Multi-feature concatenation and multi-classifier stacking: an
  interpretable and generalizable machine learning method for MDD
  discrimination with rsfMRI
Yunsong Luo Wenyu Chen Ling Zhan Jiang Qiu Tao Jia
Major depressive disorder is a serious and heterogeneous psychiatric disorder
that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which
captures multiple perspectives on brain structure, function, and connectivity,
is increasingly applied in the diagnosis and pathological research of mental
diseases. Different machine learning algorithms are then developed to exploit
the rich information in rsfMRI and discriminate MDD patients from normal
controls. Despite recent advances reported, the discrimination accuracy has
room for further improvement. The generalizability and interpretability of the
method are not sufficiently addressed either. Here, we propose a machine
learning method (MFMC) for MDD discrimination by concatenating multiple
features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD
data set that contains 2428 subjects collected from 25 different sites. MFMC
yields 96.9% MDD discrimination accuracy, demonstrating a significant
improvement over existing methods. In addition, the generalizability of MFMC is
validated by the good performance when the training and testing subjects are
from independent sites. The use of XGBoost as the meta classifier allows us to
probe the decision process of MFMC. We identify 13 feature values related to 9
brain regions including the posterior cingulate gyrus, superior frontal gyrus
orbital part, and angular gyrus, which contribute most to the classification
and also demonstrate significant differences at the group level. The use of
these 13 feature values alone can reach 87% of MFMC's full performance when
taking all feature values. These features may serve as clinically useful
diagnostic and prognostic biomarkers for mental disorders in the future.