Mild cognitive impairment (MCI) is considered a transitional state between normal aging and early Alzheimer’s disease (AD) (Lee et al., 2012). Studies have shown that individuals with MCI tend to develop AD at a rate of about 10–15% per year (Allison et al., 2014), but the probability of a healthy elderly to be diagnosed with AD is only 1∼2% (Bischkopf et al., 2002). If MCI is diagnosed at an early stage, through rehabilitation exercise and medication, the incidence of AD can be reduced by nearly one-third (Golob et al., 2007). Thus, early detection of MCI individuals makes it possible to potentially delay or prevent the transition from MCI to AD. The following are MCI clinical conversion criteria: MCI patients can be divided into MCIc and MCInc, depending on whether they become converted into AD patients within a certain period (for instance, the conversion time could be 36 months, 48 months, etc.). Interestingly, the two types of patients have similar clinical manifestations in the early stage, and the morphological differences of their brain lesions are small. To intervene in the diagnosis and treatment of AD disease earlier, the diagnosis and prediction of MCI disease have been studied from multiple perspectives such as genetics, pathology, and medical imaging. Currently, there are different opinions on biomarkers that can accurately reflect the timeliness of preclinical disease progression. However, no research has established the versatility of such markers using prediction/validation study designs. Furthermore, there are defects and difficulties in the diagnosis and classification of MCI disease development. Therefore, finding high discriminative features and establishing a robust classification mechanism is of clinical significance for the diagnosis and timely treatment of MCI diseases, especially the provision of early warning signs for high-risk MCI patients. This may guide the patients to make rational treatment decisions, and thus, even prevent them from developing AD.

Neuroimaging studies of AD patients have found atrophy of structural tissues, and abnormal connections between brain regions in structure and function (Liu et al., 2012; Dai et al., 2019; Zhang et al., 2019). Especially, neuroanatomical abnormalities have been found to spread from one brain area to another based on distinctive network patterns in neurodegenerative diseases (Yates, 2012; Pandya et al., 2017; Cauda et al., 2018). Eskildsen and his colleagues (Eskildsen et al., 2013) classified MCI and AD using cortical thickness features from structural MRI and achieved accuracies ranging from 70 to 76% depending on the conversion time. Taking advantage of the difference in the time dimension of disease, Li and his colleagues (Li et al., 2012) proposed a 4-D disease classification algorithm based on the thickness of the cerebral cortex. The classification of MCIc and MCInc achieved the highest classification accuracy (81.7%). Since most studies have reported abnormal and inconsistent brain connections, many recent studies have used the construction of a classification framework combining brain networks and machine learning to classify MCI\AD. Raamana and colleagues (Raamana et al., 2015) constructed a brain network based on the difference in cortical thickness, by taking the average clustering coefficient, boundary number, and node degree as features, and using a multi-core Bayes classifier to classify MCIc and MCInc with a classification accuracy of 64%. Our previous study (Wei et al., 2016) proposed a classification framework to distinguish MCIc from MCInc by using MRI and network features and attained the best accuracies of 76.39%.

To improve the classification effect, many studies have been dedicated to fusing different types of data, such as MRI, fMRI, positron emission tomography (PET), cerebrospinal fluid (CSF), and cognitive scoring scales. Liu et al. (2014) proposed a new multi-modal classification method combining PET and MRI with an accuracy of 67.83% for the classification of MCInc and MCIc. While Wee et al. (2012b) used multi-core SVM to integrate diffusion tensor image (DTI) and rs-fMRI functional network features to classify MCI and normal elderly people and obtained a higher classification accuracy of 96.3%, which was 7.4% higher than that of single-mode data. Besides, appropriate feature selection (Zuo et al., 2010; Chu et al., 2012) and frequency division (Wee et al., 2012a; Mascali et al., 2015) have also been proven to effectively improve classification accuracy. One of our recent studies (Zhang et al., 2019) supports this view. Essentially, our earlier study distinguished individuals with EMCI and LMCI using a functional brain network of three frequency bands and three feature selection algorithms, during the Resting States, and obtained 83.87% accuracy using the mRMR algorithm in a slow-5 band. Although most previous studies have investigated the utility of the structural MRI or rs-fMRI for classification of MCIc from MCInc, few studies have used cortical and subcortical measurements extracted from DTI/MRI, and graph measures extracted from rs-fMRI, to classify MCIc and MCInc (Mascali et al., 2015; Hojjati et al., 2018). Besides, previous studies only focused on the classification of the different groups of patients, but whether this kind of classification can truly reflect the changes in the structure and function of the brain regions in disease progression remains unverified.

To address these issues, this study aims to: (i) incorporate multiple structural and functional metrics into a combined graph theoretical and machine learning analysis, to evaluate the efficacy of a classification framework to distinguish MCInc/AD from MCIc. (ii) predict the highly sensitive brain regions of AD conversion, by comparing the difference of the brain regions between MCIc and MCInc, with that between MCIc and AD. Firstly, we proposed structural features including MRI features by FreeSurfer and nodal parameters from thickness network, and functional features derived from constructed functional brain network among time series of the brain regions with three frequency bands (full-band, slow-4, slow-5) at Resting State. Subsequently, we established a weighted network by using a kernel function, and then thresholded it to a binary network at a high discriminative range of sparsity from 8 to 44%. In the current study, the SS-LR and mRMR feature selection algorithms build upon our previous work (Wei et al., 2016; Zhang et al., 2019). We employed novel feature selection algorithms (RSFS) to find effective features, and then trained and tested the SVM classifier for classification. We also tested the reliability and stability of the best classification results by applying multiple classifiers (Random Forest, K-nearest neighbor (KNN), AdaBoost, SVM) by upsampling. Finally, we compared the selected top 10 features from the classification of MCInc vs. MCIc and those from the MCIc vs. AD group. Meanwhile, we also investigated the contribution of each modal to the multi-modal classification to explore the conversion of MCI. We hypothesized that the proposed method will improve the accuracy and the sensitivity of identifying prodromal AD, and that the high-sensitivity brain regions of the two classification groups may be inconsistent. To the best of our knowledge, this is the first study that has used cortical thickness, structural brain network, and sub-frequency functional brain network for this classification (MCInc vs. MCIc, MCIc vs. AD). Besides, another innovation of this study is the employment of the intra-subject and inter-subject design to classify the two groups of patients.

In the present study, we used structure-functional MRI and the combined graph theory with multiple machine learning methods to accurately classify patients with MCIc and MCInc/AD. Our findings demonstrated that, by including the cortical thickness features, structural brain network features, and sub-frequency (full-band, slow-4, slow-5) functional brain network features, the proposed method performed effectively in identifying MCIc subjects from MCInc/AD. In the classifications of MCIc vs. MCInc and MCIc vs. AD, the proposed RSFS algorithm achieved the best accuracies (84.71%, 89.80%) compared to other algorithms (Table 2 and Figure 3).

In Table 2, there is a gap between specificities and sensitivities due to the imbalanced data. However, our proposed method obtained the best BAC of 80.61 and 87.81% with the RSFS algorithm. We also compared the performance of multiple classifiers and verified the reliability of our results through upsampling (Supplementary Figure 2). The results indicated that the SVM classifier obtained the best accuracy, and was consistent with the results before upsampling. The balance of sensitivities and specificities has also been appropriately improved. In addition, we observed that the mRMR algorithm achieved 5.26% sensitivity in MCIc vs. AD group compared to other methods as described in Table 2. Actually, as shown in Supplementary Figures 1A,B, the SS-LR algorithm and the mRMR algorithm achieved best performance (84.71% ACC, 73.33% SEN, 90.91% SPE, 83.45% AUC at cost = 27%, K = 4 and 77.65% ACC, 53.33% SEN, 90.91% SPE, 74.45% AUC at cost = 8%, K = 20, respectively) in MCInc vs. MCIc group. The SS-LR algorithm and the mRMR algorithm achieved the best performance (71.43% ACC, 42.11% SEN, 90.00% SPE, 70.53% AUC at cost = 36%, K = 2 and 71.43% ACC, 52.63% SEN, 83.33% SPE, 70.35% AUC at cost = 33%, K = 12, respectively) in MCIc vs. AD group.

As illustrated in Tables 8, 9, the classification results obtained by the combination of sMRI and rs-fMRI in the present study are better than those of the unimodal (sMRI\rs-fMRI) approach, including those of our previous studies (Wei et al., 2016; Zhang et al., 2019). Meanwhile, we also compared the classification performances with other studies. Most previous methods that constructed brain networks only considered structural or functional features (Suk and Shen, 2014; Hu et al., 2015; Moradi et al., 2015; Raamana et al., 2015; Ardekani et al., 2016; Suk et al., 2016; Beheshti et al., 2017; Hojjati et al., 2017, 2018; Zheng et al., 2019; Gupta et al., 2020; Zhu et al., 2021), and obtained an accuracy lower than that of the present study. Only Hojjatia’s study (Hojjati et al., 2017) used graph theory and machine learning approach (mRMR, FS) to classify rs-fMRI and obtained a classification accuracy of 91.4%. However, the sample size was too small (<20), and the effect was not widely representative. Besides, the studies in Table 8, Zhang and Shen (2012) used a multi-modal multi-task learning algorithm to fuse MRI, FDG-PET, and CSF data and regressed the MMSE and ADAS-Cog scores to classify MCInc and MCIc with a classification accuracy of 73.9%. Similarly, Cui et al. (2011) combined MRI, CSF, and cognitive scoring scale features to classify MCInc and MCIc with a classification accuracy of 67.13%. Ye et al. (2012) used sMRI, ApoE, and cognitive scores to classify MCIc and MCInc using a smooth selection method based on sparse logistic regression, and obtained good classification results of 0.859 AUC. Therefore, these results may suggest that the method we have proposed could effectively help predict the conversion to Alzheimer’s disease.

Different from the previous studies, our research not only focused on the brain regions’ conversion sensitivity of the two groups of patients (MCIc vs. MCInc), but also studied the conversion sensitivity of the brain regions of the same group of patients (MCIc vs. AD). Tables 6, 7 and Figure 4 list the highly sensitive brain regions selected from the two groups. These results proved the inconsistency of the selected brain regions in the two classification groups. As shown in Table 6, there were 30% structural features, 20% structural connectivity network features, 50% functional connectivity network features. The proportion of functional connectivity network features in each frequency band is listed as follows: 1(full-band):1(slow-5):3(slow-4). In Table 7, all features came from the functional network and the proportion of the three frequency bands was 3(full-band):3(slow-5):4(slow-4). Moreover, it is worth noting that 70% of features came from betweenness centrality. Our results suggest that the betweenness centrality in a functional network carries more disease information and the top 10 selected features are more sensitive to more efficient classification for MCIc and AD. According to Tables 6, 7, it can be seen that the network parameter characteristics of all frequency bands from rs-fMRI have been selected. However, the cortical surface area (CS) was not selected for the top 10 features in two classification groups by three algorithms. More importantly, in Wei’s work (Wei et al., 2016), the selected top 10 combined structure features did not include CS. Based on the above results, we consider that CS is not an effective marker for AD disease. In future work, we will assess whether it can be excluded from the feature set. Different from our previous work on EMCI and LMCI classification (Zhang et al., 2019) the characteristics of the slow-5 band did not show high sensitivity in MCInc and MCIc classification. The reason may be that the former is mainly based on the degree of memory impairment of MCI disease, and the latter is based on the longitudinal time diagnosis status to classify whether MCI develops into AD. Therefore, we suggest that the difference in their brain activity may be reflected in different frequency bands.

Our findings converge nicely with what has been suggested by the previous studies (see Results Section), and these selected brain regions have been shown to be related to MCI conversion. The important roles of several brain regions in MCI disease have been widely recognized. Braak and Braak (1991) used structural magnetic resonance imaging (sMRI) to study AD patients. They first discovered a large number of neurofibrillary tangles in the medial temporal lobe, and the brain areas involved mainly included the olfactory cortex, hippocampus, and parahippocampal gyrus, amygdala, and cingulate cortex area, which is consistent with the conclusion that the brain atrophy of AD or MCI patients are mainly located in the medial temporal lobe (Fan et al., 2008; Das et al., 2015). In line with the previous studies (Khazaee et al., 2015; Wang et al., 2015; Pusil et al., 2019), we also found that the left calcarine fissure and the surrounding cortex are associated with MCI conversion to AD. Damage to this brain area may cause central visual diseases (such as macular avoidance and hallucinations). Studies have reported that visual impairment can affect patients’ cognition, thereby increasing the risk of dementia (Uhlmann et al., 1991; Naël et al., 2019). Besides, the top 10 highly sensitive features provided by the other two algorithms are also listed in the Supplementary Material (Supplementary Tables 3.1–3.4). Although the sensitivity was lower than that of the RSFS algorithm, the selected top 10 highly sensitive features are also important to brain areas related to AD disease. It shows that the classification framework of graph theory and machine learning methods considering structural and functional MRI provides a new view for improving MCI clinical prediction and diagnosis. Moreover, our findings suggest that the inconsistency of the selected brain regions between the two classification groups requires more attention. The transformation of MCI disease may imply that the structure of the brain area changed in the early stage of AD, and the function of the brain area later began to degenerate. Inconsistency of the brain regions obtained by the two classification groups indicates that the conversion sensitivity brain regions of the two group patients (MCInc vs. MCIc) and the same group patients (MCIc vs. AD) may be different, which further suggests that the classification between the different groups of patients provides limited information. For the follow-up within a group, it may be more meaningful for the study of diseases.

In the current study, the best performances achieved with costs of 39 and 19% based on MCInc vs MCIc group MCIc vs. AD group, respectively. The cost was defined as the ratio of the number of above-threshold edges to the total number of edges in a network. Cost range can be defined from 0 to 1, but the upper limit is generally less than 50% (Tan et al., 2019). Compared to cost = 19%, cost = 39% is the low threshold. Compared to the MCIc vs. AD group, the MCInc vs. MCIc group can be distinguished when the cost is large and there are more edges in the network. Refer to the study of Jie et al. (2014), as the threshold increases, weak connections and unimportant connections are removed, and significant differences are found between different groups of patients. Therefore, we suggested that the best classification performance of the two classification groups at different costs is due to the different topological properties of the brain network. Specifically, the larger the cost, the higher the global and local efficiency, the higher the clustering coefficient, the lower the characteristic path length, and the lower the small-world attributes (Zhang et al., 2019). The difference between brain network parameters is significant, and the topological characteristics of brain regions can be better distinguished. In the future, we will investigate the specific differences in the brain network characteristics of different groups of patients, and combine their clinical scales for predictive analysis.

However, this study has several limitations. One major limitation is the small sample size. Another limitation is the imbalanced data. Despite the promising results of using the RSFS algorithm and the SVM to screen patients with MCIc, further data collection is required to test the generalizability of the method to other patient populations. In future studies, a larger sample should be collected, and the number of subjects balanced as the scale of the ADNI database is expanding (Aisen et al., 2010). Furthermore, future studies should attempt to explore different methods of classification in different stages of AD, including the interpretability of structural and functional brain abnormalities (Ibrahim et al., 2021). The versatility in multiple data sets will be necessary to validate the robustness of the models. For the study of the topological properties of the brain, Power-264 brain regions might be considered as a template for constructing brain networks. In addition, other well-known prognostic information (DTI, ApoE status, Tau/Amyloid/FDG-PET) will be considered for classification (Gupta et al., 2020; Fan et al., 2021). In terms of subject design, we believe that the follow-up data within the subject can better reveal the brain area where the sensitive characteristics of the transformed biomarker are located. The limitation is that the data sample size is too small. If there are subjects who can collect follow-up data through cognitive training (Hernes et al., 2021) and set a baseline control at the same time, more meaningful and reliable results may be obtained.

The present study investigated the predictive power of cortical thickness features and brain connectivity network features derived from the sMRI and rs-fMRI to identify individuals with MCI from MCInc/AD for the first time. For the selection of subjects, we proposed a mixed-subject method with an inter- (horizontal) and intra-subject design (longitudinal, follow up), which is rarely used in AD classification. In this classification framework, multiple modalities integration was achieved by using graph theory and machine learning algorithms. We found that this framework improves the classification performance of identifying precursor AD (MCIc), and the high-sensitivity features derived with two classification groups are inconsistent. These findings indicate that the converted sensitivity brain regions of the two groups of patients (MCInc vs. MCIc) and the same group of patients (MCIc vs. AD) may be different, which further indicates that the former way of classifying two different groups of patients may provide limited information. Ultimately, such a classification framework integrating information from sMRI and fMRI can effectively predict the conversion of MCI, and different brain regions obtained in this framework from inter-subject and intra-subject design are probably diagnostic markers for AD.

Publicly available datasets were analyzed in this study. This data can be found here: adni.loni.usc.edu.

The studies involving human participants were reviewed and approved by Ethics approval and informed consents were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The patients/participants provided their written informed consent to participate in this study.

TZ: roles/writing – original draft, conceptualization, investigation, methodology, writing – review and editing. QL: resources. DZ: investigation. CZ: software. JY and RN: writing – review and editing. JZ and ZJ: funding acquisition and writing – review and editing. LL: conceptualization, supervision, funding acquisition, project administration, and writing – review and editing. All authors reviewed the manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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