Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging

Canonical correlation analysis (CCA) is a valuable method for interpreting cross-covariance across related datasets of different dimensionality. There are many potential applications of CCA to neuroimaging data analysis. For instance, CCA can be used for finding functional similarities across fMRI datasets collected from multiple subjects without resampling individual datasets to a template anatomy. In this paper, we introduce Pyrcca, an open-source Python module for executing CCA between two or more datasets. Pyrcca can be used to implement CCA with or without regularization, and with or without linear or a Gaussian kernelization of the datasets. We demonstrate an application of CCA implemented with Pyrcca to neuroimaging data analysis. We use CCA to find a data-driven set of functional response patterns that are similar across individual subjects in a natural movie experiment. We then demonstrate how this set of response patterns discovered by CCA can be used to accurately predict subject responses to novel natural movie stimuli

The `Parahippocampal Place Area' Responds Selectively to High Spatial Frequencies

Defining the exact mechanisms by which the brain processes visual objects and scenes remains an unresolved challenge. Valuable clues to this process have emerged from the demonstration that clusters of neurons (“modules”) in inferior temporal cortex apparently respond selectively to specific categories of visual stimuli, such as places/scenes. However, the higher-order “category-selective” response could also reflect specific lower-level spatial factors. Here we tested this idea in multiple functional MRI experiments, in humans and macaque monkeys, by systematically manipulating the spatial content of geometrical shapes and natural images. These tests revealed that visual spatial discontinuities (as reflected by an increased response to high spatial frequencies) selectively activate a well-known place-selective region of visual cortex (the “parahippocampal place area”) in humans. In macaques, we demonstrate a homologous cortical area, and show that it also responds selectively to higher spatial frequencies. The parahippocampal place area may use such information for detecting object borders and scene details during spatial perception and navigation.National Institutes of Health (U.S.) (NIH Grant R01 MH6752)National Institutes of Health (U.S.) (grant R01 EY017081)Athinoula A. Martinos Center for Biomedical ImagingNational Center for Research Resources (U.S.)Mind Research Institut

Queer In AI: A Case Study in Community-Led Participatory AI

We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and intersectional principles, and then assess the organization's impact. Queer in AI provides important lessons and insights for practitioners and theorists of participatory methods broadly through its rejection of hierarchy in favor of decentralization, success at building aid and programs by and for the queer community, and effort to change actors and institutions outside of the queer community. Finally, we theorize how communities like Queer in AI contribute to the participatory design in AI more broadly by fostering cultures of participation in AI, welcoming and empowering marginalized participants, critiquing poor or exploitative participatory practices, and bringing participation to institutions outside of individual research projects. Queer in AI's work serves as a case study of grassroots activism and participatory methods within AI, demonstrating the potential of community-led participatory methods and intersectional praxis, while also providing challenges, case studies, and nuanced insights to researchers developing and using participatory methods.Comment: To appear at FAccT 202

Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging.

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model

Pyrcca: regularized kernel canonical correlation analysis in Python and its applications to neuroimaging

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model

Eye movement-invariant representations in the human visual system.

During natural vision, humans make frequent eye movements but perceive a stable visual world. It is therefore likely that the human visual system contains representations of the visual world that are invariant to eye movements. Here we present an experiment designed to identify visual areas that might contain eye-movement-invariant representations. We used functional MRI to record brain activity from four human subjects who watched natural movies. In one condition subjects were required to fixate steadily, and in the other they were allowed to freely make voluntary eye movements. The movies used in each condition were identical. We reasoned that the brain activity recorded in a visual area that is invariant to eye movement should be similar under fixation and free viewing conditions. In contrast, activity in a visual area that is sensitive to eye movement should differ between fixation and free viewing. We therefore measured the similarity of brain activity across repeated presentations of the same movie within the fixation condition, and separately between the fixation and free viewing conditions. The ratio of these measures was used to determine which brain areas are most likely to contain eye movement-invariant representations. We found that voxels located in early visual areas are strongly affected by eye movements, while voxels in ventral temporal areas are only weakly affected by eye movements. These results suggest that the ventral temporal visual areas contain a stable representation of the visual world that is invariant to eye movements made during natural vision

Retinotopy versus Face Selectivity in Macaque Visual Cortex

Retinotopic organization is a ubiquitous property of lower-tier visual cortical areas in human and nonhuman primates. In macaque visual cortex, the retinotopic maps extend to higher-order areas in the ventral visual pathway, including area TEO in the inferior temporal (IT) cortex. Distinct regions within IT cortex are also selective to specific object categories such as faces. Here we tested the topographic relationship between retinotopic maps and face-selective patches in macaque visual cortex using high-resolution fMRI and retinotopic face stimuli. Distinct subregions within face-selective patches showed either (1) a coarse retinotopic map of eccentricity and polar angle, (2) a retinotopic bias to a specific location of visual field, or (3) nonretinotopic selectivity. In general, regions along the lateral convexity of IT cortex showed more overlap between retinotopic maps and face selectivity, compared with regions within the STS. Thus, face patches in macaques can be subdivided into smaller patches with distinguishable retinotopic properties.Mental Illness and Neuroscience Discovery (MIND) InstituteAthinoula A. Martinos Center for Biomedical ImagingNational Center for Research Resources (U.S.

Retinotopy versus Face Selectivity in Macaque Visual Cortex

Retinotopic organization is a ubiquitous property of lowertier visual cortical areas in human and nonhuman primates. In macaque visual cortex, the retinotopic maps extend to higherorder areas in the ventral visual pathway, including area TEO in the inferior temporal (IT) cortex. Distinct regions within IT cortex are also selective to specific object categories such as faces. Here we tested the topographic relationship between retinotopic maps and face-selective patches in macaque visual cortex using high-resolution fMRI and retinotopic face stimuli.Distinct subregions within face-selective patches showed either (1) a coarse retinotopic map of eccentricity and polar angle, (2) a retinotopic bias to a specific location of visual field, or (3) nonretinotopic selectivity. In general, regions along the lateral convexity of IT cortex showed more overlap between retinotopic maps and face selectivity, compared with regions within the STS. Thus, face patches in macaques can be subdivided into smaller patches with distinguishable retinotopic propertiesstatus: publishe

Eye movement-invariant representations in the human visual system.

During natural vision, humans make frequent eye movements but perceive a stable visual world. It is therefore likely that the human visual system contains representations of the visual world that are invariant to eye movements. Here we present an experiment designed to identify visual areas that might contain eye-movement-invariant representations. We used functional MRI to record brain activity from four human subjects who watched natural movies. In one condition subjects were required to fixate steadily, and in the other they were allowed to freely make voluntary eye movements. The movies used in each condition were identical. We reasoned that the brain activity recorded in a visual area that is invariant to eye movement should be similar under fixation and free viewing conditions. In contrast, activity in a visual area that is sensitive to eye movement should differ between fixation and free viewing. We therefore measured the similarity of brain activity across repeated presentations of the same movie within the fixation condition, and separately between the fixation and free viewing conditions. The ratio of these measures was used to determine which brain areas are most likely to contain eye movement-invariant representations. We found that voxels located in early visual areas are strongly affected by eye movements, while voxels in ventral temporal areas are only weakly affected by eye movements. These results suggest that the ventral temporal visual areas contain a stable representation of the visual world that is invariant to eye movements made during natural vision