  Major depressive disorder (MDD) is one of the most common mental health
conditions that has been intensively investigated for its association with
brain atrophy and mortality. Recent studies reveal that the deviation between
the predicted and the chronological age can be a marker of accelerated brain
aging to characterize MDD. However, current conclusions are usually drawn based
on structural MRI information collected from Caucasian participants. The
universality of this biomarker needs to be further validated by subjects with
different ethnic/racial backgrounds and by different types of data. Here we
make use of the REST-meta-MDD, a large scale resting-state fMRI dataset
collected from multiple cohort participants in China. We develop a stacking
machine learning model based on 1101 healthy controls, which estimates a
subject's chronological age from fMRI with promising accuracy. The trained
model is then applied to 1276 MDD patients from 24 sites. We observe that MDD
patients exhibit a $+4.43$ years ($\text{$p$} < 0.0001$, $\text{Cohen's $d$} =
0.35$, $\text{95\% CI}:1.86 - 3.91$) higher brain-predicted age difference
(brain-PAD) compared to controls. In the MDD subgroup, we observe a
statistically significant $+2.09$ years ($\text{$p$} < 0.05$, $\text{Cohen's
$d$} = 0.134483$) brain-PAD in antidepressant users compared to medication-free
patients. The statistical relationship observed is further checked by three
different machine learning algorithms. The positive brain-PAD observed in
participants in China confirms the presence of accelerated brain aging in MDD
patients. The utilization of functional brain connectivity for age estimation
verifies existing findings from a new dimension.
