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BackgroundThere is a need for outcome measures with improved responsiveness to changes in pre-dementia populations. Both cognitive and motor function play important roles in neurodegeneration; motor function decline is detectable at early stages of cognitive decline. This proof of principle study used a Pooled Index approach to evaluate improved responsiveness of the predominant outcome measure (ADAS-Cog: Alzheimer's Disease Assessment Scale-Cognitive Subscale) when assessment of motor function is added.MethodsCandidate Pooled Index variables were selected based on theoretical importance and pairwise correlation coefficients. Kruskal-Wallis and Mann-Whitney U tests assessed baseline discrimination. Standardized response means assessed responsiveness to longitudinal change.ResultsFinal selected variables for the Pooled Index include gait velocity, dual-task cost of gait velocity, and an ADAS-Cog-Proxy (statistical approximation of the ADAS-Cog using similar cognitive tests). The Pooled Index and ADAS-Cog-Proxy scores had similar ability to discriminate between pre-dementia syndromes. The Pooled Index demonstrated trends of similar or greater responsiveness to longitudinal decline than ADAS-Cog-Proxy scores.ConclusionAdding motor function assessments to the ADAS-Cog may improve responsiveness in pre-dementia populations.

BackgroundChronic kidney disease (CKD) is associated with cognitive impairment and dementia. We examined whether this relationship hold true in older adults, who have a higher prevalence of both CKD and dementia.Design, setting, participants, and measurementsWe conducted a cross-sectional secondary analysis of an established observational cohort. We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), an NIH funded, multicenter longitudinal observational study, which includes participants with normal and impaired cognition and assesses cognition with a comprehensive battery of neuropsychological tests. We included a non-probability sample of all ADNI participants with serum creatinine measurements at baseline (N = 1181). Using multivariable linear regression analysis, we related the CKD Epidemiology Collaboration equation eGFR with validated composite scores for memory (ADNI-mem) and executive function (ADNI-EF).ResultsFor the 1181 ADNI participants, the mean age was 73.7 ± 7.1 years. Mean estimated glomerular filtration rate (eGFR) was 76.4 ± 19.7; 6% had eGFR90 ml/min/1.73 m2. The mean ADNI-Mem score was 0.241 ± 0.874 and mean ADNI-EF score was 0.160 ± 1.026. In separate multivariable linear regression models, adjusted for age, sex, race education and BMI, there was no association between each 10 ml/ min/1.73 m2 higher eGFR and ADNI-Mem (β -0.02, 95% CI -0.04, 0.02, p = 0.56) or ADNI-EF (β 0.01, 95% CI -0.03, 0.05, p = 0.69) scores.ConclusionWe did not observe an association between eGFR and cognition in the older ADNI participants.

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.

'Normal aging' in the brain refers to age-related changes that occur independent of disease, in particular Alzheimer's disease. A major barrier to mapping normal brain aging has been the difficulty in excluding the earliest preclinical stages of Alzheimer's disease. Here, before addressing this issue we first imaged a mouse model and learn that the best MRI measure of dendritic spine loss, a known pathophysiological driver of normal aging, is one that relies on the combined use of functional and structural MRI. In the primary study, we then deployed the combined functional-structural MRI measure to investigate over 100 cognitively-normal people from 20-72 years of age. Next, to cover the tail end of aging, in secondary analyses we investigated structural MRI acquired from cognitively-normal people, 60-84 years of age, who were Alzheimer's-free via biomarkers. Collectively, the results from the primary functional-structural study, and the secondary structural studies revealed that the dentate gyrus is a hippocampal region differentially affected by aging, and that the entorhinal cortex is a region most resistant to aging. Across the cortex, the primary functional-structural study revealed and that the inferior frontal gyrus is differentially affected by aging, however, the secondary structural studies implicated other frontal cortex regions. Together, the results clarify how normal aging may affect the brain and has possible mechanistic and therapeutic implications.

ObjectiveIn vivo functional changes in white matter during the progression of Alzheimer's disease (AD) have not been previously reported. Our objectives are to measure changes in white matter functional connectivity (FC) in an elderly population undergoing cognitive decline as AD develops, to establish their relationship to neuropsychological scores of cognitive abilities, and to assess the performance in prediction of AD using white matter FC measures as features.MethodsAnalyses were conducted using resting state functional MRI and neuropsychological data from 383 ADNI participants, including 136 cognitive normal (CN) controls, 46 with significant memory concern, 83 with early mild cognitive impairment (MCI), 37 with MCI, 46 with late MCI, and 35 with AD dementia. FC metrics between segregated white matter tracts and discrete gray matter volumes or between white matter tracts were quantitatively analyzed and characterized, along with their relationships to 6 cognitive measures. Finally, supervised machine learning was implemented on white matter FCs to classify the participants and performance of the classification was evaluated.ResultsSignificant decreases in FC measures were found in white matter with prominent, specific, regional deficits appearing in late MCI and AD dementia patients from CN. These changes significantly correlated with neuropsychological measurements of impairments in cognition and memory. The sensitivity and specificity of distinguishing AD dementia and CN using white matter FCs were 0.83 and 0.81 respectively.Conclusions and relevanceThe white matter FC decreased in late MCI and AD dementia patients compared to CN participants, and this decrease was correlated with cognitive measures. White matter FC is valuable in the prediction of AD. All these findings suggest that white matter FC may be a promising avenue for understanding functional impairments in white matter tracts during AD progression.

Abstract Introduction We sought lipid‐metabolic biomarkers involved in the processes underlying cognitive decline and detected them in association with Alzheimer's disease (AD) phenotypes. Methods A least absolute shrinkage and selection operator logistic regression model was used to select lipids that best classified cognitive decline defined by a fast‐annual rate of cognition. Lipid summary scores were constructed as predictors of cognitive decline by using this model. Multivariable‐adjusted models tested the associations of risk score with AD phenotypes. Results A model incorporating 17 selected lipids showed good discrimination and calibration. The lipid risk score was positively associated with the baseline Alzheimer Disease Assessment Scale—13‐item cognitive subscale (ADAS‐Cog13) score and cerebrospinal tau protein level, and predicted cognitive diagnoses. Additional results showing that individuals with increased lipid risk scores had rapid change rates of ADAS‐Cog13 and brain atrophy further corroborated the predictive role of lipids. Discussion A panel of blood lipids instead of individual lipid molecules could better diagnose and predict cognitive decline.

Abstract Introduction Plasma markers have been reported to be associated with brain amyloid burden, tau pathology, or neurodegeneration. We aimed to evaluate whether plasma biomarker profiles could predict Alzheimer's disease (AD) pathology and clinical progression in older adults without dementia. Methods Cross‐sectional and longitudinal data of participants enrolled in this study were from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Plasma amyloid beta (Aβ)1‐42/Aβ1‐40 ratio was selected as the marker for amyloid pathology, p‐tau181 for tau pathology, and neurofilament light for neurodegeneration. Cut‐offs for these plasma markers were calculated with well‐established positron emission tomography and structural imaging biomarkers as reference. Older adults without dementia were categorized into eight groups at baseline by plasma amyloid/tau/neurodegeneration (A/T/N) cut‐offs. Clinical progression was analyzed using linear mixed‐effects models and Cox proportional hazard models. Results A total of 183 participants (97 cognitively normal [CN] subjects and 86 patients with mild cognitive impairment [MCI]; mean age 72.6 years, and 48.1% men) were included. Participants with A+ had significantly higher proportions of apolipoprotein E (APOE) gene ɛ4 carriers than those with A–. Brain atrophy was observed in all groups of CN, whereas cognition decline was obvious in the A+T+N+ group. Compared to A–T–N–, MCI patients with A+T+N+ had faster cognition worsening and faster brain atrophy. In the whole cohort, A+T+N+ and A+T+N– participants were at higher risk of clinical progression. Discussion Plasma A/T/N biomarker profiles may predict AD pathology and clinical progression, indicating a potential role for plasma biomarkers in clinical trials. More research is warranted to develop a robust plasma AD framework.

Abstract Aims/hypothesis Midlife obesity is a risk factor for dementia. We investigated the impact of obesity on brain structure, metabolism, and cerebrospinal fluid (CSF) core Alzheimer's disease (AD) biomarkers in healthy elderly. Methods We selected controls from ADNI2 with CSF AD biomarkers and/or fluorodeoxyglucose positron emission tomography (FDG‐PET) and 3T‐MRI. We measured cortical thickness, FDG uptake, and CSF amyloid beta (Aβ)1‐42, p‐tau, and t‐tau levels. We performed regression analyses between these biomarkers and body mass index (BMI). Results We included 201 individuals (mean age 73.5 years, mean BMI 27.4 kg/m2). Higher BMI was related to less cortical thickness and higher metabolism in brain areas typically not involved in AD (family‐wise error [FWE]

Abstract Introduction Amyloid beta (Aβ) deposition was identified to precede tau pathology and neurodegeneration in familial Alzheimer's disease (AD). But the divergence between sporadic and familial AD limits the extension of these findings to sporadic AD. Methods Longitudinal changes of biomarkers among different stages were assessed using linear mixed‐effects models. The slopes of the models were used to estimate rates of change to calculate the biomarker trajectories in sporadic AD. Results Cerebrospinal fluid (CSF) Aβ was estimated to decline 45.2 years (abnormal: 27.8 years) before dementia, and Aβ deposition seemed to increase 31.7 years (abnormal: 26.7 years) before dementia. It was estimated to take 29.0 years (CSF t‐tau), 12.2 years (memory), 11.6 years (hippocampus), 9.3 years (hypometabolism), and 6.1 years (cognition) to move from normal to dementia. Discussion The trajectory in sporadic AD is led by Aβ accumulation, followed by CSF t‐tau increase, memory deficits, brain atrophy, hypometabolism, and cognitive decline.

Abstract Background A relationship quantitative trait locus exists when the correlation between multiple traits varies by genotype for that locus. Relationship quantitative trait loci (rQTL) are often involved in gene-by-gene (G×G) interactions or gene-by-environmental interactions, making them a powerful tool for detecting G×G. Methods We performed genome-wide association studies to identify rQTL between tau and Aβ42 and ptau and Aβ42 with over 3000 individuals using age, gender, series, APOE ε2, APOE ε4, and two principal components for population structure as covariates. Each significant rQTL was separately screened for interactions with other loci for each trait in the rQTL model. Parametric bootstrapping was used to assess significance. Results We found four significant tau/Aβ42 rQTL from three unique locations and six ptau/Aβ42 rQTL from five unique locations. G×G screens with these rQTL produced four significant G×G interactions (one Aβ42, two ptau, and one tau) with four rQTL where each second locus was from a unique location. On follow-up, rs1036819 and rs74025622 were associated with Alzheimer’s disease (AD) case/control status; rs15205 and rs79099429 were associated with rate of decline. Conclusions The two most significant rQTL (rs8027714 and rs1036819) for ptau/Aβ42 are on different chromosomes and both are strong hits for pelvic organ prolapse. While diseases of the nervous system can cause pelvic organ prolapse, it is unlikely related to the ptau/Aβ42 relationship but may suggest that these two loci share a pathway. In addition to a ptau/Aβ42 rQTL and association with AD case/control status, rs1036819 is a strong rQTL for case/control status/Aβ42 and for tau/Aβ42. It resides in the ZFAT gene, which is related to autoimmune thyroid disease. For tau, rs9817620 interacts with the tau/Aβ42 rQTL rs74025622. It is in the CHL1 gene, which is a neural cell adhesion molecule and may be involved in signal transduction pathways. CHL1 is related to BACE1, which is a β-secretase enzyme that initiates production of the β-amyloid peptide involved in AD and is a primary drug target. Overall, there are numerous loci that affect the relationship between these important AD endophenotypes and some are due to interactions with other loci. Some affect the risk of AD and/or rate of progression.

Recent evidence shows that neuroinflammation plays a role in many neurological diseases including mild cognitive impairment (MCI) and Alzheimer's disease (AD), and that free water (FW) modeling from clinically acquired diffusion MRI (DTI-like acquisitions) can be sensitive to this phenomenon. This FW index measures the fraction of the diffusion signal explained by isotropically unconstrained water, as estimated from a bi-tensor model. In this study, we developed a simple but powerful whole-brain FW measure designed for easy translation to clinical settings and potential use as a priori outcome measure in clinical trials. These simple FW measures use a “safe” white matter (WM) mask without gray matter (GM)/CSF partial volume contamination (WMsafe) near ventricles and sulci. We investigated if FW inside the WMsafe mask, including and excluding areas of white matter damage such as white matter hyperintensities (WMHs) as shown on T2 FLAIR, computed across the whole white matter could be indicative of diagnostic grouping along the AD continuum. After careful quality control, 81 cognitively normal controls (NC), 103 subjects with MCI and 42 with AD were selected from the ADNIGO and ADNI2 databases. We show that MCI and AD have significantly higher FW measures even after removing all partial volume contamination. We also show, for the first time, that when WMHs are removed from the masks, the significant results are maintained, which demonstrates that the FW measures are not just a byproduct of WMHs. Our new and simple FW measures can be used to increase our understanding of the role of inflammation-associated edema in AD and may aid in the differentiation of healthy subjects from MCI and AD patients.

Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.

Abstract Background The activity of cerebrospinal fluid (CSF) β-site APP cleaving enzyme (BACE) is a potential diagnostic biomarker for Alzheimer disease (AD). Methods A total of 340 non-Hispanic Caucasian participants from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI) database were included in this study with quality-controlled CSF BACE and genotype data. Association of CSF BACE with the genetic variants of single nucleotide polymorphisms (SNPs) was assessed using PLINK under the additive genetic model. The P values of all SNPs for CSF BACE were adjusted for multiple comparisons. Results One SNP (rs1481950) in the ATP6V1H gene reached genome-wide significance for associations with CSF BACE (P = 4.88 × 10− 9). The minor allele (G) of rs1481950 was associated with higher CSF BACE activity. Although seven SNPs in SNX31, RORA, CDH23, RGS20, LRRC4C, MAPK6PS1 and LOC105378355 did not reach genome-wide significance (P

Abstract Background Plasma neurofilament light (NFL) is a promising biomarker for Alzheimer disease (AD), which increases in the early stage of AD and is associated with the progression of AD. We performed a genome-wide association study (GWAS) of plasma NFL in Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) cohort to identify novel variants associated with AD. Methods This study included 179 cognitively healthy controls (HC), 176 patients with mild cognitive impairment (MCI), and 172 patients with AD. All subjects were restricted to non-Hispanic Caucasian derived from the ADNI cohort and met all quality control (QC) criteria. Association of plasma NFL with the genetic variants was assessed using PLINK with an additive genetic model, i.e.dose-dependent effect of the minor alleles. The influence of a genetic variant associated with plasma NFL (rs7943454) on brain structure was further assessed using PLINK with a linear regression model. Results The minor allele (T) of rs7943454 in leucine zipper protein 2 gene (LUZP2) was associated with higher plasma NFL at suggestive levels (P = 1.39 × 10− 6) in a dose-dependent fashion. In contrast, the minor allele (G) of rs640476 near GABRB2 was associated with lower plasma NFL at suggestive levels (P = 6.71 × 10− 6) in a dose-dependent effect in all diagnostic groups except the MCI group. Furthermore, the minor allele (T) of rs7943454 within LUZP2 increased the onset risk of AD (odds ratio = 1.547, confidence interval 95% = 1.018–2.351) and was associated with atrophy of right middle temporal gyrus in the whole cohort in the longitudinal study (P = 0.0234). Conclusion GWAS found the associations of two single nucleotide polymorphisms (rs7943454 and rs640476) with plasma NFL at suggestive levels. Rs7943454 in LUZP2 was associated with the onset risk of AD and atrophy of right middle temporal gyrusin the whole cohort. Using an endophenotype-based approach, we identified rs7943454 as a new AD risk locus.

Abstract Introduction Relationship between self– and informant memory concerns and tau aggregation was assessed in adults at risk for Alzheimer's disease (AD). Methods Regional mean standardized uptake value ratios were extracted from [18F]flortaucipir positron emission tomography (PET) scans of 82 at‐risk adults in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Associations between self– and informant ECog memory scores and tau aggregation were analyzed on both regional and voxelwise bases. Analyses were completed both on the whole sample and restricted to amyloid‐positive individuals only. Results Memory concerns were associated with tau aggregation. Self‐perception was more associated with frontal tau. In contrast, informant scores were more associated with parietal tau. This source‐by‐region interaction was more prominent in amyloid‐positive participants and observed in both regional and voxelwise analyses. Discussion Quantitative assessment of perceived memory functioning may be useful for screening older adults at risk for Alzheimer's disease. Individuals and their informants may provide complementary information relating to the anatomical distribution of tau.

Abstract Introduction The clinical significance of ventriculomegaly in cognitively normal elderly individuals remains unclear. Methods We selected cognitively normal individuals (n = 425) from the Alzheimer's Disease Neuroimaging Initiative database and calculated Evans index (EI) based on the ratio of the frontal horn and skull diameter. We defined ventriculomegaly as EI ≥ 0.30, and the participants were stratified into EI ≥ 0.30 group and EI

Abstract Introduction It has been proposed that the signal distribution on tau positron emission tomography (PET) images could be used to define pathologic stages similar to those seen in neuropathology. Methods Three topographic staging schemes for tau PET, two sampling the temporal and occipital subregions only and one sampling cortical gray matter across the major brain lobes, were evaluated on flortaucipir F 18 PET images in a test‐retest scenario and from Alzheimer's Disease Neuroimaging Initiative 2. Results All three schemes estimated stages that were significantly associated with amyloid status and when dichotomized to tau positive or negative were 90% to 94% concordant in the populations identified. However, the schemes with fewer regions and simpler decision rules yielded more robust performance in terms of fewer unclassified scans and increased test‐retest reproducibility of assigned stage. Discussion Tau PET staging schemes could be useful tools to concisely index the regional involvement of tau pathology in living subjects. Simpler schemes may be more robust.

Abstract Introduction Cerebrovascular disease (CVD) is not currently considered a core pathological feature of Alzheimer's disease (AD), but mounting evidence suggests that concurrent CVD may exacerbate AD progression. The purpose of this study was first to examine the relationship among amyloid, CVD, and neurodegeneration and second to examine the extent to which amyloid and CVD pathology drive subsequent neurodegeneration over time. Methods Six hundred eight (224 normal controls, 291 mild cognitive impairment, 93 AD) subjects from the Alzheimer's Disease Neuroimaging Initiative with longitudinal AV45 positron emission tomography imaging and MR imaging were investigated. Results Amyloid and white matter hyperintensity (WMH) burden increased across clinical diagnosis groups (normal control

Abstract Introduction Current models posit a sequence of amyloid β (Aβ), tau, atrophy, and cognitive change leading to Alzheimer's disease, but ambiguities remain. We examined these sequences via serial mediations. Methods We studied normal controls, early mild cognitive impairment, and late mild cognitive impairment individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database for the mediation of baseline cerebrospinal fluid Aβ effects on 2‐year cognitive change via regional longitudinal atrophy rate (AR) alone or AR and tau. Results In normal controls, Aβ correlated directly with regional ARs and memory loss, with no mediations. In early mild cognitive impairment, tau and lateral temporal ARs serially mediated the influence of Aβ on memory while Aβ affected memory via hippocampal AR. Late mild cognitive impairment consistently showed serial mediations of tau followed by atrophy. However, Aβ effects on memory also continued to be specifically mediated by medial temporal ARs without intermediate tau. Discussion Biomarker sequences vary by region and disease state, suggesting the need to refine current cascade models.

Abstract Introduction Type 2 diabetes mellitus (T2DM) is a risk factor for Alzheimer's disease. Cerebrospinal fluid (CSF) amyloid β (Aβ) 1‐42 is an important Alzheimer's disease biomarker. However, it is inconclusive on how T2DM is related to CSF Aβ1‐42. Methods Participants with T2DM were selected from the Alzheimer's Disease Neuroimaging Initiative by searching keywords from the medical history database. A two‐way analysis of covariance model was used to analyze how T2DM associates with CSF Aβ1‐42 or cerebral cortical Aβ. Results CSF Aβ1‐42 was higher in the T2DM group than the nondiabetic group. The inverse relation between CSF Aβ1‐42 and cerebral cortical Aβ was independent of T2DM status. Participants with T2DM had a lower cerebral cortical Aβ in anterior cingulate, precuneus, and temporal lobe than controls. Discussion T2DM is positively associated with CSF Aβ1‐42 but negatively with cerebral cortical Aβ. The decreased cerebral cortical Aβ associated with T2DM is preferentially located in certain brain regions.

Abstract Introduction Models characterizing intermediate disease stages of Alzheimer's disease (AD) are needed to inform clinical care and prognosis. Current models, however, use only a small subset of available biomarkers, capturing only coarse changes along the complete spectrum of disease progression. We propose the use of machine learning techniques and clinical, biochemical, and neuroimaging biomarkers to characterize progression to AD. Methods We used a large multimodal longitudinal data set of biomarkers and demographic and genotype information from 1624 participants from the Alzheimer's Disease Neuroimaging Initiative. Using hidden Markov models, we characterized intermediate disease stages. We validated inferred disease trajectories by comparing time to first clinical AD diagnosis. We trained an L2‐regularized logistic regression model to predict disease trajectory and evaluated its discriminative performance on a test set. Results We identified 12 distinct disease states. Progression to AD occurred most often through one of two possible paths through these states. Paths differed in terms of rate of disease progression (by 5.44 years on average), amyloid and total‐tau (t‐tau) burden (by 10% and 69%, respectively), and hippocampal neurodegeneration (P

Abstract Introduction We aimed to evaluate the consistency of the A/T/N classification system. Methods We included healthy controls, mild cognitive impairment, and dementia patients from Alzheimer's disease Neuroimaging Initiative. We assessed subject classification consistency with different biomarker combinations and the agreement and correlation between biomarkers. Results Subject classification discordance ranged from 12.2% to 44.5% in the whole sample; 17.3%–46.4% in healthy controls; 11.9%–46.5% in mild cognitive impairment, and 1%–35.7% in dementia patients. Amyloid, but not neurodegeneration biomarkers, showed good agreement both in the whole sample and in the clinical subgroups. Amyloid biomarkers were correlated in the whole sample, but not along the Alzheimer's disease continuum (as defined by a positive amyloid positron emission tomography). Neurodegeneration biomarkers were poorly correlated both in the whole sample and along the Alzheimer's disease continuum. The relationship between biomarkers was stage‐dependent. Discussion Our findings suggest that the current A/T/N classification system does not achieve the required consistency to be used in the clinical setting.

Abstract Introduction The heterogeneity of behavioral variant frontotemporal dementia (bvFTD) calls for multivariate imaging biomarkers. Methods We studied a total of 148 dementia patients from the Feinstein Institute (Center‐A: 25 bvFTD and 10 Alzheimer's disease), Technical University of Munich (Center‐B: 44 bvFTD and 29 FTD language variants), and Alzheimer's Disease Neuroimaging Initiative (40 Alzheimer's disease subjects). To identify the covariance pattern of bvFTD (behavioral variant frontotemporal dementia–related pattern [bFDRP]), we applied principal component analysis to combined 18F‐fluorodeoxyglucose–positron emission tomography scans from bvFTD and healthy subjects. The phenotypic specificity and clinical correlates of bFDRP expression were assessed in independent testing sets. Results The bFDRP was identified in Center‐A data (24.1% of subject × voxel variance; P

Abstract Introduction We determined whether the effect of apolipoprotein E (APOE)‐ε4 genotype on Alzheimer's disease (AD) markers differs in men and women across AD stages. Methods Among normal control (NC) participants (N = 702) and participants with mild cognitive impairment (N = 576) and AD (N = 305), we examined the associations of sex and APOE‐ε4 carrier status with cortical amyloid‐β (Aβ) burden, hippocampal volume ratio (HpVR; hippocampal volume/intracranial volume × 103), brain glucose metabolism, and verbal memory. Results In NC, APOE‐ε4 related to greater Aβ burden and poorer verbal memory across sex but to smaller HpVR and hypometabolism in men only. In mild cognitive impairment, APOE‐ε4 related to smaller HpVR, hypometabolism, greater Aβ burden, and poorer verbal memory across sex. In AD, APOE‐ε4 related to greater Aβ burden in men only and smaller HpVR across sex and showed no association with hypometabolism or verbal memory. Discussion Sex differences in the association between APOE‐ε4 and AD markers vary by disease stage.

Abstract Introduction An Alzheimer's disease (AD) biomarker adjusted for age‐related brain changes should improve specificity for AD‐related pathological burden. Methods We calculated a brain‐age‐adjusted “personalized AD cortical thickness index” (pADi) in mild cognitive impairment patients from the Alzheimer's Disease Neuroimaging Initiative. We performed receiver operating characteristic analysis for discrimination between patients with and without cerebrospinal fluid evidence of AD and logistic regression in an independent sample to determine if a dichotomized pADi predicted conversion to AD dementia. Results Receiver operating characteristic area under the curve was 0.69 and 0.72 in the two samples. Three empirical methods identified the same cut‐point for pADi in the discovery sample. In the validation sample, 83% of pADi+ mild cognitive impairment patients were cerebrospinal fluid AD biomarker positive. pADi+ mild cognitive impairment patients (n = 63, 38%) were more likely to progress to AD dementia after 1 (odds ratio = 2.9) and 3 (odds ratio = 2.6) years. Discussion The pADi is a personalized, magnetic resonance imaging–derived AD biomarker that predicts progression to dementia.

Abstract Introduction We characterize long‐term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative. Methods We apply a latent time joint mixed‐effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference. Results We find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid‐β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis. Discussion The latent time joint mixed‐effects model can (1) uncover long‐term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject‐specific prognostic estimates of the time until onset of symptoms.

Midlife hypercholesterolemia is a well-known risk factor for sporadic Alzheimer’s disease (AD), and like AD, it is highly influenced by genetics with heritability estimates of 32–63%. We thus hypothesized that genetics underlying peripheral blood total cholesterol (TC) levels could influence the risk of developing AD. We created a weighted polygenic score (TC-PGS) using summary data from a meta-analysis of TC genome-wide association studies for evaluation in three independent AD-related cohorts spanning pre-clinical, clinical, and pathophysiologically proved AD. APOE-ε4 variant was purposely included in the analysis as it represents an already well-established genetic risk factor for both AD and circulating TC. We could vastly improve the performance of the score when considering p-value thresholds for inclusion in the score, sex, and statin use. This optimized score (p-value threshold of 1 × 10−6 for inclusion in the score) explained 18.2% of the variance in TC levels in statin free females compared to 6.9% in the entire sample and improved prediction of hypercholesterolemia (receiver operator characteristics analysis revealed area under the curve increase from 70.8% to 80.5%). The TC-PGS was further evaluated for association with AD risk and pathology. We found no association between the TC-PGS and either of the AD hallmark pathologies, assessed by cerebrospinal fluid levels of Aβ-42, p-Tau, and t-Tau, and 18F-NAV4694 and 18F-AV-1451 positron emission tomography. Similarly, we found no association with the risk of developing amyloid pathology or becoming cognitively impaired in individuals with amyloid pathology.

The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer’s disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD.

Abstract The introduction of mild cognitive impairment (MCI) as a diagnostic category adds to the challenges of diagnosing Alzheimer’s Disease (AD). No single marker has been proven to accurately categorize patients into their respective diagnostic groups. Thus, previous studies have attempted to develop fused predictors of AD and MCI. These studies have two main limitations. Most do not simultaneously consider all diagnostic categories and provide suboptimal fused representations using the same set of modalities for prediction of all classes. In this work, we present a combined framework, cascaded multiview canonical correlation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic categories and optimizes classification by selectively combining a subset of modalities at each level of the cascade. CaMCCo is evaluated on a data cohort comprising 149 patients for whom neurophysiological, neuroimaging, proteomic and genomic data were available. Results suggest that fusion of select modalities for each classification task outperforms (mean AUC = 0.92) fusion of all modalities (mean AUC = 0.54) and individual modalities (mean AUC = 0.90, 0.53, 0.71, 0.73, 0.62, 0.68). In addition, CaMCCo outperforms all other multi-class classification methods for MCI prediction (PPV: 0.80 vs. 0.67, 0.63).

Abstract Introduction The growing public threat of Alzheimer's disease (AD) has raised the urgency to quantify the degree of cognitive decline during the conversion process of mild cognitive impairment (MCI) to AD and its underlying genetic pathway. The aim of this article was to test genetic common variants associated with accelerated cognitive decline after the conversion of MCI to AD. Methods In 583 subjects with MCI enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI; ADNI‐1, ADNI‐Go, and ADNI‐2), 245 MCI participants converted to AD at follow‐up. We tested the interaction effects between individual single‐nucleotide polymorphisms and AD diagnosis trajectory on the longitudinal Alzheimer's Disease Assessment Scale‐Cognition scores. Results Our findings reveal six genes, including BDH1, ST6GAL1, RAB20, PDS5B, ADARB2, and SPSB1, which are directly or indirectly related to MCI conversion to AD. Discussion This genome‐wide association study sheds light on a genetic mechanism of longitudinal cognitive changes during the transition period from MCI to AD.

The genetic component of many common traits is associated with the gene expression and several variants act as expression quantitative loci, regulating the gene expression in a tissue specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction models on the genotype of 808 samples including controls, subjects with mild cognitive impairment, and patients with Alzheimer's Disease. We then dissected the imputed transcriptomic profiles by means of different unsupervised and supervised machine learning approaches to identify potential biological associations. Our analysis suggests that unsupervised and supervised methods can provide complementary information, which can be integrated for a better characterization of the underlying biological system. In particular, a variational autoencoder representation of the transcriptomic profiles, followed by a support vector machine classification, has been used for tissue-specific gene prioritizations. Interestingly, the achieved gene prioritizations can be efficiently integrated as a feature selection step for improving the accuracy of deep learning classifier networks. The identified gene-tissue information suggests a potential role for inflammatory and regulatory processes in gut-brain axis related tissues. In line with the expected low heritability that can be apportioned to eQTL variants, we were able to achieve only relatively low prediction capability with deep learning classification models. However, our analysis revealed that the classification power strongly depends on the network structure, with recurrent neural networks being the best performing network class. Interestingly, cross-tissue analysis suggests a potentially greater role of models trained in brain tissues also by considering dementia-related endophenotypes. Overall, the present analysis suggests that the combination of supervised and unsupervised machine learning techniques can be used for the evaluation of high dimensional omics data.

Pancreatic adenocarcinoma (PC) is a lethal malignancy that is familial or associated with genetic syndromes in 10% of cases. Gene-based surveillance strategies for at-risk individuals may improve clinical outcomes. However, familial PC (FPC) is plagued by genetic heterogeneity and the genetic basis for the majority of FPC remains elusive, hampering the development of gene-based surveillance programs. The study was powered to identify genes with a cumulative pathogenic variant prevalence of at least 3%, which includes the most prevalent PC susceptibility gene, BRCA2. Since the majority of known PC susceptibility genes are involved in DNA repair, we focused on genes implicated in these pathways. We performed a region-based association study using the Mixed-Effects Score Test, followed by leave-one-out characterization of PC-associated gene regions and variants to identify the genes and variants driving risk associations. We evaluated 398 cases from two case series and 987 controls without a personal history of cancer. The first case series consisted of 109 patients with either FPC (n = 101) or PC at ≤50 years of age (n = 8). The second case series was composed of 289 unselected PC cases. We validated this discovery strategy by identifying known pathogenic BRCA2 variants, and also identified SMG1, encoding a serine/threonine protein kinase, to be significantly associated with PC following correction for multiple testing (p = 3.22x10-7). The SMG1 association was validated in a second independent series of 532 FPC cases and 753 controls (pG SMG1 variant in 3 affected relatives in a FPC kindred, and we found c.103G>A to be a recurrent SMG1 variant associating with PC in both the discovery and validation series. These results suggest that SMG1 is a novel PC susceptibility gene, and we identified specific SMG1 gene variants associated with PC risk.

Background: Despite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20–40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to AD.Methods: The algorithm was developed using the open dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm.Results: The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity.Conclusions: The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.

Recent developments of tau Positron Emission Tomography (PET) allows assessment of regional neurofibrillary tangles (NFTs) deposition in human brain. Among the tau PET molecular probes, 18F-AV1451 is characterized by high selectivity for pathologic tau aggregates over amyloid plaques, limited non-specific binding in white and gray matter, and confined off-target binding. The objectives of the study are (1) to quantitatively characterize regional brain tau deposition measured by 18F-AV1451 PET in cognitively normal older adults (CN), mild cognitive impairment (MCI), and AD participants; (2) to evaluate the correlations between cerebrospinal fluid (CSF) biomarkers or Mini-Mental State Examination (MMSE) and 18F-AV1451 PET standardized uptake value ratio (SUVR); and (3) to evaluate the partial volume effects on 18F-AV1451 brain uptake.Methods: The study included total 115 participants (CN = 49, MCI = 58, and AD = 8) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed 18F-AV1451 PET images, structural MRIs, and demographic and clinical assessments were downloaded from the ADNI database. A reblurred Van Cittertiteration method was used for voxelwise partial volume correction (PVC) on PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. The parametric images of 18F-AV1451 SUVR relative to cerebellum were calculated. The ROI SUVR measurements from PVC and non-PVC SUVR images were compared. The correlation between ROI 18F-AV1451 SUVR and the measurements of MMSE, CSF total tau (t-tau), and phosphorylated tau (p-tau) were also assessed.Results:18F-AV1451 prominently specific binding was found in the amygdala, entorhinal cortex, parahippocampus, fusiform, posterior cingulate, temporal, parietal, and frontal brain regions. Most regional SUVRs showed significantly higher uptake of 18F-AV1451 in AD than MCI and CN participants. SUVRs of small regions like amygdala, entorhinal cortex and parahippocampus were statistically improved by PVC in all groups (p < 0.01). Although there was an increasing tendency of 18F-AV-1451 SUVRs in MCI group compared with CN group, no significant difference of 18F-AV1451 deposition was found between CN and MCI brains with or without PVC (p > 0.05). Declined MMSE score was observed with increasing 18F-AV1451 binding in amygdala, entorhinal cortex, parahippocampus, and fusiform. CSF p-tau was positively correlated with 18F-AV1451 deposition. PVC improved the results of 18F-AV-1451 tau deposition and correlation studies in small brain regions.Conclusion: The typical deposition of 18F-AV1451 tau PET imaging in AD brain was found in amygdala, entorhinal cortex, fusiform and parahippocampus, and these regions were strongly associated with cognitive impairment and CSF biomarkers. Although more deposition was observed in MCI group, the 18F-AV-1451 PET imaging could not differentiate the MCI patients from CN population. More tau deposition related to decreased MMSE score and increased level of CSF p-tau, especially in ROIs of amygdala, entorhinal cortex and parahippocampus. PVC did improve the results of tau deposition and correlation studies in small brain regions and suggest to be routinely used in 18F-AV1451 tau PET quantification.

Linking interindividual differences in psychological phenotype to variations in brain structure is an old dream for psychology and a crucial question for cognitive neurosciences. Yet, replicability of the previously-reported ‘structural brain behavior’ (SBB)-associations has been questioned, recently. Here, we conducted an empirical investigation, assessing replicability of SBB among heathy adults. For a wide range of psychological measures, the replicability of associations with gray matter volume was assessed. Our results revealed that among healthy individuals 1) finding an association between performance at standard psychological tests and brain morphology is relatively unlikely 2) significant associations, found using an exploratory approach, have overestimated effect sizes and 3) can hardly be replicated in an independent sample. After considering factors such as sample size and comparing our findings with more replicable SBB-associations in a clinical cohort and replicable associations between brain structure and non-psychological phenotype, we discuss the potential causes and consequences of these findings.

Introduction: Patients with transient ischemic attack (TIA) and minor stroke demonstrate cognitive impairment, and a four-fold risk of late-life dementia.Aim: To study the extent to which the rates of brain volume loss in TIA patients differ from healthy controls and how they are correlated with cognitive impairment.Methods: TIA or minor stroke patients were tested with a neuropsychological battery and underwent T1 weighted volumetric magnetic resonance imaging scans at fixed intervals over a 3 years period. Linear mixed effects regression models were used to compare brain atrophy rates between groups, and to determine the relationship between atrophy rates and cognitive function in TIA and minor stroke patients.Results: Whole brain atrophy rates were calculated for the TIA and minor stroke patients; n = 38 between 24 h and 18 months, and n = 68 participants between 18 and 36 months, and were compared to healthy controls. TIA and minor stroke patients demonstrated a significantly higher whole brain atrophy rate than healthy controls over a 3 years interval (p = 0.043). Diabetes (p = 0.012) independently predicted higher atrophy rate across groups. There was a relationship between higher rates of brain atrophy and processing speed (composite P = 0.047 and digit symbol coding P = 0.02), but there was no relationship with brain atrophy rates and memory or executive composite scores or individual cognitive tests for language (Boston naming, memory recall, verbal fluency or Trails A or B score).Conclusion: TIA and minor stroke patients experience a significantly higher rate of whole brain atrophy. In this cohort of TIA and minor stroke patients changes in brain volume over time precede cognitive decline.

Alzheimer's disease (AD) affects millions of people and is a major rising problem in health care worldwide. Recent research suggests that AD could have different subtypes, presenting differences in how the disease develops. Characterizing those subtypes could be key to deepen the understanding of this complex disease. In this paper, we used a multivariate, non-supervised clustering method over blood-based markers to find subgroups of patients defined by distinctive blood marker profiles. Our analysis on ADNI database identified 4 possible subgroups, each with a different blood profile. More importantly, we show that subgroups with different profiles have a different relationship between brain phenotypes detected in magnetic resonance imaging and disease condition.

In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer's disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods.

BackgroundMemory assessment is a key factor for the diagnosis of cognitive impairment. However, memory performance over time may be quite heterogeneous within diagnostic groups.MethodTo identify latent trajectories in memory performance and their associated risk factors, we analyzed data from Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who were classified either as cognitively normal or as Mild Cognitive Impairment (MCI) at baseline and were administered the Rey Auditory Verbal Learning test (RAVLT) for up to 9 years. Group-based trajectory modeling on the 30-minute RAVLT delayed recall score was applied separately to the two baseline diagnostic groups.ResultsThere were 219 normal subjects with mean age 75.9 (range from 59.9 to 89.6) and 52.5% male participants, and 372 MCI subjects with mean age 74.8 (range from 55.1 to 89.3) and 63.7% male participants included in the analysis. For normal subjects, six trajectories were identified. Trajectories were classified into three types, determined by the shape, each of which may comprise more than one trajectory: stable (~30% of subjects), curvilinear decline (~ 28%), and linear decline (~ 42%). Notably, none of the normal subjects assigned to the stable stratum progressed to dementia during the study period. In contrast, all trajectories identified for the MCI group tended to decline, although some participants were later re-diagnosed with normal cognition. Age, sex, and education were significantly associated with trajectory membership for both diagnostic groups, while APOE ɛ4 was only significantly associated with trajectories among MCI participants.ConclusionMemory trajectory is a strong indicator of dementia risk. If likely trajectory of memory performance can be identified early, such work may allow clinicians to monitor or predict progression of individual patient cognition. This work also shows the importance of longitudinal cognitive testing and monitoring.

Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and FDG PET, but a comprehensive and balanced comparison of the MRI and amyloid PET modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with identical neural network architectures. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.

The latent variable "δ" (for "dementia") provides an etiologically "agnostic" omnibus dementia severity metric capable of recognizing the dementing potential of any condition. Depressive symptoms are independent predictors of δ and are thereby implicated as "dementing". Serum resistin levels partially mediate the association between depressive symptoms and δ. We use a novel "off-diagonal" CHI SQ algorithm to demonstrate our ability to select individuals demented solely by depression's effect in both the Texas Alzheimer's Research and Care Consortium (TARCC) (N ≌ 3,500), and the Alzheimer's Disease Neuroimaging Initiative (ADNI (N ≌ 1,750), and demonstrate the higher resistin levels of such cases in TARCC. This approach can be adapted to any δ-related dementia risk factor or biomarker and used identify individuals who might revert back to non-demented states after its successful treatment.

The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides interpretable features and its output is a fixed length vector irrespective of the number of input points or their sample times. In this paper we use the path signature to provide features for identifying people whose diagnosis subsequently converts to Alzheimer's disease. In two separate classification tasks we distinguish converters from 1) healthy individuals, and 2) individuals with mild cognitive impairment. The data used are time-ordered measurements of the whole brain, ventricles and hippocampus from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We find two nonlinear interactions which are predictive in both cases. The first interaction is change of hippocampal volume with time, and the second is a change of hippocampal volume relative to the volume of the whole brain. While hippocampal and brain volume changes are well known in Alzheimer's disease, we demonstrate the power of the path signature in their identification and analysis without manual feature selection. Sequential data is becoming increasingly available as monitoring technology is applied, and the path signature method is shown to be a useful tool in the processing of this data.

Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing step. In this paper we use a random forest to learn the relationship between pairs of data points at different time separations. The input vector is a summary of the time series history and it includes both demographic and non-time varying variables such as genetic data. To test the method we use data from the TADPOLE grand challenge, an initiative which aims to predict the evolution of subjects at risk of Alzheimer's disease using demographic, physical and cognitive input data. The task is to predict diagnosis, ADAS-13 score and normalised ventricles volume. While the competition proceeds, forecasting methods may be compared using a leaderboard dataset selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and with standard metrics for measuring accuracy. For diagnosis, we find an mAUC of 0.82, and a classification accuracy of 0.73 compared with a benchmark SVM predictor which gives mAUC = 0.62 and BCA = 0.52. The results show that the method is effective and comparable with other methods.

Introduction: Alzheimer’s disease (AD) is a neurodegenerative disorder with a clinical presentation characterized by memory impairment and executive dysfunction. Our group previously demonstrated significant alterations in white matter microstructural metrics in AD compared to healthy older adults. We aimed to further investigate the relationship between white matter microstructure in AD and cognitive function, including memory and executive function.Methods: Diffusion tensor imaging (DTI) and neuropsychological data were downloaded from the AD Neuroimaging Initiative database for 49 individuals with AD and 48 matched healthy older adults. The relationship between whole-brain fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AxD), radial diffusivity (RD), and composite scores of memory and executive function was examined. We also considered voxel-wise relationships using Tract-Based Spatial Statistics.Results: As expected, individuals with AD had lower composite scores on tests of memory and executive function, as well as disrupted white matter integrity (low FA, high MD, AxD, and RD) relative to healthy older adults in widespread regions, including the hippocampus. When the AD and healthy older adult groups were combined, we found significant relationships between DTI metrics (FA/MD/AxD/RD) and memory scores across widespread regions of the brain, including the medial temporal regions. We also found significant relationships between DTI metrics (FA/MD/AxD/RD) and executive function in widespread regions, including the frontal areas in the combined group. However, when the groups were examined separately, no significant relationships were found between DTI metrics (FA/MD/AxD/RD) and memory performance for either group. Further, we did not find any significant relationships between DTI metrics (FA/MD/AxD/RD) and executive function in the AD group, but we did observe significant relationships between FA/RD, and executive function in healthy older adults.Conclusion: White matter integrity is disrupted in AD. In a mixed sample of AD and healthy elderly persons, associations between measures of white matter microstructure and memory and executive cognitive test performance were evident. However, no significant linear relationship between the degree of white matter disruption and level of cognitive functioning (memory and executive abilities) was found in those with AD. Future longitudinal studies of the relations between DTI metrics and cognitive function in AD are required to determine whether DTI has potential to measure progression of AD and/or treatment efficacy.

Predicting progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell’s C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

The Plexin-A 4 (PLXNA4) gene, has recently been identified in genome wide association studies (GWAS), as a novel genetic player associated with Alzheimer's disease (AD). Additionally, PLXNA4 genetic variations were also found to increase AD risk by tau pathology in vitro. However, the potential roles of PLXNA4 variants in the amyloid-β (Aβ) pathology, were not evaluated. Five targeted loci capturing the top common variations in PLXNA4, were extracted using tagger methods. Multiple linear regression models were used to explore whether these variations can affect the cerebrospinal fluid (CSF) (Aβ1−42, T-tau, and P-tau) phenotypes in the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset. We detected that two loci (rs6467431, rs67468325) were significantly associated with CSF Aβ1−42 levels in the hybrid population (rs6467431: P = 0.01376, rs67468325: P = 0.006536) and the significance remained after false discovery rate (FDR) correction (rs6467431: Pc = 0.03441, rs67468325: Pc = 0.03268). In the subgroup analysis, we further confirmed the association of rs6467431 in the cognitively normal (CN) subgroup (P = 0.01904, Pc = 0.04761). Furthermore, rs6467431-A carriers and rs67468325-G carriers showed higher CSF Aβ1−42 levels than non-carriers. Nevertheless, we did not detect any significant relationships between the levels of T-tau, P-tau and these PLXNA4 loci. Our findings provided preliminary evidence that PLXNA4 variants can confer AD risk through modulating the Aβ deposition.

Brain aging is a multifaceted process that remains poorly understood. Despite significant advances in technology, progress toward identifying reliable risk factors for suboptimal brain health requires realistically complex analytic methods to explain relationships between genetics, biology, and environment. Here we show the utility of a novel unsupervised machine learning technique – Correlation Explanation (CorEx) – to discover how individual measures from structural brain imaging, genetics, plasma, and CSF markers can jointly provide information on risk for Alzheimer’s disease (AD). We examined 829 participants (Mage: 75.3 ± 6.9 years; 350 women and 479 men) from the Alzheimer’s Disease Neuroimaging Initiative database to identify multivariate predictors of cognitive decline and brain atrophy over a 1-year period. Our sample included 231 cognitively normal individuals, 397 with mild cognitive impairment (MCI), and 201 with AD as their baseline diagnosis. Analyses revealed latent factors based on data-driven combinations of plasma markers and brain metrics, that were aligned with established biological pathways in AD. These factors were able to improve disease prediction along the trajectory from normal cognition and MCI to AD, with an area under the receiver operating curve of up to 99%, and prediction accuracy of up to 89.9% on independent “held out” testing data. Further, the most important latent factors that predicted AD consisted of a novel set of variables that are essential for cardiovascular, immune, and bioenergetic functions. Collectively, these results demonstrate the strength of unsupervised network measures in the detection and prediction of AD.

Recent data have found that aging-related hearing loss (ARHL) is associated with the development of Alzheimer’s Disease (AD). However, the nature of the relationship between these two disorders is not clear. There are multiple potential factors that link ARHL and AD, and previous investigators have speculated that shared metabolic dysregulation may underlie the propensity to develop both disorders. Here, we investigate the distribution of serum lipidomic biomarkers in AD subjects with or without hearing loss in a publicly available dataset. Serum levels of 349 known lipids from 16 lipid classes were measured in 185 AD patients. Using previously defined co-regulated sets of lipids, both age- and sex-adjusted, we found that lipid sets enriched in phosphatidylcholine and phosphatidylethanolamine showed a strong inverse association with hearing loss. Examination of biochemical classes confirmed these relationships and revealed that serum phosphatidylcholine levels were significantly lower in AD subjects with hearing loss. A similar relationship was not found in normal subjects. These data suggest that a synergistic relationship may exist between AD, hearing loss and metabolic biomarkers, such that in the context of a pathological state such as AD, alterations in serum metabolic profiles are associated with hearing loss. These data also point to a potential role for phosphatidylcholine, a molecule with antioxidant properties, in the underlying pathophysiology of ARHL in the context of AD, which has implications for our understanding and potential treatment of both disorders.

Abstract Introduction Cognitive tests and nonamyloid imaging biomarkers do not consistently identify preclinical AD. The objective of this study was to evaluate whether white matter hyperintensity (WMH) volume, a cerebrovascular disease marker, is more associated with preclinical AD than conventional AD biomarkers and cognitive tests. Methods Elderly controls enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 158) underwent florbetapir‐PET scans, psychometric testing, neuroimaging with MRI and PET, and APOE genetic testing. Elderly controls the Parkinson's progression markers initiative (PPMI, n = 58) had WMH volume, cerebrospinal fluid (CSF) Aβ1–42, and APOE status measured. Results In the ADNI cohort, only WMH volume and APOE ε4 status were associated with cerebral Aβ (standardized β = 0.44 and 1.25, P = .03 and .002). The association between WMH volume and APOE ε4 status with cerebral Aβ (standardized β = 1.12 and 0.26, P = .048 and .045) was confirmed in the PPMI cohort. Discussion WMH volume is more highly associated with preclinical AD than other AD biomarkers.