Machine Learning–Based Perivascular Space Volumetry in Alzheimer Disease
Authors: Deike Katerina , Decker Andreas , Scheyhing Paul , Harten Julia , Zimmermann Nadine , Paech Daniel , Peters Oliver , Freiesleben Silka D. , Schneider Luisa-Sophie , Preis Lukas , Priller Josef , Spruth Eike , Altenstein Slawek , Lohse Andrea , Fliessbach Klaus , Kimmich Okka , Wiltfang Jens , Bartels Claudia , Hansen Niels , Jessen Frank , Rostamzadeh Ayda , Düzel Emrah , Glanz Wenzel , Incesoy Enise I. , Butryn Michaela , Buerger Katharina , Janowitz Daniel , Ewers Michael , Perneczky Robert , Rauchmann Boris-Stephan , Teipel Stefan , Kilimann Ingo , Goerss Doreen , Laske Christoph , Munk Matthias H. , Spottke Annika , Roy Nina , Wagner Michael , Roeske Sandra , Heneka Michael T. , Brosseron Frederic , Ramirez Alfredo , Dobisch Laura , Wolfsgruber Steffen , Kleineidam Luca , Yakupov Renat , Stark Melina , Schmid Matthias C. , Berger Moritz , Hetzer Stefan , Dechent Peter , Scheffler Klaus , Petzold Gabor C. , Schneider Anja , Effland Alexander , Radbruch Alexander 
Published: April 2024

Objectives

Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD.

Materials and Methods

A convolutional neural network was trained using manually corrected, filter-based segmentations (n = 1000) to automatically segment the PVS in the centrum semiovale from interpolated, coronal T2-weighted magnetic resonance imaging scans (n = 894). These scans were sourced from the national German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study. Convolutional neural network–based segmentations and those performed by a human rater were compared in terms of segmentation volume, identified PVS clusters, as well as Dice score. The comparison revealed good segmentation quality (Pearson correlation coefficient r = 0.70 with P < 0.0001 for PVS volume, detection rate in cluster analysis = 84.3%, and Dice score = 59.0%). Subsequent multivariate linear regression analysis, adjusted for participants' age, was performed to correlate PVS volume with clinical diagnoses, disease progression, cerebrospinal fluid biomarkers, lifestyle factors, and cognitive function. Cognitive function was assessed using the Mini-Mental State Examination, the Comprehensive Neuropsychological Test Battery, and the Cognitive Subscale of the 13-Item Alzheimer’s Disease Assessment Scale.

Results

Multivariate analysis, adjusted for age, revealed that participants with AD and MCI, but not those with SCD, had significantly higher PVS volumes compared with CU participants without SCD (P = 0.001 for each group). Furthermore, CU participants who developed incident MCI within 4.5 years after the baseline assessment showed significantly higher PVS volumes at baseline compared with those who did not progress to MCI (P = 0.03). Cognitive function was negatively correlated with PVS volume across all participant groups (P ≤ 0.005 for each). No significant correlation was found between PVS volume and any of the following parameters: cerebrospinal fluid biomarkers, sleep quality, body mass index, nicotine consumption, or alcohol abuse.

Conclusions

The very early changes of PVS volume may suggest that alterations in PVS function are involved in the pathophysiology of AD. Overall, the volumetric assessment of centrum semiovale PVS represents a very early imaging biomarker for AD.