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

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)

Fingerprint Dive into the research topics of 'Emerging shifts in neuroimaging data analysis in the era of “big data”'. Together they form a unique fingerprint.

Cite this