PACmeg

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MATLAB scripts for detecting and validating phase amplitude coupling (PAC) in electrophysiological data

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# PACmeg

MATLAB scripts for detecting and validating **phase amplitude coupling (PAC)** in electrophysiological data.

Written and maintained by **[Robert Seymour](http://neurofractal.github.io)**, June 2017 - October 2019.

![PACmeg](https://raw.githubusercontent.com/neurofractal/PACmeg/master/functions/pacmeg_logo.png)

## Manuscript & Citation

If you use these scripts, please cite:

```
Seymour, R. A., Rippon, G., & Kessler, K. (2017). 
The Detection of Phase Amplitude Coupling During Sensory Processing. 
Frontiers in Neuroscience 11, 487. 
https://doi.org/10.3389/fnins.2017.00487
```

## PAC Function

The pacMEG.m function can be used to create a phase x amplitude comodulogram, for data arranged in trial x time matrix:

* **[PACmeg](https://github.com/neurofractal/PACmeg/blob/master/functions/PACmeg.m)**

```matlab
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PACmeg: a function to do PAC
%
% Author: Robert Seymour (robert.seymour@mq.edu.au)
%
%%%%%%%%%%%
% Inputs:
%%%%%%%%%%%
%
% data              = data for PAC (size: trials*time)
% cfg.Fs            = Sampling frequency (in Hz)
% cfg.phase_freqs   = Phase Frequencies in Hz (e.g. [8:1:13])
% cfg.amp_freqs     = Amplitude Frequencies in Hz (e.g. [40:2:100])
% cfg.filt_order    = Filter order used by ft_preproc_bandpassfilter
%
% amp_bandw_method  = Method for calculating bandwidth to filter the 
%                   ampltitude signal:
%                        - 'number': +- nHz either side
%                        - 'maxphase': 1.5*max(phase_freq)
%                        - 'centre_freq': +-2.5*amp_freq
% amp_bandw         = Bandwidth when cfg.amp_bandw_method = 'number'; 
%
% cfg.method        = Method for PAC Computation:
%                   ('Tort','Ozkurt','PLV','Canolty)
%
% cfg.surr_method   = Method to compute surrogates:
%                        - '[]': No surrogates
%                        - 'swap_blocks': cuts each trial's amplitude at 
%                        a random point and swaps the order around
%                        - 'swap_trials': permutes phase and amp from
%                        different trials
% cfg.surr_N        = Number of iterations used for surrogate analysis
%
% cfg.mask          = filters ALL data but masks between times [a b]
%                   (e.g. cfg.mask = [100 800]; will 
%
% cfg.avg_PAC       = Average PAC over trials ('yes' or 'no')
%
%%%%%%%%%%%
% Outputs:
%%%%%%%%%%%
%
% - MI_matrix_raw   = comodulagram matrix (size: amp*phase)
% - MI_matrix_surr  = surrogate comodulagram matrix (size: surr*amp*phase)
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
```

Currently the function only accepts data from a **single** time-series which could be obtained using an atlas-based approach (e.g. AAL atlas or HCP-MMP 1.0).

## Implemented PAC algorithms:
- **[Tort et al., (2010)](https://journals.physiology.org/doi/full/10.1152/jn.00106.2010)**
- **[Ozkurt & Schnitzler (2011)](https://www.sciencedirect.com/science/article/pii/S0165027011004730?casa_token=e2nbjg9I4DQAAAAA:A45gR6W3700DGRe7sjDM6k35c8V8RfYRlhD9Uq2-ljSs76q-UcCKQNGvBLXxk0PvX4LAkIi5)**
- **[Canolty et al., (2006)](https://science.sciencemag.org/content/313/5793/1626)**
- **[Cohen (2008)](https://doi.org/10.1016/j.jneumeth.2007.10.012)**

## Example use:

```matlab

% To produce a PAC comodulogram using the 
% Tort et al., (2010) approach, between 2-10Hz phase and 
% 30-100Hz amplitude, with 200 surrogates:

cfg                     = [];
cfg.Fs                  = 1000;
cfg.phase_freqs         = [2:1:10];
cfg.amp_freqs           = [30:2:100];
cfg.method              = 'tort';
cfg.filt_order          = 3;
cfg.surr_method         = 'swap_blocks';
cfg.surr_N              = 200;
cfg.amp_bandw_method    = 'number';
cfg.amp_bandw           = 16;

[MI_raw,MI_surr]        = PACmeg(cfg,signal);
```

## NOTE:

**Please be aware of the various methodological pitfalls in PAC analysis before applying the scripts to your own data (see Seymour, Kessler & Rippon manuscript).**

For more information/queries please raise an ISSUE within Github or email me: rob.seymour@ucl.ac.uk. I am also very keen for collaborations to help improve and expand this code.

## Data Sharing (Seymour et al., 2017)

The data discussed in Seymour et al., (2017) was shared opnely usiing [Figshare](https://figshare.com/collections/The_Detection_of_Phase_Amplitude_Coupling_During_Sensory_Processing/3819106).

Scripts can be easily adapted for your computer by modifying the sensory_PAC.m script:

```matlab
data_dir = 'path_to_data';
scripts_dir = 'path_to_scripts';
fieldtrip_dir = 'path_to_fieldtrip';
subject = {'sub-01','sub-02','sub-03','sub-04','sub-05','sub-06','sub-07',...
    'sub-08','sub-09','sub-10','sub-11','sub-12','sub-13','sub-14',...
    'sub-15','sub-16'};
```

Fieldtrip version 20161024 was used during data analysis, and can be found from the /fieldtrip folder.

## Analysis for reproducing Seymour et al., (2017)

* [1_preprocessing_elektra_frontiers_PAC.m](https://github.com/neurofractal/PACmeg/blob/master/1_preprocessing_elektra_frontiers_PAC.m) = this script performs simple preprocessing steps and removes bad trials

* [2_get_source_power.m](https://github.com/neurofractal/PACmeg/blob/master/2_get_source_power.m) = this script performs source analysis in the gamma (40-60Hz) and alpha (8-13Hz) bands.

* [3_get_VE_frontiers_PAC.m](https://github.com/neurofractal/PACmeg/blob/master/3_get_VE_frontiers_PAC.m) = this script computes a V1 virtual electrode, using the HCP-MMP-1.0 atlas. Other atlases could easy be used (e.g. the AAL atlas).

* [4_calc_pow_change.m](https://github.com/neurofractal/PACmeg/blob/master/4_calc_pow_change.m) = this script calculates the change in oscillatory power (1-100Hz) using the V1 virtual electrode.

* [5_visual_PAC_four_methods.m](https://github.com/neurofractal/PACmeg/blob/master/5_visual_PAC_four_methods.m) = this script uses 4 different methods to quantify alpha-gamma PAC. Non-parametric statistics are then applied to determine changes in PAC between baseline and grating periods.

* [6_check_non_sinusoidal.m](https://github.com/neurofractal/PACmeg/blob/master/6_check_non_sinusoidal.m) = this script checks the low-frequency phase for differences in non-sinusoidal oscillations.

* [7_simulated_PAC_analysis.m](https://github.com/neurofractal/PACmeg/blob/master/7_simulated_PAC_analysis.m) = this script simulates PAC, checks for the detection of this coupling using three approaches, and investigates how much data is needed for reliable PAC estimates.

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