Abstract

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 second generation morphometry protocol consists of an array of scan types: multi-echo MP-RAGE (MEMPR), multi-echo FLASH (MEF), T2-SPACE and a T2-SPACE based FLAIR . This overall protocol can by tailored to the needs and scan-time constraints of the individual study. For example, studies of damaged white matter may require FLAIR from T2-SPACE, PD-weighted MEF or T2-weighted T2-SPACE scans, while users interested in distinguishing the globus pallidus from surrounding structures will be more interested in T2* weighted MEF scans. In the following we show some of the advantages of these scans  they are more stable due to decreased B0 distortion and they allow dura to be distinguished from gray matter allowing its removal from gray matter volume/thickness estimates.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR014075-10
Application #
7723778
Study Section
Special Emphasis Panel (ZRG1-SSS-X (40))
Project Start
2008-09-01
Project End
2009-08-31
Budget Start
2008-09-01
Budget End
2009-08-31
Support Year
10
Fiscal Year
2008
Total Cost
$106,238
Indirect Cost

  Institution

Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199

  Publications

Esch, Lorenz; Sun, Limin; Klüber, Viktor et al. (2018) MNE Scan: Software for real-time processing of electrophysiological data. J Neurosci Methods 303:55-67
Zaretskaya, Natalia; Fischl, Bruce; Reuter, Martin et al. (2018) Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE. Neuroimage 165:11-26
Lee, Jeungchan; Mawla, Ishtiaq; Kim, Jieun et al. (2018) Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. Pain :
Ina Ly, K; Vakulenko-Lagun, Bella; Emblem, Kyrre E et al. (2018) Probing tumor microenvironment in patients with newly diagnosed glioblastoma during chemoradiation and adjuvant temozolomide with functional MRI. Sci Rep 8:17062
Ellingsen, Dan-Mikael; Napadow, Vitaly; Protsenko, Ekaterina et al. (2018) Brain Mechanisms of Anticipated Painful Movements and Their Modulation by Manual Therapy in Chronic Low Back Pain. J Pain 19:1352-1365
Sawyer, Kayle S; Maleki, Nasim; Papadimitriou, George et al. (2018) Cerebral white matter sex dimorphism in alcoholism: a diffusion tensor imaging study. Neuropsychopharmacology 43:1876-1883
Lee, Jeungchan; Protsenko, Ekaterina; Lazaridou, Asimina et al. (2018) Encoding of Self-Referential Pain Catastrophizing in the Posterior Cingulate Cortex in Fibromyalgia. Arthritis Rheumatol 70:1308-1318
Cushing, Cody A; Im, Hee Yeon; Adams Jr., Reginald B et al. (2018) Neurodynamics and connectivity during facial fear perception: The role of threat exposure and signal congruity. Sci Rep 8:2776
Izen, Sarah C; Chrastil, Elizabeth R; Stern, Chantal E (2018) Resting State Connectivity Between Medial Temporal Lobe Regions and Intrinsic Cortical Networks Predicts Performance in a Path Integration Task. Front Hum Neurosci 12:415
Kang, Dong-Wha; Kim, Dongho; Chang, Li-Hung et al. (2018) Structural and Functional Connectivity Changes Beyond Visual Cortex in a Later Phase of Visual Perceptual Learning. Sci Rep 8:5186

Showing the most recent 10 out of 1099 publications