Abstract
fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 776-787 |
| Number of pages | 12 |
| Journal | Trends in Cognitive Sciences |
| Volume | 25 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 2021 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021 Elsevier Ltd
Keywords
- biomarker
- clinical neuroscience
- fMRI
- individual differences
- reliability
- translational neuroscience