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. 2024 Mar 15;34(3):bhae088. doi: 10.1093/cercor/bhae088

Impact of sex and reproductive status on the default mode network in early midlife: implications for aging of memory circuitry and function

Dylan S Spets 1,2,3, Justine E Cohen 4,5, Kyoko Konishi 6,7,8, Sarah Aroner 9,10, Madhusmita Misra 11,12, Hang Lee 13,14,15, Jill M Goldstein 16,17,18,19,✉
PMCID: PMC10944696  PMID: 38494419

Abstract

Alterations to the resting-state default mode network (rsDMN) are early indicators of memory decline and Alzheimer’s disease (AD). Brain regions shared by the rsDMN and memory circuitry are highly sexually dimorphic. However, data are limited regarding the impact of sex and reproductive status on rsDMN connectivity and memory circuitry and function. In the current investigation, rsDMN connectivity was assessed in 180 early midlife adults aged 45 to 55 by sex and reproductive status (87 women; 93 men). Associations between left and right hippocampal connectivity of rsDMN and verbal memory encoding circuitry were examined using linear mixed models, controlled for age and parental socioeconomic status, testing interactions by sex and reproductive status. Relative to men, women exhibited greater rsDMN connectivity between the left and right hippocampus. In relation to rsDMN-memory encoding connectivity, sex differences were revealed across the menopausal transition, such that only postmenopausal women exhibited loss of the ability to decrease rsDMN left–right hippocampal connectivity during memory encoding associated with poorer memory performance. Results demonstrate that sex and reproductive status play an important role in aging of the rsDMN and interactions with memory circuitry/function. This suggests the critical importance of sex and reproductive status when studying early midlife indicators of memory decline and AD risk.

Keywords: DMN, estradiol, hippocampus, menopause, resting state

Introduction

Approximately 75% of older adults report memory-related issues (Koivisto et al. 1995). With a rapidly aging population (Howden and Meyer 2011), the burden of memory-related disorders is rising and becoming one of the greatest public health challenges of our time. Applying a sex-dependent lens to this question is essential, as women have a higher frequency of memory-related disorders, such as Alzheimer’s disease (AD), which is not fully explained by longevity (Alzheimer’s Association 2023). Moreover, several regions involved in memory circuitry [e.g. hippocampus (HIPP) and prefrontal cortex (PFC)] are enriched with sex steroid hormone receptors and are among the most sexually dimorphic regions in the brain (Goldstein et al. 2001).

There is a large body of literature demonstrating the importance of ovarian hormones, particularly estradiol, in regulating memory circuitry. Estradiol has been linked to hippocampal synaptic plasticity in rodent and nonhuman primate models (Woolley and McEwen 1994; McEwen 2002; Liu et al. 2008; Brinton 2009; Dumitriu et al. 2010; Hara et al. 2012). Our laboratory has previously demonstrated the role of estradiol in regulating memory circuitry across the reproductive transition in humans during working memory (Jacobs et al. 2017) and verbal encoding (Jacobs et al. 2016). Of particular relevance to the current study, we demonstrated the impact of declining estradiol levels on increasing bilateral HIPP connectivity during memory encoding using a verbal encoding (VE) memory task (Jacobs et al. 2016). This increase in bilateral HIPP connectivity was associated with poorer memory performance in postmenopausal women. The results lend further support to the modulating effects of estradiol (i.e. ovarian decline) on HIPP connectivity and memory function. Critically, these associations may reveal risk and resilience for memory-related disorders during midlife.

The menopausal transition is a particularly critical period for identifying preclinical targets for memory decline, as AD pathology begins 20 years prior to symptom onset (Jack et al. 2010). Previously, postmenopausal women with high genetic risk for AD presented increased amyloid deposition compared to premenopausal women and men with the same genetic risk (Mosconi et al. 2017; Scheyer et al. 2018; Rahman et al. 2020). These results further emphasized that the menopausal transition is a critical period for clinical intervention for AD and memory-related disorders. The impact of AD pathology, namely, the deposition of amyloid and tau proteins, on memory function has been well established and first appears in memory regions including the medial temporal lobe (MTL; which includes HIPP) and medial PFC (mPFC) (Leal and Yassa 2013; Grothe et al. 2017). These regions, which are also among the most sexually dimorphic of the brain (c.f. Goldstein et al. 2001), are shared by the resting-state default mode network (rsDMN).

The rsDMN is a highly circumscribed functional network of brain regions that is active when the brain is at rest and deactivated when engaged in a task (e.g. during memory encoding) (Fox et al. 2005; Fox et al. 2009; Raichle 2015). Regions of the rsDMN include the mPFC, bilateral HIPP, precuneus/posterior cingulate cortex (PCC), and bilateral angular gyri (AG)(Fig. 1) (Raichle 2015). Resting-state functional connectivity in the DMN is reliably altered in several neuropsychiatric and neurodegenerative diseases, including AD. Amyloid deposition has been associated with decreases in rsDMN functional connectivity in cognitively intact older adults (Sheline et al. 2010; Palmqvist et al. 2017) and those with prodromal AD (Koch et al. 2015). There is a robust body of literature demonstrating the importance of rsDMN connectivity in maintaining intact memory function in healthy aging (Tomasi and Volkow 2012; Vidal-Pineiro et al. 2014; Staffaroni et al. 2018) and disrupting memory function in diseased aging (Dennis and Thompson 2014). Despite its clinical relevance, the impact of sex and reproductive aging on rsDMN connectivity and memory function has been understudied.

Fig. 1.

Fig. 1

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The default mode network. Regions of the default mode network include the mPFC, left/right HIPP, PCC, and left/right AG.

A handful of studies that have investigated the impact of sex on rsDMN connectivity have reported an overall increase in connectivity in women relative to men (Biswal et al. 2010; Thurston et al. 2015; Cavedo et al. 2018; Ritchie et al. 2018; Ficek-Tani et al. 2022). Specific effects of estradiol on the reorganization of the rsDMN have also been reported (Thurston et al. 2015; Pritschet et al. 2020). A recent large study identified that sex differences in rsDMN connectivity were most prominent in early midlife around the time of the menopausal transition (Ficek-Tani et al. 2022). Another smaller study found increased HIPP connectivity in the rsDMN in late peri- and postmenopause (Thurston et al. 2015). These studies suggest a role of reproductive aging in modulating rsDMN connectivity. However, there was a lack of hormonal and reproductive data in the former (Ficek-Tani et al. 2022) and a small sample size in the latter (Thurston et al. 2015). Additionally, none of these studies investigated the impact of rsDMN connectivity on memory circuitry or function.

In the current study, we investigated rsDMN connectivity in 180 early midlife adults, made comparable by sex and serologically validated reproductive status (pre-, peri-, and postmenopause). We sought to determine differences in the functional connectivity among all nodes of the rsDMN by sex and reproductive status. We predicted that women, relative to men, would produce hyperconnectivity in the rsDMN and that the magnitude of connectivity would increase across the menopausal transition. Based on previous work (Thurston et al. 2015; Cavedo et al. 2018; Ficek-Tani et al. 2022), we hypothesized that HIPP would be a predominant node of hyperconnectivity in women.

Further, we tested whether differences in rsDMN connectivity by menopausal status would contribute to explaining the postmenopausal increase in bilateral HIPP connectivity during verbal memory encoding, which we previously found was associated with poorer memory performance (Jacobs et al. 2016). We predicted that in postmenopausal women, increases in bilateral HIPP connectivity during verbal memory encoding would be associated with HIPP hyperconnectivity during resting-state and poorer memory performance, suggesting that alterations to the rsDMN may impact the reorganization of memory circuitry and memory function across the menopausal transition.

Materials and methods

Participants

Participants in this study consisted of 180 adults ages 45 to 55 who participated in a larger study of the early antecedents to sex differences in memory decline in early midlife (MH090291, JMG, PI). They were highly comparable by sex (defined as that assigned at birth; n = 87 women and n = 93 men) and were the subset of those in Jacobs et al. (2016) with resting-state data. There were no significant differences between the subset (n = 180) compared to Jacobs et al. (2016; n = 200). Adult offspring were from the Boston and Providence cohorts (n = 17,741) of the National Collaborative Perinatal Project (NCPP; Niswander and Gordon 1972), whom we have been following for more than 20 years in the New England Family Studies (NEFS). The NCPP was a prospective study initiated in the 1950s to investigate the prenatal and familial antecedents of pediatric, neurological, and psychological disorders of childhood (Niswander and Gordon 1972). Women in NEFS were recruited from 1959 to 1966 and were representative of patients receiving prenatal care in the Boston and Providence areas. In a series of ongoing studies over 20 years, we have followed the offspring of these women to investigate the fetal programming of adult psychiatric and general medical disorders and the sex differences therein. The MassGeneral Brigham Human Research Committee and Brown University’s Institutional Review Board granted human studies participants’ approval. All volunteers gave written informed consent and were paid for their participation.

Demographic and clinical assessments

Height, weight, current medications, and smoking history were collected for each participant at intake in a hospital-based clinical research center. The body mass index (BMI) was calculated as kilograms divided by meters squared (kg/m2). Demographic information (i.e. race, ethnicity, marital status, and education) was obtained via self-report.

Steroid hormone assessments

Participants were asked to fast from midnight to the morning of their study appointment. Trained nurses inserted an intravenous (IV) line in the left arm and obtained a fasting blood draw at approximately 8:00 AM to evaluate hypothalamic–pituitary–gonadal (HPG) and hypothalamic–pituitary–adrenal (HPA) axis hormones, including serum levels of sex steroids (17-β estradiol (E2), progesterone (P), and testosterone (T)). Approximately 10 cubic centimeters (cc) of blood was sampled at Brigham and Women’s Clinical Research Center, allowed to clot for 30 to 45 min, spun, aliquoted, and stored in 2 milliliter (ml) aliquots at −20 °C. E2, P, and T concentrations were determined via liquid chromatography-mass spectroscopy (LC-MS) at the Brigham Research Assay Core (BRAC). Assay sensitivities, dynamic range, and intra-assay coefficients of variation (CV) were as follows: E2 (1 picogram (pg)/ml, 1 to 500 pg/ml, <5% RSD); P (0.05 nanogram (ng)/ml, 0.05 to 10 ng/ml, 5.75% RSD); T (1.0 ng/dl, 1 to 2,000 ng/dL, <2% RSD).

Menopausal staging

The timing of the menopausal transition between the first clinical appearance of decreased ovarian function (i.e. shorter intermenstrual time periods) to menstrual irregularity and final amenorrhea is highly variable and can take place over several years. Women in this sample were between the ages of 45 and 55 years and in various stages of ovarian decline, ranging from normal cycling to oligomenorrhea to permanent amenorrhea. Reproductive histories and hormonal evaluations were used to determine the reproductive stages of women in our sample following the Stages of Reproductive Aging Workshop (STRAW)-10 guidelines (Harlow et al. 2012). Women were categorized as late reproductive (“premenopause,” n = 31), in the menopausal transition (“perimenopause,” n = 27), or early postmenopausal (“postmenopause,” n = 29). All women who were not in menopause were scanned in the early follicular phase of their menstrual cycles. Women on hormone replacement therapy were not included in the analyses.

Face-Name Associative Memory Exam

The 12-item Face-Name Associative Memory Exam (FNAME) (Rentz et al. 2011; Papp et al. 2014; Pankratz et al. 2015) was used to measure associative memory. During the FNAME, participants studied 12 unfamiliar face–name–occupation groupings (over two learning exposures). Participants were shown the 12 faces again after a 10-min interference trial and asked to freely recall the name and occupation associated with each face. Following the recall phase, faces were presented with three multiple-choice options for the associated name and occupation. Scores were based on the total number of names and occupations recalled in each trial. Two scores were calculated: the initial learning (IL) score, based on recall at the first test and cued recall (CR) score, based on recognition during the multiple-choice test. All raw scores were converted to z-scores for analyses, and z-score composites were created by averaging the z-scores of the component measures (i.e. IL and CR). Composite scores were used in the main analyses. FNAME was selected as a measure of memory performance, as it was previously shown sensitive to early memory changes (Rentz et al. 2011; Hedden et al. 2012), particularly regarding MTL regions. As such, it is a useful tool for elucidating the impact of reproductive aging on MTL and memory function.

Verbal encoding paradigm

During functional magnetic resonance imaging (fMRI), participants performed a VE memory task (Golby et al. 2001; Stone et al. 2005). The task consisted of two conditions: “Novel” and “Repeat.” In both conditions, a pair of common nouns were presented centrally for 4,000 ms with a variable interstimulus interval (600 to 1,500 ms). Participants were asked to silently generate a sentence using both words and to remember the stimuli for a later test. In the Repeat condition, the same noun pair was presented for a block of a run and participants were instructed to generate the same sentence each time they saw the word pair. In the Novel condition, new word pairs were presented, and participants were instructed to generate a new sentence for each word pair. Participants responded to every word pair with a single button press (pointer finger) to indicate that they had successfully formed a sentence in their mind. Each participant performed two runs of the task. Each run consisted of three blocks of each condition for a total of six blocks per condition.

Image acquisition

fMRI data were acquired on a Siemens Tim Trio 3T MRI scanner (Siemens, Erlangen, Germany), equipped with a 12-channel head coil. Functional data were acquired using a T2-weighted echo-planar imaging sequence sensitive to blood oxygenation level–dependent (BOLD) contrast. The resting-state and task-based (VE) scans were acquired during the same session with the resting-state scan preceding the task-based scans. During the resting-state scan, participants were instructed to relax with their eyes open and keep their head still. Parameters for the resting-state scan were repetition time, 6,000 ms; echo time, 30 ms; field of view, 190 × 190 × 167 mm; and flip angle, 20°. Each functional volume consisted of 62 2.0 mm slices. The length of the resting-state scan was 6 min and 18 s. Parameters for the task-based (i.e. VE) scan were repetition time, 2,000 ms; echo time, 30 ms; field of view, 200 × 200 × 200 mm; and flip angle, 90°. Each functional volume consisted of 33 3 mm slices.

A T1-weighted structural image was collected using a high-resolution 3D Multi-Echo (ME) MPRAGE sagittal sequence with an isotropic resolution of 1 mm3 (repetition time, 2,350 ms; echo time, 3.32 ms; flip angle, 7°). Following acquisition, MRI data were converted to Neuroimaging Informatics Technology Initiative (NIFTI) format for preprocessing.

fMRI data analyses

Images were preprocessed and analyzed using the CONN toolbox (Whitfield-Gabrieli and Nieto-Castanon 2012), a software package built on the SPM platform. Functional images were initially motion corrected, unwarped, and coregistered to the T1 structural image. Both functional and structural images were transformed to MNI space. Scans with framewise displacement above 9 mm or global BOLD signal changes above 5 SD (Power et al. 2014; Nieto-Castanon 2022) were identified using ART (Whitfield-Gabrieli 2011) and excluded from analyses. Functional images were spatially smoothed using an 8 mm full width at half-maximum Gaussian kernel.

Additional denoising of data was performed using a standard denoising pipeline (Nieto-Castanon 2020a) and included potential confounding effects of white matter (five CompCor noise components), CSF timeseries (five CompCor noise components), motion parameters and their first-order derivatives (Friston et al. 1996), outlier scans (Power et al. 2014), session effects and their first order derivatives (two factors), and linear trends (two factors). Following this, a bandpass filter (0.008 < f < 0.09 for resting-state) was applied to eliminate high-frequency noise and focus the analysis on low-frequency activity indicative of intrinsic connections between brain regions. A bandpass filter of 0.008 < f < Inf was applied to the VE data. CONN uses a seed-driven analysis approach, where Pearson correlation coefficients are calculated between the seed time course and the time courses of all other voxels. The correlation coefficients are then converted to normally distributed scores using Fisher’s transformation to allow for second-level general linear model analysis (Whitfield-Gabrieli and Nieto-Castanon 2012).

Resting-state connectivity was assessed in the five primary regions of the DMN using region of interest (ROI)-to-ROI connectivity matrices: mPFC, left and right AG, precuneus/PCC, and left and right HIPP (Fig. 1). Fisher-transformed bivariate correlation coefficients from a weighted general linear model were used to represent functional connectivity strength for each pair of seed and target areas (Nieto-Castanon 2020b). The following contrasts were run on resting-state data to assess for sex differences (combined and separated by reproductive stage) with age included as a covariate: men vs. women, men vs. premenopausal women, men vs. perimenopausal women, men vs. postmenopausal women, pre- vs. perimenopausal women, pre- vs. postmenopausal women, and peri- vs. postmenopausal women. Contrasts were connection-level thresholded at P < 0.01 and cluster-level thresholded at p-FDR < 0.05. rsDMN ROIs were selected from the default atlas in CONN, which uses a combination of ROIs extracted from the Harvard-Oxford and Automated Anatomical (AAL) atlases. VE connectivity was assessed only between the left and right HIPP based on a priori hypotheses (Jacobs et al. 2016). Connectivity values were exported using REX toolbox (Nieto-Castanon and Whitfield-Gabrieli 2009) for resting-state and VE connectivity between left and right HIPP nodes.

Statistical analyses

Study sample characteristics [age at study visit, BMI, parental socioeconomic status (SES), education (above vs. below a 4-year college degree), race (White v. non-White), marital status (married v. unmarried), smoking history (ever smoked), and medication use (currently on medication)] were examined by sex and reproductive status (pre-, peri-, and postmenopausal). Continuous variables were compared between sex/reproductive status groups using independent-sample t-tests. Categorical variables were compared using chi-square tests except when expected cell counts were less than 5, in which case Fisher’s exact tests were used.

Connectivity values were imported into SAS 9.4 (SAS Institute, Cary, NC). Linear mixed models with a compound symmetry covariance structure and random family effect were used to examine associations between rsDMN connectivity (our independent variable) and VE left–right HIPP connectivity (our dependent variable). Differences in associations by sex and reproductive status were tested by including an interaction term between rsDMN connectivity and group status along with their main effects. A global test for interaction across reproductive status groups in women as well as pairwise interaction tests by sex and reproductive status were performed.

Based on previous findings from our lab demonstrating that bilateral HIPP connectivity was associated with poorer memory performance in postmenopausal women (Jacobs et al. 2016), we investigated whether associations between rsDMN and VE L-R HIPP connectivity differed among postmenopausal women based on memory performance. Thus, post hoc exploratory analyses were only run in postmenopausal women based on a priori hypotheses (see Jacobs et al. 2016). Analyses were stratified by performance status as in Jacobs et al. (2016), as power was limited in this subsample. Postmenopausal women were divided into tertiles based on a composite index of performance on FNAME scores and associations were examined for “low” (n = 10), “middle” (n = 9), and “high” (n = 9) performers. Associations between the free and cued recall FNAME scores were also considered, post hoc (see Supplementary Information).

Sex (total sample only) and age at study visit were included in the models a priori. Race and parental SES (a marker of early social environmental enrichment unconfounded by illness effects) were considered as potential confounders. Parental SES was related to both DMN and VE connectivity (based on our threshold of P < 0.20), but race was not and thus omitted from the final models.

One-tailed P-values are presented for overall associations by reproductive status as well as for sex and reproductive status interactions given our a priori hypotheses. Based on our previous findings in this sample (Jacobs et al. 2016), we predicted that left–right HIPP connectivity in the rsDMN would increase and that associations between VE and rsDMN left–right HIPP would become positive across the menopausal transition. Two-tailed P-values are presented for analyses by performance status in postmenopausal women, as this was considered exploratory.

Results

Participant characteristics

Demographic characteristics of the total sample with rsDMN data (n = 180) and group comparisons are reported in Table 1. Groups were comparable for BMI, parental SES, education, and medication use but differed slightly in age, race, marital status, and smoking history. Specifically, men and postmenopausal women were older than premenopausal women, there was a slightly higher percentage of White women in pre- compared to postmenopausal women, a slightly higher percentage of married women in pre- compared to perimenopausal women, and a higher prevalence of smoking history in all women (including pre-, peri-, and postmenopausal) compared to men. Models examining rsDMN-VE associations included 170 of the original 180 participants. Eight of the original 180 participants that had resting-state data were excluded based on missing VE data, one was excluded based on missing parental SES values, and one premenopausal woman was excluded as an outlier based on VE functional connectivity values greater than 2 SD above the mean. Distributions of demographic measures were similar for the full sample (n = 180) and the subsample (n = 170; see Supplementary Table 1).

Table 1.

Demographic characteristics of 180 men and women with resting-state data assessed at ages 45 to 55.

Men Women Pre Peri Post Comparison
(n = 93) (n = 87) (n = 31) (n = 27) (n = 29) P < 0.05
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
Age (years) 50.2 (2.3) 49.8 (2.1) 49.2 (1.8) 49.7 (1.9) 50.6 (2.3) Pre < Post, Men
BMI (kg/m2) 28.9 (5.3) 27.9 (5.9) 28.2 (6.1) 28.5 (6.3) 27.2 (5.6) n.s.
Parental SES 5.8 (1.8) 5.8 (1.8)a 6.2 (1.8)a 5.5 (1.8) 5.7 (1.9) n.s.
n (%) n (%) n (%) n (%) n (%)
Education 33 (35.9)a 34 (40.0)b 13 (41.9) 11 (40.7) 10 (37.0)b n.s.
Race 84 (90.3) 77 (88.5) 30 (96.8) 24 (88.9) 23 (79.3) Pre > Post
Marital status 59 (64.1)a 49 (56.3) 22 (71.0) 12 (44.4) 15 (51.7) Pre > Peri
Smoking history 26 (28.0) 46 (52.9) 15 (48.4) 14 (51.9) 17 (58.6) Men < Women,
Pre, Peri, Post
Medication use 48 (51.6) 50 (57.5) 19 (61.3) 15 (55.6) 16 (55.2) n.s.
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n.s. = nonsignificant.

Continuous variables were compared using t-tests. Categorical variables were compared using chi-square tests (or Fisher’s exact tests for comparisons with expected cell counts <5).

Education = % ≥ 4-year college.

Race/ethnicity = % non-Hispanic White.

Marital status = % married.

Smoking history = % ever smoked.

Medication use = % currently on any medications.

aMissing n = 1.

bMissing n = 2.

DMN functional connectivity

Women had increased connectivity between left and right HIPP compared to men [t(177) = 2.92, connection-level P < 0.01, cluster-level p-FDR < 0.05; Fig. 2]. Within women, there were no significant differences across reproductive status. Significant differences emerged when women were stratified by reproductive status compared to men, both premenopausal [t(177) = 2.85, connection-level P < 0.01, cluster-level p-FDR < 0.05] and postmenopausal [t(177) = 2.57), connection-level P < 0.01, cluster-level p-FDR < 0.05] women had increased connectivity between left and right HIPP compared to men (Fig. 2). There were no significant differences among any other rsDMN connections.

Fig. 2.

Fig. 2

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Left, left–right HIPP connectivity in the default mode network. Right, mean magnitudes of left–right HIPP connectivity in women, by reproductive status, and in men. Error bars represent ±1 SEM. * P < 0.05.

rsDMN-VE functional connectivity associations

There was an inverse association between rsDMN and VE left–right HIPP connectivity in men (β = −0.43) and a positive association in women (β = 0.24), although neither of these associations were significant (both Ps > 0.20). However, a significant association was revealed in postmenopausal women when analyses were conducted by reproductive status. In postmenopausal women only, there was a significant positive association between rsDMN and VE left–right HIPP connectivity (β = 1.99, P = 0.02), which was attenuated in perimenopause (β = 0.05, P = 0.45) and inverse in premenopause at a trend level (β = −0.85, P = 0.14). Differences in the association across reproductive status groups were significant (P for interaction, pre- vs. peri- vs. postmenopausal = 0.02) (Fig. 3). In pairwise interaction tests, differences were found for men vs. postmenopausal women (P = 0.004), pre- vs. postmenopausal women (P = 0.02), and peri- vs. postmenopausal women (P = 0.03).

Fig. 3.

Fig. 3

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Associations between left–right HIPP connectivity during resting-state and the verbal encoding task in pre-, peri-, and postmenopausal women.

The significant positive association in postmenopausal women was driven by the lowest tertile of memory performers (β = 3.28, P = 0.0004) (Fig. 4). No associations were present in middle (β = 0.67, P = 0.76) or high (β = 1.19, P = 0.61) performers. Similar results were achieved when examining free and cued recall scores (see Supplementary Results).

Fig. 4.

Fig. 4

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Associations between left–right HIPP connectivity during resting-state and the verbal encoding task in low-, middle-, and high-performing postmenopausal women based on tertiles of performance on the face name associative memory task.

Discussion

The present study investigated the impact of sex and reproductive status on resting-state connectivity in the DMN and associations with memory circuitry and function. In line with previous studies, we found increased connectivity in the rsDMN for women relative to men (Biswal et al. 2010; Thurston et al. 2015; Cavedo et al. 2018; Ritchie et al. 2018; Ficek-Tani et al. 2022), specifically between left and right HIPP. This relationship held true in pre- and postmenopausal women; however, there were no significant differences between perimenopausal women relative to men. This may be due to the fact that some women in the sample were in early perimenopause while others were in late perimenopause. With the sample size, we may have not had enough power to detect significant differences in a group with high variability. Although previous studies have alluded to the role of ovarian decline (i.e. reproductive aging) in modulating DMN connectivity, no previous study tested this systematically. The lack of significant rsDMN connectivity differences within reproductive status may be due, in part, to the fact that these women were in early stages of menopause. Significant differences may emerge for older postmenopausal women compared to pre- and perimenopausal woman. Even given this, significant effects were revealed in postmenopausal women regarding the impact of increased left–right HIPP rsDMN connectivity on memory performance.

The ability to decrease rsDMN connectivity while in a task-positive state (i.e. during memory encoding), is an essential feature of typical rsDMN and cognitive functioning (Fox et al. 2005; Fox et al. 2009). We found that across the menopausal transition, there was a gradual loss of the ability to decrease left–right HIPP resting-state connectivity during memory encoding, resulting in a positive association between rsDMN and VE left–right HIPP connectivity postmenopause (Fig. 3). Although associations were significant for postmenopausal women only, the significant interaction clearly illustrates that interactions between rsDMN and VE connectivity change across the menopausal transition. The positive association in postmenopausal women was driven by the lowest memory performers, underscoring the importance of decreasing resting-state connectivity during successful memory. Interestingly, middle- and high-performing postmenopausal women did not show positive associations between rsDMN and VE networks, similar to premenopausal and perimenopausal women. These results suggest that the inability to decrease left–right HIPP resting-state connectivity during memory encoding, due to ovarian decline, impacts memory function with age.

Aging is generally associated with decreases in rsDMN connectivity with the exception of the hippocampal/subcortical subsystem (Salami et al. 2014). Although resting-state connectivity between subcortical (i.e. HIPP) and cortical rsDMN nodes continues to decrease with age (c.f. Dennis and Thompson 2014), connectivity within subcortical nodes (i.e. left–right HIPP) increases. This aging-related increase in left–right HIPP connectivity is associated with decreased memory performance and alterations to memory circuitry. Mechanistically, hyperconnectivity between rsDMN left and right HIPP may prevent necessary connections between HIPP and cortical regions during memory. The current results suggest that sex and reproductive status impact these associations. The HIPP hyperconnectivity in women, reported here, may be of particular clinical relevance given that HIPP is one of the first regions to accumulate amyloid in the brain (Leal and Yassa 2013; Grothe et al. 2017) and is highly sexually dimorphic (Goldstein et al. 2001).

Given the current sample is predominantly White, there may be some concern about the generalizability of the results. However, this does not challenge the internal validity of the findings. The impact of race should be tested in larger, more diverse samples. It should be noted, however, that larger studies often lack the deep phenotyping that we were able to conduct in the current study (e.g. menopausal staging). Our sample also had a very tight age range. Given this, we were able to control for age and investigate the impact of reproductive (over chronological) aging; however, we lacked the variability to look at associations between rsDMN connectivity and memory with continuous hormonal measures. The effect of individual hormones (e.g. estradiol and dehydroepiandrosterone and its sulfate form) on rsDMN connectivity is important, and future studies need to address this question. The tight age range of our sample may also explain why no significant effects were observed in rsDMN connectivity when pre-, peri-, and postmenopausal women were directly compared. Due to this tight age range, most postmenopausal women were early in postmenopause. Significant differences may emerge when comparing pre- and perimenopausal women to those later in postmenopause.

The current study adds to a growing body of literature reporting increased rsDMN connectivity in women relative to men (Biswal et al. 2010; Thurston et al. 2015; Cavedo et al. 2018; Ritchie et al. 2018; Ficek-Tani et al. 2022) but extends and refines this through investigation of the impact of reproductive aging in women. Several studies report a general increase in rsDMN connectivity for women compared to men (Biswal et al. 2010; Ritchie et al. 2018), while others report increased connectivity for women among specific nodes (Thurston et al. 2015; Cavedo et al. 2018). In line with the present results, the HIPP is often reported as a hyperconnected node in women (Thurston et al. 2015; Cavedo et al. 2018; Ficek-Tani et al. 2022), modulated by ovarian decline (Thurston et al. 2015). A large study investigating the role of sex on rsDMN connectivity found increased connectivity in rsDMN for women relative to men among specific nodes of the DMN, including PCC-AG and PCC-HIPP (Ficek-Tani et al. 2022). Given the large age range of that sample (i.e. 36–100), the authors were able to investigate the impact of age on sex differences in connectivity. The largest sex effects were revealed in early midlife (i.e. ages 40 to 59, the decades of life corresponding to the reproductive transition). However, due to the lack of hormonal reproductive data and wide age range used to define “reproductive aging,” the authors were not able to determine if, or how, there may have been differences in rsDMN by menopausal status. A number of other previous studies lacked the inclusion of women across all menopausal stages (i.e. pre-, peri-, and postmenopause) and thus were also unable to identify how rsDMN connectivity changed across the menopausal transition. Further, none of the studies mentioned above extended the findings to investigate sex differences in the impact of rsDMN on aging of memory circuitry or function in early midlife.

Finally, our unique study systematically investigated the impact of reproductive status, controlled for chronological age, on the aging of the rsDMN and its impact on midlife memory circuitry and function. This was possible given that our groups, based on reproductive status, had variable ages within a narrow 7-year age range and were comparable for race, education, and SES. While a number of studies have investigated the role of rsDMN on memory and aging, few studies have considered the impact of sex and reproductive status. The current findings underscore the critical and complex impact of sex and reproductive status on aging of the rsDMN and memory circuitry and function. As the field continues to study the rsDMN and leverages rsDMN aberrations to inform risk and resilience for AD (Greicius et al. 2004), sex and reproductive status are essential to consider given the significant differences in brain aging between men and women.

Supplementary Material

Supplementary_Information_bhae088
supplementary_information_bhae088.docx (25KB, docx)

Acknowledgments

We would like to thank Harlyn Aizley, Emily Jacobs, and Blair Scribner-Weiss for data collection. Emily Jacobs, PhD, originally analyzed our verbal encoding memory data, which was reanalyzed with updated software for the current set of analyses.

Contributor Information

Dylan S Spets, Clinical Neuroscience Laboratory for Sex Differences in the Brain, Department of Psychiatry, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA; Innovation Center on Sex Differences in Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA.

Justine E Cohen, Clinical Neuroscience Laboratory for Sex Differences in the Brain, Department of Psychiatry, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA; Innovation Center on Sex Differences in Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA.

Kyoko Konishi, Clinical Neuroscience Laboratory for Sex Differences in the Brain, Department of Psychiatry, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA; Innovation Center on Sex Differences in Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA.

Sarah Aroner, Clinical Neuroscience Laboratory for Sex Differences in the Brain, Department of Psychiatry, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA; Innovation Center on Sex Differences in Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA.

Madhusmita Misra, Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Department of Pediatrics, Division of Pediatric Endocrinology, Massachusetts General Hospital, 55 Fruit Street Boston, MA 02114, USA.

Hang Lee, Innovation Center on Sex Differences in Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Biostatistics Center, Massachusetts General Hospital, 500 Staniford Street, Boston, MA 02114, USA.

Jill M Goldstein, Clinical Neuroscience Laboratory for Sex Differences in the Brain, Department of Psychiatry, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA; Innovation Center on Sex Differences in Medicine, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.

Author contributions

Dylan S. Spets (conceptualization, data curation, formal analysis, methodology, visualization, writing-original draft, writing—review & editing), Justine E. Cohen (conceptualization, data curation, writing-original draft), Kyoko Konishi (data curation), Sarah Aroner (data curation, formal analysis, methodology), Madhumita Misra (data curation, resources), Hang Lee (formal analysis, methodology), and Jill Goldstein (conceptualization, funding acquisition, methodology, resources, supervision, writingoriginal draft, writing—review & editing).

Funding

This work was supported by the National Institute of Mental Health [NIMH R01 MH090291 to J.M.G.] and the National Institute on Aging [NIA R01 AG235379 and, in part by, NIA R01 AG057505 & AG074008 and NIMH U54 MH118919 to J.M.G.] Additional support for D.S.S. was provided by the Stuart T. Hauser Clinical Research Training Program (CRTP) [NIMH T32 MH016259]. Additional support for K.K. was provided by ORWH-NICHD (BIRCWH) K12HD051959 and NIA K01AG081500.

Conflict of interest statement: The authors have no conflicts of interest. J.M.G. is on the scientific advisory board of and has some equity interest in Cala Health, but this has no relation to the content of this study. The relationship is managed by the MGB Office for Industry-Academia Interactions.

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