UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Temporal kernel CCA and its application in multimodal neuronal data analysis

Biebmann, F; Meinecke, FC; Gretton, A; Rauch, A; Rainer, G; Logothetis, NK; Müller, KR; (2010) Temporal kernel CCA and its application in multimodal neuronal data analysis. Machine Learning , 79 (1-2) pp. 5-27. 10.1007/s10994-009-5153-3.

Full text not available from this repository.


Data recorded from multiple sources sometimes exhibit non-instantaneous couplings. For simple data sets, cross-correlograms may reveal the coupling dynamics. But when dealing with high-dimensional multivariate data there is no such measure as the crosscorrelogram. We propose a simple algorithm based on Kernel Canonical Correlation Analysis (kCCA) that computes a multivariate temporal filter which links one data modality to another one. The filters can be used to compute a multivariate extension of the crosscorrelogram, the canonical correlogram, between data sources that have different dimensionalities and temporal resolutions. The canonical correlogram reflects the coupling dynamics between the two sources. The temporal filter reveals which features in the data give rise to these couplings and when they do so. We present results from simulations and neuroscientific experiments showing that tkCCA yields easily interpretable temporal filters and correlograms. In the exper iments, we simultaneously performed electrode recordings and functional magnetic resonance imaging (fMRI) in primary visual cortex of the non-human primate. While electrode recordings reflect brain activity directly, fMRI provides only an indirect view of neural activity via the Blood Oxygen Level Dependent (BOLD) response. Thus it is crucial for our understanding and the interpretation of fMRI signals in general to relate them to direct measures of neural activity acquired with electrodes. The results computed by tkCCA confirm recent models of the hemodynamic response to neural activity and allow for a more detailed analysis of neurovascular coupling dynamics. © The Author(s) 2009.

Type: Article
Title: Temporal kernel CCA and its application in multimodal neuronal data analysis
DOI: 10.1007/s10994-009-5153-3
URI: http://discovery.ucl.ac.uk/id/eprint/1334302
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item