2,282 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
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
Surface-based cortical registration is an important topic in medical image
analysis and facilitates many downstream applications. Current approaches for
cortical registration are mainly driven by geometric features, such as sulcal
depth and curvature, and often assume that registration of folding patterns
leads to alignment of brain function. However, functional variability of
anatomically corresponding areas across subjects has been widely reported,
particularly in higher-order cognitive areas. In this work, we present JOSA, a
novel cortical registration framework that jointly models the mismatch between
geometry and function while simultaneously learning an unbiased
population-specific atlas. Using a semi-supervised training strategy, JOSA
achieves superior registration performance in both geometry and function to the
state-of-the-art methods but without requiring functional data at inference.
This learning framework can be extended to any auxiliary data to guide
spherical registration that is available during training but is difficult or
impossible to obtain during inference, such as parcellations, architectonic
identity, transcriptomic information, and molecular profiles. By recognizing
the mismatch between geometry and function, JOSA provides new insights into the
future development of registration methods using joint analysis of the brain
structure and function.Comment: A. V. Dalca and B. Fischl are co-senior authors with equal
contribution. arXiv admin note: text overlap with arXiv:2303.0159
Hand classification of fMRI ICA noise components
We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
Objective: Endophenotypes such as brain age and fluid intelligence are
important biomarkers of disease status. However, brain imaging studies to
identify these biomarkers often encounter limited numbers of subjects and high
dimensional imaging features, hindering reproducibility. Therefore, we develop
an interpretable, multivariate classification/regression algorithm, called
Latent Similarity (LatSim), suitable for small sample size, high feature
dimension datasets. Methods: LatSim combines metric learning with a kernel
similarity function and softmax aggregation to identify task-related
similarities between subjects. Inter-subject similarity is utilized to improve
performance on three prediction tasks using multi-paradigm fMRI data. A greedy
selection algorithm, made possible by LatSim's computational efficiency, is
developed as an interpretability method. Results: LatSim achieved significantly
higher predictive accuracy at small sample sizes on the Philadelphia
Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave
superior discriminative power compared to those identified by other methods. We
identified 4 functional brain networks enriched in connections for predicting
brain age, sex, and intelligence. Conclusion: We find that most information for
a predictive task comes from only a few (1-5) connections. Additionally, we
find that the default mode network is over-represented in the top connections
of all predictive tasks. Significance: We propose a novel algorithm for small
sample, high feature dimension datasets and use it to identify connections in
task fMRI data. Our work should lead to new insights in both algorithm design
and neuroscience research. Code and demo are available at
https://github.com/aorliche/LatentSimilarity/.Comment: 12 page
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