AD_PAC: Phase-Amplitude Coupling Analysis in Alzheimer's Disease

This repository contains MATLAB code for analyzing phase-amplitude coupling (PAC) in MEG data from patients with Alzheimer's Disease (AD) and healthy controls.

Citation

If you use this code in your research, please cite our preprint:

Preprint: https://www.medrxiv.org/content/10.64898/2026.02.06.26345635v1

Overview

This pipeline processes MEG (Magnetoencephalography) data to investigate phase-amplitude coupling between hippocampal and cortical regions in Alzheimer's Disease. The analysis focuses on theta/alpha phase coupling with low-gamma amplitude, examining differences between AD patients and healthy controls.

Key Features

• Head Modeling: Automated head model construction using Brainstorm and OpenMEEG
• MEG Preprocessing: Bad channel interpolation, resampling, and trial rejection
• Source Reconstruction: LCMV beamformer-based source localization
• PAC Analysis: Cross-frequency coupling between hippocampus and cortical ROIs
• Statistical Analysis: Cluster-based permutation tests and linear mixed-effects models

Requirements

Software Dependencies

1. MATLAB (tested with recent versions)
2. FieldTrip Toolbox - For anatomy transformation and preprocessing
   • Download: https://www.fieldtriptoolbox.org/
3. Brainstorm Toolbox - For head model construction
   • Download: https://neuroimage.usc.edu/brainstorm/
4. FreeSurfer - For cortical surface extraction (must be run on MRI files before the pipeline)
   • Download: https://surfer.nmr.mgh.harvard.edu/

Data

MEG and MRI data can be downloaded from:

• OSF Repository: https://osf.io/pd4h9/overview

Data should be placed in a Dataset folder under the main project path.

PAC Code Attribution

The PAC estimation code is based on the implementation by Franziska Pellegrini and Stefan Haufe:

• GitHub: https://github.com/fpellegrini/PAC

Installation

1. Clone this repository:

   git clone https://github.com/braindatalab/AD_PAC.git
   cd AD_PAC

2. Download and install required toolboxes (FieldTrip, Brainstorm, FreeSurfer)

3. Download the MEG and MRI data from OSF and place in Dataset/ folder

4. Add required toolboxes to your MATLAB path in the main.m script

Usage

The complete analysis pipeline is orchestrated through the main.m script. The pipeline consists of five main stages:

1. Head Modeling Pipeline

anatomyTransform;      % Correct MRI transformation matrices and save as NIfTI files
removeElec;            % Remove 'elec' field from MEG files
buildHeadModels;       % Build head models and leadfields using Brainstorm and OpenMEEG
processBSFiles;        % Convert to MNI, extrapolate to high-res cortex, save leadfields

2. Preprocessing Pipeline

preprocessing;         % Resample, interpolate bad channels, segment trials, reject bad trials

3. Source Reconstruction Pipeline

sourceReconstruction_p; % LCMV beamformer source reconstruction

4. PAC Estimation Pipeline

seedRoitoCortex;       % Compute cross-site PAC between hippocampus and cortical ROIs
                       % (theta/alpha - low-gamma coupling)

5. Statistical Analysis Pipeline

prepareTable;          % Prepare table with clinical/demographic data and PAC values
powerAnalysis;         % Analyze PSD and region-wise power differences
clusterBasedModel;     % Cluster-based statistical analysis on PAC differences
linearMixedEffectsModel; % Linear mixed-effects modeling

Running the Complete Pipeline

To run the full analysis:

1. Open MATLAB and navigate to the repository directory
2. Edit main.m to set your main_path variable
3. Ensure all required toolboxes are in your MATLAB path
4. Run individual sections or the complete pipeline:

% Set the main path
main_path = './';

% Add all subdirectories to path
addpath(genpath(main_path));

% Run individual pipeline stages as needed
% (See main.m for complete pipeline)

Directory Structure

AD_PAC/
├── main.m                    # Main pipeline orchestration script
├── Dataset/                  # Data directory (not included, download from OSF)
├── HeadModelling/           # Head model construction scripts
│   ├── anatomyTransform.m
│   ├── buildHeadModels.m
│   ├── processBSFiles.m
│   └── removeElec.m
├── PreProcessing/           # MEG data preprocessing scripts
│   ├── preprocessing.m
│   ├── interpolate_bad_channels.m
│   ├── detect_bad_trials.m
│   └── plot_data.m
├── SourceReconstruction/    # Source localization scripts
│   ├── sourceReconstruction_p.m
│   ├── lcmv_meg.m
│   └── ...
├── Pac/                     # Phase-amplitude coupling analysis
│   ├── seedRoitoCortex.m
│   ├── er_pac.m
│   └── ...
└── Stat/                    # Statistical analysis scripts
    ├── prepareTable.m
    ├── powerAnalysis.m
    ├── clusterBasedModel.m
    └── linearMixedEffectsModel.m

Pipeline Details

Head Modeling

The head modeling pipeline creates forward models for MEG source localization:

• Transforms anatomical MRI data to standard space
• Constructs realistic head models using OpenMEEG
• Generates leadfield matrices for source reconstruction
• Outputs are saved for each subject and used in subsequent analysis

Preprocessing

MEG data preprocessing includes:

• Resampling to 200 Hz (configurable)
• Bad channel detection and interpolation
• Trial segmentation (2-second trials)
• Artifact rejection based on amplitude thresholds
• Quality control plots are generated for each step

Source Reconstruction

Source-level analysis using LCMV beamformer:

• Computes spatial filters for each cortical location
• Projects sensor-level MEG data to source space
• Focuses on regions of interest (ROIs) including hippocampus and cortical areas
• Produces time series for each ROI

PAC Analysis

Phase-amplitude coupling estimation:

• Computes coupling between hippocampal theta/alpha phase (4-12 Hz)
• And cortical low-gamma amplitude (30-55 Hz)
• Analyzes left/right hippocampus to ipsilateral/contralateral cortex
• Uses bispectral analysis methods (Antisymmetrized bicoherence)
• Results saved per subject for statistical analysis

Statistical Analysis

Group-level statistics:

• Prepares data tables with demographics and PAC metrics
• Power spectral density analysis
• Cluster-based permutation testing for PAC differences
• Linear mixed-effects models accounting for repeated measures
• Visualization of significant results and correlations

Output

Results are organized in the Results/ directory:

• Results/Preprocessing/: Preprocessing quality control plots
• Results/source/: Source reconstruction results per subject
• Results/SeedtoCortex/: PAC analysis results per subject
• Statistical analysis outputs including plots and model results

Troubleshooting

Common Issues

1. Missing toolboxes: Ensure FieldTrip, Brainstorm, and FreeSurfer are properly installed and in your MATLAB path
2. Data not found: Verify that the Dataset folder exists and contains the downloaded MEG/MRI data
3. Memory issues: Large datasets may require substantial RAM; consider processing subjects in batches
4. FreeSurfer preprocessing: MRI files must be processed with FreeSurfer before running the head model pipeline

Support

For questions or issues, please open an issue on the GitHub repository.

License

See LICENSE file for details.

Acknowledgments

• PAC analysis code adapted from Franziska Pellegrini and Stefan Haufe
• FieldTrip, Brainstorm, and FreeSurfer development teams
• Data contributors and participants