adding course pages and videos

docs/courses/fmri_vbm/bayesian_inference.md

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+# Bayesian inference
+
+In this talk, [Prof Chris Mathys](https://chrismathys.com/) explores the fundamentals of Bayesian inference and how it helps us extract meaningful insights from data. Whether you're new to the concept or already familiar, the talk covers the key aspects of this mathematical framework for logical reasoning about uncertain quantities, particularly in the analysis of neuroimaging data.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/vvea8DcD3xw?si=wTd6NEWoMRHyKMiN" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/contrasts_classical_inference.md

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+# Contrasts & classical inference
+
+In this talk, [Prof Christophe Phillips](https://christophephillips.github.io) provides an overview of contrast and statistical inference in the context of the mass univariate approach. The discussion builds upon concepts presented earlier on the GLM and focuses on the latter part of the analysis pipeline.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/H70QskuskdY?si=l8hqXq7OdvLNM1VM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/dcm_hypothesis_testing.md

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+# DCM: Testing hypotheses
+
+In the last lecture of the course, [Dr Peter Zeidman](https://peterzeidman.co.uk) provides a comprehensive overview of the steps involved in performing a DCM analysis for fMRI data. This talk summarises the key concepts discussed throughout the course and sheds light on the process of model inversion using Bayesian statistics.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/Yba3-2KPfSo?si=NmlOCtyCs4uCPoir" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/dcm_introduction.md

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+# Introduction to DCM
+
+In this talk, [Dr Edda Bilek](https://www.zi-mannheim.de/en/research/people/person/5186.html) provides an introductory overview of the use of DCM in neuroimaging. The talk aims to give a broad understanding of DCM, its applications, and the modelling and estimation process as they are implemented in SPM.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/GcIvdqotdpY?si=zRq-mG3B2feh4eQy" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/event_related_fmri.md

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+# Event-related fMRI
+
+In this talk, [Dr Sonia Medina](https://medicine.exeter.ac.uk/clinical-biomedical/people/profile/index.php?web_id=Sonia_Medina) discusses the importance of understanding the BOLD signal and HRF for fMRI data analysis. This talk delves into the importance of modelling fMRI data accurately and the key considerations when building design matrices for analysis.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/hpyh0JSMKxM?si=883tIMmogpq33mVf" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/experimental_design.md

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+# Experimental design
+
+In this talk, [Dr Carolin Moessnang](https://www.zi-mannheim.de/en/research/people/person/6741.html) explains the fundamental concepts of experimental designs in fMRI experiments. The goal is to provide an overview of different experimental designs and highlight important considerations when planning an fMRI study.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/8m62gGbDu7w?si=ir_g6MuUSWlbKrGi" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/glm.md

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+# The general linear model
+
+In this talk, [Dr Nadège Corbin](https://scholar.google.com/citations?user=TjIzldkAAAAJ&hl=fr) discusses the concept of the GLM in the context of fMRI analysis. The GLM is a widely used statistical approach that allows researchers to analyse fMRI time series data and make inferences about brain function. The talk outlines the key components and steps involved in creating a GLM.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/Didz0hGzx2M?si=KmkHvV_Epu7YKc6j" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/group_analysis.md

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+# Group analysis
+
+In this talk, [Dr Emma Holmes](https://emmaholmes.co.uk) provides an overview of methods for conducting group analyses in neuroimaging studies using SPM. The talk covers fixed and random effects analyses, and provides examples of different within- and between-subject designs.
+
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/1MrRnjrOfLM?si=bcQc7JB4l6gfEueh" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/preprocessing.md

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+# Spatial preprocessing
+
+In this talk, [Prof John Ashburner](https://www.fil.ion.ucl.ac.uk/~john/) provides an overview of spatial preprocessing steps for fMRI data. The talk covers realignment, distortion correction, coregistration, normalisation, and spatial smoothing.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/jgm1jzeKSg8?si=76LMuzKOReR-Pcx9" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/random_field_theory.md

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+# Random field theory 
+
+In this talk, [Prof Tom Nichols](https://www.bdi.ox.ac.uk/Team/t-e-nichols) describes how random field theory can be used to help determine the presence of real effects amidst statistical noise in neuroimaging data. The talk outlines various approaches to correcting for multiple comparisons, including family-wise error correction, false discovery rate correction, and permutation testing.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/PrYpYsu8iTw?si=eu08hMMHTvD3s3qK" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/fmri_vbm/vbm.md

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+# Voxel-based morphometry
+
+In this talk, [Dr Christian Lambert](https://profiles.ucl.ac.uk/11034-christian-lambert) focuses on the concept of structural brain analysis and explores the use of VBM and its implementation in SPM. The talk covers preprocessing fundamentals, common pitfalls, and hints for effective data analysis. Additionally, the extensions to the VBM framework, including longitudinal analysis and voxel-based quantification, are explored. 
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/lfUPuDNpUJ4?si=iUR-L6QT0_9phOwJ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+--8<-- "addons/abbreviations.md"

docs/courses/index.md

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+# SPM Course
+
+The FIL SPM course on using Statistical Parametric Mapping for neuroimaging is held each May and October as part of the [Institute of Neurology's short course programme](https://www.ucl.ac.uk/ion/education).
+
+Since 2010, we provide two separate SPM courses reflecting the different imaging modalities. There is a three-day course on SPM for EEG/MEG followed by the long-established three-day course on SPM for fMRI/VBM. Both courses are suitable for beginners and more advanced users. We advise students to gain at least some minimal familiarity with the methodology, for example, from reading introductory articles available from the SPM webpage or by following data analysis examples in the [SPM tutorials](../tutorials/index.md).