739 research outputs found
A group model for stable multi-subject ICA on fMRI datasets
Spatial Independent Component Analysis (ICA) is an increasingly used
data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
data. To date, it has been used to extract sets of mutually correlated brain
regions without prior information on the time course of these regions. Some of
these sets of regions, interpreted as functional networks, have recently been
used to provide markers of brain diseases and open the road to paradigm-free
population comparisons. Such group studies raise the question of modeling
subject variability within ICA: how can the patterns representative of a group
be modeled and estimated via ICA for reliable inter-group comparisons? In this
paper, we propose a hierarchical model for patterns in multi-subject fMRI
datasets, akin to mixed-effect group models used in linear-model-based
analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based
on i) probabilistic dimension reduction of the individual data, ii) canonical
correlation analysis to identify a data subspace common to the group iii)
ICA-based pattern extraction. In addition, we introduce a procedure based on
cross-validation to quantify the stability of ICA patterns at the level of the
group. We compare our method with state-of-the-art multi-subject fMRI ICA
methods and show that the features extracted using our procedure are more
reproducible at the group level on two datasets of 12 healthy controls: a
resting-state and a functional localizer study
Parametric oscillator based on non-linear vortex dynamics in low resistance magnetic tunnel junctions
Radiofrequency vortex spin-transfer oscillators based on magnetic tunnel
junctions with very low resistance area product were investigated. A high power
of excitations has been obtained characterized by a power spectral density
containing a very sharp peak at the fundamental frequency and a series of
harmonics. The observed behaviour is ascribed to the combined effect of spin
transfer torque and Oersted-Amp\`ere field generated by the large applied
dc-current. We furthermore show that the synchronization of a vortex
oscillation by applying a ac bias current is mostly efficient when the external
frequency is twice the oscillator fundamental frequency. This result is
interpreted in terms of a parametric oscillator.Comment: 4 pages, 4 figure
Fast Optimal Transport Averaging of Neuroimaging Data
Knowing how the Human brain is anatomically and functionally organized at the
level of a group of healthy individuals or patients is the primary goal of
neuroimaging research. Yet computing an average of brain imaging data defined
over a voxel grid or a triangulation remains a challenge. Data are large, the
geometry of the brain is complex and the between subjects variability leads to
spatially or temporally non-overlapping effects of interest. To address the
problem of variability, data are commonly smoothed before group linear
averaging. In this work we build on ideas originally introduced by Kantorovich
to propose a new algorithm that can average efficiently non-normalized data
defined over arbitrary discrete domains using transportation metrics. We show
how Kantorovich means can be linked to Wasserstein barycenters in order to take
advantage of an entropic smoothing approach. It leads to a smooth convex
optimization problem and an algorithm with strong convergence guarantees. We
illustrate the versatility of this tool and its empirical behavior on
functional neuroimaging data, functional MRI and magnetoencephalography (MEG)
source estimates, defined on voxel grids and triangulations of the folded
cortical surface.Comment: Information Processing in Medical Imaging (IPMI), Jun 2015, Isle of
Skye, United Kingdom. Springer, 201
Unsupervised Deformable Image Registration Using Cycle-Consistent CNN
Medical image registration is one of the key processing steps for biomedical
image analysis such as cancer diagnosis. Recently, deep learning based
supervised and unsupervised image registration methods have been extensively
studied due to its excellent performance in spite of ultra-fast computational
time compared to the classical approaches. In this paper, we present a novel
unsupervised medical image registration method that trains deep neural network
for deformable registration of 3D volumes using a cycle-consistency. Thanks to
the cycle consistency, the proposed deep neural networks can take diverse pair
of image data with severe deformation for accurate registration. Experimental
results using multiphase liver CT images demonstrate that our method provides
very precise 3D image registration within a few seconds, resulting in more
accurate cancer size estimation.Comment: accepted for MICCAI 201
Bayesian Joint Detection-Estimation of cerebral vasoreactivity from ASL fMRI data
International audienceAlthough the study of cerebral vasoreactivity using fMRI is mainly conducted through the BOLD fMRI modality, owing to its relatively high signal-to-noise ratio (SNR), ASL fMRI provides a more interpretable measure of cerebral vasoreactivity than BOLD fMRI. Still, ASL suffers from a low SNR and is hampered by a large amount of physiological noise. The current contribution aims at improving the re- covery of the vasoreactive component from the ASL signal. To this end, a Bayesian hierarchical model is proposed, enabling the recovery of per- fusion levels as well as fitting their dynamics. On a single-subject ASL real data set involving perfusion changes induced by hypercapnia, the approach is compared with a classical GLM-based analysis. A better goodness-of-fit is achieved, especially in the transitions between baseline and hypercapnia periods. Also, perfusion levels are recovered with higher sensitivity and show a better contrast between gray- and white matter
Idiosyncratic behaviour of tRNA-like structures in translation of plant viral RNA genomes
Supramolecular & Biomaterials Chemistr
Transmission XMCD-PEEM imaging of an engineered vertical FEBID cobalt nanowire with a domain wall
Using focused electron-beam-induced deposition, we fabricate a vertical, platinum-coated cobalt nanowire with a controlled three-dimensional structure. The latter is engineered to feature bends along the height: these are used as pinning sites for domain walls, which are obtained at remanence after saturation of the nanostructure in a horizontally applied magnetic field. The presence of domain walls is investigated using x-ray magnetic circular dichroism (XMCD) coupled to photoemission electron microscopy (PEEM). The vertical geometry of our sample combined with the low incidence of the x-ray beam produce an extended wire shadow which we use to recover the wire''s magnetic configuration. In this transmission configuration, the whole sample volume is probed, thus circumventing the limitation of PEEM to surfaces. This article reports on the first study of magnetic nanostructures standing perpendicular to the substrate with XMCD-PEEM. The use of this technique in shadow mode enabled us to confirm the presence of a domain wall without direct imaging of the nanowire
Unsupervised Fiber Bundles Registration using Weighted Measures Geometric Demons
International audienceBrain image registration aims at reducing anatomical variability across subjects to create a common space for group analysis. Multi-modal approaches intend to minimize cortex shape variations along with internal structures, such as fiber bundles. A di ficulty is that it requires a prior identi fication of these structures, which remains a challenging task in the absence of a complete reference atlas. We propose an extension of the log-Geometric Demons for jointly registering images and fi ber bundles without the need of point or ber correspondences. By representing fi ber bundles as Weighted Measures we can register subjects with di fferent numbers of fiber bundles. The ef ficacy of our algorithm is demonstrated by registering simultaneously T1 images and between 37 and 88 ber bundles depending on each of the ten subject used. We compare results with a multi-modal T1 + Fractional Anisotropy (FA) and a tensor-based registration algorithms and obtain superior performance with our approach
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