000276335 001__ 276335 000276335 005__ 20240229155000.0 000276335 0247_ $$2doi$$a10.1016/j.schres.2023.05.011 000276335 0247_ $$2pmid$$apmid:37236889 000276335 0247_ $$2ISSN$$a0920-9964 000276335 0247_ $$2ISSN$$a1573-2509 000276335 0247_ $$2altmetric$$aaltmetric:149045360 000276335 037__ $$aDKFZ-2023-01065 000276335 041__ $$aEnglish 000276335 082__ $$a610 000276335 1001_ $$aHirjak, Dusan$$b0 000276335 245__ $$aMicrostructural white matter biomarkers of symptom severity and therapy outcome in catatonia: Rationale, study design and preliminary clinical data of the whiteCAT study. 000276335 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2024 000276335 3367_ $$2DRIVER$$aarticle 000276335 3367_ $$2DataCite$$aOutput Types/Journal article 000276335 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1703247290_28909 000276335 3367_ $$2BibTeX$$aARTICLE 000276335 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000276335 3367_ $$00$$2EndNote$$aJournal Article 000276335 500__ $$a#LA:E230# / 2024 Jan:263:160-168 000276335 520__ $$aThe 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. 000276335 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0 000276335 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de 000276335 650_7 $$2Other$$aCatatonia 000276335 650_7 $$2Other$$aDTI 000276335 650_7 $$2Other$$aLongitudinal 000276335 650_7 $$2Other$$aMRI 000276335 650_7 $$2Other$$aOutcome 000276335 7001_ $$aBrandt, Geva A$$b1 000276335 7001_ $$0P:(DE-He78)4995a888dce6affae1a4f407647c2b5c$$aPeretzke, Robin$$b2$$udkfz 000276335 7001_ $$aFritze, Stefan$$b3 000276335 7001_ $$aMeyer-Lindenberg, Andreas$$b4 000276335 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus$$b5$$udkfz 000276335 7001_ $$0P:(DE-He78)64313331bb3bdc0902ff88697f402c92$$aNeher, Peter$$b6$$eLast author$$udkfz 000276335 773__ $$0PERI:(DE-600)1500726-1$$a10.1016/j.schres.2023.05.011$$gp. 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