This proposal aims to support the development of a new """"""""Connectom"""""""" diffusion imaging system, designed with advanced gradient technology (300 mT/m gradient set in an advanced 3T instrument), and optimized for the collection of in vivo structural connectivity data from healthy adult humans. Following installation and optimization of this novel system, we will scan normal human subjects, including a number of subjects recruited from the other HCP site, and begin initial development of software to analyze this data and compare, document and disseminate the results obtained against those developed by other connectomics efforts, including the other HCP site. This work will be integral part of the collaborative HCP effort to construct a map of the human connectome that represents the structural and functional connections in vivo within a brain and across individuals. As a result, this work has significant potential to dramatically advance capabilities to measure the human Connectome, by aggressively optimizing non-invasive imaging technology toward Connectome measurements. This effort builds upon existing multidisciplinary collaboration between Massachusetts General Hospital/Harvard Medical School (MGH) and the University of California-Los Angeles (UCLA), and employs a multiple PI leadership approach, providing a rigorous system of leadership, organization, and oversight to this program of bioengineering, optimization and validation that aims to improve the ability of Diffusion Spectrum Imaging (DSI) to map connectivity in the living human brain.

Public Health Relevance

By fostering investigation of human neural connectivity, the new technology developed through this project has potential to improve understanding of the structure and function relationship in the human brain, and therefore, ultimately facilitate advances in the diagnosis and treatment of many psychiatric and neurological diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01MH093765-04
Application #
8641567
Study Section
Special Emphasis Panel (ZMH1-ERB-C (04))
Program Officer
Farber, Gregory K
Project Start
2010-09-15
Project End
2015-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
4
Fiscal Year
2013
Total Cost
$1,727,999
Indirect Cost
$724,689
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Thaker, A A; Weinberg, B D; Dillon, W P et al. (2017) Entorhinal Cortex: Antemortem Cortical Thickness and Postmortem Neurofibrillary Tangles and Amyloid Pathology. AJNR Am J Neuroradiol 38:961-965
McDaniel, Patrick; Bilgic, Berkin; Fan, Audrey P et al. (2017) Mitigation of partial volume effects in susceptibility-based oxygenation measurements by joint utilization of magnitude and phase (JUMP). Magn Reson Med 77:1713-1727
Duval, Tanguy; Smith, Victoria; Stikov, Nikola et al. (2017) Scan-rescan of axcaliber, macromolecular tissue volume, and g-ratio in the spinal cord. Magn Reson Med :
Bo Zhao; Bilgic, Berkin; Adalsteinsson, Elfar et al. (2017) Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling. Conf Proc IEEE Eng Med Biol Soc 2017:3264-3268
Lindemer, Emily R; Greve, Douglas N; Fischl, Bruce et al. (2017) Differential Regional Distribution of Juxtacortical White Matter Signal Abnormalities in Aging and Alzheimer's Disease. J Alzheimers Dis 57:293-303
LaPlante, Roan A; Tang, Wei; Peled, Noam et al. (2017) The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes. Int J Comput Assist Radiol Surg 12:1829-1837
Saygin, Z M; Kliemann, D; Iglesias, J E et al. (2017) High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage 155:370-382
Bianciardi, Marta; Strong, Christian; Toschi, Nicola et al. (2017) A probabilistic template of human mesopontine tegmental nuclei from in vivo 7T MRI. Neuroimage :
Aganj, Iman; Fischl, Bruce (2017) Multimodal Image Registration through Simultaneous Segmentation. IEEE Signal Process Lett 24:1661-1665
Polimeni, Jonathan R; Renvall, Ville; Zaretskaya, Natalia et al. (2017) Analysis strategies for high-resolution UHF-fMRI data. Neuroimage :

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