Andrew Chan

Background and purpose Neuronal autoantibodies can support the diagnosis of primary autoimmune cerebellar ataxia (PACA). Knowledge of PACA is still sparce. This article aims to highlight the relevance of anti‐neurochondrin antibodies and possible therapeutical consequences in people with PACA. Methods This is a case presentation and literature review of PACA associated with anti‐neurochondrin antibodies. Results A 33‐year‐old man noticed reduced control of the right leg in May 2020. During his first clinic appointment at our institution in September 2021, he complained about gait imbalance, fine motor disorders, tremor, intermittent diplopia and slurred speech. He presented a pancerebellar syndrome with stance, gait and limb ataxia, scanning speech and oculomotor dysfunction. Within 3 months the symptoms progressed. An initial cerebral magnetic resonance imaging, June 2020, was normal, but follow‐up imaging in October 2021 and July 2022 revealed marked cerebellar atrophy (29% volume loss). Cerebrospinal fluid analysis showed lymphocytic pleocytosis of 11 x 10 3 /L (normal range 0–4) and oligoclonal bands type II. Anti‐neurochondrin antibodies (immunoglobulin G) were detected in serum (1:10,000) and cerebrospinal fluid (1:320, by cell‐based indirect immunofluorescence assay and immunoblot, analysed by the EUROIMMUN laboratory). After ruling out alternative causes and neoplasia, diagnosis of PACA was given and immunotherapy (steroids and cyclophosphamide) was started in January 2022. In March 2022 a stabilization of disease was observed. Conclusion Cerebellar ataxia associated with anti‐neurochondrin antibodies has only been described in 19 cases; however, the number of unrecognized PACAs may be higher. As anti‐neurochondrin antibodies target an intracellular antigen and exhibit a mainly cytotoxic T‐cell‐mediated pathogenesis, important therapeutic implications may result. Because of the severe and rapid clinical progression, aggressive immunotherapy was warranted. This case highlights the need for rapid diagnosis and therapy in PACA, as stabilization and even improvement of symptoms are attainable.

Brain morphometry is usually based on non‐enhanced (pre‐contrast) T1‐weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available. Existing tools such as FreeSurfer yield unreliable results when applied to contrast‐enhanced (CE) images. Consequently, these acquisitions are excluded from retrospective morphometry studies, which reduces the sample size. We hypothesize that deep learning (DL)‐based morphometry methods can extract morphometric measures also from contrast‐enhanced MRI. We have extended DL+DiReCT to cope with contrast‐enhanced MRI. Training data for our DL‐based model were enriched with non‐enhanced and CE image pairs from the same session. The segmentations were derived with FreeSurfer from the non‐enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. Global and regional cortical thickness derived from non‐enhanced and CE images were contrasted to results from FreeSurfer. Correlation coefficients of global mean cortical thickness between non‐enhanced and CE images were significantly larger with DL+DiReCT ( r = 0.92) than with FreeSurfer ( r = 0.75). When comparing the longitudinal atrophy rates between the two MS subgroups, the effect sizes between PPMS and RRMS were higher with DL+DiReCT both for non‐enhanced ( d = −0.304) and CE images ( d = −0.169) than for FreeSurfer (non‐enhanced d = −0.111, CE d = 0.085). In conclusion, brain morphometry can be derived reliably from contrast‐enhanced MRI using DL‐based morphometry tools, making additional cases available for analysis and potential future diagnostic morphometry tools.