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Comparison of Microfiber Alteration of Fornix in Idiopathic Normal Pressure Hydrocephalus Patients, Alzheimer’s Disease Patients and Healthy Volunteers via Diffusion Tensor Imaging

Purpose: The clinical and imaging findings of idiopathic normal pressure hydrocephalus (iNPH) and Alzheimer's disease (AD) patients have some overlap and are often challenging to diagnose. The fornix is an important structure in the memory function, and damage to the fornix can result in memory impairment. Our study aimed to explore any differences in the microstructural changes to the fornices of iNPH and AD patients relative to those of normal control subjects, as demonstrated by using diffusion tensor imaging (DTI).

Materials and methods: Ten normal control subjects, 10 iNPH patients and 10 AD patients underwent MRI scans (3-Tesla), and the DTI data were obtained. The DTI parameters and the diffusion fiber tractography were derived using DSI studio software. The differences in the fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity, and radial diffusivity data of the three groups were compared. A receiver operating characteristic (ROC) curve analysis was also evaluated.

Results: There was a statistically-significant lower mean FA for the iNPH and AD patients than the normal control subjects. The mean ADC of the iNPH patients was statistically significantly higher than that of both the normal control subjects and the AD patients. The mean ADC is probably the most helpful parameter evident from our results, given its high sensitivity and high negative predictive value for discriminating between iNPH and AD patients.

Conclusion: Our study revealed different microstructural changes in the fornices of iNPH and AD patients using the DTI technique. The results are probably due to differences in the pathogenesis of the diseases. Furthermore, our study demonstrated the possibility of using the DTI parameter as a supportive tool to discriminate between iNPH and AD patients with high sensitivity and a high negative predictive value.


Ngamsombat C*, Gonthong A, Wongsripuemtet J, Charnchaowanish P, Chawalparit O, Muangpaisan W and Witthiwej T

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