22 research outputs found
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
Supervised Hyperalignment for multi-subject fMRI data alignment
Hyperalignment has been widely employed in Multivariate Pattern (MVP) analysis to discover the cognitive states in the human brains based on multi-subject functional Magnetic Resonance Imaging (fMRI) datasets. Most of the existing HA methods utilized unsupervised approaches, where they only maximized the correlation between the voxels with the same position in the time series. However, these unsupervised solutions may not be optimum for handling the functional alignment in the supervised MVP problems. This paper proposes a Supervised Hyperalignment (SHA) method to ensure better functional alignment for MVP analysis, where the proposed method provides a supervised shared space that can maximize the correlation among the stimuli belonging to the same category and minimize the correlation between distinct categories of stimuli. Further, SHA employs a generalized optimization solution, which generates the shared space and calculates the mapped features in a single iteration, hence with optimum time and space complexities for large datasets. Experiments on multi-subject datasets demonstrate that SHA method achieves up to 19% better performance for multi-class problems over the state-of-the-art HA algorithms
Procrustes analysis for high-dimensional data
The Procrustes-based perturbation model \citep{Goodall} allows to minimize
the Frobenius distance between matrices by similarity transformation. However,
it suffers from non-identifiability, critical interpretation of the transformed
matrices, and non-applicability in high-dimensional data. We provide an
extension of the perturbation model focused on the high-dimensional data
framework, called the ProMises (Procrustes von Mises-Fisher) model. The
ill-posed and interpretability problems are solved by imposing a proper prior
distribution for the orthogonal matrix parameter, i.e., the von Mises-Fisher
distribution, which is a conjugate prior, resulting in a fast estimation
process. Furthermore, we present the Efficient ProMises model for the
high-dimensional framework, useful in neuroimaging, where the problem has much
more than three dimensions. We found a great improvement in functional Magnetic
Resonance Imaging connectivity analysis since the ProMises model permits to
incorporate topological brain information in the alignment's estimation
process.Comment: 20 pages, 7 figure
AN EVALUATION OF HYPERALIGNMENT ON REPRODUCIBILITY AND PREDICTION ACCURACY FOR FMRI DATA
Functional magnetic resonance imaging (fMRI) is a neuroimaging technique which measures a person's brain activity using changes in the blood flow in response to neural activity. Recently, resting state fMRI (rs-fMRI) has become a ubiquitous tool for measuring connectivity and examining the functional architecture of the human brain. Here, we used a publicly available rs-fMRI dataset to investigate the performance of the hyperalignment algorithm, on several fMRI analyses. The research employs the use of the image intra-class correlation coefficient and functional connectome fingerprinting to evaluate the reproducibility of both the unaligned and hyperaligned data, and developed a predictive model to investigate whether hyperalignment improves the prediction of certain behavioral measures. Overall, our results illustrate the utility of the hyperalignment algorithm for studying inter-individual variation in brain activity
Hyperalignment of motor cortical areas based on motor imagery during action observation
Multivariate Pattern Analysis (MVPA) has grown in importance due to its capacity to use both coarse and fine scale patterns of brain activity. However, a major limitation of multivariate analysis is the difficulty of aligning features across brains, which makes MVPA a subject specific analysis. Recent work by Haxby et al. (2011) introduced a method called Hyperalignment that explored neural activity in ventral temporal cortex during object recognition and demonstrated the ability to align individual patterns of brain activity into a common high dimensional space to facilitate Between Subject Classification (BSC). Here we examined BSC based on Hyperalignment of motor cortex during a task of motor imagery of three natural actions (lift, knock and throw). To achieve this we collected brain activity during the combined tasks of action observation and motor imagery to a parametric action space containing 25 stick-figure blends of the three natural actions. From these responses we derived Hyperalignment transformation parameters that were used to map subjects’ representational spaces of the motor imagery task in the motor cortex into a common model representational space. Results showed that BSC of the neural response patterns based on Hyperalignment exceeded both BSC based on anatomical alignment as well as a standard Within Subject Classification (WSC) approach. We also found that results were sensitive to the order in which participants entered the Hyperalignment algorithm. These results demonstrate the effectiveness of Hyperalignment to align neural responses across subject in motor cortex to enable BSC of motor imagery
Improved fMRI-based Pain Prediction using Bayesian Group-wise Functional Registration
In recent years, neuroimaging has undergone a paradigm shift, moving away
from the traditional brain mapping approach toward developing integrated,
multivariate brain models that can predict categories of mental events.
However, large interindividual differences in brain anatomy and functional
localization after standard anatomical alignment remain a major limitation in
performing this analysis, as it leads to feature misalignment across subjects
in subsequent predictive models