This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.The goals of this protocol are to develop a method of quantifying pulmonary gas flow using inhalation of helium3 and dynamic MRI. This is a developmental study. Volunteers will be imaged by 3HeMRI. 3HeMRI is a radical new method of lung imaging that promises high spatial resolution assessments of lung function that are unavailable by any other technique. With this test the lung airspaces and airways are directly visualized by inhaling the hyperpolarized gas and forming MRI images. Subjects will be instructed to breathe deeply four times and on the final inhalation the 3He will be administered. The investigators believe that the smallest possible volume of inhaled gas, followed by a 'chaser' of air, will give the best results. The 3He will not be diluted with nitrogen. The investigators will try doses of 100, 200 and 300 ml of 3He to determine whether the higher signal to noise offered by the greater quantity of gas compensated for the longer bolus. This will be assessed by measuring the signal to noise in calculated flow and volume measurements in the larger vessels. The main focus of the proposal is to develop quantitative methods to analyze the data, based on algorithms designed to evaluate blood flow. The investigators have an approved IND and intend to study 20 normal volunteers. This is a pilot study of what could turn out to be a simple, non-invasive way to examine pulmonary gas flow.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
General Clinical Research Centers Program (M01)
Project #
5M01RR000096-46
Application #
7605719
Study Section
National Center for Research Resources Initial Review Group (RIRG)
Project Start
2007-04-01
Project End
2008-03-31
Budget Start
2007-04-01
Budget End
2008-03-31
Support Year
46
Fiscal Year
2007
Total Cost
$789
Indirect Cost
Name
New York University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
121911077
City
New York
State
NY
Country
United States
Zip Code
10016
Jun, Gyungah R; Chung, Jaeyoon; Mez, Jesse et al. (2017) Transethnic genome-wide scan identifies novel Alzheimer's disease loci. Alzheimers Dement 13:727-738
Homann, O R; Misura, K; Lamas, E et al. (2016) Whole-genome sequencing in multiplex families with psychoses reveals mutations in the SHANK2 and SMARCA1 genes segregating with illness. Mol Psychiatry 21:1690-1695
Ridge, Perry G; Hoyt, Kaitlyn B; Boehme, Kevin et al. (2016) Assessment of the genetic variance of late-onset Alzheimer's disease. Neurobiol Aging 41:200.e13-200.e20
Hohman, Timothy J; Bush, William S; Jiang, Lan et al. (2016) Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium. Neurobiol Aging 38:141-150
Jun, G; Ibrahim-Verbaas, C A; Vronskaya, M et al. (2016) A novel Alzheimer disease locus located near the gene encoding tau protein. Mol Psychiatry 21:108-17
Ebbert, Mark T W; Boehme, Kevin L; Wadsworth, Mark E et al. (2016) Interaction between variants in CLU and MS4A4E modulates Alzheimer's disease risk. Alzheimers Dement 12:121-129
Hohman, Timothy J; Cooke-Bailey, Jessica N; Reitz, Christiane et al. (2016) Global and local ancestry in African-Americans: Implications for Alzheimer's disease risk. Alzheimers Dement 12:233-43
Beecham, Gary W; Dickson, Dennis W; Scott, William K et al. (2015) PARK10 is a major locus for sporadic neuropathologically confirmed Parkinson disease. Neurology 84:972-80
Wang, Li-San; Naj, Adam C; Graham, Robert R et al. (2015) Rarity of the Alzheimer disease-protective APP A673T variant in the United States. JAMA Neurol 72:209-16
Mukherjee, Shubhabrata; Walter, Stefan; Kauwe, John S K et al. (2015) Genetically predicted body mass index and Alzheimer's disease-related phenotypes in three large samples: Mendelian randomization analyses. Alzheimers Dement 11:1439-1451

Showing the most recent 10 out of 470 publications