Examples

• （Group) Bayesian Representational Similarity Analysis
  • Overview
  • Annotated Bibliography
  • Table of contents
    • Notes on data preparation
    • Notes on model assumption
    • Load some package which we will use in this demo.
  • Simulate data
    • Load an example design matrix.
      • details about design matrix
    • simulate noise
    • specify simulated similarity matrix
    • Simulate task-related signals
    • Synthesize data by adding signal with noise
  • Fit Group Bayesian RSA to simulated data
    • Prepare onset information for model fitting
    • Instantiate a GBRSA object
    • Fit model to the data
  • Evaluation
    • Result of GBRSA
    • Compare with result of traditional RSA
    • Evaluating estimated pseudo-SNR map against the simulated SNR map
    • Examine the recovery of activation pattern
  • Other usage: “decoding” task-related signal from new data
  • Model selection by cross-validation
  • Avoid false discovery
  • Summary
• Event segmentation and alignment in fMRI data
  • Overview
  • Annotated Bibliography
  • Table of Contents
  • Loading data
  • Finding event boundaries during perception
    • Warm-up: Event structure in activity patterns
    • Fitting the HMM
    • Determining the number of events with the HMM
    • Optimal segmentation with the HMM
  • Comparing model and human-labeled boundaries
  • Aligning movie and recall data
  • Summary
• Full Correlation Matrix Analysis (FCMA) demo
  • Overview
  • Table of Contents
    • Important details of simulated data
  • 1. FCMA Step 1: Feature (voxel) selection
    • 1.1 The goal
    • 1.2 Data preprocessing
    • 1.3 Understanding preprocessed data
    • 1.4 Feature selection with simplified implementation
      • Steps:
    • 1.5 Understanding the outputs of feature selection
    • 1.6 Perform feature selection with actual FCMA implementation
    • 1.7 Select n top-performing features
  • 2. FCMA Step 2: Classification using the selected features (voxels)
    • 2.1 The goal
    • 2.2 Classification steps
    • 2.3 Understanding the outputs of classification
    • 2.4 Perform classification for all outer loop folds using FCMA script
  • 3 Results Visualizations
    • 3.1 Examine classification accuracy for different top-n-mask
    • 3.2 Visualize voxels in top-n-masks
    • 3.3 Visualize functional connectivity pattern with circos plot
  • 4. Summary
• fmrisim demo script
  • Overview
  • Annotated bibliography
  • Table of Contents
    • 1. Set parameters
      • 1.1 Import necessary Python packages
      • 1.2 Load participant data*
      • 1.3 Specify participant dimensions and resolution
      • 1.4 Generate an activity template and a mask
      • 1.5 Determine noise parameters
    • 2. Generate noise
      • 2.1 Create temporal noise
      • 2.2 Create system noise
      • 2.3 Combine noise and template
      • 2.4 Fit the data to the noise parameters
    • 3. Generate signal
      • 3.1 Specify which voxels in the brain contain signal
      • 3.2 Characterize signal for voxels
      • 3.3 Generate event time course
      • 3.4 Export stimulus time course for analysis
      • 3.5 Estimate the voxel weight for each event
      • 3.6 Convolve each voxel’s time course with the Hemodynamic Response Function
      • 3.7 Establish signal magnitude
      • 3.8 Multiply the convolved response with the signal voxels
      • 3.9 Combine signal and noise
    • 4. Analyse data
      • 4.1 Pull out data for each trial
      • 4.2 Represent the data
      • 4.3 Test for univariate effect*
      • 4.4 Test for a multivariate effect
  • Summary
    • References
• Hierarchical Topographic Factor Analysis
• Overview
• Annotated bibliography
  • Table of contents:
• Getting started
• Code
  • Initialization
  • Fit HTFA to data
  • Plotting HTFA global and local node locations
  • Compute dynamic correlations
  • Generate animated chord diagrams
  • Generate animated brain network plots
• Summary
• Inverted Encoding Model
  • Overview
  • Annotated Bibliography
  • Table of Contents
  • Example 1: Reconstructing items from working memory
  • Example 2: Reconstructing 2D spatial representations with attention and contrast changes
    • Average feature reconstructions across trials
  • Example 3: Comparing SVM and IEM with simulated data and low trial numbers
  • Summary
• Inverted encoding model
• Intersubject correlation
  • Annotated bibliography
  • Table of contents
  • Example fMRI data and atlas
    • Computing intersubject correlation (ISC)
  • Temporal receptive windows
  • Intersubject network estimation (ISFC)
  • Intersubject pattern correlation (ISPC)
  • Summary
    • References
• Rapid prototyping of fMRI models with brainiak.matnormal
• Annotated Bibliography
  • Table of contents
  • Overview: Understanding kronecker-separability
  • Example 1: Matrix-normal (separable-covariance) regression
  • Example 2: Priors and marginalization, with MN-RSA
  • Example 3: latent design matrices with PCA and FA
  • Summary
• Set Up Instructions for the Real-Time fMRI Cloud-Based Framework
  • Things to do once
  • Things to do every time
  • Common Issues
• Implementing a Real-Time fMRI Cloud-Based Framework
  • Overview
  • Annotated Bibliography
  • Table of Contents
  • Before Running This Notebook
  • Import Necessary Modules and Declare Important Variables
  • Step 1: Start the ProjectInterface Web Server
  • Step 2: Start the Synthetic Data Generator
  • Step 3: Open the Web Server on the localhost
  • Alternate Step 3: Run the classification script from command line
  • Summary
• Shared response model
  • Annotated bibliography
  • Table of contents
  • Example fMRI data and atlas
  • Estimating the SRM
  • Between-subject time-segment classification
  • Summary
  • References