Heritability of white matter fiber tract shapes: A HARDI study of 198 twins

Genetic analysis of diffusion tensor images (DTI) shows great promise in revealing specific genetic variants that affect brain integrity and connectivity. Most genetic studies of DTI analyze voxel-based diffusivity indices in the image space (such as 3D maps of fractional anisotropy) and overlook tract geometry. Here we propose an automated workflow to cluster fibers using a white matter probabilistic atlas and perform genetic analysis on the shape characteristics of fiber tracts. We apply our approach to large study of 4-Tesla high angular resolution diffusion imaging (HARDI) data from 198 healthy, young adult twins (age: 20-30). Illustrative results show heritability for the shapes of several major tracts, as color-coded maps.

Automatic clustering of white matter fibers in brain diffusion mri with an application to genetics

To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion - a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.

Automatic population HARDI white matter tract clustering by label fusion of multiple tract atlases

Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults. © 2012 Springer-Verlag.

Predicting white matter integrity from multiple common genetic variants

Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20-30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ∼ 6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.

Automatic clustering and population analysis of white matter tracts using maximum density paths

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.

Visualization tools for high angular resolution diffusion imaging

There is a major effort in medical imaging to develop algorithms to extract information from DTI and HARDI, which provide detailed information on brain integrity and connectivity. As the images have recently advanced to provide extraordinarily high angular resolution and spatial detail, including an entire manifold of information at each point in the 3D images, there has been no readily available means to view the results. This impedes developments in HARDI research, which need some method to check the plausibility and validity of image processing operations on HARDI data or to appreciate data features or invariants that might serve as a basis for new directions in image segmentation, registration, and statistics. We present a set of tools to provide interactive display of HARDI data, including both a local rendering application and an off-screen renderer that works with a web-based viewer. Visualizations are presented after registration and averaging of HARDI data from 90 human subjects, revealing important details for which there would be no direct way to appreciate using conventional display of scalar images.

Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects

As connectivity analyses become more popular, claims are often made about how the brain's anatomical networks depend on age, sex, or disease. It is unclear how results depend on tractography methods used to compute fiber networks. We applied 11 tractography methods to high angular resolution diffusion images of the brain (4-Tesla 105-gradient HARDI) from 536 healthy young adults. We parcellated 70 cortical regions, yielding 70×70 connectivity matrices, encoding fiber density. We computed popular graph theory metrics, including network efficiency, and characteristic path lengths. Both metrics were robust to the number of spherical harmonics used to model diffusion (4th-8th order). Age effects were detected only for networks computed with the probabilistic Hough transform method, which excludes smaller fibers. Sex and total brain volume affected networks measured with deterministic, tensor-based fiber tracking but not with the Hough method. Each tractography method includes different fibers, which affects inferences made about the reconstructed networks.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Seasonal and spatial variations in rare earth elements to identify inter-aquifer linkages and recharge processes in an Australian catchment

With the aim of elucidating the seasonal behaviour of rare earth elements (REEs), surface and groundwaters were collected under dry and wet conditions in different hydrological units of the Teviot Brook catchment (Southeast Queensland, Australia). Sampled waters showed a large degree of variability in both REE abundance and normalised patterns. Overall REE abundance ranged over nearly three orders of magnitude, and was consistently lower in the sedimentary bedrock aquifer (18ppt<∑REE<477ppt) than in the other hydrological systems studied. Abundance was greater in springs draining rhyolitic rocks (∑REE=300 and 2054ppt) than in springs draining basalt ranges (∑REE=25 and 83ppt), yet was highly variable in the shallow alluvial groundwater (16ppt<∑REE<5294ppt) and, to a lesser extent, in streamwater (85ppt<∑REE<2198ppt). Generally, waters that interacted with different rock types had different REE patterns. In order to obtain an unbiased characterisation of REE patterns, the ratios between light and middle REEs (R(M/L)) and the ratios between middle and heavy REEs (R(H/M)) were calculated for each sample. The sedimentary bedrock aquifer waters had highly evolved patterns depleted in light REEs and enriched in middle and heavy REEs (0.17

Re-theorizing the “digital divide”: Identifying dimensions of social exclusion in contemporary media technologies

The digital divide is the disparancy in access to information, in the ability to communicate, and in the capacity to make information and communication serve full participation in the information society. Indeed, the conversation about the digital divide has developed over the last decade from a focus on connectivity and access to information and communication technologies, to a conversation that encompasses the ability to use them and to the utility that usage provides (Wei et al., 2011). However, this conversation, while transitioning from technology to the skills of the people that use them and to the fruits of their use is limited in its ability to take into account the social role of information and communication technologies (ICTs). One successful attempt in conceptualizing the social impact of the differences in access to and utilization of digital communication technologies, was developed by van Dijk (2005) whose sequential model for analyzing the divide states that: 1. Categorical inequalities in society produce an unequal distribution of resources; 2. An unequal distribution of resources causes unequal access to digital technologies; 3. Unequal access to digital technologies also depends on the characteristics of these technologies; 4. Unequal access to digital technologies brings about unequal participation in society; 5. Unequal participation in society reinforces categorical inequalities and unequal distributions of resources.” (p. 15) As van Dijk’s model demonstrates, the divide’s impact is the exclusion of individuals from participation. Still left to be defined are the “categorical inequalities,” the “resources,” the “characteristics of digital technologies,” and the different levels of “access” that result in differentiated levels of participation, as these change over time due to the evolving nature of technology and the dynamics of society. And most importantly, the meaning of “participation” in contemporary society needs to be determined as it is differentiated levels of participation that are the result of the divide and the engine of the ever-growing disparities. Our argument is structured in the following manner: We first claim that contemporary digital media differ from the previous generation of ICTs along four dimensions: They offer an abundance of information resources and communication channels when compared to the relative paucity of both in the past; they offer mobility as opposed to the stationary nature of their predecessors; they are interactive in that they provide users with the capability to design their own media environments in contrast to the dictated environs of previous architectures; and, they allow users to communicate utilizing multi forms of mediation, unlike the uniformity of sound or word that limited users in the past. We then submit that involvement in the information society calls for egalitarian access to all four dimensions of the user experience that make contemporary media different from their predecessors and that the ability to experience all four affects the levels in which humans partake in the shaping of society. The model being cyclical, we then discuss how lower levels of participation contribute to the enhancement of social inequalities. Finally, we discuss why participation is needed in order to achieve full membership in the information society and what political philosophy should govern policy solutions targeting the re-inclusion of those digitally excluded.

Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning

Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.

Cooperative diversity for performance enhancement of vehicular ad hoc networks

This project was a step forward in introducing suitable cooperative diversity transmission techniques for vehicle to vehicle communications. The contributions are intended to aid in the successful implementation of future vehicular safety and autonomous controlling systems. Several protocols were introduced for vehicles to communicate effectively without losing connectivity. This study investigated novel protocols in terms of diversity-multiplexing trade-off and outage for a range of potential vehicular safety and infotainment applications.

Isostructural co-crystals derived from molecules with different supramolecular topologies

In this article, we report the crystal structures of five halogen bonded co-crystals comprising quaternary ammonium cations, halide anions (Cl– and Br–), and one of either 1,2-, 1,3-, or 1,4-diiodotetrafluorobenzene (DITFB). Three of the co-crystals are chemical isomers: 1,4-DITFB[TEA-CH2Cl]Cl, 1,2-DITFB[TEA-CH2Cl]Cl, and 1,3-DITFB[TEA-CH2Cl]Cl (where TEA-CH2Cl is chloromethyltriethylammonium ion). In each structure, the chloride anions link DITFB molecules through halogen bonds to produce 1D chains propagating with (a) linear topology in the structure containing 1,4-DITFB, (b) zigzag topology with 60° angle of propagation in that containing 1,2-DITFB, and (c) 120° angle of propagation with 1,3-DITFB. While the individual chains have highly distinctive and different topologies, they combine through π-stacking of the DITFB molecules to produce remarkably similar overall arrangements of molecules. Structures of 1,4-DITFB[TEA-CH2Br]Br and 1,3-DITFB[TEA-CH2Br]Br are also reported and are isomorphous with their chloro/chloride analogues, further illustrating the robustness of the overall supramolecular architecture. The usual approach to crystal engineering is to make structural changes to molecular components to effect specific changes to the resulting crystal structure. The results reported herein encourage pursuit of a somewhat different approach to crystal engineering. That is, to investigate the possibilities for engineering the same overall arrangement of molecules in crystals while employing molecular components that aggregate with entirely different supramolecular connectivity.

A quantitative metric to identify critical elements within seafood supply networks

A theoretical basis is required for comparing key features and critical elements in wild fisheries and aquaculture supply chains under a changing climate. Here we develop a new quantitative metric that is analogous to indices used to analyse food-webs and identify key species. The Supply Chain Index (SCI) identifies critical elements as those elements with large throughput rates, as well as greater connectivity. The sum of the scores for a supply chain provides a single metric that roughly captures both the resilience and connectedness of a supply chain. Standardised scores can facilitate cross-comparisons both under current conditions as well as under a changing climate. Identification of key elements along the supply chain may assist in informing adaptation strategies to reduce anticipated future risks posed by climate change. The SCI also provides information on the relative stability of different supply chains based on whether there is a fairly even spread in the individual scores of the top few key elements, compared with a more critical dependence on a few key individual supply chain elements. We use as a case study the Australian southern rock lobster Jasus edwardsii fishery, which is challenged by a number of climate change drivers such as impacts on recruitment and growth due to changes in large-scale and local oceanographic features. The SCI identifies airports, processors and Chinese consumers as the key elements in the lobster supply chain that merit attention to enhance stability and potentially enable growth. We also apply the index to an additional four real-world Australian commercial fishery and two aquaculture industry supply chains to highlight the utility of a systematic method for describing supply chains. Overall, our simple methodological approach to empirically-based supply chain research provides an objective method for comparing the resilience of supply chains and highlighting components that may be critical.

Targeting glucocorticoid receptors that promote resilience in the treatment of addiction

There is strong evidence to suggest that the combination of alcohol and chronic repetitive stress leads to long-lasting effects on brain function, specifically areas associated with stress, motivation and decision-making such as the amygdala, nucleus accumbens and prefrontal cortex. Alcohol and stress together facilitate the imprinting of long-lasting memories. The molecular mechanisms and circuits involved are being studied but are not fully understood. Current evidence suggests that corticosterone (animals) or cortisol (humans), in addition to direct transcriptional effects on the genome, can directly regulate pre- and postsynaptic synaptic transmission through membrane bound glucocorticoid receptors (GR). Indeed, corticosterone-sensitive synaptic receptors may be critical sites for stress regulation of synaptic responses. Direct modulation of synaptic transmission by corticosterone may contribute to the regulation of synaptic plasticity and memory during stress (Johnson et al., 2005; Prager et al., 2010). Specifically, previous data has shown that long term alcohol (1) increases the expression of NR2Bcontaining NMDA receptors at glutamate synapses, (2) changes receptor density, and (3) changes morphology of dendritic spines (Prendergast and Mulholland; 2012). During alcohol withdrawal these changes are associated with increased glucocorticoid signalling and increased neuronal excitability. It has therefore been proposed that these synapse changes lead to the anxiety and alcohol craving associated with withdrawal (Prendergast and Mulholland; 2012). My lab is targeting this receptor system and the amygdala in order to understand the effect of combining alcohol and stress on these pathways. Lastly, we are testing GR specific compounds as potential new medications to promote the development of resilience to developing addiction.