Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Abstract

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Large-scale neuronal network breakdown underlies memory impairment in Alzheimer’s disease (AD). However, the differential trajectories of the relationships between network organisation and memory across pathology and cognitive stages in AD remain elusive. We determined whether and how the influences of individual-level structural and metabolic covariance network integrity on memory varied with amyloid pathology across clinical stages without assuming a constant relationship. Methods: Seven hundred and eight participants from the Alzheimer’s Disease Neuroimaging Initiative were studied. Individual-level structural and metabolic covariance scores in higher-level cognitive and hippocampal networks were derived from magnetic resonance imaging and [18F] fluorodeoxyglucose positron emission tomography using seed-based partial least square analyses. The non-linear associations between network scores and memory across cognitive stages in each pathology group were examined using sparse varying coefficient modelling. Results: We showed that the associations of memory with structural and metabolic networks in the hippocampal and default mode regions exhibited pathology-dependent differential trajectories across cognitive stages using sparse varying coefficient modelling. In amyloid pathology group, there was an early influence of hippocampal structural network deterioration on memory impairment in the preclinical stage, and a biphasic influence of the angular gyrus-seeded default mode metabolic network on memory in both preclinical and dementia stages. In non-amyloid pathology groups, in contrast, the trajectory of the hippocampus-memory association was opposite and weaker overall, while no metabolism covariance networks were related to memory. Key findings were replicated in a larger cohort of 1280 participants. Conclusions: Our findings highlight potential windows of early intervention targeting network breakdown at the preclinical AD stage. Funding: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). We also acknowledge the funding support from the Duke NUS/Khoo Bridge Funding Award (KBrFA/2019-0020) and NMRC Open Fund Large Collaborative Grant (OFLCG09May0035). Editor's evaluation This paper presents important information about how potential network-based structural and metabolic imaging biomarkers are associated with memory performance during distinct disease stages, in line with previous hypothetical biomarker models. The study is conceptually sound and methodologically convincing and will be of interest to neuroscientists and medical professionals involved in the study of Alzheimer's disease and related neurodegenerative conditions. https://doi.org/10.7554/eLife.77745.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Alzheimer’s disease (AD) is a neurodegenerative disease that is characterised by neuropathological accumulation of amyloid-beta (Aβ) plaques (A), intraneuronal tau neurofibrillary tangles (T), and neurodegeneration (N) in the brain (Braak and Braak, 1991; Serrano-Pozo et al., 2011). While AD is traditionally a clinical-pathologic condition, the emerging development of biomarkers to profile AD pathophysiology has led to the proposal of AD as a biological construct based on the AT(N) system (Jack et al., 2016; Jack et al., 2018). The incorporation of the AT(N) classification into the clinical continuum will offer robust disease staging by combining both pathophysiological and cognitive phenotypes which span from cognitively intact to mild cognitive impairment (MCI) before progressing to the dementia stage (Knopman et al., 2018). Studies have suggested that Aβ is the first to become abnormal in AD, followed by downstream pathophysiological changes of tauopathy, neurodegeneration, and cognitive impairment (Bateman et al., 2012; Jack et al., 2013a; Bertens et al., 2015). While neurodegeneration is widely associated with worse cognitive impairment in neurocognitive disorders, it remains unknown whether the influence of neurodegeneration on cognitive function varies with AD biomarkers status and across the AD continuum. Neurodegeneration represents neuronal injury in the forms of cerebral grey matter (GM) atrophy and hypometabolism. In AD, it is widely postulated that Aβ triggers tau-mediated toxicity leading to AD-type neurodegeneration in brain regions such as the hippocampus, the precuneus and posterior cingulate cortex (PCC), bilateral angular gyrus (ANG), and medial temporal lobes (Chételat et al., 2008; Misra et al., 2009; Mosconi et al., 2009; Kljajevic et al., 2014). Recently, amyloid and tau pathologies are also shown to have a synergistic effect on AD-type hypometabolism, involving the basal and mesial temporal, orbitofrontal, and anterior and posterior cingulate cortices (Hanseeuw et al., 2017; Pascoal et al., 2017). However, neurodegeneration may also occur prior to incident amyloid positivity (Jack et al., 2013b) and be influenced by the loss of microtubule stabilising function and toxic effects of tau pathology, independent of amyloid pathology (Ballatore et al., 2007). Advancement in brain network analysis offers insights into the functional effects of AD pathophysiology on cognitive changes. Work from our group has demonstrated that AD pathophysiologies compromise brain structure and function systematically by capitalising on the intrinsic connectivities among brain regions (Zhou et al., 2012). Accumulating evidence suggests that AD pathological deposition around neurons which impairs synaptic communication, leads to specific large-scale brain intrinsic network disorganisation (Seeley et al., 2009; Marchitelli et al., 2018). Decreased functional connectivity in the default mode network (DMN) derived from resting state functional MRI is well-described in MCI and AD (Greicius et al., 2004; Zhou et al., 2010; Chong et al., 2017; Chong et al., 2019; Zhou et al., 2017), while aberrant loss of functional connectivity in other higher-order cognitive networks such as the executive control network (ECN) and salience network (SN) are being increasingly reported (Chong et al., 2017; Brier et al., 2012; He et al., 2014). Brain networks can also be constructed based on similarity in GM structure and metabolism between brain areas across individuals, known as the GM structural and metabolic covariance network, respectively (Ripp et al., 2020; Zielinski et al., 2010; Montembeault et al., 2012). Both structural and metabolic covariance networks show convergent patterns with the intrinsic connectivity network in healthy individuals and mirror GM atrophy patterns in distinct neurodegenerative disorders (Seeley et al., 2009; Ripp et al., 2020; Lizarraga et al., 2021). Using this approach, a recent study revealed differential patterns of structural covariance networks within different amyloid pathology groups classified by cerebrospinal fluid (CSF) Aβ1–42 and P-tau181 levels (Li et al., 2019). However, existing studies on the GM structural and metabolic covariance networks were largely reliant on group-level correlation maps of cortical morphology and metabolism, which cannot be used to infer individual differences in cognition. It is postulated that network analysis at the individual level will allow direct evaluation of each individual’s structural and metabolic covariance networks, hence providing deeper understanding on the effects of brain networks on cognitive performances (Kim et al., 2016). For instance, a cube-based correlation approach to calculate the individual GM networks by computing intracortical similarities in GM morphology (Tijms et al., 2012) showed that single-subject GM graph properties were associated with individual differences of clinical progression in AD (Tijms et al., 2018; Tijms et al., 2014; Vermunt et al., 2020; Tijms et al., 2013). A network template perturbation approach was also introduced to construct an individual differential SCN using regional GM volume, though it required reference models derived from a group of normal control subjects (Liu et al., 2021). Nevertheless, the relationships between changes in individual-level network-based neurodegeneration across different amyloid pathology groups and cognitive stages, and their influence on memory impairment, remain unclear. The influence of cerebral GM loss and [18F] fluorodeoxyglucose (FDG) hypometabolism on cognitive function in AD has often been modelled as a linear relationship (Habeck et al., 2012; Bejanin et al., 2017). However, emerging evidence suggests that structural and metabolic abnormalities in AD may follow a sigmoidal curve trajectory with an initial period of acceleration and subsequent deceleration (Jack et al., 2013a; Sabuncu et al., 2011; Schuff et al., 2012). While the dynamic effects of AD biomarkers on worsening cognition can be better modelled by sigmoid-shaped curves rather than a constant across disease stages (Caroli et al., 2010), it remains largely unknown how brain structural and metabolic networks will influence cognition decline differentially in individuals stratified into different pathology groups and cognitive stages. Once these trajectories are defined across the AD continuum and subgroups, they can potentially highlight windows of opportunity for targeted intervention at the appropriate cognitive stages to improve disease outcomes. To cover these gaps, we sought to determine the differential associations of brain metabolism and GM structural networks with memory function using a neurodegeneration covariance network approach, among cognitively normal (CN), MCI, and probable AD individuals stratified by their A and T biomarker status. We used the seed partial least squares (PLS) method (Krishnan et al., 2011) to evaluate the individual-level brain network integrity. We employed the sparse varying coefficient (SVC) model which does not assume a constant relationship between brain measures and cognitive performance over different cognitive stages (Hong et al., 2015; Daye et al., 2012; Ji et al., 2019). Besides capturing the possible non-linear brain-cognition relationship, SVC also allows the selection of significant predictors with the least absolute shrinkage and selection operator (LASSO) sparse penalty while eliminating the contribution of the less important predictors. We hypothesised that individual-level brain metabolic and structural network integrity would be non-linearly associated with memory performance across the AD continuum and such trajectories would vary depending on the presence of amyloid and tau protein deposition. Based on our previous findings (Zhou et al., 2010; Chong et al., 2017; Zhang et al., 2020), we further hypothesised that the posterior DMN and the medial temporal lobe regions would play an early and dominant role affecting the memory performance in individuals with amyloid pathology. Our study provides first evidence that both hippocampal structural and ANG metabolic network integrity contributed to memory performance in the early cognitively normal stage in individuals with amyloid deposition. However, in the amyloid positive individuals with dementia, only the ANG metabolic network dominated the memory-network association. Amyloid negative individuals did not have such patterns. These findings characterise the dynamic influence of brain structural and metabolic networks on memory function across the AD continuum and underscore the importance of early intervention targeting neuronal dysfunction in the preclinical AD stage to improve memory outcomes. Results Group differences in brain metabolic and structural covariance networks We selected 812 participants (232 CN, 413 MCI, and 167 probable AD) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with 3T T1-weighted MRI and [18F]FDG PET scans to define seed regions for brain network derivation (Figure 1, step 1 and Figure 1—figure supplement 1). As our study focused on memory and AD pathology, we chose to study the individual-level structural and metabolic covariance within higher-order cognitive networks such as DMN, SN, ECN as well as the hippocampus (HIP)-based memory network (Veldsman et al., 2020; Vincent et al., 2008). We defined a set of 12 seed regions to derive these covariance networks on the basis that they have been shown to reliably produce the relevant network across imaging modalities. Specifically, the DMN included bilateral ANG, PCC, and medial prefrontal cortex (mPFC); the SN included bilateral anterior insular (INS); the ECN included bilateral dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC); the memory network included bilateral HIP. The seed coordinates were determined based on the group comparisons of the grey matter volume (GMV) probability and glucose metabolic spatial maps between CN and probable AD individuals (Supplementary file 1 and Supplementary file 4, see details in Methods). Figure 1 with 2 supplements see all Download asset Open asset Study design schematic. Seven hundred and eight participants with either healthy cognition (CN), mild cognitive impairment (MCI) or dementia were studied. Twelve brain seeds covering the key regions of hippocampus, the default mode network, the executive control network, and salience network were defined based on hypometabolism (via FDG) and grey matter atrophy (via MRI) patterns in all patients with probable AD compared to CN (step 1). Using seed-based partial least square (PLS) analysis (step 2), the covariance patterns in metabolism and grey matter volume maps were identified and used to derive the individual-level brain metabolic network scores and structural network scores for each seed. The group difference was evaluated between different cognitive stages and pathology groups (step 3). We then investigated the differential stage-dependent associations between these key brain network scores with memory performance in each of the three pathology groups (A-T-, A-T+, and A+T-/A+T + ) separately using sparse varying coefficient (SVC) modelling (step 4). Abbreviations: A=Aβ; T=tau; ‘-’ = negative; ‘+’ = positive. To derive brain structural and metabolic networks from individuals with and without amyloid pathology, we further identified 708 out of the existing 812 participants who underwent neuropsychological assessments, and lumbar puncture, in addition to [18F]FDG PET and T1-weighted MRI scans to form the main dataset (Table 1). Using seed PLS (Figure 1, step 2, see details in Methods), we identified the structural and metabolic covariance network patterns associated with each seed at the group-level (Figure 2A and Figure 3A). We projected the original individual GMV and metabolic maps onto the covariance network maps to derive the individual brain structural or metabolic network scores, which reflected how strongly each brain network pattern was manifested in the individual’s metabolic and structural brain networks. Figure 2 with 1 supplement see all Download asset Open asset The integrity of brain metabolic networks in participants with and without amyloid pathology across cognitive stages. (A) Brain slices of metabolic covariance networks associated with each brain seed. Brain metabolic network resemabled canonical brain networks. The intensity of colorbar represents bootstrap ratios, derived from dividing the weight of the singular-vector by the bootstrapped standard error. (B) Individual-level brain metabolic network scores (z-score) were lower in individuals with worse cognition and amyloid pathology. Z-scores were calculated within all the subjects. Summary of individual-level metabolic network scores (mean and median) were presented in half-violin plots. ‘*’ indicates significant group difference (p<0.05). Thick lines indicate group differences in brain scores of all the seven networks between different cognitive stages (grey dashed lines) or pathology groups (dark lines). Abbreviations: HIP = hippocampus; ANG = angular gyrus; PCC = posterior cingulate cortex; mPFC = media prefrontal cortex; INS = insular; DLPFC = dorsolateral prefrontal cortex; PPC = posterior parietal cortex; CN = cognitively normal; MCI = mild cognitive impairment; pAD = probable AD; A = β-amyloid; T=tau; ‘+’=positive; ‘-’=negative. Figure 3 with 1 supplement see all Download asset Open asset The integrity of brain structural networks in participants with and without amyloid pathology across cognitive stages. (A) Brain slices of structural covariance networks associated with each brain seed. The intensity of colorbar represents bootstrap ratios, derived from dividing the weight of the singular-vector by the bootstrapped standard error. (B) Individual-level brain structural network scores (z-score) were lower in individuals with worse cognition and amyloid pathology. Z-scores were calculated within all the subjects. Summary of individual-level structural network scores (mean and median) were presented in half-violin plots. ‘*’ indicates significant group difference (p<0.05). Thick lines indicate group differences in brain scores of all the networks between different cognitive stages (grey dashed lines) or pathology groups (dark lines). Thin lines indicate group differences in brain scores of specific networks. Abbreviations: HIP = hippocampus; ANG = angular gyrus; PCC = posterior cingulate cortex; mPFC = media prefrontal cortex; INS = insular; DLPFC = dorsolateral prefrontal cortex; PPC = posterior parietal cortex; CN = cognitively normal; MCI = mild cognitive impairment; pAD = probable AD; A = β-amyloid; T=tau; ‘+’ = positive; ‘-’ = negative. Table 1 Subject demographics for the main study cohort. A-T-A-T+A+T-/A+T +CNMCIprobable ADCNMCIprobable ADCNMCIprobable ADN307448075785225128Age, years65.12~85.1656.08~88.5169.56~90.5056.53~84.4755.15~88.8360.79~80.7660.19~90.0855.38~91.5755.96~90.4672.24±4.5469.78±7.20d77.37±9.11m71.95±5.8770.21±8.1674.57±7.7075.37±6.5973.17±6.9374.12±8.19Gender (M/F)12/1839/354/045/3538/376/137/48127/9872/56Handedness (R/L)29/160/144/069/1167/87/079/6203/22118/10Education, years16.63±2.6816.45±2.5917.50±1.2916.88±2.6716.00±2.6516.71±2.4316.34±2.3616.12±2.7415.78±2.71APOE e4 (+/-)6/2416/580/416/6417/581/638/47md140/85cd93/35cmMemory1.28±0.66md0.75±0.64cd–0.12±0.68cm1.17±0.57md0.55±0.62cd–0.40±0.67cm0.97±0.63md0.20±0.63cd–0.87±0.53cmMMSE28.70±1.68d28.62±1.31d25.75±2.36cm29.15±0.99md28.29±1.64cd23.86±2.19cm29.02±1.17md27.81±1.86cd23.21±2.24cmCDR-SOB0.02±0.09md1.22±0.60cd4.38±3.04cm0.05±0.15md1.20±0.76cd4.57±1.40cm0.05±0.17md1.53±0.90cd4.64±1.70cmICV1523.77±152.571520.05±127.781540.83±36.501554.01±128.101559.14±147.961566.75±221.761524.87±148.291558.61±148.251549.78±163.05 Note: Data on age are range and mean ± SD. Data on education, ICV, and memory are mean ± SD. Data on memory are in z-scores. Abbreviations: CN = cognitively normal; MCI = mild cognitive impairment; AD = Alzheimer's disease; A = β-amyloid; T = tau; ‘+’ = positive; ‘-’ = negative; y = years; M = male; F = female; R = right; L = left; MMSE = Mini-Mental State Exam; CDR-SOB = Clinical Dementia Rating Sum of Box; ICV = intracranial volume. Superscripts (‘c’, ‘m’, ‘d’) represent significant group difference with CN, MCI and probable AD, respectively. First, we compared the brain metabolic and structural network scores between different pathology groups and cognitive stages (Figure 1, step 3). In participants with amyloid pathology (A+T-/A+T + ), the probable AD group had lower metabolic and structural network scores than the CN and MCI groups in all the networks (Figure 2B right and 3B right). No such difference was observed in participants without amyloid pathology. At the same cognitive stage, we observed slightly different patterns in structural and metabolic networks. Specifically, at the same cognitive stage, amyloid positive (A+T-/A+T + ) MCI individuals had lower metabolic and structural network scores than the MCI individuals without amyloid and tau pathology (A-T-) for all the networks (Figures 2B and 3B). The amyloid positive CN individuals had comparable structural network scores but lower metabolic network scores than the CN individuals without amyloid and tau pathology. In contrast, CN individuals with amyloid pathology (A+T-/A+T + ) showed lower structural integrity in the HIP-based memory network, the mPFC-based anterior DMN and the INS-based SN than the CN individuals with tau pathology only (A-T+). In addition, CN individuals with tau pathology (A-T+) had lower structural mPFC-based anterior DMN scores than the CN group without tau and amyloid pathology (A-T-). Divergent stage-dependent trajectories of the association between hippocampal structural network integrity and memory performance in the three pathology groups Next, we sought to determine the differential non-linear trajectories of the association between brain network integrity and memory impairment in different pathology groups across the three cognitive stages using the SVC model (Figure 1, step 4; Hong et al., 2015). Note that we did not assume a constant relationship here; instead, the network-memory association could vary across cognitive stages. Instead of analysing each brain measure in separate models, the SVC analysis allows all variables to be entered as predictors in the same multivariate model, with the identification of the most important predictors and the elimination of the less important predictors (i.e. feature selection) implemented by minimising the penalised least squares function. To characterise the possible stage-dependent trajectories using SVC modelling, we ordered the participants by their cognitive stages (i.e. CN → MCI → dementia; Figure 1—figure supplement 2A) in each of the three pathology groups (A-/T-, A-/T+and A+T-/A+T + ). Within each stage, the participants were then ordered by their global cognition or dementia severity (i.e. no impairment → severe impairment). Specifically, the participants within the CN group were ordered by decreasing MMSE scores, while the participants within the MCI and dementia groups were ordered by increasing CDR-sum of boxes (SOB) scores. Participants with the same MMSE or CDR-SOB scores were further ordered by increasing age (i.e. young → old). Ordered participants were distributed evenly into bins (i.e. 10 subjects/bin). In our SVC models, the dependent variable was the ADNI memory composite score. Predictors included all the 14 FDG/GMV regional network scores with gender, education years, APOE ε4, intracranial volume (ICV), and scanning site as nuisance variables. We performed the SVC modelling for each pathology group separately to find the key predictors and the trajectories of their associations with memory along the disease progression (see details in Methods). The SVC models identified the HIP-based structural memory network score as a key predictor of memory impairment in all three pathology groups (Figures 4A and 5A). We found that the lower HIP structural network scores, the lower the ADNI-mem scores (indicated by positive beta coefficient). The strength of this association was higher (i.e. higher beta coefficient) in the amyloid pathology group than the other two A-groups. Figure 4 with 1 supplement see all Download asset Open asset Brain metabolic and structural networks had differential stage-dependent associations with memory in amyloid positive individuals. Data from the main dataset (panel A) and validation dataset 1 (panel B) exhibited consistent stage-dependent memory-network association trajectory from cognitively normal to dementia stage in participants with amyloid pathology (i.e. A+T-/A+T + group). Both hippocampal-seeded structural network (left, in blue) and angular gyrus-seeded default mode metabolic network (right, in red) integrity contributed significantly to memory performance in early cognitively normal stage. Such impact decreased in MCI stage for both metabolic and structural networks. In contrast, only the metabolic network had a major influence on memory in late dementia stage. Solid curves represent the mean associations (beta coefficients) of brain network scores with memory as a function of advancing AD continuum estimated from 100 replicates. The dashed curves represent the point-wise 2* standard errors of the solid curves estimated from 100 replicates. The participants were ordered by their cognitive stages (i.e. CN → MCI → probable AD). Within each cognitive stage, the participants were then ordered by general cognition (MMSE for CN) or dementia severity (CDR for MCI and dementia) (i.e. no impairment → severe impairment). Participants with the same level of impairment/severity were further ordered by increasing age (i.e. young → old). Ordered participants were distributed evenly into bins (i.e. 10 subjects/bin). Abbreviations: CN = cognitively normal; MCI = mild cognitive impairment; HIP = hippocampus; ANG = angular gyrus. Figure 5 with 4 supplements see all Download asset Open asset Stage-dependent association of brain hippocampal structural network with memory performance in A-T- and A-T +pathology groups. Data from the main dataset (panel A) and validation dataset 1 (panel B) exhibited consistent stage-dependent memory-network association trajectory from cognitively normal stage to dementia stage in participants with A-T- and A-T +pathology. The hippocampus-memory association was much weaker overall in non-amyloid/non-tau and tau only groups compared to amyloid positive group (Figure 4). The memory-network association was the lowest in early cognitively normal stage and gradually increased with clinical progression in both groups, while the tau only group had stronger associations in dementia stage. Solid curves represent the mean associations (beta coefficients) of brain network scores with memory as a function of advancing AD continuum estimated from 100 replicates. The dashed curves represent the point-wise 2* standard errors of the solid curves estimated from 100 replicates. The participants were ordered by their cognitive stages (i.e. CN → MCI → probable AD). Within each cognitive stage, the participants were then ordered by general cognition (MMSE for CN) or dementia severity (CDR for MCI and dementia) (i.e. no impairment → severe impairment). Participants with the same level of impairment/severity were further ordered by increasing age (i.e. young → old). Ordered participants were distributed evenly into bins (i.e. 10 subjects/bin). Abbreviations: CN = cognitively normal; MCI = mild cognitive impairment; HIP = hippocampus. More importantly, not only was the relationship between the HIP structural network and memory performance non-linearly dependent on cognitive stages as hypothesised, but such non-linear trajectories were also different across the three pathology groups (Figures 4A and 5A). Specifically, in the amyloid pathology group, the strength of this association was highest in early CN stage, and decreased from late CN to early MCI stage (Figure 4A, left). The strength of this association remained stable in MCI and then decreased in the dementia stage. The two amyloid negative groups had the opposite pattern of the amyloid positive group (Figure 5A). Specifically, in the A-T- group, the strength of the association between the HIP structural network and memory performance was lowest in early CN stage and increased in the late CN stage. It then remained stable in the MCI stage before a further increase in the dementia stage. Similarly, in the A-T +group, the strength of such association was low in the CN stage and gradually increased in the MCI stage, reaching the highest in the late MCI and dementia stage. Our findings suggest that the influence of the HIP-based structural network integrity on memory performance begins early in the preclinical AD stage and the strength of this influence gradually decreased as the cognitive stages progress. On the other hand, the influence of the HIP network integrity on memory is weaker in individuals without Aβ pathology and peaks in the dementia stage. The stronger hippocampus-memory association in the preclinical AD stage supports the current strategy of early intervention to attain better cognitive outcomes. Furthermore, demographical and genetic variables such as gender, education years and APOE ε4 genotype showed differential stage- and pathology-dependent associations with memory performance (Figure 5—figure supplement 2). Females and fewer years of education were associated with memory impairment in A-/T- and A-/T+groups respectively. These associations were highest in the early CN stage and gradually decreased in late CN stage before increasing in the late MCI and probable AD stages. In contrast, females, fewer years of education and APOE ε4 carriers in the amyloid pathology group were associated with memory impairment with a differential trajectory (i.e. highest in the early CN stage and gradually decreased afterwards), although the strength of this association was relatively lower overall compared to those in the A-/T- and A-/T+groups. Stage-dependent association between angular gyrus metabolic network integrity and memory performance in amyloid pathology group The SVC models identified the ANG-based metabolic network score (i.e. DMN) to be associated with memory impairment only in the amyloid pathology group (Figure 4A, right). We f