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Functional MRI

Welcome to the landing page for all things related to functional MRI (fMRI) in our lab. Whether you're a new student, a researcher, or someone interested in learning more about fMRI, you'll find everything you need here—from getting started with your work environment to data analysis.

  • First steps

    Everything you need to know before you start scanning, including MRI booking, invoicing, training and ethical approval.

    Get started

  • MR8 Equipment Reference

    Hardware descriptions and connection diagrams for the MR8 suite: stimulus PC, trigger boxes, projection system, audio, and eyetracker.

    View equipment

  • Scanning Procedure

    Step-by-step protocol for conducting fMRI scans, from participant registration to data export and cleanup.

    View procedures

  • Data Analysis

    The step-by-step workflow we use to pre-process and analyze fMRI data.

    Start analyzing

  • fMRI Task

    You need to code your fMRI task and you don't know where to start? Check out this fMRI task template from the Hoplab Github repositories.

    See the repo

Data Management

Make sure to follow the lab's Research Data Management guidelines throughout your project. See the temporary RDM guidelines for current recommended practices on data storage and organization.

Here are some helpful links to external resources for fMRI data analysis, tools, and tutorials:

Brain Atlases and Templates

Standard brain atlases are essential for defining ROIs, reporting results, and comparing across studies. Below are commonly used resources:

Atlas repositories

Resource Description Link
TemplateFlow Centralised repository of brain templates and atlases in standardised spaces (MNI, fsaverage, etc.). Used by fMRIPrep. templateflow.org
OSF Atlas Collection Curated collection of brain atlases and parcellations hosted on OSF. osf.io/4mw3a
neuromaps Python toolbox for mapping, transforming, and comparing brain annotations across MNI, fsaverage, and other coordinate systems. neuromaps docs

Commonly used atlases in the lab

Atlas Type Description
Glasser (HCP-MMP1) Multi-modal parcellation 360-region cortical parcellation from the Human Connectome Project. Based on architecture, function, connectivity, and topography.
Schaefer Functional parcellation Data-driven parcellations available in 100–1000 region versions. Aligned to the Yeo 7/17 network parcellation.
Harvard-Oxford Probabilistic anatomical Probabilistic atlas based on manual segmentation. Available in cortical and subcortical versions. Distributed with FSL.

Which atlas to choose?

The choice of atlas depends on your research question. For ROI-based analyses, Glasser and Schaefer provide finer-grained and more functionally meaningful parcellations. For reporting peak coordinates and comparing with older literature, Harvard-Oxford remains a common choice. See the ROIs page for how to create and use ROIs from these atlases.

Python Tools for fMRI

While the lab's primary analysis pipeline uses MATLAB and SPM, Python offers powerful complementary tools. Python-specific examples are included in the relevant analysis pages:

  • GLM and results visualisation: nilearn for running GLMs and plotting brain maps — see the GLM page
  • ROI extraction: nilearn's maskers for extracting signal from ROIs — see the ROIs page
  • File handling: nibabel for loading, manipulating, and saving NIfTI files
  • Surface plots: nilearn surface plotting (with plotly engine) for interactive and publication-quality visualisations — see the GLM page
  • Cross-space transformations: neuromaps for converting between MNI and fsaverage spaces