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      Hormone seasonality in medical records suggests circannual endocrine circuits

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          Significance

          We provide a dataset of millions of hormone tests from medical records that shows seasonality with a winter−spring peak in hormones for reproduction, growth, metabolism, and stress adaptation. Together with a long history of studies on a winter−spring peak in human function and growth, the hormone seasonality indicates that, like other animals, humans may have a physiological peak season for basic biological functions. We further use the specific seasonal phases of the hormones to suggest a model for a circannual clock in humans and animals that can keep track of the seasons, similar in spirit to the circadian clock that keeps track of time of day.

          Abstract

          Hormones control the major biological functions of stress response, growth, metabolism, and reproduction. In animals, these hormones show pronounced seasonality, with different set-points for different seasons. In humans, the seasonality of these hormones remains unclear, due to a lack of datasets large enough to discern common patterns and cover all hormones. Here, we analyze an Israeli health record on 46 million person-years, including millions of hormone blood tests. We find clear seasonal patterns: The effector hormones peak in winter−spring, whereas most of their upstream regulating pituitary hormones peak only months later, in summer. This delay of months is unexpected because known delays in the hormone circuits last hours. We explain the precise delays and amplitudes by proposing and testing a mechanism for the circannual clock: The gland masses grow with a timescale of months due to trophic effects of the hormones, generating a feedback circuit with a natural frequency of about a year that can entrain to the seasons. Thus, humans may show coordinated seasonal set-points with a winter−spring peak in the growth, stress, metabolism, and reproduction axes.

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          Most cited references71

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          FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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            The minimal preprocessing pipelines for the Human Connectome Project.

            The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines. Copyright © 2013 Elsevier Inc. All rights reserved.
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              The WU-Minn Human Connectome Project: an overview.

              The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                16 February 2021
                2 February 2021
                2 February 2021
                : 118
                : 7
                : e2003926118
                Affiliations
                [1] aDepartment of Molecular Cell Biology, Weizmann Institute of Science , 76100 Rehovot, Israel;
                [2] bDepartment of Computer Science, Weizmann Institute of Science , 76100 Rehovot, Israel
                Author notes
                1To whom correspondence may be addressed. Email: uri.alon@ 123456weizmann.ac.il .

                Edited by Satchidananda Panda, Salk Institute for Biological Studies, San Diego, CA, and accepted by Editorial Board Member David J. Mangelsdorf December 23, 2020 (received for review March 4, 2020)

                Author contributions: A. Tendler, A.B., A. Tanay, and U.A. designed research; A. Tendler, A.B., O.K., Y.K.K., L.M., T.M., M.R., and A.M. performed research; A. Tanay contributed new reagents/analytic tools; A.B. and N.M.-C. analyzed data; and A.B. and U.A. wrote the paper.

                Author information
                https://orcid.org/0000-0003-0176-876X
                https://orcid.org/0000-0003-4493-7244
                https://orcid.org/0000-0002-4479-3423
                Article
                202003926
                10.1073/pnas.2003926118
                7896322
                33531344
                25722e99-7342-47b1-9e0b-89c880ded4b5
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 8
                Categories
                435
                Biological Sciences
                Systems Biology

                systems endocrinology,gonadal axis,thyroid axis,growth axis,hpa axis

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