Connecting the dots: information visualization and text analysis of the Searchlight Project newsletters

This report is the product of the Pardee Center’s work on the Searchlight:Visualization and Analysis of Trend Data project sponsored by the Rockefeller Foundation. Part of a larger effort to analyze and disseminate on-the-ground information about important societal trends as reported in a large number of regional newsletters developed in Asia, Africa and the Americas specifically for the Foundation, the Pardee Center developed sophisticated methods to systematically review, categorize, analyze, visualize, and draw conclusions from the information in the newsletters.The Rockefeller Foundatio

Nonparametric statistical inference for functional brain information mapping

An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate analysis frameworks. Two most prominent MVPA methods for information mapping are searchlight decoding and classifier weight mapping. The new MVPA brain mapping methods, however, have also posed new challenges for analysis and statistical inference on the group level. In this thesis, I discuss why the usual procedure of performing t-tests on MVPA derived information maps across subjects in order to produce a group statistic is inappropriate. I propose a fully nonparametric solution to this problem, which achieves higher sensitivity than the most commonly used t-based procedure. The proposed method is based on resampling methods and preserves the spatial dependencies in the MVPA-derived information maps. This enables to incorporate a cluster size control for the multiple testing problem. Using a volumetric searchlight decoding procedure and classifier weight maps, I demonstrate the validity and sensitivity of the new approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, the new results showed a higher sensitivity and spatial specificity. The second goal of this thesis is the comparison of the two widely used information mapping approaches -- the searchlight technique and classifier weight mapping. Both methods take into account the spatially distributed patterns of activation in order to predict stimulus conditions, however the searchlight method solely operates on the local scale. The searchlight decoding technique has furthermore been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. In this thesis, I compare searchlight decoding with linear classifier weight mapping, both using the formerly proposed non-parametric statistical framework using a simulation and ultra-high-field 7T experimental data. It was found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, the weight mapping method was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, such global multivariate methods provide a substantial improvement for characterizing structure-function relationships

Philanthropic Sourcing, Diligence, and Decision Making: An Equity-Oriented Approach

These are times that demand courageous giving to provide essential fuel for social change efforts in areas ranging from public health and the environment to inequality, racial equity, and much more. The ever-rising scale and urgency of the challenges facing our communities and our planet are a clarion call for funders to take giant leaps forward.Sourcing and diligence processes are at the heart of funders' ability to meet the moment. By "sourcing," we mean finding and elevating nonprofits and initiatives to fund, while "diligence" refers to the vetting process donors conduct before making a contribution. Together, sourcing and diligence are the means to an important end: providing information for making decisions on giving. But that's not all—sourcing and diligence processes can also help donors meet and build trust with those who are leading the hard and ongoing work of social change. In addition, they can energize donors about what is possible, helping them see how their support contributes to an arc of impact that is larger than any one individual's reach.In this article, we offer practical sourcing and diligence guidance for donors who want to increase their contributions to social change efforts—whether they are just getting started or have been at this work for decades. This information will help donors make their grantmaking more inclusive and equitable, and, importantly, it will help donors get started, "learn while doing," and improve over time

A data driven approach to understanding the organization of high-level visual cortex

The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain