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Major depressive disorder (MDD) is one of the most common mental healthconditions that has been intensively investigated for its association withbrain atrophy and mortality. Recent studies reveal that the deviation betweenthe predicted and the chronological age can be a marker of accelerated brainaging to characterize MDD. However, current conclusions are usually drawn basedon structural MRI information collected from Caucasian participants. Theuniversality of this biomarker needs to be further validated by subjects withdifferent ethnic/racial backgrounds and by different types of data. Here wemake use of the REST-meta-MDD, a large scale resting-state fMRI datasetcollected from multiple cohort participants in China. We develop a stackingmachine learning model based on 1101 healthy controls, which estimates asubject's chronological age from fMRI with promising accuracy. The trainedmodel is then applied to 1276 MDD patients from 24 sites. We observe that MDDpatients 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 astatistically significant $+2.09$ years ($\text{$p$} < 0.05$, $\text{Cohen's$d$} = 0.134483$) brain-PAD in antidepressant users compared to medication-freepatients. The statistical relationship observed is further checked by threedifferent machine learning algorithms. The positive brain-PAD observed inparticipants in China confirms the presence of accelerated brain aging in MDDpatients. The utilization of functional brain connectivity for age estimationverifies existing findings from a new dimension.

Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants