Joint SVD-Hyperalignment for multi-subject FMRI data alignment

Chen, Po Hsuan; Guntupalli, J. Swaroop; Haxby, James V.; Ramadge, Peter J.

doi:10.1109/mlsp.2014.6958912

Cited by 31 publications

(91 citation statements)

References 17 publications

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“…This methodology should improve analytical sensitivity, as demonstrated in Chen et al (2015). More importantly, this result shows culturally distinct visual cortex representations using methodology tolerant to anatomical misalignment.…”

Section: Resultsmentioning

confidence: 69%

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Cultural specialization of visual cortex

Ksander

Paige

Johndro

et al. 2018

Social Cognitive and Affective Neuroscience

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A growing body of evidence suggests culture influences how individuals perceive the world around them. This study investigates whether these cultural differences extend to a simple object viewing task and visual cortex by examining voxel pattern representations with multi-voxel pattern analysis (MVPA). During functional magnetic resonance imaging scanning, 20 East Asian and 20 American participants viewed photos of everyday items, equated for familiarity and conceptual agreement across cultures. Whole brain searchlight mapping with non-parametric statistical evaluation tested whether these stimuli evoked multi-voxel patterns that were distinct between cultural groups. We found that participants’ cultural identities were successfully predicted from stimuli representations in visual cortex Brodmann areas 18 and 19. This result demonstrates culturally specialized visual cortex during a basic perceptual task ubiquitous to everyday life.

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Section: Resultsmentioning

confidence: 69%

“…In order to account for this concern analytically, another ROI analysis was performed with shared response modeling [SRM; Chen et al (2015); also see Cohen et al (2017) for a broader review].…”

Section: Resultsmentioning

confidence: 99%

Cultural specialization of visual cortex

Ksander

Paige

Johndro

et al. 2018

Social Cognitive and Affective Neuroscience

View full text Add to dashboard Cite

show abstract

“…It can be any kernel function [26] that maps the voxels from original nonlinear space to a linear embedded space. Further, this function can be any feature selection/ranking function [5,6,16]. In order to employ the original data, this function can be considered as a linear mapping, where Φ(x) = x.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Since task-based fMRI datasets can provide better spatial resolution in comparison with other modalities, most of the previous studies employed fMRI datasets in order to study human brains [19]. A crucial step in fMRI analysis is creating a model that is generalized across subjects [5,6,7,18,20,39,41]. In other words, utilizing multi-subject fMRI data is necessary to validate the generated results across subjects [20,41].…”

Section: Introductionmentioning

confidence: 99%

“…The next step in the standard pipeline is employing binary classification methods, such as regularized Support Vector Machine (SVM) algorithm, for generating the cognitive models, i.e., decision surfaces [5,19,26,29,41,42]. Feature selection is a usual subtask, which must be applied before the classification analysis, to reduce the sparsity problem and increase the performance of the final model [6].…”

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confidence: 99%

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Multi-Objective Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis

Yousefnezhad

Zhang

2018

Neuroinform

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In order to decode human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques.

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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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Joint SVD-Hyperalignment for multi-subject FMRI data alignment

Cited by 31 publications

References 17 publications

Cultural specialization of visual cortex

Cultural specialization of visual cortex

Multi-Objective Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis

Enhanced hyperalignment via spatial prior information

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