Juan E. Iglesias Gonzalez, Ph.D. - US grants
Affiliations: | 2011 | University of California, Los Angeles, Los Angeles, CA |
Area:
Computer vision, machine learning, deep learning, neural computation, neuro imagingWe are testing a new system for linking grants to scientists.
The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Juan E. Iglesias Gonzalez is the likely recipient of the following grants.Years | Recipients | Code | Title / Keywords | Matching score |
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2020 | Fischl, Bruce (co-PI) [⬀] Iglesias Gonzalez, Juan Eugenio |
RF1Activity Code Description: To support a discrete, specific, circumscribed project to be performed by the named investigator(s) in an area representing specific interest and competencies based on the mission of the agency, using standard peer review criteria. This is the multi-year funded equivalent of the R01 but can be used also for multi-year funding of other research project grants such as R03, R21 as appropriate. |
Open-Source Software For Multi-Scale Mapping of the Human Brain @ Massachusetts General Hospital Project Summary (maximum 30 lines) The BRAIN initiative seeks to develop and apply technologies in order to understand of how brain cells interact in both time and space to give rise to brain function. A key deliverable in BRAIN is a systematic census of neuronal and glial cell types, which is a prerequisite to understand how these cells interact and change in healthy aging and in disease. Moreover, such a census will provide a common reference cell taxonomy, which is crucial to harmonize studies at different sites and achieve the goals of BRAIN. A necessary companion of the census is a reference coordinate system, which enables us to understand the spatio-anatomical context in which cells interact, as well as their connectivity. Building such a coordinate system requires advanced spatial alignment (registration) tools, since virtually every lab technique used in microscopic brain cell phenotyping ? particularly in human brain ? requires blocking and/or sectioning of samples, hence distorting the structure of tissue. Due to the difficulty of providing support for datasets and acquisition setups different to the original, most publicly available techniques to recover the lost tissue structure (?3D reconstruction?) rely on very simple techniques, such as vanilla pairwise registration of neighboring sections. Moreover, conventional reconstruction methods are notoriously slow, and no available method is designed to 3D reconstruct whole human brains. In this interdisciplinary project, which lies at the nexus of computer science, MRI physics, histology, optical imaging, anatomy and statistics, we propose to extend, robustify, test, distribute and support our recently developed, state-or-the-art techniques that will enable the constructions of a coordinate system capable of representing multi-scale maps of human brain anatomy and function. This includes algorithms and software for: image analysis of ex vivo MRI; construction of laminar models of the human cerebral cortex; 3D reconstruction of microscopic images and alignment to the laminar models; surface based analysis of microscopy data on the laminar structure; and alignment of ex vivo and in vivo images to accurately transfer information from microscopy to MRI studies of the living brain, in health and in disease. The tools we propose to build and disseminate will combine modern deep learning techniques with principled Bayesian inference, and have the potential to deliver accurate registration at the macroscopic, mesoscopic, and microscopic level, with high throughput delivered using cutting-edge machine learning algorithms. Effective dissemination of these tools, along with companion test data, will be achieved through our widespread package FreeSurfer. The distributed tools will not only enable the construction of a cell census with rich spatial information at human brain scale (including a novel laminar model), but will also have a tremendous impact in other areas of neuroimaging, including overarching goals of BRAIN such as: linking cellular-level activity to functional MRI, atlas building, or connecting axonal anatomy to diffusion MRI. |
0.909 |
2021 | Iglesias Gonzalez, Juan Eugenio | R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
@ Massachusetts General Hospital Project Summary Title: Diagnosing the undiagnosable: studies of Alzheimer disease mimics and confounders via neuropathometry of dissection photos with 3D scanning Summary: While most patients with late life dementia have Alzheimer?s disease (AD), there are conditions that overlap or even mimic AD, confounding clinical diagnosis, and thus representing a barrier to accurate predictions of rate of progression and to effective therapeutics. Examples of these diseases include concomitant TDP-43 pathology, Dementia with Lewy bodies (DLB), and microvascular lesions associated with poorly defined white matter lesions. A critical barrier to studying these diseases is that there currently is no reliable premortem biomarker. Here we propose a collaboration with two Alzheimer?s Research Centers to evaluate anatomical signatures of these three conditions, in contrast to AD, in order to enable research into them, and ultimately port back to MRI in order to directly enhance clinical care. Specifically, we propose to use advanced machine learning (ML) techniques to perform volumetric photographic scanning post mortem (at autopsy), on patients seen at the Massachusetts Alzheimer Disease Research Center (MADRC). Reconstructing imaging volumes from dissection photographs, which are routinely acquired at brain banks and neuropathology departments, will enable us to correlate neuropathology with macroscopic measurements (e.g., volume and shape of brain structures, cortical thickness) without the need for magnetic resonance imaging (MRI) data. This is crucial because diagnostic MRI is not always acquired close to autopsy, or at all, and ex vivo MRI is expensive, technically challenging, and not available at many research sites. Therefore, our technique has the potential of greatly increasing sample sizes, especially with asymptomatic individuals who were not scanned in life, and who would likely manifest the earliest and purest neuropathological changes. Our tools will combine ML with 3D shape scanning, which is an increasingly inexpensive technology ($1,000 - $10,000 for a scanner), to produce very accurate reconstructions of the brain shape. Moreover, we will also build an ?atlas? version of the tool, that replaces 3D scanning by a probabilistic atlas, thus enabling analysis of retrospective data. We will develop the tools in collaboration with a second ADRC, the University of Washington ADRC, which has slice photographs for approximately one thousand cases. The new tools will be used to closely study a prospective cohort at MADRC, consisting of 200 subjects. We seek to identify neuroimaging signatures of the AD mimics mentioned above, which can be ported to in vivo MRI scanning. Moreover, we will also distribute and maintain the tools as part of our neuroimaging package FreeSurfer (over 40,000 worldwide licenses), so they can be used by research sites around the world to augment neuropathology with macroscopic morphometric measures at little or no cost. |
0.909 |