AUTHOR=Lorenzi Roberta Maria , Palesi Fulvia , Castellazzi Gloria , Vitali Paolo , Anzalone Nicoletta , Bernini Sara , Cotta Ramusino Matteo , Sinforiani Elena , Micieli Giuseppe , Costa Alfredo , D’Angelo Egidio , Gandini Wheeler-Kingshott Claudia A. M. 

TITLE=Unsuspected Involvement of Spinal Cord in Alzheimer Disease

JOURNAL=Frontiers in Cellular Neuroscience

VOLUME=14

YEAR=2020

URL=https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2020.00006

DOI=10.3389/fncel.2020.00006

ISSN=1662-5102

ABSTRACT=<p><bold>Objective</bold>: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer’s disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important <italic>in vivo</italic> imaging biomarker contributing to understanding neurodegeneration associated with dementia.</p><p><bold>Methods</bold>: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume 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 (<italic>ρ</italic> &gt; 0.7) were removed, and the best subset identified for patients’ classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (<italic>p</italic> ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors.</p><p><bold>Results</bold>: 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 gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance.</p><p><bold>Discussion</bold>: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD.</p>