The roadmap for estimation of cell-typespecific neuronal activity from noninvasive measurements

Abstract

The computational properties of the human brain arise from an intricate interplay between billions of neurons connected in complex networks.However, our ability to study these networks in healthy human brain is limited by the necessity to use non-invasive technologies.This is in contrast to animal models where a rich, detailed viewof cellular-level brain functionwith cell-type-specificmolecular identity has become available due to recent advances in microscopic optical imaging and genetics.Thus, a central challenge facing neuroscience today is leveraging these mechanistic insights from animal studies to accurately draw physiological inferences from non-invasive signals in humans.On the essential path towards this goal is the development of a detailed ‘bottom-up’ forward model bridging neuronal activity at the level of cell-type-specific populations to non-invasive imaging signals.The general idea is that specific neuronal cell types have identifiable signatures in theway they drive changes in cerebral blood flow, cerebral metabolic rate of O2 (measurablewith quantitative functionalMagnetic Resonance Imaging), and electrical currents/potentials (measurable with magneto/electroencephalography).This forward model would then provide the ‘ground truth’ for the development of new tools for tackling the inverse problem—estimation of neuronal activity from multimodal non-invasive imaging data.