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Unlike past studies, we attempted to estimate accurate prediction models for late\u2010life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi\u2010modal imaging and non\u2010imaging whole brain and network\u2010based features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Late\u2010life depression patients (medicated post\u2010recruitment) (<jats:italic>n<\/jats:italic>\u2009=\u200933) and older non\u2010depressed individuals (<jats:italic>n<\/jats:italic>\u2009=\u200935) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi\u2010modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A learning method called alternating decision trees estimated the most accurate prediction models for late\u2010life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini\u2010mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Combinations of multi\u2010modal imaging and\/or non\u2010imaging measures may help better predict late\u2010life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi\u2010modal imaging measures\u2014rather than region\u2010based differences\u2014are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late\u2010life depression and identify preliminary steps toward personalized late\u2010life depression treatment. 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