Publications

Generic acquisition protocol for quantitative MRI of the spinal cord

Eva Alonso-Ortiz

Mihael Abramovic

Carina Arneitz

Nicole Atcheson

Laura Barlow

Robert L. Barry

Markus Barth

Marco Battiston

Christian Büchel

Matthew Budde

Virginie Callot

Anna J. E. Combes

Benjamin De Leener

Maxime Descoteaux

Paulo Loureiro de Sousa

Marek Dostál

Adam Dvorak

Falk Eippert … (voir 71 de plus)

Karla R. Epperson

Kevin S. Epperson

Patrick Freund

Jürgen Finsterbusch

Alexandru Foias

Michela Fratini

Issei Fukunaga

Claudia A. M. Gandini Wheeler-Kingshott

Giancarlo Germani

Guillaume Gilbert

Federico Giove

Charley Gros

Francesco Grussu

Akifumi Hagiwara

Pierre-Gilles Henry

Tomáš Horák

Masaaki Hori

James Joers

Kouhei Kamiya

Haleh Karbasforoushan

Miloš Keřkovský

Ali Khatibi

Joo-Won Kim

Nawal Kinany

Hagen Kitzler

Shannon Kolind

Yazhuo Kong

Petr Kudlička

Paul Kuntke

Nyoman D. Kurniawan

Slawomir Kusmia

René Labounek

Maria Marcella Laganà

Cornelia Laule

Christine S. Law

Christophe Lenglet

Tobias Leutritz

Yaou Liu

Sara Llufriu

Sean Mackey

Eloy Martinez-Heras

Loan Mattera

Igor Nestrasil

Kristin P. O’Grady

Nico Papinutto

Daniel Papp

Deborah Pareto

Todd B. Parrish

Anna Pichiecchio

Ferran Prados

Àlex Rovira

Marc J. Ruitenberg

Rebecca S. Samson

Giovanni Savini

Maryam Seif

Alan C. Seifert

Alex K. Smith

Seth A. Smith

Zachary A. Smith

Elisabeth Solana

Yuichi Suzuki

George Tackley

Alexandra Tinnermann

Jan Valosek

Dimitri Van De Ville

Marios C. Yiannakas

Kenneth A. Weber

Nikolaus Weiskopf

Richard G. Wise

Patrik O. Wyss

Junqian Xu

Optimized and standardized MRI acquisition protocols for the spinal cord, compatible with Siemens, GE and Philips scanners.

2021-08-15

Nature Protocols (publié)

Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Eva Alonso-Ortiz

Mihael Abramovic

Carina Arneitz

Nicole Atcheson

Laura Barlow

Robert L. Barry

Markus Barth

Marco Battiston

Christian Büchel

Matthew Budde

Virginie Callot

Anna J. E. Combes

Benjamin De Leener

Maxime Descoteaux

Paulo Loureiro de Sousa

Marek Dostál

Julien Doyon

Adam Dvorak

Falk Eippert … (voir 71 de plus)

Karla R. Epperson

Kevin S. Epperson

Patrick Freund

Jürgen Finsterbusch

Alexandru Foias

Michela Fratini

Issei Fukunaga

Claudia A. M. Gandini Wheeler-Kingshott

Giancarlo Germani

Guillaume Gilbert

Federico Giove

Charley Gros

Francesco Grussu

Akifumi Hagiwara

Pierre-Gilles Henry

Tomáš Horák

Masaaki Hori

James Joers

Kouhei Kamiya

Haleh Karbasforoushan

Miloš Keřkovský

Ali Khatibi

Joo-Won Kim

Nawal Kinany

Hagen H. Kitzler

Shannon Kolind

Yazhuo Kong

Petr Kudlička

Paul Kuntke

Nyoman D. Kurniawan

Slawomir Kusmia

René Labounek

Maria Marcella Laganà

Cornelia Laule

Christine S. Law

Christophe Lenglet

Tobias Leutritz

Yaou Liu

Sara Llufriu

Sean Mackey

Eloy Martinez-Heras

Loan Mattera

Igor Nestrasil

Kristin P. O'Grady

Nico Papinutto

Daniel Papp

Deborah Pareto

Todd B. Parrish

Anna Pichiecchio

Ferran Prados

Àlex Rovira

Marc J. Ruitenberg

Rebecca S. Samson

Giovanni Savini

Maryam Seif

Alan C. Seifert

Alex K. Smith

Seth A. Smith

Zachary A. Smith

Elisabeth Solana

Y. Suzuki

George Tackley

Alexandra Tinnermann

Jan Valosek

Dimitri Van De Ville

Marios C. Yiannakas

Kenneth A. Weber II

Nikolaus Weiskopf

Richard G. Wise

Patrik O. Wyss

Junqian Xu

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assess… (voir plus)ing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.

2021-08-15

Scientific Data (publié)

Hybrid Harmony: A Multi-Person Neurofeedback Application for Interpersonal Synchrony

Phoebe Chen

Sophie Hendrikse

Kaia Sargent

Michele Romani

Matthias Oostrik

Tom F. Wilderjans

Sander Koole

Guillaume Dumas

David Medine

Suzanne Dikker

Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple … (voir plus)individuals during social interaction. For example, so-called “hyperscanning” research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.

2021-08-11

Frontiers in Neuroergonomics (publié)

Evaluation of distortion correction methods in diffusion MRI of the spinal cord

Haykel Snoussi

Emmanuel Caruyer

Olivier Commowick

Benoit Combes

Elise Bannier

Anne Kerbrat

Christian Barillot

2021-08-08

ArXiv (prépublication)

Learning function from structure in neuromorphic networks

Laura E. Suárez

Blake A. Richards

Guillaume Lajoie

Bratislav Misic

The connection patterns of neural circuits in the brain form a complex network. Collective signaling within the network manifests as pattern… (voir plus)ed neural activity, and is thought to support human cognition and adaptive behavior. Recent technological advances permit macro-scale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging, and use reservoir computing to implement these connectomes as artificial neural networks. We then train these neuromorphic networks to learn a cognitive task. We show that biologically realistic neural architectures perform optimally when they display critical dynamics. We find that performance is driven by network topology, and that the modular organization of large-scale functional systems is computationally relevant. Throughout, we observe a prominent interaction between network structure and dynamics, such that the same underlying architecture can support a wide range of learning capacities across dynamical regimes. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity, conceptually bridging neuroscience and artificial intelligence.

2021-08-08

Nature Machine Intelligence (inconnu)

Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information

Luke Y. Prince

Matthew M. Tran

Dorian Grey

Lydia Saad

Helen Chasiotis

Jeehyun Kwag

Michael M. Kohl

Blake A. Richards

Neurons can carry information with both the synchrony and rate of their spikes. However, it is unknown whether distinct subtypes of neurons … (voir plus)are more sensitive to information carried by synchrony versus rate, or vice versa. Here, we address this question using patterned optical stimulation in slices of somatosensory cortex from mouse lines labelling fast-spiking (FS) and regular-spiking (RS) interneurons. We used optical stimulation in layer 2/3 to encode a 1-bit signal using either the synchrony or rate of activity. We then examined the mutual information between this signal and the interneuron responses. We found that for a synchrony encoding, FS interneurons carried more information in the first five milliseconds, while both interneuron subtypes carried more information than excitatory neurons in later responses. For a rate encoding, we found that RS interneurons carried more information after several milliseconds. These data demonstrate that distinct interneuron subtypes in the neocortex have distinct sensitivities to synchrony versus rate codes.

2021-08-04

Communications Biology (publié)

Barriers and facilitators to patient engagement in patient safety from patients and healthcare professionals' perspectives: A systematic review and meta-synthesis.

Zahra Chegini

Morteza Arab‐Zozani

Sheikh Mohammed Shariful Islam

Georgia Tobiano

S. A. Rahimi

AIMS To explore patients' and healthcare professionals' (HCPs) perceived barriers and facilitators to patient engagement in patient safety. … (voir plus) METHODS We conducted a systematic review and meta-synthesis from five computerized databases, including PubMed/MEDLINE, Embase, Web of Science, Scopus and PsycINFO, as well as grey literature and reference lists of included studies. Data were last searched in December 2019 with no limitation on the year of publication. Qualitative and Mix-methods studies that explored HCPs' and patients' perceptions of barriers and facilitators to patient engagement in patient safety were included. Two authors independently screened the titles and the abstracts of studies. Next, the full texts of the screened studies were reviewed by two authors. Potential discrepancies were resolved by consensus with a third author. The Mixed Methods Appraisal Tool was used for quality appraisal. Thematic analysis was used to synthesize results. RESULTS Nineteen studies out of 2616 were included in this systematic review. Themes related to barriers included: patient unwillingness, HCPs' unwillingness, and inadequate infrastructures. Themes related to facilitators were: encouraging patients, sharing information with patients, establishing trustful relationship, establishing patient-centred care and improving organizational resources. CONCLUSION Patients have an active role in improving their safety. Strategies are required to address barriers that hinder or prevent patient engagement and create capacity and facilitate action.

2021-08-01

Nursing Forum (publié)

Sequoia: A Software Framework to Unify Continual Learning Research

Pau Rodríguez

J. Hurtado

Dominic Zhao

Ryan Lindeborg

Timothee LESORT

David Vázquez

Laurent Charlin

Irina Rish

Massimo Caccia

The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non… (voir plus)-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting www.github.com/lebrice/Sequoia.

2021-08-01

ArXiv (prépublication)

openreview.net

An Advanced Noise Reduction and Edge Enhancement Algorithm

Shih-Chia Huang

Quoc-Viet Hoang

Trung-Hieu Le

Yan-Tsung Peng

Ching-Chun Huang

Cheng Zhang

Benjamin C. M. Fung

Kai-Han Cheng

Sha-Wo Huang

Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments,… (voir plus) especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.

2021-07-31

Italian National Conference on Sensors (publié)

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting

Yi Cheng

Siyao Li

Bang Liu

Ruihui Zhao

Sujian Li

Chenhua Lin

Yefeng Zheng

This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficu… (voir plus)lty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.

2021-07-31

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (publié)

Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval

Zijing Ou

Qinliang Su

Jianxing Yu

Bang Liu

Jingwen Wang

Ruihui Zhao

Changyou Chen

Yefeng Zheng

With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale informatio… (voir plus)n retrieval. To generate high-quality hashing code, both semantics and neighborhood information are crucial. However, most existing methods leverage only one of them or simply combine them via some intuitive criteria, lacking a theoretical principle to guide the integration process. In this paper, we encode the neighborhood information with a graph-induced Gaussian distribution, and propose to integrate the two types of information with a graph-driven generative model. To deal with the complicated correlations among documents, we further propose a tree-structured approximation method for learning. Under the approximation, we prove that the training objective can be decomposed into terms involving only singleton or pairwise documents, enabling the model to be trained as efficiently as uncorrelated ones. Extensive experimental results on three benchmark datasets show that our method achieves superior performance over state-of-the-art methods, demonstrating the effectiveness of the proposed model for simultaneously preserving semantic and neighborhood information.

2021-07-31

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (publié)