Evidence map›Paper›PMID 41263959›Full record

ReviewDer Nervenarzt2026

Trajectories of affective disorders: neurobiological mechanisms during symptom change.

Ulrich W Ebner-Priemer, Judith Alferink, Michael Bauer, Udo Dannlowski, Irina Falkenberg, Andreas J Forstner, Tim Hahn, Markus Junghöfer, Tilo Kircher, Luisa Klotz and 7 more

Abstract readReview
In one paragraph

Review in Der Nervenarzt, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

17 authors.

Ulrich W Ebner-PriemerKarlsruher Institut für Technologie (KIT), Institute of Sports and Sports Science, Karlsruhe, Germany.
Judith AlferinkDepartment of Psychiatry, University of Münster, Münster, Germany.
Michael BauerDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Udo DannlowskiInstitute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, A9a, 48149, Münster, Germany.
Irina FalkenbergDepartment of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany.
Andreas J ForstnerInstitute for Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany.
Tim HahnInstitute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, A9a, 48149, Münster, Germany.
Markus JunghöferInstitute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany.
Tilo KircherDepartment of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany.
Luisa KlotzDepartment of Neurology with Institute of Translational Neurology, University of Münster, Münster, Germany.
Julia MartiniDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Eva MennigenDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Igor NenadićDepartment of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany.
Carmine ParianteDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Andrea PfennigDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Michael ZillerDepartment of Psychiatry, University of Münster, Münster, Germany.
Susanne MeinertInstitute for Translational Psychiatry, University of Münster, Albert-Schweitzer-Campus 1, A9a, 48149, Münster, Germany. s.meinert@uni-muenster.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective treatment of affective disorders (AD) requires a deep understanding of the underlying neurobiological mechanisms. However, in machine-learning-based analyses, cross-sectional studies have failed to identify robust individual-level biomarkers. Research Domain A of CRC/TRR393 addresses this gap by implementing longitudinal, multimodal studies using real-time mobile assessments. Central to this effort is the identification of "inflection signals"-clinically meaningful symptom changes marking transitions from euthymia to depressive or (hypo)manic episodes. These critical windows are captured through digital phenotyping and ecological momentary assessments and followed up by in-depth neurobiological profiling. Six projects examine the dynamic interplay of behavioral, cognitive-emotional, molecular, immune, and neural mechanisms during these transitions. Project A01 validates early-warning models using digital phenotypes and machine learning. Project A02 maps structural and functional brain changes in relation to disease course and risk factors. Project A03 investigates the role of microglial immune activation in recurrent depression. Project A04 investigates neurobiological alterations after inflection signals using intensive, multimodal data acquisition conducted both in laboratory settings and in the participants' personal environments. Project A05 adds molecular and immunological profiling and integrates findings from human and animal data. Project A06 studies trajectories from bipolar at-risk states to full-blown illness. Together, these projects form the empirical foundation for mechanism-based interventions (Domain C) and theoretical modeling of symptom trajectories (Domain B).

Indexed as

BrainMood DisordersDisease ProgressionHumansLongitudinal StudiesMachine LearningAffective symptomsBiomarkersDigital monitoringMood disordersRisk factors

Identifiers

PMID41263959
PMCPMC12953256

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.