Denoising scanner effects from multimodal MRI data using linked independent component analysis

Pooling magnetic resonance imaging (MRI) data across research studies, or utilizing shared data from imaging repositories, presents exceptional opportunities to advance and enhance reproducibility of neuroscience research. However, scanner confounds hinder pooling data collected on different scanners or across software and hardware upgrades on the same scanner, even when all acquisition protocols are harmonized. These confounds reduce power and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach that implements a data-driven linked independent component analysis (LICA) to identify scanner-related effects for removal from multimodal MRI to denoise scanner effects. We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters. Our proposed denoising method shows a greater reduction of scanner-related variance compared with standard GLM confound regression or ICA-based single-modality denoising. Although we did not test it here, for combining data across different scanners, LICA should prove even better at identifying scanner effects as between-scanner variability is generally much larger than within-scanner variability. Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Ageing and the Ipsilateral M1 BOLD Response: A Connectivity Study.

Young people exhibit a negative BOLD response in ipsilateral primary motor cortex (M1) when making unilateral movements, such as button presses. This negative BOLD response becomes more positive as people age. In this study, we investigated why this occurs, in terms of the underlying effective connectivity and haemodynamics. We applied dynamic causal modeling (DCM) to task fMRI data from 635 participants aged 18-88 from the Cam-CAN dataset, who performed a cued button pressing task with their right hand. We found that connectivity from contralateral supplementary motor area (SMA) and dorsal premotor cortex (PMd) to ipsilateral M1 became more positive with age, explaining 44% of the variability across people in ipsilateral M1 responses. In contrast, connectivity from contralateral M1 to ipsilateral M1 was weaker and did not correlate with individual differences in rM1 BOLD. Neurovascular and haemodynamic parameters in the model were not able to explain the age-related shift to positive BOLD. Our results add to a body of evidence implicating neural, rather than vascular factors as the predominant cause of negative BOLD-while emphasising the importance of inter-hemispheric connectivity. This study provides a foundation for investigating the clinical and lifestyle factors that determine the sign and amplitude of the M1 BOLD response in ageing, which could serve as a proxy for neural and vascular health, via the underlying neurovascular mechanisms

Lunar Wormbot: Design and Development of a Ground Base Robotic Tunneling Worm for Operation in Harsh Environments

From 1969 to 1972, the National Aeronautics and Space Administration (NASA) sent Apollo missions to the moon to conduct various exploration experiment. A few of the missions were directed to the study and sampling of moon soil, otherwise known as lunar regolith. The extent of the sample acquisition was limited due to the astronauts' limited ability to penetrate the moon's surface to a depth greater than three meters. However. the samples obtained were sufficient enough to provide key information pertaining to lunar regolith material properties that would further assist in future exploration endeavors. Analysis of the collected samples showed that the properties of lunar regolith may lead to knowledge of processed materials that will be beneficial for future human exploration or colonization. However, almost 40 years after the last Apollo mission, limited infonnation is known about regions underneath the moon's surface. Future lunar missions will require hardware that possesses the ability to burrow to greater depths in order to collect samples for subsequent analysis. During the summer of 2010, a team (Dr. Jessica Gaskin, Michael Kuhlman. Blaze Sanders, and Lafe Zabowski) from the NASA Robotics Academy at Marshall Space Flight Center (MSFC) was given the task of designing a robot to function as a soil collection and analysis device. Working with the National Space Science and Technology Center (NSSTC), the team was able to propose an initial design, build a prototype, and test the various subsystems of the prototype to be known as the "Lunar Wormbot" (LW). The NASA/NSSTC team then transferred the project to a University of Alabama in Huntsville (UAH) Mechanical and Aerospace Engineering (MAE) senior design class for further development. The UAH team was to utilize the NASA Systems Engineering Engine Design Process in the continuance of the Lunar Wormbot project. This process was implemented in order to coordinate the efforts of the team and guide the design of the project to ensure a high quality product that met requirements within the academic year timeframe. When the transition from the NASA NSSTC team to the UAH team occurred in August 2010, the scope and requirements were provided to the UAH team. The main objective for the UAH team was to design and fabricate a robotic burrowing prototype using peristaltic or earthworm-like motion with the purpose of collecting soil samples. The team was tasked with the design of a sub-system of the LW called the locomotive, or active, segment. Through the design process, the team extensively reviewed the requirements and functions to be performed of the LW, which led to the proposal of a final design. The present paper provides the details of the development of the design up to and including the Critical Design Review (CDR) of the Lunar Wormbot. This document briefly describes thc overall system and its function but primarily focuses on the design and implementation of the locomotive segment. Content presented includes: general design and system functionality, technical drawings, system analysis, manufacturing methods, and general project costs

Development and Validation of a Knee-Thigh-Hip LSDYNA Model of a 50th Percentile Male.

With the introduction of air bags, occupant safety in frontal car crashes has been improved for upper regions of the body, such as the head and thorax. These improvements, however, have not helped improve the safety for the lower extremities, increasing their percentage of injuries in car crashes. Though lower extremity injuries are usually not life threatening, they can have long lasting physical and psychosocial consequences. An LSDYNA finite element model of the knee-thigh-hip (KTH) of a 50th percentile adult male was developed for exploring the mechanics of injuries to the KTH during frontal crash crashes. The model includes a detailed geometry of the bones, the mass of the soft tissue, and a discrete element representation of the ligaments and muscles of the KTH. The bones were validated using physical tests obtained from the National Highway Traffic and Safety Administration\u27s (NHTSA) test database. The geometry, the material properties and the failure mechanisms of bone materials were verified. A validation was also performed against a whole-body cadaver test to verify contributions of passive muscle and ligament forces. Failure mechanisms in the tests and simulations were compared to ensure that the model provides a useful tool for exploring fractures and dislocations in the KTH resulting from frontal vehicle crashes. The validated model was then used to investigate injury mechanisms during a frontal car crash at different occupant positions. The role of muscle forces on these fracture mechanisms was explored and simulations of frontal impacts were then reproduced with the KTH complex at different angles of thigh flexion, adduction and abduction. Results show that the failure mechanism of the lower limb can significantly depend on the occupant position prior to impact. Failure mechanisms in the simulations were compared to results found in literature to ensure the model provides a useful tool for predicting fractures in the lower limb resulting from out-of-position frontal vehicle crashes. The FE model replicate injury criteria developed for ligament failure and suggested lowering the actual used axial femur force threshold for KTH injures both in neutral and out-of-position KTH axial impacts

1992 NASA/ASEE Summer Faculty Fellowship Program

For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers

Towards human-relevant preclinical models: fluid-dynamics and three-dimensionality as key elements

The activity of research of this thesis focuses on the relevance that appropriate in vitro fully humanized models replicating physiological microenvironments and cues (e.g., mechanical and fluidic) are essential for improving human biology knowledge and boosting new compound testing. In biomedical research, the high percentage of the low rate of successful translation from bench to bedside failure is often attributed to the inability of preclinical models in generating reliable results. Indeed, it is well known that 2D models are far from being representative of human complexity and, on the other side, although animal tests are currently required by regulatory organizations, they are commonly considered unpredictive. As a matter of fact, there is a growing awareness that 3D human tissue models and fluid-dynamic scenarios are better reproducers of the in vivo context. Therefore, during this PhD, I have worked to model and validate technologically advanced fluidic platforms, where to replicate biological processes in a systemic and dynamic environment to better assess the pharmacokinetics and the pharmacodynamics of drug candidates, by considering different case studies. First, skin absorption assays have been performed accordingly to the OECD Test Guidelines 428 comparing the standard diffusive chamber (Franz Diffusion Cell) to a novel fluidic commercially available organ on chip platform (MIVO), demonstrating the importance of emulating physiological fluid flows beneath the skin to obtain in vivo-like transdermal penetration kinetics. On the other hand, after an extensive research analysis of the currently available intestinal models, which resulted insufficient in reproducing chemicals and food absorption profiles in vivo, a mathematical model of the intestinal epithelium as a novel screening strategy has been developed. Moreover, since less than 8% of new anticancer drugs are successfully translated from preclinical to clinical trials, breast, and ovarian cancer, which are among the 5 most common causes of death in women, and neuroblastoma, which has one of the lowest survival rates of all pediatric cancers, have been considered. For each, I developed and optimized 3D ECM-like tumor models, then cultured them under fluid-dynamic conditions (previously predicted by CFD simulations) by adopting different (customized or commercially available) fluidic platforms that allowed to mimic u stimuli (fluid velocity and the fluid flow-induced shear stress) and investigate their impact on tumor cells viability and drug response. I provided evidence that such an approach is pivotal to clinically reproduce the complexity and dynamics of the cancer phenomenon (onset, progression, and metastasis) as well as to develop and validate traditional (i.e., platin-based drugs, caffein active molecule) or novel treatment strategies (i.e., hydroxyapatite nanoparticles, NK cells-based immunotherapies)

A machine learning approach to parameter inference in gravitational-wave signal analysis

openGravitational Wave (GW) physics is now in its golden age thanks to modern interferometers. The fourth observing run is now ongoing with two of the four second-generation detectors, collecting GW signals coming from Compact Binary Coalescences (CBCs). These systems are formed by black holes and/or neutron stars which lose energy and angular momentum in favour of GW emission, spiraling toward each other until they merge. The characteristic waveform has a chirping behaviour, with a frequency increasing with time. These GW signals are gold mines of physical information on the emitting system. The data analysis of these signals has two main aspects: detection and parameter estimation. For what concerns detection, two approaches are used right now: matched filtering, which compares numerical waveform with raw interferometers' output to highlight the signal, and the study of bursts, which highlights the coherence of arbitrary signals in different detectors. Both these techniques need to be fast enough to allow for electromagnetic follow-up with a relatively short delay. The offline parameter inference process is based on Bayesian techniques and is rather lengthy (individual processing Markov Chain Monte Carlo runs can take a month or more). My thesis has the goal of introducing a fast parameter estimation for unmodeled (burst) methods which produce only phenomenological, de-noised waveforms with, at best, a rough estimate of only a few parameters. The implementation of an approach for fast parameter inference in this unmodeled analysis, taking as input the reconstructed waveform, could be extremely useful for multimessenger observations. In this context, Keith et al. (2021a) proposed to use Physics Informed Neural Networks (PINNs) in GW data analysis. These PINNs are a machine learning approach which includes physical prior information in the algorithm itself. Taking a clean chirping waveform as input, the algorithm of Keith et al. (2021a) demonstrated a successful application of this concept and was able to reconstruct the compact object's orbits before coalescence with great detail, starting only from a parameterized Post-Newtonian model. The PINN environment could become a key tool to infer parameters from GW signals with a simple physical ansatz. As part of my thesis work, I reviewed in detail GW physics and the PINN environment and I updated the algorithm described in Keith et al. (2021a). Their ground-breaking work introduces PINNs for the first time in the analysis of GW signals, however it does so without considering some important details. In particular, I noted that the algorithm of Keith et al. (2021a) spans a very constrained parameter space. In this thesis I introduce some of the missing details and I recode the algorithm from scratch. My implementation includes the learning of the phenomenological differential equation that describes the frequency evolution over time of the chirping GW, within a different, but more physical, parameter space. As a test, starting from a waveform as training data, and from the Newtonian approximation of the GW chirp, I infer the chirp mass, the GW phase and the frequency exponent in the differential equation. The resulting algorithm is robust and uses realistic physical conditions. This is a necessary first step to realize parameter inference with PINNs on real gravitational wave data

URI Graduate School Course Catalog 1974-1975

This is a digitized, downloadable version of the University of Rhode Island course catalog.https://digitalcommons.uri.edu/course-catalogs/1007/thumbnail.jp