993 research outputs found
Spectral Characterization of functional MRI data on voxel-resolution cortical graphs
The human cortical layer exhibits a convoluted morphology that is unique to
each individual. Conventional volumetric fMRI processing schemes take for
granted the rich information provided by the underlying anatomy. We present a
method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC)
graphs, which encode the cortical morphology at the resolution of voxels in
3-D. We study graph spectral energy metrics associated to fMRI data of 100
subjects from the Human Connectome Project database, across seven tasks.
Experimental results signify the strength of CHC graphs' Laplacian eigenvector
bases in capturing subtle spatial patterns specific to different functional
loads as well as experimental conditions within each task.Comment: Fixed two typos in the equations; (1) definition of L in section 2.1,
paragraph 1. (2) signal de-meaning and normalization in section 2.4,
paragraph
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Multi-view graph embedding has become a widely studied problem in the area of
graph learning. Most of the existing works on multi-view graph embedding aim to
find a shared common node embedding across all the views of the graph by
combining the different views in a specific way. Hub detection, as another
essential topic in graph mining has also drawn extensive attentions in recent
years, especially in the context of brain network analysis. Both the graph
embedding and hub detection relate to the node clustering structure of graphs.
The multi-view graph embedding usually implies the node clustering structure of
the graph based on the multiple views, while the hubs are the boundary-spanning
nodes across different node clusters in the graph and thus may potentially
influence the clustering structure of the graph. However, none of the existing
works in multi-view graph embedding considered the hubs when learning the
multi-view embeddings. In this paper, we propose to incorporate the hub
detection task into the multi-view graph embedding framework so that the two
tasks could benefit each other. Specifically, we propose an auto-weighted
framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain
network analysis. The MVGE-HD framework learns a unified graph embedding across
all the views while reducing the potential influence of the hubs on blurring
the boundaries between node clusters in the graph, thus leading to a clear and
discriminative node clustering structure for the graph. We apply MVGE-HD on two
real multi-view brain network datasets (i.e., HIV and Bipolar). The
experimental results demonstrate the superior performance of the proposed
framework in brain network analysis for clinical investigation and application
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
Persistent Homology in Sparse Regression and its Application to Brain Morphometry
Sparse systems are usually parameterized by a tuning parameter that
determines the sparsity of the system. How to choose the right tuning parameter
is a fundamental and difficult problem in learning the sparse system. In this
paper, by treating the the tuning parameter as an additional dimension,
persistent homological structures over the parameter space is introduced and
explored. The structures are then further exploited in speeding up the
computation using the proposed soft-thresholding technique. The topological
structures are further used as multivariate features in the tensor-based
morphometry (TBM) in characterizing white matter alterations in children who
have experienced severe early life stress and maltreatment. These analyses
reveal that stress-exposed children exhibit more diffuse anatomical
organization across the whole white matter region.Comment: submitted to IEEE Transactions on Medical Imagin
Computing Scalable Multivariate Glocal Invariants of Large (Brain-) Graphs
Graphs are quickly emerging as a leading abstraction for the representation
of data. One important application domain originates from an emerging
discipline called "connectomics". Connectomics studies the brain as a graph;
vertices correspond to neurons (or collections thereof) and edges correspond to
structural or functional connections between them. To explore the variability
of connectomes---to address both basic science questions regarding the
structure of the brain, and medical health questions about psychiatry and
neurology---one can study the topological properties of these brain-graphs. We
define multivariate glocal graph invariants: these are features of the graph
that capture various local and global topological properties of the graphs. We
show that the collection of features can collectively be computed via a
combination of daisy-chaining, sparse matrix representation and computations,
and efficient approximations. Our custom open-source Python package serves as a
back-end to a Web-service that we have created to enable researchers to upload
graphs, and download the corresponding invariants in a number of different
formats. Moreover, we built this package to support distributed processing on
multicore machines. This is therefore an enabling technology for network
science, lowering the barrier of entry by providing tools to biologists and
analysts who otherwise lack these capabilities. As a demonstration, we run our
code on 120 brain-graphs, each with approximately 16M vertices and up to 90M
edges.Comment: Published as part of 2013 IEEE GlobalSIP conferenc
Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space
Diffusion MRI requires sufficient coverage of the diffusion wavevector space,
also known as the q-space, to adequately capture the pattern of water diffusion
in various directions and scales. As a result, the acquisition time can be
prohibitive for individuals who are unable to stay still in the scanner for an
extensive period of time, such as infants. To address this problem, in this
paper we harness non-local self-similar information in the x-q space of
diffusion MRI data for q-space upsampling. Specifically, we first perform
neighborhood matching to establish the relationships of signals in x-q space.
The signal relationships are then used to regularize an ill-posed inverse
problem related to the estimation of high angular resolution diffusion MRI data
from its low-resolution counterpart. Our framework allows information from
curved white matter structures to be used for effective regularization of the
otherwise ill-posed problem. Extensive evaluations using synthetic and infant
diffusion MRI data demonstrate the effectiveness of our method. Compared with
the widely adopted interpolation methods using spherical radial basis functions
and spherical harmonics, our method is able to produce high angular resolution
diffusion MRI data with greater quality, both qualitatively and quantitatively.Comment: 15 pages, 12 figure
Abnormal connectional fingerprint in schizophrenia: a novel network analysis of diffusion tensor imaging data
The graph theoretical analysis of structural magnetic resonance imaging (MRI) data has received a great deal of interest in recent years to characterize the organizational principles of brain networks and their alterations in psychiatric disorders, such as schizophrenia. However, the characterization of networks in clinical populations can be challenging, since the comparison of connectivity between groups is influenced by several factors, such as the overall number of connections and the structural abnormalities of the seed regions. To overcome these limitations, the current study employed the whole-brain analysis of connectional fingerprints in diffusion tensor imaging data obtained at 3 T of chronic schizophrenia patients (n = 16) and healthy, age-matched control participants (n = 17). Probabilistic tractography was performed to quantify the connectivity of 110 brain areas. The connectional fingerprint of a brain area represents the set of relative connection probabilities to all its target areas and is, hence, less affected by overall white and gray matter changes than absolute connectivity measures. After detecting brain regions with abnormal connectional fingerprints through similarity measures, we tested each of its relative connection probability between groups. We found altered connectional fingerprints in schizophrenia patients consistent with a dysconnectivity syndrome. While the medial frontal gyrus showed only reduced connectivity, the connectional fingerprints of the inferior frontal gyrus and the putamen mainly contained relatively increased connection probabilities to areas in the frontal, limbic, and subcortical areas. These findings are in line with previous studies that reported abnormalities in striatal–frontal circuits in the pathophysiology of schizophrenia, highlighting the potential utility of connectional fingerprints for the analysis of anatomical networks in the disorder
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