Transcript Identification of the auditory thalamus using multi

#MO85
Identification of the auditory thalamus using multi-modal structural analyses
J. T. Devlin1, E. Sillery1, H. Johansen-Berg1, T. E. J. Behrens1, P. M. Matthews1, D. A. Hall2, and D. R. Moore2
1Centre
for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, U. K.
2MRC Institute of Hearing Research, University Park, Nottingham, U.K.
Objective
Automatic identification of MGB
Manual identification of MGB
Functional investigations of the ascending auditory system in
humans are limited due to difficulty identifying the medial
geniculate body (MGB) from surrounding thalamic nuclei.
Here we use high resolution proton-density and diffusionweighted structural images to develop a highly reliable,
accurate method to identify MGB in individuals.
 In the PD, but not the T1 scan, MGB was distinct from the
lateral geniculate nucleus (Figure 2), allowing independent
manual identification by two authors (JTD, HJB).
 To identify MGB, the following method was used:
1. An ROI was defined in standard
space for each hemisphere to
conservatively encompass both LGN
and MGB [x= ±10 to 26, y= –22 to
–30, z= –2 to –10].
1. Find the coronal section showing
the substantia nigra (SN) meeting
at the midline, just inferior to the
third ventricle (V3).
Background
LV
Th
Unlike other sensory systems, substantial processing of
auditory signals occurs subcortically, before ever reaching the
cortex. Sounds first reach the nervous system via the hair cells of
the cochlea whose movements transduce the acoustic energy into
a neural signal carried by the auditory nerve into the brain.
Information arrives at the cochlear nucleus of the brainstem
which projects to the brainstem and inferior colliculus (IC). The
IC connects to the medial geniculate body (MGB) of the
thalamus, which in turn projects to primary auditory cortex
(PAC). Together, the IC, MGB, and PAC constitute three of the
main regions of the ascending auditory system (Figure 1).
Imaging these subcortical structures presents a challenge due to
the effects of pulsatile motion, the small size of the relevant
nuclei, and the difficulty identifying these regions anatomically.
Here we address the latter problem using multi-modal structural
imaging techniques to reliably identify MGB in individuals.
 An alternate, unbiased method is to separate the medial and
lateral geniculate nuclei based on their distinct connectivity
profiles:
Pu
V3
IC
Hi
SN
Interpeduncular fossa
2. Move 6-10mm posteriorally until the
LGN appears, a tear-dropped shaped
high intensity region superior to the body
of the hippocampus and inferolateral to
the majority of the thalamus (Th).
Y= –22
LV
HG
V3 Th
PC
LGN
MGB
2. Probabilistic tractography [6] was run
from each voxel in the mask. The
resulting connectivity profiles were
entered into a cross-correlation matrix
and the cells of the matrix were sorted
to bring similar items together,
yielding clusters of voxels with similar
connectivity [7].
3. Projecting these back onto T1 images revealed a lateral and a medial
cluster (see Figure 3).
Hi
3. The MGB is immediately medial to
LGN and appears as an oval
region of high intensity between the
body of the hippocampus (Hi) and the
third ventricle. This typically was
visible on 1-3 slices.
MGB
SN
Right hemisphere
Y= –28
Abbrevs: HG= Heschl’s gyrus, Hi= body of the
hippocampus, IC= internal capsule, LGN=
lateral geniculate nucleus, LV= lateral
ventricle, MGB= medial geniculate nucleus,
PC= posterior commissure, Pu= putamen,
SN= substantia nigra, Th= thalamus, V3=
third ventricle
Left hemisphere
HG
HG
Y= –28
HG
Th
MGB
IC
Figure 1: A schematic diagram of the ascending auditory
pathway. IC= inferior colliculus, MGB= medial geniculate
body, HG=Heschl’s gyrus (site of primary auditory cortex). Note
neither contralateral nor top-down projections are shown.
Method
 Grey matter typically has 20% higher proton density than white
matter [1], suggesting that grey-white contrast could be
improved in a proton-density (PD) scan relative to T1-weighted
images [2]
 In addition, connectivity patters differ across thalamic nuclei,
suggesting that MBG might be identified according to its
connectivity profile [3]
 Five neurologically normal volunteers (3F, 2M) participated in
two separate scanning sessions:
1. 5-10 high resolution proton-density (PD) scans using a
fast spin echo protocol (coronal acquisition, 800m2 inplane resolution, 2mm slice thickness, TR= 6 sec,
effective TE = 9.5 mse), implemented on a 1.5T
Siemens Sonata.
2. Three diffusion-weighted scans using an EPI protocol
(TR= 15sec, TE=106.2ms, b-value=1000s/mm2, 60
directions, 5-10 non-diffusion weighted images, 1.9mm2
in-plane resolution, 2.5mm slice thickness) optimized
for tractography [4] implemented on a 3T Varian
scanner. Data collection was cardiac gated to reduce
pulsatile motion artefacts.
In addition, a standard T1-weighted structural scan (3D
Turbo FLASH, TR=12ms, TE=5.6ms, 1mm3 isotropic
voxels) was also acquired.
 To compensate for the reduction in SNR associated with
smaller voxel sizes, the PD scans were aligned using a rigidbody registration [5] and averaged.
 DWI scans were realigned, corrected for eddy current
distortions, and averaged using the FMRIB Diffusion Toolkit
(www.fmrib.ox.ac.uk/fsl/fdt)
Figure 2: The medial (red) and lateral (blue) geniculate appear as distinct
regions of high intensity in the PD image (left) and indistinct regions of
low intensity in the T1 image (right). Bottom row: The regions are labelled
in the left hemisphere of each participant but are visible in the right of the
PD image.
 Inter-rater reliability was based on comparing centres-of-gravity
(COG) for corresponding MGB masks. On average across 10
hemispheres, these were separated by only 1.8mm.
Table 1: Standard space coordinates for the COG of MGB
Subject
LH
RH
1
-14 -28 -8
14 -28
2
-14 -24 -10
16 -24
3
-14 -30 -8
16 -28
4
-14 -26 -8
16 -28
5
-16 -28 -6
16 -28
-8
-8
-8
-8
-6
Figure 3: The top (RH) and bottom (LH) rows show the sorted
cross-correlation matrices for each subject and below them are
the corresponding clusters of voxels with similar connectivity
profiles. All images are shown on the individual’s T1 scan (in
standard space).
 The COG of the medial cluster was (±14 –25 –6) while the
lateral cluster was centred at (±22 –27 –6).
 These results were, on average, one voxel anterior and superior
relative to the manual identifications of the MGB. This may,
in part, be due to difficulty getting a precise registration
between the DWI and T1 images due to macroscopic
susceptibility effects.
 The mean standard space coordinates for the COG of MGB
were [±16, –28, –8].
Discussion
We have demonstrated two methods for reliably identifying MGB based solely on structural data. The first relies on
differences in proton density between grey and white matter [1] while the second is based on the distinct connectivity profiles of
the medial and lateral geniculate. In both cases, data acquisition required less than one hour using commonly available pulse
sequences on standard hardware, a clear advantage over approaches that rely on either extremely long acquisitions (e.g. 13
hours, [8]) or very high fields (4-8T, [8, 9]).
These data also provide further validation for using DWI to structurally segment separate anatomical regions. Previously
Johansen-Berg et al. [7] showed that this method produced excellence correspondence between DWI and functional parcellation
of SMA and pre-SMA. Here we show that the same technique also matches visually identified anatomical borders.
The ability to reliably identify MGB anatomically will facilitate functional studies of the ascending auditory system such as
those investigating spatial localisation [10] or laterality effects [11]. These methods will complement, extend, and objectify
further functional characterisations of this increasingly interesting nuclear group.
References
1. Jackson, E. F., Ginsberg, L. E., Schomer, D. F., & Leeds, N. E. (1997). A review of MRI pulse sequences and techniques used in neuroimaging. Surgical Neurology, 47, 185-199.
2. Fujita, N., Tanaka, H., Takanashi, M., Hirabuki, N., Abe, K., Yoshimura, H., & Nakamura, H. (2001). Lateral geniculate nucleus: anatomic and functional identification by use of MR imaging. AJNR Am J
Neuroradiol, 22(9), 1719-1726.
3. Behrens, T. E. J., Johansen-Berg, H., Woolrich, M. W., Smith, S. M., Wheeler-Kingshott, C. A., Boulby, P. A., Barker, G. J., Sillery, E. L., Sheehan, K., Ciccarelli, O., Thompson, A. J., Brady, J. M., &
Matthews, P. M. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci, 6(7), 750-757.
4. Jones, D. K., Horsfield, M. A., & Simmons, A. (1999). Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med, 42(3), 515-525.
5. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825-841.
6. Behrens, T. E. J., Woolrich, M. W., Jenkinson, M., Johansen-Berg, H., Nunes, R. G., Clare, S., Matthews, P. M., Brady, J. M., & Smith, S. M. (2003). Characterization and propagation of uncertainty in
diffusion-weighted MR imaging. Magn Reson Med, 50(5), 1077-1088.
7. Johansen-Berg, H., Behrens, T. E., Robson, M. D., Drobnjak, I., Rushworth, M. F., Brady, J. M., Smith, S. M., Higham, D. J., & Matthews, P. M. (2004). Changes in connectivity profiles define functionally
distinct regions in human medial frontal cortex. Proc Natl Acad Sci U S A.
8. Deoni, S. C., Josseau, M. J., Rutt, B. K., & Peters, T. M. (2005). Visualization of thalamic nuclei on high resolution, multi-averaged T(1) and T(2) maps acquired at 1.5 T. Hum Brain Mapp.
9. Bourekas, E. C., Christoforidis, G. A., Abduljalil, A. M., Kangarlu, A., Chakeres, D. W., Spigos, D. G., & Robitaille, P. M. (1999). High resolution MRI of the deep gray nuclei at 8 Tesla. J Comput Assist
Tomogr, 23(6), 867-874.
10. Krumbholz, K., Schonwiesner, M., Rubsamen, R., Zilles, K., Fink, G. R., & von Cramon, D. Y. (2005). Hierarchical processing of sound location and motion in the human brainstem and planum temporale.
Eur J Neurosci, 21(1), 230-238.
11. Devlin, J. T., Raley, J., Tunbridge, E., Lanary, K., Floyer-Lea, A., Narain, C., Cohen, I., Behrens, T. E. J., Jezzard, P., Matthews, P. M., & Moore, D. R. (2003). Functional asymmetry for auditory processing in
human primary auditory cortex. J Neurosci, 23(37), 11516-11522.