Analysis of the relationship between the degree of migraine with right-to-left shunt and changes in white matter lesions and brain structural volume

doi:10.21203/rs.3.rs-4810764/v1

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Analysis of the relationship between the degree of migraine with right-to-left shunt and changes in white matter lesions and brain structural volume

https://doi.org/10.21203/rs.3.rs-4810764/v1

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Objective

To investigate the location of white matter lesions (WMLs) in migraineurs with right-to-left shunt (RLS); the relationships among the severity of WMLs, changes in brain structural volume and RLS shunts; and the relationships among the severity of WMLs, changes in brain structural volume and degree of headache in RLS migraine patients.

Methods

A total of 102 migraineurs with RLS admitted to the affiliated Central Hospital of Dalian University of Technology from December 2018 to December 2022 were enrolled in this study. RLS flow and the 6-item Headache Impact Test (HIT-6) scores were recorded to reflect the degree of headache. The brain structural volumes of 102 migraineurs with RLS were calculated from T1-weighted images using artificial intelligence, and the brain structural volumes of healthy controls matched according to age and sex were also calculated. The correlations among WML location, RLS, headache degree, WML severity and brain structural volume changes in migraineurs were analysed.

Results

1. The WMLs of migraineurs with RLS were concentrated mainly in the white matter of the lateral ventricular margin and deep white matter. Subcortical WMLs were concentrated mainly in the parietal lobe, occipital lobe and frontal lobe. 2. There were no significant differences in the WML variables of cerebral white matter high signal volume, ratio of high-signal white matter volume to whole-brain white matter volume (%) or Fazekas score among migraineurs with different RLS flows, but there were significant differences in WML variables among migraineurs with RLS with different HIT-6 grades and MIDAS grades. RLS flow, HIT-6 score and MIDAS grade were not correlated with the WML variables measured in this study. 3. There was a significant difference in the volume of the precentral gyrus between migraineurs with RLS and normal controls (P < 0.001), and there was a significant difference between migraineurs with different RLS flows and different HIT-6 scores and peripheral cerebrospinal fluid volumes. There was also a positive correlation between frontal pole structural volume and RLS flow. The volume of the precentral gyrus was negatively correlated with RLS flow, whereas the volume of the pons gyrus was positively correlated with the HIT-6 score. The volume of the temporal pole was negatively correlated with the HIT-6 score.

Conclusion

1. The WMLs of migraineurs with RLS were concentrated mainly in the white matter of the lateral ventricular margin and deep white matter. Subcortical WMLs were concentrated mainly in the parietal lobe, occipital lobe and frontal lobe. 2. There was no correlation between WML severity and RLS flow in migraineurs with RLS. 3. There was no correlation between WML severity and migraine severity in migraineurs with RLS. 4. Volume changes occur in some brain structures of migraineurs with RLS. 5. Shunt flow and the degree of headache in migraineurs with RLS were correlated with structural volume changes in specific brain regions.

Migraine

Right-to-left shunt

Degree of headache

White matter lesion

Volume of brain structure

Migraine is a common disease of the nervous system that is characterized by recurrent, unilateral, moderate-to-severe pulsatile headache accompanied by photophobia, phonophobia, nausea, vomiting and other symptoms^[1]. In China, it has been reported that migraineurs may experience aura symptoms^{[2, 3]}. Previous studies have shown that the annual incidence of migraine in the general adult population is as high as 33 million. The incidence of migraine is greater in women than in men. At the peak age for migraine, the incidence in women is 2–3 times greater than that in men^[4–6]. The 2016 Global Burden of Disease (GBD) study revealed that the overall annual prevalence of migraine is 14.4%, with a prevalence of 18.9% for women and 9.8% for men^[7]. The pathogenesis of migraine includes sensitization, trigeminal neurovascular system (TVS) damage, calcitonin gene-related peptide (CGRP), and cerebral spreading depression (CSD).

Cardiac right-to-left shunt (RLS) diseases include patent foramen ovale (PFO), ventricular septal defect (VSD), atrial septal defect (ASD), patent ductus arteriosus (PDA) and pulmonary arteriovenous malformation (PAVM). PFO is the most common RLS disease seen in the clinic, accounting for approximately 95% of all circulatory RLS diseases. RLSs can be classified into three levels: small shunts, medium shunts and large shunts. RLS is closely related to migraine. The incidence of RLS in patients with aura migraine is reportedly 2.5 times greater than that in healthy individuals^[8]. The mechanism for this relationship may involve the entrance of a microthrombus into the systemic circulation directly without being filtered by the pulmonary circulation, and the inability of haemoglobin to exchange oxygen molecules in the lungs causing hypoxemia, which affects the cerebrovascular system and trigeminal nervous system, leading to migraine^[9]. In addition, RLS can activate platelets and trigger the release of vasoactive substances such as serotonin and CGRP, which aggravate stimulation of the cerebrovascular system and trigeminal nervous system^[10]. Finally, the correlation between RLS and migraine may be genetic. One study revealed that some families exhibit autosomal dominant inheritance of RLS associated with aura migraines. Therefore, there may be a specific genetic basis for the abnormality of the atrial septum that is associated with migraine^[11].

According to the 2016 GBD study, migraine is the second most common nervous system dysfunction^[7] and is associated with anxiety, depression and sleep disorders. Some studies have also shown that migraine may increase the risk of cognitive impairment and cardiovascular and cerebrovascular diseases^{[12, 13]}. Migraine attacks often affect the daily activities of patients and seriously affect their work, study and social roles. A questionnaire on the degree of disability related to migraine can be used to quantitatively evaluate the degree of disability caused by migraine. In addition to being used in research, these questionnaires can also help doctors assess the degree of headache in migraineurs. A variety of scales are used to reflect the degree of migraine disability, the most common of which is the 6-item Headache Impact Test (HIT-6). Because the HIT-6 scale is easily understood by the public, it is often used on the internet to help patients understand the burden caused by headaches.

In the context of imaging research, an increasing number of studies have confirmed that migraine with RLS can lead to white matter lesions (WMLs) in migraineurs^[14–16]. A study on the relationship between RLS and WMLs by Iwasaki et al.^[17] revealed that the presence of RLS is the only independent risk factor for WMLs in migraineurs with RLS. The possible pathogenesis of WMLs in migraineurs with RLS includes abnormal embolism, in which microemboli enter the systemic circulation directly from the venous system, resulting in abnormal embolism and WMLs in migraineurs^[18], and vasoactive substances, such as interleukins and serotonin, which directly enter the arterial system due to RLS and stimulate intracranial sensitive neurovascular tissues, resulting in abnormal contraction and relaxation of intracranial blood vessels, headache and WMLs^[19]. However, to date, few correlations have been reported between WML severity and RLS flow or headache in migraineurs with RLS. An increasing number of studies have identified functional connectivity changes in the brain regions involved in pain regulation, sensory discrimination, pain cognition and pain emotion in migraineurs through functional magnetic resonance imaging (fMRI), and some brain regions exhibit abnormal activation patterns.

This study focused on migraineurs with RLS to explore where WMLs are more likely to occur, the correlations among RLS flow, WML severity and brain structural volume changes, and the correlations among headache degree, WML severity and brain structural volume changes.

The subjects of this study were migraineurs with RLS examined at Dalian Central Hospital from December 1, 2018, to December 1, 2022.

2.1 Data selection

The inclusion criteria for patients were as follows: 1. headache symptoms were consistent with the International Classification of Headache Disorders, 3rd edition (ICHD-III) proposed by the International Headache Society revised in 2018; 2. a contrast-transcranial Doppler (c-TCD) examination clearly revealed RLS; and 3. a plain MRI scan of the skull, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and fluid-attenuated inversion recovery (FLAIR) sequences, was performed.

The exclusion criteria were as follows: 1. history of cerebrovascular disease; 2. severe intracranial and extracranial macrovascular stenosis and occlusion confirmed by imaging; 3. WMLs with other causes, such as multiple sclerosis; 4. hypertension, diabetes, tumours and other serious medical diseases; and 5. space-occupying lesions observed on head MRI.

Grouping criteria

To study the correlations among RLS flow, headache degree and WML severity, 102 migraineurs with cardiac RLS were divided into two groups according to RLS flow and headache degree.

To study the correlations among RLS flow, headache degree and changes in brain structural volume, 102 migraineurs with RLS were included in the study group, and healthy individuals matched by age and sex comprised the control group.

2.2 Research methods

2.2.1 RLS detection methods

A transcranial Doppler (TCD) device was used for c-TCD ultrasonography examination. The patients were examined in the supine position using a 2-MHz pulsed wave TCD probe with a depth of 50–60 mm, and the unilateral (left or right) middle cerebral artery (MCA) was monitored through the temporal bone window. Ten millilitres of activated normal saline were injected into the median cubital vein of each patient, and the number of microembolic signals (MESs) within 20 s was counted. Saline (10 ml) was reactivated by pellet injection, the patients were instructed to perform the standard Valsalva manoeuvre, and the number of microemboli within 20 s was counted. MESs were divided into RLS small flows (1–10 MESs), RLS medium flows (11–25 MESs) and RLS large flows (> 25 MESs).

2.2.2 Detection of WMLs and volume changes in 157 brain regions

All the subjects were scanned with a GE Pioneer 3.0T MR scanner. All the subjects underwent conventional T1WI, T2WI and FLAIR imaging. The scanning parameters were as follows: repetition time (TR), 1,750 ms; echo time (TE), 24.0 ms; layer thickness, 6 mm; layer number, 20; layer spacing, 1 mm; matrix, 256 × 256; field of view (FOV), 220 mm × 220 mm; and scanning time, 160 s. 2. T2WI parameters: TR, 6,859 ms; TE, 152.2 ms; layer thickness, 6 mm; layer spacing, 1 mm; layer number, 20; matrix, 256 × 256; FOV, 220 mm × 220 mm; and scanning time, 70 s. 3. FLAIR parameters: TR, 12,000 ms; TE, 140 ms; layer thickness, 6 mm; layer spacing, 1 mm; layer number, 20; matrix, 256 × 256; FOV, 220 mm × 220 mm; and scanning time, 160 s.

WMLs exhibited high signal intensity on T2WI and FLAIR images and equal or low signal intensity on T1WI images. To evaluate the severity of WMLs, the intelligent analysis method was applied to high signals in white matter to automatically and synchronously correlate the patients' past examination data and accurately register and compare the lesions. The high-signal volumes of the lateral ventricular margin, periventricular white matter, deep white matter, and subcortical white matter were accurately quantified; the percentage of high-signal cerebral white matter compared with the whole-brain white matter was determined; and the Fazekas score, which effectively indicates ischaemic changes, demyelination changes and other pathological processes, was calculated according to authoritative guidelines. Finally, the high-signal report of brain white matter was generated automatically according to the intelligent diagnosis results. The high signal intensity of white matter in the margin of the lateral ventricle was located ≤ 3 mm from the surface of the ventricle; the high signal intensity of periventricular white matter was 3–13 mm away from the surface of the ventricle; the high signal intensity of deep white matter was located between those of periventricular white matter and subcortical white matter; and the high signal intensity of subcortical white matter was ≤ 4 mm from the cortical medulla junction (Fig. 1).

The whole-brain partition of each patient was based on the T1WI partition and was automatically extracted by a deep learning model trained on the United Imaging platform. Automatic segmentation of the whole brain produced 157 subregions, and the left and right parts of each brain structure were also identified. Once the automatic segmentation results of the deep learning model were obtained, they were evaluated by two senior radiologists with more than 5 years of experience in radiation diagnosis. The intelligent analysis function of brain volume change generated a follow-up curve according to the patient's previous examination results, compared it with the population distribution of big data, and automatically calculated the volume and volume proportions of 157 brain regions (Fig. 2).

2.2.3 Statistical analysis

The data used in this study were analysed via SPSS 26.0 software, GraphPad and RStudio.

Analysis of WML distribution in migraineurs: The chi-square test was used to compare counting data groups. Continuous variables with a normal distribution are expressed as the means ± standard deviations and were analysed using ANOVA. Continuous variables that did not have a normal distribution are expressed as the median (Q1 and Q3), and the Kruskal‒Wallis H test was used for analysis. Multiple hypothesis test correction was performed with Dunn's test.

WML difference analysis: Continuous variables with a normal distribution are expressed as the mean ± standard deviation. Continuous variables with a non-normal distribution are expressed as the median (Q1 and Q3). Categorical variables are expressed as numbers (percentages). The comparison of continuous variables between two groups was performed with a t test for variables that satisfied the criteria of independence, a normal distribution and homogeneity of variance; otherwise, the Mann‒Whitney U test was used. For comparisons among three or four groups, an ANOVA was used for variables that satisfied the independence, normal distribution and homogeneity of variance criteria; otherwise, the Kruskal‒Wallis H test was used. For comparisons of categorical variables, the chi-square test was used. P < 0.05 was considered statistically significant.

Analysis of the differences in brain volume between migraineurs and normal controls: Continuous variables with a normal distribution are expressed as the average ± standard deviation; the independent sample t test was used for analysis, and the difference between the two groups was calculated as the difference in the average. Continuous variables that did not have a normal distribution are expressed as the median (Q1 - Q3), and the Mann‒Whitney U test was used for analysis; the difference between the two groups was calculated as the median difference. Multiple hypothesis tests were corrected by the false discovery rate (FDR). P < 0.001 indicated statistical significance.

Analysis of the differences among RLS grade, HIT-6 score and changes in brain structural volume: Two-way ANOVA was used for analysis. Multiple hypothesis tests included two-stage Benjamini, Krieger and Yekutieli FDR correction. P < 0.05 was considered statistically significant.

The correlation between WMLs and brain volume was statistically analysed via Spearman correlation analysis. P < 0.001 was considered statistically significant.

3.1. Distribution of WMLs in migraineurs with RLS

The WMLs of migraineurs with RLS were mainly concentrated in the white matter of the lateral ventricular margin and deep white matter (Table 1). Subcortical WMLs were concentrated mainly in the parietal lobe, frontal lobe and occipital lobe (Fig. 3).

Table 1

WML distribution in migraineurs with RLS
Lesion site	Occurrence of WML	Volume (mm3)	Proportion (%)
High signal in lateral ventricular margin white matter	102(100%)	2127.1(1566.0,3332.1)	0.44(0.34,0.72)
High signal in periventricular white matter	98(96.1%)	366.0(176.8,1242.6)	0.08(0.04,0.27)
High signal in deep white matter	102(100%)	586.3(345.9,1129.1)	0.13(0.08,0.23)
High signal in subcortical white matter	98(96.1%)	129.6(79.8,323.1)	0.03(0.02,0.07)

3.2. Analysis of the differences in WML variables with different RLS grades

There was no significant difference in any WML variable (volume of high-signal cerebral white matter, percentage of high-signal cerebral white matter volume in the whole-brain white matter volume, Fazekas score) among migraine patients with different RLS grades.

3.3. Analysis of the differences in WML variables across different HIT-6 grades

There were significant differences in 6 WML variables among migraineurs with RLS with different HIT-6 grades. The 6 variables were right parietal white matter high signal volume (mm³), right temporal white matter high signal volume (mm³), right cerebellar white matter high signal volume (mm³), right parietal white matter percentage of whole-brain white matter high signal (%), right temporal white matter percentage of whole-brain white matter high signal (%), and right cerebellar white matter percentage of whole-brain white matter high signal (%) (Table 2).

The differential white matter high signals were compared in pairs; the volume and volume proportion of white matter high signals in the same region were consistent (P < 0.05) (Table 3).

Table 2

Difference analysis of WML variables across different HIT-6 grades
Lesion site	Overall (n = 102)	HIT-6 I (n = 3)	HIT-6 II (n = 13)	HIT-6 III (n = 23)	HIT-6 IV (n = 63)	P
Right parietal white matter high signal volume	267.8 (92.3,852.3)	62.0 (35.4,81.2)	376.5 (138.5,862.8)	193.8 (68.9,351.2)	289.8 (129.2,1282.7)	0.039
Right temporal white matter high signal volume	32.8 (0.0,97.8)	22.4 (0.0,35.1)	66.7 (0.0,239.0)	7.4 (0.0,36.9)	55.4 (1.8,132.9)	0.023
Right cerebellar white matter high signal volume	0.0 (0.0,0.0)	0.0 (0.0,2.6)	0.0 (0.0,0.0)	0.0 (0.0,0.0)	0.0 (0.0,0.0)	0.017
Right parietal white matter percentage of whole brain white matter	5.4 (2.1,18.8)	1.0 (0.8,1.6)	7.2 (2.9,19.9)	4.3 (1.6,8.5)	5.9 (2.3,27.4)	0.037
Right temporal white matter percentage of whole brain white matter	0.7 (0.0,2.1)	0.4 (0.0,0.7)	1.4 (0.0,4.9)	0.2 (0.0, 0.8)	1.1 (0.0,2.7)	0.027
Right cerebellar white matter percentage of whole brain white matter	0.0 (0.0,0.0)	0.0 (0.0,0.0)	0.0 (0.0,0.0)	0.0 (0.0,0.0)	0.0 (0.0,0.0)	0.017

Table 3

Comparison of differences in WML variables across different HIT-6 grades
Differential WML position	P	Ⅰ vs. Ⅱ	Ⅰ vs. Ⅲ	Ⅰ vs. Ⅳ	Ⅱ vs. Ⅲ	Ⅱ vs. Ⅳ	Ⅲ vs. Ⅳ
Right parietal white matter high signal volume	0.039	0.014	0.088	0.017	0.125	0.586	0.132
Right temporal white matter high signal volume	0.023	0.245	0.940	0.279	0.023	0.731	0.005
Right cerebellar white matter high signal volume	0.017	0.003	0.007	0.002	0.461	0.754	0.509
Right parietal white matter percentage of whole brain white matter	0.037	0.012	0.071	0.013	0.145	0.615	0.149
Right temporal white matter percentage of whole brain white matter	0.027	0.232	0.974	0.276	0.024	0.688	0.006
Right cerebellar white matter percentage of whole brain white matter	0.017	0.004	0.007	0.004	0.321	0.396	0.321

3.4. Correlation analysis between RLS grades and WML variables

No correlation was found between the RLS grade and the WML variables measured in this study.

3.5. Correlation analysis between HIT-6 grades and WML variables

No correlation was found between HIT-6 grades and the WML variables measured in this study.

3.6. Analysis of the difference in brain structural volume between migraineurs and normal controls

The differences in brain structural volume between migraineurs and normal controls were analysed. The differences in the superior frontal gyrus, middle frontal gyrus, frontal pole, medial orbitofrontal lobe, lateral orbitofrontal lobe, orbital part, inferior parietal lobule, supramarginal gyrus, anterior cuneiform lobe, superior temporal gyrus, inferior temporal gyrus, temporal pole, lateral occipital gyrus, fusiform gyrus, rectangular gyrus, lingual gyrus, anterior cingulate gyrus, entorhinal cortex, parahippocampal gyrus, precentral gyrus, postcentral gyrus, paracentral lobule, optic chiasm, putamen, globus pallidus, caudate nucleus, nucleus accumbens, ventral diencephalon, pons, cerebellar grey matter, choroid plexus, corpus callosum, cerebral white matter, cerebellar white matter, lateral ventricle, third ventricle, fourth ventricle and peripheral cerebrospinal fluid were statistically significant (P < 0.001).

The brain volume of migraineurs was significantly greater than that of normal subjects in the frontal lobe (frontal pole, lateral orbitofrontal lobe, orbital part), parietal lobe (inferior parietal lobule), temporal lobe (superior temporal gyrus, inferior temporal gyrus, anterior cuneiform lobe), occipital lobe (supramarginal gyrus, lingual gyrus, lateral occipital gyrus), cingulate gyrus (isthmus of the cingulate gyrus), subcortical grey matter structures (ventral diencephalon, caudate nucleus), pons, corpus callosum (anterior, middle, posterior), peripheral cerebrospinal fluid, optic chiasm, and ventricles (lateral ventricle, third ventricle). The brain volume of migraineurs was significantly smaller than that of normal subjects in the frontal lobe (precentral gyrus, superior frontal gyrus, middle frontal gyrus, and medial orbitofrontal gyrus), paracentral lobule, parietal lobe (postcentral gyrus), temporal lobe (parahippocampal gyrus, temporal pole, slope part of superior temporal gyrus, entorhinal cortex, fusiform gyrus), occipital lobe (rectangular gyrus), cingulate gyrus (anterior cingulate gyrus and posterior cingulate gyrus), subcortical grey matter structures (putamen, globus pallidus, and nucleus accumbens), choroid plexus, cerebral white matter, cerebellar grey matter, cerebellar white matter, and fourth ventricle (Tables 4 and 5).

Table 4

Regions of increased volume of brain structure
Brain structure	Research group	Control group	Discrepancy	p
Total frontal pole	3.21 ± 0.60	2.34 ± 0.27	0.87	< 0.001
Left frontal pole	1.70 ± 0.34	1.06 ± 0.14	0.64	< 0.001
Right frontal pole	1.51 ± 0.33	1.28 ± 0.17	0.23	< 0.001
Total orbital part	5.75 ± 0.95	4.79 ± 0.58	0.95	< 0.001
Left orbital part	2.77 ± 0.51	2.23 ± 0.28	0.54	< 0.001
Right orbital part	2.98 ± 0.54	2.57 ± 0.37	0.41	< 0.001
Total caudate nucleus	6.84 ± 1.10	6.53 ± 0.76	0.31	< 0.001
Total ventral diencephalon	8.26 ± 1.36	5.03 ± 0.44	3.23	< 0.001
Left ventral diencephalon	4.18 ± 0.76	2.52 ± 0.22	1.66	< 0.001
Right ventral diencephalon	4.08 ± 0.67	2.51 ± 0.22	1.57	< 0.001
Total lingual gyrus	14.18 ± 2.04	13.17 ± 1.57	1.01	< 0.001
Right superior temporal gyrus	13.17 ± 1.80	11.42 ± 1.22	1.75	< 0.001
Total inferior temporal gyrus	23.12 ± 3.02	21.40 ± 2.77	1.72	< 0.001
Left inferior temporal gyrus	11.77 ± 1.86	10.91 ± 1.54	0.86	< 0.001
Right inferior temporal gyrus	11.35 ± 1.49	10.49 ± 1.54	0.86	< 0.001
Left lateral occipital gyrus	12.30 ± 1.46	11.32 ± 1.50	0.98	< 0.001
Peripheral cerebrospinal fluid	370.70 ± 44.25	289.00 ± 50.14	81.70	< 0.001
Optic chiasm	0.44 ± 0.13	0.19 ± 0.04	0.25	< 0.001
Anterior part of corpus callosum	1.20 ± 0.24	0.89 ± 0.13	0.30	< 0.001
Anterior midbody of corpus callosum	1.07 ± 0.20	0.67 ± 0.19	0.40	< 0.001
Middle part of corpus callosum	1.11 ± 0.20	0.65 ± 0.16	0.46	< 0.001
Posterior midbody of corpus callosum	0.76 ± 0.17	0.55 ± 0.09	0.21	< 0.001
Lateral part of left orbitofrontal lobe	7.77 (7.18, 8.57)	6.88 (6.48, 7.45)	0.89	< 0.001
Isthmus of total cingulate gyrus	4.88 (4.35, 5.46)	4.59 (4.20, 5.11)	0.29	< 0.001
Isthmus of right cingulate gyrus isthmus	2.46 (2.21, 2.81)	2.29 (2.06, 2.58)	0.17	< 0.001
Right caudate nucleus	3.40 (3.06, 3.84)	3.17 (2.99, 3.46)	0.23	< 0.001
Total lateral ventricle	18.63 (14.73, 26.65)	14.31 (10.90, 20.57)	4.32	< 0.001
Left lateral ventricle	9.91 (7.67, 14.46)	7.62 (5.89, 11.27)	2.28	< 0.001
Right lateral ventricle	8.72 (7.19, 12.64)	6.76 (4.75, 9.44)	1.96	< 0.001
Inferior horn of total lateral ventricle	0.98 (0.68, 1.27)	0.45 (0.30, 0.67)	0.53	< 0.001
Inferior horn of left lateral ventricle	0.46 (0.29, 0.62)	0.24 (0.12, 0.37)	0.23	< 0.001
Inferior horn of right lateral ventricle	0.50 (0.32, 0.64)	0.22 (0.14, 0.34)	0.28	< 0.001
Total inferior parietal lobule	28.57 (26.23, 30.80)	26.42 (24.50, 28.79)	2.15	< 0.001
Left lingual gyrus	7.10 (6.42, 7.70)	6.48 (5.89, 7.18)	0.63	< 0.001
Right anterior cuneiform lobe	10.54 (9.59, 11.38)	9.67 (9.17, 10.75)	0.87	< 0.001
Total supramarginal gyrus	22.89 (21.00, 25.71)	21.17 (19.34, 22.91)	1.72	< 0.001
Right supramarginal gyrus	11.72 (10.64, 12.58)	10.01 (9.10, 10.88)	1.71	< 0.001
Total superior temporal gyrus	25.74 (22.92, 27.41)	23.00 (21.67, 24.85)	2.74	< 0.001
Left superior temporal gyrus	12.51 (11.28, 13.48)	11.60 (10.83, 12.51)	0.91	< 0.001
Third ventricle	1.51 (1.24, 2.02)	1.04 (0.81, 1.31)	0.47	< 0.001
Pons	19.24 (18.22, 21.09)	14.56 (13.58, 15.65)	4.68	< 0.001
Posterior part of corpus callosum	1.27 (1.16, 1.49)	0.97 (0.89, 1.08)	0.29	< 0.001

Table 5

Regions with decreased brain structure volume
Brain structure	Research group		Control group	Discrepancy	p
Total precentral gyrus	23.42 ± 2.45		27.71 ± 2.48	-4.29	< 0.001
Left precentral gyrus	11.99 ± 1.45		13.94 ± 1.28	-1.95	< 0.001
Right precentral gyrus	11.42 ± 1.27		13.77 ± 1.38	-2.35	< 0.001
Total postcentral gyrus	18.42 ± 2.31		20.17 ± 2.16	-1.75	< 0.001
Left postcentral gyrus	9.38 ± 1.29		10.26 ± 1.09	-0.88	< 0.001
Right postcentral gyrus	9.05 ± 1.37		9.91 ± 1.29	-0.86	< 0.001
Total paracentral lobule	6.62 ± 0.98		7.79 ± 0.79	-1.17	< 0.001
Left paracentral lobule	3.00 ± 0.54		3.73 ± 0.40	-0.74	< 0.001
Right paracentral lobule	3.62 ± 0.56		4.06 ± 0.49	-0.44	< 0.001
Left superior frontal gyrus	20.99 ± 2.42		22.16 ± 2.53	-1.17	< 0.001
Caudal part of the total middle frontal gyrus	9.70 ± 1.42		11.68 ± 1.60	-1.99	< 0.001
Caudal part of right middle frontal gyrus	4.74 ± 0.78		5.68 ± 0.95	-0.95	< 0.001
Total anterior cingulate gyrus	3.63 ± 0.93		4.34 ± 0.68	-0.71	< 0.001
Left anterior cingulate gyrus	1.98 ± 0.55		2.40 ± 0.42	-0.41	< 0.001
Right anterior cingulate gyrus	1.65 ± 0.48		1.94 ± 0.40	-0.30	< 0.001
Total posterior cingulate gyrus	5.78 ± 0.91		6.48 ± 0.94	-0.70	< 0.001
Right posterior cingulate gyrus	2.93 ± 0.54		3.33 ± 0.58	-0.40	< 0.001
Left parahippocampal gyrus	1.60 ± 0.36		2.06 ± 0.24	-0.46	< 0.001
Total putamen	8.84 ± 1.21		9.45 ± 1.19	-0.61	< 0.001
Right putamen	4.35 ± 0.66		4.66 ± 0.60	-0.31	< 0.001
Total globus pallidus	3.33 ± 0.73		3.77 ± 0.45	-0.44	< 0.001
Right globus pallidus	1.48 ± 0.42	1.95 ± 0.24		-0.48	< 0.001
Left nucleus accumbens	0.41 ± 0.11	0.53 ± 0.14		-0.12	< 0.001
Right nucleus accumbens	0.47 ± 0.13	0.54 ± 0.13		-0.07	< 0.001
Total choroid plexus	0.98 ± 0.25	1.15 ± 0.46		-0.18	< 0.001
Left choroid plexus	0.50 ± 0.16	0.59 ± 0.23		-0.09	< 0.001
Total rectangular gyrus	3.80 ± 1.24	5.21 ± 0.86		-1.40	< 0.001
Left rectangular gyrus	1.99 ± 0.69	2.42 ± 0.45		-0.43	< 0.001
Right rectangular gyrus	1.82 ± 0.64	2.79 ± 0.47		-0.97	< 0.001
Total temporal pole	4.10 ± 0.73	5.20 ± 0.46		-1.09	< 0.001
Left temporal pole	1.93 ± 0.48	2.61 ± 0.26		-0.68	< 0.001
Right temporal pole	2.18 ± 0.40	2.59 ± 0.26		-0.41	< 0.001
Slope part of total superior temporal gyrus	3.81 ± 0.91	4.36 ± 0.65		-0.55	< 0.001
Total cerebral white matter	368.40 ± 42.38	433.80 ± 43.97		-65.40	< 0.001
Left cerebral white matter	184.10 ± 21.20	216.80 ± 22.04		-32.70	< 0.001
Right cerebral white matter	184.40 ± 21.44	217.00 ± 22.03		-32.60	< 0.001
Total superior frontal gyrus	39.22 (36.90, 43.03)	42.55 (39.07, 45.80)		-3.33	< 0.001
Right superior frontal gyrus	18.75 (17.31, 20.57)	20.50 (18.86, 21.89)		-1.75	< 0.001
Caudal part of left middle frontal gyrus	4.97 (4.46, 5.48)	5.90 (5.44, 6.59)		-0.93	< 0.001
Left medial orbitofrontal lobe	4.56 (4.03, 4.98)	4.97 (4.67, 5.44)		-0.41	< 0.001
Left posterior cingulate gyrus	2.95 (2.52, 3.15)	3.14 (2.84, 3.39)		-0.18	< 0.001
Total parahippocampal gyrus	2.94 (2.55, 3.38)	4.09 (3.72, 4.31)		-1.15	< 0.001
Right parahippocampal gyrus	1.30 (1.15, 1.55)	1.99 (1.81, 2.18)		-0.69	< 0.001
Left putamen	4.49 (4.18, 4.77)	4.69 (4.44, 5.17)		-0.20	< 0.001
Total nucleus accumbens	0.88 (0.74, 0.99)	1.07 (0.93, 1.17)		-0.20	< 0.001
Total entorhinal cortex	3.27 (2.82, 3.66)	3.66 (3.41, 4.06)		-0.39	< 0.001
Left entorhinal cortex	1.60 (1.30, 1.95)	1.97 (1.76, 2.17)		-0.37	< 0.001
Total fusiform gyrus	15.64 (14.42, 17.40)		17.84 (16.92, 19.48)	-2.20	< 0.001
Left fusiform gyrus	8.01 (7.17, 8.95)		9.19 (8.40, 9.94)	-1.18	< 0.001
Right fusiform gyrus	7.65 (6.93, 8.38)		8.77 (8.29, 9.72)	-1.12	< 0.001
Slope part of left superior temporal gyrus	1.75 (1.33, 2.1432)		2.24 (2.01, 2.46)	-0.49	< 0.001
Total cerebellar grey matter	93.13 (87.83, 98.17)		99.22 (92.71, 104.10)	-6.09	< 0.001
Left cerebellar grey matter	46.03 (43.22, 49.03)		49.56 (46.22, 52.27)	-3.53	< 0.001
Right cerebellar grey matter	46.90 (43.77, 49.68)		49.26 (46.35, 51.98)	-2.36	< 0.001
Total cerebellar white matter	19.42 (17.48, 21.03)		25.15 (23.66, 26.57)	-5.73	< 0.001
Left cerebellar white matter	9.74 (8.75, 10.62)		13.16 (12.42, 13.90)	-3.43	< 0.001
Right cerebellar white matter	9.87 (8.80, 10.31)		12.04 (11.19, 12.93)	-2.17	< 0.001
Fourth ventricle	0.84 (0.72, 0.96)		1.65 (1.36, 1.97)	-0.81	< 0.001

3.7. Analysis of the differences in brain structural volume changes across different RLS grades

There were statistically significant differences in total lateral ventricle volume, right cerebral white matter volume, left cerebral white matter volume, total cerebellar grey matter volume and peripheral cerebrospinal fluid volume between different RLS grades (Table 6). In the pairwise comparisons of these differential brain structures, the statistical results were consistent (P < 0.05) (Table 7).

Table 6

Analysis of the differences in brain structural volume changes across different RLS grades
Brain structure	Control group (n = 102)	RLS I (n = 24)	RLS II (n = 30)	RLS III (n = 48)	P
Total lateral ventricle	14.3(10.9,20.6)	17.2(14.7,27.1)	19.0(13.9,25.5)	19.8(15.8,28.5)	< 0.001
Peripheral cerebrospinal fluid	278.3(251.6,320.1)	387.2(356.2,407.4)	357.7(339.8,387.7)	367.7(346.1,397.7)	< 0.001
Total cerebellar grey matter	99.2(92.7,104.1)	93.3(88.8,105.1)	94.4(88.7,97.6)	91.7(87.4,97.9)	< 0.001
Left cerebral white matter	216.8 ± 22.04	181.1 ± 22.39	186.6 ± 24.07	184.0 ± 18.80	< 0.001
Right cerebral white matter	217.0 ± 22.03	181.2 ± 24.10	185.9 ± 23.40	185.0 ± 18.93	< 0.001
Total cerebral white matter	433.8 ± 44.0	362.3 ± 46.2	372.5 ± 47.3	369.0 ± 37.5	< 0.001

Table 7

Comparison of the changes in brain structural volume among different RLS grades
Differential brain regions	p	0 vs. I	0 vs. II	0 vs. III	I vs. II	I vs. III	II vs. III
Total lateral ventricle	< 0.001	0.2483	0.2634	0.0321	0.6723	0.6723	0.5721
Peripheral cerebrospinal fluid	< 0.001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	0.0041
Total cerebellar grey matter	< 0.001	0.521	0.1415	0.0026	0.3954	0.1312	0.3764
Left cerebral white matter	< 0.001	< 0.0001	< 0.0001	< 0.0001	0.1043	0.2084	0.2084
Right cerebral white matter	< 0.001	< 0.0001	< 0.0001	< 0.0001	0.1641	0.1653	0.4107
Total cerebral white matter	< 0.001	< 0.0001	< 0.0001	< 0.0001	0.003	0.02	0.0915

3.8. Analysis of the differences in brain structural volume changes across different HIT-6 grades

There were statistically significant differences in total lateral ventricle volume, total cerebral white matter volume, total cerebellar white matter volume, left cerebral white matter volume, right cerebral white matter volume and peripheral cerebrospinal fluid volume among different HIT-6 grades (Table 8). In the pairwise comparisons of these differential brain structures, the statistical results were consistent (P < 0.05) (Table 9).

Table 8

Analysis of the differences in brain structural volume changes across different HIT-6 grades
Brain structure	HIT-6 0 (n = 102)	HIT-6 I (n = 3)	HIT-6 II (n = 13)	HIT-6 III (n = 23)	HIT-6 IV (n = 63)	p
Total lateral ventricle	14.3 (10.9,20.6)	20.2 (5.5,26.2)	16.9 (14.7,26.1)	17.5 (11.3,30.2)	19.6 (15.4,29.1)	< 0.001
Total cerebral white matter	433.8 ± 44.0	399.7 ± 45.0	371.6 ± 42.8	359.6 ± 40.1	369.5 ± 43.1	< 0.001
Total cerebellar white matter	25.2 (23.7,26.6)	21.0 (17.9,21.0)	19.8 (17.8,21.7)	20.0 (17.5,20.7)	19.0 (17.5,21.0)	< 0.001
Left cerebral white matter	216.8 ± 22.04	198.0 ± 21.12	185.1 ± 21.43	179.5 ± 20.61	184.8 ± 21.44	< 0.001
Right cerebral white matter	217.0 ± 22.03	201.7 ± 23.89	186.5 ± 21.50	180.1 ± 19.58	184.7 ± 21.96	< 0.001
Peripheral cerebrospinal fluid	278.3 (251.6,320.1)	386.7 (279.2,396.5)	375.1 (347.0,410.2)	364.2 (342.3,386.8)	369.5 (344.5,398.3)	< 0.001

Table 9

Comparison of the changes in brain structural volume among different HIT-6 grades
Differential brain structure	p	0 vs. Ⅰ	0 vs. Ⅱ	0 vs. Ⅲ	0 vs. Ⅳ	Ⅰ vs. Ⅱ	Ⅰ vs. Ⅲ	Ⅰ vs. Ⅳ	Ⅱ vs. Ⅲ	Ⅱ vs. Ⅳ	Ⅲ vs. Ⅳ
Total lateral ventricle	< 0.001	0.890	0.696	0.696	0.014	0.890	0.696	0.696	0.890	0.696	0.696
Total cerebral white matter	< 0.001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0002	< 0.0001	< 0.0001	< 0.0002	< 0.0003	< 0.0004
Total cerebellar white matter	< 0.001	0.943	0.534	0.304	0.037	0.942	0.942	0.942	0.942	0.942	0.942
Left cerebral white matter	< 0.001	0.943	0.534	0.304	0.037	0.942	0.942	0.942	0.942	0.942	0.942
Right cerebral white matter	< 0.001	0.055	< 0.0001	< 0.0001	< 0.0001	0.072	0.015	0.042	0.118	0.411	0.118
Peripheral cerebrospinal fluid	< 0.001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	0.004	0.046	0.014	0.008	0.041	0.045

3.9. Analysis of the correlation between RLS flow and brain structural volume changes

The brain regions with significantly strong correlations with RLS flow were the frontal pole, orbital part, lateral orbitofrontal lobe, superior frontal gyrus, caudal part of the middle frontal gyrus, medial orbitofrontal lobe, precentral gyrus, postcentral gyrus, anterior cuneiform lobe, superior temporal gyrus, inferior temporal gyrus, slope part of the superior temporal gyrus, temporal pole, fusiform gyrus, entorhinal cortex, parahippocampal gyrus, lateral occipital gyrus, supramarginal gyrus, lingual gyrus, rectangular gyrus, cingulate gyrus, ventral diencephalon, globus pallidus, nucleus accumbens, optic chiasm, cerebellar grey matter, cerebellar white matter, cerebral white matter, and fourth ventricle (P < 0.001) (Table 10). The volumes of the frontal lobe (frontal pole, orbital part, and lateral orbitofrontal lobe), temporal lobe (anterior cuneiform lobe, superior temporal gyrus, and inferior temporal gyrus), occipital lobe (lateral occipital gyrus, supramarginal gyrus, and lingual gyrus), optic chiasm, subcortical grey matter structures (ventral diencephalon), pons, corpus callosum, lateral ventricle, third ventricle and peripheral cerebrospinal fluid were positively correlated with RLS flow. The volumes of the frontal lobe (superior frontal gyrus, caudal part of the middle frontal gyrus, medial orbitofrontal lobe, precentral gyrus), parietal lobe (postcentral gyrus), temporal lobe (slope part of the superior temporal gyrus, temporal pole, fusiform gyrus, entorhinal cortex, parahippocampal gyrus), paracentral lobule, occipital lobe (rectangular gyrus), cingulate gyrus, subcortical grey matter structures (globus pallidus, nucleus accumbens), cerebellar grey matter, cerebellar white matter, cerebral white matter, and fourth ventricle were negatively correlated with RLS flow.

Table 10

Analysis of the correlation between RLS flow and brain structural volume changes
Brain structure	Relativity	95% confidence interval
Total ventral diencephalon	0.758	[0.693, 0.811]
Right ventral diencephalon	0.750	[0.683, 0.804]
Left ventral diencephalon	0.744	[0.676, 0.800]
Left frontal pole	0.719	[0.645, 0.779]
Middle part of corpus callosum	0.698	[0.620, 0.762]
Optic chiasm	0.696	[0.618, 0.761]
Pons	0.673	[0.590, 0.742]
Anterior midbody of corpus callosum	0.632	[0.541, 0.708]
Total frontal pole	0.622	[0.530, 0.700]
Posterior midbody of corpus callosum	0.569	[0.468, 0.655]
Anterior part of corpus callosum	0.566	[0.465, 0.653]
Peripheral cerebrospinal fluid	0.562	[0.461, 0.650]
Left orbital part	0.519	[0.411, 0.613]
Posterior part of corpus callosum	0.509	[0.400, 0.604]
Total orbital part	0.472	[0.357, 0.572]
Inferior horn of total lateral ventricle	0.456	[0.340, 0.559]
Inferior horn of right lateral ventricle	0.453	[0.337, 0.556]
Right superior temporal gyrus	0.445	[0.328, 0.549]
Third ventricle	0.430	[0.311, 0.535]
Inferior horn of left lateral ventricle	0.407	[0.285, 0.515]
Right supramarginal gyrus	0.395	[0.272, 0.505]
Total superior temporal gyrus	0.390	[0.266, 0.500]
Lateral part of left orbitofrontal lobe	0.375	[0.251, 0.488]
Right orbital part	0.353	[0.227, 0.468]
Right frontal pole	0.351	[0.225, 0.466]
Left lingual gyrus	0.293	[0.162, 0.414]
Left lateral occipital gyrus	0.278	[0.146, 0.400]
Left superior temporal gyrus	0.264	[0.131, 0.387]
Total lingual gyrus	0.262	[0.130, 0.386]
Total inferior temporal gyrus	0.261	[0.128, 0.385]
Right lateral ventricle	0.256	[0.122, 0.380]
Right inferior temporal gyrus	0.250	[0.117, 0.375]
Total lateral ventricle	0.245	[0.112, 0.370]
Right anterior cuneiform lobe	0.245	[0.111, 0.369]
Fourth ventricle	-0.700	[-0.764, -0.622]
Left superior frontal gyrus	-0.244	[-0.369, -0.110]
Right postcentral gyrus	-0.272	[-0.394, -0.140]
Total globus pallidus	-0.267	[-0.390, -0.134]
Left nucleus accumbens	-0.374	[-0.486, -0.250]
Total anterior cingulate gyrus	-0.361	[-0.475, -0.235]
Right paracentral lobule	-0.355	[-0.470, -0.229]
Left fusiform gyrus	-0.394	[-0.504, -0.272]
Right cerebellar grey matter	-0.258	[-0.382, -0.125]
Right superior frontal gyrus	-0.292	[-0.412, -0.161]
Total superior frontal gyrus	-0.283	[-0.405, -0.151]
Total cerebellar grey matter	-0.281	[-0.403, -0.149]
Medial part of left orbitofrontal lobe	-0.279	[-0.401, -0.148]
Caudal part of right middle frontal gyrus	-0.421	[-0.528, -0.301]
Right fusiform gyrus	-0.416	[-0.524, -0.296]
Left entorhinal cortex	-0.410	[-0.518, -0.289]
Left cerebellar white matter	-0.669	[-0.739, -0.586]
Total parahippocampal gyrus	-0.662	[-0.733, -0.577]
Right parahippocampal gyrus	-0.651	[-0.723, -0.564]
Total cerebellar white matter	-0.642	[-0.716, -0.554]
Total temporal pole	-0.605	[-0.685, -0.510]
Right rectangular gyrus	-0.595	[-0.677, -0.499]
Left temporal pole	-0.592	[-0.675, -0.495]
Right cerebellar white matter	-0.590	[-0.673, -0.492]
Right precentral gyrus	-0.585	[-0.668, -0.486]
Left parahippocampal gyrus	-0.573	[-0.659, -0.473]
Total precentral gyrus	-0.563	[-0.650, -0.461]
Left anterior cingulate gyrus	-0.344	[-0.459, -0.217]
Left rectangular gyrus	-0.334	[-0.450, -0.206]
Total postcentral gyrus	-0.331	[-0.448, -0.203]
Left postcentral gyrus	-0.325	[-0.443, -0.197]
Total entorhinal cortex	-0.323	[-0.441, -0.194]
Right posterior cingulate gyrus	-0.321	[-0.439, -0.192]
Slope part of total superior temporal gyrus	-0.315	[-0.433, -0.185]
Total nucleus accumbens	-0.309	[-0.429, -0.180]
Total posterior cingulate gyrus	-0.308	[-0.427, -0.178]
Left cerebellar grey matter	-0.297	[-0.417, -0.166]
Right anterior cingulate gyrus	-0.294	[-0.415, -0.164]
Left paracentral lobule	-0.555	[-0.643, -0.452]
Left cerebral white matter	-0.530	[-0.622, -0.423]
Total cerebral white matter	-0.527	[-0.619, -0.420]
Right cerebral white matter	-0.521	[-0.615, -0.413]
Total rectangular gyrus	-0.509	[-0.604, -0.399]
Total paracentral lobule	-0.505	[-0.601, -0.395]
Caudal part of total middle frontal gyrus	-0.500	[-0.596, -0.389]
Right globus pallidus	-0.492	[-0.590, -0.381]
Right temporal pole	-0.485	[-0.584, -0.372]
Left precentral gyrus	-0.480	[-0.579, -0.367]
Caudal part of left middle frontal gyrus	-0.470	[-0.570, -0.356]
Total fusiform gyrus	-0.435	[-0.540, -0.317]
Slope part of left superior temporal gyrus	-0.432	[-0.538, -0.313]

3.10. Analysis of the correlation between HIT-6 grades and brain structural volume changes

The brain regions with significant strong correlations with HIT-6 grades were the frontal pole, orbital part, lateral orbitofrontal lobe, precentral gyrus, superior frontal gyrus, caudal part of the middle frontal gyrus, medial orbitofrontal gyrus, paracentral lobule, superior temporal gyrus, inferior temporal gyrus, parahippocampal gyrus, entorhinal cortex, temporal pole, slope part of the superior temporal gyrus, fusiform gyrus, supramarginal gyrus, lingual gyrus, rectangular gyrus, cingulate gyrus, ventral diencephalon, globus pallidus, nucleus accumbens, putamen, optic chiasm, corpus callosum, lateral ventricle, third ventricle, fourth ventricle, peripheral cerebrospinal fluid, cerebellar grey matter, cerebellar white matter, and cerebral white matter (P < 0.001) (Table 11). The volumes of the frontal lobe (frontal pole, orbital part, lateral orbitofrontal lobe), temporal lobe (superior temporal gyrus, inferior temporal gyrus), occipital lobe (supramarginal gyrus, lingual gyrus), subcortical grey matter (ventral diencephalon), optic chiasm, third ventricle, pons, corpus callosum, lateral ventricle and peripheral cerebrospinal fluid were positively correlated with the HIT-6 grade. The volumes of the frontal lobe (precentral gyrus, superior frontal gyrus, caudal part of the middle frontal gyrus, medial orbitofrontal gyrus), parietal lobe (postcentral gyrus), paracentral lobule, temporal lobe (parahippocampal gyrus, entorhinal cortex, temporal pole, slope part of the superior temporal gyrus, fusiform gyrus), occipital lobe (rectangular gyrus), cingulate gyrus, subcortical grey matter structures (globus pallidus, nucleus accumbens, putamen), cerebellar grey matter, cerebellar white matter, cerebral white matter and fourth ventricle were negatively correlated with HIT-6 grades.

Table 11

Analysis of the correlation between HIT-6 grades and brain structural volume changes
Brain structure	Relativity	95% confidence interval
Total ventral diencephalon	0.803	[0.748, 0.847]
Right ventral diencephalon	0.802	[0.747, 0.846]
Left ventral diencephalon	0.781	[0.721, 0.830]
Left frontal pole	0.769	[0.706, 0.820]
Optic chiasm	0.761	[0.697, 0.814]
Middle part of corpus callosum	0.742	[0.673, 0.798]
Pons	0.690	[0.610, 0.755]
Total frontal pole	0.684	[0.604, 0.751]
Anterior midbody of corpus callosum	0.682	[0.601, 0.750]
Peripheral cerebrospinal fluid	0.623	[0.531, 0.700]
Anterior part of corpus callosum	0.559	[0.456, 0.646]
Posterior midbody of corpus callosum	0.551	[0.448, 0.640]
Posterior part of corpus callosum	0.533	[0.427, 0.625]
Left orbital part	0.505	[0.395, 0.601]
Right superior temporal gyrus	0.472	[0.358, 0.572]
Total orbital part	0.463	[0.347, 0.564]
Inferior horn of total lateral ventricle	0.462	[0.347, 0.564]
Third ventricle	0.462	[0.346, 0.563]
Inferior horn of right lateral ventricle	0.457	[0.341, 0.559]
Right frontal pole	0.418	[0.298, 0.525]
Inferior horn of left lateral ventricle	0.414	[0.293, 0.522]
Right supramarginal gyrus	0.391	[0.268, 0.501]
Total superior temporal gyrus	0.376	[0.252, 0.488]
Lateral part of left orbitofrontal lobe	0.372	[0.248, 0.485]
Right orbital part	0.351	[0.224, 0.466]
Left lingual gyrus	0.313	[0.183, 0.431]
Left lateral occipital gyrus	0.301	[0.171, 0.421]
Right lateral ventricle	0.288	[0.157, 0.409]
Total lingual gyrus	0.284	[0.152, 0.405]
Total lateral ventricle	0.276	[0.144, 0.398]
Left lateral ventricle	0.259	[0.126, 0.383]
Total inferior temporal gyrus	0.247	[0.114, 0.372]
Right inferior temporal gyrus	0.245	[0.111, 0.370]
Left cerebellar white matter	-0.746	[-0.802, -0.679]
Fourth ventricle	-0.740	[-0.797, -0.671]
Total cerebellar white matter	-0.710	[-0.772, -0.635]
Total parahippocampal gyrus	-0.657	[-0.729, -0.572]
Right parahippocampal gyrus	-0.657	[-0.729, -0.571]
Right cerebellar white matter	-0.645	[-0.718, -0.557]
Right rectangular gyrus	-0.636	[-0.712, -0.547]
Right precentral gyrus	-0.632	[-0.708, -0.542]
Total temporal pole	-0.629	[-0.705, -0.538]
Total precentral gyrus	-0.622	[-0.699, -0.529]
Left temporal pole	-0.612	[-0.691, -0.518]
Right cerebral white matter	-0.582	[-0.666, -0.483]
Total cerebral white matter	-0.581	[-0.666, -0.483]
Left cerebral white matter	-0.578	[-0.663, -0.479]
Left paracentral lobule	-0.560	[-0.648, -0.458]
Left parahippocampal gyrus	-0.557	[-0.645, -0.455]
Left precentral gyrus	-0.544	[-0.634, -0.440]
Total rectangular gyrus	-0.542	[-0.632, -0.437]
Right globus pallidus	-0.537	[-0.628, -0.431]
Caudal part of total middle frontal gyrus	-0.516	[-0.610, -0.408]
Right temporal pole	-0.508	[-0.603, -0.398]
Total paracentral lobule	-0.502	[-0.598, -0.392]
Caudal part of left middle frontal gyrus	-0.465	[-0.566, -0.350]
Caudal part of right middle frontal gyrus	-0.456	[-0.559, -0.340]
Left nucleus accumbens	-0.442	[-0.546, -0.324]
Slope part of left superior temporal gyrus	-0.425	[-0.531, -0.306]
Right fusiform gyrus	-0.422	[-0.529, -0.302]
Total fusiform gyrus	-0.421	[-0.528, -0.301]
Right posterior cingulate gyrus	-0.381	[-0.493, -0.257]
Total posterior cingulate gyrus	-0.379	[-0.491, -0.255]
Left entorhinal cortex	-0.375	[-0.487, -0.250]
Total anterior cingulate gyrus	-0.374	[-0.487, -0.250]
Total nucleus accumbens	-0.372	[-0.484, -0.247]
Left anterior cingulate gyrus	-0.371	[-0.484, -0.247]
Left fusiform gyrus	-0.364	[-0.477, -0.238]
Left rectangular gyrus	-0.354	[-0.468, -0.227]
Total postcentral gyrus	-0.347	[-0.462, -0.220]
Medial part of left orbitofrontal lobe	-0.346	[-0.461, -0.219]
Right paracentral lobule	-0.344	[-0.459, -0.217]
Total globus pallidus	-0.336	[-0.452, -0.208]
Slope part of total superior temporal gyrus	-0.327	[-0.444, -0.198]
Right postcentral gyrus	-0.319	[-0.437, -0.190]
Left postcentral gyrus	-0.303	[-0.423, -0.173]
Left posterior cingulate gyrus	-0.297	[-0.417, -0.166]
Right anterior cingulate gyrus	-0.289	[-0.410, -0.158]
Total entorhinal cortex	-0.284	[-0.406, -0.153]
Right superior frontal gyrus	-0.262	[-0.385, -0.129]
Total putamen	-0.262	[-0.385, -0.129]
Right putamen	-0.259	[-0.383, -0.127]
Total superior frontal gyrus	-0.252	[-0.376, -0.119]
Left putamen	-0.245	[-0.370, -0.111]
Left cerebellar grey matter	-0.245	[-0.37, -0.111]
Right nucleus accumbens	-0.242	[-0.368, -0.109]

In this study, the WMLs of migraineurs with RLS were found to be concentrated mainly in the lateral ventricular marginal white matter and deep white matter. A retrospective study of 425 headache patients (303 women; 242 migraineurs, 183 tension-type headache patients) revealed an increased prevalence of deep WMLs in migraineurs with RLS ^14]. Mark C. Kruit et al.^[20] reported that the incidence of deep WMLs in female migraineurs was greater than that in control individuals and that deep WMLs increased with increasing migraine attack frequency but were not related to migraine subtype; in addition, there was no correlation between periventricular WML severity and sex, migraine frequency or migraine subtype in migraineurs. At present, there are few studies on the distribution of WMLs in lateral ventricular marginal white matter and deep white matter in migraineurs with RLS, and more studies are needed to determine the pathogenesis.

In addition, we found that subcortical WMLs were concentrated in the parietal frontal lobe and occipital lobe in migraineurs with RLS. A study by Signorielloe et al.^[21] revealed that PFO may be associated with WMLs in migraineurs and that WMLs are more likely to occur in the occipital lobe; in particular, visual aura was associated with occipital lobe lesions. Another study showed that in migraineurs^[22], RLS was associated with near-cortical WMLs, mainly in the frontal and parietal lobes, which are located in the blood supply area of the anterior cerebral artery. However, the exact mechanism underlying this effect is not clear. The WMLs near the cortex may be caused by the mechanism of embolization. With changes in chest pressure, microemboli intermittently enter the brain due to the RLS in the heart. This mechanism may occur because the anterior cerebral artery is the direct continuation of the end of the internal carotid artery, and the blood flow resistance is lower than that in other large intracranial arteries; thus, the microemboli can easily enter the distribution area of the anterior cerebral artery and then distribute along the blood vessels to the farthest end. However, a limitation of this study is that migraineurs without RLS were not included in the control group, preventing a better reflection of the WML distribution characteristics of migraine patients with RLS.

With respect to the relationship between WML severity and RLS flow in migraineurs with RLS, a multicentre study in 2018 involving 334 migraineurs^[23] reported that WML severity in migraineurs with RLS was not associated with RLS flow. The conclusions of this study are consistent with those of previous studies. Similarly, another study revealed that WMLs do not increase with increasing RLS flow^[24]. Park et al.^[14] reported a correlation between RLS flow and deep WMLs (OR = 3.240, P < 0.01), and RLS was an independent risk factor for the severity of small deep WMLs. The varying conclusions of these studies may be related to differences in the race of the participants, the definition and classification of WMLs, age, MRI equipment, setting parameters and research methods.

This study found no relationship between WML severity and headache severity in migraineurs with RLS. A study by Junyan Huo et al.^[27] suggested that the severity of WMLs in migraineurs with RLS was not related to the severity or duration of headache. This finding is consistent with the results of previous studies^{[22, 25–32]}.

Currently, some neuroscientists believe that the pathophysiology of migraine has evolved from the initial vasodilation hypothesis to brain dysfunction involving pain and other organ processing^[33]. Neuroscientists have used fMRI to observe the brain under visual, olfactory, cognitive, motor and other stimuli, which can induce migraine attacks, increasing the understanding of the pathogenesis of migraine. Under pain stimulation, abnormal activation has been observed in the brain regions involved in pain regulation, sensory discrimination, pain cognition and pain emotion. The activation of the thalamus, hippocampus, temporal pole, middle cingulate gyrus and fusiform gyrus increased, and the activation of brain regions such as the secondary somatosensory cortex and precentral gyrus decreased. Under olfactory stimulation, cortical structures related to smell, such as the temporal pole and superior temporal gyrus, are abnormally activated^[36]. In addition, the rostral structure of the pontine, which is closely related to the trigeminal pain pathway, is abnormally activated, which explains the symptoms of osmophobia in migraineurs and why a specific smell can induce migraine attacks. Under visual stimulation, the visual cortex is significantly activated^{[37, 38]}, which may explain photophobia during migraine attacks and why visual stimulation can induce headache attacks. Abnormalities in brain networks and functional connections, including the occipital lobe, sensorimotor network, bilateral lateral and inferior cerebellum, cingulate network, default mode network and frontoparietal network, can also be observed in migraineurs at rest^{[39, 40]}. In recent years, studies on the brain networks of migraineurs and models of dynamic functional connectors have shown that the thalamus, occipital lobe and basal nucleus play important roles in transmitting pain, regulating vision and integrating pain^{[41, 42]}.

An increasing number of studies have revealed evidence of structural abnormalities in grey matter in migraineurs, suggesting that grey matter is related to the neural network involved in pain management. In some studies, surface-based morphology (SBM) and voxel-based morphology (VBM) were used, and a significant decrease was observed in grey matter volume in some regions, such as the left precentral gyrus, right superior temporal gyrus and right inferior frontal gyrus, which participate in the pain loop^[43], and the volume of grey matter in visual areas V3 and V5 of the right occipital cortex decreased^[44]. The volume of the spinal trigeminal nucleus, which is involved in the transmission and regulation of intracranial vascular and meningeal trauma information, and the cerebellum, which is involved in pain information, decreased^[45]. However, other studies have shown that the thicknesses of certain areas of the cortex can also be increased in migraineurs^[46–48].

Diffusion tensor imaging (DTI) can reveal the structure of white matter, especially the course and structure of the axons of nerve cells. Planchuelo-Gómez et al.^[49] reported a positive correlation between the course of chronic migraine and bilateral external fractional anisotropy (FA) and a negative correlation between the onset time of chronic migraine and the average radial diffusivity (RD) value of the bilateral external capsule. These findings indicate that there are differences in white matter structure between paroxysmal migraine and chronic migraine. Compared with that of patients with paroxysmal migraine, the axonal integrity of patients with chronic migraine is impaired in the early stage of headache attack. Porcaro et al.^[50] analysed the DTI parameters of the whole hypothalamus and its subregions in 20 patients with aura migraine during headache attack and 20 healthy controls. Compared with those in the healthy control group, the mean diffusivity (MD), axial diffusivity (AD) and RD in the hypothalamus of patients with aura migraine changed significantly. These findings indicate that the hypothalamus plays an important role in the pathogenesis of aura migraines.

In summary, migraine can affect the white matter and grey matter of the human brain, but studies on the volume changes in these 157 brain regions in migraineurs with RLS are lacking. In this study, the brain structural volume of migraineurs with RLS changed significantly in the paracentral lobule, precentral gyrus, postcentral gyrus, inferior parietal lobule, supramarginal gyrus, anterior cuneiform lobe, temporal pole, superior temporal gyrus, inferior temporal gyrus, lateral occipital gyrus, fusiform gyrus, rectangular gyrus, superior frontal gyrus, middle frontal gyrus, frontal pole, medial orbitofrontal lobe, lateral orbitofrontal lobe, orbital part, lingual gyrus, cingulate gyrus, entorhinal cortex, parahippocampal gyrus, optic chiasm, globus pallidus, caudate nucleus, nucleus accumbens, putamen, ventral diencephalon, pons, cerebellar grey matter, choroid plexus, corpus callosum, cerebral white matter, cerebellar white matter, lateral ventricle, third ventricle, fourth ventricle, and peripheral cerebrospinal fluid. The volumes of the frontal pole, temporal pole, slope part of the superior temporal gyrus, fusiform gyrus, rectangular gyrus, anterior cuneiform lobe, lateral occipital gyrus, supramarginal gyrus, lingual gyrus, optic chiasm, pons, ventral diencephalon, corpus callosum, third ventricle, peripheral cerebrospinal fluid, entorhinal cortex, cingulate gyrus, parahippocampal gyrus, globus pallidus and nucleus accumbens were also significantly correlated with RLS flow and headache severity. These findings indicate that the human brain exhibits adaptive changes in response to migraine. However, the mechanism underlying this phenomenon is not clear. Previous studies have shown that some symptoms of migraine can be caused by the excitation of dopaminergic neurons and that migraineurs are highly sensitive to dopamine receptors^[51–53]. Dopamine receptors are distributed in the caudate nucleus, putamen, amygdala, nucleus accumbens, lateral papillary nucleus, Calleja island, hypothalamus, hippocampus, medial temporal lobe, optic tract, cerebral cortex, telencephalon, frontal cortex, and retina, among others.. This finding is highly consistent with the changes in brain volume observed in this study, which may indicate that some changes in brain structural volume in this study may be related to the involvement of dopamine in the pathogenesis of migraine.

An in-depth study of the factors related to brain structural volume changes in migraineurs with RLS may provide clues for exploring the pathogenesis of migraine with RLS. This study is novel in that, to date, no correlation study on the changes in brain structural volume in migraineurs with RLS has been performed. However, the sample size included in this study was small and therefore prone to bias errors. In addition, the changes in brain structural volume in migraineurs without RLS were not compared with those in migraineurs with RLS to determine the specificity of brain structural volume changes.

Migraine is a complex disease that can be affected by different psychological conditions, different environments, and biochemical and neurophysiological factors ^[38]. The threshold of headache differs depending on the individual. Even in the same patient, the threshold of headache will change under different conditions. Moreover, headache can be caused by a variety of factors or one decisive factor, such as fluctuations in oestrogen, which plays a decisive role in menstrual migraine^[54]. All the above factors may have had an impact on the results of the study.

The main limitations of this study are as follows: 1. With respect to imaging methods, the thickness and spacing of head MR images are relatively large, which results in some lesions being missed, which impacts the results of the study. 2. In this study, migraineurs without RLS were not included in the control group to reflect the specificity of WMLs and brain structural volume changes in migraineurs with RLS. 3. The small sample size is the main limitation of this study. The sample size is small because this study was a single-centre study, and strict inclusion and exclusion criteria were implemented to determine the number of subjects. All migraineurs with RLS had to meet the international diagnostic criteria for headache classification, and drug abuse and other types of headache had to be excluded, slowing the case inclusion speed. Subsequent collection of cases will continue to expand the sample size and allow further analysis.

Our understanding of the relationships among migraine with RLS, WMLs and brain structural volume changes is constantly developing, and many studies on related mechanisms and manifestations on neuroimaging, including structural and functional imaging, are ongoing. The changes in brain structure and function in migraineurs vary. Therefore, it is important to explore whether migraineurs with RLS have specific bioimaging changes, the causal relationship between imaging changes and migraine, and whether occlusion of the PFO can affect the brain structure or function of migraineurs to improve migraine symptoms. Therefore, it will be necessary to use multimode magnetic resonance technology to perform larger sample, multicentre and prospective studies in the future.

1. The WMLs of migraineurs with RLS were concentrated mainly in the white matter of the lateral ventricular margin and deep white matter. Subcortical WMLs were concentrated mainly in the parietal lobe, occipital lobe and frontal lobe. 2. There was no correlation between WML severity and RLS flow in migraineurs with RLS. 3. There was no correlation between the severity of WMLs and the degree of migraine in migraineurs with RLS. 4. Volume changes were found in the brain structure of migraineurs with RLS. 5. The RLS flow and degree of headache in migraineurs with RLS were correlated with structural volume changes in some brain regions.

Author contribution statements

XP, HR, LX, FL, WS, LC and ZC contributed to the study conception and design. XP and HR wrote the first draft of the manuscript. LX and FL performed the statistical analyses. WS, CL and ZC organized the database. JW and FS performed the imaging analyses and contributed to data collection. SM and HZ edited the manuscript. All the authors contributed to manuscript revision and read and approved the submitted version.

Acknowledgements

We would like to thank our colleagues for their assistance in the writing of this article. I would also like to thank my supervisor Professor Shubei Ma and Hongling Zhao for repeatedly revising this article and my unit for providing me with the platform. We would also like to thank the United Imaging platform on which the model was trained.

I would like to thank American Journal Experts (www.aje.cn) for English language editing.

To analyse the relationships among the degree of migraine with RLS, white matter lesions and brain structural volume, 102 migraineurs with RLS were selected. The RLS flow and HIT-6 score were recorded to reflect the degree of headache. The brain structural volumes were calculated with artificial intelligence. The WMLs of migraineurs with RLS were concentrated mainly in the white matter of the lateral ventricular margin and deep white matter. There were significant differences in WML variables among migraineurs with RLS with different HIT-6 grades and MIDAS grades. There was a significant difference in the volume of brain structures, and there was a significant difference among migraineurs with different RLS flows, HIT-6 grades and peripheral cerebrospinal fluid volumes. There was a positive correlation between frontal pole volume and RLS flow. The volume of specific brain structures was negatively correlated with RLS flow but positively correlated with HIT-6 grade. The volume of specific brain structures was negatively correlated with the HIT-6 grade. There was no correlation between WML severity and RLS flow or migraine severity. Volume changes occurred in parts of brain structures of migraineurs with RLS. Shunt flow and the degree of migraine in migraineurs with RLS were correlated with structural volume changes in some brain regions.

All methods were carried out in accordance with relevant guidelines and regulations. Research involving human participants, human material, or human data was performed in accordance with the Declaration of Helsinki.

Ethics approval and consent to participate

This study was approved by the ethics committee of Dalian Municipal Central Hospital (approval number: 2022-039-60). All methods were carried out in accordance with relevant guidelines and regulations. All the human subjects in this study provided written informed consent for their participation in our research.

Conflicts of interest

The authors declare that there are no 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 potential conflicts of interest.

Funding

This work was supported by the Dalian Central Hospital “peak plan” science and technology project (Grant ID: 2022ZZ215).

Data availability

The datasets used and analysed during the current study are available from the corresponding author ([email protected]) on reasonable request.

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Analysis of the relationship between the degree of migraine with right-to-left shunt and changes in white matter lesions and brain structural volume

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and methods

2.1 Data selection

2.2 Research methods

2.2.1 RLS detection methods

2.2.2 Detection of WMLs and volume changes in 157 brain regions

2.2.3 Statistical analysis

3. Results

3.1. Distribution of WMLs in migraineurs with RLS

3.2. Analysis of the differences in WML variables with different RLS grades

3.3. Analysis of the differences in WML variables across different HIT-6 grades

3.4. Correlation analysis between RLS grades and WML variables

3.5. Correlation analysis between HIT-6 grades and WML variables

3.6. Analysis of the difference in brain structural volume between migraineurs and normal controls

3.7. Analysis of the differences in brain structural volume changes across different RLS grades

3.8. Analysis of the differences in brain structural volume changes across different HIT-6 grades

3.9. Analysis of the correlation between RLS flow and brain structural volume changes

3.10. Analysis of the correlation between HIT-6 grades and brain structural volume changes

4. Analysis and Discussion

Conclusion

Declarations

References

Additional Declarations

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