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Background: The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism emerged as a risk factor for Alzheimer's disease (AD). However, little was known about its effects on the process of potential AD.Objective: To explore the effects of the Val66Met polymorphism on cognition, cerebrospinal fluid (CSF), and neuroimaging markers in non-demented elderly individuals.Methods: A total of 1,081 adults without dementia (375 healthy subjects and 706 individuals with mild cognitive impairment) were recruited from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to test the influence of BDNF Val66Met polymorphism on cognitive impairment, brain structure atrophy, and change in the levels of CSF biomarkers. Moreover, we also conducted our study in abnormal amyloid-β (A+) subgroup and normal amyloid-β (A-) subgroup, as well as in APOEɛ4 carriers and non-carriers.Results: The BDNF Val66Met polymorphism had significant association with atrophy of the entorhinal cortex and Mini-Mental State Examination (MMSE) scores in the non-demented elderly and A + subgroup, while no association was found in A-subgroup. What is more, there was a significant effect of interaction between BDNF Val66Met and amyloid-β load in MMSE. In addition, significant associations of BDNF Val66Met with the entorhinal cortex and ventricular volumes were found among APOEɛ4 non-carriers, but not APOEɛ4 carriers.Conclusions: The BDNF Val66Met polymorphism is associated with cognitive impairment and brain atrophy among the non-demented elderly, APOEɛ4 non-carriers and A + subgroup, implying the potential of the Val66Met polymorphism as an important genetic factor for AD-related neurodegeneration. [ABSTRACT FROM AUTHOR]

Dementia can be empirically described by the latent dementia phenotype "δ" and its various composite "homologs". We have explored δ's blood-based protein biomarkers in the Texas Alzheimer's Research and Care Consortium (TARCC) study. However, it would be convenient to replicate those associations in the Alzheimer's Disease Neuroimaging Initiative (ADNI). To this end, we have engineered a δ homolog from observed cognitive performance measures common to both projects. Our findings were replicated in randomly selected 50% splits of TARCC data (Group 1, N = 1,747; Group 2, N = 1,755), and then independently in ADNI (N = 1,737). The new δ homolog, i.e., "dT2A" (d-TARCC to ADNI), fit the data of both studies well, and was strongly correlated with dementia severity, as rated by the Clinical Dementia Rating Scale "sum of boxes" (TARCC: r = 0.99, p < 0.001; ADNI: r = 0.96, p < 0.001). dT2A achieved an area under the receiver operating characteristic curve of 0.981 (0.976-0.985) for the discrimination of Alzheimer's disease from normal controls in TARCC, and 0.988 (0.983-0.993) in ADNI. dT2A is the 12th δ homolog published to date, and opens the door to independent replications across these and similar studies. [ABSTRACT FROM AUTHOR]

Background: Both ongoing local metabolic activity (LMA) and corresponding functional connectivity (FC) with remote brain regions are progressively impaired in Alzheimer's disease (AD), particularly in the posterior default mode network (pDMN); however, it is unknown how these impairments interact. It is well known that decreasing mean synaptic activity of a region, i.e., decreasing LMA, reduces the region's sensitivity to afferent input from other regions, i.e., FC.Objective: We hypothesized progressive decoupling between LMA and FC in AD, which is linked to amyloid-β pathology (Aβ).Methods: Healthy adults (n=20) and Aβ+patients without memory impairment (n=9), early MCI (n=21), late MCI (n=18) and AD (n=22) were assessed by resting-state fMRI, FDG-PET, and AV-45-PET to measure FC, LMA, and Aβ of the pDMN. Coupling between LMA and FC (rLA/FC) was estimated by voxelwise correlation.Results: RLMA/FC decreased with disease severity (F=20.09, p<0.001). This decrease was specifically associated with pDMN Aβ (r=-0.273, p=0.029) but not global Aβ (r=-0.112, p=0.378) and with the impact of Aβ on FC (i.e., rAβ/FC,r=-0.339; p=0.006). In multiple regression models rLMA/FC was also associated with memory impairment, reduced cognitive speed and flexibility, outperforming global Aβ, pDMN Aβ, pDMN LMA, and pDMN FC, respectively.Conclusion: Results demonstrate increasing decoupling of LMA from its FC in AD. Data suggest that decoupling is driven by local Aβ and contributes to memory decline. [ABSTRACT FROM AUTHOR]

Background: We previously operationally-defined subtle cognitive decline (SCD) in preclinical Alzheimer's disease (AD) using total scores on neuropsychological (NP) tests. NP process scores (i.e., provide information about how a total NP score was achieved) may be a useful tool for identifying early cognitive inefficiencies prior to objective impairment seen in mild cognitive impairment (MCI) and dementia.Objective: We aimed to integrate process scores into the SCD definition to identify stages of SCD and improve early detection of those at risk for decline.Methods: Cognitively "normal" participants from the Alzheimer's Disease Neuroimaging Initiative were classified as "early" SCD (E-SCD; >1 SD below norm-adjusted mean on 2 process scores or on 1 process score plus 1 NP total score), "late" SCD (L-SCD; existing SCD criteria of >1 SD below norm-adjusted mean on 2 NP total scores in different domains), or "no SCD" (NC). Process scores considered in the SCD criteria were word-list intrusion errors, retroactive interference, and learning slope. Cerebrospinal fluid AD biomarkers were used to examine pathologic burden across groups.Results: E-SCD and L-SCD progressed to MCI 2.5-3.4 times faster than the NC group. Survival curves for E-SCD and L-SCD converged at 7-8 years after baseline. The combined (E-SCD+L-SCD) group had improved sensitivity to detect progression to MCI relative to L-SCD only. AD biomarker positivity increased across NC, SCD, and MCI groups.Conclusions: Process scores can be integrated into the SCD criteria to allow for increased sensitivity and earlier identification of cognitively normal older adults at risk for decline prior to frank impairment on NP total scores. [ABSTRACT FROM AUTHOR]

We examined moderation effects of sex and diagnosis on the effect of positive florbetapir positron emission tomography (PET) amyloid-β (Aβ) scan (A+) on hippocampus subfield volumes in 526 normal control (NC) and early mild cognitive impairment (eMCI) participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI2; ADNI-GO). Regression moderation models showed that women- but not men- with NC designation did not show reduced subiculum volumes despite A+. At the eMCI stage, A+ was detrimental across sexes. Findings were significant while accounting for the effects of age, cognition at screening, education, and APOE4 carrier status. These findings suggest that women with A+ have early neural resistance to Alzheimer's disease-related amyloid burden. [ABSTRACT FROM AUTHOR]

T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer's disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer's Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer's Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended. [ABSTRACT FROM AUTHOR]

Background: There are no agreed-upon variables for predicting progression from unimpaired cognition to amnestic mild cognitive impairment (aMCI), or from aMCI to Alzheimer's disease (AD).Objective: Use ADNI data to develop a 'Framingham-like' prediction model for a 4-year period.Methods: We developed models using the strongest baseline predictors from six domains (demographics, neuroimaging, CSF biomarkers, genetics, cognitive tests, and functional ability). We chose the best predictor from each domain, which was dichotomized into more versus less harmful.Results: There were 224 unimpaired individuals and 424 aMCI subjects with baseline data on all predictors, of whom 37 (17% ) and 150 (35% ) converted to aMCI and AD, respectively, during 4 years of follow-up. For the unimpaired, CSF tau/Aβ ratio, hippocampal volume, and a memory score predicted progression. For those aMCI at baseline, the same predictors plus APOE4 status and functional ability predicted progression. Demographics and family history were not important predictors for progression for either group. The fit statistic was good for the unimpaired-aMCI model (C-statistic 0.80) and very good for the aMCI-AD model (C-statistic 0.91). Among the unimpaired, those with no harmful risk factors had a 4-year predicted 2% risk of progression, while those with the most harmful risk factors had a predicted 35% risk. The aMCI subjects with no harmful risk factors had a predicted 1% risk of progression those with all six harmful risk factors had a predicted 90% risk.Conclusion: Our parsimonious model accurately predicted progression from unimpaired to aMCI with three variables, and from aMCI to AD with five variables. [ABSTRACT FROM AUTHOR]

Background: Cognitive reserve is the acquired capacity reflecting a functional brain adaptability/flexibility in the context of aging. Educational attainment is thought to be among the most important factors that contribute to cognitive reserve.Objective: The aim of this study is to investigate the relationships among duration of education and Alzheimer's disease (AD) related neuroimaging biomarkers such as amyloid-β deposition, glucose metabolism, and brain volumes in each stage of AD.Methods: We reanalyzed a part of the datasets of the Alzheimer's Disease Neuroimaging Initiative. Participants were between 55 and 90 years of age and diagnosed as one of the following: healthy controls (HC), mild cognitive impairment (MCI), or AD. Multiple regression analyses were conducted to examine the relationships among duration of education and amyloid-β deposition (n = 825), brain metabolism (n = 1,304), and brain volumes (n = 1,606) among three groups using data for 18F-Florbetapir (AV-45) imaging, fludeoxyglucose (FDG) positron emission tomography, and T1-weighted magnetic resonance imaging.Results: Duration of education had no correlations with amyloid-β deposition or brain metabolism in any groups. However, duration of education was positively associated with the total brain volume only in participants with MCI.Conclusions: Our findings suggest that education may exert a protective effect on total brain volume in the MCI stage but not in HC or AD. Thus, education may play an important role in preventing the onset of dementia through brain reserve in MCI. [ABSTRACT FROM AUTHOR]

Immune mechanisms may be important in the pathogenesis of Alzheimer's disease (AD). Yet, studies comparing cerebrospinal fluid (CSF) and plasma immune marker levels of healthy and demented individuals have yielded conflicting results. We analyzed CSF from 101 members of the parental history-positive PREVENT-AD cohort of healthy aging adults, and 237 participants without dementia from the initial cohort of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1). Following recent practice, we used the biomarkers total-tau and amyloid-β1-42 to allocate participants from each study into four stages of AD pathogenesis: Stage 0 (no abnormality), Stage 1 (reduced amyloid-β1-42), Stage 2 (reduced amyloid-β1-42 and increased total-tau), or "Suspected Non-Alzheimer Pathology" (elevated total-tau only). Investigating the PREVENT-AD participants' CSF assay results for 19 immune/inflammatory markers, we found six that showed a distinct bi-directional relationship with pathogenetic stage. Relative to Stage 0, these were diminished at Stage 1 but strongly increased at Stage 2. Among the ADNI participants (90 healthy controls and 147 with mild cognitive impairment), we found that 23 of 83 available CSF markers also showed this distinct pattern. These results support recent observations that immune activation may become apparent only after the onset of both amyloid and tau pathologies. Unexpectedly, they also suggest that immune marker activity may diminish along with earliest appearance of amyloid-β plaque pathology. These findings may explain discordant results from past studies, and suggest the importance of characterizing the extent of AD pathology when comparing clinical groups. [ABSTRACT FROM AUTHOR]

Background/objective: The APOE ɛ4 allele and increased vascular risk have both been independently linked to cognitive impairment and dementia. Since few studies have characterized how these risk factors affect everyday functioning, we investigated the relationship between APOE ɛ4 genotype and elevated pulse pressure (PP) on functional change in cognitively normal participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI).Methods: 738 normally aging participants underwent APOE genotyping, and baseline PP was calculated from blood pressure indices. The Functional Activities Questionnaire (FAQ) was completed by participants' informant at baseline and 6, 12, 24, 36, and 48-month follow-up visits. Multiple linear regression and multilevel modeling were used to examine the effects of PP and APOE ɛ4 genotype on cross-sectional and longitudinal FAQ scores, respectively.Results: Adjusting for demographic and clinical covariates, results showed that both APOE ɛ4 status and elevated PP predicted greater functional difficulty trajectories across four years of follow-up. Interestingly, however, elevated PP was associated with greater functional decline over time in ɛ4 non-carriers versus carriers.Conclusion: Results show that, although APOE ɛ4 status is the prominent predictor of functional difficulty for ɛ4 carriers, an effect of arterial stiffening on functional difficulty was observed in non-carriers. Future studies are needed in order to clarify the etiology of the association between PP and different brain aging processes, and further explore its utility as a marker of dementia risk. The present study underscores the importance of targeting modifiable risk factors such as elevated PP to prevent or slow functional decline and pathological brain aging. [ABSTRACT FROM AUTHOR]

Neurofibrillary tangle (NFT) pathology is linked to neurodegeneration in the medial temporal lobe (MTL). Using a tailored pipeline, we correlated atrophy rate, as measured from retrospective longitudinal MRI, with NFT burden, measured from 18F-AV-1451 PET, within MTL regions of earliest NFT pathology. In amyloid-β positive but not amyloid-β negative individuals, we found significant correlation between 18F-AV-1451 uptake and atrophy rate that was strongest in the transentorhinal cortex, the first region with NFT pathology. This supports the role of NFTs in driving neurodegeneration and the utility of 18F-AV-1451 PET and structural measurement of transentorhinal cortex in tracking early tau-mediated disease progression. [ABSTRACT FROM AUTHOR]

Joint analysis of amyloid and metabolic PET patterns across healthy, mild cognitive impairment (MCI), and Alzheimer's disease (AD) subjects was performed using baseline 18F-florbetapir and 18F-FDG PET of 684 subjects from the ADNI (251 normal, 204 stable MCI, 85 AD converters, and 144 AD). Correlation between regional amyloid and metabolic uptake was measured and predictive value of PET profile regarding AD conversion in cognitively impaired subjects was assessed using survival analysis and support vector machine classification (SVM). The highest correlations were found in the temporal cortex, precuneus, and posterior cingulum. With respect to normal controls, amyloid load increase was diffuse and early in MCI subjects, whereas metabolism decrease occurred later and predominated in temporo-parietal, precuneus, and cingulate cortices. Five-year AD conversion rates in cognitively impaired subjects were 5%, 22%, 42%, and 78% in amyloid-/FDG-, amyloid-/FDG+, amyloid+/FDG-, and amyloid+/FDG+ subjects respectively (mean follow-up 37±14 months). Using SVM, the combination of ADAS-cog score, amyloid PET, and FDG PET yielded better performance in predicting AD conversion (77% accuracy; 58% positive predictive value; 88% negative predictive value) than ADAS-cog (72%; 52%; 86%), amyloid PET (72%; 52%; 87%), and FDG PET (67%; 47%; 84%). This study attests the complementary value of amyloid and FDG PET in MCI assessment and the efficiency of combined cognitive, amyloid, and metabolic scores to predict AD conversion. [ABSTRACT FROM AUTHOR]

There is a growing recognition of sex differences in Alzheimer's disease (AD). Females show an advantage over males on tests of verbal memory, which are used to diagnose AD and its precursor, amnestic mild cognitive impairment (aMCI). Women retain this advantage in aMCI despite reduced hippocampal volume and temporal lobe glucose metabolism. Here we examined whether this female advantage endures despite evidence of AD-specific pathology, cortical amyloid-β (Aβ) deposition measured with [18F]AV45 (florbetapir) positron emission tomography. Participants with normal cognition (N = 304), aMCI (N = 515), and AD dementia (N = 175) were drawn from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Across and within diagnostic groups, we conducted linear regressions to examine the interaction of sex with cortical Aβ burden on immediate and delayed recall on the Rey Auditory Verbal Learning Test (RAVLT) adjusting for age, education, and APOE4. In the overall group, sex by cortical Aβ interaction was significant for delayed recall only. Overall, delayed recall performance was significantly better in women versus men among those with low to moderate Aβ burden, but women and men performed similarly among those with high Aβ burden. In diagnosis-stratified analyses, a significant sex by cortical Aβ interaction was observed for delayed recall in the aMCI group, but not in the normal or AD dementia groups. Thus, women maintain a verbal memory advantage over men in aMCI despite similar levels of AD pathology. Although this advantage may benefit women by delaying verbal memory impairment until more advanced pathology, it may also delay diagnosis of aMCI and treatment intervention. [ABSTRACT FROM AUTHOR]

Background and Purpose: Ventricular enlargement in elderly raises a challenging differential diagnosis to physicians. While Alzheimer's disease is the most common form of dementia, idiopathic normal pressure hydrocephalus (iNPH) constitutes a potentially reversible syndrome. iNPH has a unique pathophysiology pertaining to cerebrospinal fluid (CSF) dynamics and periventricular white matter. We aimed to determine the effects of iNPH on periventricular white matter bundles and to further characterize its ventricular and sulcal CSF distribution by using diffusion tensor tractography (DTT) and CSF volumetrics on high resolution T1-weighted magnetic resonance imaging data.Methods: Deterministic DTT and validated volumetric parcellation were performed on 20 healthy elderly, 13 Alzheimer's disease (AD), and 9 iNPH patients. The superior thalamic radiation, corticospinal tract, and dentatorubrothalamic tract were traced and quantified using DTI studio software. Cloud-based volumetric parcellation was also performed on 138 healthy subjects across the lifespan, 13 AD, and 9 iNPH-patients. Ventricular and sulcal CSF volumes in the three groups were compared.Results: Combining increased mean diffusivity of the superior thalamic radiation with ventricular volume resulted in clear separation of iNPH from the AD and age-matched healthy subject groups. Additionally, ventricular to sulcal CSF ratio, utilizing fully automated methods, was significantly greater in the iNPH patients compared to AD and healthy age-matched controls.Conclusions: Combined microstructural (DTT) and macrostructural (ventricular volume) changes is a promising radiological approach in studying ventriculomegaly. Automated estimation of the disproportionate ventricular and sulcal CSF ratio in patients presenting with ventriculomegaly may be important as radiologic markers in differentiating iNPH from other causes of ventriculomegaly. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to verify the reliability and generalizability of an automatic tool for the detection of Alzheimer-related hypometabolic pattern based on a Support-Vector-Machine (SVM) model analyzing 18F-fluorodeoxyglucose (FDG) PET data.Methods: The SVM model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetries. It was trained on a homogeneous dataset from a memory clinic center and tested on an independent multicentric dataset drawn from the Alzheimer's Disease Neuroimaging Initiative. Subjects were included in the study and classified based on a diagnosis confirmed after an adequate follow-up time.Results: The accuracy of the discrimination between patients with Alzheimer Disease (AD), in either prodromal or dementia stage, and normal aging subjects was 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed, and the accuracy was 89.8% in the multicentric training set and 88.0% in the monocentric testing set. The classification rate was also evaluated in different subgroups, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with non-confirmed memory concern. The percent of pattern detections increased from 77% in early prodromal AD to 91% in AD dementia, while it was about 10% for healthy controls and non-AD patients.Conclusions: The present findings show a good level of reproducibility and generalizability of a model for detecting the hypometabolic pattern in AD and confirm the accuracy of FDG-PET in Alzheimer disease. [ABSTRACT FROM AUTHOR]

Purpose: To assess disease-related patterns of in vivo pathology in 11 patients with Corticobasal Syndrome (CBS) compared to 20 healthy controls and 33 mild cognitive impairment (MCI) patients due to Alzheimer’s disease.Methods: We assessed tau aggregates with [18F]AV1451 PET, amyloid-β depositions with [18F]AV45 PET, and volumetric microstructural changes with MRI. We validated for [18F]AV1451 standardised uptake value ratio (SUVRs) against input functions from arterial metabolites and found that SUVRs and arterial-derived distribution volume ratio (DVRs) provide equally robust measures of [18F]AV1451 binding.Results: CBS patients showed increases in [18F]AV1451 SUVRs in parietal (P < 0.05) and frontal (P < 0.05) cortices in the affected hemisphere compared to healthy controls and in precentral (P = 0.008) and postcentral (P = 0.034) gyrus in the affected hemisphere compared to MCI patients. Our data were confirmed at the histopathological level in one CBS patient who underwent brain biopsy and showed sparse tau pathology in the parietal cortex co-localizing with increased [18F]AV1451 signal. Cortical and subcortical [18F]AV45 uptake was within normal levels in CBS patients. In parietal and frontal cortices of the most affected hemisphere we found also grey matter loss (P < 0.05), increased mean diffusivity (P < 0.05) and decreased fractional anisotropy (P < 0.05) in CBS patients compared to healthy controls and MCI patients. Grey matter loss and white matter changes in the precentral gyrus of CBS patients were associated with worse motor symptoms.Conclusions: Our findings demonstrate disease-related patterns of in vivo tau and microstructural pathology in the absence of amyloid-β, which distinguish CBS from non-affected individuals and MCI patients. [ABSTRACT FROM AUTHOR]

Purpose: We estimated whether amyloid involvement in subcortical regions may predict cognitive impairment, and established an amyloid staging scheme based on degree of subcortical amyloid involvement.Methods: Data from 240 cognitively normal older individuals, 393 participants with mild cognitive impairment, and 126 participants with Alzheimer disease were acquired at Alzheimer’s Disease Neuroimaging Initiative sites. To assess subcortical involvement, we analyzed amyloid deposition in amygdala, putamen, and caudate nucleus. We staged participants into a 3-stage model based on cortical and subcortical amyloid involvement: 382 with no cortical or subcortical involvement as stage 0, 165 with cortical but no subcortical involvement as stage 1, and 203 with both cortical and subcortical involvement as stage 2.Results: Amyloid accumulation was first observed in cortical regions and spread down to the putamen, caudate nucleus, and amygdala. In longitudinal analysis, changes in MMSE, ADAS-cog 13, FDG PET SUVR, and hippocampal volumes were steepest in stage 2 followed by stage 1 then stage 0 (p value <0.001). Stage 2 showed steeper changes in MMSE score (β [SE] = −0.02 [0.004], p < 0.001), ADAS-cog 13 (0.05 [0.01], p < 0.001), FDG PET SUVR (−0.0008 [0.0003], p = 0.004), and hippocampal volumes (−4.46 [0.65], p < 0.001) compared to stage 1.Conclusions: We demonstrated a downward spreading pattern of amyloid, suggesting that amyloid accumulates first in neocortex followed by subcortical structures. Furthermore, our new finding suggested that an amyloid staging scheme based on subcortical involvement might reveal how differential regional accumulation of amyloid affects cognitive decline through functional and structural changes of the brain. [ABSTRACT FROM AUTHOR]

Background: Early onset dementias (EOD) are rare neurodegenerative dementias that present before 65 years. Genetic factors have a substantially higher pathogenetic contribution in EOD patients than in late onset dementia.Objective: To identify known and/or novel rare variants in major candidate genes associated to EOD by high-throughput sequencing. Common-risk variants of apolipoprotein E (APOE) and prion protein (PRNP) genes were also assessed.Methods: We studied 22 EOD patients recruited in Memory Clinics, in the context of studies investigating genetic forms of dementia. Two methodological approaches were applied for the target-Next Generation Sequencing (NGS) analysis of these patients. In addition, we performed progranulin plasma dosage, C9Orf72 hexanucleotide repeat expansion analysis, and APOE genotyping.Results: We detected three rare known pathogenic mutations in the GRN and PSEN2 genes and eleven unknown-impact mutations in the GRN, VCP, MAPT, FUS, TREM2, and NOTCH3 genes. Six patients were carriers of only common risk variants (APOE and PRNP), and one did not show any risk mutation/variant. Overall, 69% (n = 9) of our early onset Alzheimer's disease (EAOD) patients, compared with 34% (n = 13) of sporadic late onset Alzheimer's disease (LOAD) patients and 27% (n = 73) of non-affected controls (ADNI, whole genome data), were carriers of at least two rare/common risk variants in the analyzed candidate genes panel, excluding the full penetrant mutations.Conclusion: This study suggests that EOD patients without full penetrant mutations are characterized by higher probability to carry polygenic risk alleles that patients with LOAD forms. This finding is in line with recently reported evidence, thus suggesting that the genetic risk factors identified in LOAD might modulate the risk also in EOAD. [ABSTRACT FROM AUTHOR]

The common p.D358A variant (rs2228145) in IL-6R is associated with risk for multiple diseases and with increased levels of soluble IL-6R in the periphery and central nervous system (CNS). Here, we show that the p.D358A allele leads to increased proteolysis of membrane bound IL-6R and demonstrate that IL-6R peptides with A358 are more susceptible to cleavage by ADAM10 and ADAM17. IL-6 responsive genes were identified in primary astrocytes and microglia and an IL-6 gene signature was increased in the CNS of late onset Alzheimer's disease subjects in an IL6R allele dependent manner. We conducted a screen to identify variants associated with the age of onset of Alzheimer's disease in APOE ɛ4 carriers. Across five datasets, p.D358A had a meta P = 3 ×10-4 and an odds ratio = 1.3, 95% confidence interval 1.12 -1.48. Our study suggests that a common coding region variant of the IL-6 receptor results in neuroinflammatory changes that may influence the age of onset of Alzheimer's disease in APOE ɛ4 carriers. [ABSTRACT FROM AUTHOR]

Background: The question that whether the presence of osteoarthritis (OA) can modify the effects of apolipoprotein E4 (APOE4) genotype on longitudinal change in cognitive performance among non-demented older people remains unclear.Objective: To examine whether the association of APOE4 genotype with change in verbal episodic memory over time is modified by the presence of OA among non-demented older people.Methods: Longitudinal data from 1,400 non-demented older people were obtained from the Alzheimer's Disease Neuroimaging Initiative database. The sample included 466 healthy individuals and 934 mild cognitive impairment. The effects of the OA×APOE4 genotype interaction term on longitudinal change in cognitive performance were examined using linear mixed-effects regression models. Global cognition was assessed by the Mini-Mental State Examination score and Clinical Dementia Rating-Sum of Boxes. Verbal episodic memory was evaluated by the Rey Auditory Verbal Learning Test (RAVLT) immediate recall and delayed recall score.Results: We found that OA interacted with APOE4 genotype to influence longitudinal change in verbal episodic memory (as assessed by RAVLT immediate recall score) but not global cognition. Specifically, the OA-/APOE4+ group had a steeper decline in RAVLT immediate recall score compared with the OA+/APOE4+ group. However, there was no difference in RAVLT immediate recall score between OA-/APOE4-and OA+/APOE4-individuals.Conclusion: Our study suggested that the association of APOE4 genotype with change in RAVLT immediate recall score over time is modified by the presence of OA at earliest stages of the disease. [ABSTRACT FROM AUTHOR]

Background: Structural brain imaging metrics and gene expression biomarkers have previously been used for Alzheimer's disease (AD) diagnosis and prognosis, but none of these studies explored integration of imaging and gene expression biomarkers for predicting mild cognitive impairment (MCI)-to-AD conversion 1-2 years into the future.Objective: We investigated advantages of combining gene expression and structural brain imaging features for predicting MCI-to-AD conversion. Selection of the differentially expressed genes (DEGs) for classifying cognitively normal (CN) controls and AD patients was benchmarked against previously reported results.Methods: The current work proposes integrating brain imaging and blood gene expression data from two public datasets (ADNI and ANM) to predict MCI-to-AD conversion. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated in the two independents patient cohorts.Results: Combining DEGs and imaging biomarkers for predicting MCI-to-AD conversion yielded 0.832-0.876 receiver operating characteristic (ROC) area under the curve (AUC), which exceeded the 0.808-0.840 AUC from using the imaging features alone. With using only three DEGs, the CN versus AD predictive model achieved 0.718, 0.858, and 0.873 cross-validation AUC for the ADNI, ANM1, and ANM2 datasets.Conclusion: For the first time we show that combining gene expression and imaging biomarkers yields better predictive performance than using imaging metrics alone. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated to produce consistent results in the two independents patient cohorts. Using an improved feature selection, we show that predictive models with fewer gene expression probes can achieve competitive performance. [ABSTRACT FROM AUTHOR]

Previous studies report hippocampal volume loss can help predict conversion from normative aging to mild cognitive impairment to dementia. Additionally, a growing literature indicates that stress-related allostatic load may increase disease vulnerability. The current study examined the relationship between stress-related cytokines (ie, interleukin-6 [IL-6]), cognition as measured by Mini-Mental State Examination (MMSE) scores, and hippocampal volume. Mixed models were employed to examine both within- (across time) and between-subject effects of IL-6 and hippocampal volume on MMSE score among 566 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The within-subject analysis found left hippocampal volume significantly (p = .009) predicted MMSE score. Between-subject analysis found the effect of IL-6 on MMSE was moderated by right hippocampal volume (p = .001). These results replicate previous findings and also extend prior work demonstrating stress-related cytokines may play a role in Alzheimer's disease progression. [ABSTRACT FROM AUTHOR]

Background: APOEɛ4 allele confers greatest genetic risk for Alzheimer's disease (AD), yet mechanisms underlying this risk remain elusive. APOE is involved in lipid metabolism, and literature suggest relationships between high total cholesterol, APOE, and AD. Further investigation is needed to elucidate the potential role of total cholesterol in AD risk.Objective: To investigate the relationship between total cholesterol and APOE-related AD risk in the Alzheimer's Disease Neuroimaging Initiative.Methods: Participants (N = 1,534) were classified as controls (cognitively normal; N = 404), early mild cognitive impairment (MCI; N = 294), late MCI (N = 539), or AD (N = 297). Total cholesterol levels were compared across APOE genotype and diagnosis. Mendelian randomization was performed to examine causality between total cholesterol and AD risk using APOE as a genetic instrument.Results: Total cholesterol was higher in APOE4+ compared to APOE3 and APOE2+ (ps < 0.04) carriers. Those with AD and late MCI (ps < 0.001) had higher total cholesterol than the control group. Comparing APOE4+ to APOE3 carriers, the predicted odds ratios per mg/dL greater total cholesterol were 1.11 for MCI (95% confidence interval, 1.04-7.32), 1.05 for early MCI (1.01-3.22), 1.13 for late MCI (1.05-11.70), 1.21 for AD (1.09-54.05), and 1.13 for composite dementia (MCI or AD; 1.06-11.59) (ps < 0.05, F-statistics > 10).Conclusion: Higher total cholesterol may be a significant contributor to AD risk, particularly in APOE4 carriers who, based on existing literature, tend to have impaired cholesterol metabolism. Our findings highlight a possible mechanism by which APOE confers AD risk and indicate potential for AD risk modification through maintenance of healthy total cholesterol levels. [ABSTRACT FROM AUTHOR]

Background: With the rapid development of neurobiology and neuroimaging technologies, mounting evidence shows that Alzheimer's disease (AD) is caused by the build-up of two abnormal proteins, amyloid-β plaques (A) and neurofibrillary tangles (T). Over time, these AD-related neuropathological burdens begin to spread throughout the brain, which results in the characteristic progression of symptoms in AD.Objective: Although tremendous efforts have been made to link biological indicators to the progression of AD, limited attention has been paid to investigate the multi-factorial role of socioeconomic status (SES) in the prevalence or incidence of AD. There is high demand to explore the synergetic effect of sex and SES factors in moderating the neurodegeneration process caused by the accumulation of A and T biomarkers.Methods: We carry out a meta-data analysis on the longitudinal neuroimaging data, clinical outcomes, genotypes, and demographic data in Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu).Results: Our major findings include 1) education and occupation show resilience effects at the angular gyrus, superior parietal lobule, lateral occipital-temporal sulcus, and posterior transverse collateral sulcus where we found significant slowdown of neurodegeneration due to higher education level or more advanced occupation rank; 2) A and T biomarkers manifest different spatial patterns of brain resilience; 3) BDNF (brain-derived neurotrophic factor) single nucleotide polymorphism (SNP) rs10835211 shows strong association to the identified resilience effect; 4) the identified resilience effect is associated with the clinical manifestation in memory, learning, and organization performance.Conclusion: Several brain regions manifest resilience from SES to A and T biomarkers. BDNF SNPs have a potential association with the resilience effect from SES. In addition, cognitive measures of learning and memory demonstrate the resilience effect. [ABSTRACT FROM AUTHOR]

Background: Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer's disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD.Objective: To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI.Methods: We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI.Results: The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient's Mini-Mental State Examination scores.Conclusion: The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory. [ABSTRACT FROM AUTHOR]

Background: The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer's disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), which often demonstrates classification accuracy equivalent or superior to other ML methods. Further, when faced with many potential features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different approaches for variable selection.Objective: The present study sought to compare different methods for statistical classification and for automated and theoretically guided feature selection techniques in the context of predicting conversion from MCI to dementia.Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate different influences of automated feature preselection on LR and support vector machine (SVM) classification methods, in classifying conversion from MCI to dementia.Results: The present findings demonstrate how similar performance can be achieved using user-guided, clinically informed pre-selection versus algorithmic feature selection techniques.Conclusion: These results show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM's necessity or value for many clinical researchers. [ABSTRACT FROM AUTHOR]

Hippocampal atrophy and hypometabolism of the posterior cingulate cortex (PCC), early markers of Alzheimer's disease (AD), have been shown to be associated in late mild cognitive impairment and early AD via atrophy of connecting cingulum fibers. Recently, a direct association of hippocampal atrophy and PCC hypometabolism has been shown in cognitively normal elderly. We aimed to investigate if this association might be modulated by partly non-hippocampogenic alterations of parahippocampal cingulum (PHC) integrity. 45 cognitively healthy elderly aged 59 to 89 years were included from the Alzheimer's Disease Neuroimaging Initiative. Hippocampal volumes and PCC glucose metabolism were measured using MRI and FDG-PET. PHC fibers connecting the hippocampus and the PCC were reconstructed using diffusion weighted MRI and measures of diffusivity were calculated. Using robust linear regression, interaction effects of PHC diffusivity and hippocampal volume on PCC metabolism were calculated. For both hemispheres, significant interaction effects were found for PHC mean diffusivity. Interaction effects were such that the association of hippocampal volume and PCC metabolism was higher in subjects with increased mean diffusivity in PHC fibers. In cognitively normal elderly, compromised integrity of the PHC may increase the risk of PCC hypometabolism due to hippocampal atrophy. Spared PHC fiber integrity may protect against PCC hypometabolism due to hippocampal atrophy. [ABSTRACT FROM AUTHOR]

Background: Anosognosia, or unawareness of memory deficits, is a common manifestation of Alzheimer's disease (AD), but greatly variable in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) subjects. Self-referential network (SRN) is responsible for self-referential processing and considered to be related to AD progression.Objective: Our aim is to explore connectivity changes of SRN and its interaction with memory-related network and primary sensorimotor network (SMN) in the AD spectrum.Methods: About 444 Alzheimer's Disease Neuroimaging Initiative subjects (86 cognitively normal [CN]; 156 SCD; 146 aMCI; 56 AD) were enrolled in our study. The independent component analysis (ICA) method was used to extract the SRN, SMN, and memory-related network from all subjects. The alteration of functional connectivity (FC) within SRN and its connectivity with memory-related network/SMN were compared among four groups and further correlation analysis between altered FC and memory awareness index as well as episodic memory score were performed.Results: Compared with CN group, individuals with SCD exhibited hyperconnectivity within SRN, while aMCI and AD patients showed hypoconnectivity. Furthermore, aMCI patients and AD patients both showed the interruption of the FC between the SRN and memory-related network compared to CN group. Pearson correlation analysis showed that disruptive FC within SRN and its interaction with memory-related network were related to memory awareness index and episodic memory scores.Conclusion: In conclusion, impaired memory awareness and episodic memory in the AD spectrum are correlated to the disconnection within SRN and its interaction with memory-related network. [ABSTRACT FROM AUTHOR]

Background: Histopathologic studies have demonstrated differential amyloid-β (Aβ) burden between cortical sulci and gyri in Alzheimer's disease (AD), with sulci having a greater Aβ burden.Objective: To characterize Aβ deposition in the sulci and gyri of the cerebral cortex in vivo among subjects with normal cognition (NC), mild cognitive impairment (MCI), and AD, and to evaluate if these differences could improve discrimination between diagnostic groups.Methods: T1-weighted 3T MR and florbetapir (amyloid) positron emission tomography (PET) data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). T1 images were segmented and the cortex was separated into sulci/gyri based on pial surface curvature measurements. T1 images were registered to PET images and regional standardized uptake value ratios (SUVr) were calculated. A linear mixed effects model was used to analyze the relationship between clinical variables and amyloid PET SUVr measurements in the sulci/gyri. Receiver operating characteristic (ROC) analysis was performed to define amyloid positivity. Logistic models were used to evaluate predictive performance of clinical diagnosis using amyloid PET SUVr measurements in sulci/gyri.Results: 719 subjects were included: 272 NC, 315 MCI, and 132 AD. Gyral and sulcal Aβ increased with worsening cognition, however there was a greater increase in gyral Aβ. Females had a greater gyral and sulcal Aβ burden. Focusing on sulcal and gyral Aβ did not improve predictive power for diagnostic groups.Conclusion: While there were significant differences in Aβ deposition in cerebral sulci and gyri across the AD spectrum, these differences did not translate into improved prediction of diagnosis. Females were found to have greater gyral and sulcal Aβ burden. [ABSTRACT FROM AUTHOR]

Background: Individuals with mild cognitive impairment (MCI) are at high risk of progression to Alzheimer's disease (AD) dementia, but some remain stable. There is a need to identify those at higher risk of progression to improve patient management and outcomes.Objective: To evaluate the trajectory of plasma neurofilament light chain (pNFL) prior to progression from MCI to AD dementia, the performance of pNFL, in combination with the Mini-Mental State Examination (MMSE), as a predictor of progression from MCI to AD dementia and to inform clinicians on the use of pNFL as a predictive biomarker.Methods: Participants (n = 440) with MCI and longitudinal follow-up (mean = 4.2 years) from the AD Neuroimaging Initiative dataset were included. pNFL as a marker for neurodegeneration and the MMSE as a cognitive measure were investigated as simple/practical predictors of progression. The risk of progressing from MCI to AD dementia associated with pNFL and MMSE scores was assessed using Cox and logistic regression models.Results: The current risk of progression to AD dementia was 37%higher in individuals with high pNFL (> 56 ng/L) compared to those with average pNFL (≤40 ng/L). A combination of baseline pNFL and MMSE could differentiate those who progressed within 5 years (AUC = 0.75) from stable individuals. Including change in MMSE over 6-12 months further improved the model (AUC = 0.84).Conclusion: Our findings reveal that combining pNFL with a simple dementia screener (MMSE) can reliably predict whether a person with MCI is likely to progress to AD dementia within 5 years. [ABSTRACT FROM AUTHOR]

Body mass index (BMI) is a risk factor for Alzheimer's disease (AD) although the relationship is complex. Obesity in midlife is associated with increased risk for AD, whereas evidence supports both higher and lower BMI increasing risk for AD in late life. This study examined the influence of individual differences in genetic risk for AD to further clarify the relationship between late-life BMI and conversion to AD. Participants included 52 individuals diagnosed as having mild cognitive impairment (MCI) at baseline who converted to AD within 24 months and 52 matched MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. BMI was measured at baseline. Genetic risk for AD was assessed via genome-wide polygenic risk scores. Conditional logistic regression models were run to determine if BMI and polygenic risk predicted conversion to AD. Results showed an interaction between BMI and genetic risk, such that individuals with lower BMI and higher polygenic risk were more likely to convert to AD relative to individuals with higher BMI. These results remained significant after adjusting for cerebrospinal fluid biomarkers of AD. Exploratory sex-stratified analyses revealed this relationship only remained significant in males. These results show that higher genetic risk in the context of lower BMI predicts conversion to AD in the next 24 months, particularly among males. These findings suggest that genetic risk for AD in the context of lower BMI may serve as a prodromal risk factor for future conversion to AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal–Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments. [ABSTRACT FROM AUTHOR]

Background: Cerebrospinal fluid t-tau (CSF t-tau) is a measure of neurodegeneration in Alzheimer's disease (AD) and has been increasingly demonstrated to be a non-specific biomarker within the AD continuum.Objective: We sought to test whether t-tau influences the longitudinal effects of amyloid-β (Aβ) and phospho-tau (p-tau) on memory and executive function (EF) in mild cognitive impairment (MCI).Methods: 319 MCI individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with baseline and 2-year CSF Aβ, p-tau, t-tau, and neuropsychological assessments were studied. Mediation and moderation analyses evaluated the role of t-tau in the effects of Aβ and p-tau on memory and EF over 2 years.Results: We found that high baseline p-tau but not Aβ was associated with higher t-tau and lower memory scores at 2 years follow-up. The association between p-tau and memory impairment was partially mediated by t-tau, whereby higher p-tau was indirectly associated with lower memory via higher t-tau. t-tau also moderated the association between p-tau and memory. When t-tau level was relatively lower, higher p-tau was associated with lower memory scores at 2 years. When t-tau level was higher, the memory scores were low regardless of the p-tau level.Conclusion: Tau-induced neurodegeneration is one key pathway by which AD pathology (p-tau) affects memory impairment. Furthermore, in individuals with lower levels of tau-induced neurodegeneration, higher levels of p-tau were required for memory impairment. Our findings suggest that t-tau plays a significant role in how early AD pathology affects cognitive outcomes. [ABSTRACT FROM AUTHOR]

Objective: Neuropsychiatric symptoms (NPS) are often present in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia. NPS are associated with structural and functional changes in the brain such as atrophy, regional hypometabolism, and hypoperfusion, considered proxies of neurodegeneration. Our objective was to evaluate the association between NPS and regional cerebral tau burden, a more direct representation of neurodegeneration, in cognitively normal (CN), MCI, and AD dementia individuals.Methods: Cross-sectional NPS were assessed using the Neuropsychiatric Inventory (NPI) in 410 CN, 199 MCI, and 61 AD dementia participants who underwent flortaucipir tau positron emission tomography as part of the AD Neuroimaging Initiative (ADNI). Total NPI score and two factors of NPS (affective and hyperactive) were used in analyses. Linear regression models with backward elimination were employed with NPI as dependent variable and regional tau or tau-amyloid interaction as predictor of interest. Covariates included education, age, sex, Rey Auditory Verbal Learning Test Total Learning, and Trail Making Test B.Results: There were significant associations (p < 0.05) between the NPI variables (total score, Affective factor) and entorhinal and precuneus tau across all participants. These associations were also significant for the tau-amyloid interaction. These effects were significant in cognitively symptomatic participants (MCI and AD dementia), but not in CN participants.Conclusions: Increased tau burden in the entorhinal and precuneus cortices was modestly associated with greater NPS in MCI and AD dementia. Further evaluation of NPS and their effect on early-stage AD could aid in finding new interventions and slowing disease progression. [ABSTRACT FROM AUTHOR]

Background: Cross-sectional studies have shown lower cerebral blood flow (CBF) in Alzheimer's disease (AD), but longitudinal CBF changes in AD are still unknown.Objective: To reveal the longitudinal CBF changes in normal control (NC) and the AD continuum using arterial spin labeling perfusion magnetic resonance imaging (ASL MRI).Methods: CBF was calculated from two longitudinal ASL scans acquired 2.22±1.43 years apart from 140 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At the baseline scan, the cohort contained 41 NC, 74 mild cognitive impairment patients (MCI), and 25 AD patients. 21 NC converted into MCI and 17 MCI converted into AD at the follow-up. Longitudinal CBF changes were assessed using paired-t test for non-converters and converters separately at each voxel and in the meta-ROI. Age and sex were used as covariates.Results: CBF reductions were observed in all subjects. Stable NC (n = 20) showed CBF reduction in the hippocampus and precuneus. Stable MCI patients (n = 57) showed spatially more extended CBF reduction patterns in hippocampus, middle temporal lobe, ventral striatum, prefrontal cortex, and cerebellum. NC-MCI converters showed CBF reduction in hippocampus and cerebellum and CBF increase in caudate. MCI-AD converters showed CBF reduction in hippocampus and prefrontal cortex. CBF changes were not related with longitudinal neurocognitive changes.Conclusion: Normal aging and AD continuum showed common longitudinal CBF reductions in hippocampus independent of disease and its conversion. Disease conversion independent longitudinal CBF reductions escalated in MCI subjects. [ABSTRACT FROM AUTHOR]

Background: Altered cerebral blood flow (CBF) has been linked to increased risk for Alzheimer's disease (AD). However, whether altered CBF contributes to AD risk by accelerating cognitive decline remains unclear. It also remains unclear whether reductions in CBF accelerate neurodegeneration and development of small vessel cerebrovascular disease.Objective: To examine associations between CBF and trajectories of memory performance, regional brain atrophy, and global white matter hyperintensity (WMH) volume.Method: 147 Alzheimer's Disease Neuroimaging Initiative participants free of dementia underwent arterial spin labeling (ASL) magnetic resonance imaging (MRI) to measure CBF and serial neuropsychological and structural MRI examinations. Linear mixed effects models examined 5-year rate of change in memory and 4-year rate of change in regional brain atrophy and global WMH volumes as a function of baseline regional CBF. Entorhinal and hippocampal CBF were examined in separate models.Results: Adjusting for demographic characteristics, pulse pressure, apolipoprotein E ɛ4 positivity, cerebrospinal fluid p-tau/Aβ ratio, and neuronal metabolism (i.e., fluorodeoxyglucose standardized uptake value ratio), lower baseline entorhinal CBF predicted faster rates of decline in memory as well as faster entorhinal thinning and WMH progression. Hippocampal CBF did not predict cognitive or brain structure trajectories.Conclusion: Findings highlight the importance of early cerebrovascular dysfunction in AD risk and suggest that entorhinal CBF as measured by noninvasive ASL MRI is a useful biomarker predictive of future cognitive decline and of risk of both. [ABSTRACT FROM AUTHOR]

Background: Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. Methods: This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. Results: The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status. Conclusions: This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset. [ABSTRACT FROM AUTHOR]

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

Background: There is intense interest in the development of blood-based biomarkers, not only that can differentiate Alzheimer's disease (AD) from controls, but that can also predict conversion from mild cognitive impairment (MCI) to AD. Serum biomarkers carry the potential advantage over imaging or spinal fluid markers both in terms of cost and invasiveness.Objective: Our objective was to measure the potential for serum lipid markers to differentiate AD from age-matched healthy controls as well as to predict conversion from MCI to AD.Methods: Using a publicly-available dataset, we examined the relationship between baseline serum levels of 349 known lipids from 16 classes of lipids to differentiate disease state as well as to predict the conversion from MCI to AD.Results: We observed that several classes of lipids (cholesteroyl ester, phosphatidylethanolamine, lysophosphatidylethanolamine, and acylcarnitine) differentiated AD from normal controls. Among these, only two classes, phosphatidylethanolamine (PE) and lysophosphatidylethanolamine (lyso-PE), predicted time to conversion from MCI to AD. Low levels of PE and high levels of lyso-PE result in two-fold faster median time to progression from MCI to AD, with hazard ratios 0.62 and 1.34, respectively.Conclusion: These data suggest that serum PE and lyso-PE may be useful biomarkers for predicting MCI to AD conversion. In addition, since PE is converted to lyso-PE by phospholipase A2, an important inflammatory mediator that is dysregulated in AD, these data suggest that the disrupted serum lipid profile here may be related to an abnormal inflammatory response early in the AD pathologic cascade. [ABSTRACT FROM AUTHOR]

Background: APOE4 has been hypothesized to increase Alzheimer's disease risk by increasing neuroinflammation, though the specific neuroinflammatory pathways involved are unclear.Objective: Characterize cerebrospinal fluid (CSF) proteomic changes related to APOE4 copy number.Methods: We analyzed targeted proteomic data from ADNI CSF samples using a linear regression model adjusting for age, sex, and APOE4 copy number, and additional linear models also adjusting for AD clinical status or for CSF Aβ, tau, or p-tau levels. False discovery rate was used to correct for multiple comparisons correction.Results: Increasing APOE4 copy number was associated with a significant decrease in a CRP peptide level across all five models (q < 0.05 for each), and with significant increases in ALDOA, CH3L1 (YKL-40), and FABPH peptide levels (q < 0.05 for each) except when controlling for AD clinical status or neurodegeneration biomarkers (i.e., CSF tau or p-tau). In all models except the one controlling for CSF Aβ levels, though not statistically significant, there was a consistent inverse direction of association between APOE4 copy number and the levels of all 24 peptides from all 8 different complement proteins measured. The odds of this happening by chance for 24 unrelated peptides would be less than 1 in 16 million.Conclusion: Increasing APOE4 copy number was associated with decreased CSF CRP levels across all models, and increased CSF ALDOA, CH3L1, and FABH levels when controlling for CSF Aβ levels. Increased APOE4 copy number may also be associated with decreased CSF complement pathway protein levels, a hypothesis for investigation in future studies. [ABSTRACT FROM AUTHOR]

Background: In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer's disease. Due to memory constraints, many of the proposed CNNs work at a 2D slice-level or 3D patch-level.Objective: Here, we propose a subject-level 3D CNN that can extract the neurodegenerative patterns of the whole brain MRI and converted into a probabilistic Dementia score.Methods: We propose an efficient and lightweight subject-level 3D CNN featuring dilated convolutions. We trained our network on the ADNI data on stable Dementia of the Alzheimer's type (sDAT) from stable normal controls (sNC). To comprehensively evaluate the generalizability of our proposed network, we performed four independent tests which includes testing on images from other ADNI individuals at various stages of the dementia, images acquired from other sites (AIBL), images acquired using different protocols (OASIS), and longitudinal images acquired over a short period of time (MIRIAD).Results: We achieved a 5-fold cross-validated balanced accuracy of 88%in differentiating sDAT from sNC, and an overall specificity of 79.5%and sensitivity 79.7%on the entire set of 7,902 independent test images.Conclusion: Independent testing is essential for estimating the generalization ability of the network to unseen data, but is often lacking in studies using CNN for DAT classification. This makes it difficult to compare the performances achieved using different architectures. Our comprehensive evaluation highlighting the competitive performance of our network and potential promise for generalization. [ABSTRACT FROM AUTHOR]

Background: Predicting the prognosis of mild cognitive impairment (MCI) has outstanding clinical value, and the hippocampal volume is a reliable imaging biomarker of AD diagnosis.Objective: We aimed to longitudinally assess hippocampal sub-regional difference (volume and asymmetry) among progressive MCI (pMCI), stable MCI (sMCI) patients, and normal elderly.Methods: We identified 29 pMCI, 52 sMCI, and 102 normal controls (NC) from the ADNI database. All participants underwent neuropsychological assessment and 3T MRI scans three times. The time interval between consecutive MRI sessions was about 1 year. Volumes of hippocampal subfield were measured by Freesurfer. Based on the analysis of variance, repeated measures analyses, and receiver operating characteristic curves, we compared cross-sectional and longitudinal alteration sub-regional volume and asymmetry index.Results: Compared to NC, both MCI groups showed significant atrophy in all subfields. At baseline, pMCI have a smaller volume than sMCI in the bilateral subiculum, molecular layer (ML), the molecular and granule cell layers of the dentate gyrus, cornu ammonis 4, and right tail. Furthermore, repeated measures analyses revealed that pMCI patients showed a faster volume loss than sMCI in bilateral subiculum and ML. After controlling for age, gender, and education, most results remained unchanged. However, none of the hippocampal sub-regional volumes performed better than the whole hippocampus in ROC analyses, and no asymmetric difference between pMCI and sMCI was found.Conclusion: The faster volume loss in subiculum and ML suggest a higher risk of disease progression in MCI patients. The hippocampal asymmetry may have smaller value in predicting the MCI prognosis. [ABSTRACT FROM AUTHOR]

Background: The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). One strategy to boost the power of trials is to enroll individuals who are more likely to progress targeted using data-driven predictive models.Objective: To investigate if machine learning (ML) models can effectively predict clinical disease progression (cognitive decline) in mild-to-moderate AD patients during the timeframe of a phase III clinical trial.Methods: Data from 202 participants with a diagnosis of AD at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to train ML classifiers that can differentiate between individuals who had declining cognitive function (DC) and individuals with stable cognitive function (SC). DC was defined as any downward change in the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog) score over 12 months of follow-up. SC was defined by the absence of decline in ADAS-cog. Trained models were applied to data from 77 participants from the placebo arm of the phase III trial of Semagacestat (LFAN study) to identify subgroups of SC versus DC.Results: Only 74.8% of ADNI participants and 63.6% of LFAN participants had cognitive decline after one year of follow up. K-nearest neighbors (kNN) classifier had an accuracy of 68.3%, sensitivity of 80.1%, and specificity of 33.3% for identifying decliners in ADNI (training sample). In LFAN (validation sample), the model showed an overall accuracy of 61.3%, sensitivity of 65.5%, and specificity of 47.0% in identifying decliners at the 12 months of follow-up. The model had a positive predictive value of 80.8%, which was 17.2% more than the base prevalence of decliners.Conclusions: Machine learning predictive models can be effectively used to boost the power of clinical trials by reducing the sample size. [ABSTRACT FROM AUTHOR]

Background: The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data.Methods: This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine.Results: The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data.Conclusion: The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings. [ABSTRACT FROM AUTHOR]

Applications of high-dimensional regression often involve multiple sources or types of covariates. We propose methodology for this setting, emphasizing the "wide data" regime with large total dimensionality p and sample size n ≪ p . We focus on a flexible ridge-type prior with shrinkage levels that are specific to each data type or source and that are set automatically by empirical Bayes. All estimation, including setting of shrinkage levels, is formulated mainly in terms of inner product matrices of size n × n . This renders computation efficient in the wide data regime and allows scaling to problems with millions of features. Furthermore, the proposed procedures are free of user-set tuning parameters. We show how sparsity can be achieved by post-processing of the Bayesian output via constrained minimization of a certain Kullback–Leibler divergence. This yields sparse solutions with adaptive, source-specific shrinkage, including a closed-form variant that scales to very large p. We present empirical results from a simulation study based on real data and a case study in Alzheimer's disease involving millions of features and multiple data sources. for this article are available online. [ABSTRACT FROM AUTHOR]

Purpose: To assess relationships between biomarkers for Alzheimer's disease (AD) and their potential contributions to AD.Methods: Biomarkers and cognitive evaluations were assessed longitudinally in 179 patients with mild cognitive impairment, from the Alzheimer's Disease Neuroimaging Initiative from 2003 to 2006, and were used to examine, at any given time, the joint contributions of hippocampal volume, whole brain volume, and brain glucose metabolism on clinical AD progression, using the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog). Marginal structural models were applied, and an inverse-probability of treatment weight estimation was used to account for time-dependent confounding between study variables.Results: At any given time, population-level differences (e.g., 1-standard deviation [SD] increase) in brain glucose metabolism (-1.036; 95% confidence interval [95% CI], -1.608, -0.464) and hippocampal volume (-1.537; 95% CI, -2.399, -0.674) independently reduced mean (ADAS-Cog), whereas a 1-SD increase in whole brain volume did not (0.372; 95% CI, -0.283, 1.027). The effects of brain glucose metabolism differed in subgroups defined by baseline covariates (e.g., age), but no subgroup effects were observed for hippocampal volume and brain volume.Conclusions: Brain glucose metabolism and hippocampal volume represent relevant biological markers in subjects at risk for AD. [ABSTRACT FROM AUTHOR]

With rapid aging of world population, Alzheimer's disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer's disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer's disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer's Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer's disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849. [ABSTRACT FROM AUTHOR]

Background: APOEɛ4 and sex have been linked to increased risk for conversion to Alzheimer's disease (AD). However, the relationship between APOEɛ4 gene dose, sex, and AD biomarkers remains understudied.Objective: To investigate the effect of APOEɛ4 dose on AD biomarkers in a sample of older adults with mild cognitive impairment (MCI), and to examine whether APOEɛ4 dose modifies AD risk differently in MCI women and men.Methods: We examined cross-sectional AD biomarkers for participants with MCI (n = 930, 55-96 years old) from three large aging cohorts. Region of interest MRI volumes, global cognition, and episodic memory were analyzed by number of APOEɛ4 alleles and stratified by sex.Results: Across all participants, number of APOEɛ4 alleles was associated with smaller hippocampal and amygdala volumes and poorer cognition. When stratified by sex, women showed an APOEɛ4 dose effect for bilateral hippocampal and left amygdala volumes and cognition. In contrast, men showed an APOEɛ4 dose effect for hippocampal volumes with a trend in amygdala, but cognition did not differ between men with 1 and 2 APOEɛ4 alleles. Women with 2 APOEɛ4 alleles had poorer memory between 65-69 and poorer global cognition between 70-74 compared to men with 2 APOEɛ4 alleles.Conclusion: APOEɛ4 confers a dose effect on AD biomarkers in patients with MCI, and the number of APOEɛ4 alleles has a greater detrimental impact in women than men, which may be specific to a critical time window. [ABSTRACT FROM AUTHOR]

Background: The percentage of verbal forgetting (VF%) measure of the Rey Auditory Verbal Learning Test (RAVLT) has been proposed to differentiate patients diagnosed clinically with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB).Objective: To determine if VF% aligns with gold-standard biomarker and autopsy evidence of AD and DLB neuropathology.Methods: Clinical, cognitive, sociodemographic, and biomarker data were collected from 315 patients with baseline cognitive impairment and 485 normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). AD markers included reduced cerebrospinal fluid (CSF) amyloid-β, elevated total-tau and phosphorylated-tau, hippocampal atrophy, and the presence of amyloid plaques and neurofibrillary tangles at autopsy. DLB markers included reduced CSF α-synuclein, preserved hippocampus, atrophied putamen, occipital glucose metabolism, and the presence of Lewy bodies at autopsy. Cognitively impaired participants were classified as ADVF% (n = 190) or DLBVF% (n = 125) based on their RAVLT VF% scores using a 75% cut-off (≥75%  = ADVF%, <75%  = DLBVF%). Postmortem data were available for 13 ADVF% participants, 13 DLBVF% patients, and six healthy controls.Results: ADVF% and DLBVF% participants did not differ on CSF or neuroimaging biomarkers, with the exception of total tau levels which were higher in ADVF%. In the subset of participants with autopsy data, comorbid AD and DLB pathology was most frequent in ADVF% participants, and pure DLB pathology was most frequent in DLBVF% participants, however, these differences were not statistically significant.Conclusion: The RAVLT VF% measure does not reliably align with AD and DLB neuropathology in ADNI participants. [ABSTRACT FROM AUTHOR]

Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer's disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer's disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer's disease. However, the impact of amyloid-β deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer's disease in 318 cognitively normal individuals aged 70-85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer's disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer's disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer's disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to 'suspected non-Alzheimer's pathophysiology'. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer's disease. [ABSTRACT FROM AUTHOR]