Microstructural white matter biomarkers of symptom severity and therapy outcome in catatonia: rationale, study design and preliminary clinical data of the whiteCAT study
The number of magnetic resonance imaging (MRI) studies on neuronal correlates of catatonia has dramatically increased in the last 10 years, but conclusive findings on white matter (WM) tracts alterations underlying catatonic symptoms are still lacking. Therefore, we conduct an interdisciplinary long...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
January 2024
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| In: |
Schizophrenia research
Year: 2024, Volume: 263, Pages: 160-168 |
| ISSN: | 1573-2509 |
| DOI: | 10.1016/j.schres.2023.05.011 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.schres.2023.05.011 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0920996423001913 
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| Author Notes: | Dusan Hirjak, Geva A. Brandt, Robin Peretzke, Stefan Fritze, Andreas Meyer-Lindenberg, Klaus H. Maier-Hein, Peter F. Neher |
| Summary: | The number of magnetic resonance imaging (MRI) studies on neuronal correlates of catatonia has dramatically increased in the last 10 years, but conclusive findings on white matter (WM) tracts alterations underlying catatonic symptoms are still lacking. Therefore, we conduct an interdisciplinary longitudinal MRI study (whiteCAT) with two main objectives: First, we aim to enroll 100 psychiatric patients with and 50 psychiatric patients without catatonia according to ICD-11 who will undergo a deep phenotyping approach with an extensive battery of demographic, psychopathological, psychometric, neuropsychological, instrumental and diffusion MRI assessments at baseline and 12 weeks follow-up. So far, 28 catatonia patients and 40 patients with schizophrenia or other primary psychotic disorders or mood disorders without catatonia have been studied cross-sectionally. 49 out of 68 patients have completed longitudinal assessment, so far. Second, we seek to develop and implement a new method for semi-automatic fiber tract delineation using active learning. By training supportive machine learning algorithms on the fly that are custom tailored to the respective analysis pipeline used to obtain the tractogram as well as the WM tract of interest, we plan to streamline and speed up this tedious and error-prone task while at the same time increasing reproducibility and robustness of the extraction process. The goal is to develop robust neuroimaging biomarkers of symptom severity and therapy outcome based on WM tracts underlying catatonia. If our MRI study is successful, it will be the largest longitudinal study to date that has investigated WM tracts in catatonia patients. |
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| Item Description: | Online verfügbar: 24. Mai 2023, Artikelversion: 18. Dezember 2023 Gesehen am 15.03.2024 |
| Physical Description: | Online Resource |
| ISSN: | 1573-2509 |
| DOI: | 10.1016/j.schres.2023.05.011 |