Alzheimer's Disease Neuroimaging Initiative Cohort Research Articles

Hypertension and cerebral blood flow in the development of Alzheimer's disease.

We investigated the interactive associations between amyloid and hypertension on the entorhinal cortex (EC) tau and atrophy and the role of cerebral blood flow (CBF) as a shared mechanism by which amyloid and hypertension contribute to EC tau and regional white matter hyperintensities (WMHs). We analyzed data from older adults without dementia participating in the Add-Tau study (NCT02958670, n=138) or Alzheimer's Disease Neuroimaging Initiative (ADNI) (n=523) who had available amyloid-positron emission tomography (PET), tau-PET, fluid-attenuated inversion recovery (FLAIR), and T1-weighted magnetic resonance imaging (MRI). A subsample in both cohorts had available arterial spin labeling (ASL) MRI (Add-Tau: n=78; ADNI: n=89). The detrimental effects of hypertension on AD pathology and EC thickness were more pronounced in the Add-Tau cohort. Increased amyloid burden was associated with decreased occipital gray matter CBF in the ADNI cohort. In both cohorts, lower regional gray matter CBF was associated with higher EC tau and posterior WMH burden. Reduced cerebral perfusion may be one common mechanism through which hypertension and amyloid are related to increased EC tau and WMH volume. Hypertension is associated with increased entorhinal cortex (EC) tau, particularly in the presence of amyloid. Decreased cortical cerebral blood flow (CBF) is associated with higher regional white matter hyperintensity volume. Increasing amyloid burden is associated with decreasing CBF in the occipital lobe. MTL CBF and amyloid are synergistically associated with EC tau.

Hippocampal volume maximally modulates the relationship between subsyndromal symptomatic depression and cognitive impairment in non-demented older adults

BackgroundSubsyndromal symptomatic depression (SSD) is associated with an elevated risk of cognitive impairment in non-demented older adults. Given that hippocampal and middle temporal gyrus atrophy have been shown to cause SSD, our study aimed to investigate the effect of hippocampal volume on the association between SSD and cognitive impairment. Methods338 non-demented older adults from the ADNI (Alzheimer's Disease Neuroimaging Initiative) cohort who underwent cognitive assessments, questionnaires on depressive symptoms and MRI brain were studied. SSD group is defined as a score of 1–5 based on Geriatric Depression Scale scores. We conducted causal mediation analyses to investigate the effect of hippocampal volume on cognitive performance cross-sectionally. ResultsThe SSD group displayed lower left and right hippocampal volume (p<0.01) than the non-SSD group. SSD was linked to poorer cognition and smaller hippocampal volume. We found that hippocampal volume partially mediated the effect of SSD on cognitive performance including the global cognition and the cognitive section of Alzheimer's Disease Assessment Scale, with mediation percentages ranging from 6.45 % to 30.46 %. In addition, we found that the thickness of the left middle temporal, right entorhinal and right fusiform gyrus, brain regions linked to AD, mediate the relationship between SSD and cognition with mediation percentages ranging from 8.67 % to 21.44 %. LimitationsOur article didn't differentiate between mild cognitive impairment and normal population. ConclusionThe associations of SSD and cognitive impairment are linked to alterations in Alzheimer's Disease related brain regions.

The neutrophil to lymphocyte ratio associates with markers of Alzheimer's disease pathology in cognitively unimpaired elderly people.

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. 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 < 0.001), had a higher proportion of males (49.5% vs. 36.8%, p = 0.042), higher BMIs (27.94 vs. 25.79, p < 0.001), higher prevalence of hypertensive history (47.7% vs. 16.3%, p < 0.001), and a greater percentage of Aβ-positivity (34.2% vs. 20.0%, p = 0.009). In the ADNI cohort, we found cross-sectional associations between the NLR and CSF Aβ42 (β = -12.193, p = 0.021), but not t-tau or p-tau. In the NYU cohort, we found cross-sectional associations between the NLR and CSF t-tau (β = 26.812, p = 0.019) and p-tau (β = 3.441, p = 0.015), but not Aβ42. In the NYU cohort alone, subjects classified as Aβ + (n = 38) displayed a stronger association between the NLR and t-tau (β = 100.476, p = 0.037) compared to Aβ- subjects or the non-stratified cohort. In both cohorts, the same associations observed in the cross-sectional analyses were observed after incorporating longitudinal CSF data. We report associations between the NLR and Aβ42 in the older ADNI cohort, and between the NLR and t-tau and p-tau in the younger NYU cohort. Associations persisted after adjusting for comorbidities, suggesting a direct link between the NLR and AD. However, changes in associations between the NLR and specific AD biomarkers may occur as part of immunosenescence.

Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer's Disease Conversion: Evidence from a 2-Cohort Longitudinal Study.

To explore the complementary relationship between magnetic resonance imaging (MRI) radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer's disease (AD), our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD. This longitudinal multicentric cohort study included 2 independent cohorts: the Sino Longitudinal Study on Cognitive Decline (SILCODE) project and the Alzheimer Disease Neuroimaging Initiative (ADNI). We collected comprehensive assessments, MRI, plasma samples, and amyloid positron emission tomography data. A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator. The optimal model's performance and generalizability were assessed across populations in 2 cross-racial cohorts. A total of 897 subjects were included, including 635 from the SILCODE cohort (mean [SD] age, 64.93 [6.78] years; 343 [63%] female) and 262 from the ADNI cohort (mean [SD] age, 73.96 [7.06] years; 140 [53%] female). The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset, respectively. A calibration analysis displayed excellent consistency between the prognosis and actual observation. The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker. Importantly, built upon data-driven analyses commonly seen in previous radiomics studies, our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.

An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification.

Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Prevalence and Clinical Implications of a β-Amyloid–Negative, Tau-Positive Cerebrospinal Fluid Biomarker Profile in Alzheimer Disease

Knowledge is lacking on the prevalence and prognosis of individuals with a β-amyloid-negative, tau-positive (A-T+) cerebrospinal fluid (CSF) biomarker profile. To estimate the prevalence of a CSF A-T+ biomarker profile and investigate its clinical implications. This was a retrospective cohort study of the cross-sectional multicenter University of Gothenburg (UGOT) cohort (November 2019-January 2021), the longitudinal multicenter Alzheimer Disease Neuroimaging Initiative (ADNI) cohort (individuals with mild cognitive impairment [MCI] and no cognitive impairment; September 2005-May 2022), and 2 Wisconsin cohorts, Wisconsin Alzheimer Disease Research Center and Wisconsin Registry for Alzheimer Prevention (WISC; individuals without cognitive impairment; February 2007-November 2020). This was a multicenter study, with data collected from referral centers in clinical routine (UGOT) and research settings (ADNI and WISC). Eligible individuals had 1 lumbar puncture (all cohorts), 2 or more cognitive assessments (ADNI and WISC), and imaging (ADNI only) performed on 2 separate occasions. Data were analyzed on August 2022 to April 2023. Baseline CSF Aβ42/40 and phosphorylated tau (p-tau)181; cognitive tests (ADNI: modified preclinical Alzheimer cognitive composite [mPACC]; WISC: modified 3-test PACC [PACC-3]). Exposures in the ADNI cohort included [18F]-florbetapir amyloid positron emission tomography (PET), magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose PET (FDG-PET), and cross-sectional tau-PET (ADNI: [18F]-flortaucipir, WISC: [18F]-MK6240). Primary outcomes were the prevalence of CSF AT biomarker profiles and continuous longitudinal global cognitive outcome and imaging biomarker trajectories in A-T+ vs A-T- groups. Secondary outcomes included cross-sectional tau-PET. A total of 7679 individuals (mean [SD] age, 71.0 [8.4] years; 4101 male [53%]) were included in the UGOT cohort, 970 individuals (mean [SD] age, 73 [7.0] years; 526 male [54%]) were included in the ADNI cohort, and 519 individuals (mean [SD] age, 60 [7.3] years; 346 female [67%]) were included in the WISC cohort. The prevalence of an A-T+ profile in the UGOT cohort was 4.1% (95% CI, 3.7%-4.6%), being less common than the other patterns. Longitudinally, no significant differences in rates of worsening were observed between A-T+ and A-T- profiles for cognition or imaging biomarkers. Cross-sectionally, A-T+ had similar tau-PET uptake to individuals with an A-T- biomarker profile. Results suggest that the CSF A-T+ biomarker profile was found in approximately 5% of lumbar punctures and was not associated with a higher rate of cognitive decline or biomarker signs of disease progression compared with biomarker-negative individuals.

Relationships of Hypnotics with Incident Dementia and Alzheimer's Disease: A Longitudinal Study and Meta-Analysis.

Evidence describing the association between hypnotics use and dementia risk is conflicting. It is unknown if the controversy is related to the type or dose of hypnotics or if hypnotics affect different populations. We sought to derive lessons learned and future projections based on evidence from longitudinal studies. In the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, 1,543 older adults without dementia (mean age = 73.3 years, female = 45%) were followed for four years. The association between hypnotics and the risk of Alzheimer's disease (AD) was investigated using Cox proportional hazards regressions. Next, electronic databases were searched until March 2022 to conduct the evidence synthesis of the associations of hypnotics with incident risk of dementia. In the ADNI cohort, ever use of hypnotics was associated with an increased risk of AD (hazard ratio = 1.96, 95% confidence intervals = 1.23-3.11, p < 0.01). This association was significant for benzodiazepines and Z-drugs but not for melatonin. The association was stronger in long-term (more than one year) users and those with high cumulative doses. A meta-analysis of 26 longitudinal studies with 3,942,018 participants revealed a correlation between the use of hypnotics and the risk of dementia (relative risk = 1.23, 95% confidence intervals = 1.13-1.33, p < 0.001, median risk difference = 4%). It is a linear dose-response relationship, if a person takes the daily recommended dose for 100 days, their risk of developing dementia increases by 5% relative to non-users. According to subgroup analyses, neither association was significant among patients with a history of insomnia. Individuals who use hypnotics, especially high-dose or long-term users, are at a higher risk of dementia and AD. The main issue with conclusion credibility is heterogeneity.

Association of CSF GAP-43 With the Rate of Cognitive Decline and Progression to Dementia in Amyloid-Positive Individuals.

To test the associations between the presynaptic growth-associated protein 43 (GAP-43), quantified in CSF, and biomarkers of Alzheimer disease (AD) pathophysiology, cross-sectionally and longitudinally. In this retrospective study, GAP-43 was measured in participants from the AD Neuroimaging Initiative (ADNI) cohort using an in-house ELISA method, and levels were compared between groups, both cross-sectionally and longitudinally. Linear regression models tested the associations between biomarkers of AD (amyloid beta [Aβ] and tau pathologies, neurodegeneration, and cognition) adjusted by age, sex, and diagnosis. Linear mixed-effect models evaluated how baseline GAP-43 predicts brain hypometabolism, atrophy, and cognitive decline over time. Cox proportional hazard regression models tested how GAP-43 levels and Aβ status, at baseline, increased the risk of progression to AD dementia over time. This study included 786 participants from the ADNI cohort, which were further classified in cognitively unimpaired (CU) Aβ-negative (nCU- = 197); CU Aβ-positive (nCU+ = 55), mild cognitively impaired (MCI) Aβ-negative (nMCI- = 228), MCI Aβ-positive (nMCI+ = 193), and AD dementia Aβ-positive (nAD = 113). CSF GAP-43 levels were increased in Aβ-positive compared with Aβ-negative participants, independent of the cognitive status. In Aβ-positive participants, high baseline GAP-43 levels led to worse brain metabolic decline (p = 0.01), worse brain atrophy (p = 8.8 × 10-27), and worse MMSE scores (p = 0.03) over time, as compared with those with low GAP-43 levels. Similarly, Aβ-positive participants with high baseline GAP-43 had the highest risk to convert to AD dementia (hazard ratio [HR = 8.56, 95% CI 4.94-14.80, p = 1.5 × 10-14]). Despite the significant association with Aβ pathology (η2 Aβ PET = 0.09, P Aβ PET < 0.001), CSF total tau (tTau) and phosphorylated tau (pTau) had a larger effect size on GAP43 than Aβ PET (η2 pTau-181 = 0.53, P pTau-181 < 0.001; η2 tTau = 0.59, P tTau < 0.001). High baseline levels of CSF GAP-43 are associated with progression in Aβ-positive individuals, with a more aggressive neurodegenerative process, faster rate of cognitive decline, and increased risk of converting to dementia.

Association of rs2072446 in the NGFR gene with the risk of Alzheimer's disease and amyloid-β deposition in the brain.

Alzheimer's disease (AD) is the most common form of dementia with a complex genetic background. The cause of sporadic AD (sAD) remains largely unknown. Increasing evidence shows that genetic variations play a crucial role in sAD. P75 neurotrophin receptor (p75NTR, encoded by NGFR) plays a critical role in the pathogenesis of AD. Yet, the relationship between NGFR gene polymorphisms and AD was less studied. This study aims to analyze the relationship of NGFR gene polymorphism with the risk of AD in the Chinese Han population and amyloid-β deposition in the ADNI cohort. This case-control association study was conducted in a Chinese Han cohort consisting of 366 sporadic AD (sAD) patients and 390 age- and sex-matched controls. Twelve tag-SNPs were selected and genotyped with a multiplex polymerase chain reaction-ligase detection reaction (PCR-LDR) method. The associations between tag-SNPs and the risk of AD were analyzed by logistic regression. Moreover, another cohort from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database was included to examine the association of one tag-SNP (rs2072446) with indicators of amyloid deposition. Kaplan-Meier survival analysis and Cox proportional hazards models were used to test the predictive abilities of rs2072446 genotypes for AD progression. The mediation effects of Aβ deposition on this association were subsequently tested by mediation analyses. After multiple testing corrections, one tag-SNP, rs2072446, was associated with an increased risk of sAD (additive model, OR=1.79, Padjustment =0.0144). Analyses of the ADNI cohort showed that the minor allele (T) of rs2072446 was significantly associated with the heavier Aβ burden, which further contributed to an increased risk of AD progression in APOE ε4 non-carrier. Our study found that rs2072446 in NGFR is associated with both the risk of sAD in the Chinese Han population and the amyloid burden in the ADNI cohort, which reveals the role of p75NTR in AD from a genetic perspective.

Personal history of breast cancer, cognitive function, and conversion in women from the ADNI cohort

AbstractBackgroundSome women with a personal history of breast cancer (PHBC) may experience higher rates of objective/subjective cancer‐related cognitive impairment (CRCI) shortly after treatment, while others have persistent CRCI as many as 20 years after. It is unclear how this impacts cognitive aging years after diagnosis. We explored whether remote PHBC is associated with a specific pattern of cognitive decline or increased dementia diagnosis rates in women.MethodWe performed secondary analysis of data from women enrolled in the Alzheimer Disease Neuroimaging Initiative (ADNI) from October, 2005 ‐ September, 2018. A review of records identified 33 cognitively normal (CN) and 20 mild cognitive impairment (MCI) participants with PHBC, and 268 CN and 270 MCI participants without PHBC. We compared overall rates of PHBC in the MCI/CN groups as well as rates of conversion to MCI (from CN) or Alzheimer’s dementia (AD). Baseline scores on cognitive tests were corrected for age, education, and sex and averaged into composite scores: Structured‐Verbal‐Memory (Logical Memory Learning/Delay) Unstructured‐Verbal‐Memory (RAVLT Learning/Delay/Recognition), Attention/Processing‐Speed (Trails A+B/Number‐Cancellation) and Language (Animal‐fluency/Boston Naming). One‐way ANOVA and χ2 examined differences in PHBC rates, cognitive performance, and dementia conversion.ResultRates of PHBC did not differ significantly between CN(11%) and MCI(7%), (p=0.08). Among women with MCI, those absent PHBC performed significantly worse than those with PHBC on Unstructured‐Verbal‐Memory (p=0.012). Follow‐up examination of individual RAVLT scores revealed intact recognition in the MCI+PHBC women. Women with MCI alone, converted to AD at significantly higher rates than MCI+PHBC (p=0.04). Cognitively normal women with/without PHBC did not differ in cognitive test performance or conversation rates.ConclusionResults suggest no increased risk of MCI or AD in women with a PHBC. In fact, women with MCI+PHBC show lower rates of conversion to AD, a pattern of memory impairment less consistent with AD, and better overall cognition relative to those with MCI alone. This may indicate that the etiology of memory problems in women with PHBC may be related to persistent CRCI rather than a neurodegenerative process like AD, but further research is needed to understand specific factors that contribute to the development of CRCI and their impact on cognitive aging.

Head-to-Head Comparison of 8 Plasma Amyloid-β 42/40 Assays in Alzheimer Disease

Blood-based tests for brain amyloid-β (Aβ) pathology are needed for widespread implementation of Alzheimer disease (AD) biomarkers in clinical care and to facilitate patient screening and monitoring of treatment responses in clinical trials. To compare the performance of plasma Aβ42/40 measured using 8 different Aβ assays when detecting abnormal brain Aβ status in patients with early AD. This study included 182 cognitively unimpaired participants and 104 patients with mild cognitive impairment from the BioFINDER cohort who were enrolled at 3 different hospitals in Sweden and underwent Aβ positron emission tomography (PET) imaging and cerebrospinal fluid (CSF) and plasma collection from 2010 to 2014. Plasma Aβ42/40 was measured using an immunoprecipitation-coupled mass spectrometry developed at Washington University (IP-MS-WashU), antibody-free liquid chromatography MS developed by Araclon (LC-MS-Arc), and immunoassays from Roche Diagnostics (IA-Elc); Euroimmun (IA-EI); and Amsterdam University Medical Center, ADx Neurosciences, and Quanterix (IA-N4PE). Plasma Aβ42/40 was also measured using an IP-MS-based method from Shimadzu in 200 participants (IP-MS-Shim) and an IP-MS-based method from the University of Gothenburg (IP-MS-UGOT) and another immunoassay from Quanterix (IA-Quan) among 227 participants. For validation, 122 participants (51 cognitively normal, 51 with mild cognitive impairment, and 20 with AD dementia) were included from the Alzheimer Disease Neuroimaging Initiative who underwent Aβ-PET and plasma Aβ assessments using IP-MS-WashU, IP-MS-Shim, IP-MS-UGOT, IA-Elc, IA-N4PE, and IA-Quan assays. Discriminative accuracy of plasma Aβ42/40 quantified using 8 different assays for abnormal CSF Aβ42/40 and Aβ-PET status. A total of 408 participants were included in this study. In the BioFINDER cohort, the mean (SD) age was 71.6 (5.6) years and 49.3% of the cohort were women. When identifying participants with abnormal CSF Aβ42/40 in the whole cohort, plasma IP-MS-WashU Aβ42/40 showed significantly higher accuracy (area under the receiver operating characteristic curve [AUC], 0.86; 95% CI, 0.81-0.90) than LC-MS-Arc Aβ42/40, IA-Elc Aβ42/40, IA-EI Aβ42/40, and IA-N4PE Aβ42/40 (AUC range, 0.69-0.78; P < .05). Plasma IP-MS-WashU Aβ42/40 performed significantly better than IP-MS-UGOT Aβ42/40 and IA-Quan Aβ42/40 (AUC, 0.84 vs 0.68 and 0.64, respectively; P < .001), while there was no difference in the AUCs between IP-MS-WashU Aβ42/40 and IP-MS-Shim Aβ42/40 (0.87 vs 0.83; P = .16) in the 2 subcohorts where these biomarkers were available. The results were similar when using Aβ-PET as outcome. Plasma IPMS-WashU Aβ42/40 and IPMS-Shim Aβ42/40 showed highest coefficients for correlations with CSF Aβ42/40 (r range, 0.56-0.65). The BioFINDER results were replicated in the Alzheimer Disease Neuroimaging Initiative cohort (mean [SD] age, 72.4 [5.4] years; 43.4% women), where the IP-MS-WashU assay performed significantly better than the IP-MS-UGOT, IA-Elc, IA-N4PE, and IA-Quan assays but not the IP-MS-Shim assay. The results from 2 independent cohorts indicate that certain MS-based methods performed better than most of the immunoassays for plasma Aβ42/40 when detecting brain Aβ pathology.

A 4-Year Follow-Up of Subjects with Visually Equivocal Amyloid Positron Emission Tomography Findings from the Alzheimer's Disease Neuroimaging Initiative Cohort.

To date, the clinical significance of visually equivocal amyloid positron emission tomography (PET) has not been well established. We studied the clinical significance of equivocal amyloid PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Subjects with F-18 florbetapir PET scans at baseline who were followed up for 4 years were selected. Clinical characteristics, imaging biomarkers, cognitive function, and rate of conversion to AD were compared in subjects with visually equivocal findings. Of 249 subjects who completed the follow-up, 153 (61.4%), 20 (8.0%), and 129 (30.5%) were F-18 florbetapir-negative, -equivocal, and -positive, respectively. The mean standardized uptake value ratios (SUVR) of F-18 florbetapir PET were 0.75 ± 0.04, 0.85 ± 0.10, and 1.00 ± 0.09 for each group (p <0.001 between groups), and 15.0%, 70.0%, and 98.7% of patients were quantitatively above the positive threshold. The change in the SUVR of F-18 florbetapir PET was higher in the equivocal (6.09 ± 3.61%, p <0.001) and positive (3.13 ± 4.38%, p <0.001) groups than the negative group (0.88 ± 4.28%). Among the subjects with normal or subjective memory impairment and mild cognitive impairment, 5.3% with negative amyloid PET and 37.5% with positive amyloid PET converted to AD over the 4-year period. None of the equivocal amyloid PET subjects converted to AD during this period. Approximately 8% of subjects from the ADNI cohort showed visually equivocal amyloid PET scans with intermediate load and rapid accumulation of amyloid, but did not convert to AD during the 4-year follow-up.

TNFRSF1B Gene Variants and Related Soluble TNFR2 Levels Impact Resilience in Alzheimer's Disease.

Tumor necrosis factor receptor 2 (TNFR2) promotes neuronal survival downstream. This longitudinal study evaluated whether the TNFRSF1B gene encoding TNFR2 and levels of its soluble form (sTNFR2) affect Alzheimer disease (AD) biomarkers and clinical outcomes. Data analyzed included 188 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had mild cognitive impairment (MCI) and AD dementia. Further, a replication study was performed in 48 patients with MCI with positive AD biomarkers who were treated at a memory clinic. Cerebrospinal fluid (CSF) sTNFR2 levels along with two related TNFRSF1B gene single nucleotide polymorphisms (SNPs) rs976881 and rs1061622 were assessed. General linear models were used to evaluate the effect of CSF sTNFR2 levels and each SNP in relationship to CSF t-tau and p-tau, cognitive domains, MRI brain measures, and longitudinal cognitive changes after adjustments were made for covariates such as APOE ε4 status. In the ADNI cohort, a significant interaction between rs976881 and CSF sTNFR2 modulates CSF t-tau and p-tau levels; hippocampal and whole brain volumes; and Digit Span Forwards subtest scores. In the replication cohort, a significant interaction between rs976881 and CSF sTNFR2 modulates CSF p-tau. A significant interaction between rs976881 and CSF sTNFR2 also impacts Clinical Dementia Rating Sum of Boxes scores over 12 months in the ADNI cohort. The interaction between TNFRSF1B variant rs976881 and CSF sTNFR2 levels was noted to modulate multiple AD-associated severity markers and cognitive domains. This interaction impacts resilience-related clinical outcomes in AD and lends support to sTNFR2 as a promising candidate for therapeutic targeting to improve clinical outcomes of interest.

Cohort-Specific Optimization of Models Predicting Preclinical Alzheimer's Disease, to Enhance Screening Performance in the Middle of Preclinical Alzheimer's Disease Clinical Studies.

Models that can predict brain amyloid beta (Aβ) status more accurately have been desired to identify participants for clinical trials of preclinical Alzheimer's disease (AD). However, potential heterogeneity between different cohorts and the limited cohort size have been the reasons preventing the development of reliable models applicable to the Asian population, including Japan. We aim to propose a novel approach to predict preclinical AD while overcoming these constraints, by building models specifically optimized for ADNI or for J-ADNI, based on the larger samples from A4 study data. This is a retrospective study including cognitive normal participants (CDR-global = 0) from A4 study, Alzheimer Disease Neuroimaging Initiative (ADNI), and Japanese-ADNI (J-ADNI) cohorts. The model is made up of age, sex, education years, history of AD, Clinical Dementia Rating-Sum of Boxes, Preclinical Alzheimer Cognitive Composite score, and APOE genotype, to predict the degree of amyloid accumulation in amyloid PET as Standardized Uptake Value ratio (SUVr). The model was at first built based on A4 data, and we can choose at which SUVr threshold configuration the A4-based model may achieve the best performance area under the curve (AUC) when applied to the random-split half ADNI or J-ADNI subset. We then evaluated whether the selected model may also achieve better performance in the remaining ADNI or J-ADNI subsets. When compared to the results without optimization, this procedure showed efficacy of AUC improvement of up to approximately 0.10 when applied to the models "without APOE;" the degree of AUC improvement was larger in the ADNI cohort than in the J-ADNI cohort. The obtained AUC had improved mildly when compared to the AUC in case of literature-based predetermined SUVr threshold configuration. This means our procedure allowed us to predict preclinical AD among ADNI or J-ADNI second-half samples with slightly better predictive performance. Our optimizing method may be practically useful in the middle of the ongoing clinical study of preclinical AD, as a screening to further increase the prior probability of preclinical AD before amyloid testing.

Association of Initial β-Amyloid Levels With Subsequent Flortaucipir Positron Emission Tomography Changes in Persons Without Cognitive Impairment

Tau accumulation in Alzheimer disease (AD) is closely associated with cognitive impairment. Quantitating tau accumulation by positron emission tomography (PET) will be a useful outcome measure for future clinical trials in the AD spectrum. To investigate the association of β-amyloid (Aβ) on PET with subsequent tau accumulation on PET in persons who were cognitively unimpaired (CU) to gain insight into temporal associations between Aβ and tau accumulation and inform clinical trial design. This cohort study included individuals aged 65 to 85 years who were CU and had participated in the Mayo Clinic Study of Aging, with serial cognitive assessments, serial magnetic resonance imaging, 11C-Pittsburgh compound B (Aβ) PET scans, and 18F-flortaucipir PET scans, collected from May 2015 to March 2020. Persons were excluded if they lacked follow-up PET scans. A similarly evaluated CU group from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were also studied. These data were collected from September 2015 to March 2020. Participants were stratified by index Aβ levels on PET into low Aβ (≤8 centiloid [CL]), subthreshold Aβ (9-21 CL), suprathreshold Aβ (22-67 CL), and high Aβ (≥68 CL). Changes over a mean of 2.7 (range, 1.1-4.1) years in flortaucipir PET in entorhinal, inferior temporal, and lateral parietal regions of interest and an AD meta-region of interest (ROI). A total of 167 people were included (mean age, 74 [range, 65-85] years; 75 women [44.9%]); 101 individuals were excluded lacking follow-up, and 114 individuals from the ADNI were also studied (mean [SD] age, 74.14 [5.29] years; 64 women [56.1%]). In the Mayo Clinic Study of Aging, longitudinal flortaucipir accumulation rates in the high Aβ group were greater than the suprathreshold, subthreshold, and low Aβ groups in the entorhinal ROI (suprathreshold, 0.025 [95% CI, 0.013-0.037] standardized uptake value ratio [SUVR] units; subthreshold, 0.026 [95% CI, 0.014-0.037] SUVR units; low Aβ, 0.034 [95% CI, 0.02-0.049] SUVR units), inferior temporal ROI (suprathreshold, 0.025 [95% CI, 0.014-0.035] SUVR units; subthreshold, 0.027 [95% CI, 0.017-0.037] SUVR units; low Aβ, 0.035 [95% CI, 0.022-0.047] SUVR units), and the AD meta-ROI (suprathreshold, 0.023 [95% CI, 0.013-0.032] SUVR units; subthreshold, 0.025 [95% CI, 0.016-0.034] SUVR units; low Aβ, 0.032 [95% CI, 0.021-0.043] SUVR units) (all P < .001). Flortaucipir accumulation rates in the subthreshold and suprathreshold Aβ groups in temporal regions were nonsignificantly elevated compared with the low Aβ group. In the ADNI cohort, the variance was larger than in the Mayo Clinic Study of Aging but point estimates for annualized flortaucipir accumulation in the inferior temporal ROI were very similar. An estimated 216 participants who were CU per group with PET Aβ of 68 CL or more would be needed to detect a 25% annualized reduction in flortaucipir accumulation rate in the AD meta-ROI with 80% power. Substantial flortaucipir accumulation in temporal regions is greatest in persons aged 65 to 85 years who were CU and had high initial Aβ PET levels, compared with those with lower Aβ levels. Recruiting persons who were CU and exhibiting Aβ of 68 CL or more on an index Aβ PET is a feasible strategy to recruit for clinical trials in which a change in tau PET signal is an outcome measure.

Surface-based analysis of cortical thickness and volume loss in Alzheimer’s disease

Background and Hypothesis: Magnetic resonance imaging (MRI) has become a useful tool in monitoring the progression of Alzheimer's disease. Previous surface-based analysis has focused on changes in cortical thickness associated with the disease1. The objective of this study is to analyze MRI-derived cortical reconstructions for patterns of atrophy in terms of both cortical thickness and cortical volume. We hypothesize that Alzheimer’s Disease progression will be associated with a more significant change in volume than thickness.&#x0D; Experimental Design or Project Methods: MRI data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). All subjects with baseline and two-year 3T MRI scans were included. Segmentation of MRIs into gray and white matter was performed with FreeSurfer2,3,4,5. Subjects whose scans did not segment accurately were excluded. Surfaces were then registered to a common atlas with Ciftify6, and anatomically-constrained Multimodal Surface Matching (aMSM) was used to analyze longitudinal changes in each subject7. This produced continuous surface maps showing changes in cortical surface area and thickness. These maps were multiplied to create cortical volume maps8. Permutation Analysis of Linear Models (PALM) was used to perform two-sample t-tests comparing the maps of the Alzheimer’s and control groups9.&#x0D; Results: Preliminary analysis of nine Alzheimer’s subjects and nine control subjects produced surface maps displaying patterns that were expected given previous research findings10,11. There was increased volume and thickness loss in Alzheimer’s subjects relative to controls, with relatively high loss in structures of the medial temporal lobe. Future analysis of a larger sample will determine whether statistically significant differences exist between the Alzheimer’s and control groups in terms of thickness loss and volume loss.&#x0D; Conclusion and Potential Impact: If significant results are found, surface-based analysis of cortical volume may allow for detection of atrophy at an earlier stage in disease progression than would be possible based on cortical thickness.&#x0D; &#x0D; References&#x0D; 1. Clarkson MJ, Cardoso MJ, Ridgway GR, Modat M, Leung KK, Rohrer JD, Fox NC, Ourselin S. A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage. 2011 Aug 1; 57(3):856-65.&#x0D; 2. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179194.&#x0D; 3. Fischl B, Sereno M, Dale A. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207. &#x0D; 4. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341-355.&#x0D; 5. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004;23 Suppl 1:S69-84.&#x0D; 6. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC, Jenkinson M, WU-Minn HCP Consortium. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013 Oct 15;80:105-24.&#x0D; 7. Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-65.&#x0D; 8. Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform 2011;5:4.&#x0D; 9. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397&#x0D; 10. Matsuda, H. MRI morphometry in Alzheimer’s disease. Ageing Research Reviews. 2016 Sep;30:17-24.&#x0D; 11. Risacher SL, Shen L, West JD, Kim S, McDonald BC, Beckett LA, Harvey DJ, Jack CR Jr, Weiner MW, Saykin AJ. Alzheimer's Disease Neuroimaging Initiative (ADNI). Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging. 2010 Aug;31(8):1401-18.

Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework.

A long-standing question is how to best use brain morphometric and genetic data to distinguish Alzheimer's disease (AD) patients from cognitively normal (CN) subjects and to predict those who will progress from mild cognitive impairment (MCI) to AD. Here, we use a neural network (NN) framework on both magnetic resonance imaging-derived quantitative structural brain measures and genetic data to address this question. We tested the effectiveness of NN models in classifying and predicting AD. We further performed a novel analysis of the NN model to gain insight into the most predictive imaging and genetics features and to identify possible interactions between features that affect AD risk. Data were obtained from the AD Neuroimaging Initiative cohort and included baseline structural MRI data and single nucleotide polymorphism (SNP) data for 138 AD patients, 225 CN subjects, and 358 MCI patients. We found that NN models with both brain and SNP features as predictors perform significantly better than models with either alone in classifying AD and CN subjects, with an area under the receiver operating characteristic curve (AUC) of 0.992, and in predicting the progression from MCI to AD (AUC=0.835). The most important predictors in the NN model were the left middle temporal gyrus volume, the left hippocampus volume, the right entorhinal cortex volume, and the APOE (a gene that encodes apolipoprotein E) ɛ4 risk allele. Furthermore, we identified interactions between the right parahippocampal gyrus and the right lateral occipital gyrus, the right banks of the superior temporal sulcus and the left posterior cingulate, and SNP rs10838725 and the left lateral occipital gyrus. Our work shows the ability of NN models to not only classify and predict AD occurrence but also to identify important AD risk factors and interactions among them.

Whole brain white matter histogram analysis of diffusion tensor imaging data detects microstructural damage in mild cognitive impairment and alzheimer's disease patients.

Amnestic mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease (AD). However, the clinical conversion from MCI to AD is unpredictable. Hence, identification of noninvasive biomarkers able to detect early changes induced by dementia is a pressing need. To explore the added value of histogram analysis applied to measures derived from diffusion tensor imaging (DTI) for detecting brain tissue differences between AD, MCI, and healthy subjects (HS). Prospective. A local cohort (57 AD, 28 MCI, 23 HS), and an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (41 AD, 58 MCI, 41 HS). 3T. Dual-echo turbo spin echo (TSE); fluid-attenuated inversion recovery (FLAIR); modified-driven-equilibrium-Fourier-transform (MDEFT); inversion-recovery spoiled gradient recalled (IR-SPGR); diffusion tensor imaging (DTI). Normal-appearing white matter (NAWM) masks were obtained using the T1 -weighted volumes for tissue segmentation and T2 -weighted images for removal of hyperintensities/lesions. From DTI images, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AXD), and radial diffusivity (RD) were obtained. NAWM histograms of FA, MD, AXD, and RD were derived and characterized estimating: peak height, peak location, mean value (MV), and quartiles (C25, C50, C75), which were compared between groups. Receiver operating characteristic (ROC) and area under ROC curves (AUC) were calculated. To confirm our results, the same analysis was repeated on the ADNI dataset. One-way analysis of variance (ANOVA), post-hoc Student's t-test, multiclass ROC analysis. For the local cohort, C25 of AXD had the maximum capability of group discrimination with AUC of 0.80 for "HS vs. patients" comparison and 0.74 for "AD vs. others" comparison. For the ADNI cohort, MV of AXD revealed the maximum group discrimination capability with AUC of 0.75 for "HS vs. patients" comparison and 0.75 for "AD vs. others" comparison. AXD of NAWM might be an early marker of microstructural brain tissue changes occurring during the AD course and might be useful for assessing disease progression. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.

AMYLOID-BETA MODULATES CEREBRAL METABOLIC NETWORK IN RATS AND HUMANS

The amyloid hypothesis proposes that the amyloid pathology affects a series of downstream AD pathophysiology; changes in the brain metabolism, aggregation of neurofibrillary tangles, dysfunctional network, structural changes, and cognitive decline. This is supported by the typical pattern of amyloid-beta (Aβ) deposition that overlaps with the regional hypometabolism based on FDG-PET. Felix et al. have reported abnormal metabolic network in MCI Aβ+ individuals compared to Aβ- individuals. However, the interregional association between Aβ and FDG as well as interregional metabolic network (IMN) is still elusive. Here, we used Alzheimer's disease neuroimaging initiative (ADNI) database and McGill-R-Thy1-APP (Tg) to investigate the difference in IMN between Aβ+ and Aβ-. Furthermore, we characterized the difference in interregional association between Aβ and metabolism (IAMN) between Aβ+ and Aβ-. A total of 392 subjects from ADNI cohort (228 CN- = Aβ-, 164 MCI+ = Aβ+) was used and 17 (7 WT = Aβ-, 10 Tg = Aβ+) were used for this study. The ROC-based Aβ cut-off (1.286) was used to characterize CN- = Aβ- or MCI+ = Aβ+ from CN (287) and AD (176). Human IMN was generated based on the 201 nodes in 228 CN- and 164 MCI+. Rat IMN was generated based on 61 nodes in 7 WT and 10 Tg. The same regions were used to generate IAMN. Fisher's Z-transformation is applied to compare the network association between CN- and MCI+ as well as WT and Tg. MCI+ showed greater association compared to CN- in hippocampus, medial and lateral temporal cortex, superior parietal cortex, and fornix in IMN. Similarly, Tg showed greater association compared to WT in basal temporal cortex, parietotemporal cortex, and hypothalamus in IMN. Furthermore, regional Aβ association in lateral frontal cortex, lateral temporal cortex, and PCC/Precuneus in IMN metabolism were greater in MCI+ compared to CN-. Analogous regional Aβ associations were greater in Tg compared to WT in IMN.

AMYLOID-BETA MODULATES CEREBRAL METABOLIC NETWORK IN RATS AND HUMANS

The amyloid hypothesis proposes that the amyloid pathology affects a series of downstream AD pathophysiology; changes in the brain metabolism, aggregation of neurofibrillary tangles, dysfunctional network, structural changes, and cognitive decline. This is supported by the typical pattern of amyloid-beta (Aβ) deposition that overlaps with the regional hypometabolism based on FDG-PET. Felix et al. have reported abnormal metabolic network in MCI Aβ+ individuals compared to Aβ- individuals. However, the interregional association between Aβ and FDG as well as interregional metabolic network (IMN) is still elusive. Here, we used Alzheimer's disease neuroimaging initiative (ADNI) database and McGill-R-Thy1-APP (Tg) to investigate the difference in IMN between Aβ+ and Aβ-. Furthermore, we characterized the difference in interregional association between Aβ and metabolism (IAMN) between Aβ+ and Aβ-. A total of 392 subjects from ADNI cohort (228 CN- = Aβ-, 164 MCI+ = Aβ+) was used and 17 (7 WT = Aβ-, 10 Tg = Aβ+) were used for this study. The ROC-based Aβ cut-off (1.286) was used to characterize CN- = Aβ- or MCI+ = Aβ+ from CN (287) and AD (176). Human IMN was generated based on the 201 nodes in 228 CN- and 164 MCI+. Rat IMN was generated based on 61 nodes in 7 WT and 10 Tg. The same regions were used to generate IAMN. Fisher's Z-transformation is applied to compare the network association between CN- and MCI+ as well as WT and Tg. MCI+ showed greater association compared to CN- in hippocampus, medial and lateral temporal cortex, superior parietal cortex, and fornix in IMN. Similarly, Tg showed greater association compared to WT in basal temporal cortex, parietotemporal cortex, and hypothalamus in IMN. Furthermore, regional Aβ association in lateral frontal cortex, lateral temporal cortex, and PCC/Precuneus in IMN metabolism were greater in MCI+ compared to CN-. Analogous regional Aβ associations were greater in Tg compared to WT in IMN.