1,125 research outputs found
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
Mapping hybrid functional-structural connectivity traits in the human connectome
One of the crucial questions in neuroscience is how a rich functional
repertoire of brain states relates to its underlying structural organization.
How to study the associations between these structural and functional layers is
an open problem that involves novel conceptual ways of tackling this question.
We here propose an extension of the Connectivity Independent Component Analysis
(connICA) framework, to identify joint structural-functional connectivity
traits. Here, we extend connICA to integrate structural and functional
connectomes by merging them into common hybrid connectivity patterns that
represent the connectivity fingerprint of a subject. We test this extended
approach on the 100 unrelated subjects from the Human Connectome Project. The
method is able to extract main independent structural-functional connectivity
patterns from the entire cohort that are sensitive to the realization of
different tasks. The hybrid connICA extracted two main task-sensitive hybrid
traits. The first, encompassing the within and between connections of dorsal
attentional and visual areas, as well as fronto-parietal circuits. The second,
mainly encompassing the connectivity between visual, attentional, DMN and
subcortical networks. Overall, these findings confirms the potential ofthe
hybrid connICA for the compression of structural/functional connectomes into
integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5
figure
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
We introduce TempoCave, a novel visualization application for analyzing
dynamic brain networks, or connectomes. TempoCave provides a range of
functionality to explore metrics related to the activity patterns and modular
affiliations of different regions in the brain. These patterns are calculated
by processing raw data retrieved functional magnetic resonance imaging (fMRI)
scans, which creates a network of weighted edges between each brain region,
where the weight indicates how likely these regions are to activate
synchronously. In particular, we support the analysis needs of clinical
psychologists, who examine these modular affiliations and weighted edges and
their temporal dynamics, utilizing them to understand relationships between
neurological disorders and brain activity, which could have a significant
impact on the way in which patients are diagnosed and treated. We summarize the
core functionality of TempoCave, which supports a range of comparative tasks,
and runs both in a desktop mode and in an immersive mode. Furthermore, we
present a real-world use case that analyzes pre- and post-treatment connectome
datasets from 27 subjects in a clinical study investigating the use of
cognitive behavior therapy to treat major depression disorder, indicating that
TempoCave can provide new insight into the dynamic behavior of the human brain
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Functional brain networks are well described and estimated from data with
Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance
estimators. Comparing functional connectivity of subjects in two populations
calls for comparing these estimated GGMs. Our goal is to identify differences
in GGMs known to have similar structure. We characterize the uncertainty of
differences with confidence intervals obtained using a parametric distribution
on parameters of a sparse estimator. Sparse penalties enable statistical
guarantees and interpretable models even in high-dimensional and low-sample
settings. Characterizing the distributions of sparse models is inherently
challenging as the penalties produce a biased estimator. Recent work invokes
the sparsity assumptions to effectively remove the bias from a sparse estimator
such as the lasso. These distributions can be used to give confidence intervals
on edges in GGMs, and by extension their differences. However, in the case of
comparing GGMs, these estimators do not make use of any assumed joint structure
among the GGMs. Inspired by priors from brain functional connectivity we derive
the distribution of parameter differences under a joint penalty when parameters
are known to be sparse in the difference. This leads us to introduce the
debiased multi-task fused lasso, whose distribution can be characterized in an
efficient manner. We then show how the debiased lasso and multi-task fused
lasso can be used to obtain confidence intervals on edge differences in GGMs.
We validate the techniques proposed on a set of synthetic examples as well as
neuro-imaging dataset created for the study of autism
Metrics for Graph Comparison: A Practitioner's Guide
Comparison of graph structure is a ubiquitous task in data analysis and
machine learning, with diverse applications in fields such as neuroscience,
cyber security, social network analysis, and bioinformatics, among others.
Discovery and comparison of structures such as modular communities, rich clubs,
hubs, and trees in data in these fields yields insight into the generative
mechanisms and functional properties of the graph.
Often, two graphs are compared via a pairwise distance measure, with a small
distance indicating structural similarity and vice versa. Common choices
include spectral distances (also known as distances) and distances
based on node affinities. However, there has of yet been no comparative study
of the efficacy of these distance measures in discerning between common graph
topologies and different structural scales.
In this work, we compare commonly used graph metrics and distance measures,
and demonstrate their ability to discern between common topological features
found in both random graph models and empirical datasets. We put forward a
multi-scale picture of graph structure, in which the effect of global and local
structure upon the distance measures is considered. We make recommendations on
the applicability of different distance measures to empirical graph data
problem based on this multi-scale view. Finally, we introduce the Python
library NetComp which implements the graph distances used in this work
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