Evidence map›Paper›PMID 32771618›Full record

ArticleNeuroImage2020

Joint embedding: A scalable alignment to compare individuals in a connectivity space.

Karl-Heinz Nenning, Ting Xu, Ernst Schwartz, Jesus Arroyo, Adelheid Woehrer, Alexandre R Franco, Joshua T Vogelstein, Daniel S Margulies, Hesheng Liu, Jonathan Smallwood and 2 more

Abstract read
In one paragraph

Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
–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.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Hierarchical Flows of Human Cortical Activity.bioRxiv : the preprint server for biology · 2026
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  10. Multiscale comparative connectomics.Imaging neuroscience (Cambridge, Mass.) · 2025
    Article
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  12. Consecutive-Contrastive Spherical U-Net: Enhancing Reliability of Individualized Functional Brain Parcellation for Short-Duration fMRI Scans.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024
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4 · The record

Corrections and comments

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

12 authors.

Karl-Heinz NenningComputational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria. Electronic address: karl-heinz.nenning@meduniwien.ac.at.
Ting XuCenter for the Developing Brain, Child Mind Institute, New York, NY, USA. Electronic address: ting.xu@childmind.org.
Ernst SchwartzComputational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Jesus ArroyoCenter for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
Adelheid WoehrerDivision of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria.
Alexandre R FrancoCenter for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA; Department of Psychiatry, NYU Langone School of Medicine, New York, NY, USA.
Joshua T VogelsteinDepartment of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
Daniel S MarguliesCentre National de la Recherche Scientifique, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France.
Hesheng LiuA.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Jonathan SmallwoodDepartment of Psychology, Queen's University, Kingston, Ontario, Canada.
Michael P MilhamCenter for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA.
Georg LangsComputational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: georg.langs@meduniwien.ac.at.

Funding

Project 5: ComputationalP50MH109429 · NIMH · NATHAN S. KLINE INSTITUTE FOR PSYCH RES · PI STEPHANIE Ruggiano JONES · 2017 to 2026
$25.3M
Defining Neuronal Circuits and Cellular Processes Underlying Resting fMRI SignalsR01MH111439 · NIMH · NATHAN S. KLINE INSTITUTE FOR PSYCH RES · PI MILHAM, MICHAEL PETER, SCHROEDER, CHARLES E · 2016 to 2020
$5.1M
Reproducible imaging-based brain growth charts for psychiatryR01MH120482 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI MILHAM, MICHAEL PETER, SATTERTHWAITE, THEODORE · 2019 to 2023
$3.5M
C-PAC: A configurable, compute-optimized, cloud-enabled neuroimaging analysis software for reproducible translational and comparativeR24MH114806 · NIMH · CHILD MIND INSTITUTE, INC. · PI CRADDOCK, RICHARD CAMERON, MILHAM, MICHAEL PETER · 2018 to 2020
$1.6M
Austrian Science Fund FWF I 2714Austrian Science Fund FWF I 3925NIMH NIH HHS P50 MH109429NIMH NIH HHS R01 MH111439NIMH NIH HHS R01 MH120482NIMH NIH HHS R24 MH114806
6 · The paper itself

Abstract

A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual representations across datasets to be aligned. Using Human Connectome Project data, we evaluated the joint embedding approach by comparing it to the previously established orthonormal alignment model. Alignment using joint embedding substantially increased the similarity of functional representations across individuals while simultaneously capturing their distinct profiles, allowing individuals to be more discriminable from each other. Additionally, we demonstrated that the common space established using resting-state fMRI provides a better overlap of task-activation across participants. Finally, in a more challenging scenario - alignment across a lifespan cohort aged from 6 to 85 - joint embedding provided a better prediction of age (r2 = 0.65) than the prior alignment model. It facilitated the characterization of functional trajectories across lifespan. Overall, these analyses establish that joint embedding can simultaneously capture individual neural representations in a common connectivity space aligning functional data across participants and populations and preserve individual specificity.

Indexed as

ConnectomeAdultAlgorithmsBrainFemaleHumansImage Processing, Computer-AssistedIndividualityMagnetic Resonance ImagingMaleNerve NetNeural PathwaysCommon spaceFunctional alignmentFunctional gradientIndividual differencesJoint embeddingLifespan

Identifiers

PMID32771618
PMCPMC7779372

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.