Functional changes in the dorsal attention network in perimenopausal women: a resting-state functional MRI study

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Methods: A total of 25 perimenopausal women and 25 premenopausal women underwent sex hormone level, scale, and cognition assessments, as well as magnetic resonance imaging (MRI) scans. The resting state fMRI data were acquired using a 3.0 Tesla magnetic resonance scanner, and the differences in DAN functional connection between these two groups were evaluated by independent component analysis (ICA). Gray matter volume (GMV) values of brain regions (regions of interest [ROI]) with differences in DAN functional connection were extracted, and the differences in GMV between the two groups were compared. Correlation analysis was performed between the connection strengths of the DAN functional connection and GMV values of ROIs with sex hormone levels and clinical and neuropsychological assessments in the two groups. Results: Compared with the premenopausal group, the brain regions with enhanced functional connection in the perimenopausal group were the right inferior parietal lobule (IPL) and the right angular gyrus (AG) in the DAN. There were no differences in GMV values between the two groups. Correlation analysis showed that connection strengths of the right IPL negatively correlated with the estradiol level and positively correlated with the reaction time of the STROOP color-word test in perimenopausal women. Conclusions: The ICA demonstrated that the DAN functional changes may stimulate the brain's compensatory mechanisms to compensate for physiological and psychological problems in women during the reproductive transition period. Our findings provide evidence for understanding the changes in brain function in perimenopausal women. dorsal attention network (DAN) perimenopausal period magnetic resonance imaging (MRI) estrogen cognitive function Figures Figure 1 Figure 2 Background During perimenopause, nearly two-thirds of women often complain about lack of concentration, slow movement, forgetfulness, and subjective cognitive difficulties[ 1 ]. A cross-sectional study of 130 perimenopausal women found that those who reported frequent forgetting scored lower in attention determination[ 2 ]. Another cross-sectional study of 75 perimenopausal women found that the degree of perceived memory difficulty in perimenopausal women was associated with poor performance in working memory and complex attention tests[ 3 ]. An analysis of a large longitudinal cohort (N ≈ 2000) showed that during the perimenopausal period, cognitive process speed, speech encoding (immediate paragraph recall), and overall situational memory (delayed paragraph recall) temporarily decreased[ 4 – 5 ]. Another cohort study (N ≈ 400) found that compared to premenopausal, the encoding of the word list during the postmenopausal period (immediate recall) was lower, and the verbal memory during the perimenopausal period (delayed recall) decreased compared to premenopausal. In this analysis, anxiety, depression, and stress symptoms were not associated with cognitive performance, and adjusting for them did not alter the negative association between premenopausal and perimenopausal language encoding or memory[ 6 ]. Studies have found that perimenopausal cognitive dysfunction in women occurs in the context of estrogen withdrawal. During a 2-year observation period using 18F-FDG-PET, it was found that the metabolism of brain regions involved in learning and memory was significantly reduced in perimenopausal women, such as the hippocampus, parahippocampal gyrus, temporal lobe, medial prefrontal cortex, and posterior cingulate cortex. After estrogen replacement therapy, the low metabolism in these brain regions was alleviated, and memory function was improved[ 7 – 9 ]. In a study evaluating regional cerebral blood flow, postmenopausal women who received estrogen treatment showed increased cerebral blood flow in resting-state brain regions; postmenopausal women who did not receive estrogen treatment did not experience this effect[ 7 ]. Enhanced hippocampal activation and reduced parahippocampal activation in perimenopausal women receiving hormone therapy suggest that such treatment during the perimenopausal period increases state-dependent and memory-dependent processes to improve speech recognition[ 10 ]. Previous studies by our group using resting-state functional magnetic resonance imaging (fMRI) have shown that cognitive-related brain regions in perimenopausal women undergo spontaneous changes in brain activity[ 11 , 12 ]. In recent years, fMRI has been used as a popular tool in neuroscience to evaluate the therapeutic effects of hormone therapy on cognitive function in perimenopausal women and to indicate that perimenopausal women may benefit from specific hormone therapies in cognitive control, speech, and working memory[ 13 ]. For most functional magnetic resonance imaging studies related to perimenopausal women, which have focused on changes in brain function under stimulation or after hormone therapy[ 14 – 19 ], few studies have focused on hormonal fluctuations in women during the perimenopausal period and their effects on brain networks. The DAN plays a vital role in cognitive processing and consists mainly of the bilateral parietal and frontal lobes, which are responsible for top-down attentional orientations and are involved in exogenous attentional orientations. However, it is not yet clear how DAN changes during the women’s reproductive transition. Therefore, in this study, we applied fMRI and used independent component analysis (ICA) to identify differences in DAN between premenopausal and perimenopausal women, explore the relationship between serum hormone levels and DAN in perimenopausal women, and evaluate the relationship between DAN functional connectivity strength, serum estrogen levels, and cognitive function. Methods Participants The inclusion criteria were as follows: (a) female candidates aged 45–55 years; (b) right-handed; (c) education of more than 12 years; and (d) a diagnosis of perimenopause based on the Stages of Reproductive Aging Workshop (STRAW) + 10: menstrual cycle change more significant than 7 days, repeated for 10 menstrual cycles or an amenorrhea interval ≥ 60 days, and follicle-stimulating hormone (FSH) range 11–45 IU/L. Premenopausal women were recruited based on not meeting the diagnosis criterion of STRAW + 10; they had a regular ovulation day based on the rhythm method and FSH < 11IU/L. The exclusion criteria were as follows: (a) history of neoplasms of the female genital organs, uterectomy, or oophorectomy; (b) presence of neurological and psychiatric disorders or history of brain trauma, smoking or alcohol dependence, or other diseases that may affect brain structure and function; (c) presence of mood disorder (such as depression or anxiety disorders); (d) history of hormone administration; (e) color-blindness; or (f) MRI contraindications. Finally, a total of 50 participants met the requirements for the fMRI experiment, including 25 women in the perimenopausal group (average age, 53.19 ± 3.82 years) and 25 women in the premenopausal group (average age, 47.67 ± 3.48 years). Sex hormone level measurement All participants underwent measurement of the levels of sex hormones, including follicle-stimulating hormone(FSH), luteinizing hormone(LH), estradiol(E2), progesterone(P), testosterone(T), and prolactin. Samples were collected for sex hormone measurements by blood collection from the elbow vein at 8:00–9:00 am within 3 days of the start of menstruation. The collected venous blood was analyzed by chemiluminescence analysis, and the concentrations of the six abovementioned sex hormones were determined. Participants with an abnormal menstrual cycle or amenorrhea completed the blood sex hormone testing at 8:00–9:00 am on the day of the experiment. Scale and Cognition evaluations All participants completed the menopause rating scale (MRS) and the patient health questionnaire (PHQ-9) to evaluate their menopausal status and the presence of depressive symptoms. All participants employed the computer-based STROOP color-word test, in which incongruent color words were shown in the center of a computer monitor, and participants were required to choose from four matching color words according to the ink color of the color word in the center. fMRI acquisition and processing All participants underwent conventional MRI examinations and resting-state fMRI scans at a 3.0 T MR scanner (Discovery MR 750, GE, US) with an eight-channel head coil. The participants were 3 days postmenstrual period for the fMRI scan. For participants with amenorrhea, there was no such time restriction. For MRI scanning, we adopted three-dimensional brain volume (T1 3D-BRAVO) (TR/TE = 8.1/3.1 ms, flip angle = 13°, FOV = 256 mm × 256 mm, matrix = 256 × 256, slice = 176, slice thickness = 1 mm) to exclude organic lesions such as cerebral infarction and tumor. For the resting state fMRI scans, a single-shot gradient echo planner imaging sequence was used: TR/TE = 2000/30 ms, FOV = 220 mm × 220 mm, matrix = 64 × 64, slice = 32, slice thickness = 3 mm, slice gap = 0.9 mm, and measurements = 180. Before the MRI scanning, sponge pads were placed on both sides of the participant’s ears to keep the head fixed, and the participant was told to keep closed eyes, quiet, and awake during the scanning. The fMRI data were analyzed using the Data Processing Assistant for Resting-State fMRI (DPARSFA http://restfmri.net/forum/DPARSF ) software. Considering the required time for the BOLD signal to stabilize and the adaption of participants to the environment, the first 10 phases of the acquired images were removed to eliminate potential noise interference, and the remaining data for 170 phases of images were used for preprocessing. The acquisition time difference between layers of the images and the head movement was corrected. Images with translation ≥ 2 mm and rotation ≥ 2° were excluded. No participants were excluded due to apparent head movements in this experiment. The images from all participants were assigned to the Montreal Neurological Institute (MNI) template, and then all data were resampled to obtain the functional image data for 2 × 2 × 2 mm 3 voxels. The full width at half maximum (FWHM) Gaussian kernel of 4×4×4 mm 3 was used to space smooth the fMRI images. Identification of resting-state networks (RSNs) The independent component analysis (ICA) of the smoothed data was performed using MICA software tools (Stable and Consistent Group ICA of fMRI Toolbox, version1.2, http://www.nitrc.org/projects/cogicat/ ) based on MatlabR2012a (MathWorks Inc., http://www.mathworks.com ) platform. Data reduction was performed using triple principal component analyses. They were using the MICA software to calculate and recognize the independent components. The data for each participant were segmented into 20 spatially independent components. The arithmetic operation of ICA was 100 times. Each subject’s independent components, including time series and spatial diagrams, were obtained, reconstructed, and transformed by Fisher z. After careful observation and analysis, the independent components consistent with the template reported in the literature were selected [ 20 ]. Structural MRI preprocessing Structural MRI data were preprocessed using voxel-based morphometry and the Statistical Parametric Mapping (SPM12, London, UK) software. The data preprocessing steps included segmenting into gray matter (GM), white matter, and cerebrospinal fluid and registering the GM DARTEL template to the tissue probability map in the MNI space. Each voxel ’s gray matter volume (GMV) was obtained by multiplying the GM concentration map by the nonlinear determinants derived from the spatial normalization step. The GMV represents the probability that each voxel is genetically modified for individual brain sizes. Finally, the GMV maps were smoothed with a Gaussian kernel of 4×4×4 mm 3 FWHM. Statistical analysis All statistical analyses were performed using SPSS 20.0 software (SPSS, Chicago, IL). The independent two-sample t-test was used to compare the age, years of education, MRS score, PHQ-9 score, the accuracy rate and reaction time of Color-Word STROOP, and sex hormone levels between the two groups. P < 0.05 indicated a statistical difference. The statistical analysis module of Matlab-based statistical parametric mapping (SPM12, Wellcome Department of Imaging Neuroscience, London, UK) was used to analyze RSNs. Specifically, a general linear model was used to detect any significant statistical differences in RSNs between premenopausal and perimenopausal women. Age and years of education were controlled as covariates, as they may affect the results. For multiple comparison correction, AlphaSim correction with voxel level P < 0.01 and the corrected threshold was P < 0.05. Correlation analyses of function connectivity (FC) values of the DAN with GMV values, MRS scores, PHQ-9 scores, the accuracy rate and reaction time of the Stroop color-word test, and sex hormone levels were conducted in the perimenopausal group. After group analysis, the regions showing significant FC changes between the two groups were identified, and the mean FC of each region was extracted in the perimenopausal group. Spearman partial correlation analyses were conducted to evaluate the relationship between the mean FC values and GMV values of these regions with MRS score, PHQ-9 score, the accuracy rate and reaction time of STROOP color-word, and sex hormone levels, with age and years of education considered nuisance covariate. Results Demographic and clinical data The demographic characteristics, scale, STROOP assessments, and sex hormone data for the two groups are summarized in Table 1 . There were statistical differences in age, MRS score, PRL, FSH, E2, and the reaction time of the STROOP color word (P 0.05). Table 1 Demographic, sex hormone levels and behavioral data between perimenopausal and premenopausal groups Perimenopausal group (n = 25) Premenopausal group (n = 25) P value Age (years) 53.19 ± 3.82 47.67 ± 3.48 <0.001 Educations (years) 13.00 ± 4.58 15.07 ± 4.20 0.201 PHQ−9 score 2.38 ± 1.63 3.20 ± 3.60 0.409 MRS score 18.50 ± 1.55 10.93 ± 2.84 <0.001 PRL(ng/ml) 12.05 ± 8.60 19.01 ± 5.88 0.014 FSH(IU/L) 24.25 ± 10.58 8.07 ± 3.78 <0.001 E 2 (pg/mol) 22.84 ± 11.00 101.47 ± 70.34 <0.001 T(ng/dl) 35.56 ± 10.27 30.84 ± 10.33 0.303 P(ng/ml) 0.17 ± 0.18 0.31 ± 0.34 0.161 LH(mIU/ml) 28.00 ± 26.52 14.82 ± 12.96 0.093 The reaction time of STROOP(ms) 1354.75 ± 261.17 1094.37 ± 146.16 0.002 The accuracy rate of STROOP(%) 97.88 ± 3.81 96.73 ± 4.38 0.097 PHQ−9, Patient Health Questionnaire-9; MRS, Menopause Rating Scale; PRL, prolactin; FSH, follicle stimulating hormone; E2, estradiol; T, testosterone; P, progesterone; LH, luteotropic hormone. Comparison of FC in the resting-state DAN The results of comparisons of brain regions with different FC in the DAN between the perimenopausal and premenopausal groups are summarized in Table 2 . Compared with the premenopausal group, after adjusted by AlphaSim, with clusters ≥ 74 and P < 0.05, our results revealed that the brain regions with enhanced FC in the DAN included the right inferior parietal lobule (IPL) and the right angular gyrus (AG) (Fig. 1 ). We extracted the FC values of the above two brain regions in both groups and found that the FC values were significantly higher in the perimenopausal group than in the premenopausal group (Table 3 , P < 0.05). Table 2 Brain regions with different functional connections in the DAN between the perimenopausal and premenopausal groups Brain regions BA Cluster size(voxel) MINI Coordination (x,y,z) Peak t score Right inferior parietal lobule 40 83 52,−52,36 3.72 Right angular gyrus 39 82 48,−68,36 3.89 DAN, dorsal attention network; BA, Brodmann area; MNI, Montreal Neurological Institute coordinate system or template; x, y, z, coordinates of primary peak locations in the MNI space. Table 3 Functional connection values differences in DAN between the perimenopausal and premenopausal groups Groups Brain region Perimenopausal group Premenopausal group t -Value P- value Perimenopausal > permenopausal Right inferior parietal lobule 1.89 ± 0.53 1.37 ± 0.48 2.896 0.007 Right angular gyrus 0.85 ± 0.36 0.58 ± 0.33 2.192 0.037 DAN, dorsal attention network. Comparison of GMV in the resting-state DAN GMV values were extracted for the two brain regions that showed no significant differences in GMV values of DAN between the perimenopausal and postmenopausal groups (P > 0.05). Correlation analysis Spearman partial correlation analysis, showed that in the perimenopausal group, the FC value of the right inferior parietal lobule was significantly and negatively correlated with the E2 level (P = 0.003, correlation coefficient: -0.585) and positively correlated with the reaction time of STROOP (P = 0.001, correlation coefficient: 0.636; Figure 2 ). The GMV values of the two ROIs in DAN showed no significant correlations with sex hormone levels, scales, and STROOP data. Moreover, Spearman’s partial correlation analysis showed no significant correlations between the GMV values of the two ROIs in DAN and age in the perimenopausal and premenopausal groups. There were no significant correlations between age and sex hormone levels in the two groups. Discussion Previous studies have considered that perimenopause is a transitional state of female reproductive aging. Perimenopause is characterized by unique endocrine characteristics that affect the aging of multiple organ systems, including the brain; its essence is transforming the nervous system[ 21 ]. The physiological and psychological symptoms that occur during perimenopause are disrupted by various hormone regulatory systems, including fluctuations in serum hormones such as estrogen, which may affect the structure and function of the central nervous system through a hormone receptor network[ 22 , 23 ]. Therefore, the present study investigates the relationship between cognitive and resting-state functional connectivity in perimenopausal women and explores the relationship between serum hormone levels and DAN, which is of great clinical importance. New data-driven analysis techniques have rapidly become efficient and powerful tools for exploring large-scale networks in the human brain. Independent Component Analysis (ICA) is highly suitable for analyzing resting-state functional magnetic resonance imaging data and evaluating the connectivity of resting-state brain networks participating in oscillatory activities without prior selection of regions of Interest[ 24 – 26 ]. In this study, the ICA method was used to evaluate the changes in DAN in perimenopausal women, and correlation analysis was used to evaluate the relationship between estrogen levels, cognitive function, and DAN. This study showed that DAN functional connectivity was increased in perimenopausal women compared to premenopausal women and that DAN was significantly associated with estrogen levels and cognitive function. The concept of anatomical and functional attention networks in the human brain was first proposed by Corbetta and Shulman et al. [ 27 ]. Fox et al. first assessed the DAN using rs-fMRI. They found that the functional organization of the DAN can be represented by the correlation structure of spontaneous activity, essentially defining the DAN in a way that is broadly consistent with task-based models[ 28 ]. The DAN is involved in top-down, autonomous attentional control, driven from the frontoparietal lobes and responding in the occipital lobes[ 29 ]. In this study, compared with premenopausal women, perimenopausal women showed an increase in functional connectivity in the right IPL node of the DAN, which was significantly positively correlated with the reaction time of STROOP. Many cognitive processes are realized through spatially distributed neural networks in the human brain, and the IPL, an area of hetero-modal convergence of various brain networks, is central to realizing critical cognitive operations at different levels of the neural processing hierarchy. These psychological operations include lower-level processes, such as spatial attention, and significantly more complex, higher-level processes in the human species, such as semantic memory and social communication patterns[ 30 ]. In short, the IPL is involved in a wide range of cognitive functions, including attention, action-related functions, self-perception, memory, and social cognition, or with a focus on cognitive control of language[ 31 ]. Shaywitz et al. found that postmenopausal women who received estrogen treatment showed increased activation of the IPL and performed better in storing speech materials[ 32 ]. This study found enhanced functional connectivity in the right IPL of the DAN in perimenopausal women, which may indicate a decline in cognitive function and a longer reaction time to complete the STROOP task compared to premenopausal women. However, the enhanced functional connectivity required for perimenopausal women with decreasing serum estrogen levels to complete the task with the same speed and accuracy may be due to the early compensatory response to cognitive impairment in the initial phase of the DAN with decreasing estrogen levels. This study also showed that perimenopausal women had enhanced functional connectivity of the right AG in DAN compared to premenopausal women. In many meta-analytic reviews, the AG is consistently activated across various tasks. The AG plays a role in semantic processing, word reading and comprehension, number processing, default mode networks, memory retrieval, attentional and spatial cognition, reasoning, and social cognition. The AG is a cross-modal hub where fused multi-sensory information is combined and integrated to understand and make sense of events, manipulate mental representations, solve familiar problems, and redirect attention to relevant information[ 33 ]. The AG is located in the posterior part of the subparietal lobule, and the two are anatomically closely related and highly integrated in function. The current findings may indicate that during perimenopause, women experience changes in functional connectivity within the DAN network caused by decreasing estrogen levels in the inferior parietal lobule and the angular gyrus. In this study, there was no significant difference in the accuracy of cognitive tasks performed by perimenopausal women compared to premenopausal women, which may be attributed to the enhanced connectivity of the IPL and AG of the DAN in compensating for cognitive impairment. E2 is a form of estrogen that acts on multiple brain regions and is associated with establishing neuroendocrine phenomena and behavioral patterns[ 34 , 35 ]. It involves cognitive function, emotional regulation, learning, and memory[ 36 , 37 ]. Studies have shown that perimenopausal women treated with estrogen have increased activity in relevant brain regions and significantly improved working memory and emotional processing[ 38 , 39 ]. Studies have shown that E2 receptors are distributed in the parietal cortex[ 40 ], essential in advanced cognitive functions. In this study, we found that functional connectivity strength values in the right inferior parietal lobule of the DAN were negatively correlated with E2 levels, suggesting that estrogens play a role in DAN-related tasks, affecting DAN activity and subsequently affecting cognitive function. When E2 levels decrease, DAN requires more network nodes to exert more robust connectivity to compensate for neurodegenerative changes and maintain good cognitive performance. Additionally, some studies suggest that GM atrophy may be related to age[ 41 ]. However, this study found no significant correlation between GMV values at the DAN key nodes and age or sex hormone levels. The slight age difference between the perimenopausal and premenopausal groups or the small sample size may have contributed to this. Limitation The study has several limitations that need to be addressed in future research. Firstly, the sample size was small. Although fMRI is rarely used to study brain DAN changes in perimenopausal women, a larger sample size could make the results more representative. Secondly, longitudinal studies are also necessary. In future studies, women's brain changes and cognitive function changes can be longitudinally followed from premenopausal to perimenopausal and postmenopausal periods. The effects of estrogen on women's brain DNA can be dynamically observed. Conclusion Resting-state fMRI was used to assess differences in DAN between premenopausal and perimenopausal women. The results showed that cognitive function was associated with more excellent DAN connectivity in perimenopausal women, particularly in the right IPL and right AG. Correlation analyses assessed the relationship between estrogen levels, cognitive function, and DAN. The study found that DAN activity and functional compensation differed between perimenopausal and premenopausal women. Additionally, there was a significant correlation between the DAN's estradiol levels and network nodes, which affect cognitive function. In conclusion, the relationship between estradiol and DAN and the changes in the abnormal patterns of DAN may help to understand the functional changes of the brain in perimenopausal women. This study could provide new insights for the diagnosis and clinical intervention of perimenopausal cognitive dysfunction. Abbreviations DAN Dorsal Attention Network MRI Magnetic Resonance Imaging ICA Independent Component Analysis GMV Gray Matter Volume FC Function Connectivity IPL Inferior Parietal Lobule AG Angular Gyrus Declarations Acknowledgements The authors are deeply grateful to all participants involved in this study, and also thank all of the doctors and researchers who participated in the study. Author contributions LNN and LHJ designed the study, ZY and FWQ collected data, LNN and ZY wrote the manuscript, LNN and LHJ revised the manuscript. All authors read and approved the final manuscript. Funding This study was supported by the National Social Science Fund of China [grant no. 15BSH065]. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Declarations Ethics approval and consent to participate This study was conducted with approval from the Ethics Committee of Second Hospital of Tianjin Medical University. The participants provided their written informed consent to participate in this study. This study was conducted in accordance with the declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Greendale GA, Karlamangla AS, Maki PM. The Menopause Transition and Cognition. JAMA. 2020;323(15):1495–6. Unkenstein AE, Bryant CA, Judd FK, et al. Understanding women's experience of memory over the menopausal transition: subjective and objective memory in pre-, peri-, and postmenopausal women. Menopause. 2016;23(12):1319–29. Weber MT, Mapstone M, Staskiewicz J, et al. Reconciling subjective memory complaints with objective memory performance in the menopausal transition. Menopause. 2012;19(7):735–41. Greendale GA, Huang MH, Wight RG, et al. 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Enhanced Neuroactivation during Working Memory Task in Postmenopausal Women Receiving Hormone Therapy: A Coordinate-Based Meta-Analysis. Front Hum Neurosci. 2015;9:35. Rettberg JR, Yao J, Brinton RD. Estrogen: a master regulator of bioenergetic systems in the brain and body. Front Neuroendocrinol. 2014;35(1):8–30. Halkur Shankar S, Ballal S, Shubha R. Study of normal volumetric variation in the putamen with age and sex using magnetic resonance imaging. Clin Anat. 2017;30(4):461–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4436654","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305334126,"identity":"443f8b49-98fe-47f0-a6aa-9bccc4a203da","order_by":0,"name":"Ningning Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACZhBRIAFksDEcYGCw4eHnbyBGiwFUywGGNBnJGQeIscoARLAxAK05bGPQkIBfsTk77+HXBQYWidvZ2RIPf6g5z2PAcIDxw8cc3Fosm/nSrGcYSCTubGY7cODAsds85swNzJIzt+Fx0mEeM2MeoJYNh9kbDhxgu81j2XCAjZmXeC3/zvEYHEggqMX4MUQL0GEH2w4QpcWMGajFGKgl4cDZvmQeyRkHm/H75fwZ4888FXWyG84fM/5Q8c3Onp+/+eCHj3i0AAGbBJoAYwNe9UDA/IGQilEwCkbBKBjhAACbsFJZZUEVKwAAAABJRU5ErkJggg==","orcid":"","institution":"Second Hospital of Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ningning","middleName":"","lastName":"Liu","suffix":""},{"id":305334127,"identity":"00176ae2-04ff-4226-a824-bb8e68c7684b","order_by":1,"name":"Yue Zhang","email":"","orcid":"","institution":"Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhang","suffix":""},{"id":305334128,"identity":"f7ee4de3-0698-4c05-8d0c-8b59cfbfc996","order_by":2,"name":"Weiqing Fu","email":"","orcid":"","institution":"Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiqing","middleName":"","lastName":"Fu","suffix":""},{"id":305334130,"identity":"f0a6e9f0-4c60-466e-882d-65a7bcb9facb","order_by":3,"name":"Huijun Liu","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huijun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-17 12:12:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4436654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4436654/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57865924,"identity":"39893cfa-968f-47b5-b678-f12e0e6c069c","added_by":"auto","created_at":"2024-06-06 15:48:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":967567,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions with enhancing functional connection within DAN between the perimenopausal and premenopausal groups\u003c/p\u003e\n\u003cp\u003eDAN, dorsal attention network.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4436654/v1/d5fd46094682d19f3ce788fe.png"},{"id":57865922,"identity":"ead84d87-7792-4e7e-9ff5-cb71f8c9b4bf","added_by":"auto","created_at":"2024-06-06 15:48:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48327,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis results between functional connection values and clinical information in perimenopausal group. (A) Scatter plots between functional connection value of the right IPL in DAN and E2 in perimenopausal women. (B) Scatter plots between functional connection value of the right IPL in DAN and the reaction time of STROOP in perimenopausal women.\u003c/p\u003e\n\u003cp\u003eR.IPL, right inferior parietal lobule; DAN, dorsal attention network; E2, estradiol.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4436654/v1/3fd0a02378ce27bbc22f68d6.png"},{"id":58909471,"identity":"123f47ee-1033-478c-8f38-f67770479243","added_by":"auto","created_at":"2024-06-24 04:02:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1520840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4436654/v1/31223faa-b544-45a5-b309-e3f4f9e8c58f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional changes in the dorsal attention network in perimenopausal women: a resting-state functional MRI study","fulltext":[{"header":"Background","content":"\u003cp\u003eDuring perimenopause, nearly two-thirds of women often complain about lack of concentration, slow movement, forgetfulness, and subjective cognitive difficulties[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA cross-sectional study of 130 perimenopausal women found that those who reported\u003c/p\u003e \u003cp\u003efrequent forgetting scored lower in attention determination[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Another cross-sectional study of 75 perimenopausal women found that the degree of perceived memory difficulty in perimenopausal women was associated with poor performance in working memory and complex attention tests[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. An analysis of a large longitudinal cohort (N\u0026thinsp;\u0026asymp;\u0026thinsp;2000) showed that during the perimenopausal period, cognitive process speed, speech encoding (immediate paragraph recall), and overall situational memory (delayed paragraph recall) temporarily decreased[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Another cohort study (N\u0026thinsp;\u0026asymp;\u0026thinsp;400) found that compared to premenopausal, the encoding of the word list during the postmenopausal period (immediate recall) was lower, and the verbal memory during the perimenopausal period (delayed recall) decreased compared to premenopausal. In this analysis, anxiety, depression, and stress symptoms were not associated with cognitive performance, and adjusting for them did not alter the negative association between premenopausal and perimenopausal language encoding or memory[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have found that perimenopausal cognitive dysfunction in women occurs in the context of estrogen withdrawal. During a 2-year observation period using 18F-FDG-PET, it was found that the metabolism of brain regions involved in learning and memory was significantly reduced in perimenopausal women, such as the hippocampus, parahippocampal gyrus, temporal lobe, medial prefrontal cortex, and posterior cingulate cortex. After estrogen replacement therapy, the low metabolism in these brain regions was alleviated, and memory function was improved[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In a study evaluating regional cerebral blood flow, postmenopausal women who received estrogen treatment showed increased cerebral blood flow in resting-state brain regions; postmenopausal women who did not receive estrogen treatment did not experience this effect[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Enhanced hippocampal activation and reduced parahippocampal activation in perimenopausal women receiving hormone therapy suggest that such treatment during the perimenopausal period increases state-dependent and memory-dependent processes to improve speech recognition[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies by our group using resting-state functional magnetic resonance imaging (fMRI) have shown that cognitive-related brain regions in perimenopausal women undergo spontaneous changes in brain activity[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, fMRI has been used as a popular tool in neuroscience to evaluate the therapeutic effects of hormone therapy on cognitive function in perimenopausal women and to indicate that perimenopausal women may benefit from specific hormone therapies in cognitive control, speech, and working memory[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For most functional magnetic resonance imaging studies related to perimenopausal women, which have focused on changes in brain function under stimulation or after hormone therapy[\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], few studies have focused on hormonal fluctuations in women during the perimenopausal period and their effects on brain networks. The DAN plays a vital role in cognitive processing and consists mainly of the bilateral parietal and frontal lobes, which are responsible for top-down attentional orientations and are involved in exogenous attentional orientations. However, it is not yet clear how DAN changes during the women\u0026rsquo;s reproductive transition. Therefore, in this study, we applied fMRI and used independent component analysis (ICA) to identify differences in DAN between premenopausal and perimenopausal women, explore the relationship between serum hormone levels and DAN in perimenopausal women, and evaluate the relationship between DAN functional connectivity strength, serum estrogen levels, and cognitive function.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe inclusion criteria were as follows: (a) female candidates aged 45\u0026ndash;55 years; (b) right-handed; (c) education of more than 12 years; and (d) a diagnosis of perimenopause based on the Stages of Reproductive Aging Workshop (STRAW)\u0026thinsp;+\u0026thinsp;10: menstrual cycle change more significant than 7 days, repeated for 10 menstrual cycles or an amenorrhea interval\u0026thinsp;\u0026ge;\u0026thinsp;60 days, and follicle-stimulating hormone (FSH) range 11\u0026ndash;45 IU/L. Premenopausal women were recruited based on not meeting the diagnosis criterion of STRAW\u0026thinsp;+\u0026thinsp;10; they had a regular ovulation day based on the rhythm method and FSH\u0026thinsp;\u0026lt;\u0026thinsp;11IU/L. The exclusion criteria were as follows: (a) history of neoplasms of the female genital organs, uterectomy, or oophorectomy; (b) presence of neurological and psychiatric disorders or history of brain trauma, smoking or alcohol dependence, or other diseases that may affect brain structure and function; (c) presence of mood disorder (such as depression or anxiety disorders); (d) history of hormone administration; (e) color-blindness; or (f) MRI contraindications. Finally, a total of 50 participants met the requirements for the fMRI experiment, including 25 women in the perimenopausal group (average age, 53.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82 years) and 25 women in the premenopausal group (average age, 47.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48 years).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSex hormone level measurement\u003c/h2\u003e \u003cp\u003eAll participants underwent measurement of the levels of sex hormones, including follicle-stimulating hormone(FSH), luteinizing hormone(LH), estradiol(E2), progesterone(P), testosterone(T), and prolactin. Samples were collected for sex hormone measurements by blood collection from the elbow vein at 8:00\u0026ndash;9:00 am within 3 days of the start of menstruation. The collected venous blood was analyzed by chemiluminescence analysis, and the concentrations of the six abovementioned sex hormones were determined. Participants with an abnormal menstrual cycle or amenorrhea completed the blood sex hormone testing at 8:00\u0026ndash;9:00 am on the day of the experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eScale and Cognition evaluations\u003c/h2\u003e \u003cp\u003eAll participants completed the menopause rating scale (MRS) and the patient health questionnaire (PHQ-9) to evaluate their menopausal status and the presence of depressive symptoms. All participants employed the computer-based STROOP color-word test, in which incongruent color words were shown in the center of a computer monitor, and participants were required to choose from four matching color words according to the ink color of the color word in the center.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003efMRI acquisition and processing\u003c/h2\u003e \u003cp\u003eAll participants underwent conventional MRI examinations and resting-state fMRI scans at a 3.0 T MR scanner (Discovery MR 750, GE, US) with an eight-channel head coil. The participants were 3 days postmenstrual period for the fMRI scan. For participants with amenorrhea, there was no such time restriction. For MRI scanning, we adopted three-dimensional brain volume (T1 3D-BRAVO) (TR/TE\u0026thinsp;=\u0026thinsp;8.1/3.1 ms, flip angle\u0026thinsp;=\u0026thinsp;13\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;256 mm \u0026times; 256 mm, matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, slice\u0026thinsp;=\u0026thinsp;176, slice thickness\u0026thinsp;=\u0026thinsp;1 mm) to exclude organic lesions such as cerebral infarction and tumor. For the resting state fMRI scans, a single-shot gradient echo planner imaging sequence was used: TR/TE\u0026thinsp;=\u0026thinsp;2000/30 ms, FOV\u0026thinsp;=\u0026thinsp;220 mm \u0026times; 220 mm, matrix\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, slice\u0026thinsp;=\u0026thinsp;32, slice thickness\u0026thinsp;=\u0026thinsp;3 mm, slice gap\u0026thinsp;=\u0026thinsp;0.9 mm, and measurements\u0026thinsp;=\u0026thinsp;180. Before the MRI scanning, sponge pads were placed on both sides of the participant\u0026rsquo;s ears to keep the head fixed, and the participant was told to keep closed eyes, quiet, and awake during the scanning.\u003c/p\u003e \u003cp\u003eThe fMRI data were analyzed using the Data Processing Assistant for Resting-State fMRI (DPARSFA \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://restfmri.net/forum/DPARSF\u003c/span\u003e\u003cspan address=\"http://restfmri.net/forum/DPARSF\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software. Considering the required time for the BOLD signal to stabilize and the adaption of participants to the environment, the first 10 phases of the acquired images were removed to eliminate potential noise interference, and the remaining data for 170 phases of images were used for preprocessing. The acquisition time difference between layers of the images and the head movement was corrected. Images with translation\u0026thinsp;\u0026ge;\u0026thinsp;2 mm and rotation\u0026thinsp;\u0026ge;\u0026thinsp;2\u0026deg; were excluded. No participants were excluded due to apparent head movements in this experiment. The images from all participants were assigned to the Montreal Neurological Institute (MNI) template, and then all data were resampled to obtain the functional image data for 2 \u0026times; 2 \u0026times; 2 mm\u003csup\u003e3\u003c/sup\u003e voxels. The full width at half maximum (FWHM) Gaussian kernel of 4\u0026times;4\u0026times;4 mm\u003csup\u003e3\u003c/sup\u003e was used to space smooth the fMRI images.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of resting-state networks (RSNs)\u003c/h2\u003e \u003cp\u003eThe independent component analysis (ICA) of the smoothed data was performed using MICA software tools (Stable and Consistent Group ICA of fMRI Toolbox, version1.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nitrc.org/projects/cogicat/\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/cogicat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on MatlabR2012a (MathWorks Inc., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mathworks.com\u003c/span\u003e\u003cspan address=\"http://www.mathworks.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) platform. Data reduction was performed using triple principal component analyses. They were using the MICA software to calculate and recognize the independent components. The data for each participant were segmented into 20 spatially independent components. The arithmetic operation of ICA was 100 times. Each subject\u0026rsquo;s independent components, including time series and spatial diagrams, were obtained, reconstructed, and transformed by Fisher z. After careful observation and analysis, the independent components consistent with the template reported in the literature were selected [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStructural MRI preprocessing\u003c/h2\u003e \u003cp\u003eStructural MRI data were preprocessed using voxel-based morphometry and the Statistical Parametric Mapping (SPM12, London, UK) software. The data preprocessing steps included segmenting into gray matter (GM), white matter, and cerebrospinal fluid and registering the GM DARTEL template to the tissue probability map in the MNI space. Each voxel \u0026rsquo;s gray matter volume (GMV) was obtained by multiplying the GM concentration map by the nonlinear determinants derived from the spatial normalization step. The GMV represents the probability that each voxel is genetically modified for individual brain sizes. Finally, the GMV maps were smoothed with a Gaussian kernel of 4\u0026times;4\u0026times;4 mm\u003csup\u003e3\u003c/sup\u003e FWHM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS 20.0 software (SPSS, Chicago, IL). The independent two-sample t-test was used to compare the age, years of education, MRS score, PHQ-9 score, the accuracy rate and reaction time of Color-Word STROOP, and sex hormone levels between the two groups. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a statistical difference.\u003c/p\u003e \u003cp\u003eThe statistical analysis module of Matlab-based statistical parametric mapping (SPM12, Wellcome Department of Imaging Neuroscience, London, UK) was used to analyze RSNs. Specifically, a general linear model was used to detect any significant statistical differences in RSNs between premenopausal and perimenopausal women. Age and years of education were controlled as covariates, as they may affect the results. For multiple comparison correction, AlphaSim correction with voxel level P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and the corrected threshold was P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eCorrelation analyses of function connectivity (FC) values of the DAN with GMV values, MRS scores, PHQ-9 scores, the accuracy rate and reaction time of the Stroop color-word test, and sex hormone levels were conducted in the perimenopausal group. After group analysis, the regions showing significant FC changes between the two groups were identified, and the mean FC of each region was extracted in the perimenopausal group. Spearman partial correlation analyses were conducted to evaluate the relationship between the mean FC values and GMV values of these regions with MRS score, PHQ-9 score, the accuracy rate and reaction time of STROOP color-word, and sex hormone levels, with age and years of education considered nuisance covariate.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical data\u003c/h2\u003e \u003cp\u003eThe demographic characteristics, scale, STROOP assessments, and sex hormone data for the two groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were statistical differences in age, MRS score, PRL, FSH, E2, and the reaction time of the STROOP color word (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no statistical differences in years of education, PHQ-9 scores, T, P, LH, and the accuracy rate of STROOP color-word between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, sex hormone levels and behavioral data between perimenopausal and premenopausal groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerimenopausal group (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePremenopausal group (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e47.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducations (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ\u0026minus;9 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRL(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.05\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e19.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH(IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.25\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003csub\u003e2\u003c/sub\u003e(pg/mol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.84\u0026thinsp;\u0026plusmn;\u0026thinsp;11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e101.47\u0026thinsp;\u0026plusmn;\u0026thinsp;70.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT(ng/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e35.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH(mIU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.00\u0026thinsp;\u0026plusmn;\u0026thinsp;26.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.82\u0026thinsp;\u0026plusmn;\u0026thinsp;12.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe reaction time of STROOP(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1354.75\u0026thinsp;\u0026plusmn;\u0026thinsp;261.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1094.37\u0026thinsp;\u0026plusmn;\u0026thinsp;146.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe accuracy rate of STROOP(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e97.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e96.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePHQ\u0026minus;9, Patient Health Questionnaire-9; MRS, Menopause Rating Scale; PRL, prolactin; FSH, follicle stimulating hormone; E2, estradiol; T, testosterone; P, progesterone; LH, luteotropic hormone.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of FC in the resting-state DAN\u003c/h2\u003e \u003cp\u003eThe results of comparisons of brain regions with different FC in the DAN between the perimenopausal and premenopausal groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared with the premenopausal group, after adjusted by AlphaSim, with clusters\u0026thinsp;\u0026ge;\u0026thinsp;74 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, our results revealed that the brain regions with enhanced FC in the DAN included the right inferior parietal lobule (IPL) and the right angular gyrus (AG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We extracted the FC values of the above two brain regions in both groups and found that the FC values were significantly higher in the perimenopausal group than in the premenopausal group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBrain regions with different functional connections in the DAN between the perimenopausal and premenopausal groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster size(voxel)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMINI Coordination (x,y,z)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeak \u003cem\u003et\u003c/em\u003e score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight inferior parietal lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e52,\u0026minus;52,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight angular gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e48,\u0026minus;68,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eDAN, dorsal attention network; BA, Brodmann area; MNI, Montreal Neurological Institute coordinate system or template; x, y, z, coordinates of primary peak locations in the MNI space.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional connection values differences in DAN between the perimenopausal and premenopausal groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrain region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerimenopausal\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePerimenopausal\u0026thinsp;\u0026gt;\u0026thinsp;permenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight inferior parietal lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRight angular gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDAN, dorsal attention network.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of GMV in the resting-state DAN\u003c/h2\u003e \u003cp\u003eGMV values were extracted for the two brain regions that showed no significant differences in GMV values of DAN between the perimenopausal and postmenopausal groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003eSpearman partial correlation analysis, showed that in the perimenopausal group, the FC value of the right inferior parietal lobule was significantly and negatively correlated with the E2 level (P\u0026thinsp;=\u0026thinsp;0.003, correlation coefficient: -0.585) and positively correlated with the reaction time of STROOP (P\u0026thinsp;=\u0026thinsp;0.001, correlation coefficient: 0.636;\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The GMV values of the two ROIs in DAN showed no significant correlations with sex hormone levels, scales, and STROOP data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, Spearman\u0026rsquo;s partial correlation analysis showed no significant correlations between the GMV values of the two ROIs in DAN and age in the perimenopausal and premenopausal groups. There were no significant correlations between age and sex hormone levels in the two groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have considered that perimenopause is a transitional state of female reproductive aging. Perimenopause is characterized by unique endocrine characteristics that affect the aging of multiple organ systems, including the brain; its essence is transforming the nervous system[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The physiological and psychological symptoms that occur during perimenopause are disrupted by various hormone regulatory systems, including fluctuations in serum hormones such as estrogen, which may affect the structure and function of the central nervous system through a hormone receptor network[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, the present study investigates the relationship between cognitive and resting-state functional connectivity in perimenopausal women and explores the relationship between serum hormone levels and DAN, which is of great clinical importance.\u003c/p\u003e \u003cp\u003eNew data-driven analysis techniques have rapidly become efficient and powerful tools for exploring large-scale networks in the human brain. Independent Component Analysis (ICA) is highly suitable for analyzing resting-state functional magnetic resonance imaging data and evaluating the connectivity of resting-state brain networks participating in oscillatory activities without prior selection of regions of Interest[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, the ICA method was used to evaluate the changes in DAN in perimenopausal women, and correlation analysis was used to evaluate the relationship between estrogen levels, cognitive function, and DAN. This study showed that DAN functional connectivity was increased in perimenopausal women compared to premenopausal women and that DAN was significantly associated with estrogen levels and cognitive function.\u003c/p\u003e \u003cp\u003eThe concept of anatomical and functional attention networks in the human brain was first proposed by Corbetta and Shulman et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Fox et al. first assessed the DAN using rs-fMRI. They found that the functional organization of the DAN can be represented by the correlation structure of spontaneous activity, essentially defining the DAN in a way that is broadly consistent with task-based models[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The DAN is involved in top-down, autonomous attentional control, driven from the frontoparietal lobes and responding in the occipital lobes[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, compared with premenopausal women, perimenopausal women showed an increase in functional connectivity in the right IPL node of the DAN, which was significantly positively correlated with the reaction time of STROOP. Many cognitive processes are realized through spatially distributed neural networks in the human brain, and the IPL, an area of hetero-modal convergence of various brain networks, is central to realizing critical cognitive operations at different levels of the neural processing hierarchy. These psychological operations include lower-level processes, such as spatial attention, and significantly more complex, higher-level processes in the human species, such as semantic memory and social communication patterns[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In short, the IPL is involved in a wide range of cognitive functions, including attention, action-related functions, self-perception, memory, and social cognition, or with a focus on cognitive control of language[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Shaywitz et al. found that postmenopausal women who received estrogen treatment showed increased activation of the IPL and performed better in storing speech materials[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This study found enhanced functional connectivity in the right IPL of the DAN in perimenopausal women, which may indicate a decline in cognitive function and a longer reaction time to complete the STROOP task compared to premenopausal women. However, the enhanced functional connectivity required for perimenopausal women with decreasing serum estrogen levels to complete the task with the same speed and accuracy may be due to the early compensatory response to cognitive impairment in the initial phase of the DAN with decreasing estrogen levels.\u003c/p\u003e \u003cp\u003eThis study also showed that perimenopausal women had enhanced functional connectivity of the right AG in DAN compared to premenopausal women. In many meta-analytic reviews, the AG is consistently activated across various tasks. The AG plays a role in semantic processing, word reading and comprehension, number processing, default mode networks, memory retrieval, attentional and spatial cognition, reasoning, and social cognition. The AG is a cross-modal hub where fused multi-sensory information is combined and integrated to understand and make sense of events, manipulate mental representations, solve familiar problems, and redirect attention to relevant information[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The AG is located in the posterior part of the subparietal lobule, and the two are anatomically closely related and highly integrated in function. The current findings may indicate that during perimenopause, women experience changes in functional connectivity within the DAN network caused by decreasing estrogen levels in the inferior parietal lobule and the angular gyrus. In this study, there was no significant difference in the accuracy of cognitive tasks performed by perimenopausal women compared to premenopausal women, which may be attributed to the enhanced connectivity of the IPL and AG of the DAN in compensating for cognitive impairment.\u003c/p\u003e \u003cp\u003eE2 is a form of estrogen that acts on multiple brain regions and is associated with establishing neuroendocrine phenomena and behavioral patterns[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It involves cognitive function, emotional regulation, learning, and memory[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Studies have shown that perimenopausal women treated with estrogen have increased activity in relevant brain regions and significantly improved working memory and emotional processing[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Studies have shown that E2 receptors are distributed in the parietal cortex[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], essential in advanced cognitive functions. In this study, we found that functional connectivity strength values in the right inferior parietal lobule of the DAN were negatively correlated with E2 levels, suggesting that estrogens play a role in DAN-related tasks, affecting DAN activity and subsequently affecting cognitive function. When E2 levels decrease, DAN requires more network nodes to exert more robust connectivity to compensate for neurodegenerative changes and maintain good cognitive performance.\u003c/p\u003e \u003cp\u003eAdditionally, some studies suggest that GM atrophy may be related to age[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, this study found no significant correlation between GMV values at the DAN key nodes and age or sex hormone levels. The slight age difference between the perimenopausal and premenopausal groups or the small sample size may have contributed to this.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThe study has several limitations that need to be addressed in future research. Firstly, the sample size was small. Although fMRI is rarely used to study brain DAN changes in perimenopausal women, a larger sample size could make the results more representative. Secondly, longitudinal studies are also necessary. In future studies, women's brain changes and cognitive function changes can be longitudinally followed from premenopausal to perimenopausal and postmenopausal periods. The effects of estrogen on women's brain DNA can be dynamically observed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResting-state fMRI was used to assess differences in DAN between premenopausal and perimenopausal women. The results showed that cognitive function was associated with more excellent DAN connectivity in perimenopausal women, particularly in the right IPL and right AG. Correlation analyses assessed the relationship between estrogen levels, cognitive function, and DAN. The study found that DAN activity and functional compensation differed between perimenopausal and premenopausal women. Additionally, there was a significant correlation between the DAN's estradiol levels and network nodes, which affect cognitive function. In conclusion, the relationship between estradiol and DAN and the changes in the abnormal patterns of DAN may help to understand the functional changes of the brain in perimenopausal women. This study could provide new insights for the diagnosis and clinical intervention of perimenopausal cognitive dysfunction.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDAN \u0026nbsp;Dorsal Attention Network\u003c/p\u003e\n\u003cp\u003eMRI \u0026nbsp;Magnetic Resonance Imaging\u003c/p\u003e\n\u003cp\u003eICA \u0026nbsp;Independent Component Analysis\u003c/p\u003e\n\u003cp\u003eGMV \u0026nbsp;Gray Matter Volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFC \u0026nbsp;Function Connectivity\u003c/p\u003e\n\u003cp\u003eIPL \u0026nbsp;Inferior Parietal Lobule\u003c/p\u003e\n\u003cp\u003eAG \u0026nbsp;Angular Gyrus\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are deeply grateful to all participants involved in this study, and also thank all of the doctors and researchers who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLNN and LHJ designed the study, ZY and FWQ collected data, LNN and ZY wrote the manuscript, LNN and LHJ revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Social Science Fund of China [grant no. 15BSH065].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted with approval from the Ethics Committee of Second Hospital of Tianjin Medical University. The participants provided their written informed consent to participate in this study. This study was conducted in accordance with the declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGreendale GA, Karlamangla AS, Maki PM. The Menopause Transition and Cognition. JAMA. 2020;323(15):1495\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnkenstein AE, Bryant CA, Judd FK, et al. Understanding women's experience of memory over the menopausal transition: subjective and objective memory in pre-, peri-, and postmenopausal women. 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Aberrant functional connectivity of resting state networks associated with trait anxiety. Psychiatry Res. 2015;234(1):25\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci. 2002;3(3):201\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox MD, Corbetta M, Snyder AZ, et al. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A. 2006;103(26):10046\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen CY, Chen VC, Yeh DC, et al. Association of functional dorsal attention network alterations with breast cancer and chemotherapy. Sci Rep. 2019;9(1):104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNumssen O, Bzdok D, Hartwigsen G. Functional specialization within the inferior parietal lobes across cognitive domains. Elife. 2021;10:e63591.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabassi Mofrad F, Schiller NO. Cognitive demand modulates connectivity patterns of rostral inferior parietal cortex in cognitive control of language. Cogn Neurosci. 2020;11(4):181\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaywitz SE, Shaywitz BA, Pugh KR, et al. Effect of estrogen on brain activation patterns in postmenopausal women during working memory tasks. JAMA. 1999;281(13):1197\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeghier ML. The angular gyrus: multiple functions and multiple subdivisions. Neuroscientist. 2013;19(1):43\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLentini E, Kasahara M, Arver S, et al. Sex differences in the human brain and the impact of sex chromosomes and sex hormones. 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Fertil Steril. 2010;93(6):1929\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi K, Huang X, Han Y, et al. Enhanced Neuroactivation during Working Memory Task in Postmenopausal Women Receiving Hormone Therapy: A Coordinate-Based Meta-Analysis. Front Hum Neurosci. 2015;9:35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRettberg JR, Yao J, Brinton RD. Estrogen: a master regulator of bioenergetic systems in the brain and body. Front Neuroendocrinol. 2014;35(1):8\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalkur Shankar S, Ballal S, Shubha R. Study of normal volumetric variation in the putamen with age and sex using magnetic resonance imaging. Clin Anat. 2017;30(4):461\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dorsal attention network (DAN), perimenopausal period, magnetic resonance imaging (MRI), estrogen, cognitive function","lastPublishedDoi":"10.21203/rs.3.rs-4436654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4436654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo evaluate the functional changes of the dorsal attention network (DAN) in perimenopausal women using functional magnetic resonance imaging (fMRI) and\u003c/p\u003e\n\u003cp\u003ethe relationship between sex hormones and cognitive function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 25 perimenopausal women and 25 premenopausal women underwent sex hormone level, scale, and cognition assessments, as well as magnetic\u003c/p\u003e\n\u003cp\u003eresonance imaging (MRI) scans. The resting state fMRI data were acquired using a 3.0 Tesla magnetic resonance scanner, and the differences in DAN functional connection between these two groups were evaluated by independent component analysis (ICA). Gray matter volume (GMV) values of brain regions (regions of interest [ROI]) with differences in DAN functional connection were extracted, and the differences in GMV between the two groups were compared. Correlation analysis was performed between the connection strengths of the DAN functional connection and GMV values of ROIs with sex hormone levels and clinical and neuropsychological assessments in the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCompared with the premenopausal group, the brain regions with enhanced functional connection in the perimenopausal group were the right inferior parietal lobule (IPL) and the right angular gyrus (AG) in the DAN. There were no differences in GMV values between the two groups. Correlation analysis showed that connection strengths of the right IPL negatively correlated with the estradiol level and positively correlated with the reaction time of the STROOP color-word test in perimenopausal women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe ICA demonstrated that the DAN functional changes may stimulate the brain's compensatory mechanisms to compensate for physiological and psychological problems in women during the reproductive transition period. Our findings provide evidence for understanding the changes in brain function in perimenopausal women.\u003c/p\u003e","manuscriptTitle":"Functional changes in the dorsal attention network in perimenopausal women: a resting-state functional MRI study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 15:48:09","doi":"10.21203/rs.3.rs-4436654/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ac4495a-76e0-4ac7-8d43-27c1df0a16cf","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-22T13:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-06 15:48:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4436654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4436654","identity":"rs-4436654","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}