980 research outputs found
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
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The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.
ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
We present DeepTract, a deep-learning framework for estimating white matter
fibers orientation and streamline tractography. We adopt a data-driven approach
for fiber reconstruction from diffusion weighted images (DWI), which does not
assume a specific diffusion model. We use a recurrent neural network for
mapping sequences of DWI values into probabilistic fiber orientation
distributions. Based on these estimations, our model facilitates both
deterministic and probabilistic streamline tractography. We quantitatively
evaluate our method using the Tractometer tool, demonstrating competitive
performance with state-of-the art classical and machine learning based
tractography algorithms. We further present qualitative results of
bundle-specific probabilistic tractography obtained using our method. The code
is publicly available at: https://github.com/itaybenou/DeepTract.git
PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traits
Our understanding of the structure of the brain and its relationships with
human traits is largely determined by how we represent the structural
connectome. Standard practice divides the brain into regions of interest (ROIs)
and represents the connectome as an adjacency matrix having cells measuring
connectivity between pairs of ROIs. Statistical analyses are then heavily
driven by the (largely arbitrary) choice of ROIs. In this article, we propose a
novel tractography-based representation of brain connectomes, which clusters
fiber endpoints to define a data adaptive parcellation targeted to explain
variation among individuals and predict human traits. This representation leads
to Principal Parcellation Analysis (PPA), representing individual brain
connectomes by compositional vectors building on a basis system of fiber
bundles that captures the connectivity at the population level. PPA reduces
subjectivity and facilitates statistical analyses. We illustrate the proposed
approach through applications to data from the Human Connectome Project (HCP)
and show that PPA connectomes improve power in predicting human traits over
state-of-the-art methods based on classical connectomes, while dramatically
improving parsimony and maintaining interpretability. Our PPA package is
publicly available on GitHub, and can be implemented routinely for diffusion
tensor image data
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