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Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4 219 samples collected from 1 180 participants from the Alzheimer's Disease Neuroimaging Initiative. The findings revealed that these BA features exhibited significant correlations with clinical stages, encompassing cognitively normal (CN), early and late mild cognitive impairment, and AD, as well as cognitive performance. Importantly, these associations are more pronounced in men than women. Among participants with progressive disease stages (n = 660), BAs underwent early changes in men, occurring before AD. By incorporating BA features into diagnostic and predictive models, positive enhancements are achieved for all models. The area under the receiver operating characteristic curve improved from 0.78 to 0.91 for men and from 0.76 to 0.83 for women for the differentiation of CN and AD. Additionally, the key findings are validated in a subset of participants (n = 578) with cerebrospinal fluid amyloid-beta and tau levels. These findings underscore the role of BAs in AD progression, offering potential improvements in the accuracy of AD prediction.

Abstract Determining the genetic architecture of Alzheimer’s disease pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we perform a genome-wide association study of cortical tau quantified by positron emission tomography in 3046 participants from 12 independent studies. The CYP1B1-RMDN2 locus is associated with tau deposition. The most significant signal is at rs2113389, explaining 4.3% of the variation in cortical tau, while APOE4 rs429358 accounts for 3.6%. rs2113389 is associated with higher tau and faster cognitive decline. Additive effects, but no interactions, are observed between rs2113389 and diagnosis, APOE4, and amyloid beta positivity. CYP1B1 expression is upregulated in AD. rs2113389 is associated with higher CYP1B1 expression and methylation levels. Mouse model studies provide additional functional evidence for a relationship between CYP1B1 and tau deposition but not amyloid beta. These results provide insight into the genetic basis of cerebral tau deposition and support novel pathways for therapeutic development in AD.

Abstract Background Blood-derived mitochondrial DNA copy number (mtDNA-CN) is a proxy measurement of mitochondrial function in the peripheral and central systems. Abnormal mtDNA-CN not only indicates impaired mtDNA replication and transcription machinery but also dysregulated biological processes such as energy and lipid metabolism. However, the relationship between mtDNA-CN and Alzheimer disease (AD) is unclear. Methods We performed two-sample Mendelian randomization (MR) using publicly available summary statistics from GWAS for mtDNA-CN and AD to investigate the causal relationship between mtDNA-CN and AD. We estimated mtDNA-CN using whole-genome sequence data from blood and brain samples of 13,799 individuals from the Alzheimer’s Disease Sequencing Project. Linear and Cox proportional hazards models adjusting for age, sex, and study phase were used to assess the association of mtDNA-CN with AD. The association of AD biomarkers and serum metabolites with mtDNA-CN in blood was evaluated in Alzheimer’s Disease Neuroimaging Initiative using linear regression. We conducted a causal mediation analysis to test the natural indirect effects of mtDNA-CN change on AD risk through the significantly associated biomarkers and metabolites. Results MR analysis suggested a causal relationship between decreased blood-derived mtDNA-CN and increased risk of AD (OR = 0.68; P = 0.013). Survival analysis showed that decreased mtDNA-CN was significantly associated with higher risk of conversion from mild cognitive impairment to AD (HR = 0.80; P = 0.002). We also identified significant associations of mtDNA-CN with brain FDG-PET (β = 0.103; P = 0.022), amyloid-PET (β = 0.117; P = 0.034), CSF amyloid-β (Aβ) 42/40 (β=-0.124; P = 0.017), CSF t-Tau (β = 0.128; P = 0.015), p-Tau (β = 0.140; P = 0.008), and plasma NFL (β=-0.124; P = 0.004) in females. Several lipid species, amino acids, biogenic amines in serum were also significantly associated with mtDNA-CN. Causal mediation analyses showed that about a third of the effect of mtDNA-CN on AD risk was mediated by plasma NFL (P = 0.009), and this effect was more significant in females (P

Abstract Background Current AT(N) stratification for Alzheimer’s disease (AD) accounts for complex combinations of amyloid (A), tau proteinopathy (T) and neurodegeneration (N) signatures. Understanding the transition between these different stages is a major challenge, especially in view of the recent development of disease modifying therapy. Methods This is an observational study, CSF levels of Tau, pTau181, pTau217, Aβ38/40/42, sAPPα/β, BACE1 and neurogranin were measured in the BALTAZAR cohort of cognitively impaired patients and in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Biomarkers levels were related to the AT(N) framework. (A) and (T) were defined in BALTAZAR with CSF Aβ42/40 ratio and pTau217 respectively, and in ADNI with amyloid and tau PET. (N) was defined using total CSF tau in both cohorts. Results As expected, CSF Aβ42 decreased progressively with the AD continuum going from the A-T-N- to the A + T + N + profile. On the other hand, Tau and pTau181 increased progressively with the disease. The final transition from A + T + N- to A + T + N + led to a sharp increase in Aβ38, Aβ42 and sAPP levels. Synaptic CSF biomarkers BACE1 and neurogranin, were lowest in the initial A + T-N- stage and increased with T + and N + . CSF pTau181 and total tau were closely related in both cohorts. Conclusions The early transition to an A + phenotype (A + T-N-) primarily impacts synaptic function. The appearance of T + and then N + is associated with a significant and progressive increase in pathological Alzheimer's disease biomarkers. Our main finding is that CSF pTau181 is an indicator of N + rather than T + , and that N + is associated with elevated levels of BACE1 protein and beta-amyloid peptides. This increase may potentially fuel the amyloid cascade in a positive feedback loop. Overall, our data provide further insights into understanding the interconnected pathological processes of amyloid, tau, and neurodegeneration underlying Alzheimer's disease.

Abstract Objectives Traumatic brain injury (TBI) is associated with sleep deficits, but it is not clear why some report sleep disturbances and others do not. The objective of this study was to assess the associations between axonal injury, sleep, and memory in chronic and acute TBI. Methods Data were acquired from two independent datasets which included 156 older adult veterans (69.8 years) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with prior moderate‐to‐severe TBIs and 90 (69.2 years) controls and 374 (39.6 years) from Transforming Research and Clinical Knowledge in TBI (TRACK‐TBI) with a recent mild TBI (mTBI) and 87 controls (39.6 years), all who completed an MRI, memory assessment, and sleep questionnaire. Results Older adults with a prior TBI had a significant association between axonal injury and sleep disturbances [β = 9.52, 95% CI (4.1, 14.9), p = 0.01]. Axonal injury predicted changes in memory over 1‐year in TBI [β = −8.72, 95% CI (−18, −2.7), p = 0.03]. We externally validated those findings in TRACK‐TBI where axonal injury within 2 weeks after mTBI was significantly associated with higher sleep disturbances in the TBI group at 2 weeks[β = −7.2, 95% CI (−14, −0.50), p = 0.04], 6 months [β = −16, 95% CI (−24, −7.6), p ≤ 0.01], and 12 months post‐injury [β = −11, 95% CI (−19, −0.85), p = 0.03]. These associations were not significant in controls. Interpretations Axonal injury, specifically to the left anterior internal capsule is robustly associated with sleep disturbances in multiple TBI populations. Early assessment of axonal injury following mTBI could identify those at risk for persistent sleep disturbances following injury.

Abstract Alzheimer’s disease (AD) is characterized by progressive episodic memory dysfunction. A prominent hallmark of AD is gradual brain atrophy. Despite extensive research on brain pathology, the understanding of spinal cord pathology in AD and its association with cognitive decline remains understudied. We analyzed serial magnetic resonance imaging (MRI) scans from the ADNI data repository to assess whether progressive cord atrophy is associated with clinical worsening. Cervical cord morphometry was measured in 45 patients and 49 cognitively normal controls (CN) at two time points over 1.5 years. Regression analysis examined associations between cord atrophy rate and cognitive worsening. Cognitive and functional activity performance declined in patients during follow-up. Compared with controls, patients showed a greater rate of decline of the anterior–posterior width of the cross-sectional cord area per month (− 0.12%, p = 0.036). Worsening in the mini-mental state examination (MMSE), clinical dementia rating (CDR), and functional assessment questionnaire (FAQ) was associated with faster rates of cord atrophy (MMSE: r = 0.320, p = 0.037; CDR: r = − 0.361, p = 0.017; FAQ: r = − 0.398, p = 0.029). Progressive cord atrophy occurs in AD patients; its rate over time being associated with cognitive and functional activity decline.

Abstract Introduction Sleep disorders, particularly insomnia and obstructive sleep apnea, are associated with dyslipidemia in the general population. The study’s aim was to explore the association between pathological Cholesterol and Triglyceride levels, and sleep and nighttime behavior disorders (SNBD) in older adults, whether they might predict SNBD onset, and to emphasize the role of body mass index (BMI) in this association. Methods Alzheimer’s Disease Neuroimaging Initiative (ADNI) population with complete Cholesterol, Triglyceride, SNBD, and neurocognitive data were included. Logistic regression was performed to study the association between hypercholesterolemia, hypertriglyceridemia, and SNBD at baseline and at 12 months. Relevant confounders, particularly BMI, were adjusted for. Results Among the 2,216 included cases, 1,045 (47%) were females, and the median age was 73 years (IQR: 68, 78). At baseline, 357 (16%) had SNBD and 327 (18%) at 12 months; 187 of them were incident cases. There were more cases of baseline SNBD in the hypertriglyceridemia group than in those without (19% vs. 14%, P-value = 0.003). Similarly, more follow-up SNBD cases had hypertriglyceridemia at baseline (21% vs. 16%, P-value = 0.025). SNBD cases at baseline had significantly higher serum Triglyceride levels than those without (132 vs. 118mg/dL, P-value

Abstract Background Plasma neurofilament light chain (NfL) is a blood biomarker of neurodegeneration, including Alzheimer’s disease. However, its usefulness may be influenced by common conditions in older adults, including amyloid-β (Aβ) deposition and cardiometabolic risk factors like hypertension, diabetes mellitus (DM), impaired kidney function, and obesity. This longitudinal observational study using the Alzheimer’s Disease Neuroimaging Initiative cohort investigated how these conditions influence the prognostic capacity of plasma NfL. Methods Non-demented participants (cognitively unimpaired or mild cognitive impairment) underwent repeated assessments including the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) scores, hippocampal volumes, and white matter hyperintensity (WMH) volumes at 6- or 12-month intervals. Linear mixed-effect models were employed to examine the interaction between plasma NfL and various variables of interest, such as Aβ (evaluated using Florbetapir positron emission tomography), hypertension, DM, impaired kidney function, or obesity. Results Over a mean follow-up period of 62.5 months, participants with a mean age of 72.1 years (n = 720, 48.8% female) at baseline were observed. Higher plasma NfL levels at baseline were associated with steeper increases in ADAS-Cog scores and WMH volumes, and steeper decreases in hippocampal volumes over time (all p-values

Abstract Objective To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI. Methods A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort. Results Based on multivariate logistic regression, clinical dementia rating and Alzheimer’s disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P

Abstract Background The cerebrospinal fluid (CSF), primarily generated by the choroid plexus (ChP), is the major carrier of the glymphatic system. The alternations of CSF production and the ChP can be associated with the Alzheimer’s disease (AD). The present work investigated the roles of the ChP in the AD based on a proposed ChP image segmentation pipeline. Methods A human-in-the-loop ChP image segmentation pipeline was implemented with intermediate and active learning datasets. The performance of the proposed pipeline was evaluated on manual contours by five radiologists, compared to the FreeSurfer and FastSurfer toolboxes. The ChP volume and blood flow were investigated among AD groups. The correlations between the ChP volume and AD CSF biomarkers including phosphorylated tau (p-tau), total tau (t-tau), amyloid-β42 (Aβ42), and amyloid-β40 (Aβ40) was investigated using three models (univariate, multiple variables, and stepwise regression) on two datasets with 806 and 320 subjects. Results The proposed ChP segmentation pipeline achieved superior performance with a Dice coefficient of 0.620 on the test dataset, compared to the FreeSurfer (0.342) and FastSurfer (0.371). Significantly larger volumes (p

Abstract Background Leveraging Alzheimer’s disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR. Methods We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women). Results The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline. Conclusion These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

Abstract To uncover molecular changes underlying blood-brain-barrier dysfunction in Alzheimer’s disease, we performed single nucleus RNA sequencing in 24 Alzheimer’s disease and control brains and focused on vascular and astrocyte clusters as main cell types of blood-brain-barrier gliovascular-unit. The majority of the vascular transcriptional changes were in pericytes. Of the vascular molecular targets predicted to interact with astrocytic ligands, SMAD3, upregulated in Alzheimer’s disease pericytes, has the highest number of ligands including VEGFA, downregulated in Alzheimer’s disease astrocytes. We validated these findings with external datasets comprising 4,730 pericyte and 150,664 astrocyte nuclei. Blood SMAD3 levels are associated with Alzheimer’s disease-related neuroimaging outcomes. We determined inverse relationships between pericytic SMAD3 and astrocytic VEGFA in human iPSC and zebrafish models. Here, we detect vast transcriptome changes in Alzheimer’s disease at the gliovascular-unit, prioritize perturbed pericytic SMAD3-astrocytic VEGFA interactions, and validate these in cross-species models to provide a molecular mechanism of blood-brain-barrier disintegrity in Alzheimer’s disease.

Abstract The etiopathogenesis of late-onset Alzheimer’s disease (AD) is increasingly recognized as the result of the combination of the aging process, toxic proteins, brain dysmetabolism, and genetic risks. Although the role of mitochondrial dysfunction in the pathogenesis of AD has been well-appreciated, the interaction between mitochondrial function and genetic variability in promoting dementia is still poorly understood. In this study, by tissue-specific transcriptome-wide association study (TWAS) and further meta-analysis, we examined the genetic association between mitochondrial solute carrier family (SLC25) genes and AD in three independent cohorts and identified three AD-susceptibility genes, including SLC25A10, SLC25A17, and SLC25A22. Integrative analysis using neuroimaging data and hippocampal TWAS-predicted gene expression of the three susceptibility genes showed an inverse correlation of SLC25A22 with hippocampal atrophy rate in AD patients, which outweighed the impacts of sex, age, and apolipoprotein E4 (ApoE4). Furthermore, SLC25A22 downregulation demonstrated an association with AD onset, as compared with the other two transcriptome-wide significant genes. Pathway and network analysis related hippocampal SLC25A22 downregulation to defects in neuronal function and development, echoing the enrichment of SLC25A22 expression in human glutamatergic neurons. The most parsimonious interpretation of the results is that we have identified AD-susceptibility genes in the SLC25 family through the prediction of hippocampal gene expression. Moreover, our findings mechanistically yield insight into the mitochondrial cascade hypothesis of AD and pave the way for the future development of diagnostic tools for the early prevention of AD from a perspective of precision medicine by targeting the mitochondria-related genes.

Abstract Background Past research suggests that low testosterone levels relate to poorer cognitive function and higher Alzheimer’s disease (AD) risk; however, these findings are inconsistent and are mostly derived from male samples, despite similar age-related testosterone decline in females. Both animal and human studies demonstrate that testosterone’s effects on brain health may be moderated by apolipoprotein E ε4 allele (APOE-ε4) carrier status, which may explain some previous inconsistencies. We examined how testosterone relates to cognitive function in older women versus men across healthy aging and the AD continuum and the moderating role of APOE-ε4 genotype. Methods Five hundred and sixty one participants aged 55–90 (155 cognitively normal (CN), 294 mild cognitive impairment (MCI), 112 AD dementia) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who had baseline cognitive and plasma testosterone data, as measured by the Rules Based Medicine Human DiscoveryMAP Panel were included. There were 213 females and 348 males (self-reported sex assigned at birth), and 52% of the overall sample were APOE-ε4 carriers. We tested the relationship of plasma testosterone levels and its interaction with APOE-ε4 status on clinical diagnostic group (CN vs. MCI vs. AD), global, and domain-specific cognitive performance using ANOVAs and linear regression models in sex-stratified samples. Cognitive domains included verbal memory, executive function, processing speed, and language. Results We did not observe a significant difference in testosterone levels between clinical diagnostic groups in either sex, regrardless of APOE-ε4 status. Across clinical diagnostic group, we found a significant testosterone by APOE-ε4 interaction in females, such that lower testosterone levels related to worse global cognition, processing speed, and verbal memory in APOE-ε4 carriers only. We did not find that testosterone, nor its interaction with APOE-ε4, related to cognitive outcomes in males. Conclusions Findings suggest that low testosterone levels in older female APOE-ε4 carriers across the aging-MCI-AD continuum may have deleterious, domain-specific effects on cognitive performance. Although future studies including additional sex hormones and longitudinal cognitive trajectories are needed, our results highlight the importance of including both sexes and considering APOE-ε4 carrier status when examining testosterone’s role in cognitive health.

Abstract This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer’s disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing. The results, based on a combined dataset from four public sources comprising 3844 data points, demonstrate the model's efficacy with a mean absolute error (MAE) of 2.05 for brain age gap estimation. Moreover, the model's generalizability is showcased by training on three datasets and testing on a separate one, yielding a remarkable MAE of 2.4. Furthermore, leveraging BAG as the sole biomarker, our method achieves an accuracy of 92% and an AUC of 0.87 in Alzheimer's disease detection on the ADNI dataset. These findings underscore the potential of our approach in assisting with early detection and disease monitoring, emphasizing the strong correlation between BAG and AD.

IntroductionBrain age is a machine learning-derived estimate that captures lower brain volume. Previous studies have found that brain age is significantly higher in mild cognitive impairment and Alzheimer’s disease (AD) compared to healthy controls. Few studies have investigated changes in brain age longitudinally in MCI and AD. We hypothesized that individuals with MCI and AD would show heightened brain age over time and across the lifespan. We also hypothesized that both MCI and AD would show faster rates of brain aging (higher slopes) over time compared to healthy controls.MethodsWe utilized data from an archival dataset, mainly Alzheimer’s disease Neuroimaging Initiative (ADNI) 1 with 3Tesla (3 T) data which totaled 677 scans from 183 participants. This constitutes a secondary data analysis on existing data. We included control participants (healthy controls or HC), individuals with MCI, and individuals with AD. We predicted brain age using a pre-trained model and tested for accuracy. We investigated cross-sectional differences in brain age by group [healthy controls or HC, mild cognitive impairment (MCI), and AD]. We conducted longitudinal modeling of age and brain age by group using time from baseline in one model and chronological age in another model.ResultsWe predicted brain age with a mean absolute error (MAE)

BackgroundThe relationship between white matter hyperintensities (WMH) and the core features of Alzheimer’s disease (AD) remains controversial. Further, due to the prevalence of co-pathologies, the precise role of WMH in cognition and neurodegeneration also remains uncertain.MethodsHerein, we analyzed 1803 participants with available WMH volume data, extracted from the ADNI database, including 756 cognitively normal controls, 783 patients with mild cognitive impairment (MCI), and 264 patients with dementia. Participants were grouped according to cerebrospinal fluid (CSF) pathology (A/T profile) severity. Linear regression analysis was applied to evaluate the factors associated with WMH volume. Modeled by linear mixed-effects, the increase rates (Δ) of the WMH volume, cognition, and typical neurodegenerative markers were assessed. The predictive effectiveness of WMH volume was subsequently tested using Cox regression analysis, and the relationship between WMH/ΔWMH and other indicators such as cognition was explored through linear regression analyses. Furthermore, we explored the interrelationship among amyloid-β deposition, cognition, and WMH using mediation analysis.ResultsHigher WMH volume was associated with older age, lower CSF amyloid-β levels, hypertension, and smoking history (all p ≤ 0.001), as well as cognitive status (MCI, p

Abstract Background Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer’s disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans. Methods In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD. Results The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results. Conclusions We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

Abstract Background Alzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Given that there is currently no cure, it is critical to detect AD in its early stage during the disease progression. Recently, many statistical learning methods have been presented to identify cognitive decline with temporal data, but few of these methods integrate heterogeneous phenotype and genetic information together to improve the accuracy of prediction. In addition, many of these models are often unable to handle incomplete temporal data; this often manifests itself in the removal of records to ensure consistency in the number of records across participants. Results To address these issues, in this work we propose a novel approach to integrate the genetic data and the longitudinal phenotype data to learn a fixed-length “enriched” biomarker representation derived from the temporal heterogeneous neuroimaging records. Armed with this enriched representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD. Conclusion The proposed method shows improved prediction performance when applied to data derived from Alzheimer’s Disease Neruoimaging Initiative cohort. In addition, our approach can be easily interpreted to allow for the identification and validation of biomarkers associated with cognitive decline.

Abstract Recent evidence suggests that Alzheimer’s disease (AD) genetic risk variants (rs1582763 and rs6591561) of the MS4A locus are genome-wide significant regulators of soluble TREM2 levels such that the minor allele of the protective variant (rs1582763) is associated with higher sTREM2 and lower AD risk while the minor allele of (rs6591561) relates to lower sTREM2 and higher AD risk. Our group previously found that higher sTREM2 relates to higher Aβ40, worse blood–brain barrier (BBB) integrity (measured with the CSF/plasma albumin ratio), and higher CSF tau, suggesting strong associations with amyloid abundance and both BBB and neurodegeneration complicate interpretation. We expand on this work by leveraging these common variants as genetic tools to tune the interpretation of high CSF sTREM2, and by exploring the potential modifying role of these variants on the well-established associations between CSF sTREM2 as well as TREM2 transcript levels in the brain with AD neuropathology. Biomarker analyses leveraged data from the Vanderbilt Memory & Aging Project (n = 127, age = 72 ± 6.43) and were replicated in the Alzheimer’s Disease Neuroimaging Initiative (n = 399, age = 73 ± 7.39). Autopsy analyses were performed leveraging data from the Religious Orders Study and Rush Memory and Aging Project (n = 577, age = 89 ± 6.46). We found that the protective variant rs1582763 attenuated the association between CSF sTREM2 and Aβ40 (β = -0.44, p-value = 0.017) and replicated this interaction in ADNI (β = -0.27, p = 0.017). We did not observe this same interaction effect between TREM2 mRNA levels and Aβ peptides in brain (Aβ total β = -0.14, p = 0.629; Aβ1-38, β = 0.11, p = 0.200). In contrast to the effects on Aβ, the minor allele of this same variant seemed to enhance the association with blood–brain barrier dysfunction (β = 7.0e-4, p = 0.009), suggesting that elevated sTREM2 may carry a much different interpretation in carriers vs. non-carriers of this allele. When evaluating the risk variant (rs6591561) across datasets, we did not observe a statistically significant interaction against any outcome in VMAP and observed opposing directions of associations in ADNI and ROS/MAP on Aβ levels. Together, our results suggest that the protective effect of rs1582763 may act by decoupling the associations between sTREM2 and amyloid abundance, providing important mechanistic insight into sTREM2 changes and highlighting the need to incorporate genetic context into the analysis of sTREM2 levels, particularly if leveraged as a clinical biomarker of disease in the future.

Abstract Background An elevated neutrophil–lymphocyte ratio (NLR) in blood has been associated with Alzheimer’s disease (AD). However, an elevated NLR has also been implicated in many other conditions that are risk factors for AD, prompting investigation into whether the NLR is directly linked with AD pathology or a result of underlying comorbidities. Herein, we explored the relationship between the NLR and AD biomarkers in the cerebrospinal fluid (CSF) of cognitively unimpaired (CU) subjects. Adjusting for sociodemographics, APOE4, and common comorbidities, we investigated these associations in two cohorts: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the M.J. de Leon CSF repository at NYU. Specifically, we examined associations between the NLR and cross-sectional measures of amyloid-β42 (Aβ42), total tau (t-tau), and phosphorylated tau181 (p-tau), as well as the trajectories of these CSF measures obtained longitudinally. Results A total of 111 ADNI and 190 NYU participants classified as CU with available NLR, CSF, and covariate data were included. Compared to NYU, ADNI participants were older (73.79 vs. 61.53, p

Abstract Objectives Mild cognitive impairment presenting with an amnestic syndrome (aMCI) and amyloid positivity is considered due to AD. Many subjects, however, can show an overall very slow progression relevant for differential diagnosis, prognosis, and treatment. This study assessed PET biomarkers, including brain glucose metabolism, tau, and amyloid load, in a series of comparable aMCI at baseline, clinically evaluated at follow‐up. Methods We included 72 aMCI subjects from Geneva Memory Center (N = 31) and ADNI cohorts (N = 41), selected based on available FDG‐PET, tau‐PET, amyloid‐PET, and clinical follow‐up (2.3 years ± 1.2). A data‐driven algorithm classified brain metabolic patterns into subtypes that were then compared for clinical and PET biomarker measures and cognitive decline. Voxel‐wise comparisons were performed both with FDG‐PET and tau‐PET data. Results The algorithm classified three metabolic subtypes, namely “Hippocampal‐sparing with cortical hypometabolism” (Type1; N = 27), “Hippocampal and cortical hypometabolism” (Type 2; N = 23), and “Medial temporal hypometabolism” (Type 3; N = 22). Amyloid positivity and tau accumulation in the medial temporal and neocortical regions characterized Type 1 and Type 2, whereas Type 3 showed no significant tau pathology, variable amyloid positivity, and stability at follow‐up. All tau‐positive patients, independently of the FDG‐based subtype, showed faster cognitive decline. Interpretation aMCI subjects can differ in metabolic patterns, tau and amyloid pathology, and clinical progression. Here, we complemented with PET tau biomarker the specific brain hypometabolic patterns at the individual level in the prodromal phase, contributing to the patient's classification. Tau PET is the most accurate biomarker in supporting or excluding the AD diagnosis in aMCI across metabolic subtypes and also predicting the risk of decline.

Abstract Background Irregular word reading has been used to estimate premorbid intelligence in Alzheimer’s disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities. Method Six hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer’s Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory. Results EMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances. Conclusions While the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum.

Abstract Background Alzheimer’s Disease (AD) is a devastating disease that destroys memory and other cognitive functions. There has been an increasing research effort to prevent and treat AD. In the US, two major data sharing resources for AD research are the National Alzheimer’s Coordinating Center (NACC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI); Additionally, the National Institutes of Health (NIH) Common Data Elements (CDE) Repository has been developed to facilitate data sharing and improve the interoperability among data sets in various disease research areas. Method To better understand how AD-related data elements in these resources are interoperable with each other, we leverage different representation models to map data elements from different resources: NACC to ADNI, NACC to NIH CDE, and ADNI to NIH CDE. We explore bag-of-words based and word embeddings based models (Word2Vec and BioWordVec) to perform the data element mappings in these resources. Results The data dictionaries downloaded on November 23, 2021 contain 1,195 data elements in NACC, 13,918 in ADNI, and 27,213 in NIH CDE Repository. Data element preprocessing reduced the numbers of NACC and ADNI data elements for mapping to 1,099 and 7,584 respectively. Manual evaluation of the mapping results showed that the bag-of-words based approach achieved the best precision, while the BioWordVec based approach attained the best recall. In total, the three approaches mapped 175 out of 1,099 (15.92%) NACC data elements to ADNI; 107 out of 1,099 (9.74%) NACC data elements to NIH CDE; and 171 out of 7,584 (2.25%) ADNI data elements to NIH CDE. Conclusions The bag-of-words based and word embeddings based approaches showed promise in mapping AD-related data elements between different resources. Although the mapping approaches need further improvement, our result indicates that there is a critical need to standardize CDEs across these valuable AD research resources in order to maximize the discoveries regarding AD pathophysiology, diagnosis, and treatment that can be gleaned from them.

Abstract Background Individuals on the preclinical Alzheimer's continuum, particularly those with both amyloid and tau positivity (A + T +), display a rapid cognitive decline and elevated disease progression risk. However, limited studies exist on brain atrophy trajectories within this continuum over extended periods. Methods This study involved 367 ADNI participants grouped based on combinations of amyloid and tau statuses determined through cerebrospinal fluid tests. Using longitudinal MRI scans, brain atrophy was determined according to the whole brain, lateral ventricle, and hippocampal volumes and cortical thickness in AD-signature regions. Cognitive performance was evaluated with the Preclinical Alzheimer's Cognitive Composite (PACC). A generalized linear mixed-effects model was used to examine group × time interactions for these measures. In addition, progression risks to mild cognitive impairment (MCI) or dementia were compared among the groups using Cox proportional hazards models. Results A total of 367 participants (48 A + T + , 86 A + T − , 63 A − T + , and 170 A − T − ; mean age 73.8 years, mean follow-up 5.1 years, and 47.4% men) were included. For the lateral ventricle and PACC score, the A + T − and A + T + groups demonstrated statistically significantly greater volume expansion and cognitive decline over time than the A − T − group (lateral ventricle: β = 0.757 cm3/year [95% confidence interval 0.463 to 1.050], P

Abstract Background Neuropsychiatric symptoms (NPS) are prevalent in cognitively impaired individuals including Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI). Whereas several studies have reported the associations between NPS with AD pathologic biomarkers and cerebral small vessel disease (SVD), but it remains unknown whether AD pathology and SVD contribute to different sub-syndromes independently or aggravate same symptoms synergistically. Method We included 445 cognitively impaired individuals (including 316 MCI and 129 AD) with neuropsychiatric, cerebrospinal fluid (CSF) biomarkers (Aβ42, p-tau, and t-tau) and multi-model MRI data. Psychiatric symptoms were accessed by using the Neuropsychiatric Inventory (NPI). Visual assessment of SVD (white matter hyperintensity, microbleed, perivascular space, lacune) on MRI images was performed by experienced radiologist. Linear regression analyses were conducted to test the association between neuropsychiatric symptoms with AD pathology and CSVD burden after adjustment for age, sex, education, apolipoprotein E (APOE) ε4 carrier status, and clinical diagnosis. Results The NPI total scores were related to microbleed (estimate 2.424; 95% CI [0.749, 4.099]; P =0.005). Considering the sub-syndromes, the hyperactivity was associated with microbleed (estimate 0.925; 95% CI [0.115, 1.735]; P =0.025), whereas the affective symptoms were correlated to CSF level of Aβ42 (estimate -0.006; 95% CI [-0.011, -0.002]; P =0.005). Furthermore, we found the apathy sub-syndrome was associated with CSF t-tau/Aβ42 (estimate 0.636; 95% CI [0.078, 1.194]; P =0.041) and microbleed (estimate 0.693; 95% CI [0.046, 1.340]; P =0.036). In addition, we found a significant interactive effect between CSF t-tau/Aβ42 and microbleed (estimate 0.993; 95% CI [0.360, 1.626]; P =0.019) on severity of apathy sub-syndrome. Conclusion Our study showed that CSF Aβ42 was associated with affective symptoms, but microbleed was correlated with hyperactivity and apathy, suggesting the effect of AD pathology and SVD on different neuropsychiatric sub-syndromes.

Abstract Background Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes. Results We conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors. Conclusion Several scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level $$\alpha $$ α .

Abstract Background Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. Methods This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. Results The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p

Abstract Background Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer’s disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression. Methods This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. Results On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers. Conclusions The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

BackgroundStable mild cognitive impairment (sMCI) and progressive mild cognitive impairment (pMCI) represent two distinct subtypes of mild cognitive impairment (MCI). Early and effective diagnosis and accurate differentiation between sMCI and pMCI are crucial for administering targeted early intervention and preventing cognitive decline. This study investigated the intrinsic dysconnectivity patterns in sMCI and pMCI based on degree centrality (DC) and effective connectivity (EC) analyses, with the goal of uncovering shared and distinct neuroimaging mechanisms between subtypes.MethodsResting-state functional magnetic resonance imaging combined with DC analysis was used to explore the functional connectivity density in 42 patients with sMCI, 31 patients with pMCI, and 82 healthy control (HC) participants. Granger causality analysis was used to assess changes in EC based on the significant clusters found in DC. Furthermore, correlation analysis was conducted to examine the associations between altered DC/EC values and cognitive function. Receiver operating characteristic curve analysis was performed to determine the accuracy of abnormal DC and EC values in distinguishing sMCI from pMCI.ResultsCompared with the HC group, both pMCI and sMCI groups exhibited increased DC in the left inferior temporal gyrus (ITG), left posterior cerebellum lobe (CPL), and right cerebellum anterior lobe (CAL), along with decreased DC in the left medial frontal gyrus. Moreover, the sMCI group displayed reduced EC from the right CAL to bilateral CPL, left superior temporal gyrus, and bilateral caudate compared with HC. pMCI demonstrated elevated EC from the right CAL to left ITG, which was linked to episodic memory and executive function. Notably, the EC from the right CAL to the right ITG effectively distinguished sMCI from pMCI, with sensitivity, specificity, and accuracy of 0.5806, 0.9512, and 0.828, respectively.ConclusionThis study uncovered shared and distinct alterations in DC and EC between sMCI and pMCI, highlighting their involvement in cognitive function. Of particular significance are the unidirectional EC disruptions from the cerebellum to the temporal lobe, which serve as a discriminating factor between sMCI and pMCI and provide a new perspective for understanding the temporal-cerebellum. These findings offer novel insights into the neural circuit mechanisms involving the temporal-cerebellum connection in MCI.

IntroductionAlzheimer’s disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown.MethodsParticipants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer’s Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer’s Disease Neuroimaging Initiative database (n = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes.ResultsWe found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 – 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 – 1057.5) to 400.9 (95% CI: 306.9 – 516.8).DiscussionOur findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.

Abstract Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS‐C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue‐specific enrichment analysis indicate that the protective effect of PGS‐C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high‐cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS‐C.

Abstract Purpose Mild cognitive impairment (MCI) can be the prodromal phase of Alzheimer's disease (AD) where appropriate intervention might prevent or delay conversion to AD. Given this, there has been increasing interest in using magnetic resonance imaging (MRI) and neuropsychological testing to predict conversion from MCI to AD. Recent evidence suggests that the choroid plexus (ChP), neural substrates implicated in brain clearance, undergo volumetric changes in MCI and AD. Whether the ChP is involved in memory changes observed in MCI and can be used to predict conversion from MCI to AD has not been explored. Method The current study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate whether later progression from MCI to AD (progressive MCI [pMCI], n = 115) or stable MCI (sMCI, n = 338) was associated with memory scores using the Rey Auditory Verbal Learning Test (RAVLT) and ChP volumes as calculated from MRI. Classification analyses identifying pMCI or sMCI group membership were performed to compare the predictive ability of the RAVLT and ChP volumes. Finding The results indicated a significant difference between pMCI and sMCI groups for right ChP volume, with the pMCI group showing significantly larger right ChP volume (p = .01, 95% confidence interval [−.116, −.015]). A significant linear relationship between the RAVLT scores and right ChP volume was found across all participants, but not for the two groups separately. Classification analyses showed that a combination of left ChP volume and auditory verbal learning scores resulted in the most accurate classification performance, with group membership accurately predicted for 72% of the testing data. Conclusion These results suggest that volumetric ChP changes appear to occur before the onset of AD and might provide value in predicting conversion from MCI to AD.

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

IntroductionThis study aimed to investigate the cognitive profile and prospective cognitive changes in non-demented adults with elevated Modified Dementia Risk Scores (MDRS), while also exploring the potential relationship between these associations and cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD) pathology and neuroinflammation.MethodsWithin the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database, 994 participants without dementia were assessed on MDRS, CSF biomarkers and cognition. We examined the associations of the MDRS with CSF biomarkers and cognitive scores using linear regressions. Causal mediation analyses were conducted to analyze the associations among MDRS, brain pathologies, and cognition. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) study was used to validate the mediation effects and to investigate the longitudinal association between MDRS and cognitive decline.ResultsThe results revealed that higher MDRS were linked to poorer cognitive performance (Model 1: PFDR

Abstract Objective To explore the functional connectivity (FC) characteristics of the episodic memory network (EMN) in amnestic mild cognitive impairment (aMCI) patients with different levels of executive function (EF). Methods This study included 76 participants from the Alzheimer's Disease Neuroimaging Initiative database, comprising 23 healthy controls (HCs) and 53 aMCI patients. Based on EF levels, aMCI patients were categorized into aMCI‐highEF and aMCI‐lowEF groups. Cognitive function scores, pathological markers (cerebrospinal fluid β‐amyloid, total tau protein, phosphorylated tau protein, AV45‐PET, and FDG‐PET), and functional magnetic resonance imaging were collected and compared among the three groups. Seed‐based FC analysis was used to examine differences in the EMN among the groups, and partial correlation analysis was employed to investigate the relationship between changes in FC and cognitive function scores as well as pathological markers. Results Compared to the aMCI‐highEF group, the aMCI‐lowEF group exhibited more severe cognitive impairment, decreased cerebral glucose metabolism, and elevated AV45 levels. Significant FC differences in the left superior temporal gyrus (STG) of the EMN were observed among the three groups. Post hoc analysis revealed that the aMCI‐lowEF group had increased FC in the left STG compared to the HCs and aMCI‐highEF groups, with statistically significant differences. Correlation analysis showed a significant negative correlation between the differences in FC in the left STG of aMCI‐highEF and aMCI‐lowEF groups and Rey Auditory Verbal Learning Test forgetting scores. Receiver operator characteristic curve analysis indicated an area under the curve of 0.741 for distinguishing between aMCI‐highEF and aMCI‐lowEF groups based on FC of left STG, with a sensitivity of 0.808 and a specificity of 0.667. Conclusion aMCI‐lowEF exhibits characteristic changes in FC within the EMN, providing theoretical support for the role of EF in mediating EMN alterations and, consequently, impacting episodic memory function.

IntroductionMild cognitive impairment (MCI) is an important stage in Alzheimer’s disease (AD) research, focusing on early pathogenic factors and mechanisms. Examining MCI patient subtypes and identifying their cognitive and neuropathological patterns as the disease progresses can enhance our understanding of the heterogeneous disease progression in the early stages of AD. However, few studies have thoroughly analyzed the subtypes of MCI, such as the cortical atrophy, and disease development characteristics of each subtype.MethodsIn this study, 396 individuals with MCI, 228 cognitive normal (CN) participants, and 192 AD patients were selected from ADNI database, and a semi-supervised mixture expert algorithm (MOE) with multiple classification boundaries was constructed to define AD subtypes. Moreover, the subtypes of MCI were obtained by using the multivariate linear boundary mapping of support vector machine (SVM). Then, the gray matter atrophy regions and severity of each MCI subtype were analyzed and the features of each subtype in demography, pathology, cognition, and disease progression were explored combining the longitudinal data collected for 2 years and analyzed important factors that cause conversion of MCI were analyzed.ResultsThree MCI subtypes were defined by MOE algorithm, and the three subtypes exhibited their own features in cortical atrophy. Nearly one-third of patients diagnosed with MCI have almost no significant difference in cerebral cortex from the normal aging population, and their conversion rate to AD are the lowest. The subtype characterized by severe atrophy in temporal lobe and frontal lobe have a faster decline rate in many cognitive manifestations than the subtype featured with diffuse atrophy in the whole cortex. APOE ε4 is an important factor that cause the conversion of MCI to AD.ConclusionIt was proved through the data-driven method that MCI collected by ADNI baseline presented different subtype features. The characteristics and disease development trajectories among subtypes can help to improve the prediction of clinical progress in the future and also provide necessary clues to solve the classification accuracy of MCI.

IntroductionThe Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies.MethodsAll Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated.ResultsThe smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used.ConclusionsFWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.

Abstract Background Glymphatic dysfunction is a crucial pathway for dementia. Alzheimer’s disease (AD) pathologies co-existing with cerebral small vessel disease (CSVD) is the most common pathogenesis for dementia. We hypothesize that AD pathologies and CSVD could be associated with glymphatic dysfunction, contributing to cognitive impairment. Method Participants completed with amyloid PET, diffusion tensor imaging (DTI), and T2 fluid-attenuated inversion-recovery (FLAIR) sequences were included from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). White matter hyperintensities (WMH), the most common CSVD marker, was evaluated from T2FLAIR images and represented the burden of CSVD. Amyloid PET was used to assess Aβ aggregation in the brain. We used diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, the burden of enlarged perivascular spaces (PVS), and choroid plexus volume to reflect glymphatic function. The relationships between WMH burden/Aβ aggregation and these glymphatic markers as well as the correlations between glymphatic markers and cognitive function were investigated. Furthermore, we conducted mediation analyses to explore the potential mediating effects of glymphatic markers in the relationship between WMH burden/Aβ aggregation and cognition. Results One hundred and thirty-three participants along the AD continuum were included, consisting of 40 CN − , 48 CN + , 26 MCI + , and 19 AD + participants. Our findings revealed that there were negative associations between whole-brain Aβ aggregation (r = − 0.249, p = 0.022) and WMH burden (r = − 0.458, p

Abstract Background The pathophysiology of Alzheimer’s disease (AD) involves $$\beta$$ β -amyloid (A $$\beta$$ β ) accumulation. Early identification of individuals with abnormal $$\beta$$ β -amyloid levels is crucial, but A $$\beta$$ β quantification with positron emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expensive. Methods We propose a machine learning framework using standard non-invasive (MRI, demographics, APOE, neuropsychology) measures to predict future A $$\beta$$ β -positivity in A $$\beta$$ β -negative individuals. We separately study A $$\beta$$ β -positivity defined by PET and CSF. Results Cross-validated AUC for 4-year A $$\beta$$ β conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A $$\beta$$ β definitions. Although not trained for the clinical status-change prediction, the CSF-based model excelled in predicting future mild cognitive impairment (MCI)/dementia conversion in cognitively normal/MCI individuals (AUCs, respectively, 0.76 and 0.89 with a separate dataset). Conclusion Standard measures have potential in detecting future A $$\beta$$ β -positivity and assessing conversion risk, even in cognitively normal individuals. The CSF-based definition led to better predictions than the PET-based definition.

Abstract Parkinson’s disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model’s decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Abstract Background Functional decline associated with dementia, including in Alzheimer’s disease (AD), is not uniform across individuals, and respective heterogeneity is not yet fully explained. Such heterogeneity may in part be related to genetic variability among individuals. In this study, we investigated whether the SNP rs6859 in nectin cell adhesion molecule 2 (NECTIN2) gene (a major risk factor for AD) influences trajectories of cognitive decline in older participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Methods We retrospectively analyzed records on 1310 participants from the ADNI database for the multivariate analysis. We used longitudinal measures of Mini-Mental State Examination (MMSE) scores in participants, who were cognitively normal, or having AD, or other cognitive deficits to investigate the trajectories of cognitive changes. Multiple linear regression, linear mixed models and latent class analyses were conducted to investigate the association of the SNP rs6859 with MMSE. Results The regression coefficient per one allele dose of the SNP rs6859 was independently associated with MMSE in both cross-sectional (-2.23, p

Abstract Various plasma biomarkers for amyloid-β (Aβ) have shown high predictability of amyloid PET positivity. However, the characteristics of discordance between amyloid PET and plasma Aβ42/40 positivity are poorly understood. Thorough interpretation of discordant cases is vital as Aβ plasma biomarker is imminent to integrate into clinical guidelines. We aimed to determine the characteristics of discordant groups between amyloid PET and plasma Aβ42/40 positivity, and inter-assays variability depending on plasma assays. We compared tau burden measured by PET, brain volume assessed by MRI, cross-sectional cognitive function, longitudinal cognitive decline and polygenic risk score (PRS) between PET/plasma groups (PET−/plasma−, PET−/plasma+, PET+/plasma−, PET+/plasma+) using Alzheimer’s Disease Neuroimaging Initiative database. Additionally, we investigated inter-assays variability between immunoprecipitation followed by mass spectrometry method developed at Washington University (IP-MS-WashU) and Elecsys immunoassay from Roche (IA-Elc). PET+/plasma+ was significantly associated with higher tau burden assessed by PET in entorhinal, Braak III/IV, and Braak V/VI regions, and with decreased volume of hippocampal and precuneus regions compared to PET−/plasma-. PET+/plasma+ showed poor performances in global cognition, memory, executive and daily-life function, and rapid cognitive decline. PET+/plasma+ was related to high PRS. The PET−/plasma+ showed intermediate changes between PET−/plasma− and PET+/plasma+ in terms of tau burden, hippocampal and precuneus volume, cross-sectional and longitudinal cognition, and PRS. PET+/plasma− represented heterogeneous characteristics with most prominent variability depending on plasma assays. Moreover, IP-MS-WashU showed more linear association between amyloid PET standardized uptake value ratio and plasma Aβ42/40 than IA-Elc. IA-Elc showed more plasma Aβ42/40 positivity in the amyloid PET-negative stage than IP-MS-WashU. Characteristics of PET−/plasma+ support plasma biomarkers as early biomarker of amyloidopathy prior to amyloid PET. Various plasma biomarker assays might be applied distinctively to detect different target subjects or disease stages.

Abstract Alzheimer’s disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer’s disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.