ArticleResearch square2024
Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.
Kevin Guo, Nikhil Chaudhari, Tamara Jafar, Nahian Chowdhury, Paul Bogdan, Andrei Irimia
Abstract readPreprint
In one paragraphArticle in Research square, 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
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
6 authors.
Kevin GuoThomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California.
Nikhil ChaudhariCorwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California.
Tamara JafarNeuroscience Graduate Program, University of Southern California.
Nahian ChowdhuryNeuroscience Graduate Program, University of Southern California.
Paul BogdanMing Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California.
Andrei IrimiaEthel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California.
Funding
National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8MResearch Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Katherine P Rankin · 2019 to 2026
$36.9MResearch Education ComponentP30AG062421 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI BRADFORD C DICKERSON · 2019 to 2026
$36.5MUCSD Shiley-Marcos Alzheimer's Disease Research Center P30P30AG062429 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DOUGLAS R GALASKO · 2019 to 2026
$34.9MWisconsin Alzheimer's Disease Research CenterP30AG062715 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Sanjay Asthana · 2019 to 2026
$34.5MResearch Education ComponentP30AG062677 · NIA · MAYO CLINIC ROCHESTER · PI KEJAL KANTARCI · 2019 to 2026
$33.5MResearch Education ComponentP30AG066514 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Margaret Sewell · 2020 to 2026
$31.0MYale Alzheimer Disease Research CenterP30AG066508 · NIA · YALE UNIVERSITY · PI STEPHEN M STRITTMATTER · 2020 to 2026
$30.2MResearch Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PHILIP L DE JAGER · 2020 to 2026
$30.1MResearch Education ComponentP30AG066468 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI C. Elizabeth Shaaban · 2020 to 2026
$29.4MResearch Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3MUniversity of Washington Alzheimer's Disease Research CenterP30AG066509 · NIA · UNIVERSITY OF WASHINGTON · PI Amanda D. Boyd · 2020 to 2026
$29.0MNIA NIH HHS P20 AG068024NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062677NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066462NIA NIH HHS P30 AG066468NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066508NIA NIH HHS P30 AG066509NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG066518NIA NIH HHS P30 AG066519NIA NIH HHS P30 AG066530NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072946NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072973NIA NIH HHS P30 AG072975NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072977NIA NIH HHS P30 AG072978NIA NIH HHS P30 AG072979NIA NIH HHS R01 AG079280NIA NIH HHS R01 AG079957NIA NIH HHS RF1 AG082201NIA NIH HHS U24 AG072122NINDS NIH HHS R01 NS100973
6 · The paper itselfAbstract
The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI). Cognitively normal (CN) adults (
Identifiers
PMID39483910
PMCPMC11527355
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