Publications

Learning Robust Kernel Ensembles with Kernel Average Pooling
Pouya Bashivan
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2022-09-29
ArXiv (preprint)
doi.org
openreview.net
SPeCiaL: Self-Supervised Pretraining for Continual Learning
Lucas Caccia
Joelle Pineau
2022-09-27
Continual Semi-Supervised Learning (published)
doi.org
arxiv.org
From analytic to synthetic-organizational pluralisms: A pluralistic enactive psychiatry
Christophe Gauld
Kristopher Nielsen
Manon Job
Hugo Bottemanne
Guillaume Dumas
Reliance on sole reductionism, whether explanatory, methodological or ontological, is difficult to support in clinical psychiatry. Rather, p… (see more)sychiatry is challenged by a plurality of approaches. There exist multiple legitimate ways of understanding human functionality and disorder, i.e., different systems of representation, different tools, different methodologies and objectives. Pluralistic frameworks have been presented through which the multiplicity of approaches in psychiatry can be understood. In parallel of these frameworks, an enactive approach for psychiatry has been proposed. In this paper, we consider the relationships between the different kinds of pluralistic frameworks and this enactive approach for psychiatry. We compare the enactive approach in psychiatry with wider analytical forms of pluralism. On one side, the enactive framework anchored both in cognitive sciences, theory of dynamic systems, systems biology, and phenomenology, has recently been proposed as an answer to the challenge of an integrative psychiatry. On the other side, two forms of explanatory pluralisms can be described: a non-integrative pluralism and an integrative pluralism. The first is tolerant, it examines the coexistence of different potentially incompatible or untranslatable systems in the scientific or clinical landscape. The second is integrative and proposes to bring together the different levels of understanding and systems of representations. We propose that enactivism is inherently a form of integrative pluralism, but it is at the same time a component of the general framework of explanatory pluralism, composed of a set of so-called analytical approaches. A significant number of mental health professionals are already accepting the variety of clinical and scientific approaches. In this way, a rigorous understanding of the theoretical positioning of psychiatric actors seems necessary to promote quality clinical practice. The study of entanglements between an analytical pluralism and a synthetic-organizational enactivist pluralism could prove fruitful.
2022-09-26
Frontiers in Psychiatry (published)
doi.org
Lesions in putative language and attention regions are linked to more severe strokes in patients with higher white matter hyperintensity burden
Anna K. Bonkhoff
Sungmin Hong
Martin Bretzner
Markus D. Schirmer
Robert W. Regenhardt
E. Murat Arsava
Kathleen L. Donahue
Marco J. Nardin
Adrian V. Dalca
Anne-Katrin Giese
Mark R. Etherton
Brandon L. Hancock
Steven J. T. Mocking
Elissa C. McIntosh
John Attia
Oscar R. Benavente
John W. Cole
Amanda Donatti
Christoph J. Griessenauer
Laura Heitsch … (see 37 more)
Lukas Holmegaard
Katarina Jood
Jordi Jimenez-Conde
Steven J. Kittner
Robin Lemmens
Christopher R. Levi
Caitrin W. McDonough
James F. Meschia
Chia-Ling Phuah
Arndt Rolfs
Stefan Ropele
Jonathan Rosand
Jaume Roquer
Tatjana Rundek
Ralph L. Sacco
Reinhold Schmidt
Pankaj Sharma
Agnieszka Slowik
Martin Söderholm
Alessandro Sousa
Tara M. Stanne
Daniel Strbian
Turgut Tatlisumak
Vincent Thijs
Achala Vagal
Johan Wasselius
Daniel Woo
Ramin Zand
Patrick F. McArdle
Bradford B. Worrall
Christina Jern
Arne G. Lindgren
Jane Maguire
Polina Golland
Danilo Bzdok
Ona Wu
Natalia S. Rost
To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in… (see more) lesion pattern–specific ways. MR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3–6 months after stroke were obtained from the MRI–Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning–based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score >2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age2, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts. A total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location–specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location. Higher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.
2022-09-26
Neurology (published)
doi.org
Multilevel development of cognitive abilities in an artificial neural network
Konstantin Volzhenin
Jean-Pierre Changeux
Guillaume Dumas
Multiple biological mechanisms support the unique ability of the brain to develop complex cognitive abilities. Nevertheless, it remains uncl… (see more)ear which mechanisms are necessary and sufficient. We propose a neurocomputational model of the developing brain spanning sensorimotor, cognitive, and conscious levels. The model solves three tasks of increasing complexity: from visual recognition to cognitive manipulation and maintenance of conscious percepts. Results highlight two fundamental mechanisms for the multilevel development of cognitive abilities in biological neural networks: 1) synaptic epigenesis, with Hebbian learning at the local scale and reinforcement learning at the global scale; and 2) self-organized dynamics, through spontaneous activity and balanced excitatory/inhibitory ratio of neurons. We emphasize how these core features of human intelligence could guide future development in artificial intelligence.
2022-09-26
Proceedings of the National Academy of Sciences of the United States of America (published)
doi.org
Preserved neural dynamics across animals performing similar behaviour
Mostafa Safaie
Joanna C. Chang
Junchol Park
Lee E. Miller
Joshua T. Dudman
Matthew G. Perich
Juan A. Gallego
Animals of the same species often exhibit similar behaviours that are advantageously adapted to their body and their environment. These beha… (see more)viours are shaped by selection pressures over evolutionary timescales at the species level, yet each individual produces these behaviours using a different, uniquely constructed brain. It remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of a given individual. Here, we hypothesised that the adaptive behaviour of a species requires specific neural population ‘latent dynamics’. These latent dynamics should thus be preserved and identifiable across individuals within a species, regardless of the idiosyncratic aspects of each individual’s brain. Using recordings of neural populations from monkey and mouse motor cortex, we show that individuals from the same species share surprisingly similar neural dynamics when they perform the same behaviour. The similarity in neural population dynamics extends beyond cortical regions to the dorsal striatum, an evolutionarily older structure, and also holds when animals con-sciously plan future movements without overt behaviour. These preserved dynamics are behaviourally-relevant, allowing decoding of intended and ongoing movements across individuals. We posit that these emergent neural population dynamics result from evolutionarily-imposed constraints on brain development, and reflect a fundamental property of the neural basis of behaviour.
2022-09-26
Spiral (preprint)
doi.org
Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning
Jean-Pierre R. Falet
Joshua Durso-finley
Brennan Nichyporuk
Julien Schroeter
Francesca Bovis
Maria-Pia Sormani
Doina Precup
Tal Arbel
Douglas Lorne Arnold
Disability progression in multiple sclerosis remains resistant to treatment. The absence of a suitable biomarker to allow for phase 2 clinic… (see more)al trials presents a high barrier for drug development. We propose to enable short proof-of-concept trials by increasing statistical power using a deep-learning predictive enrichment strategy. Specifically, a multi-headed multilayer perceptron is used to estimate the conditional average treatment effect (CATE) using baseline clinical and imaging features, and patients predicted to be most responsive are preferentially randomized into a trial. Leveraging data from six randomized clinical trials ( n  = 3,830), we first pre-trained the model on the subset of relapsing-remitting MS patients ( n  = 2,520), then fine-tuned it on a subset of primary progressive MS (PPMS) patients ( n  = 695). In a separate held-out test set of PPMS patients randomized to anti-CD20 antibodies or placebo ( n  = 297), the average treatment effect was larger for the 50% (HR, 0.492; 95% CI, 0.266-0.912; p  = 0.0218) and 30% (HR, 0.361; 95% CI, 0.165-0.79; p  = 0.008) predicted to be most responsive, compared to 0.743 (95% CI, 0.482-1.15; p  = 0.179) for the entire group. The same model could also identify responders to laquinimod in another held-out test set of PPMS patients ( n  = 318). Finally, we show that using this model for predictive enrichment results in important increases in power.
2022-09-25
Nature Communications (published)
doi.org
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
Samuel Horváth
Maziar Sanjabi
Lin Xiao
Peter Richtárik
Michael G. Rabbat
The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to o… (see more)vercoming the communication bottleneck in Federated Learning (FL). Such methods are usually implemented by having clients perform one or more epochs of local training per round while randomly reshuffling their finite dataset in each epoch. Data imbalance, where clients have different numbers of local training samples, is ubiquitous in FL applications, resulting in different clients performing different numbers of local updates in each round. In this work, we propose a general recipe, FedShuffle, that better utilizes the local updates in FL, especially in this regime encompassing random reshuffling and heterogeneity. FedShuffle is the first local update method with theoretical convergence guarantees that incorporates random reshuffling, data imbalance, and client sampling — features that are essential in large-scale cross-device FL. We present a comprehensive theoretical analysis of FedShuffle and show, both theoretically and empirically, that it does not suffer from the objective function mismatch that is present in FL methods that assume homogeneous updates in heterogeneous FL setups, such as FedAvg (McMahan et al., 2017). In addition, by combining the ingredients above, FedShuffle improves upon FedNova (Wang et al., 2020), which was previously proposed to solve this mismatch. Similar to Mime (Karimireddy et al., 2020), we show that FedShuffle with momentum variance reduction (Cutkosky & Orabona, 2019) improves upon non-local methods under a Hessian similarity assumption.
2022-09-25
TMLR (accepted)
doi.org
openreview.net
Social belonging: brain structure and function is linked to membership in sports teams, religious groups, and social clubs
Carolin Kieckhaefer
Leonhard Schilbach
Danilo Bzdok
Human behavior across the life span is driven by the psychological need to belong, right from kindergarten to bingo nights. Being part of so… (see more)cial groups constitutes a backbone for communal life and confers many benefits for the physical and mental health. Capitalizing on the neuroimaging and behavioral data from ∼40,000 participants from the UK Biobank population cohort, we used structural and functional analyses to explore how social participation is reflected in the human brain. Across 3 different types of social groups, structural analyses point toward the variance in ventromedial prefrontal cortex, fusiform gyrus, and anterior cingulate cortex as structural substrates tightly linked to social participation. Functional connectivity analyses not only emphasized the importance of default mode and limbic network but also showed differences for sports teams and religious groups as compared to social clubs. Taken together, our findings establish the structural and functional integrity of the default mode network as a neural signature of social belonging.
2022-09-25
Cerebral Cortex (unknown)
doi.org
Tackling bias in AI health datasets through the STANDING Together initiative
Shaswath Ganapathi
Johannes Palmer
J. Alderman
Melanie Calvert
Cyrus Espinoza
Jacqui Gath
Marzyeh Ghassemi
Katherine Heller
Francis McKay
Alan Karthikesalingam
S. Kuku
Maxine E. Mackintosh
Sinduja Manohar
Bilal Mateen
Rubeta Matin
Melissa D. McCradden
Lauren Oakden-Rayner
Johan Ordish
Russell Pearson
S. Pfohl … (see 8 more)
Negar Rostamzadeh
Elizabeth Sapey
Neil J. Sebire
Viknesh Sounderajah
Charlotte Summers
Darren E. Treanor
Alastair Denniston
Xiaoxuan Liu
2022-09-25
Nature Network Boston (published)
doi.org
The 5-year longitudinal diagnostic profile and health services utilization of patients treated with electroconvulsive therapy in Quebec: a population-based study
Simon Lafrenière
Fatemeh Gholi-Zadeh-Kharrat
Caroline Sirois
Victoria Massamba
Louis Rochette
Camille Brousseau-Paradis
Simon Patry
Christian Gagné
Morgane Lemasson
Geneviève Gariépy
Chantal Mérette
Elham Rahme
Alain Lesage
2022-09-25
Social Psychiatry and Psychiatric Epidemiology (published)
doi.org
OSSEM: One-Shot Speaker Adaptive Speech Enhancement Using Meta Learning
Cheng Yu
Szu-wei Fu
Tsun-An Hsieh
Yu Tsao
Mirco Ravanelli
Although deep learning (DL) has achieved notable progress in speech enhancement (SE), further research is still required for a DL-based SE s… (see more)ystem to adapt effectively and efficiently to particular speakers. In this study, we propose a novel meta-learning-based speaker-adaptive SE approach (called OSSEM) that aims to achieve SE model adaptation in a one-shot manner. OSSEM consists of a modified transformer SE network and a speaker-specific masking (SSM) network. In practice, the SSM network takes an enrolled speaker embedding extracted using ECAPA-TDNN to adjust the input noisy feature through masking. To evaluate OSSEM, we designed a modified Voice Bank-DEMAND dataset, in which one utterance from the testing set was used for model adaptation, and the remaining utterances were used for testing the performance. Moreover, we set restrictions allowing the enhancement process to be conducted in real time, and thus designed OSSEM to be a causal SE system. Experimental results first show that OSSEM can effectively adapt a pretrained SE model to a particular speaker with only one utterance, thus yielding improved SE results. Meanwhile, OSSEM exhibits a competitive performance compared to state-of-the-art causal SE systems.
2022-09-17
Interspeech 2022 (published)
doi.org
arxiv.org