Publications

Processing of social and monetary rewards in autism spectrum disorders

Sarah Baumeister

Carolin Moessnang

Nico Bast

Sarah Hohmann

Pascal Aggensteiner

Anna Kaiser

Julian Tillmann

David Goyard

Tony Charman

Sara Ambrosino

Simon Baron-Cohen

Christian Beckmann

Sven Bölte

Thomas Bourgeron

Annika Rausch

Daisy Crawley

Flavio Dell’Acqua

Guillaume Dumas

Sarah Durston

Christine Ecker … (voir 21 de plus)

Dorothea L. Floris

Vincent Frouin

Hannah Hayward

Rosemary Holt

Mark Johnson

Emily J. H. Jones

Meng-Chuan Lai

Michael V. Lombardo

Luke Mason

Beth Oakley

Marianne Oldehinkel

Antonio Persico

Antonia San José Cáceres

Thomas Wolfers

Eva Loth

Declan Murphy

Jan K. Buitelaar

Heike Tost

Andreas Meyer-Lindenberg

Tobias Banaschewski

Daniel Brandeis

Background Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous … (voir plus)neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD. Aims Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD. Method Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age). Results Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD. Conclusions Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD.

2023-02-28

The British Journal of Psychiatry (publié)

doi.org

Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning

Mohammad Karami

Hervé Lombaert

David Rivest‐henault

2023-02-28

Computerized Medical Imaging and Graphics (publié)

doi.org

arxiv.org

Scalable Regret for Learning to Control Network-Coupled Subsystems With Unknown Dynamics

Sagar Sudhakara

Aditya Mahajan

Ashutosh Nayyar

Yi Ouyang

In this article, we consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems … (voir plus)connected over a network. Our goal is to minimize and quantify the regret (i.e., loss in performance) of our learning and control strategy with respect to an oracle who knows the system model. Upfront viewing the interconnected subsystems globally and directly using existing LQG learning algorithms for the global system results in a regret that increases super-linearly with the number of subsystems. Instead, we propose a new Thompson sampling-based learning algorithm which exploits the structure of the underlying network. We show that the expected regret of the proposed algorithm is bounded by

2023-02-28

IEEE Transactions on Control of Network Systems (publié)

doi.org

arxiv.org

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

David Bergeron

Christian Iorio-Morin

Marco Bonizzato

Guillaume Lajoie

Nathalie Orr Gaucher

Éric Racine

Alexander G. Weil

2023-02-28

Journal of Child Neurology (publié)

doi.org

Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

Rashid Hussain Khokhar

Benjamin C. M. Fung

Farkhund Iqbal

Khalil Al-Hussaeni

Mohammed Hussain

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting… (voir plus) with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article, we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.

2023-02-27

ACM Transactions on Knowledge Discovery from Data (publié)

doi.org

A case–control study on predicting population risk of suicide using health administrative data: a research protocol

JianLi Wang

Fatemeh Gholi Zadeh Kharrat

Jean-François Pelletier

Louis Rochette

Eric Pelletier

Pascale Lévesque

Victoria Massamba

Camille Brousseau-Paradis

Mada Mohammed

Geneviève Gariépy

Christian Gagné

Alain Lesage

2023-02-26

BMJ Open (publié)

doi.org

DisKeyword: Tweet Corpora Exploration for Keyword Selection

Sacha Lévy

Reihaneh Rabbany

2023-02-26

Web Search and Data Mining (publié)

doi.org

Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship

Andres Ferraro

Gustavo Ferreira

Fernando Diaz

Georgina Born

2023-02-21

ArXiv (prépublication)

doi.org

arxiv.org

Steerable Equivariant Representation Learning

Sangnie Bhardwaj

Willie McClinton

Tongzhou Wang

Guillaume Lajoie

Chen Sun

Phillip Isola

Dilip Krishnan

Pre-trained deep image representations are useful for post-training tasks such as classification through transfer learning, image retrieval,… (voir plus) and object detection. Data augmentations are a crucial aspect of pre-training robust representations in both supervised and self-supervised settings. Data augmentations explicitly or implicitly promote invariance in the embedding space to the input image transformations. This invariance reduces generalization to those downstream tasks which rely on sensitivity to these particular data augmentations. In this paper, we propose a method of learning representations that are instead equivariant to data augmentations. We achieve this equivariance through the use of steerable representations. Our representations can be manipulated directly in embedding space via learned linear maps. We demonstrate that our resulting steerable and equivariant representations lead to better performance on transfer learning and robustness: e.g. we improve linear probe top-1 accuracy by between 1% to 3% for transfer; and ImageNet-C accuracy by upto 3.4%. We further show that the steerability of our representations provides significant speedup (nearly 50x) for test-time augmentations; by applying a large number of augmentations for out-of-distribution detection, we significantly improve OOD AUC on the ImageNet-C dataset over an invariant representation.

2023-02-21

ArXiv (prépublication)

doi.org

openreview.net

Interpret Your Care: Predicting the Evolution of Symptoms for Cancer Patients

Rupali Bhati

Jennifer Jones

Audrey Durand

Cancer treatment is an arduous process for patients and causes many side-effects during and post-treatment. The treatment can affect almost … (voir plus)all body systems and result in pain, fatigue, sleep disturbances, cognitive impairments, etc. These conditions are often under-diagnosed or under-treated. In this paper, we use patient data to predict the evolution of their symptoms such that treatment-related impairments can be prevented or effects meaningfully ameliorated. The focus of this study is on predicting the pain and tiredness level of a patient post their diagnosis. We implement an interpretable decision tree based model called LightGBM on real-world patient data consisting of 20163 patients. There exists a class imbalance problem in the dataset which we resolve using the oversampling technique of SMOTE. Our empirical results show that the value of the previous level of a symptom is a key indicator for prediction and the weighted average deviation in prediction of pain level is 3.52 and of tiredness level is 2.27.

2023-02-18

ArXiv (prépublication)

doi.org

arxiv.org

LAGrad: Statically Optimized Differentiable Programming in MLIR

Mai Jacob Peng

Christophe Dubach

2023-02-16

International Conference on Compiler Construction (publié)

doi.org

Spatio-temporal hard attention learning for skeleton-based activity recognition

Bahareh Nikpour