Frequently asked questions about activity flow mapping, Part 2

coleetal2016fig1a
The activity flow mapping procedure. See Cole et al. (2016; Nature Neuroscience) for more info.

I recently posted a brief summary of the Cole et al. (2016; Nature Neuroscience) paper, along with answers to some frequently asked questions. I’m including answers to additional frequently asked questions here. This gets into more of the lesser-known implications of the activity flow mapping findings.

What can explain the success of activity flow mapping at a fine-grained mechanistic level? What could possibly explain the ability to predict held-out activations at a distance?

The most parsimonious explanation is that fMRI BOLD signals strongly reflect action potential propagation. Action potential propagation is the only known mechanism for the brain to synchronize distal locations (i.e., implement activity flow). Note that this interpretation is still consistent with the previous suggestion that fMRI BOLD primarily reflects local field potentials, given that local field potentials are caused by incoming action potentials. This direct causal link between local field potentials and action potentials suggests it is likely quite rare for local field potential variance to be dissociated from incoming action potential variance at the neural level. Even if sub-threshold local field potential variance drives fMRI signals frequently, however, functional connectivity estimates likely isolate the variance that is most strongly tied to action potentials. This is because, as mentioned, action potentials are the only known (non-artifactual) mechanism for creating the long-distance correlations observed with fMRI functional connectivity. I think this is an important point to consider moving forward when interpreting fMRI functional connectivity estimates.

Given the dependence of functional connectivity estimates on action potentials, the success of the activity flow mapping approach suggests that task-evoked activations likely also largely reflect action potentials. However, it is possible that an activation in a given region is largely reflecting local field potentials (i.e., there is little spiking output from that region), with those local field potentials being driven by action potentials from other regions.

Given that you are using fMRI (which relates to neural-activity-induced changes in blood flow), can you rule out the possibility that the “flow” in activity flow mapping is actually blood flow?

Vasculature-based blood flow likely influences highly local correlations in fMRI BOLD signals. We had actually considered this issue in our methodology, under the assumption that such blood flow effects would likely not extend beyond 9 millimeters. Thus, activity flow estimates less than 9 millimeters were excluded. Note that this is likely overly cautious, since blood flow effects are unlikely to extend as far as 9 millimeters (it’s effects are likely restricted to within ~2.5 millimeters, with the largest effects due to draining veins). Thus, the results suggest the most likely (non-artifactual) cause of fMRI BOLD correlation over distances greater than ~2.5 millimeters is activity flow via action potential propagation.

You talk of rest and task as if they’re different things, yet a premise of the paper is that it’s the same networks that drive both.  Why is rest not a task?

I definitely think of rest as a sort of (less constrained) cognitive task. We focused on resting-state fMRI data here for several pragmatic reasons. First, this is the convention in the literature, largely because it’s easier to collect resting-state data than task data. Second, PET imaging has been used to show that rest is the state with the lowest overall metabolic demanding conscious state. This suggests it may be the best-available cognitive baseline. That said, we are really using resting-state functional connectivity as a proxy to true intrinsic functional connectivity, which would not be influenced by the idiosyncrasies of resting state. This might involve deriving a cross-state functional connectivity map, as we did in a recent study, such that the resulting map was not strongly biased by any single mental state.

How is it possible for activity flow mapping to accurately predict task activations when we know resting-state functional connectivity estimates are corrupted via a large number of artifacts, especially motion-related artifacts?

The activity flow mapping results should be surprising if you believe fMRI functional connectivity is corrupted by artifacts more than fMRI task activations (which some studies have suggested). Task fMRI activations are thought to be largely immune to artifacts such as motion that influence functional connectivity estimates, due to the benefit of experimental control dissociating task-related activations from the timing of various artifacts. The success of the activity flow mapping approach demonstrates that these artifacts may not be as bad as we thought, especially as far as correspondence to task activation is concerned. Note, however, that activity flow mapping worked better when multiple regression was used to estimate connectivity – a method thought to reduce the influence of such artifacts on the data. Nonetheless, the approach worked quite well using the standard Pearson correlation approach to functional connectivity, suggesting much of the variance included in standard resting-state functional connectivity is likely non-artifactual.

 

 

 

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