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Course Material for COGNESTIC 2022

The Cognitive Neuroscience Skills Training In Cambridge (COGNESTIC) is a 2-week course that provides researchers with training in state-of-the-art methods for neuroimaging and neurostimulation. You can find more information on the COGNESTIC webpage.

Below you will find documents, videos and web links that will be used for the course or can be used for preparation.

Introduction and Open Science
Rik Henson & Olaf Hauk

Websites

OSF
UKRN
BIDS

Suggested reading

Munafo et al, 2017, problems in science
Button et al, 2013, power in neuroscience
Poldrack et al, 2017, reproducible neuroimaging
Marek et al, 2022, power in neuroimaging association studies

Suggested viewing

Open Cognitive Neuroscience (will give this talk live on day)
Statistical power in neuroimaging
PayWall: open access
Comedian's Perspective on science and media

Tutorial slides and scripts

Open Science Talk Slides


Structural MRI - VBM and Surface-based Analysis
Marta Correia

Software

FSL Freesurfer

Datasets

Freesurfer tutorial data
Subset of the CamCAN dataset (~3GB) https://www.cam-can.org/, please sign data user agreement if using

Suggested reading

Introduction to GLM for structural MRI analysis
Good et al, 2001, A VBM study of ageing
Smith et al, 2004, Structural MRI analysis in FSL
Dale et al, 1999, Cortical surface-based analysis I
Fischl et al, 1999, Cortical surface-based analysis II

Suggested viewing

Using the command line
Introduction to MRI Physics and image contrast
Slides

Tutorial slides and scripts

FSLVBM slides
FSLVBM tutorial
FSLVBM bash script

FreeSurfer Cortical Thickness slides
Freesurfer tutorials
FS check location script
FS visualising the output script
FS group analysis script
FS ROI analysis script


Diffusion MRI I - DTI Model Fitting and Group Analysis
Marta Correia

Software

FSL

Datasets

BTC_preop

Suggested reading

FSL Diffusion Toolbox Wiki
Le Bihan et al, 2015, What water tells us about biological tissues
Soares et al, 2013, A short guide to Diffusion Tensor Imaging
Smith et al, 2006, Tract-based spatial statistics (TBSS)

Suggested viewing

Introduction to Diffusion MRI - Part I
Slides

Tutorial slides and scripts

FSL DTI and TBSS slides
DTI and group analysis in TBSS tutorial
FSL DTI pipeline script
FSL TBSS script
FSL Group QC script


Diffusion MRI II - Tractography and Structural Connectivity
Marta Correia

Software

MRtrix3

Datasets

BTC_preop

Suggested reading

MRtrix3 documentation
MR Diffusion Tractography

Suggested viewing

Introduction to Diffusion MRI - Part II
Slides

Tutorial slides and scripts

MRtrix Tractography slides
MRtrix Tractography tutorials
MRtrix preprocessing script
MRtrix CSD Tractography script
MRtrix connectome script


fMRI I - Data management, structure, manipulation
Dace Apšvalka

Software

HeudiConv, PyBIDS, NiBabel, Nilearn

Datasets

Wakeman Multimodal

Suggested reading

Gorgolewski et al., 2016, The brain imaging data structure (BIDS)

Suggested viewing

BIDS for MRI: Structure and Conversion by Taylor Salo (13:39)
fMRI Data Structure & Terminology by Martin Lindquist and Tor Wager (6:47)

Slides and scripts

https://github.com/dcdace/COGNESTIC-fMRI


fMRI II - Quality control & Pre-processing
Dace Apšvalka

Software

MRIQC, fMRIprep, Nipype

Datasets

Wakeman Multimodal

Suggested reading

Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods
Ashburner J & Friston KJ (2004), Rigid body registration
Maclaren et al. (2013), Prospective Motion Correction in Brain Imaging: A Review
Sladky et al. (2011), Slice-timing effects and their correction in functional MRI
Friston et al. (2000), To Smooth or Not to Smooth?: Bias and Efficiency in fMRI Time-Series Analysis
Esteban et al., 2018, fMRIPrep: a robust preprocessing pipeline for functional MRI

Suggested viewing

fMRI Artifacts and Noise by Martin Lindquist and Tor Wager (11:57)
Pre-processing I by Martin Lindquist and Tor Wager (10:17)
Pre-processing II by Martin Lindquist and Tor Wager (7:42)

Slides and scripts

https://github.com/dcdace/COGNESTIC-fMRI


fMRI III - Subject Level Analysis
Dace Apšvalka

Software

Nipype, Nilearn, SPM12

Datasets

Wakeman Multimodal

Suggested reading

Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear approach
Poline & Brett (2012), Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: does love last forever?
Monti (2011), Statistical analysis of fMRI time-series: a critical review of the GLM approach
Nichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative review
Chumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fields
Woo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations
Lindquist (2008), The Statistical Analysis of fMRI Data

Suggested viewing

The General Linear Model by Martin Lindquist and Tor Wager (12:24)
GLM applied to fMRI by Martin Lindquist and Tor Wager (11:21)
Model Building I – conditions and contrasts by Martin Lindquist and Tor Wager (11:48)
Model Building II – temporal basis sets by Martin Lindquist and Tor Wager (11:08)
Model Building III- nuisance variables by Martin Lindquist and Tor Wager (13:58)
GLM Estimation by Martin Lindquist and Tor Wager (9:11)
Noise Models- AR models by Martin Lindquist and Tor Wager (9:57)
Inference- Contrasts and t-tests by Martin Lindquist and Tor Wager (11:05)
Multiple Comparisons by Martin Lindquist and Tor Wager (9:03)
FWER Correction by Martin Lindquist and Tor Wager (16:11)
FDR Correction by Martin Lindquist and Tor Wager (5:25)
More about multiple comparisons by Martin Lindquist and Tor Wager (14:39)

Slides and scripts

https://github.com/dcdace/COGNESTIC-fMRI


fMRI IV - Group Level Analysis & Reporting
Dace Apšvalka

Software

Nilearn, PySurfer, SPM12

Datasets

Wakeman Multimodal

Suggested reading

Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data
Nichols et al. (2017), Best practices in data analysis and sharing in neuroimaging using MRI
Poldrack et al. (2008), Guidelines for reporting an fMRI study
Gorgolewski et al. (2016), NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain
Markiewicz et al. (2021), The OpenNeuro resource for sharing of neuroscience data

Suggested viewing

Group-level Analysis I by Martin Lindquist and Tor Wager (7:05)
Group-level Analysis II by Martin Lindquist and Tor Wager (10:09)
Group-level Analysis III by Martin Lindquist and Tor Wager (14:01)

Slides and scripts

https://github.com/dcdace/COGNESTIC-fMRI


Connectivity for fMRI
Rik Henson

Software

SPM12

Datasets

Wakeman Multimodal

Suggested reading

Resting-state functional Connectivity
Simple Intro to DCM
fMRI preprocessing in SPM12 (for demo)
SPM12 manual (Chapter 36)

Suggested viewing

fMRI Functional Connectivity, including DCM
Bayesian Model Comparison (for DCM for fMRI/MEEG)

Slides and scripts


Eye-tracking
Edwin Dalmaijer

Software

Python NumPy, SciPy, Matplotlib

Datasets

Example Data EyeLink

Suggested reading

https://doi.org/10.3758/s13428-021-01762-8 Paper on eye-tracking reporting standards (great for beginners and experts alike)

Suggested viewing

https://www.youtube.com/watch?v=F5eBln42VyM Talk at the MRC CBU on how to hack pupillometry studies

Slides and scripts


EEG/MEG I – Pre-processing
Olaf Hauk

Software

MNE-Python
MNE Installation for Cognestic

Datasets

Sample dataset in MNE-Python. Tutorials
MNE Installation for Cognestic

Suggested reading

Digitial Filtering
Filtering How To
Maxwell Filtering
General EEG/MEG Literature

Suggested viewing

Introduction to EEG/MEG Preprocessing
What are we measuring with M/EEG?

Slides and scripts

Notebooks and Slides


EEG/MEG II – Source Estimation
Olaf Hauk

Software

MNE-Python
MNE Installation for Cognestic

Datasets

Sample dataset in MNE-Python. Tutorials
MNE Installation for Cognestic

Suggested reading

Linear source estimation and spatial resolution
General EEG/MEG Literature

Suggested viewing

Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM

Slides and scripts

Notebooks and Slides


EEG/MEG III – Time-Frequency and Functional Connectivity
Olaf Hauk

Software

MNE-Python
MNE Installation for Cognestic

Datasets

Sample dataset in MNE-Python. Tutorials
MNE Installation for Cognestic

Suggested reading

Tutorial on Functional Connectivity
Analyzing Neural Time Series Data
General EEG/MEG Literature

Suggested viewing

Introduction to time-frequency and functional connectivity analysis
Time-Frequency Analysis of EEG Time Series

Slides and scripts

Notebooks and Slides


Graph Theory
Caroline Nettekoven Zoom link

Software

Brain Connectivity Toolbox in Matlab, BCT Documentation

Datasets

Coding exercises
Exercise solutions

Suggested reading

Complex brain networks: graph theoretical analysis of structural and functional systems

Suggested viewing

Slides

Slides and scripts


MVPA/RSA I
Daniel Mitchell

Software

The Decoding Toolbox in Matlab. (This might not be accessible from the CBU internet connection, so please download it in advance or use a difffernt wifi connection)

Datasets

The Decoding Toolbox example dataset
(See toolbox webpage for a lower resolution alternative)

Suggested reading

Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide
Hebart et al. (2014) The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

Suggested viewing

Excellent presentations from Martin Hebart's MVPA course, on:
Introduction to MVPA
Introduction to classification. (I've suggested these two, but the others are worth a look too.)

Slides and scripts

Slides for morning session - MVPA
These functions should be saved in a subfolder of the Decoding Toolbox demos folder
These functions should be saved in the top-level folder
(Please see the 3rd slide for an overview of the file structure expected by the demo scripts)


MVPA/RSA II
Daniel Mitchell

Software

The RSA toolbox in Matlab
For the demos, please download this alternative version: https://git.fmrib.ox.ac.uk/hnili/rsa
(Note that the toolbox development has recently switched to Python. We will not be demoing this version, but you can find it here: Version 3.0)

Datasets

Group-averged example data from Mitchell & Cusack (2016) Semantic and emotional content of imagined representations in human occipitotemporal cortex

Suggested reading

Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems neuroscience
Kriegeskorte & Kievit (2013) Representational geometry: integrating cognition, computation, and the brain
Nili et al. (2014) A toolbox for representational similarity analysis

EEG/MEG:
Tutorial on EEG/MEG decoding
Temporal Generalization

Suggested viewing

Martin Hebart's lecture on RSA

Slides and scripts

Slides for afternoon session - RSA
These functions should be saved in rsa-master/Demos
EEGMEG Notebooks and Slides


Statistics in R
Peter Watson

Software

R

Datasets

Data&Code Readme

Suggested reading

Statistical Methods for Psychology (Howell)
Introduction to R
Discovering statistics using R
An introduction to statistical learning with applications in R

Suggested viewing

CBU Statistics Lectures

Slides and scripts


Brain Stimulation
Ajay Halai

Software

SIMNIBS (also requires access to Matlab, FSL and Freesurfer to run certain functions, see SIMNIBS installation guide) and k-wave

Datasets

tutorial_data

Suggested reading

Approaches to brain stimulation ; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 2

Suggested viewing

slides

Slides and scripts

scripts


DCM for M/EEG
Pranay Yadav & Rik Henson

Software

SPM12

Datasets

Wakeman Multimodal

Suggested reading

Preprocessing M/EEG in SPM12
Simple Intro to DCM

Suggested viewing

Talk on DCM for M/EEG
MEEG connectivity other than DCM (not demo'ed, and related to Hauk talks above)

Slides and scripts



None: COGNESTIC2022 (last edited 2022-09-29 12:07:21 by AjayHalai)