Publications

Adapting to the COVID‐19 pandemic in cohort studies: Validation of online assessments of cognition and neuropsychiatric symptoms in an aging population
Firoza Z Lussier
Stijn Servaes
Min Su Kang
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Nesrine Rahmouni
Alyssa Stevenson
Tharick A. Pascoal
Suzanne King
Danilo Bzdok
Serge Gauthier
Pedro Rosa‐Neto
The occurrence of the COVID‐19 pandemic has had a significant impact on cohort studies, particularly those whose subjects are at higher ri… (voir plus)sk of developing complications from the virus. As such, assessment methods must be adapted to minimize COVID‐19 exposure risk. The TRIAD (Translational Biomarkers of Aging and Dementia) cohort assessed N=292 individuals during initial COVID‐19 lockdown measures by telephone interview to rate cognition, neuropsychiatric symptoms, and impact of the pandemic. To increase speed and efficiency of data collection, we aim to follow these individuals by means of online survey. Here, we present a validation of our online assessment tools by comparing data obtained through both methods (phone interview and online survey) in the same subjects.
2021-12-30
Alzheimer's & Dementia (publié)
doi.org
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning
Samin Yeasar Arnob
Riashat Islam
Doina Precup
2021-12-30
ArXiv (prépublication)
arxiv.org
arxiv.org
Single-Shot Pruning for Offline Reinforcement Learning
Samin Yeasar Arnob
Riyasat Ohib
Sergey Plis
Doina Precup
2021-12-30
arXiv (prépublication)
doi.org
arxiv.org
Tau‐PET is associated with knowledge of COVID‐19, COVID‐19‐related distress, and change in sleep quality during the pandemic
Firoza Z Lussier
Stijn Servaes
Min Su Kang
Gleb Bezgin
Mira Chamoun
Jenna Stevenson
Nesrine Rahmouni
Alyssa Stevenson
Tharick A. Pascoal
Suzanne King
Guillaume Elgbeili
Danilo Bzdok
Serge Gauthier
Pedro Rosa‐Neto
While the global COVID‐19 pandemic has hindered many human research operations, it has allowed for the investigation of novel scientific q… (voir plus)uestions. Particularly, the effects of the pandemic and its resulting social isolation on elderly individuals and their association with Alzheimer’s disease biomarkers remains a broad and open question. Here, we sought to investigate whether knowledge of COVID‐19, pandemic‐related distress, and changes in sleep quality were associated with in vivo tau deposition in an AD‐enriched cohort.
2021-12-30
Alzheimer's & Dementia (publié)
doi.org
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
Enoch Tetteh
Joseph Viviano
Yoshua Bengio
David Krueger
Joseph Paul Cohen
Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (voir plus)ve been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing.
2021-12-26
arXiv (prépublication)
doi.org
arxiv.org
Fall 2021 Resurgence and COVID-19 Seroprevalence in Canada: Modelling waning and boosting COVID-19 immunity in Canada, A Canadian Immunization Research Network Study
David W. Dick
Lauren Childs
Zhilan Feng
Jing Li
Gergely Röst
David L Buckeridge
Nick H. Ogden
Jane Heffernan
2021-12-22
Vaccines (publié)
doi.org
Generative Models of Brain Dynamics -- A review
Mahta Ramezanian Panahi
Germán Abrevaya
Jean-christophe Gagnon-audet
Vikram Voleti
Irina Rish
Guillaume Dumas
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-tw… (voir plus)entieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.
2021-12-21
ArXiv (prépublication)
arxiv.org
arxiv.org
Recovery after stroke: the severely impaired are a distinct group
Anna K. Bonkhoff
Thomas Hope
Danilo Bzdok
Adrian G. Guggisberg
Rachel L. Hawe
Sean P. Dukelow
François Chollet
David J. Lin
Christian Grefkes
Howard Bowman
Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both sha… (voir plus)red and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.
2021-12-21
Journal of Neurology Neurosurgery & Psychiatry (publié)
doi.org
The Myelin-Weighted Connectome in Parkinson's Disease
Tommy Boshkovski
Julien Cohen-Adad
Bratislav Misic
Isabelle Arnulf
Jean-Christophe Corvol
Marie Vidailhet
Stéphane Lehéricy
Nikola Stikov
Matteo Mancini
Even though Parkinson's disease (PD) is typically viewed as largely affecting gray matter, there is growing evidence that there are also str… (voir plus)uctural changes in the white matter. Traditional connectomics methods that study PD may not be specific to underlying microstructural changes, such as myelin loss. The primary objective of this study is to investigate the PD‐induced changes in myelin content in the connections emerging from the basal ganglia and the brainstem. For the weighting of the connectome, we used the longitudinal relaxation rate as a biologically grounded myelin‐sensitive metric. We computed the myelin‐weighted connectome in 35 healthy control subjects and 81 patients with PD. We used partial least squares to highlight the differences between patients with PD and healthy control subjects. Then, a ring analysis was performed on selected brainstem and subcortical regions to evaluate each node's potential role as an epicenter for disease propagation. Then, we used behavioral partial least squares to relate the myelin alterations with clinical scores. Most connections (~80%) emerging from the basal ganglia showed a reduced myelin content. The connections emerging from potential epicentral nodes (substantia nigra, nucleus basalis of Meynert, amygdala, hippocampus, and midbrain) showed significant decrease in the longitudinal relaxation rate (P  0.05). This effect was not seen for the medulla and the pons. The myelin‐weighted connectome was able to identify alteration of the m
2021-12-21
Movement Disorders (inconnu)
doi.org
A Cost-Efficient Metadata Scheme for High-Performance Deduplication Systems
Yuxuan Mo
Yu Hua
Pengfei Li
Qin Cao
Xue Liu
Data deduplication has been widely used in backup systems to eliminate redundant data, which speeds up the backup process and reduces the st… (voir plus)orage overhead. Deduplication packs multiple chunks into a large, fixed-size container as a storage unit to maintain the locality and achieve efficient compression. We observe that the traditional containers have low filling ratios due to a large amount of metadata generated by small files. Unfilled containers require more space to store a backup, which decreases the storage efficiency and reduces restore performance. In order to address this problem, we propose a Metadata region Adaptive Container Structure, called MACS. MACS maintains a tag to record the length of metadata region in the container. The boundary between meta-data region and data region is dynamically decided to ensure the maximum space efficiency of the containers. Moreover, we propose a container metadata length-based indexing and cache replacement strategy to allow MACS to be practical in data backup systems. We demonstrate the advantages of MACS with three real world backup datasets. MACS achieves over 95% average container filling ratio, which is significantly higher than existing designs. MACS further achieves better restore performance than the traditional container structure. When combined with existing rewriting method, MACS achieves an efficient trade-off between deduplication ratio and restore performance.
2021-12-19
2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) (publié)
doi.org
Faults in deep reinforcement learning programs: a taxonomy and a detection approach
Amin Nikanjam
Mohammad Mehdi Morovati
Foutse Khomh
Houssem Ben Braiek
2021-12-19
Automated Software Engineering (publié)
doi.org
arxiv.org
Robustness of Markov perfect equilibrium to model approximations in general-sum dynamic games
Jayakumar Subramanian
Amit Sinha
Aditya Mahajan
Dynamic games (also called stochastic games or Markov games) are an important class of games for modeling multi-agent interactions. In many … (voir plus)situations, the dynamics and reward functions of the game are learnt from past data and are therefore approximate. In this paper, we study the robustness of Markov perfect equilibrium to approximations in reward and transition functions. Using approximation results from Markov decision processes, we show that the Markov perfect equilibrium of an approximate (or perturbed) game is always an approximate Markov perfect equilibrium of the original game. We provide explicit bounds on the approximation error in terms of three quantities: (i) the error in approximating the reward functions, (ii) the error in approximating the transition function, and (iii) a property of the value function of the MPE of the approximate game. The second and third quantities depend on the choice of metric on probability spaces. We also present coarser upper bounds which do not depend on the value function but only depend on the properties of the reward and transition functions of the approximate game. We illustrate the results via a numerical example.
2021-12-19
2021 Seventh Indian Control Conference (ICC) (publié)
doi.org