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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Long-term Depression01:03

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over time, all...
Long-term Depression01:05

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.

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Related Experiment Video

Updated: Jul 8, 2026

Robotically Delivered fMRI-Guided Personalized Transcranial Magnetic Stimulation Therapy for Treatment-Resistant Depression
13:18

Robotically Delivered fMRI-Guided Personalized Transcranial Magnetic Stimulation Therapy for Treatment-Resistant Depression

Published on: April 10, 2026

A Longitudinal Study on Predicting Treatment Response in Depression Using Self-Reported Symptoms.

Yoojin Song1, Sang Jin Rhee2, Daun Shin3

  • 1Department of Psychiatry, Kangwon National University Hospital, Chuncheon, Korea.

Journal of Korean Medical Science
|July 7, 2026
PubMed
Summary

Predicting depression treatment response is challenging. Self-reported symptoms like phobic anxiety, beyond standard depression scores, significantly improve prediction accuracy, aiding clinical decisions.

Keywords:
AnxietyDepressionMood DisordersSelf ReportsTreatment Outcome

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Area of Science:

  • Psychiatry
  • Clinical Psychology
  • Mental Health Research

Background:

  • Depression is a disabling condition with unpredictable treatment outcomes.
  • Accurate prediction of treatment response is crucial for effective patient management.

Purpose of the Study:

  • To investigate if self-reported initial symptoms can predict treatment response in patients with depression.
  • To compare the predictive performance of models based on demographic data versus subjective symptom reports.

Main Methods:

  • 157 patients with moderate to severe depression (MDD or bipolar disorder) were recruited.
  • Patients were categorized into responders (n=89) and non-responders (n=68) based on symptom changes.
  • Logistic regression analysis identified predictors of treatment response, comparing model performances.

Main Results:

  • No baseline differences were found in diagnoses, medications, or objective symptom scores between responders and non-responders.
  • Non-responders reported higher initial levels of obsessive-compulsive symptoms, interpersonal sensitivity, phobic anxiety, and paranoid ideation.
  • Models incorporating subjective symptoms demonstrated superior predictive performance (AUROC=0.725) compared to demographic models (AUROC=0.666). Phobic anxiety was a significant independent predictor (OR=1.93).

Conclusions:

  • Self-reported symptoms, particularly phobic anxiety, are vital for predicting depression treatment response.
  • Integrating patient-reported subjective symptoms into predictive models enhances accuracy and clinical utility.