Mapping cerebral atrophy and hypometabolism on F-FDG PET/CT scans for detecting Alzheimer's disease in the Malaysian population using a Malaysian brain atlas template.

INTRODUCTION

Studies are lacking in evaluating brain atrophy patterns in the Malaysian population. This study aimed to compare the patterns of cerebral atrophy and impaired glucose metabolism on F-FDG PET/CT imaging in various stages of AD in a Klang Valley population by using voxelbased morphometry in SPM12.

MATERIALS AND METHODS

F-FDG PET/CT images of 14 healthy control (HC) subjects (MoCA score > 26 (mean+SD~ 26.93+0.92) with no clinical evidence of cognitive deficits or neurological disease) and 16 AD patients (MoCA ≤22 (mean+SD~18.6+9.28)) were pre-processed in SPM12 while using our developed Malaysian healthy control brain template. The AD patients were assessed for disease severity using ADAS-Cog neuropsychological test. KNE96 template was used for registration-induced deformation in comparison with the ICBM templates. All deformation fields were corrected using the Malaysian healthy control template. The images were then nonlinearly modified by DARTEL to segment grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) to produce group-specific templates. Age, intracranial volume, MoCA score, and ADASCog score were used as variables in two sample t test between groups. The inference of our brain analysis was based on a corrected threshold of p<0.001 using Z-score threshold of 2.0, with a positive value above it as hypometabolic. The relationship between regional atrophy in GM and WM atrophy were analysed by comparing the means of cortical thinning between normal control and three AD stages in 15 clusters of ROI based on Z-score less than 2.0 as atrophied.

RESULTS

One-way ANOVA indicated that the means were equal for TIV, F(2,11) = 1.310, p=0.309, GMV, F(2,11) = 0.923, p=0.426, WMV, F(2,11) = 0.158, p=0.856 and CSF, F(2,11) = 1.495 p=0.266. Pearson correlations of GM, WM and CSF volume between HC and AD groups indicated the presence of brain atrophy in GM (p=-0.610, p<0.0001), WM (p=-0.178, p=0.034) and TIV (p=-0.374, p=0.042) but showed increased CSF volume (p=0.602, p<0.0001). Voxels analysis of the 18FFDG PET template revealed that GM atrophy differs significantly between healthy control and AD (p<0.0001). Zscore comparisons in the region of GM & WM were shown to distinguish AD patients from healthy controls at the prefrontal cortex and parahippocampal gyrus. The atrophy rate within each ROI is significantly different between groups (c=35.9021, df=3, p<0.0001), Wilcoxon method test showed statistically significant differences were observed between Moderate vs. Mild AD (p<0.0001), Moderate AD vs. healthy control (p=0.0005), Mild AD vs. HC (p=0.0372) and Severe AD vs. Moderate AD (p<0.0001). The highest atrophy rate within each ROI between the median values ranked as follows severe AD vs. HC (p<0.0001) > mild AD vs. HC (p=0.0091) > severe AD vs. moderate AD (p=0.0143).

CONCLUSION

We recommend a reliable method in measuring the brain atrophy and locating the patterns of hypometabolism using a group-specific template registered to a quantitatively validated KNE96 group-specific template. The studied regions together with neuropsychological test approach is an effective method for the determination of AD severity in a Malaysian population.