15 citations · 20 across the 6 of their papers we have counts for
6 papers
Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging
Snigdha Panigrahi, Natasha Stewart, Chandra Sekhar Sripada +1
Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative t…
Supervised PCA: A Multiobjective Approach
Alexander Ritchie, Laura Balzano, Daniel Kessler +2
Methods for supervised principal component analysis (SPCA) aim to incorporate label information into principal component analysis (PCA), so that the extracted features are more use…
Image Response Regression via Deep Neural Networks
Daiwei Zhang, Lexin Li, Chandra Sripada +1
Delineating the associations between images and a vector of covariates is of central interest in medical imaging studies. To tackle this problem of image response regression, we pr…
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images
Pascal Sturmfels, Saige Rutherford, Mike Angstadt +3
Given the wide success of convolutional neural networks (CNNs) applied to natural images, researchers have begun to apply them to neuroimaging data. To date, however, exploration o…
Which Findings from the Functional Neuromaging Literature Can We Trust?
Daniel Kessler, Michael Angstadt, Chandra Sripada
In their recent "Cluster Failure" paper, Eklund and colleagues cast doubt on the accuracy of a widely used statistical test in functional neuroimaging. Here, we leverage nonparamet…
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine
Takanori Watanabe, Daniel Kessler, Clayton Scott +2
Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using neuroimaging methods to…