A quantitative model for human neurovascular coupling with translated mechanisms from animals

• Author summaryThe neurovascular coupling (NVC) is the basis for functional magnetic resonance imaging (fMRI), since the NVC connects neural activity with the observed hemodynamic changes. This connection is highly complex, which warrants a model-based analysis. However, even though NVC-data from several species and many relevant variables are available, a mathematical model for all these data is still missing. Herein, we combine experimental data from mice, monkeys, and humans, to develop a comprehensive model for NVC. Importantly, our new approach to modelling propagates the qualitative insights from each species to the subsequent analysis of data from other species. In mice, we unravel the role of different neuronal sub-populations when producing a biphasic response to prolonged sensory stimulations. The qualitative role of these sub-populations is preserved when analysing primate data. These primate data add knowledge on the interplay between local field potential (LFP) and vascular changes. Similarly, these pre-clinical qualitative insights are propagated to analysis of human data, which contain additional insights regarding blood flow and volume in arterioles and venules, during both positive and negative responses. This work illustrates how data with complementary information from different species can be combined, so that qualitative insights from animals are preserved in the quantitative analysis of human data. Neurons regulate the activity of blood vessels through the neurovascular coupling (NVC). A detailed understanding of the NVC is critical for understanding data from functional imaging techniques of the brain. Many aspects of the NVC have been studied both experimentally and using mathematical models; various combinations of blood volume and flow, local field potential (LFP), hemoglobin level, blood oxygenation level-dependent response (BOLD), and optogenetics have been measured and modeled in rodents, primates, or humans. However, these data have not been brought together into a unified quantitative model. We now present a mathematical model that describes all such data types and that preserves mechanistic behaviors between experiments. For instance, from modeling of optogenetics and microscopy data in mice, we learn cell-specific contributions; the first rapid dilation in the vascular response is caused by NO-interneurons, the main part of the dilation during longer stimuli is caused by pyramidal neurons, and the post-peak undershoot is caused by NPY-interneurons. These insights are translated and preserved in all subsequent analyses, together with other insights regarding hemoglobin dynamics and the LFP/BOLD-interplay, obtained from other experiments on rodents and primates. The model can predict independent validation-data not used for training. By bringing together data with complementary information from different species, we both understand each dataset better, and have a basis for a new type of integrative analysis of human data.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)