ArticlebioRxiv : the preprint server for biology2024
Automatic Geometry-based Estimation of the Locus Coeruleus Region on T
Iman Aganj, Jocelyn Mora, Bruce Fischl, Jean C Augustinack
Abstract readPreprint
In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
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4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
4 authors.
Iman AganjAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, MA 02129, USA.ORCID 0000-0002-4673-1293 Jocelyn MoraAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, MA 02129, USA.
Bruce FischlAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, MA 02129, USA.
Jean C AugustinackAthinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, MA 02129, USA.
Funding
Functionally guided adult whole brain cell atlas in human and NHPUM1MH130981 · NIMH · ALLEN INSTITUTE · PI Ed Lein, Hongkui Zeng · 2022 to 2026
$91.9MMapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7MBRAIN CONNECTS: The center for Large-scale Imaging of Neural Circuits (LINC)UM1NS132358 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Suzanne N Haber, Elizabeth M. C. Hillman · 2023 to 2026
$17.5MEPIDEMIOLOGY OF DEMENTIA IN THE FRAMINGHAM STUDYR01AG008122 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA, SESHADRI, SUDHA · 1989 to 2015
$14.4MMRI, Genetics &Cognitive Precursors of AD &DementiaR01AG016495 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA · 1999 to 2016
$12.5MThe Human Connectome Project (HCP)U01MH093765 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2010 to 2014
$12.1MTraining and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI BRUCE R ROSEN · 2020 to 2026
$10.9MProject 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8MImaging and Analysis Techniques to Construct a Cell Census Atlas of the Human Brain Admin SupplementU01MH117023 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI BOAS, DAVID A, FISCHL, BRUCE · 2018 to 2022
$7.6MAn Acquisition and Analysis Pipeline for Integrating MRI and Neuropathology in TBI-related Dementia and VCIDU24NS135561 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Bruce Fischl, Susie Yi Huang · 2023 to 2026
$6.1MNeuropathology of CTE and Delayed Effects of TBI: Toward In-Vivo DiagnosticsU01NS086625 · NINDS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DAMS-O'CONNOR, KRISTEN · 2014 to 2017
$6.1MFreeSurfer Development, Maintenance, and HardeningR01EB023281 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Bruce Fischl, Douglas Nowlin Greve · 2016 to 2026
$5.6MNIA NIH HHS R01 AG008122NIA NIH HHS R01 AG016495NIA NIH HHS R01 AG057672NIA NIH HHS R01 AG064027NIA NIH HHS R01 AG070988NIA NIH HHS R01 AG072056NIA NIH HHS R21 AG082082NIA NIH HHS R56 AG064027NIA NIH HHS R56 AG068261NIA NIH HHS RF1 AG068261NIA NIH HHS RF1 AG072056NIA NIH HHS RF1 AG082223NIBIB NIH HHS P41 EB015896NIBIB NIH HHS P41 EB030006NIBIB NIH HHS R01 EB006758NIBIB NIH HHS R01 EB019956NIBIB NIH HHS R01 EB023281NIBIB NIH HHS R21 EB018907NIMH NIH HHS RF1 MH121885NIMH NIH HHS RF1 MH123195NIMH NIH HHS U01 MH093765NIMH NIH HHS U01 MH117023NIMH NIH HHS U54 MH091657NIMH NIH HHS UM1 MH130981NINDS NIH HHS R01 NS070963NINDS NIH HHS R01 NS083534NINDS NIH HHS R01 NS105820NINDS NIH HHS R21 NS072652NINDS NIH HHS U01 NS086625NINDS NIH HHS U01 NS132181NINDS NIH HHS U24 NS135561NINDS NIH HHS UM1 NS132358
6 · The paper itselfAbstract
The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T
Indexed as
expected label value (ELV)image segmentationlocus coeruleus (LC)magnetic resonance imaging (MRI)U-Net
Identifiers
PMID38328208
PMCPMC10849695
What OpenQuestion holds
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