title: PUNCH: Population Characterization of Heterogeneity.
abstract: Neuropsychiatric disorders are notoriously heterogeneous in their presentation, which precludes straightforward and objective description of the differences between affected and typical populations that therefore makes finding reliable biomarkers a challenge. This difficulty underlines the need for reliable methods to capture sample characteristics of heterogeneity using a single continuous measure, incorporating the multitude of scores used to describe different aspects of functioning. This study addresses this challenge by proposing a general method of identifying and quantifying the heterogeneity of any clinical population using a severity measure called the PUNCH (Population Characterization of Heterogeneity). PUNCH is a decision level fusion technique to incorporate decisions of various phenotypic scores, while providing interpretable weights for scores. We provide applications of our framework to simulated datasets and to a large sample of youth with Autism Spectrum Disorder (ASD). Next we stratify PUNCH scores in our ASD sample and show how severity moderates findings of group differences in diffusion weighted brain imaging data; more severely affected subgroups of ASD show expanded differences compared to age and gender matched healthy controls. Results demonstrate the ability of our measure in quantifying the underlying heterogeneity of the clinical samples, and suggest its utility in providing researchers with reliable severity assessments incorporating population heterogeneity.
authors.affiliation: Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Birkan
authors.initials: B
authors.lastname: Tunc
authors.affiliation: Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Yasser
authors.initials: Y
authors.lastname: Ghanbari
authors.affiliation: Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Alex R
authors.initials: AR
authors.lastname: Smith
authors.affiliation: Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
authors.forename: Juhi
authors.initials: J
authors.lastname: Pandey
authors.affiliation: Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
authors.forename: Aaron
authors.initials: A
authors.lastname: Browne
authors.affiliation: Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: Robert T
authors.initials: RT
authors.lastname: Schultz
authors.affiliation: Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: Ragini.Verma@uphs.upenn.edu.
authors.forename: Ragini
authors.initials: R
authors.lastname: Verma
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000175:diagnosis*; D003198:Computer Simulation; D003658:Decision Making, Computer-Assisted; D006801:Humans; D008297:Male; D010641:Phenotype; D011153:Population; D012720:Severity of Illness Index
keywords: Autism Spectrum Disorders; Heterogeneity; Severity measure