NR427-SD-TUB7

MRI Biomarkers Improve Disease Progression Modeling-Based Prediction of Cognitive Decline

Tuesday, Dec. 3 12:45PM - 1:15PM Room: NR Community, Learning Center Station #7



FDA Discussions may include off-label uses.

Participants
Mostafa Mehdipour Ghazi, Copenhagen, Denmark (Abstract Co-Author) Employee, Biomediq A/S
Mads Nielsen, PhD, Copenhagen, Denmark (Presenter) Stockholder, Biomediq A/S Research Grant, Nordic Bioscience A/S Research Grant, SYNARC Inc Research Grant, AstraZeneca PLC
Akshay Pai, Copenhagen, Denmark (Abstract Co-Author) Stockholder, Cerebriu A/S
Marc Modat, PhD, London, United Kingdom (Abstract Co-Author) Co-founder, BrainMiner Ltd
Jorge Cardoso, PhD, Hertforshire, United Kingdom (Abstract Co-Author) Nothing to Disclose
Sebastien Ourselin, PhD, Hertforshire, United Kingdom (Abstract Co-Author) Nothing to Disclose
Lauge Sorensen, Copenhagen, Denmark (Abstract Co-Author) Employee, Biomediq A/S; Employee, Cerebriu A/S

For information about this presentation, contact:

mehdipour@biomediq.com

PURPOSE

To investigate if volumetric MRI biomarkers help across both parametric and nonparametric Alzheimer's disease (AD) progression modeling using neuropsychological tests for decline prediction of mini-mental state examination (MMSE) score in converting and stable mild cognitive impairment (MCI) subjects.

METHOD AND MATERIALS

The study dataset consisted of yearly visits (2005-2016) for 372 Alzheimer's Disease Neuroimaging Initiative subjects with normal cognition, MCI, and AD, including the following measurements: FreeSurfer-based T1-weighted brain MRI volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, all normalized with intracranial volume, and cognitive tests of MMSE, CDR-SB, and ADAS-Cog. Two state-of-the-art disease progression modeling methods, a nonparametric [DOI:10.1016/j.media.2019.01.004] and a parametric [DOI:10.1016/j.neurobiolaging.2014.03.043], were trained on the data with and without MRI biomarkers using 336 subjects and were subsequently applied to predict month 24 to 60 MMSE scores for 36 independent test subjects based on only their baseline and month 12 visits.

RESULTS

The predictive power and prognostic capability of the AD progression modeling methods were assessed using the per-visit mean absolute error (MAE) and area under the ROC curve (AUC) of predicted MMSE scores for stable (MCI-to-MCI) and converting (MCI-to-AD) test subjects. The MAE results for month 24 to 60 were as follows: parametric-MRI 1.23 to 4.41 (stable), 1.54 to 11.57 (converting); parametric+MRI 1.09 to 4.39 (stable), 1.72 to 10.98 (converting); nonparametric-MRI 0.93 to 5.27 (stable), 1.62 to 8.28 (converting); nonparametric+MRI 0.23 to 0.46 (stable), 1.63 to 6.79 (converting). The AUC results for month 24 to 60 were as follows (p < 0.01): parametric-MRI 0.90 for all visits; parametric+MRI 0.89 to 0.91; nonparametric-MRI 0.86 to 0.89; nonparametric+MRI 0.85 to 0.95.

CONCLUSION

MRI measurements improve neuropsychological assessment-based disease progression modeling performance of both parametric and non-parametric methods in MMSE decline prediction. Predictions from both utilized methods can significantly discriminate between stable MCI and MCI converting to AD.

CLINICAL RELEVANCE/APPLICATION

Neuropsychological test-based disease progression modeling benefits from including volumetric MRI measurements. These types of models can be applied to predict cognitive decline and clinical status.

Printed on: 10/29/20