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Published in final edited form as: Alzheimers Dement. 2013 Jul 18;10(2):162–170. doi: 10.1016/j.jalz.2013.04.507

Global brain hypoperfusion and oxygenation in amnestic mild cognitive impairment

Jie Liu a,b, Yong-Sheng Zhu a,b, Muhammad Ayaz Khan a,b, Estee Brunk a, Kristin Martin-Cook c, Myron F Weiner c,d, C Munro Cullum c,d, Hanzhang Lu f, Benjamin D Levine a,b, Ramon Diaz-Arrastia e, Rong Zhang a,b,c,*
PMCID: PMC3859724  NIHMSID: NIHMS507775  PMID: 23871763

Abstract

Background

To determine if global brain hypoperfusion and oxygen hypometabolism occur in patients with amnestic mild cognitive impairment (aMCI).

Methods

Thirty-two aMCI and 21 normal subjects participated. Total cerebral blood flow (TCBF), cerebral metabolic rate of oxygen (CMRO2) and brain tissue volume were measured using color-coded duplex ultrasonography (CDUS), near-infrared spectroscopy (NIRS), and MRI. TCBF was normalized by total brain tissue volume (TBV) for group comparisons (nTCBF). Cerebrovascular resistance (CVR) was calculated as mean arterial pressure divided by TCBF.

Results

Reductions in nTCBF by 9%, CMRO2 by 11%, and increase in CVR by 13% were observed in aMCI relative to normal subjects. No group differences in TBV were observed. nTCBF was correlated with CMRO2 in normal controls, but not in aMCI.

Conclusions

Global brain hypoperfusion, oxygen hypometabolism and neurovascular decoupling observed in aMCI suggest that changes in cerebral hemodynamics occur early at prodromal stage of Alzheimer’s disease, which can be assessed using low cost and bed-side available CDUS and NIRS technology.

Keywords: mild cognitive impairment, cerebral blood flow, cerebral metabolic rate of oxygen, ultrasonography, near-infrared spectroscopy, MRI

1. Introduction

Alzheimer’s disease (AD) may begin years and even decades before its clinical appearance 1. For early detection or treatment of AD, the term mild cognitive impairment (MCI), or the amnestic type of MCI (aMCI) in particular, has been coined to describe a prodromal stage of AD 2. The etiology of sporadic AD is not clear and is likely to be multi-factorial involving both genetic and environmental factors 3.

Brain perfusion is fundamentally important for normal neuronal function. Brain hypoperfusion, even insufficient to produce ischemic cell death, can affect brain protein synthesis and lead to neuronal dysfunction 4. Accumulating evidence suggests brain hypoperfusion/cerebrovascular dysfunction may play an important role in AD onset and progression 4. In this regard, regional brain hypoperfusion and hypometabolism have been observed in aMCI patients mainly in the temporal-parietal lobe and posterior cingulate cortex 5. However, increase rather than decrease in regional brain perfusion also has been observed in aMCI and have been interpreted to reflect a compensatory effect 6. These observations reflect the complexity of changes in regional brain perfusion and metabolism at prodromal stage of AD.

So far, few studies have measured global brain perfusion in patients with aMCI and AD 7–9. To our knowledge, only one study reported that reduction in global brain perfusion in aMCI patients was related to the risk of AD conversion 9. Large population-based studies have demonstrated that global brain hypoperfusion as indicated by a low cerebral blood flow (CBF) velocity in the middle cerebral artery (MCA) was related to the high risks for developing AD in older adults 10. The color-coded duplex ultrasonography (CDUS) and spatially resolved near-infrared spectroscopy (NIRS) are well established methods for measurement of CBF and brain tissue oxygenation in human subjects 11, 12. These methods are non-invasive, low cost and bedside available and therefore have a great potential to be used in large population studies. Using these methods, this study tested the hypothesis that reductions in global brain perfusion and cerebral metabolic rate for oxygen utilization (CMRO2) occur in aMCI patients relative to normal control subjects. Obtaining this information may help us to better understand the role of cerebrovascular dysfunction in AD onset and progression 4.

2. Methods

2.1. Participants

Thirty-two aMCI subjects and 21 normal cognitive controls were recruited from local newspaper advertisements, senior centers and the UT Southwestern Medical Center Alzheimer’s Disease Center. The diagnosis of aMCI was based on Petersen criteria 1 as modified by the Alzheimer's Disease Neuroimaging Initiative (ADNI) project (http://adni-info.org). Further clinical evaluation was performed according to the Alzheimer’s Disease Cooperative Study (ADCS) recommendations (http://www.adcs.org) using standard diagnostic criteria. The results of cognitive assessment using the Mini-Mental State Examination (MMSE) 13, Clinical Dementia Rating (CDR) scale 14, and Wechsler Memory Scale Logical Memory (LM) for immediate and delayed recalls are shown in Table 1. Inclusion criteria were normal subjects and patients with aMCI both sex and age 55∼80. Exclusion criteria were major psychiatric disorders, major or unstable medical conditions, uncontrolled hypertension, diabetes mellitus or chronic inflammatory diseases. Subjects with heart pacemaker or any metal plates or pins in their body which prevented them from MRI were also excluded. Group demographics and clinical features are presented in Table 1. All subjects and/or their study partners signed the informed consent approved by the Institutional Review Boards of the UT Southwestern Medical Center and Texas Health Presbyterian Hospital of Dallas.

Table 1.

Demographics and clinical characteristics of study participants

NC
n=21		 aMCI
n=32		 P Value
Age, yr 67 (7) 67 (7) 0.802
Education, yrs 16 (3) 16 (3) 0.874
Female sex, No. (%) 13 (62%) 19 (59%) >0.999
Race, No. (%)
  Caucasian 19 (90%) 29 (91%) >0.999
  African American 2 (10%) 3 (9%) >0.999
Height, cm 169 (7) 167 (10) 0.432
Weight , kg 75 (16) 79 (17) 0.461
Body mass index, kg/m2 26.1 (4.2) 28.0 (4.3) 0.122
Hematocrit, % 41.7 (3.8) 41.4 ( 3.3) 0.767
ETCO2, mmHg 40.1 (2.9) 40.0 (3.9) 0.953
Blood pressure, mmHg
  Systolic BP 120 (12) 123 (11) 0.220
  Diastolic BP 72 (9) 74 (7) 0.250
  Mean arterial pressure 88 (9) 91 (8) 0.194
  Pulse pressure 48 (7) 49 (10) 0.591
Heart rate, bpm 64 (8) 61 (9) 0.192
Cardiac output (echo), L/min 3.62 (0.88) 3.39 (1.00) 0.388
Medical history
  Treated hypertension 9 (43%) 14 (44%) >0.999
  Hypercholesterolemia 7 (33%) 11 (34%) >0.999
  Hypothyroidism 3 (14%) 4 (13%) >0.999
Medication use
  Calcium channel blocker 3 (14%) 5 (16%) >0.999
  β-blocker 3 (14%) 4 (13%) >0.999
  ARBs 2 (10%) 3 (9%) >0.999
  Ace Inhibitors 4 (19%) 6 (19%) >0.999
  Diuretics 3 (14%) 4 (13%) >0.999
  Statin 4 (19%) 6 (19%) >0.999
Psychometric test scores
  MMSE 29.1 (0.8) 28.9 (1.4) 0.669
  CDR 0 0.5 -
  LM immediate recall 14.9 (1.8) 10.8 (2.3) <0.001
  LM delayed recall 14.5 (2.6) 8.2 (2.2) <0.001
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*

Values are the mean (standard deviation) or number (percentage). NC = normal controls; aMCI = amnestic mild cognitive impairment; ETCO2 = End-tidal CO2; BP = blood pressure; ARBs= angiotensin receptor blockers; MMSE = Mini-Mental State Examination score; CDR= clinical dementia rating, LM = Wechsler Memory Scale Logical Memory subtest.

2.2. MRI Measurement of Bain Tissue Volume

Magnetization-prepared rapid acquisition gradient echo (MPRAGE) images were acquired on a 3T system (Philips Achieva MR system) to measure cortical and sub-cortical brain tissue volumes. Sagittal images were obtained with FOV 256×256mm, matrix size 256×256 and slice thickness 1mm without gap. Image sequence parameters were TR = 8.1 ms, TE = 3.7 ms, TI = 1100 ms, shot interval 2100 ms, and FA = 12° with a SENSE factor of 2. A total of 140 images were collected to cover the whole brain. MPRAGE images were processed using FreeSurfer software (http://nmr.mgh.harvard.edu/martinos). Details of the procedures for measuring brain tissue volume were published previously 15. Total brain tissue volume (TBV) was obtained as a sum of measured cortical and subcortical gray matter (GM) and white matter (WM) volumes including the brainstem and cerebellum. Intracranial volume (ICV) was measured using the atlas based spatial normalization procedures to delineate cerebral spinal fluid (CSF)/skull borders 15. Individual TBV, GM, and WM volumes were divided by ICV to obtain the normalized values (nTBV, nGM, and nWM).

2.3. Measurement of Brain Perfusion, Tissue Oxygenation and Systemic Hemodynamics

A 3–12 MHz linear array transducer on the CDUS system (CX-50, Phillips Healthcare) was used for CBF measurements. The CBF measurements for the internal carotid artery (ICA) were performed at least 1 cm above the carotid bulb (Figure 1 A), and for vertebral artery (VA) between the C4 and C6 intertransverse segments (Figure 1 B). A straight vessel segment with a parallel wall view was identified where the luminal diameter (D) remained the same for a length of at least 0.5 cm to enhance the uniformity of Doppler sample volume. The sample volume was positioned at this site to cover the entire vessel lumen (Figure 1 C&D) to measure the angle-corrected mean velocity (i.e., spatially averaged blood flow velocity across the whole vessel lumen assuming a laminar flow)16. For calculation of volumetric blood flow, at least 5 complete cardiac cycles of consecutive blood flow velocity waveforms were recorded to obtain the time-averaged mean velocity (TAMV) (Figure 1 C&D). For vessel diameter measurement, the distance between the parallel internal layers at the sample volume site was measured (Figure 1 A&B). Specifically, pulsatile changes of the vessel diameter were recorded continuously for about 6 seconds on high-resolution B-mode video with a 21 Hz frame rate in a longitudinal view for both ICA and VA. Changes of vessel diameter at the sampling segment (with a length of ∼ 0.5cm) were measured using an edge-detection and wall-tracking technology to obtain time averaged vessel diameter from 3 consecutive cardiac cycles (Brachial and Carotid Analyzer, Medical Imaging Applications). Both the velocity and diameter measurements were repeated 3 times for each vessel with their mean values used for CBF calculation. This procedure was taken to reduce the intrinsic CBF variability associated with respiratory and other low frequency oscillations 17. Blood flow of each vessel was calculated as the product of TAMV and the cross-sectional area (A) of the vessels as CBF = TAMV × A × 60 = TAMV × [(D/2)2 × π] × 60. Total CBF (TCBF) was obtained as a sum of CBFs of bilateral ICAs and VAs and was normalized by TBV (nTCBF) for group comparisons.

Figure 1.

Figure 1

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Measurements of cerebral blood flow in internal carotid artery (ICA) and vertebral artery (VA). A. Diameter of ICA on high-resolution B-mode video was measured using an automated wall-tacking and edge-detection software (see text); B. Diameter of VA was measured in the same way; C. Measurement of time-averaged mean velocity (TAMV) at ICA; D. Measurement of TAMV at VA.

Spatially resolved NIRS (NIRO-200NX, Hamamatsu Photonics) was used to measure brain tissue oxygenation (i.e., the ratio of intravascular oxygenated to total hemoglobin concentration) expressed as a tissue oxygenation index (TOI)12. This technology is based on the photon diffusion theory to assess the slope of light attenuation versus the distances traveled by the light to calculate absolute brain tissue oxygen saturation, and has been validated and used extentively to assess brain tissue oxygenation under a variety of clinical conditions 18. It has been shown that TOI can provide a quantitative estimation of cerebral venous oxygenation under certain model assumption 19. Thus, cerebral oxygen extraction fraction (OEF) can be estimated as OEF =(SaO2-TOI)/ SaO2; and CMRO2 can be calculated as CMRO2 = nTCBF×(SaO2-TOI) × (Ca/ρ) based on the Fick’s law 20, where Ca is the maximal amount of oxygen that a unit volume of blood can carry (8.337 µmol/ml) 21, and ρ is a constant of brain tissue density 1.06 g/ml 22. Six aMCI subjects (3 females) and 3 controls (2 females) had no TOI measurements for technical problems.

Brachial arterial pressure was measured using a sphygmomanometer (Tango+, Suntech) and finger arterial blood oxygen saturation (SaO2) was measured using a pulse oximeter (Biox 3700, Ohmeda). Cerebrovascular resistance (CVR) was calculated as mean arterial pressure (MAP) divided by TCBF (CVR = MAP/TCBF). Electrocardiogram (Hewlett-Packard) was measured to determine heart rate (HR). End-tidal CO2 (ETCO2) was monitored with a capnography (Capnogard, Novamatrix). Cardiac output was measured with echocardiography from the apical 4-chamber view using a modified Simpson's rule method 23.

All data collections were performed in the supine position after at least 20 minutes of rest in an environmentally controlled laboratory with ambient temperature of 23°C to allow stabilization of systemic and cerebral hemodynamics. Subjects were asked to refrain from high intensity exercise, alcohol, or caffeinated beverage at least 24 hours before test. The time used for vascular imaging and blood flow measurement using CDUS was about 20 minutes for each individual subject. HR, TOI, and ETCO2 were recorded continuously using a data acquisition software (Acknowledge, BIOPAC Systems) and averaged for data analysis. Thus, the total time used for the test including supine rest was about 40 minutes. During data collection, subjects were instructed not to speak and were not cognitively engaged (e.g. reading, watching TV etc.). The experimental protocol was the same for aMCI and normal subjects to minimize potential influences of differences in cognitive activity on the CBF and TOI measurements 24.

Clinical cognitive assessments, measurements of cerebral hemodynamics and MRI were performed during 3 visits. The time intervals among these visits were more than a week but less than 3 months.

2.4. Measurement Reproducibility

Fifteen individuals were randomly selected from the 21 normal subjects to assess the CBF and TOI measurement reproducibility. Two measurements were obtained within a time period of 3 months. The coefficients of variation between the measurements for TCBF and TOI were 4.7 % and 5.5%, respectively. Bland-Altman plots confirmed that test-retest differences were distributed within mean difference ± 2 standard deviation (SD) without systemic bias for both TCBF (−3 ± 48 ml/min) and TOI (0.2 ± 7.2 %).

2.5. Statistical Analysis

Data were presented as mean ± SD. Group comparisons were performed using independent samples t-tests for continuous variables or Chi-square tests for categorical variables. The relationship between brain perfusion and CMRO2 was examined using the Pearson product-moment correlation analysis to assess neurovascular coupling. A P value < 0.05 was considered statistically significant.

3. Results

The aMCI and normal subjects were similar with respect to age, sex, education, body size, hematocrit, ETCO2, blood pressure, and cardiac output (Table 1). No differences in either absolute (TBV, GM, and WM) or normalized brain volumes (nTBV, nGM, and nWM) were observed between the two groups (Table 2).

Table 2.

Measurements of brain volume, perfusion, and tissue oxygenation

NC
n=21		 aMCI
n=32		 P Value
Brain volume
  ICV, ml 1516 (212) 1550 (212) 0.561
  TBV, ml 1110 (98) 1140(107) 0.299
  GM, ml 608 (47) 611 (56) 0.852
  WM, ml 502 (56) 529 (57) 0.088
  nTBV, %ICV 73.9 (6.9) 74.1 (5.3) 0.938
  nGM, %ICV 40.6 (4.6) 39.7 (3.1) 0.393
  nWM, %ICV 33.3 (2.7) 34.3 (2.7) 0.177
Regional brain perfussion
  Diameter, mm RICA 4.55 (0.74) 4.35 (0.61) 0.290
LICA 4.49 (0.52) 4.48 (0.66) 0.950
RVA 3.12 (0.62) 3.04 (0.45) 0.581
LVA 3.21 (0.47) 3.20 (0.48) 0.925
  TAMV, cm/s RICA 24.3 (7.7) 24.4 (6.8) 0.938
LICA 22.5 (7.5) 21.6 (6.6) 0.663
RVA 15.0 (4.7) 14.6 (5.8) 0.773
LVA 15.0 (3.0) 14.5 (3.9) 0.600
  CBF, ml/min RICA 233 (80) 211 (46) 0.211
LICA 207 (58) 201 (64) 0.756
RVA 73 (36) 64 (30) 0.349
LVA 76 (31) 70 (27) 0.492
Global brain perfusion
  TCBF, ml/min 588 (101) 547 (97) 0.142
  nTCBF, ml/min/100ml 53.1 (8.7) 48.1 (8.3) 0.038
  CVR, mmHg•min/ml 0.152 (0.025) 0.171 (0.033) 0.029
Brain tissue oxygenaton indices
  SaO2, % 97.0 (1.6) 97.2 (1.3) 0.570
  TOI, % 66.4 (4.1) 65.5 (5.6) 0.562
  OEF, % 31.4 (4.3) 32.5 (5.9) 0.524
  CMRO2, µmol/100g/min 127.1 (17.1) 113.6 (15.7) 0.010
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*

Values are the mean (standard deviation). NC = cognitively normal controls; aMCI = amnestic mild cognitive impairment; ICV = intracranial volume; TBV = total brain-tissue volume; nTBV = normalized TBV; WM = white matter; nWM = normalized WM; GM = grey matter; nGM = normalized GM; RICA = right internal carotid artery; LICA = left internal carotid artery; RVA = right vertebral artery; LVA = left vertebral artery; TAMV = time-averaged mean velocity; CBF = cerebrovascular blood flow; TCBF = total CBF; nTCBF = normalized TCBF; CVR = cerebrovascular resistance; SaO2 = arterial blood oxygen saturation; TOI = tissue oxygenation index; OEF = oxygen extraction fraction = (SaO2-TOI)/ SaO2; CMRO2 = cerebral metabolic rate of oxygen = nTCBF×(SaO2-TOI) × (8.337/1.06).

No group difference in diameter, blood flow velocity, or CBF was found in bilateral ICAs and VAs, although a trend of lower TCBF (about 7%) was observed in aMCI subjects relative to normal controls (547 ± 97 vs. 588 ± 101 ml/min, P = 0.142) (Table 2). Notably, nTCBF was reduced significantly by 9% (48.1 ± 8.3 vs. 53.1 ± 8.7 ml/min/100 ml, P < 0.05, Figure 2 A) in aMCI relative to the normal controls associated with an significant increase in CVR by 13% (0.171 ± 0.033 vs. 0.152 ± 0.025 mmHg·min/ml, P < 0.05, Figure 2 B).

Figure 2.

Figure 2

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Box plots of (A) normalized total cerebral blood flow (nTCBF), (B) cerebrovascular resistance (CVR), and (C) cerebral metabolic rate of oxygen (CMRO2) in cognitively normal controls (NC) and amnestic mild cognitive impairment (aMCI) group. The horizontal dotted and solid lines within the box represent the mean and median, respectively. *P < 0.05.

Both TOI and OEF also showed no group differences. However, CMRO2 was reduced significantly by 11% in aMCI relative to normal controls (113.6 ± 15.7 vs. 127.1 ± 17.1 µmol/100g/min, P < 0.05, Figure 2 C).

Finally, a significant correlation between nTCBF and CMRO2 (r = 0.556, P < 0.05, Figure 3A) was observed in normal controls but not in aMCI (Figure 3B).

Figure 3.

Figure 3

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Linear correlation analyses between cerebral metabolic rate of oxygen (CMRO2) and normalized TCBF (nTCBF) in cognitively normal controls (NC) (A, solid dots) and amnestic mild cognitive impairment (aMCI) group (B, open circles). CMRO2 showed a significant correlation to TCBF in NC but not aMCI group. r = Pearson’s correlation coefficient.

4. Discussion

In this study, we found that global brain perfusion and CMRO2 were reduced and cerebrovascular resistance was increased significantly in aMCI patients as compared to normal controls. Furthermore, a linear correlation between nTCBF and CMRO2 was found in normal subjects but not in aMCI suggesting the presence of neurovascular decoupling in these patients. Taken together, these findings suggest that cerebral hemodynamics or CBF regulation is compromised at prodromal stage of AD which can be assessed using low cost and bed-side available CDUS and NIRS technology.

4.1. Methodological considerations

Numerous imaging modalities have been used to measure brain perfusion and metabolic rate of oxygen or glucose, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), and perfusion MRI 25. These methods in general are expensive, not bedside-available and are used mainly at academic research centers. In addition, these modalities either require injection of radioactive tracers (PET and SPECT) or are not feasible in patients who may have claustrophobia or metal implants in their body (MRI). This study demonstrated the feasibility of using non-invasive, low-cost, bedside available CDUS and NIRS methods to measure brain perfusion and CMRO2 in aMCI patients. Further studies using these technologies to assess changes in cerebral hemodynamics in aMCI or AD in longitudinal or interventional studies are likely to establish its value in clinical study of AD.

Two technical issues related to the use of CDUS for measuring CBF deserve attention. Firstly, in this study, blood velocity in a given vessel was measured using the time-averaged mean velocity (TAMV) across the whole vessel lumen over at least five complete cardiac cycles. Thus, the obtained TAMV represents both the spatial (across the whole vessel lumen) and temporal (≥ 5 cardiac cycles) averaged blood velocity 16. Under steady-state conditions, TAMV is the most accurate index used for CBF calculation when compared with other methods 26. Secondly, for CBF calculation based on the product of TAMV and the vessel luminal area, the importance of vessel diameter measurement cannot be overemphasized. In the present study, high-resolution (≈0.01mm) longitudinal view of both ICA and VA were acquired and the vessel diameter was measured using an automated edge-detection and wall-tracking software (Figure 1 A & B) 27. These procedures enhanced the accuracy and reliability of vessel diameter measurements by avoiding the potential manual measurement errors and/or the rater’s subjective bias. Furthermore, the vessel diameter waveforms over the consecutive cardiac cycles were averaged to minimize the influences of diameter pulsatility on the blood flow calculation. With these improvements in the methodology, TCBF measured using CDUS was remarkably consistent with the reported values using the standard Kety-Schmidt method 28.

Spatially resolved NIRS was used to measure brain tissue oxygenation expressed as TOI 12. It should be realized that TOI reflects a mixture of intravascular oxygenation status from the venous, arterial and capillaries with a proportion of about 75: 20: 5 in adults 29. Under steady-state conditions, changes in TOI reflect primarily changes in cerebral venous oxygen saturation 30, 31. In the present study, no group differences in arterial oxygen saturation were observed. Thus, CMRO2 can be estimated based on the Fick’s law 20 by using TOI as a surrogate for cerebral venous oxygen saturation.

It is likely that CMRO2 assessed in this study reflects primarily cortex oxygen utilization since TOI measured from the forehead is likely to be lower than the internal jugular vein oxygen saturation which also incorporates the blood from the deep brain structures that generally extract less oxygen 32. In addition, assessment of CMRO2 assumed that the proportional contributions of blood oxygenation from the venous, arterial and capillaries to the TOI did not differ between patients with aMCI and the normal controls. Verification of this assumption needs to be determined in future studies.

4.2. Brain hypoperfusion, increases in CVR and reduction of CMRO2 in aMCI

Regional brain hypoperfusion and reduction in metabolic rate of glucose have been observed in aMCI patients in the temporal-parietal lobe and posterior cingulate cortex similar to those observed in patients with AD 33, 34. These observations have been interpreted to reflect a reduced neuronal activity in these regions at a prodromal stage of AD35. However, changes in regional brain perfusion or metabolism in aMCI patients are inconsistent and likely to be a dynamic process associated with AD progression 6. In addition, changes in brain perfusion or metabolism could be complicated by the presence of heterogeneous regional neural and/or vascular compensatory mechanisms 36. For example, increases rather than decreases in brain perfusion 37 or neuronal activity 38 have been observed in the prefrontal lobe or hippocampus in aMCI patients despite the presence of regional brain atrophy 39. Thus, spatially heterogeneous changes in regional brain perfusion or metabolism in aMCI may lead to non-net changes in total brain perfusion.

Using the CDUS method, a few studies have documented global brain hypoperfusion in AD patients 7, 8; and only one study of aMCI has reported a reduced TCBF in those patients who converted to AD after 2 years of follow-up when compared to non-converters 9. These findings are consistent with the large population-based studies which showed that low CBF velocity measured using transcranial Doppler was related to a high risk of developing AD in older adults 10. The present study demonstrated for the first time that global volumetric CBF measured using CDUS was reduced in aMCI patients when compared to normal controls with similar demographic characteristics and cardiovascular risk factors, and that reduction in CBF in aMCI was associated with a significant increase in cerebrovascular resistance.

Interestingly, increases in cerebrovascular resistance also have been observed in aMCI patients in a recent study based on TCD measurement of CBF velocity in the MCA. In addition, the obtained cerebrovascular resistance index for aMCI was between the normal controls and patients with AD 40. The present study extends these findings by having a larger sample size and used global measurement of volumetric CBF. Of note, since blood pressure was similar between the groups, increases in cerebrovascular resistance suggest presence of cerebral vasoconstriction in aMCI. It is possible that impairment of cerebral endothelial function and/or brain amyloid-β deposition on the vessel wall may lead to cerebral vasoconstriction in aMCI 41–43.

Furthermore, associated with changes in cerebral hemodynamics, a recent study found that cardiac baroreflex gain was also reduced in aMCI patients who had values between the normal controls and AD 44. The underlying mechanism(s) between changes in cerebral hemodynamics and baroreflex function in aMCI are not clear. However, these findings do suggest that progressive changes in peripheral and central hemodynamics are associated with AD progression 44.

Finally, in this study, we found that CMRO2 was reduced in aMCI patients. Since SaO2 and TOI did not differ between the groups, reduction in CMRO2 in aMCI could be simply due to a reduction in CBF, reflecting decreases in brain neuronal activity. Conversely, a primary reduction in CBF due to vascular dysfunction can lead to reduction in CMRO2 4. The cause-effect relationship cannot be determined in this study.

4.3. Neurovascular decoupling in aMCI

Neurovascular coupling generally refers to increases in regional brain blood flow in response to brain activation 4. Under resting conditions, global brain perfusion also is related to the brain volume and/or brain oxidative metabolic rate 45. This view of neurovascular coupling is consistent with the findings that in the normal subjects, brain perfusion (nTCBF) was correlated with CMRO2 (Figure 3A). The new findings of the present study are that the neurovascular coupling relationship observed in normal subjects appears to be diminished in patients with aMCI (Figure 3B). The underlying mechanisms for these observations cannot be determined in this study. It is possible that the presence of compensatory mechanisms or dysregulation of brain perfusion or metabolism may have led to the observed neurovascular decoupling in aMCI.

4.4. Strengths and Limitations

Strengths of this study include that all normal subjects were strictly screened and matched to aMCI patients, which allowed us to study the main influence of aMCI by balancing other confounding factors which may affect brain perfusion and metabolism (Table 1). Another strength of this study is the use of low-cost and bedside available CDUS and NIRS methods to measure brain perfusion and CMRO2. Both of these methods are readily available in clinical settings and can be applied in large population studies. In addition, the automated method used for vessel diameter measurement is likely to enhance the accuracy and reliability of CBF measurements. However, it should be noted that both brain perfusion and CMRO2 values overlapped considerably among individuals with and without aMCI even though statistically significant differences between group-averaged mean values were observed (Figure 2 and Figure 3). Thus, further studies are needed to establish the sensitivity and specificity of using these measurements for clinical screening of individuals with aMCI or early AD. In addition, a major limitation of these methods is its low spatial resolution for CBF and brain tissue oxygenation measurement. Using these methods, this study was only able to differentiate CBF between the internal carotid and vertebral arteries (Table 2). Potential changes in more specific regional brain perfusion and metabolism may not be detectable either because of the presence of collateral blood flow and/or neuronal/vascular compensatory mechanisms 5,6. Another limitation is the use of TOI as a surrogate of cerebral venous oxygenation for CMRO2 calculation, which may lead to an underestimated or overestimated absolute CMRO2. However, measurement of TOI was performed in the same way in aMCI as in the normal controls. Thus, a systematic bias, if it indeed existed, should have had less influence on the group differences observed in the present study. Of note, we also did not find any correlations between the measured Logical Memory scores (both immediate and delayed recall) and cerebral hemodynamics in this study (data not shown). Whether changes in cerebral hemodynamics in aMCI indeed are correlated with changes in cognitive function needs to be determined in longitudinal studies. In this regard, the cross-sectional nature of this study also limits its ability to draw conclusions regarding the cause-effect relationship between changes in cerebral hemodynamics and aMCI.

4.5. Summary of Main Findings

This study demonstrated for the first time that global brain perfusion and CMRO2 were reduced and cerebrovascular resistance was increased significantly in aMCI patients when compared to normal controls similar in age, sex and education. Furthermore, we observed that global brain perfusion was correlated with CMRO2 in the normal controls but not in aMCI suggesting the presence of neurovascular decoupling in these patients. Collectively, these findings suggest that compromised cerebral hemodynamics and/or CBF dysregulation occur early at prodromal stage of AD, which may contribute to AD onset or progression.

Acknowledgments

This work was support by the NIH grant R01AG033106-01 and NIA grant P30 AG12300. Jie Liu conducted the statistical analysis and drafted the manuscript; Jie Liu, Yong-Sheng Zhu, and Muhammad Ayaz Khan: data acquisition, analysis and interpretation; Estee Brunk and Kristin Martin-Cook: subject’s screening, cognitive data collection, clinical support and study coordination; Myron F. Weiner, Ramon Diaz-Arrastia, C. Munro Cullum and Benjamin D. Levine: clinical diagnosis and support; Ramon Diaz-Arrastia, Hanzhang Lu and Rong Zhang: study concept and experimental design, interpretation of data and study supervision. All authors edited and revised the manuscript and approved final submission with no conflict of interest to disclose. We sincerely thank all our study participants for their willingness, time and effort devoted to this study and Mr. Kyle Armstrong, Ms. Rosemary Parker and all members of study team for their excellent technical support.

Footnotes

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References

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