Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
This addresses a critical bottleneck in neuroscience for researchers aggregating multi-subject fMRI data, enabling more flexible and accurate functional alignment, though it is incremental as it builds on existing alignment methods.
The paper tackled the problem of aligning fMRI data across subjects when datasets are not temporally-aligned, such as when subjects have missing responses or different stimulus sequences, by proposing a graph-based decoding model with kernel-based optimization for nonlinear feature extraction, achieving superior performance to state-of-the-art methods on five datasets.
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.