[{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"inpress","citation":{"ista":"Walker SJ, Lowenstein ED, Douglass AM, Thomas CMP, Madara JC, Kucukdereli H, Barbosa-Meillon EA, Tao J, Resch JM, Lowell BB. A hypothalamic circuit for anticipating future changes in energy balance. Neuron.","apa":"Walker, S. J., Lowenstein, E. D., Douglass, A. M., Thomas, C. M. P., Madara, J. C., Kucukdereli, H., … Lowell, B. B. (n.d.). A hypothalamic circuit for anticipating future changes in energy balance. <i>Neuron</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.neuron.2026.05.010\">https://doi.org/10.1016/j.neuron.2026.05.010</a>","ieee":"S. J. Walker <i>et al.</i>, “A hypothalamic circuit for anticipating future changes in energy balance,” <i>Neuron</i>. Elsevier.","chicago":"Walker, Samuel J., Elijah D. Lowenstein, Amelia M. Douglass, Callum M.P. Thomas, Joseph C. Madara, Hakan Kucukdereli, Eunice A. Barbosa-Meillon, Jenkang Tao, Jon M. Resch, and Bradford B. Lowell. “A Hypothalamic Circuit for Anticipating Future Changes in Energy Balance.” <i>Neuron</i>. Elsevier, n.d. <a href=\"https://doi.org/10.1016/j.neuron.2026.05.010\">https://doi.org/10.1016/j.neuron.2026.05.010</a>.","mla":"Walker, Samuel J., et al. “A Hypothalamic Circuit for Anticipating Future Changes in Energy Balance.” <i>Neuron</i>, Elsevier, doi:<a href=\"https://doi.org/10.1016/j.neuron.2026.05.010\">10.1016/j.neuron.2026.05.010</a>.","short":"S.J. Walker, E.D. Lowenstein, A.M. Douglass, C.M.P. Thomas, J.C. Madara, H. Kucukdereli, E.A. Barbosa-Meillon, J. Tao, J.M. Resch, B.B. Lowell, Neuron (n.d.).","ama":"Walker SJ, Lowenstein ED, Douglass AM, et al. A hypothalamic circuit for anticipating future changes in energy balance. <i>Neuron</i>. doi:<a href=\"https://doi.org/10.1016/j.neuron.2026.05.010\">10.1016/j.neuron.2026.05.010</a>"},"year":"2026","doi":"10.1016/j.neuron.2026.05.010","quality_controlled":"1","OA_type":"green","publisher":"Elsevier","keyword":["hunger","hypothalamus","AGRP neurons","neuroscience","metabolism","homeostasis","feeding","food intake","energy balance","appetite"],"pmid":1,"article_type":"original","OA_place":"repository","oa":1,"status":"public","department":[{"_id":"AmDo"}],"language":[{"iso":"eng"}],"month":"06","abstract":[{"lang":"eng","text":"AgRP neurons cause hunger, the drive to seek and consume food. Their activation by fasting is key for survival and is thought to be triggered by feedback when energy stores are low. However, we know that environmental cues can also regulate AgRP neurons since cues that predict future food intake rapidly inhibit AgRP neurons, but is the converse true: can the prediction of future fasting rapidly activate AgRP neurons? Here, we show in mice that such rapid fasting activation of AgRP neurons does occur. This rapid activation is driven by excitatory input from paraventricular hypothalamic (PVH) neurons expressing Sim2, which are bidirectionally sensitive to predictions of future energy state. Thus, cognitively processed contextual information conveyed by PVHSim2 neurons strongly activates AgRP neurons. Lastly, chronic silencing of PVHSim2 neurons causes persistent hypophagia. This PVHSim2-to-AgRP-neuron circuit, by anticipating and preventing negative energy balance, provides an important new dimension of hunger regulation."}],"fulldoi":"https://doi.org/10.1016/j.neuron.2026.05.010","date_updated":"2026-06-16T08:35:11Z","day":"03","date_created":"2026-06-08T09:24:25Z","type":"journal_article","article_processing_charge":"No","title":"A hypothalamic circuit for anticipating future changes in energy balance","publication_identifier":{"eissn":[" 1097-4199"],"issn":["0896-6273"]},"date_published":"2026-06-03T00:00:00Z","external_id":{"pmid":["42235510"]},"publication":"Neuron","author":[{"first_name":"Samuel J.","full_name":"Walker, Samuel J.","last_name":"Walker"},{"first_name":"Elijah D.","last_name":"Lowenstein","full_name":"Lowenstein, Elijah D."},{"orcid":"0000-0001-5398-6473","first_name":"Amelia May Barnett","full_name":"Douglass, Amelia May Barnett","id":"de5f6fda-80fb-11ef-996f-a8c4ecd8e289","last_name":"Douglass"},{"first_name":"Callum M.P.","full_name":"Thomas, Callum M.P.","last_name":"Thomas"},{"full_name":"Madara, Joseph C.","first_name":"Joseph C.","last_name":"Madara"},{"last_name":"Kucukdereli","first_name":"Hakan","full_name":"Kucukdereli, Hakan"},{"first_name":"Eunice A.","full_name":"Barbosa-Meillon, Eunice A.","last_name":"Barbosa-Meillon"},{"first_name":"Jenkang","full_name":"Tao, Jenkang","last_name":"Tao"},{"full_name":"Resch, Jon M.","last_name":"Resch","first_name":"Jon M."},{"first_name":"Bradford B.","full_name":"Lowell, Bradford B.","last_name":"Lowell"}],"scopus_import":"1","acknowledgement":"We thank all members of the B.B.L. laboratory for helpful discussions. We\r\nthank the BADERC and BNORC transgenic cores (NIH P30DK057521 and\r\nP30DK046200) for performing embryo injections to generate knockin mouse\r\nlines. We also thank the BIDMC Energy Balance Core (supported by NIH\r\nS10OD028635 and the Boston Area Diabetes Endocrinology Research Centers, P30DK135043), where Marissa Cortopassi performed indirect calorimetry experiments and Alexander Banks assisted with data analysis and interpretation. Confocal imaging was performed at BIDMC’s Confocal Imaging\r\nCore. We thank Chen Wu for assistance in designing knockin mouse lines.\r\nThis work was supported by the NIH (R01DK134427, R01DK096010, and\r\nR01DK075632 to B.B.L.). Authors were supported by an EMBO Long-Term\r\nFellowship (770-2018, S.J.W.), a T32 Postdoctoral Training Fellowship\r\n(5T32DK007516, E.D.L.), the Charles A. King Trust Postdoctoral Research\r\nFellowship program (A.M.D.), and a K99 Career Development Award\r\n(K99HL144923, J.M.R.).","_id":"21955","oa_version":"Preprint","main_file_link":[{"url":"https://doi.org/10.1101/2025.09.27.678865","open_access":"1"}]},{"author":[{"id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph M","last_name":"Sommer","full_name":"Sommer, Christoph M","orcid":"0000-0003-1216-9105"},{"first_name":"Christoph","full_name":"Straehle, Christoph","last_name":"Straehle"},{"full_name":"Köthe, Ullrich","first_name":"Ullrich","last_name":"Köthe"},{"full_name":"Hamprecht, Fred A.","first_name":"Fred A.","last_name":"Hamprecht"}],"publication":"2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro","_id":"9943","conference":{"name":"ISBI: International Symposium on Biomedical Imaging","location":"Chicago, Illinois, USA","start_date":"2011-03-30","end_date":"2011-04-02"},"oa":1,"main_file_link":[{"url":"https://www.researchgate.net/publication/224241106_Ilastik_Interactive_learning_and_segmentation_toolkit","open_access":"1"}],"oa_version":"Preprint","publication_identifier":{"issn":["1945-7928"],"isbn":["978-1-4244-4127-3"],"eissn":["1945-8452"]},"publisher":"Institute of Electrical and Electronics Engineers","title":"Ilastik: Interactive learning and segmentation toolkit","extern":"1","keyword":["image segmentation","biomedical imaging","three dimensional displays","neurons","retina","observers","image color analysis"],"date_published":"2011-06-09T00:00:00Z","year":"2011","doi":"10.1109/isbi.2011.5872394","date_created":"2021-08-19T11:49:58Z","quality_controlled":"1","article_processing_charge":"No","type":"conference","department":[{"_id":"Bio"}],"status":"public","month":"06","language":[{"iso":"eng"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","citation":{"chicago":"Sommer, Christoph M, Christoph Straehle, Ullrich Köthe, and Fred A. Hamprecht. “Ilastik: Interactive Learning and Segmentation Toolkit.” In <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Micro</i>. Institute of Electrical and Electronics Engineers, 2011. <a href=\"https://doi.org/10.1109/isbi.2011.5872394\">https://doi.org/10.1109/isbi.2011.5872394</a>.","mla":"Sommer, Christoph M., et al. “Ilastik: Interactive Learning and Segmentation Toolkit.” <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Micro</i>, Institute of Electrical and Electronics Engineers, 2011, doi:<a href=\"https://doi.org/10.1109/isbi.2011.5872394\">10.1109/isbi.2011.5872394</a>.","ista":"Sommer CM, Straehle C, Köthe U, Hamprecht FA. 2011. Ilastik: Interactive learning and segmentation toolkit. 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro. ISBI: International Symposium on Biomedical Imaging.","apa":"Sommer, C. M., Straehle, C., Köthe, U., &#38; Hamprecht, F. A. (2011). Ilastik: Interactive learning and segmentation toolkit. In <i>2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro</i>. Chicago, Illinois, USA: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/isbi.2011.5872394\">https://doi.org/10.1109/isbi.2011.5872394</a>","ieee":"C. M. Sommer, C. Straehle, U. Köthe, and F. A. Hamprecht, “Ilastik: Interactive learning and segmentation toolkit,” in <i>2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro</i>, Chicago, Illinois, USA, 2011.","ama":"Sommer CM, Straehle C, Köthe U, Hamprecht FA. Ilastik: Interactive learning and segmentation toolkit. In: <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Micro</i>. Institute of Electrical and Electronics Engineers; 2011. doi:<a href=\"https://doi.org/10.1109/isbi.2011.5872394\">10.1109/isbi.2011.5872394</a>","short":"C.M. Sommer, C. Straehle, U. Köthe, F.A. Hamprecht, in:, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Micro, Institute of Electrical and Electronics Engineers, 2011."},"fulldoi":"https://doi.org/10.1109/isbi.2011.5872394","abstract":[{"lang":"eng","text":"Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org."}],"publication_status":"published","date_updated":"2023-02-23T14:13:38Z","day":"09"}]