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Convolutional GLM
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fMRI
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Block design fMRI
Block design fMRI
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Visual inspection
Removal of dummy scans
Realignment
Slice timing correction
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Normalisation
Smoothing
Batch interface
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Statistical modelling
First-level modelling - block design
Event-related fMRI
Event-related fMRI
Preprocessing
Categorical Modelling
Parametric Modelling
Bayesian Analysis
Group-level fMRI
Group-level fMRI
One-sample t-test
Two-sample t-test
Factorial
VBM
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M/EEG
M/EEG
EEG mismatch negativity data
Advanced topics in artefact removal
M/EEG convolution modelling
MEG source localisation
Multimodal
Multimodal
EEG
MEG
fMRI
Fusion
Multimodal, multisubject data fusion
Multimodal, multisubject data fusion
Preprocessing
Evoked analysis
Time-frequency analysis
fMRI
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References
OPM
OPM
Sensor level: Evoked response
Source level: Evoked response
Coregistration
Simulation
Supported file formats
DCM
DCM
DCM for fMRI - 1st level (Attention)
DCM for fMRI - 2nd level (Motor)
DCM for fMRI - 1st level (Language)
DCM for fMRI - 2nd level (Language)
DCM for evoked responses
DCM for induced responses
DCM for phase coupling
DCM for steady state responses
DCM for cross-spectral densities
DAiSS
DAiSS
Beamforming: MEGIN/Elekta/Neuromag
Beamforming: OPM
Beamforming: Simulated Data
Courses
Courses
M/EEG
M/EEG
Course schedule
Datasets
Lecture recordings
Lecture recordings
Introduction & resources
What are we measuring with M/EEG?
Data preprocessing (talk & demo)
General linear model (GLM) and classical inference
Convolutional GLM
Multiple comparisons - problems & solutions
Analysis of optically pumped magnetometer (OPM) data (talk & demo)
Bayesian inference
M/EEG source analysis (talk & demo)
The principles of dynamic causal modelling (DCM)
DCM for evoked responses
DCM for cross-spectral densities (talk & DCM demo)
Bayesian model selection and averaging
DAiSS: An SPM toolbox for alternate source imaging approaches (talk & demo)
Group M/EEG data analysis
fMRI/VBM
fMRI/VBM
Course schedule
Datasets
Preparatory materials
Learning objectives
Lecture recordings
Lecture recordings
Spatial preprocessing
General linear model
Contrasts & classical inference
Group analysis
Random field theory
Voxel-based morphometry
Experimental design
Event-related fMRI
Bayesian inference
Introduction to DCM
DCM: Testing hypotheses
Earlier recordings
Reference documentation
Reference documentation
M/EEG
M/EEG
M/EEG preprocessing
Sensor-level analysis
Source imaging
DCM for evoked responses
Development
Development
Git
Guidelines
Testing
Compilation
Convolutional
GLM
ΒΆ
In this talk,
Dr Ashwani Jha
describes advanced applications of the
GLM
.
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