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Published March 21, 2016 | Published
Book Section - Chapter Open

Axonal diameter and density estimated with 7-Tesla hybrid diffusion imaging in transgenic Alzheimer rats

Abstract

Diffusion-weighted MR imaging (DWI) is a powerful tool to study brain tissue microstructure. DWI is sensitive to subtle changes in the white matter (WM), and can provide insight into abnormal brain changes in diseases such as Alzheimer's disease (AD). In this study, we used 7-Tesla hybrid diffusion imaging (HYDI) to scan 3 transgenic rats (line TgF344-AD; that model the full clinico-pathological spectrum of the human disease) ex vivo at 10, 15 and 24 months. We acquired 300 DWI volumes across 5 q-sampling shells (b=1000, 3000, 4000, 8000, 12000 s/mm^2). From the top three b-value shells with highest signal-to-noise ratios, we reconstructed markers of WM disease, including indices of axon density and diameter in the corpus callosum (CC) – directly quantifying processes that occur in AD. As expected, apparent anisotropy progressively decreased with age; there were also decreases in the intra- and extra-axonal MR signal along axons. Axonal diameters were larger in segments of the CC (splenium and body, but not genu), possibly indicating neuritic dystrophy – characterized by enlarged axons and dendrites as previously observed at the ultrastructural level (see Cohen et al., J. Neurosci. 2013). This was further supported by increases in MR signals trapped in glial cells, CSF and possibly other small compartments in WM structures. Finally, tractography detected fewer fibers in the CC at 10 versus 24 months of age. These novel findings offer great potential to provide technical and scientific insight into the biology of brain disease.

Additional Information

© 2016 Society of Photo-Optical Instrumentation Engineers SPIE. Algorithm development and image analysis for this study were funded, in part, by grants to PT from the NIBIB (R01 EB008281, R01 EB008432) and by the NIA, NIBIB, NIMH, and the National Library of Medicine (AG016570, AG040060, EB01651, MH097268, LM05639 to PT) and by an NINDS grant to TT (R01 NS076794). Data collection and sharing for this project was funded by NIH Grant R01 AG034499-05. NIBIB & the Beckman Institute at Caltech provided essential support for the scanner & personnel (NIBIB EB000993). TT and TMW were supported by an NIH/NINDS grant (5R01NS076794-05). This work was also supported in part by a Consortium grant (U54 EB020403) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative and R01 EB008432.

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August 20, 2023
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