220 research outputs found
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimaging
data analysis. Their main virtue is their ability to model high-dimensional
datasets, e.g. multivariate analysis of activation images or resting-state time
series. Supervised learning is typically used in decoding or encoding settings
to relate brain images to behavioral or clinical observations, while
unsupervised learning can uncover hidden structures in sets of images (e.g.
resting state functional MRI) or find sub-populations in large cohorts. By
considering different functional neuroimaging applications, we illustrate how
scikit-learn, a Python machine learning library, can be used to perform some
key analysis steps. Scikit-learn contains a very large set of statistical
learning algorithms, both supervised and unsupervised, and its application to
neuroimaging data provides a versatile tool to study the brain.Comment: Frontiers in neuroscience, Frontiers Research Foundation, 2013, pp.1
Structured Learning in Time-dependent Cox Models
Cox models with time-dependent coefficients and covariates are widely used in
survival analysis. In high-dimensional settings, sparse regularization
techniques are employed for variable selection, but existing methods for
time-dependent Cox models lack flexibility in enforcing specific sparsity
patterns (i.e., covariate structures). We propose a flexible framework for
variable selection in time-dependent Cox models, accommodating complex
selection rules. Our method can adapt to arbitrary grouping structures,
including interaction selection, temporal, spatial, tree, and directed acyclic
graph structures. It achieves accurate estimation with low false alarm rates.
We develop the sox package, implementing a network flow algorithm for
efficiently solving models with complex covariate structures. Sox offers a
user-friendly interface for specifying grouping structures and delivers fast
computation. Through examples, including a case study on identifying predictors
of time to all-cause death in atrial fibrillation patients, we demonstrate the
practical application of our method with specific selection rules.Comment: 49 pages (with 19 pages of appendix),9 tables, 3 figure
Towards a Multi-Subject Analysis of Neural Connectivity
Directed acyclic graphs (DAGs) and associated probability models are widely
used to model neural connectivity and communication channels. In many
experiments, data are collected from multiple subjects whose connectivities may
differ but are likely to share many features. In such circumstances it is
natural to leverage similarity between subjects to improve statistical
efficiency. The first exact algorithm for estimation of multiple related DAGs
was recently proposed by Oates et al. 2014; in this letter we present examples
and discuss implications of the methodology as applied to the analysis of fMRI
data from a multi-subject experiment. Elicitation of tuning parameters requires
care and we illustrate how this may proceed retrospectively based on technical
replicate data. In addition to joint learning of subject-specific connectivity,
we allow for heterogeneous collections of subjects and simultaneously estimate
relationships between the subjects themselves. This letter aims to highlight
the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2
Flexible Bayesian Product Mixture Models for Vector Autoregressions
Bayesian non-parametric methods based on Dirichlet process mixtures have seen
tremendous success in various domains and are appealing in being able to borrow
information by clustering samples that share identical parameters. However,
such methods can face hurdles in heterogeneous settings where objects are
expected to cluster only along a subset of axes or where clusters of samples
share only a subset of identical parameters. We overcome such limitations by
developing a novel class of product of Dirichlet process location-scale
mixtures that enable independent clustering at multiple scales, which result in
varying levels of information sharing across samples. First, we develop the
approach for independent multivariate data. Subsequently we generalize it to
multivariate time-series data under the framework of multi-subject Vector
Autoregressive (VAR) models that is our primary focus, which go beyond
parametric single-subject VAR models. We establish posterior consistency and
develop efficient posterior computation for implementation. Extensive numerical
studies involving VAR models show distinct advantages over competing methods,
in terms of estimation, clustering, and feature selection accuracy. Our resting
state fMRI analysis from the Human Connectome Project reveals biologically
interpretable connectivity differences between distinct intelligence groups,
while another air pollution application illustrates the superior forecasting
accuracy compared to alternate methods
Fast Temporal Wavelet Graph Neural Networks
Spatio-temporal signals forecasting plays an important role in numerous
domains, especially in neuroscience and transportation. The task is challenging
due to the highly intricate spatial structure, as well as the non-linear
temporal dynamics of the network. To facilitate reliable and timely forecast
for the human brain and traffic networks, we propose the Fast Temporal Wavelet
Graph Neural Networks (FTWGNN) that is both time- and memory-efficient for
learning tasks on timeseries data with the underlying graph structure, thanks
to the theories of multiresolution analysis and wavelet theory on discrete
spaces. We employ Multiresolution Matrix Factorization (MMF) (Kondor et al.,
2014) to factorize the highly dense graph structure and compute the
corresponding sparse wavelet basis that allows us to construct fast wavelet
convolution as the backbone of our novel architecture. Experimental results on
real-world PEMS-BAY, METR-LA traffic datasets and AJILE12 ECoG dataset show
that FTWGNN is competitive with the state-of-the-arts while maintaining a low
computational footprint. Our PyTorch implementation is publicly available at
https://github.com/HySonLab/TWGNNComment: arXiv admin note: text overlap with arXiv:2111.0194
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