Metaanalytic connectivity modeling: Delineating the functional connectivity of the human amygdala

Jennifer L. Robinson, Angela R. Laird, David C. Glahn, William R. Lovallo, Peter T. Fox

Research output: Contribution to journalArticle

211 Scopus citations

Abstract

Functional neuroimaging has evolved into an indispensable tool for noninvasively investigating brain function. A recent development of such methodology is the creation of connectivity models for brain regions and related networks, efforts that have been inhibited by notable limitations. We present a new method for ascertaining functional connectivity of specific brain structures using metaanalytic connectivity modeling (MACM), along with validation of our method using a nonhuman primate database. Drawing from decades of neuroimaging research and spanning multiple behavioral domains, the method overcomes many weaknesses of conventional connectivity analyses and provides a simple, automated alternative to developing accurate and robust models of anatomically-defined human functional connectivity. Applying MACM to the amygdala, a small structure of the brain with a complex network of connections, we found high coherence with anatomical studies in nonhuman primates as well as human-based theoretical models of emotive-cognitive integration, providing evidence for this novel method's utility.

Original languageEnglish (US)
Pages (from-to)173-184
Number of pages12
JournalHuman Brain Mapping
Volume31
Issue number2
DOIs
StatePublished - Feb 2010

Keywords

  • Brainmap
  • CoCoMac
  • Meta-analysis
  • PET
  • fMRI

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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