Topic: Principal component analysis

Despite the large number of active neurons in the cortex, the activity of neuronal populations is expected to lie on a low-dimensional manifold for different brain regions [1]. Variants of principal component analysis (PCA) are commonly used to assess this manifold. However, these methods are limited by the assumption that the data follows a Gaussian distribution and neglect additional features such as the curvature of the manifold. Hence, their performance as generative models tends to be subpar. To construct a generative model that entirely learns the statistics of neural activity with no a

Accumulating evidence from electrocorticogram (ECoG) recordings and imaging challenge traditional views of brain oscillations as spatially stationary periodic sources. Understanding the spatio-temporal dynamics of cortical activity requires novel measurement and analysis methodologies. We recorded broadband local field potential (LFP) across the whole neocortex in freely behaving rats using high-density flexible surface arrays using active transistors. 3D tracking and spatio-spectral analysis of recordings over long sessions enabled automatic and accurate brain and behavioral states segmentati

Psychedelics (5-HT2AR agonists) show great promise in treating mental disorders but their effects on neuronal population activity are not clear. Latent dimensions identified by Principal Component Analysis (PCA) capture the dynamics of this interrelated neuronal population activity. We asked whether latent dimensions emerge or disappear as a result of psychedelic administration (acute), and across other naturally occurring brain state transitions (sleep, arousal). Contrastive PCA (cPCA) [Abid et al. (2018), Nature Comm.] was applied to spontaneous brain activity recordings in rodents before