Timothy Robert‐Fitzgerald
A5005359973- Multiple Sclerosis Research Studies
- Functional Brain Connectivity Studies
- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Bioinformatics and Genomic Networks
- Memory and Neural Mechanisms
- Mosquito-borne diseases and control
- Gene expression and cancer classification
- Neurogenesis and neuroplasticity mechanisms
- Digital Imaging for Blood Diseases
- Malaria Research and Control
- Genetic Syndromes and Imprinting
- Machine Learning in Healthcare
- Neuroinflammation and Neurodegeneration Mechanisms
- Tuberculosis Research and Epidemiology
- Cytokine Signaling Pathways and Interactions
- Cell Image Analysis Techniques
- Systemic Sclerosis and Related Diseases
- Ultrasound Imaging and Elastography
- Image Processing Techniques and Applications
- Cancer-related molecular mechanisms research
- Neural and Behavioral Psychology Studies
University of Pennsylvania
2020-2024
California University of Pennsylvania
2022-2023
Penn Center for AIDS Research
2021-2023
Magnetic resonance imaging (MRI) is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field strength, MRI scanners could potentially lower financial technical barriers to neuroimaging reach underserved or disabled populations, but sensitivity these devices for MS lesions unknown. We sought determine if white matter can be detected on portable 64mT scanner, compare automated lesion segmentations total volume between...
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across regions individuals during aging diseases. The genetic underpinnings these remain largely unknown. We apply a stochastic multivariate factorization method diverse population 50,699 (12 studies 130 sites) derive data-driven, multi-scale PSCs regional size. were significantly correlated with 915 genomic loci the discovery set, 617 which...
Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these have varied. Current state-of-the-art comparing observed group-level maps (after averaging intensities at each image location across multiple subjects) against spatial null models of maps. However, typically make strong and potentially unrealistic assumptions, such as covariance stationarity. To address issues, this article we propose using subject-level data a...
Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption related depression in MS.
When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities - that is, in how covary at the voxel level. Previous studies have shown local covariance structures or intermodal coupling (IMCo), can be summarized for two and two-modality IMCo reveals otherwise undiscovered patterns neurodevelopment certain diseases. However, previous methods based on slopes of weighted linear regression lines, which...
Abstract A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate low-resolution images acquired on (MRI) scanners with lower field strength. In low-resource settings where low-field are more common and there a shortage radiologists manually interpret MRI scans, it...
Abstract Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across regions individuals during aging diseases. The genetic underpinnings these remain largely unknown. We apply a stochastic multivariate factorization method diverse population 50,699 (12 studies, 130 sites) derive data-driven, multi-scale PSCs regional size. were significantly correlated with 915 genomic loci the discovery set,...
Abstract Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these have varied. Current state-of-the-art comparing observed group-level maps (after averaging intensities at each image location across multiple subjects) against spatial null models of maps. However, typically make strong and potentially unrealistic assumptions, such as covariance stationarity. To address issues, this paper we propose using subject-level...
Abstract Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to structural covariance patterns across regions individuals. We present a mega-analysis of with magnetic resonance imaging 50,699 healthy diseased individuals (12 studies, 130 sites, 12 countries) over their lifespan (ages 5 through 97). Patterns (PSC) were highly heritable (0.05< h2 <0.78) significantly associated 1610 independent significant variants after...
Abstract A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images (MRI). These methods, however, may not translate low resolution acquired on MRI scanners with lower field strength. In low-resource settings where low-field are more common and there a shortage radiologists manually interpret scans, it critical...
Abstract When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities – that is, in how covary at the voxel level. Previous studies have shown local covariance structures or intermodal coupling (IMCo), can be summarized for two and two-modality IMCo reveals otherwise undiscovered patterns neurodevelopment certain diseases. However, previous methods based on slopes of weighted linear regression...
Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression.
Abstract Magnetic resonance imaging is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field MRI scanners could potentially lower financial technical barriers to neuroimaging reach underserved or disabled populations. However, sensitivity for MS lesions unknown. We sought determine if white matter can be detected on 64mT MRI, compare automated lesion segmentations total burden between paired 3T scans, identify...