Deciphering white matter microstructural alterations in catatonia according to ICD-11: replication and machine learning analysis

Abstract

Catatonia is a severe psychomotor disorder characterized by motor, affective and cognitive-behavioral abnormalities. Although previous magnetic resonance imaging (MRI) studies suggested white matter (WM) dysconnectivity in the pathogenesis of catatonia, it is unclear whether microstructural alterations of WM tracts connecting psychomotor regions might contribute to a better classification of catatonia patients. Here, diffusion-weighted MRI data were collected from two independent cohorts (whiteCAT/replication cohort) of patients with (n = 45/n = 13) and without (n = 56/n = 26) catatonia according to ICD-11 criteria. Catatonia severity was examined using the Northoff (NCRS) and Bush-Francis (BFCRS) Catatonia Rating Scales. We used tract-based spatial statistics (TBSS), tractometry (TractSeg) and machine-learning (ML) to classify catatonia patients from tractometry values as well as tractomics features generated by the newly developed tool RadTract. Catatonia patients showed fractional anisotropy (FA) alterations measured via TractSeg in different corpus callosum segments (CC_1, CC_3, CC_4, CC_5 and CC_6) compared to non-catatonia patients across both cohorts. Our classification results indicated a higher level of performance when trained on tractomics as opposed to traditional tractometry values. Moreover, in the CC_6, we successfully trained two classifiers using the tractomics features identified in the whiteCAT data. These classifiers were applied separately to the whiteCAT and replication cohorts, demonstrating comparable performance with Area Under the Receiver Operating Characteristics (AUROC) values of 0.79 for the whiteCAT cohort and 0.76 for the replication cohort. In contrast, training on FA tractometry resulted in lower AUROC values of 0.66 for the whiteCAT cohort and 0.51 for the replication cohort. In conclusion, these findings underscore the significance of CC WM microstructural alterations in the pathophysiology of catatonia. The successful use of an ML based classification model to identify catatonia patients has the potential to improve diagnostic precision.

Fig. 1. Between-group differences in white matter.

Significant differences (areas highlighted in red) in FA values of CC_6 in both cohorts along the tract (a whiteCAT cohort; b replication cohort).

Fig. 2. Per-tract classification results.

The classifier's performance in CC_6 remained consistent across both cohorts. Additionally, in the whiteCAT cohort, tractomics demonstrated superior classification accuracy compared to tractometry features for all tracts, except for ST_PO_right.

Fig. 3. Relationship between white matter and catatonia signs.

Location of significant correlation between FA values and NCRS motor (NCRS_mot) in catatonia patients of the whiteCAT cohort (left) and between FA and NCRS behavioral (NCRS_be) in catatonia patients of the replication cohort (right).

Fig. 4. Relationship between white matter and catatonia signs.

Location of significant correlation between tractomics and NCRS motor (NCRS_mot; left side) and BFCRS total score (BFCRS_total; ride side) in catatonia patients of the whiteCAT cohort.