Schroeder, K. E. | Search bioRxiv

bioRxiv2026

Thymus Involution across the Human Menstrual Cycle

Structured AbstractO_ST_ABSImportanceC_ST_ABSThymic involution in humans has traditionally been conceptualized as a linear, age-associated process. Animal studies suggest transient hormone-dependent fluctuations in thymus size during reproductive transitions, including across the estrous cycle, but translational evidence in humans remains limited. ObjectiveTo determine whether thymus size changes across the menstrual cycle in healthy women and whether these changes are associated with fluctuations in estradiol and progesterone levels. DesignObservational repeated-measures study using quantitative computed tomography (qCT). Data were collected as part of an imaging study examining menstrual cycle-related physiological variation. Participants underwent assessments during menses and the early luteal phase within the same menstrual cycle. Mixed-effects models with robust variance estimation evaluated associations between cycle phase, hormonal contraception, reproductive hormone levels, and thymus size. Thymus segmentation was independently conducted by 2 blinded raters. SettingSingle-center university-based imaging study conducted at the University of Iowa. ParticipantsThirty-one non-smoking women with regular menstrual cycles were included, of whom n = 16 used oral hormonal birth control and n = 15 did not use hormonal contraception. Exclusion criteria included pregnancy, breastfeeding, postmenopausal status, diabetes, body mass index greater than 30 kg/m2, hysterectomy, or use of long-term noncyclic hormonal contraception. Participants tracked menstrual cycles using temperature monitoring and ovulation kits before completing visits during menses and the early luteal phase. All participants provided informed consent before study participation. Main Outcomes and MeasuresPrimary outcome was thymus size measured in mm3 using ultra-low-dose qCT imaging. Secondary outcomes included serum estradiol and progesterone levels. ResultsEstradiol levels increased from menses to the early luteal phase independent of hormonal contraceptive status. Progesterone levels were significantly lower among women using hormonal birth control. Thymus size differed significantly by hormonal contraceptive group, with larger thymus volumes observed among women not using hormonal birth control. Among women not using hormonal contraception, 66.7% demonstrated thymic involution from menses to the early luteal phase, compared with 37.5% of women using hormonal contraception. Estradiol and progesterone significantly interacted in predicting thymus size. Conclusions and RelevanceThese findings provide first-in-human evidence suggesting that thymus involution demonstrates short-term dynamics across the menstrual cycle and may be influenced by reproductive hormones and hormonal contraception. Key PointsO_ST_ABSQuestionC_ST_ABSDoes thymus size change dynamically across the human menstrual cycle in association with fluctuations in reproductive hormones and hormonal contraception use? FindingsIn this repeated-measures quantitative computed tomography study of n = 31 healthy women, thymus size demonstrated within-person variation across the menstrual cycle. Greater thymic involution was associated with larger increases in estradiol, whereas increases in thymus size were associated with larger increases in progesterone; thymus size also differed significantly by hormonal contraceptive status. MeaningThese findings suggest that thymic involution in humans may demonstrate short-term hormone-dependent dynamics beyond age-related atrophy, with potential implications for sex differences in immune regulation and inflammatory disease.

physiology↗

bioRxiv2019

Robust neural control of virtual locomotion enabled by a novel decoding strategy

Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this, we developed a self-motion BMI based on cortical activity as monkeys performed non-reaching arm movements: cycling a hand-held pedal to progress along a virtual track. Unlike during reaching, we found no high-variance dimensions that directly correlated with to-be-decoded variables. Yet we could decode a single variable - self-motion - by non-linearly leveraging structure that spanned many high-variance neural dimensions. Resulting online BMI-control success rates approached those during manual control. These findings make two broad points regarding how to build decode algorithms that harmonize with the empirical structure of neural activity in motor cortex. First, even when decoding from the same cortical region (e.g., arm-related motor cortex) different movement classes may need to employ very different strategies. Although correlations between neural activity and hand velocity are prominent during reaching tasks, they are not a fundamental property of motor cortex and cannot be counted on to be present in general. Second, although one generally desires a low-dimensional readout, it can be beneficial to leverage a multi-dimensional high-variance subspace. Fully embracing this approach requires highly non-linear approaches tailored to the task at hand, but can produce near-native levels of performance. Significance StatementMany BMI decoders have been constructed for controlling movements normally performed with the arm. Yet it is unclear how these will function beyond the reach-like scenarios where they were developed. Existing decoders implicitly assume that neural covariance structure, and correlations with to-be-decoded kinematic variables, will be largely preserved across tasks. We find that the correlation between neural activity and hand kinematics, a feature typically exploited when decoding reach-like movements, is essentially absent during another task performed with the arm: cycling through a virtual environment. Nevertheless, the use of a different strategy, one focused on leveraging the highest-variance neural signals, supported high performance real-time BMI control.

neuroscience↗