Mild cognitive impairment (MCI) is a heterogeneous syndrome defined as deficits on neurocognitive testing but without significant damage to activities of daily living (ADLs) (Winblad et al., 2004). Objective evidence cognitive or functional decline, labeled “subjective cognitive decline,” does not always accompany subjective awareness of cognitive impairment (Tao et al., 2021). MCI reflects that cognitive function deteriorates in neuropsychological testing, but ADL is intact relatively (Petersen, 2006).

One characteristic of MCI is heterogeneous, which means that about 5–15% of the patients are at the risk of getting dementia every year. At the same time, about half of the patients keep stable at 5 years, and symptoms sort out over time in the minority (Dunne et al., 2021). The collaboration of Cohort Studies of Memory in an International Consortium (COSMIC), which spread standard diagnostic criteria to estimate the prevalence of MCI more reliably across different regions, confirmed the prevalence of MCI in adults over 60 years up to 6% across 11 studies (Sachdev et al., 2015).

The guideline date updated by the American Academy of Neurology estimated a prevalence of 6.7% in 65–69-year-olds and 25% for 80–84-year-olds (Livingston et al., 2017). Jia et al. estimated that overall MCI prevalence was 15.5% in China, representing 38.77 million people (Jia et al., 2020). Consistently, 10 million new instances of dementia are enlisted, and it is assessed that 13,500,000 individuals are associated with the risk of dementia by 2050 (WHO., 2022). It presents an opportunity for reducing vascular risk and altering behavioral patterns when a diagnosis of MCI is made. Given diagnostic and therapeutic practice variation, things could change if a more definitive diagnosis is available. Therefore, standard national guidance is eagerly needed for using neuroimaging in MCI (Gillis et al., 2019; McWhirter et al., 2020). There is a need for precise prognosis methods to ensure that the diagnosis of MCI is a possibility for people to prevent the risk of Alzheimer's disease (AD). To improve the specificity and sensitivity of MCI diagnosis, we need cognitive testing and neuroimaging with more evidence.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging tool to detect brain activity (Biswal et al., 1995; Smitha et al., 2017). As shown in previous research, rs-fMRI has been confirmed as a reliable instrument to explore the brain's mechanism, reflecting the signature of the brain's neural network (van den Heuvel and Hulshoff Pol, 2010; Pan et al., 2021). Regional homogeneity (ReHo) is a test-retest method to detect the characteristic of brain connectome (Biswal, 2012; Cheng et al., 2019; Geng et al., 2019; Hou et al., 2021). The support vector machine (SVM) is among the most popular methods in the machine learning (ML) that has exceeded practical neuroimage analysis in the past 20 years (Orru et al., 2012; Pisner and Schnyer, 2020). SVM has been applied in different fields and addresses various classification problems because of its relative simplicity and flexibility (Gori et al., 2015; Cheng et al., 2021; Shan et al., 2021; Gao et al., 2022). There have been hundreds of studies using ML to accurately classify patients with heterogeneous mental and neurodegenerative disorders, which make improvements in the early diagnosis.

In brain disorders research, SVMs are deployed by multivoxel pattern analysis (MVPA) and solve plenty of clinical problems because of their relative simplicity. Even if data are high dimensional, there is a lower risk of overfitting in SVMs (Zhang et al., 2020). Recently, SVMs have been applied in precision medicine. SVM analysis can predict the diagnosis and prognosis, particularly for patients with brain diseases, such as Alzheimer's (Franzmeier et al., 2020; Xie et al., 2022), schizophrenia's (Chand et al., 2022; Lei et al., 2022), and depression's (Al-Hakeim et al., 2019; Bone et al., 2021). The results would make improvement in the diagnostic accuracy.

More research participation is required to increase the investment in neuroimaging biomarkers to achieve this goal. To determine whether there are distinct or specific alterations in MCI that can be used to differentiate MCI from healthy controls, we examined specific or distinctive alterations in MCI. The study hypothesized that patients with MCI have abnormal ReHo in the brain region and the abnormal region serves as a biomarker to aid in diagnosing and prediction of MCI.

Mild cognitive impairment could be a prodromal stage of dementia. Over the past decade, neuroimaging has gained increasing attention as a predictor of mental disorders (Grueso and Viejo-Sobera, 2021). The association between MCI and right caudate was assessed in fMRI comprising 53 MCI patients with a range of demographic characteristics, and HCs were matched to patients. As shown in the results, when compared with the HCs, patients with MCI had poorer scores in all cognition tests, such as ADAS-Cog test (p < 0.05), HIS (p < 0.05), CDR (p < 0.05), and MoCA (p < 0.05). There was no significance in the ADL Scale and HRSD between the two groups (p > 0.05). Patients with MCI at times will ultimately advance to dementia, yet the cognitive symptoms have not ultimately shown.

Compared with HCs, the ReHo of patients with MCI in the right caudate is lower than HCs. The caudate nucleus plays a critical role in various higher neurological functions. It is a region that is not only in executive functioning but also related to learning, memory, motivation, and emotion (Fisher et al., 2006; Grahn et al., 2008). One can consider the head of the caudate nucleus as the cognitive and emotional portion (Seger and Cincotta, 2005; Graff-Radford et al., 2017). It is possible that the caudate was restricted from overactivity due to its significant involvement in the working memory task. Ekman et al. found in patients with Parkinson's disease and MCI, blood-oxygen-level-dependent (BOLD) signals are reduced in the right caudate and frontal cortex, as well as impaired presynaptic function in the caudate (Ekman et al., 2012). Working memory is updated by the caudate and anterior cingulate cortex and their functional changes are associated with Parkinson's disease with MCI. Specifically, Qiu et al. found that MCI smokers showed decreased functional connectivity of right caudate to left inferior parietal lobule (Qiu et al., 2022). Compared to controls, caudate volumes were lower in MCI (4.43% right) (Madsen et al., 2010).

Nevertheless, in the early stages, although biomarkers might be found in the result of fMRI, it stays trying for the location of MCI to dementia progression in clinical practice (Zhang et al., 2012). For this reason, finding the contrast between those patients with MCI and healthy subjects is significant. To tackle this issue, researchers established neuroimaging datasets from patients with MCI, HCs, and different variables, such as demographic, genetic, and cognitive measurements (Pellegrini et al., 2018). SVM can characterize non-linear choice limits in high-layered variable space by tackling a quadratic improvement issue (Grueso and Viejo-Sobera, 2021). This technique can provide a valuable insight into a disease that occurs with neural patterns, such as autism (Gori et al., 2015), attention deficit hyperactivity disorder (Park et al., 2016), and schizophrenia (Cheng et al., 2015; Pina-Camacho et al., 2015; Arbabshirani et al., 2017). In the study, the decreased ReHo of right caudate was a potential biomarker to diagnose MCI by SVM. The SVM results showed a diagnostic accuracy of 68.6% (83/121). The ReHo was decreased in the right caudate, with a sensitivity of 62.26% (33/53) and a specificity of 58.82% (40/68). The level of prediction may seem modest, but it must be viewed in the context of other risk factors that patients may have for progression over the next 20–30 years. Since these patients usually coexist, it is crucial to understand how they interact. The results suggest that future therapy studies should directly verify the ability to deliver compounds to the caudate nucleus as the cognitive and emotional portion.

In summary, this study is the first to demonstrate the relation between the right cerebrum and MCI using ML based on fMRI imaging data. The right cerebrum may play an important role in early intervention in patients with MCI. It would be interesting to combine ReHo value and MCI to fully investigate its diagnostic accuracy.

They can serve as an objective and reliable complementary tool to improve diagnosis accuracy and ultimately predict the MCI. In addition, by classifying biomarkers, it is possible to revise clinical diagnosis in cases of uncertain diagnosis. The results add to a superior comprehension of the mechanism in the cognitive decline of patients with MCI.

With regard to the research methods, some limitations need to be acknowledged. First, cross-sectional design limits our ability to interpret causal relationships between patients with MCI and HCs. This study needs to be confirmed in the future to increase the likelihood of generalizing its results to MCI patients with different clinical characteristics. Second, several factors confound our study, such as illness duration, medication, lifestyle, and diet. In the future, we will explore potential variations. Third, there was no information available about whether changes occurred in the caudate before the development of MCI. Understanding the cause and effect may be possible through long-term follow-up observations. Fourth, we do not make the use of the medial temporal lobe atrophy (MTA) score (Scheltens' scale) in distinguishing patients with MCI and AD from those without impairment.

In conclusion, the results suggest that abnormal neural activity in the right cerebrum plays a vital role in the process of MCI. Moreover, the ReHo in right caudate may serve as a neuroimaging biomarker for MCI, which can provide objective guidance on diagnosing and managing MCI in the future.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Medical Ethics Committee of Wuxi Mental Health Center. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

GW, YZ, and FS: conception and design. YG: statistical analysis. YZ: drafting of the manuscript. XZ, JH, and SW: conduction. XC and ML: critical revision of the manuscript. All authors read and approved the final paper.

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.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.