Abstract
Introduction Volumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer grey matter (GM) structure volumes and its possible bias on functional relationships.
Methods T1-weighted images from 1077 participants (4321 timepoints) from the Alzheimer’s Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery (FLAIR) images. Mixed effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overal WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance.
Results Participants with higher WMH volumes had higher overalps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (P < 0.0001). When not corrected for WMHs, caudate volumes increased with age (P < 0.0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimer’s disease patients. After correcting for WMHs, caudate volumes decreased with age (P < 0.0001), and Alzheimer’s disease patients had lower caudate volumes than cognitively healthy individuals (P < 0.01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (P < 0.0001).
Conclusions Presence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
* Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-ontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
