{"doi":"10.1113/jp280630","title":"Neurovascular coupling: Sending this signal here, hope you pick it up loud and clear","abstract":"Because the brain has high metabolic activity but very limited energy reserves, it needs a continual supply of blood to provide O2 and glucose to support energy demands. As a result, brain function relies on the precise regulation of cerebral blood flow (CBF). Normally, CBF varies regionally based on energy consumption. With physiological challenges, changes in CBF occur at regional, segmental and temporal levels, supporting a continually integrating network of perfusion. Neurovascular coupling (NVC) can be defined as the functional coupling between activity of neurons and local CBF (Iadecola, 2017). Although this definition is deceivingly simple, decades of study have revealed a signalling process with layers of complexity (multiple cell types and mediators, multiple segments of the circulation, etc.). Ongoing investigations continue to provide novel insight into mechanistic details of NVC, its impact on brain health and its clinical implications. The haemodynamic component of NVC is the basis for functional magnetic resonance imaging, used commonly for brain mapping in neuroscience and clinically. Signalling networks within and between cells are engaged during normal NVC. Mechanisms include neural activation, glutamatergic signalling, production of nitric oxide (NO) and other mediators, with signalling to adjacent cells and dilatation of the local vasculature (Faraci & Breese, 1993; Iadecola, 2017). Distal components of the vascular tree (perhaps capillary endothelial cells) act as sensors of neural activity, sending electrical signals to the upstream vasculature, including vessels on the brain surface (pial arteries and arterioles). Because of the known distribution of vascular resistance in the cerebral circulation (Iadecola, 2017), an effective physiological response that maintains microvascular perfusion pressure requires such integration, resulting in the dilatation of resistance vessels on the pial surface in addition to microvessels deep within the parenchyma. NVC has been studied in diverse models from across the phylogenetic tree, with representatives from vertebrate classes including teleosts, birds and mammals (e.g. rodents, lagomorphs, New and Old World monkeys). In the past, mechanistic insight has predominantly been obtained using in vivo animal models or in vitro approaches (e.g. brain slices, isolated vessels). By contrast, there has been relatively little work addressing NVC signalling mechanisms in humans. As reported in this issue of The Journal of Physiology, the study by Hoiland et al. (2020) sought to help fill this gap, taking advantage of the integrated NVC response by measuring blood flow velocity (assumed to be an accurate index of quantitative blood flow per unit time) in the posterior cerebral artery during visual stimulation. The approach involved quantifying effects of a systemically administered NO synthase (NOS) inhibitor, NG-monomethyl-l-arginine (l-NMMA). l-NMMA reduced the NVC response by ∼30%, without significant changes in total CBF, blood gases, arterial pressure or whole brain metabolism. By contrast, haemodilution or phenylephrine-induced blood pressure elevation did not alter NVC. In relation to NO, the study by Hoiland et al. (2020) confirms the concept that NO plays an essential role in NVC based on non-human preclinical models. It also confirms work conducted in humans that evaluated the role of NO in NVC in the retina (an extension of the central nervous system) (Dorner et al. 2003). From early mechanistic studies, a role for NO has been repeatedly implicated in NVC and regulation of CBF. This concept has now translated from preclinical models to human brain and retina. Strengths of the work by Hoiland et al. (2020) include the investigation of awake humans using a natural visual stimulus to activate NVC. A careful and systematic quantification of various determinants of CBF was included. Limitations include the use of a single approach to implicate NO. Quantitative estimates of t","journal":"The Journal of Physiology","year":2020,"id":130848,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9485,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":583664,"name":"Frank M. Faraci","orcid":"0000-0002-0203-1690","position":0,"is_corresponding":true}],"reference_count":5,"raw_metadata":null,"created_at":"2026-07-18T23:15:56.698770Z","pmid":"32869875","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}