16:30 |
109. |
In Vivo
Measurement of Cortical Anisotropy by Diffusion-Weighted
Imaging Correlates with Cortex Type
Alfred Anwander1,
André Pampel1, Thomas R. Knösche1
1Max Planck Institute for Human
Cognitive and Brain Sciences, Leipzig, Germany
High resolution
diffusion-weighted imaging in conjunction with highly
sensitive phase array acquisition coils can identify
different anisotropic orientation depending on the cortex
type. Motor cortex shows radial anisotropy while primary
somatosensory cortex shows tangential anisotropy. This might
relate to a strong wiring between neighboring cortical
areas. |
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16:42 |
110. |
Skeleton
Thickness Biases Statistical Power in Skeleton-Based
Analyses of Diffusion MRI Data
Richard A E
Edden1,2, Derek K. Jones3
1Russell H
Morgan Department of Radiology and Radiological Science, The
Johns Hopkins University, Baltimore, MD, United States;
2FM Kirby Research Center for Functional MRI, Kennedy
Krieger Institute, Baltimore, MD, United States; 3CUBRIC,
School of Psychology, Cardiff University, Cardiff, Wales,
United Kingdom
DTI provides rotationally
invariant information. Additionally, DTI acquisitions are
optimised to ensure that data are statistically rotationally
invariant so that parameter variance is independent of the
orientation of the fibre population within the brain.
Against this backdrop, we focus on skeletonization-based
methods for group comparisons of DTI data and show that they
can reintroduce rotational dependence. Specifically, the
power to detect group differences in a fibre can depend on
its orientation. While the cause/solution to this problem
are trivial, the effect on statistical inference is not –
and should be viewed in the light of the increasing
popularity of skeletonization-based methods. |
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16:54 |
111. |
Sex-Linked White Matter Microstructure of the Social and the
Analytic Brain
Kun-Hsien
Chou1, I-Yun Chen2, Chun-Wei Lan3,
Ya-Wei Cheng2, Ching-Po Lin2,3,
Woei-Chyn Chu1
1Institute
of Biomedical Engineering, National Yang-Ming University,
Taipei, Taiwan; 2Institute of Neuroscience,
National Yang-Ming University, Taipei, Taiwan; 3Institute
of Biomedical imaging and Radiological Sciences, National
Yang-Ming University, Taipei, Taiwan
Empathizing, driven by the
social brain, means the capacity to predict and to respond
to the behavior of agents by inferring their mental status
with an appropriate emotion. Systemizing, based on the
analytic brain, is the capacity to predict and to respond to
the behavior of non-agentive deterministic systems by
analyzing input-operation-output relations and inferring the
rules of systems. However WM associated with the social and
analytic brain as indicated by sex differences remains to be
investigated. In this study, we demonstrated WM
microstructures with sexual dimorphism, which may reflected
the neural underpinning of the social and analytic brain. |
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17:06 |
112. |
Diffusion
Tensor Imaging of Brain White Matter Changes Across the
Lifespan
Catherine Lebel1,
Myrlene Gee1, Richard Camicioli2,
Marguerite Wieler2, Wayne Martin2,
Christian Beaulieu1
1Biomedical Engineering,
University of Alberta, Edmonton, Alberta, Canada; 2Neurology,
University of Alberta, Edmonton, Alberta, Canada
Lifespan studies of the
normal human brain link the development processes of
childhood with the degenerative processes of old age. Many
diffusion tensor imaging (DTI) studies evaluate changes over
narrow age ranges; few examine the lifespan. We used DTI to
measure age-related changes in 12 white matter tracts in 392
healthy volunteers aged 5-83 years. Fractional anisotropy
increased until adulthood, then decreased, while mean
diffusivity followed an opposite trend. Trend reversals
occurred between 18-43 years. Frontal-temporal connections
demonstrated prolonged development and late reversals, while
the fornix and corpus callosum develop earliest and have the
most prolonged periods of decline. |
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17:18 |
113. |
Partial
Volume Effect as a Hidden Covariate in Tractography Based
Analyses of Fractional Anisotropy: Does Size Matter?
Sjoerd B. Vos1, Derek K. Jones2, Max
A. Viergever1, Alexander Leemans1
1Image Sciences Institute,
University Medical Center, Utrecht, Netherlands; 2CUBRIC,
School of Psychology, Cardiff University, Cardiff, United
Kingdom
Diffusion tensor imaging has
been used extensively to investigate brain aging. Fiber
tractography has shown a relation between age and fractional
anisotropy (FA) along fiber tracts. Partial volume effects
are known to affect tractography, and may also influence FA
calculations along tracts. In this study, simulations and
experiments have been performed to test whether tract volume
is a covariate in FA calculations. A strong correlation
between tract volume and FA has been found in both the
simulations and experiments, proving that partial volume
effects affect FA calculations, and that size is indeed a
hidden covariate in tractography based FA analyses. |
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17:30 |
114. |
Microstructural Correlations of White Matter Tracts in the
Human Brain
Michael Wahl1,
Yi-Ou Li1, Joshua Ng1, Sara C. LaHue1,
Shelly R. Cooper1, Elliott H. Sherr2,
Pratik Mukherjee1
1Radiology,
University of California, San Francisco, San Francisco, CA,
United States; 2Neurology, University of
California, San Francisco, San Francisco, CA, United States
In this 3T DTI study of 44
normal adult volunteers, we use quantitative fiber tracking
to demonstrate that specific patterns of microstructural
correlation exist between white matter tracts and may
reflect phylogenetic and functional similarities between
tracts. Inter-tract correlation matrices computed from
tract-based measures of fractional anisotropy (FA), mean
diffusivity, axial diffusivity, and radial diffusivity,
reveal that there are significant variations in correlations
between tracts for each of these four DTI parameters.
Data-driven hierarchical clustering of FA correlational
distances show that neocortical association pathways grouped
separately from limbic association pathways, and that
projection pathways grouped separately from association
pathways. |
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17:42 |
115. |
A Novel
Clustering Algorithm for Application to Large Probabilistic
Tractography Data Sets
Robert Elton Smith1,2,
Jacques-Donald Tournier1,2, Fernando Calamante1,2,
Alan Connelly1,2
1Brain Research
Institute, Florey Neuroscience Institutes (Austin),
Heidelberg West, Victoria, Australia; 2Department
of Medicine, The University of Melbourne, Melbourne,
Victoria, Australia
Current clustering
methodologies are not able to process very large data sets,
such as those generated using probabilistic tractography. We
propose a novel clustering algorithm designed specifically
to handle a very large number of tracks, which is therefore
ideally suited for processing whole-brain probabilistic
tractography data. A hierarchical clustering stage
identifies major white matter structures from the large
number of smaller clusters generated. The method is
demonstrated on a 1,000,000 track whole-brain in-vivo data
set. |
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17:54 |
116. |
A
Scalable Approach to Streamline Tractography Clustering
Eelke
Visser1,2, Emil Nijhuis1,3, Marcel P.
Zwiers1,2
1Donders Institute for Brain,
Cognition and Behaviour, Radboud University Nijmegen,
Nijmegen, Netherlands; 2Department of Psychiatry,
Radboud University Nijmegen Medical Centre, Nijmegen,
Netherlands; 3Department of Technical Medicine,
University of Twente, Enschede, Netherlands
Finding clusters among the
many streamlines produced by tractography algorithms can
improve interpretability and can provide a starting point
for further analysis. A problem with many clustering methods
is their handling of large datasets. We propose to overcome
this problem by repeatedly clustering complementary
subselections of streamlines. The execution time of the
algorithm scales linearly with the number of streamlines,
while working memory usage remains constants. The method
produces anatomically plausible and coherent clusters in a
single subject. When applied to a large group dataset,
results are similar and consistent across subjects. |
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18:06 |
117. |
Validation of DTI Measures of Primary Motor Area Cortical
Connectivity
Yurui Gao1,
Ann S. Choe2, Xia Li3, Iwona
Stepniewska4, Adam Anderson
1BME,
VUIIS, Nashville, TN, United States; 2BME, VUIIS,
United States; 3EECS, VUIIS, United States;
4Psychological Sciences at Vanderbilt, United States
Since DTI tractography is
used to examine the neural connectivity between specialized
cortical regions of the brain, it is important to evaluate
the agreement between the connectivity derived from DTI
tractography and corresponding histological information. We
reconstruct the projection regions connecting to the primary
motor cortex (M1) of the squirrel monkey, based on
histological segmentation and compare these regions with the
locations of the terminals of DTI fibers penetrating the
same M1 region. Quantitative comparison shows an approximate
agreement but also limits of applying DTI tractography to
predict M1 connectivity. |
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18:18 |
118. |
High
Resolution Tractography in Macaque Visual System –
Validation Against in Vivo Tracing
Laura
M. Parkes1,2, Hamied A. Haroon1,2,
Mark Augarth3, Nikos K. Logothetis2,3,
Geoff J. M. Parker1,2
1School of Cancer and Imaging
Sciences, University of Manchester, Manchester, United
Kingdom; 2Biomedical Imaging Institute,
University of Manchester, Manchester, United Kingdom; 3Max
Planck Institute for Biological Cybernetics, Tubingen,
Germany
The aim is to validate the
connections identified with high angular resolution
diffusion imaging in the post-mortem macaque visual system
against true connections from the many detailed in vivo
tracer studies. A probabilistic tractography approach is
used, and comparisons are made between identified
connections at different thresholds of connection strength,
and the true connections. The accuracy of connections
increases up until an acceptance threshold of 5%, beyond
which accuracy is not greatly affected. 72% of connections
were correctly identified at 5% threshold. The majority of
false connections involved areas of higher level processing,
particularly parietal and temporal regions. |
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