Alzheimer’s disease (AD), a neurodegenerative disease primarily affecting the elderly, is marked by beta-amyloid plaques and neurofibrillary tau protein tangles (Rosales-Corral et al., 2012). There are no effective therapies that have been shown to change the pathophysiology or course of AD that cause the disease’s progression (Harrington, 2012). Depending on the symptoms displayed, AD can be classified as mild, moderate, or severe. Mild cognitive impairment (MCI), as an intermediate stage between normal aging and AD (Chandra et al., 2019), is mainly defined as neurocognitive impairment and does not meet the diagnostic criteria for dementia. Very mild cognitive impairment (VMCI) represents a transitional state between the cognitive changes of normal aging and the very early onset of dementia and is becoming increasingly recognized as a risk factor for Alzheimer’s disease (Chandra et al., 2019). Previous studies demonstrated that MCI is a significant risk factor for AD and that approximately 10 to 15% of MCI will eventually progress to AD (Petersen et al., 2001; Ronald et al., 2001). This is the reason why early assessment of MCI may help researchers discover the pathophysiological mechanisms of AD and provide new information for better treatment in the future (Weller and Budson, 2018; Gupta et al., 2020).

Neuroimaging research has been crucial in the clinical diagnosis of MCI and AD, and magnetic resonance imaging (MRI) modalities have proven useful in the identification of biomarkers for AD and MCI pathology (McKhann et al., 2011). AD progression has been tracked with structural imaging techniques to demonstrate that atrophy spreads to the medial temporal lobe, parahippocampal, fusiform gyri, and orbitofrontal cortex (Li et al., 2011). Atrophy in AD subjects is distributed in the frontal, parietal, and temporal lobes, whereas atrophy in MCI subjects is in the frontal and temporal lobes (Guo et al., 2010). The aforementioned research on AD has focused on gray matter (GM) or white matter (WM) structures.

Blood oxygenation-level-dependent (BOLD) signals have primarily focused on the GM but are rarely reported on the WM (Mazerolle et al., 2020; Abramian et al., 2021). Gawryluk and colleagues comprehensively demonstrated the possibility that non-invasive fMRI techniques could detect the WM functional signals in the human brain, which laid a good foundation for the study of WM functional images in the future (Gawryluk et al., 2014). The WM BOLD signal has recently been shown to play an important role in understanding cerebral intrinsic activity (Ji et al., 2017; Ding et al., 2018; Li et al., 2019; Wang et al., 2020). Ji et al. (2017) demonstrated that the BOLD signal in the white matter varies with physiological states, closely correlating with structural features. Ding et al. (2018) have found that BOLD signals in specific WM tracts are functionally correlated with specific GM regions during different tasks. The BOLD signals reflect changes in cerebral blood flow and volume and are used to study dynamic representations of brain activity (Chen and Pike, 2009; Wang et al., 2021). Blood flow and blood volume in WM are approximately 25% of those in GM (Rostrup et al., 2000; Helenius et al., 2003). In both GM and WM, resting-state BOLD fluctuations have similar low-frequency (0.01–0.1 Hz) signal power (Gore et al., 2019; Wang et al., 2022). Changes in BOLD signals in the WM reflect a distinct response to neuronal activity because they vary with baseline neural activity (Abramian et al., 2020). Recent studies have shown that sensory stimulation evokes reliable BOLD responses along with a subset of fiber pathways (Huang et al., 2018). After brief stimulation, WM bundles also show a transient signal response similar to GM but not identical to the characteristics of the hemodynamic response function of GM (Wu et al., 2017). Thus, there is increasing evidence that WM exhibits resting-state fluctuations and a transient signal response (Ding et al., 2016). These studies provide evidence that BOLD signals in WM are associated with functional activity in the human brain and deserve more attention from the neuroimaging community (Gore et al., 2019).

The amplitude of low-frequency fluctuation (ALFF) has been used to measure the amplitude of low-frequency oscillations in resting-state fMRI, and it is defined as the total power within the frequency range from 0.01 to 0.1 Hz (Han et al., 2011; Di et al., 2013; Yang et al., 2018). The BOLD signals of resting-state fMRI are thought to represent spontaneous brain activity and are a good indicator of the regional intensity of spontaneous variations of ALFF (Fox and Raichle, 2007). Previous research (Han et al., 2011; Chen et al., 2015; Wang et al., 2016) has suggested that the ALFF measure can be used as a predictor for disease states such as MCI, tinnitus, and depression. A propensity for ALFF values to decrease as AD progresses can be seen in the right median and paracingulate gyri, bilateral inferior cerebellar lobes (which extend to the posterior cerebellum), bilateral precuneus, and the right anterior cingulate (Yang et al., 2018; Luo et al., 2020).

One potential area for indicators is structure-based neuroimaging research, with voxel-based morphometry (VBM) in particular serving as a well-established technique for identifying brain atrophy. In early AD prodromal stages, compared to healthy senior controls, VBM studies have found a high similarity in the olfactory cortex and hippocampus (Pennanen et al., 2004; Vasavada et al., 2015). Several atrophies in the temporal, parietal, posterior cingulate, frontal cortices, insula, and Rolandic eyes have been documented during the senile stage of AD (Duarte et al., 2006). Lower GM volume was demonstrated by Roquet et al. (2017) in the anterior cuneus, frontal lobe, insula, and extratemporal lobe. Axonal atrophy and demyelination were the findings of two early studies that described alterations in WM macro- and microstructure in AD (Brun and Englund, 1986). Additionally, earlier research showed that ALFF and its association with fractional asymmetry in WM supported the idea that structure–function coupling is a fundamental organizing principle in healthy subjects (Honey et al., 2009; Wang et al., 2015).

In the past, functional activity in predetermined brain areas has been examined using a mix of structural and functional brain imaging. The integration of functional and structural MRI, for instance, may provide complementary information to improve the diagnosis of MCI relative to either alone (error rate: 6% with both versus 15% with fMRI alone and 35% with sMRI alone), according to a classification study (Kim and Lee, 2013). In order to comprehend the aberrant changes in brain diseases, it was essential to combine the anatomical and functional information from neuroimaging techniques.

The current study aimed to examine the functional and structural changes within the whole brain in AD by combining ALFF and VBM analysis. First, we separately investigated the abnormal ALFF and VBM within the whole brain. Then, the ALFF/VBM ratio would be adopted here as the regional-level measure. In addition, we analyzed the studies of structural-functional association via direct ALFF–VBM comparisons. Furthermore, we analyzed the abnormal changes in whole-brain ALFF–VBM coupling in the MCI and VMCI groups. Finally, we estimated the relationships between the above abnormal regions and the clinical manifestations of AD.

In this study, we used 243 subjects in the publicly available OASIS-3 dataset¹, with 53 MCI, 90 VMCI, and 100 normal control (NC) subjects. We collected resting-state functional images and T1-weighted anatomical images for each subject. All data were collected from the University of Washington Knight AD Research Center in various studies conducted over the past 15 years. Written informed consent was obtained from all participants before collecting fMRI data. The dementia spectrum status of all patients was assessed using the Clinical Dementia Rating (CDR) scale (Morris, 1993). According to the CDR, all AD patients were divided into NC (CDR = 0), VMCI (CDR = 0.5), and MCI (CDR = 1), respectively. The severity of cognitive impairment was assessed based on the Mini-Mental State Examination (MMSE; Folstein et al., 1975). Detailed demographic and clinical information is described in Table 1.

In this study, we investigated the abnormal neuroimaging changes in the early stages of AD by combining whole-brain ALFF and VBM. We adopted novel measures (ALFF/VBM ratio) to investigate the whole-brain structure–function coupling abnormalities. For the MCI and VMCI groups, significant differences in ALFF were observed in VM-VIII, PUT.R, CR-VIII in the GM, and ORBinf-WM, PUT-WM, and PCL-WM. Additionally, we found the structure–function coupling clusters distributed in CR-VIII, PHG.R, and HP.L in the GM, and CUN.R and the frontal lobe in the WM. More importantly, as the typical focal areas in AD, PHG.R exhibited a negative correlation with the severity of cognitive impairment, indicating the important applications of the ALFF/VBM ratio in brain disorders. Findings from WM functional signals provided a novel perspective for understanding AD pathophysiology.

Using ALFF values from resting-state fMRI signals, we estimated the VMCI- and MCI-related changes in intrinsic and spontaneous brain activity. We found that there were widespread differences in ALFF values among the VMCI, MCI, and healthy elderly patients in the different brain regions of the frontal, temporal, and parietal lobes and in subcortical areas. A previous study showed decreased ALFF in the left hippocampus and PCC (Sorg et al., 2007; Wang et al., 2011). Different ALFF values in the GM were observed in the PUT.R, and VM-VII among the NC, VMCI, and MCI groups. Also, there was a significant abnormality in the PUT.R. The abnormal ALFF values in the PUT.R were significantly attributed to the memory decline over time and more severe AD pathology. Previous studies have shown that the PUT, as part of the striatum, is active during working memory tasks (Dahlin et al., 2008) and probabilistic learning tasks (Bellebaum et al., 2008). Cross-sectional studies have reported disrupted functional connectivity involving the PUT in individuals with MCI compared to their healthy counterparts (Teipel et al., 2007; Bai et al., 2011; Han et al., 2011). Our findings of ALFF changes in PUT, in line with two previous studies, likely strengthen the evidence for memory decline over time and more severe AD pathology. In the first study, it was reported that atrophy of the PUT but not the caudate was significantly associated with cognitive decline in patients with probable AD (de Jong et al., 2008). In the second study, the other reported abnormally high perfusion in the PUT but not the caudate in MCI compared to AD patients (Alexopoulos et al., 2012). Thus, our finding of abnormal ALFF in the PUT, together with our recognition, suggests that the PUT is an important brain region that deserves our attention. In addition, our study also found that ALFF in cerebellar VM-VII is abnormal in three groups. Such an approach was published in the 1990s, when research demonstrated that patients with cerebellar damage had cognitive deficits (Schmahmann, 1991). More recently, much evidence has shown that the cerebellum affects not only visuospatial and verbal functions and declarative memory but also more complex behavioral regulatory processes, namely executive function (Mak et al., 2016; Myers et al., 2017; Beuriat et al., 2020; Liu et al., 2021). A previous study demonstrated that patients with cognitive impairment had reduced fractional ALFF values in the cerebellar regions closely related to cognitive function compared to HC (Fan et al., 2019). The present study found that the widely distributed cerebellar ALFF changed with the severity of cognitive impairment, suggesting the important role of the cerebellum in normal cognitive functional activity. We also found abnormalities in CR-VIII. L, CR-VII. R in the GM between NC and the other two disease groups. According to previous studies, the cerebellum is involved in motor, balance, and cognitive functions (Wagner and Luo, 2020), so we think that cerebellar abnormalities are linked to the dysfunction in MCI and VMCI.

As for the WM results, three clusters (ORBinf-WM, PUT-WM, and PCL-WM) were consistently significantly abnormal across the three groups. We analyzed significant differences in ALFF values. ORBinf-WM is a part of the orbitofrontal cortex (OFC) and prefrontal cortex regions in the frontal lobe. It shares extensive connections with other cortices such as motor, primary sensory, and association cortices, the hippocampus, the amygdala, and other areas such as olfactory, auditory, and periaqueductal grey areas. The extensive connections of ORBinf-WM indicate that ORBinf-WM has extensive and complex functions (Cavada et al., 2000; Kringelbach, 2005; Barbas, 2007). Therefore, the abnormal differences in ALFF in ORBinf-WM of our MCI and VMCI patients imply that they might lead to a series of clinical manifestations such as poorer cognitive, behavioral, mental, and mood performance. Especially in the MCI, it will manifest in hallucinations, psychosis, depression, anxiety, apathy, sleep problems, and sexual desire dysfunction. The PCL-WM in the WM regions is distributed in the anterior portion of the paracentral lobule involved in the frontal lobe, which is often referred to as the supplementary motor area. The posterior portion is considered part of the parietal lobe and deals with the somatosensory system of the distal limbs⁶, demonstrating a potential relationship between associative memory and cognitive disorders. It can be inferred that as the disease develops, neural damage strengthens these three brain regions in the WM. VBM is a reliable method to compare typical brain regions in GM and WM between different groups of patients. Results among the three groups showed more GM atrophy in parts of the parietal and temporal lobes, PUT, insula, parahippocampal gyrus, and frontal lobe of both hemispheres. The above results are consistent with the research of Michaela Defrancesco et al. who reported disrupted WM integrity in the frontal, temporal, and parietal lobes and in subcortical structures in patients with MCI. In addition, AD subjects showed reduced GM density in the frontal lobe, left parietal lobe, left putamen, left insula, left temporal lobe, and right parahippocampal gyrus in comparison to MCI patients (Defrancesco et al., 2014). MCI compared to VMCI, showed mild GM atrophy in areas such as the middle temporal lobe and medial frontal lobe, both of which are mainly involved in verbal memory and naming. In addition, atrophy of these brain regions and limbic system structures has been reported in MCI patients years before conversion to AD in numerous previous volumetric MRI studies (Ferreira et al., 2011). Regions of the default mode network (DMN), hippocampus, insula, and middle temporal gyrus showed significant GM loss in MCI, suggesting structural impairment in patients with MCI (Tisserand et al., 2004). In WM, our results are consistent with several previous findings showing disruption of WM integrity in frontal, temporal, parietal, and subcortical structures in patients with MCI and VMCI (Wang et al., 2006; Liu et al., 2013; Bao et al., 2022). These outcomes suggest that prefrontal and temporal cortical regions are of particular relevance in cognitive decline in healthy elderly individuals (Defrancesco et al., 2014).

As a major AD symptom seen in clinics (Jaroudi et al., 2017), the PHG and PH regions exhibited abnormally increased structure–function coupling values in the VMCI and MCI groups (Figure 3). The role of the parahippocampal gyrus in AD has long been known, and our research discovered an abnormal ALFF/VBM ratio in the PHG.R and HP.L between MCI and VMCI patients, indicating that the coupling technique may have some validity (Van Hoesen et al., 2000). The medial temporal lobe, which is primarily made up of the hippocampus, perirhinal and entorhinal cortices, and parahippocampal cortex involved in episodic memory, is where the early neuropathological changes that are characteristic of AD (de Leon et al., 1997; Dickerson and Eichenbaum, 2010; Eichenbaum et al., 2012). The PHG, which includes the middle temporal gyrus (MTG), angular gyrus, posterior cingulate cortex/precuneus/cuneus (Pcc/Pcu/Cun), and medial frontal cortex, is the area of the brain most commonly impacted by AD (MPFC; Greicius et al., 2004; Buckner et al., 2008). Additionally, there was a strong correlation between the structure–function coupling values of the PHG.R cluster and HP.L and our clinical heterogeneity in the MMSE results. It has been proposed that the DMN participates in activities that are inwardly directed, such as retrieving autobiographical memories and planning for the future (Chen et al., 2015). The PHG mainly receives direct associative input from the posterior associative brain areas, which are a component of the DMN. It then strongly projects to the hippocampus, a hub region in the episodic memory network that is located in the caudal two-thirds of the entorhinal cortex (Lavenex and Amaral, 2000; Witter et al., 2000; Spaniol et al., 2009; Chen et al., 2014). Understanding the functions of the PHG and the brain regions involved in the DMN in impaired episodic memory in AD/MCI is strengthened by knowledge of disease severity-related changes in connectivity between them. CUN-WM and FG-WM were both found to have distinct abnormal brain regions compared to NC and the other two cases. This was also seen in the ALFF and VBM findings of our study, adding to the mounting evidence that this area of the brain is abnormal in the early stages of AD.

Our study has four limitations that need to be considered. First, we did not include the subjects’ vascular risk factors (such as hypertension, hyperlipidemia, diabetes, obesity, excessive smoking, and mental stress) in the covariates of the regression analysis. The vascular risk factors may influence cognitive function in the elderly, which may be related to AD (Chong et al., 2017). Second, we have only analyzed rs-fMRI data. In future studies, the combination of multimodal neuroimaging and biological information may provide a more comprehensive understanding of the progression patterns in AD. Third, our study lacked extensive neuropsychological testing, and more exhaustive neuropsychological testing will be necessary to examine more cognitive aspects of patients to further explore the underlying mechanisms in the brain. Finally, we found that the clinical correlation coefficients between abnormal clusters and the MMSE in Figure 4 did not pass the correction for multiple comparisons. Further studies are needed to replicate the correlation plot results.

This study investigated the changes in whole-brain neuroimaging in the early stages of AD by combining ALFF and VBM measures and further adopted the novel coupling index (ALFF/VBM ratio) to investigate the whole-brain structure–function coupling abnormalities. Compared to NC, MCI, and VMCI groups, significant ALFF differences were distributed in the VM-VIII, PUT.R, and CR-VIII in the GM, ORBinf-WM, PUT-WM, and PCL-WM. Moreover, we observed the coupling differences distributed in the CR-VIII, PHG.R, and HP.L in the GM, and CUN.R and frontal lobe in the WM, which were closely associated with MMSE. The above findings demonstrated the important applications of the ALFF/VBM ratio in brain disorders and provided a novel opinion for understanding the pathophysiology of AD from the perspective of WM functional signals.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

The studies involving human participants were reviewed and approved by University of Washington Knight AD Research Center and School of Life Science and Technology, University of Electronic Science and Technology of China. 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.

RZ performed all data analysis and wrote the whole manuscript. RZ, PW, and BB contributed to the study design and sorting of the experimental data, modifying the manuscript. LL, FZ, PH, JW, and HL contributed to the analysis and interpretation of data. All authors contributed to the article and approved the submitted version.

This work was supported by the China MOST2030 Brain Project No. 2022ZD0208500 and the National Natural Science Foundation of China (grant numbers: 61871420; 62171101). The funding sources had no role in the design, methods, subject recruitment, data collection, analysis, or preparation of the 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.