Not all voxels are created equal: reducing estimation bias in regional NODDI metrics using tissue-weighted means

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of neurites relating to their organisation and processing capacity that are essential for effective neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analysed in regions-of-interest (ROI) to identify brain behaviour associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional method induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. The tissue-weighted mean robustly identified disease-related differences in ROIs such as the fornix (p<0.05, Bonferroni corrected), some of which were absent using the conventional mean. The tissue-weighted mean may generate new insight into microstructural disease-related effects in regions typically confounded by partial volume, representing a promising tool for the study of microstructural correlates of aging and neurodegenerative diseases.