Imputing Brain Measurements Across Data Sets via Graph Neural Networks
Yixin Wang Wei Peng Susan F. Tapert Qingyu Zhao Kilian M. Pohl
Publicly available data sets of structural MRIs might not contain specific
measurements of brain Regions of Interests (ROIs) that are important for
training machine learning models. For example, the curvature scores computed by
Freesurfer are not released by the Adolescent Brain Cognitive Development
(ABCD) Study. One can address this issue by simply reapplying Freesurfer to the
data set. However, this approach is generally computationally and labor
intensive (e.g., requiring quality control). An alternative is to impute the
missing measurements via a deep learning approach. However, the
state-of-the-art is designed to estimate randomly missing values rather than
entire measurements. We therefore propose to re-frame the imputation problem as
a prediction task on another (public) data set that contains the missing
measurements and shares some ROI measurements with the data sets of interest. A
deep learning model is then trained to predict the missing measurements from
the shared ones and afterwards is applied to the other data sets. Our proposed
algorithm models the dependencies between ROI measurements via a graph neural
network (GNN) and accounts for demographic differences in brain measurements
(e.g. sex) by feeding the graph encoding into a parallel architecture. The
architecture simultaneously optimizes a graph decoder to impute values and a
classifier in predicting demographic factors. We test the approach, called
Demographic Aware Graph-based Imputation (DAGI), on imputing those missing
Freesurfer measurements of ABCD (N=3760) by training the predictor on those
publicly released by the National Consortium on Alcohol and Neurodevelopment in
Adolescence (NCANDA, N=540)...