Background and purpose Magnetic resonance imaging (MRI) of the brain in scrub typhus meningoencephalitis is non-specific, and in the majority of the cases, conventional MRI fails to detect any abnormality. However, autopsy reports depict central nervous system involvement in almost all patients. There is therefore a need for research on the quantitative assessment of brain parenchyma that can detect microstructural abnormalities. The study aimed to assess the microstructural integrity changes of scrub typhus meningoencephalitis by using different diffusion tensor imaging (DTI) parameters. Methods This was a retrospective analysis of scrub typhus meningoencephalitis. Seven patients and seven age- and sex-matched healthy controls were included. Different DTI parameters such as apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative anisotropy (RA), trace, volume ratio (VR) and geodesic anisotropy (GA) were obtained from six different regions of subcortical white matter at the level of the centrum semiovale. Intergroup significant difference was determined by one-way analysis of variance followed by Tukey’s post hoc test. Receiver operating characteristic curves were constructed to determine the accuracy of the DTI matrices. Results There was a significant decrease in FA, RA and GA as well as an increase in ADC and VR in the subcortical white matter in patients with scrub typhus meningoencephalitis compared to controls ( p < 0.001). The maximum sensitivity of the DTI parameters was 85.7%, and the maximum specificity was 81%. Conclusion There was an alteration of subcortical white-matter integrity in scrub typhus meningoencephalitis that represents the axonal degeneration, myelin breakdown and neuronal degeneration. DTI may be a useful tool to detect white-matter abnormalities in scrub typhus meningoencephalitis in clinical practice, particularly in patients with negative conventional MRI.
Subcortical White Matter Changes with Normal Aging Detected by Multi-Shot High Resolution Diffusion Tensor Imaging
