Abstract:
Brain activity and structure span many scales, as do experimental measurements that probe them. In contrast to the tendency to investigate single phenomena ever deeper and more narrowly, multiscale modeling aims to bring together multiple phenomena and measurements in a unified framework so they can be interrelated and become mutually illuminating.
Here, the use of multiscale neural field theory (NFT) is outlined, based on the actual physical brain. NFT is shown to predict a wide variety of phenomena, as verified against experiment, including spontaneous and evoked activity and their variations over time and between arousal states. Its results can be used to monitor brain state and to analyze activity in terms of the brain that underlies it, rather than via phenomenological measures such as band powers and evoked response "components". Viewing brain activity in terms of natural spatially extended modes of oscillation (akin to pure notes of a musical instrument) yields further insights into activity patterns, resonances (e.g., the alpha rhythm) and the structures that support them. Indeed, modal analysis enables phenomenological patterns such as "resting state networks" (RSNs) to be explained as superpositions of just a few modes - i.e., RSNs are akin to musical chords.
Thus NFT achieves many aims of multiscale modeling, binding brain structure, function, and activity into a unified picture, and enabling measurements and phenomena at many scales to be interrelated. This permits new insights to be obtained, more to be extracted from data, and novel hypotheses to be formulated.
