Our Statistical Parametric Mapping (SPM) software consists of a suite of tools for analysing brain imaging data, which may be images from different cohorts or time series from the same subject (fMRI, PET, EEG, MEG, etc).
SPM has been freely available to the brain imaging community since its launch in 1991 as an open source, academic software. Much of our efforts go into developing new functionality and offering support to our online community. As with any software project, ongoing maintenance is essential to ensure that SPM continues to work effectively within an ever-changing computing and research environment.
Diverse image types
We prioritise making SPM applicable to a wide range of brain images. Efforts have gone into extending the software to make it applicable to patient scans acquired within hospitals. This has included developing super-resolution techniques for dealing with thicker sliced scans, as well as extending SPM’s segmentation/normalisation approach to better handle patient MR and CT scans. Other work has been on dealing with quantitative MRI, and further modifications are needed for scans acquired at 7T.
Dynamic Causal Modelling (DCM)
DCM is a framework embedded in the SPM software for investigating the neural circuitry that gives rise to functional MRI, EEG or MEG measurements. It was invented, at the Centre, by Friston, Harrison and Penny (2003). Our recent focus has been to develop DCM in two key areas:
New statistical tools for group analysis, namely the Parametric Empirical Bayes (PEB) framework. This enables people to ask: what is the effect of an experimental manipulation on particular neural connections? How does this differ between patient groups, or vary across people according to particular cognitive or clinical scores? Furthermore, can we predict a person’s diagnosis or clinical outcome from their connection strengths? (Friston et al., 2016; Zeidman et al., 2019).
Neurovascular coupling refers to the connections between neural activity and changes in cerebral blood flow (CBF). Alterations to these mechanisms occur due to ageing and a variety of clinical conditions including Alzheimer’s disease. We have been developing modelling tools for investigating neurovascular coupling, through multi-modal fusion of MRI and MEG data. This may also help us to better understand the source of the functional MRI signal, which itself depends on neurovascular coupling. (Friston et al., 2017; Jafarian et al., 2019).
- The ultimate trick?: Comment on: “The Markov blanket trick: On the scope of the free energy principle and active inference” by Raja et al. Physics of Life Reviews, 43, 10-16 DOI: 10.1016/j.plrev.2022.07.007
- Neural Networks special issue on Artificial Intelligence and Brain Science Neural Networks, 155, 328-329 DOI: 10.1016/j.neunet.2022.08.018
- View all publications by the SPM team