3,837 research outputs found
The Potential of the Human Connectome as a Biomarker of Brain Disease
The human connectome at the level of fiber tracts between brain regions has
been shown to differ in patients with brain disorders compared to healthy
control groups. Nonetheless, there is a potentially large number of different
network organizations for individual patients that could lead to cognitive
deficits prohibiting correct diagnosis. Therefore changes that can distinguish
groups might not be sufficient to diagnose the disease that an individual
patient suffers from and to indicate the best treatment option for that
patient. We describe the challenges introduced by the large variability of
connectomes within healthy subjects and patients and outline three common
strategies to use connectomes as biomarkers of brain diseases. Finally, we
propose a fourth option in using models of simulated brain activity (the
dynamic connectome) based on structural connectivity rather than the structure
(connectome) itself as a biomarker of disease. Dynamic connectomes, in addition
to currently used structural, functional, or effective connectivity, could be
an important future biomarker for clinical applications.Comment: Perspective Article for special issue on Magnetic Resonance Imaging
of Healthy and Diseased Brain Network
Simple connectome inference from partial correlation statistics in calcium imaging
In this work, we propose a simple yet effective solution to the problem of
connectome inference in calcium imaging data. The proposed algorithm consists
of two steps. First, processing the raw signals to detect neural peak
activities. Second, inferring the degree of association between neurons from
partial correlation statistics. This paper summarises the methodology that led
us to win the Connectomics Challenge, proposes a simplified version of our
method, and finally compares our results with respect to other inference
methods
Genetic and Neuroanatomical Support for Functional Brain Network Dynamics in Epilepsy
Focal epilepsy is a devastating neurological disorder that affects an
overwhelming number of patients worldwide, many of whom prove resistant to
medication. The efficacy of current innovative technologies for the treatment
of these patients has been stalled by the lack of accurate and effective
methods to fuse multimodal neuroimaging data to map anatomical targets driving
seizure dynamics. Here we propose a parsimonious model that explains how
large-scale anatomical networks and shared genetic constraints shape
inter-regional communication in focal epilepsy. In extensive ECoG recordings
acquired from a group of patients with medically refractory focal-onset
epilepsy, we find that ictal and preictal functional brain network dynamics can
be accurately predicted from features of brain anatomy and geometry, patterns
of white matter connectivity, and constraints complicit in patterns of gene
coexpression, all of which are conserved across healthy adult populations.
Moreover, we uncover evidence that markers of non-conserved architecture,
potentially driven by idiosyncratic pathology of single subjects, are most
prevalent in high frequency ictal dynamics and low frequency preictal dynamics.
Finally, we find that ictal dynamics are better predicted by white matter
features and more poorly predicted by geometry and genetic constraints than
preictal dynamics, suggesting that the functional brain network dynamics
manifest in seizures rely on - and may directly propagate along - underlying
white matter structure that is largely conserved across humans. Broadly, our
work offers insights into the generic architectural principles of the human
brain that impact seizure dynamics, and could be extended to further our
understanding, models, and predictions of subject-level pathology and response
to intervention
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
This paper considers the problem of brain disease classification based on
connectome data. A connectome is a network representation of a human brain. The
typical connectome classification problem is very challenging because of the
small sample size and high dimensionality of the data. We propose to use
simultaneous approximate diagonalization of adjacency matrices in order to
compute their eigenstructures in more stable way. The obtained approximate
eigenvalues are further used as features for classification. The proposed
approach is demonstrated to be efficient for detection of Alzheimer's disease,
outperforming simple baselines and competing with state-of-the-art approaches
to brain disease classification
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
There is no consensus on how to construct structural brain networks from
diffusion MRI. How variations in pre-processing steps affect network
reliability and its ability to distinguish subjects remains opaque. In this
work, we address this issue by comparing 35 structural connectome-building
pipelines. We vary diffusion reconstruction models, tractography algorithms and
parcellations. Next, we classify structural connectome pairs as either
belonging to the same individual or not. Connectome weights and eight
topological derivative measures form our feature set. For experiments, we use
three test-retest datasets from the Consortium for Reliability and
Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare
pairwise classification results to a commonly used parametric test-retest
measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure
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