## Abstract Previous research has found that functional connectivity (FC) can accurately predict the identity of a subject performing a task and the type of task being performed. These results are replicated using a large dataset collected at the OSU Center for Cognitive and Behavioral Brain Imaging. This work introduces a novel perspective on task and subject identity prediction: BOLD Variability (BV). Conceptually, BV is a region-specific measure based on the variance within each brain region. BV is simple to compute, interpret, and visualize. This work shows that both FC and BV are predictive of task and subject, even across scanning sessions separated by multiple years. Subject differences rather than task differences account for the majority of changes in BV and FC. Similar to results in FC, BV is reduced during cognitive tasks relative to rest. ## Data We include all relevant data for the project in the OSF Storage directory. You will find the file `Preprocessing Brain Connectivity - Sheet1.xlsx`, which contains the preprocessing options and filename for each dataset, and the accompanying datasets. Each dataset is a `.mat` file that can be loaded using MATLAB: e.g., `load( ‘tc_ica_gm_task_out_180523.mat’)` Each `.mat` file contains: readme: a short description of preprocessing options used session_idx: [1×193 double], which session the subject was scanned in subjs: {1×193 cell}, subject id tasks: {1×9 cell}, list of tasks tc: {193×9 cell}, the time course for each subject and task The time course for the first subject, first task, and first ROI can be accessed as `tc{1,1}(:,1)`. ## Code Code for analysis can be found [here](https://github.com/geebioso/PredictingTaskSubject). Note that code is intended to illustrate our analyses and results visualization, not as production code to be downloaded and used publically.