9,432 research outputs found
Mapping Topographic Structure in White Matter Pathways with Level Set Trees
Fiber tractography on diffusion imaging data offers rich potential for
describing white matter pathways in the human brain, but characterizing the
spatial organization in these large and complex data sets remains a challenge.
We show that level set trees---which provide a concise representation of the
hierarchical mode structure of probability density functions---offer a
statistically-principled framework for visualizing and analyzing topography in
fiber streamlines. Using diffusion spectrum imaging data collected on
neurologically healthy controls (N=30), we mapped white matter pathways from
the cortex into the striatum using a deterministic tractography algorithm that
estimates fiber bundles as dimensionless streamlines. Level set trees were used
for interactive exploration of patterns in the endpoint distributions of the
mapped fiber tracks and an efficient segmentation of the tracks that has
empirical accuracy comparable to standard nonparametric clustering methods. We
show that level set trees can also be generalized to model pseudo-density
functions in order to analyze a broader array of data types, including entire
fiber streamlines. Finally, resampling methods show the reliability of the
level set tree as a descriptive measure of topographic structure, illustrating
its potential as a statistical descriptor in brain imaging analysis. These
results highlight the broad applicability of level set trees for visualizing
and analyzing high-dimensional data like fiber tractography output
Characterising population variability in brain structure through models of whole-brain structural connectivity
Models of whole-brain connectivity are valuable for understanding neurological function. This thesis
seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically
acquired diffusion data. We propose new approaches for studying these models. The aim is to
develop techniques which can take models of brain connectivity and use them to identify biomarkers
or phenotypes of disease.
The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified
to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections
are traced between 77 regions of interest, automatically extracted by label propagation from
multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract
are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data.
These are compared in subsequent studies.
To date, most whole-brain connectivity studies have characterised population differences using graph
theory techniques. However these can be limited in their ability to pinpoint the locations of differences
in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include
a spectral clustering approach for comparing population differences in the clustering properties of
weighted brain networks. In addition, machine learning approaches are suggested for the first time.
These are particularly advantageous as they allow classification of subjects and extraction of features
which best represent the differences between groups.
One limitation of the proposed approach is that errors propagate from segmentation and registration
steps prior to tractography. This can cumulate in the assignment of false positive connections, where
the contribution of these factors may vary across populations, causing the appearance of population
differences where there are none. The final contribution of this thesis is therefore to develop a common
co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject
into a single probabilistic model of diffusion for the population. This allows tractography to be
performed only once, ensuring that there is one model of connectivity. Cross-subject differences can
then be identified by mapping individual subjects’ anisotropy data to this model. The approach is
used to compare populations separated by age and gender
Taxonomic Features and Comparison of the Gut Microbiome from Two Edible Fungus-Farming Termites (Macrotermes falciger, M. natalensis) Harvested in the Vhembe District of Limpopo, South Africa
Background Termites are an important food resource for many human populations around the world, and are a good supply of nutrients. The fungus-farming ‘higher’ termite members of Macrotermitinae are also consumed by modern great apes and are implicated as critical dietary resources for early hominins. While the chemical nutritional composition of edible termites is well known, their microbiomes are unexplored in the context of human health. Here we sequenced the V4 region of the 16S rRNA gene of gut microbiota extracted from the whole intestinal tract of two Macrotermes sp. soldiers collected from the Limpopo region of South Africa. Results Major and minor soldier subcastes of M. falciger exhibit consistent differences in taxonomic representation, and are variable in microbial presence and abundance patterns when compared to another edible but less preferred species, M. natalensis. Subcaste differences include alternate patterns in sulfate-reducing bacteria and methanogenic Euryarchaeota abundance, and differences in abundance between Alistipes and Ruminococcaceae. M. falciger minor soldiers and M. natalensissoldiers have similar microbial profiles, likely from close proximity to the termite worker castes, particularly during foraging and fungus garden cultivation. Compared with previously published termite and cockroach gut microbiome data, the taxonomic representation was generally split between termites that directly digest lignocellulose and humic substrates and those that consume a more distilled form of nutrition as with the omnivorous cockroaches and fungus-farming termites. Lastly, to determine if edible termites may point to a shared reservoir for rare bacterial taxa found in the gut microbiome of humans, we focused on the genus Treponema. The majority of Treponemasequences from edible termite gut microbiota most closely relate to species recovered from other termites or from environmental samples, except for one novel OTU strain, which clustered separately with Treponema found in hunter-gatherer human groups. Conclusions Macrotermes consumed by humans display special gut microbial arrangements that are atypical for a lignocellulose digesting invertebrate, but are instead suited to the simplified nutrition in the fungus-farmer diet. Our work brings to light the particular termite microbiome features that should be explored further as avenues in human health, agricultural sustainability, and evolutionary research
Connectivity of the Superficial Muscles of the Human Perineum: A Diffusion Tensor Imaging-Based Global Tractography Study.
Despite the importance of pelvic floor muscles, significant controversy still exists about the true structural details of these muscles. We provide an objective analysis of the architecture and orientation of the superficial muscles of the perineum using a novel approach. Magnetic Resonance Diffusion Tensor Images (MR-DTI) were acquired in 10 healthy asymptomatic nulliparous women, and 4 healthy males. Global tractography was then used to generate the architecture of the muscles. Micro-CT imaging of a male cadaver was performed for validation of the fiber tracking results. Results show that muscles fibers of the external anal sphincter, from the right and left side, cross midline in the region of the perineal body to continue as transverse perinea and bulbospongiosus muscles of the opposite side. The morphology of the external anal sphincter resembles that of the number '8' or a "purse string". The crossing of muscle fascicles in the perineal body was supported by micro-CT imaging in the male subject. The superficial muscles of the perineum, and external anal sphincter are frequently damaged during child birth related injuries to the pelvic floor; we propose the use of MR-DTI based global tractography as a non-invasive imaging technique to assess damage to these muscles
Dispersal and Concentration: Patterns of Latino Residential Settlement
Uses 1990 and 2000 Census data to determine how trends in residential settlement patterns among the Hispanic population changed over the course of a decade
Inventory and Characterization of the Riparian Zone of the Current and Jacks Fork Rivers
The ecological, recreational, and economic value of the 134 mile (216 km) riparian corridor within the Ozark National Scenic Riverways (ONSR) is of great interest to land managers and conservationists. Recent interest in applying ecosystem management to forest systems has necessitated a fresh look at the tools and methods in use to assess existing patterns of plant community structure and diversity. The purpose and objective of the study described in this report was to initiate a series of vegetation studies that could be integrated with existing research and management infonnation on the riparian vegetation in the ONSR. Defining the compositional and spatial attributes of the riparian corridor were at the core of our research efforts. We used multivariate analysis and ordination techniques to characterize the composition and distribution of woody and herbaceous vegetation within the ONSR
Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data
A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student’s t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer’s disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM)
Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications
Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic
resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of
Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity
underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the
use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to
cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers
have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic,
and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity
across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power
and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited.
Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral
reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains
in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163
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