Neural detection of socially valued community members
This repository hosts the online supplement for the paper: "Neural detection of socially valued community members" (Morelli, Leong, Carlson, Kullar, & Zaki, in press)
For a preprint of the paper, please contact Sylvia Morelli at firstname.lastname@example.org.
Social Network Nominations
Nomination Matrices: Adjacency matrices of nominations for each of the 8 social network questions for the larger sample of 197 participants, as well as a matrix that represents the weighted average of these 8 questions
Factor Analysis: Factor analysis on indegree for each of the eight questions, using the full sample (i.e., 97 participants)
Pre-Scan Ratings of Dorm Relationships
Pre-Scan Ratings: Anonymized data of scanner participants' ratings of each dorm member on various dimensions
Face Selection Algorithm: Script for selecting 30 target faces for each participant based on their pre-scan ratings
Face Selection Files: 30 target faces selected for each participant produced by the face selection algorithm
Face Viewing Task: Main script to run the face-viewing task (but missing the folder of target photos to maintain anonymity)
Face Viewing Task Output: Recorded onsets & durations for stimuli, as well as button presses
Preprocessing scripts: SPM preprocessing scripts for all tasks (including face viewing)
First-level scripts for parametric analyses: SPM subject-level scripts for parametric modulation
First-level scripts for hub categories: SPM subject-level scripts used to generate hub categories (median split, terciles, & quartiles) for univariate and multivariate prediction analyses
Functional Reward Localizer
Modified Monetary Incentive Delay Task: Main script to run the modified MID (but missing the folder of photos to maintain anonymity)
MID Output: Recorded onsets & durations for stimuli, as well as button presses
Preprocessing scripts: SPM preprocessing scripts for all tasks (including MID)
First-level scripts:SPM subject-level scripts
Data for the prediction analyses reported in the paper can be downloaded here. Each participant's subfolder (SN_XXX) contains three pairs of .img/.hdr files. Each pair contains a t-map associated with a particular hub category:
- spmT_0001 - High hub category
- spmT_0002 - Middle hub category
- spmT_0003 - Low hub category
ROI masks used for the analyses can be found here
UnivariatePrediction.m: Follows a leave-one-participant-out cross-validation procedure to predict hub category from the average t-values of held-out data in a given ROI.
MultivariatePrediction.m: Follows a leave-one-participant-out cross-validation procedure to train a LASSO-PCR algorithm to predict hub category from neural patterns of held-out data in a given ROI.
- MultivariatePrediction_zSpace.m: Same analysis with the mean ROI signal removed
Compare_RMSE.m: Compares univariate and multivariate prediction accuracy using root mean squared error (RMSE).
ParcelSearchLightAnalysis.m: Prediction analyses using whole-brain parcellation ROIs.
The following folders contain scripts for additional control analyses.
Median_scripts: Analyses when splitting data into two bins
Quartile_scripts: Analyses when splitting data into four bins
NoControlScripts: Analyses when not controlling for personal nomination and closeness
WS_prediction: Within-Subject Prediction Analyses
Multivariate pattern weights learned by LASSO-PCR algorithm for each ROI can be found here ([roi_name.nii])
To run the prediction scripts, you will need to download the following toolboxes: