Diffusion Studies of Brain Anatomy
Monday 3 May 2010
Victoria Hall 16:30-18:30 Moderators: Alexander L. G. Leemans and Carlo Pierpaoli

16:30 109.

In Vivo Measurement of Cortical Anisotropy by Diffusion-Weighted Imaging Correlates with Cortex Type
Alfred Anwander1, André Pampel1, Thomas R. Knösche
Max 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.

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.

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.

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
Biomedical 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.

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
Image 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.

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.

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.

17:54 116

A Scalable Approach to Streamline Tractography Clustering
Eelke Visser1,2, Emil Nijhuis1,3, Marcel P. Zwiers1,2
Donders 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.

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.

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
School 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.



Back to Main Meeting

Back to Home