So, a fairly emphatic null result. Many connections we wouldn't expect to correlate with behaviour, but we don't even see associations for connections with dorsal attention, default mode, or frontoparietal regions which the scene regions show connectivity biases towards. Why not?

BUT, how about the connectivity with the subcortical structures?

Also no.

Many of the connectivity biases were with cortical networks throughout the rest of the brain - do these correlate any better with behaviour?

Still no. Again, we get a mix of small positive and negative correlations - no clear association between connectivity and behaviour.

Turns out no. For connectivity between our main ROIs, we just get a mix of small positive and negative correlations with behaviour, most of which aren't significant.

We measured resting-state functional connectivity of scene regions to other ROIs, and to cortical networks & subcortical structures throughout the brain. As before, OPA connects more with early visual & dorsal attention networks, RSC more with default mode & frontoparietal networks & hippocampus.

We selected behavioural measures of scene recognition, spatial memory, and navigational ability - there was substantial individual variation in performance on all tasks.

Nice to see the Trends in Neurosciences contents alert today includes a correction to a paper titled "Where is the error?". Turns out the answer is Box 2.

Component scores show many object features varying along components - including animacy, shape, size, and category. But representations do not straightforwardly align with previous accounts - many components embodied multiple features, and many features were present in multiple components.

Component loadings indicate continuous graded representations of each component across the visual brain. #1 runs anterior-posterior, #2 runs medial-lateral, and further components variously identify repeating medial-lateral and anterior-posterior gradients.

Despite reduction in dimensionality (from 66 stimulus features down to just 5 components), PLSR accurately predicts neural responses to objects held out from model fitting throughout ventral visual cortex, as well some lateral occipital and early visual regions.

We use Partial Least Squares Regression to predict brain responses from the stimulus features while also reducing dimensionality. We identify a small number (5) of latent components which capture a low-dimensional representation of the object properties and neural responses.