Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease, leading to severe disabilities due to progressive cognitive impairments. With the population aging, the incidence of AD is rising, which imposes a heavy burden on societies. Mild cognitive impairment (MCI) is the prodromal form of AD and is a higher at-risk state for AD [1]. Despite the increase of neuroimaging studies related to AD and MCI over the past decades, the brain structural change underlying the disease has not been fully elucidated.

Gray matter (GM) changes are closely associated with tau pathology in AD patients with abnormal accumulation of amyloids $\beta$ [2, 3]. Therefore, GM atrophy pattern-based morphometric measures have increasingly received attention in AD research [4, 5]. As a non-invasive and non-radiative imaging technique, structural magnetic resonance imaging (sMRI) is an alternative method to find neuroimaging evidence for early diagnosis of AD [6]. Data from sMRI of AD suggest that GM atrophy exists in the temporal lobe and prefrontal cortex (PFC) during MCI and further extends to frontal, parietal, and occipital cortexes during the progression from MCI to AD [7, 8, 9].

A study found amyloid deposits in the frontal and parietal lobes in patients with mild AD [10]. More amyloid deposits may be associated with faster cortical damage [5]. Therefore, the frontal lobe should be regarded as an important site of AD-associated GM atrophy. The frontal lobe plays an important role in various cognitive processes and is essential for memory, attention, executive function (EF), and self-awareness [11]. Prefrontal cortex (PFC) integrates complex information to form the physiologic constructs of memory, perception, and intricate functions [12], which damage will severely affect executive function (EF) and working memory (WM) [13]. Among the healthy population, larger PFC volume was found to be associated with better execution performance [14]. The middle frontal cortex plays an important role in the maintenance of working memory and response inhibition [15]. The inferior frontal gyrus is pivotal for interference control processes [16]. Moreover, cognitive function is a complex neural process, requiring collaborative effects of different brain regions to operate as a functional brain network. Cognitive decline is closely related to cortical atrophy [17]. However, the relationship between frontal subregions atrophy and cognitive decline in AD or MCI is still unclear.

A reliable and accurate brain partition atlas is crucial for quantitative studies of brain structure in AD. The human Brainnetome (BN-246) atlas [18] is a tractography-based and cross-validated atlas with fine delineation of brain regions and precise boundary localization, containing information on both anatomical and functional connections. Using voxel-based morphometry (VBM), Long et al. [4] indicated that the BN-246 atlas is superior to other widely used atlases in distinguishing patients with MCI from cognitively normals (CN). Several studies used the BN-246 atlas to identify the connection between different parts of the cerebral cortex and diseases [4, 19, 20].

However, the pattern of atrophy in frontal brain regions based on the BNA-246 atlas and its relationship with the cognitive impairments in MCI and AD has not been extensively investigated. We hypothesized that: (1) The atrophy trajectory of the frontal lobe in AD is different from previous studies. (2) The volume of some subregions has a better diagnostic ability to distinguish AD from CN. (3) There is a correlation between certain subregions’ volume and cognitive impairment. This study aimed to determine the trajectory of atrophy in frontal subregions and to find which subregion can better differentiate between groups.

In this study, we first compared grey matter volume (GMV) in frontal regions and subregions between AD, MCI, and CN groups. Then, the AUC results demonstrated that the right MFG_7_1 has a satisfactory ability to distinguish between AD and CN. Furthermore, after adjustment for age, gender, years of education, and eTIV, we found that the right IFG_6_1, the left IFG_6_3, the right IFG_6_3, the right IFG_6_5, the right OrG_6_5, and the right OrG_6_6 with ADAS-cog 13 score and slightly correlated with MMSE score in patients with AD.

In our study, widespread cortical atrophy was found in the bilateral frontal lobes in patients with MCI, which was exacerbated by disease progression [7, 8, 9]. The right dorsal area 9/46 (right MFG_7_1) showed a more severe atrophy and its volume is the best one to distinguish AD patients from CN. In addition, PrG subregion atrophy was found in patients with MCI. Machine learning has suggested that the precentral gyrus is one of several regions affected by AD [28]. However, no studies reported decreased PrG volume in MCI patients through voxel-based morphometry. Compared with previous studies of MCI [7, 9], our results suggested more extensive frontal cortical deficits. Using magnetic resonance elastography (MRE), Lucy et al. [29] identified stiffness deficits in the frontal operculum and PrG. PrG_6_6 corresponds to the caudal ventrolateral area 6 [18], and controls muscle contraction and speech [30]. Moreover, it may control eye blinking [31], and an abnormally high eye blink rate may be characteristic of subjects with MCI [32].

The frontal cortex is a heterogeneous region with multiple functional subdivisions, and damage to different subdivisions can impair different cognitive functions, including memory, language, response inhibition, and cognitive flexibility. ADAS-cog 13, whose scores range from 0 to 85, with higher scores indicating more severe cognitive impairment. MMSE, the score ranges from 0 to 30, with lower scores indicating more severe cognitive impairment. Among patients with AD, the volume of these subregions was more robustly correlated with ADAS-cog 13 than with the MMSE score. The reason may be the greater involvement of memory, language, and orientation in the ADAS-cog 13 than in the MMSE. Due to the ceiling effect, MMSE may lack sensitivity to early cognitive impairment, which may lead to the absence of any correlations between subregion volumes and MMSE score in MCI. Despite our findings, Han et al. [33] reported that volume reduction in the left inferior frontal gyrus is related to the severity of symptoms in MCI. Differences in the MCI groups may warrant this discrepancy between the studies. Han et al. [33] recruited amnestic MCI patients, whereas MCI includes more heterogeneous groups of patients [34].

Broca’s area is essential for speech production [35]. It has been divided into BA 44 and BA 45 [36], usually in the left hemisphere. However, the right BA 44 also plays an important role in successful language. The right BA 44 is also involved in language processing in both visual and auditory ways [37]. Moreover, BA 44 is considered the core area of syntax processing [38]. There is growing evidence that cross-hemispheric communication is also important for syntactic learning and processing [39, 40]. Chen L et al. [40] suggested that right BA 44 may transfer information to left BA44 for promoting syntactic operations. In our study, the BA 44 was further subdivided into ventral (IFG_6_1), dorsal (IFG_6_6), and operculum parts (IFG_6_5) subregions [41]. BA 45 was subdivided into caudal (IFG_6_3) and rostral (IFG_6_4) areas. Molnar-Szakacs et al. [42] reported that the dorsal part of area 45 is active during observation and imitation, whereas the ventral part is active just during imitation, but not during observation. In this study, cognitive function was correlated with right IFG_6_1 and right IFG_6_3 volumes, but not with IFG_6_6 and IFG_6_4 volumes, which warrants further parcellation within the BA 44 and BA 45.

The right BA 44 might function beyond phonological processing. The operculum (IFG_6_5) is crucial for successful response inhibition and task-switching in the stop-signal task [43, 44]. The inability to maintain directed attention in the presence of interfering stimuli may be the mechanism underlying frontal lobe memory deficit [45]. Meanwhile, the right IFG_6_5 is involved in integrating exteroceptive and interoceptive signals, which are necessary for interoceptive awareness. The defect may be responsible for the lack of insight, which is a frequent symptom of dementia [18, 46]. Inferior frontal sulcus areas of the left hemisphere (left IFG_6_2) are associated with language and working memory [47].

The right IFG and its connections with the striatum may be the basis of cognitive control, whereas the right ventrolateral prefrontal cortex (VLPFC) is a critical area in control [16, 44]. VLPFC is a component of the ventral attention (VANet), dorsal attention (DANet), and salience (SNet) networks. These brain network patterns are important for cognitive flexibility [48, 49]. Cognitive flexibility, also known as behavioral flexibility, relies on both inhibitory control and WM. Transient inactivation of the right VLPFC, including right IFG_6_3, right IFG_6_4, right OrG_6_2, and right OrG_6_6 subregions, impairs audiovisual working memory performance [50]. In this study, the right OrG_6_5 and OrG_6_6 were correlated with cognitive function. OrG_6_5 is a part of the orbitofrontal cortex (OFC), whose lesions usually affect flexible cognitive processes such as learning, recognition, and drive response [51, 52]. Therefore, the impaired function of these areas can affect different aspects of cognition.

Although MMSE items cover several cognitive domains, they are unlikely to identify more subtle cognitive changes related to frontal subregions, as MMSE is a global scale of cognitive impairment. The most important disadvantage of MMSE is low sensitivity to MCI. More sensitive and precise measures of frontal lobe function, such as verbal fluency test and false memory test [53, 54], are recommended for future studies. It has been shown that some brain networks are disrupted in AD [55]. Cognitive function depends on the normal function of multiple regions in the brain network. Cognitive impairment is also associated with abnormal connectivity between different brain regions [56]. Changes in the functional connectivity of the intrinsic network can be detected by resting state fMRI. Whether there is a compensatory mechanism between brain regions can be explored by task-state fMRI. The combination of multimodal imaging, such as structural and functional MRI, can provide a great deal of information to expand our understanding of the mechanisms of brain changes in AD. Furthermore, the BNA-246 atlas we used in this study was developed in the Chinese population, and the results of this study need to be further verified among Chinese patients with MCI and AD.

(1) There is atrophy in the frontal superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbital gyrus and precentral gyrus in MCI. Dividing the frontal lobe into 34 subregions can more accurately and objectively analyze the structural changes of the frontal lobe in MCI and AD patients. (2) The 9/46 area of the middle frontal gyrus is an important part of the structural changes of the frontal lobe in AD patients, which helps to promote the exploration of the pathological mechanism of the frontal lobe in AD patients. (3) Atrophy of certain frontal subregion cortex is associated with cognitive dysfunction in AD patients.

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

All authors contributed significantly to this research and preparation of the manuscript. Conceived and designed the experiments—CS, XD, WY. Performed the experiments and analyzed the data—CS, HD, DR. All authors have been involved in the drafting, critical revision, and final approval of the manuscript for publication. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors read and approved the final manuscript.

The study has been approved by Medical Ethics Committee of Affiliated Hospital of North Sichuan Medical College with the IRB number (2022ER452-1). The patient consent form was waived which was approved by IRB.

We thank all of the study participants and their families that participated in the neuroimaging research. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. We also appreciate their contributions. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

This research received no external funding.

The authors declare no conflict of interest.