Age-dependent cut-offs for pathological deep gray matter and thalamic volume loss using Jacobian integration

• Published in: NeuroImage clinical 2020-01, Vol.28, p.102478-102478, Article 102478
• Main Authors: Opfer, Roland, Krüger, Julia, Spies, Lothar, Hamann, Marco, Wicki, Carla A., Kitzler, Hagen H., Gocke, Carola, Silva, Diego, Schippling, Sven
• Format: Article
• Language: English
• Subjects: Aging, multiple sclerosis; Brain atrophy; Deep gray matter volume loss; Gray matter volume loss; Jacobian integration; Regular; Siena; Thalamic volume loss
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2020.102478

•Longitudinal MRI data of 189 healthy controls and 127 MS patients were analyzed.•Deep gray matter (deep GMVL) thalamic volume loss (ThalaVL) was assessed.•Magnitude of measurement error was estimated by means of 162 MRI scan-rescans.•Age-dependent cut-offs for pathological deep GMVL and ThalaVL are provided.•Result help interpreting deep GMVL and ThalaVL measurements in individual MS patients. Several recent studies indicate that deep gray matter or thalamic volume loss (VL) might be promising surrogate markers of disease activity in multiple sclerosis (MS) patients. To allow applying these markers to individual MS patients in clinical routine, age-dependent cut-offs distinguishing physiological from pathological VL and an estimation of the measurement error, which provides the confidence of the result, are to be defined. Longitudinal MRI scans of the following cohorts were analyzed in this study: 189 healthy controls (HC) (mean age 54 years, 22% female), 98 MS patients from Zurich university hospital (mean age 34 years, 62% female), 33 MS patients from Dresden university hospital (mean age 38 years, 60% female), and publicly available reliability data sets consisting of 162 short-term MRI scan-rescan pairs with scan intervals of days or few weeks. Percentage annualized whole brain volume loss (BVL), gray matter (GM) volume loss (GMVL), deep gray matter volume loss (deep GMVL), and thalamic volume loss (ThalaVL) were computed deploying the Jacobian integration (JI) method. BVL was additionally computed using Siena, an established method used in many Phase III drug trials. A linear mixed effect model was used to estimate the measurement error as the standard deviation (SD) of model residuals of all 162 scan-rescan pairs For estimation of age-dependent cut-offs, a quadratic regression function between age and the corresponding annualized VL values of the HC was computed. The 5th percentile was defined as the threshold for pathological VL per year since 95% of HC subjects exhibit a less pronounced VL for a given age. For the MS patients BVL, GMVL, deep GMVL, and ThalaVL were mutually compared and a paired t-test was used to test whether there are systematic differences in VL between these brain regions. Siena and JI showed a high agreement for BVL measures, with a median absolute difference of 0.1% and a correlation coefficient of r = 0.78. Siena and GMVL showed a similar standard deviation (SD) of the scan-rescan error of 0.28% and 0.29%, respectively. For