La propension à l’interdisciplinarité des étudiants en situation d’innovation

L’article vise à qualifier le comportement des étudiants lorsqu’ils sont confrontés à l’interdisciplinarité en situation d’innovation. Nous montrons que les étudiants disposent d’une appétence pour les travaux de groupe réalisés avec d’autres élèves du supérieur spécialisés dans d’autres disciplines. Cette propension à l’interdisciplinarité diffère selon le type d’étudiant et les établissements. Les résultats avancés dans cet article proviennent du traitement des données issues de la plateforme Studyka. Cette plateforme en ligne permet à des étudiants de se mettre en relation, quels que soient leur spécialité et leur établissement pour travailler ensemble et relever un challenge autour de l’innovation.The article provides insight on students’ strategies when they have to work in an interdisciplinary group working on an innovative project. We show that students are willing to work in a team made up of students studying in other disciplines. This inclination for interdisciplinary approaches depends both on the type of students and the institutions. The outcomes are the result of processing data obtained through the Studyka platform. This online platform provides opportunities for the students to work together, whatever their skills and institution, and to deal with a challenge in innovation

Loss of brain inter-frequency hubs in Alzheimer's disease

Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-beta deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.Comment: 27 pages, 6 figures, 3 tables, 3 supplementary figure

Limited usefulness of neurocognitive functioning indices as predictive markers for treatment response to methylphenidate or neurofeedback@home in children and adolescents with ADHD

IntroductionEarlier studies exploring the value of executive functioning (EF) indices for assessing treatment effectiveness and predicting treatment response in attention-deficit/hyperactivity disorder (ADHD) mainly focused on pharmacological treatment options and revealed rather heterogeneous results. Envisioning the long-term goal of personalized treatment selection and intervention planning, this study comparing methylphenidate treatment (MPH) and a home-based neurofeedback intervention (NF@Home) aimed to expand previous findings by assessing objective as well as subjectively reported EF indices and by analyzing their value as treatment and predictive markers.MethodsChildren and adolescents (n = 146 in the per protocol sample) aged 7–13 years with a formal diagnosis of an inattentive or combined presentation of ADHD were examined. We explored the EF performance profile using the Conners Continuous Performance Task (CPT) and the BRIEF self-report questionnaire within our prospective, multicenter, randomized, reference drug-controlled NEWROFEED study with sites in five European countries (France, Spain, Switzerland, Germany, and Belgium). As primary outcome for treatment response, the clinician-rated ADHD Rating Scale-IV was used. Patients participating in this non-inferiority trial were randomized to either NF@home (34–40 sessions of TBR or SMR NF depending on the pre-assessed individual alpha peak frequency) or MPH treatment (ratio: 3:2). Within a mixed-effects model framework, analyses of change were calculated to explore the predictive value of neurocognitive indices for ADHD symptom-related treatment response.ResultsFor a variety of neurocognitive indices, we found a significant pre-post change during treatment, mainly in the MPH group. However, the results of the current study reveal a rather limited prognostic value of neurocognitive indices for treatment response to either NF@Home or MPH treatment. Some significant effects emerged for parent-ratings only.DiscussionCurrent findings indicate a potential value of self-report (BRIEF global score) and some objectively measured neurocognitive indices (CPT commission errors and hit reaction time variability) as treatment markers (of change) for MPH. However, we found a rather limited prognostic value with regard to predicting treatment response not (yet) allowing recommendation for clinical use. Baseline symptom severity was revealed as the most relevant predictor, replicating robust findings from previous studies

Analyse des lésions cérébrales ischémiques en phase aiguë, par imagerie par résonance magnétique de diffusion (méthodes, intégration logicielle et évaluations cliniques)

Dans un premier temps, nous avons développé des techniques de segmentation automatique de l'infarctus à la phase aigue de l' AIC à partir d'images IRM pondérées en diffusion (DWI). Deux méthodes ont été développées avec comme idée directrice, le croisement des informations issues des images DWI et celles de cartographie du coefficient apparent de diffusion (ADC). La première méthode est basée sur des critères d'intensité des images en question, avec une mise en œuvre faisant appel à l'algorithme EM. La seconde méthode intègre en supplément aux données les informations issues d'un atlas probabiliste des lésions attendues suites à un AIC de l'artère cérébrale moyenne. Nous nous sommes ensuite intéressés à l'estimation de la zone à risque de croissance : la pénombre ischémique. La méthode de référence que nous avons utilisée, appelée NeurInfarct , est basée sur une mesure de disparité (mismatch) entre les mesures d'ADC et de DWI. Dans le cadre d'un tranfert de technologie, j'ai développé une solution logicielle, NeurInfarct 1.0.0 , répondant à des critères d'usage clinique ainsi qu'aux normes qualité des règlementations européennes (CE) et américaines (FDA). La création de ce logiciel a permis la mise en place de plusieurs études cliniques afin d'évaluer ses performances sur de larges bases de données mono et multi centriques, auxquelles j'ai collaboré au tout premier plan. Ces études nous ont permis d'évaluer de manière soutenue les performances des approches méthodologiques proposées et d'envisager des améliorations. Les méthodes d'estimation de la pénombre ischémique à partir de cartes ADC bénéficient également d'informations a priori issues de l'intégration d'un atlas probabiliste des lésions ischémiques.We first developed new automatic image segmentation techniques of the ischemic lesion from diffusion-weighted MRI image (DWI) data acquired at the acute phase. We propose two approaches that both combine DWI data and apparent diffusion coefficient maps (ADC). The first of these segmentation methods isolates the ischemic lesions using a model designed with voxel-intensity criteria and an implementation running the EM algorithm. Indeed, irreversible lesions are revealed by both signaIs of hyper-intensity in DWI images and a relative diminution of the ADC. The second approach complements the imaging data with a probabilistic atlas of the expected lesion territory following an occlusion of the middle cerebral artery. We then worked on the prediction of potential infarct growth and the determination of the so-called ischemic penumbra region. The reference method that was used, called "NeurInfarct", is based on the detection of a regional mismatch between ADC and DWI measures 1 have brought a significant contribution to the technology translation during my thesis: I have developed a software application, NeurInfarct 1.0.0 , complying with the clinical quality standards from European (CE) and American (FDA) regulations. Through this software, several mono and multi centric clinical studies were initiated to evaluate the performances of the approach on larger databases I have contributed and actively collaborated to these studies. This manuscript details the results from two of these studies. Similarly to the segmentation of acute lesions, the methods used for the estimation of the ischemic penumbra from ADC maps benefited from the addition of a priori information extracted from the probabilistic atlas of ischemic lesions.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

La propension à l’interdisciplinarité des étudiants en situation d’innovation

The article provides insight on students’ strategies when they have to work in an interdisciplinary group working on an innovative project. We show that students are willing to work in a team made up of students studying in other disciplines. This inclination for interdisciplinary approaches depends both on the type of students and the institutions. The outcomes are the result of processing data obtained through the Studyka platform. This online platform provides opportunities for the students to work together, whatever their skills and institution, and to deal with a challenge in innovation

Comparative performance evaluation of data-driven causality measures applied to brain networks

International audienceIn this article, several well-known data-driven causality methods are revisited and comparatively evaluated. These are the Granger-Geweke Causality (GGC), the Partial Directed Coherence (PDC), the Directed Transfer Function (DTF) and the Direct Directed Transfer Function (dDTF). The robustness of the four causality measures against two degradation factors is quantitatively evaluated. These are: the presence of realistic biological/electronic noise at various SNR levels, as recorded on a MagnetoEncephalography (MEG) machine, and the presence of a weak node in the brain network where the causality analysis is applied. The causality measures are evaluated in terms of the relative estimation error and the compromise between true and fictitious causal density in the brain network. Both parametric and non-parametric causality analysis is performed. It is illustrated that the non-parametric method is a promising alternative to the more commonly applied MVAR-model based causality analysis. It is also demonstrated that, in the presence of both tested degradation factors, the DTF method is the most robust in terms of low estimation error, while the PDC in terms of low fictitious causal density. The dDTF provides lower fictitious causal density and higher spectral selectivity as compared to DTF, at high enough SNR. The GGC exhibits the worst compromise of performance. An application of the causality measures to a set of MEG resting-state experimental data is accordingly presented. It is demonstrated that significant contrast between the Eyes-Closed and Eyes-Open rest condition in the alpha frequency band allows to detect significant causality between the occipital cortex and the thalamus

Point spread functions of hippocampus sources using wMNE.

<p>The left side displays the PSF maps of three point sources (blue dots) using wMNE. Each PSF map is normalized (i.e., normalization of the given point source’s column of the resolution matrix) to better visualize each spatial distribution. By averaging the PSF maps over all of the hippocampus and after normalization, we obtain the PSF maps that are displayed on the right side. Note that the colorbar does not start from zero and that the structure sizes are modified to make the sources more visible. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059856#pone-0059856-g001" target="_blank">Figure 1B</a> for a medial view of the relative position of the structures.</p

Simulation setup illustrations.

<p>A. Schematic view of the simulation setup. The red and blue Gaussians correspond to the distributions that modulate the neocortical (respectively, subcortical) simulated fields that are generated from the activation of patches; these fields are illustrated with neocortical and hippocampal tessellations. The summation of both activations is used to estimate the actual generators. D_t is the time between the maximum of the two Gaussians. The variation in D_t allows variations in the ratio R_c between the cortical and the subcortical activations. B. The lower part displays the anatomical model that has the mentioned structures and is included to give to the reader a better idea of their positions. “P” stands for posterior and “A” anterior.</p

Simulated magnetic fields and sensitivity distributions.

<p>A. Plot of normalized distributions (with fitted Gaussian distributions) of the simulated fields for patches of 3 cm² that belong to the four structures. These distributions account for the DMD of each structure, the geometry of the patches and the gain matrix of the sources that belong to the patches. B. Normalized averaged sensitivity distributions (normalized average root mean-squared (RMS) contribution to sensors) over 7 subjects. The corresponding maps are displayed for the hippocampus and the amygdala. Note that the x-axis is logarithmic and that the colormap is scaled at the subcortical level to better evaluate the distributions of the subcortical sources. These distributions are calculated using the gain vector at each source location.</p

Average PSF and CTF maps over all hippocampus sources.

<p>Average PSF (1^st line) and CTF (2^nd line) maps are shown for the three inverse kernels: wMNE, sLORETA and dSPM. Note that the colorbar does not start from zero and that the structure sizes are modified to make the sources more visible. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059856#pone-0059856-g001" target="_blank">Figure 1B</a> for a medial view of the relative position of the structures.</p