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Related Experiment Video

Updated: Feb 10, 2026

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice

Published on: October 19, 2019

Identifying predictive behavioral markers: A demonstration using automatically reinforced self-injurious behavior.

Louis P Hagopian^1,2, Griffin W Rooker^1,2, Gayane Yenokyan³

¹The Kennedy Krieger Institute.

Journal of Applied Behavior Analysis

|May 22, 2018

Summary

This summary is machine-generated.

This study introduces predictive behavioral markers (PBMs) to forecast treatment response, similar to predictive biomarkers in medicine. Two PBMs accurately predicted automatically reinforced self-injurious behavior (ASIB), showing promise for personalized treatment selection.

Keywords:

automatically reinforced self-injurious behavior conditional effectiveness conditional probability analysis precision medicine predictive behavioral markers

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Area of Science:

Behavioral science
Precision medicine
Clinical psychology

Background:

Predictive biomarkers (PBioMs) enable personalized medical treatments.
Objective behavioral measures can potentially serve a similar predictive role in treatment response.

Purpose of the Study:

To translate precision medicine methods for identifying PBioMs to the identification of predictive behavioral markers (PBMs).
To examine the accuracy of PBMs in predicting response to treatment for automatically reinforced self-injurious behavior (ASIB).

Main Methods:

Adapted methods from precision medicine used for identifying PBioMs.
Evaluated two specific PBMs for their accuracy in predicting treatment response in ASIB.

Main Results:

Both identified PBMs demonstrated good to excellent accuracy in predicting response to treatment for ASIB.
The findings support the utility of PBMs in a clinical context.

Conclusions:

The approach of identifying PBMs is compatible with applied behavior analysis.
Variables related to problem behavior mechanisms and treatment action mechanisms are promising candidates for future PBMs.
This methodology can guide individualized treatment selection and enhance understanding of behavior and treatment efficacy.