The authors have declared that no competing interests exist.
Conceived and designed the experiments: VN KH. Performed the experiments: VN. Analyzed the data: MCA KH VN. Contributed reagents/materials/analysis tools: KH MTL. Wrote the paper: MCA KH VN. Provided expert interpretation of data analysis and edited the manuscript: MS MSO.
Dynamic cerebral autoregulation (dCA) is impaired following stroke. However, the relationship between dCA, brain atrophy, and functional outcomes following stroke remains unclear. In this study, we aimed to determine whether impairment of dCA is associated with atrophy in specific regions or globally, thereby affecting daily functions in stroke patients.
We performed a retrospective analysis of 33 subjects with chronic infarctions in the middle cerebral artery territory, and 109 age-matched non-stroke subjects. dCA was assessed via the phase relationship between arterial blood pressure and cerebral blood flow velocity. Brain tissue volumes were quantified from MRI. Functional status was assessed by gait speed, instrumental activities of daily living (IADL), modified Rankin Scale, and NIH Stroke Score.
Compared to the non-stroke group, stroke subjects showed degraded dCA bilaterally, and showed gray matter atrophy in the frontal, parietal and temporal lobes ipsilateral to infarct. In stroke subjects, better dCA was associated with less temporal lobe gray matter atrophy on the infracted side (
Cerebral autoregulation (CA) modulates cerebral blood flow in order to meet regional perfusion demands despite variations in arterial blood pressure (BP) associated with daily activities
Both impaired dCA
Noninvasive assessment of dCA often entails examining the coupling between continuous BP and cerebral blood flow velocity (BFV), measured by transcranial Doppler ultrasound (TCD). However, finding computational methods for the accurate quantification of this relationship is a challenge to reliable dCA assessment. Multimodal pressure-flow (MMPF) analysis
This study applied the MMPF-derived dCA measure to examine the relationship between dCA, regional brain tissue volumes, and functional status in a retrospective analysis of elderly subjects with chronic large vessel infarctions in the middle cerebral artery (MCA) territory, and in age-matched non-stroke subjects. We hypothesize that worse perfusion regulation is associated with enhanced gray matter atrophy in the temporal lobe, and worse long-term functional status in the elderly with chronic ischemic infarctions.
All subjects signed informed consent and the study was approved by the Institutional Review Board at Beth Israel Deaconess Medical Center (BIDMC). Participants were recruited from community advertisement, Beth Israel Deaconess Medical Center, Joslin Diabetes Clinic patient registries and from the Harvard Cooperative Program on Aging research subject registry.
The data for this retrospective analysis of 142 subjects were selected from a database of records prospectively collected at the Syncope and Falls in the Elderly Laboratory and the Magnetic Resonance Imaging Center at BIDMC. The database was composed of records from three completed projects spanning January 2002 to February 2008: Cerebral vasoregulation in the elderly with stroke (March 2003–April 2005); Cerebral vasoregulation in diabetes (January 2002–December 2005); and Cerebral perfusion and cognitive decline in type 2 diabetes (January 2006–December 2008). Grant numbers and awarding institutions are provided in the financial disclosures section. All stroke subjects included in the current project were recruited for the vasoregulation in the elderly study while diabetic non-stroke subjects were from the vasoregulation in diabetes and cognitive decline in diabetes studies. Non-diabetic non-stroke subjects were recruited in all three studies. The subjects were selected for the present cohort, only if they completed both TCD and MRI measurements, and met the inclusion/exclusion criteria detailed below and in
Included stroke subjects had large vessel hemispheric MCA infarcts affecting
All diabetic subjects were required to be diagnosed with type-II diabetes mellitus (DM) and to have been treated for at least 1 year prior to participation. Non-diabetic controls were age- and sex- matched to diabetic and stroke subjects from their respective studies with no clinical history of stroke and no focal deficits on neurological examination. Non-diabetic participants were required to have normal fasting glucose.
Subjects were excluded if they had intracranial or subarachnoid hemorrhage on MRI or CT or carotid artery stenosis (for control group, over 50% by medical history and MR angiobgraphy and for the stroke group, bilateral stenosis or stenosis contralateral to stroke). Other exclusion criteria included myocardial infarction within 6 months and other clinically important cardiac diseases; arrhythmias; significant nephropathy; kidney or liver transplant; renal or congestive heart failure; type I DM; or neurological or other systemic disorders. Incompatibility with 3 Tesla MRI, including claustrophobia, metal implants, pacemakers, and arterial stents was also an excluding factor.
Collectively, the three studies recruited 358 subjects (157 healthy controls, 115 diabetic controls, 86 stroke). We excluded 145 subjects (71 healthy control, 33 diabetic control, 41 stroke) because they either withdrew consent, they met exclusion criteria (listed above), or they did not get permission from their primary care provider. Of those subjects excluded, 24 subjects (14 healthy controls, 8 diabetic controls, 2 stroke) were excluded due to a poor temporal insonation window. From the remaining 213 subjects, 142 of them (52 healthy control, 57 diabetic control, 33 stroke) had complete TCD and MRI recordings and were used for the final analysis in the present study. Subjects with diabetes, hypertension, or both were included as part of the non-stroke group in order to control for dCA impairment associated with risk factors related to stroke. Demographic information of the selected cohort is listed in
Stroke | Control | ||
Age (years) | 63.4 |
65.3 |
0.23 |
Male/Female | 19/14 | 61/48 | 0.87 |
Race (W/A/AI/AA/U) | 29/1/0/3/0 | 93/8/1/6/1 | 0.01 |
Smokers (Y/N) (Current,Past) | (8/25), (28/5) | (4/72), (42/67) | |
Packs per year | 33.0 |
6.9 |
|
stroke side (R/L)(M,F) | (11/8),(7/7) | (38/23),(25/23) | 0.74 |
Hypertens./Normotens. | 23/10 | 36/73 | |
Diabetes Mellitues (M/F) | 0/0 | 35/22 | – |
Mean BP (mmHg) | 87 |
84 |
0.23 |
Systolic BP (mmHg) | 131 |
124 |
0.02 |
Diastolic BP (mmHg) | 63 |
62 |
0.69 |
Body mass index (kg/m2) | 27.7 |
26.7 |
0.29 |
Gait Speed (m/s) | 0.88 |
1.1 |
|
IADL (counts per score) | (6,10,4,7) | (33,21,3,2) | |
Mini mental state exam | 26.7 |
27.6 |
0.03 |
NIH stroke scale | 2.5 |
– | – |
mRS (counts per 0,1,2,3,4) | (11,13,3,4,0) | – | – |
BFV (NS/RND2) (cm/s) | 39.4 |
45.1 |
0.15 |
BFV (SS/RND1) (cm/s) | 40.8 |
45.2 |
0.29 |
7.3 |
14.9 |
0.02 |
|
4.4 |
12.8 |
0.02 |
|
CO2R (SS/RND1) | 0.18 |
0.88 |
0.45 |
CO2R (NS) | 0.94 |
1.28 |
0.75 |
End-tidal CO2 (mmHg) | 35.4 |
36.7 |
0.09 |
MCA (SS/RND1)(mm) | 2.27 |
2.59 |
0.02 |
MCA (NS/RND2)(mm) | 2.44 |
2.58 |
0.27 |
ICA (SS/RND1) (mm) | 5.18 |
5.3 |
0.61 |
ICA (NS/RND2) (mm) | 5.18 |
5.3 |
0.51 |
White blood cells (k/ul) | 6.96 |
6.62 |
0.37 |
Hemoglobin (g/dL) | 13.8 |
13.6 |
0.60 |
Hematocrit (%) | 40.1 |
40.1 |
0.98 |
Cholesterol | 179 |
190 |
0.19 |
LDL (mg/dL) | 95 |
100 |
0.42 |
triglycerides (mg/dL) | 147 |
166 |
0.40 |
infarct (volume/ICV) |
2.25 |
– | – |
Values are mean
indicates stroke and control groups are significantly different after controlling for false discovery rate. Abbreviations: W = white; A = Asian; AI = American Indian; AA = African American; U = unknown; R/L = right/left; M/F = male/female; IADL = instrumental activities of daily living; mRS = modified Rankin scale; BP = blood pressure; BFV = blood flow velocity;
The stroke group consisted of 33 subjects that were 6.71
Experiments were conducted in the morning after a thirty-minute rest during instrumentation. Baseline recordings of 5–10 minutes were collected during resting conditions when subjects were supine, awake and breathing regularly at their normal respiratory frequency. Vasoreactivity to CO2 (CO2R) was measured using 3 minutes of hyperventilation followed by 3 minutes re-breathing 5% CO2 in an air bag. Vasoreactivity was calculated as the slope of the regression of CO2 on BFV over baseline, hyperventilation, and rebreathing conditions. BFVs in both MCAs were measured from trans-temporal windows using TCD (MultiDop X4; Neuroscan, Sterling, VA). BP was recorded from the finger using the volume-clamp technique with a Finapres device (Finapres, Ohmeda Monitoring Systems, Englewood, CO) and corroborated by sphygmomanometer measurements. BP, BFV, respiration and end-tidal CO2 measurements (Capnomac Ultima, Ohmeda Monitoring Systems, Englewood, CO) were recorded at 500 HZ. Signals were decimated to 50 Hz before analysis.
The MRI studies were performed on a 3-Tesla GE Signa Vhi or Excite MRI scanner using a quadrature and phase array head coils (GE Medical Systems, Milwaukee, WI). Anatomical 3D magnetization prepared rapid gradient echo (MP-RAGE) images were used to quantify brain volumes with the Statistical Parametric Mapping software (SPM, University College,A London, UK) using spatial normalization and tissue classification. An anatomical template (Laboratory of Neuro Imaging, University of California, Los Angles, USA) was applied to measure GM and white matter (WM) in frontal, temporal, parietal, and occipital lobes. Normalized volumes (regional volume/global intracranial volume, cm3/cm3) of GM, and WM were used for analysis. Vessel diameters were derived from 3D MR angiography (time of flight, TOF) using the Medical Image Processing, Analysis, and Visualization (MIPAV) software from the Biomedical Imaging Research Services Section, NIH, Bethesda, MD, at 3 locations and averaged. Diameters Internal carotid arteries (ICA) and MCAs were computed from a single-slice transverse view with conservatively estimated accuracy (
Functional status was assessed in both stroke and non-stroke groups by gait speed (measured by a 12-minute walking test at preferred walking speed), Instrumental Activities of Daily Living
Multimodal pressure-flow (MMPF) analysis was used for non-invasive assessment of dCA. Details on the development and performance of the method have been published previously
The MMPF analysis for this study was performed according to the following four major steps:
Decomposition of BP and BFV signals into multiple empirical modes.Central to the MMPF method is the Hilbert-Huang transform
Selection of empirical modes for dominant oscillations in BP at 0.1–0.3 Hz and corresponding oscillations in BFV.In order to determine a meaningful phase relationship between the BP and BFV time series, empirical modes from both series must be selected from within the same frequency band. Previous studies have shown that dCA can be assessed from respiratory-induced pressure-flow variations during spontaneous respiration
Calculation of instantaneous phases of extracted BP and BFV oscillations.Since each empirical mode has zero mean and is sufficiently narrow-band, the complex part of each mode can be calculated by the Hilbert transform
Calculation of the mean BP-BFV phase difference (
For all subjects, MRI and TCD measurements were made on both left and right sides and analyses were conducted by stroke side and non-stroke side. Since non-stroke subjects did not have an affected side, each non-stroke subject was randomly assigned a ÒstrokeÓ side (RND1) and a Ònon-strokeÓ side (RND2). The side assignment was implemented in order to have a left-side-stroke/right-side-stroke probability that approximately matched the distribution of the stroke group. Univariate group differences were determined by one -, or two-tailed t-test, or
To constrain the number of variables under consideration, regression analyses were limited to those areas directly affected by stroke (i.e. MCA territory on the stroke side/RND1). Linear regression models were tested for the effects of
Regression parameters were estimated using the traditional least squares estimator as well as the Theil-Sen robust regression estimator
Robust regression parameter inference, including simultaneous 95% confidence intervals and hypothesis test statistics, was estimated by bootstrapping of observations. For each robust regression, bootstrapping consisted of n = 600 resamples of the stroke group, with replacement, of the multivariate observations. Regression parameter estimates, standard errors, bootstrap confidence intervals, and hypothesis test statistics were calculated using functions written by R.R. Wilcox
Relationships between GM and IADL and between
For groups of hypothesis tests that included multiple variables (differences between groups and between sides), the threshold of significance was adjusted to maintain a false discovery rate
Demographic characteristics, mean BP, MCA and ICA diameters on the non-stroke side, mean BFV and CO2 vasoreactivity and laboratory results were similar between the stroke and non-stroke groups (
Stroke Group | Control Group | |||
Region | S side | NS side | RND1 | RND2 |
Front. GM | 5.2 |
5.6 |
5.5 |
5.5 |
Temp. GM | 4.4 |
4.7 |
4.6 |
4.7 |
Par. GM | 3.0 |
3.2 |
3.2 |
3.2 |
Occ. GM | 1.8 |
1.8 |
1.8 |
1.8 |
Temp. WM | 2.0 |
2.1 |
2.1 |
2.1 |
Front.WM | 4.1 |
4.5 |
4.4 |
4.4 |
Par. WM | 2.5 |
2.6 |
2.7 |
2.7 |
Occ. WM | 1.7 |
1.1 |
1.8 |
1.8 |
Gray matter (GM) and white matter (WM) volumes for stroke (S) and non-stroke (NS) groups by side. Values are mean
indicates values are significantly different from stroke side of stroke group by Wilcoxon signed-rank test, with false discovery rate of 0.05.
BP-BFV phase shift (
The fraction of subjects with either current or previous smoking history was significantly different for stroke and non stroke groups (
The main results for least squares regressions are summarized in
Response | Predictor | Group | Model fit | Effect test |
TGM |
|
Stroke | ||
Non-stroke | ||||
Gait |
|
Stroke | ||
Non-stroke | ||||
IADL |
|
Stroke | ||
Non-stroke |
Regression on temporal lobe grey matter (TGM), gait speed (Gait), instrumental activities of daily living survey (IADL). Estimates were calculated with regression equations controlling for age, BMI, mean arterial pressure, sex, and infarct volume. The
Response | Predictor | Estimate | S.E. | |
TGM |
|
6.8 |
3.97 |
0.047 |
Gait |
|
4.05 |
3.04 |
0.097 |
Gait | TGM | 30.0 | 23.0 | 0.080 |
Gait | TGM |
28.9 | 14.7 | 0.060 |
Theil-Sen estimates for regressions with
indicates results without controlling for infarct volume.
Using least squares regression, a larger
Residuals of least squares regression on relative temporal lobe gray matter (GM) volume against BP-BFV phase difference (
In agreement with the least squares estimates, the Theil-Sen estimates showed a significant effect (
Using the least squares estimator, a larger
The Theil-Sen estimate for the relationship between
Ordinal logistic regression showed that a larger
In contrast with our findings of associations between
Similar to our results with least squares regression, the Theil-Sen estimate for a relationship between gait speed and temporal GM was marginally significant, (
IADL was not significantly associated with temporal GM (Lack of fit:
In order to examine the confounding effect of infarct volume on linear regressions observed above, linear regressions of infarct volume on gait speed and temporal lobe GM were examined, after correcting for age, sex, mean BP and BMI. Least squares regression showed that larger infarct volumes were associated with significantly slower gait speed (model fit:
This study examined the relationships among dCA, brain structural volumes, and functional status in subjects with chronic ischemic stroke using a nonlinear dCA assessment computed using the MMPF method. Both traditional least squares regression and robust regression were used to test the hypothesis that better dCA function is associated with less GM atrophy and better functional status. Supporting this hypothesis, smaller
Poor clinical outcomes have been shown to be associated with impaired dCA following both brain injury
Atrophy of brain tissue continues following the acute stroke period and extends from periinfarct zones to contralateral and remote cortical and subcortical regions that are functionally connected to the infarct site
Both impaired CA
In the present study, although all MCA-territory brain regions showed significant atrophy for stroke subjects, the relationships between dCA impairment, GM atrophy, and functional status were most prominent for temporal lobe GM. These relationships were not observed in age-matched, diabetic, or non-diabetic non-stroke subjects, and were independent of BP. However, diabetic subjects also had impaired dCA compared to healthy nonstroke subjects, which suggests that associations between dCA, functional impairment, and temporal lobe GM are stroke-specific and not due to normal aging or stroke-independent dCA impairment.
Temporal lobe structures such as the insular cortex and amygdala are key centers of the autonomic network
Disrupted autonomic regulation after stroke may also influence the clinical outcomes measured in this study, independent from dCA. Sympathetic reflex activity is attenuated with chronic stroke and this attenuation is correlated with functional motor capacity
We hypothesize that treatments aimed at the improvement of dCA may play a role in optimizing the functional performance and quality of life in elderly people with chronic ischemic stroke. Therefore, the dCA status and the potential effects of treatments (such as antihypertensive medications
The results of least squares regression were in agreement with robust regression estimates for the relationship between
Including infarct volume in the linear models had a considerable effect on robust regression results. When including infarct volume, the regressions of
The size and variability of the sample may also explain why no significant differences in CO2 reactivity between groups were detected (
Due to the retrospective nature of the present study, the sample selected for analysis was not population-based. Specifically, our non-stroke group included individuals with diabetes, hypertension, or both while all stroke subjects were non-diabetic. However, diabetes is a major risk factor for stroke and hypertension
The study design has also limited the elucidation of causality between impaired cerebral autoregulation, brain tissue loss and poor functional outcomes in stroke patients. Since stroke itself can cause impairment of dCA, brain tissue loss and worse functional outcomes, the cross-sectional study design cannot demonstrate a causal link between dCA, tissue loss and functional outcome. Therefore, future work should include a longitudinal study that would evaluate the time course and relationship between cerebral autoregulation and functional outcomes.
There are likely associations between dCA, temporal lobe GM, gait speed, and IADL, indicating that dCA may impact GM atrophy and functional recovery following stroke. The relationships between dCA, temporal lobe GM and functional status were independent of age, sex, BMI, mean BP and mean BFV, but it is unclear how infarct volume is associated with these parameters. Therefore, dCA impairment may be an important factor underlying perfusion adaptation to daily activities and progression of regional atrophy and functional recovery in patients with stroke.
The authors would like to thank the reviewers for their helpful comments on the improvement of this manuscript. We would also like to thank Sara Monti for help with the preparation of this manuscript.