Increased cerebral blood volume in small arterial vessels is a correlate of Amyloid-β related cognitive decline.

Abstract

The protracted accumulation of Amyloid-beta (Aβ) is the major pathologic hallmark of Alzheimer's disease (AD) and may trigger secondary pathological processes that include neurovascular damage. This study was aimed at investigating long-term effects of Aβ-burden on arteriolar cerebral-blood-volume (CBVa), possibly present before manifestation of dementia.

Aβ-burden was assessed by 11C-Pittsburgh-Compound-B positron-emission-tomography (PiB-PET) in 22 controls and 18 persons with mild-cognitive-impairment (MCI), (ages: 75(±6) years). After two years, inflow-based vascular-space-occupancy (iVASO) at ultra-high fieldstrength of 7-Tesla was administered for measuring CBVa, and neuropsychological testing for cognitive decline.

CBVa was significantly elevated in MCI compared to cognitively normal controls, and regional CBVa related to local Aβ-deposition. For both MCI and controls, Aβ-burden and follow-up CBVa in several brain regions synergistically predicted cognitive decline over two years. Orbitofrontal CBVa was associated with APOE4 carrier status.

Increased CBVa may reflect long-term effects of region-specific pathology associated with Aβ-deposition. Additional studies are needed to clarify the role of the arteriolar system, and the potential of CBVa as a biomarker for Aβ related vascular downstream pathology.

1. Introduction

Late-onset Alzheimer’s Disease (AD) is characterized by a decade-long preclinical phase and eventually leads to mild cognitive impairment (MCI) as its first clinical manifestation (Albert, et al., 2011). While neuropathological changes in AD are complex and include cerebrovascular disease, neuroinflammation and aggregation of pathological tau and other proteins (Elahi and Miller, 2017,Serrano-Pozo, et al., 2011), Aβ-burden is considered to represent earliest brain pathology in AD and possibly trigger a multitude of pathologic downstream processes (Jack, et al., 2010,Ossenkoppele, et al., 2015,Rabinovici, et al., 2017,Villemagne, et al., 2018). The advent of positron emission tomography (PET) tracers such as 11C-Pittsburgh Compound B (PiB), has provided the means to non-invasively assess the extent of regional Amyloid-beta (Aβ) burden in old-aged populations (Klunk, et al., 2004,Seo, et al., 2017,van Bergen, et al., 2018b). By now, a concatenation of studies have consistently demonstrated that the risk for cognitive decline and dementia is closely related to the cerebral accumulation of Aβ, as measured by PET, cerebrospinal fluid or post-mortem examination (Jansen, et al., 2015,Mormino, et al., 2009,Roberts, et al., 2017). Moreover, progression of pathology in AD is linked to neurovascular dysfunction(Iadecola, 2004,Iturria-Medina, et al., 2016), as reflected by reduced cerebral blood flow (CBF) and also altered blood brain barrier (BBB) permeability (Bell and Zlokovic, 2009,Kisler, et al., 2017,Leeuwis, et al., 2017,Ostergaard, et al., 2013). The co-occurrence of AD-typical neuro-metabolic change and vascular pathology may support the idea of synergistic interaction of vascular change and neurodegeneration as an aggravating factor for pathology in AD (Gelber, et al., 2012,Schreiner, et al., 2018,Snowdon and Nun, 2003,Tyas, et al., 2007a,Tyas, et al., 2007b). Interestingly, Apolipoprotein E e4 (APOE4), which is the strongest known genetic risk factor for AD (Corder, et al., 1993,Verghese, et al., 2013), is both associated with accelerated accumulation of Aβ, reduced CBF and impaired BBB function (Janelidze, et al., 2017,Thambisetty, et al., 2010). Particularly, reduced brain perfusion (CBF) (Michels, et al., 2016,Thambisetty, et al., 2010) and impaired cerebrovascular reactivity (Suri, et al., 2015) have been observed in cognitively normal APOE4 carriers. Therefore it is important to assess the association between APOE4 and cerebrovascular impairments in AD. While the accumulation of Aβ in sporadic AD is mainly caused by decreased clearance (Mawuenyega, et al., 2010), clearance capacity of Aβ from the brain depends upon interstitial spinal fluid (ISF) flow in the paravascular space surrounding arterioles, which is primarily driven by the arterial pulsation wave from the small blood vessels (Iliff, et al., 2013,Iliff, et al., 2012,Xie, et al., 2013). This indicates that abnormalities in arterioles may contribute to accumulation and deposition of Aβ and possibly other toxic solutes related to neurodegenerative brain dysfunction. Cerebral-blood volume (CBV) is a measure of the quantity of blood in a unit of tissue (Hua, et al., 2018). While CBV generally is linked to regional tissue-metabolism, arteriolar CBV (CBVa) particularly reflects the autoregulatory capacity of arteriolar vessel resistance, and thus may be used to investigate cerebrovascular functionality within grey matter regions (Aaslid, et al., 1989,Gröhn, et al., 1998,Grubb, et al., 1974,Petrella and Provenzale, 2000,Rane, et al., 2016). CBVa may be assessed by inflow vascular space occupancy (iVASO) MRI (Donahue, et al., 2010,Hua, et al., 2011a), and application of ultra high fieldstrength of 7 Tesla (7T) provides high signal to noise ratios (SNR) (Hua, et al., 2017,Wu, et al., 2016). Recently, reductions in total CBV have been demonstrated in patients with AD-dementia (Harris, et al., 1996,Lacalle-Aurioles, et al., 2014,Nielsen, et al., 2017b,Uh, et al., 2010). Based on these earlier findings, we hypothesized that CBVa may be a particularly sensitive indicator of AD related grey-matter pathology and thus reflect vascular downstream effects of Aβ in the pre-dementia stage. To test this hypothesis, a non-demented study population comprising cognitive performance levels from normal to MCI, received 11C-PiB-PET for assessment of regional Aβ-burden, and as a follow-up after two years, iVASO MRI at ultra-high field strength of 7T. In addition, genetic risk for AD as indicated by APOE4 carrier status and cognitive decline within the study period, were used as covariates.

2. Methods

2.1. Participant characteristics

We recruited 18 patients with MCI and 22 cognitively unimpaired old-aged adults as controls. MCI patients and healthy controls were recruited as part of an ongoing study at our hospital, and matched by age and sex, as reported in detail earlier (Gietl, et al., 2015,Schreiner, et al., 2016,Schreiner, et al., 2014,Steininger, et al., 2014). Exclusion criteria were: any present medication that may affect cognition, general magnetic resonance imaging (MRI) exclusion criteria, contraindications against venipuncture, clinically relevant changes in red blood cell count, any acute severe medical, neurological or psychiatric condition, present or past drug abuse, allergy to the PET tracer 11C-PiB, significant earlier exposure to radiation. The study was conducted in accordance with guidelines issued by the local ethics committee (Kantonale Ethikkommission Zurich), as well as with the declaration of Helsinki (World_Medical_Association, 1991). All procedures were approved by the Kantonale Ethikkommission Zurich. All participants gave written informed consent before scanning. Each participant had two visits approximately 2 years apart (730±277 days) and received neuropsychological testing at both time-points (TP1 and TP2). PET-studies were performed at study inclusion, the 7 Tesla MR sequences were administered during the follow-up visit, resulting in an average delay of 803±203 days between PET and 7T-MRI measures. Demographic data and factors associated with vascular risk are summarized in Table 1. Isoforms of the APOE gene were assessed for all participants. Twelve of the participants carried one e4-allele and two participants carried two e4-alleles.

Table 1.

Demographics and test performance of the study population.

	Controls a	MCI patients a	P value b
N	22	18	N/A
Sex (Female)	8	6	1
Age (year)	72±5 c	75±7	0.08
Education (year)	13.64±2.56	15.06±3.28	0.13
Number of APOE4 alleles	7	9	N/A
Body mass index (BMI)	25.24±4.13	24.57±3.83	0.60
Arterial hypertension (N: yes; no)	4; 18	8; 10	0.07
Hypercholesterinemia (N: yes; no)	4; 18	9; 9	0.03
Present smokers (N: yes; no)	1; 21	0; 18	1
History of past smoking (N: yes; no)	9; 13	10; 8	0.36
MMSE, % change per year	0.57±3.6	−0.63±5.04	0.4
Boston Naming Test (BNT), % change	−1.46±4.46	0.19±10.63	0.54
Digit Span backward, % change	−1.35±21.17	4.89±26.83	0.42
Trail Making B / A (TMT B/A), % change	4.11±35.41	−9.21±67.44	0.45
Verbal learning and memory (VLMT), % change	−8.43±28.9	−31.49±100.72	0.35
Cognitive performance score (Z), %SD change	−0.9±37.45	−6.19±71.54	0.77

^aMean ± standard deviation.

^bP values from two-sample t-tests between the two groups, or from χ²-test for categorical variables. Neuropsychological scores indicate average % changes in test performance between study inclusion (TP1) and follow-up (TP2).

2.2. Neuropsychological testing

All participants received detailed clinical and neuropsychological examination before they were included in the present study. Participants were categorized either as cognitively normal or MCI according to established criteria (Albert, et al., 2011). Cognitive performance of study participants was assessed at TP1 and TP2 based on four cognitive tests: (1) The Revised Boston Naming Test (BNT) (Nicholas, et al., 1988); (2) Digit Spans Backward (Gregoire and Van der Linden, 1997); (3) Trail Making Test (TMT) B/A (Tombaugh, 2004); (4) Verbal Learning And Memory Test (VLMT): delayed recall (Lange, et al., 2002). These measures were z-score transformed and averaged to generate a single global cognitive score for each participant. Longitudinal changes in the cognitive scores were normalized by the time duration (in years) between the two visits.

2.3. MRI

All scans were performed on a 7T Philips MRI scanner (Philips Healthcare, Best, The Netherlands). A 32- channel phased-array head coil (Nova Medical, Wilmington, MA) was used for RF reception and a head-only quadrature coil for transmit. High-resolution anatomical images were acquired with a 3D magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence (Marques, et al., 2010,Van de Moortele, et al., 2009) (voxel=0.75mm isotropic). Total scan time on the 7T instrument was 50:42min, including 10:43min for performing the MP2RAGE sequence, and 8:19min for obtaining inflow vascular space occupancy (iVASO) data.

3D iVASO MRI was performed to measure regional GM CBVa. In iVASO MRI, arterial and arteriolar blood signal is zeroed out (nulled) by applying a spatially selective inversion in regions containing major feeding arteries. The difference signal between the arterial blood nulled scan and a control scan without blood nulling can then be used to calculate CBVa (Donahue, et al., 2010,Hua, et al., 2011c,Hua, et al., 2014,Wu, et al., 2016). To account for the heterogeneity of vascular transit times, interleaved nulling and control images are acquired at multiple post-inversion delay times (TI), and a biophysical model for multi-TI iVASO signals is used to numerically fit absolute CBVa from the dynamic multi-TI time course on a voxel-by-voxel basis so that the vascular transit time can be different in each voxel (Hua, et al., 2011c). Crushing gradients can be incorporated to suppress signals from fast-flowing blood in large arteries, and thereby sensitize this method to CBVa predominantly in the pial arteries and arterioles, which we refer to here as arteriolar blood. The iVASO approach was originally developed in single-slice mode using a gradient-echo (GRE) echo-planar-imaging (EPI) readout, and has now been extended to a 3D sequence with whole brain coverage (Hua, et al., 2017,Hua, et al., 2013). The following iVASO parameters were used: Time of repetition (TR)/TI = 10000/1383, 5000/1093, 3800/884, 3100/714, 2500/533, and 2000/356 ms; 3D fast GRE readout (TE = 2.2 ms); voxel = 3.5×3.5×5 mm³, 20 slices; parallel imaging acceleration (SENSE) = 2×2; crusher gradients of b = 0.3 s/mm². A reference scan (TR=20s, other parameters identical) was obtained to determine the scaling factor M0 in iVASO images so that absolute CBVa values can be calculated.

2.4. MRI data analysis

The statistical parametric mapping (SPM) software package (Version 8, Wellcome Trust Centre for Neuroimaging, London, United Kingdom; http://www.fil.ion.ucl.ac.uk/spm/) and other in-house code programmed in Matlab (MathWorks, Natick, MA, USA) were used for image analysis. iVASO images were motion corrected using the realignment routine in SPM. As the nulling and control scans in iVASO are acquired in an interleaved order and the MR signal difference between nulling and control images is usually less than 1-3% of the total signal (because it reflects the arteriolar blood volume in the voxel) (Hua, et al., 2011c), we aligned all nulling and control images to the first control image acquired in the series. Anatomical images were co-registered with iVASO images and normalized to the Montreal Neurological Institute (MNI) space using SPM. GM, white matter (WM) and cerebrospinal fluid (CSF) maps were generated from the anatomical images using the SPM segmentation algorithm. No spatial smoothing was performed in the analysis. The surround subtraction method (Lu, et al., 2006) was used to calculate the difference signal from the nulling and control iVASO images. Partial volume effects of WM and CSF on the iVASO difference signal in GM were corrected (Johnson, et al., 2005) (results without partial volume correction are also reported). A signal-to-noise ratio (SNR) threshold of one standard deviation below the mean SNR was used to exclude voxels with insufficient SNR from further analysis (Hua, et al., 2011c). In our data, about 10% voxels were excluded, most of which are close to the skull and the sinus regions. This SNR threshold was applied on each subject’s data. If a voxel falls below the threshold in one subject, it will be excluded from all subjects in the group analysis. In our data, most of these voxels overlapped in multiple subjects. Whole-brain GM CBVa maps were numerically fitted from the iVASO difference signals at all TIs with the iVASO equations (Hua, et al., 2011c) using in-house Matlab code.

2.5. PiB–Positron Emission Tomography

PiB-PET was used to estimate individual brain Aβ-plaque-load as described in earlier publications of ours (Quevenco, et al., 2017,van Bergen, et al., 2016). In brief, an individual dose of approximately 350MBq of carbon-labelled PiB was injected into the cubital vein. A standard quantitative filtered back projection algorithm including necessary corrections was applied. Cortical late frame (minutes 50-70) values were divided by the cerebellar gray matter average, resulting in PiB-PET relative standard uptake values (SUVr; matrix=128×128×47, voxel size=2.3×2.3×3.3 mm³). Measures of individual regional brain Aß-load were derived from the ratio of regional PiB-SUV, referenced to cerebellar SUV, after co-registration of PET images to the 7T MP2RAGE volumes using SPM 12 software.

2.6. Statistics

Group differences in GM CBVa maps were examined using analysis of variance with age, sex, education, regional GM volume from anatomical scans and motion parameters estimated from the motion correction (realignment) routine in SPM accounted for as covariates in the analysis. Significant clusters of increased or decreased GM CBVa were identified using in-house Matlab code implemented using the threshold-free cluster enhancement (TFCE) method (Smith and Nichols, 2009). Briefly, permutation testing was conducted by testing the TFCE output against the null distribution across permutations and obtaining the 95th percentile in the null distribution as the corrected P < 0.05 level. The IBASPM116 atlas (Lancaster, et al., 1997,Lancaster, et al., 2000,Maldjian, et al., 2004,Maldjian, et al., 2003,Tzourio-Mazoyer, et al., 2002) (PickAtlas software, Wake Forest University, North Carolina, USA) was used to identify anatomical regions within the significant clusters. Effect size was estimated with Cohen’s d. Correlations between CBVa values and β-Amyloid (PiB ratio) in each region, as well as APOE4 carrier-status were evaluated using data combined from all participants (including both MCI and controls). Partial correlations were calculated with age, sex and education as covariates. All statistical tests were corrected for multiple comparisons by controlling the false-discovery rate (adjusted P < 0.05) (Benjamini and Hochberg, 1995). Multiple regression was carried out using Matlab to test the potential synergistic effects from GM CBVa and β-Amyloid (reflected in the β3 term in the following equation) on longitudinal cognitive decline using the following model:

$$\text{Cognitive}\text{decline}(\%\text{per year})=\beta 0+\beta 1\times \text{CBVa}+\beta 2\times \mathrm{A}\beta +\beta 3\times \text{CBVa}\times \mathrm{A}\beta +\beta 4\times \text{Sex}+\beta 5\times \text{Age}+\beta 6\times \text{Education}+\beta 7\times \text{Age}\times \mathrm{A}\beta .$$

In addition, multilevel regression analysis using data from the control and MRI subjects as two separate groups was also performed.

3. Results

As shown in Table 1, age, sex and education levels were matched between MCI patients and controls (P > 0.1). No significant differences were found in motion parameters derived from the SPM realignment routine between the two groups. Performance in the VLMT and MMSE tests differed between controls and MCI both at study inclusion (TP1) and follow-up (TP2) (Supplement 3, Table 7, Figure S2). While there was considerable variability in % changes of performance from TP1 to TP2 on an individual level (Table 1), no significant changes between TP1 and TP2 resulted for any of the investigated tests on a group level (Table 7, Figure S2). None of the participants progressed to AD-dementia from TP1 to TP2. Participants of the MCI group had a higher prevalence of hypercholesterinemia, and a trend for higher prevalence of arterial hypertension (Table 1). An individual subject level iVASO CBVa map from one control subject is shown in Figure 1 to demonstrate typical data quality.

Figure 1.

Individual subject level CBVa map obtained using iVASO MRI in one control subject.

Table 2 summarizes the main findings in the group comparisons. The average GM CBVa values in controls were all in normal range (Hua, et al., 2018,Hua, et al., 2011c), providing validation for our measurements. Widespread elevation of GM CBVa was detected in many brain regions in MCI patients compared to controls with relative changes of 17.0-122.0% and effect sizes of 0.75-1.56. Most of these changes were detected in both hemispheres in corresponding regions, although the cluster sizes varied between the left and right hemispheres in some regions. Significant reduction of GM CBVa was also observed in a few brain regions, but the spatial extent was much smaller than increased CBVa. Some brain regions showed both decreased and increased GM CBVa values in different sub-regions. No significant difference was found in mean GM CBVa over the whole brain (including all GM voxels, not just significant clusters) between patients and controls. The partial volume correction procedure did not seem to have a major effect on the measured CBVa values (Table 2b). Figure 2A displays the regions with significant increased or decreased GM CBVa in MCI patients on MNI normalized anatomical images, with an intensity reflecting the relative changes in each significant voxel. Figure 2B shows the areas with significantly increased PiB-PET retention in MCI patients on MNI normalized anatomical images.

Table 2a.

GM CBVa in MCI patients compared to controls in various brain regions.

Region a	Hemisphere	Cluster Sizeb	Cluster Peak c (mm, MNI)			CBVa (ml blood/100ml tissue)				Relative Change (%) d	Effect Size e	Adjusted P Value
						MCI		control
			x	y	z	mean	std	mean	std
Frontal_Sup_Orb	R	89	22	16	−14	2.75	1.88	1.32	1.11	108.2	1.00	0.015
Frontal_Sup_Orb	L	86	−18	22	−14	1.85	1.28	1.01	0.93	82.1	0.79	0.042
Frontal_Inf_Oper	R	239	54	12	14	1.54	0.43	1.08	0.25	41.8	1.40	0.049
Frontal_Inf_Oper	L	372	−46	14	2	1.34	0.28	1.09	0.14	23.7	1.26	0.004
Frontal_Inf_Tri	R	498	52	22	24	2.27	1.62	1.25	0.99	81.3	0.82	0.042
Frontal_Inf_Tri	L	752	−56	22	2	1.90	0.79	1.27	0.52	49.5	1.01	0.013
Frontal_Inf_Orb	R	557	32	30	−8	2.65	1.63	1.19	0.82	122.0	1.23	0.005
Frontal_Inf_Orb	L	200	−48	20	−8	2.15	1.44	1.07	0.99	100.8	0.93	0.019
Frontal_Sup_Medial	R	504	4	42	36	2.44	1.24	1.31	0.91	86.1	1.10	0.050
Frontal_Sup_Medial	L	390	−12	48	14	2.13	1.14	1.20	0.75	78.3	1.04	0.048
Frontal_Mid_Orb	R	346	2	22	−12	1.85	1.04	1.12	0.60	65.3	0.93	0.018
Frontal_Mid_Orb	L	178	−6	38	−8	1.89	0.91	1.27	0.67	49.1	0.82	0.028
Rolandic_Oper	R	265	40	−28	18	1.70	0.65	1.21	0.36	40.2	1.02	0.050
Rolandic_Oper	L	417	−54	−2	4	1.63	0.52	1.23	0.20	33.1	1.15	0.010
Supp_Motor_Area	R	136	2	−4	54	1.20	0.26	0.99	0.12	21.3	1.17	0.008
Supp_Motor_Area	L	78	0	−6	56	1.22	0.31	1.00	0.18	22.2	0.94	0.022
Olfactory	R	161	4	14	−14	2.31	1.19	1.13	0.81	103.9	1.24	0.003
Olfactory	L	146	−4	20	−14	2.06	1.11	1.08	0.80	90.1	1.07	0.008
Rectus	R	81	10	18	−14	2.46	1.17	1.16	1.01	111.4	1.24	0.002
Rectus	L	59	−8	22	−12	2.16	1.17	1.11	0.86	94.7	1.09	0.007
Insula	R	979	36	18	−12	2.39	1.13	1.23	0.69	93.5	1.33	0.002
Insula	L	761	−36	24	8	2.25	1.30	1.33	0.78	69.6	0.93	0.022
Cingulum_Ant	R	311	12	36	−8	2.16	1.12	1.29	0.94	66.8	0.88	0.029
Cingulum_Ant	L	504	−8	34	18	2.22	1.13	1.32	0.81	67.9	0.97	0.022
Cingulum_Mid	R	499	6	−8	44	1.21	0.16	1.04	0.07	17.0	1.56	0.001
Cingulum_Mid	L	338	−4	−2	40	1.30	0.23	1.08	0.11	20.3	1.35	0.003
Hippocampus	R	56	28	−36	8	1.78	0.93	1.05	0.57	69.2	1.01	0.014
Hippocampus	L	84	−22	−26	−12	1.76	0.84	1.08	0.65	63.9	0.96	0.013
Lingual	R	35	22	−50	2	2.35	0.88	1.79	0.69	31.4	0.75	0.047
Lingual	L	47	−22	−56	−4	1.37	0.47	1.08	0.15	26.9	0.94	0.035
Caudate	R	209	14	10	−12	2.34	1.65	1.16	1.17	101.4	0.88	0.026
Caudate	L	178	−10	18	−10	2.26	1.61	1.17	1.31	93.3	0.78	0.039
Putamen	R	422	24	22	−8	1.93	1.25	1.06	0.79	82.7	0.90	0.025
Putamen	L	441	−28	−2	−4	2.40	1.38	1.51	1.01	58.7	0.78	0.044
Temporal_Sup	R	518	54	0	−14	1.88	0.86	1.25	0.50	50.2	0.97	0.019
Temporal_Sup	L	955	−56	4	−2	1.83	0.91	1.17	0.36	56.3	1.05	0.017
Temporal_Pole_Sup	R	272	50	14	−12	2.46	1.33	1.30	0.76	88.5	1.16	0.006
Temporal_Pole_Sup	L	205	−58	6	−4	2.00	1.20	1.01	0.64	98.1	1.12	0.009

^aThe brain regions were labeled according to the IBASPM 116 atlas (please see Methods for references).

^bNumber of voxels that show significant group difference in this region.

^cLocation of the voxel with the maximum (peak) T-score in the cluster in the MNI space.

^dRelative change was defined as 100 * (mean CBVa in MCI – mean CBVa in controls) / (mean CBVa in controls) %.

^eEffect size was estimated with Cohen's d = (mean CBVa in MCI – mean CBVa in controls) / s, where s is the pooled standard deviation of the two groups. std: standard deviation.

Table 2b.

GM CBVa in MCI patients compared to controls in various brain regions (without partial volume correction).

Region a	Hemisphere	Cluster Sizeb	Cluster Peak c (mm, MNI)			CBVa (ml blood/100ml tissue)				Relative Change (%) d	Effect Size e	Adjusted P Value
						MCI		control
			x	y	z	mean	std	mean	std
Frontal_Sup_Orb	R	89	22	16	−14	2.48	1.70	1.22	1.02	104.1	0.98	0.020
Frontal_Sup_Orb	L	86	−18	22	−14	1.67	1.16	0.93	0.86	78.5	0.76	0.046
Frontal_Inf_Oper	R	239	54	12	14	1.36	0.38	0.97	0.22	40.3	1.36	0.050
Frontal_Inf_Oper	L	372	−46	14	2	1.21	0.25	0.98	0.13	22.8	1.22	0.005
Frontal_Inf_Tri	R	498	52	22	24	2.04	1.46	1.13	0.89	80.6	0.81	0.043
Frontal_Inf_Tri	L	752	−56	22	2	1.68	0.70	1.17	0.48	43.9	0.92	0.016
Frontal_Inf_Orb	R	557	32	30	−8	2.34	1.44	1.08	0.75	116.0	1.19	0.006
Frontal_Inf_Orb	L	200	−48	20	−8	1.92	1.29	0.99	0.91	94.9	0.89	0.023
Frontal_Sup_Medial	R	504	4	42	36	2.22	1.13	1.18	0.82	87.9	1.12	0.051
Frontal_Sup_Medial	L	390	−12	48	14	1.90	1.02	1.08	0.68	75.7	1.01	0.051
Frontal_Mid_Orb	R	346	2	22	−12	1.66	0.93	1.01	0.54	65.0	0.93	0.020
Frontal_Mid_Orb	L	178	−6	38	−8	1.68	0.81	1.14	0.61	47.1	0.80	0.032
Rolandic_Oper	R	265	40	−28	18	1.54	0.58	1.12	0.33	37.4	0.96	0.054
Rolandic_Oper	L	417	−54	−2	4	1.45	0.46	1.12	0.19	29.5	1.04	0.015
Supp_Motor_Area	R	136	2	−4	54	1.08	0.23	0.91	0.11	19.1	1.07	0.012
Supp_Motor_Area	L	78	0	−6	56	1.10	0.28	0.90	0.16	22.5	0.95	0.023
Olfactory	R	161	4	14	−14	2.10	1.08	1.04	0.75	100.9	1.21	0.006
Olfactory	L	146	−4	20	−14	1.87	1.01	0.99	0.73	89.1	1.06	0.009
Rectus	R	81	10	18	−14	2.20	1.05	1.05	0.92	109.3	1.22	0.002
Rectus	L	59	−8	22	−12	1.91	1.03	1.01	0.78	89.1	1.04	0.010
Insula	R	979	36	18	−12	2.11	1.00	1.12	0.63	88.7	1.28	0.004
Insula	L	761	−36	24	8	2.00	1.16	1.19	0.70	67.8	0.92	0.025
Cingulum_Ant	R	311	12	36	−8	1.95	1.02	1.17	0.85	67.4	0.88	0.033
Cingulum_Ant	L	504	−8	34	18	1.97	1.00	1.19	0.72	65.8	0.95	0.025
Cingulum_Mid	R	499	6	−8	44	1.10	0.15	0.93	0.06	17.5	1.60	0.004
Cingulum_Mid	L	338	−4	−2	40	1.16	0.20	0.98	0.10	18.3	1.23	0.007
Hippocampus	R	56	28	−36	8	1.61	0.85	0.95	0.52	69.2	1.01	0.018
Hippocampus	L	84	−22	−26	−12	1.57	0.75	0.96	0.58	62.8	0.95	0.016
Lingual	R	35	22	−50	2	2.09	0.78	1.65	0.63	26.3	0.64	0.048
Lingual	L	47	−22	−56	−4	1.22	0.42	1.00	0.13	22.0	0.79	0.036
Caudate	R	209	14	10	−12	2.10	1.48	1.06	1.07	98.9	0.86	0.030
Caudate	L	178	−10	18	−10	2.02	1.44	1.06	1.19	90.0	0.76	0.039
Putamen	R	422	24	22	−8	1.72	1.11	0.96	0.71	79.8	0.88	0.027
Putamen	L	441	−28	−2	−4	2.17	1.25	1.39	0.93	55.8	0.75	0.045
Temporal_Sup	R	518	54	0	−14	1.69	0.77	1.14	0.46	48.8	0.94	0.024
Temporal_Sup	L	955	−56	4	−2	1.64	0.82	1.05	0.33	56.0	1.05	0.020
Temporal_Pole_Sup	R	272	50	14	−12	2.23	1.21	1.20	0.70	86.3	1.14	0.009
Temporal_Pole_Sup	L	205	−58	6	−4	1.78	1.07	0.92	0.58	94.1	1.09	0.011

^aThe brain regions were labeled according to the IBASPM 116 atlas (please see Methods for references).

^bNumber of voxels that show significant group difference in this region.

^cLocation of the voxel with the maximum (peak) T-score in the cluster in the MNI space.

^dRelative change was defined as 100 * (mean CBVa in MCI – mean CBVa in controls) / (mean CBVa in controls) %.

^eEffect size was estimated with Cohen's d = (mean CBVa in MCI – mean CBVa in controls) / s, where s is the pooled standard deviation of the two groups.

std: standard deviation.

Figure 2.

(A) Map of relative CBVa changes between MCI patients and controls overlaid on MNI normalized anatomical images. The relative change is defined as [100 × (MCI – control)/control] %. Only voxels that show significant CBVa difference between the two groups (adjusted P < 0.05) are highlighted.

(B) Brain regions with abnormal PiB-PET retention overlaid on MNI normalized anatomical images. Only voxels that show significant PiB-PET difference between MCI patients and controls (adjusted P < 0.05) are highlighted.

(C) Co-localization of GM CBVa abnormality and β-amyloid deposit. Voxels that show significant correlation between GM CBVa values and PiB ratios are highlighted overlaid on MNI normalized anatomical images.

From the correlation analysis, many regions with increased CBVa values co-localized with regions with increased amyloid β deposition, with significant positive correlations between the two in several brain regions (Table 3, Figure 2C). Scatter plots from two of these regions are shown in Figure 3. In most of these regions, synergistic effects from GM CBVa and amyloid β on longitudinal cognitive decline was found using multiple regression (Table 4 and Figure S1). In regions with only significant CBVa or amyloid β changes, no such synergistic effects were found. In addition, GM CBVa in two sub-regions in the orbitofrontal cortex also showed significant positive correlation with APOE4 carrier-status (Supplement 1, Table 5, Figure 3). Here, by applying multiple regression analysis, synergistic effects from GM CBVa and APOE4 on cognitive decline in multiple brain regions were found (Supplement 2, Table 6). Sex, age, and education were included as covariates in the correlation analysis.

Table 3.

Correlation between GM CBVa and β-amyloid in multiple brain regions in all participants.

Region a	Estimate coefficient	Standard error	p-value
Frontal_Sup_Orb	2.32	0.38	<0.001
Frontal_Inf_Oper	0.31	0.14	0.006
Frontal_Inf_Orb	4.69	2.36	0.01
Frontal_Sup_Medial	3.33	1.32	0.006
Frontal_Mid_Orb	1.16	0.23	<0.001
Rolandic_Oper	0.67	0.26	0.001
Supp_Motor_Area	0.17	0.10	0.05
Cingulum_Ant	2.64	1.36	0.01
Cingulum_Mid	0.23	0.06	<0.001
Hippocampus	4.49	2.46	0.03
Lingual	1.70	0.86	0.01

^aThe brain regions were labeled according to the IBASPM 116 atlas (please see Methods for references).

Figure 3.

Scatter plots show correlations between CBVa and β-amyloid, and boxplots show associations between CBVa, the number of APOE-e4 alleles, and between β-amyloid and the number of APOE-e4 alleles in the superior orbitofrontal cortex (Frontal_Sup_Orb, a-c) and the middle orbitofrontal cortex (Frontal_Mid_Orb, d-f), respectively. These two regions showed significant correlations between CBVa and both β-amyloid (Table 3) and number of APOE-e4 alleles (Supplement 1, Table 5). Several additional regions showed significant correlation between CBVa and β-amyloid (Table 3), but not between CBVa and number of APOE-e4 alleles. The trendlines and adjusted R² and P were obtained from linear regression using data combined from all participants.

Table 4a.

Multiple regression shows synergistic effects from GM CBVa and β-amyloid on cognitive decline in multiple brain regions.

	Standardized coefficient b,c			p-value			R2
Region a	CBVa (β1)	Aβ (β2)	CBVax Aβ (β3)	CBVa (β1)	Aβ (β2)	CBVax Aβ (β3)
Frontal_Sup_Orb
Model 1 d (full)	−1.47	−0.62	−1.77	0.05	0.05	0.05	0.31
Model 2 e (restricted)		−0.27			0.05		0.15
F-test (p) f							0.05
Frontal_Inf_Orb
Model 1 d (full)	−1.46	−1.02	−1.76	0.05	0.03	0.05	0.39
Model 2 e (restricted)		−0.51			0.05		0.16
F-test (p) f							0.01
Frontal_Sup_Medial
Model 1 d (full)	−1.52	−0.97	−2.14	0.02	0.02	0.01	0.36
Model 2 e (restricted)		−0.45			0.04		0.15
F-test (p) f							0.01
Frontal_Mid_Orb
Model 1 d (full)	−0.70	−1.02	−1.75	0.05	0.02	0.05	0.45
Model 2 e		−0.43			0.05		0.17
(restricted)
F-test (p) f							0.002
Supp_Motor_Area
Model 1 d (full)	−1.39	−1.84	−2.47	0.03	0.03	0.03	0.37
Model 2 e (restricted)		−0.38			0.04		0.15
F-test (p) f							0.01
Hippocampus
Model 1 d (full)	−3.52	−0.71	−4.78	0.01	0.05	0.01	0.35
Model 2 e (restricted)		−0.29			0.05		0.15
F-test (p) f							0.05
Lingual
Model 1 d (full)	−2.76	−1.19	−2.96	0.05	0.03	0.05	0.33
Model 2 e (restricted)		−0.37			0.04		0.15
F-test (p) f							0.03

^aThe brain regions were labeled according to the IBASPM 116 atlas (please see Methods for references).

^bThe estimated coefficients correspond to β1, β2 and β3 in Eq. [1] for CBVa, Aβ and CBVa×Aβ, respectively. The synergistic effect from CBVa and Aβ is reflected in the β3 term.

^cThe Standardized coefficient can be used as an estimate of relative contribution to longitudinal cognitive decline from each term.

^dModel 1: Cognitive decline (% per year) = β0 + β1×CBVa + β2×Aβ + β3×CBVa×Aβ + β4×Sex + β5×Age + β6×Education

^eModel 2: Cognitive decline (% per year) = β0 + β2×Aβ + β4×Sex + β5×Age + β6×Education

^fP-Value from R² change F-test to assess whether the R² from the two models are significantly different.

4. Discussion

By applying ultra-high field strength MRI at 7T to a study population of non-demented older adults, significantly elevated CBVa in patients with MCI could be observed. Interestingly, for the entire study population, an association between high local deposition of Aβ, as measured by 11C-PiB-PET, and elevated CBVa, as measured by iVASO at 7T two years later, was observable in many brain regions. Moreover, Aβ-burden and CBVa at follow-up in several brain regions synergistically predicted cognitive performance over two years, when applying a linear regression model. Consistently, CBVa in the orbitofrontal cortex also showed a synergistic interaction with APOE4 carrier-status regarding its relationship to cognitive change.

For this study, ultra-high field strength MRI at 7T was used to acquire MP2RAGE structural images and 3D iVASO data for measuring whole brain absolute CBVa maps (Donahue, et al., 2010,Hua, et al., 2017,Hua, et al., 2011c,Hua, et al., 2014,Marques, et al., 2010,Van de Moortele, et al., 2009,Wu, et al., 2016). The combination of these technologies allows for high-resolution assessment of the homeostasis of pialarteries and arterioles in fine brain regions (Hua, et al., 2018). Regional distribution of Aβ was investigated by 11C-PiB-PET, as used by many earlier studies to investigate disease burden in preclinical and clinical stages of sporadic AD (Jansen, et al., 2015,Klunk, et al., 2004). The significantly higher burden of Aβ and its distribution pattern in the MCI-group is consistent with earlier reports on pathological change in the pre-dementia stage of AD (Jansen, et al., 2015,Klunk, et al., 2004,Roberts, et al., 2017). Information on regional Aβ was complemented by assessment of the APOE4 genotype, which is the strongest known genetic risk factor for sporadic AD (Corder, et al., 1993,Kantarci, et al., 2012,Strittmatter, et al., 1993). As a general indicator of cognitive performance in the study population, four German language versions of neuropsychological tests were performed to generate a composite score that integrates episodic memory, language, working memory and executive function, as a representation of major domains affected early in AD (Albert, et al., 2011). Our finding of lower test scores at follow-up in some study participants within the control group may reflect subtle cognitive changes associated with normal aging (Harada, et al., 2013), or alternatively could be caused by non-specific variation within the high-performance range of the used tests.

To our knowledge, this is the first report of microvascular abnormalities specifically in pial arteries and arterioles in gray matter (measured with CBVa) in an elderly, non-demented population at risk for AD. While reductions in CBV and CBF have been reported earlier for AD dementia (Harris, et al., 1996,Hauser, et al., 2013,Lacalle-Aurioles, et al., 2014,Nielsen, et al., 2017b,Uh, et al., 2010), investigations of neurovascular dysfunction in early, prodromal disease stages so far mainly have been focused on total CBV and CBF, which include arteriolar, capillary and venular vessels (Gietl, et al., 2015,Kisler, et al., 2017,Leeuwis, et al., 2017). Here, our findings of increased CBVa in older patients with MCI may be consistent with earlier reports suggesting an interaction of microvascular abnormalities and neurodegenerative pathology in AD (Iadecola and Nedergaard, 2007,Kisler, et al., 2017,Schreiner, et al., 2018). The close relationship between CBVa changes and local Aβ deposits in our data is in agreement with earlier considerations of consecutive vascular damage, impaired cerebrovascular autoregulation and effects attributable to Aβ toxicity (Brickman, et al., 2015,Zlokovic, 2011). In addition, our finding of a significant interactive effect of CBVa and Aβ in several brain regions on cognitive performance over two years, as measured by linear regression analysis, corroborates the relevance of vascular change for functional decline due to AD-pathology (Helzner, et al., 2009). Such interactive effect was not found in regions without overlapping CBVa or Aβ changes. Concordantly, we also find a synergistic effect of CBVa and APOE4 on cognitive performance over time, which may indicate relevance of increased CBVa regarding the individual risk to develop AD-dementia. Our observation of a relationship between increased CBVa with Aβ and also APOE4 may reflect impaired cerebrovascular regulation, possibly caused by a direct impact of toxic Aβ aggregates on cerebral vessel walls (Kisler, et al., 2017). Considering reports on a significant role of interstitial bulk flow for clearance of Aβ and other neurodegenerative proteins from the central nervous system that involves bidirectional cerebrospinal fluid movement through paravascular spaces of pial arteries (Iliff, et al., 2012), further pre-clinical studies are needed to investigate whether vasodilatation and thus increased CBVa might be caused by direct toxic impact of Aβ. Considering pathological changes of CBVa as a potential consequence of initial Aβ-toxicity, iVASO might represent a marker of secondary cerebrovascular "downstream" pathology, as demanded recently for prospective clinical trials that allow for co-morbid pathology in AD (Rabinovici, et al., 2017). Alternatively, impaired arterial pulsation might reduce paravascular drainage of Aβ, and as such could be consistent with the notion of a relationship between deteriorated microvascular hemodynamics and buildup of AD-pathology (Iadecola, 2003,Nielsen, et al., 2017b). Moreover, our observation of an association between APOE4, increased CBVa and cognitive decline might also confirm earlier reports of APOE4 as a modulator of harmful Abeta effects on cognitive function (Kantarci, et al., 2012). Consistently, brain regions with significant correlations between CBVa and β-amyloid in our study included the neocortex and hippocampal formation, which are considered particularly susceptible for AD pathology (Murray, et al., 2015).

The significant CBVa increase observed here may appear contradictory with the well-established finding of reduced regional CBF in MCI in the literature (Alsop, et al., 2010,Dvorak, et al., 1999,Hirao, et al., 2005,Roher, et al., 2011,Ruitenberg, et al., 2005,Thomas, et al., 2015). Aβ has long been known as a powerful vasoconstrictor (Niwa, et al., 2001). However, we think that our increased CBVa finding may be congruent with CBF reduction for the reasons explained subsequently. Note that CBV measured by MRI reflects the fractional volume of all blood vessels in a given voxel (i.e. the unit of CBV: ml blood per 100 ml tissue). Therefore, CBV is proportional to the cross-sectional vessel diameter, length of blood vessels and overall number of blood vessels in the voxel. A decrease in cross-sectional vessel diameter often leads to a decrease in CBF (vasoconstriction). However, if the vessel density in the voxel increases (thus increased number of vessels) possibly due to angiogenesis or others, the overall CBV measured in the voxel can be increased. Indeed, recent studies in tau-overexpressing mice showed increased blood vessel volume (increased CBV) but reduced blood vessel diameter (reduced CBF) compared to controls (Bennett, et al., 2018). In the cortex of these mice, overall blood vessel density was increased compared to controls even when the cortical atrophy is accounted for, and blood flow was altered and at times obstructed in some vessels. A qPCR assay revealed that several genes related to hypoxia and angiogenesis were increased more than twofold in these mice. Although these results were obtained in tau-overexpressing mice (rather than amyloid based models), they indicate a possible explanation for the concomitant CBVa increase and CBF reduction. Besides, the uncoupling of CBF and CBV is often seen in circumstances in which there is increased metabolic demand or hypoxia, and angiogenesis (Mishra, 2016,Puro, et al., 2016); for instance, in ischemia, the pial arteries and arterioles are typically dilated to compensate for a lack of blood flow, so a situation of reduced CBF, with increased CBVa. Another possibility is elevated microvascular tortuosity (Thore, et al., 2007), thus increased length of blood vessels. These hypotheses should be validated in future studies possibly combining MRI with other imaging techniques such as optical imaging. Second, as CBVa represents arteriolar fractions of total CBV (sum of arteriolar, capillary and venular CBV), a distinct increase of CBVa might indicate a shift of volumes within the cerebrovascular system. Different types of blood vessels have distinct functions and physiology, and can be affected differentially by the AD pathology. The arterioles are the most actively regulated blood vessels, and thus may be more sensitive to metabolic disturbances in the brain (Iadecola and Nedergaard, 2007,Ito, et al., 2005,Ito, et al., 2001,Kim, et al., 2007,Takano, et al., 2006). For instance, significant reduction in CBVa has been observed in schizophrenia patients (Hua, et al., 2017,Hua, et al., 2015), whereas no substantial changes in capillaries was indicated in histopathological studies in schizophrenia (Kreczmanski, et al., 2009,Kreczmanski, et al., 2005,Uranova, et al., 2010). It should be noted that small diameter (<150 microns) pial arteries and arterioles (CBVa) only represents 10-20% of total CBV (Hua, et al., 2018,Piechnik, et al., 2008,Sharan, et al., 1989,van Zijl, et al., 1998) (depending on vessel diameter), thus an 100% increase in CBVa would only translate to 10-20% in total CBV change, which is within the typical physiological range. The fact that CBVa values in controls were all within the normal range (Hua, et al., 2018) and that CBVa in the cerebellum (which is not expected to be affected at this stage) did not show group differences, also provides some validation for our results. Further studies are needed to investigate whether increase of CBVa in AD risk populations might possibly result in reduced total CBV in clinically manifest AD, as reported earlier (Eskildsen, et al., 2017,Hauser, et al., 2013,Lacalle-Aurioles, et al., 2014,Nielsen, et al., 2017b,Yoshiura, et al., 2009). It is also worth noting that the synergetic effects between CBVa and other parameters were restricted to several small brain regions, which might be in agreement with the recent observation of regional AD-like biomarker patterns in at-risk populations (Wirth, et al., 2017). Moreover, in our study higher prevalences of hyperlipidemia were observable for the MCI group, which may be consistent with earlier reports on an association between serum hypercholesterolemia and increased risk for the development of AD amyloid pathology (Pappolla, et al., 2003).

It is important to discuss methodological factors for iVASO MRI, which might interact with MCI pathology to bias the study results. First, the presence of amyloid may introduce additional susceptibility effects at high field that can affect MR signals. Fortunately, in iVASO, CBVa is calculated from the difference signal between the arteriolar blood nulled image and the subsequent control image. Therefore, all static signal that is not flowing such as GM and WM should cancel out upon subtraction (Hua, et al., 2011b). Second, differences in cortical thickness among groups or over time may lead to different partial volume effects in the CBVa results. As this is an important confounding factor especially in this population in which substantial brain atrophy is expected, we corrected such partial volume effects in our analysis using a previously published method (Johnson, et al., 2005) (see Methods section). In addition, GM volume derived from anatomical images was included as a covariate in all statistical analyses to account for any residual partial volume effects. As shown in the Results section, all major findings were consistent with and without the partial volume correction step. It is important to realize that iVASO MRI does not measure CBV in the capillaries (Hua, et al., 2018), but only in the arterial compartment. This predominant arterial origin of the iVASO signal was validated previously by measuring the transverse (T2) relaxation time of the iVASO difference signal, which is highly sensitive to blood oxygenation level (Hua, et al., 2011b). Given the importance of capillaries for tissue perfusion in healthy brain and in AD and MCI (Eskildsen, et al., 2017,Nielsen, et al., 2017a), future studies should also investigate potential abnormalities in capillaries in MCI using specialized methods.

One fundamental limitation of this pilot study is the time difference between the PET imaging and the MRI scans. To assess long-term effects of Aβ, in the current study 7T MRI was administered approximately two years after the initial 11C-PiB-PET. Considering the gradual accumulation of Aβ in AD, which takes almost two decades before the threshold-level of pathological Aβ characterizing the clinical syndrome is reached (Dubois, et al., 2016,Roberts, et al., 2017), the chosen temporal delay of two years should allow for detecting long-term effects of Aβ on CBVa, while still remaining within the AD predementia phase. However, as currently not much is known on possible determinants of Aβ-dynamics in clinical populations, our findings of a relationship between local Aβ and CBVa might be affected by possibly existing differences in cerebral Aβ-accumulation between study participants. In this regard, our preliminary results should be interpreted with caution, and both PET and MRI will be administrated at the initial and the follow-up visits in subsequent studies to validate the current findings. Also, as our assessment of individual cognitive decline per year doesn't account for practice effects or regression to the mean, effects associated with cognition need to be interpreted with caution and replicated by prospective longitudinal studies.

Another important limitation of the study is that all variables, including APOE4 status, were treated as linear in the multiple regression analysis. While such linear mixed models are commonly adopted in similar studies (Mormino, et al., 2014), they may not reflect accurate relationship between cognitive decline and these variables. More detailed modeling is merited in subsequent studies to explore and describe more precise relationships between these measures.

Finally, it is possible that PET SUVr may show correlation with CBF simply because the PET tracer reaches its target via the blood flow. However, as we assessed the steady state effect with cortical late-frames for estimating SUVr, i.e. 50-70 minutes after 11C-PiB injection, influence from the dynamic relationship between 11C-PiB-SUVr and CBF at earlier time points should be negligible. Indeed, it has been shown that 11C-PiB retention did not correlate with regional CBF (Chen, et al., 2015). Moreover, according to the central volume theorem, CBF=CBVa/ATT (ATT: arterial transit time), such dynamic interaction between CBF and PET SUVr should primarily be driven by changes in ATT. In iVASO, heterogeneity of ATT is accounted for with data acquired at multiple TIs and the fitting algorithm in the iVASO theory (Hua, et al., 2011c). Therefore, the correlation between CBVa and PET SUVr observed in our data should be minimally affected by the potential interaction between CBF and PET SUVr. While in our data only regional interactions between 11C-PiB SUVr and CBVa were observable, the possibility of a general confounding effect on quantification of the iVASO signal should nevertheless be considered. Also, potential interindividual differences in the susceptibility to Aβ-burden might be a confounding factor (van Bergen, et al., 2018a). Furthermore, because of the highly specialized experimental setup including both 7T MRI and 11C-PiB, which needed to be performed in the vicinity of the cyclotron used for manufacturing the tracer, a multi-center approach was not feasible resulting in a relatively small sample size. However, while the application of ultra-high field strength MRI for maximizing SNR may have increased power to detect early Aβ-related CBVa changes, future larger studies are needed to reproduce our findings in a more clinical setting.

5. Conclusion

Taken together, we here provide evidence on increases in CBVa that are observable in old aged adults with MCI and closely relate to local Aβ, APOE4 and cognitive decline within two years. While our data support earlier considerations on a close relationship between AD-pathology and neurovascular dysfunction, additional research is needed to investigate whether elevated CBVa in the pre-dementia stage of AD may represent a marker of secondary vascular pathology associated with Aβ-accumulation and thus resulting damage to the neurovascular unit. Alternatively, increased CBVa in the pre-dementia stage may represent a compensatory mechanism aimed at maintaining stable cerebral perfusion and oxygen supply despite spreading AD pathology.

Supplementary Material

2

Figure S1

Example of a scatterplot showing the relationship between cognitive decline and β-amyloid, and synergistic effects from CBVa and β-amyloid on cognitive decline. Multiple regression results with (Model 1) and without (Model 2) accounting for the synergistic effects from CBVa are shown in (a) and (b), respectively. Quantitative results are shown in Table 4.

Model 1: Cognitive decline (change % per year) = β0 + β1×CBVa + β2×Aβ + β3×CBVa×Aβ + β4×Sex + β5×Age + β6×Education + β7×Age×Aβ

Model 2: Cognitive decline (change % per year) = β0 + β2×Aβ + β4×Sex + β5×Age + β6×Education + β7×Age×Aβ

3

Figure S2

(A) and B). Mean (±SD) test performance of healthy controls (n=21) at study inclusion (A1) and follow-up (A2), as well as MCI (n=19) at study inclusion (B1) and follow up (B2). Indicated are test scores of Mini Mental State Examination (MMSE), Boston Naming Test (BNT), Digit Spans backward (DS Bw), Trailmaking Tests B divided by A (TMT B div A), Verbal Learning and Memory Test, delayed recall (VLMT del).

C). Mean (±SEM) values of the global cognitive score, at study inclusion (TP1) and follow-up (TP2), for healthy controls (CTR, A, n=21), and MCI (B, n=19). "n.s." = non-significant differences (p>0.05), "***" = p<0.001.

4

Supplement 1, Table 5

Correlation between GM CBVa in multiple brain regions and number of APOE4 alleles in all participants.

5

Supplement 2, Table 6

Multiple regression shows synergistic effects from GM CBVa and APOE4 on cognitive decline in multiple brain regions.

6

Supplement 3, Table 7

Average neuropsychological test scores for healthy controls (A) and MCI (B) at study inclusion (TP1) and followup (TP2). Indicated are mean (±SD) performance in the Mini Mental State Examination, Boston Naming Test, Digit Spans backward, Trailmaking Tests B divided by A, Verbal Learning and Memory Test, delayed recall and the global cognitive score (mean, ±SEM). (C) t-tests for differences in test-performance between controls and MCI at TP1 and TP2.

Table 4b.

Multilevel regression analysis on the synergistic effects from GM CBVa and β-amyloid on cognitive decline using data from the control and MCI subjects as two separate groups.

	Standardized coefficient b,c			p-value			R2
Region a	CBVa (β1)	Aβ (β2)	CBVax Aβ (β3)	CBVa (β1)	Aβ (β2)	CBVax Aβ (β3)
Frontal_Sup_Orb
Model 1 d (full)	−1.40	−0.58	−1.85	0.05	0.05	0.04	0.32
Model 2 e (restricted)		−0.25			0.05		0.16
F-test (p) f							0.05
Frontal_Inf_Orb
Model 1 d (full)	−1.40	−0.95	−1.85	0.05	0.03	0.05	0.39
Model 2 e (restricted)		−0.57			0.05		0.15
F-test (p) f							0.01
Frontal_Sup_Medial
Model 1 d (full)	−1.46	−0.95	−2.22	0.02	0.02	0.01	0.32
Model 2 e (restricted)		−0.46			0.03		0.17
F-test (p) f							0.02
Frontal_Mid_Orb
Model 1 d (full)	−0.66	−0.99	−1.84	0.04	0.02	0.04	0.41
Model 2 e (restricted)		−0.44			0.04		0.16
F-test (p) f							0.005
Supp_Motor_Area
Model 1 d (full)	−1.38	−1.82	−2.52	0.03	0.03	0.03	0.37
Model 2 e (restricted)		−0.46			0.04		0.15
F-test (p) f							0.01
Hippocampus
Model 1 d (full)	−3.46	−0.67	−4.85	0.01	0.04	0.01	0.37
Model 2 e (restricted)		−0.25			0.05		0.15
F-test (p) f							0.04
Lingual
Model 1 d (full)	−2.72	−1.15	−3.03	0.04	0.03	0.04	0.32
Model 2 e (restricted)		−0.42			0.04		0.16
F-test (p) f							0.05

^aThe brain regions were labeled according to the IBASPM 116 atlas (please see Methods for references).

^bThe estimated coefficients correspond to β1, β2 and β3 in Eq. [1] for CBVa, Aβ and CBVa×Aβ, respectively. The synergistic effect from CBVa and Aβ is reflected in the β3 term.

^cThe Standardized coefficient can be used as an estimate of relative contribution to longitudinal cognitive decline from each term.

^dModel 1: Cognitive decline (% per year) = β0 + β1×CBVa + β2×Aβ + β3×CBVa×Aβ + β4×Sex + β5×Age +β6×Education

^eModel 2: Cognitive decline (% per year) = β0 + β2×Aβ + β4×Sex + β5×Age + β6×Education

^fP-Value from R² change F-test to assess whether the R² from the two models are significantly different.

Highlights.

• 11C-PiB-PET for Aβ and 7 Tesla iVASO MRI for CBVa in non-demented old aged adults.

• Increased CBVa is a correlate of cognitive decline associated with Aβ-burden.

• Local Aβ-deposits indicate brain regions with increased CBVa after two years.

• High orbitofrontal CBVa is associated with APOE4 carrier status.

• CBVa may represent a downstream marker of metabolic change associated with Aβ.