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Multi‐center study of regulation of cerebral perfusion using spontaneous time‐series data shows impairment of CO2 vasoreactivity in MCI & AD patients
Author(s) -
Marmarelis Vasilis,
Billinger Sandra A.,
Chui Helena C.,
Joe Elizabeth B.,
Shin Dae,
Cardim Danilo,
Hashem Suhaib,
Kaufman Carolyn S.,
Cullum Munro,
Kelley Brendan,
Zhang Rong
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.054737
Subject(s) - cerebral blood flow , medicine , transcranial doppler , vasomotor , cardiology , cerebral perfusion pressure , perfusion , breathing , cognitive impairment , ventilation (architecture) , cerebral autoregulation , blood pressure , nuclear medicine , anesthesia , autoregulation , mechanical engineering , disease , engineering
Background Three AD Centers have joined together to study the regulation of cerebral perfusion at resting conditions using spontaneous time‐series data with a modeling methodology that previously yielded indications of reduced dynamic vasomotor CO2 reactivity in amnestic MCI patients [ Marmarelis et al., J. Alzh. Dis. 56:89–105, 2017 ] and significant correlation of this reduction with functional impairment of the chemoreflex in these MCI patients [ Marmarelis et al., J. Alzh. Dis. 75:1‐16, 2020 ]. The goal of this multi‐center study is to confirm these preliminary findings with larger cohorts of MCI patients, AD patients and age‐matched cognitively normal controls (NC), and to examine the time‐course of these impairments with longitudinal data over 5 years. Method Quantitative results were obtained through dynamic modeling of the effects of spontaneous changes in arterial blood pressure (ABP) and end‐tidal CO2 (etCO2) upon cerebral blood flow velocity (CBFV) in the middle cerebral arteries measured via transcranial Doppler. The obtained subject‐specific input‐output predictive models were used to compute indices (physio‐markers) that quantify the Dynamic Vasomotor Reactivity (DVR) in each subject/patient and to compare differences between NC, and MCI/AD patients. Result The obtained DVR indices were significantly different for patients (9 MCI and 4 AD taken together due to small numbers) vs. 32 controls (p=0.026). Notably, the delineation between patients and controls improved (p=0.009) after a session of slow paced‐breathing (8 breaths/minute) that led to larger average DVR for controls and to reduction of inter‐subject variability of DVR indices. This is illustrated in Figure 1, where the average model‐predicted responses (representing the CBFV response to a unit‐step change of etCO2) are shown for 13 MCI/AD patients (red line) and 32 controls (blue line) before and after the paced‐breathing session. Further improvement in delineating patients from controls is achieved (p=9x10 ‐5 ) when the DVR indices from the two sessions were averaged for each subject/patient. Conclusion Significantly lower DVR index under resting spontaneous conditions was observed in 13 MCI/AD patients relative to 32 NC before (p= 0.026) and after (p=0.009) slow paced‐breathing session. Averaging of the DVR indices obtained from data before and after paced‐breathing improved substantially this delineation (p=9x10 ‐5 ).