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Cervical spinal cord atrophy and Alzheimer’s disease

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.


Introduction
Dementia is one of the most debilitating cognitive neurodegenerative disorders affecting the central nervous system in elderly people and having a significant impact on daily life activities. Moreover, with an ageing population the incidence of dementia is growing. Clinically, several forms of dementia-like diseases that differently impair multiple cognitive and behavioral domains are defined. Alzheimer's disease (AD) is the most common cause of dementia and it is responsible for 60% to 80 % of cases worldwide 1 .
It is known that AD is associated with an extracellular deposit of β -amyloid plaques in the brain and cerebral vessels, but also to the presence of intracellular neurofibrillary tangles, which appear like paired helical filaments with hyperphosphorylated tau proteins. Tau tangles have been identified as the cause of cortical neurons' degeneration while β -amyloid oligomers have an important role in synaptic impairment, hence β -amyloid plaques deposition is suggested to raise later during the AD progression 2,3 .
This neuronal degeneration explained by pathophysiology leads to macroscopic atrophy of specific brain structures, such as the hippocampi and the medial temporal lobes 4 , which can be detected using Magnetic Resonance Imaging (MRI) techniques. Indeed, several MRI studies have demonstrated significant atrophy of white matter, gray matter and specific brain structures such as the hippocampi, thalami and amygdalae in AD patients suggesting that these structures are informative in identifying dementia disorders 5,6 . Indeed, hippocampi have been proposed as in vivo non-invasive imaging biomarkers of AD while other structures may be useful in distinguishing between different subtypes of dementia 7 .
Recently, numerous MRI investigations have tried to identify new in vivo biomarkers for dementia and other neurological diseases. Optical Coherence Tomography studies have been used to assess that retinal ganglion cell degeneration can be associated to early stages of AD. Previous studies of other diseases associated with neurodegeneration, such as multiple sclerosis 8 , amyotrophic lateral sclerosis 9 , and spinal cord injury 10 , have revealed that atrophy of the spinal cord is indicative of widespread alterations of the central nervous system and might be considered as a relevant imaging biomarker in a wider range of neurodegenerative diseases. Nevertheless, this kind of alteration has never been investigated and reported in dementia patients. Hence, the main aim of the present work was to assess for the first time -to the best of our knowledge-whether spinal cord volume is significantly reduced in AD patients compared to healthy controls (HC), hypothesizing that the neurodegeneration typical of AD significantly spreads to all components of the central nervous system. Furthermore, in case of a positive outcome, it would be important to quantify the role of spinal cord features in distinguishing between AD and HC to drive design of future studies. We propose to do so by a machine learning approach for features selection, that is increasingly applied to improve diagnostic accuracy by quantitative imaging 11,12 .

Subjects
A total of 66 subjects including 31 AD patients and 35 HC, as a reference group, were analyzed. 7 subjects (4 HC and 3 AD) were excluded from the study due to post-processing issues. The study was accomplished in accordance with the Declaration of Helsinki and with the approbation of the local ethic committee of the IRCCS Mondino Foundation, upon signature of the written informed consent by the subjects.

Spinal Cord analysis
For each subject, the 3DT1-w volume was resized removing the brain and centering the FOV on the spine. Once a single volume of interest(VOI) comprising the same spinal cord regions for each 3DT1-w was defined(matrix=176x240x96 voxels), the process was automatized for the whole dataset. The resized 3DT1-w volumes were analyzed with the Spinal Cord
WM, GM and all other brain structures volumes were calculated in mm 3 . Total intracranial volume, as the sum of WM, GM and CSF, was also calculated to account for different brain sizes.

Machine learning analysis
Classification between AD and HC was performed using a machine learning approach implemented with Orange(https://orange.biolab.si/).
A total of 22 features were extracted from the above MRI morphometric analysis. Given the large number of parameters extracted compared to the sample size of our AD and HC groups, a feature reduction approach was adopted in order to control for overfitting issues. The Spearman correlation coefficient 25 was obtained in Matlab between pairs of all calculated metrics. When pairs of metrics had a correlation coefficient greater than 0.7, one metric was kept while the other was eliminated.
Ranking was implemented with the ReliefF algorithm 26 on the uncorrelated features to identified the best subset able to classify AD from HC, and particularly to investigate the contribution of spinal cord metrics to the task. In order to identify a unique subset of features, 30% of instances was employed for ranking. Data were normalized by span to avoid a polarization of the results due to the different scale of features, as for WM compared to CSA. The remaining 70% of instances was further divided into 70% for the Random Forest algorithm application and 30% to test its ) and specificity(Spec= ), using the previously-identified best features.
Among several machine learning algorithms, RF was selected for its robustness against a reduced number of input features and the capacity to weight features runtime, providing features relevance in a classification task 27,28 . The Receiving Operating Characteristics (ROC) curve was then obtained to visually discriminate between AD and HC and the Area Under the Curve was also calculated to quantify the overall ability of RF to discriminate between AD and HC.

Statistical analysis
Statistical tests were performed using the Statistical Package For Social Sciences (SPSS) software, version 21 (IBM, Armonk, New York). All continuous data were tested for normality using a Shapiro-Wilk test 29 . Age and MMSE were compared between AD and HC using a two-tailed Kruskal-Wallis test 30 while gender was compared using a chi-squared test 31 . A multivariate regression model with gender, age and total intracranial volume as covariates was used to compare all morphometric metrics between AD and HC. Two-sided p<0.05 was considered statistically significant.
Furthermore, to assess the power of the best features in explaining the variance of the MMSE, a linear regression model was implemented using the MMSE score as the dependent variable and the best features as predictors. These independent features were used in two ways: i) each predictor was used alone to determine its specific contribution to MMSE; ii) all features were used in a backward approach to identify which of them explained the greatest percentage of MMSE variance. A threshold of p<0.01(two-tailed) was considered statistically significant.

Subjects
Population demographics and neuropsychological scores are reported in Table 1. Significant differences were found in MMSE between HC and AD patients.

Morphometric changes in AD patients
All results are reported in Table 2 and Table 3. AD patients compared to HC showed atrophy in all brain structures. Moreover, all patients for all investigated spinal cord segments showed reduced CSA at all vertebral levels, while CSV was significantly reduced only in correspondence of vertebrae C1 and C2.

AD classification based on morphometric data
Results of the correlation analysis are reported in Figure 3, and show that brain volumes are not significantly correlated with any spinal cord metrics.
Features that were considered independent from each other and that were entered in the feature selection analysis are reported in Table 4. The best features selected by the RF algorithm for the AD versus HC classification task are reported in Table 5   Moreover, our results indicate that the spinal cord CSA23 contributes significantly to discriminate between HC and AD patients. Usually, only atrophy of brain regions is investigated in dementia 39,40 but our findings reveals that a better discriminative power is achievable combining information of both brain and spinal cord structures. In light of the only animal model study reported to date 35 , which shows that C2-C3 is selectively affected by greater morphological alterations our results become of significant value.
It is well known that hippocampal atrophy is a key feature to detect dementia and is considered a biomarker of AD progression 41 ; our results are consistent with this as both hippocampi are picked up as top features for the classification task. Moreover, it has now been recognized that other brain structures play a key role to identify AD patients and to distinguish between different subtypes of dementia 7 . Our results are, indeed, indicating that patients present significantly different brain volumes with respect to HC, and all segmented brain structures, except for the right amygdala, are statistically significantly atrophic in AD. In this context, though, our work demonstrates that volumes of all cervical vertebral segments are reduced in AD, with only the CSV of the first and second vertebrae being significantly atrophic with respect to HC. These results are coherent with results obtained for cerebral structures and suggest the existence of a remarkable reduction in the volume of spinal cord in dementia. This hypothesis is further supported by significant CSA reduction for all vertebrae in patients. Moreover, the fact that CSA23 is particularly sensitive to pathological changes in AD is in accordance with other studies in neurodegenerative diseases such as amyotrophic lateral sclerosis 9 and could be seen as a corroborating evidence of significant correlation between spinal cord atrophy and neurodegeneration. Considering that the 5xFAD animal study showed that there are significant alterations in myelin microstructure, uncorrelated with the amyloid deposits, one has to be careful in interpreting these findings as purely due to axonal loss induced by Wallerian degeneration. It is important to assert that our findings reveal the importance of spinal cord changes in the study of dementia and suggest that CSA23 should be considered as a contributing in vivo imaging biomarker to distinguish between dementia patients and HC.
Few recent studies have combined several MRI findings with machine learning approaches to attempt the classification of dementia subtypes and prediction of disease progression. Accuracy of about 80% 42,43 was achieved when AD and HC were classified while more fluctuating results were reported when more subtypes of dementia were considered. In the present study a RF algorithm with the "leave-one-out" approach was chosen to discriminate between AD and HC because RF is robust with small numbers of subjects and performs features weighting runtime with good sensitivity and specificity. Our findings has shown that spinal cord morphometric measures (CSA and CSV) alone cannot directly discriminate between AD and HC, but CSA23 was identified as one of the six best features useful to distinguish between these groups of subjects. Classifier accuracy was good and reached its best performance, around 76%, when both volume of brain structures, such as LHip, RHip, WM and GM, and CSA23 were included in the classification procedure. In addition, the ROC curve between AD and HC (shown in Figure 4) reported high performance with AUC of 86%. The RF sensitivity and specificity, of 74% and 78% respectively, showed a remarkable ability in correcting identify healthy and pathological cases. Furthermore, the RF feature weighting (reported in Table 6) demonstrated that CSA23 had a considerable weight in the classification procedure, for example higher than GM, highlighting that it should be considered as an additional biomarker together with the more conventional volumes of subcortical regions 31 .
These results indicate the yet unexplored potential influence that spinal cord features can play in the diagnosis of dementia.
Finally, our data shows that also clinical aspects of AD are partially explained by spinal cord atrophy. Given the exploratory nature of the present study, in absence of specific motor and sensory tests, it was decided to assess whether spinal cord atrophy could be correlated with the variance of the MMSE, which is a global test, clinically used to assess AD severity. Indeed, 43% of the MMSE variance was explained with a multiple regression model implemented with all the best features included as independent variables, whereas CSA23 alone explained 13% of the MMSE variance.    Pathological class (AD = 1) was considered as the target class. The curve shows higher performance (bold red line) than the majority algorithm (diagonal). TN rate is the rate of true negative and FP rate is the rate of false positives.