Publications

Age differences in the functional architecture of the human brain
Roni Setton
Laetitia Mwilambwe-Tshilobo
Manesh Girn
Amber W. Lockrow
Giulia Baracchini
Colleen Hughes
Alexander J. Lowe
Benjamin N. Cassidy
Jian Li
Wen-Ming Luh
Danilo Bzdok
Richard M. Leahy
Tian Ge
Daniel S. Margulies
Bratislav Misic
Boris C. Bernhardt
W. Dale Stevens
Felipe De Brigard
Prantik Kundu
Gary R. Turner … (see 2 more)
R. Nathan Spreng
Montreal Neurological
The intrinsic functional organization of the brain changes into older adulthood. Age differences are observed at multiple spatial scales, fr… (see more)om global reductions in modularity and segregation of distributed brain systems, to network-specific patterns of dedifferentiation. Whether dedifferentiation reflects an inevitable, global shift in brain function with age, circumscribed, experience dependent changes, or both, is uncertain. We employed a multi-method strategy to interrogate dedifferentiation at multiple spatial scales. Multi-echo (ME) resting-state fMRI was collected in younger (n=181) and older (n=120) healthy adults. Cortical parcellation sensitive to individual variation was implemented for precision functional mapping of each participant, while preserving group-level parcel and network labels. ME-fMRI processing and gradient mapping identified global and macroscale network differences. Multivariate functional connectivity methods tested for microscale, edge-level differences. Older adults had lower BOLD signal dimensionality, consistent with global network dedifferentiation. Gradients were largely age-invariant. Edge-level analyses revealed discrete, network-specific dedifferentiation patterns in older adults. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal control network regions showed greater connectivity; and the dorsal attention network was more integrated with heteromodal regions. These findings highlight the importance of multi-scale, multi-method approaches to characterize the architecture of functional brain aging.
2021-03-30
bioRxiv (preprint)
doi.org
All in This Together? A Preregistered Report on Deservingness of Government Aid During the COVID-19 Pandemic
Aengus Bridgman
Eric Merkley
Peter John Loewen
Taylor Owen
Derek Ruths
2021-03-30
Journal of Experimental Political Science (published)
doi.org
Fasting alters the gut microbiome reducing blood pressure and body weight in metabolic syndrome patients
András Maifeld
Hendrik Bartolomaeus
Ulrike Löber
Ellen G. Avery
Nico Steckhan
Lajos Markó
Nicola Wilck
Ibrahim Hamad
Urša Šušnjar
Anja Mähler
Christoph Hohmann
Chia-Yu Chen
Holger Cramer
Gustav Dobos
Till Robin Lesker
Till Strowig
Ralf Dechend
Danilo Bzdok
Markus Kleinewietfeld
Andreas Michalsen … (see 2 more)
Dominik N. Müller
Sofia K. Forslund
Abstract Periods of fasting and refeeding may reduce cardiometabolic risk elevated by Western diet. We show that in hypertensive metabolic s… (see more)yndrome (MetS) patients (n=35), a 5-day fast followed by a modified DASH diet (Dietary Approach to Stop Hypertension) reduced systolic blood pressure (SBP), antihypertensive medication need, and body-mass index (BMI) at three months post intervention compared to a modified DASH diet alone (n=36). Fasting altered the gut microbiome, impacting bacterial taxa and gene modules associated with short-chain fatty acid production. Cross-system analyses revealed a positive correlation of circulating mucosa-associated invariant T (MAIT) cells, non-classical monocytes and CD4+ effector T cells with SBP. Furthermore, regulatory T cells (Tregs) positively correlated with BMI and weight. Machine learning could predict sustained SBP-responsiveness within the fasting group from baseline immunome data, identifying CD8+ effector T cells, Th17 cells and Tregs as important contributors to the model. The high-resolution multi-omics data highlights fasting as a promising non-pharmacological intervention in MetS.
2021-03-29
Nature Communications (published)
doi.org
Loneliness and Neurocognitive Aging
R. Nathan Spreng
Danilo Bzdok
2021-03-28
Advances in Geriatric Medicine and Research (published)
doi.org
Mapping gene transcription and neurocognition across human neocortex
Justine Y. Hansen
Ross D. Markello
Jacob W. Vogel
Jakob Seidlitz
Danilo Bzdok
Bratislav Misic
2021-03-24
Nature Human Behaviour (published)
doi.org
Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework
Cécile Aenishaenslin
Barbara Häsler
André Ravel
E. Jane Parmley
Sarah Mediouni
Houda Bennani
Katharina D. C. Stärk
David L. Buckeridge
It is now widely acknowledged that surveillance of antimicrobial resistance (AMR) must adopt a “One Health” (OH) approach to successfull… (see more)y address the significant threats this global public health issue poses to humans, animals, and the environment. While many protocols exist for the evaluation of surveillance, the specific aspect of the integration of a OH approach into surveillance systems for AMR and antimicrobial Use (AMU), suffers from a lack of common and accepted guidelines and metrics for its monitoring and evaluation functions. This article presents a conceptual framework to evaluate the integration of OH in surveillance systems for AMR and AMU, named the Integrated Surveillance System Evaluation framework (ISSE framework). The ISSE framework aims to assist stakeholders and researchers who design an overall evaluation plan to select the relevant evaluation questions and tools. The framework was developed in partnership with the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). It consists of five evaluation components, which consider the capacity of the system to: [1] integrate a OH approach, [2] produce OH information and expertise, [3] generate actionable knowledge, [4] influence decision-making, and [5] positively impact outcomes. For each component, a set of evaluation questions is defined, and links to other available evaluation tools are shown. The ISSE framework helps evaluators to systematically assess the different OH aspects of a surveillance system, to gain comprehensive information on the performance and value of these integrated efforts, and to use the evaluation results to refine and improve the surveillance of AMR and AMU globally.
2021-03-23
Frontiers in Veterinary Science (published)
doi.org
Beyond Correlation versus Causation: Multi-brain Neuroscience Needs Explanation
Quentin Moreau
Guillaume Dumas
2021-03-18
Trends in Cognitive Sciences (published)
doi.org
Factors influencing consumer choice: a study of apparel and sustainable cues from Canadian and Indian consumers’ perspectives
Osmud Rahman
Benjamin C. M. Fung
Devender Kharb
2021-03-18
International Journal of Fashion Design, Technology and Education (published)
doi.org
Intention estimation and controllable behaviour models for traffic merges
Aditya Mahajan
Takayasu Kumano
Yuji Yasui
This work focuses on decision making for automated driving vehicles in interaction rich scenarios like traffic merges in a flexibly assertiv… (see more)e yet safe manner. We propose a Q-learning based approach, that takes in active intention inferences as additional inputs besides the directly observed state inputs. The outputs of Q-function are processed to select a decision by a modulation function, which can control how assertively or defensively the agent behaves.
2021-03-17
SICE Journal of Control Measurement and System Integration (published)
doi.org
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers
Alex Lamb
Anirudh Goyal
Agnieszka Słowik
Michael Mozer
Philippe Beaudoin
Yoshua Bengio
Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previ… (see more)ous layer. A downside to this approach is that each layer (or module, as multiple modules can operate in parallel) is tasked with processing the entire hidden state, rather than a particular part of the state which is most relevant for that module. Methods which only operate on a small number of input variables are an essential part of most programming languages, and they allow for improved modularity and code re-usability. Our proposed method, Neural Function Modules (NFM), aims to introduce the same structural capability into deep learning. Most of the work in the context of feed-forward networks combining top-down and bottom-up feedback is limited to classification problems. The key contribution of our work is to combine attention, sparsity, top-down and bottom-up feedback, in a flexible algorithm which, as we show, improves the results in standard classification, out-of-domain generalization, generative modeling, and learning representations in the context of reinforcement learning.
2021-03-17
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
doi.org
proceedings.mlr.press
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Siddarth Srinivasan
Sandesh Adhikary
Jacob Miller
Guillaume Rabusseau
Byron Boots
Modeling joint probability distributions over sequences has been studied from many perspectives. The physics community developed matrix prod… (see more)uct states, a tensor-train decomposition for probabilistic modeling, motivated by the need to tractably model many-body systems. But similar models have also been studied in the stochastic processes and weighted automata literature, with little work on how these bodies of work relate to each other. We address this gap by showing how stationary or uniform versions of popular quantum tensor network models have equivalent representations in the stochastic processes and weighted automata literature, in the limit of infinitely long sequences. We demonstrate several equivalence results between models used in these three communities: (i) uniform variants of matrix product states, Born machines and locally purified states from the quantum tensor networks literature, (ii) predictive state representations, hidden Markov models, norm-observable operator models and hidden quantum Markov models from the stochastic process literature,and (iii) stochastic weighted automata, probabilistic automata and quadratic automata from the formal languages literature. Such connections may open the door for results and methods developed in one area to be applied in another.
2021-03-17
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
doi.org
proceedings.mlr.press
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou
Sharan Vaswani
Issam Laradji
Simon Lacoste-Julien
We propose a stochastic variant of the classical Polyak step-size (Polyak, 1987) commonly used in the subgradient method. Although computing… (see more) the Polyak step-size requires knowledge of the optimal function values, this information is readily available for typical modern machine learning applications. Consequently, the proposed stochastic Polyak step-size (SPS) is an attractive choice for setting the learning rate for stochastic gradient descent (SGD). We provide theoretical convergence guarantees for SGD equipped with SPS in different settings, including strongly convex, convex and non-convex functions. Furthermore, our analysis results in novel convergence guarantees for SGD with a constant step-size. We show that SPS is particularly effective when training over-parameterized models capable of interpolating the training data. In this setting, we prove that SPS enables SGD to converge to the true solution at a fast rate without requiring the knowledge of any problem-dependent constants or additional computational overhead. We experimentally validate our theoretical results via extensive experiments on synthetic and real datasets. We demonstrate the strong performance of SGD with SPS compared to state-of-the-art optimization methods when training over-parameterized models.
2021-03-17
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
doi.org
proceedings.mlr.press