ethanknights / Knightsetal_fMRI-Cattell-Compensation

Knights, E., Henson, R. N., Morcom, A. M., Mitchell, D. J., & Tsvetanov, K. A. (2023). Neural Evidence of Functional Compensation for Fluid Intelligence in Healthy Ageing. bioRxiv.

Knights et al. (2023). Neural Evidence of Functional Compensation for Fluid Intelligence in Healthy Ageing.

This repository contains code accompanying the multivariate Bayesian machine-learning project described in the following preprint:
Knights, E., Henson, R. N., Morcom, A. M., Mitchell, D. J., & Tsvetanov, K. A. (2023). Neural Evidence of Functional Compensation for Fluid Intelligence in Healthy Ageing. bioRxiv, 2023-09.

Modelling Quickstart

Clone & download processed fMRI/behavioural data with shell from root:

git clone https://github.com/ethanknights/Knightsetal_fMRI-Cattell-Compensation

cd Knightsetal_fMRI-Cattell-Compensation

./setup.sh

Use R to perform univariate/multivariate modelling analysis (see README sections 2/4 in 'Analysis Pipline') e.g.:

setwd('R')

source('load_data.R')

source('plot_ROI.R') #For interaction: activation ~ age * bhv

Prerequisites

Software

• FSL v4.0+
• Matlab (tested with r2019a)
  • SPM12
    https://www.fil.ion.ucl.ac.uk/spm/software/spm12/
  • Commonality Analysis tools
    https://github.com/kamentsvetanov/CommonalityAnalysis
  • BrainNet toolbox
    https://www.nitrc.org/projects/bnv/
• R
  • MASS
  • BayesFactor
  • ggeffects
  • Popular manipulation/visualisation packages (e.g., ggplot2, dplyr, tidyverse)
• Python
  • gl package (to plot t-statistic maps in mricroGL)

Datasets

Raw data for Machine-Learning

• For performing Multivariate Bayes and the preceding whole-brain univariate analysis (e.g. generating second-level models, defining ROIs, extracting timeseries), download the Cattell first-level model images derived from automatic analysis (see Taylor et al. 2017 NeuroImage) by submitting a Cam-CAN data-request:
  https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess
  
  

Summary Data for Modelling

• For hypothesis testing using R download data from the Open Science Framework (OSF).

Linux/Mac

./setup.sh  #Download, extract & move the summary datasets from the OSF, to their required destinations

Windows

• Manaully download/extract the summary_data.zip from https://osf.io/v7kmh/ and move each .csv to the following corresponding analysis directories:

Univariate

mkdir R/csv
mv univariate.csv R/csv/

Univariate (Vascular control)

mkdir R_vascular/csv
mv univariate_vascular_RSFA.csv R_vascular/csv/

MVB

# Cuneal Cortex
mkdir MVB/R/dropMVBSubjects-1/70voxel_model-sparse/csv
mv MVB_cuneal.csv  MVB/R/dropMVBSubjects-1/70voxel_model-sparse/csv/

# Frontal cortex
mkdir MVB/R/dropMVBSubjects-1/control-frontalROI_70voxel_model-sparse/csv
mv MVB_frontal.csv MVB/R/dropMVBSubjects-1/control-frontalROI_70voxel_model-sparse/csv

MVB (Multivariate mapping)

# Cuneal Cortex
mv MVB_multvariateMapping-Cuneal/extradata_ShuffledGroupFVals.csv MVB/R/dropMVBSubjects-1/70voxel_model-sparse/csv/

# Frontal cortex
mv MVB_multvariateMapping-Frontal/extradata_ShuffledGroupFVals.csv MVB/R/dropMVBSubjects-1/control-frontalROI_70voxel_model-sparse/csv

Analysis Pipeline

1) Whole-brain Univariate Analysis

Use matlab to run fMRI preprocessing and generate the group univariate contrasts (i.e., second level model generation, TFCE correction, ROI definition and timeseries extraction):

pipeline.m

/* Alternatively, download the summary datasets described earlier in the Prerequisties section.

Use Python to plot TFCE-corrected t-statistic maps with MRIcroGL.

./python/mosaic_MRICRON_HardBIGEasy.py

gl.overlayload('./nii_HardBIGEasy/Intercept_tfce197.nii')
gl.minmax(2, 0, 10000)
gl.colorname (1,"1red")

./python/mosaic_MRICRON_Covariates.py:

gl.overlayload('/nii_Covariates/tval_Age_tfce197.nii')
gl.minmax(1, 0, 250)
gl.colorname (1,"2green")

gl.overlayload('nii_Covariates/tval_PC6_tfce197.nii')
gl.minmax(2, 0, 250)
gl.colorname (2,"1red")

2) Univariate Modelling

In R, generate the regression models & plots:

setwd('R')
source('loadData.R')

#For interaction: Bhv ~ age * gender
source('plot_bhv.R')

#For interaction: Activation ~ age * bhv
source('plot_ROI.R')

#For the control vascular RSFA analysis:
setwd('R_vascular')
source('load_data_RSFA.R')
source('plot_ROI_RSFA.R')

3) Multivariate Bayes

Use matlab to run the MVB machine-learning pipeline:

cd MVB

setupDir.m
wrapper.m
doPostProcessing.m

/* Alternatively, download the summary datasets described earlier in the Prerequisties section.

4) Multivariate Modelling

In R, generate the logistic regression models & plots for the ROI datasets independently:

#CHOOSE ONE ROI: 
setwd('./MVB/R/dropMVBSubjects-1/70voxel_model-spare') #Cuneal Cortex
setwd('./MVB/R/dropMVBSubjects-1/control_frontalROI_70voxel_model-spare') #Frontal Cortex

#Load data into dataframe 'df'
source('run_loadData.R')

For testing the multvariate mapping, use raincloud package:

source('run_MVB_shuffledMapping.R') 

For logistic regression of Boost likelihood, use MASS/polr packages:

source('run_fMRI_MVB.R')

How to Acknowledge

Please cite:
Knights, E., Henson, R. N., Morcom, A. M., Mitchell, D. J., & Tsvetanov, K. A. (2023). Neural Evidence of Functional Compensation for Fluid Intelligence in Healthy Ageing. bioRxiv, 2023-09.