Article in Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
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 registry
The 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.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
6 authors.
Yixin WangDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Wei PengDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Yu ZhangDepartment of Bioengineering, Lehigh University, Bethlehem, PA, USA.
Ehsan AdeliDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Qingyu ZhaoDepartment of Radiology, Weill Cornell Medicine, New York, USA.
Kilian M PohlDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Funding
TRACKING HIV INFECTION AND ALCOHOL ABUSE CNS COMORBIDITY WITH NEUROIMAGINGU01AA017347 · NIAAA · SRI INTERNATIONAL · PI PFEFFERBAUM, ADOLF, SULLIVAN, EDITH VIONI · 2007 to 2021
$16.0M
CNS DEFICITS: INTERACTION OF AGE AND ALCOHOLISMR01AA005965 · NIAAA · STANFORD UNIVERSITY · PI PFEFFERBAUM, ADOLF, ZAHR, NATALIE M · 1985 to 2025
$13.0M
NCANDA: Data Analysis Resource - Uploading Legacy Data to NDAU24AA021697 · NIAAA · STANFORD UNIVERSITY · PI Adolf Pfefferbaum, Kilian Maria Pohl · 2016 to 2026
$12.2M
CEREBELLAR STRUCTURE AND FUNCTION IN ALCOHOLISMR01AA010723 · NIAAA · STANFORD UNIVERSITY · PI SULLIVAN, EDITH VIONI, ZAHR, NATALIE M · 1996 to 2025
$8.3M
Cerebellar Structure and Function in AlcoholismR37AA010723 · NIAAA · STANFORD UNIVERSITY · PI SULLIVAN, EDITH VIONI · 2010 to 2019
$5.4M
Tracking HIV Infection & Alcohol Abuse CNS Comorbidity with NeuroimagingR01AA017347 · NIAAA · SRI INTERNATIONAL · PI Adolf Pfefferbaum · 2022 to 2026
$5.2M
CNS DEFICITS: INTERACTION OF AGE AND ALCOHOLISMR37AA005965 · NIAAA · STANFORD UNIVERSITY · PI PFEFFERBAUM, ADOLF · 1996 to 2005
$4.4M
Establishing Multimodal Brain Biomarkers Using Data-driven Analyticsfor Treatment Selection in DepressionR01MH129694 · NIMH · STANFORD UNIVERSITY · PI Yu Zhang · 2023 to 2026
$2.8M
Interpretable Deep Forecasting of Hazardous Substance Use during High SchoolR01DA057567 · NIDA · STANFORD UNIVERSITY · PI Kilian Maria Pohl, Susan F. Tapert · 2022 to 2026
$2.3M
Longitudinal Analysis of Diffusion Tensor Imaging to Discover Adolescent Alcohol Use EffectR00AA028840 · NIAAA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Qingyu Zhao · 2024 to 2026
$747k
Assess Neural Circuits and Subtypes Underlying Dimensions of Neuropsychiatric Symptoms in Alzheimer's DiseaseR21AG080425 · NIA · LEHIGH UNIVERSITY · PI ZHANG, YU · 2023 to 2024
$426k
Identifying Transdiagnostic Functional Connectivity Biomarkers for Cognitive Health and PsychopathologyR21MH130956 · NIMH · LEHIGH UNIVERSITY · PI BERDICHEVSKY, YEVGENY · 2023 to 2024
Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning. · full record | OpenQuestion