| Title: | Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness |
| Journal: | Science Advances |
| Published: | 6 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39504381/ |
| DOI: | https://doi.org/10.1126/sciadv.adn1862 |
Publication 14202
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Abstract
A primary aim of computational psychiatry is to establish predictive models linking individual differences in brain functioning with symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and associated with poor outcomes. Recent work suggests that thousands of participants may be necessary for the accurate and reliable prediction of cognition, questioning the utility of most patient collection efforts. Here, using a transfer learning framework, we train a model on functional neuroimaging data from the UK Biobank to predict cognitive functioning in three transdiagnostic samples (ns = 101 to 224). We demonstrate prediction performance in all three samples comparable to that reported in larger prediction studies and a boost of up to 116% relative to classical models trained directly in the smaller samples. Critically, the model generalizes across datasets, maintaining performance when trained and tested across independent samples. This work establishes that predictive models derived in large population-level datasets can boost the prediction of cognition across clinical studies.</p>
12 Keywords
- Adult
- Aged
- Brain
- Cognition
- Cognitive Dysfunction
- Female
- Humans
- Magnetic Resonance Imaging
- Male
- Mental Disorders
- Middle Aged
- Reproducibility of Results
21 Authors
- Sidhant Chopra
- Elvisha Dhamala
- Connor Lawhead
- Jocelyn A. Ricard
- Edwina R. Orchard
- Lijun An
- Pansheng Chen
- Naren Wulan
- Poornima Kumar
- Arielle Rubenstein
- Julia Moses
- Lia Chen
- Priscila Levi
- Alexander Holmes
- Kevin Aquino
- Alex Fornito
- Ilan Harpaz-Rotem
- Laura T. Germine
- Justin T. Baker
- B. T. Thomas Yeo
- Avram J. Holmes
1 Application
| Application ID | Title |
|---|---|
| 25163 | Structural genetic contributions brain function and behavior |
Enabling scientific discoveries that improve human health