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

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

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

Population modeling with machine learning can enhance measures of mental health

Background: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? Results: Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data. Conclusion: Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations

Domain wall pinning in a circular cross-section wire with modulated diameter

Domain wall propagation in cylindrical nanowires with modulations of diameter is a key phenomenon to design physics-oriented devices, or a disruptive three-dimensional magnetic memory. This chapter presents a combination of analytical modelling and micromagnetic simulations, with the aim to present a comprehensive panorama of the physics of pinning of domain walls at modulations, when moved under the stimulus of a magnetic field or a spin-polarized current. For the sake of considering simple physics, we consider diameters of a few tens of nanometers at most, and accordingly domain walls of transverse type. Modeling with suitable approximations provides simple scaling laws, while simulations are more accurate, refining the results and defining the range of validity of the models. While pinning increases with the relative change of diameter, a key feature is the much larger efficiency of pinning at an increase of diameter upon considering current rather than field, due to the drastic decrease of current density related to the increase of diameter.Comment: 37 pages, 14 figures, overview chapte

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