They may also have implications for brain stimulation. For example: if we increase excitability in a cortical area (with TMS) we may see a decrease in its fMRI connectivity. What we like here is that these are testable hypotheses: and so we will soon see if (any of) this holds in humans!
17/n
We believe our results may (partly) reframe how we interpret fMRI connectivity
▶️ fMRI connectivity ≠direct communication strength
▶️ fMRI connectivity is supported by distributed slow neuronal coupling
▶️ Hyper/hypoconnectivity (eg., in brain disorders) may reflect cortical hypo/hyperexcitability
16/n
Thus our work suggests that
1️⃣ cortical excitability inversely modulates fMRI connectivity
2️⃣ fMRI coupling rests on distributed, slow neuronal fluctuations (i.e. QPPs, CAPs, neuromodulation pulses..)
3️⃣cortical excitability gates local coupling by weakening or facilitating that slow synchrony
15/n
Notably, biophysical modelling supports this framework. Using a simple three-node model we found that local excitability changes are sufficient to reproduce the direction of the low-frequency coherence effects across perturbations.
This offers a plausible mechanistic account of our results!
14/n
So these results suggest that
▶️slow, shared LFP fluctuations provide a neuronal scaffold for fMRI connectivity
▶️cortical excitability gates how strongly regions participate on this process: shifts in cortical excitability weaken or facilitate this coupling, leading to
hypo/hyperconnectivity
13/n
