150 research outputs found
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
We propose two strategies to improve the quality of tractography results
computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both
methods are based on the same PDE framework, defined in the coupled space of
positions and orientations, associated with a stochastic process describing the
enhancement of elongated structures while preserving crossing structures. In
the first method we use the enhancement PDE for contextual regularization of a
fiber orientation distribution (FOD) that is obtained on individual voxels from
high angular resolution diffusion imaging (HARDI) data via constrained
spherical deconvolution (CSD). Thereby we improve the FOD as input for
subsequent tractography. Secondly, we introduce the fiber to bundle coherence
(FBC), a measure for quantification of fiber alignment. The FBC is computed
from a tractography result using the same PDE framework and provides a
criterion for removing the spurious fibers. We validate the proposed
combination of CSD and enhancement on phantom data and on human data, acquired
with different scanning protocols. On the phantom data we find that PDE
enhancements improve both local metrics and global metrics of tractography
results, compared to CSD without enhancements. On the human data we show that
the enhancements allow for a better reconstruction of crossing fiber bundles
and they reduce the variability of the tractography output with respect to the
acquisition parameters. Finally, we show that both the enhancement of the FODs
and the use of the FBC measure on the tractography improve the stability with
respect to different stochastic realizations of probabilistic tractography.
This is shown in a clinical application: the reconstruction of the optic
radiation for epilepsy surgery planning
Reducing variability in along-tract analysis with diffusion profile realignment
Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction
of the brain's white matter structures through tractography. Analyzing dMRI
measures along the trajectory of white matter bundles can provide a more
specific investigation than considering a region of interest or tract-averaged
measurements. However, performing group analyses with this along-tract strategy
requires correspondence between points of tract pathways across subjects. This
is usually achieved by creating a new common space where the representative
streamlines from every subject are resampled to the same number of points. If
the underlying anatomy of some subjects was altered due to, e.g. disease or
developmental changes, such information might be lost by resampling to a fixed
number of points. In this work, we propose to address the issue of possible
misalignment, which might be present even after resampling, by realigning the
representative streamline of each subject in this 1D space with a new method,
coined diffusion profile realignment (DPR). Experiments on synthetic datasets
show that DPR reduces the coefficient of variation for the mean diffusivity,
fractional anisotropy and apparent fiber density when compared to the unaligned
case. Using 100 in vivo datasets from the HCP, we simulated changes in mean
diffusivity, fractional anisotropy and apparent fiber density. Pairwise
Student's t-tests between these altered subjects and the original subjects
indicate that regional changes are identified after realignment with the DPR
algorithm, while preserving differences previously detected in the unaligned
case. This new correction strategy contributes to revealing effects of interest
which might be hidden by misalignment and has the potential to improve the
specificity in longitudinal population studies beyond the traditional region of
interest based analysis and along-tract analysis workflows.Comment: v4: peer-reviewed round 2 v3 : deleted some old text from before
peer-review which was mistakenly included v2 : peer-reviewed version v1:
preprint as submitted to journal NeuroImag
White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions
Electrical brain stimulation is currently being investigated as a therapy for
neurological disease. However, opportunities to optimize such therapies are
challenged by the fact that the beneficial impact of focal stimulation on both
neighboring and distant regions is not well understood. Here, we use network
control theory to build a model of brain network function that makes
predictions about how stimulation spreads through the brain's white matter
network and influences large-scale dynamics. We test these predictions using
combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data
who volunteered to participate in an extensive stimulation regimen. We posit a
specific model-based manner in which white matter tracts constrain stimulation,
defining its capacity to drive the brain to new states, including states
associated with successful memory encoding. In a first validation of our model,
we find that the true pattern of white matter tracts can be used to more
accurately predict the state transitions induced by direct electrical
stimulation than the artificial patterns of null models. We then use a targeted
optimal control framework to solve for the optimal energy required to drive the
brain to a given state. We show that, intuitively, our model predicts larger
energy requirements when starting from states that are farther away from a
target memory state. We then suggest testable hypotheses about which structural
properties will lead to efficient stimulation for improving memory based on
energy requirements. Our work demonstrates that individual white matter
architecture plays a vital role in guiding the dynamics of direct electrical
stimulation, more generally offering empirical support for the utility of
network control theoretic models of brain response to stimulation
Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics
International audienceBrain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but very little is known about interregional differences of their intrinsic dynamics at a macro-scale in human. We developed here a new method aiming at mapping the dynamical properties of cortical microcircuits non-invasively using the coupling between robotized transcranial magnetic stimulation and elec-troencephalography. We recorded the responses evoked by the stimulation of 18 cortical targets largely covering the accessible neocortex in 22 healthy volunteers. Specific data processing methods were developed to map the local source activity of each cortical target, which showed interregional differences with very good interhemi-spheric reproducibility. Functional signatures of cortical microcircuits were further studied using spatio-temporal decomposition of local source activities in order to highlight principal brain modes. The identified brain modes revealed that cortical areas with similar intrinsic dynamical properties could be distributed either locally or not, with a spatial signature that was somewhat reminiscent of resting state networks. Our results provide the proof of concept of " functional cytoarchitectonics " , that would guide the parcellation of the human cortex using not only its cytoarchitecture but also its intrinsic responses to local perturbations. This opens new avenues for brain modelling and physiopathology readouts
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