Processing...

Understanding multivariate brain activity: evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

PLOS Computational Biology, 2020-08-18

Video Abstract (AI generated) Paper Preprint PLOS Computational Biology

Previous studies have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We first replicate the classical finding that TCNCs impair population codes in a standard neuronal population. We then extend our analysis to fMRI data, and show that voxelwise TCNCs do not impair and can even improve MVPA performance when TCNCs are strong or the number of voxels is large. We also confirm these results using standard information-theoretic analyses in computational neuroscience. Further computational analyses demonstrate that the discrepancy between the effect of TCNCs in neuronal and voxel populations can be explained by tuning heterogeneity and pool sizes. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

A Variable Clock Underlies Internally Generated Hippocampal Sequences

Video Abstract (AI generated) Paper Preprint

Humans have the ability to retrieve memories with various degrees of specificity, and recent advances in reinforcement learning have identified benefits to learning when past experience is represented at different levels of temporal abstraction. How this flexibility might be implemented in the brain remains unclear. We analyzed the temporal organization of rat hippocampal population spiking to identify potential substrates for temporally flexible representations. We examined activity both during locomotion and during memory-retrieval-associated population events known as sharp wave-ripples (SWRs). We found that spiking during SWRs is rhythmically organized with higher event-to-event variability than spiking during locomotion-associated population events. Decoding analyses using clusterless methods further suggest that similar spatial experience can be replayed in multiple SWRs, each time with a different rhythmic structure whose periodicity is sampled from a lognormal distribution. This variability is preserved despite the decline in SWR rates that occurs as environments become more familiar: in more familiar environments the width of the lognormal distribution increases, further enhancing the range of temporal variability. We hypothesize that the variability in temporal organization of hippocampal spiking provides a mechanism for retrieving remembered experiences with various degrees of specificity. ### Competing Interest Statement The authors have declared no competing interest.