Volume of contrast‐based Substantia Nigra segmentation is decreased in dementia patients compared to healthy controls

Abstract

AbstractBackgroundThe substantia nigra (SN) is susceptible to neurodegeneration in Alzheimer’s disease. Automatic segmentation of the SN using MRI permits in vivo morphological assessment of the SN in cohort studies. Here we evaluate the multi‐brain segmentation (MB, Brudfors, 2020) to learn a probabilistic tissue prior of the SN using neuromelanin‐sensitive MRI while systematically studying effects of (A) SN tissue contrast; and (B) volumetric differences between healthy subjects and memory patients.MethodWe studied 50 3T single‐site T1w FLASH MRI from cognitively healthy older adults from the DZNEDELCODE cohort and a second ageing cohort in Magdeburg and 25 multi‐site diagnosed memory patients (MCI or AD). We quantified local SN signal contrast and image noise as a quality metric (obtained from CAT/SPM toolbox). We learnt tissue priors for each 10, 20, 30 and 50 older adults with the highest SN tissue contrast. For each tissue prior, we obtained model‐based SN tissue segments and calculated (integrated) SN volume. We compared SN volume between tissue prior segmentations and correlated it to age and quality. We coregistered all patients to the N = 50 tissue prior and compared volumetric differences between healthy adults and patients using voxel‐based morphometry (VBM).ResultThe template‐space N = 50 SN prior (in red) was visually larger than all other priors (Fig 1A). Patients visually had a lower SN volume than healthy from N = 50 (Fig 1B). The N = 50 native space SN tissue prior volume was significantly higher than all other tissue prior volumes (p>0.01, Fig 2A). SN volume correlated with quality for N = 50 (p = 0.02, Fig 2B). The N = 50 sample SN contrast (Fig 2C) did not differ between patients and healthy adults. Patients had a decreased volume in a cluster overlapping with the SN (N = 268, pFDR corrected = 0.02 (Fig 2D).ConclusionHere we demonstrate that model‐based learning of a probabilistic SN tissue prior using intensity‐based generative approach to segmentation is feasable in a heterogenous cohort. Additionally, using this prior, we showed that while there is no difference of the SN contrast, there is a significant decrease in SN volume consistent with previous analyses (Krohn et al 2022).