Altered Functional Connectivity Patterns of Parietal Subregions Contribute to Cognitive Dysfunction in Patients with White Matter Hyperintensities

Abstract

Background:

The white matter hyperintensities (WMHs) are considered as one of the core neuroimaging findings of cerebral small vessel disease and independently associated with cognitive deficit. The parietal lobe is a heterogeneous area containing many subregions and play an important role in the processes of neurocognition.

Objective:

To explore the relationship between parietal subregions alterations and cognitive impairments in WHMs.

Methods:

Resting-state functional connectivity (rs-FC) analyses of parietal subregions were performed in 104 right-handed WMHs patients divided into mild (n = 39), moderate (n = 37), and severe WMHs (n = 28) groups according to the Fazekas scale and 36 healthy controls. Parietal subregions were defined using tractographic Human Brainnetome Atlas and included five subregions for superior parietal lobe, six subregions for inferior parietal lobe (IPL), and three subregions for precuneus. All participants underwent a neuropsychological test battery to evaluate emotional and general cognitive functions.

Results:

Differences existed between the rs-FC strength of IPL_R_6_2 with the left anterior cingulate gyrus, IPL_R_6_3 with the right dorsolateral superior frontal gyrus, and the IPL_R_6_5 with the left anterior cingulate gyrus. The connectivity strength between IPL_R_6_3 and the left anterior cingulate gyrus were correlated with AVLT-immediate and AVLT-recognition test in WMHs.

Conclusion:

We explored the roles of parietal subregions in WMHs using rs-FC. The functional connectivity of parietal subregions with the cortex regions showed significant differences between the patients with WMHs and healthy controls which may be associated with cognitive deficits in WMHs.

INTRODUCTION

Cerebral small vessel disease (CSVD) is a generic term that refers to intracranial vascular disease with different clinical manifestations and neuroimaging features based on diverse pathomechanisms, which is present in most every individual aged 60 years or older and contributes to 25%of strokes and 45%of dementia [1]. CSVD can be asymptomatic and also manifest cognitive dysfunction, mood disorders, and motor and gait dysfunction [2]. CSVD is diagnosed on the basis of brain imaging biomarkers. The white matter is the most vulnerable region to suffer hypoxia/hypoperfusion due to the watershed effect and thus white matter hyperintensities (WMHs) are considered as one of the core neuroimaging findings of CSVD, which is defined as patchy or confluent hyperintensities on T2-weighted (T2WI) or T2 fluid attenuated inversion recovery (FLAIR) images, without cavitation in subcortical white or deep grey matter regions [3]. It is known that WMHs were independently associated with general cognitive function, such as processing speed and executive functioning [4, 5]. However, the underlying neural mechanism and cognitive impairment of WMHs is still largely unknown. Consequently, it is urgently needed to understand how WMHs exert their action on the aging brain and lead to clinical symptoms.

Advances in structural and functional neuroim-aging recently have begun to shed light on neural mechanisms in WMHs, and most research mainly focuses on the white matter and network analysis [6]. To our knowledge, the parietal lobe (PL) has received relatively little attention. However, the PL has a unique place in the human brain, which is at the crossroad between the frontal, occipital, and temporal lobes. And increasing knowledge of the PL have enlightened us to its crucial roles in the processes of neurocognition, including perception, attention, working memory, motor planning and control, mental calculation, language, self-awareness, and emotion [7]. Intuitively, the distribution of WMHs lesions near the PL could affect information transfer in white matter tracts or cell function in gray matter and disrupt the anatomical connectivity with other brain regions, explaining the impaired cognitive function in patients with WMHs [5]. An ischemic stroke and dementia cohorts study found the higher WMHs burden is associated with a thinner cerebral cortex in inferior parietal regions [8]. Furthermore, the changes in functional activity of PL, especially in the attention and executive function, are more studied in neuropsychiatric diseases [9, 10]. Patients with mild cognitive impairment showed decreased resting-state activity in the precuneus, and hyperactivation in the inferior parietal lobule (IPL) and superior parietal lobule (SPL) [11]. Functional connectivity refers to relevance degree of activities between anatomically separated but functionally correlated brain regions, which is defined as the temporal dependency of neuronal activation patterns [12]. Reduced connectivity between the pulvinar and posterior parietal cortex may explain impairments to autobiographical memory, self-referential processing, and socioaffective domains in posttraumatic stress disorder [13, 14]. All this evidence indicated the vital role of PL in the mechanisms of cognitive impairment in patients with WMHs.

The PL is a heterogeneous area and can be divided into two large parts traditionally: the anterior parietal cortex, mainly including the postcentral gyrus (PoCG) responsible for somatosensory; and the posterior parietal cortex, consisting of the SPL and IPL as well as precuneus responsible for advanced cognitive functions [14, 15]. However, neuroimaging studies suggested functional clusters that do not conform to the Brodmann demarcation of the PL [16]. Jiang et al. used both anatomical connec-tivity and task-dependent coactivation patterns separately to reparcellate the PL and identified five subregions for superior parietal lobule, six subregions for inferior parietal lobule, three subregions for precuneus, and four subregions for PoCG [17 –20]. Each subregion has different structural, functional, and networks patterns, which implied each subregion contributes differently to cognitive functions. Take the SPL for example. The two anterior subregions were primarily involved in action processes whereas the three posterior subregions were primarily in visual perception, spatial cognition, working memory, and attention. Besides, structural and functional abnormalities of each subregion showed differently in many neurological conditions including mild to severe cognitive impairment [21]. Therefore, revealing the specific association of parietal subregions with cognitive impairment in CSVD with WMHs will provide important references for the diagnosis and treatment precisely.

In this study, we explored the alterations of resting-state functional connectivity (rs-FC) of parietal subregions. In view that PoCG is mainly focused on sensory functions, we mainly analyzed rs-FC of the subregions in SPL, IPL, and precuneus. Additionally, correlation analyses were performed to reveal the associations between neuroimaging changes and clinical characteristics. Based on prior studies, we predicted that the functional connectivity patterns of parietal subregions would be differentially altered and associated with cognitive deficits in WMHs.

MATERIAL AND METHODS

Participants

104 right-handed patients with WMHs were rec-ruited from the Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China. The inclusion criteria were: 1) age between 40 and 80 years; 2) diagnosis of CSVD with WMHs made by experienced clinicians according to confluent hyperintensities in periventricular and deep WM on T2WI and T2-FLAIR. The exclusion criteria included: 1) intracranial and extracranial vascular stenosis over 50%; 2) trial of Org 10172 in Acute Stroke Treatment classification suggestive of cardiogenic stroke; 3) non-CSVD-related WMHs (e.g., multiple sclerosis); 4) other neuropsychiatric or severe systemic disease; and 5) MRI contraindications. According to the Fazekas scale on periventricular hyperintensity and deep white matter hyperintense signals, WMH severity was expressed as the sum of Fazekas scores in periventricular plus deep white matter. WMHs subjects were further div-ided into three subgroups, including the mild WMHs group scored 1–2 (n = 39), the moderate WMHs gr-oup scored 3–4 (n = 37), and the severe WMHs group scored 5–6 (n = 28) [22, 23]. Thirty-six healthy controls (HC) matched for sex ratio, age, and education years were recruited according to the same exclusion criteria supplemented no diagnosis of WMHs. The study was approved by the Anhui Medical University Ethics Committee and all participants provided written informed consent.

Neuropsychological measurements

All participants underwent a neuropsychological battery test of the Chinese Cerebral Small Vessel Disease Clinical Evaluation Study to evaluate the emotion and cognitive functions, including the Montreal Cognitive Assessment (MoCA) for the global cognitive function [24], Generalized Anxiety Disorder-7 [25] for emotional state, Patient Health Questionnaire-9 for somatic symptoms [26], Auditory Verbal Learning Test (AVLT) for memory [27], Symbol Digit Modalities Test [28], Digital Span, Stroop Color Word Test [29] and Trail Making Test (TMT) [30] for executive function and response speed, and Boston Naming Test (BNT) [31] for language, respectively.

MRI acquisition

Structural and functional MRI were obtained using a 3-T scanner (Discovery GE750w at Information Science Center of the University of Science and Technology of China). Participants were instructed to keep their eyes closed without sleeping and thinking during MRI scanning. The parameters of the different sequences were set as follow:

T1-weighted images (T1WI): TR = 8.16 ms; TE = 3.18 ms; Flip Angle = 12°; FOV = 256×256 mm²; slice thickness = 1 mm, no Gap; voxel size = 1×1×1 mm³; sections = 188.

T2FLAIR: TR = 8,000 ms; TE = 165 ms; TI = 2,000 ms; Flip Angle = 111°; matrix size = 512×512, FOV = 256×256 mm²; slice thic-kness = 5 mm, Gap = 1 mm; total slices = 20.

Enhanced gradient echo T2 star weighted angiography (ESWAN): TR = 52.189 ms; TE = 2.856 ms; Flip Angle = 12°; matrix size = 256×256, FOV = 220×220 mm²; slice thickness = 2 mm, no Gap.

Rs-fMRI: TR = 2,400 ms; TE = 30 ms; Flip Angle = 90°; matrix size = 64×64, FOV = 192×192 mm²; slice thickness = 3 mm, no Gap; 46 continuous slices (one voxel = 3×3×3 mm³).

WMHs volume

A cluster-based UBO Detector was used to calculate the total volume of WMHs (https://cheba.unsw.edu.au/group/neuroimagingpipeline) [32]. Firstly, we registered individual T2FLAIR image to their corresponding T1WI, then warped the registered image to the standard template by removing non-brain tissue and inhomogeneity. Lastly, we distinguished the WMHs and non-WMHs through a k-nearest neighbor-based algorithm. Any WMHs <12 mm from lateral ventricles were considered as PVH, and the remaining WMHs were DWMH.

fMRI data processing

Rs-fMRI data were preprocessed using Data Processing Assistant for Resting-State Functional MR Imaging toolkit (DPARSF; http://rfmri.org/content/dparsf) and Statistical Parametric Mapping (SPM12; https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) [33]. Pre-processing mainly included removing the first 10 time points to facilitate magnetization equilibrium, correcting the slice timing, realigning to the first volume for head motion, normalizing to the Montreal Neurological Institute (MNI) template, smoothing at a Gaussian kernel of 8 mm FWHM [34], detrending linear and regressing out motion parameters, white matter, cerebrospinal fluid, and global signals. Then, the data were filtered with a temporal band-pass of 0.01–0.1 Hz.

Definition of the parietal subregions

Parietal subregions were defined using tractogra-phic Human Brainnetome Atlas (http://atlas.brainnetome.org/bnatlas.html) which is constructed using whole brain anatomical connectivity-based parcellation [35]. We extracted the SPL, IPL, and precuneus, including 5, 6, and 4 subregions in each hemisphere respectively as the seed areas for resting-state functional connectivity analyses (Fig. 1). The details of parietal subregions are shown in the Supplementary Material.

Fig. 1

Parietal subregions in left hemisphere. Parietal subregions were defined using tractographic Human Brainnetome Atlas and included five subregions for superior parietal lobule (SPL), six subregions for inferior parietal lobule (IPL), three subregions for precuneus (PCun).

Functional connectivity analyses

To identify the changed functional connectivity patterns of each parietal subregion in WMHs, the whole brain functional connectivity analysis of each parietal subregion was performed, which included the following steps: extracting the mean time series of each parietal subregion, measuring the strength of the functional connectivity through Pearson’s correlations between the averaged time series of each parietal subregion and voxels in the remaining of the brain, applying the Fisher’s z transformation to normalize the original correlation maps and performing the One-way analysis of covariance (ANCOVA) and independent samples t-tests to determine areas with significantly altered functional connectivity to each parietal subregion in WMHs. Lastly, statistical maps were corrected with Gaussian Random Field method at threshold of voxel p < 0.001, cluster p < 0.001.

Statistical and correlation analyses

Demographic and neuropsychological data were analyzed using SPSS23.0 (SPSS, Inc., Chicago, IL, USA). One-way analysis of variance and independent sample t-tests were used to assess differences in age, education, neuropsychological tests score, and CSVD neuroimaging markers, while the Chi-square test was used in gender differences among the four groups. Pearson’s correlation analyses were performed to explore as the associations between changed functional connectivities and neuropsychological scores in all WMHs. The significance was set at p < 0.05.

RESULTS

General characteristics of the participants

The clinical and neuropsychological characteristics of the participants were summarized in Table 1. There was no significant difference between the four groups in the age, education years, and sex. Among the vascular risk factors, only hypertension differed between the four groups. As for neuropsychological characteristics, patients with WMHs demonstrated lower scores in the MoCA test, AVLT-immediate, AVLT-delay, AVLT-recognition, TMT-A, and BNT compared to the HC group, and these cognitive functions showed a trend of progressive decline as WMHs worsens. No significant difference in the emotion assessments was seen between the four groups. Besides, All CSVD groups displayed greater total WMH volume and the number of lacunes and cerebral microbleeds (CMBs) than the HC group.

Table 1

Demographics, cerebral small vessel disease neuroimaging manifestations, and neuropsychological tests of participants in the four groups (mean (SD))

	HC (n = 36)	Mild WMHs (n = 39)	Moderate WMHs (n = 37)	Severe WMHs (n = 28)	F/χ²	p
Age (y)	60.58 (5.98)	63.77 (8.23)	65.08 (10.03)	65.04 (6.90)	2.424	0.068
Female	18	16	14	16	3.011	0.390
Education (y)	9.31 (3.32)	8.41 (4.16)	8.00 (3.84)	7.75 (3.88)	1.083	0.359
Hypertension	8	21	22	21	19.568	< 0.001^***
Diabetes	2	8	7	4	3.905	0.272
Hyperlipidemia	8	10	9	8	0.356	0.949
Smoking history	9	18	15	11	3.808	0.283
Drinking history	12	9	16	12	4.346	0.226
Fazekas	0.00 (0.00)^a,b,c	1.64 (0.49)^a,d,e	3.59 (0.50)^b,d,f	5.43 (0.50)^c,e,f	958.855	< 0.001^***
WMHs volume	/	11457.35 (12493.05)^d,e	18067.56 (10873.84)^d,f	30658.380 (12033.91)^e,f	21.696	< 0.001^***
Lacunes	0.00 (0.00)^a,b,c	0.51 (0.91)^a	0.89 (1.29)^b	0.79 (1.23)^c	5.626	0.001^**
Microbleeds	0.31 (0.59)^c	2.26 (3.61)^e	1.50 (2.35)^f	6.29 (14.96)^c,e,f	3.389	0.021^*
MoCA	22.33 (3.09)^c	21.16 (4.32)^e	21.06 (3.79)^f	17.81 (4.91)^c,e,f	6.850	< 0.001^***
AVLT-study	8.15 (1.59)^c	7.63 (2.24)^e	7.31 (1.52)	6.55 (2.00)^c,e	3.776	0.012^*
AVLT-immediate	9.40 (2.30)^b,c	8.17 (3.84)^e	7.88 (2.65)^b,^f	6.17 (3.27)^c,e,f	5.320	0.002^**
AVLT-delay	9.06 (2.41)^b,c	7.95 (3.76)	7.31(2.67)^b	6.50 (3.13)^c	3.731	0.013^*
AVLT-recognition	13.63 (1.33)^c	13.53 (3.68)^e	13.16 (1.87)^f	11.13 (3.62)^c,e,f	4.734	0.004^**
TMT-A	63.34 (24.76)^a,b,c	80.72 (33.12)^a,e	79.12 (33.73)^b,f	105.82 (37.72)^c,e,f	8.233	< 0.001^***
TMT-B	129.65 (44.44)^c	155.35 (70.69)^e	158.16 (73.16)^f	207.55 (81.24)^c,e,f	6.301	0.001^**
BNT	13.94 (0.98)^a,b,c	12.89 (1.54)^a	13.25 (1.68)^b	12.74 (1.40)^c	4.797	0.003^**
PHQ-9	3.31 (4.78)^c,e	3.74 (4.56)^e	5.00 (4.62)	7.39 (5.81)^c,e	4.272	0.006^**
GAD-7	2.40 (3.24)^c,e	2.61 (4.10)^e	2.81 (3.43)^f	5.29 (5.48)^c,e,f	3.282	0.023^*

^aHealthy control group versus mild WMHs group significantly different (p < 0.05), ^bHealthy control group versus moderate WMHs group significantly different (p < 0.05), ^cHealthy control group versus severe WMHs group significantly different (p < 0.05), ^dMild WMHs group versus moderate WMHs group significantly different (p < 0.05), ^eMild WMHs group versus severe WMHs group significantly different (p < 0.05), ^fModerate WMHs group versus severe WMHs group significantly different (p < 0.05). SD, standard deviation; HC, healthy control; WMHs, white matter hyperintensities; MoCA, Montreal Cognitive Assessment; AVLT, Chinese Auditory Learning Test; TMT, Trial Making Test; BNT, Boston Naming Test; PHQ, Patient Health Questionnaire; GAD, Generalized Anxiety Disorder. Volume are in cubic millimeters. ^***significant at 0.001 level, ^**significant at 0.01 level, and ^*significant at 0.05 level (2-tailed).

Imaging manifestations

We illustrated the lesion load of the lacunes and CMBs in subcortical, deep and infratentorial to better understand the CSVD imaging markers in patients with WMHs (Table 2).

Table 2

Distribution of neuroimaging manifestations in HC and patients with WMHs (mean (SD))

	Lacunes				CMBs
	HC (n = 36)	Mild WMHs (n = 39)	Moderate WMHs (n = 37)	Severe WMHs (n = 28)	HC (n = 32)	Mild WMHs (n = 27)	Moderate WMHs (n = 30)	Severe WMHs (n = 21)
Subcortical
Frontal	0.00 (0.00)	0.18 (0.51)	0.11 (0.39)	0.14 (0.45)	0.03 (0.18)	0.11 (0.32)	0.13 (0.57)	0.33 (0.80)
Parietal	0.00 (0.00)	0.03 (0.16)	0.11 (0.39)	0.00 (0.00)	0.09 (0.30)	0.19 (0.68)	0.13 (0.35)	0.33 (0.91)
Occipital	0.00 (0.00)	0.00 (0.00)	0.05 (0.23)	0.04 (0.19)	0.03 (0.18)	0.07 (0.27)	0.07 (0.25)	0.57 (1.36)
Temporal	0.00 (0.00)	0.08 (0.27)	0.05 (0.33)	0.21 (0.50)	0.03 (0.18)	0.41 (1.15)	0.23 (0.63)	1.48 (3.12)
Any subcortical	0.00 (0.00)	0.03 (0.16)	0.08 (0.28)	0.11 (0.31)	0.00 (0.00)	0.04 (0.19)	0.00 (0.00)	0.19 (0.87)
Deep
Basal ganglia	0.00 (0.00)	0.18 (0.51)	0.38 (0.79)	0.18 (0.61)	0.03 (0.18)	0.22 (0.51)	0.17 (0.38)	0.71 (1.23)
Thalamus	0.00 (0.00)	0.00 (0.00)	0.03 (0.16)	0.00 (0.00)	0.00 (0.00)	0.07 (0.27)	0.17 (0.46)	0.43 (1.33)
Internal capsule	0.00 (0.00)	0.00 (0.00)	0.03 (0.16)	0.00 (0.00)	0.03 (0.18)	0.44 (0.84)	0.23 (0.77)	0.62 (1.72)
Any deep	0.00 (0.00)	0.00 (0.00)	0.03 (0.16)	0.11 (0.31)	0.03 (0.18)	0.11 (0.32)	0.20 (0.66)	0.24 (0.89)
Infratentorial
Cerebellum	0.00 (0.00)	0.00 (0.00)	0.03 (0.16)	0.00 (0.00)	0.03 (0.18)	0.15 (0.36)	0.13 (0.35)	0.86 (3.50)
Pons	0.00 (0.00)	0.03 (0.16)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.11 (0.42)	0.03 (0.18)	0.14 (0.48)
Mesencephalon	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.15 (0.46)	0.03 (0.18)	0.14 (0.65)
Any	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	0.04 (0.19)	0.00 (0.00)	0.29 (1.10)
Any	0.00 (0.00)	0.51 (0.91)	0.89 (1.29)	0.79 (1.23)	0.31 (0.59)	2.11 (3.53)	1.53 (2.36)	6.33 (14.96)

HC, healthy control; WMHs, white matter hyperintensities; SD, standard deviation; CMBs, cerebral microbleeds.

Changed functional connectivity

Whole brain functional connectivity analyses of parietal subregions identified significantly changed functional connectivities of IPL_R_6_2 with the left anterior cingulate gyrus (Fig. 2A), IPL_R_6_3 with the right dorsolateral superior frontal gyrus (dlSFG) (Fig. 3A), and the IPL_R_6_5 with the left anterior cingulate gyrus between the four groups (Fig. 4A). Further post-analysis showed that these imaging differences were more significant between the severe WMHS group and HC (Fig. 2B , 4B).

Fig. 2

The changed functional connectivity of IPL_R_6_2 with the left anterior cingulate gyrus (MNI: 9 36 9) in patients with WMHs. IPL, inferior parietal lobule; WMHs, white matter hyperintensities; HC, healthy control.

Fig. 3

The changed functional connectivity of IPL_R_6_3 with the right dorsolateral superior frontal gyrus (MNI: 18 –3 57) in patients with WMHs. IPL, inferior parietal lobule; WMHs, white matter hyperintensities; HC, healthy control.

Fig. 4

The functional connectivity of IPL_R_6_5 with the left anterior cingulate gyrus (MNI: –9 36 –6) changed significantly in patients with WMHs. IPL, inferior parietal lobule; WMHs, white matter hyperintensities; HC, healthy control.

Correlation analysis

The connectivity strength between IPL_R_6_3 and the left anterior cingulate gyrus was correlated with AVLT-immediate (p = 0.024, r = 0.233), AVLT-recognition (p = 0.007, r = 0.281) in WMHs (Fig. 5). The other correlation analysis results without statistically significance were included in the Supplementary Material.

Fig. 5

The correlation between the functional connectivity strength between IPL_R_6_3 and the left anterior cingulate gyrus and AVLT-immediate, AVLT-recognition in WMHs. IPL, inferior parietal lobule; WMHs, white matter hyperintensities; AVLT, Auditory Verbal Learning Test.

DISCUSSION

The current study explored the roles of parietal subregions in WMHs using rs-FC. The functional connectivity of IPL_R_6_2 with the left anterior cingulate gyrus, IPL_R_6_3 with the right dorsolateral superior frontal gyrus, and IPL_R_6_5 with the left anterior cingulate gyrus showed significant difference between the four groups.

The principal findings suggest parietal subregions showed differentially disrupted functional connectivity in WMHs, mainly focus on the right IPL. The IPL is an important node in the parietal association cortex and implicated in multimodal integration and advanced cognition [36]. Interestingly, the IPL is greatly expanded and matures late in human development compared to other primates, which implies it is unusual [37]. The IPL is also known for its hemispheric differences in cognitive functions; predominantly the right IPL is responsible for spatial attention processing and mathematical cognition, and the left IPL is for tool use and language and semantic processing [16 , 38–41]. Later studies found different perceptual and cognitive processes may selectively involve subregions of the RIPL that can be functionally aggregated and tried to look for modalities to parcellate the RIPL [42 –44]. According to tractographic Human Brainnetome Atlas, the RIPL is divided into six subregions with a unique pattern of structural and functional connectivities. IPL_R_6_2 and IPL_R_6_5 are combined into the anterior angular gyrus (AG) and both showed altered functional connections with the left anterior cingulate gyrus [20]. The dorsal anterior IPL_R_6_2 subregion in the AG is mainly involved in cognition, attention, and reasoning and the ventral anterior IPL_R_6_5 subregion is in social cognition and observation. Elman et al. explored the neural correlates of episodic retrieval and found episodic retrieval of recently learned locations activated a circumscribed region within the anterior angular gyrus while familiar locations activated more posterior regions in the posterior angular gyrus [45]. Another recent study also showed the AG connectivity was significantly reduced within the DMN in mild cognitive impairment [46]. Taken together, these findings would promote our understanding of the role of the IPL in cognitive diseases.

In addition, IPL_R_6_3 exhibited decreased connectivity with the right dlSFG and the functional connectivity strength was significantly correlation with AVLT-immediate and AVLT-recognition in WMH group. IPL_R_6_3, located in the rostrodor-sal supramarginal gyrus (SMG) area, mainly involves in sensorimotor processing, tool use, action observation, and imitation, and connects with the sensori-motor regions, frontal regions, temporal lobes, cingulate cortex, and basal ganglia [20, 47]. IPL_ R_6_3 is also suggested as a part of the mirror neuron system that might explain the human capacity to learn by understanding others’ actions and their intentions behind them [48, 49]. A VBM analysis in the posterior cortical atrophy characterized by progressive impairment in visuospatial and perceptual function patients revealed a significant correlation between total learning and grey matter density in the right supramarginal gyrus, which is consistent with our results [50]. Guidali et al. ran four TMS experiments by different short-term memory tasks (verbal, spatial, motor, visual) and found SMG is one of the key nodes of the short-term memory network involved in retaining an abstract representation of serial order information, independently from the content information [51]. Moreover, SMG is recognized as an important part of the salience network, especially in the bottom-up perceptual salient stimulus processing [52, 53], while disrupted salience network connectivity and the relevance with cognitive impairment were largely reported in mild cognitive impairment and AD patients [46]. Therefore, these results indicate that IPL_R_6_3 is critically important for the learning and short memory. Besides, we found the prevalence of hypertension was higher in WMHs groups relative to the controls, which may influence the blood oxygenation level dependent (BOLD) signal. Hypertension will place enormous stress on the cerebral circulation and cause changes to the structure and function of cerebral blood vessels, making it a major risk factor for vascular cognitive impairment [54]. Studies in older adults with hypertension show reduced blood flow, particularly in prefrontal, and occipital-temporal regions, which was associated with white matter degradation, gray matter atrophy, and cognitive deficits. While the blood flow is the main factor of the BOLD signal in fMRI [55]. However, the specific effect of hypertension on cerebral BOLD signal is still unclear, which is needed to further explore or correct.

Some limitations should be further consideration. First, the sample size was relatively small. These findings should be validated in a larger population. Second, this is a correlational study that could not explore the cause and effect relationship among functional alterations, WMH burden, and cognitive impairments. Also, the results might be affected by multiple confounding factors, including mental and medical status, and medication condition. Third, the impact of other imaging types of CSVD, including lacunes and cerebral microbleeds, on functional networks was not analyzed, which need future studies to understand the relationship underlying the onset of cognitive impairments in CSVD. Furthermore, as our neuropsychological test battery was focused on typical cognitive profile of CSVD patients and lacked tests of cognitive functions specific to parietal lobe, it is worth considering additional cognitive measures to investigate the relationship between parietal subregions alterations and cognitive impairments in WHMs.

In conclusion, we have identified abnormal functional connectivity of PL subregions in CSVD patients with WMH. These findings have important implications for the underlying neurobiology of CSVD and add the new potential biomarker for detecting CSVD in the future.

Footnotes

ACKNOWLEDGMENTS

We would like to thank the Information Science Laboratory Center of the University of Science and Technology of China for their measurement services.

This work study supported by the National Key Research and Development Program of China (2016YFC1300604).

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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