2,258 research outputs found
A group model for stable multi-subject ICA on fMRI datasets
Spatial Independent Component Analysis (ICA) is an increasingly used
data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
data. To date, it has been used to extract sets of mutually correlated brain
regions without prior information on the time course of these regions. Some of
these sets of regions, interpreted as functional networks, have recently been
used to provide markers of brain diseases and open the road to paradigm-free
population comparisons. Such group studies raise the question of modeling
subject variability within ICA: how can the patterns representative of a group
be modeled and estimated via ICA for reliable inter-group comparisons? In this
paper, we propose a hierarchical model for patterns in multi-subject fMRI
datasets, akin to mixed-effect group models used in linear-model-based
analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based
on i) probabilistic dimension reduction of the individual data, ii) canonical
correlation analysis to identify a data subspace common to the group iii)
ICA-based pattern extraction. In addition, we introduce a procedure based on
cross-validation to quantify the stability of ICA patterns at the level of the
group. We compare our method with state-of-the-art multi-subject fMRI ICA
methods and show that the features extracted using our procedure are more
reproducible at the group level on two datasets of 12 healthy controls: a
resting-state and a functional localizer study
CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
Spatial Independent Component Analysis (ICA) is an increasingly used
data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
data. To date, it has been used to extract meaningful patterns without prior
information. However, ICA is not robust to mild data variation and remains a
parameter-sensitive algorithm. The validity of the extracted patterns is hard
to establish, as well as the significance of differences between patterns
extracted from different groups of subjects. We start from a generative model
of the fMRI group data to introduce a probabilistic ICA pattern-extraction
algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and
canonical correlation analysis, our method is auto-calibrated and identifies
the group-reproducible data subspace before performing ICA. We compare our
method to state-of-the-art multi-subject fMRI ICA methods and show that the
features extracted are more reproducible
Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging
Independent component analysis (ICA) is a powerful computational tool for
separating independent source signals from their linear mixtures. ICA has been
widely applied in neuroimaging studies to identify and characterize underlying
brain functional networks. An important goal in such studies is to assess the
effects of subjects' clinical and demographic covariates on the spatial
distributions of the functional networks. Currently, covariate effects are not
incorporated in existing group ICA decomposition methods. Hence, they can only
be evaluated through ad-hoc approaches which may not be accurate in many cases.
In this paper, we propose a hierarchical covariate ICA model that provides a
formal statistical framework for estimating and testing covariate effects in
ICA decomposition. A maximum likelihood method is proposed for estimating the
covariate ICA model. We develop two expectation-maximization (EM) algorithms to
obtain maximum likelihood estimates. The first is an exact EM algorithm, which
has analytically tractable E-step and M-step. Additionally, we propose a
subspace-based approximate EM, which can significantly reduce computational
time while still retain high model-fitting accuracy. Furthermore, to test
covariate effects on the functional networks, we develop a voxel-wise
approximate inference procedure which eliminates the needs of computationally
expensive covariance estimation. The performance of the proposed methods is
evaluated via simulation studies. The application is illustrated through an
fMRI study of Zen meditation.Comment: 36 pages, 5 figure
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data
is indispensable for generating valid and general inferences from patterns
distributed across human brains. The disparities in anatomical structures and
functional topographies of human brains warrant aligning fMRI data across
subjects. However, the existing functional alignment methods cannot handle well
various kinds of fMRI datasets today, especially when they are not
temporally-aligned, i.e., some of the subjects probably lack the responses to
some stimuli, or different subjects might follow different sequences of
stimuli. In this paper, a cross-subject graph that depicts the
(dis)similarities between samples across subjects is used as a priori for
developing a more flexible framework that suits an assortment of fMRI datasets.
However, the high dimension of fMRI data and the use of multiple subjects makes
the crude framework time-consuming or unpractical. To address this issue, we
further regularize the framework, so that a novel feasible kernel-based
optimization, which permits nonlinear feature extraction, could be
theoretically developed. Specifically, a low-dimension assumption is imposed on
each new feature space to avoid overfitting caused by the
highspatial-low-temporal resolution of fMRI data. Experimental results on five
datasets suggest that the proposed method is not only superior to several
state-of-the-art methods on temporally-aligned fMRI data, but also suitable for
dealing `with temporally-unaligned fMRI data.Comment: 17 pages, 10 figures, Proceedings of the Association for the
Advancement of Artificial Intelligence (AAAI-20
Directional clustering through matrix factorization
This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction. This directional distance measure arises in several applications, including document classification and human brain imaging. Using ideas from the field of constrained low-rank matrix factorization and sparse approximation, a novel approach is presented that differs from classical clustering methods, such as seminonnegative matrix factorization, K-EVD, or k-means clustering, yet combines some aspects of all these. As in nonnegative matrix factorization and K-EVD, the matrix decomposition is iteratively refined to optimize a data fidelity term; however, no positivity constraint is enforced directly nor do we need to explicitly compute eigenvectors. As in k-means and K-EVD, each optimization step is followed by a hard cluster assignment. This leads to an efficient algorithm that is shown here to outperform common competitors in terms of clustering performance and/or computation speed. In addition to a detailed theoretical analysis of some of the algorithm's main properties, the approach is empirically evaluated on a range of toy problems, several standard text clustering data sets, and a high-dimensional problem in brain imaging, where functional magnetic resonance imaging data are used to partition the human cerebral cortex into distinct functional regions
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