Activation Likelihood Estimation:
An Approach to Meta-Analysis in
Imaging Research
Charles Ellis, PhD CCC-SLP
Heather Bonilha, PhD CCC-SLP
Department of Health Sciences & Research
Medical University of South Carolina
Annual Meeting of the American Speech-Language Hearing Association
San Diego, CA
November 18, 2011
Presentation Objectives
• To provide an overview of activation likelihood
estimation (ALE) as a meta-analytic technique for
imaging data.
• To provide an introduction to the components of the
BrainMap database used in neuroimaging metaanalyses.
• To provide an overview of current uses of the ALE
and BrainMap to complete meta-analyses and how
this approach can be used in aphasia and related
disorder.
Historical Perspectives
• In the 19th & 20th centuries aphasiology
shaped how the brain produced and
comprehended language.
• A model of language emerged with two
loosely defined areas (Broca’s and
Wernicke’s) playing primary roles in
language production and
comprehension.
Historical Perspectives
• In the 1980s, non-invasive functional
brain imaging techniques emerged (PET,
fMRI) shifting the emphasis on
investigating neural bases of normal
language.
• These techniques enabled investigations
of specific components of brain and their
associations with language.
Meta-analyses
• Combines the results of several studies
that address a set of related research
hypotheses.
– Common measure of effect size, for which a
weighted average might be the output.
– Aim is to estimate the true "effect size" as
opposed to a smaller "effect size" derived in a
single study under a given single set of
assumptions and conditions.
• “Analysis of analyses”
Levels of Evidence
Level
Description
Ia
Well designed meta-analysis of >1 randomized
controlled trial
Ib
Well-designed randomized controlled study
IIa
Well-designed controlled study without
randomization
IIb
Well-designed quasi-experimental study
III
Well-designed non-experimental studies. i.e.,
correlational and case studies
IV
Expert committee report, consensus conference,
clinical experience of respected authorities
Phillips et al. (2001). Oxford Centre for Evidence-based Medicine Levels of Evidence
Levels of Evidence
Imaging Meta-analysis
• Formal, statistical integration in which
studies are collected, coded, and
interpreted in an analytical and
unbiased manner.
• Widely adopted practice of reporting the
brain locations of task-induced
activations as 3D (x, y, z) coordinates in
stereotactic space.
Purpose of
Imaging Meta-analyses
• Tool for identifying reliable patterns of
activation across studies.
• Multiple studies are combined to assess
concordance and guide interpretation
Activation Likelihood
Estimation (ALE)
• ALE is a coordinate-based method of
meta-analysis.
– Tool for integrating the neuroimaging data
when consistent regions of activation are
identified across a group of studies.
– Peak coordinates are collected from
studies that share a similar feature of
interest
Activation Likelihood
Estimation (ALE)
• ALE made possible by community-wide
standards of spatial normalization and
the reporting of peak activation
locations in stereotactic coordinates.
• Approach allows researchers to
compare results across studies.
Activation Likelihood
Estimation (ALE)
• The ALE method was originally
developed and validated by Turkeltaub
et al. (2002) in a meta-analysis of single
word reading.
• BrainMap developers obtained the
algorithm from the Georgetown
University CSL group, ported the code
into Java, and created a graphical user
interface (GingerALE).
Activation Likelihood
Estimation (ALE)
• ALE uses 3D Gaussian distributions
summed to create a whole-brain statistical
map that estimates the likelihood of
activation for that task determined by the
studies.
ALE Statistic
•
•
•
•
3-D coordinates in stereotactic space
are pooled from a number of like
studies.
Once all coordinates refer to locations in
a single stereotactic space, the ALE
analysis begins.
Each reported coordinate (focus) is
modeled by a 3-D Gaussian distribution.
Xi denotes the event that the ith focus is
located in a given voxel.
– The probability of Xi occurring at voxel x, y, z
is “ale statistic
•
An ALE statistic is computed at every
voxel in the brain.
•
”
Activation Likelihood
Estimation (ALE)
• Imaging coordinates are modeled to
accommodate spatial uncertainty
associated with coordinates and analyzed
where they converge.
• ALE applications:
–
–
–
–
Merge previous results in a retrospective fashion
Generate or test a new hypothesis
Identify a previously unspecified region
Resolve conflicting views or validate a new paradigm
Uses of ALE
• ALE is applied to studies that share similar
aspects of evoked brain function.
• Function-Based Analyses
– Function-based meta-analyses involve
pooling studies with similar experimental
designs
• Studies may also be segregated into different
collections to evaluate the functional specificity
of the task or process being investigated.
Uses of ALE
• Structure-Based Analyses
– Structure-Based Analyses focuses on
specific anatomical regions and attempts to
identify global coactivation patterns across
a diverse range of tasks.
• Groups of coordinates that coactivate across
experiments can be pooled to identify
functionally connected networks in the brain.
BrainMap
• BrainMap was originally conceived by
Peter Fox in 1987
– Original funding from the James S. McDonnell
Foundation (1988–1990).
– Continued development funded by:
• Office of Naval Research (1991–1992)
• EJLB Foundation (1992–1996)
• National Library of Medicine (2000–2003).
– Currently funded by the Human Brain Project
of the National Institute of Mental Health.
BrainMap
• BrainMap project was designed to
create tools for large-scale data mining
and meta-analysis of the brain mapping
literature. (Fox and Lancaster, 2002; Laird et al., 2005a)
BrainMap
• BrainMap is a community accessible
database that allows users to relate
behavioral functions to specific brain
locations through retrieval and
visualization of peak coordinates and
their associated metadata.
BrainMap
• These metadata allow each coordinate
to be linked with how the observed
activation was experimentally derived, a
formulation that lends itself to very rich
data mining.
BrainMap Hierarchy
• Papers report experimental results
drawn from one or more subject
populations, the members of which
have been functionally imaged during
one or more scanning sessions, with
each session being composed of one or
more behavioral conditions.
BrainMap Hierarchy
• Experiments are defined operationally by the
production of a statistical parametric image
(SPI).
– Each SPI is created by a statistical contrast of
functional images selected based on criteria
defining specific subsets of the populations,
sessions, and conditions.
• Experiments create one SPI. From each SPI
one or more functional activations (locations)
are extracted.
Presentation Summary
• ALE is a methodology for data mining and completion
of meta-analyses of imaging data in studies of
aphasia.
• Meta-analyses can provide clinicians and researchers
an understanding of function-based and structurebased activity in individuals with aphasia.
• Clinician-researchers engaged in aphasia research are
encouraged to input their data into the BrainMap
database to facilitate meta-analyses of aphasia
research and in particular aphasia recovery patterns.
Acknowledgements
•
Dr. Ellis is supported by a career development award (CDA# 07012-3) from the Veterans Health Administration Health Services
Research and Development program.
•
Dr. Bonilha is supported by a career development award (KL2)
from NIH/NCRR Grant number UL1 RR029882.
Thank you for you attendance
Questions?????