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

Selection of mouse cells with amplified metallothionein genes retaining their glucocorticoid inducibility

AbstractTwo new mouse cell mutants, resistant to either 80 or 100 mM CdCl2, were isolated to study the regulation of transcription by the glucocorticoid hormones. Their metallothionein mt-1+ and mt-2+ genes were amplified coordinately to a maximum of 30 copies per cell. By Southern blot analysis, no gross rearrangement was detectable near the mt+ loci. Contrary to other mutants previously isolated, the metallothionein-specific mRNAs of these mutants are inducible by dexamethasone

Information Literacy in Students Entering Higher Education in the French Speaking Community of Belgium: lessons learned from an evaluation

Although universities are providing more and more information literacy training for their undergraduate students, the students’ real level of information literacy at the beginning of their studies has never been assessed. Hence EduDOC has decided to team up with the CIUF ‘Library’ Commission in order to organize a wide study aiming at objectively describing this initial level of information literacy, at identifying the students’ main weaknesses, as well as allowing instructors to adjust their training on this basis. The questionnaire was based on a similar study carried out in Québec and contains 20 questions grouped in five themes relating to information search steps. It was sent in September 2007 to a random sample of students entering a higher education institution in the French Speaking Community of Belgium for the first time. The students’ rather poor results confirm that organizing an information literacy program is imperative if students are to perform well in their studies.Peer reviewe

Automatically building morphometric anatomical atlases from 3D medical images : Application to a skull atlas

In this article, we present a method for building entirely automatically a morphometric anatomical atlas from 3D medical image s acquired by CT-Scan or MR . We detail each step of the method, including the non-rigid registration algorithm, 3D lines averaging , and statistical analysis processes . We apply the method to obtain a quantitative atlas of crest lines of the skull . Finally, we use the resulting atlas to study a craniofacia l disease : we show how we can obtain qualitative and quantitative results by contrasting a skull affected by a deformation of th e mandible with the atlas .Dans cet article, nous présentons une méthode pour construire de manière automatique des atlas anatomiques morphométriques à partir d'images médicales tridimensionnelles obtenues par scanographie ou imagerie par résonance magnétique. Nous en détaillons les différentes étapes, en particulier les algorithmes de mise en correspondance non-rigide, de moyenne et d'analyse statistique de lignes caractéristiques tridimensionnelles. Nous appliquons la méthode à la construction d'un atlas morphométrique des lignes de crête du crâne. Nous montrons alors comment la comparaison automatique entre l'atlas et un crâne présentant une déformation mandibulaire permet d'obtenir des résultats qualitatifs et quantitatifs utilisables par un médecin

Magnetic Anisotropy of a Single Cobalt Nanoparticle

Using a new microSQUID set-up, we investigate magnetic anisotropy in a single 1000-atoms cobalt cluster. This system opens new fields in the characterization and the understanding of the origin of magnetic anisotropy in such nanoparticles. For this purpose, we report three-dimensional switching field measurements performed on a 3 nm cobalt cluster embedded in a niobium matrix. We are able to separate the different magnetic anisotropy contributions and evidence the dominating role of the cluster surface.Comment: 4 pages, 8 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

Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework

International audienceIdentifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-Detection-Estimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool

Joint T1 and Brain Fiber Log-Demons Registration Using Currents to Model Geometry

International audienceWe present an extension of the diffeomorphic Geometric Demons algorithm which combines the iconic registration with geometric constraints. Our algorithm works in the log-domain space, so that one can efficiently compute the deformation field of the geometry. We represent the shape of objects of interest in the space of currents which is sensitive to both location and geometric structure of objects. Currents provides a distance between geometric structures that can be defined without specifying explicit point-to-point correspondences. We demonstrate this framework by registering simultaneously T1 images and 65 fiber bundles consistently extracted in 12 subjects and compare it against non-linear T1, tensor, and multi-modal T1+ Fractional Anisotropy (FA) registration algorithms. Results show the superiority of the Log-domain Geometric Demons over their purely iconic counterparts