
==== Front
Neuroimage Clin
Neuroimage Clin
NeuroImage : Clinical
2213-1582
Elsevier

S2213-1582(24)00099-8
10.1016/j.nicl.2024.103660
103660
Regular Article
Multimodal analysis of disease onset in Alzheimer’s disease using Connectome, Molecular, and genetics data
Oh Sewook a1
Kim Sunghun abc1
Lee Jong-eun ac
Park Bo-yong cd
Hye Won Ji e
Park Hyunjin hyunjinp@skku.edu
abc⁎
a Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
b Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea
c Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
d Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
e Department of Computer Engineering, Pukyong National University, Busan, Republic of Korea
⁎ Corresponding author. hyunjinp@skku.edu
1 Equal contributions as cofirst authors.

24 8 2024
2024
24 8 2024
43 10366026 5 2024
23 8 2024
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© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Highlights

• Integrating fMRI, PET, and SNP data offers a multifaceted approach to AD onset.

• Multimodal analysis links neuroimaging and genetics to reveal complex mechanisms.

• Serial mediation shows SNP affect Aβ (PET) and FC (fMRI) finally affecting AD onset.

Alzheimer’s disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each modality. We aimed to identify multimodal biomarkers of AAO in AD using an extended version of sparse canonical correlation analysis, in which we integrated two imaging modalities, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), and genetic data in the form of single-nucleotide polymorphisms (SNPs) obtained from the Alzheimer’s disease neuroimaging initiative database. These three modalities cover low-to-high-level complementary information and offer multiscale insights into the AAO. We identified multivariate markers of AAO in AD using fMRI, PET, and SNP. Furthermore, the markers identified were largely consistent with those reported in the existing literature. In particular, our serial mediation analysis suggests that genetic variants influence the AAO in AD by indirectly affecting brain connectivity by mediation of amyloid-beta protein accumulation, supporting a plausible path in existing research. Our approach provides comprehensive biomarkers related to AAO in AD and offers novel multimodal insights into AD.

Keywords

Alzheimer’s disease
Age at onset
Multimodal imaging
Imaging genetics
Mediation analysis
fMRI
PET
SNP
==== Body
pmc1 Introduction

Alzheimer’s disease (AD) poses a significant challenge for current healthcare systems due to its progressive and incapacitating characteristics. The increasing prevalence of AD imposes a substantial burden on affected individuals, caregivers, and society (Bergvall et al., 2011). Early diagnosis and prediction of the onset of AD is essential for improving its management and therapeutic interventions. Although the severity of cognitive impairment exhibits considerable heterogeneity, ranging from mild cognitive impairment (MCI) to severe dementia (Libon et al., 2010, Wang et al., 2023), the factors contributing to this diversity remain incompletely elucidated. Possible answers may lie in the integration of multiple factors with complementary information. Age at onset (AAO) is a strong predictor of cognitive decline even after controlling for covariates such as sex and family history of dementia (Jacobs et al., 1994). Understanding the temporal trajectory of the progression of AD can inform the timing of interventions, enabling tailored prevention and treatment strategies. Similar to the heterogeneity of symptom severity in AD, AAO encompasses a wide spectrum from early onset before 65 years to manifestation later in life, presenting a varied landscape (Liang et al., 2023). Multimodal analysis that integrates neuroimaging and genetic information is well suited to explore this heterogeneity in AAO.

Magnetic resonance imaging (MRI) provides a noninvasive window into the human brain, allowing the interrogation of brain structure and function in vivo. Resting-state functional MRI (rs-fMRI) allows the investigation of whole-brain intrinsic functional networks and reveals their functional organization (Glover, 2011). Specifically, rs-fMRI has identified alterations in functional connectivity (FC) patterns associated with AD progression and onset (Filippi et al., 2017, Gour et al., 2014, Sheline and Raichle, 2013, Zhang et al., 2010). Previous studies have consistently shown reductions in connectivity between the frontoparietal regions (Greicius et al., 2004, Jones et al., 2011). Furthermore, the FC patterns between early- and late-onset AD are different in the default mode network, and anterior temporal and dorsolateral prefrontal networks are correlated with cognitive performance (Gour et al., 2014). AD and amnestic MCI disrupt the organization of the brain network, leading to changes in functional segregation (He et al., 2023). Additionally, significant reductions in the FC gradient dispersion, both globally and in specific brain modules, decrease the complexity of brain communication and potentially affect cognitive function. (He et al., 2023).

Several studies have reported that extracellular amyloid plaques and intraneuronal tau neurofibrillary tangles are associated with AD (Ashrafian et al., 2021, He et al., 2018, Huang and Jiang, 2009). These pathological hallmarks can be assessed in vivo using positron emission tomography (PET) imaging. Autopsy studies and PET imaging have shown that amyloid-beta (Aβ) pathology follows a distinct spatial distribution, which aligns with large-scale functional networks in the brain (Braak et al., 2006, Cho et al., 2016, Grothe et al., 2016). Studies have highlighted the importance of examining specific brain regions for amyloid accumulation to understand the severity and progression of the disease. The Aβ pathway in AD underscores the cortical regions where Aβ accumulation begins during the initial preclinical stage (Hampel et al., 2021). Individuals with significant Aβ accumulation exhibited accelerated functional brain aging, particularly during the presymptomatic phase of autosomal dominant AD, and this acceleration is more pronounced in those with substantial Aβ pathology (Gonneaud et al., 2021).

Existing genetic studies on AD have compiled various characteristics, including clinical information such as survival time, family history, and AAO, and identified genetic risk factors associated with AD (Marioni et al., 2018, Wang et al., 2015, Wightman et al., 2021). In particular, research employing genetic risk scores derived from more than a million single-nucleotide polymorphisms (SNPs), including apolipoprotein E (APOE) variants, has revealed an oligogenic structure that markedly influences the onset of AD (Zhang et al., 2020). Several studies have identified specific genes associated with AAO in AD (Lutz et al., 2010, Naj et al., 2014). Among these are APOE, a gene widely known to be related to the risk of AD onset, and the APP gene, which is closely involved in the processing of Aβ (Basun et al., 2008, van der Flier et al., 2011). Notably, phenotypes are influenced by more than just genetic variations, underscoring some of the limitations of these approaches. We sought to overcome these limitations using multimodal integration to identify biomarkers associated with AAO in AD.

Multimodal integration is still necessary to better understand the relationships between neuroimaging and genetics, as well as the pathology underlying various conditions. A previous study identified genetic variants for AD enriched by imaging endophenotypes, which illuminated biological pathways from genetic factors to brain traits and phenotypic outcomes (Kim et al., 2022b). Another study on multimodal neuroimaging phenotypes identified a genetic locus that significantly affects disease progression and protection, highlighting the potential of imaging-derived endophenotypes to understand genetic influences on complex diseases (Scelsi et al., 2018). These findings suggest that understanding pathology through multimodal integration will aid in the early diagnosis and development of therapeutic interventions. Despite these advances, existing multimodal studies often lack comprehensive integration that spans from low-level genetics to high-level clinical outcomes.

Exploring the intricate interplay among multiple modalities, such as neuroimaging and genetic predispositions, affecting AAO is nontrivial because the modalities involved are high-dimensional. Recent advances in imaging genetics have addressed this issue using canonical correlation analysis (CCA)-based methods to explore high-dimensional multivariate associations across modalities (Witten & Tibshirani, 2009). Won et al. (2020) identified neuroimaging and genetic markers and accurately predicted the AAO of neurodegenerative disorders, demonstrating the potential to improve early diagnosis through the integrated analysis of imaging and genetic data (Won et al., 2020). Building on this foundation, we aimed to explore the multivariate associations between SNPs and multimodal neuroimaging (e.g., PET and fMRI) related to AAO in AD. By integrating large-scale brain molecular imaging, connectomics, and genetic variants, we aimed to uncover the complex interplay between molecular deposition, brain network alterations, and genetic predispositions that contribute to AAO in AD. This comprehensive approach offers a promising avenue to advance our understanding of AAO in AD.

2 Methods

2.1 Study participants

Imaging and phenotypic data were obtained from the Alzheimer’s disease neuroimaging initiative (ADNI) database (adni.loni.usc.edu) (Weiner et al., 2013). ADNI was launched in 2003 as a public–private partnership led by the principal investigator, Michael W. Weiner. The primary goal of ADNI is to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of MCI and early AD. The criteria for MCI and AD followed the guidelines established by the ADNI (Alzheimer’s disease neuroimaging initiative, 2024). For MCI, participants must express a subjective memory concern, which can be self-reported, recalled by a study partner, or noted by a clinician. Abnormal memory function must be documented by scoring below education-adjusted cutoffs on the Logical Memory II subscale. Additionally, participants must have a Mini-Mental State Examination (MMSE) score between 24 and 30, inclusive, and a Clinical Dementia Rating (CDR) score of 0.5, with a Memory Box score of at least 0.5. Compared to MCI, the criteria for AD include the following differences: participants must have an MMSE score between 20 and 24, inclusive, and a CDR score of 0.5 or 1. Participants were chosen based on the following criteria. Among 1,913 participants from ADNI-2/GO/3 phases, we excluded subjects 1) without SNP data (n = 465), 2) without complete imaging modalities (n = 656), 3) missing imaging modalities within two years before the age at onset (n = 686), and 4) who were already diagnosed with AD at baseline (n = 40), resulting in 66 participants (Supplementary Fig. S1). We focused on 66 patients with cognitive impairment from the ADNI database to investigate potential correlations between genetic and imaging factors and the AAO in AD. Demographic information on the main study participants is described in Table 1.Table 1 Demographic information of the study participants. Demographic information, including the number of participants, age, sex, MMSE score, APOE4 status, and education level, is described.

Number of participants	66	
Age of onset
(mean ± SD)	76.99 ± 8.25	
Sex(male: female)	39:27	
MMSE(mean ± SD)	23.80 ± 5.44	
APOE4
(carrier: noncarrier)	34:32	
Education (mean ± SD)	15.89 ± 2.44	
Abbreviations: ADNI, Alzheimer’s disease neuroimaging initiative; MMSE, mini-mental state examination; APOE4, apolipoprotein E4; SD, standard deviation.

2.2 Data acquisition

2.2.1 MRI

The MRI scans were obtained from 30 sites using a total of 12 scanners from GE, Philips, and Siemens. MRI data from the ADNI-2/GO phase were acquired using 3 T scanners across multiple sites. T1w images were obtained using a 3D magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence (repetition time [TR], 6.8 ms; echo time [TE], 3.2 ms; flip angle, 9°). FLAIR images were obtained using a 2D FLAIR sequence (TR, 9000 ms; TE, 90 ms; inversion time [TI], 2500 ms; flip angle, 150°). The rs-fMRI data were acquired using a 2D echo planar imaging (EPI) sequence (TR, 3,000 ms; TE, 30 ms; flip angle, 80°; number of volumes, 140; voxel size, 3.31 mm isotropic). MRI data from the ADNI-3 phase were also acquired using 3 T scanners across multiple sites. T1w images were obtained using a 3D MPRAGE sequence (TR, 2,300 ms; TE, 2.98 ms). FLAIR images were obtained using a 3D FLAIR sequence (TR, 4800 ms; TE, 119 ms; TI, 1650 ms; flip angle, 120°). rs-fMRI data were acquired using an EPI sequence (TR, 3,000 ms; TE, 30 ms; flip angle, 90°; number of volumes, 197; voxel size, 3.4 mm isotropic).

2.2.2 PET

The AV45-PET scans from ADNI-2/GO/3 were obtained from 30 sites using a total of 18 scanners from GE, Philips, and Siemens. All PET-acquiring sites also underwent inter-site and inter-scanner calibration to ensure data quality (Jagust et al., 2015). The AV45-PET images were acquired following a standardized dynamic protocol lasting 50 − 70 min post intravenous injection of 370 ± 37 MBq of [18F] AV45. The scan consisted of a total of 20 min, divided into four 5 min frames. The PET and fMRI images were obtained within the same session (i.e., visit). For further details about PET protocols, please refer to the ADNI website (Alzheimer’s disease neuroimaging initiative, 2024).

2.2.3 Gene

Genetic data were obtained from genomic deoxyribonucleic acid samples extracted from the peripheral blood of ADNI participants. Illumina chips were used for genotyping and intensity data were processed using GenomeStudio software from Illumina. Specifically, ADNI1 samples were genotyped using the Illumina Human610-Quad BeadChip and intensity data were processed using GenomeStudio v2009.1. ADNI-2/GO samples were genotyped using Illumina HumanOmniExpress BeadChip and intensity data were processed using GenomeStudio v2009.1. The II3 samples were genotyped using the Illumina Infinium Global Screening Array v2 (GSA2) and intensity data were processed using GenomeStudio v2.0.4 (Illumina) (Saykin et al., 2010). The SNP data remain constant across time points, being measured only once. Although the genetic data for the patients are fixed, slight variations may occur depending on the phase during which they were measured. To address this, we included only those genes consistently expressed across all phases (i.e., ADNI-1/2/GO/3).

2.3 Data preprocessing

2.3.1 MRI

The fMRI data were preprocessed using fMRIprep version 23.2.0, which automatically preprocesses the fMRI data to minimize manual intervention, ensuring high-quality results with minimal processing (Esteban et al., 2019). Corrections for intensity nonuniformity were applied to the T1w images, which were then skull-stripped. An anatomical T1w-reference map was generated after registration of multiple T1w images. Brain tissue segmentation (cerebrospinal fluid, white matter, and gray matter) was performed using FSL (Jenkinson et al., 2012). The brain surfaces were reconstructed using FreeSurfer (Fischl, 2012) with a FLAIR image to improve the refinement of the pial surfaces. Volume-based spatial normalization to the MNI standard space was performed through nonlinear registration with ANTs (Avants et al., 2009). Preprocessing was performed for each rs-fMRI session. A reference volume was generated, and slice timing and head-motion corrections were performed. The fMRI reference was then coregistered with the T1w reference with six degrees of freedom and subsequently registered onto the standard space. The first four volumes (12 s) were removed to adjust for the delay in the hemodynamic response. A bandpass filter (0.008 − 0.1 Hz) was applied to remove noise, and further effects of head motion, white matter, and cerebrospinal fluid signals were regressed out. Finally, spatial smoothing with an isotropic Gaussian kernel of 5 mm full width at half maximum (FWHM) was applied.

2.3.2 PET

The AV45-PET images were preprocessed as follows (Jagust et al., 2015). Raw PET images were coregistered across different frames to reduce the motion effect and then processed by averaging the frames over 5-min intervals. These images were reoriented to conform to a standard image grid of 160 × 160 × 96 voxels with 1.5 mm3 voxels and intensity normalization. Spatial smoothing was performed with 8 mm FWHM. The PET images were coregistered to the corresponding T1w image with six degrees of freedom and subsequently nonlinearly registered onto the standard space.

2.3.3 Gene

We identified SNPs associated with AD that were common in ADNI-1/2/GO/3 genome-wide association studies (GWAS). The SNP imputation approach was used to address the limited overlap caused by different genotyping arrays for each phase of the ADNI dataset. All imputation and quality control processes were performed according to the ENIGMA protocol (https://enigma.ini.usc.edu/). Before imputation, we eliminated any strands of ambiguous SNPs and re-screened for low minor allele frequency (<0.01), missingness, and Hardy − Weinberg equilibrium (<1e-6). We used the Michigan imputation server (Das et al., 2016) to impute the entire genetic dataset using 1000 Genomes phase 3 v5 (Consortium, 2015) as a reference panel, and phasing was performed using Eagle v2.3, using the American population (Loh et al., 2016). PLINK 1.9 (Chang et al., 2015) was used for quality control. Finally, a categorical file was created by recording the binary file, which included the number of copies of the minor alleles of each variant for each participant.

2.4 Estimation of AAO

The ADNI database provides longitudinal data that provide an estimated timeline for AD conversion. Considering the inherent uncertainty in identifying the precise moment of disease conversion, we adopted a midpoint imputation method. Midpoint imputation refers to imputing interval-censored time to an event using the midpoint of the interval (Kim et al., 2022a). Therefore, the approximate AAO in AD was deduced from the interval between the final diagnosis of MCI and the initial diagnosis of AD. Sometimes imaging data could be missing at the time of onset, and thus, we expanded the time frame to include subjects whose imaging was taken within two years before the onset to have enough samples. The two-year window was chosen because a typical follow-up interval of PET scans in ADNI was around two years (Alzheimer’s disease neuroimaging initiative, 2024).

2.5 FC manifolds

We estimated cortex-wide FC gradients and subcortical manifold degrees from rs-fMRI data using the Schaefer atlas (Schaefer et al., 2018) with 200 parcels for the cortex and the Desikan-Killiany atlas (Desikan et al., 2006) for the subcortex area (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus). We calculated the FC matrices by determining Pearson’s correlations between the time-series data of the two distinct brain regions for each participant. Fisher’s r-to-z transformation was applied to these matrices to normalize the distribution. We then created a group-level connectivity matrix by averaging the individual matrices. This matrix was refined by preserving only the strongest 10 % of connections per row after thresholding. Subsequently, an affinity matrix was constructed using a normalized angle kernel to highlight the similarity in connectivity patterns across cortical areas. We derived low-dimensional representations (i.e., gradients) from this matrix using diffusion map embedding (Coifman & Lafon, 2006), a method known for its noise resilience and computational efficiency compared with other nonlinear manifold learning techniques (Tenenbaum et al., 2000, Von Luxburg, 2007). We used diffusion map parameters following prior research (Park, Hong, et al., 2021). To mitigate potential demographic biases in the ADNI dataset, we aligned individual gradients onto the ADNI-to-HCP group-level gradients template (Kim et al., 2024). Specifically, we generated group-level gradient templates from the averaged FC of the ADNI dataset. Subsequently, the ADNI group-level gradients template was further aligned with those from the independent Human Connectome Project (HCP) database (Van Essen et al., 2012). For individual gradient alignment, a Procrustes rotation was performed (Langs et al., 2015). The entire gradient estimation process was facilitated using the BrainSpace toolbox (Vos de Wael et al., 2020). We focused on the first three cortical gradients, G1, G2, and G3, because they are biologically interpretable (Choi et al., 2024, Nenning et al., 2023). Finally, we calculated the functional manifold eccentricity, defined as the Euclidean distance between the central points (i.e., 3-element vector due to G1, G2, and G3 averaged over the regions), computed from the group template and individual gradients (Park et al., 2021a). This scalar measure quantifies how an individual gradient deviates from the group gradient to provide a simple summary of global brain organization. For subcortical regions, we calculated the subcortical-weighted manifold degree by multiplying the subcortico-cortical FC by the cortical gradients (i.e., G1, G2, and G3). We then calculated the nodal degree of the subcortical-weighted manifolds, referred to as S1, S2, and S3 (Park et al., 2021a, Park et al., 2021b). Finally, we computed the subcortical manifold degree eccentricity with S1, S2, and S3 using the same procedure as for the cortical functional manifold eccentricity. Although the ADNI fMRI data adopted standardized protocol, site-specific differences can persist even with standardized MRI scanning protocol (Pouwels et al., 2023). To address these site effects, we applied longitudinal ComBat harmonization to the longitudinal data of 792 individuals across multiple sessions (Supplementary Fig. S1, (Beer et al., 2020)). We controlled for the site effects from the FC manifold eccentricity in both cortex and subcortex.

2.6 Amyloid-beta deposit estimation

To evaluate amyloid biomarkers, AV45-PET imaging was used to provide detailed measurements of these pathological features. The standardized uptake value ratio (SUVR) for AV45-PET images was calculated using the entire cerebellum as the reference region. Voxel values corresponding to the regions of interest (ROIs) defined by the atlases, specifically the Schaefer atlas (Schaefer et al., 2018) for cortical regions and the Desikan − Killiany atlas (Desikan et al., 2006) for subcortical areas, were averaged and further divided by the reference value. These values within each ROI provided regional SUVR values.

2.7 SNP identification

After data preprocessing, we obtained 5,410,468 SNPs. Unlike traditional approaches that analyze each SNP individually, we curated the SNPs associated with AD collected from the GWAS Catalog (Sollis et al., 2023). We used SNPs related to AD that have been discovered in other studies to identify novel SNPs related to AAO. The GWAS catalog revealed 2,589 SNPs linked to AD, which were then used to extract relevant SNPs from our genetic dataset. After quality control and filtering, the final dataset contained 314 SNPs.

2.8 Extended objective-specific sparse canonical correlation analysis

We adapted the existing object-specific sparse CCA (os-SCCA) (Won et al., 2020) to incorporate two neuroimaging modalities and SNP related to the AAO in AD. This algorithm uses objective-specific vectors associated with a target task. It enables the identification of loading vectors relevant to the target task rather than simply seeking correlations between neuroimaging and genetic data. We performed z-score standardization for both eccentricity and SUVR. We considered six (i.e., 4 combination 2) possible pair-wise associations in our model. Specifically, given datasets X1∈Rn×p,X2∈Rn×q,X3∈Rn×r, and X4∈Rn×1, where X1 denotes p features of the FC manifold eccentricity, X2 denotes q features of the SUVR, X3 denotes r features of the SNP data, and X4 denotes a feature related to the target objective (i.e., AAO in AD), the objective function is as follows:max.u1,u2,u3u1TX1TX2u2+u1TX1TX3u3+u1TX1TX4u4+u2TX2TX3u3+u2TX2TX4u4+u3TX3TX4u4

s.t.uiTXiTXjuj≤1i<j,‖u1‖≤c1,‖u2‖≤c2,‖u3‖≤c3

where ui(i≠4) are the corresponding canonical vectors of Xi and u4 is the scalar for the target objective. The final optimization formulation with a sparsity term can be rewritten as follows:min.u1,u2,u3-u1TX1TX2u2-u1TX1TX3u3-u1TX1TX4u4-u2TX2TX3u3-u2TX2TX4u4-u3TX3TX4u4+λ1‖u1‖+λ2‖u2‖+λ3‖u3‖

, where λ1,λ2 and λ3 were L1 regularization parameters, which control the sparsity of the canonical loading vectors. Due to the limited number of samples, we used nested four-fold cross-validation (CV) for hyperparameter selection. For the outer loop, all data were split into training and test sets. For the inner loop, the training set was also subjected to a four-fold split into inner training and inner validation sets. All parameters were jointly optimized using a nested four-fold CV:scorek=16∑i=13∑j=i+14corrXiku-ik,Xjku-jk

CV=14∑k=14scorek

The scorek refers to the average correlation coefficient across all pairs of observed values within each fold, where Xik and Xjk denote the individual features of each possible pair in the k-th subset of the test set. u-ik and u-jk denote the estimated canonical loading vectors from the datasets, except for k-th subset. We selected hyperparameters from the best CV scores. Then, we averaged the estimated canonical loading vectors from the nested four-fold CV. For robustness, we repeated the same procedure 25 times and subsequently averaged the results. The train/test size and mean AAO for each fold can be found in Supplementary Table S1.

2.9 Multimodal canonical score

We computed a summary score, denoted as the canonical score for each modality, using the weighted sum of the loading and feature vectors. The canonical score was generated by multiplying the features by the canonical loading values derived from the optimized model (i.e., Xiui). This process yielded three canonical scores: FC eccentricity, SUVR, and SNP. The AAO was not considered because it is scalar; therefore, no summary was required. It represents a score that recapitulates important brain regional features or genetic factors in the process in which the four modalities interact. We denoted the canonical score for SNP as the genetic canonical score, the canonical score for functional manifolds as the FC canonical score, and the canonical score for Aβ SUVR as the Aβ canonical score.

2.10 Serial mediation analysis

We performed a standard four-variable serial mediation analysis (Hayes, 2009) using the lavaan R package (Rosseel, 2012), where associations between the genetic canonical score, FC canonical score, Aβ canonical score, and AAO in AD were evaluated. We explored the intricate relationships among genetic factors, molecular markers, and FC. We determined the significance of the mediation effect by bootstrapping for 5000 iterations. All regression weights were standardized.

2.11 Sensitivity analysis

Our study considered only 66 samples due to the limited number of samples that met the inclusion criteria. Considering the relatively small sample size, we broadened our inclusion criteria to include an additional 40 participants who had already been diagnosed with AD upon their entry into the study. Although this decision increased the sample size, it may have overestimated the AAO due to the retrospective nature of the data for these participants. The analysis was performed using an expanded dataset. Demographic information for this expanded dataset is provided in Supplementary Table S2. Additionally, we conducted a sensitivity analysis by adjusting the threshold of the FC matrix for the 66-subject setting to examine whether the threshold level affects the outcomes. This analysis was motivated by a recent study that emphasized the selection of hyperparameters for the FC analysis (Luppi et al., 2024). Our choice of a 10 % threshold was based on existing FC gradient studies (Dong et al., 2021, Hong et al., 2019, Kim et al., 2024, Margulies et al., 2016), but we also conducted sensitivity analyses using 5 % and 20 % thresholds to ensure the robustness of our results.

3 Results

3.1 Whole-brain FC manifolds

Our FC gradients showed smooth transitions across the cortical surface and explained 48.3 % of group-averaged FC. The three gradients were largely consistent with those reported in previous studies (Huntenburg et al., 2018, Margulies et al., 2016, Nenning et al., 2023). Functional gradients map high-dimensional FC information onto a low-dimensional space yielding a few eigenvectors that depict hierarchical brain organization. This hierarchy is reflected in neocortical geometry, where the topology of functional gradients aligns with geodesic distances between sensory and default mode regions (Leech et al., 2023, Margulies et al., 2016, Smallwood et al., 2021). The estimated functional manifolds (i.e., gradients) show smoothly varying spatial patterns where the first three functional eigenvectors were biologically interpretable (Margulies et al., 2016, Vos de Wael et al., 2020). The first gradient extended from the primary sensory to the association cortices, reflecting the large-scale functional hierarchy. The second gradient spanned from the visual to the somatomotor regions and the third gradient distinguished the multiple demand networks and the rest of the brain (Supplementary Fig. S2A). Studies have indicated that the three gradients are associated with cognitive function (Karapanagiotidis et al., 2020, Mckeown et al., 2023, Song et al., 2023). Describing brain processes with these functional gradients may help us understand how brain processes emerge from the macroscopic organization of the cortex. To summarize the functional organization, we calculated the functional manifold eccentricities of the three FC gradients (Fig. 1A). This measure reflected how far the gradient of each node was located from the center of the template manifold. Beyond cortical connectivity patterns, we computed subcortical-weighted manifold degrees by mapping the influence of subcortical regions onto the cortical manifold space (Supplementary Fig. S2B). This approach evaluates connectivity patterns within the subcortico-corticalareas in the macroscale context of cortico-cortical connectivity Subsequently, the eccentricities of these subcortical manifolds were calculated (Fig. 1A). These combined manifolds (i.e., cortical manifold eccentricity and subcortical-weighted manifold degree eccentricity) provide comprehensive functional representations of the entire brain.Fig. 1 Three modalities that serve as a multimodal input to our SCCA approach. (A) Functional connectivity gradients (G1, G2, and G3) are estimated from the functional connectivity matrix computed from the resting-state functional MRI (rs-fMRI). These gradients accounted for approximately 48.3% information of cortico-cortical connectivity data. Subsequently, the functional manifold eccentricity and the subcortical-weighted manifold degree eccentricity are computed. (B) Regional SUVR values are estimated from the AV45-PET. Averaged SUVR values are shown in the cortical and subcortical regions. (C) SNPs are obtained from genetic data and following imputation and quality control procedures, a total of 314 SNPs are identified. Abbreviations: fMRI, functional magnetic resonance imaging; PET, positron emission tomography; SNP, single-nucleotide polymorphism; SUVR, standardized uptake value ratio; AV45, [18F] Florbetapir; ADNI, Alzheimer’s disease neuroimaging initiative.

3.2 Whole-brain AV45-PET SUVR

We found that certain brain regions demonstrated elevated SUVR measurements, which correlate with the common patterns of Aβ plaque accumulation observed in neurodegenerative disorders (Chapleau et al., 2022, Villeneuve et al., 2015). Accumulated amyloid deposition was identified in the frontal, medial prefrontal regions, temporal cortices, and precuneus, as well as in the putamen, and pallidum regions of the averaged AV45-PET SUVR map (Fig. 1B). We obtained 314 SNPs from genetic data following the imputation and quality control procedures given in the Methods (Fig. 1C).

3.3 Multimodal canonical loadings

We analyzed multimodal canonical loadings as AAO markers using the SCCA model. The brain regions selected from the corresponding modalities are mapped in Fig. 2 and the selected SNPs are listed in Table 2. Overall, canonical loadings of the functional manifolds were predominantly located in the frontoparietal regions, visual cortex, entorhinal cortex, amygdala, caudate, thalamus, pallidum, and putamen. In AV45-PET, the frontoparietal, motor, visual cortex, hippocampus, caudate, thalamus, and amygdala regions showed high canonical loading values.Fig. 2 Canonical loadings in two imaging modalities. (A) Estimated canonical loadings of functional manifolds on the fMRI imaging side are plotted on the cortical surface and subcortical structures. (B) Similarly, on the PET imaging side, canonical loadings are plotted on the cortical surface and subcortical structures. Abbreviations: SUVR, standardized uptake value ratio; AV45, [18F] Florbetapir;

Table 2 Top 15 identified SNPs.

Chromosome number	rsID for the SNP	Loading	BP	MA	Associated gene	
1	rs551379	0.00303	85,492,639	C	DDAH1	
3	rs9846480	0.00592	138,306,554	G	NME9	
4	rs6448799	0.00308	11,628,425	C	LINC02360,HS3ST1	
6	rs9462027	0.00393	34,829,464	G	BLTP3A	
7	rs7810606	0.00403	143,411,065	T	EPHA1-AS1	
7	rs10270490	0.00697	37,579,541	T	NECAP1P1, ELMO1	
11	rs10792830	0.00524	86,127,766	G	RNU6-560P, PICALM	
12	rs249153	0.00505	94,930,613	T	NDUFA12	
15	rs11637445	0.00297	67,699,268	G	MAP2K5	
17	rs4277405	0.00639	63,471,557	C	PPIAP55, CYB561	
17	rs4311	0.00378	63,483,402	C	ACE	
17	rs7225787	0.00315	49,351,211	A	ZNF652	
19	rs6859	0.00460	44,878,777	G	NECTIN2	
21	rs4817090	0.00561	26,161,943	T	APP	
21	rs2154481	0.00394	26,101,558	T	APP	
Abbreviations: BP, base-pair location in hg38 coordinates; MA, minor allele of variant.

Our comprehensive analyses also uncovered significant genetic variants, including SNPs rs4817090 and rs2154481 in APP, rs6859 in NECTIN2, and rs4311 in ACE (Table 2). We reported the top 15 SNPs out of the 314 absolute loadings to focus on the dominant SNPs. These genes are associated with different modalities in which multimodal relationships are maximized. APP is linked to Aβ accumulation and alterations in FC patterns and is also associated with early-onset AD (EOAD) (Su et al., 2017, Zhang et al., 2011, Zheng et al., 2018). NECTIN2 is also associated with cognitive decline and late-onset AD (LOAD) (Rajendrakumar et al., 2024). ACE is related to a decrease in LOAD, cognitive scores, Aβ accumulation, and changes in the FC pattern (Belbin et al., 2011, Kehoe et al., 2003, Ohrui et al., 2004, Wang et al., 2012).

3.4 Results of serial mediation analysis

We identified a model that demonstrated significant direct effects of canonical scores ([a], [b], [c], and [f]; all p < 0.05) (Fig. 3). We found that the indirect effect of the mediation pathways from the genetic canonical score to the FC canonical score and then to the AAO in AD was significant ([dc] = 0.121, p < 0.05). Additionally, the pathways from the genetic canonical score to the Aβ canonical score and then to the AAO in AD were also significant ([ae] = 0.139, p < 0.05). Importantly, this model showed a smaller direct effect ([f] = 0.188, p < 0.05) of the genetic canonical score on the AAO score compared to an indirect pathway through Aβ canonical score and FC canonical score ([abc] = 0.466, p < 0.05). Together, these genetic factors mediate FC changes through molecular burden and ultimately contribute to AAO.Fig. 3 Results of the mediation analysis. Serial mediation analysis is used to assess the direct and indirect effects of the genetic canonical score on the AAO in AD, mediated by the Aβ canonical score and FC canonical score. Reported values are regression weights with p-values in asterisks. * denotes a p-value < 0.05. Abbreviations: Aβ, amyloid-beta; FC, functional connectivity; AAO, age at onset;

3.5 Results of the sensitivity analysis

The results of the expanded dataset (n = 106) were in general consistent with the main findings. We examined the canonical loadings of functional manifolds, which notably highlighted regions, including the frontoparietal visual, and entorhinal cortices; accumbens; amygdala; thalamus; and pallidum (Supplementary Fig. S3A). In AV45-PET scans, high canonical loadings were observed in the frontoparietal regions, motor and visual cortices, hippocampus, caudate, thalamus, and amygdala (Supplementary Fig. S3B). Our analysis also revealed significant genetic variants, including SNPs rs4817090 and rs2154481 in APP, rs6859 in NECTIN2, and rs4311 in ACE, similar to the main findings (Supplementary Table S3). Furthermore, we identified the SNP rs405509 and rs584007 in APOE, a widely recognized and potent gene associated with AD. We also performed serial mediation analysis using expanded datasets (Supplementary Fig. S3C). We observed significant mediated pathways where the genetic canonical score influences the Aβ canonical score and then affects the FC canonical score. The canonical FC score was significantly associated with AAO in patients with AD. The indirect effects of genetic factors on AAO through Aβ scores and FC scores were more substantial ([abc] = 0.387, p < 0.05) than the direct effects ([f] = 0.066, p < 0.05). The results of the different FC thresholding were also consistent with the main findings. The canonical loadings of the functional manifolds were still predominantly located in the frontoparietal regions, visual cortex, entorhinal cortex, amygdala, caudate, thalamus, pallidum, and putamen. The canonical loading profiles of the 5 and 10 % thresholding were similar, but there were minor differences in the entorhinal cortex and the hippocampus in the subcortex when we compared the profiles between the 10 and 20 % thresholds (Supplementary Figs. S4-S5). The canonical loadings of the PET were consistently observed in the frontoparietal regions, motor and visual cortices, hippocampus, caudate, thalamus, and amygdala. The identified SNPs stayed consistently among different FC thresholds where rs4817090, rs2154481, rs6859, and rs4311 were reported (Supplementary Tables S4-S5).

4 Discussion

The etiology of AD is influenced by the accumulation of Aβ and tau proteins, which disrupts brain connectivity (Hampel et al., 2021). Given the hereditary nature of AD, multiple genes and their variants modulate the disease susceptibility and AAO in AD (Eid et al., 2019, Naj, A. C., Schellenberg, G. D., & Consortium, A. s. D. G, 2017). In this study, we used AV45-PET SUVR to assess the spatial distribution of amyloid plaques and jointly explored alterations in functional manifolds. Simultaneously, SNPs were used to explore the relationship between genetic variations that induce protein synthesis. This study revealed multiple imaging biomarkers and SNPs associated with AAO in AD, highlighting a polygenic risk profile for the disease. Our SCCA model was designed not only to identify correlations between imaging and genetic markers, but also to integrate a clinical phenotype (i.e., AAO). We identified several markers that demonstrated interactions between functional manifolds: AV45-PET SUVR, SNP, and AAO. The intricate relationship between genetic predisposition and neuroimaging alterations was further validated through mediation analysis, which identified specific pathways that link these factors to AAO in AD.

The regions highlighted by the canonical loadings of the functional manifolds were largely relevant to AD. Changes in the lateral prefrontal cortex, temporal lobes, and parietal cortex affect core cognitive functions, such as executive function, language, and spatial processing (Jones & Graff-Radford, 2021). These areas are involved in large-scale neural networks that are essential for controlling cognitive function (Friedman and Robbins, 2022, Haber et al., 2022). The lateral prefrontal cortex plays a role in working memory, attention, and problem-solving, and changes in these regions can lead to executive dysfunction (Chen et al., 2009). This is highlighted in atypical AD phenotypes, which target parietal-temporal-frontal networks that are crucial for executive functions (Jacobs et al., 2012, Jones and Graff-Radford, 2021). In our analysis, we found frontoparietal regions, such as prefrontal cortex, are affected. This suggests that the involvement of the prefrontal cortex in AD, especially within higher-order cognitive networks, contributes significantly to AD onset. The findings of AV45 PET SUVR canonical loading and amyloid plaque accumulation emphasize the impact of amyloid pathology in various brain regions, including the temporal lobes, frontal and cingulate cortices, entorhinal cortex, motor cortex, and subcortical areas, such as the hippocampus. These findings highlight the marked amyloid burden in these regions, underscoring their significant role in AD pathology. Studies have confirmed the crucial role of amyloid deposits in the progression of AD. In particular, amyloid pathology affects the overall cortex and specific cortical regions such as the precuneus, anterior and posterior cingulate, frontal median, temporal, parietal, and occipital cortices (Camus et al., 2012). One study highlighted two critical interactions between Aβ and tau in the progression of AD, with the initial key moment marked by the emergence of neocortical amyloid-beta in regions connected to the entorhinal cortex (Lee et al., 2022). Furthermore, an fMRI study showed that Aβ positive MCI patients exhibit increased hippocampal activity and faster disease progression, highlighting the importance of Aβ in the prediction of AD outcomes (Huijbers et al., 2015). Aβ protein isolated from the AD brain disrupts hippocampal plasticity and alters neurotransmitter release, while APP fragments form aggregates that impair synaptic activity (Wang et al., 2017). These studies collectively reinforce the contribution of amyloid deposition to the development of AD, which affects a wide range of brain regions, including those identified using AV45-PET SUVR canonical loading.

The genetic mechanisms by which SNPs influence AD are multifaceted. We highlighted the top 15 SNPs associated with multiple genes, including APP, NECTIN2, and ACE, from both the main and sensitivity analyses. Mutations in APP are significant in patients with EOAD (Basun et al., 2008). The APP processing pathway generates peptides that accumulate as amyloid plaques in the brains of patients with AD (Zheng & Koo, 2006). Mutations in APP are one of the primary causes of EOAD and serve as a critical factor in explaining the relationship between Aβ and AAO. The APP gene exhibited the highest canonical loading value in the sensitivity analysis. NECTIN2, also known as PVRL2, has various effects on health-related phenotypes (Yashin et al., 2018). In the context of AD, this gene is significantly correlated with cognitive decline and an increased risk of AD (Rajendrakumar et al., 2024). ACE, where rs4311 is located, breaks down Aβ and plays a role in the regulation of ACE protein levels according to the rs4311 variant (Domingues-Montanari et al., 2011, Miners et al., 2011). Previous studies have found that ACE significantly contributes to AAO in AD (Kehoe et al., 2004). Furthermore, the APOE gene, where rs584007 is located, was notably prominent in the sensitivity analysis. APOE is strongly associated with the risk of AD onset and particularly contributes to EOAD (van der Flier et al., 2011). Moreover, APOE is involved with Aβ and FC, specifically, it contributes to the clearance of Aβ and is known to influence alterations in FC (Castellano et al., 2011, Verghese et al., 2013).

In our serial mediation analysis, we showed the influence of genetic factors at the molecular level on higher-order phenotypic observations. Our results indicate that, while SNPs affect the AAO in AD, they also exhibit serial mediation pathways. Specifically, our findings reveal that SNPs primarily influence Aβ burden, which subsequently affects changes in FC, ultimately contributing to AAO in AD. Previous studies have reported a linked relationship between Aβ deposition in the brain cortex and its advocation of alterations in FC (Elman et al., 2016, Yu et al., 2021). Furthermore, the role of previously identified SNPs, such as APP and APOE, in mediating the impact of Aβ on FC was reaffirmed through mediation analysis. The discovery of SNPs that have not been previously associated with these processes also sheds light on their potential for further analysis. In summary, AAO is mediated by specific neuropathological changes, particularly the accumulation of Aβ and FC changes due to genetic factors. Our findings largely replicate a plausible path in well-known open ADNI datasets, reinforcing existing findings. However, our mediation analysis was employed on cross-sectional data in this study. Consequently, our findings should not be construed as establishing a direct causal pathway from genes to molecular pathology to FC. This methodological choice was made to explore potential relationships.

One limitation of our study was the complexity associated with interpreting the results from the six pair-wise multimodal associations. Given this multiplicity, the direct interpretation of canonical vectors (i.e., the sign of canonical loadings) poses a significant challenge. This complexity may obscure our understanding of the specific contributions of each brain area (or SNP). Furthermore, the expanded criteria of AAO in AD in sensitivity analyses could cause bias in estimating AAO. Therefore, AAO may not accurately reflect the actual onset of pathological changes. Finally, our study used data from the ADNI database; further validation using an independent dataset is necessary.

5 Conclusion

The integration of neuroimaging and genetic data illuminates the intricate interplay between functional alterations, genetic predisposition, and AAO in AD. Our findings demonstrate that specific SNPs correlate with amyloid plaque distribution and subsequent changes in FC, ultimately influencing clinical phenotypes. This low-to-high-level pathway highlights the importance of considering multiple biological levels to fully understand AD. We discovered significant associations among amyloid plaques, FC manifolds, and specific SNPs, emphasizing the polygenic nature of the disease. Our study contributes to ongoing efforts to unravel the multifaceted etiology of AD and may provide more personalized and effective interventions in the future.

CRediT authorship contribution statement

Sewook Oh: Methodology, Investigation, Conceptualization. Sunghun Kim: Writing – original draft, Methodology, Investigation. Jong-eun Lee: Writing – review & editing. Bo-yong Park: Writing – review & editing, Investigation. Ji Hye Won: Writing – review & editing. Hyunjin Park: Writing – original draft, Supervision.

Appendix A Supplementary data

The following are the Supplementary data to this article:Supplementary Data 1

Data availability

We used open data of ADNI.

Acknowledgments

Data collection and sharing for this project were funded by the 10.13039/100014041 Alzheimer’s Disease Neuroimaging Initiative (ADNI) (10.13039/100000002 National Institutes of Health Grant U01 AG024904 ) and the 10.13039/100000005 DOD ADNI (Department of Defense award number W81XWH-12-2-0012 ). ADNI is funded by the 10.13039/100000049 National Institute on Aging , the 10.13039/100000070 National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; 10.13039/100007742 BioClinica , Inc.; 10.13039/100005614 Biogen ; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; 10.13039/501100003037 Elan Pharmaceuticals, Inc.; 10.13039/100004312 Eli Lilly and Company ; EuroImmun; 10.13039/100007013 F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; 10.13039/100006775 GE Healthcare ; 10.13039/501100015725 IXICO Ltd .; Janssen Alzheimer Immunotherapy 10.13039/100006190 Research & Development , LLC.; 10.13039/100004331 Johnson & Johnson Pharmaceutical 10.13039/100006190 Research & Development LLC.; Lumosity; 10.13039/501100013327 Lundbeck ; Merck & Co., Inc.; 10.13039/100007054 Meso Scale Diagnostics , LLC.; NeuroRx Research; Neurotrack Technologies; 10.13039/100008272 Novartis Pharmaceuticals Corporation ; 10.13039/100004319 Pfizer Inc.; Piramal Imaging; 10.13039/501100011725 Servier ; 10.13039/100008373 Takeda Pharmaceutical Company ; and Transition Therapeutics. The Canadian Institute of 10.13039/100005622 Health Research provided funds to support the ADNI clinical sites in Canada. Private-sector contributions were provided by the 10.13039/100000009 Foundation for the National Institutes of Health (www.fnih.org). The grantee organization was the 10.13039/100009804 Northern California Institute for Research and Education , and the study was coordinated by the Alzheimer’s Therapeutic Research Institute at the 10.13039/100006034 University of Southern California . ADNI data were disseminated by the Laboratory for Unk Imaging at the University of Southern California.

Author contributions

S.O., S.K., and H.P. designed the study, analyzed the data, and wrote the manuscript. J.W. and B.P. reviewed the manuscript. H.P. is the corresponding author of this study and is responsible for the integrity of the data analysis. All authors reviewed and approved the final version of the manuscript for publication.

Code availability

The codes for gradient generation are available at https://github.com/MICA-MNI/BrainSpace.

Funding

This study was supported by 10.13039/501100001321 National Research Foundation (NRF-2020M3E5D2A01084892), 10.13039/501100010446 Institute for Basic Science (IBS-R015-D1 ), AI Graduate School Support Program (2019-II190421 ), ICT Creative Consilience program (IITP-2024-2020-0-01821), the Artificial Intelligence Innovation Hub program (RS-2021-II212068 ), and the Institute for Information and Communications Technology Planning and Evaluation (IITP), funded by the Korean Government (MSIT) (No. 2022-0-00448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks).

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2024.103660.
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