How does activity in the brain relate to things in the world? How does the brain represent stimuli, actions, and concepts? The brain processes information in a distributed manner, so models of neuronal representations need to take into account the full activity pattern in a brain region, not just the level of average activity. In the fMRI field there are many analysis methods that attempt to build and evaluate such models, coming under a variety of names, including multi-voxel pattern, decoding, classification, representational similarity analysis, population receptive field models, and encoding models. Our lab is working on improving the statistical methods in this new field. We have provided a general framework, showning that nearly all representational techniques can be understood as testing specific aspects of the distribution of neurons or voxels in the space of experimental conditions (Diedrichsen & Kriegeskorte, 2017).
Representational similarity analysis (RSA)
In collaboration with Niko Kriegeskorte's lab, we are developing tools and methods for representational similarity analysis, openly available within the RSA toolbox. We have focussed on the crossnobis distance estimate, a bias free, reliable, and easily interpretable measure of dissimilarity (Walther et al., 2015). We have in detail described the distributional properties of this measure (Diedrichsen et al., 2016), and ways to use these to make optimal inferences in subsequent analyses.
Pattern component modelling (PCM)
Because we often want to fit and compare more complex representational models, RSA or encoding models often do not provide an optimal approach (Diedrichsen & Kriegeskorte, 2017). We have therefore developed a hierarchical Bayesian approach that allows the decomposition of activity patterns into different representational components or feature sets (Diedrichsen et al., 2011, 2013, 2017, 2018). PCM can efficiently estimate the weights of these proportions and also fit nonlinear representational models. It allows for both RSA-style or Encoding-style representational models, and extends both approaches by providing advanced techniques of model fitting and evaluation. The fully optimized Matlab PCM toolbox with extensive documentation is on Github.
- Diedrichsen, J. (2018). Representational models and the feature fallacy. In M. S. Gazzaniga (Ed.), The Cognitive Neurosciences.
- Diedrichsen, J., Zareamoghaddam, H., & Provost, S. (2016). The distribution of crossvalidated mahalanobis distances. ArXiv.
- Diedrichsen, J., Yokoi, A., & Arbuckle, S. A. (2018). Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns. Neuroimage. 180(Pt A), 119-133.
- Arbuckle, S. A., Yokoi, A., Pruszynski, J. A., & Diedrichsen, J. (in press). Stability of representational geometry across a wide range of fMRI activity levels. Neuroimage.
- Diedrichsen, J., & Kriegeskorte, N. (2017). Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS Comput Biol.
- Walther, A., Nilli, H., Ejaz, N., Alink, A., Kriegeskorte, N., Diedrichsen, J. (2016). Reliability of dissimilarity measures for multivariate fMRI pattern analysis. Neuroimage.
- Diedrichsen, J., Wiestler, T., & Ejaz, N. (2013). A multivariate method to determine the dimensionality of neural representation from population activity. Neuroimage.
- Diedrichsen, J., Ridgway, G., Friston, K.J., Wiestler, T., (2011). Comparing the similarity and spatial structure of neural representations: A pattern-component model. Neuroimage.