also increases with adult age, in the absence of disease. Previous studies have reported association between age-related DGM iron accumulation and decline in some aspects of neurocognitive function. However, previous studies have typically focused on a limited number of neurocognitive outcome measures, often biased towards motor functioning, and have relied on less than optimal MRI methods to estimate iron concentration, such as MRI relaxometry. Further, although decline in structural and functional brain connectivity appears to contribute to neurocognitive decline in healthy aging, the role of iron in this decline is not clear. In this project we test a model of the influence of age-related DGM iron accumulation on neurocognitive function, proposing that age-related DGM iron contributes to oxidative stress and consequently to a decline in network connectivity. The research will investigate the effects of DGM iron using Quantitative Susceptibility Mapping (QSM), a novel and validated technique that has several advantages over previous methods for estimating GM iron (e.g., relaxometry). This research comprises imaging and neurocognitive testing of 270 healthy, community-dwelling individuals, with 45 individuals in each of six age decades: 20s, 30s, 40s, 50s, 60s, and 70s. The participants in their 60s and 70s will be tested at two time points, approximately 3 years apart. Aim 1 will test the hypothesis that iron in the head of the caudate will have a greater mediating or moderating influence, on the relation between age and the neurocognitive measures, relative to other DGM regions, and that this influence will extend to measures of the efficiency of decision processes (drift rate). Aim 2 will test the hypothesis that age-related increase in DGM iron, particularly in the head of the caudate, influences structural and functional connectivity in a serial manner, with regionally specific associations among DGM iron, the white matter (WM) integrity of frontostriatal circuits, and the functional connectivity of associated resting-state networks (RSNs). Aim 3 will test the hypothesis that the age-related influences of DGM iron identified in Aims 1 and 2, in cross-sectional analyses, can be confirmed longitudinally, across a three-year interval. The project will consequently contribute to a more comprehensive theoretical model, than presently available, of the influence of DGM iron on the relation between age and neurocognitive performance. The findings will also be relevant to assessing the potential role of DGM iron as a biomarker of neurodegenerative disease.

Project Summary The proposed technologies effectively address a significant shortcoming in current MRI protocols, that is the incapability of existing protocols to acquire a complete set of high-resolution, artifact-free, multi- contrast and quantitative MR images from challenging patients (e.g., Parkinson?s disease (PD) patients; stroke patients; pediatric populations) within clinically-feasible time. Here we will develop and integrate multidisciplinary approaches to maximize the translatability of advanced MRI technologies to clinical uses for challenging patients. Aim 1A) We will incorporate fast scan strategies, motion-correction and distortion- correction modules into our recently developed multiplexed sensitivity encoded (MUSE) DTI and fMRI in a novel way, to enable high-resolution connectivity mapping (at 0.85 mm isotropic resolution, in contrast to 1.5mm to 4mm that are standard with conventional DTI and fMRI protocols). At this resolution, critical network nodes (e.g., motor and non-motor subregions of subthalamic nucleus (STN)) and pathways that are important for patient care (e.g., improved MRI guidance for deep brain stimulation) can be much more reliably resolved. Aim 1B) We will develop an innovative multi-echo-pathway MRI method to significantly reduce the scan time of multi-contrast MRI and parametric imaging (e.g., achieving simultaneous T1 and T2 parametric mapping within 3 min: ~ 4-fold improvement than conventional protocols). Aim 1C) We will develop motion artifact correction schemes that are suitable for high-resolution multi-contrast MRI in challenging patients. Aim 2A) We plan to first evaluate the MRI technologies in healthy adult volunteers in two ways. First, data obtained with our methods and conventional, more time-consuming protocols will be quantitatively compared. Second, new knowledge that can only be produced from our high-resolution data (e.g., imaging motor- subregions of STN) will be confirmed with theta-burst transcranial magnetic simulation (TMS) neuro- modulation of motor networks. Aim 2B) We plan to evaluate the proposed imaging technologies in PD patients in three ways. First, in a cross-sectional study, we will acquire and compare imaging data from 1) those who are at high risk of PD conversion (with positive family history, hyposmia, rapid eye movement sleep behavior disorder, constipation, and impairments in instrumental daily activities), 2) early-stage PD patients (Hoehn and Yahr scale 1 and 2), and 3) advanced-stage PD patients (Hoehn and Yahr scale 3 and 4). Difference in brain structure and function across three populations will be assessed. Second, in a longitudinal study, imaging data obtained from subjects with high risk of conversion to PD in year 2 and year 5 of the project will be compared to measure prodromal brain signal abnormalities and their correlation with longitudinal behavioral and MRI signal changes. Third, we will compare imaging data obtained from PD patients and non-PD patients (mainly essential tremor) to evaluate the differential diagnosis accuracy of the proposed imaging technologies.