Neuroanatomy of dementia in Down Syndrome revealed using a voxel‐based morphometry approach to computed tomography. (20th December 2022)

• Record Type: Journal Article
• Authors: Zhang, Linda; Sanchez, Beatriz; Moldenhauer, Fernando; de Asua, Diego Real; Brudfors, Mikael; Foulon, Chris; del Ser, Teodoro; Ashburner, John; Nachev, Parashkev; Strange, Bryan A
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.062219
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: People with Down syndrome (DS) are more likely to develop Alzheimer's disease than the general population, but comorbidities and variable intellectual disability complicate diagnosis and disease monitoring. Although neuroimaging may help elucidate the pathogenesis of dementia, studies using magnetic resonance imaging (MRI) in DS subjects with dementia are limited due to greater motion artefact in those with severe cognitive impairment. Computed tomography (CT) is faster, thus less impacted by motion, but poor tissue contrast makes analysis of cortical and subcortical changes difficult. In this study, we use CTseg, an algorithm able to robustly spatially normalise and segment CT scans, to analyse grey matter (GM) and white matter (WM) differences in 98 DS subjects with and without cognitive impairment due to dementia. Methods: The CT scans of 120 DS patients from the Adult Down Syndrome unit at the Hospital Universitario La Princesa were selected. Dementia level was classified by retrospective review of medical records as 0 (cognitively stable), 1 (mild dementia), 2 (moderate dementia), or 3 (advanced dementia) according to the DSM‐V operational criteria. Premorbid functionality was assessed using Part 1 ("Level of 'best' ability") of the Dementia Screening Questionnaire for Individuals with Intellectual Disabilities (DSQIID). Those with artefacts, faulty scans, or incidental brain findings were excluded, leaving a final sample of 98 subjects (mean age: 48±8.5 years; 45% female; 62 cognitively stable). CT scans were segmented into GM, WM, and cerebrospinal fluid using CTseg (https://github.com/WCHN/CTseg ). Voxel‐based morphometry (VBM) analyses were carried out separately on smoothed and modulated GM and WM images, correlating with dementia level whilst controlling for age, sex, DSQIID, and total intracranial volume. Results: GM volume was significantly negatively correlated with dementia level in the bilateral parahippocampal gyri, extending into hippocampus, and amygdalae. By contrast, significant WM reductions associated with dementia level were primarily parietal and occipitotemporal and right‐lateralised in the superior and inferior longitudinal fasciculi (p<0.001, FWE‐corrected). Conclusions: Our study uses a novel algorithm that allows the analysis of CT scans using VBM methods, enabling the inclusion of a sizeable sample of DS subjects with dementia, a group difficult to study with neuroimaging due to practical constraints.