{"doi":"10.1177/0271678x231153728","title":"Dynamic cerebral autoregulation measured by diffuse correlation spectroscopy","abstract":"Dynamic cerebral autoregulation (dCA) can be derived from spontaneous oscillations in arterial blood pressure (ABP) and cerebral blood flow (CBF). Transcranial Doppler (TCD) measures CBF-velocity and is commonly used to assess dCA. Diffuse correlation spectroscopy (DCS) is a promising optical technique for non-invasive CBF monitoring, so here we aimed to validate DCS as a tool for quantifying dCA. In 33 healthy adults and 17 acute ischemic stroke patients, resting-state hemodynamic were monitored simultaneously with high-speed (20 Hz) DCS and TCD. dCA parameters were calcaulated by a transfer function analysis using a Fourier decomposition of ABP and CBF (or CBF-velocity). Strong correlation was found between DCS and TCD measured gain (magnitude of regulation) in healthy volunteers (r = 0.73, p < 0.001) and stroke patients (r = 0.76, p = 0.003). DCS-gain retained strong test-retest reliability in both groups (ICC 0.87 and 0.82, respectively). DCS and TCD-derived phase (latency of regulation) did not significantly correlate in healthy volunteers (r = 0.12, p = 0.50) but moderately correlated in stroke patients (r = 0.65, p = 0.006). DCS-derived phase was reproducible in both groups (ICC 0.88 and 0.90, respectively). High-frequency DCS is a promising non-invasive bedside technique that can be leveraged to quantify dCA from resting-state data, but the discrepancy between TCD and DCS-derived phase requires further investigation.","journal":"Journal of Cerebral Blood Flow & Metabolism","year":2023,"id":337968,"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":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9665,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":726154,"name":"Michael T. Mullen","orcid":"0000-0001-8300-7731","position":1,"is_corresponding":false},{"id":1070376,"name":"Farhan Kahn","orcid":null,"position":2,"is_corresponding":false},{"id":1069950,"name":"Izad-Yar Rasheed","orcid":"0000-0002-0271-5320","position":3,"is_corresponding":false},{"id":246784,"name":"Steven R. Messé","orcid":"0000-0003-3108-5441","position":4,"is_corresponding":false},{"id":799710,"name":"Ashwin B. Parthasarathy","orcid":"0000-0003-2024-3432","position":5,"is_corresponding":false},{"id":313769,"name":"Arjun G. Yodh","orcid":"0000-0003-4744-2706","position":6,"is_corresponding":false},{"id":726155,"name":"Christopher G. Favilla","orcid":"0000-0003-0871-9549","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T01:10:31.133446Z","pmid":"36703572","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":[]}