Publications

Meta-matching: a simple framework to translate phenotypic predictive models from big to small data
Tong He
Lijun An
Jiashi Feng
Danilo Bzdok
Avram J Holmes
Simon B. Eickhoff
B.T. Thomas Yeo
There is significant interest in using brain imaging data to predict non-brain-imaging phenotypes in individual participants. However, most … (voir plus)prediction studies are underpowered, relying on less than a few hundred participants, leading to low reliability and inflated prediction performance. Yet, small sample sizes are unavoidable when studying clinical populations or addressing focused neuroscience questions. Here, we propose a simple framework – “meta-matching” – to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in boutique studies. The key observation is that many large-scale datasets collect a wide range inter-correlated phenotypic measures. Therefore, a unique phenotype from a boutique study likely correlates with (but is not the same as) some phenotypes in some large-scale datasets. Meta-matching exploits these correlations to boost prediction in the boutique study. We applied meta-matching to the problem of predicting non-brain-imaging phenotypes using resting-state functional connectivity (RSFC). Using the UK Biobank (N = 36,848), we demonstrated that meta-matching can boost the prediction of new phenotypes in small independent datasets by 100% to 400% in many scenarios. When considering relative prediction performance, meta-matching significantly improved phenotypic prediction even in samples with 10 participants. When considering absolute prediction performance, meta-matching significantly improved phenotypic prediction when there were least 50 participants. With a growing number of large-scale population-level datasets collecting an increasing number of phenotypic measures, our results represent a lower bound on the potential of meta-matching to elevate small-scale boutique studies.
2020-08-10
bioRxiv (prépublication)
doi.org
Hidden population modes in social brain morphology: Its parts are more than its sum
Hannah Kiesow
R. Nathan Spreng
Avram J. Holmes
M. Mallar Chakravarty
Andre F. Marquand
B.T. Thomas Yeo
Danilo Bzdok
The complexity of social interactions is a defining property of the human species. Many social neuroscience experiments have sought to map … (voir plus)perspective taking’, ‘empathy’, and other canonical psychological constructs to distinguishable brain circuits. This predominant research paradigm was seldom complemented by bottom-up studies of the unknown sources of variation that add up to measures of social brain structure; perhaps due to a lack of large population datasets. We aimed at a systematic de-construction of social brain morphology into its elementary building blocks in the UK Biobank cohort (n=~10,000). Coherent patterns of structural co-variation were explored within a recent atlas of social brain locations, enabled through translating autoencoder algorithms from deep learning. The artificial neural networks learned rich subnetwork representations that became apparent from social brain variation at population scale. The learned subnetworks carried essential information about the co-dependence configurations between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as feelings of loneliness. Our population-level evidence indicates that hidden subsystems of the social brain underpin interindividual variation in dissociable aspects of social lifestyle.
2020-08-06
bioRxiv (prépublication)
doi.org
''COGITO in Space'': a thought experiment in exo-neurobiology
Daniela de Paulis
Stephen Whitmarsh
Robert Oostenveld
Guillaume Dumas
Michael Sanders
2020-08-03
(publié)
doi.org
SeroTracker: a global SARS-CoV-2 seroprevalence dashboard
Rahul K. Arora
Abel Joseph
Jordan Van Wyk
Simona Rocco
Austin Atmaja
Ewan May
Tingting Yan
Niklas Bobrovitz
Jonathan Chevrier
Matthew P. Cheng
Tyler Williamson
David L Buckeridge
2020-08-03
Lancet. Infectious Diseases (Print) (publié)
doi.org
BDD-based optimization for the quadratic stable set problem
Jaime E. González
Andr'e Augusto Cire
Andrea Lodi
Louis-Martin Rousseau
2020-07-31
Discrete Optimization (publié)
doi.org
Dynamic planning of redundant robots within a set-based task-priority inverse kinematics framework.
Daniele Di Vito
Mathieux Bergeron
David Meger
Gregory Dudek
Gianluca Antonelli
This work presents the dynamic planning of redundant robots by merging a global and local planner. The global planner is implemented as a sa… (voir plus)mpling-based algorithm which works in the reduced-dimensionality of the robot workspace applying the Cartesian constraints only. The output trajectory is then checked within a framework of set-based task priority inverse kinematics verifying the fulfillment of the other task constraints. The inverse kinematics framework is used also in real-time as local motion control to ensure a reactive behaviour to address, e.g., mismatch between the apriori information and on-line perception acquisition. During the movement, the motion planner runs in background to adapt to changes in the environment or, in general, to continuously optimize the path. The proposed method is experimentally validated with a Kinova Jaco2 7 degrees of freedom manipulator.
2020-07-31
Conference on Control Technology and Applications (publié)
doi.org
Optimal Local and Remote Controllers With Unreliable Uplink Channels: An Elementary Proof
Mohammad Afshari
Aditya Mahajan
Recently, a model of a decentralized control system with local and remote controllers connected over unreliable channels was presented in [… (voir plus)1]. The model has a nonclassical information structure that is not partially nested. Nonetheless, it is shown in [1] that the optimal control strategies are linear functions of the state estimate (which is a nonlinear function of the observations). Their proof is based on a fairly sophisticated dynamic programming argument. In this article, we present an alternative and elementary proof of the result which uses common information-based conditional independence and completion of squares.
2020-07-31
IEEE Transactions on Automatic Control (publié)
doi.org
arxiv.org
Precision, Equity, and Public Health and Epidemiology Informatics – A Scoping Review
David L Buckeridge
2020-07-31
Yearbook of Medical Informatics (publié)
doi.org
Renewal Monte Carlo: Renewal Theory-Based Reinforcement Learning
Jayakumar Subramanian
Aditya Mahajan
An online reinforcement learning algorithm called renewal Monte Carlo (RMC) is presented. RMC works for infinite horizon Markov decision pro… (voir plus)cesses with a designated start state. RMC is a Monte Carlo algorithm that retains the key advantages of Monte Carlo—viz., simplicity, ease of implementation, and low bias—while circumventing the main drawbacks of Monte Carlo—viz., high variance and delayed updates. Given a parameterized policy
2020-07-31
IEEE Transactions on Automatic Control (publié)
doi.org
Inferring disease subtypes from clusters in explanation space
Marc-Andre Schulz
Matt Chapman-Rounds
Manisha Verma
Danilo Bzdok
Konstantinos Georgatzis
Identification of disease subtypes and corresponding biomarkers can substantially improve clinical diagnosis and treatment selection. Discov… (voir plus)ering these subtypes in noisy, high dimensional biomedical data is often impossible for humans and challenging for machines. We introduce a new approach to facilitate the discovery of disease subtypes: Instead of analyzing the original data, we train a diagnostic classifier (healthy vs. diseased) and extract instance-wise explanations for the classifier’s decisions. The distribution of instances in the explanation space of our diagnostic classifier amplifies the different reasons for belonging to the same class–resulting in a representation that is uniquely useful for discovering latent subtypes. We compare our ability to recover subtypes via cluster analysis on model explanations to classical cluster analysis on the original data. In multiple datasets with known ground-truth subclasses, particularly on UK Biobank brain imaging data and transcriptome data from the Cancer Genome Atlas, we show that cluster analysis on model explanations substantially outperforms the classical approach. While we believe clustering in explanation space to be particularly valuable for inferring disease subtypes, the method is more general and applicable to any kind of sub-type identification.
2020-07-29
Scientific Reports (publié)
doi.org
Deriving Differential Target Propagation from Iterating Approximate Inverses
Yoshua Bengio
2020-07-28
ArXiv (prépublication)
arxiv.org
arxiv.org
Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning
Joseph Paul Cohen
Lan Dao
Paul Morrison
Karsten Roth
Yoshua Bengio
Beiyi Shen
Almas F Abbasi
Hoshmand Kochi Mahsa
Marzyeh Ghassemi
Haifang Li
Tim Q Duong
Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-ray… (voir plus)s (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model’s ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online.
2020-07-27
Cureus (publié)
doi.org
arxiv.org