Correlation Between the Fractional Amplitude of Low-Frequency Fluctuation and Cognitive Defects in Alzheimer’s Disease

Abstract

Background:

The fractional amplitude of low-frequency fluctuations (fALFFs) can detect spontaneous brain activity. However, the association between abnormal brain activity and cognitive function, amyloid protein (Aβ), and emotion in Alzheimer’s disease (AD) patients remains unclear.

Objective:

This study aimed to survey alterations in fALFF in different frequency bands and the relationship between abnormal brain activity, depressive mood, and cognitive function to determine the potential mechanism of AD.

Methods:

We enrolled 34 AD patients and 32 healthy controls (HC). All the participants underwent resting-state magnetic resonance imaging, and slow-4 and slow-5 fALFF values were measured. Subsequently, the study determined the correlation of abnormal brain activity with mood and cognitive function scores.

Results:

AD patients revealed altered mfALFF values in the slow-5 and slow-4 bands. In the slow-4 band, the altered mfALFF regions were the right cerebellar crus I, right inferior frontal orbital gyrus (IFOG), right supramarginal gyrus, right precuneus, angular gyrus, and left middle cingulate gyrus. Elevated mfALFF values in the right IFOG were negatively associated with Montreal Cognitive Assessment scores, Boston Naming Test, and Aβ_1–42 levels. The mfALFF value of the AD group was lower than the HC group in the slow-5 band, primarily within the right inferior parietal lobule and right precuneus.

Conclusions:

Altered mfALFF values in AD patients are linked with cognitive dysfunction. Compared with HCs, Aβ_1–42 levels in AD patients are related to abnormal IFOG activity. Therefore, mfALFF could be a potential biomarker of AD.

Keywords

Alzheimer’s disease cerebellum cognitive function resting-state functional MRI

INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by an overall decline of cognitive abilities, such as language, memory, reasoning, and calculation. 1 AD is the most common reason behind dementia. The number of AD cases is increasing with the aging global population. It is estimated that 130 million people will suffer from AD by 2050, 2 resulting in a significant burden on society and families. To date, the etiology and pathophysiological mechanism of AD remain unclear, and there is no effective drug treatment. 3 Early and accurate identification of AD is thus crucial, with the search for biomarkers of particular importance.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a simple, rapid, non-invasive brain imaging technique reflecting the spontaneous activity of neurons in vivo by determining changes in the endogenous blood oxygen level-dependent (BOLD) signal. As rs-fMRI can detect intrinsic internal changes and abnormal brain function under pathological conditions, such as neurological and mental disorders, it could diagnose AD.4,5, 4,5 rs-fMRI methods, including functional connectivity (FC), graph theory, and independent component analysis, can investigate functional alterations in AD patients.5,6, 5,6 However, these methods mainly reflect brain networks, including the default mode network (DMN), salience network, cognitive network, etc. The amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) are commonly utilized rs-fMRI measures.7,8, 7,8 The fALFF is a modified version of ALFF. fALFF detects spontaneous brain activity and is almost noise-free and more sensitive than ALFF. The low-frequency range can be divided into four bands: slow-5 : 0.01–0.027 Hz, slow-4 : 0.027–0.073 Hz, slow-3 : 0.073–0.198 Hz, and slow-2 : 0.198–0.25 Hz. Among these, the slow-4 and slow-5 bands are more sensitive to understanding the pathological features of neuropsychiatric disorders.9 –11 Studies have indicated widespread ALFF differences between AD and HC across the frontal, temporal, and parietal cortices, within the frequency range of 0.01–0.08 Hz. AD patients depict reduced activity within the medial parietal lobe region and lentiform nucleus. Simultaneously, compared to controls, elevated activity in the lateral temporal areas and superior frontal and parietal regions was observed. 12 Recently, the characteristics across different subbands have attracted more attention, particularly the slow 4 and 5 bands.

Some studies have demonstrated ALFF abnormalities in the classical frequency band of AD patients in the posterior cingulate gyrus, left hippocampus, and left anteromedial frontal cortex. Abnormal brain activity in the slow-5 frequency band in areas such as the angular gyrus and right hippocampus has also been reported. 13 Many previous neuroimaging studies have reported structural and functional abnormalities within the brains of AD patients. 14 For example, AD patients depict reduced volumes of the hippocampus, amygdala, and cingulate cortex and abnormalities in the central executive network, DMN, and salience network.15,16, 15,16 However, correcting the gray matter volume loss significantly affected the functional analysis. Thus, gray matter loss can partially lead to functional imaging results in AD. 10

Moreover, whether the abnormal ALFF in AD patients is associated with mood is unclear. The fALFF values of the right fusiform gyrus, left caudate nucleus, right middle temporal gyrus (MTG), and supplementary motor area (SMA) are significantly lower in AD patients with depression than those without. 17 These results highlight the impact of depressive mood on AD.

There is a correlation between the early AD onset and depression, 18 along with many similarities between AD pathogenesis and depression. 19 Anxiety and depression may reflect the emotional response of AD patients to the early stage of the disease, and the neurodegeneration of neural regions and circuits that process emotions can cause anxiety and depression. 20 Depressed patients with impaired executive function may be more susceptible to AD. 21 Despite numerous studies on AD and depression,22,23, 22,23 there are few studies on the relationship between emotion and brain activity in AD patients.

The cerebellum is associated with cognitive function in AD patients 24 and with mood in patients with depression. 25 However, no study has explored altered cerebellum activity in AD patients and whether the changes are linked with mood. The current study aims to address these research gaps.

The two main hypotheses about AD pathogenesis are the deposition of Aβ and hyperphosphorylation of tau protein. 26 Aβ, deposited as neuroinflammatory plaques, induces AD by damaging neuronal cells.27,28, 27,28 Aβ is an essential marker in the cerebrospinal fluid (CSF) of AD patients. Aβ_1–42 shows high diagnostic accuracy, sensitivity, and specificity of 85–90% and can identify patients with early AD.29,30, 29,30

Previous imaging studies on AD require more CSF biomarker data. Moreover, the correlation between abnormal brain activity and cerebrospinal fluid biomarkers among AD patients has yet to be studied. In this study, all the AD patients underwent CSF examination, comprehensive cognitive function assessment, and mood assessment using the Hamilton depression rating scale (HAMD). The correlation between CSF biomarkers, cognitive function, HAMD score, and brain activity was analyzed in AD patients and healthy controls.

MATERIALS AND METHODS

Participants

All the participants were recruited from Nanjing Brain Hospital, affiliated with Nanjing Medical University, between May 2018 and May 2021. Participants included 34 AD patients (14 males and 20 females) and 32 healthy controls (HCs) (14 males and 18 females) matched for age, sex, and education.

All the participants underwent clinical and neuropsychological assessment and fMRI. AD patients also underwent CSF examination.

All the participants were right-handed. The AD patients satisfied the clinical diagnostic criteria proposed by the National Institute on Aging-Alzheimer’s Association (NIA-AA).31,32, 31,32 The inclusion criteria for AD included: (1) patients had significant memory impairment, corroborated by the Rey Auditory Verbal Learning Test (AVLT) score; (2) the Mini-Mental State Examination (MMSE) affected the general cognition test performance (MMSE score < 24) and activities of daily living (ADL); (3) a CDR (Clinical Dementia Rating) value of 1.

For the HC group, the inclusion criteria were: (1) no serious nervous system or mental diseases; (2) MRI showing no softening lesions, masses, or other abnormalities; (3) no abnormal nervous system findings on examination; (4) no complaints of memory decline or cognitive impairment; and (5) a CDR value of 0.

The exclusion criteria for all the participants were: (1) dementia due to other causes, such as Lewy bodies, frontotemporal dementia, etc.; and (2) MRI contraindications.

This study was approved by the Institutional Ethical Committee for Clinical Research of Nanjing Brain Hospital (No. 2018-KY063-01) and carried out following the Declaration of Helsinki. Informed consent was obtained from each participant.

Clinical and neuropsychological assessment

All the participants underwent professional cognitive and psychological assessment, such as (1) assessment of depression: 17-item Hamilton Depression Rating Scale (HAMD17); (2) assessment of general cognitive status: MMSE and Montreal Cognitive Assessment (MoCA); (3) assessment of memory: Auditory Verbal Learning Test (AVLT); (4) assessment of visuospatial ability: Clock Drawing Test (CDT); (5) assessment of attention: Digit Symbol Switching Test (SMDT) and digit Span test (DST); (6) assessment of word naming ability: Boston Naming Test (BNT); and (7) assessment of verbal fluency: Verbal fluency test (VFT).

CSF biomarkers

CSF samples were obtained from the 34 AD patients. The levels of Aβ protein (Aβ_1–40, Aβ_1–42), total tau (t-tau), and phosphorylated tau (p-tau) were analyzed using the INNO-BAIAAlzBio3 immunoassay kit (Ghent, Belgium). CSF samples were not collected from the control group since lumbar puncture to obtain CSF is invasive.

MRI

Imaging was performed with a Siemens 3.0 T singer magnetic resonance scanner (Siemens, Germany). Participants wore earplugs to reduce noise and were instructed to close their eyes, stay awake and quiet as much as possible, and try not to think. The T1WI scanning parameters (acquired using a three-dimensional magnetization prepared rapid gradient echo (3D-MPRAGE))) were: repetition time (TR) = 1900 ms; echo time (TE) = 2.48 ms; flip angle (FA) = 9°; thickness = 1.0 mm; field of view (FOV) = 256 mm×256 mm. rs-fMRI was performed using a single echo planar imaging (EPI) sequence with the following parameters: 240-time points; TE = 30 ms; TR = 2000 ms; FOV = 240 mm×240 mm; matrix = 64×64; FA = 90°; slice number = 36; and thickness = 4.0 mm.

Data preprocessing

The fMRI data were preprocessed with the Data Processing Assistant of Resting State fMRI (DPABI, http://rfmri.org/DPABI and rest1.8, http://restfmri.net/forum/) in MATLAB 2013b 33 with the following steps: (1) format conversion; (2) to reduce instability of the initial data, the first 10 time points were removed for each participant; (3) time correction; (4) head movement correction: the maximum displacement of head movement for all participants was <2 mm and the rotation angle of each axis was <than 2° (excluding the 2 HC participants having excessive head movement who were excluded. During imaging analysis, 30 participants were in the HC group and 34 were in the AD group.); (5) covariate regression; (6) spatial standard and image segmentation: all the images were normalized to the Montreal Neurological Institute (MNI) template space and resampled to 3×3×3 mm³ voxels; (7) linear drift removal; and (8) spatial smoothing with a Gaussian filter of 6 mm full width at half maximum (FWHM).

fALFF measurement

fALFF analysis was performed using DPABI software. The time series of each voxel was transformed into a frequency domain using a Fast Fourier Transform. The square root of each power spectrum frequency was computed, and the averaged square root became the ALFF value, ranging from 0.01–0.08 Hz. The images were band-pass filtered using the corresponding frequency band to calculate ALFF. The fALFF value was obtained by dividing the ALFF values from 0.01 to 0.025 Hz. The ALFF and the fALFF were measured at different frequency bands: 1. 0.01–0.027 Hz (Slow 5) and 2. 0.027–0.073 Hz (Slow 4). Finally, the resulting fALFF maps were normalized using each voxel divided by the fALFF mean values of the whole-brain signal, leading to mfALFF spatial maps.

Statistical analysis

Statistical analysis was performed using the Statistical Package for the Social Sciences 26 (SPSS version 26; IBM, USA, https://www.ibm.com/analytics/spss-statistics-software). Quantitative data were analyzed using the two-sample t-test, and gender data using the χ ² test. The results with p < 0.05 were considered statistically significant.

Imaging analysis was performed using DPABI and REST software in the MATLAB platform. Statistical tests were performed to compare imaging data between the AD and HC groups. The two sample t-test helped compare slow-5 and slow-4 frequency band data with p < 0.001 (AlphaSim correction for multiple comparisons) and a cluster size > 20 voxels for statistical significance. The Slice viewer tool in REST helped determine the specific anatomical location of the brain regions, which showed statistically significant differences at the corresponding MNI coordinates. The voxels of brain regions showing significant statistical effects were presented using the REST software.

We extracted the mfALFF values of the regions indicating significant changes in the AD group using DPABI software. SPSS software was used to perform a partial correlation analysis between these mfALFF values and neuropsychological scale scores and CSF biomarkers, with covariates ranging from gender to age and years of education.

RESULTS

Participant characteristics

The general characteristics and neuropsychological evaluation results for the two groups are represented in Table 1. No statistically significant group differences were observed in gender, age, or education. Compared with HCs, the AD group had significantly lower neuropsychological assessment scale scores (p < 0.05). Moreover, the HAMD score of the AD group was significantly lower than the HC group (p < 0.05).

Table 1

Demographic and neuropsychological scale data for the AD and HC groups

	AD n=34	HC n=32	t/χ ²	p
Age (y)	62.68±8.62	63.69±7.30	–0.513	0.610^a
Gender (male/female)	14/20	14/18	0.045	0.833^b
Education level (y)	9.06±5.25	11.59±4.77	–2.051	0.360^a
CSF t-tau (pg/ml)	484.75±216.48	–	–	–
CSF p-tau (pg/ml)	130.21±109.41	–	–	–
CSF Aβ_1–40 (pg/ml)	10043.77±2499.49	–	–	–
CSF Aβ_1–42 (pg/ml)	485.03±174.20	–	–	–
MMSE	14.06±6.01	27.34±2.67	–11.487	<0.001^a
MoCA	8.97±4.89	24.75±4.98	–12.989	<0.001^a
HAMD	5.38±4.37	2.75±3.27	2.405	0.019^a
AVLT	10.15±8.15	26.59±4.81	–9.903	<0.001^a
CDT	10.45±10.65	26.91±7.11	–7.332	<0.001^a
SDMT	6.59±9.95	35.06±14.87	–9.194	<0.001^a
DST	4.97±2.34	9.31±3.38	–6.092	<0.001^a
BNT	12.56±5.78	23.84±5.21	–8.312	<0.001^a
VFT	7.74±4.00	19.03±5.44	–9.657	<0.001^a

MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; HAMD, Hamilton Rating Scale for Depression-17.AVLT, Auditory Verbal Learning Test; CDT, Clock Drawing Test; SDMT, Symbol Digit Modalities Test; DST, Digit Span Test; BNT, Boston Naming Test; VFT, Verbal Fluency Test. ^a p-value obtained by two-sample t-tests. ^b p-value obtained by the chi-square test.

Group differences in mfALFF values

For the slow-5 band, the mfALFF value of the AD group was lower than the HC group, primarily in the right inferior parietal lobule and right precuneus (corrected by AlphaSim, voxels≥20, p < 0.001; Fig. 1 and Table 2).

Fig. 1

In the slow-5 band, the mfALFF value of the AD group was decreased compared to the HC group. This was primarily in the right inferior parietal lobule and right precuneus (corrected by AlphaSim, voxels≥20, p < 0.001); Warm colors show elevated mfALFF values compared to HCs, while cool colors indicate reduced mfALFF values.

Table 2

Comparison of mfALFF values in the slow-5 band between the AD and HC groups

Brain region	MNI coordinates			t-value	Cluster size
	X	Y	Z
AD < HC
R-inferior parietal lobule	30	–54	51	–4.2	36
R-precuneus	3	–51	54	–5.1	30

MNI, Montreal Neurological Institute; Cluster size > 20 voxels in two-sample t-test, Significance set at p < 0.001 corrected by AlphaSim.

For the slow-4 band, more abnormal brain regions were present. Compared with the HC group, the AD group highlighted increased mfALFF values in the right cerebellar crus I and right inferior frontal orbital gyrus (IFOG). However, decreased mfALFF values were present in the right supramarginal gyrus (SMG), right precuneus, angular gyrus (AG) and left middle cingulate gyrus (MCG) (corrected by AlphaSim, voxels≥20, p < 0.001; Fig. 2 and Table 3). All the results included age, gender, education, and grey matter volume as covariates.

Fig. 2

In the slow-4 band, compared with the HC group, the AD group depicted elevated mfALFF values in the right cerebellar crus I and right inferior frontal orbital gyrus (IFOG), but decreased mfALFF values within the right supramarginal gyrus (SMG), right precuneus, angular gyrus (AG), and left middle cingulate gyrus (MCG) (corrected by AlphaSim, voxels≥20, p < 0.001). Warm colors show elevated mfALFF values compared to HCs, while cool colors indicate decreased mfALFF values.

Table 3

The comparison of mfALFF values in the slow-4 band between the AD and HC groups

Brain region	MNI coordinates			t-value	Cluster size
	X	Y	Z
AD>HC
R-cerebellar crus I	18	–78	–27	4.9	22
R-inferior frontal orbital gyrus	24	15	–21	4.5	40
AD<HC
R-supramarginal gyrus	54	–42	39	–4.8	51
R-precuneus	6	–63	33	–5.8	158
R-angular gyrus	33	–60	48	–5.0	91
L-angular gyrus	–39	–57	45	–5.2	30
L-middle cingulate gyrus	0	–45	48	–4.5	62

MNI, Montreal Neurological Institute; Cluster size > 20 voxels in two-sample t-test, Significance set at p < 0.001 corrected by AlphaSim.

Relationship between altered mfALFF values, neuropsychological scale scores, and CSF biomarkers

Pearson correlation analysis was performed between mfALFF values of abnormal brain regions in the AD group and neuropsychological scale scores and CSF biomarkers, such as age, sex, and years of education as covariates. For the slow-5 band, no significant correlations were observed. However, for the slow-4 band, altered mfALFF values were negatively correlated in the right IFOG with MoCA scores (r = –0.408, p = 0.031) and BNT (r = –0.400, p = 0.035) and Aβ_1–42 (r = –0.546, p = 0.003) levels. Furthermore, reduced fALFF values in the right SMG were positively associated with MoCA (r = 0.454, p = 0.015), VFT (r = 0.414, p = 0.029), BNT (r = 0.476, p = 0.010), and MMSE (r = 0.414, p = 0.029). In contrast, decreased mfALFF values in the left AG were positively associated with BNT (r = 0.400, p = 0.035) and VFT (r = 0.396, p = 0.037).

Fig. 3

In the slow-4 band, significant correlations were found between altered mfALFF values and neuropsychological assessment scores and CSF biomarkers in the AD group (Bonferroni corrected, p < 0.05). R.IFOG: right inferior frontal orbital gyrus; R.crusI: right cerebellar crusI; R.SMG: right supramarginal gyrus; L.AG: left angular gyrus; (A, B, C) The mfALFF value of the right IFOG was negatively correlated with MoCA, BNT, and Aβ_1–42; (D, E, F, G) The mfALFF value of right SMG was positively correlated with MoCA, VFT, BNT, and MMSE; (H–I) The mfALFF value of left AG was positively associated with BNT and VFT.

DISCUSSION

The present study analyzed alterations in mfALFF values in the slow-4 (0.027–0.073) and slow-5 (0.01–0.027 Hz) bands in AD patients. The study explored the relationship between mfALFF value changes, neuropsychological scale scores, and CSF biomarkers. AD patients showed abnormal brain activity in both frequency bands, mainly distributed in the right inferior parietal lobule, right precuneus, right IFOG, right SMG, right cerebellar crus I, AG, and left MCG. The correlation analysis of mfALFF values with neuropsychological scale scores and CSF biomarkers showed higher correlations with abnormal brain regions in the slow-4 band. Specifically, altered mfALFF values within the right IFOG were significantly correlated with Aβ1–42. These findings suggest that the right Crus I, angular gyrus, right SMG, and right IFOG in the slow-4 band are potential biomarkers for diagnosing AD. These findings are essential in diagnosing and understanding AD pathogenesis.

The low-frequency band (ALFF) is a tool for assessing local spontaneous brain activity through the average amplitude value of the low-frequency fluctuation signal in the brain. The low-frequency oscillation of resting fMRI can be categorized into slow-4 (0.027–0.073 Hz) and slow-5 (0.01–0.027 Hz). 9 Slow-4 and slow-5 ALFF were preferentially distributed across specific brain regions. Moreover, ALFF in the slow-5 frequency band could have a higher accuracy in distinguishing between HC, MCI, and AD.11,13, 11,13 Some studies have shown that slow-4 and slow-5 possess higher sensitivity than classical frequency regarding emotion and cognition. Under the slow-4 band, the unipolar depression group revealed elevated mfALFF values in the left superior temporal gyrus compared to HC. In contrast, the right inferior parietal lobule (IPL) indicated elevated mfALFF values in the bilateral postcentral gyrus, and the right IPL was significantly positively associated with Verbal Fluency Test scores. 34 Many ALFF studies in AD have been conducted in the 0.01 to 0.08 Hz frequency band, although the slow-4 and slow-5 bands are more sensitive to neuropsychiatric pathologies. There are few studies on the correlation between ALFF values of abnormal brain regions, neuropsychological scales, and CSF biomarkers in AD patients. All the AD patients in this study underwent CSF examination and comprehensive cognitive assessment. Moreover, the correlation was analyzed between abnormal brain activity in slow-4 and slow-5 frequency bands, neuropsychological scale scores, and CSF biomarkers.

The prevalence of depression in AD patients is high (about 40%). Depression increases the risk of dementia, causing several negative consequences such as impaired activities of daily life, decreased quality of life, and excessive caregiver burden.35,36, 35,36 Our study observed that HAMD scores were significantly lower in the AD group than in the HCs, confirming the relationship between AD and depressive mood. Moreover, without gray matter volume as a covariate, elevated mfALFF values were observed in the crus I region in AD patients, negatively correlating with HAMD scores (See the Supplementary material). Until recently, the role of the cerebellum in cognitive function has been neglected. Previous studies on AD and depression have considered the cerebellum, with some deliberately excluding the cerebellum from imaging analysis.

As early as 1998, Schmahmann described cerebellum cognitive emotional syndrome, indicating that the cerebellum is involved in higher cognitive function. 37 However, few cases have been reported. The cerebellum is involved in the mechanism of depressive emotion, and a wide range of emotional networks are linked with the co-activation of some cerebellum regions. 38 Previous studies have shown decreased connections of cerebellar subregions with the executive, default mode, and affective-limbic networks among people with depression. 25 The cerebellum regulates emotion, 22 and cognitive and emotional changes could occur during cerebellar dysfunction. Altered FC of the cerebellum has been reported in AD patients. There is evidence of reduced FC between the cerebellum and the caudate nucleus, frontal lobe, temporal lobe, etc., positively correlating with cognitive function decline. 24 A randomized controlled study shows that stimulating the cerebellum can improve cognitive function in AD patients. 39 There are some similarities between these prior findings and this study. Our results suggest that the cerebellum plays a vital role in AD development. Therefore, the cerebellum could become an essential imaging basis for evaluating AD and providing novel targets for AD interventions.

The present study indicated decreased mfALFF in the right IFOG and a negative association between mfALFF and cognitive assessment (MoCA, BNT). The term orbital part of the inferior frontal gyrus indicates one of three parts of the inferior frontal gyrus that overlie the insula and create the lower boundary of the gyrus with the lateral fissure. IFOG is essential in language. An intact right inferior frontal gyrus function is necessary for accurate and efficient phonological decisions within a healthy brain. 40 Resting-state functional connectivity analyses depicted significantly elevated functional connections of the right pars-orbital part of the inferior frontal gyrus (IFG) with the right caudate, the right pars-opercular part of IFG, and the left inferior temporal gyrus in Bai-Mandarin bilinguals compared to monolinguals. 41 Frontal lobe activity affects cognitive function. 42 The right IFC damage impairs independent measures of executive control by disrupting inhibition (specifically of responses and task sets). Cognitive inhibition is a component of executive control that can be localized to a specific PFC subregion, the right IFC. 43 Our study provides evidence of the role of IFOG in cognition.

The inferior parietal lobule includes the supramarginal and angular gyrus and is part of the default network. The supramarginal gyrus is associated with cognition. Several studies have depicted that the brain activity of the supramarginal gyrus is decreased among cognitively impaired patients. 4 The right SMG contributes to accurate and efficient phonological decisions in a healthy brain. 44 Previous studies have reported changes in phosphorylation levels during AD development, which could be the underlying mechanism for preserving pathological memory in AD patients. 45 The angular gyrus is located at the junction of the occipital, temporal, and parietal lobes. It is a vital interface for transmitting and integrating information across different networks, playing an essential regulatory role in cognitive processes. 46 ¹⁸F-THK5351 PET imaging analysis in AD has shown that the specific retention of ¹⁸F-THK5351 is primarily present in the angular gyrus, inferior temporal cortex, and parieto-occipital region among AD patients. 47 In reports of effective non-pharmacological treatment of AD patients, significant changes in angular gyrus activity have been depicted using magnetoencephalography. 48 These results support our findings that AD patients have abnormal activity in the bilateral angular gyrus. Moreover, this study observed that abnormal activity in the left angular gyrus and right supramarginal gyrus was significantly associated with VFT and BNT. Therefore, abnormal activity and metabolism of the angular gyrus could be involved in the cognitive decline mechanism in AD.

The pathological features of AD include extracellular aggregation of amyloid plaques and accumulating intracellular neurofibrillary tangles. 49 Three core CSF biomarkers for diagnosing AD, namely Aβ_1–42, total tau (T-tau), and phosphorylated tau (P-tau)181, are included in the diagnostic criteria for AD. 50 Biomarkers are essential for accurately identifying preclinical AD and can facilitate the underlying disease treatment. CSF biomarkers are considered more accurate than blood markers for diagnosing AD but require invasive examinations. The present study observed a negative correlation between mfALFF values in the inferior frontal gyrus and Aβ_1–42. This could provide a new biological marker for the noninvasive diagnosis of AD.

This study has several limitations. First, the sample size was relatively small, and results using small samples could be biased. In the future, we will continue to collect data for additional analysis. Second, it is unknown how fALFF changes in AD patients with age, as the data are cross-sectional. Further studies will investigate dynamic changes in brain activity across AD patients.

Conclusion

Overall, this study provides evidence of a wide range of brain activity abnormalities across AD patients, particularly in the slow-4 band. The brain areas showing abnormal activity were associated with cognitive function and Aβ_1–42. Specifically, increased mfALFF values within the right IFOG were negatively correlated with MoCA, VFT, and Aβ_1–42 levels. These findings support the neurobiological significance of altered mfALFF values in relation to cognitive function in AD patients. We speculate there is a link between the cerebellum and AD, while depressive mood could play a role in AD development.

AUTHOR CONTRIBUTIONS

Jingping Shi (Investigation; Project administration); Xuemei Zhang (Data curation; Formal analysis; Investigation; Methodology; Software; Supervision; Writing – original draft; Writing – review & editing); Jie You (Data curation); Qun Yao (Formal analysis); Xinyang Qi (Supervision); Junrong Li (Validation; Visualization).

Footnotes

ACKNOWLEDGMENTS

The authors thank all the participants in this study and MogoEdit () for its English editing while preparing this manuscript.

FUNDING

Nanjing Medical Science and Technique Development Key project (No. ZKX21034) supported this study.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The data supporting this study’s findings are available upon reasonable request from the corresponding author. However, due to privacy or ethical restrictions, the data are not publicly available.

The supplementary material is available in the electronic version of this article: .

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