Medinria sti4/11/2023 ![]() Additionally, a set of age-specific DTI templates are included. Subjects range in age from 10 days (from date of birth) through 22 years. This longitudinal DTI dataset includes raw and processed diffusion data from 498 low resolution (3 mm) DTI datasets from 274 unique subjects, and 193 high resolution (2.5 mm) DTI datasets from 152 unique subjects. In this work, we describe the DTI data acquisition protocols, data processing steps, quality assessment procedures, and data included in the database, along with database access requirements. This paper focuses on the DTI component of the NIH MRI study of normal brain development. The study was designed with the final goal of providing a controlled-access database open to qualified researchers and clinicians, which could serve as a powerful tool for elucidating typical brain development and identifying deviations associated with brain-based disorders and diseases, and as a resource for developing computational methods and image processing tools. The study began in 1999 with data collection commencing in 2001 and concluding in 2007. The NIH MRI Study of normal brain development sought to characterize typical brain development in a population of infants, toddlers, children and adolescents/young adults, covering the socio-economic and ethnic diversity of the population of the United States. ![]() Walker, Lindsay Chang, Lin-Ching Nayak, Amritha Irfanoglu, M Okan Botteron, Kelly N McCracken, James McKinstry, Robert C Rivkin, Michael J Wang, Dah-Jyuu Rumsey, Judith Pierpaoli, Carlo ![]() The diffusion tensor imaging ( DTI) component of the NIH MRI study of normal brain development (Peds DTI). By comparison, the high resolution and the FA with high contrast of gray matter and white matter derived from sequence D (b=800 s/mm2, spatial resolution=1.0 mmÃ-1.0 mm Ã-1.0 mm, numbers of direction=33, NSA=3) qualified in its application in tracking both thick and thin fibers, making it an optimal DTI setting for rhesus macaques. The fractional anisotropy (FA) derived from sequence C (b=1 000s/mm2, spatial resolution=0.55 mmÃ-0.55 mmÃ-2.5 mm, direction number=33, NSA=3) was too fuzzy to be used in tracking white matter fibers. In this study, we analyzed diffusion tensor imaging ( DTI) results of brain white matter in rhesus macaques (Macaca mulatta) with four different parameter settings and found that the sequence A (b=1 000 s/mm2, spatial resolution=1.25 mmÃ-1.25 mmÃ- 1.25 mm, numbers of direction=33, NSA=3) and B (b=800 s/mm2, spatial resolution=1.25 mmÃ-1.25 mmÃ-1.25 mm, numbers of direction=33, NSA=3) could accurately track coarse fibers. MO, Yin CHAO, Fang SONG, Ming LIU, Ci-Rong LIU, Hui-Lang QIAN, Xi-Ying ZHAO, Xu-Dong Parameter comparison of white matter diffusion tensor imaging ( DTI) in rhesus macaques (Macaca mulatta) Although technological advances are necessary to enable the robust and routine measurement of this electrical activity-dependent movement of water molecules perpendicular to axons, the proposed model of d DTI defines the vectorial parameters that will need to be measured to bring this much needed technique to fruition. ![]() Specifically, the proposed model, termed “ionic DTI modelâ€, was formulated as follows.•First, based on theoretical calculations, we calculated the molecular water flow accompanying the ionic flow perpendicular to the principal axis of fiber tracts produced by electrical conduction along excited myelinated and non-myelinated axons.•Based on the changes in molecular water flow we estimated the signal changes as well as the changes in fractional anisotropy of axonal tracts while performing a functional task.•The variation of fractional anisotropy in axonal tracts could allow mapping the active fiber tracts during a functional task. The potential changes in water mobility accompanying the movement of ions during the propagation of action potentials along axonal tracts are taken into account. It is termed dynamic diffusion tensor imaging (d DTI) and is based on a hypothesized “anisotropy reduction due to axonal excitation†(“AREXâ€). Here, a modification of the DTI method is presented to enable the mapping of active fibers. Measurements of water molecule diffusion along fiber tracts in CNS by diffusion tensor imaging ( DTI) provides a static map of neural connections between brain centers, but does not capture the electrical activity along axons for these fiber tracts. The ionic DTI model (i DTI) of dynamic diffusion tensor imaging (d DTI) ![]()
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