Background to Open Science
Rik Henson

Viewing

Open Cognitive Neuroscience

Primer on Python
Kshipra Gurunandan

Viewing

Introduction to Python and notebooks

Structural MRI I and II - VBM and surface-based analysis
Marta Correia

Viewing

Introduction to MRI Physics and image contrast
Slides

Diffusion MRI I - Preprocessing, model fitting and group analysis
Marta Correia

Viewing

Introduction to Diffusion MRI - Part I
Slides

Diffusion MRI II - Tractography and the anatomical connectome
Marta Correia

Viewing

Introduction to Diffusion MRI - Part II
Slides

fMRI I - Data Management
Dace Apšvalka

Viewing

fMRI Data Structure & Terminology (6:47)
Brain imaging data structure (11:07)

fMRI II - Preprocessing
Dace Apšvalka

Viewing

fMRI Artifacts and Noise (11:57)
Pre-processing I (10:17)
Pre-processing II (7:42)

fMRI III - Analysis
Dace Apšvalka

Viewing

GLM applied to fMRI (11:21)
Model Building – conditions and contrasts (11:48)
Model Building - nuisance variables (13:58)
Multiple Comparisons (9:03)
Group-level Analysis I (7:05)

fMRI Connectivity
Petar Raykov

Viewing

Functional Connectivity in fMRI

Network Analysis
Rik Henson

Viewing

Introduction to Network Neuroscience

EEG/MEG I – Measurement and Pre-processing
Olaf Hauk

Viewing

1. Overview of EEG/MEG data processing from raw data to source estimates
Event-related paradigm, sample dataset, power spectrum, pre-processing, artefact correction, epoching and averaging, visualization, source estimation.
2. The generation of EEG/MEG signals
Dipole sources, volume currents, sensor types (EEG, magnetometers, gradiometers) and their leadfields.
3.Frequency and temporal filtering of EEG/MEG data
Frequency spectrum, temporal smoothing, relationship between frequency and time domain, filters (low-/high-/band-pass, Notch), aliasing, decibels.
4. Differential sensitivity of EEG and MEG
Volume conduction, sensor types and their leadfields, sensitivity maps, dipoles vs spatially extended sources.
5.Event-related potentials and fields
Averaging, evoked and induced activity, number of trials, artefact rejection, parametric designs, regression.
Fore more on this topic see here.

EEG/MEG II – Head Modelling and Source Estimation
Olaf Hauk

Viewing

1. The EEG/MEG forward model
Basic formulation of the EEG/MEG forward problem, linear equation, basics of head modelling, examples of sensory evoked responses.
2. The EEG/MEG inverse problem
Non-uniqueness, under-determinedness, examples of non-uniqueness, source estimates for sensorily evoked activity.
3. The spatial resolution of linear EEG/MEG source estimation
Leakage and blurring, resolution matrix, point-spread functions (PSFs), cross-talk functions (CTFs), examples of PSFs and CTFs, regions-of-interest for source estimation.
4. Noise and regularisation in EEG/MEG source estimates
Over- and under-fitting, smoothing, regularisation parameter, data whitening, noise covariance matrix.
Fore more on this topic see here.

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

Viewing

1. Frequency spectra and the Fourier analysis
Periodic basis functions, Fourier Decomposition, frequency spectrum, Nyquist Theorem, steady state response.
2. Time-frequency analysis and wavelets
Fourier analysis, wavelets, trade-off between time and frequency resolution, wavelets, number of cycles, evoked and induced activity, beta bursts.
3.The basics of functional connectivity methods
Types of connectivity, amplitude envelope correlation, resting state analysis, Hilbert envelope, phase-locking, coherence, SNR bias, time-resolved connectivity.
Fore more on this topic see here.

EEG/MEG IV – Further Topics and BIDS
Olaf Hauk & Máté Aller

Viewing

1. Primer on group statistics for EEG/MEG data
Regions-of-interest (ROI) analysis, multiple comparison problem, cluster-based permutation tests, problems estimating cluster extent, MNE-Python tutorial.
2. Primer on decoding and RSA with EEG/MEG data
Basics of linear decoding, temporal generalisation, interpreting decoding weights, back-projection, representational similarity analysis (RSA).
3. Primer on multimodal integration
Types of neural “activity”, differential sensitivity of EEG/MEG vs fMRI, source weighting and priors, estimating deep sources with EEG/MEG.
Fore more on this topic see here.

MVPA/RSA I and II
Daniel Mitchell & Máté Aller

Viewing

Excellent presentations from Martin Hebart's MVPA course, on:
Introduction to MVPA
Introduction to classification.
If the links don't work, download from here and here.
Martin Hebart's lecture on RSA. If the link fails, download from here.
Primer on decoding and RSA with EEG/MEG data

Background to Open Science
Rik Henson

Websites

OSF
UKRN
BIDS

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

Viewing

Statistical power in neuroimaging
PayWall: open access
Comedian's Perspective on science and media

Slides

Open Science Talk Slides

Primer on Python
Kshipra Gurunandan

Software

Python, Pandas, NumPy, Matplotlib, Seaborn

Datasets

Wakeman Multimodal

Useful references

Python concepts with examples, Quick reference, Cheatsheets

Slides and scripts

To be added

Structural MRI I - Voxel-based morphometry
Marta Correia

Software

FSL

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

Structural MRI II - Surface-based analyses
Marta Correia

Software

Freesurfer

Suggested reading

Dale et al, 1999, Cortical surface-based analysis I
Fischl et al, 1999, Cortical surface-based analysis II

Suggested viewing

Using the command line

Diffusion MRI I - Preprocessing, Model Fitting and Group Analysis
Marta Correia

Software

dipy, FSL

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)

Diffusion MRI II - Tractography and the Anatomical Connectome
Marta Correia

Software

dipy

Suggested reading

MR Diffusion Tractography

fMRI I - Data Management
Dace Apšvalka

Software

HeudiConv, PyBIDS, NiBabel, Nilearn

Websites

Brain Imaging Data Structure
BIDS Starter Kit
BIDS Specification v1.9.0

Suggested reading

The brain imaging data structure (BIDS), Gorgolewski et al., 2016
The past, present, and future of the brain imaging data structure (BIDS), Poldrack et al., 2024

fMRI II - Pre-processing
Dace Apšvalka

Software

MRIQC, fMRIprep, NiPype

Suggested reading

Functional Magnetic Resonance Imaging Methods, Chen & Glover, 2015
Quality control in functional MRI studies with MRIQC and fMRIPrep, Provins et al., 2023
fMRIPrep: a robust preprocessing pipeline for functional MRI, Esteban et al., 2018
Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python, Gorgolewski et al., 2011

fMRI III - Analysis
Dace Apšvalka

Software

Nilearn

Suggested reading

The Statistical Analysis of fMRI Data, Lindquist, 2008
Controlling the familywise error rate in functional neuroimaging: a comparative review, Nichols & Hayasaka, 2003
Analysis of task-based functional MRI data preprocessed with fMRIPrep, Esteban et al., 2020
Guidelines for reporting an fMRI study, Poldrack et al., 2008

Suggested viewing

Model Building - temporal basis sets (11:08)
GLM Estimation (9:11)
Noise Models- AR models (9:57)
Inference- Contrasts and t-tests (11:05)
Multiple Comparisons by Martin Lindquist and Tor Wager (9:03)
FWER Correction (16:11)
FDR Correction (5:25)
More about multiple comparisons (14:39)

fMRI Connectivity
Petar Raykov

Software

Nilearn

Datasets

movie dataset

Reading

Resting-state functional Connectivity
Learning and comparing functional connectomes across subjects

Viewing

fMRI Functional Connectivity in fMRI
Overview of Effective Connectivity (not covered in person)

Tutorial slides and scripts

Functional Connectivity Nilearn Practical
DCM tutorial in SPM (not covered in-person)

Brain Network Analysis
Rik Henson

Software

Python 3.7+, nxviz, python-louvain

Datasets

Reading

- (Review article) Bullmore, E., Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10, 186–198 (2009). https://doi.org/10.1038/nrn2575
- (Textbook reference for more information) Alex Fornito, Andrew Zalesky, and Edward Bullmore. Fundamentals of brain network analysis. Academic press, 2016.

Viewing

Understanding your brain as a network and as art by Prof. Dani Bassett.

Slides

https://github.com/isebenius/COGNESTIC_network_analysis/ Slides

EEG/MEG I – Measurement and Pre-processing
Olaf Hauk

Software and datasets

This will be part of a download that will become available later.
MNE-Python software homepage
MNE stand-alone installation instructions for COGNESTIC
Jupyter script to download sample datasets in MNE-Python

Essential and suggested viewing

0. Overview of EEG/MEG data processing from raw data to source estimates
Event-related paradigm, sample dataset, power spectrum, pre-processing, artefact correction, epoching and averaging, visualization, source estimation.

1. A brief history of timing
A brief overview of the history of bioelectromagnetism, EEG and MEG.
2. The generation of EEG/MEG signals
Dipole sources, volume currents, sensor types (EEG, magnetometers, gradiometers) and their leadfields.
3. Basics of EEG/MEG artefact correction
Physiological and non-physiological artefacts, data decompositions, frequency/temporal/spatial filtering.
4. Frequency and temporal filtering of EEG/MEG data
Frequency spectrum, temporal smoothing, relationship between frequency and time domain, filters (low-/high-/band-pass, Notch), aliasing, decibels.
5. Topographical artefact correction of EEG/MEG data
Independent Component Analysis (ICA), Signal Space Projection (SSP), eye movement and heart beat artefacts.
6. Maxfiltering of MEG data
Signal Space Separation, options of Maxfilter software (e.g. movement compensation).
7. Differential sensitivity of EEG and MEG
Volume conduction, sensor types and their leadfields, sensitivity maps, dipoles vs spatially extended sources.
8. Event-related potentials and fields
Averaging, evoked and induced activity, number of trials, artefact rejection, parametric designs, regression.
+ Origin, significance, and interpretation of EEG (Michael X Cohen)
+ Analysing MEG data with MNE-Python and its ecosystem (Alex Gramfort)
+ List of EEG/MEG lectures

MNE-Python tutorials:
Overview of MNE-Python processing pipeline from preprocessing to source estimation
Preprocessing

Suggested reading

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

Slides and scripts

TBA

EEG/MEG II – Head Modelling and Source Estimation
Olaf Hauk

Software and datasets

This will be part of a download that will become available later.
MNE-Python software homepage
MNE stand-alone installation instructions for COGNESTIC
Jupyter script to download sample datasets in MNE-Python

Essential and suggested viewing

0. Overview of EEG/MEG data processing from raw data to source estimates
Event-related paradigm, sample dataset, power spectrum, pre-processing, artefact correction, epoching and averaging, visualization, source estimation.

1. The EEG/MEG forward model
Basic formulation of the EEG/MEG forward problem, linear equation, basics of head modelling, examples of sensory evoked responses.
2. Source spaces for EEG/MEG source estimation
Cortical surface, volumetric source space, spatial sampling, spatial normalisation, subcortical areas, source orientation.
3. Head models for EEG/MEG source estimation <<BR>>Volume conduction, Boundary Element Method (BEM), Finite Element Method (FEM), head model accuracy.
4. The EEG/MEG inverse problem
Non-uniqueness, under-determinedness, examples of non-uniqueness, source estimates for sensorily evoked activity.
5. The spatial resolution of linear EEG/MEG source estimation
Leakage and blurring, resolution matrix, point-spread functions (PSFs), cross-talk functions (CTFs), examples of PSFs and CTFs, regions-of-interest for source estimation.
6. Comparison of spatial resolution for linear EEG/MEG source estimation methods
Point-spread functions (PSFs), cross-talk functions (CTFs), resolution metrics (localisation error, spatial deviation), combination of EEG and MEG, PSFs and CTFs for minimum-norm type methods and beamformers, comparison of resolution metrics for minimum-norm type methods and beamformers.
7. Noise and regularisation in EEG/MEG source estimates
Over- and under-fitting, smoothing, regularisation parameter, data whitening, noise covariance matrix.
+ List of EEG/MEG lectures

MNE-Python Tutorials:
Forward Models and Source Spaces
Source Estimation

Suggested reading

Linear source estimation and spatial resolution
Comparison of common head models (e.g. BEM)
Guidelines for head modelling (incl. FEM)
General EEG/MEG Literature

Slides and scripts

TBA

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

Software and datasets

This will be part of a download that will become available later.
MNE-Python software homepage
MNE stand-alone installation instructions for COGNESTIC
Jupyter script to download sample datasets in MNE-Python

Essential and suggested viewing

1. The basics of signals in the frequency domain
Oscillations, periodic signals, sine and cosine, polar representation, complex numbers.
2. Frequency spectra and the Fourier analysis
Periodic basis functions, Fourier Decomposition, frequency spectrum, Nyquist Theorem, steady state response.
3. Time-frequency analysis and wavelets
Fourier analysis, wavelets, trade-off between time and frequency resolution, wavelets, number of cycles, evoked and induced activity, beta bursts.
4. The basics of functional connectivity methods
Types of connectivity, amplitude envelope correlation, resting state analysis, Hilbert envelope, phase-locking, coherence, SNR bias, time-resolved connectivity.
5. Spatial resolution (leakage) and connectivity
Connectivity in sensor and source space, point-spread and cross-talk, (non-)zero-lag signals, orthogonalisation, imaginary part of coherency, source space parcellations.

Suggested reading

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

Slides and scripts

TBA

EEG/MEG IV – Statistics and BIDS
Olaf Hauk & Máté Aller

Software

MNE-Python
MNE Installation for Cognestic

Datasets

Sample dataset in MNE-Python. Tutorials
MNE Installation for Cognestic
M/EEG combined dataset Download Datasets

Suggested reading

Estimating subcortical sources from EEG/MEG
Tutorial on converting MEG data to BIDS format
Example using MNE-BIDS-Pipeline for processing combined M/EEG data

Suggested viewing

Talk on Multimodal Integration

Slides and scripts

Notebooks Exercises Slides1 Slides2
Notebooks mne-bids-pipeline Slides mne-bids-pipeline

MVPA/RSA I
Daniel Mitchell

Software

Python 3.7+, including numpy, matplotlib, & scikit-learn.
(To visualise MRI data, you can use your software of choice, although for nifti format data you might like to consider MRIcroN or MRIcroGL.)

Datasets

Reading

Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide

Slides and scripts

To be added nearer the time.

MVPA/RSA II
Daniel Mitchell & Máté Aller

Software

Python implementation of the RSA Toolbox: Version 3.0

Datasets

Example data included with RSA toolbox

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
Schutt et al. (2023) Statistical inference on representational geometries
EEG/MEG:
Tutorial on EEG/MEG decoding
Temporal Generalization Interpretation of Weight Vectors

Slides and scripts

We will demo the RSA toolbox using the jupyter notebooks in the "demos" folder of the toolbox.
EEGMEG Notebooks EEG/MEG Slides