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Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics

Abstract Background Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable. Method We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains. Results The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu. Conclusion For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder.

clinical situation. So, clinicians need to combine the patient's head structure imaging and other more objective examinations for comprehensive evaluation and judgment. Current studies have found that structural and morphological changes in the brain are closely related to cognitive decline (McInnes et al., 2017). An increasing number of studies have found that brain atrophy is closely related to cognition, and many characteristics become predictors of cognitive states.
Brain atrophy refers to reduced brain capacity due to the loss of nerve cells. It has some signs from thinner cerebral gyrus, wider, and deeper cerebral sulcus in neuroimaging. At present, it is generally believed that brain atrophy can lead to cognitive impairment such as aphasia, memory, and executive dysfunction. It occurs 10 years before cognitive impairment is detectable (Tondelli et al., 2012), and it is accompanied by the whole process of human brain aging and in different diseases. There are various patterns of brain atrophy, the whole-brain atrophy pattern, the localized pattern atrophy, or the cortical and subcortical atrophy pattern. The correlation between atrophy pattern and dementia type also has great heterogeneity. Brain atrophy patterns do not follow the same trajectory in each type of dementia, such as Alzheimer's disease (AD), dementia with Lewy bodies, frontotemporal dementia, and vascular dementia (C. H. Li et al., 2020;B. Zhang et al., 2021). At different stages of the diseases, the pattern and progress of atrophy are also controversial. Even in the same type of dementia, the location and degree of brain atrophy have great heterogeneity in different patients (Poulakis et al., 2018). At present, it is still necessary to explore the correlation between the features of brain atrophy and cognitive impairment and discover the essential features to establish the prediction model of cognitive impairment.
There are many ways to obtain the features of brain atrophy.
Most studies collected brain atrophy feature information from highresolution 3D sequences. However, they always base on scientific research needs and in small-scale populations. The technical bottleneck of analysis is high and not universal. Clinical brain imaging examination is convenient and efficient, which is one of the necessary examinations for the etiological diagnosis of patients with cognitive impairment. Based on years of clinical exploration, clinicians have formed several simple, feasible, and relatively objective visual rating scales for brain atrophy assessment (Cho et al., 2009;Del Brutto et al., 2015;Koedam et al., 2011) and multiple linear measurement ratios (Uribe-San-Martín et al., 2018;Y. Zhang et al., 2008). However, these assessment ways are based on the specific type of dementia and a single specific assessment scale which was not comprehensive enough. Therefore, it is necessary to integrate the brain atrophy features of various assessment programs to explore the most closed brain atrophy indicators to establish dementia or cognitive domain models.
Previous studies focused on the relationship between brain atrophy and the severity of dementia, but few studies explored the relationship between brain atrophy and overall cognitive function, and the relationship between brain atrophy and the cognitive domains of word-fluency, execution, language, and memory, and there are great controversy among these results. Oosterman et al. (2012) found that patients with severe medial temporal lobe atrophy had worse executive function. Other studies found that corpus callosum atrophy rather than medial temporal lobe atrophy in AD patients was significantly associated with executive dysfunction (Meguro et al., 2003). Some studies found that medial temporal lobe atrophy was more associated with situational memory or language dysfunction than executive dysfunction (Pleizier et al., 2012;. Therefore, the correlation between brain atrophy ratio and various cognitive domains still needs further exploration. In this study, we plan to integrate multiple highdimensional brain atrophy features of visual assessment and linear measurement, but routine statistical analysis has limitations in doing so. With the development of artificial intelligence and the great advantage of machine learning for multidimensional data processing, feature modeling based on multidimensional clinical data can potentially reveal the causal relationship between clinical features and diseases, which has been widely used to establish disease diagnosis and prediction models (Deo, 2015). Therefore, it is very meaningful to extract more comprehensive features of brain atrophy and to explore the predictive efficacy of dementia, cognitive function and memory, word-fluency, execution, and visualspatial ability disorders to guide clinical diagnosis and treatment.
In this study, in order to establish a screening model that can be convenient and feasible for clinician, we extracted multidimensional brain atrophy characteristics based on the clinical MR data of nonstroke elderly patients and used machine learning method (Luo et al., 2016) to comprehensively explore the correlation between brain atrophy ratio and dementia, subcognitive domain, and general cognitive function.

Inclusion and exclusion criteria
Elderly (

Clinical data collection
The general information of all subjects, including gender, age, and edu-    each, and the sum of each item was the total CSVD score, 0-4 points.

Consistency assessment
All the grade indexes and linear measurement indexes of brain atrophy were completed by two neurologists with systematic training and ensuring that intra-class correlation coefficient (ICC) > 0.8, p < .01.

Statistical analysis
All statistical analyses were completed based on software R 3. 6. 3 version. Demographic data were calculated as means ± SD or quartile IQR.
Statistical analyses were used the t-test or Wilcoxon rank sum test.
Spearman or Pearson correlation coefficient was used to analyze the correlation between brain atrophy index and cognitive function. The brain atrophy data and demographic data (age, gender, and education level) of all subjects were divided into 4/5 training set and 1/5 test set through the calculation of machine learning by R caret. The fivefold cross-validation training models were used, and the following five After adding points on each independent variable to the total score, it is converted to the probability of predicting dementia or overall cognitive impairment and subcognitive domain impairment. ROC curve is drawn to evaluate the accuracy of the model, and the calibration curve is used to verify the model.

Comparison of general information
There was no significant difference between the dementia group and the normal cognitive group in age, gender, education years, WMH, EPVS, CMB, lacunar infarction, and CSVD total score (Table 1).

Results of correlation analysis between brain atrophy and cognition
From the graph, the general cognitive function and subcognitive domain were correlated with multiple brain atrophy indexes. The score of MMSE, immediate memory, word fluency, and visualspatial function negatively correlated with temporal horn ratio (p < .01) and positively correlated with the genu of corpus callosum (p < .01). The positive correlation coefficient between executive function and temporal horn ratio was the largest (r = .351, p < .01), and the negative correlation coefficient between executive function and the genu of corpus callosum was the largest (r = −.289, p < .01). The detailed results are shown in Figure 1.

3.3
Results of dementia prediction model based on machine learning algorithm

Prediction results of general cognition and subcognitive domains based on machine learning algorithm
When predicting the overall cognitive impairment, the SVM model

Predictive efficacy of dementia, general cognition, and subcognitive domains
Construction of nomogram to predict the overall cognitive impairment: Based on the important feature distribution of the random forest

DISCUSSION
It is urgent to establish a convenient screening tool for dementia, and brain atrophy is closely related to human brain structure and advanced cognitive function, and it is widely used as an important factor in dementia screening model. Previous studies mostly established prediction models based on high-resolution structural images of the whole brain or magnetic resonance data of diffusion tensor imaging of 3DT1 (Lee et al., 2018;Savarraj et al., 2021;Tam et al., 2019).
However, the acquisition of these features was very complicated and was not suitable for clinicians. Therefore, based on clinical routine magnetic resonance sequence, we used visual assessment and simple linear measurement features to establish the prediction model of dementia, general cognitive function, and memory, language, execution, and visuospatial domain. The model was verified by internal data and established nomogram to predict dementia, general cognitive dysfunction, and cognitive domain dysfunction in the individual level.

Dementia prediction model
Previous dementia prediction models are mainly based on the demographic characteristics (Hou et al., 2019;Kivipelto et al., 2006) and neuropsychological assessment scale characteristics. Due to the close correlation between dementia and brain structure and function, models established by clinical features combined with imaging features were more explanatory and high prediction efficiency. Some studies have found that brain atrophy played an important role in predicting dementia (Frenzel et al., 2019;Kivipelto et al., 2006). Grassi and Frenzel et al. established model in which sensitivity and specificity could reach more than 0.8 by incorporating cortical atrophy index into multimodal MRI feature and other features (Frenzel et al., 2019;Grassi et al., 2018). Through the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, Tam et al. used machine learning method to extract characteristics of multiple subtypes of brain atrophy and neuropsychological measurement scores to establish prediction model for the conversion of MCI patients to AD within 3 years. The study found that compared with MMSE score alone, the combination of MMSE score and multimodal structural brain atrophy data could improve the prediction accuracy (Tam et al., 2019). However, it is difficult to illustrate clearly on the brain atrophy features extracted based on machine learning methods, and obtain 3D brain structure scanning data for clinicians. In some studies, models established based on clinical visual scale and linear measurement data also had excellent sensitivity and specificity. These features included temporal horn ratio, corpus callosum genu thickness, suprasellar cistern ratio, and so on. Studies varied in the extraction of brain atrophy features. Among them, temporal horn ratio, suprasellar cistern ratio, and corpus callosum genu thickness were used as predictors to establish prediction models in most researches (Frenzel et al., 2019;Grassi et al., 2018;Hou et al., 2019;Kivipelto et al., 2006;Savarraj et al., 2021). Some studies found that cella media index was significantly different between AD and normal elderly (Chrzan et al., 2019). In this study, the XGBoost machine learning model predicted dementia with AUC of 0.8, and we found that temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu were selected as the atrophy characteristics of dementia.
In this study, our prediction model is essentially a classifier for dementia, and four major cognitive domain disorders are based on cross-sectional data. However, many other studies are based on the prediction model established by similar methods (Shigemizu et al., 2019;Shimoda et al., 2021), which can be used for dementia screening rather than accurate diagnosis. Considering the sample size, we did not make a more detailed classification of dementia types. The common types are AD, vascular dementia, and mixed dementia. However, according to recent studies (Reith & Mühl-Benninghaus, 2015), the current distribution of dementia types is more coincident with that in clinical practices. We constructed a dementia screening model based on clinical practicability for the community elderly because precise classification diagnoses are difficult for community doctors.
The study found that education was closely related to cognitive impairment (Wilson et al., 2019). Stern (2006) proposed that education level was an important factor affecting cognitive reverse (CR). Some studies believed that education could improve CR, thereby enhancing individual resistance to cognitive impairment and coping with cognitive decline caused by age (Brayne et al., 2010). Through the analysis of the importance of features, this study found that education is more important in model establishment. Thus, it is included as one of the predictors. We combined the important features of brain atrophy with educational to establish dementia prediction model, AUC reaching to 0.8.

Prediction model of general cognitive dysfunction and subcognitive domain dysfunction
At present, few studies were involved in the models of general cognition and subcognitive domain disorders based on clinical brain atrophy features, among which most were cohort studies. A study based on 3D whole brain volume MR data found that all brain volume and local brain volume were closely related to the decline of overall cognitive function (Jokinen et al., 2020). Another study found that medial temporal lobe atrophy and subcortical atrophy could independently predict memory function and executive function decline (Jokinen et al., 2012). The atrophy of the hippocampus and olfactory cortex can predict the decline of memory function (Stoub et al., 2010). A 10-year follow-up study of 60 healthy elders found that hippocampal volume could predict changes in executive function (Aljondi et al., 2019). A few studies extracted the nondementia population over the age of 70 (n = 921) for 12 years of follow-up by using visual scale assessment of brain atrophy (frontal lobe, parietal lobe, temporal lobe, and occipital lobe), lacunar, and WMH to establish a model. The study found that cortical atrophy was relative to cognitive decline (Sacuiu et al., 2018). Other studies found that frontal lobe atrophy could predict decreased word fluency (Söderlund et al., 2004). Our study also found a correlation between brain atrophy and cognition, which could be used to predict general cognitive function, memory, execution, language, and visualspatial subcognitive domains. However, these studies were conducted by cross-sectional or follow-up analysis based on partial brain atrophy data or 3DMRI data.
Among them, there were few studies on the prediction of cognitive dysfunction by combining multiple brain atrophy indicators. Our study only selected multiple and relatively comprehensive measurable brain atrophy data to establish models and without any neuropsychological scale. Our models' prediction performance in internal verification were acceptable. Moreover, we also established nomogram of general cognitive impairment and subcognitive domain impairment on the individual level, with AUC reaching to 0.8.
The machine learning methods applied in this study were all from the methods recommended in the biomedical guidelines (Luo et al., 2016). In this study, the machine learning model of XGBoost showed its best prediction effect for dementia. Since XGBoost supported parallel and distributed computing, it runs fast and can adjust multiple hyperparameters, which can effectively reduce overfitting (Shimoda et al., 2021). Moreover, the XGBoost algorithm also can standardize the regularization term to prevent the model from overfitting. Finally, the XGBoost algorithm model used can automatically screen the importance of predictive variables while ignoring the interference of irrelevant variables, which greatly improved the effectiveness of our research (Wang et al., 2022).
This algorithm also has good prediction results for single cognitive domain-word fluency. In the prediction of executive function and visual space function, the prediction results of logical regression are better.
The L2 regularized logistic regression used in this study can better solve the problem of multiple collinearities. GBM has a good prediction effect in predicting single cognitive domain-immediate memory, which may be related to the robustness of GBM for data and can effectively deal with multidimensional data.
Certainly, limitations exist. (1) This study is a single-center study, which still needs larger multicenter data to expand the sample size, so as to train more optimized models. (2) The dementia screening model established in this study had good predictive effect in internal validation and could continue external validation in follow-up cohort or other cohorts. (3) This study established a two-class prediction model based on normal cognition and dementia. We need to further collect data to establish a three-class prediction model of normal cognition, MCI, and dementia. (4) The brain atrophy evaluation index selected in this study was a linear measurement index. Although it was practical, more objective brain atrophy evaluation indexes based on clinical MRI needed to be developed.

CONFLICT OF INTEREST
The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT
The data that support the findings of this study is available from the corresponding author, upon reasonable request.

PEER REVIEW
The peer review history for this article is available at https://publons.