Introduction: Hippocampal volumetry can discriminate normal subjects from patients with amnestic mild cognitive impairment (MCI) or Alzheimer disease (AD). We have analyzed the effects of different methods of hippocampal volume (HV) adjustment on the diagnostic accuracy of this technique.

Methods: Cross-sectional analysis of 148 subjects of the ADNI database (48 normal, 66 MCI, 34 AD). Brain volumes were calculated from 3T MRI scans with gm extractor, a fully automated script based on FSL. A series of logistic regression models was obtained using 9 volumes of reference and 3 methods of adjustment (normalization, covariance, bilinear regression). Diagnostic accuracy was evaluated with the receiver operating characteristic curve method. External validity was assessed with 10-fold cross-validation.

Results: The models with the highest area under the curve (AUC) were those including the HV normalized by total intracranial volume (TIV). The differences with bilinear regression and the covariance method adjusted by TIV were minor and not statistically significant. The lowest AUCs corresponded to the models based on raw (unadjusted) HVs. The results were qualitatively similar in two clinical settings (normal versus MCI, and normal versus AD), but the differences were higher in the normal versus MCI context.

Conclusion: The accuracy of hippocampal volumetry for the differential diagnosis between normal subjects and patients with MCI or AD was maximized by normalizing the HV by the TIV. Our results do not exclude the potential superiority of non-linear models.