Evidence map›Paper›PMID 29100150›Full record

ArticleJournal of affective disorders2018

Disrupted engagement of networks supporting hot and cold cognition in remitted major depressive disorder.

Jonathan P Stange, Lisanne M Jenkins, Elissa J Hamlat, Katie L Bessette, Sophie R DelDonno, Leah R Kling, Alessandra M Passarotti, K Luan Phan, Heide Klumpp, Kelly A Ryan and 1 more

Registry-linked trialOpen access · greenAbstract read
In one paragraph

Article in Journal of affective disorders, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03977038 (Investigation of Effects of Repetitive Transcranial Magnetic Stimulation), which is not on this map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 17% of its field
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.

NCT03977038 completednot on this mapstarted 2020, after this paper: background citation

Investigation of Effects of Repetitive Transcranial Magnetic Stimulation (rTMS) on "Hot" and "Cold" Cognitive Systems In Treatment Resistant Depression (TRD)

Typeobservational_patient_registrySponsorSunnybrook Health Sciences CentreRan2020 to 2022Enrolled25ConditionsMajor Depressive Disorder, Treatment Resistant DepressionArmsrepetitive transcranial magnetic stimulation
3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Remission is insufficient: predictors and mechanistic models of recurrence in late-life depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Review
  4. Neurophysiological Markers of Regulation Success in Everyday Life in Depression.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Disruption of Neural Homeostasis as a Model of Relapse and Recurrence in Late-Life Depression.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2019
    Review
  15. Article
  16. Multidimensional imaging techniques for prediction of treatment response in major depressive disorder.Progress in neuro-psychopharmacology & biological psychiatry · 2019
    Article
  17. 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

11 authors at 2 institutions in 1 country.

Jonathan P StangeUniversity of Illinois at Chicago, Chicago, IL, USA.
Lisanne M JenkinsUniversity of Illinois at Chicago, Chicago, IL, USA.
Elissa J HamlatUniversity of Illinois at Chicago, Chicago, IL, USA.
Katie L BessetteUniversity of Illinois at Chicago, Chicago, IL, USA.
Sophie R DelDonnoUniversity of Illinois at Chicago, Chicago, IL, USA.
Leah R KlingUniversity of Illinois at Chicago, Chicago, IL, USA.
Alessandra M PassarottiUniversity of Illinois at Chicago, Chicago, IL, USA.
K Luan PhanUniversity of Illinois at Chicago, Chicago, IL, USA.
Heide KlumppUniversity of Illinois at Chicago, Chicago, IL, USA.
Kelly A RyanUniversity of Michigan Medical Center, Ann Arbor, MI, USA.
Scott A LangeneckerUniversity of Illinois at Chicago, Chicago, IL, USA. Electronic address: slangenecker@psych.uic.edu.
University of Illinois Chicago · USUniversity of Michigan–Ann Arbor · US

Funding

Strengthening Stakeholder Engagement in Human Research Protections.UL1TR002003 · NCATS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI KARNIK, NIRANJAN SUBHASH, MERMELSTEIN, ROBIN J. · 2016 to 2024
$33.7M
Center for Clinical and Translational ScienceUL1TR000050 · NCATS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI AZAR, DIMITRI T, MERMELSTEIN, ROBIN J. · 2012 to 2014
$9.8M
Training in the Neuroscience of Mental HealthT32MH067631 · NIMH · UNIVERSITY OF ILLINOIS AT CHICAGO · PI RASENICK, MARK M. · 2004 to 2024
$4.8M
A High-End 3 Tesla Human MRI Scanner Dedicated to ResearchS10RR028898 · NCRR · UNIVERSITY OF ILLINOIS AT CHICAGO · PI ZHOU, XIAOHONG JOE · 2010 to 2010
$2.9M
Dimensional RDoC Modeling across the Range of Negative Mood DysfunctionR01MH101487 · NIMH · UNIVERSITY OF ILLINOIS AT CHICAGO · PI LANGENECKER, SCOTT A · 2013 to 2016
$2.5M
Neurobiological Intermediate Phenotypes of Major Depressive DisorderR01MH091811 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANGENECKER, SCOTT A · 2010 to 2014
$2.3M
From Networks to the Real World: Integrating Neural and Autonomic Processes of LossK23MH112769 · NIMH · UNIVERSITY OF SOUTHERN CALIFORNIA · PI STANGE, JONATHAN P · 2017 to 2021
$931k
NCATS NIH HHS UL1 TR000050NCATS NIH HHS UL1 TR002003NCRR NIH HHS S10 RR028898NIMH NIH HHS K23 MH112769NIMH NIH HHS R01 MH091811NIMH NIH HHS R01 MH101487NIMH NIH HHS T32 MH067631
6 · The paper itself

Abstract

backgroundMajor depressive disorder (MDD) is characterized by dysfunction in cognitive and emotional systems. However, the neural network correlates of cognitive control (cold cognition) and emotion processing (hot cognition) during the remitted state of MDD (rMDD) remain unclear and not fully probed, which has important implications for identifying intermediate phenotypes of depression risk.

methods43 young adults with rMDD and 33 healthy controls (HCs) underwent fMRI while completing separate tasks of cold cognition (Parametric Go/No-Go test) and hot cognition (Facial Emotion Processing Test). Two 2 group (rMDD, HC) × 2 event (sad/fearful faces, correct rejections) factorial models of activation were calculated in SPM8. Functional activation was evaluated in the salience and emotional network (SEN) and the cognitive control network (CCN), including hypothesized interaction between group and task within the CCN.

resultsIndividuals with rMDD demonstrated greater spatial extent of suprathreshold activation within the SEN during sad faces relative to HCs. There were several regions within the CCN in which HCs showed greater activation than rMDD during correct rejections of lures, whereas individuals with rMDD showed greater activation than HCs during sad or fearful faces. LIMITATIONS: Results were not directly compared with active MDD.

conclusionsThese results provide evidence of deficient CCN engagement during cognitive control in rMDD (dysfunctional cold cognition). Elevated SEN activation during sad faces could represent heightened salience of negative emotional faces in rMDD; elevated CCN activation during emotional faces in rMDD could represent compensatory regulatory control. These group differences may represent vulnerability factors, scars of prior depressive episodes, or processes maintaining wellness.

Indexed as

Brain MappingCase-Control StudiesCognitionEmotionsFacial ExpressionFemaleHumansMagnetic Resonance ImagingMajor Depressive DisorderMalePhotic StimulationRemission InductionYoung AdultCognitionCognitive controlEmotionfMRIMajor depressive disorderSadness

Identifiers

PMID29100150
PMCPMC6026853
OpenAlexW2764309797

What OpenQuestion holds

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Registered trials

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.