Evidence map›Paper›PMID 42140957›Full record

ArticleTranslational psychiatry2026

Neural Responses to Affective Sentences Reveal Signatures of Depression.

Aditya Kommineni, Woojae Jeong, Kleanthis Avramidis, Colin McDaniel, Myzelle Hughes, Thomas McGee, Elsi Kaiser, Kristina Lerman, Idan A Blank, Dani Byrd and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational psychiatry, 2026. 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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

16 authors.

Aditya KommineniMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA. akommine@usc.edu.ORCID http://orcid.org/0009-0002-0056-8336
Woojae JeongMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.ORCID http://orcid.org/0000-0001-5415-2040
Kleanthis AvramidisThomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA.
Colin McDanielBrain and Creativity Institute, University of Southern California, Los Angeles, USA.ORCID http://orcid.org/0000-0003-3880-164X
Myzelle HughesBrain and Creativity Institute, University of Southern California, Los Angeles, USA.
Thomas McGeeDepartment of Psychology, University of California Los Angeles, Los Angeles, USA.
Elsi KaiserDepartment of Linguistics, University of Southern California, Los Angeles, USA.
Kristina LermanThomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA.
Idan A BlankDepartment of Psychology, University of California Los Angeles, Los Angeles, USA.ORCID http://orcid.org/0000-0001-7057-8391
Dani ByrdDepartment of Linguistics, University of Southern California, Los Angeles, USA.ORCID http://orcid.org/0000-0003-3319-5871
Assal HabibiBrain and Creativity Institute, University of Southern California, Los Angeles, USA.ORCID http://orcid.org/0000-0002-1420-3568
B Rael CahnBrain and Creativity Institute, University of Southern California, Los Angeles, USA.
Sudarsana KadiriMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.
Takfarinas MedaniMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.
Richard M LeahyMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.
Shrikanth NarayananMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, USA.ORCID http://orcid.org/0000-0002-1052-6204

Funding

United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) N660012324006
6 · The paper itself

Abstract

Major Depressive Disorder (MDD) is a highly prevalent mental health condition, and a deeper understanding of its neurocognitive foundations is essential for identifying how core functions such as emotional and self-referential processing are affected. We investigate how depression symptoms alters the temporal dynamics of emotional processing by measuring neural responses to self-referential affective sentences using surface electroencephalography (EEG) in healthy and depressed individuals. Our results reveal significant group-level differences in neural activity during sentence viewing, suggesting disrupted integration of emotional and self-referential information in depression. Deep learning model trained on these responses achieves an area under the receiver operating curve (AUC) of 0.72 in distinguishing healthy from depressed participants, and 0.65 in differentiating depressed subgroups with and without suicidal ideation symptoms. Spatial ablations highlight anterior electrodes associated with semantic and affective processing as key contributors. These findings suggest stable, stimulus-driven neural signatures of depression symptoms that may inform future screening tools.

Indexed as

AffectBrainEmotionsMajor Depressive DisorderAdultDeep LearningElectroencephalographyFemaleHumansMaleSemanticsSuicidal IdeationYoung Adult

Identifiers

PMID42140957
PMCPMC13346668

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.