COGNESTIC 2022
Cognitive Neuroscience Skills Training In Cambridge
19-30 September 2022
MRC Cognition and Brain Sciences Unit, University of Cambridge
Please note the webpage for COGNESTIC 2023 is now available.
The Cognitive Neuroscience Skills Training In Cambridge (COGNESTIC) will provide researchers with training in state-of-the-art methods for neuroimaging and neurostimulation. It will be held at the MRC Cognition & Brain sciences Unit (CBU), University of Cambridge. Our tutors are experts using and developing MRI, fMRI, EEG/MEG and TMS analysis methods, as well as in statistical methods and open science. Our programme will focus on demos of neuroimaging methods, with pre-recorded lectures on the background theory being made available in advance. Additional tours will be offered of the CBU’s facilities to learn tips about data acquisition, as well as special guest lectures on applications to cognitive and clinical neuroscience. There will also be ample time for participants to mingle with tutors and attendees. The course will focus on principles of methods that are relevant to cognitive neuroscientists, and will use open source software packages (but it is not a course on a particular software). Basic coding skills in Matlab and/or Python are desirable but not required. Attendees can bring their own laptops to follow the demos (after downloading datasets and software in advance), but this is not required.
Registration for COGNESTIC is now closed.
If you are looking for a PhD position then please visit our studentships page. The schedule below contains links to individual scientists who you can contact directly.
Please contact olaf.hauk@mrc-cbu.cam.ac.uk for further information.
Schedule
Click on a title of a session to find more information about course material and suggested reading, and click on the name of the presenter to learn more about them.
Mon, 19 |
Tue, 20 |
Wed, 21 |
Thu, 22 |
Fri, 23 |
Sat, 24 |
|
9-12.00 |
Introduction and Open Science | Diffusion MRI I | fMRI I | fMRI III | Connectivity for fMRI | Lab visits
(fMRI, EEG/MEG, TMS) 10-12.00 |
13.30-16.30 |
Structural MRI | Diffusion MRI II | fMRI II | fMRI IV |
Eye-tracking | |
17.00Special Talk |
Large-scale, multimodal imaging: the CamCAN example | Human Cognitive Neuroscience and how it is taught | Brain plasticity for alternative hand control: From phantoms to robotic fingers | Applying neuroimaging to understand rare genomic disorders | Nibbles and drinks in the CBU garden
16.30-19.00 All welcome |
Mon, 26 |
Tue, 27 |
Wed, 28 |
Thu, 29 |
Fri, 30 |
Sat, 1 |
|
9-12.00 |
EEG/MEG I – Pre-processing | EEG/MEG III – Time-Frequency and Functional Connectivity | MVPA/RSA I | Statistics in R | DCM for M/EEG I | |
13.30-16.30 |
EEG/MEG II – Source Estimation | Graph Theory | MVPA/RSA II | Brain Stimulation |
DCM for M/EEG II | |
17.00Special Talk |
Localising and understanding the neural systems for processing spoken words | Using MRI protocols from the Human Connectome Project for precision imaging of the multiple demand system | Development, Disorders and Data Science | Dynamic causal models of dementia |
Selected Topics
Structural MRI:
Pre-recorded lectures: principles of magnetic resonance, spatial encoding in MRI, image formation and k-space, MRI tissue contrast.
Demo: voxel based morphometry (VBM) using FSL, brain parcellation and cortical thickness analysis with Freesurfer, group-level analysis.
Diffusion MRI:
Pre-recorded lectures: principles of water diffusion in the brain, diffusion tensor model, advanced diffusion models, introduction to tractography, limitations and artefacts.
Demo: pre-processing and quality control using FSL and mrtrix, diffusion tensor model fitting, group-level analysis, tractography and structural connectivity using mrtrix.
fMRI:
Data management (BIDS), Quality control (MRIQC), Pre-processing (fMRIPrep, Nipype, SPM), Statistical analysis (Nilearn, SPM).
EEG/MEG:
Pre-processing (Maxfilter, filtering), artefact correction/rejection, averaging, inverse and forward problem, source estimation, source space and parcellations, head models, spatial resolution, minimum-norm estimation, beamforming, time-frequency and functional connectivity analysis, Fourier analysis, wavelets, coherence and phase-locking, using MNE Python.
MVPA/RSA:
Classification, types of classifiers, cross-validation, representational similarity analysis (RSA), representational dissimilarity matrix (RDM), types of dissimilarity, multidimensional scaling (MDS)
Graph Theory:
Topology, graph analysis, network neuroscience, brain connectivity, random networks, network analysis, Erdős–Rényi networks, small worldness, structural covariance networks, morphometric similarity networks, degree distribution, hubs, rich clubs, clustering, modules, null models, speech networks; Brain Connectivity Toolbox
Eye-tracking:
Experiment builders (OpenSesame, PsychoPy), experiment coding (Python), experimental paradigms, EDF data processing, pupil analysis, gaze analysis
Brain Stimulation:
TMS(+fMRI), CS, single-pulse, rTMS, experimental paradigms, excitatory/inhibitory stimulation, simulation of non-invasive brain stimulation (TMS and ultrasound), ultrasound stimulation (SimNIBs software)
Functional Connectivity:
Time-frequency analysis, wavelets, coherence, phase-locking
Effective Connectivity:
Functional vs effective connectivity for fMRI; Dynamic Causal Modelling (DCM) of fMRI; DCM for evoked responses in M/EEG
General Statistics:
Introduction to R, common statistical models in R including general linear models, generalised linear models, repeated measure anova and generalised linear mixed models, Power Analysis, Bayesian Inference and sequential designs