Improving accuracy and power with transfer learning using a meta-analytic database

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e. to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.Comment: MICCAI, Nice : France (2012

Decoding Visual Percepts Induced by Word Reading with fMRI

International audienceWord reading involves multiple cognitive processes. To infer which word is being visualized, the brain first processes the visual percept, deciphers the letters, bigrams, and activates different words based on context or prior expectation like word frequency. In this contribution, we use supervised machine learning techniques to decode the first step of this processing stream using functional Magnetic Resonance Images (fMRI). We build a decoder that predicts the visual percept formed by four letter words, allowing us to identify words that were not present in the training data. To do so, we cast the learning problem as multiple classification problems after describing words with multiple binary attributes. This work goes beyond the identification or reconstruction of single letters or simple geometrical shapes and addresses a challenging estimation problem, that is the prediction of multiple variables from a single observation, hence facing the problem of learning multiple predictors from correlated inputs

Probing Brain Context-Sensitivity with Masked-Attention Generation

Two fundamental questions in neurolinguistics concerns the brain regions that integrate information beyond the lexical level, and the size of their window of integration. To address these questions we introduce a new approach named masked-attention generation. It uses GPT-2 transformers to generate word embeddings that capture a fixed amount of contextual information. We then tested whether these embeddings could predict fMRI brain activity in humans listening to naturalistic text. The results showed that most of the cortex within the language network is sensitive to contextual information, and that the right hemisphere is more sensitive to longer contexts than the left. Masked-attention generation supports previous analyses of context-sensitivity in the brain, and complements them by quantifying the window size of context integration per voxel.Comment: 2 pages, 2 figures, CCN 202

Vous m'en mettrez un peu plus ? D'un projet de mise en conformité à un rêve de Learning and Research Center

L’idée de développement d’un learning center ou d’une “bibliothèque augmentée” au sein du campus du centre-ville qui abrite la Faculté de Philosophie et Lettres de l’Université de Liège n’est pas neuve dans le chef d’ULiège Library. Elle remonte au moins à 2005. Divers projets ont été élaborés et parfois ont connu des débuts de concrétisation pour finalement avorter pour différentes raisons. Néanmoins, si la ligne initiale est restée constante depuis le début, la réflexion autour de ce projet ainsi que ses conditions de faisabilité ont fortement évolué depuis ses premières ébauches. Cette présentation revient sur l’historique de ce projet en en décrivant les enjeux, contraintes, et objectifs au regard du contexte institutionnel. Seront ensuite exposés les éléments de contexte qui ont offert l’opportunité de relancer ce projet, l’état d’avancement actuel des réflexions portant sur l’élaboration et la mise en oeuvre de ce projet de learning center et de ses nombreuses composantes. Parmi celles-ci, seront abordés notamment la question de la définition institutionnelle d’un concept spécifique de learning center, de son périmètre conceptuel ou encore ses interrelations avec d’autres aspects tels que les services à la recherche et l’éco-responsabilité. L’état des lieux de la réflexion autour de la planification du projet de learning center ULiège Library tentera de rendre compte de cette diversité complexe

How reliable and useful is Cabell's Blacklist ? A data-driven analysis

In scholarly publishing, blacklists aim to register fraudulent or deceptive journals and publishers, also known as "predatory", to minimise the spread of unreliable research and the growing of fake publishing outlets. However, blacklisting remains a very controversial activity for several reasons: there is no consensus regarding the criteria used to determine fraudulent journals, the criteria used may not always be transparent or relevant, and blacklists are rarely updated regularly. Cabell's paywalled blacklist service attempts to overcome some of these issues in reviewing fraudulent journals on the basis of transparent criteria and in providing allegedly up-to-date information at the journal entry level. We tested Cabell's blacklist to analyse whether or not it could be adopted as a reliable tool by stakeholders in scholarly communication, including our own academic library. To do so, we used a copy of Walt Crawford's Gray Open Access dataset (2012-2016) to assess the coverage of Cabell's blacklist and get insights on their methodology. Out of the 10,123 journals that we tested, 4,681 are included in Cabell's blacklist. Out of this number of journals included in the blacklist, 3,229 are empty journals, i.e. journals in which no single article has ever been published. Other collected data points to questionable weighing and reviewing methods and shows a lack of rigour in how Cabell applies its own procedures: some journals are blacklisted on the basis of 1 to 3 criteria, identical criteria are recorded multiple times in individual journal entries, discrepancies exist between reviewing dates and the criteria version used and recorded by Cabell, reviewing dates are missing, and we observed two journals blacklisted twice with a different number of violations. Based on these observations, we conclude with recommendations and suggestions that could help improve Cabell's blacklist service.Comment: 38 page

Broadband Setup for Magnetic-Field-Induced Domain Wall Motion in Cylindrical Nanowires

In order to improve the precision of domain wall dynamics measurements, we develop a coplanar waveguide-based setup where the domain wall motion should be triggered by pulses of magnetic field. The latter are produced by the Oersted field of the waveguide as a current pulse travels toward its termination, where it is dissipated. Our objective is to eliminate a source of bias in domain wall speed estimation while optimizing the field amplitude. Here, we present implementations of this concept for magnetic force microscopy (MFM) and synchrotron-based investigation

Measuring fish activities as additional environmental data during a hydrographic survey with a multi-beam echo sounder

International audienceThe modern multi-beam echo sounders (MBES) are advanced instrumentation for active underwater acoustic surveys that can be boarded on oceanic vessels as well on light crafts. Although their versatility allows scientists to perform various environmental studies, their potential is seldom fully exploited. A single data acquisition cruise is not only able to display the seabed backscatter, but also provide an estimation of the fish activities from an underwater site thanks to water column imagery. This work is aiming at developing some (automatic) signal processing techniques to detect, analyse and classify objects observed in the water column with a focus on fish activities to provide fish accumulation and classification but also some comparative analyses along with the seafloor classification

Improved brain pattern recovery through ranking approaches

International audienceInferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture multivariate statistical effects distributed across voxels and brain regions. Up to now, much effort has been made to improve decoding by incorporating prior knowledge in the form of a particular regularization term. In this paper we demonstrate that further improvement can be made by accounting for non-linearities using a ranking approach rather than the commonly used least-square regression. Through simulation, we compare the recovery properties of our approach to linear models commonly used in fMRI based decoding. We demonstrate the superiority of ranking with a real fMRI dataset

Imaging genetics: bio-informatics and bio-statistics challenges

International audienceThe IMAGEN study -- a very large European Research Project -- seeks to identify and characterize biological and environmental factors that in uence teenagers mental health. To this aim, the consortium plans to collect data for more than 2000 subjects at 8 neuroimaging centres. These data comprise neuroimaging data, behavioral tests (for up to 5 hours of testing), and also white blood samples which are collected and processed to obtain 650k single nucleotide polymorphisms (SNP) per subject. Data for more than 1000 subjects have already been collected. We describe the statistical aspects of these data and the challenges, such as the multiple comparison problem, created by such a large imaging genetics study (i.e., 650k for the SNP, 50k data per neuroimage).We also suggest possible strategies, and present some rst investigations using uni or multi-variate methods in association with re-sampling techniques. Specically, because the number of variables is very high, we rst reduce the data size and then use multivariate (CCA, PLS) techniques in association with re-sampling techniques