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Background: Mild cognitive impairment (MCI) is a heterogeneous condition and MCI patients are at increased risk of progression to dementia due to Alzheimer’s disease (AD). Objective: In this study, we aim to evaluate the associations between polygenic risk scores (PRSs) and 1) time to AD progression from MCI, 2) changes in longitudinal cognitive impairment, and 3) biomarkers from cerebrospinal fluid and imaging. Methods: We constructed PRS by using 40 independent non-APOE SNPs from well-replicated AD GWASs and tested its association with the progression time from MCI to AD by using 767 MCI patients from the ADNI study and 1373 patients from the NACC study. PRSs calculated with other methods were also computed. Results: We found that the PRS constructed with SNPs that reached genome-wide significance predicted the progression from MCI to AD (beta = 0.182, SE = 0.061, p = 0.003) after adjusting for the demographic and clinical variables. This association was replicated in the NACC dataset (beta = 0.094, SE = 0.037, p = 0.009). Further analyses revealed that PRS was associated with the increased ADAS-Cog11/ADAS-Cog13/ADASQ4 scores, tau/ptau levels, and cortical amyloid burdens (PiB-PET and AV45-PET), but decreased hippocampus and entorhinal cortex volumes (p

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.

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.

Background: Blood plasma proteins have been associated with Alzheimer’s disease (AD), but understanding which proteins are on the causal pathway remains challenging. Objective: Investigate the genetic overlap between candidate proteins and AD using polygenic risk scores (PRS) and interrogate their causal relationship using bi-directional Mendelian randomization (MR). Methods: Following a literature review, 31 proteins were selected for PRS analysis. PRS were constructed for prioritized proteins with and without the apolipoprotein E region (APOE+/–PRS) and tested for association with AD status across three cohorts (n = 6,244). An AD PRS was also tested for association with protein levels in one cohort (n = 410). Proteins showing association with AD were taken forward for MR. Results: For APOE ɛ3, apolipoprotein B-100, and C-reactive protein (CRP), protein APOE+ PRS were associated with AD below Bonferroni significance (pBonf, p

Background: Abnormal default mode network (DMN) was associated with the progress of Alzheimer’s disease (AD). Rather than treat the DMN as a unitary network, it can be further divided into three subsystems with different functions. Objective: It remains unclear the interactions of DMN subsystems associated with the progress of cognitive impairments and AD pathological features. Methods: This study has recruited 187 participants, including test data and verification data. Firstly, an imaging analysis approach was utilized to investigate disease-related differences in the interactions of DMN subsystems in test data (n = 149), including 42 cognitively normal subjects, 43 early mild cognitive impairment (EMCI), 32 late mild cognitive impairment (LMCI), and 32 AD patients. Brain-behavior-pathological relationships regarding to the interactions among DMN subsystems were then further examined. Secondly, DMN subsystems abnormalities for classifying AD spectrum population in the independent verification data (n = 38). Results: This study found that the impaired cognition relates to disturbances in the interactions between DMN subsystems but preferentially in core subsystem, and the abnormal regulatory processes of core subsystem were significantly associated with the levels of cerebrospinal fluid Aβ and tau in AD-spectrum patients. Meantime, the nonlinear relationship between dysfunctional core subsystem and impaired cognition was observed as one progresses through the stages of MCI to AD. Importantly, this classification presented a higher sensitivity and specificity dependent on the core-centered connection abnormalities. Conclusion: The abnormal interaction patterns of DMN subsystems at an early stage of AD appeared and presented as core-centered connection abnormalities, which were the potential neuroimaging features for monitoring the development of AD.

Background: The pathophysiological process of amyloid-β, tau deposition, and neurodegeneration of Alzheimer’s disease (AD) begin in a preclinical phase, while anxiety is associated with an increased risk of AD in preclinical phase. Objective: To examine the relationships between anxiety and amyloid-β, tau deposition, and neurodegeneration. To test the hypothesis that anxiety could predict clinical progression in the elderly without dementia. Methods: 1,400 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were included in the study and were studied over a median period of 3 years. In multivariable models, the cross-sectional and longitudinal associations between anxiety and amyloid-β PET, tau PET, and FDG PET SUVRs in participants without dementia were explored using Spearman rank correlation, logistic regression model, multiple linear regression model, Kaplan-Meier survival curves, and Cox proportional hazards model. The association between baseline anxiety and clinical progression was also explored. Results: There was a positive correlation between anxiety and amyloid-β deposition (r = 0.11, p = 0.0017) and a negative correlation between anxiety and neurodegeneration (r = –0.13, p = 0.00022). MCI participants with anxiety showed a faster clinical progression of dementia (HR = 1.56, p = 0.04). Non-anxious participants with more amyloid-β deposition or more severe neurodegeneration displayed accelerated development into anxiety (HR = 2.352, p

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 ε4 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 cerebrovascular and neuronal changes, even after adjusting for well-established AD risk factors and in a sample with relatively low vascular risk burden.

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.

Background: It is suggested that not all individuals with elevated Aβ will develop dementia or cognitive impairment. Environment or lifestyle might modulate the association of amyloid pathology with cognition. Insomnia is a risk factor of cognitive disorders including Alzheimer’s disease. Objective: To investigate if insomnia moderated the relationship between amyloid-β (Aβ) and longitudinal cognitive performance in non-demented elders. Methods: A total of 385 Alzheimer’s Disease Neuroimaging Initiative participants (mean age = 73 years, 48% females) who completed 4 + neuropsychological evaluations and a [18F] florbetapir positron emission tomography scan were followed up to 8 years. Linear mixed-effects regression models were used to examine the interactions effect between insomnia and Aβ on longitudinal cognitive sores, including four domains (memory [MEM], executive function [EF], language [LAN], and visuospatial function [VS]). Results: The Aβ-positive status (A+) but not insomnia independently predicted faster cognitive decline in all domains. Furthermore, the relationship between Aβ and cognitive decline was moderated by insomnia (MEM: χ2 = 4.05, p = 0.044, EF: χ2 = 4.38, p = 0.036, LAN: χ2 = 4.56, p = 0.033, and VS: χ2 = 4.12, p = 0.042). Individuals with both elevated Aβ and insomnia experienced faster cognitive decline than those with only elevated Aβ or insomnia. Conclusion: These data reinforced the values of insomnia management in preventing dementia, possibly by interacting Aβ metabolism. Future efforts are warranted to determine whether sleep improvement will postpone the onset of dementia, specifically among populations in stages of preclinical or prodromal AD.

Background: Noninvasive identification of amyloid β (Aβ) is important in mild cognitive impairment (MCI) patients for better clinical management. This study aimed to evaluate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical and genetic profiles.Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to the training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from T1-weighted images of magnetic resonance imaging (MRI). A previously defined cutoff (< 192 pg/mL) was applied for CSF Aβ 42 status. After feature selection, random forest with subsampling methods were trained to predict the CSF Aβ 42 with three models: 1) a radiomics model; 2) a clinical model based on clinical and genetic profiles including demographics, APOE ε4 genotype, and neuropsychological tests; and 3) a combined model based on radiomics and clinical profiles. The prediction performance of the classifier was validated in the test set using the area under the receiver operating characteristic curve (AUC). Results: The radiomics model identified 33 radiomics features to predict CSF Aβ 42 , which showed an AUC of 0.674 in the best performing radiomics model in the test set. The clinical model identified 6 clinical features to predict CSF Aβ 42 , which showed an AUC of 0.758 in the best performing clinical model in the test set. The combined model based on radiomics and clinical profiles identified a total of 37 features (32 from radiomics and 5 from clinical features), showing an AUC of 0.823 in the best performing combined model test set, which showed the highest performance among the three models. Conclusions: Radiomics model from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for the invasive and costly Aβ test.

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

Background Effect of serum calcium level to the incidence of mild cognitive impairment (MCI) conversion to early Alzheimer's disease (AD) remains uncertain. Objective To investigate association between baseline serum calcium and the MCI conversion in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) study cohort. Methods In this sub-analysis of J-ADNI study, we reviewed data from MCI participants at baseline regarding their conversion to early AD during the 3 years of observation period and assessed the associated factors including serum calcium level. In addition, we compared our results from the J-ADNI study with the corresponding results from the North American (NA)-ADNI. Results Of 234 eligible MCI participants from the J-ADNI cohort, 121 (51.7%) converted to AD during the first 36 months of observation. Using univariate analysis, being female, having shorter years of education, and lower serum calcium level were correlated with increased risk of MCI-to-AD conversion exclusively in J-ADNI cohort. The lower corrected serum calcium level remained as one of conversion-associated factors in the J-ADNI cohort even after adjustment for multiple confounding variables, although this was not observed in the NA-ADNI cohort. Conclusion Our findings suggest that lower serum calcium may be associated with an increased risk of MCI conversion to AD in Japanese cohorts. The reason for this correlation remains unclear and further external validation using other Asian cohorts is needed. It would be interesting for future AD studies to obtain serum calcium levels and other related factors, such as vitamin D levels, culture-specific dietary or medication information.

Background: Posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are two of the most common variants of atypical Alzheimer’s disease (AD). Both PCA and LPA are associated with relative sparing of hippocampus compared to neocortex, although hippocampal atrophy is observed. It is unclear whether regional patterns of hippocampal subfield involvement differ between PCA and LPA, and whether they differ from typical AD. Objective: To assess volume of specific subfields of the hippocampus in PCA, LPA, and typical AD. Methods: Fifty-nine patients with PCA and 77 patients with LPA were recruited and underwent T1-weighted MRI and Pittsburgh Compound B (PiB) PET at Mayo Clinic. Thirty-six probable AD patients and 100 controls were identified from the Alzheimer’s Disease Neuroimaging Initiative. Hippocampal subfield volumes were calculated using Freesurfer, and volumes were compared between PCA, LPA, AD, and controls using Kruskal-Wallis and Dunn tests. Results: The LPA and PCA groups both showed the most striking abnormalities in CA4, presubiculum, molecular layer of the hippocampus, molecular and granule cell layers of the dentate gyrus, and the hippocampal-amygdala transition area, although atrophy was left-sided in LPA. PCA showed smaller volume of right presubiculum compared to LPA, with trends for smaller volumes of right parasubiculum and fimbria. LPA showed a trend for smaller volumes of left CA1 compared to PCA. The AD group showed smaller volumes of the right subiculum, CA1, and presubiculum compared to LPA. Conclusion: Patterns of hippocampal subfield atrophy differ across the different syndromic variants of AD.

Background: Agitation in Alzheimer’s disease (AD) has been hypothesized to be an expression of anxiety, but whether anxiety early in the course of dementia could be a risk factor for developing later agitation is unknown. Objective: We used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to examine the longitudinal relationship between anxiety and incident agitation in individuals with a diagnosis of AD at baseline or during follow-up. Methods: Longitudinal neuropsychiatric symptom data from AD individuals who were agitation-free at study baseline (N = 272) were analyzed using mixed effects regression models to test the longitudinal relationship between baseline and incident anxiety with incident agitation. Results: Anxiety at baseline was not associated with subsequent agitation, but there was a positive linear relationship between incident anxiety and agitation over the study duration. Baseline apathy and delusions were consistently associated with subsequent agitation and greater disease severity and illness duration also appeared to be risk factors for agitation. Conclusion: Our findings support the concept that anxiety and agitation are likely to be distinct rather than equivalent constructs in mild-moderate AD. Future longitudinal cohort studies are needed to replicate these findings and further characterize potential risk factors for agitation, such as apathy and delusions.

Background: As Alzheimer’s disease (AD) and cerebral small vessel disease (CSVD) commonly coexist, the interaction between two has been of the considerable interest. Objective: We determined whether the association of Aβ and tau with cognitive decline differs by the presence of significant CSVD. Methods: We included 60 subcortical vascular cognitive impairment (SVCI) from Samsung Medical Center and 82 Alzheimer’s disease-related cognitive impairment (ADCI) from ADNI, who underwent Aβ (florbetaben or florbetapir) and tau (flortaucipir, FTP) PET imaging. They were retrospectively assessed for 5.0±3.9 and 5.6±1.9 years with Clinical Dementia Rating-sum of boxes (CDR-SB)/Mini-Mental State Examination (MMSE). Mixed effects models were used to investigate the interaction between Aβ/tau and group on CDR-SB/MMSE changes. Results: The frequency of Aβ positivity (45% versus 54.9%, p = 0.556) and mean global FTP SUVR (1.17±0.21 versus 1.16±0.17, p = 0.702) were not different between the two groups. We found a significant interaction effect of Aβ positivity and SVCI group on CDR-SB increase/MMSE decrease (p = 0.013/p

Background: Hypometabolism detected by fluorodeoxyglucose F18 positron emission tomography ([18F] FDG PET) is an early neuropathologic changes in Alzheimer’s disease (AD) and provides important pathologic staging information. Objective: This study aimed to discover genetic interactions that regulate longitudinal glucose metabolic decline in AD-related brain regions. Methods: A total of 586 non-Hispanic white individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 1/GO/2 cohorts that met all quality control criteria were included in this study. Genome-wide association study of glucose metabolic decline in regions of interest (ROIs) was performed with linear regression under the additive genetic model. Results: We identified two novel variants that had a strong association with longitudinal metabolic decline in different ROI. Rs4819351-A in gene 1-acylglycerol-3-phosphate O-acyltransferase 3 (AGPAT3) demonstrated reduced metabolic decline in right temporal gyrus (p = 3.97×10–8, β= –0.016), while rs13387360-T in gene LOC101928196 demonstrated reduced metabolic decline in left angular gyrus (p = 1.69×10–8, β= –0.027). Conclusion: Our results suggest two genome-wide significant SNPs (rs4819351, rs13387360) in AGPAT3 and LOC101928196 as protective loci that modulate glucose metabolic decline. These two genes should be further investigated as potential therapeutic target for neurodegeneration diseases.

Background Although aging is the strongest risk factor for the development of Alzheimer's disease (AD), it remains uncertain if the blood DNA methylation clock, which reflects the effect of biological aging on DNA methylation (DNAme) status of blood cells, may be used as a surrogate biomarker for AD pathology in the central nervous system (CNS). Objective We aimed to develop a practical model to predict for A/T/N-based AD biomarkers as the prediction targets using the aging acceleration of blood cells. Methods We obtained data of North American ADNI study participants (n = 317) whose blood DNA methylation microarray (Illumina HumanMethylation EPIC Beadchips) and cerebrospinal fluid (CSF) AD biomarkers (Aβ, t-tau, and p-tau) were recorded simultaneously. Methylation clock was calculated to conduct machine learning, in order to predict binary statuses (+ or -) for A (corresponding to the lowered CSF Aβ), T (the elevated CSF p-tau), or N (the elevated CSF t-tau). The predictive performance of the models was evaluated by area under curve (AUC) in the test subset within ADNI. Results Among the 317 included samples, 194 (61.2%) were A+, 247 (77.9%) were T+, and 104 (32.8%) were N+. The degree of blood aging acceleration showed weak positive correlation with the CSF Aβ levels, even after adjustment with APOE genotype and other covariates. However, the contribution of aging acceleration to improve the predictive performance of models was not significant for any of A+, T+, or N+. Conclusion Our exploratory attempts could not demonstrate the substantial utility of the peripheral blood cells' methylation clock as a predictor for A/T/N-based CSF biomarkers of AD, and further additional work should be conducted to determine whether the blood DNAme signatures including methylation clock have substantial utility in detecting underlying amyloid, tau or neurodegeneration pathology of AD.

White matter hyperintensities (WMH) is mainly caused by cerebrovascular injury and may also increase the possibilities of progression to Alzheimer's disease. The present study aims to determine whether plasma neurofilament light (NFL) protein levels could predict the progression of WMH volume in elderly persons without dementia. The present study enrolled 1029 non-dementia participants from the Alzheimer's Disease Neuroimaging Initiative in which all had measurements of plasma NFL and WMH at baseline and 589 had longitudinal measurements during follow-up. Spearman correlation analyses and regression models were used to assess cross-sectional and longitudinal associations between plasma NFL and WMH. Plasma NFL concentration had a moderately strong correlation with WMH at baseline (r = 0.17, p < 0.001). Longitudinal analyses showed that higher baseline plasma NFL concentration was associated with accelerated progression of WMH (β=0.015, p = 0.007). Furthermore, higher change rates of plasma NFL could predict faster progression of WMH in the future (β=0.581, p = 0.002). The results of the study suggest that plasma NFL level might be used as a noninvasive biomarker to track variation trend in WMH in elderly persons without dementia.

BACKGROUND: Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer’s disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches. However, a key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge regards how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). OBJECTIVE: To address these two challenges, we propose a novel multi-task learning framework based on CNN. METHODS: First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog). RESULTS: We applied this novel CNN-MSCC system on the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods. CONCLUSION: Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.

BACKGROUND The role of cerebral microbleeds (CMBs) in cognitive impairment remains controversial. OBJECTIVE To investigate the possible links between the presence, progression, number, and location of CMBs and cognition. METHODS We assessed 792 subjects from the Alzheimer's Disease Neuroimaging Initiative who underwent both brain 3 Tesla MRI scans and cognitive testing. The association between CMBs and cognitive change was explored using linear mixed-effects models (LME). RESULTS Presence and number of CMBs were associated with memory (β= -0.03, p = 0.015; β= -0.01, p = 0.003), executive function (β= -0.04, p = 0.010; β= -0.01, p = 0.014), and global cognitive function (β= -0.06, p = 0.025; β= -0.03, p

Apolipoproteins (APOs) have been implicated in the pathogenesis of Alzheimer's disease (AD). In the present study, we aimed to investigate if patterns of cerebrospinal fluid (CSF) APOs (APOA-I, APOC-III, APOD, APOE, APOH, and APOJ) levels are associated with changes over time in cognition, memory performance, neuroimaging markers, and AD-related pathologies (CSF Aβ42, t-tau, and p-tau) in non-demented older adults. At baseline, a total of 241 non-demented older adults with CSF APOs data was included in the present analysis. Hierarchical agglomerative cluster analysis including the six CSF APOs was carried out. Among non-demented older adults, we identified two clusters. Compare with the first cluster, the second cluster had higher levels of APOs in CSF. Additionally, the second cluster showed a more benign disease course, including slower cognitive decline and slower p-tau accumulation in CSF. Our data highlight the importance of APOs in the pathogenesis of AD.

Background: APOE ɛ4 and sex have been linked to increased risk for conversion to Alzheimer’s disease (AD). However, the relationship between APOE ɛ4 gene dose, sex, and AD biomarkers remains understudied. Objective: To investigate the effect of APOE ɛ4 dose on AD biomarkers in a sample of older adults with mild cognitive impairment (MCI), and to examine whether APOE ɛ4 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 APOE ɛ4 alleles and stratified by sex. Results: Across all participants, number of APOE ɛ4 alleles was associated with smaller hippocampal and amygdala volumes and poorer cognition. When stratified by sex, women showed an APOE ɛ4 dose effect for bilateral hippocampal and left amygdala volumes and cognition. In contrast, men showed an APOE ɛ4 dose effect for hippocampal volumes with a trend in amygdala, but cognition did not differ between men with 1 and 2 APOE ɛ4 alleles. Women with 2 APOE ɛ4 alleles had poorer memory between 65–69 and poorer global cognition between 70–74 compared to men with 2 APOE ɛ4 alleles. Conclusion: APOE ɛ4 confers a dose effect on AD biomarkers in patients with MCI, and the number of APOE ɛ4 alleles has a greater detrimental impact in women than men, which may be specific to a critical time window.

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%

BACKGROUND: Clinical-pathological Alzheimer’s disease (AD) subtypes may help distill heterogeneity in patient presentation. To date, no studies have utilized neuropsychological and biological markers to identify preclinical subtypes with longitudinal stability. OBJECTIVE: The objective of this study was to empirically derive AD endophenotypes using a combination of cognitive and biological markers. METHODS: Hierarchical cluster analysis grouped dementia-free older adults using memory, executive and language abilities, and cerebrospinal fluid amyloid-(3 and phosphorylated tau. Brain volume differences, neuropsychological trajectory, and progression to dementia were compared, controlling for age, gender, education, and apolipoprotein E4 (ApoE4). RESULTS: Subgroups included asymptomatic-normal (n = 653) with unimpaired cognition and subthreshold biomarkers, typical AD (TAD; n = 191) showing marked memory decline, high ApoE4 rates and abnormal biomarkers, and atypical AD (AAD; n = 132) with widespread cognitive decline, intermediate biomarker levels, older age, less education and more white matter lesions. Cognitive profiles showed longitudinal stability with corresponding patterns of cortical atrophy, despite nearly identical rates of progression to AD dementia. CONCLUSION: Two clinical-pathological AD subtypes are identified with potential implications for preventative efforts. Keywords: Alzheimer’s disease, atypical AD, cluster analysis, heterogeneity, neuroimaging

Our aim was to examine whether the influence of apolipoprotein E4 (APOE4) genotype on cognitive decline differs in male and female across the Alzheimer's disease (AD) continuum. Among individuals with normal cognition (NC; n = 415), mild cognitive impairment (MCI; n = 870), and AD (n = 334), we investigated the longitudinal associations of APOE4 genotype and sex with cognitive decline over 13 years. Our cognitive outcomes were Rey Auditory Verbal Learning Test (RAVLT) total learning score and delayed recall and Mini-Mental State Examination (MMSE) score. There were significant effects of the APOE4×sex interaction on change in verbal memory in the MCI group, but not the NC or AD group. Specifically, among individuals with MCI, female APOE4 carriers had a steeper decline in RAVLT total learning score, but not delayed recall or MMSE score compared to all other groups (APOE4 + /Male, APOE4-/Female, APOE4-/Male). In conclusion, female APOE4 carriers have faster rates of memory decline than their male counterparts among MCI individuals.

Resting-state fMRI studies have demonstrated that Alzheimer's disease (AD) is associated with aberrant organization and function of large-scale brain networks. However, the nature of the disruption of cross-network interactions in the key neurocognitive networks in the brain remains unclear. In this paper, we examined the 'triple-network model', including the default mode network (DMN), salience network (SN), and central executive network (CEN), to identify the cross-network interactions in late mild cognitive impairment (LMCI) and AD. With resting-state fMRI, we tested cross-network functional connectivity among the DMN, SN, and CEN in 33 AD patients, 24 LMCI, and 25 well-matched normal control subjects. Then, we identified the most influential brain regions affected by AD and LMCI. Finally, we investigated the relationship between aberrant functional connectivity and clinical cognitive dysfunction. We found the cross-network functional connectivity of the SN-centered 'triple-network model' was significantly impaired in the AD group and the alterations were negatively correlated with the Mini-Mental State Examination (MMSE) scores. For the LMCI group, the functional connectivity of the SN-centered 'triple-network model' also changed compared to AD; however, we found no correlation with MMSE score. As predicted, the abnormal connections among the three networks mainly overlap with the key nodes of the three networks. Overall, our findings suggested that the interactions of the SN-centered 'triple-network model' are impaired in AD patients and that these alterations contribute to the decline in cognitive function. This 'triple-network model' provides new insights into AD and provides more information about the dysregulation of brain networks in AD.

BACKGROUND: Hippocampal atrophy (HA) is one of the biomarkers for Alzheimer's disease (AD).OBJECTIVE: To identify the best biomarkers and develop models for prediction of HA over 24 months using baseline data.METHODS: The study included healthy elderly controls, subjects with mild cognitive impairment, and subjects with AD, obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) databases. Predictor variables included cognitive and neuropsychological tests, amyloid-β, tau, and p-tau from cerebrospinal fluid samples, apolipoprotein E, and features extracted from magnetic resonance images (MRI). Least-mean-squares regression with elastic net regularization and least absolute deviation regression models were tested using cross-validation in ADNI 1. The generalizability of the models including only MRI features was evaluated by training the models with ADNI 1 and testing them with AIBL. The models including the full set of variables were not evaluated with AIBL because not all needed variables were available in it.RESULTS: The models including the full set of variables performed better than the models including only MRI features (root-mean-square error (RMSE) 1.76-1.82 versus 1.93-2.08). The MRI-only models performed well when applied to the independent validation cohort (RMSE 1.66-1.71). In the prediction of dichotomized HA (fast versus slow), the models achieved a reasonable prediction accuracy (0.79-0.87).CONCLUSIONS: These models can potentially help identifying subjects predicted to have a faster HA rate. This can help in selection of suitable patients into clinical trials testing disease-modifying drugs for AD.

Background Biomarkers may soon be used to predict decline in older individuals. Extended follow-up studies are needed to determine the stability of such biomarker-based predictions. Objective To examine the long-term performance of baseline cognitive, neuroimaging, and cerebrospinal fluid (CSF) biomarker-assisted prognosis in patients with mild cognitive impairment. Methods Established, biomarker-defined, cohorts of subjects with mild cognitive impairment were examined for progression to dementia. Subjects with a baseline volumetric MRI, lumbar puncture, and Rey Auditory Verbal Learning Test were included. Dementia-free survival time in each biomarker-defined risk group was determined with Kaplan-Meier survival curves. The influence of each risk factor or combination of factors on dementia-free survival was examined with Cox proportional hazard analyses. Results 185 subjects were followed longitudinally for a mean (SD) 4.3 (2.8) years. 59% of participants converted within the follow-up period and the median dementia-free survival time was 2.8 years. Each individual risk factor predicted conversion to dementia (HR 1.9-3.7). The joint presence of any two risk factors increased risk for conversion (HR 7.1-11.0), with the presence of medial temporal atrophy and memory impairment showing the greatest risk for decline. Concordant atrophy, memory impairment, and abnormal CSF amyloid and tau was associated with the highest risk for conversion (HR 15.1). The presence of medial temporal atrophy was associated with the shortest dementia-free survival time, both alone and in combination with memory impairment, abnormal CSF amyloid and tau, or both. Conclusion These results suggest that baseline biomarker-assisted predictions of decline to dementia are stable over the long term, and that combinations of complementary biomarkers can improve the accuracy of these predictions.

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.

Background Although a growing body of evidence shows an important role of apolipoproteins in the pathogenesis of Alzheimer's disease (AD), the association of apolipoprotein C-III (APOC-III) with cognitive decline is not clear. Objective To examine whether higher cerebrospinal fluid (CSF) and plasma APOC-III levels were associated with better cognitive performance over time in the early stage of AD. Methods Baseline CSF and plasma APOC-III levels were analyzed in relation to cross-sectionally and longitudinally cognitive performance over a 12-year period. Data were extracted from the Alzheimer's Disease Neuroimaging Initiative database, and 234 subjects (89 subjects with normal cognition (NC) and 145 subjects with mild cognitive impairment (MCI)) with CSF APOC-III measurements and 454 subjects (58 subjects with NC and 396 subjects with MCI) with plasma APOC-III measurements were included. Results In the cross-sectional study, we did not find a significant relationship between CSF APOC-III and cognitive performance in pooled individuals with MCI and NC. However, longitudinal analysis found that higher baseline CSF APOC-III was significantly associated with slower cognitive decline over a 12-year period in individuals with MCI, but not the healthy controls, after controlling for several covariates and Alzheimer biomarkers. Plasma APOC-III levels showed a mild correlation with CSF APOC-III levels, but were not associated with longitudinal cognitive changes in the pooled sample or in diagnosis-stratified analyses. Conclusions Higher CSF APOC-III levels are significantly associated with slower cognitive decline over a 12-year period among individuals with MCI.