COGNESTIC2023 - Methods
Self: COGNESTIC2023

Course Material for COGNESTIC 2023

The Cognitive Neuroimaging Skills Training In Cambridge (COGNESTIC) is a 2-week course that provides researchers with training in state-of-the-art methods for neuroimaging analysis and related methods. 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.

Software Installation Instructions

Access to much of the COGNESTIC-23 materials is available via Virtual Machine (VM). You will need at least 70GB of free space on your local hard drive, and at least 4GB of RAM. For instructions on how to install and set up the Cognestic23 VM see section ‘1 COGNESTIC Virtual Machine (full hands-on)’ for instructions. Data for the Structural MRI and Diffusion MRI are located inside the VM.

Full installation instructions can be found here. The installation can take some time (potentially more than an hour, depending on your download speed), so please reserve some time for this ahead of the event.



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


Primer on Python
Edwin Dalmijer

Websites

Python, NumPy, SciPy, Matplotlib, PsychoPy

Suggested reading

None

Suggested viewing

saliency-mapping of Taylor Swift music videos

Tutorial slides and scripts

None


Structural MRI I - Voxel-based morphometry
Marta Correia

Software

FSL

Datasets

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

Suggested viewing

Introduction to MRI Physics and image contrast
Slides

Tutorial slides and scripts

Intro to command line
VBM slides
FSL VBM tutorials
FSL VBM script
Hands on exercises for structural and diffusion MRI


Structural MRI II - Surface-based analyses
Marta Correia

Software

Freesurfer

Datasets

Freesurfer tutorial data

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

Tutorial slides and scripts

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


Diffusion MRI I - The Diffusion Tensor Model
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 tutorials
FSL DTI pipeline script
FSL TBSS script
FSL group QC script


Diffusion MRI II - Tractography and the Anatomical Connectome
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 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)
The Past, Present, and Future of 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/fMRI-COGNESTIC-23/


fMRI II - Quality control & Pre-processing
Dace Apšvalka

Software

MRIQC, fMRIprep

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/fMRI-COGNESTIC-23/


fMRI III - Subject Level Analysis
Dace Apšvalka

Software

Nilearn

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/fMRI-COGNESTIC-23/


fMRI IV - Group Level Analysis & Reporting
Dace Apšvalka

Software

Nilearn

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/fMRI-COGNESTIC-23/


fMRI Connectivity
Petar Raykov

Software

Nilearn Python

Datasets

movie dataset

Suggested reading

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

Suggested 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
Isaac Sebenius

Software

Python 3.7+, nxviz, python-louvain

Datasets

Suggested 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.

Suggested viewing

- Introduction to Network Neuroscience, minutes 0:00-48:30. A wonderful introduction to brain networks by Prof. Bratislav Misic.
- Understanding your brain as a network and as art by Prof. Dani Bassett.

Slides and scripts

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


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 Download Datasets

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 Exercises Slides1 Slides2 Slides3


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 Download Datasets

Suggested reading

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

Suggested viewing

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

Slides and scripts

Notebooks Exercises Slides1 Slides2 Slides3


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 Download Datasets

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 Exercises Slides1 Slides2Slides3


EEG/MEG IV – Advanced Topics
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

CoSMoMVPA using Octave. (These are not included in the virtual machine; you will need to install them yourself. If you have Matlab, you are welcome to use it instead of Octave, but the demos will be in Octave because it is open source.)
To visualise MRI data, you can use your software of choice, although for nifti format data you might like to consider MRIcroN.

Datasets

Tutorial data from CoSMoMVPA toolbox

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
Oosterhof et al. (2016) CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

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.
(Note: on 25/9/23-26/9/23 the above links stopped working due to a temporary issue with the host website. If this happens again, let me know.)

Slides and scripts

We will be using the demos from the "examples" folder of the CoSMoMVPA toolbox.
Exercises can be copied from the files here, pasted into an empty Octave file, and you can try to fill in the missing snippets.
extra example 1
extra example 2
slides


MVPA/RSA II
Daniel Mitchell

Software

Python implementation of the RSA Toolbox: Version 3.0

Datasets

Example data included with RSA toolbox

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

Suggested viewing

Martin Hebart's lecture on RSA
(Note: on 25/9/23-26/9/23 this link stopped working due to a temporary issue with the host website. If this happens again, let me know.)

Slides and scripts

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


Brain Stimulation, Pethysmography, Electromyography
Ajay Halai, Alexis Deighton McIntyre, Hristo Dimitrov

Software

Brain Stimulation:
E-field modelling for non-invasive brain stimulation using SimNIBS
Plethysmography:
Speech breathing-oriented toolbox for breath-belt data (MATLAB)

Datasets

Suggested reading

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

Plethysmography:
Heck, D. H., McAfee, S. S., Liu, Y., Babajani-Feremi, A., Rezaie, R., Freeman, W. J., ... & Kozma, R. (2017). Breathing as a fundamental rhythm of brain function. Frontiers in neural circuits, 10, 115. https://doi.org/10.3389/fncir.2016.00115
Varga, S., & Heck, D. H. (2017). Rhythms of the body, rhythms of the brain: Respiration, neural oscillations, and embodied cognition. Consciousness and Cognition, 56, 77-90. https://doi.org/10.1016/j.concog.2017.09.008
Allen, M., Varga, S., & Heck, D. H. (2022). Respiratory rhythms of the predictive mind. Psychological Review. https://doi.org/10.1037/rev0000391

EMG:
Consensus for experimental design in electromyography
Tutorial high-density EMG
Noninvasive Neural Interfacing With Wearable Muscle Sensors

Suggested viewing

Brain Stimulation: SimNIBs tutorial and SimNIBS youtube videos

Slides and scripts

Brain Stimulation: slides
Plethysmography: slides



None: COGNESTIC2023 (last edited 2024-06-11 15:57:44 by OlafHauk)