title: Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.
abstract: OBJECTIVE
  Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features.
  
  
  METHODS
  Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models.
  
  
  RESULTS
  A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental 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.
  
  
  CONCLUSIONS
  Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life 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-modal imaging measures-rather than region-based differences-are 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-life depression and identify preliminary steps toward personalized late-life depression treatment.
authors.affiliation: Department of Bioengineering, University of Pittsburgh, PA, USA.
authors.forename: Meenal J
authors.initials: MJ
authors.lastname: Patel
authors.affiliation: Department of Psychiatry, University of Pittsburgh School of Medicine, PA, USA.
authors.forename: Carmen
authors.initials: C
authors.lastname: Andreescu
authors.affiliation: Department of Radiology, University of Pittsburgh Medical Center, PA, USA.
authors.forename: Julie C
authors.initials: JC
authors.lastname: Price
authors.affiliation: Department of Psychiatry, University of Pittsburgh School of Medicine, PA, USA.
authors.forename: Kathryn L
authors.initials: KL
authors.lastname: Edelman
authors.affiliation: Department of Psychiatry, University of Pittsburgh School of Medicine, PA, USA.|Department of Neurology, University of Pittsburgh, PA, USA.|Department of Neuroscience, University of Pittsburgh, PA, USA.
authors.forename: Charles F
authors.initials: CF
authors.lastname: Reynolds
authors.affiliation: Department of Bioengineering, University of Pittsburgh, PA, USA.|Department of Psychiatry, University of Pittsburgh School of Medicine, PA, USA.
authors.forename: Howard J
authors.initials: HJ
authors.lastname: Aizenstein
mesh_terms: D000328:Adult; D017668:Age of Onset; D000368:Aged; D001931:Brain Mapping / Q000379:methods; D003866:Depressive Disorder / Q000175:diagnosis* / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000473:pathology; D059906:Neuroimaging / Q000379:methods*; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests
keywords: diagnosis; imaging; late-life depression; learning; prediction; treatment response