Complex biomarker discovery in neuroimaging data: Finding a needle in a haystack

•We review data mining approaches for discovering four types of complex biomarkers.•Linear biomarkers capture linear combinations that are related to the phenotype.•Combinatorial biomarkers capture biomarkers for heterogeneous samples in a study.•Pathway biomarkers study the role of known subsystems for a given disorder.•Network biomarkers capture the role of brain network structure in a phenotype.

Neuropsychiatric disorders such as schizophrenia, bipolar disorder and Alzheimer's disease are major public health problems. However, despite decades of research, we currently have no validated prognostic or diagnostic tests that can be applied at an individual patient level. Many neuropsychiatric diseases are due to a combination of alterations that occur in a human brain rather than the result of localized lesions. While there is hope that newer imaging technologies such as functional and anatomic connectivity MRI or molecular imaging may offer breakthroughs, the single biomarkers that are discovered using these datasets are limited by their inability to capture the heterogeneity and complexity of most multifactorial brain disorders. Recently, complex biomarkers have been explored to address this limitation using neuroimaging data. In this manuscript we consider the nature of complex biomarkers being investigated in the recent literature and present techniques to find such biomarkers that have been developed in related areas of data mining, statistics, machine learning and bioinformatics.