Journal of Cerebral Blood Flow & Metabolism (2008) 28, 1652–1655 & 2008 ISCBFM All rights reserved 0271-678X/08 $30.00 www.jcbfm.com Brief Communication Total cerebral blood flow in relation to cognitive function: The Rotterdam Scan Study Mariëlle MF Poels1,2, Mohammad Arfan Ikram1, Meike W Vernooij1,2, Gabriel P Krestin2, Albert Hofman1, Wiro J Niessen2,3, Aad van der Lugt2 and Monique MB Breteler1 1 Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands; Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands; 3 Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands 2 Cerebral hypoperfusion has been associated with worse cognitive function. We investigated the association between cerebral blood flow and cognition and whether this association is independent of brain volume. In 892 participants, aged 60 to 91 years, of the population-based Rotterdam Scan study, we measured total cerebral blood flow (tCBF) and brain volume using magnetic resonance imaging. Lower tCBF was associated with worse information-processing speed, executive function, and global cognition. However, after correcting tCBF for brain volume, these associations disappeared. The association between tCBF and cognition may be mediated or confounded by brain atrophy. Future studies on tCBF should take into account brain atrophy. Journal of Cerebral Blood Flow & Metabolism (2008) 28, 1652–1655; doi:10.1038/jcbfm.2008.62; published online 25 June 2008 Keywords: cerebral blood flow; cerebral hypoperfusion; cognition; MRI Introduction Elderly persons often suffer from deterioration of cognitive function. Vascular risk factors may contribute to cognitive impairment by affecting blood flow to the brain (Meyer et al, 1988). Moreover, it has been suggested that cerebral hypoperfusion precedes and possibly contributes to the onset of clinical dementia (Ruitenberg et al, 2005). To assess perfusion at the brain tissue level is difficult as most measurement techniques are invasive and complex. Phase-contrast magnetic resonance imaging (MRI) enables fast and accurate measurement of total cerebral blood flow (tCBF) and has shown to be applicable in population-based studies (Spilt et al, Correspondence: Dr MMB Breteler, Department of Epidemiology, Erasmus MC University Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands. E-mail: m.breteler@erasmusmc.nl The Rotterdam study is supported by the Erasmus MC University Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research (NWO); the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science; the Ministry of Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. This study was further financially supported by the Netherlands Organization for Scientific Research (NWO) Grant nos. 948-00-010 and 918-46-615. Received 14 March 2008; revised and accepted 23 May 2008; published online 25 June 2008 2002). Previous studies showed that lower tCBF assessed with phase-contrast MRI was related to poorer cognition, in particular, information processing speed, and dementia (Rabbitt et al, 2006; Spilt et al, 2005). However, these studies did not assess whether this association was independent of brain atrophy. It can be hypothesized that smaller brain volume leads to decreased cerebral metabolic demand and as such confounds the association between CBF and cognitive function. Thus, the aim of our study was to investigate whether diminished CBF is associated with specific domains of cognitive function independent of brain volume. Materials and methods Participants This study is embedded within the Rotterdam study, a large population-based cohort study in the Netherlands (Hofman et al, 2007). The original study population consisted of 7,983 participants aged 55 years and older from the Ommoord area, a suburb of Rotterdam. In the year 2000, the cohort was expanded with the addition of 3,011 persons (Z55 years) (Hofman et al, 2007). From August 2005 to May 2006, we randomly selected 1,073 members of this cohort expansion for participation in the Rotterdam Scan study, a population-based brain-imaging study. After exclusion of individuals who were demented Total cerebral blood flow and cognitive function MMF Poels et al 1653 or had MRI contraindications, 975 persons were found to be eligible, of whom 907 participated and gave written informed consent. Because of physical inabilities (e.g., back pain), imaging could not be performed or completed in 12 individuals. Therefore, a total of 895 complete MR examinations were performed. The institutional review board approved the study. Magnetic Resonance Imaging Scan Protocol Magnetic resonance imaging of the brain was performed on a 1.5 T MRI scanner (General Electric Healthcare, Milwaukee, WI, USA), using an 8-channel head coil. For flow measurement, 2D phase-contrast imaging was performed as described previously (Vernooij et al, 2007). In brief, a sagittal 2D phase-contrast MRI angiographic scout image was performed. On this scout image, a transverse imaging plane perpendicular to both the precavernous portion of the internal carotid arteries and the middle part of the basilar artery was chosen for a 2D gradient-echo phase-contrast sequence (repetition time = 20 ms, echo time = 4 ms, field of view = 19 cm2, matrix = 256  160, flip angle = 81, number of excitations = 8, bandwidth = 22.73 kHz, velocity encoding = 120 cm/sec, slice thickness = 5 mm). For an example, see (Vernooij et al, 2007). Acquisition time was 51 secs and no cardiac gating was performed (Spilt et al, 2002). We further performed three high-resolution axial MRI sequences, that is, a T1weighted sequence, a proton-density-weighted sequence, and a fluid attenuated inversion recovery sequence (Vernooij et al, 2007). Measurement of tCBF and Total Brain Perfusion Flow was calculated from the phase-contrast images using interactive data language-based custom software (Cinetool version 4; General Electric Healthcare) (Vernooij et al, 2007). Two independent, experienced technicians drew all the manual regions of interest and performed subsequent flow measurements (interrater correlations (n = 533) > 0.94 for all vessels) (Vernooij et al, 2007). In three persons, tCBF could not be measured because of incorrect positioning of the phase-contrast imaging plane, leaving a total of 892 persons in our analysis. We calculated total brain perfusion (in mL/min per 100 mL) by dividing tCBF (mL/min) by each individual’s brain volume (mL) and multiplying the obtained result by 100 (Vernooij et al, 2007). classify scans into brain tissue and cerebrospinal fluid using the multispectral MR intensities. All segmentation results were visually inspected and if needed manually corrected. To remove noncerebral tissue, for example, eyes, skull, and cerebellum, we applied nonrigid registration (Rueckert et al, 1999) to register to each brain a template scan in which these tissues were manually masked. Brain volume was calculated by summing up all the voxels across the whole brain to yield volumes in milliliters. Cognitive Function Cognitive function was assessed with a neuropsychological test battery comprising the MMSE (mini-mental state examination), the Stroop test, the LDST (letter-digit substitution task; number of correct digits in 1 min), the WFT (word fluency test; animal categories), and a 15-WLT (15-word verbal learning test; based on Rey’s recall of words) (Prins et al, 2005). For each participant, z-scores were calculated for each test separately (individual test score minus mean test score divided by the s.d.), except for MMSE. To obtain more robust measures, we constructed compound scores for information-processing speed, executive function, memory, and global cognitive function. The compound score for information-processing speed was the average of the z-scores for the Stroop reading and Stroop color-naming subtask and the LDST. Executive function included the z-scores of the Stroop interference subtask, the LDST and the WFT (number of animals in 1 min). The compound score for memory was the average of the z-scores for the immediate and delayed recall of the 15-WLT. For global cognitive function, we used the average of the z-scores of the Stroop test (average of the reading, color-naming, and interference subtask), the LDST, the WFT, and the immediate and delayed recall of the 15-WLT (Prins et al, 2005). Covariates We assessed the level of education and current smoking by interview. Systolic and diastolic blood pressures were measured twice on the right arm with a random-zero sphygmomanometer. The mean of the two readings was used in the analyses. Diabetes mellitus was defined as the use of blood glucose-lowering medication or fasting serum glucose level Z7.0 mmol/L. Carotid plaque score was assessed by Doppler ultrasound (van Popele et al, 2001). Assessment of Brain Volume Data Analysis For the assessment of brain volume, the structural MRI scans (T1-weighted, PD-weighted, and fluid attenuated inversion recovery) were transferred to a Linux workstation. Preprocessing steps and the classification algorithm have been described elsewhere (Vrooman et al, 2007). In summary, preprocessing included coregistration, nonuniformity correction, and variance scaling. We used the k-nearest-neighbor classifier (Anbeek et al, 2005) to We evaluated the association of both tCBF (mL/min) and total brain perfusion (mL/min per 100 mL brain tissue) s.d. increase with cognitive function using multiple linear regression models. All analyses were adjusted for age, sex, and education. To examine whether associations were independent of vascular risk factors, we additionally adjusted for current smoking, systolic and diastolic blood pressure, diabetes mellitus, and carotid plaque score. Journal of Cerebral Blood Flow & Metabolism (2008) 28, 1652–1655 Total cerebral blood flow and cognitive function MMF Poels et al 1654 Results Characteristics of the study population are shown in Table 1. Lower tCBF was associated with worse performance on tests of information-processing speed, executive function, and global cognition, but not with the MMSE score and memory performance (Table 2). Total brain volume was a strong determinant of tCBF (per s.d. increase in brain volume 36.00 mL/min increase in tCBF; 95% confidence interval 30.00;42.10). The associations of tCBF with cognition disappeared on correcting for brain volume (Table 2). Adjustments for vascular risk factors did not change any of these associations (Table 2). Discussion We found that persons with low tCBF performed significantly worse on tasks assessing informationprocessing speed, executive function, and global cognitive function, compared with persons with higher tCBF. However, total brain perfusion, indicating the flow in mL per 100 mL of brain tissue volume, was not associated with cognitive function. Adjustments for vascular risk factors did not change the results. Before interpreting the results, some methodological issues need to be addressed. The strengths of our study are its population-based setting, the high response rate, and the large sample size. A limitation is the cross-sectional design, which restricts our interpretation of the data with respect to cause and consequence. Furthermore, we only assessed average brain perfusion. Hence, we cannot exclude that brain perfusion in distinct brain regions may relate differently to cognitive performance. Finally, we could not measure blood flow into the cerebellum as we measured blood flow in the basilar artery at the level after the anterior and posterior inferior cerebellar arteries arise. It can be hypothesized that cerebral hypoperfusion causes brain atrophy that subsequently leads to cognitive decline (de la Torre 1999; Meyer et al, 1988). Conversely, it may also be that because of a diminished demand, brain atrophy itself affects CBF. Thus, the association between tCBF and cognitive function may be mediated or confounded by brain atrophy. In the past, CBF velocity measured by transcranial Doppler ultrasonography has been used as a proxy measure for CBF. Several studies using CBF velocity reported that subjects with greater CBF velocity were less likely to have dementia (Ruitenberg et al, 2005). Furthermore, a greater CBF velocity was found to be related with larger hippocampal and amygdalar volumes (Ruitenberg et al, 2005). More recently, associations of tCBF with speed, executive function (Rabbitt et al, 2006), and dementia (Spilt et al, 2005) were found using phasecontrast MRI. Our data are in line with these studies, as we also found the strongest associations for cognitive domains of speed and executive function Table 1 Characteristics of the study population Characteristics Participants (n = 892) Men, n 441 (49.4) Age, years 67.5 (5.5) Primary education, n 38 (4.4%) Systolic blood pressure, mm Hg 143.8 (18.5) Diastolic blood pressure, mm Hg 81.0 (10.2) Diabetes mellitus, n 85 (9.6%) Current smokers, n 267 (29.9%) 3.0 (1.0–5.0) Plaques in carotid artery, range: 0–12a Mini-mental state examination, score 27.9 (1.8) Brain volume, mL 976.8 (114) Total cerebral blood flow, mL/min 497.4 (86.2) Total brain perfusion, mL/min per 100 mL brain 51.2 (8.8) tissue Values are means (s.d.) or numbers (percentages). a Median interquartile range. Table 2 Association of tCBF and total brain perfusion with cognitive function (z-scores), using linear regression models (n = 892) Difference in test scores (95% CI) per s.d. increase in flow measure MMSE tCBF Model 1 Model 2 Z-score informationprocessing speed 0.08 ( 0.04;0.19) 0.09 ( 0.03;0.20) Total brain perfusion Model 1 0.07 ( 0.05;0.19) Model 2 0.08 ( 0.04;0.20) Z-score executive function Z-score memory Z-score global cognition 0.08 (0.03;0.14) 0.07 (0.02;0.13) 0.07 (0.02;0.12) 0.06 (0.01;0.11) 0.00 ( 0.07;0.06) 0.00 ( 0.07;0.06) 0.05 (0.01;0.10) 0.05 (0.01;0.10) 0.04 ( 0.02;0.09) 0.04 ( 0.02;0.09) 0.00 ( 0.05;0.05) 0.00 ( 0.05;0.05) 0.03 ( 0.04;0.09) 0.03 ( 0.03;0.09) 0.02 ( 0.02;0.07) 0.02 ( 0.02;0.07) CI, confidence interval; MMSE, mini-mental state examination; tCBF, total cerebral blood flow. Model 1 = adjusted for age, sex, and level of education. Model 2 = additionally adjusted for systolic blood pressure, diastolic blood pressure, current smoking, diabetes mellitus, and plaque score. Journal of Cerebral Blood Flow & Metabolism (2008) 28, 1652–1655 Total cerebral blood flow and cognitive function MMF Poels et al (Rabbitt et al, 2006; Spilt et al, 2005). However, none of those previous studies assessed whether the associations between CBF and cognitive function were independent of brain volume. We went a step further by correcting for brain volume, and found no associations between total brain perfusion and cognitive function. Thus far, only a few small studies reported that regional patterns of hypoperfusion in the brain may relate to cognitive decline or dementia independent of global differences (Johnson et al, 2005; Kogure et al, 2000). As mentioned, we could not evaluate this in our study. Further studies are needed to investigate this. In conclusion, our findings show that the relation between total CBF and worse performance on several domains of cognitive function is dependent on brain volume. Our study emphasizes that future studies on tCBF should take into account brain atrophy. Disclosure/conflict of interest The authors state no conflict of interest, and have no involvement, financial or otherwise, that might potentially bias their work. References Anbeek P et al (2005) Probabilistic segmentation of brain tissue in MR imaging. Neuroimage 27:795–804 de la Torre JC (1999) Critical threshold cerebral hypoperfusion causes Alzheimer’s disease? Acta Neuropathol 98:1–8 Hofman A et al (2007) The Rotterdam Study: objectives and design update. Eur J Epidemiol 22:819–29 Johnson NA et al (2005) Pattern of cerebral hypoperfusion in Alzheimer disease and mild cognitive impairment measured with arterial spin-labeling MR imaging: initial experience. Radiology 234:851–9 Kogure D et al (2000) Longitudinal evaluation of early Alzheimer’s disease using brain perfusion SPECT. J Nucl Med 41:1155–62 Meyer JS et al (1988) Cognition and cerebral blood flow fluctuate together in multi-infarct dementia. Stroke 19:163–9 Prins ND et al (2005) Cerebral small-vessel disease and decline in information processing speed, executive function and memory. Brain 128:2034–41 Rabbitt P et al (2006) Losses in gross brain volume and cerebral blood flow account for age-related differences in speed but not in fluid intelligence. Neuropsychology 20:549–57 Rueckert D et al (1999) Nonrigid registration using freeform deformations: application to breast MR images. IEEE Trans Med Imaging 18:712–21 Ruitenberg A et al (2005) Cerebral hypoperfusion and clinical onset of dementia: the Rotterdam Study. Ann Neurol 57:789–94 Spilt A et al (2002) Reproducibility of total cerebral blood flow measurements using phase contrast magnetic resonance imaging. J Magn Reson Imaging 16:1–5 Spilt A et al (2005) Late-onset dementia: structural brain damage and total cerebral blood flow. Radiology 236:990–5 van Popele NM et al (2001) Association between arterial stiffness and atherosclerosis: the Rotterdam Study. Stroke 32:454–60 Vernooij MW et al (2007) Total cerebral blood flow and total brain perfusion in the general population: the Rotterdam Scan Study. J Cereb Blood Flow Metab 28:412–9 Vrooman HA et al (2007) Multi-spectral brain tissue segmentation using automatically trained k-nearestneighbor classification. Neuroimage 37:71–81 1655 Journal of Cerebral Blood Flow & Metabolism (2008) 28, 1652–1655