䉬

Human Brain Mapping 10:120 –131(2000) 䉬

Automated Talairach Atlas Labels For
Functional Brain Mapping
Jack L. Lancaster,* Marty G. Woldorff, Lawrence M. Parsons,
Mario Liotti, Catarina S. Freitas, Lacy Rainey, Peter V. Kochunov,
Dan Nickerson, Shawn A. Mikiten, and Peter T. Fox
Research Imaging Center, University of Texas Health Science Center at San Antonio䉬䉬

Abstract: An automated coordinate-based system to retrieve brain labels from the 1988 Talairach Atlas,
called the Talairach Daemon (TD), was previously introduced [Lancaster et al., 1997]. In the present study,
the TD system and its 3-D database of labels for the 1988 Talairach atlas were tested for labeling offunctional activation foci. TD system labels were compared with author-designated labels of activation
coordinates from over 250 published functional brain-mapping studies and with manual atlas-derived
labels from an expert group using a subset of these activation coordinates. Automated labeling by the TD
system compared well with authors’ labels, with a 70% or greater label match averaged over all locations.
Author-label matching improved to greater than 90% within a search range of ⫾5 mm for most sites. An
adaptive grey matter (GM) range-search utility was evaluated using individual activations from the M1
mouth region (30 subjects, 52 sites). It provided an 87% label match to Brodmann area labels (BA 4 & BA6) within a search range of ⫾5 mm. Using the adaptive GM range search, the TD system’s overall matchwith authors’ labels (90%) was better than that of the expert group (80%). When used in concert withauthors’ deeper knowledge of an experiment, the TD system provides consistent and comprehensive
labels for brain activation foci. Additional suggested applications of the TD system include interactive
labeling, anatomical grouping of activation foci, lesion-deficit analysis, and neuroanatomy education.
Hum. Brain Mapping 10:120 –131, 2000. © 2000 Wiley-Liss, Inc.
Keywords: Talairach Daemon, volume occupancy, Talairach Labels, brain labels
䉬䉬

INTRODUCTION
It is common practice in brain mapping experiments
to report locations of functional and anatomical sites
Edited by: Karl Friston, Associate Editor.
Contract grant sponsor: NIMH; Contract grant number: 5 P01
MH52176-07; Contract grant sponsor: NLM; Contract grant number:
1 RO1 LM06858-01.
*Correspondence to: Jack L. Lancaster, Ph.D., University of Texas
Health Science Center at San Antonio, Research Imaging Center,
7703 Floyd Curl Drive, San Antonio, Texas 78284.
E-mail: jlancaster@uthscsa.edu
Web address for this project: http://nc.uthscsa.edu/projects/
tnlairachdaemon.html
Received 1 November 1999; Accepted 28 April 2000.
© 2000 Wiley-Liss, Inc.

using standardized x-y-z coordinates [Fox et al., 1985;
Friston et al., 1989, 1991; Fox 1995]. Widespread use of
Talairach coordinates [Talairach et al., 1988] fostered
the development of the BrainMap威 database that encodes and queries the locations of functional neuroimaging findings using these coordinates [Fox et al.,1994]. The Talairach Daemon (TD) system [Lancasteret al., 1997] expands this concept by providing easy
Internet access to a 3-dimensional (3-D) database ofbrain labels accessed by Talairach coordinates. The
Talairach labels database uses a volume-filling hierarchical naming scheme to organize labels for brain
structures ranging from hemispheres to cytoarchitectural regions [Freitas et al., 1996, Lancaster et al., 1997].
This scheme is reflected in the database name, Volume

䉬 Automated Talairach Atlas Labels 䉬

Occupancy Talairach Labels (VOTL). The focus of thisreport is an evaluation of the accuracy of the TD
system for obtaining Talairach labels for functional
activation sites.
Brain atlases define and catalog rudimentary spatial
features of brain structure using traditional nomenclature. Atlas labels provide a consistent terminology for
qualitative description of regional brain structures.
This is exemplified by the broad use of the 1988 Talairach atlas by the human brain mapping community[Steinmetz et al., 1989; Fox 1995]. Subcortical structures such as thalamus, caudate, and lentiform areeasily identified by visual comparison of atlas sections
with high-resolution 3-D MR images. Unlike the subcortical region, however, visual labeling in the cortex
is highly problematic. Visual identification of gyri inserial MR section images is tedious, subject to reproducibility problems [Sobel et al., 1993], and subject tofailure for secondary and tertiary sulci, that are notalways present [Ono et al., 1990]. Gyri identification
can be improved by use of surface rendering [Watson
et al., 1993] and/or surface flattening [Dale and Sereno, 1993; Drury and VanEssen, 1997] to create images
with better definition of sulcal and gyral boundaries.
These processing methods, however, are neither timeefficient nor readily available and provide little support for standardized labeling of individual brains.
The coordinate-based labeling scheme of the TD system, with concise gyral definitions, provides a consistent labeling alternative to visual labeling of gyri.
Visual labeling is hampered by vague boundary
definitions for many brain structures. For example,
complete boundaries of lobes and Brodmann areas are
not found in popular atlases due to lack of consensus
for their definition, including the 1988 Talairach atlas.
Users must therefore select among several possiblelabels near non-delineated boundaries, increasing inter-user variability and making labeling susceptible toobserver expectancy-based bias. The VOTL databaseprovides explicit boundaries for each labeled brain
structure to manage this problem (see Methods).
Visual labeling is problematic even when explicit
atlas labels are available for lobes, gyri and Brodmannareas. Boundaries for these regions are not easy toidentify in high-resolution MR images, more difficultin group-average MR images, and practically impossible to identify in low resolution images (PET,
SPECT, and fMRI), whether from individual subjects
or group averages. Numerous automated methods,
based on computerized mapping of atlas region labels
onto medical images, have been developed to facilitatelabeling [Bohm et al., 1983, 1991; Evans et al., 1991;
Roland et al., 1994; Collins et al., 1994, 1995]. However,䉬

none of these atlas-to-image label-mapping methods
fully label the brain volume, nor are they simple to use
or widely distributed. Alternatively, an efficient image-to-atlas mapping method is provided in the TD
system to retrieve 1988 Talairach atlas labels for activation coordinates [Lancaster et al., 1997].
A coordinate-based automated labeling system forfunctional activation sites should provide appropriate
labels regardless of methodological differences. To test
this capability in the TD system, it was compared witha large set of published functional activations thatreported both coordinates and labels.
METHODS
The methods section is divided into development
and evaluation subsections. The development subsection describes: (1) creation of a 3-D Talairach Atlas
from the published atlas, (2) formulation of the VOTL
labeling scheme for this 3-D atlas, and (3) features of
the TD system software. The evaluation subsection
focuses on three important areas: (1) labeling by the
TD system vs. Talairach labels from published functional brain studies, (2) labeling by the TD system vs.
knowledgeable users of the Talairach atlas, and (3)labeling accuracy of the TD system in a PET activation
study.
TD System Development
3-D Talairach Atlas
The 1988 Talairach Atlas contains a series of highdetail color tracings (coronal, axial, and sagittal sections) derived from MR images of a 60-year old righthanded European female. Axial section images werechosen for developing the 3-D atlas because this section format is the most common acquired by tomographic medical imagers. There are twenty-seven axial
sections ranging from z ⫽ ⫹ 65 mm and to z ⫽ - 40
mm. The section spacing ranges from 2 mm near the
AC to 5 mm for the slices near the top of the brain.
Each section image was digitized with x-y resolutionof 0.43 mm, and each major structure segmented using
a combination of color discrimination (Adobe PhotoShop™, San Jose, CA) and thresholding (Alice™,
PAREXEL, Boston, MA). The digitized section images
served as reference planes to interpolate a continuous
3-D brain atlas using pixel (x & y) and section (z)locations relative to the AC or origin. Section images
were assigned the z-coordinate designated in the atlas.
All images were carefully registered during digitiza-

121 䉬䉬 Lancaster et al. 䉬

Figure 1.
A typical set of 3-D data used to create the volume occupancy (VOTL) database around the z ⫽⫹1 level. Openings in Lobe through Cell levels were provided to emphasize the 3-D nature of dataat each level.

tion, to guarantee that the x-y coordinate of the origin
was maintained at a consistent location and that the xand y-axes were properly aligned. A contiguous 1-mm
3-D Talairach atlas volume was created from the reference images using resampling in the x and y directions and nearest neighbor interpolation in the z direction. Care was taken to guarantee that the brain
dimensions matched that previously published for theatlas brain [Lancaster et al., 1995] (L-R ⫽ 136 mm,
A-P ⫽ 172 mm, and S-I ⫽ 118 mm). Small regional
differences were seen, but these were usually less than
1 mm. Since most axial sections of the 1988 Talairach
atlas were incomplete on the right, right-side data wasmade by reflecting left-side data about the y-axis for
all images.

(volumes of interest - VOIs) from the 3-D Talairach
atlas is based on volume occupancy (Fig. 1). Morespecifically a volume-filling, hierarchical, anatomicallabeling scheme is used, wherein each VOI is definedusing 3-D coordinates (for location) and a unique code(for anatomical label). The VOIs in the computerized
3-D Talairach atlas were organized into five hierarchical levels: Hemisphere, Lobe, Gyrus, Tissue type, andCell type (Table I). Rules and procedures wereadopted for the VOTL labeling scheme to explicitly
define boundaries for labeled brain structures [Freitas
et al., 1996, Lancaster et al., 1997], and the entire brain
volume fully labeled at each hierarchical level (Fig. 1).
The rules and procedures for the VOTL labelingscheme are outlined below:

Volume Occupancy Talairach Labels
Database (VOTL)

Hemisphere Level. The largest anatomical structures inthe brain (cerebrum, cerebellum, and brainstem) are
assigned to the hemisphere level. The outer borders ofhemisphere structures were extended slightly to include small invaginations (Fig. 2). The TD system

The anatomical structure-naming scheme devisedfor organizing the numerous 3-D anatomical regions

䉬

122 䉬

䉬 Automated Talairach Atlas Labels 䉬

TABLE I. Volume-occupancy talairach labels
Level
Hemisphere(Level 1)
Lobe(Level 2)
Gyrus(Level 3)
Tissue Type
(Level 4)
Cell Type(Level 5)

Label
Cerebrum (R/L)
Lobes
Gyri

Cerebellum(R/L)
In Progress

Brainstem(R/L)
In Progress

Extra-Nuclear““

Sub-Lobar
Sub-Gyral

Nuclei

GM

WM

WM

CSF

GM

WM

WM

CSF

“

“

BA

—

—

—

SubNuc.

—

—

Space

“

“

BA - Brodmann Area; WM - White Matter; GM - Gray Matter; CSF - Cerebral Spinal Fluid

returns “*” or “interhemispheric” labels for coordinates falling outside hemisphere level structures. The
left- and right-side attribute is assigned at this level.

Lobe Level. For the cerebrum, the lobe level consists ofthe four main lobes (Frontal, Temporal, Parietal, andOccipital), a single lobular equivalent (Limbic lobe)

Figure 2.
Example of VOTL region labels for a Talairach atlas section image at the z ⫽ ⫹ 1 level. Lobe labels
are illustrated with patterned color fills. Several Brodmann areas (cell level) are illustrated on the
top left using solid color fills. Several gyral level structures are illustrated on the bottom left usingbold color outlines.

䉬

123 䉬

䉬 Lancaster et al. 䉬

deeper within the brain, and a sub-lobar region. Using
this labeling scheme, cingulate is within the limbic
lobe and insula within the sub-lobar region. Outerboundaries of lobes were defined following corticalboundaries. Since explicit inner lobe boundaries were
not present in the Talairach atlas, inner boundaries
were defined using lines connecting the deepest extent
of bounding sulci in axial section images (Fig. 2). The
sub-lobar region was assigned to the volume of thecerebrum not specifically designated as a lobe, completing the lobe-level volume-occupancy labeling forthe cerebrum. The cerebellum and brainstem labeling(in progress) will follow this general scheme.
Gyrus Level. This level includes gyri for lobes andvarious deep gray-matter structures within the sublobar region. Outer gyral boundaries follow lobar
boundaries, while inner boundaries were defined using lines drawn between bounding sulcal pits in axial
section images. Since inner lobar boundaries often
extended deeper than inner gyral boundaries a subgyral label was assigned to the region between the
inner boundary of a gyrus and the inner boundary of
its lobe (Fig. 2). Boundaries for sub-lobar gyrus-levelstructures (Caudate, Thalamus, Lentiform, etc.) were
well delineated in the Talairach Atlas and used without modification. There are forty-eight different gyrus
level labels.
Tissue Level. This level provides labels for gray matter, white matter, and cerebral spinal fluid (CSF). The
lateral, third, and fourth ventricles were the only CSFdesignated regions since non-ventricular CSF was notlabeled in the Talairach atlas and is too variable tolabel consistently. Brain regions normally associatedwith gray matter (cortex and nuclei) were labeled asgray matter. Brain regions not labeled as gray matteror CSF were designated as white matter.
Cell Level. The brain was labeled by cell-type in thecortex using Brodmann’s scheme [Garey, 1994] andtracts, spaces, and sub-nuclear regions for other portions of the brain. Forty-seven Brodmann areas (BA)labels were defined. Boundaries between adjacentBrodmann areas are not defined in the Talairach Atlas,
so explicit BA boundaries were established that conformed to their original descriptions as closely as possible [Garey, 1994]. The BA boundaries were set at asulcal pit or gyral crown when possible (Fig. 2). Carewas taken to provide continuity of BA boundaries
between adjacent sections [Freitas et al., 1996]. Other
cell-level labels (e.g., for sub-lobar regions) closely
follow explicit Talairach Atlas labels. Labels for tracts䉬and spaces have not yet been created. The insularregion had not been assigned a Brodmann area, so it
was designated as BA 13. There are sixty-six different
cell level labels.
Cerebellum Labels
Several sources [Schmahmann et al., 1999;
Courchesne et al., 1989; Press et al., 1989, 1990; Angevine et al., 1961] are being used for development of
cerebellar labels. The cerebellum labels will not comefrom a single brain, but rather from a consensus basedon numerous sources. A working version of cerebellar
labels is in place. Since numerous refinements are
anticipated cerebellar labels were not evaluated in thepresent study.

Talairach Daemon System Software
The general operating scheme of the TD system isthat a user sends Talairach coordinates to the TD
system server via a TD client. The server looks uplabels in the VOTL database and sends coordinateswith labels back to the user. The TD server is Internet
accessible and supports multiple concurrent requests.
It was implemented as a multi-threaded application,with network communication using Berkeley Sockets,with a query/response protocol using ASCII strings,
and a streamlined query/response processing method.
A variety of freely distributed client applications, including Java versions, provide Internet access to TD
databases (http://ric.uthscsa.edu/projects/talairachdaemon.html). The client-server database environment was named the Talairach Daemon for the atlas
space it references and the fact that the server is aUNIX daemon process [Freitas et al., 1996; Lancaster etal., 1997]. A disk-based version of the TD system (client & VOTL database) has also been developed tosupport high-speed labeling for large groups of coordinates.
Newer TD clients provide an adaptive range-searchutility to find nearby GM labels. Searching is performed in a cubic region centered on the coordinate ofinterest. The search is started using a 3x3x3-mm3 cube,that is, with a search range of ⫹/- 1-mm along major
coordinate directions. If no GM label is found, thesearch range is expanded to 5⫻5⫻5 mm3 (⫹/- 2 mm).
Expansion of the search range is continued in thismanner to a maximum range of 11⫻11⫻11 mm3 (⫹/5 mm). If a single GM label is found at any searchrange, its VOTL label and search range (⫹/- mm) arereturned, and the search terminated. If multiple GM
labels are encountered within a search range, the label

124 䉬䉬 Automated Talairach Atlas Labels 䉬with the highest frequency of occurrence is used. In
cases of ties the search range is enlarged until the tie isbroken. If no GM label is found, “No GM” is returned.
This GM search algorithm has been successful for all
Talairach coordinates tested to date, and there havebeen no cases of ties.
TD System Evaluation
TD vs. Author Labels
A data set containing author-designated labels andTalairach coordinates for activation sites widely distributed throughout the brain was compared with labels obtained from the VOTL database by the TD
system. Mostly group-mean data (⬎5000 points; published prior to 1996) from numerous laboratories
around the world were retrieved from the BrainMap威database [Fox et al., 1994; Fox and Lancaster, 1998]. Alldata from our research group were removed to eliminate potential bias. Additionally, all points with missing or incompatible label descriptions were removed
resulting in a refined data set of approximately 2500
points. The refined data set (113 authors and ⬎ 250
studies) provided a wide range of TD system labels forcomparison: all lobes, 33 gyri, 42 Brodmann areas, and17 sublobar labels (nuclei, ventricles, and corpus callosum).
Multiple author-designated labels were often seen
for the same anatomical region, so an author-to-VOTL
label-mapping scheme was designated to support likelabel comparisons (Table II). This table indicates that
author’s labels paired reasonably well with gyrus andcell level labels. All label comparisons were therefore
made at the “Gyrus” or “Cell” levels since hemisphere, lobe, and tissue labels can generally be inferred from these. A 670-coordinate subset of labels forten commonly referenced brain regions was selected
for a detailed comparison with the TD system (Fig. 3,
Legend). To characterize distances from coordinates toauthor-designated structures, label-matching scores
were calculated as a function of search range from 0 to⫹/- 5mm.
TD vs. Human for Atlas Labels
It was proposed that the TD system would provide
a reliable method to obtain Talairach labels for functional activation studies, and that automated labeling
could be done as well as or better than a user withatlas in hand. To test these propositions TD-derived
labels were compared with labels looked up in the1988 Talairach atlas by three knowledgeable users

䉬(ML, LP, and MW). A set of approximately 100 labels
was targeted for testing by this group. The set of 670
author-labeled coordinates (See Previous Section)
were sorted by author label and like labels groupedusing Table II as a guide. A subset of widely distributed structures was selected including motor, sensory,
association, and limbic areas, encompassing numerous Brodmann areas, as well as several deep graystructures. Coordinates were equally distributed between left and right hemispheres, and labels distributed across all lobes. Data for each structure wereselected to include as many authors as possible tominimize bias. The resulting test set from 51 authors
contained 106 labels in 16 different gyrus-level and 19
different cell-level structures.
Since the TD system was based on atlas axial section
images, human labelers (testers) were instructed toinitially use axial sections to determine a label for eachpoint’s Talairach coordinate. They then reviewed
coronal and sagittal section images to determine if thesame label would be obtained. Testers were instructed
to indicate the nearest gray matter label if a coordinate
fell outside gray matter. The testers were provided
with a 1988 Talairach atlas axial section image similar
to Figure 2 as an example of the VOTL scheme for lobe
and gyrus labeling (included lobe, sub-lobar, gyraland sub-gyral region bounds outlined in color).
Testers were also briefed on how Brodmann areaboundaries were determined for the VOTL scheme.
The method for looking up labels using x-y-z coordinates was left to the testers. Each tester indicated alevel of difficulty (easy, medium, and hard) for eachlabel and the total labeling time required.

Brodmann Area Labels for Cortical Activations
The accuracy of the TD system was tested using
Talairach coordinates taken from a previously reported O-15 PET water study designed to activate M1
mouth motor areas [Fox et al., 1997, 1999]. The change
distribution analysis (CDA) method [Fox et al., 1988]
was used to determine significant activation foci ineach of 30 subjects [Fox et al., 1999]. All subjects were
healthy, right-handed, native English speakers between the ages of 21 and 49 (mean ⫽ 32; SD ⫽ 7).
Statistically significant activations (uncorrected p ⬍0.001) for BA 4 or 6 were isolated for testing using
visual inspection (PTF). Fifty-two different x-y-z coordinates were selected, 24 on the right and 28 on the left
side. Talairach coordinates for each activation sitewere submitted to the TD system for labeling. If no125 䉬䉬 Lancaster et al. 䉬

TABLE II. Author-to-TD label mapping
Author label
CuneateCuneusCuneus-striate junction
Precuneate cortex
Precuneus
Calcarine (sulcus)
Primary visual (area or cortex)
StriatePrimary motor
Motor area
Motor cortex
Motor hand area
InsulaInsular cortex
Insular gyrus
Insular region
Sylvian-insular
Globus pallidus
Lenticular nucleus
Lenticulate
Lentiform nucleus
Putamen
Pallidum
CaudateCaudate nucleus
Caudate, head
Caudatum
Caudatus
Anterior cingulate
Anterior cingulate cortex
Primary auditory
Heschl’s gyrus
Frontal eye fields
Wernicke’s area
Broca’s area
Somatosensory
ThalamusAmygdala
SMA
Parahippocampal gyrus
Uncus

Corresponding TD label

Hierarchical level

CuneusGyrus

Precuneus

Gyrus

Brodmann area 17

Cell

Brodmann area 4

Cell

Brodmann areas 4 or 6
Insula (BA 13)

Gyrus (cell)

Lentiform

Gyrus

Caudate

Gyrus

Anterior cingulate (BA 24,32)

Gyrus (cell)

Brodmann area 41, 42

Cell

Brodmann area 8
Brodmann area 22
Brodmann area 44, 45
Brodmann area 1, 2, 3ThalamusAmygdala
Brodmann area 6
Parahippocampal gyrus
Brodmann area 34

Cell
Cell
Cell
Cell
Gyrus
Gyrus
Cell
Gyrus
Cell

GM label was found, the adaptive range-search utility
was invoked to provide a nearby GM label.
RESULTSTD vs. Author Labels
Label matching between TD system (VOTL) labels
and author-designated labels for ten regions (670 activation sites) is presented in Figure 3. The graph in䉬

this figure indicates the probability of matching thelabel designated by authors as a function of searchrange. For the Cuneus and Thalamus (gyrus-level labels) the initial match (search range ⫽ 0 mm) was
about 70%. Label matching rose to 80% for a searchrange of ⫾2 mm and to over 90% for a ⫾3-mm range.
Label matching for Lentiform (cell-level) and Amygdala (gyrus-level) labels followed a similar trend. The
primary motor area (BA 4) and the parahippocampal
gyrus had considerably lower initial label matching126 䉬䉬 Automated Talairach Atlas Labels 䉬

Figure 3.
Percent match between TD and ten author-designated labels as a by initial percent match (search range ⫽ 0 mm). The numbers after
function of the search range in mm. Data was calculated from 670 each label are organized as follows: (# of different first authors, #points reported by numerous authors. The legend data is ordered of points for the label).

scores (32%). Label matching showed little improvement for a ⫾ 1-mm range but approached 80% for a ⫾3-mm range. For the four remaining regions (caudate,insula, anterior cingulate, and primary visual – BA 17),
label matching scores fell between the values for the
worst (primary motor area) and the best (Thalamus)
label matches. Author-TD label matching was above
90% for half of the regions evaluated within a searchrange of ⫾ 4 mm. For eight of ten of the regions, label
matching was greater than 90% using a search rangeof ⫾ 5 mm. These results are for group-mean data, andindividual subject results are presented in the Brodmann Area Labels section below.
TD vs. Human for Atlas Labels
Difficulty ratings provided by the human labelers(testers) showed that similar numbers of sites were oflow (47%) and medium (40%) difficulty. Tester’s impression of difficulty was mirrored by their author
label matching scores (Table III, columns 1 & 2). Categorization of difficulty by level was not obviously
associated with any specific label, nor did it appear to䉬be consistent among testers. Interestingly, the TD system (Raw – without range search) had its highest
matching score for sites in the tester’s medium-difficulty category. Both the testers and the TD system hadlowest matching scores for sites in the high-difficulty
category. Testers’ author-matching scores for labels
derived from atlas axial sections were similar to TD
raw scores, which were also from axial sections (Table
III, columns 1 & 3).
Testers’ scores improved somewhat when all atlassections (axial, sagittal, and coronal) were used todetermine a best label, with the score averaged acrossall sites increasing from 73% to 80%. Author-matchingscores for the TD system improved dramatically when
the range-search utility was used to find nearest GM
labels, with scores rising by as much as 37% for thehigh difficulty sites. The score averaged across all sitesrose from 71% to 90%. This trend is similar to thatfound for the TD-to-author evaluation with 670 points(Fig. 3). Percentage matching to author’s labels by the
TD system, using the adaptive GM range-search utility, exceeded that by the tester group in all difficulty
rating categories (Table III, columns 4 vs. 2). The time127 䉬䉬 Lancaster et al. 䉬

TABLE III. Manual vs. TD system matching to author labels
using talairach coordinates
Percentage match to authors’ labels
Testers
Difficulty rating
All sites (regardless of rating)
Low (47% of sites)
Medium (40% of sites)High (13% of sites)

TD system

Axial
section*Allsections**

Raw

Range
search

73%
83%
72%
40%

80%
88%
76%
64%

71%
72%
77%
47%

90%
92%
89%
84%

* Average scores for three experts manually labeling 106 coordinates using axial section images fromthe 1988 Talairach Atlas.
** Average scores of expert’s best label using all (axial, coronal, and sagittal) section images.

to obtain the axial labels for the 106 sites was overthree hours for each tester, while the TD system, with
the GM range-search utility, completed this task inless than one minute.
Median scores for label agreement between testers
and the TD system (without range search) dropped level-by-level (hemisphere: 98%, lobe: 83%, gyrus: 63%, andcell: 55%). Label consistency among testers (all agreed)
dropped similarly (hemisphere: 96%, lobe: 72%, gyrus:
42%, and cell: 53%). Incomplete agreement at the hemisphere level and diminished agreement at the gyrus
level were due to inconsistent nomenclature among users. Full label matching disparity (all testers disagreed)
was seen only at the gyrus (13%) and cell (10%) levels.
These data demonstrate a potentially high level of variability among users for manually obtaining labels for
activation sites from the Talairach atlas. There were no
reproducibility problems observed with the TD system.
A consensus (at least 2 agreed) among testers’ best
labels was seen for 94% of the sites. Consensus labels
are less sensitive to task-related user variability andprovide a good data set to evaluate the potential performance for the manual-labeling task. The consensus
labels compared well with labels from the TD range
search (89% agreement) and provided a better overall
match with author designated labels (86%). However,
the TD system (with adaptive range-search utility)
had a slightly higher author label-matching score(90%) than that of testers’ consensus labeling.

Brodmann Area Labels for Cortical Activations
The TD system returned matching Brodmann labels(BA 4 or 6) for 40% of the coordinates without using
the range search. The label match increased rapidly to䉬56%, 79%, and 85% with search ranges of ⫾1, ⫾2, and⫾3 mm. It rose slightly after that with a final label
match of 87% at ⫾5 mm. Seven errant labels wereseen, all postcentral gyrus (BA 2 and 3), and five of
these occurred at a search range of zero. Similar numbers of errant labels were seen for left (4) and right (3)sides. Label matching as a function of search range
was better than that recorded for the 670 group-mean
coordinates taken from the BrainMap database (Fig. 3,
Primary Motor).
DISCUSSION
The large set of author labels selected for testing
provided reasonable anatomical labeling accuracy, adiversity of sites distributed throughout the brain, andnumerous methodological challenges, all importantfor evaluating automated labeling of functional activation sites. Authors were not assumed to all correctly
label sites. However, their labels were considered tobe more nearly correct than labels obtained from Talairach coordinates alone [Roland, et al., 1997]. This
assumption was based on additional information
available to authors (i.e., experimental design, overlays of functional maps on MR images, etc.).
Automated labeling by the TD system performed aswell as or better than our experts, or even the consensus of 2 of 3 experts, when compared with author’s
labels. It should be noted, however, that neither
achieved a 100% match with author-designated labels.
The fact that the TD system can achieve an author
match of 90% for many labels indicates that it can be avaluable asset to brain mappers seeking to standardize labeling of activation foci. However, it is recommended that the TD system be used with caution, andthat users treat labels as candidate labels to be re-128 䉬䉬 Automated Talairach Atlas Labels 䉬viewed for appropriateness. Questionable labels
should be resolved by inspecting overlays of functional maps on high-resolution MR images. This isespecially important for sites in highly variable regions such as the occipital-cerebellar boundary. For
example, if the TD system reports labels from thecerebellum that are obviously in the occipital lobe
(based on overlays on MR images), it is useful toexpand the search range to produce an extended list oflabel options. This approach is similar to how authors
use the Talairach atlas when such confusion arises, toreview nearby labels and select the most likely candidate.

nates closer to Talairach labels), coupled with therange-search utility, should produce even better results. Another design goal of the GM range-searchutility was to accommodate methodological difference
between different laboratories. The improved author
matching (71 to 90%) for all labels (51 authors) using
the adaptive GM range-search utility (Table III, column 4 vs. 3) shows that this strategy works reasonably
well.

Brain Labeling Accuracy

The BrainMap威 database provides access to brain
findings from more than 200 research papers, 700
experiments, and 7000 locations in the human brain[Fox et al., 1994; Fox and Lancaster, 1998]. Data for theevaluation of the TD system was taken from this database. Access to TD labels is provided in the BrainMap威 Search & View client, where a user can quickly
review candidate Talairach labels for coordinates frompublished brain function studies (Fig. 4). A futureenhancement of the BrainMap威 database is automatic
entry of VOTL labels of each coordinate to support
searching using VOTL labels.

The degree of feature correspondence between individual brain images and an atlas varies throughout
the brain [Ono et al., 1990; Mazziotta et al., 1995; Zilles
et al., 1997; Roland et al., 1997]. For visual labeling, amore informed choice can be made after a survey ofnearby features in both brain image and atlas. A label
is then selected, along with a qualitative estimate ofconfidence level. However, there is no guarantee thatmultiple users will choose the same label given identical information or that the label will be correct. Accordingly, the Talairach atlas labels provided by the
TD system should be treated as candidate labels. Intrinsic coordinate-based TD-system labeling errors are
expected to be smallest for larger structures seen at thehemisphere and lobe level and largest for smaller
structures and near boundaries (Fig. 3). However, using the adaptive GM range-search utility, the TDsystem matching with author’s labels exceeded 90%
for many labels (Fig. 3 and Table III).
Label errors for coordinate-based labeling methods
can come from incomplete anatomical matching by
global spatial normalization as well as methodologicaldifferences [Strother et al., 1994]. While several nonlinear, high degree-of-freedom 3-D regional spatial
normalization algorithms have been developed [Collins et al., 1994; Friston et al., 1995; Woods et al., 1998,
1998a, Kochunov et al., 1999], their accuracy remains
unproved, and they have not been implemented toregionally match the standard 1988 Talairach atlas. A
design goal of the adaptive GM range-search utility
was to accommodate residual errors following spatial
normalization. The notable improvement in label accuracy (56 to 87%) for M1 hand motor PET activation
studies, using linear affine spatial normalization [Lancaster, et al., 1995], shows that this strategy can workreasonably well. While label errors were reduced,
more accurate spatial normalization (bringing coordi-䉬Common Talairach Daemon System Uses
BrainMap姞

Anatomical Organization of Coordinate Data
Statistical analyses of brain function studies often
result in a long list of coordinates for activation foci.
For software that provides Talairach coordinates in an
x-y-z list format (SPM; MEDx, Sensor Systems, Sterling, VA) it is helpful to retrieve VOTL database labels
using the TD system and to reorganize the list anatomically. This sorting feature was used to categorize
activation sites for testing in this report.
Lesion-Deficit Analysis
The TD system provides a means to tabulate andstandardize labels for the volume occupied by a brain
lesion in Talairach space. This standardization supports correlation of standard anatomical nomenclature
with measures of neurological deficit (NIH/NINDS 2
RO1 NS 21889-16).
Education
A Java applet called the Talairach Daemon wasdeveloped as an educational tool (http://ric.uthscsa.
edu/projects/talairachdaemon.html). It contains acomplete hand-detailed version of the 1988 Talairach129 䉬

䉬 Lancaster et al. 䉬

Figure 4.
The BrainMap™ Search & View 4.1 application provides direct access to the VOTL (Talairach
labels) database to obtain anatomical labels. A coordinate returned following a search on Brodmannarea 10 was used to automatically retrieve its hierarchical label.

atlas axial sections and overlays for all VOTL structures.
The interactive labeling features of the TD applet provide excellent examples for teaching brain anatomy.

ous Internet distributed client applications, provides
an extensive informatics resource for the human functional brain mapping community.

CONCLUSIONSREFERENCES

The Talairach Daemon system provides rapid accessto Talairach atlas labels for functional activation fociusing Talairach coordinates. Many gyri and cell-level
labels in the volume occupancy Talairach labels(VOTL) database directly map to common author labels. VOTL labels matching author’s labels were
found within ⫾5 mm of author-designated coordinates for most activation foci. The GM range-searchutility greatly enhanced the TD system, enabling it toperform better than a group of three knowledgeable
users, when attempting to match published Talairach
coordinates with labels. Using the GM range-searchutility, Brodmann area labels for the M1 hand motor
area activations (BA 4 and 6) were correctly identified
in 87% of the subjects. The TD system, with its numer-

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