Sex and age-related differences in cerebral blood flow investigated using pseudo-continuous arterial spin labeling magnetic resonance imaging

Results

Visualization of cerebral blood flow maps at different ages

Figure 1 shows a representative axial slice of derived CBF maps from brains of male and female participants within each age decade of our cohort. CBF maps derived with (first row) or without (second row) NESMA filtering of the ASL images are displayed. It is readily seen that, as expected, NESMA substantially reduces random variation in derived CBF maps. Further, visual inspection indicates that CBF varies with age, with the most pronounced variations occurring in the GM regions; this is clearly visible from the CBF maps derived after NESMA filtering. It is also observed that older participants have lower GM CBF as compared to young participants within the third, fourth, and fifth age decades. However, CBF varies minimally with age in the WM regions.

Figure 1. Cerebral blood flow (CBF) maps derived from pCASL imaging datasets with or without the NESMA-ASL filter. Corresponding GM and WM masks generated using FSL-FASL are also displayed. Results are shown for fourteen representative male and female participants within each age decade.

Effects of age and sex on CBF

Figure 2 shows linear relationships between CBF measurements using NESMA-filtered ASL images and age in the GM ROIs for men and women separately, and for all participants taken together. GM CBF was found to decrease with age, with regional variation among regions (Figure 2). Statistical analysis of all participants showed that all brain structures exhibited significant (p < 0.05) decreases of CBF with age except for the GM within the parietal lobes (Table 1). In addition, the most rapid decline in CBF with respect to age was found in the frontal lobes, while the slowest decline was in the temporal lobes (Table 1). For all GM ROIs, women exhibited significantly higher CBF values as compared to men (Figure 3). For women, all GM ROIs showed significant decreases in CBF with age (Table 2). For men, the frontal lobes showed significant decreases with respect to age, while the whole brain showed a non-significant trend towards a decrease in CBF with age (Table 2). Furthermore, women exhibited steeper slopes in the decline in CBF with age as compared to men for all GM ROIs with, for both men and women, the frontal lobes exhibiting the steepest decline. The rate with respect to age for the decline in CBF for women was found to be significantly different from the rate for men in the cerebellum, the whole brain, and the occipital and temporal lobes. We note that the interactions between age and sex were not significant in any ROI after FDR correction. Finally, although qualitative CBF results derived from NESMA-filtered and from unfiltered ASL images were similar in GM regions, the finding of a significant dependence on age was greatly strengthened with NESMA filtering in several brain structures including the occipital and parietal lobes, and the cerebellum (Table 1). This is consistent with the effect of the NESMA filter to decrease image noise, and hence variance of parameter estimation [15].

Figure 2. Regressions of NESMA-CBF with age and sex shown for the six gray matter (GM) regions investigated. For each structure, the coefficient of determination, R², and p-value, obtained after FDR correction, are reported. Most regions investigated showed linearly decreasing CBF with age.

Table 1. Significance (p-value) and slope of the variation in CBF as a function of age and sex for the linear regression analysis for each GM and WM brain region studied.

NESMA filtered

Unfiltered

Gray matter (GM)

White matter (WM)

Gray matter (GM)

White matter (WM)

Age

Sex

Age

Sex

Age

Sex

Age

Sex

p-value

Slope

p-value

Slope

p-value

Slope

p-value

Slope

p-value

Slope

p-value

Slope

p-value

Slope

p-value

Slope

Whole Brain

< 0.01

-0.11

< 0.01

-4.95

< 0.01

0.08

< 0.05

-1.90

< 0.01

-0.11

< 0.01

-4.92

< 0.01

0.07

< 0.1

-1.41

Frontal Lobes

< 0.01

-0.16

< 0.01

-3.85

< 0.05

0.04

< 0.05

-1.44

< 0.01

-0.15

< 0.01

-3.85

< 0.05

0.02

> 0.1

-1.04

Occipital Lobes

< 0.05

-0.10

< 0.01

-8.32

< 0.01

0.06

< 0.01

-2.56

< 0.1

-0.10

< 0.01

-8.35

< 0.01

0.04

< 0.01

-1.96

Parietal Lobes

< 0.1

-0.08

< 0.01

-5.20

< 0.01

0.06

< 0.05

-1.66

> 0.1

-0.08

< 0.01

-5.32

< 0.05

0.04

< 0.05

-1.20

Temporal lobes

< 0.05

-0.07

< 0.01

-4.82

< 0.01

0.05

< 0.01

-1.84

< 0.05

-0.07

< 0.01

-4.82

< 0.05

0.03

< 0.01

-1.34

Cerebellum

< 0.05

-0.09

< 0.01

-5.64

< 0.05

0.04

< 0.01

-2.12

< 0.1

-0.10

< 0.01

-5.38

< 0.05

0.02

< 0.01

-1.85

All p-values presented are obtained after FDR correction. Results are shown for both CBF maps derived with or without the NESMA filtering of the ASL images.

Figure 3. Comparison of mean CBF values obtained from NESMA-filtered ASL images for men and women in the indicated GM and WM regions. Mean CBF values for women are overall significantly greater than mean CBF values for men.

Table 2. Significance (p-value) and slope of the variation in NESMA-CBF as a function of age and sex for the GM and WM regions studied, for men and for women.

	Men					Women
	GM		WM			GM		WM
	p-value	Slope	p-value	Slope		p-value	Slope	p-value	Slope
Whole Brain	< 0.1	-0.07	< 0.01	0.11		< 0.01	-0.18	> 0.1	0.05
Frontal Lobes	< 0.01	-0.12	< 0.05	0.05		< 0.01	-0.21	> 0.1	0.03
Occipital Lobes	> 0.1	-0.04	< 0.05	0.06		< 0.05	-0.20	< 0.1	0.05
Parietal Lobes	> 0.1	-0.03	< 0.01	0.07		< 0.05	-0.16	> 0.1	0.04
Temporal Lobes	> 0.1	-0.02	< 0.01	0.05		< 0.01	-0.16	> 0.1	0.03
Cerebellum	> 0.1	-0.04	> 0.1	0.02		< 0.01	-0.17	< 0.05	0.04
All p-values presented are obtained after FDR correction.

Figure 4 shows linear relationships between CBF measurements using NESMA-filtered ASL images and age in the WM ROIs for men and women separately, and for all participants taken together. WM CBF increased slightly but significantly with age, both for the whole brain and across all examined regions (Figure 4, Table 1). Further, for all WM ROIs, women exhibited significantly higher CBF values as compared to men (Figure 3). Women exhibited a steeper and more statistically significant increase with respect to age in the cerebellum, and a smaller rate of increase with respect to age as compared to men in the occipital lobes that approached significance (Figure 3, Table 2). For men, increases in WM CBF as a function of age were seen in all brain regions investigated except in the cerebellum (Table 2). Furthermore, men exhibited relatively larger rates with respect to age for the decline in CBF as compared to women for all ROIs studied except in the cerebellum. The interactions between age and sex were not significant in all ROIs after FDR correction. Table 3 provides a detailed summary of GM and WM CBF values. Finally, results of CBF derived from NESMA-filtered ASL images as compared to those derived from unfiltered ASL images were substantially different in WM regions. Indeed, the regression coefficients calculated from CBF maps derived from filtered ASL images exhibited, overall, higher values in most WM regions examined (Table 1). Here as well, significance with age was strengthened with NESMA-filtering in most ROIs, including within the parietal lobes and temporal lobes.

Figure 4. Regressions of CBF values obtained from NESMA-filtered ASL images with age and sex shown for the six white matter (WM) regions investigated. For each structure, the coefficient of determination, R², and p-value, obtained after FDR correction, are reported. Most regions investigated showed linearly increasing CBF with age. This trend is more pronounced for men (blue) as compared to women (red).

Table 3. The mean and standard deviation (SD) of CBF values, derived from NESMA filtered ASL images, for all participants as well as for men or women, for each ROI and for each age interval as well as across the entire age range.

Mean ± SD CBF values (mL/100g/min)
			20–29 yrs.	30–39 yrs.	40–49 yrs.	50–59 yrs.	60–69 yrs.	70–79 yrs.	80–89 yrs.	All
All Participants	GM	Whole Brain	36.4 ± 6.40	34.5 ± 6.45	32.9 ± 6.39	31.9 ± 5.50	31.6 ± 5.77	31.2 ± 4.58	26.1 ± 3.71	33.0 ± 6.17
		Frontal Lobes	37.3 ± 6.14	34.9 ± 5.76	33.7 ± 7.14	31.2 ± 6.15	30.8 ± 6.05	30.6 ± 4.92	24.0 ± 2.80	33.0 ± 6.66
		Occipital Lobes	41.3 ± 9.41	39.4 ± 9.92	36.9 ± 8.56	38.7 ± 8.02	36.7 ± 8.26	35.7 ± 5.75	31.7 ± 5.65	37.9 ± 8.43
		Parietal Lobes	36.4 ± 7.66	35.5 ± 8.85	32.9 ± 8.57	33.1 ± 5.68	31.7 ± 7.74	33.5 ± 6.87	28.3 ± 5.04	33.8 ± 7.59
		Temporal Lobes	36.9 ± 6.76	35.4 ± 6.10	33.5 ± 5.98	33.0 ± 6.05	33.9 ± 6.28	33.3 ± 5.66	29.7 ± 4.21	34.2 ± 6.14
		Cerebellum	35.3 ± 6.82	31.7 ± 7.98	32.6 ± 6.26	30.6 ± 6.74	31.5 ± 5.41	30.6 ± 3.59	25.1 ± 3.71	32.0 ± 6.43
	WM	Whole Brain	16.6 ± 2.92	17.2 ± 4.35	16.6 ± 3.10	17.3 ± 3.38	19.2 ± 4.84	22.1 ± 4.97	18.7 ± 1.90	18.0 ± 4.13
		Frontal Lobes	12.8 ± 2.25	14.3 ± 3.16	13.7 ± 2.13	13.2 ± 2.55	14.6 ± 3.44	15.7 ± 3.27	14.4 ± 1.36	14.0 ± 2.77
		Occipital Lobes	15.0 ± 2.67	14.7 ± 3.47	15.1 ± 3.29	16.9 ± 2.59	17.4 ± 3.31	17.4 ± 3.34	16.3 ± 5.19	15.9 ± 3.29
		Parietal Lobes	14.4 ± 2.94	14.4 ± 4.14	14.4 ± 3.40	15.1 ± 2.32	17.4 ± 3.59	17.8 ± 3.56	15.3 ± 1.91	15.4 ± 3.45
		Temporal Lobes	13.5 ± 2.32	13.8 ± 2.81	13.7 ± 2.63	14.8 ± 2.97	15.1 ± 3.10	16.1 ± 3.10	15.3 ± 2.58	14.4 ± 2.82
		Cerebellum	20.8 ± 1.95	18.7 ± 2.99	19.7 ± 3.00	20.0 ± 3.35	21.4 ± 2.44	22.8 ± 2.26	18.8 ± 2.73	20.4 ± 2.87
Men	GM	Whole Brain	32.4 ± 4.17	31.3 ± 5.58	31.6 ± 6.57	30.6 ± 5.49	29.3 ± 5.92	30.9 ± 4.04	25.0 ± 3.65	30.9 ± 5.18
		Frontal Lobes	33.9 ± 4.84	32.6 ± 5.94	33.3 ± 7.06	29.7 ± 6.34	29.8 ± 7.41	30.4 ± 4.65	23.5 ± 3.23	31.3 ± 6.05
		Occipital Lobes	34.9 ± 4.29	35.1 ± 6.79	34.3 ± 7.87	36.8 ± 7.6	31.1 ± 3.76	34.4 ± 3.27	29.5 ± 4.59	34.5 ± 5.95
		Parietal Lobes	32.3 ± 4.91	31.6 ± 7.24	31.7 ± 9.10	31.7 ± 5.91	29.4 ± 8.42	32.9 ± 6.84	27.2 ± 5.60	31.6 ± 6.65
		Temporal Lobes	32.6 ± 4.72	32.8 ± 5.32	32.0 ± 5.97	32.2 ± 6.20	30.9 ± 4.94	33.2 ± 5.06	28.9 ± 4.78	32.2 ± 5.16
		Cerebellum	31.1 ± 4.66	28.6 ± 5.71	30.1 ± 6.51	30.0 ± 7.17	28.5 ± 5.75	30.7 ± 3.70	23.4 ± 1.82	29.6 ± 5.50
	WM	Whole Brain	15.1 ± 1.88	15.5 ± 3.84	15.9 ± 3.97	17.1 ± 3.59	18.5 ± 4.06	22.1 ± 5.64	18.1 ± 1.83	17.3 ± 4.35
		Frontal Lobes	11.8 ± 1.87	13.2 ± 3.34	13.2 ± 2.69	12.8 ± 2.59	15.9 ± 3.02	14.8 ± 3.47	14.2 ± 1.58	13.4 ± 2.83
		Occipital Lobes	13.6 ± 1.84	13.7 ± 3.34	13.5 ± 3.37	16.8 ± 2.78	15.2 ± 2.96	17.1 ± 3.53	14.8 ± 5.22	14.9 ± 3.32
		Parietal Lobes	13.2 ± 1.96	13.4 ± 3.53	13.8 ± 4.12	15.3 ± 2.4	17.1 ± 3.13	17.1 ± 4.13	15.4 ± 2.34	14.8 ± 3.42
		Temporal Lobes	12.5 ± 1.59	12.9 ± 2.7	12.4 ± 2.55	14.8 ± 3.08	13.5 ± 2.21	15.7 ± 3.42	14.9 ± 3.01	13.7 ± 2.83
		Cerebellum	19.9 ± 1.78	18.6 ± 3.02	18.3 ± 3.22	19.5 ± 3.84	20.8 ± 1.75	21.9 ± 2.04	17.6 ± 1.36	19.6 ± 2.90
Women	GM	Whole Brain	40.8 ± 5.59	40.0 ± 3.63	34.5 ± 6.27	35.3 ± 4.68	32.8 ± 5.86	31.8 ± 6.16	29.4 ± n/a	36.0 ± 6.28
		Frontal Lobes	41.2 ± 5.18	39.7 ± 2.36	34.3 ± 7.77	35.4 ± 3.55	31.2 ± 5.99	31.0 ± 6.16	25.4 ± n/a	35.4 ± 6.9
		Occipital Lobes	48.5 ± 8.34	45.8 ± 12.9	40.2 ± 8.80	43.8 ± 7.85	39.5 ± 8.68	38.2 ± 9.16	38.0 ± n/a	42.7 ± 9.06
		Parietal Lobes	41.0 ± 7.84	43.4 ± 9.01	34.4 ± 8.27	36.9 ± 3.16	32.8 ± 7.94	34.9 ± 7.75	31.5 ± n/a	36.8 ± 7.96
		Temporal Lobes	41.7 ± 5.39	38.8 ± 5.14	35.5 ± 5.85	34.9 ± 6.37	35.4 ± 6.73	33.6 ± 7.60	32.1 ± n/a	36.9 ± 6.33
		Cerebellum	40.0 5.75	37.3 ± 9.08	35.9 ± 4.40	32.1 ± 6.52	33.0 ± 5.03	30.6 ± 3.91	30.2 ± n/a	35.4 ± 6.19
	WM	Whole Brain	18.2 ± 3.07	20.3 ± 3.76	17.5 ± 1.17	17.8 ± 3.40	19.5 ± 5.53	22.2 ± 4.04	20.4 ± n/a	19.1 ± 3.60
		Frontal Lobes	13.9 ± 2.21	16.8 ± 1.73	14.3 ± 0.98	14.2 ± 2.65	13.9 ± 3.72	17.6 ± 2.12	15.1 ± n/a	14.8 ± 2.53
		Occipital Lobes	16.4 ± 2.76	15.8 ± 3.71	17.2 ± 1.62	17.2 ± 2.54	18.5 ± 3.11	18.1 ± 3.33	20.7 ± n/a	17.4 ± 2.70
		Parietal Lobes	15.7 ± 3.42	16.1 ± 6.14	15.3 ± 2.21	14.7 ± 2.49	17.6 ± 4.07	19.3 ± 1.53	15.2 ± n/a	16.3 ± 3.38
		Temporal Lobes	14.7 ± 2.56	14.8 ± 2.78	15.4 ± 1.71	14.9 ± 3.29	15.9 ± 3.36	16.9 ± 2.60	16.5 ± n/a	15.4 ± 2.50
		Cerebellum	21.7 ± 1.74	18.8 ± 3.38	21.6 ± 1.06	21.3 ± 1.08	21.6 ± 2.84	24.5 ±1.66	22.6 ± n/a	21.7 ± 2.37

Materials and Methods

Participants

Investigation has been conducted in accordance with the ethical standards and according to the Declaration of Helsinki and according to national and international guidelines and has been approved by the authors' institutional review board. Participants were drawn from two ongoing healthy aging cohorts at the National Institute on Aging (NIA). 15 volunteers from the Baltimore Longitudinal Study of Aging (BLSA) [66, 67], and 52 from the Genetic and Epigenetic Signatures of Translational Aging Laboratory Testing (GESTALT) were enrolled in this study. Thirteen additional participants from our home institution were recruited. The study populations, experimental design, and measurement protocols of the BLSA have been previously reported [66, 67]. The BLSA is a longitudinal cohort study funded and conducted by the NIA Intramural Research Program (IRP). Established in 1958, the BLSA enrolls community-dwelling adults with no major chronic conditions or functional impairments. The GESTALT study is also a study of healthy volunteers, initiated in 2015, and funded and conducted by the NIA IRP. The goal of the BLSA and GESTALT studies is to evaluate multiple biomarkers related to aging. We note that the inclusion and exclusion criteria for these two studies are essentially identical. Participants underwent testing at the NIA's clinical research unit and were excluded if they had metallic implants, or neurologic or medical disorders. Participants underwent a Mini Mental State Examination (MMSE) and achieved a score > 25. The final cohort consisted of 80 cognitively unimpaired volunteers ranging in age from 22 to 88 years (mean ± standard deviation 49.2 ± 18.7 years) of which 47 were men (49.7 ± 19.2 years) and 33 were women (48.4 ± 18.3 years). Data from four additional subjects were not included due to excessive motion artifacts. The number of participants per age-decade was: 17 (8 women) within 20-29 years, 11 (4 women) within 30-39 years, 16 (7 women) within 40-49 years, 11 (3 women) within 50-59 years, 9 (6 women) within 60-69 years, 12 (4 women) within 70-79 years, and 4 (1 woman) within 80-89 years. Figure 5 provides a detailed distribution of the number of participants per age-decade and sex. Experimental procedures were performed in compliance with our local Institutional Review Board, and participants provided written informed consent.

Figure 5. Number of participants per age decade and sex within the study cohort.

Data acquisition

MRI scans were performed on a 3T whole body Philips MRI system (Achieva, Best, The Netherlands) using the internal quadrature body coil for transmission and an eight-channel phased-array head coil for reception. For each participant, multi-slice single shot 2D echo-planar imaging (EPI) pCASL imaging datasets were acquired following the consensus recommendations of the ISMRM Perfusion Study Group and the European ASL in Dementia consortium [4]. This consisted of control, labeled, and proton density (PD) images acquired with incorporation of background suppression, FoV of 220 mm × 210 mm × 120 mm, and spatial resolution of 2.5 mm × 2.5 mm × 5 mm with reconstruction to 1 mm × 1 mm × 1 mm through linear interpolation after scanner reconstruction. 24 slices were acquired in ascending order to avoid slice ordering confounds associated with interleaved order schemes, and with minimal temporal slice spacing to ensure similar post-labeling duration (PLD) for all slices. Other experimental parameters were: echo time (TE) of 15 ms, repetition time (TR) of 7.5 s, labeling duration of 1.8 s, PLD of 2 s, SENSE factor of 2.3, flip angle of 90°, label distance of 8.5 cm, and 30 signal averages [4]. We note that the PD image was acquired with identical TE and TR as for the control and labeled images. The total acquisition time was ~12 min.

Data processing

After careful visual inspection of data quality for each participant [4], a whole-brain CBF map was generated from the corresponding pCASL dataset using NESMA noise filtering to improve accuracy and precision in CBF determination [15]. Briefly, NESMA-ASL restores the amplitude of an index voxel by incorporating the intensities of voxels with similar multispectral signal patterns, that is, intensities from pCASL images. The similarity between two voxels across the pCASL images is calculated using the relative Euclidean distance within a large search window centered on the index voxel. The size of the search window must be sufficiently large to ensure inclusion of an adequate number of similar voxels, and sufficiently restricted to ensure that the transmission and reception radiofrequency fields and noise standard deviation are approximately constant within the window. Voxels exhibiting relative Euclidean distance lower than 5% are considered as being similar to the index voxel [15]. Finally, CBF maps derived from unfiltered ASL images were also generated for comparison with those derived from the NESMA-filtered ASL images. All CBF maps were calculated based on the following equation [4].

$\text{CBF}=6000\times \frac{\lambda ·\left(I_C-I_L\right)·exp\left(\text{PLD}/T_{1,Blood}\right)}{2\alpha ·T_{1,Blood}·I_{PD}·\left(1-exp\left(-\text{LD}/T_{1,Blood}\right)\right)}\left[\text{mL}/100\text{ g}/\text{min}\right],$

where I_C, I_L, and I_PD are the control, labeled, and PD images, respectively. Here, λ is the partition coefficient between brain tissue and blood with value set to 0.9 mL/g, α is the labeling efficiency with value fixed to 0.85, T_1,Blood = 1.65 s (at 3T) representing the longitudinal relaxation time of blood, and LD and PLD are the label duration and post labeling delay, respectively.

The PD image of each participant was nonlinearly registered to the Montreal Neurological Institute (MNI) space with 1 mm × 1 mm × 1 mm voxel resolution, using FNIRT as implemented in the FMRIB Software library (FSL) [68]. Using FSL, FAST segmentation was performed to generate WM and GM masks. Figure 1 shows examples of GM and WM masks for fourteen participants within different age decades. Six regions of interest (ROIs) were defined from the MNI structural atlas corresponding to the whole brain, and the frontal, parietal, temporal, and occipital lobes, and the cerebellum. In each ROI, only voxels with at least 90% of GM or WM, as defined from the FSL-FAST WM and GM masks, were considered to minimize partial volume effects. The mean CBF value within each ROI was then calculated.

Statistical analysis

For each ROI, the effects of age and sex on CBF were investigated using multiple linear regression with the mean CBF value within each WM or GM ROI as the dependent variable and age and sex as the independent variables. The initial model incorporated an interaction term between sex and age which was removed if found not to be significant. The resulting parsimonious model was then constructed without this interaction term. This analysis was conducted on CBF values derived both with and without use of NESMA-ASL filtering to define the possible effect of filtering on our results.

Further, the effect of age on CBF for men and for women separately was also investigated with the mean CBF value within each WM or GM ROI as the dependent variable and age as the independent variable. For all statistical analyses, the threshold for statistical significance was p < 0.05 after correction for multiple ROI comparisons (i.e. 12 ROIs) using the false discovery rate (FDR) method [69, 70]. All calculations were performed with MATLAB (MathWorks, Natick, MA, USA).

Author Contributions

MB, RGS: Research design, Analysis, Paper writing JSRA, NK, RWK, ACR, LEC, WQ, LF, SMR: Analysis, Paper writing, Paper editing.

Acknowledgments

We gratefully acknowledge Christopher M. Bergeron, Denise Melvin, and Linda Zukley for their assistance with data acquisition, participant recruitment, and logistics.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This work was supported by the Intramural Research Program of the National Institute on Aging of the National Institutes of Health.

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