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Data Mining in the Era of Big Data: The BrainMap Database as a Resource for Characterizing Brain Networks in Psychiatric Illness

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Brain Network Dysfunction in Neuropsychiatric Illness

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Abstract

Neuroimaging meta-analysis provides an exciting opportunity to evaluate consensus and generate new hypotheses using data mined from the published literature. This chapter provides a review of meta-analysis techniques available via the BrainMap Project, including activation likelihood estimation (ALE), independent component analysis (ICA), and clustering. Prior work emphasizing clinical utility of these meta-analytic methods are discussed.

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Authors and Affiliations

  1. Department of Psychology, Florida International University, Miami, FL, USA

    Katherine L. Bottenhorn

  2. Department of Physics, Florida International University, Miami, FL, USA

    Angela R. Laird

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  1. Katherine L. Bottenhorn
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Correspondence to Angela R. Laird .

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Editors and Affiliations

  1. Department of Psychiatry & Behavioral Neurosciences, Wayne State U School of Medicine, Detroit, MI, USA

    Vaibhav A. Diwadkar

  2. Heinrich-Heine Univ Düsseldorf, Institut für Klinische Neurowissenschaft, Institute of Neuroscience and Medicine Brain & Behaviour (INM-7), Research Centre Jülich Jülich, North Rhine - Westphalia, Düsseldorf, Nordrhein-Westfalen, Germany

    Simon B. Eickhoff

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Bottenhorn, K.L., Laird, A.R. (2021). Data Mining in the Era of Big Data: The BrainMap Database as a Resource for Characterizing Brain Networks in Psychiatric Illness. In: Diwadkar, V.A., B. Eickhoff, S. (eds) Brain Network Dysfunction in Neuropsychiatric Illness. Springer, Cham. https://doi.org/10.1007/978-3-030-59797-9_7

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