Hormonal dynamics shape brain structural plasticity across the menstrual cycle: Insights from dense-sampling structural brain imaging of females with and without endometriosis

Hormonal dynamics shape brain structural plasticity across the menstrual cycle: Insights from dense-sampling structural brain imaging of females with and without endometriosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Hormonal dynamics shape brain structural plasticity across the menstrual cycle: Insights from dense-sampling structural brain imaging of females with and without endometriosis Carina Heller, Christian Gaser, Lejla Colic, Nooshin Javaheripour, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3750023/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Nature Neuroscience → Version 1 posted You are reading this latest preprint version Abstract Gonadal hormone fluctuations in females have been associated with symptoms of mental health, yet the underlying brain mechanisms remain understudied. Recent advances in neuroscience have shifted the paradigm towards longitudinal tracking, enabling the detection of subtle changes overlooked in conventional cross-sectional analyses. This dense-sampling approach acknowledges the rhythmic nature of gonadal hormone production. Our study employed three densely sampled females who underwent brain imaging and venipuncture (5 to 7 days per week) over the full menstrual cycle to investigate the impact of gonadal hormone variation on brain structure. In two healthy females with typical menstrual cycles, progesterone and progesterone/estradiol ratios were inversely associated with spatiotemporal structural brain patterns across the cycle. To probe the neural effects of hormonal dysregulation, we densely sampled a participant with endometriosis, an endocrine disorder affecting 10% of females in their reproductive years. Here, the spatiotemporal brain pattern was associated only with estradiol fluctuations. Our findings suggest that gonadal hormones are associated with short-term brain structural changes, with distinctions observed between typical and endometriosis cycles. This emphasizes the consideration of individual hormonal dynamics in understanding fluctuations in brain structural plasticity. Biological sciences/Neuroscience Health sciences/Anatomy/Nervous system/Brain Health sciences/Diseases/Endocrine system and metabolic diseases/Neuroendocrine diseases structural MRI menstrual cycle endometriosis estrogen progesterone precision medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The brain functions as an endocrine organ and is modulated by gonadal hormones, including endogenous estrogen and progesterone 1,2 . Based on ex vivo and rodent data, high number of estrogen receptors are found in the hippocampus, amygdala, claustrum, thalamus, hypothalamus, and the fifth layer of the temporal cortex 3 , whereas progesterone receptors are present in the hippocampus, amygdala, cerebellum, hypothalamus, and the frontal cortex 4 . Investigating the effect of endogenous hormones on brain neuroplasticity in vivo in human neuroscience has often been narrow in scope. Conventionally, data collection involved gathering information from multiple individuals simultaneously and then analyzing this data across individuals to establish mean comparisons and hormone-brain connections. However, this method, referred to as cross-sectional analysis, overlooks the intricate and rhythmic nature of hormone production within the body. Recent years have witnessed a paradigm shift in neuroimaging studies, with an alternative approach involving the longitudinal tracking of individual subjects over extended periods of weeks and months increasing sensitivity to detect associations among fluctuations in gonadal hormones and brain structure 5,6 . An emerging trend has centered on the comprehensive monitoring of the female menstrual cycle over time periods ranging from days to weeks and months, aiming to enhance our understanding of hormone-induced effects within the human brain 7–14 . This approach enriches our insights into the multifaceted impact of hormones on human brain function and structure by detecting subtle changes that could be overlooked in less frequent sampling. Densely sampled neuroimaging studies, tracking a single individual across a complete menstrual cycle, have primarily focused on investigating functional networks and connectivity 9,11–14 . To date, only two densely-sampled neuroimaging studies have examined structural changes, especially in the hippocampus and the medial temporal lobe 7,8 . Of interest is the recent longitudinal, less densely sampled, study with 27 female participants, each undergoing six scans throughout their menstrual cycle, that reported associations between estradiol, progesterone and the medial temporal lobe 15 . These structural neuroimaging studies are beginning to reshape our understanding of the dynamics of human brain structural fluctuations. However, a whole-brain approach has not yet been adopted, which would provide a broader perspective on the range of brain structures that change across the menstrual cycle in response to hormonal fluctuations. To expand our understanding of the impact of estrogens and progesterone on the brain’s structure, it is essential to broaden the scope of our research beyond individuals with typical menstrual cycle patterns. Including participants with endocrine disorders such as endometriosis, a condition characterized by a unique hormonal profile 16–18 , will provide a more nuanced understanding of the complex interplay between gonadal hormones and their influence on brain structure. Endometriosis, a chronic and inflammatory gynecological disorder, affects approximately 10 to 15% of females in their reproductive years 19 . It is defined by the presence and growth of ectopic endometrial stroma and glands outside the uterine cavity, typically within the peritoneal cavity. This pathological phenomenon can result in various clinical manifestations, including chronic pelvic pain, dysmenorrhea, dyspareunia, and infertility 20,21 . The condition is known to be influenced by hormonal dysregulations, prominently featuring an estrogen dependency 16,17 and progesterone resistance 18 . This means that the development, growth, and maintenance of endometriotic lesions are strongly influenced and sustained by estrogen within the body. In this context, estradiol synthesis is increased while its inactivation is decreased, resulting in elevated local concentrations of this hormone 22–24 . Furthermore, the endometriotic lesions can become resistant to the regulatory actions of progesterone. Consequently, even in the presence of progesterone, these tissues may continue to grow, bleed, and cause inflammation rather than responding to the inhibitory effects typically exerted by progesterone 25–27 . The current study employed three densely sampled females who underwent extensive and standardized brain imaging and venipuncture (5 to 7 days per week) over the full menstrual cycle to investigate the impact of endogenous hormone variation on brain structure. First, we densely sampled a healthy female with a typical menstrual cycle, referred to as ‘typical cycle.’ We compared this ‘typical cycle’ dataset of one female to the densely-sampled open-access 28andMe dataset of another female, which probes the extent to which endogenous fluctuations in sex hormones across a complete reproductive cycle influences the brain 8–14 . This dataset will be referred to as ‘28andMe (typical) cycle.’ The extensive brain imaging and venipuncture of the two complete menstrual cycles enabled us to test and reconfirm the hypothesis that brain volume fluctuates during the typical menstrual cycle. To extend the relevance of our findings and to probe the neural effects of hormonal dysregulation, we repeated these procedures in a female participant diagnosed with endometriosis. This dataset will be referred to as ‘endometriosis cycle.’ The addition of the endometriosis cycle allowed us to examine whether hormone-brain interactions are different between typically cycling females and those with endometriosis. An overview of the study procedures and timelines for all three participants is presented in Figure 1 . Whole-brain analyses in the present study revealed patterns of regions in the brain sharing similar structural changes across the menstrual cycle for the three individuals (see Figure 2 ). These spatiotemporal patterns of all three participants encompassed overlapping brain regions including the thalamus, globus pallidus, putamen, and caudate nucleus (see Figure 3 ). Distinct relationships between these spatiotemporal brain patterns and hormonal levels emerged across the typical menstrual cycles and the endometriosis cycle. While within the two typical menstrual cycles an inverse relationship between progesterone levels, progesterone/estradiol ratios and spatiotemporal brain patterns was observed, higher levels of estradiol corresponded with the spatiotemporal brain patterns across the menstrual cycle in endometriosis. To probe generalizability of our findings, we repeated these analyses in one male and one female using oral contraceptives across the time course of five weeks, when gonadal hormones were either low or progesterone was selectively suppressed. As expected, no relationship between spatiotemporal brain patterns and gonadal hormones were observed. 2. Results 2.1 Endocrine assessments and menstrual cycle patterns Gonadal hormones were assessed throughout the full menstrual cycle. Analyses of hormone serum concentrations in the typical cycle and the 28andMe (typical) cycle confirmed the expected rhythmic changes of a natural menstrual cycle (see Table 1 ). In the typical cycle, the 25 test sessions covered 15 days of the follicular phase and 10 days of the luteal phase. In the 28andMe (typical) cycle, the 30 test sessions covered 14 days of the follicular phase and 16 days of the luteal phase. The ratios between progesterone and estradiol concentrations suggested a typical hormonal balance during the luteal phase. The gonadal hormone concentrations in the endometriosis cycle also followed the rhythmic changes of a menstrual cycle. The 25 test sessions covered 17 days of the follicular phase and eight days of the luteal phase. As predicted, the progesterone/estradiol ratio suggested an estradiol dominance during the luteal phase. The menstrual cycles covered during the experiment lasted 24 and 23 days, representing a shorter menstrual cycle (£ 24 days) - typical in endometriosis. Progesterone concentrations surpassed 15.9 nmol/l, suggesting an ovulatory cycle in all three participants 28 . Figure 4 displays hormonal values for each participant relative to the day of ovulation. An alternative display is provided in the Supplementary Figure 1 . To test whether hormonal profiles differed between participants, a one-way Multivariate Analysis of Variance (MANOVA) was conducted with estradiol, progesterone, and the progesterone/estradiol ratio as dependent variables, and the three individuals (endometriosis cycle, typical cycle, 28andMe (typical) cycle) as fixed factors. The analysis revealed a significant main effect among the three individuals (Pillai’s trace: F (6,152) = 4.63, p < 0.001 ɳ 2 = 0.15; Roy’s largest root: F (3,76) = 10.23, p < 0.001, ɳ 2 = 0.29). Post-hoc Analyses of Variance (ANOVAs) indicated significant differences among the three individuals in estradiol ( F (2,77) = 8.86, p < 0.001, ɳ 2 = 0.19) and the progesterone/estradiol ratio ( F (2,77) = 5.96, p = 0.004, ɳ 2 = 0.13). Progesterone did not show a significant difference among individuals ( F (2,77) = 2.28, p = 0.1, ɳ 2 = 0.06). Post-hoc one-tailed t -tests, corrected using the Bonferroni method, further revealed that the endometriosis cycle had significantly higher estradiol levels compared to the typical cycle ( p = 0.003) and the 28andMe (typical) cycle ( p < 0.001). A similar pattern was observed for progesterone/estradiol ratio, with the endometriosis cycle showing significantly lower progesterone/estradiol ratios compared to the typical cycle ( p = 0.044) and the 28andMe (typical) cycle ( p = 0.002). Differences in hormonal values are displayed in Figure 5 . 2.2 Dynamics in structural brain changes T 1 -weigthed images were acquired of each individual across the full menstrual cycle. Singular Value Decomposition (SVD) analysis was used to extract spatiotemporal patterns. The spatial patterns represent the brain regions (structural) that share similar spatial changes over time. Spatiotemporal pattern 1 (STP1), the pattern explaining the highest variance, was used for further analyses. AutoRegressive Integrated Moving Average (ARIMA) models were then employed to investigate the relationship between STP1 in brain structure and gonadal hormones. ARIMA models are usually used to understand and predict time patterns in time series data. For each individual, the ARIMA models were initially specified as ARIMA(0,0,0) for estradiol, progesterone, and progesterone/estradiol ratio as predictor variables. This specification implies the deliberate exclusion of autoregressive (p), differencing (d), or moving average (q) terms, simplifying the models into basic linear regression without inherent time series complexities. Subsequently, ARIMA models with an autoregressive (AR) lag of order 1 were applied to account for lagged temporal patterns in the spatial map. ARIMA(1,0,0) model represents a pure AR model, examining the relationship between the current values of gonadal hormones and the temporal patterns in the spatial map from the following day (lag 1). Regarding the two typical cycles, the ARIMA(0,0,0) models with estradiol as a predictor were not significant. Similarly, the ARIMA(1,0,0) models, which included a time lag and employed estradiol as predictors, were not significant. These findings imply the absence of direct temporal patterns in brain structure associated with variations in estradiol. On the other hand, progesterone (28andMe (typical) cycle: p < 0.001, p FDR < 0.001; typical cycle: p = 0.013, p FDR < 0.039) and the progesterone/estradiol ratio (28andMe (typical) cycle: p < 0.001, p FDR < 0.001; typical cycle: p = 0.009, p FDR = 0.038) were significant predictors in the ARIMA(0,0,0) models, indicating a linear association between these hormones and the temporal pattern in the spatial map. This suggests that higher progesterone and higher progesterone/estradiol ratios correspond with lower STP1 levels across the typical cycles. The parameter estimates of -0.006 (28andMe (typical) cycle) and -0.005 (typical cycle) implies that for every one-unit increase in progesterone, a corresponding units change of -0.006 and -0.005, respectively, in the spatiotemporal brain pattern is expected. Likewise, this applies to the progesterone/estradiol ratio. For every one-unit increase in the progesterone/estradiol ratio, a -0.002 unit change in the spatiotemporal brain pattern is expected. The ARIMA(1,0,0) models were not significant, suggesting no relation of the current progesterone value nor the progesterone/estradiol ratio with the following two values of the temporal pattern in the spatial map. For the endometriosis cycle, the ARIMA(0,0,0) model revealed estradiol as a statistically significant predictor of the temporal pattern in the spatial map ( p = 0.010, p FDR = 0.038). This suggests that elevated estradiol levels correspond with higher spatiotemporal brain pattern levels across the menstrual cycle in the individual with endometriosis. The estimated parameter of approximately 0.0002 implies that for every one-unit increase in estradiol, we anticipate a corresponding change of 0.0002 units in the temporal brain pattern in the spatial map. In contrast, progesterone and the progesterone/estradiol ratio were not significant ( p = 0.137, p FDR = 0.205). When a time lag was incorporated into the ARIMA model (1,0,0) the results were not statistically significant. These results suggest the absence of lagged temporal patterns in the spatial map associated with fluctuations in gonadal hormones for the endometriosis cycle. Results of all ARIMA(0,0,0) models are displayed in Figure 6 and Table 2 . 2.3 Control analyses To probe the generalizability of our densely sampled datasets, we repeated the SVD and time-series regression analyses in a male participant and in a female using oral contraceptives. Both individuals were scanned over the time course of five weeks, resulting in 25 test sessions per individual (see Supplementary Figure 2 ). The female was placed on a daily regimen of 0.03 mg ethinyl-estradiol and 2 mg dienogest (Maxim, Jenapharm) for three months before the assessment, which selectively suppressed circulating progesterone. The concentration and dynamic range of estradiol during oral contraception intake was similar to a typical cycle (see Supplementary Figure 3 ). As expected, the ARIMA(0,0,0) models did not yield significant results after FDR correction (see Supplementary Figure 4 ) in either the male or in the oral contraceptives dataset, regardless of the hormone: estradiol (male: p = 0.136, p FDR = 0.206; oral contraceptives: p = 0.021, p FDR = 0.053), progesterone (male: p = 0.771, p FDR = 0.776; oral contraceptives: p = 0.105, p FDR = 0.197), and progesterone/estradiol ratio (male: p = 0.503, p FDR = 0.629; oral contraceptives: p = 0.305, p FDR = 0.416). Similarly, the ARIMA(1,0,0) models, which included a time lag, were not significant. Spatiotemporal brain patterns for the male and the female using oral contraceptives are displayed in Supplementary Figure 5 . 2.4 Sensitivity analyses Furthermore, previous work reported gray matter volume changes across the menstrual cycle in less densely sampled cohorts. Changes were reported in whole-brain gray matter volume 29,30 , in subcortical structures, including the hippocampus 15,31 , putamen, and globus pallidus 31 , as well as in cerebrospinal fluid (CSF) 30 . We assessed whole-brain gray matter volume changes as well as changes in CSF in all five datasets (typical cycle, 28andMe (typical) cycle, endometriosis cycle, male, and oral contraceptives). Whole-brain gray matter volume encompassed bilateral cerebral cortex volume and the volumes of thalamus, caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, accumbens nucleus, and ventral diencephalon. The numerical values of the whole-brain gray matter volumes and CSF were demeaned to center around zero to enhance the sensitivity and specificity of our measurements. Next, cubic regression curve estimations were used to check whether demeaned volumes fluctuated significantly across the testing sessions. Whole-brain gray matter volume and CSF fluctuated significantly across the menstrual cycle in the typical cycle (whole-brain gray matter: F (3,21) = 14.46, p < 0.001, p FDR < 0.001, R 2 = 0.67; CSF: F (3,21) = 7.36, p = 0.001, p FDR = 0.001, R 2 = 0.51), the 28andMe (typical) cycle (whole-brain gray matter: F (3,26) = 5.05, p = 0.007, p FDR = 0.009, R 2 = 0.37; CSF: F (3,26) = 4.71, p = 0.009, p FDR = 0.009, R 2 = 0.35), and the endometriosis cycle (whole-brain gray matter: F (3,20) = 5.58, p = 0.006, p FDR = 0.012, R 2 = 0.46; CSF: F (3,20) = 3.65, p = 0.030, p FDR = 0.030, R 2 = 0.35). As expected, whole-brain gray matter volume and CSF did not fluctuate in the male (whole-brain gray matter: F (3,21) = 3.00, p = 0.053, p FDR = 0.11, R 2 = 0.30; CSF: F (3,21) = 0.90, p = 0.46, p FDR = 0.46, R 2 = 0.11). Additionally, in the female using oral contraceptives, when progesterone was selectively suppressed, whole-brain volume and CSF did not fluctuate significantly (whole-brain gray matter: F (3,21) = 2.01, p = 0.14, p FDR = 0.14, R 2 = 0.22; CSF: F (3,21) = 3.23, p = 0.043, p FDR = 0.086, R 2 = 0.32). 3. Discussion Despite recent efforts to elucidate the association between gonadal hormones and brain structure fluctuations, whole-brain approaches are scarce. Moreover, investigations of hormone-brain interactions in non-typical cycles remain understudied. In the present study, we applied dense-sampling brain imaging and venipuncture, utilizing a whole-brain singular value decomposition analytical approach to assess brain structure in both the typical and endometriosis cycle. We reported divergent impacts of gonadal hormones on structural brain dynamics, suggesting differences between individuals with endometriosis and those with typical menstrual cycles. In females with typical menstrual cycles, progesterone exhibited the most pronounced effect on volumetric brain changes throughout the menstrual cycle. In contrast, in the endometriosis cycle, estradiol showed the most pronounced effect. This whole-brain analysis underscores the multifaceted interactions between gonadal hormones and brain structure, with implications in both pathological and typical cycles. The spatiotemporal patterns identified in the brain across the menstrual cycle for the three individuals encompassed regions such as the thalamus, globus pallidus, putamen, and caudate nucleus – brain areas characterized by increased distributions of estradiol and progesterone receptors 3,4 . Notably, these patterns exhibited distinct relationships with hormonal fluctuations across the typical menstrual cycles and the endometriosis cycle, underscoring the complexity of hormone-induced brain changes. In individuals with typical menstrual cycles, no associations between estradiol and changes in brain structure were observed. In contrast, progesterone levels and progesterone/estradiol ratios were associated with cycle-dependent brain volume fluctuations. This suggests a heightened sensitivity to progesterone in individuals with typical menstrual cycles. These findings align with the recently published results in a less densely sampled cohort of 27 female participants scanned six times throughout their menstrual cycle 15 . In the context of the endometriosis cycle, our study revealed an association between estradiol levels and brain volume changes across the menstrual cycle. High levels of estradiol were found to be associated with increased volumes of regions including the thalamus, globus pallidus, and caudate nucleus. Incorporating time lags into the analysis revealed no significant associations between gonadal hormones and brain structure, suggesting that hormonal fluctuations did not exhibit patterns with delayed effects within the endometriosis cycle. Our findings are aligned with the previous hormonal evidence that characterizes endometriosis with elevated estradiol levels 20,32 . This association may be attributed to an estradiol dependency and progesterone resistance commonly observed in endometriosis 16–18 . In support, the individual with endometriosis had elevated estradiol levels and estradiol dominance in the luteal phase of the menstrual cycle, suggesting a greater exposure to estradiol on the brain. Estrogen is believed to have a neuroprotective role, promoting brain health and protecting against cognitive decline 33–35 . However, while estradiol levels within the physiological range stimulate brain activity, especially within the hippocampus, supraphysiological levels of estradiol (equivalent to levels during early pregnancy) show opposite effects 36 . To date, little is known about the impact of prolonged high estradiol exposure during the reproductive years on long-term health outcomes. This underscores the importance of further research to elucidate the longitudinal relationships between gonadal hormones, reproductive health, and long-term well-being in individuals with hormonal dysregulations. Since this study is a longitudinal study with a dense-sampling design with a limited sample of only three individuals, interpretations and explanations of the reported associations should be made cautiously in generalizing the findings to the broader population. By focusing on single participants, we aimed to mitigate the intraindividual variability of hormonal fluctuations instead of the interindividual variability, often obscured in studies with larger and more heterogeneous samples. Our approach provides a more precise examination and clearer picture of the specific patterns of hormonal fluctuations and their potential impact on the brain, offering a higher level of sensitivity and temporal resolution towards precision imaging and medicine. In support, we expanded the scope of our study by including additional analyses of one male and one female using oral contraceptives over a densely sampled period of five weeks. In these two individuals, where gonadal hormones were either lower or progesterone was selectively suppressed, no significant associations between spatiotemporal patterns of the brain and hormonal fluctuations were observed. By contrasting periods of natural hormonal fluctuations with those of lower or suppressed gonadal hormones, we gain a more comprehensive understanding of the specificity of the observed relationships. This comparative aspect enhances the generalizability of our findings, suggesting that the associations between gonadal hormones and brain structure may be context-dependent, influenced by the presence or absence of natural hormonal variations. Further research with larger and more diverse samples is necessary to validate and expand these initial findings, addressing potential interindividual variations and enhancing the generalizability of the observed associations. Despite the small sample size, our findings offer valuable first insights into the dynamic impact of unique hormonal fluctuations on brain organization throughout the menstrual cycle and speak towards a future of personalized and precision medicine. In summary, by adopting a dense-sampling approach, this study enriches our comprehension of the intricate relationship between hormones and the human brain, offering novel insights into the dynamics of whole-brain organization and variations of the menstrual cycle. Future research, with larger and more diverse cohorts, can build upon these insights, advancing our understanding of the dynamic relationship between hormonal and brain structure fluctuations. The results underscore the importance of considering individual hormonal dynamics in understanding brain plasticity during the menstrual cycle as hormonal dysregulations, characteristic of specific conditions, may modulate structural brain changes differently. 4. Online Methods Dense-sampling, longitudinal datasets were acquired from two female participants in Jena, Germany. These datasets are referred to as the ‘endometriosis cycle’ and ‘typical cycle’. To extend our findings, we also leveraged the open-access 28andMe dataset of one female, which probes the extent to which endogenous fluctuations in sex hormones across a complete reproductive cycle influence the brain 8–14 . The data was acquired in Santa Barbara, California, and is referred to as ‘28andMe (typical) cycle’. For the purposes of control analyses and to probe generalizability of our findings, additional dense-sampling, longitudinal datasets of one male and one female using oral contraceptives were acquired across the time course of five weeks in Jena, Germany. Detailed descriptions of the participants are found in the Supplementary Figure 2 and Supplementary Figure 3 . 4.1 Participants The study procedures for the participants in Jena, Germany, were: A healthy female (37 years of age, Caucasian) underwent most weekday testing for five consecutive weeks (January 9 – February 12, 2023) while freely cycling, resulting in n = 25 test sessions. The female participant (‘typical cycle’) had a history of regular menstrual cycles (last half-year mean length = 27.1 days, SD = 0.64, range = 26 – 28 days), no history of psychiatric, neurological, and endocrine diagnoses, breastfeeding or pregnancy, and no history of alcohol, or drug abuse, but current use of nicotine. A second female participant (30 years of age, Caucasian) diagnosed with endometriosis (‘endometriosis cycle’) participated in this dense-sampling, longitudinal study. She received the diagnosis seven months prior to the assessments (October 28, 2022) after a cyst surgery in the pelvic area. The participant was tracking her menstrual cycle length and reported a mean menstrual cycle length of 24.4 days ( SD = 1.67, range = 23 – 27 days) during that time. Otherwise, the female participant had no history of psychiatric or neurological disorders, breastfeeding or pregnancy, and no history of smoking, alcohol, or drug abuse. The participant underwent testing from Monday to Friday for five consecutive weeks (June 12– July 14, 2023) while freely cycling, resulting in n = 25 test sessions. Participants gave written informed consent, and the Friedrich Schiller University Jena Ethics Committee approved the study. The study procedure for the third participant was the following: The healthy female participant (23 years of age, Caucasian, ‘28andMe (typical) cycle’) underwent testing for 30 consecutive days (July 9 – August 7, 2018), while freely cycling. She had a history of regular menstrual cycles (no missed periods, cycle occurring every 26–28 days) and had not taken hormone-based medication in the 12 months prior to the first study. The participant had no history of psychiatric or neurological disorders, breastfeeding, or pregnancy, and no history of smoking, alcohol, or drug abuse. The participant gave written informed consent, and the study was approved by the University of California, Santa Barbara Human Subjects Committee. 4.2 Image acquisition For datasets collected in Jena (typical cycle, endometriosis cycle, male, oral contraceptives), scans were collected daily at 7.30 a.m. local time. The imaging dataset for the typical cycle was acquired on a 3T MRI scanner (Prisma Fit , Siemens Medical Solutions, Erlangen, Germany) with a 64-channel head coil. The imaging datasets for the endometriosis cycle, male, and female on oral contraceptives were acquired on a 3T MRI scanner (Prisma, Siemens Medical Solutions, Erlangen, Germany) with a 64-channel head coil. Structural MRI for the datasets were acquired with T 1 -weighted (T1w) magnetization prepared - rapid gradient echo (MPRAGE) sequence with the generalized autocalibrating partially parallel acquisitions (GRAPPA) acceleration. Scan parameters were: echo time (TE) = 2.22 ms, repetition time (TR) = 2400 ms, inversion time (TI) = 1000 ms, flip angle = 8°, matrix size = 320 x 320 pixels, field of view (FOV) = 256 mm, band width = 220 Hz/pixel, and slice thickness = 0.80 mm. For the 28andMe (typical) cycle dataset, scans were collected on a 3T MRI scanner (Prisma, Siemens Medical Solutions, Erlangen, Germany) equipped with a 64-channel head coil. Structural scans were acquired using a T1w MPRAGE sequence with the GRAPPA acceleration with the following parameters: TE = 2.31 ms, TR = 2500 ms, TI = 934 ms, flip angle = 7°, matrix size = 320 x 320 pixels, FOV = 255 mm, band width = 210 Hz/pixel, and slice thickness = 0.80 mm. 4.3 Image preprocessing and data reduction The parameters used to acquire the images (e.g., sizes, space directions, space origin), and the quality of the images (e.g., motion artifacts, ringing, ghosting of the skull or eyeballs, cut-offs, signal drops, and other artifacts) were visually inspected. One scan from the endometriosis cycle had to be removed due to artefacts in subcortical structures, corpus callosum, and cingulate gyrus. The final datasets consisted of 24 T1w images for the endometriosis cycle, 25 T1w images for the typical cycle, 25 T1w images for the male, 25 T1w images for the female on oral contraceptives, and 30 T1w images for the 28andMe (typical) cycle. The T1w images were converted from Dicom to Nifti files using dcm2niix (Chris Rorden, version v1.0.20170724, https://www.nitrc.org/projects/mricrogl/) and then preprocessed in SPM12 (http://www.fil.ion.ucl.ac.uk/spm) and the CAT12 (https://neuro-jena.github.io/cat) 37 toolbox using the longitudinal pipeline approach in Matlab R2021b (The MathWorks Inc., Natick, MA, USA). All T1w images were corrected for bias-field inhomogeneities 38,39 and tissue-classified into gray matter, white matter, and cerebrospinal fluid 40 , which also included an approach accounting for partial volume effects 41 by applying adaptive maximum a posteriori estimations 42 and a hidden Markov Random Field Model 43 . The resulting gray and white matter partitions were spatially normalized to MNI space Geodesic Shooting Registration 44 . Subsequently, the normalized tissue segments were smoothed using a 6 mm full width at half maximum (FWHM) Gaussian Kernel. Singular Value Decomposition (SVD) was then used to extract spatiotemporal patterns from the preprocessed images by decomposing the three-dimensional image sets into spatial patterns (maps) and their associated temporal dynamics (time course) for each participant separately (Jacobian determinant maps) using an in-house Matlab script. The spatial patterns represent the brain regions (structural) that share similar spatial changes over time. By using SVD, we can identify and analyze these patterns, revealing coherent time courses across the brain rather than being restricted to an expected change over time. For each individual, the SVD analysis yielded four brain spatiotemporal patterns exceeding a threshold of 1, representing similar spatiotemporal patterns in the brain (see Supplementary Figure 6 ). Spatiotemporal pattern 1 (for better understanding now referred to as STP1) exhibited the highest variance explained (40% for endometriosis cycle, 45% for typical cycle, 54% for 28andMe (typical) cycle, 44% for male, 27% for female on oral contraceptives) and was thus chosen for subsequent analysis for each individual. By selecting STP1, we prioritized the dominant structural patterns within each dataset, allowing us to focus on the most relevant information. STP1 of the investigated three participants encompassed overlapping brain regions, which included subcortical structures such as the thalamus, globus pallidus, putamen, and caudate nucleus, suggesting that these brain structures exhibit common spatiotemporal patterns across the participants. 4.4 Image preprocessing for additional control analyses Sequence Adaptive Multimodal SEGmentation (SAMSEG) 45 was used to segment whole-brain gray matter volume. Initially, a subject-specific template was created by spatially co-registering all 3D T1w MRPAGE volumes through an iterative process 46 . The co-registered 3D volumes were then employed to implement longitudinal SAMSEG 47 . The final segmentations were used to extract the volume of the bilateral cerebral cortex, thalamus, caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, accumbens nucleus, ventral diencephalon as well as CSF directly for each measurement day and each participant separately. Whole-brain gray matter volume was calculated by adding the volumes of the bilateral cerebral cortex, thalamus, caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, accumbens nucleus, and ventral diencephalon. The numerical values of the whole-brain gray matter volumes and CSF were then demeaned to center around zero. Demeaning the volumes is essential for removing potential sources of systematic bias or shift. It helps eliminate variations caused by external factors or measurement errors. As a result, this process enhances the sensitivity and specificity. 4.5 Endocrine procedure For the datasets acquired in Jena, Germany, a daily blood draw immediately followed the MRI session at ~8.30 a.m. One 4.9 ml blood sample was collected in a S-Monovette ® Serum-GEL (Sarstedt) with a clotting activator/gel each test session. The sample was clotted at room temperature and centrifugated (2500 x g for 10 minutes) within two hours. Estradiol (pmol/ml), LH (IU/l), FSH (IU/l), and progesterone serum concentrations (ng/ml) were determined at the Department of Clinical Chemistry and Laboratory Medicine, Jena University Hospital, Jena, Germany. Estradiol was assessed with the electrochemiluminescence immunoassay (ECLIA) Elecsys ® Estradiol III Assay. Assay antibodies, measuring ranges (defined by the limit of detection and the maximum of the master curve), and intra-assay precision coefficients of variation for estradiol were the following: antibodies, two biotinylated monoclonal anti-estradiol antibodies (rabbit), 2.5 ng/ml and 4.5 ng/ml; measuring range, 18.4 – 11,010 pmol/l (5 – 3000 pg/ml); intra-assay precision, ≤ 8.4% variation coefficient. LH was assessed with the ECLIA Elecsys ® LH Assay. Assay antibodies, measuring ranges, and intra-assay coefficients of variation for LH were the following: antibodies, biotinylated monoclonal anti-LH antibody (mouse), 2.0 mg/l; measuring range, 0.3 – 200 mIU/ml (0.3 – 200 IU/l); intra-assay precision, ≤ 2.2% variation coefficient. FSH was assessed with the ECLIA Elecsys ® FSH Assay. Assay antibodies, measuring ranges, and intra-assay coefficients of variation for FSH were the following: antibodies, biotinylated monoclonal anti-FSH antibody (mouse), 0.5 mg/l; measuring range, 0.3 – 200 mIU/ml (0.3 – 200 IU/l); intra-assay precision, ≤ 2.1% variation coefficient. Progesterone was assessed with the ECLIA Elecsys ® Progesterone III Assay. Assay antibodies, measuring ranges, and intra-assay coefficients of variation for progesterone were the following: antibodies, biotinylated monoclonal anti-progesterone antibody (recombinant sheep), 30 ng/ml; measuring range, 0.159 – 191 nmol/l (0.05 – 60 ng/ml); intra-assay precision, ≤ 20.7% variation coefficient. All assays were determined on the cobas ® e 402/801 analyzer (Roche Diagnostics GmbH, Mannheim, Germany) and were used according to the manufacturer's instructions. For the 28andMe (typical) cycle dataset acquired in Santa Barbara, CA, USA, a licensed phlebotomist inserted a saline-lock intravenous line into the dominant or non-dominant hand or forearm. One 10 ml blood sample was collected in a vacutainer SST (BD Diagnostic Systems) each session. The sample was clotted at room temperature for 45 minutes until centrifugation (2000 x g for 10 minutes) and then aliquoted into three 1 ml microtubes. Serum samples were stored at -20°C until assayed. Serum concentrations were determined at the Brigham and Women’s Hospital Research Assay Core. Estradiol and progesterone were assessed via liquid chromatography mass-spectrometry. Assay sensitivities, dynamic range, and intra-assay coefficients of variation (respectively) were as follows: estradiol, 1 pg/ml, 1–500 pg/ml, < 5% relative standard deviation (RSD); progesterone, 0.05 ng/ml, 0.05–10 ng/ml, 9.33% RSD. FSH and LH levels were determined via chemiluminescent assay (Beckman Coulter). The assay sensitivity, dynamic range, and intra-assay coefficient of variation were as follows: FSH, 0.2 mIU/ml, 0.2–200 mIU/ml, 3.1–4.3%; LH, 0.2 mIU/ml, 0.2–250 mIU/ml, 4.3–6.4%. 4.6 Psychological measures To track mood variations across the menstrual cycle, both positive affect and negative affect was assessed along with state anxiety. A detailed description can be found in the Supplementary Description 1 and Supplementary Figure 7 . 4.7 Statistical approach Statistical analyses were performed using R software (https://www.r-project.org), Statistical Package for Social Sciences (SPSS) version 27, and GraphPad Prism 8. First, a one-way MANOVA was conducted with estradiol, progesterone, and progesterone/estradiol ratio as dependent variables. The fixed factors were the three individuals (endometriosis cycle, typical cycle, 28andMe (typical) cycle). Post-hoc ANOVAs and one-tailed t -tests were performed and Bonferroni-corrected. Second, spearman correlations were performed between the psychological measures of positive affect, negative affect, and state anxiety, with the hormones of estradiol, progesterone, and progesterone/estradiol ratio. False Discovery Rate (FDR) was used to correct for multiple comparisons 48 . Correlations were only performed in the endometriosis cycle and the typical cycle as only these two individuals completed the same psychological assessments. Comparable psychological assessments were performed in the 28andMe (typical) cycle; details regarding these assessments can be found elsewhere 13 . Next, an autoregressive modeling approach was employed for the dependent variable (STP1) and predictors (estradiol, progesterone, and progesterone/estradiol ratio). For each individual, the AutoRegressive Integrated Moving Average (ARIMA) models were initially specified as ARIMA(0,0,0) for the predictor variables of estradiol, progesterone, and progesterone/estradiol ratio separately. This specification implies the deliberate exclusion of autoregressive (p), differencing (d), or moving average (q) terms, simplifying the models into basic linear regression structures without inherent time series complexities. Subsequently, ARIMA models with an autoregressive (AR) lag of order 1 were applied to account for the spatiotemporal patterns. ARIMA(1,0,0) model represents a pure AR model, examining the relationship between today’s values of gonadal hormones and brain spatiotemporal patterns of the following day (lag 1). All ARIMA models were FDR corrected for multiple comparisons 48 . Lastly, cubic regression curve estimations were used to check whether demeaned whole-brain gray matter and CSF volume fluctuated significantly across the testing sessions. Cubic regressions were corrected for multiple comparisons using the FDR-method 48 . Declarations Author contributions: CH was responsible for the study concept and design, acquired the MRI data in Jena, Germany, and psychological questionnaires, processed and analyzed the data, performed the statistical analysis, and wrote the manuscript. CG analyzed the MRI data and was involved in the critical revision of the manuscript. LC acquired the data and was involved in the critical revision of the manuscript. NJ, FC, and PR assisted with the interpretation of the results and were involved in the critical revision of the manuscript. CJK and ACB collected the blood samples and were involved in the critical revision of the manuscript. HG and FJL assisted with the statistical analysis and the critical revision of the manuscript. MK analyzed the blood samples. LP and EGJ acquired the MRI data in Santa Barbara, USA, assisted with the interpretation of the findings, and were involved in the critical revision of the final manuscript. ZK, MW, and IC assisted with the study concept and design and were involved in the critical revision of the final manuscript. DG acquired, processed, and analyzed the MRI data, supervised the inspection of the anatomical data, and was involved in the critical revision of the final manuscript. Acknowledgment: This work was supported by the Friedrich Schiller University Jena (IMPULSE Project) to CH, and by the Interdisciplinary Center of Clinical Research of the Medical Faculty Jena (LC). The present work is endorsed by the German Center for Mental Health. The funder played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript. Competing interest: MW is a member of the following advisory boards and gave presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische Heilmittel Heel GmbH, Germany. MW has further conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation. MW did not receive any financial compensation from the companies mentioned above. All other authors declare no financial or non-financial competing interests. Data availability: The datasets generated in Jena, Germany, are available from the corresponding author on request. The dataset generated in Santa Barbara, CA, USA, is available at https://openneuro.org/datasets/ds002674. Code availability: Code is available from the corresponding author on request. References Juraska, J. M., Sisk, C. L. & DonCarlos, L. L. Sexual differentiation of the adolescent rodent brain: Hormonal influences and developmental mechanisms. Horm. Behav. 64 , 203–210 (2013). Rehbein, E., Hornung, J., Sundström Poromaa, I. & Derntl, B. Shaping of the Female Human Brain by Sex Hormones: A Review. 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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57 , 289–300 (1995). Tables Table 1. Gonadal hormones by cycle stage in the endometriosis cycle, the typical cycle, and the 28andMe (typical) cycle. Follicular Luteal Mean ± SD Mean ± SD Endometriosis Cycle Typical Cycle 28andMe (Typical) Cycle Endometriosis Cycle Typical Cycle 28andMe (Typical) Cycle Estradiol (pmol/l) 912.41 ± 791.6 584.40 ± 434.6 307.01 ± 284.9 596.75 ± 275.3 403.20 ± 147.3 301.74 ± 105.3 Progesterone (nmol/l) 1.54 ± 1.8 1.85 ± 1.7 0.39± 0.4 20.40 ± 12.6 38.05 ± 20.4 28.57 ± 16.3 Progesterone/Estradiol Ratio a 1.54 ± 1.8 5.61 ± 6.6 2.30 ± 3.0 31.69 ± 10.0 90.89 ± 30.7 91.85 ± 50.1 Note. a = The Progesterone/Estradiol ratio is usually measured during the luteal phase of the menstrual cycle. Table 2 . Linear time series regression between the spatiotemporal brain pattern and gonadal hormones. Cycle ARIMA (p,d,q) Outcome Predictor Estimate SE T p p FDR Endometriosis Cycle (0,0,0) STP1 Constant -0.129 0.059 -2.177 0.040 --- Estradiol 1.73E-4 6.16E-5 2.800 0.010 0.038 R 2 = 0.263; RMSE = 0.183 (0,0,0) STP1 Constant -0.045 0.048 -0.939 0.358 --- Progesterone 0.006 0.004 1.748 0.094 0.197 R 2 = 0.122; RMSE = 0.200 (0,0,0) STP1 Constant -0.049 0.052 -0.936 0.359 --- Progesterone/Estradiol Ratio 0.004 0.003 1.543 0.137 0.206 R 2 = 0.098; RMSE = 0.203 Typical Cycle (0,0,0) STP1 Constant -0.020 0.074 0.275 0.785 --- Estradiol -3.98E-5 1.19E-4 -0.333 0.742 0.776 R 2 = 0.005; RMSE = 0.208 (0,0,0) STP1 Constant 0.074 0.046 1.627 0.117 --- Progesterone -0.005 0.002 -2.687 0.013 0.039 R 2 = 0.240; RMSE = 0.182 (0,0,0) STP1 Constant 0.088 0.047 1.858 0.076 --- Progesterone/Estradiol Ratio -0.002 0.001 -2.840 0.009 0.038 R 2 = 0.260; RMSE = 0.179 28andMe (Typical) Cycle (0,0,0) STP1 Constant 0.015 0.064 0.242 0.811 --- Estradiol -1.86E-4 0.001 -0.288 0.776 0.776 R 2 = 0.003; RMSE = 0.189 (0,0,0) STP1 Constant 0.106 0.035 3.014 0.005 --- Progesterone -0.006 0.001 -4.530 <0.001 <0.001 R 2 = 0.423; RMSE = 0.144 (0,0,0) STP1 Constant 0.11 0.036 3.084 0.005 --- Progesterone/Estradiol Ratio -0.002 4.54E-4 -4.55 <0.001 <0.001 R 2 = 0.425; RMSE = 0.143 Note. STP1 = spatiotemporal pattern 1 of brain regions that share a similar structural pattern across the measured time. ARIMA = autoregressive integrated moving average modeling. p = autoregressive order. d = integrated order. q = moving average order. SE = standard error. AR = autoregressive. RMSE = root mean squared error. T = T -statistic. p = p -value. p FDR = false discovery rate corrected p -value. Significant p -values indicated in bold. Additional Declarations Yes there is potential Competing Interest. MW is a member of the following advisory boards and gave presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische Heilmittel Heel GmbH, Germany. MW has further conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation. MW did not receive any financial compensation from the companies mentioned above. 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Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Güllmar","suffix":""}],"badges":[],"createdAt":"2023-12-13 19:40:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3750023/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3750023/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41593-025-02066-2","type":"published","date":"2025-09-26T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":48474063,"identity":"da22260e-bdff-4763-9533-7baccbff94b7","added_by":"auto","created_at":"2023-12-19 16:28:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":822397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTimeline of the data collection.\u003c/strong\u003e MRI and endocrine assessments were acquired simultaneously for each participant on each test day. Purple timeline bars represent the endometriosis and the typical cycle acquired in Jena, Germany. The orange timeline bar represents the 28andMe (typical) cycle acquired in Santa Barbara, California, USA.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3750023/v1/36511ceb7ab440cb590401b2.jpg"},{"id":48474064,"identity":"3c75c5eb-5420-4e8c-b305-e0f1488842fc","added_by":"auto","created_at":"2023-12-19 16:28:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2032654,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal brain pattern in the endometriosis cycle, the typical cycle, and the 28andMe (typical) cycle.\u003c/strong\u003e Displayed is spatiotemporal pattern1 (STP1), which represents the dominant pattern explaining the most variance. The top shows the spatial maps that represent brain regions that share a similar spatial pattern across the menstrual cycle (spatial pattern). Warm colors indicate positive associations, while cool colors represent negative associations of the spatial pattern with the temporal pattern across the cycle. The bottom shows the associated time course of each spatial map (temporal pattern). Individual eigenvectors of each test day associated to the spatial map are displayed.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3750023/v1/15441af1c57a58be4188c6eb.jpg"},{"id":48474065,"identity":"7b746211-d388-49af-9337-526ac38d7c8e","added_by":"auto","created_at":"2023-12-19 16:28:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1636463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverlapping brain regions that share a common spatial pattern across the menstrual cycle in the three participants. \u003c/strong\u003eSpatiotemporal patterns of all three participants overlap in several brain regions, including the thalamus, globus pallidus, putamen, and caudate nucleus.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3750023/v1/fdcbdef55218227393006e81.jpg"},{"id":48474830,"identity":"d19b4168-10bd-4de1-b792-608daaf0b81b","added_by":"auto","created_at":"2023-12-19 16:36:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":504839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHormone concentrations of estradiol, progesterone, and the ratio of progesterone to estradiol relative to the day of ovulation for each participant. \u003c/strong\u003eCycle length and ovulation (day of ovulation = day 0) were standardized to account for variations in cycle length among the three participants. Ovulation was confirmed through assessment of luteinizing hormone (LH). Positive numbers display days after ovulation (luteal phase), while negative numbers display days before ovulation (follicular phase). Hormone levels suggest a typical hormonal balance in the typical cycle and the 28andMe (typical) cycle, while hormone levels in the endometriosis cycle suggest an estradiol dominance in the luteal phase.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3750023/v1/cf8d9e86bbc0cd15de17cb04.jpg"},{"id":48474068,"identity":"becbe7da-aa31-4841-9423-88201547451e","added_by":"auto","created_at":"2023-12-19 16:28:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":198944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePost-hoc tests display differences in estradiol, progesterone, and the progesterone/estradiol ratio among the three individuals.\u003c/strong\u003e The box-and-whisker plots show the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentile, the minimum and maximum values, and individual outliers. Bonferroni correction was applied. *** \u0026lt; 0.001. ** \u0026lt; 0.01. * \u0026lt; 0.05. n.s. = non-significant.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3750023/v1/ce10157bb68b6c54472d1f0c.jpg"},{"id":48474066,"identity":"8640cebd-2d0c-4d48-98bf-58cfe395c9e2","added_by":"auto","created_at":"2023-12-19 16:28:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":739815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear time series regression analyses with hormonal values as predictors of brain spatiotemporal pattern1 (STP1) in the endometriosis cycle, the typical cycle, and the 28andMe (typical) cycle.\u003c/strong\u003e AutoRegressive Integrated Moving Average (ARIMA) models were employed to investigate the relationship between spatiotemporal patterns in brain structure and sex steroid hormones of estradiol, and progesterone, as well as the progesterone/estradiol ratio. Models were initially specified as ARIMA(0,0,0). This specification implies the deliberate exclusion of autoregressive (p), differencing (d), or moving average (q) terms, simplifying the models into basic linear regression without inherent time series complexities. Data are displayed in standardized units. p = p-value. Significant regressions are indicated in bold, after false discovery rate correction for multiple comparisons (p\u003csub\u003eFDR\u003c/sub\u003e). The black lines represent the best-linear-fit for the data with 95% confidence bands in gray.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3750023/v1/13949335a9afb9f0c6a75f38.jpg"},{"id":92304515,"identity":"05e46ac7-2320-4fd2-97e1-33c01043d896","added_by":"auto","created_at":"2025-09-27 07:07:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7251447,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3750023/v1/4af87e8c-661b-4098-bab3-00b1b401c1c3.pdf"},{"id":48474069,"identity":"ddcb3683-d96a-4de1-8f31-9cd2c6ecb4a5","added_by":"auto","created_at":"2023-12-19 16:28:29","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2861704,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"SupplementaryMaterialnew.docx","url":"https://assets-eu.researchsquare.com/files/rs-3750023/v1/ccf7c90e36f727215a450ebd.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nMW is a member of the following advisory boards and gave presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische Heilmittel Heel GmbH, Germany. MW has further conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation. MW did not receive any financial compensation from the companies mentioned above. All other authors declare no financial or non-financial competing interests.","formattedTitle":"Hormonal dynamics shape brain structural plasticity across the menstrual cycle: \r\nInsights from dense-sampling structural brain imaging of females with and without endometriosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe brain functions as an endocrine organ and is modulated by gonadal hormones, including endogenous estrogen and progesterone \u003csup\u003e1,2\u003c/sup\u003e. Based on \u003cem\u003eex vivo\u003c/em\u003e and rodent data, high number of estrogen receptors are found in the hippocampus, amygdala, claustrum, thalamus, hypothalamus, and the fifth layer of the temporal cortex \u003csup\u003e3\u003c/sup\u003e, whereas progesterone receptors are present in the hippocampus, amygdala, cerebellum, hypothalamus, and the frontal cortex \u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eInvestigating the effect of endogenous hormones on brain neuroplasticity \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003ein human neuroscience has often been narrow in scope. Conventionally, data collection involved gathering information from multiple individuals simultaneously and then analyzing this data across individuals to establish mean comparisons and hormone-brain connections. However, this method, referred to as cross-sectional analysis, overlooks the intricate and rhythmic nature of hormone production within the body. Recent years have witnessed a paradigm shift in neuroimaging studies, with an alternative approach involving the longitudinal tracking of individual subjects over extended periods of weeks and months increasing sensitivity to detect associations among fluctuations in gonadal hormones and brain structure \u003csup\u003e5,6\u003c/sup\u003e. An emerging trend has centered on the comprehensive monitoring of the female menstrual cycle over time periods ranging from days to weeks and months, aiming to enhance our understanding of hormone-induced effects within the human brain \u003csup\u003e7\u0026ndash;14\u003c/sup\u003e. This approach enriches our insights into the multifaceted impact of hormones on human brain function and structure by detecting subtle changes that could be overlooked in less frequent sampling. Densely sampled neuroimaging studies, tracking a single individual across a complete menstrual cycle, have primarily focused on investigating functional networks and connectivity \u003csup\u003e9,11\u0026ndash;14\u003c/sup\u003e. To date, only two densely-sampled neuroimaging studies have examined structural changes, especially in the hippocampus and the medial temporal lobe\u003csup\u003e7,8\u003c/sup\u003e. Of interest is the recent longitudinal, less densely sampled, study with 27 female participants, each undergoing six scans throughout their menstrual cycle, that reported associations between estradiol, progesterone and the medial temporal lobe\u003csup\u003e15\u003c/sup\u003e. These structural neuroimaging studies are beginning to reshape our understanding of the dynamics of human brain structural fluctuations. However, a whole-brain approach has not yet been adopted, which would provide a broader perspective on the range of brain structures that change across the menstrual cycle in response to hormonal fluctuations.\u003c/p\u003e\n\u003cp\u003eTo expand our understanding of the impact of estrogens and progesterone on the brain\u0026rsquo;s structure, it is essential to broaden the scope of our research beyond individuals with typical menstrual cycle patterns. Including participants with endocrine disorders such as endometriosis, a condition characterized by a unique hormonal profile \u003csup\u003e16\u0026ndash;18\u003c/sup\u003e, will provide a more nuanced understanding of the complex interplay between gonadal hormones and their influence on brain structure. Endometriosis, a chronic and inflammatory gynecological disorder, affects approximately 10 to 15% of females in their reproductive years \u003csup\u003e19\u003c/sup\u003e. It is defined by the presence and growth of ectopic endometrial stroma and glands outside the uterine cavity, typically within the peritoneal cavity. This pathological phenomenon can result in various clinical manifestations, including chronic pelvic pain, dysmenorrhea, dyspareunia, and infertility \u003csup\u003e20,21\u003c/sup\u003e. The condition is known to be influenced by hormonal dysregulations, prominently featuring an estrogen dependency \u003csup\u003e16,17\u003c/sup\u003e and progesterone resistance \u003csup\u003e18\u003c/sup\u003e. This means that the development, growth, and maintenance of endometriotic lesions are strongly influenced and sustained by estrogen within the body. In this context, estradiol synthesis is increased while its inactivation is decreased, resulting in elevated local concentrations of this hormone \u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. Furthermore, the endometriotic lesions can become resistant to the regulatory actions of progesterone. Consequently, even in the presence of progesterone, these tissues may continue to grow, bleed, and cause inflammation rather than responding to the inhibitory effects typically exerted by progesterone \u003csup\u003e25\u0026ndash;27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe current study employed three densely sampled females who underwent extensive and standardized brain imaging and venipuncture (5 to 7 days per week) over the full menstrual cycle to investigate the impact of endogenous hormone variation on brain structure. First, we densely sampled a healthy female with a typical menstrual cycle, referred to as \u0026lsquo;typical cycle.\u0026rsquo; We compared this \u0026lsquo;typical cycle\u0026rsquo; dataset of one female to the densely-sampled open-access 28andMe dataset of another female, which probes the extent to which endogenous fluctuations in sex hormones across a complete reproductive cycle influences the brain \u003csup\u003e8\u0026ndash;14\u003c/sup\u003e. This dataset will be referred to as \u0026lsquo;28andMe (typical) cycle.\u0026rsquo; The extensive brain imaging and venipuncture of the two complete menstrual cycles enabled us to test and reconfirm the hypothesis that brain volume fluctuates during the typical menstrual cycle. To extend the relevance of our findings and to probe the neural effects of hormonal dysregulation, we repeated these procedures in a female participant diagnosed with endometriosis. This dataset will be referred to as \u0026lsquo;endometriosis cycle.\u0026rsquo; The addition of the endometriosis cycle allowed us to examine whether hormone-brain interactions are different between typically cycling females and those with endometriosis. An overview of the study procedures and timelines for all three participants is presented in \u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eWhole-brain analyses in the present study revealed patterns of regions in the brain sharing similar structural changes across the menstrual cycle for the three individuals (see \u003cstrong\u003eFigure 2\u003c/strong\u003e). These spatiotemporal patterns of all three participants encompassed overlapping brain regions including the thalamus, globus pallidus, putamen, and caudate nucleus (see \u003cstrong\u003eFigure 3\u003c/strong\u003e). Distinct relationships between these spatiotemporal brain patterns and hormonal levels emerged across the typical menstrual cycles and the endometriosis cycle. While within the two typical menstrual cycles an inverse relationship between progesterone levels, progesterone/estradiol ratios and spatiotemporal brain patterns was observed, higher levels of estradiol corresponded with the spatiotemporal brain patterns across the menstrual cycle in endometriosis. To probe generalizability of our findings, we repeated these analyses in one male and one female using oral contraceptives across the time course of five weeks, when gonadal hormones were either low or progesterone was selectively suppressed. As expected, no relationship between spatiotemporal brain patterns and gonadal hormones were observed.\u003c/p\u003e"},{"header":"2. Results ","content":"\u003cp\u003e\u003cem\u003e2.1 Endocrine assessments and menstrual cycle patterns\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGonadal hormones were assessed throughout the full menstrual cycle. Analyses of hormone serum concentrations in the typical cycle and the 28andMe (typical) cycle confirmed the expected rhythmic changes of a natural menstrual cycle (see \u003cstrong\u003eTable 1\u003c/strong\u003e). In the typical cycle, the 25 test sessions covered 15 days of the follicular phase and 10 days of the luteal phase. In the 28andMe (typical) cycle, the 30 test sessions covered 14 days of the follicular phase and 16 days of the luteal phase. The ratios between progesterone and estradiol concentrations suggested a typical hormonal balance during the luteal phase. The gonadal hormone concentrations in the endometriosis cycle also followed the rhythmic changes of a menstrual cycle. The 25 test sessions covered 17 days of the follicular phase and eight days of the luteal phase. As predicted, the progesterone/estradiol ratio suggested an estradiol dominance during the luteal phase. The menstrual cycles covered during the experiment lasted 24 and 23 days, representing a shorter menstrual cycle (\u0026pound; 24 days) - typical in endometriosis. Progesterone concentrations surpassed 15.9 nmol/l, suggesting an ovulatory cycle in all three participants \u003csup\u003e28\u003c/sup\u003e. \u003cstrong\u003eFigure 4\u003c/strong\u003e displays hormonal values for each participant relative to the day of ovulation. An alternative display is provided in the \u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTo test whether hormonal profiles differed between participants, a one-way Multivariate Analysis of Variance (MANOVA) was conducted with estradiol, progesterone, and the progesterone/estradiol ratio as dependent variables, and the three individuals (endometriosis cycle, typical cycle, 28andMe (typical) cycle) as fixed factors. The analysis revealed a significant main effect among the three individuals (Pillai\u0026rsquo;s trace: \u003cem\u003eF\u003c/em\u003e(6,152) = 4.63, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 \u003cem\u003eɳ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.15; Roy\u0026rsquo;s largest root: \u003cem\u003eF\u003c/em\u003e(3,76) = 10.23, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eɳ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e= 0.29). Post-hoc Analyses of Variance (ANOVAs) indicated significant differences among the three individuals in estradiol (\u003cem\u003eF\u003c/em\u003e(2,77) = 8.86, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003eɳ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.19) and the progesterone/estradiol ratio (\u003cem\u003eF\u003c/em\u003e(2,77) = 5.96, \u003cem\u003ep\u003c/em\u003e = 0.004, \u003cem\u003eɳ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.13). Progesterone did not show a significant difference among individuals (\u003cem\u003eF\u003c/em\u003e(2,77) = 2.28, \u003cem\u003ep\u003c/em\u003e = 0.1, \u003cem\u003eɳ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e= 0.06). Post-hoc one-tailed \u003cem\u003et\u003c/em\u003e-tests, corrected using the Bonferroni method, further revealed that the endometriosis cycle had significantly higher estradiol levels compared to the typical cycle (\u003cem\u003ep\u003c/em\u003e = 0.003) and the 28andMe (typical) cycle (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). A similar pattern was observed for progesterone/estradiol ratio, with the endometriosis cycle showing significantly lower progesterone/estradiol ratios compared to the typical cycle (\u003cem\u003ep\u003c/em\u003e = 0.044) and the 28andMe (typical) cycle (\u003cem\u003ep\u003c/em\u003e = 0.002). Differences in hormonal values are displayed in \u003cstrong\u003eFigure 5\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Dynamics in structural brain changes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e-weigthed images were acquired of each individual across the full menstrual cycle. Singular Value Decomposition (SVD) analysis was used to extract spatiotemporal patterns. The spatial patterns represent the brain regions (structural) that share similar spatial changes over time. Spatiotemporal pattern 1 (STP1), the pattern explaining the highest variance, was used for further analyses. AutoRegressive Integrated Moving Average (ARIMA) models were then employed to investigate the relationship between STP1 in brain structure and gonadal hormones. ARIMA models are usually used to understand and predict time patterns in time series data. For each individual, the ARIMA models were initially specified as ARIMA(0,0,0) for estradiol, progesterone, and progesterone/estradiol ratio as predictor variables. This specification implies the deliberate exclusion of autoregressive (p), differencing (d), or moving average (q) terms, simplifying the models into basic linear regression without inherent time series complexities. Subsequently, ARIMA models with an autoregressive (AR) lag of order 1 were applied to account for lagged temporal patterns in the spatial map. ARIMA(1,0,0) model represents a pure AR model, examining the relationship between the current values of gonadal hormones and the temporal patterns in the spatial map from the following day (lag 1).\u003c/p\u003e\n\u003cp\u003eRegarding the two typical cycles, the ARIMA(0,0,0) models with estradiol as a predictor were not significant. Similarly, the ARIMA(1,0,0) models, which included a time lag and employed estradiol as predictors, were not significant. These findings imply the absence of direct temporal patterns in brain structure associated with variations in estradiol. On the other hand, progesterone (28andMe (typical) cycle: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e\u0026lt; 0.001; typical cycle: \u003cem\u003ep\u003c/em\u003e = 0.013, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e\u0026lt; 0.039) and the progesterone/estradiol ratio (28andMe (typical) cycle: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e\u0026lt; 0.001; typical cycle: \u003cem\u003ep\u003c/em\u003e = 0.009, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.038) were significant predictors in the ARIMA(0,0,0) models, indicating a linear association between these hormones and the temporal pattern in the spatial map. This suggests that higher progesterone and higher progesterone/estradiol ratios correspond with lower STP1 levels across the typical cycles. The parameter estimates of -0.006 (28andMe (typical) cycle) and -0.005 (typical cycle) implies that for every one-unit increase in progesterone, a corresponding units change of -0.006 and -0.005, respectively, in the spatiotemporal brain pattern is expected. Likewise, this applies to the progesterone/estradiol ratio. For every one-unit increase in the progesterone/estradiol ratio, a -0.002 unit change in the spatiotemporal brain pattern is expected. The ARIMA(1,0,0) models were not significant, suggesting no relation of the current progesterone value nor the progesterone/estradiol ratio with the following two values of the temporal pattern in the spatial map.\u003c/p\u003e\n\u003cp\u003eFor the endometriosis cycle, the ARIMA(0,0,0) model revealed estradiol as a statistically significant predictor of the temporal pattern in the spatial map (\u003cem\u003ep\u003c/em\u003e = 0.010, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.038). This suggests that elevated estradiol levels correspond with higher spatiotemporal brain pattern levels across the menstrual cycle in the individual with endometriosis. The estimated parameter of approximately 0.0002 implies that for every one-unit increase in estradiol, we anticipate a corresponding change of 0.0002 units in the temporal brain pattern in the spatial map. In contrast, progesterone and the progesterone/estradiol ratio were not significant (\u003cem\u003ep\u003c/em\u003e = 0.137, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.205). When a time lag was incorporated into the ARIMA model (1,0,0) the results were not statistically significant. These results suggest the absence of lagged temporal patterns in the spatial map associated with fluctuations in gonadal hormones for the endometriosis cycle. Results of all ARIMA(0,0,0) models are displayed in \u003cstrong\u003eFigure 6\u003c/strong\u003e and \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3 Control analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo probe the generalizability of our densely sampled datasets, we repeated the SVD and time-series regression analyses in a male participant and in a female using oral contraceptives. Both individuals were scanned over the time course of five weeks, resulting in 25 test sessions per individual (see \u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e). The female was placed on a daily regimen of 0.03 mg ethinyl-estradiol and 2 mg dienogest (Maxim, Jenapharm) for three months before the assessment, which selectively suppressed circulating progesterone. The concentration and dynamic range of estradiol during oral contraception intake was similar to a typical cycle (see \u003cstrong\u003eSupplementary Figure 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAs expected, the ARIMA(0,0,0) models did not yield significant results after FDR correction (see \u003cstrong\u003eSupplementary Figure 4\u003c/strong\u003e) in either the male or in the oral contraceptives dataset, regardless of the hormone: estradiol (male: \u003cem\u003ep \u003c/em\u003e= 0.136, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.206; oral contraceptives: \u003cem\u003ep \u003c/em\u003e= 0.021, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.053), progesterone (male: \u003cem\u003ep \u003c/em\u003e= 0.771, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.776; oral contraceptives: \u003cem\u003ep \u003c/em\u003e= 0.105, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.197), and progesterone/estradiol ratio (male: \u003cem\u003ep \u003c/em\u003e= 0.503, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.629; oral contraceptives: \u003cem\u003ep \u003c/em\u003e= 0.305, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = 0.416). Similarly, the ARIMA(1,0,0) models, which included a time lag, were not significant. Spatiotemporal brain patterns for the male and the female using oral contraceptives are displayed in \u003cstrong\u003eSupplementary Figure 5\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4 Sensitivity analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, previous work reported gray matter volume changes across the menstrual cycle in less densely sampled cohorts. Changes were reported in whole-brain gray matter volume \u003csup\u003e29,30\u003c/sup\u003e, in subcortical structures, including the hippocampus \u003csup\u003e15,31\u003c/sup\u003e, putamen, and globus pallidus \u003csup\u003e31\u003c/sup\u003e, as well as in cerebrospinal fluid (CSF) \u003csup\u003e30\u003c/sup\u003e. We assessed whole-brain gray matter volume changes as well as changes in CSF in all five datasets (typical cycle, 28andMe (typical) cycle, endometriosis cycle, male, and oral contraceptives). Whole-brain gray matter volume encompassed bilateral cerebral cortex volume and the volumes of thalamus, caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, accumbens nucleus, and ventral diencephalon. The numerical values of the whole-brain gray matter volumes and CSF were demeaned to center around zero to enhance the sensitivity and specificity of our measurements. Next, cubic regression curve estimations were used to check whether demeaned volumes fluctuated significantly across the testing sessions. Whole-brain gray matter volume and CSF fluctuated significantly across the menstrual cycle in the typical cycle (whole-brain gray matter: \u003cem\u003eF\u003c/em\u003e(3,21) = 14.46, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e\u0026lt; 0.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e= 0.67; CSF: \u003cem\u003eF\u003c/em\u003e(3,21) = 7.36, \u003cem\u003ep \u003c/em\u003e= 0.001, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e= 0.51), the 28andMe (typical) cycle (whole-brain gray matter: \u003cem\u003eF\u003c/em\u003e(3,26) = 5.05, \u003cem\u003ep \u003c/em\u003e= 0.007, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.009, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e= 0.37; CSF: \u003cem\u003eF\u003c/em\u003e(3,26) = 4.71, \u003cem\u003ep \u003c/em\u003e= 0.009, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.009, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e= 0.35), and the endometriosis cycle (whole-brain gray matter: \u003cem\u003eF\u003c/em\u003e(3,20) = 5.58, \u003cem\u003ep \u003c/em\u003e= 0.006, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.012, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e= 0.46; CSF: \u003cem\u003eF\u003c/em\u003e(3,20) = 3.65, \u003cem\u003ep \u003c/em\u003e= 0.030, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.030, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.35). As expected, whole-brain gray matter volume and CSF did not fluctuate in the male (whole-brain gray matter: \u003cem\u003eF\u003c/em\u003e(3,21) = 3.00, \u003cem\u003ep \u003c/em\u003e= 0.053, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.11, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e= 0.30; CSF: \u003cem\u003eF\u003c/em\u003e(3,21) = 0.90, \u003cem\u003ep \u003c/em\u003e= 0.46, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.46, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e= 0.11). Additionally, in the female using oral contraceptives, when progesterone was selectively suppressed, whole-brain volume and CSF did not fluctuate significantly (whole-brain gray matter: \u003cem\u003eF\u003c/em\u003e(3,21) = 2.01, \u003cem\u003ep \u003c/em\u003e= 0.14, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.14, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2 \u003c/sup\u003e= 0.22; CSF: \u003cem\u003eF\u003c/em\u003e(3,21) = 3.23, \u003cem\u003ep \u003c/em\u003e= 0.043, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR \u003c/sub\u003e= 0.086, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.32).\u003c/p\u003e"},{"header":"3.\tDiscussion ","content":"\u003cp\u003eDespite recent efforts to elucidate the association between gonadal hormones and brain structure fluctuations, whole-brain approaches are scarce. Moreover, investigations of hormone-brain interactions in non-typical cycles remain understudied. In the present study, we applied dense-sampling brain imaging and venipuncture, utilizing a whole-brain singular value decomposition analytical approach to assess brain structure in both the typical and endometriosis cycle. We reported divergent impacts of gonadal hormones on structural brain dynamics, suggesting differences between individuals with endometriosis and those with typical menstrual cycles. In females with typical menstrual cycles, progesterone exhibited the most pronounced effect on volumetric brain changes throughout the menstrual cycle. In contrast, in the endometriosis cycle, estradiol showed the most pronounced effect. This whole-brain analysis underscores the multifaceted interactions between gonadal hormones and brain structure, with implications in both pathological and typical cycles.\u003c/p\u003e\n\u003cp\u003eThe spatiotemporal patterns identified in the brain across the menstrual cycle for the three individuals encompassed regions such as the thalamus, globus pallidus, putamen, and caudate nucleus \u0026ndash; brain areas characterized by increased distributions of estradiol and progesterone receptors \u003csup\u003e3,4\u003c/sup\u003e. Notably, these patterns exhibited distinct relationships with hormonal fluctuations across the typical menstrual cycles and the endometriosis cycle, underscoring the complexity of hormone-induced brain changes. In individuals with typical menstrual cycles, no associations between estradiol and changes in brain structure were observed. In contrast, progesterone levels and progesterone/estradiol ratios were associated with cycle-dependent brain volume fluctuations. This suggests a heightened sensitivity to progesterone in individuals with typical menstrual cycles. These findings align with the recently published results in a less densely sampled cohort of 27 female participants scanned six times throughout their menstrual cycle \u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the context of the endometriosis cycle, our study revealed an association between estradiol levels and brain volume changes across the menstrual cycle. High levels of estradiol were found to be associated with increased volumes of regions including the thalamus, globus pallidus, and caudate nucleus. Incorporating time lags into the analysis revealed no significant associations between gonadal hormones and brain structure, suggesting that hormonal fluctuations did not exhibit patterns with delayed effects within the endometriosis cycle. Our findings are aligned with the previous hormonal evidence that characterizes endometriosis with elevated estradiol levels \u003csup\u003e20,32\u003c/sup\u003e. This association may be attributed to an estradiol dependency and progesterone resistance commonly observed in endometriosis \u003csup\u003e16\u0026ndash;18\u003c/sup\u003e. In support, the individual with endometriosis had elevated estradiol levels and estradiol dominance in the luteal phase of the menstrual cycle, suggesting a greater exposure to estradiol on the brain. Estrogen is believed to have a neuroprotective role, promoting brain health and protecting against cognitive decline \u003csup\u003e33\u0026ndash;35\u003c/sup\u003e. However, while estradiol levels within the physiological range stimulate brain activity, especially within the hippocampus, supraphysiological levels of estradiol (equivalent to levels during early pregnancy) show opposite effects \u003csup\u003e36\u003c/sup\u003e. To date, little is known about the impact of prolonged high estradiol exposure during the reproductive years on long-term health outcomes. This underscores the importance of further research to elucidate the longitudinal relationships between gonadal hormones, reproductive health, and long-term well-being in individuals with hormonal dysregulations.\u003c/p\u003e\n\u003cp\u003eSince this study is a longitudinal study with a dense-sampling design with a limited sample of only three individuals, interpretations and explanations of the reported associations should be made cautiously in generalizing the findings to the broader population. By focusing on single participants, we aimed to mitigate the intraindividual variability of hormonal fluctuations instead of the interindividual variability, often obscured in studies with larger and more heterogeneous samples. Our approach provides a more precise examination and clearer picture of the specific patterns of hormonal fluctuations and their potential impact on the brain, offering a higher level of sensitivity and temporal resolution towards precision imaging and medicine. In support, we expanded the scope of our study by including additional analyses of one male and one female using oral contraceptives over a densely sampled period of five weeks. In these two individuals, where gonadal hormones were either lower or progesterone was selectively suppressed, no significant associations between spatiotemporal patterns of the brain and hormonal fluctuations were observed. By contrasting periods of natural hormonal fluctuations with those of lower or suppressed gonadal hormones, we gain a more comprehensive understanding of the specificity of the observed relationships. This comparative aspect enhances the generalizability of our findings, suggesting that the associations between gonadal hormones and brain structure may be context-dependent, influenced by the presence or absence of natural hormonal variations.\u003c/p\u003e\n\u003cp\u003eFurther research with larger and more diverse samples is necessary to validate and expand these initial findings, addressing potential interindividual variations and enhancing the generalizability of the observed associations. Despite the small sample size, our findings offer valuable first insights into the dynamic impact of unique hormonal fluctuations on brain organization throughout the menstrual cycle and speak towards a future of personalized and precision medicine.\u003c/p\u003e\n\u003cp\u003eIn summary, by adopting a dense-sampling approach, this study enriches our comprehension of the intricate relationship between hormones and the human brain, offering novel insights into the dynamics of whole-brain organization and variations of the menstrual cycle. Future research, with larger and more diverse cohorts, can build upon these insights, advancing our understanding of the dynamic relationship between hormonal and brain structure fluctuations. The results underscore the importance of considering individual hormonal dynamics in understanding brain plasticity during the menstrual cycle as hormonal dysregulations, characteristic of specific conditions, may modulate structural brain changes differently.\u003c/p\u003e"},{"header":"4.\tOnline Methods ","content":"\u003cp\u003eDense-sampling, longitudinal datasets were acquired from two female participants in Jena, Germany. These datasets are referred to as the \u0026lsquo;endometriosis cycle\u0026rsquo; and \u0026lsquo;typical cycle\u0026rsquo;. To extend our findings, we also leveraged the open-access 28andMe dataset of one female, which probes the extent to which endogenous fluctuations in sex hormones across a complete reproductive cycle influence the brain \u003csup\u003e8\u0026ndash;14\u003c/sup\u003e. The data was acquired in Santa Barbara, California, and is referred to as \u0026lsquo;28andMe (typical) cycle\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003eFor the purposes of control analyses and to probe generalizability of our findings, additional dense-sampling, longitudinal datasets of one male and one female using oral contraceptives were acquired across the time course of five weeks in Jena, Germany. Detailed descriptions of the participants are found in the \u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e and \u003cstrong\u003eSupplementary Figure 3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.1 Participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study procedures for the participants in Jena, Germany, were: A healthy female (37 years of age, Caucasian) underwent most weekday testing for five consecutive weeks (January 9 \u0026ndash; February 12, 2023) while freely cycling, resulting in \u003cem\u003en\u003c/em\u003e = 25 test sessions. The female participant (\u0026lsquo;typical cycle\u0026rsquo;) had a history of regular menstrual cycles (last half-year mean length = 27.1 days, \u003cem\u003eSD\u003c/em\u003e = 0.64, range = 26 \u0026ndash; 28 days), no history of psychiatric, neurological, and endocrine diagnoses, breastfeeding or pregnancy, and no history of alcohol, or drug abuse, but current use of nicotine. A second female participant (30 years of age, Caucasian) diagnosed with endometriosis (\u0026lsquo;endometriosis cycle\u0026rsquo;) participated in this dense-sampling, longitudinal study. She received the diagnosis seven months prior to the assessments (October 28, 2022) after a cyst surgery in the pelvic area. The participant was tracking her menstrual cycle length and reported a mean menstrual cycle length of 24.4 days (\u003cem\u003eSD\u003c/em\u003e = 1.67, range = 23 \u0026ndash; 27 days) during that time. Otherwise, the female participant had no history of psychiatric or neurological disorders, breastfeeding or pregnancy, and no history of smoking, alcohol, or drug abuse. The participant underwent testing from Monday to Friday for five consecutive weeks (June 12\u0026ndash; July 14, 2023) while freely cycling, resulting in \u003cem\u003en\u003c/em\u003e = 25 test sessions. Participants gave written informed consent, and the Friedrich Schiller University Jena Ethics Committee approved the study.\u003c/p\u003e\n\u003cp\u003eThe study procedure for the third participant was the following: The healthy female participant (23 years of age, Caucasian, \u0026lsquo;28andMe (typical) cycle\u0026rsquo;) underwent testing for 30 consecutive days (July 9 \u0026ndash; August 7, 2018), while freely cycling. She had a history of regular menstrual cycles (no missed periods, cycle occurring every 26\u0026ndash;28 days) and had not taken hormone-based medication in the 12 months prior to the first study. The participant had no history of psychiatric or neurological disorders, breastfeeding, or pregnancy, and no history of smoking, alcohol, or drug abuse. The participant gave written informed consent, and the study was approved by the University of California, Santa Barbara Human Subjects Committee. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2 Image acquisition \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor datasets collected in Jena (typical cycle, endometriosis cycle, male, oral contraceptives), scans were collected daily at 7.30 a.m. local time. The imaging dataset for the typical cycle was acquired on a 3T MRI scanner (Prisma\u003csup\u003eFit\u003c/sup\u003e, Siemens Medical Solutions, Erlangen, Germany) with a 64-channel head coil. The imaging datasets for the endometriosis cycle, male, and female on oral contraceptives were acquired on a 3T MRI scanner (Prisma, Siemens Medical Solutions, Erlangen, Germany) with a 64-channel head coil. Structural MRI for the datasets were acquired with T\u003csub\u003e1\u003c/sub\u003e-weighted (T1w) magnetization prepared - rapid gradient echo (MPRAGE) sequence with the generalized autocalibrating partially parallel acquisitions (GRAPPA) acceleration. Scan parameters were: echo time (TE) = 2.22 ms, repetition time (TR) = 2400 ms, inversion time (TI) = 1000 ms, flip angle = 8\u0026deg;, matrix size = 320 x 320 pixels, field of view (FOV) = 256 mm, band width = 220 Hz/pixel, and slice thickness = 0.80 mm.\u003c/p\u003e\n\u003cp\u003eFor the 28andMe (typical) cycle dataset, scans were collected on a 3T MRI scanner (Prisma, Siemens Medical Solutions, Erlangen, Germany) equipped with a 64-channel head coil. Structural scans were acquired using a T1w MPRAGE sequence with the GRAPPA acceleration with the following parameters: TE = 2.31 ms, TR = 2500 ms, TI = 934 ms, flip angle = 7\u0026deg;, matrix size = 320 x 320 pixels, FOV = 255 mm, band width = 210 Hz/pixel, and slice thickness = 0.80 mm.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.3 Image preprocessing and data reduction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe parameters used to acquire the images (e.g., sizes, space directions, space origin), and the quality of the images (e.g., motion artifacts, ringing, ghosting of the skull or eyeballs, cut-offs, signal drops, and other artifacts) were visually inspected. One scan from the endometriosis cycle had to be removed due to artefacts in subcortical structures, corpus callosum, and cingulate gyrus. The final datasets consisted of 24 T1w images for the endometriosis cycle, 25 T1w images for the typical cycle, 25 T1w images for the male, 25 T1w images for the female on oral contraceptives, and 30 T1w images for the 28andMe (typical) cycle. \u003c/p\u003e\n\u003cp\u003eThe T1w images were converted from Dicom to Nifti files using dcm2niix (Chris Rorden, version v1.0.20170724, https://www.nitrc.org/projects/mricrogl/) and then preprocessed in SPM12 (http://www.fil.ion.ucl.ac.uk/spm) and the CAT12 (https://neuro-jena.github.io/cat) \u003csup\u003e37\u003c/sup\u003e toolbox using the longitudinal pipeline approach in Matlab R2021b (The MathWorks Inc., Natick, MA, USA). All T1w images were corrected for bias-field inhomogeneities \u003csup\u003e38,39\u003c/sup\u003e and tissue-classified into gray matter, white matter, and cerebrospinal fluid \u003csup\u003e40\u003c/sup\u003e, which also included an approach accounting for partial volume effects \u003csup\u003e41\u003c/sup\u003e by applying adaptive maximum a posteriori estimations \u003csup\u003e42\u003c/sup\u003e and a hidden Markov Random Field Model \u003csup\u003e43\u003c/sup\u003e. The resulting gray and white matter partitions were spatially normalized to MNI space Geodesic Shooting Registration \u003csup\u003e44\u003c/sup\u003e. Subsequently, the normalized tissue segments were smoothed using a 6 mm full width at half maximum (FWHM) Gaussian Kernel. Singular Value Decomposition (SVD) was then used to extract spatiotemporal patterns from the preprocessed images by decomposing the three-dimensional image sets into spatial patterns (maps) and their associated temporal dynamics (time course) for each participant separately (Jacobian determinant maps) using an in-house Matlab script. The spatial patterns represent the brain regions (structural) that share similar spatial changes over time. By using SVD, we can identify and analyze these patterns, revealing coherent time courses across the brain rather than being restricted to an expected change over time. For each individual, the SVD analysis yielded four brain spatiotemporal patterns exceeding a threshold of 1, representing similar spatiotemporal patterns in the brain (see \u003cstrong\u003eSupplementary Figure 6\u003c/strong\u003e). Spatiotemporal pattern 1 (for better understanding now referred to as STP1) exhibited the highest variance explained (40% for endometriosis cycle, 45% for typical cycle, 54% for 28andMe (typical) cycle, 44% for male, 27% for female on oral contraceptives) and was thus chosen for subsequent analysis for each individual. By selecting STP1, we prioritized the dominant structural patterns within each dataset, allowing us to focus on the most relevant information. STP1 of the investigated three participants encompassed overlapping brain regions, which included subcortical structures such as the thalamus, globus pallidus, putamen, and caudate nucleus, suggesting that these brain structures exhibit common spatiotemporal patterns across the participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.4 Image preprocessing for additional control analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSequence Adaptive Multimodal SEGmentation (SAMSEG) \u003csup\u003e45\u003c/sup\u003e was used to segment whole-brain gray matter volume. Initially, a subject-specific template was created by spatially co-registering all 3D T1w MRPAGE volumes through an iterative process \u003csup\u003e46\u003c/sup\u003e. The co-registered 3D volumes were then employed to implement longitudinal SAMSEG \u003csup\u003e47\u003c/sup\u003e. The final segmentations were used to extract the volume of the bilateral cerebral cortex, thalamus, caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, accumbens nucleus, ventral diencephalon as well as CSF directly for each measurement day and each participant separately. Whole-brain gray matter volume was calculated by adding the volumes of the bilateral cerebral cortex, thalamus, caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, accumbens nucleus, and ventral diencephalon. The numerical values of the whole-brain gray matter volumes and CSF were then demeaned to center around zero. Demeaning the volumes is essential for removing potential sources of systematic bias or shift. It helps eliminate variations caused by external factors or measurement errors. As a result, this process enhances the sensitivity and specificity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.5 Endocrine procedure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the datasets acquired in Jena, Germany, a daily blood draw immediately followed the MRI session at ~8.30 a.m. One 4.9 ml blood sample was collected in a S-Monovette\u003csup\u003e\u0026reg;\u003c/sup\u003e Serum-GEL (Sarstedt) with a clotting activator/gel each test session. The sample was clotted at room temperature and centrifugated (2500 x g for 10 minutes) within two hours. Estradiol (pmol/ml), LH (IU/l), FSH (IU/l), and progesterone serum concentrations (ng/ml) were determined at the Department of Clinical Chemistry and Laboratory Medicine, Jena University Hospital, Jena, Germany. Estradiol was assessed with the electrochemiluminescence immunoassay (ECLIA) Elecsys\u003csup\u003e\u0026reg;\u003c/sup\u003e Estradiol III Assay. Assay antibodies, measuring ranges (defined by the limit of detection and the maximum of the master curve), and intra-assay precision coefficients of variation for estradiol were the following: antibodies, two biotinylated monoclonal anti-estradiol antibodies (rabbit), 2.5 ng/ml and 4.5 ng/ml; measuring range, 18.4 \u0026ndash; 11,010 pmol/l (5 \u0026ndash; 3000 pg/ml); intra-assay precision, \u0026le; 8.4% variation coefficient. LH was assessed with the ECLIA Elecsys\u003csup\u003e\u0026reg;\u003c/sup\u003e LH Assay. Assay antibodies, measuring ranges, and intra-assay coefficients of variation for LH were the following: antibodies, biotinylated monoclonal anti-LH antibody (mouse), 2.0 mg/l; measuring range, 0.3 \u0026ndash; 200 mIU/ml (0.3 \u0026ndash; 200 IU/l); intra-assay precision, \u0026le; 2.2% variation coefficient. FSH was assessed with the ECLIA Elecsys\u003csup\u003e\u0026reg;\u003c/sup\u003e FSH Assay. Assay antibodies, measuring ranges, and intra-assay coefficients of variation for FSH were the following: antibodies, biotinylated monoclonal anti-FSH antibody (mouse), 0.5 mg/l; measuring range, 0.3 \u0026ndash; 200 mIU/ml (0.3 \u0026ndash; 200 IU/l); intra-assay precision, \u0026le; 2.1% variation coefficient. Progesterone was assessed with the ECLIA Elecsys\u003csup\u003e\u0026reg;\u003c/sup\u003e Progesterone III Assay. Assay antibodies, measuring ranges, and intra-assay coefficients of variation for progesterone were the following: antibodies, biotinylated monoclonal anti-progesterone antibody (recombinant sheep), 30 ng/ml; measuring range, 0.159 \u0026ndash; 191 nmol/l (0.05 \u0026ndash; 60 ng/ml); intra-assay precision, \u0026le; 20.7% variation coefficient. All assays were determined on the cobas\u003csup\u003e\u0026reg;\u003c/sup\u003e e 402/801 analyzer (Roche Diagnostics GmbH, Mannheim, Germany) and were used according to the manufacturer\u0026apos;s instructions.\u003c/p\u003e\n\u003cp\u003eFor the 28andMe (typical) cycle dataset acquired in Santa Barbara, CA, USA, a licensed phlebotomist inserted a saline-lock intravenous line into the dominant or non-dominant hand or forearm. One 10 ml blood sample was collected in a vacutainer SST (BD Diagnostic Systems) each session. The sample was clotted at room temperature for 45 minutes until centrifugation (2000 x g for 10 minutes) and then aliquoted into three 1 ml microtubes. Serum samples were stored at -20\u0026deg;C until assayed. Serum concentrations were determined at the Brigham and Women\u0026rsquo;s Hospital Research Assay Core. Estradiol and progesterone were assessed via liquid chromatography mass-spectrometry. Assay sensitivities, dynamic range, and intra-assay coefficients of variation (respectively) were as follows: estradiol, 1 pg/ml, 1\u0026ndash;500 pg/ml, \u0026lt; 5% relative standard deviation (RSD); progesterone, 0.05 ng/ml, 0.05\u0026ndash;10 ng/ml, 9.33% RSD. FSH and LH levels were determined via chemiluminescent assay (Beckman Coulter). The assay sensitivity, dynamic range, and intra-assay coefficient of variation were as follows: FSH, 0.2 mIU/ml, 0.2\u0026ndash;200 mIU/ml, 3.1\u0026ndash;4.3%; LH, 0.2 mIU/ml, 0.2\u0026ndash;250 mIU/ml, 4.3\u0026ndash;6.4%.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.6 Psychological measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo track mood variations across the menstrual cycle, both positive affect and negative affect was assessed along with state anxiety. A detailed description can be found in the\u003cstrong\u003e Supplementary Description 1 \u003c/strong\u003eand\u003cstrong\u003e Supplementary Figure 7\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.7 Statistical approach\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R software (https://www.r-project.org), Statistical Package for Social Sciences (SPSS) version 27, and GraphPad Prism 8. First, a one-way MANOVA was conducted with estradiol, progesterone, and progesterone/estradiol ratio as dependent variables. The fixed factors were the three individuals (endometriosis cycle, typical cycle, 28andMe (typical) cycle). Post-hoc ANOVAs and one-tailed \u003cem\u003et\u003c/em\u003e-tests were performed and Bonferroni-corrected. Second, spearman correlations were performed between the psychological measures of positive affect, negative affect, and state anxiety, with the hormones of estradiol, progesterone, and progesterone/estradiol ratio. False Discovery Rate (FDR) was used to correct for multiple comparisons \u003csup\u003e48\u003c/sup\u003e. Correlations were only performed in the endometriosis cycle and the typical cycle as only these two individuals completed the same psychological assessments. Comparable psychological assessments were performed in the 28andMe (typical) cycle; details regarding these assessments can be found elsewhere \u003csup\u003e13\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003eNext, an autoregressive modeling approach was employed for the dependent variable (STP1) and predictors (estradiol, progesterone, and progesterone/estradiol ratio). For each individual, the AutoRegressive Integrated Moving Average (ARIMA) models were initially specified as ARIMA(0,0,0) for the predictor variables of estradiol, progesterone, and progesterone/estradiol ratio separately. This specification implies the deliberate exclusion of autoregressive (p), differencing (d), or moving average (q) terms, simplifying the models into basic linear regression structures without inherent time series complexities. Subsequently, ARIMA models with an autoregressive (AR) lag of order 1 were applied to account for the spatiotemporal patterns. ARIMA(1,0,0) model represents a pure AR model, examining the relationship between today\u0026rsquo;s values of gonadal hormones and brain spatiotemporal patterns of the following day (lag 1). All ARIMA models were FDR corrected for multiple comparisons \u003csup\u003e48\u003c/sup\u003e. Lastly, cubic regression curve estimations were used to check whether demeaned whole-brain gray matter and CSF volume fluctuated significantly across the testing sessions. Cubic regressions were corrected for multiple comparisons using the FDR-method \u003csup\u003e48\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contributions:\u003c/p\u003e\n\u003cp\u003eCH was responsible for the study concept and design, acquired the MRI data in Jena, Germany, and psychological questionnaires, processed and analyzed the data, performed the statistical analysis, and wrote the manuscript. CG analyzed the MRI data and was involved in the critical revision of the manuscript. LC acquired the data and was involved in the critical revision of the manuscript. NJ, FC, and PR assisted with the interpretation of the results and were involved in the critical revision of the manuscript. CJK and ACB collected the blood samples and were involved in the critical revision of the manuscript. HG and FJL assisted with the statistical analysis and the critical revision of the manuscript. MK analyzed the blood samples. LP and EGJ acquired the MRI data in Santa Barbara, USA, assisted with the interpretation of the findings, and were involved in the critical revision of the final manuscript. ZK, MW, and IC assisted with the study concept and design and were involved in the critical revision of the final manuscript. DG acquired, processed, and analyzed the MRI data, supervised the inspection of the anatomical data, and was involved in the critical revision of the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgment:\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Friedrich Schiller University Jena (IMPULSE Project) to CH, and by the Interdisciplinary Center of Clinical Research of the Medical Faculty Jena (LC). The present work is endorsed by the German Center for Mental Health. The funder played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interest:\u003c/p\u003e\n\u003cp\u003eMW is a member of the following advisory boards and gave presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische Heilmittel Heel GmbH, Germany. MW has further conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation. MW did not receive any financial compensation from the companies mentioned above. All other authors declare no financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003eData availability:\u003c/p\u003e\n\u003cp\u003eThe datasets generated in Jena, Germany, are available from the corresponding author on request. The dataset generated in Santa Barbara, CA, USA, is available at https://openneuro.org/datasets/ds002674.\u003c/p\u003e\n\u003cp\u003eCode availability:\u003c/p\u003e\n\u003cp\u003eCode is available from the corresponding author on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJuraska, J. M., Sisk, C. L. \u0026amp; DonCarlos, L. L. Sexual differentiation of the adolescent rodent brain: Hormonal influences and developmental mechanisms. \u003cem\u003eHorm. Behav.\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 203\u0026ndash;210 (2013).\u003c/li\u003e\n\u003cli\u003eRehbein, E., Hornung, J., Sundstr\u0026ouml;m Poromaa, I. \u0026amp; Derntl, B. 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Within-subject template estimation for unbiased longitudinal image analysis. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 1402\u0026ndash;1418 (2012).\u003c/li\u003e\n\u003cli\u003eCerri, S., Hoopes, A., Greve, D. N., M\u0026uuml;hlau, M. \u0026amp; Van Leemput, K. A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis. \u003cem\u003eLect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)\u003c/em\u003e \u003cstrong\u003e12449 LNCS\u003c/strong\u003e, 119\u0026ndash;128 (2020).\u003c/li\u003e\n\u003cli\u003eBenjamini, Y. \u0026amp; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. \u003cem\u003eJournal of the Royal Statistical Society. Series B (Methodological)\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 289\u0026ndash;300 (1995).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Gonadal hormones by cycle stage in the endometriosis cycle, the typical cycle, and the 28andMe (typical) cycle.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"736\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.395573997233747%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.80221300138312%\" colspan=\"3\"\u003e\n \u003cp\u003eFollicular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.80221300138312%\" colspan=\"3\"\u003e\n \u003cp\u003eLuteal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"16\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" colspan=\"3\"\u003e\n \u003cp\u003eMean \u0026plusmn; \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" colspan=\"3\"\u003e\n \u003cp\u003eMean \u0026plusmn; \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"16\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.446601941747574%\" rowspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\" rowspan=\"2\"\u003e\n \u003cp\u003eEndometriosis Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\" rowspan=\"2\"\u003e\n \u003cp\u003eTypical\u003cbr\u003e\u0026nbsp;Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\" rowspan=\"2\"\u003e\n \u003cp\u003e28andMe\u003cbr\u003e\u0026nbsp;(Typical) Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\" rowspan=\"2\"\u003e\n \u003cp\u003eEndometriosis Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\" rowspan=\"2\"\u003e\n \u003cp\u003eTypical\u003cbr\u003e\u0026nbsp;Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\" rowspan=\"2\"\u003e\n \u003cp\u003e28andMe\u003cbr\u003e\u0026nbsp;(Typical) Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"18\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"16\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.446601941747574%\"\u003e\n \u003cp\u003eEstradiol (pmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e912.41\u0026nbsp;\u0026plusmn;\u0026nbsp;791.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e584.40\u0026nbsp;\u0026plusmn;\u0026nbsp;434.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e307.01 \u0026plusmn; 284.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e596.75\u0026nbsp;\u0026plusmn;\u0026nbsp;275.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e403.20\u0026nbsp;\u0026plusmn;\u0026nbsp;147.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e301.74 \u0026plusmn; 105.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"15\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.446601941747574%\"\u003e\n \u003cp\u003eProgesterone (nmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e1.54\u0026nbsp;\u0026plusmn;\u0026nbsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e1.85\u0026nbsp;\u0026plusmn;\u0026nbsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e0.39\u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e20.40\u0026nbsp;\u0026plusmn;\u0026nbsp;12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e38.05\u0026nbsp;\u0026plusmn;\u0026nbsp;20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e28.57 \u0026plusmn; 16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"15\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.446601941747574%\"\u003e\n \u003cp\u003eProgesterone/Estradiol Ratio\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e1.54\u0026nbsp;\u0026plusmn;\u0026nbsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e5.61\u0026nbsp;\u0026plusmn;\u0026nbsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e2.30 \u0026plusmn; 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e31.69\u0026nbsp;\u0026plusmn;\u0026nbsp;10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e90.89\u0026nbsp;\u0026plusmn;\u0026nbsp;30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.592233009708737%\"\u003e\n \u003cp\u003e91.85 \u0026plusmn; 50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"15\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNote. \u003csup\u003ea\u003c/sup\u003e = The Progesterone/Estradiol ratio is usually measured during the luteal phase of the menstrual cycle.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"15\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"15\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Linear time series regression between the spatiotemporal brain pattern and gonadal hormones.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"780\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.197916666666666%\" rowspan=\"2\"\u003e\n \u003cp\u003eCycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.895833333333334%\" rowspan=\"2\"\u003e\n \u003cp\u003eARIMA (p,d,q)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\" rowspan=\"2\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.15625%\" rowspan=\"2\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.197916666666666%\" rowspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eEndometriosis Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.895833333333334%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.3125%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.15625%\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e-2.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eEstradiol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e1.73E-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e6.16E-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e2.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.263; \u003cem\u003eRMSE\u003c/em\u003e = 0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e-0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eProgesterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e1.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.122; \u003cem\u003eRMSE\u003c/em\u003e = 0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e-0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eProgesterone/Estradiol Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e1.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.098; \u003cem\u003eRMSE\u003c/em\u003e = 0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.197916666666666%\" rowspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eTypical Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.895833333333334%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.3125%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.15625%\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eEstradiol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e-3.98E-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e1.19E-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e-0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.005; \u003cem\u003eRMSE\u003c/em\u003e = 0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eProgesterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e-2.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.240; \u003cem\u003eRMSE\u003c/em\u003e = 0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e1.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eProgesterone/Estradiol Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e-2.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.260; \u003cem\u003eRMSE\u003c/em\u003e = 0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.197916666666666%\" rowspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e28andMe (Typical) Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.895833333333334%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.3125%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.15625%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.026041666666666%\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eEstradiol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e-1.86E-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e-0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.003; \u003cem\u003eRMSE\u003c/em\u003e = 0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e3.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eProgesterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e-4.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.423; \u003cem\u003eRMSE\u003c/em\u003e = 0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\"\u003e\n \u003cp\u003e(0,0,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003eSTP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e3.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.143695014662757%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.873900293255133%\"\u003e\n \u003cp\u003eProgesterone/Estradiol Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.436950146627566%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e4.54E-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e-4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.290322580645162%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.3841642228739%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.160058737151248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.30690161527166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.90748898678414%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.22760646108664%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e= 0.425; \u003cem\u003eRMSE\u003c/em\u003e = 0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.397944199706314%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\n \u003cp\u003eNote. STP1 = spatiotemporal pattern 1 of brain regions that share a similar structural pattern across the measured time. ARIMA = autoregressive integrated moving average modeling. p = autoregressive order. d = integrated order. q = moving average order. SE = standard error. AR = autoregressive. \u003cem\u003eRMSE\u003c/em\u003e = root mean squared error.\u003cem\u003e\u0026nbsp;T\u0026nbsp;\u003c/em\u003e= \u003cem\u003eT\u003c/em\u003e-statistic. \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= \u003cem\u003ep\u003c/em\u003e-value. \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u0026nbsp;\u003c/sub\u003e= false discovery rate corrected \u003cem\u003ep\u003c/em\u003e-value. Significant \u003cem\u003ep\u003c/em\u003e-values indicated in bold.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"75\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"structural MRI, menstrual cycle, endometriosis, estrogen, progesterone, precision medicine","lastPublishedDoi":"10.21203/rs.3.rs-3750023/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3750023/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Gonadal hormone fluctuations in females have been associated with symptoms of mental health, yet the underlying brain mechanisms remain understudied. Recent advances in neuroscience have shifted the paradigm towards longitudinal tracking, enabling the detection of subtle changes overlooked in conventional cross-sectional analyses. This dense-sampling approach acknowledges the rhythmic nature of gonadal hormone production. Our study employed three densely sampled females who underwent brain imaging and venipuncture (5 to 7 days per week) over the full menstrual cycle to investigate the impact of gonadal hormone variation on brain structure. In two healthy females with typical menstrual cycles, progesterone and progesterone/estradiol ratios were inversely associated with spatiotemporal structural brain patterns across the cycle. To probe the neural effects of hormonal dysregulation, we densely sampled a participant with endometriosis, an endocrine disorder affecting 10% of females in their reproductive years. Here, the spatiotemporal brain pattern was associated only with estradiol fluctuations. Our findings suggest that gonadal hormones are associated with short-term brain structural changes, with distinctions observed between typical and endometriosis cycles. This emphasizes the consideration of individual hormonal dynamics in understanding fluctuations in brain structural plasticity.","manuscriptTitle":"Hormonal dynamics shape brain structural plasticity across the menstrual cycle: \nInsights from dense-sampling structural brain imaging of females with and without endometriosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-19 16:28:24","doi":"10.21203/rs.3.rs-3750023/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-neuroscience","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"neuro","sideBox":"Learn more about [Nature Neuroscience](http://www.nature.com/neuro/)","snPcode":"","submissionUrl":"","title":"Nature Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f1ebc17e-b953-469e-92f1-cf79215ef36a","owner":[],"postedDate":"December 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":27476146,"name":"Biological sciences/Neuroscience"},{"id":27476147,"name":"Health sciences/Anatomy/Nervous system/Brain"},{"id":27476148,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Neuroendocrine diseases"}],"tags":[],"updatedAt":"2025-09-27T07:07:19+00:00","versionOfRecord":{"articleIdentity":"rs-3750023","link":"https://doi.org/10.1038/s41593-025-02066-2","journal":{"identity":"nature-neuroscience","isVorOnly":false,"title":"Nature Neuroscience"},"publishedOn":"2025-09-26 04:00:00","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2023-12-19 16:28:24","video":"","vorDoi":"10.1038/s41593-025-02066-2","vorDoiUrl":"https://doi.org/10.1038/s41593-025-02066-2","workflowStages":[]},"version":"v1","identity":"rs-3750023","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3750023","identity":"rs-3750023","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}