Graph analysis and modularity of brain functional connectivity networks: searching for the optimal threshold

Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.Comment: 15 pages, 7 figure

Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package

For statistical analysis of functional magnetic resonance imaging (fMRI) data sets, we propose a data-driven approach based on independent component analysis (ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data sets, spatial dimension being much greater than temporal dimension, spatial ICA is the computationally tractable approach generally proposed. However, for some neuroscientific applications, temporal independence of source signals can be assumed and temporal ICA becomes then an attractive exploratory technique. In this work, we use a classical linear algebra result ensuring the tractability of temporal ICA. We report several experiments on synthetic data and real MRI data sets that demonstrate the potential interest of our R package

Retinotopic and lateralized processing of spatial frequencies in human visual cortex during scene categorization.

International audienceUsing large natural scenes filtered in spatial frequencies, we aimed to demonstrate that spatial frequency processing could not only be retinotopically mapped but could also be lateralized in both hemispheres. For this purpose, participants performed a categorization task using large black and white photographs of natural scenes (indoors vs. outdoors, with a visual angle of 24° × 18°) filtered in low spatial frequencies (LSF), high spatial frequencies (HSF), and nonfiltered scenes, in block-designed fMRI recording sessions. At the group level, the comparison between the spatial frequency content of scenes revealed first that, compared with HSF, LSF scene categorization elicited activation in the anterior half of the calcarine fissures linked to the peripheral visual field, whereas, compared with LSF, HSF scene categorization elicited activation in the posterior part of the occipital lobes, which are linked to the fovea, according to the retinotopic property of visual areas. At the individual level, functional activations projected on retinotopic maps revealed that LSF processing was mapped in the anterior part of V1, whereas HSF processing was mapped in the posterior and ventral part of V2, V3, and V4. Moreover, at the group level, direct interhemispheric comparisons performed on the same fMRI data highlighted a right-sided occipito-temporal predominance for LSF processing and a left-sided temporal cortex predominance for HSF processing, in accordance with hemispheric specialization theories. By using suitable method of analysis on the same data, our results enabled us to demonstrate for the first time that spatial frequencies processing is mapped retinotopically and lateralized in human occipital cortex

Guidance for evaluating integrated surveillance of antimicrobial use and resistance

Antimicrobial resistance (AMR) resulting from antimicrobial use (AMU) is an emerging threat to global health. One of the key elements for a better understanding and management of AMU and AMR is to develop effective and efficient integrated surveillance systems that consider the complex epidemiology of these issues and the impacts of resistance on humans, animals and the environment. Consequently, for this project, an international consortium of experts from multiple fields called CoEvalAMR was formed with the objectives to study user needs, characterise and compare existing tools for the evaluation of integrated AMU and AMR surveillance, apply them to case studies, and elaborate guidance on the purpose-fit selection and the use of the tools. For the comparison of evaluation tools, questions were extracted from existing tools and attributed to themes, to assess the user needs, interviews were conducted with national key stakeholders, and we applied a series of different evaluation tools to understand and document their strengths and weaknesses. The guidance was refined iteratively. From 12 evaluation tools, 1117 questions/indicators were extracted and attributed to seven emerging themes. Twenty-three experts were interviewed, who suggested to increase the ease-of-use, grant open access, provide web-based interfaces and allow results to be automatically generated. Respondents also wished for tools providing the flexibility to conduct a rapid review, or an in-depth analysis of the surveillance system, depending on the evaluation objectives. The case studies emphasised that proper evaluations require adequate resources, typically requiring the involvement of several assessors and/or stakeholders, and can take weeks or months to complete. The resulting web-based guidance comprises six main sections: 1. Introduction to surveillance evaluation, 2. Evaluation of surveillance for AMU and AMR, 3. Evaluation tools, 4. Support for selecting an evaluation tool, 5. Case studies and 6. Directory of existing tools. The audience for the guidance is personnel working in public, private, and non-governmental organisations, from public health, animal health, plant health and environmental health, at local, national and international levels. We conclude that the field is challenged by opposing user needs for reduction and simplicity versus system approaches allowing the synthesis of that knowledge to sufficiently reflect the complexity of AMU and AMR ecology for real-world decisions. The CoEvalAMR web platform allows a better understanding of the different evaluation tools and assists users in the selection of an approach that corresponds to their evaluation needs. The CoEvalAMR consortium continues to address remaining gaps and consolidate evaluation tools and approaches in the future

Dans quelles mesures développer l’excellence dans les universités françaises ?

Since 2012, the policy of excellence in French universities has been characterized by the implementation of major accounting and budgetary reforms, university autonomy, centers of excellence, the construction of large research universities with a high international profile, the culture of evaluation and new benchmarks such as international rankings. The objective of this study is to try to define the measures for developing excellence in French universities. These measures will focus on research, training and management. Excellence is characterized by the culture of evaluation, the measurement of performance and efficiency in relation to the achievement of objectives and the measurement of results. The survey carried out in the excellence laboratories of the Grenoble site with a sample of academics and international visitors will make it possible to identify the main criteria of excellence, the levers and their effects at the local level to achieve excellence.Depuis 2012, la politique d’excellence menée dans les universités françaises s’est caractérisée par la mise en œuvre de grandes réformes sur le plan comptable et budgétaire, l’autonomie des universités, les pôles d’excellence, la construction de grandes universités de recherche à forte visibilité internationale, la culture de l’évaluation et de nouveaux référentiels tel que les classements internationaux. L’objectif de cette étude est de tenter de définir dans quelles mesures développer l’excellence dans les universités françaises. Ces mesures s’articuleront autour de la recherche, de la formation et du management. L’excellence est caractérisée par la culture de l’évaluation, la mesure de la performance et de l’efficience rapportés à l’atteinte des objectifs et la mesure des résultats. L’enquête menée dans les laboratoires d’excellence du site grenoblois auprès d’un échantillon d’universitaires et de visiteurs internationaux permettra d’identifier les principaux critères d’excellence, les leviers et leurs effets au niveau local pour atteindre l’excellence

Community detection in weighted brain connectivity networks beyond the resolution limit

AbstractGraph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or "communities", that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules (Nicolini and Bifone, 2016), in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymptotical Surprise with leading community detection methods currently in use and show its superior sensitivity in the detection of small modules even in the presence of noise and intersubject variability such as those observed in fMRI data. We apply our novel approach to functional connectivity networks from resting state fMRI experiments, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales. Finally, we discuss the implications of these findings for the identification of connector hubs, the brain regions responsible for the integration of the different network elements, showing that the improved resolution afforded by Asymptotical Surprise leads to a different classification compared to current methods

Event-related fMRI adaptation paradigm on real and synesthetic colors

The subjective experience of color by synesthetes when viewing achromatic letters and numbers supposedly relates somehow to real color experience. Using fMRI, we tried to specify the degree of coactivation by real and synesthetic colors, by evaluating each color center individually and applying adaptation protocols across real and synesthetic colors. Indeed, fMRI activation of the same voxels by real and synesthetic colors would not be enough to prove that the same neurons are involved, given the relatively weak anatomical resolution of the BOLD signal (≈ 3mm). We therefore performed fMRI event-related adaptation protocols on 10 synesthetes in order to measure possible cross-adaptation effects when mixing real and synesthetic colors. However, to start with, we did not find any region that was activated both by real and synesthetic colors. We did not observe any clear adaptation for synesthetic colors in color ROIs, but we also did not observe any systematic color adaptation in retinotopic V4 or in color ROIs, so we could not test rigorously our hypothesis of adaptation across real and synesthetic colors. This technical report should be read in complement to "The neural bases of grapheme-color synesthesia are not localized in real color-sensitive areas", by Jean-Michel Hupé, Cécile Bordier & Michel Dojat, published in Cerebral Cortex 2011; doi:10.1093/cercor/bhr236

A BOLD signature of eyeblinks in the visual cortex.

International audienceWe are usually unaware of the brief but large illumination changes caused by blinks, presumably because of blink suppression mechanisms. In fMRI however, increase of the BOLD signal was reported in the visual cortex, e.g. during blocks of voluntary blinks (Bristow, Frith and Rees, 2005) or after spontaneous blinks recorded during the prolonged fixation of a static stimulus (Tse, Baumgartner and Greenlee, 2010). We tested whether such activation, possibly related to illumination changes, was also present during standard fMRI retinotopic and visual experiments and was large enough to contaminate the BOLD signal we are interested in. We monitored in a 3T scanner the eyeblinks of 14 subjects who observed three different types of visual stimuli, including periodic rotating wedges and contracting/expanding rings, event-related Mondrians and graphemes, while fixating. We performed event-related analyses on the set of detected spontaneous blinks. We observed large and widespread BOLD responses related to blinks in the visual cortex of every subject and whatever the visual stimulus. The magnitude of the modulation was comparable to visual stimulation. However, blink-related activations lay mostly in the anterior parts of retinotopic visual areas, coding the periphery of the visual field well beyond the extent of our stimuli. Blinks therefore represent an important source of BOLD variations in the visual cortex and a troublesome source of noise since any correlation, even weak, between the distribution of blinks and a tested protocol could trigger artifactual activities. However, the typical signature of blinks along the anterior calcarine and the parieto-occipital sulcus allows identifying, even in the absence of eyetracking, fMRI protocols possibly contaminated by a heterogeneous distribution of blinks

AnalyzeFMRI: an R package to perform statistical analysis on FMRI datasets

International audienceAnalyzeFMRI is a developing package, initiated by J. Marchini, for the processing and analysis of large structural Magnetic Resonance Imaging (MRI) and Functional MRI (FMRI) datasets. In this presentation, we first introduce MRI and fMRI to enlight the data specificities and the main image processing steps. We then describe the current package version and the functionnalities we have recently added, mainly NFTI format management, cross-platform visualization based on Tcl/TK components and temporal and spatial IC analysis. We illustrate our presentation with examples coming from human visual experiments [1,2], especially demonstrating the interest of spatial and temporal IC analysis [3] compared to standard general linear model [4]. We conclude about the interest of the AnalyzeFMRI package for the exploration of MRI data and outline our plans for future extension