The study included patients aged 46–70 years with cognitive and other cerebral complaints, brain changes on MRI corresponded to CSVD (WMH, lacunes, enlarged perivascular spaces, microbleeds and cerebral atrophy) . Patients with low WMH burden (Fazekas scale score 1) were included in the study if they had AH stage 2 or 3 and/or ≥ 1 lacuna.
Exclusion criteria: 1) CI due to probable Alzheimer's disease according to the U.S. National Institute on Aging criteria [37; 38]; 2) patients with small subcortical infarcts/lacunes < 3 months after an acute cerebrovascular event; 3) CSVD due to other independent causes (genetic, inflammatory, thrombophilic, systemic, toxic, history of severe migraines); 4) a different cause of stroke and concomitant brain pathology other than CSVD; 5) > 50% atherosclerotic stenosis of the extra- or intracranial arteries; 6) serious medical condition – cardiac (ejection fraction < 50%), endocrine (diabetes mellitus (DM) type 1 or 2 with severe vascular complications, uncompensated thyroid disorder), renal (chronic kidney disease with glomerular filtration rate < 30 ml/min), etc.; 7) contraindications for MRI.
The control group consisted of volunteers with no clinical or MRI evidence of vascular and degenerative brain pathology, no AH in the medical history and during Ambulatory Blood Pressure Monitoring (ABPM), and matched for age and gender. Controls with AH according to ABPM were excluded from the study, in accordance with the European Society of Hypertension recommendations: daytime BP was ≥ 135/85 mmHg and/or night-time BP was ≥ 120/70 mmHg, or if BP increased by more than 24% over time during exertion .
In total 53 patients (37 women, average age 60.1 ± 6.8 years) and 17 healthy volunteers (12 women, average age 56.7 ± 6.7 years) were enrolled in the study. The study was approved by the Local Ethics Committee of the Research Centre of Neurology № №2–4/16 dated 17.02.2016 and performed in accordance with the principles of the Declaration of Helsinki. All subjects signed an informed consent form for participation in the study.
Traditional vascular risk factors, such as AH , hypercholesterolemia, obesity, DM and smoking were assessed in the patients and controls.
All participants underwent ABPM with an automated device (LLC DMS Advanced Technologies, Moscow) based on oscillometric method. Patients underwent ABPM during hospitalization with BP measurement every 30 min during the day (8:00 am to 10:00 pm) and every 60 min during the night (10:00 pm to 8:00 am). The ABPM device inflatable cuff was placed on the non-dominant upper limb. In all cases, at least 70% of the measurements were suitable for analysis. We calculated mean 24-hour systolic BP (SBP) and diastolic BP (DBP); mean, standard deviation (SD) and maximal values of awake and asleep SBP and DBP; and BP load parameters as the percentage of readings in a given period (24-h, day, or night), which exceed the normal levels for awake and asleep SBP and DBP .
The grade of AH was determined from the medical history and was adjusted according to ABPM results. During hospitalization patients continued their AHT.
Imaging was carried out in a Siemens MAGNETOM Verio 3T scanner (Siemens Medical Systems, Erlangen, Germany) with a standard 12-channel matrix head coil. To evaluate STRIVE criteria , patients and the control group underwent axial spin echo T2-weighed imaging (TR 4000 ms; TE 118 ms; slice thickness 5.0 mm; duration: 2 min 02 s); sagittal 3D T2 FLAIR (TR 6000 ms; TE 395 ms; 1.0 mm3 cubic voxel; duration: 7 min 12 s); sagittal 3D Т1-mpr (TR 1900 ms; TE 2,5 ms; 1.0 mm3 cubic voxel; duration: 4 min 16 s); diffusion MRI (DWI) using axial spin-echo echo-planar imaging sequence with two b-values − 0, 1000 s/mm2 (TR – 4000 ms, TE – 100 ms, slice thickness – 4 mm, duration: – 1 min 20 s); axial susceptibility weighted imaging sequence (SWI) with magnitude and phase images reconstruction (TR 28 ms; TE 20 ms; slice thickness 1.2 mm; duration: 8 min 12 s).
Two neuroradiologists evaluated MR images in a standardized manner, blinded to clinical information. No STRIVE criteria were found in volunteers from control group. There were no acute or recent small lacunar infarcts based on DWI analysis in patients with CSVD. MRI presence of lacunes, white matter hyperintensities, microbleeds, and perivascular spaces were summed in a score of 0–4 representing all SVD features combined [41; 42].
The Fazekas Scale  was used to quantify T2 FLAIR white matter hyperintensities (WMH) (score 0–3) as well as semi-automatic WMHs segmentation using LST toolbox (http://www.applied-statistics.de/lst.htm) for SPM12 (http://www.fil.ion.ucl.ac.uk/spm) with further manual correction using ITK-SNAP viewer (http://itksnap.org). The obtained data were saved as a binary mask, which was taken into consideration when the NAWM mask was subsequently created to calculate BBB permeability.
DCE-MRI was performed for BBB leak assessment: after two Т1 volumetric interpolated breath-hold examination (T1-VIBE) acquisitions (flip angles 2 and 15) for pre-contrast T1 maps, we injected gadodiamide (Omniscan; GE Healthcare) 0.2 mL/kg (i.e., 0.1 mmol/kg body weight) at a rate 3 mL/second intravenously via injection pump and then repeated the 3D T1-weighted sequence sequentially 100 times for 15 min 33 sec. The scanning parameters were: TR – 8.6 msec, TE – 4 msec, field of view – 250 mm, matrix – 256x230 pixels, flip angle – 15 degrees, slice thickness – 3.6 mm.
The entire dataset underwent preliminary processing using the NordicNeuroLab software (NordicICE, Norway). This included automatic correction of motion artefacts, correction of pre- and post-contrast data in the dynamic series, concentration of contrast agent in brain tissue calculation using relative signal change and T1 mapping. Individual vascular input functions were derived semi-automatically from the superior sagittal sinus ). The haematocrit, contrast agent dose and relaxivity of the contrast agent was set individually for each patient. The Patlak pharmacokinetic model was used to assess the low BBB permeability in CSVD resulting in Ktrans (volume transfer coefficient), Vp (fractional blood plasma volume) maps, and AUC (area under the curve - corresponding to increased contrast transit time in the brain) maps.
Once permeability parameter maps were obtained, further data processing was performed in SPM12 (http://www.fil.ion.ucl.ac.uk/spm). This included the following steps: coregistration of each subject’s permeability parameter maps and the T1 images; segmenting the T1 images into grey matter and white matter, followed by correction of the obtained images using WMH masks based on a MatLab script (https://matlab.ru/), resulting in the binary images of the corrected grey and white matter. Permeability parameters were calculated in ITK-SNAP (http://itksnap.org) separately for the grey matter, NAWM and WMH by superimposing the relevant masks over the individual permeability maps.
Statistical analysis was performed using IBM SPSS 23.0 (IBM SPSS Statistics, version 23.0, IBM Corp., Armonk, NY, USA) and R 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria) software. Data are presented as n (%) for categorical variables or as mean ± standard deviation (SD) or median [interquartile range (IQR)] for quantitative data. Differences between groups were determined using χ2, independent samples t-test, univariate analysis of variance or Kruskal–Wallis test where appropriate. In all cases, two-way statistical criteria were used. The null hypothesis was rejected if p < 0.05. Pearson's correlation coefficient and Spearman's correlation were used to assess the relationship between parameters.