Enhanced hyperalignment via spatial prior information

Andreella, Angela; Finos, Livio; Lindquist, Martin A.

doi:10.1002/hbm.26170

Cited by 5 publications

(1 citation statement)

References 58 publications

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“…For this latter point, we envision a use for this technique when using multi-atlas registration, where a single common template is not employed and instead template information is carried from a collection of labeled atlases to subjects (Rezende et al, 2019). Our proposed regression approach differs a great deal from functional HA approaches and related functional alignment techniques (Xu et al, 2012;Andreella et al, 2022;Wang et al, 2022) since: (i) explicit alignment is not a goal of the analysis; (ii) aggregate connectivity effects are considered under the assumption that they are exchangeable within levels of covariates; and (iii) in principle our approach can be implemented entirely in subject space using voxel-level data.…”

Section: Introductionmentioning

confidence: 99%

Regression models for partially localized fMRI connectivity analyses

Smith,

Zhao,

Lindquist

et al. 2023

Front. Neuroimaging

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BackgroundBrain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated.MethodsIn this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches.ResultsWe apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model—which uses only partial location information about pairs of ROI's—is comparably as high as the repeatability obtained using full location information.DiscussionWhile our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.

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Section: Introductionmentioning

confidence: 99%

Regression models for partially localized fMRI connectivity analyses

Smith,

Zhao,

Lindquist

et al. 2023

Front. Neuroimaging

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Enhanced hyperalignment via spatial prior information

Andreella

Finos

Lindquist

2022

Human Brain Mapping

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Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, even after alignment to a standard anatomical template. Hyperalignment, based on sequential Procrustes orthogonal transformations, has been proposed as a method of aligning shared functional information into a common high-dimensional space and thereby improving inter-subject analysis. Though successful, current hyperalignment algorithms have a number of shortcomings, including difficulties interpreting the transformations, a lack of uniqueness of the procedure, and difficulties performing whole-brain analysis. To resolve these issues, we propose the ProMises (Procrustes von Mises-Fisher) model. We reformulate functional alignment as a statistical model and impose a prior distribution on the orthogonal parameters (the von Mises-Fisher distribution). This allows for the embedding of anatomical information into the estimation procedure by penalizing the contribution of spatially distant voxels when creating the shared functional high-dimensional space. Importantly, the transformations, aligned images, and related results are all unique. In addition, the proposed method allows for efficient whole-brain functional alignment. In simulations and application to data from four fMRI studies we find that ProMises improves inter-subject classification in terms of between-subject accuracy and interpretability compared to standard hyperalignment algorithms.

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Procrustes-based distances for exploring between-matrices similarity

et al. 2023

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The statistical shape analysis called Procrustes analysis minimizes the Frobenius distance between matrices by similarity transformations. The method returns a set of optimal orthogonal matrices, which project each matrix into a common space. This manuscript presents two types of distances derived from Procrustes analysis for exploring between-matrices similarity. The first one focuses on the residuals from the Procrustes analysis, i.e., the residual-based distance metric. In contrast, the second one exploits the fitted orthogonal matrices, i.e., the rotational-based distance metric. Thanks to these distances, similarity-based techniques such as the multidimensional scaling method can be applied to visualize and explore patterns and similarities among observations. The proposed distances result in being helpful in functional magnetic resonance imaging (fMRI) data analysis. The brain activation measured over space and time can be represented by a matrix. The proposed distances applied to a sample of subjects—i.e., matrices—revealed groups of individuals sharing patterns of neural brain activation. Finally, the proposed method is useful in several contexts when the aim is to analyze the similarity between high-dimensional matrices affected by functional misalignment.

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Enhanced hyperalignment via spatial prior information

Cited by 5 publications

References 58 publications

Regression models for partially localized fMRI connectivity analyses

Regression models for partially localized fMRI connectivity analyses

Enhanced hyperalignment via spatial prior information

Procrustes-based distances for exploring between-matrices similarity

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