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New paper

26.11.20 08:22 PM By Dimitris Pinotsis

Linking non-invasive brain imaging data, laminar dynamics and top-down control

We present a simplified neural mass model for estimating the laminar dynamics that contribute to non-invasively recorded time frequency data. Using two independent MEG datasets, they give evidence for deep cortical layers contributing to inter-individual variability in visually induced oscillations. Their study links non-invasive brain imaging data, laminar dynamics and top-down control. Click here for full paper.

A) We scored alternative GLMs where predictors of variability in V1 included any combination of the connections (arrows) in Fig. 1a. We found that for the data from ref. 15 V1 size could be best predicted by the recurrent connectivity of deep inhibitory interneurons, a22 (brown arrow). Evidence in favour of a GLM including a22 was very strong p > 0.95. B) Same as in a for data from ref. 20. V1 size variability reported in ref. 20 could be best predicted by the inhibitory drive to deep pyramidal cells, a31 (brown arrow). Evidence for the corresponding GLM was weak p > 0.5.

Dimitris Pinotsis

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