Improving Brain Imaging for Mental Illness Diagnosis
Author Information
Author(s): Roberto Limongi, Alexandra B. Skelton, Lydia H. Tzianas, Angelica M. Silva
Primary Institution: Brandon University
Hypothesis
Understanding the causes of longitudinal changes in computational phenotypes will improve their construct validity and test-retest reliability.
Conclusion
Integrating brain imaging with active inference can enhance the reliability and validity of computational phenotypes in mental illness.
Supporting Evidence
- Computational psychiatry aims to provide mechanistic explanations for mental disorders.
- Longitudinal changes in computational phenotypes indicate structural variations over time.
- Active inference can help understand the causal relationships between cognitive processes and brain signals.
Takeaway
This study looks at how we can better understand mental illnesses by using brain scans and new methods to track changes over time.
Methodology
The review discusses various computational models and their application in understanding mental illnesses through brain imaging.
Limitations
The review highlights that computational phenotypes currently lack sufficient test-retest reliability and construct validity.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website