J Mov Disord.
2020 Jan;13(1):39-46. 10.14802/jmd.19030.
Automated Brainstem Segmentation Detects Differential Involvement in Atypical Parkinsonian Syndromes
- Bocchetta
1 - Iglesias J2
- Chelban V3,4
- Jabbari E3
- Lamb R3
- Russell LL1
- Greaves CV1
- Neason M1
- Cash DM1,2
- Thomas DL5,6
- Warren JD1
- Woodside J3
- Houlden H3
- Morris HR3
- Rohrer JD1
- Affiliations
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- 1Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- 2Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- 3Department of Movement Disorders, UCL Queen Square Institute of Neurology, University College London, London, UK
- 4Department of Neurology and Neurosurgery, Institute of Emergency Medicine, Chisinau, Republic of Moldova
- 5Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- 6Leonard Wolfson Experimental Neurology Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- KMID: 2501668
- DOI: http://doi.org/10.14802/jmd.19030
Abstract
Objective
Brainstem segmentation has been useful in identifying potential imaging biomarkers for diagnosis and progression in atypical parkinsonian syndromes (APS). However, the majority of work has been performed using manual segmentation, which is time consuming for large cohorts.
Methods
We investigated brainstem involvement in APS using an automated method. We measured the volume of the medulla, pons, superior cerebellar peduncle (SCP) and midbrain from T1-weighted MRIs in 67 patients and 42 controls. Diagnoses were corticobasal syndrome (CBS, n = 14), multiple system atrophy (MSA, n = 16: 8 with parkinsonian syndrome, MSA-P; 8 with cerebellar syndrome, MSA-C), progressive supranuclear palsy with a Richardson’s syndrome (PSP-RS, n = 12), variant PSP (n = 18), and APS not otherwise specified (APS-NOS, n = 7).
Results
All brainstem regions were smaller in MSA-C (19–42% volume difference, p < 0.0005) and in both PSP groups (18–33%, p < 0.0005) than in controls. MSA-P showed lower volumes in all regions except the SCP (15–26%, p < 0.0005). The most affected region in MSA-C and MSA-P was the pons (42% and 26%, respectively), while the most affected regions in both the PSP-RS and variant PSP groups were the SCP (33% and 23%, respectively) and midbrain (26% and 24%, respectively). The brainstem was less affected in CBS, but nonetheless, the pons (14%, p < 0.0005), midbrain (14%, p < 0.0005) and medulla (10%, p = 0.001) were significantly smaller in CBS than in controls. The brainstem was unaffected in APS-NOS.
Conclusion
Automated methods can accurately quantify the involvement of brainstem structures in APS. This will be important in future trials with large patient numbers where manual segmentation is unfeasible.
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