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Updated: Jul 11, 2025
Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression
Published on: May 19, 2015
Predicting new onset thought disorder in early adolescence with optimized deep learning implicates
Nina de Lacy1,2, Michael J Ramshaw1,2
1Huntsman Mental Health Institute, Salt Lake City, UT 84103.
Artificial intelligence accurately predicted thought disorder (TD) onset in adolescents using multimodal data. Structural brain differences and psychosocial factors identified youth at risk, informing potential interventions.
Area of Science:
- Neuroscience
- Psychiatry
- Artificial Intelligence
Background:
- Thought disorder (TD) is a key indicator for schizophrenia risk.
- Early identification of at-risk youth is crucial.
- Few studies prospectively predict TD onset in general youth populations.
Conclusions:
- Identified robust, person-level signatures for early adolescent TD.
- Left putamen structural differences are a candidate biomarker interacting with psychosocial stressors.
- Interventions targeting sleep and psychosocial stressors may modulate TD risk.
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