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

Gowtham Atluri, Kanchana Padmanabhan, Gang Fang, Michael S Steinbach, Jeffrey R. Petrella, Kelvin O Lim, Angus MacDonald, Nagiza F. Samatova, P. Murali Doraiswamy, Vipin Kumar

Research output: Contribution to journalReview articlepeer-review

46 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)123-131
Number of pages9
JournalNeuroImage: Clinical
StatePublished - 2013

Bibliographical note

Funding Information:
Lim has received research grants Schizophrenia R01 MH0606621 and NIDA CSIA P20DA024196 . Kumar and Steinbach have received NSF research grant IIS-0916439.


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