Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task
Bibliographic Data
| ID | 15516564 |
|---|---|
| Authors | Jesper Pilmeyer (0000-0002-8419-8207, Kempenhaeghe, corresponding author), Rolf Lamerichs (Philips (Netherlands)), Faroeq Ramsaransing (Kempenhaeghe), Jacobus F A Jansen (0000-0002-5271-8060, Maastricht University), Marcel Breeuwer (0000-0003-1822-8970, Eindhoven University of Technology), Svitlana Zinger (0000-0001-9357-3067, Kempenhaeghe) |
| Year | 2024 |
| Volume | 15 |
| Pages | 1255370-1255370 |
| Publication date | 2024-03-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2024.1255370 |
| PMID | 38585483 |
| OpenAlex | W4393096585 |
| Language | EN |
| References cited | 73 |
The dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts
Cognition · Default mode network · Depression (economics · Functional magnetic resonance imaging · Major depressive disorder · Physical medicine and rehabilitation · Advanced MRI Techniques and Applications · Advanced Neuroimaging Techniques and Applications · Computer Science · Functional Brain Connectivity Studies · Medicine · Neuroscience · Psychology · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |