
==== Front
Neuroimage Clin
Neuroimage Clin
NeuroImage : Clinical
2213-1582
Elsevier

S2213-1582(24)00113-X
10.1016/j.nicl.2024.103672
103672
Regular Article
Progressive brain structural abnormality in cerebral small vessel disease assessed with MR imaging by using causal network analysis
Mu Ronghua a1
Qin Xiaoyan a1
Zheng Wei a1
Yang Peng a
Huang Bingqin ab
Zhu Xiqi xiqi.zhu@ymun.edu.cn
cd⁎
a Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China
b Graduate School, Guilin Medical University, 541002 Guilin, China
c Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, 533000 Baise, China
d Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical university for Nationalities, 533000 Baise, China
⁎ Corresponding author. xiqi.zhu@ymun.edu.cn
1 These authors have contributed equally to this work.

12 9 2024
2024
12 9 2024
44 10367210 5 2024
19 8 2024
8 9 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• Right hippocampus is an early abnormal site in CSVD affecting broader brain regions.

• GMV reduction in CSVD progresses from hippocampus to wider cortical-subcortical areas.

• CaSCN reveals causal links between hippocampal atrophy and brain regions in CSVD.

Aims

Cerebral small vessel disease (CSVD) is a complex condition characterized by a combination of microcirculation disorders and neurodegenerative processes, CSVD is associated with structural abnormalities in multiple brain regions. However, the progressive pattern of structural changes remains unknown.

Methods

In order to detail the progressive structural changes in CSVD patients according to the degree of cognitive impairment, we recruited 121 CSVD patients and 104 healthy controls (HCs). Voxel-based morphometry was employed to measure the gray matter volume (GMV) of each participant. According to the VICCCS-2 diagnostic criteria, patients were initially divided into three stage groups, then we investigated the GMV changes in each stage and their causal relationships using causal structure covariance network (CaSCN) analysis.

Results

Overall, patients with CSVD presented stage-specific GMV alterations compared with HCs. With the worsening of cognitive impairment, the decrease in gray matter volume starts from the right hippocampus and gradually spreads to the cortical-subcortical brain regions. Importantly, the right hippocampus in CSVD patients plays a driving role in the directional network and forms both positive and negative causal effect networks with cortical-subcortical brain regions.

Conclusions

This study reveals the significance of the right hippocampus as an early pathological area in CSVD patients and its causal impact on brain GMV changes with disease progression, shedding light on structural brain damage hierarchy and compensatory mechanisms.

Keywords

Causal structural covariance network
Gray matter
Magnetic resonance imaging
Hippocampus gyrus
Cerebral small vessel disease
Abbreviations

CaSCN causal structure covariance network

CSVD Cerebral small vessel disease

DWI diffusion weighted imaging

FDR false discovery rate

FLAIR fluid-attenuated inversion recovery

FOV field of vision

GC Granger causality

GMV Gray matter volume

HCs healthy controls

MMSE Mini-mental State Examination

MNI Montreal Neurologic Institute

MoCA Montreal Cognitive Assessment

ROI Region of Interest

SWI susceptibility weighted imaging

TE echo time

TIV total intracranial volume

TR repetition time
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pmc1 Introduction

Cerebral small vessel disease (CSVD) is a heterogeneous pathological condition that affects small blood vessels in the brain, representing a significant contributor to cognitive impairment (Wardlaw et al., 2019). In the context of vascular disease and dementia, the pathological basis of how brain atrophy affects cognitive function is mainly related to neuronal loss, cortical thinning, white matter rarefaction and atrophy associated with subcortical vascular pathology, small artery sclerosis, venous collagenosis, and secondary neurodegenerative changes (Wardlaw et al., 2013). Studies have indicated that CSVD may lead to disruption of cortical-subcortical connections, thereby accelerating the development of brain atrophy (Jagust et al., 2008). In patients with cerebrovascular disease, those with atrophy in multiple brain regions experience a more significant decline in cognitive function compared to patients with atrophy restricted to fewer brain regions (Xu et al., 2019). Previous research has shown that white matter changes and infarctions caused by CSVD can remotely affect brain structure, leading to the appearance of brain atrophy and cortical thinning (Duering et al., 2012). Furthermore, patients with CSVD and cognitive impairment have reduced gray matter volume (GMV) in the frontal, parietal, and occipital lobes, which undoubtedly has a negative impact on cognitive function (Lambert et al., 2015). Previous studies underscore the crucial role of brain atrophy in predicting and monitoring CSVD progression, showing that the rate of brain atrophy differs significantly in patients with small vessel disease compared to their counterparts, further highlighting the cognitive implications of CSVD (Liu et al., 2020, Duering et al., 2023, Nitkunan et al., 2011). However, detailed information about the progressive structural changes and how initial structural changes spread to other brain regions is still unknown. Exploring the initial structural changes and the causal relationships between these regions is of great significance in shedding light on the gradual neurodegenerative processes in CSVD.

Understanding the complex relationship between brain atrophy and CSVD requires advanced analytical methods. The causal structure covariation network (CaSCN) analysis, as proposed by Zhang et al., is a promising method that can delineate the evolving characteristics of structural networks and elucidate the causal impacts of morphological alterations among distinct brain regions during disease progression. This is achieved through the integration of Granger causality (GC) analysis and structural covariance network techniques (Zhang et al., 2017). The utilization of CaSCN enables researchers to gain a comprehensive understanding of the operational mechanisms of brain atrophy associated with CSVD, forecast its future trajectories, and delineate causal pathways among potential influencing factors. Through the application of CaSCN, researchers have been able to uncover critical causal relationships between morphological alterations in various brain regions across different neurological and psychiatric disorders. For example, in epilepsy, a decrease in hippocampal gray matter volume has been shown to causally influence the temporal and frontal cortical regions as well as subcortical structures (Zhang et al., 2017). Similarly, in schizophrenia, the thalamus and frontal lobe play pivotal roles in driving disease progression (Jiang et al., 2018). Parkinson’s disease research highlights the identification of crucial caudate-linked CaSCNs within cortical-subcortical networks, with these networks displaying diverse progression patterns (Li et al., 2022). In cases of depression, progressive morphological changes in the ventral medial prefrontal cortex and hippocampus have been found to causally affect other brain regions (Han et al., 2023). These findings collectively demonstrate the versatility and power of CaSCN in elucidating the complex and evolving structural network alterations across a range of brain disorders. Nevertheless, the progressive morphological changes during the course of CSVD and the causal connections among these structural alterations are still ambiguous.

The aim of this study is to investigate the alterations in GMV across different stages of cognitive impairment in patients with CSVD and to explore the causal relationships between these changes. The study involves 121 CSVD patients and 104 healthy controls (HCs) matched for age, gender, and education. First, we analyzed the overall GMV changes between all CSVD patients and HCs. Next, we examined GMV variations across different stages to identify critical points in the progression of CSVD and evaluate the patterns of these changes. Finally, using comprehensive neurocognitive assessments, we established whole-brain voxel-wise and Region of Interest (ROI)-based causal structure covariance networks to investigate the causal impacts of these critical points on other brain regions.

The aim of this study is to examine the alterations in GMV during each stage according to the severity of cognitive impairment in patients with CSVD and to delve into the causal associations between these variations. The study included 121 CSVD patients and 104 age-, gender-, and education-matched healthy controls (HCs). Initially, we will investigate GMV fluctuations in different stages to pinpoint pivotal moments in CSVD and assess the evolving change patterns. Subsequently, utilizing two neurocognitive assessments for holistic cognitive evaluation, we will establish comprehensive whole-brain voxel-wise and Region of Interest (ROI)-based causal structure covariance networks to analyze the causal impact of crucial points and other affected brain regions.

2 Materials and methods

2.1 Participants

The study was approved by the Ethics Committee of Nanxishan Hospital of Guangxi Zhuang Autonomous Region (2020NXSYEC-006), and all participants provided written informed consent after they were given a complete description of the study. From May 2020 to June 2021, 269 right-handed participants were recruited in our hospital. All participants underwent neuropsychological examinations by an experienced neurologist following the diagnostic guidelines of VICCCS-2 (Skrobot et al., 2018), including a medical interview, and their cognitive function was assessed using a batteries of cognitive assessment scales. The inclusion criteria include age 40 and above; meeting clinical diagnostic criteria for cerebral small vessel disease, including presumed vascular-origin lacunar infarcts, cerebral microbleeds, presumed vascular-origin white matter lesions, and perivascular spaces enlargement. The imaging diagnosis of CSVD follows the definition from previous studies (Duering et al., 2023). Participants may have exhibited cognitive impairment reported either by themselves or by immediate family members. In cases where the cognitive impairment was reported by family members, it was often because the participant was either unaware of the extent of their impairment or unable to accurately describe it, particularly in more advanced stages of cognitive decline. These impairments, such as memory deficits and other cognitive domain impairments, persisted for more than 3 months but did not meet the DSM-5 criteria for psychiatric disorders such as schizophrenia, depression, or anxiety. Additionally, healthy participants who were matched for age, gender, had normal cognitive abilities, and without known neurological diseases were included as the control group. Exclusion criteria include neuropsychological and psychiatric disorders, intellectual disabilities, primary and metastatic brain tumors, history of brain surgery, standard contraindications for MRI scans, any history of substance abuse or alcohol abuse, incomplete data acquisition (e.g., incomplete MRI scans and key clinical assessments), and poor data quality (e.g., significant head movement and signal artifacts during scans). The specific inclusion/exclusion criteria for participants are illustrated in Fig. 1.Fig. 1 Procedure of inclusion and exclusion of the participants.

2.2 Clinical assessment

All participants underwent cognitive domain neuropsychological tests before MRI examination. The Beijing version of Montreal Cognitive Assessment (MoCA) (Yu et al., 2012) and Mini-mental State Examination (MMSE) (Jia et al., 2021) scale were used to test multiple cognitive domains. On these scales, lower scores indicate greater cognitive impairment. Specifically, for the MoCA, a score below 26 is generally considered indicative of mild cognitive impairment, with lower scores reflecting more severe impairment. Similarly, for the MMSE, a score of 27 or below suggests potential cognitive decline, with lower scores corresponding to more pronounced cognitive deficits. Based on a previous sensitivity study on vascular cognitive impairment, we defined vascular cognitive impairment in three stages: stage I as none; stage II as mild; and stage III as major (MMSE ≥ 27, 23–26, <23; MoCA ≥ 25, 20–24, <20) (Webb et al., 2014). To visualize the paired relationship between MoCA and MMSE scores of each patient, we plotted the group membership across the three stages with paired points (Figure S1).

2.3 Image acquisition

3D T1-weighted structural imaging data were acquired using a 3.0 T MRI imager (Ingenia 3.0CX; Philips Healthcare, Best, The Netherlands) with a 32-channel coils of the head were used to perform MRI scans. The three-dimensional T1-weighted imaging utilized a fast field echo sequence, with a 6.4 ms repetition time (TR) and a 3.0 ms echo time (TE). The field of vision (FOV) was 240 mm × 240 mm × 180 mm, the reconstruction voxel was 1.1 mm × 1.1 mm × 1.1 mm, the reconstruction matrix was 512 × 512, and the slice thickness was 1.1 mm. These data were used for analysis after quality assurance. In addition, we also scanned 3D T2, 3D fluid-attenuated inversion recovery (FLAIR), susceptibility weighted imaging (SWI), and diffusion weighted imaging (DWI) sequences for the imaging diagnosis of CSVD, and detailed scanning parameters can be found in Table S1.

2.4 Imaging diagnostic criteria for CSVD

The imaging diagnosis of CSVD follows the definition from previous studies (Duering et al., 2023). Briefly, CSVD is diagnosed based on several characteristic imaging features observed on MRI, which include lacunes, white matter hyperintensities, enlarged perivascular spaces, and cerebral microbleeds. Lacunes are defined as small, subcortical, fluid-filled cavities that appear similar to cerebrospinal fluid in signal. White matter hyperintensities are areas of increased signal on T2-weighted and FLAIR images, typically found in the white matter. Enlarged perivascular spaces are fluid-filled spaces that follow the course of penetrating vessels and appear similar to cerebrospinal fluid in signal intensity. Cerebral microbleeds are small, hypointense lesions on SWI sequences, usually associated with a blooming artifact.

2.5 Data preprocessing

High-spatial-resolution T1-weighted MRI data preprocessing was performed using a morphological processing toolbox (CAT12; https://neuro-jena.github.io/cat/) that was embedded in statistical parametric mapping software (SPM12; https://www.fil.ion.ucl.ac.uk/spm/software/spm12/). Initially, all images were reoriented to adjust the image origins at the anterior commissure by manual setting after artifact checking and format conversion. Secondly, T1-weighted images were normalized to Montreal Neurologic Institute (MNI) space and resampled to a volume image resolution of 1 mm × 1 mm × 1 mm with a default template. Then, all images were segmented into three categories—gray matter, white matter, and cerebrospinal fluid. The segmented gray matter images were nonlinearly modulated to compensate for spatial normalization inference. Following the verification of data quality and sample homogeneity, the segmented gray matter images were smoothed using an 8 mm full-width at half maximum Gaussian kernel. The gray matter optimal threshold mask, derived from data across all subjects, was then applied to remove non-gray matter voxels. The resulting smoothed gray matter images were utilized as the GMV for subsequent group comparisons.

2.6 Voxel-based morphometric analysis: overall GMV alteration patterns in patients with CSVD

To examine the overall GMV alterations in CSVD patients, a two-sample t-test was performed on the gray matter images between the CSVD group and HCs (P < 0.05, false discovery rate (FDR) correction). The age, gender, years of education, and total intracranial volume (TIV) of participants were served as covariates in the above analysis. Brain regions that survived the FDR correction were considered as ROIs.

2.7 Voxel-based morphometric analysis: stage-specific atrophy patterns in patients with cognitive impairment

To investigate the progressive patterns of GMV alterations in CSVD patients, we used two grouping strategies to separate the patients and examined and verified the progressive patterns. All patients were categorized into three subgroups according to the progressive stage of MoCA score (stage I, ≥25 scores; stage II, 20–24 scores; and stage III, <20 scores). Then, gray matter images of each subgroup were compared with those of HCs by using a two-sample t test (P < 0.05, FDR corrected). To ensure that any differences were not due to an arbitrary strategy, we also reclassified all patients into 3 subgroups based on their MMSE scores (stage I, ≥27 scores; stage II, 23–26 scores; and stage III, <23 scores) to further verify the results, as a validation strategy. The age, gender, years of education, and TIV of subjects were regressed as covariates in these analyses.

2.8 Voxel-wise causal structural network analysis

The main objective of the study is to test whether an early atrophied brain region exerts a causal influence over other distributed brain networks in patients with potential cognitive impairment. The sequenced data were treated as analogous to time-series information, which is used for characterizing the progressive structural alterations of CSVD based on cross-sectional data. Pseudo-time series were generated by ordering cross-sectional data points along a hypothetical timeline that represents the progression of brain atrophy. This method assumes that the degree of gray matter atrophy can be used as a proxy for the progression stage, with more atrophied regions representing later stages. Similar to GC analysis applied in functional MR imaging data analysis, the pseudo-time series was used to construct seed-based CaSCN. As a region showing early gray matter atrophy, the most atrophied right hippocampus (MNI coordinates: 36, −22, 12) was selected as the seed by comparing the patients with cognitive impairment and HCs to construct CaSCNs for CSVD. The seed region was selected from the previously mentioned voxel-based morphometric analysis. The average gray matter values within the right hippocampus were extracted from sequenced morphometric data and used as the pseudo-time-series. Signed-path coefficient GC analysis disposed with an fMRI toolbox (REST; https://www.restfmri.net) was performed on a voxel-wise basis for all the voxels in the mask of the brain areas with reduced GMV. As the seed exhibited the reduction of GMV in patients with cognitive impairment, a positive GC value indicated that the same GMV alteration (reduced) in the regions lagged behind the seed atrophy, which may suggest the reduction is driven by the seed. A negative GC value indicates that regions with an opposite alteration (increased) lagged behind the seed atrophy, which may imply a compensatory effect (Jiang et al., 2018). Individual age, gender, years of education and TIV were regressed as covariates in CaSCN analyses. The GC map was Z-value-transformed to present statistical significance (Z-value > 2.57, GC value > 0.24, and P < 0.05, FDR corrected).

2.9 ROI-wise causal structural network analysis

To further explore the causal relationships among the ROIs identified from the CaSCN analysis, we conducted an ROI-to-ROI GC analysis. This analysis aimed to investigate the temporal precedence relationships among brain regions based on the previously identified overlapping CaSCN maps. We employed signed-path coefficient GC analysis to construct an ROI-wise causal network, which delineated the directional causal interactions among the selected ROIs. To ensure consistency with the voxel-wise CaSCN analysis, we applied the same threshold criteria, setting the GC value at greater than 0.24 and a significance level of P < 0.05 (FDR corrected). For each ROI, we calculated both the binary and weighted “in-degree” and “out-degree” values. The in-degree value represents the number (or strength) of connections projecting into a given ROI (i.e., the number of head ends adjacent to the node), while the out-degree value represents the number (or strength) of connections projecting from the ROI to other regions (i.e., the number of tail ends adjacent to the node). Additionally, we calculated the out-in degree difference by subtracting the in-degree values from the out-degree values for each ROI. This metric was used to identify ROIs that serve as primary causal sources (higher out-in degree values) or causal targets (lower out-in degree values) within the network. The statistical analysis pipeline can be clearly seen in Fig. 2.Fig. 2 Statistical analysis pipeline.

3 Results

3.1 Demographic and clinical characteristics

Table 1 summarizes the demographic and clinical information. The CSVD patients and HCs showed no significant difference in age, gender, or educational level. More detailed information in each stage of the main and validation grouping strategies are presented in Tables S2 and S3 respectively, which shows that the above indicators of the CSVD subgroup are relatively well matched with the HCs.Table 1 Demographic and clinical information of CSVD and HC groups.

Characteristic	CSVD
(n = 121)	HC
(n = 104)	Statistic	P-value	
Age (years)	55.4 ± 5.5	53.8 ± 7.1	−2.233	0.064a	
Gender (Male/Female)	59/62	54/50	0.224	0.636b	
Educational level (years)	11.5 ± 3.0	12.1 ± 4.4	1.223	0.223a	
MoCA score	22.78 ± 4.04	27.27 ± 1.13	12.791	<0.001a	
MMSE score	23.56 ± 4.62	28.76 ± 1.12	10.320	<0.001a	
Abbreviations: CSVD, cerebral small vessel disease; HC, healthy control; MoCA, Montreal Cognitive Assessment; MMSE, Mini-Mental State Examination.

Values are presented as mean ± standard deviation

Significant differences are highlighted in bold.

a Independent Samples t-test.

b Chi-square t-test.

3.2 Voxel-based morphometric analysis of overall GMV alteration in CSVD patients

Compared to HCs, patients with CSVD exhibited significant reductions in GMV in several brain regions, including the right hippocampus, right amygdala, left caudate, left putamen, bilateral thalamus, and various cortical areas including the frontal, parietal, temporal, and occipital lobes. No significant increase in GMV was observed in any region of the CSVD group. Detailed regions are provided in Fig. 3 and Table S4.Fig. 3 MR images show reduced gray matter volume in patients with CSVD. Reduced gray matter volume (blue) images are overlaid on an axial template, and compared to the HCs, no brain regions with increased volume were found. Color bar represents T values from two-sample t test (P < 0.05, FDR correction), with blue indicating regions of significant GMV reduction.

3.3 Stage-specific GMV alteration patterns in CSVD patients

The analysis of GMV alterations across different stages of cognitive impairment revealed distinct atrophy patterns. Based on MoCA scores, patients in Stage I exhibited early atrophy confined to the right hippocampus. In Stage II, atrophy expanded to include the right calcarine, left caudate, left hippocampus, left postcentral, and left superior temporal gyrus in temporal pole. Stage III patients displayed further GMV reduction in additional regions including right amygdala, left putamen, bilateral thalamus as well as bilateral frontal, parietal, temporal, and occipital lobes (Fig. 4A). Similar progressive patterns were observed when using MMSE scores to define stages (Fig. 4B). On the whole, with increased illness duration of CSVD patients, regions with GMV decrease expanded from the right hippocampus (stage I) to the left caudate, right calcarine and left superior temporal gyrus in temporal pole (stage II), and then to other brain regions (stage III). Detailed results are provided in Tables S5 and S6.Fig. 4 MR images show stage-specific gray matter volume reductions in CSVD patients with cognitive impairment relative to the HCs. Reduced gray matter volume images were overlaid on a sagittal template. Color bar represents T values from two-sample t test (P < 0.05, FDR correction), where the color scale indicates the degree of GMV reduction. A: Subjects with CSVD were categorized into three subgroups according to cognitive impairment (MoCA scores) degree (stage AI, ≥25 scores; stage AII, 20–24 scores; stage AIII, <20 scores). B: All patients with CSVD were reclassified into three groups based on the MMSE grouping strategy (stage BI, ≥27 scores; BII, 23–26 scores; BIII, <23 scores).

3.4 Voxel-wise CaSCN of GMV atrophy pattern in patients with CSVD

The voxel-wise CaSCN with the right hippocampus as the seed revealed distinct causal relationships. Positive GC values indicated that reduced GMV in the right hippocampus was associated with subsequent GMV reduction in the bilateral caudate nucleus, bilateral parahippocampal gyrus, bilateral temporal lobe, as well as left frontal, parietal, and occipital lobe. This suggests that early atrophy in the hippocampus may propagate through interconnected networks, potentially leading to widespread cortical and subcortical atrophy, which could underlie the progressive cognitive decline seen in CSVD patients. Conversely, negative GC values suggested compensatory mechanisms might be at play, where increased GMV was observed in regions such as the bilateral lenticular nucleus and various cortical regions following hippocampal atrophy. These changes could reflect neuroplastic responses aimed at maintaining cognitive function despite ongoing structural damage. Detailed results are provided in Table 2 and Fig. 5.Table 2 Brain regions showing causal effect from the seed of the right hippocampus by using CaSCN analysis.

Brain regions	MNI coordinates (x,y,z)	GC value	Z-value	Number of voxels	
Positive causal effect from the seed of the right hippocampus	
Caudate nucleus, left	−18	24	3	11.37	4.08	172	
Caudate nucleus, right	18	21	6	11.89	3.86	219	
Inferior frontal gyrus, orbital part, left	−22.5	22.5	−22.5	1.73	2.87	21	
Inferior occipital gyrus, left	−40.5	−72	−12	5.75	3.64	93	
Inferior temporal gyrus, left	−34.5	0	−48	0.54	4.88	1148	
Middle frontal gyrus, left	−30	34.5	25.5	17.75	4.43	302	
Middle frontal gyrus, orbital part, left	10.5	49.5	−6	6.01	3.23	65	
Parahippocampal gyrus, left	−21	−21	−21	3.19	4.23	467	
Parahippocampal gyrus, right	22.5	−18	−24	3.08	5.54	314	
Superior temporal gyrus, left	−49.5	3	−1.5	6.54	2.93	41	
Supplementary motor area, left	−13.5	−6	64.5	15.66	3.12	22	
Temporal pole: superior temporal gyrus, right	55.5	9	−15	3.71	2.84	43	
Thalamus, right	15	−31.5	3	8.56	3.3	112	


	
Negative causal effect from the seed of the right hippocampus	
Inferior frontal gyrus, opercular part, right	39	16.5	33	−35.07	−6.65	491	
Inferior frontal gyrus, triangular part, left	−42	12	19.5	−21.77	−4.72	563	
Inferior parietal, but supramarginal and angular gyri, left	−28.5	−69	45	−17.95	−2.82	32	
Middle frontal gyrus, right	42	42	4.5	−18.29	−4.83	432	
Precuneus, right	3	−63	22.5	−22.7	−4.79	2222	
Superior frontal gyrus, dorsolateral, left	−22.5	−4.5	51	−24.21	−3.79	158	
Supramarginal gyrus, right	58.5	−43.5	25.5	−17.67	−3.50	144	
Rolandic operculum, right	49.5	−21	18	−13.79	−2.90	32	
Lenticular nucleus, pallidum, left	−15	1.5	−4.5	−10.08	−3.61	44	
Lenticular nucleus, pallidum, right	19.5	1.5	−4.5	−9.56	−3.60	39	
Calcarine fissure and surrounding cortex, left	−13.5	−99	−12	−7.37	−3.79	206	
Superior frontal gyrus, dorsolateral, right	25.5	25.5	43.5	−6.13	−5.05	399	
Inferior temporal gyrus, right	60	−36	−16.5	−4.71	−2.99	23	
Abbreviations: CaSCN, causal structural covariance network; MNI, Montreal Neurologic Institute; GC, Granger causality; z values, standardized transformation of GC values.

Fig. 5 Causal networks show causal effects of gray matter atrophy pattern in patients with CSVD. Causal networks were constructed by applying Granger causality (GC) analysis to sequenced morphometric data according to the ranks of cognitive impairment (MoCA scores) duration from low to high. The right hippocampus (Montreal Neurologic Institute coordinates: 36, −22, 12) was used as the seed region on the basis of voxel-based morphometric analysis. Color bar represents standardized transformation of GC values. The cold color bar represents positive GC values, implying that the GMV of these regions decreased after the GMV reduction of the right hippocampus. Similarly, the warm color bar represents negative GC values, indicating that the GMV of these regions increased after the GMV decrease in the right hippocampus.

Further analysis based on MMSE scores confirmed these findings. Positive GC values in regions including the bilateral thalamus and left cuneus imply that these areas might also be vulnerable to secondary atrophy due to hippocampal degeneration, while negative GC values in areas like the right anterior cingulate and left caudate nucleus could indicate regions that engage in compensatory activity (Figure S2, Table S7).

3.5 ROI-wise CaSCN of GMV atrophy pattern in patients with CSVD

The ROI-wise CaSCN demonstrated distinct causal relationships among brain regions. Using MoCA scores, we observed that the right hippocampus exerted strong positive causal influences on several regions including the bilateral parahippocampal gyrus and the left inferior temporal gyrus. This pattern suggests that hippocampal atrophy might drive secondary atrophy in these regions, potentially affecting various cognitive functions. Negative causal effects observed in cortical-subcortical regions might indicate an increase in GMV as a compensatory response, potentially reflecting neural plasticity or a shift in functional load to these areas (Fig. 6, Fig. 7).Fig. 6 Bivariate signed-path coefficient Granger causality analysis was performed on a region-of-interest (ROI)–based causal structural covariance network to characterize causal relationships among ROIs. Abbreviations: HIP, hippocampus; PHG, parahippocampal; STG, Superior temporal gyrus; THA, thalamus; ITG, inferior temporal gyrus; ORBinf, inferior frontal gyrus (orbital part); IOG; inferior occipital gyrus; SMA; supplementary motor area; CAU, caudate nucleus; TPOsup, temporal pole (superior temporal gyrus); MFG, middle frontal gyrus; ORBmid, Middle frontal gyrus(orbital part); PAL, lenticular nucleus(pallidum); CAL, calcarine fissure and surrounding cortex; ROL, rolandic operculum; IPL, inferior parietal (but supramarginal and angular gyri); SMG, supramarginal gyrus; IFGtriang, inferior frontal gyrus (triangular part); SFGdor, superior frontal gyrus (dorsolateral); PCUN, precuneus; IFGoperc, inferior frontal gyrus, opercular part; L, left; R, right.

Fig. 7 Region-of-interest (ROI)–based causal structural covariance network analysis shows causal relationship among ROIs. According to the MoCA score, the binary (weighted) out-degree and in-degree values of each Region of Interest respectively represent the sum of the number (strength) of paths projecting to other nodes and the sum of the number (strength) of paths projected from other nodes to itself; moreover, the line graph illustrates the difference between out-degree and in-degree values to determine causal source levels. Abbreviations: HIP, hippocampus; PHG, parahippocampal; STG, Superior temporal gyrus; THA, thalamus; ITG, inferior temporal gyrus; ORBinf, inferior frontal gyrus (orbital part); IOG; inferior occipital gyrus; SMA; supplementary motor area; CAU, caudate nucleus; TPOsup, temporal pole (superior temporal gyrus); MFG, middle frontal gyrus; ORBmid, Middle frontal gyrus (orbital part); PAL, lenticular nucleus(pallidum); CAL, calcarine fissure and surrounding cortex; ROL, rolandic operculum; IPL, inferior parietal (but supramarginal and angular gyri); SMG, supramarginal gyrus; IFGtriang, inferior frontal gyrus (triangular part); SFGdor, superior frontal gyrus (dorsolateral); PCUN, precuneus; IFGoperc, inferior frontal gyrus, opercular part; L, left; R, right.

Similarly, when analyzing MMSE scores, positive causal effects on regions such as the right parahippocampal gyrus and left thalamus further emphasize the hippocampus's central role in the atrophic cascade. Negative causal influences in cortical-subcortical areas highlight regions that may be involved in compensatory mechanisms, which could be a key factor in preserving cognitive functions in the face of CSVD-related damage (Figure S3).

4 Discussion

In this study, we used CaSCN analysis based on T1-weighted structural images to evaluate how changes in brain region morphological structures relate to the progression of CSVD patients, and how these changes extend to causal relationships between other brain regions. The severity of the disease is correlated with a decrease in hippocampal GMV. A directional network shows that the hippocampus is a hub. Changes in the morphological structure of the hippocampus may have causal relationships with all other nodes, receiving more causal effects from other nodes and being identified as a causal target. Specifically, the right hippocampus exhibits extensive positive causal influences on various cortical-subcortical substructures, including the bilateral hippocampal gyrus, left temporal cortex, right thalamus, and more. However, it also showing negative causal effects on corresponding cortical-subcortical regions. Overall, our findings reveal gradual morphological changes originating from the progression of CSVD and further elucidate potential details about disease progression.

In the overall comparison of GMV, we found that in CSVD patients, there was a common reduction of GMV in the right hippocampus and various cortical-subcortical substructures, consistent with previous morphometric studies. Given that CSVD is a chronic progressive condition, a critical question need to be investigated is whether the pathology of CSVD impacts all brain regions simultaneously or if there are variations in susceptibility across different structures. In this study, we used two commonly used cognitive assessment scales, comparing gray matter maps of CSVD patients at different stages with age-, gender-, and education-matched HCs to enhance our understanding of dynamic GMV changes in the progression of CSVD. Interestingly, we observed morphological changes in the right hippocampus in the early stages of CSVD. Specifically, we found a progression from the right hippocampus to the right calcarine, left caudate, and left superior temporal gyrus in the temporal pole, and then to various cortical-subcortical substructures.Our study uncovered that in cognitively intact CSVD patients, alterations in the volume of the right hippocampus precede changes in other regions. This finding implies that hippocampal atrophy initiates during the early phases of CSVD and continues surreptitiously throughout the progression of cognitive decline (Geinisman et al., 1995). Previous studies have shown that changes in hippocampal volume are related to mild cognitive impairment or vascular cognitive impairment caused by CSVD (Kim et al., 2015, Wong et al., 2021, Wong et al., 2019). Recent research has confirmed that the size and structural changes in the hippocampus are independent factors in cognitive impairment in patients with sporadic CSVD, indicating the importance of the hippocampus in CSVD-related cognitive impairment and may help in early detection and intervention in the development of vascular cognitive impairment (Perosa et al., 2024). The study by Liu and colleagues (Liu et al., 2020) did not reveal significant volume differences between CSVD patients with normal cognition and controls. However, extensive brain volume alterations were observed in CSVD patients with cognitive impairment, which is consistent with our results. The brain structural atrophy seen in CSVD patients involves several important brain regions that affect multiple functional systems, including cognitive function, emotional control, language ability, memory, perception, and executive function, possibly associated with the complex pathophysiological mechanisms of CSVD (Pantoni, 2010, Inoue et al., 2023, Elahi et al., 2023). Our findings show significant differences in brain structural changes in CSVD patients at different stages of the disease, especially with early-stage reduction in gray matter in the right hippocampus and significant impact on cognitive function. This suggests that the hippocampus plays an important role in cognitive impairment caused by CSVD, providing clues for early detection and intervention in cognitive impairment.

It is essential that, as the central hub of the directional network, the hippocampus demonstrates positive causal effects on frontal, temporal, and occipital regions, where positive causality values may indicate that the reduction in gray matter volume in the hippocampal area precedes that of other regions. This is to be expected, as neurodegenerative changes occur in specific areas and then spread to others. Gray matter atrophy in the hippocampus can lead to damage in the fiber microstructure connecting to different cortical regions and serves as a predictor of brain structural atrophy (Biesbroek et al., 2017). CSVD related cognitive impairment is associated with damage to the cortical-subcortical subcircuit, and the impairment in the cortical-subcortical subcircuit is closely linked to extensive regions of the hippocampus (Reiländer et al., 2024). The pathology may lead to interruption of cortical-subcortical connections and exacerbate brain atrophy (Jagust et al., 2008). CSVD patients with mild cognitive impairment exhibit impaired regions involving the default mode network in a deactivated state (Papma et al., 2012), indicating the critical role played by the default mode network in the pathology of CSVD and reveals that the network disruptions caused by CSVD may be a potential mechanism affecting cognitive function. Li et al. showed that the default mode network and salience network are reliably affected in sporadic CSVD during resting-state, and abnormalities within the salience network may be related to alterations within the default mode network (Li et al., 2023). Furthermore, functional neuroimaging studies report that the dorsal attention network is also commonly affected in sporadic CSVD, where the cognitive impairment resulting from reduced functional connectivity may stem from the damage to prefrontal-cortical subcircuits and long-range fiber connections caused by CSVD (Ter Telgte et al., 2018). Qing et al. utilizing source-based morphometry, structural covariance, and Granger causality analysis, found that the hippocampus is one of the core regions originating brain atrophy in MCI and AD patients, suggesting that regional brain atrophy may be an early sign or precursor of more widespread brain degeneration (Qing et al., 2021). Kantarci et al. found a significant correlation between damage to fiber bundles connected to the medial temporal lobe and reduced cognitive function, associated with the integrity of the medial temporal lobe structure and its connectivity to other brain regions (Kantarci et al., 2011). In conclusion, our study reveals potential causal relationships between the hippocampus and various structural changes in cortical and subcortical brain regions with GMV alterations, indicating a progressive pathological process of structural damage in CSVD.

In addition, parts of the brain areas from the right hippocampus to the frontal-temporal-parietal region showed negative granger causal effects. It indicates that the decrease in GMV in the hippocampal area leads to an increase in volume in these brain areas, which may be a result of compensatory and regulatory mechanisms. Ter et al. reported that cognitive function in CSVD patients can adapt to physiological activities by utilizing and developing compensatory networks (Ter Telgte et al., 2018). The study suggest that decreased executive function, working memory, attention, and motor function in CSVD patients can be maintained through activation of related brain areas or networks to support normal physiological activities (Fan et al., 2024). Moreover, a recent study on cortical thickness suggested that increased cortical thickness is related to neuroplasticity mechanisms in patients with Alzheimer’s disease (Phan et al., 2024). Sheng et al. found compensatory mechanisms in the structural networks of early patients with Alzheimer's disease, indicating different compensation patterns at the regional and whole brain levels (Sheng et al., 2021). Our findings are consistent with these previous studies, suggesting that the decrease in volume in other brain areas driven by the hippocampus as the central hub can result in compensatory increases in corresponding brain areas to compensate for functional deficits in damaged regions. This indicates the presence of compensatory and adaptive mechanisms between brain areas, which is crucial for understanding the development patterns of cognitive impairments in CSVD.

Our study provides valuable insights into the potential causal relationships between early atrophied brain regions and other distributed brain networks in patients with CSVD, but it is important to acknowledge several limitations that may impact the interpretation and generalizability of our findings. Firstly, the relatively small sample size limits the reliability of our results, as the conclusions drawn are based on a single dataset, which necessitates validation using an independent dataset in the future. Secondly, the recruitment source is another critical factor to consider; all participants were recruited from the local community, where individuals tend to present with fewer clinical symptoms compared to those recruited from memory clinics, thus limiting our ability to fully interpret the underlying reasons and consequences of cognitive impairment in CSVD patients. Thirdly, while the use of pseudo-time series data to infer causal relationships is innovative, it has inherent limitations; the assumption that the degree of gray matter atrophy can serve as a proxy for the temporal progression of the disease may not fully capture the complex and non-linear nature of neurodegenerative processes. Furthermore, CaSCN results cannot directly reflect the true sequence of morphological changes, as they are based on cross-sectional rather than longitudinal data, highlighting the need for longitudinal studies to more accurately elucidate the causal relationships between morphological changes in CSVD. Additionally, although our focus is on causal structure covariance networks, it is crucial to highlight that insights emerging from mapping intrinsic brain connectivity networks provide a potentially mechanistic framework for understanding aspects of human behavior. Recent studies have shown that intrinsic connectivity networks are integral to understanding how brain regions interact and influence cognitive functions, offering a deeper understanding of the observed changes in GMV and their implications for cognitive decline in CSVD. Integrating these connectivity network perspectives could enhance our interpretation of the causal relationships observed in our study. Lastly, as CSVD is a chronic neurodegenerative disease, a variety of measures, including the total burden of imaging markers, are necessary to evaluate disease severity, as these markers may be related to changes in brain structure, providing additional context for understanding the progression of cognitive impairment in CSVD patients. In conclusion, while our study offers important insights, these limitations underscore the need for further research to validate and extend our findings, particularly through larger sample sizes, independent datasets, and longitudinal studies.

5 Conclusion

We explored initial GMV alterations, progressive structural alteration pattern and causal relationships of them using CaSCN analysis in CSVD patients in this study. Our research results indicate that the right hippocampus is an important early pathological area in CSVD patients, and as the duration of the disease increases, the right hippocampus has causal effects on the GMV of other brain areas. It is essential for comprehending the hierarchical structural brain damage, regulatory compensatory mechanisms, and the pivotal role of the hippocampus in the disease.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 82460226), the Natural Science Foundation of Guangxi Autonomous Region (Grant No. 2023GXNSFAA026383), the Health Development Program of Guangxi Zhuang Autonomous Region (Grant No. Z20191023), and the Excellence Project of Nanxishan Hospital in Guangxi Zhuang Autonomous Region (Grant No. NY2019003).

CRediT authorship contribution statement

Ronghua Mu: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Xiaoyan Qin: Writing – original draft, Visualization, Supervision, Resources, Project administration, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Wei Zheng: Writing – review & editing, Writing – original draft, Software, Resources, Project administration, Methodology, Investigation, Data curation, Conceptualization. Peng Yang: Supervision, Software, Resources, Formal analysis, Data curation, Conceptualization. Bingqin Huang: Supervision, Software, Methodology, Investigation, Data curation, Conceptualization. Xiqi Zhu: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Supplementary Data 1

Data availability

Data will be made available on request.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2024.103672.
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