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

Area of Science:

• Neurology
• Radiology
• Biomarker Discovery

Background:

• Deep gray matter and thalamic volume loss (VL) are potential markers for multiple sclerosis (MS) disease activity.
• Clinical application requires age-dependent thresholds and reliable measurement error estimation.

Purpose of the Study:

• To define age-dependent cut-offs for physiological versus pathological VL.
• To estimate measurement error for deep gray matter and thalamic VL.
• To compare VL in different brain regions in MS patients.

Main Methods:

• Longitudinal MRI scans from healthy controls (HC) and MS patients were analyzed.
• Jacobian integration (JI) and Siena methods were used to compute volume loss (BVL, GMVL, deep GMVL, ThalaVL).
• Linear mixed-effects models and quadratic regression were used to estimate measurement error and age-dependent cut-offs.

Main Results:

• JI and Siena showed high agreement for BVL. Scan-rescan error was higher for deep GMVL and thalamic VL.
• Age-dependent cut-offs for pathological thalamic VL were established.
• MS patients showed significantly greater deep GMVL compared to BVL.

Conclusions:

• Deep gray matter volume loss (deep GMVL) is feasible for assessing MS patients using JI.
• Age and measurement error must be considered for clinical application.
• Deep GMVL can serve as a complementary marker to BVL in MS.