Social isolation is linked to classical risk factors of Alzheimer’s disease-related dementias
Kimia Shafighi
Sylvia Villeneuve
P. Rosa-Neto
AmanPreet Badhwar
Judes Poirier
Vaibhav Sharma
Yasser Iturria-Medina
Patricia P. Silveira
Laurette Dubé
David C. Glahn
Alzheimer’s disease and related dementias is a major public health burden – compounding over upcoming years due to longevity. Recently, … (see more)clinical evidence hinted at the experience of social isolation in expediting dementia onset. In 502,506 UK Biobank participants and 30,097 participants from the Canadian Longitudinal Study of Aging, we revisited traditional risk factors for developing dementia in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individuals’ social capital and various indicators of Alzheimer’s disease and related dementias risk, which replicated across both population cohorts. The quality and quantity of daily social encounters had deep connections with key aetiopathological factors, which represent 1) personal habits and lifestyle factors, 2) physical health, 3) mental health, and 4) societal and external factors. Our population-scale assessment suggest that social lifestyle determinants are linked to most neurodegeneration risk factors, highlighting them promising targets for preventive clinical action.
Haptics-based Curiosity for Sparse-reward Tasks
Sai Rajeswar
Cyril Ibrahim
Nitin Surya
Florian Golemo
David Vazquez
Pedro O. Pinheiro
Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary for tasks tha… (see more)t involve contact-rich motion. In this work, we leverage surprise from mismatches in haptics feedback to guide exploration in hard sparse-reward reinforcement learning tasks. Our approach, Haptics-based Curiosity (\method{}), learns what visible objects interactions are supposed to ``feel" like. We encourage exploration by rewarding interactions where the expectation and the experience do not match. We test our approach on a range of haptics-intensive robot arm tasks (e.g. pushing objects, opening doors), which we also release as part of this work. Across multiple experiments in a simulated setting, we demonstrate that our method is able to learn these difficult tasks through sparse reward and curiosity alone. We compare our cross-modal approach to single-modality (haptics- or vision-only) approaches as well as other curiosity-based methods and find that our method performs better and is more sample-efficient.
Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge
Uzay Macar
Enamundram Naga Karthik
Charley Gros
Andreanne Lemay
This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion … (see more)Segmentation. An overview of the data preprocessing steps applied is provided along with a brief description of the pipelines used, in terms of the architecture and the hyperparameters. Our code for this work can be found at: https://github.com/ivadomed/ms-challenge-2021.
Decision Models and Technology Can Help Psychiatry Develop Biomarkers
Daniel S. Barron
Justin T. Baker
Kristin S. Budde
Simon B. Eickhoff
Karl J. Friston
Peter T. Fox
Paul Geha
Stephen Heisig
Avram J. Holmes
Jukka-Pekka Onnela
Albert Powers
David Silbersweig
John H. Krystal
On the estimation of discrete choice models to capture irrational customer behaviors
Sanjay Dominik Jena
Andrea Lodi
Claudio Sole
The random utility maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economi… (see more)cs has provided strong empirical evidence of irrational choice behaviors, such as halo effects, that are incompatible with this framework. Models belonging to the random utility maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. In particular, we propose a column-generation method to gradually refine the discrete choice model based on partially ranked preference sequences. Extensive computational experiments indicate that our model, explicitly accounting for irrational preferences, can significantly boost the predictive accuracy on both synthetic and real-world data instances. Summary of Contribution: In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. Specifically, we show how to use partially ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a treelike data structure containing the customer behaviors. Furthermore, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach by comparing it against rank-based methods with only rational preferences and with more general benchmarks from the literature. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average when tested on a real-world data set from a large chain of grocery and drug stores.
Simple Video Generation using Neural ODEs
David Kanaa
Vikram Voleti
Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely chall… (see more)enging. It is a common belief that a key step towards solving this task resides in modelling accurately both spatial and temporal information in video signals. A promising direction to do so has been to learn latent variable models that predict the future in latent space and project back to pixels, as suggested in recent literature. Following this line of work and building on top of a family of models introduced in prior work, Neural ODE, we investigate an approach that models time-continuous dynamics over a continuous latent space with a differential equation with respect to time. The intuition behind this approach is that these trajectories in latent space could then be extrapolated to generate video frames beyond the time steps for which the model is trained. We show that our approach yields promising results in the task of future frame prediction on the Moving MNIST dataset with 1 and 2 digits.
Social belonging: Brain structure and function is linked to membership in sports teams, religious groups and social clubs
Carolin Kieckhaefer
Leonhard Schilbach
Learning Neural Causal Models with Active Interventions
Nino Scherrer
Olexa Bilaniuk
Yashas Annadani
Anirudh Goyal
Patrick Schwab
Bernhard Schölkopf
Michael Curtis Mozer
Stefan Bauer
Nan Rosemary Ke
Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science. The appealing … (see more)scaling properties of neural networks have recently led to a surge of interest in differentiable neural network-based methods for learning causal structures from data. So far, differentiable causal discovery has focused on static datasets of observational or interventional origin. In this work, we introduce an active intervention-targeting mechanism which enables quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting and is applicable for both discrete and continuous optimization formulations of learning the underlying directed acyclic graph (DAG) from data. We examine the proposed method across multiple frameworks in a wide range of settings and demonstrate superior performance on multiple benchmarks from simulated to real-world data.
Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps
Fang Frank Yu
Susie Yi Huang
Thomas Witzel
Ashwin Kumar
Congyu Liao
Tanguy Duval
Berkin Bilgic
Purpose A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast… (see more) parametric maps. We sought to reduce the acquisition time for quantitative susceptibility mapping (QSM) and macromolecular tissue volume (MTV) by acquiring both contrasts simultaneously by leveraging their redundancies. The Joint Virtual Coil concept with generalized autocalibrating partially parallel acquisitions (JVC-GRAPPA) was applied to reduce acquisition time further. Methods Three adult volunteers were imaged on a 3T scanner using a multi-echo 3D GRE sequence acquired at three head orientations. MTV, QSM, R2*, T1, and proton density maps were reconstructed. The same sequence (GRAPPA R=4) was performed in subject #1 with a single head orientation for comparison. Fully sampled data was acquired in subject #2, from which retrospective undersampling was performed (R=6 GRAPPA and R=9 JVC-GRAPPA). Prospective undersampling was performed in subject #3 (R=6 GRAPPA and R=9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. Results Subject #1’s multi-orientation and single-orientation MTV maps were not significantly different based on RMSE. For subject #2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject #3. Using QSM, R2*, and MTV, the contributions of myelin and iron content to susceptibility was estimated. Conclusion We have developed a novel strategy to simultaneously acquire data for the reconstruction of five intrinsically co-registered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 minutes using JVC-GRAPPA.
Quantitative 7-Tesla Imaging of Cortical Myelin Changes in Early Multiple Sclerosis
Valeria Barletta
Elena Herranz
Constantina A. Treaba
Ambica Mehndiratta
Russell Ouellette
Gabriel Mangeat
Tobias Granberg
Jacob A. Sloane
Eric C Klawiter
Caterina Mainero
Cortical demyelination occurs early in multiple sclerosis (MS) and relates to disease outcome. The brain cortex has endogenous propensity fo… (see more)r remyelination as proven from histopathology study. In this study, we aimed at characterizing cortical microstructural abnormalities related to myelin content by applying a novel quantitative MRI technique in early MS. A combined myelin estimation (CME) cortical map was obtained from quantitative 7-Tesla (7T) T2* and T1 acquisitions in 25 patients with early MS and 19 healthy volunteers. Cortical lesions in MS patients were classified based on their myelin content by comparison with CME values in healthy controls as demyelinated, partially demyelinated, or non-demyelinated. At follow-up, we registered changes in cortical lesions as increased, decreased, or stable CME. Vertex-wise analysis compared cortical CME in the normal-appearing cortex in 25 MS patients vs. 19 healthy controls at baseline and investigated longitudinal changes at 1 year in 10 MS patients. Measurements from the neurite orientation dispersion and density imaging (NODDI) diffusion model were obtained to account for cortical neurite/dendrite loss at baseline and follow-up. Finally, CME maps were correlated with clinical metrics. CME was overall low in cortical lesions (p = 0.03) and several normal-appearing cortical areas (p 0.05) in the absence of NODDI abnormalities. Individual cortical lesion analysis revealed, however, heterogeneous CME patterns from extensive to partial or absent demyelination. At follow-up, CME overall decreased in cortical lesions and non-lesioned cortex, with few areas showing an increase (p 0.05). Cortical CME maps correlated with processing speed in several areas across the cortex. In conclusion, CME allows detection of cortical microstructural changes related to coexisting demyelination and remyelination since the early phases of MS, and shows to be more sensitive than NODDI and relates to cognitive performance.
Sleep spindles track cortical learning patterns for memory consolidation
Marit Petzka
Alex Chatburn
G. Balanos
Bernhard P Staresina
Sleep spindles track cortical learning patterns for memory consolidation
Marit Petzka
Alex Chatburn
George M. Balanos
Bernhard P. Staresina