Emerging shifts in neuroimaging data analysis in the era of “big data”

Danilo Bzdok, Marc Andre Schulz, Martin Lindquist

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Advances in positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have revolutionized our understanding of human cognition and its neurobiological basis. However, a modern imaging setup often costs several million dollars and requires highly trained technicians to conduct data acquisition. Brain-imaging studies are typically laborious in logistics and data management, and require costly-to-maintain infrastructure. The often small numbers of scanned participants per study have precluded the deployment of and potential benefits from advanced statistical methods in neuroimaging that tend to require more data (Bzdok and Yeo, NeuroImage 155:549–564, 2017; Efron and Hastie, Computer age statistical inference, 2016). In this chapter we discuss how the increased information granularity of burgeoning neuroimaging data repositories—in both number of participants and measured variables per participant—will motivate and require new statistical approaches in everyday data analysis. We put particular emphasis on the implications for the future of precision psychiatry, where brain-imaging has the potential to improve diagnosis, risk detection, and treatment choice by clinical-endpoint prediction in single patients. We argue that the statistical properties of approaches tailored for the data-rich setting promise improved clinical translation of empirically justified single-patient prediction in a fast, cost-effective, and pragmatic manner.

Original languageEnglish (US)
Title of host publicationPersonalized Psychiatry
Subtitle of host publicationBig Data Analytics in Mental Health
PublisherSpringer International Publishing
Number of pages1
ISBN (Electronic)9783030035532
ISBN (Print)9783030035525
DOIs
StatePublished - Jan 1 2019

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Neuroimaging
Imaging techniques
Brain
Positron emission tomography
Costs and Cost Analysis
Information management
Logistics
Costs
Data acquisition
Statistical methods
Positron-Emission Tomography
Cognition
Psychiatry
Magnetic Resonance Imaging
Big data
Prediction
Brain imaging
Setup cost
Data management
Functional magnetic resonance imaging

Keywords

  • Big data
  • Brain-imaging studies
  • MRI
  • Neuroimaging

ASJC Scopus subject areas

  • Medicine(all)
  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)
  • Computer Science(all)

Cite this

Bzdok, D., Schulz, M. A., & Lindquist, M. (2019). Emerging shifts in neuroimaging data analysis in the era of “big data”. In Personalized Psychiatry: Big Data Analytics in Mental Health Springer International Publishing. https://doi.org/10.1007/978-3-030-03553-2_6

Emerging shifts in neuroimaging data analysis in the era of “big data”. / Bzdok, Danilo; Schulz, Marc Andre; Lindquist, Martin.

Personalized Psychiatry: Big Data Analytics in Mental Health. Springer International Publishing, 2019.

Research output: Chapter in Book/Report/Conference proceedingChapter

Bzdok, D, Schulz, MA & Lindquist, M 2019, Emerging shifts in neuroimaging data analysis in the era of “big data”. in Personalized Psychiatry: Big Data Analytics in Mental Health. Springer International Publishing. https://doi.org/10.1007/978-3-030-03553-2_6
Bzdok D, Schulz MA, Lindquist M. Emerging shifts in neuroimaging data analysis in the era of “big data”. In Personalized Psychiatry: Big Data Analytics in Mental Health. Springer International Publishing. 2019 https://doi.org/10.1007/978-3-030-03553-2_6
Bzdok, Danilo ; Schulz, Marc Andre ; Lindquist, Martin. / Emerging shifts in neuroimaging data analysis in the era of “big data”. Personalized Psychiatry: Big Data Analytics in Mental Health. Springer International Publishing, 2019.
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