Correlation Between Non-Stenotic Parent Artery Plaques and Lesion Imaging Features in Lenticulostriate Recent Small Subcortical Infarction: A High-Resolution Magnetic Resonance Imaging Study

doi:10.21203/rs.3.rs-4725387/v1

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Correlation Between Non-Stenotic Parent Artery Plaques and Lesion Imaging Features in Lenticulostriate Recent Small Subcortical Infarction: A High-Resolution Magnetic Resonance Imaging Study

https://doi.org/10.21203/rs.3.rs-4725387/v1

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Previous studies have shown that lesions from recent small subcortical infarction (RSSI) with accompanying responsible vessel stenosis are closer to the openings of perforating branches and involve more slices on axial Magnetic Resonance Imaging (MRI). However, it remains unclear whether similar lesion characteristics are also associated with non-stenotic responsible vessel plaques that can be detected only by high-resolution MRI (HR-MRI). We enrolled consecutive patients with RSSI in the lenticulostriate artery (LSA) territory between January 2020 and December 2022. Patients with no visible stenosis in the ipsilateral middle cerebral artery (MCA) who completed HR-MRI were enrolled. Lesion characteristics including the lowest slice involved (LS), total number of slices involved (TNS) and maximal axial diameter (MAD) of RSSI lesions were evaluated by serialized axial levels of diffusion-weighted imaging (DWI). HR-MRI was used to assess the presence of dorsal superior plaques in the M1 segment of responsible MCA. Logistic regression analysis was performed to identify the lesion characteristics associated with the presence of dorsal superior plaques in the MCA. A receiver-operating characteristic (ROC) curve was employed to estimate the predictive efficacy of these parameters for MCA plaques using area under the ROC curve (AUC) and the optimal cut-off point. A total of 112 patients were included in the final analysis, with 57 (50.89%) having plaques detected on the dorsal superior wall of MCA. The average age of these patients was 57.08 years (57.08 ± 11.90 years), and 78 (69.64%) were male. Multivariate logistic regression showed that LS was significantly associated with MCA plaques (odds ratio [OR]: 0.674, 95% confidence interval [CI]: 0.485–0.937, p = 0.019). The optimal cut-off point (LS ≤ 1) was achieved by ROC curve analysis (AUC = 0.64, p = 0.007). The results indicate that in RSSI patients without responsible vascular stenosis, an LS ≤ 1 is significantly associated with the presence of dorsal superior plaques on the responsible vessel wall as detected by HR-MRI.

Biological sciences/Neuroscience

Health sciences/Neurology

Recent small subcortical infarction

High-resolution magnetic resonance imaging

Lowest slice involved

Total number of slices involved

Plaques

Recent small subcortical infarction (RSSI) is characterized as an isolated ischemic lesion in the territory of the perforating artery with an maximal axial diameter (MAD) of ≤ 20 mm on diffusion weighted imaging (DWI), which is typically accompanied by clinical symptoms related to the lesions occurring in the past few weeks¹, accounting for approximately 30% of ischemic stroke². RSSI is a heterogeneous condition. Pathological studies demonstrated that atherosclerotic plaques of parent artery disease (PAD) can extend or protrude into the orifice of the penetrating branch, leading to stenosis or occlusion of the orifice. Otherwise, Isolated atherosclerotic plaques can also be found at the orifice of the penetrating artery, resulting in orifice obstructive lesions^3,4. The pathological features described above primarily occur at the proximal end of the perforating artery orifice, leading to infarctions that affect the entire penetrating artery. These infarctions are characterized by larger size and greater involvement of slices, and are therefore defined as proximal RSSIs (pRSSIs)^5–7. The more common pathological changes occurring in the distal portion of perforating arteries are lipohyalinosis or fibrinoid necrosis of the vessel wall. These changes typically lead to smaller infarct lesions that affect fewer layers, referred to as distal RSSI (dRSSI)^8,9. Previous studies have shown that, compared to dRSSI, pRSSI is more commonly associated with stenotic PAD visible on traditional vascular imaging, such as magnetic resonance angiography (MRA), computed tomography angiography (CTA), or digital subtraction angiography (DSA)^10,11, This aligns with the distribution characteristics of different mechanisms observed in the aforementioned pathological studies. Based on this, it can be inferred that in RSSI patients with non-stenotic PAD (nsPAD), the morphological features of the lesions at the distal and proximal ends may be related to the mechanisms of small vessel disease and atherosclerosis occurring at the proximal end of the perforating artery, respectively. With the clinical application of high-resolution magnetic resonance imaging (HR-MRI) in recent years, it is possible to identify potential atherosclerotic plaques in nsPAD^12,13. This study aims to investigate whether the presence of responsible plaques visible on HR-MRI in nsPAD is related to proximal type of imaging features of RSSI.

Patient selection

Consecutive patients with RSSI in the lenticulostriate artery (LSA) territory admitted to the Department of Neurology of the First Affiliated Hospital of Zhengzhou University between January 2020 and December 2022 were enrolled retrospectively. Patients included in the study met the following criteria: (1) Age ≥ 18 years old; (2) The time from onset to admission ≤ 72 hours; (3) 3.0T head MRI examination including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), DWI, apparent diffusion coefficient (ADC) and fluid-attenuated inversion recover (FLAIR); (4) An isolated infarction in the LSA territory associated with clinical symptoms, with a maximum axial diameter (MAD) ≤ 20 mm on DWI; (5) Prerequisite HR-MRI examination of the responsible middle cerebral artery (MCA); (6) signed informed consent. The exclusion criteria were as follows: (1) None of the MRA, CTA or DSA were completed; (2) Abnormal signal of the responsible MCA M1 segment inspected by cerebrovascular examination, or ipsilateral carotid artery stenosis of ≥ 50%; (3) Confirmed cardio-aortic embolism, or other non-atherosclerotic vascular diseases, including arteritis, moyamoya disease, and arterial dissection; (4) Poor image quality affecting the evaluation. The screening process flowchart is shown in Fig. 1.

Ethics statement

The study was approved by the Ethics Committee of the first Affiliated Hospital of Zhengzhou University (2022-KY-1334-002). The study was conducted in strict accordance with the relevant guidelines and regulations set forth by the committee.

All procedures performed in studies involving human participants complied with institutional and/or National Research Council ethical standards. Only participants who provided a written informed consent were included in the study. All the tests were performed in accordance with the Declaration of Helsinki.

Clinical data

The following clinical data were collected at admission: age, gender, past history including hypertension (defined as blood pressure ≥ 140/90 mmHg at rest on repeated measurements or the use of antihypertensive medication), hyperlipidemia (defined as overnight fasting cholesterol [TC] level ≥ 5.2 mmol/L or triglyceride [TG] level ≥ 1.7 mmol/L, or the use of lipid-lowering medication), diabetes mellitus (DM) (defined as serum glucose level ≥ 11.1 mmol/L in a 2-hour oral glucose tolerance test or the use of antidiabetic medication), coronary artery disease (CHD), stroke, current smoking and drinking status; Blood biochemical indicators including blood routine index, fasting blood glucose(FBG), serum lipid level, homocysteine (HCY), glycosylated hemoglobin (HbA1c), hemoglobin, blood creatinine level(Cr), blood urea nitrogen(BUN), uric acid(UA), etc.; Clinical assessment including the National Institutes of Health Stroke Scale (NIHSS) score at admission.

Imaging assessment

Imaging protocol

Brain MRI examinations were performed in a 3.0 T scanner. The short axial cross-sections were constructed automatically with 0.5 mm slice thickness and 1.0 mm interstice gap. The imaging parameters were used included the following: DWI: b = 0 and 1,000 s/mm²;FLAIR: repetition time/echo time = 8,400 ms/155 ms, inversion time = 2,100 ms; Time-of-flight (TOF) MRA: repetition time/echo time = 20.0 ms/3.7 ms, flip angle = 20°. The 3D HR-MRI was performed using a variant of a 3D turbo spin echo technique known as T1-weighted-space (MAGNETOM Prisma; Siemens Healthcare, Erlangen, Germany). Maximum intensity projection images and source images of 3D time-of-flight MRA were used to ensure that cross-section images were perpendicular to infarction locations. The HR-MRI scan includes three sequences: (1) Pre-contrast T1-weighted fast-spin-echo sequence: repetition time/echo time = 430 ms/10 ms, echo train length = 9, number of excitations = 6; (2) T2-weighted fast-spin-echo sequence: repetition time/echo time = 2,500 ms/63 ms; echo train length = 13; number of excitations = 2, and (3) post-contrast T1-weighted fast-spin-echo.

Imaging feature evaluation

(1) RSSI lesion features: Based on axial DWI, this study was performed by a previously reported serialized axial image template with 6 representative layers ranging from level 1 at the proximal origin of the LSA from the MCA to level 6 at the distal ramification zone (Fig. 2). the lowest slice involved (LS), total number of slices involved (TNS)¹⁴ and MAD were recorded. (2) Responsible MCA plaques on HR-MRI: All eccentric plaques were classified into 1 of the 4 quadrants based on their plaque orientation being centered on the superior, inferior, dorsal, or ventral side of the vessel (Fig. 2). The quadrant with the thickest plaque was defined as the location of the plaque. If the thickness of the plaques in the involved quadrants was similar, it was considered that the corresponding quadrants were all involved¹⁵. On account of most of penetrating arteries, which may be considered as flow dividers, arising dorsally from the upper part of MCA wall¹⁶. Plaque was defined as a focal dorsal superior wall thickening with or without gadolinium enhancement identified on HR-MRI T2 and proton density sections compared to nearby or contralateral MCA¹⁷. (3) White matter hyperintensities (WMHs): The white matter hyperintensities (WMHs) were evaluated by Fazekas scale via Flair¹⁸. The scores were as follows: Periventricular hyperintensity (PVH) was graded as 0 = absence, 1 point = “caps” or pencil-thin lining, 2 points = smooth “halo”, 3 points = irregular PVH extending into the deep white matter. Deep white matter hyperintensity (DWMH) was rated as 0 = absence, 1 point = punctate foci, 2 points = beginning confluence of foci, 3 points = large confluent areas. The combination of the two types ≥ 2 points was defined as severe WMHs (sWMHs). All the imaging parameters were evaluated independently by 2 professional physicians who were blinded to the research. The differences arising should be resolved by consultation.

Statistical analysis

The Shapiro-Wilk test was performed to assess the normal distribution of continuous variables. Continuous variables conforming to the normal distribution were expressed as the mean ± standard deviation (SD). Categorical data were expressed as counts and percentages. Data with non-normal distribution and rank variables were described as median and interquartile range (IQR). The normally distributed continuous variables were analyzed by independent sample t-test. Continuous variables non-normally distributed and rank variables were analyzed by Mann-Whitney U-test. The chi-square test was performed to assess the categorical variables. The inter-observer agreement for imaging analysis was assessed by Cohen's kappa (𝜅). A value of 𝜅 > 0.8 would indicate almost perfect agreement (Supplementary Table S1 on line). Receiver-operating characteristic (ROC) curve was conducted using MedCalc.v22.023 (MedCalc Software, Mariakerke, Belgium) to estimate predictive efficacy of RSSI lesions for MCA plaques by area under the ROC curve (AUC) and the optimal cut-off point. In the univariate analyses, statistical significance was defined as a p value of < 0.1. Collinearity diagnosis was performed for all associated variables in the univariate analyses. Collinearity was considered as tolerance ≤ 0.1 or variance inflation factor (VIF) > 10. Variables included in the multivariate logistic regression analysis were removed via backward stepwise. A 2-sided value of p < 0.05 was considered to indicate a statistically significant difference. Statistical analyses were conducted using SPSS 21.0 (SPSS, Inc., Chicago, IL, USA).

Baseline characteristics

This study included 152 RSSI patients who met the inclusion criteria. With 30 cases excluded, a total of 112 patients were included in the final analysis. Baseline characteristics showed that there were no evident differences between the finally included patients and the excluded patients excepting current smoking and drinking status, low density lipoprotein, PVH, and NIHSS scores (Supplementary Table S2 on line). Of the 112 patients enrolled, 57 (50.89%) were found to have responsible MCA plaques on HR-MRI. Among the patients, 78 (69.64%) were male, with a mean age of 57.08 ± 11.90 years. The median NIHSS score on admission was 3 (interquartile range [IQR]: 2–5). The median lesion scores for LS, TNS, and MAD were 3 (IQR: 2–4), 3 (IQR: 2–5), and 14.68 mm (IQR: 10.75–18.72), respectively. Additional baseline characteristics are detailed in Table 1.

Table 1

Demographic and baseline characteristics of RSSI patients
	Total (n = 112)	Plaque (n = 57)	Non-plaque (n = 55)	p value
Demographic data
Age, year (mean ± SD)	57.08 ± 11.90	57.54 ± 11.12	56.60 ± 12.74	0.677
Male, n (%)	78 (69.64)	35 (61.40)	43 (78.18)	0.054^*
Clinical features
Hypertension, n (%)	78 (69.64)	44 (77.19)	34 (61.82)	0.077^*
Diabetes, n (%)	25 (22.32)	14 (24.56)	11 (20.00)	0.562
Hyperlipidemia, n (%)	26 (23.21)	15 (26.32)	11 (20.00)	0.429
CHD, n (%)	10 (8.93)	4 (7.02)	6 (10.91)	0.470
Stroke history, n (%)	20 (17.86)	11 (19.30)	9 (16.36)	0.685
Smoking, n (%)	33 (29.46)	16 (28.07)	17 (30.91)	0.742
Drinking, n (%)	26 (23.21)	11 (19.30)	15 (27.27)	0.318
NIHSS score (median [IQR])	3 (2, 5)	3 (2, 5)	2 (2, 4)	0.195
SBP, mmHg (mean ± SD)	150.34 ± 21.00	150.46 ± 21.70	150.22 ± 20.47	0.953
DBP, mmHg (mean ± SD)	89.83 ± 14.30	89.72 ± 15.90	89.95 ± 12.58	0.934
Lab tests
TC, mmol/L (mean ± SD)	4.34 ± 1.04	4.40 ± 1.08	4.27 ± 1.01	0.514
TG, mmol/L (median [IQR])	1.30 (0.96, 1.68)	1.32 (0.96, 1.76)	1.20 (0.95, 1.62)	0.650
HDL, mmol/L (mean ± SD)	1.10 ± 0.26	1.12 ± 0.28	1.08 ± 0.24	0.399
LDL, mmol/L (mean ± SD)	2.73 ± 0.93	2.78 ± 0.96	2.69 ± 0.90	0.602
HCY, mmol/L (median [IQR])	14.58 (11.95, 18.77)	14.43 (11.50, 18.29)	15.20 (12.00, 19.00)	0.453
FBG, mmol/L (median [IQR])	5.47 (4.87, 6.81)	6.00 (4.97, 7.35)	5.21 (4.58, 6.25)	0.036^*
HbA1C, % (median [IQR])	5.90 (5.50, 6.90)	6.00 (5.50, 7.39)	5.80 (5.50, 6.50)	0.199
Hemoglobin, g/L (median [IQR])	142.00 (133.00, 151.00)	140.00 (133.00, 153.00)	142.00 (133.00, 151.00)	0.954
NLR (median [IQR])	2.27 (1.70, 3.24)	2.33 (1.71, 3.62)	2.23 (1.66, 3.11)	0.218
BUN, mmol/L (median [IQR])	4.60 (3.65, 5.59)	4.54 (3.58, 5.68)	4.68 (3.80, 5.20)	0.935
Cr, µmol/L (mean ± SD)	69.30 ± 15.22	69.41 ± 14.43	69.19 ± 16.07	0.941
UA, µmol/L (mean ± SD)	299.55 ± 76.57	293.35 ± 72.30	304.96 ± 80.74	0.449
GFR, mL/min/1.73m² (mean ± SD)	96.03 ± 12.70	96.86 ± 13.77	95.23 ± 11.65	0.500
Imaging examinations
LS (median [IQR])	3 (2, 4)	2 (1, 4)	3 (2, 4)	0.009^*
TNS (median [IQR])	3 (2, 5)	4 (3, 5)	3 (2, 4)	0.028^*
PVH (median [IQR])	1 (1, 2)	1 (1, 2)	1 (1, 2)	0.081^*
DWMH (median [IQR])	1 (0, 2)	1 (0, 2)	1 (0, 2)	0.473
sWMH, n (%)	50 (44.64)	27 (47.37)	23 (41.82)	0.555
MAD, mm (median [IQR])	14.68 (10.75, 18.72)	14.93 (10.98, 18.16)	14.35 (10.04, 19.09)	0.963
^*p value < 0.1.
Abbreviations: SD, standard deviation; IQR, interquartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL, high density lipoprotein; LDL, low density lipoprotein; NLR, neutrophil/lymphocyte; GFR, glomerular filtration rate.

Factors Associated with MCA Plaques

The univariate analysis indicated that the plaque group had a higher proportion of males (p = 0.054), a greater prevalence of hypertension history (p = 0.077), elevated fasting blood sugar levels (p = 0.036), increased PVH (p = 0.081), and higher TNS (p = 0.028), along with lower LS (p = 0.009) (Table 1). There was no collinearity among the associated variables (VIF < 10) in the univariate analysis (Supplementary Table S3 on line). These variables were selected for further analysis. Multivariate logistic regression analysis revealed that LS was independently associated with the presence of MCA plaques on HR-MRI (odds ratio [OR]: 0.674, 95% confidence interval [CI]: 0.485–0.937, p = 0.019), as shown in Table 2.

Table 2

Multivariate logistic regression analysis for related variables of MCA plaques
Variables	OR(95%CI)	p value
Gender	0.444 (0.182, 1.084)	0.075
LS	0.674 (0.485, 0.937)	0.019^*
TNS	1.072 (0.535, 2.149)	0.845
Hypertension history	2.365 (0.968, 5.780)	0.059
FBG	1.634 (0.998, 2.676)	0.051
PVH	0.444 (0.182, 1.084)	0.075
^*p value < 0.05.

ROC Curve and Optimal Cut-off Point for Predicting MCA Plaques

ROC curve was conducted to estimate predictive efficacy of LS for MCA plaques using the AUC and the optimal cut-off point. As shown in Fig. 3, the AUC was 0.640 (p = 0.007), with the optimal cut-off point identified at LS = 1. This suggests that an LS value of ≤ 1 provides a better predictive value for the presence of MCA plaques.

Clinical Characteristics for Different Imaging Features of RSSI

According to the optimal cut-off point, RSSI with LS ≤ 1 was defined as pRSSI, and as dRSSI with LS > 1 (Fig. 4). Among the 112 patients, 24 (21.43%) were classified as pRSSI and 88 (78.57%) were dRSSI. Univariate analysis showed that MCA plaques and diabetes history were more common in the pRSSI group than dRSSI group, and the level of FBG (p = 0.093), NIHSS score (p = 0.025), and MAD (p = 0.026) were higher in the pRSSI group (Table 3). Diabetes history, NIHSS score, MAD, and MCA plaques were substantially associated with pRSSI in the multivariate logistic regression analysis, as shown in Table 4.

Table 3

Clinical characteristics of pRSSI and dRSSI
	pRSSI (n = 24)	dRSSI (n = 88)	p value
Demographic data
Age, year (mean ± SD)	55.96 ± 12.17	57.39 ± 11.88	0.605
Male, n (%)	15 (62.50)	63 (71.59)	0.391
Clinical features
Hypertension, n (%)	16 (66.67)	62 (70.45)	0.721
Diabetes, n (%)	10 (41.67)	15 (17.05)	0.010^*
Hyperlipidemia, n (%)	7 (29.17)	19 (21.59)	0.436
CHD, n (%)	4 (16.67)	6 (6.82)	0.273
Stroke history, n (%)	6 (25.00)	14 (15.91)	0.465
Smoking, n (%)	6 (25.00)	27 (30.68)	0.588
Drinking, n (%)	5 (20.83)	21 (23.86)	0.755
NIHSS score (median [IQR])	4 (2, 8)	3 (2, 4)	0.025^*
SBP, mmHg (mean ± SD)	150.17 ± 25.81	150.40 ± 19.67	0.964
DBP, mmHg (median [IQR])	90.00 (79.25, 99.75)	70.00 (59.00, 77.75)	0.787
Lab tests
TC, mmol/L (mean ± SD)	4.34 ± 1.10	4.33 ± 1.03	0.966
TG, mmol/L (median [IQR])	1.42 (0.86, 1.96)	1.29 (0.97, 1.64)	0.843
HDL, mmol/L (median [IQR])	1.04 (0.86, 1.14)	1.08 (0.92, 1.33)	0.287
LDL, mmol/L (mean ± SD)	2.78 ± 1.00	2.72 ± 0.92	0.809
HCY, mmol/L (median [IQR])	13.28 (10.63, 18.44)	14.82 (12.10, 18.77)	0.284
FBG, mmol/L (median [IQR])	6.35 (4.89, 8.59)	5.40 (4.83, 6.40)	0.093^*
HbA1C, % (median [IQR])	6.00 (5.43, 7.95)	5.90 (5.50, 6.65)	0.460
Hemoglobin, g/L (median [IQR])	144.00 (134.25,153.75)	142.00 (133, 150.45)	0.620
NLR (median [IQR])	2.27 (1.83, 3.37)	2.27 (1.67, 3.20)	0.501
BUN, mmol/L (median [IQR])	4.68 (3.43, 5.85)	4.55 (3.80, 5.20)	0.604
Cr, µmol/L (median [IQR])	68.50 (51.25, 81.50)	70.00 (59.00, 77.75)	0.537
UA, µmol/L (mean ± SD)	289.50 ± 89.10	302.30 ± 73.12	0.471
GFR, mL/min/1.73m² (mean ± SD)	97.10 ± 12.20	95.75 ± 12.90	0.655
Imaging examinations
MCA plaque, n (%)	19 (79.17)	38 (43.18)	0.002^*
PVH (median [IQR])	1 (1, 2)	1 (1, 2)	0.201
DWMH (median [IQR])	1 (0, 2)	1 (0, 2)	0.631
sWMH, n (%)	12 (50.00)	38 (43.18)	0.551
MAD, mm (median [IQR])	17.64 (14.15, 19.10)	13.93 (10.10, 18.03)	0.026^*
^*p value < 0.1.
Abbreviations: SD, standard deviation; IQR, interquartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL, high density lipoprotein; LDL, low density lipoprotein; NLR, neutrophil/lymphocyte; GFR, glomerular filtration rate.

Table 4

Multivariate logistic regression analysis for risk factors of pRSSI
Variables	OR (95% CI)	p value
MCA plaque	4.998 (1.512, 16.519)	0.008^*
Diabetes history	6.189 (1.758, 21.785)	0.005^*
MAD	1.147 (1.028, 1.280)	0.015^*
NIHSS score	1.273 (1.042, 1.556)	0.018^*
FBG	0.990 (0.740, 1.324)	0.946
^*p value < 0.05.

In this study, we found that atherosclerotic plaques were observed on HR-MRI in 50.9% of RSSI patients in the LSA territory with no stenosis of ipsilateral MCA using contrast-enhanced 3D, despite the absence of ipsilateral MCA stenosis. The LS of RSSI lesions was independently associated with MCA plaques, with the optimal cut-off point of LS ≤ 1.

Yoon et al. reported the detection of MCA plaques by HR-MRI in 51.3% of patients with RSSI in 39 patients without MCA stenosis⁶. Another study from China also found that HR-MRI could identify approximately 46.9% of MCA plaques in a similar population¹⁹. The result in our study (50.9%) was consistent with the above studies. Our results, showing a 50.9% prevalence of MCA plaques, align with these earlier findings, reinforcing the utility of HR-MRI in diagnosing vascular pathology in patients with RSSI in the absence of visible MCA stenosis.

Previous studies had shown that there was prominent etiological heterogeneity in RSSI. The main pathological etiologies have been identified: one involves atherosclerotic plaques affecting the perforating artery at the proximal end of the parent artery or at the orifice of the perforating artery, and the other involves small vessel disease mechanisms such as fibrinoid degeneration or lipohyalinosis at the distal end of the perforating artery^3,9. The pathomechanism of the former involves atherosclerotic plaques in the parent artery or isolated atherosclerotic plaques at the orifice of the penetrating artery, obstructing the orifice and resulting in orifice obstructive lesions, commonly presented as proximal RSSI (pRSSI) lesions. These pRSSI lesions are close to the orifice of the penetrating branch (e.g., the first layer of the lenticulostriate arteries or the ventral side of the pons), have a longer diameter, and typically involve more than three layers. In contrast, the latter etiology often appears as distal RSSI (dRSSI) lesions, which are located away from the opening of the penetrating branch, leading to smaller infarctions ^10,14. Previous studies have shown that pRSSI is more commonly associated with any degree of PAD detectable by conventional vascular examinations compared to dRSSI^20,21. Our previous work indicated that LS ≤ 2 and TNS ≥ 3 were the most likely lesion types for RSSI combined with PAD¹¹. These results can be well explained by pathological findings. However, limited by traditional vascular imaging, previous studies have overlooked the potential non-stenotic atherosclerotic lesions in the parent artery proximal to the perforator opening. Even in RSSI patients with non-stenotic parent artery lesions, different etiologies of potential non-stenotic atherosclerotic lesions in the parent artery proximal to the perforator opening and distal small vessel disease may cause similar proximal and distal type lesions. With the clinical application of HR-MRI, more potential atherosclerotic plaques in non-stenotic large arteries that couldn’t be detected by traditional vascular imaging have been discovered. The result of this study was consistent with the conclusion of previous studies in RSSI patients with PAD, that is, the lower the involved level, the higher the probability of MCA plaques. Microscopic anatomy studies had shown that most perforating arteries arose vertically from the dorsal superior wall of MCA¹⁶. In patients with LSA occlusion caused by MCA plaques, lower LS was observed in a HR-MRI vessel wall imaging study²². Besides, Yang et al. combined LS and TNS to predict MCA plaques, and concluded that when LS = 1 or 2 and TNS ≥ 3, it was more likely to appear MCA plaques in RSSI patients²³. According to the MRI serialized axial image template, the territory of LSA was divided into 6 representative levels from the proximal origin of the LSA branching from the MCA to the distal end. The lower the involved level of the lesion, the closer the lesion is to the orifice of the penetrating artery. MCA plaques involved the origin of the perforator, causing infarction of the entire supply area of the perforating artery, which indicated that pRSSI was probably caused by occlusion of the perforating artery by MCA plaques.

Furthermore, although most studies reported that TNS ≥ 3 was associated with PAD^11,19, a study from Korea observed that the size of infarction focus (including TNS) had relatively lower sensitivity and specificity in predicting MCA plaques⁶. In our study, we also did not find an independent correlation between TNS and MCA plaques. MCA plaques involving the perforator opening lead to a lower level of involvement, but the number of involved levels is highly correlated with the course and variation of the perforating artery. If the perforator artery is longer, its blood supply area is relatively larger, and the number of involved levels is higher when the opening is blocked. Conversely, if the perforator artery is shorter, the number of involved levels may be smaller (Fig. 5). According to the results of this study, LS may be more specific than TNS in predicting MCA plaques.

The application of HR-MRI is highly significant for identifying the etiology of RSSI in clinical practice. However, due to its high cost, HR-MRI technology is not widely available in many hospitals, making it challenging to popularize. The lesion imaging pattern discovered in this study offers a simple and feasible method for etiological classification of RSSI patients with non-stenotic responsible MCA. This allows for the convenient selection of pRSSI patients who are more suitable for HR-MRI examination to identify MCA plaques, aiding physicians in making further clinical decisions.

There were some limitations in this study. First, although HR-MRI can sensitively detect non-stenotic MCA plaques, it is still unable to identify microaneurysms or atherosclerosis at the orifice or proximal site of perforating arteries. Therefore, HR-MRI cannot completely distinguish the pathogenesis between MCA plaque-related RSSI and those caused by lesions at the orifice of perforating arteries. Second, the outcome variable of this study was the presence of plaques on the dorsal superior wall of the MCA trunk. However, some perforating arteries originate not from the MCA trunk but from the upper and lower branches of the MCA, which may introduce some errors. Third, this was a retrospective study from a hospital-based prospective registry with a relatively small sample size, resulting in insufficient test efficiency. Larger scale and prospective studies are needed to further confirm the relevant conclusions.

In lenticulostriate RSSI patients without stenosis in the responsible MCA, an LS ≤ 1 is significantly associated with the presence of responsible plaques in the ipsilateral vessel as detected by HR-MRI.

Competing interests

The authors declare no competing interests.

Additional information

Correspondence and requests for materials should be addressed to D.D. Z.

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Author Contribution

Y.G. and Y.M. X. conceptualized and developed the project. D.D.Z. contributed to writing the manuscript. D.D.Z. and C.Z. were responsible for data collection. D.D.Z. and S.W. performed statistical analysis and figure generation. Y.G. and Y.L.T. provided assistance with English editing. All authors reviewed and approved the final version of the manuscript.

Data Availability

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

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Correlation Between Non-Stenotic Parent Artery Plaques and Lesion Imaging Features in Lenticulostriate Recent Small Subcortical Infarction: A High-Resolution Magnetic Resonance Imaging Study

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Factors Associated with MCA Plaques

ROC Curve and Optimal Cut-off Point for Predicting MCA Plaques

Clinical Characteristics for Different Imaging Features of RSSI

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