Abstract
Objective Brain atrophy is an established biomarker for dementia. We hypothesise that spinal cord atrophy is an important in vivo imaging biomarker for neurodegeneration associated with dementia.
Methods 3DT1 images of 31 Alzheimer’s disease (AD) and 35 healthy control (HC) subjects were processed to calculate volumes of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord (per vertebra as well as the C2-C3 pair (CSA23 and CSV23)). Correlated features (ρ>0.7) were removed, and best subset identified for patients’ classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p<=0.05). Linear regression was implemented to assess the explained variance of the Mini Mental State Examination (MMSE) score as dependent variable with best features as predictors.
Results Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and grey matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance.
Discussion Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC. Results show that CSA23 has a considerable weight in classification tasks warranting further investigations.