This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Near Infrared Spectroscopy (NIRS) is a non-invasive, non-ionizing, and inexpensive monitoring and imaging technique that uses near-infrared light to probe tissue optical properties. Regional variations in oxy- and deoxy-hemoglobin concentration as well as cellular scattering can be imaged by monitoring spatial-temporal variations in the light absorption and scattering properties of tissue, giving NIRS the special ability to directly measure the hemodynamic, metabolic, and neuronal responses to brain activation. These capabilities make NIRS a useful complement to fMRI and EEG/MEG in studies of normal physiology and pathology, as evidenced by our publications and dissemination in the past grant cycles of this program. We have developed real-time continuous-wave (CW) imaging instrumentation for measuring brain activation with improved spatial resolution afforded by overlapping measurements and signal processing to reduce the interference from systemic physiological fluctuations. Further, we developed a prototype time-domain (TD) imaging system that affords better depth sensitivity than CW and the ability to quantify baseline physiological properties of the brain. Current needs of the NIRS community include: 1) tools to facilitate interpretation of the brain activation images in the context of brain anatomy in adults and infants;and 2) access to portable TD imaging instrumentation that enables multispectral measurements with an image acquisition rate of >2 Hz (necessary to overcome physiological interference). Further, the good temporal resolution of NIRS has enabled us to comprehend well and design algorithms to filter the signal interference that arises from heart rate, respiration, and slower blood pressure fluctuations. This interference is common to fMRI and thus we propose to transfer know-how from our NIRS effort to fMRI to better filter this systemic physiological interference, enabling exploration of these systemic factors (including cerebral autoregulation) and improving the contrast- to-background ratio in fMRI studies of brain activation.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR014075-13
Application #
8362809
Study Section
Special Emphasis Panel (ZRG1-SBIB-L (40))
Project Start
2011-06-01
Project End
2012-05-31
Budget Start
2011-06-01
Budget End
2012-05-31
Support Year
13
Fiscal Year
2011
Total Cost
$379,810
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Lee, Jeungchan; Mawla, Ishtiaq; Kim, Jieun et al. (2018) Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. Pain :
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