For purposes of comparison, here's another approach that's super interesting ( https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168033/ ). So both frameworks are looking at system structure/dynamics and then attempting to deduce/quantify some aspect of them that (hopefully) gives us some insight into "Mind" or "Consciousness". But to me, grounding such inquiries directly in neuroscience seems awkward, because brain only runs Mind. Uncovering a bunch of implementation details about that from FMRIs probably won't give us a lot of interesting insight at the right level. To an extent some implementation details are important and might shed light on the architecture/algorithm of Consciousness/Mind, but evolved systems are also going to be cluttered up with total hacks that evolution brute-forced where implementation is kinda arbitrary.
All of which is to say, IIT being "platform agnostic" and proposing stuff like a thermostat is more conscious than a rock, a dog moreso than a thermostat, and a human more than a dog is not just a cool trick. Being able to approach this kind of intuitive problem in a somewhat rigorous way seems like a necessary requirement for a serious scientific theory of mind. I like the Ising and graph theory modeling approach, but the implementation details of {neuron-counting, FMRI, lesions on functional areas, etc} is probably a distraction if you're trying to understand mind rather than medicine.