TY - JOUR
T1 - Unsuspected Involvement of Spinal Cord in Alzheimer Disease
AU - Lorenzi, Roberta Maria
AU - Palesi, Fulvia
AU - Castellazzi, Gloria
AU - Vitali, Paolo
AU - Anzalone, Nicoletta
AU - Bernini, Sara
AU - Cotta Ramusino, Matteo
AU - Sinforiani, Elena
AU - Micieli, Giuseppe
AU - Costa, Alfredo
AU - D’Angelo, Egidio
AU - Gandini Wheeler-Kingshott, Claudia A.M.
N1 - Funding Information:
We thank University of Pavia and Mondino Foundation (Pavia, Italy) (supported by the Italian Ministry of Health, RC2014-2017) for funding; The UK Multiple Sclerosis Society and UCL-UCLH Biomedical Research Centre for ongoing support of the Queen Square MS Centre. CG receives funding from ISRT, Wings for Life and the Craig H. Neilsen Foundation (the INSPIRED study), from the MS Society (#77), Wings for Life (#169111), Horizon2020 (CDS-QUAMRI, #634541). This research has received funding from the European Union’s
Funding Information:
Funding. We thank University of Pavia and Mondino Foundation (Pavia, Italy) (supported by the Italian Ministry of Health, RC2014- 2017) for funding; The UK Multiple Sclerosis Society and UCL-UCLH Biomedical Research Centre for ongoing support of the Queen Square MS Centre. CG receives funding from ISRT, Wings for Life and the Craig H. Neilsen Foundation (the INSPIRED study), from the MS Society (#77), Wings for Life (#169111), Horizon2020 (CDS-QUAMRI, #634541). This research has received funding from the European Union?s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2) for the work of FP and ED?A. ECTRIMS and the Multiple Sclerosis International Federation (MSIF) supported the work of GC with funding (ECTRIMS Postdoctoral Research Fellowship Program, MSIF Du Pr? grant).
Publisher Copyright:
© Copyright © 2020 Lorenzi, Palesi, Castellazzi, Vitali, Anzalone, Bernini, Cotta Ramusino, Sinforiani, Micieli, Costa, D’Angelo and Gandini Wheeler-Kingshott.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/1/30
Y1 - 2020/1/30
N2 - Objective: 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 in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 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 (ρ > 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 (p ≤ 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. 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 gray 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, 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.
AB - Objective: 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 in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 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 (ρ > 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 (p ≤ 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. 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 gray 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, 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.
KW - Alzheimer’s diagnosis
KW - brain atrophy
KW - cross-sectional area (CSA)
KW - dementia biomarker
KW - dementia—Alzheimer’s disease
KW - sensorymotor function impairment
KW - spinal cord atrophy
KW - spinal cord toolbox
UR - http://www.scopus.com/inward/record.url?scp=85079500247&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079500247&partnerID=8YFLogxK
U2 - 10.3389/fncel.2020.00006
DO - 10.3389/fncel.2020.00006
M3 - Article
AN - SCOPUS:85079500247
VL - 14
JO - Frontiers in Cellular Neuroscience
JF - Frontiers in Cellular Neuroscience
SN - 1662-5102
M1 - 6
ER -