Mapping Between fMRI Responses to Movies and their Natural Language Annotations

1 code implementation • 13 Oct 2016 • Kiran Vodrahalli, Po-Hsuan Chen, YIngyu Liang, Christopher Baldassano, Janice Chen, Esther Yong, Christopher Honey, Uri Hasson, Peter Ramadge, Ken Norman, Sanjeev Arora

Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli.

Scene Classification Sentence +2

A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis

no code implementations • 29 Sep 2016 • Hejia Zhang, Po-Hsuan Chen, Janice Chen, Xia Zhu, Javier S. Turek, Theodore L. Willke, Uri Hasson, Peter J. Ramadge

In this work, we examine a searchlight based shared response model to identify shared information in small contiguous regions (searchlights) across the whole brain.

General Classification

A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation

no code implementations • 17 Aug 2016 • Po-Hsuan Chen, Xia Zhu, Hejia Zhang, Javier S. Turek, Janice Chen, Theodore L. Willke, Uri Hasson, Peter J. Ramadge

We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality.

Anatomy

A Reduced-Dimension fMRI Shared Response Model

no code implementations • NeurIPS 2015 • Po-Hsuan (Cameron) Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James Haxby, Peter J. Ramadge

Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity.