MRI and CSF biomarkers in normal, MCI, and AD subjects: diagnostic discrimination and cognitive correlations

Neuroprotective potentials of Lead phytochemicals against Alzheimer's disease with focus on oxidative stress-mediated signaling pathways: Pharmacokinetic challenges, target specificity, clinical trials and future perspectives

BACKGROUND

Alzheimer's diseases (AD) and dementia are among the highly prevalent neurological disorders characterized by deposition of beta amyloid (Aβ) plaques, dense deposits of highly phosphorylated tau proteins, insufficiency of acetylcholine (ACh) and imbalance in glutamatergic system. Patients typically experience cognitive, behavioral alterations and are unable to perform their routine activities. Evidence also suggests that inflammatory processes including excessive microglia activation, high expression of inflammatory cytokines and release of free radicals. Thus, targeting inflammatory pathways beside other targets might be the key factors to control- disease symptoms and progression.

PURPOSE

This review is aimed to highlight the mechanisms and pathways involved in the neuroprotective potentials of lead phytochemicals. Further to provide updates regarding challenges associated with their use and their progress into clinical trials as potential lead compounds.

METHODS

Most recent scientific literature on pre-clinical and clinical data published in quality journals especially on the lead phytochemicals including curcumin, catechins, quercetin, resveratrol, genistein and apigenin was collected using SciFinder, PubMed, Google Scholar, Web of Science, JSTOR, EBSCO, Scopus and other related web sources.

RESULTS

Literature review indicated that the drug discovery against AD is insufficient and only few drugs are clinically approved which have limited efficacy. Among the therapeutic options, natural products have got tremendous attraction owing to their molecular diversity, their safety and efficacy. Research suggest that natural products can delay the disease onset, reduce its progression and regenerate the damage via their anti-amyloid, anti-inflammatory and antioxidant potentials. These agents regulate the pathways involved in the release of neurotrophins which are implicated in neuronal survival and function. Highly potential lead phytochemicals including curcumin, catechins, quercetin, resveratrol, genistein and apigenin regulate neuroprotective signaling pathways implicated in neurotrophins-mediated activation of tropomyosin receptor kinase (Trk) and p75 neurotrophins receptor (p75NTR) family receptors.

CONCLUSIONS

Phytochemicals especially phenolic compounds were identified as highly potential molecules which ameliorate oxidative stress induced neurodegeneration, reduce Aβ load and inhibit vital enzymes. Yet their clinical efficacy and bioavailability are the major challenges which need further interventions for more effective therapeutic outcomes.

Neurochemical Ameliorating of the Hippocampus in Dyslipidemic Alzheimer Patients Following Silymarin; a Double-Blind Placebo-Controlled Randomized Clinical Trial

Background

Amyloid-beta (Aβ) production is a normal physiological process, and an imbalance in Aβ production/excretion rate is the basis of the plaque load increase in AD. LRP1 is involved in both central clearance of Aβ from the CNS and transport of Aβ toward peripheral organs. In this study, the effect of silymarin combination compared to rosuvastatin and placebo on neuro-metabolites and serum levels of LRP1 and Aβ1-42 proteins and oxidative stress enzymes and lipid and cognitive tests of Iranian AD patients.

Methods

In this double-blind placebo-controlled study, thirty-six mild AD patients were divided into groups (n=12) of silymarin 140mg, placebo, and rosuvastatin 10mg. Medications were administered 3 times a day for 6 months. Clinical tests, lipid profile (TG, HDL, TC, and LDL), Aβ1-42, and LRP1 markers were measured at the beginning and end of the intervention. Magnetic resonance spectroscopy (MRS) was used to measure metabolites. Using SPSS software a one-way ANOVA test was used to compare the means of the quantitative variables and Pearson and Spearman's correlations to measure the correlation. GraphPad Prism software was used for drawing graphs. P < 0.05 was considered a significant.

Results

The levels of LRP1 and Aβ1-42 in the silymarin group were significantly increased compared to the other groups (P < 0.05). NAA/mI in the silymarin group had a significant increase compared to both placebo and rosuvastatin groups (P < 0.05). Right and left hippocampal mI/Cr directly correlated with TG (r = 0.603, P = 0.003 and r = 0.595, P = 0.004, respectively). NAA/Cr of the right and left hippocampus was inversely related to TG (r = -0.511, P = 0.0033, and r = -0.532, P = 0.0021, respectively). NAA/Cr and NAA/mI of bilateral hippocampi directly correlated with HDL (P < 0.05). An inverse correlation was observed between the Aβ1-42 and mI/Cr of the right and left hippocampus (r = -0.661, P = 0.000 and r = -0.638, P = 0.000, respectively).

Conclusion

Donepezil and silymarin improved lipid profile associated with increased NAA/Cr, and decreased mI/Cr, in AD patients. Biomarker NAA/mI can be clinically significant in examining AD pathology. Measurement of the lipid factors and neurometabolites can be a suitable method for monitoring this disease.

Detecting conversion from mild cognitive impairment to Alzheimer's disease using FLAIR MRI biomarkers

Mild cognitive impairment (MCI) is the prodromal phase of Alzheimer's disease (AD) and while it presents as an imperative intervention window, it is difficult to detect which subjects convert to AD (cMCI) and which ones remain stable (sMCI). The objective of this work was to investigate fluid-attenuated inversion recovery (FLAIR) MRI biomarkers and their ability to differentiate between sMCI and cMCI subjects in cross-sectional and longitudinal data. Three types of biomarkers were investigated: volume, intensity and texture. Volume biomarkers included total brain volume, cerebrospinal fluid volume (CSF), lateral ventricular volume, white matter lesion volume, subarachnoid CSF, and grey matter (GM) and white matter (WM), all normalized to intracranial volume. The mean intensity, kurtosis, and skewness of the GM and WM made up the intensity features. Texture features quantified homogeneity and microstructural tissue changes of GM and WM regions. Composite indices were also considered, which are biomarkers that represent an aggregate sum (z-score normalization and summation) of all biomarkers. The FLAIR MRI biomarkers successfully identified high-risk subjects as significant differences (p < 0.05) were found between the means of the sMCI and cMCI groups and the rate of change over time for several individual biomarkers as well as the composite indices for both cross-sectional and longitudinal analyses. Classification accuracy and feature importance analysis showed volume biomarkers to be most predictive, however, best performance was obtained when complimenting the volume biomarkers with the intensity and texture features. Using all the biomarkers, accuracy of 86.2 % and 69.2 % was achieved for normal control-AD and sMCI-cMCI classification respectively. Survival analysis demonstrated that the majority of the biomarkers showed a noticeable impact on the AD conversion probability 4 years prior to conversion. Composite indices were the top performers for all analyses including feature importance, classification, and survival analysis. This demonstrated their ability to summarize various dimensions of disease into single-valued metrics. Significant correlation (p < 0.05) with phosphorylated-tau and amyloid-beta CSF biomarkers was found with all the FLAIR biomarkers. The proposed biomarker system is easily attained as FLAIR is routinely acquired, models are not computationally intensive and the results are explainable, thus making this pipeline easily integrated into clinical workflow.

Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach

BACKGROUND

Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer's disease (AD) biomarkers over time.

METHODS

Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies.

RESULTS

Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile.

CONCLUSIONS

Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification.

Literature review on the interdisciplinary biomarkers of multi-target and multi-time herbal medicine therapy to modulate peripheral systems in cognitive impairment

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease characterized by the deposition of amyloid-beta (Aβ) peptide and neurofibrillary tangles in the brain. The approved drug for AD has certain limitations such as a short period of cognitive improvement effect; moreover, the development of drug for AD therapeutic single target for Aβ clearance in brain ended in failure. Therefore, diagnosis and treatment of AD using a multi-target strategy according to the modulation of the peripheral system, which is not only limited to the brain, is needed. Traditional herbal medicines can be beneficial for AD based on a holistic theory and personalized treatment according to the time-order progression of AD. This literature review aimed to investigate the effectiveness of herbal medicine therapy based on syndrome differentiation, a unique theory of traditional diagnosis based on the holistic system, for multi-target and multi-time treatment of mild cognitive impairment or AD stage. Possible interdisciplinary biomarkers including transcriptomic and neuroimaging studies by herbal medicine therapy for AD were investigated. In addition, the mechanism by which herbal medicines affect the central nervous system in connection with the peripheral system in an animal model of cognitive impairment was reviewed. Herbal medicine may be a promising therapy for the prevention and treatment of AD through a multi-target and multi-time strategy. This review would contribute to the development of interdisciplinary biomarkers and understanding of the mechanisms of action of herbal medicine in AD.

Statistical Parametric Mapping in Amyloid Positron Emission Tomography

Alzheimer's disease (AD), the most common cause of dementia, has limited treatment options. Emerging disease modifying therapies are targeted at clearing amyloid-β (Aβ) aggregates and slowing the rate of amyloid deposition. However, amyloid burden is not routinely evaluated quantitatively for purposes of disease progression and treatment response assessment. Statistical Parametric Mapping (SPM) is a technique comparing single-subject Positron Emission Tomography (PET) to a healthy cohort that may improve quantification of amyloid burden and diagnostic performance. While primarily used in 2-[18F]-fluoro-2-deoxy-D-glucose (FDG)-PET, SPM's utility in amyloid PET for AD diagnosis is less established and uncertainty remains regarding optimal normal database construction. Using commercially available SPM software, we created a database of 34 non-APOE ε4 carriers with normal cognitive testing (MMSE > 25) and negative cerebrospinal fluid (CSF) AD biomarkers. We compared this database to 115 cognitively normal subjects with variable AD risk factors. We hypothesized that SPM based on our database would identify more positive scans in the test cohort than the qualitatively rated [11C]-PiB PET (QR-PiB), that SPM-based interpretation would correlate better with CSF Aβ42 levels than QR-PiB, and that regional z-scores of specific brain regions known to be involved early in AD would be predictive of CSF Aβ42 levels. Fisher's exact test and the kappa coefficient assessed the agreement between SPM, QR-PiB PET, and CSF biomarkers. Logistic regression determined if the regional z-scores predicted CSF Aβ42 levels. An optimal z-score cutoff was calculated using Youden's index. We found SPM identified more positive scans than QR-PiB PET (19.1 vs. 9.6%) and that SPM correlated more closely with CSF Aβ42 levels than QR-PiB PET (kappa 0.13 vs. 0.06) indicating that SPM may have higher sensitivity than standard QR-PiB PET images. Regional analysis demonstrated the z-scores of the precuneus, anterior cingulate and posterior cingulate were predictive of CSF Aβ42 levels [OR (95% CI) 2.4 (1.1, 5.1) p = 0.024; 1.8 (1.1, 2.8) p = 0.020; 1.6 (1.1, 2.5) p = 0.026]. This study demonstrates the utility of using SPM with a "true normal" database and suggests that SPM enhances diagnostic performance in AD in the clinical setting through its quantitative approach, which will be increasingly important with future disease-modifying therapies.

Association of Depressive Symptoms and Cognition in Older Adults Without Dementia Across Different Biomarker Profiles

BACKGROUND

Depression is a late-life risk factor for cognitive decline. Evidence suggests an association between Alzheimer's disease (AD) associated pathologic changes and depressive symptoms.

OBJECTIVE

To investigate the influence of AT(N) biomarker profile (amyloid-β [A], p-tau [T], and neurodegeneration [N]) and gender on cross-sectional associations between subclinical depressive symptoms and cognitive function among older adults without dementia.

METHODS

Participants included 868 individuals without dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Depressive symptoms were measured using the Geriatric Depression Scale (GDS). ADNI neuropsychological composite scores assessed memory and executive function (EF). PET, cerebrospinal fluid, and MRI modalities classified the study sample into biomarker profiles: normal biomarkers (A-T-N-), AD continuum (A+T±N±), and suspect non-AD pathology (SNAP; A-T±N-or A-T-N±). Multivariate regression models were used to investigate associations between GDS and cognitive domains.

RESULTS

GDS was negatively associated with memory (β= -0.156, p < 0.001) and EF (β= -0.147, p < 0.001) in the whole sample. When classified by biomarker profile, GDS was negatively associated with memory and EF in AD continuum (memory: β= -0.174, p < 0.001; EF: β= -0.129 p = 0.003) and SNAP (memory: β= -0.172, p = 0.005; EF: β= -0.197, p = 0.001) subgroups. When stratified by sex, GDS was negatively associated with memory (β= -0.227, p < 0.001) and EF (β= -0.205, p < 0.001) in men only.

CONCLUSION

The association between subclinical depressive symptoms and cognitive function is highly influenced by the AT(N) biomarker profile.

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Background:

The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease.

Objective:

To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques.

Methods:

We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques.

Results:

Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients.

Conclusion:

Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

AD-Related CSF Biomarkers Across Distinct Levels of Cognitive Impairment: Correlations With Global Cognitive State

AIM

Associations between cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) with the severity of cognitive impairment are unclear. We examined the correlations between CSF biomarkers and cognitive performance in the AD continuum.

METHODS

We studied 143 elderly patients: cognitively unimpaired (n = 51), mild cognitive impairment (MCI) amnestic (n = 55) and nonamnestic (n = 20), and mild AD (n = 17) assessed with the Cambridge Cognitive Test (CAMCOG). We correlated total CAMCOG and its subdomains with CSF Aβ42, T-tau, p-tau levels, and Aβ42/p-tau.

RESULTS

In the total sample, T-tau and Aβ42/p-tau correlated with the total CAMCOG (P < .01); all biomarkers correlated with memory (P < .001); T-tau correlated with language (P < .01).

CONCLUSION

Memory and T-tau levels may be the most suitable parameters to reflect cognitive/CSF biomarker correlations. At present, such correlations are of little use in routine clinical practice.

A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease

Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Recently, many sMRI‐based deep learning methods have been developed for AD diagnosis. Some of these methods utilize neural networks to extract high‐level representations on the basis of handcrafted features, while others attempt to learn useful features from brain regions proposed by a separate module. However, these methods require considerable manual engineering. Their stepwise training procedures would introduce cascading errors. Here, we propose the parallel attention‐augmented bilinear network, a novel deep learning framework for AD diagnosis. Based on a 3D convolutional neural network, the framework directly learns both global and local features from sMRI scans without any prior knowledge. The framework is lightweight and suitable for end‐to‐end training. We evaluate the framework on two public datasets (ADNI‐1 and ADNI‐2) containing 1,340 subjects. On both the AD classification and mild cognitive impairment conversion prediction tasks, our framework achieves competitive results. Furthermore, we generate heat maps that highlight discriminative areas for visual interpretation. Experiments demonstrate the effectiveness of the proposed framework when medical priors are unavailable or the computing resources are limited. The proposed framework is general for 3D medical image analysis with both efficiency and interpretability.

Multi-auxiliary domain transfer learning for diagnosis of MCI conversion

In the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has a higher risk of progression to AD, so the prediction of whether an MCI subject will progress to AD (known as progressive MCI, PMCI) or not (known as stable MCI, SMCI) within a certain period is particularly important in practice. It is known that such a task could benefit from jointly learning-related auxiliary tasks such as differentiating AD from PMCI or PMCI from normal control (NC) in order to take full advantage of their shared commonality. However, few existing methods along this line fully consider the correlations between the target and auxiliary tasks according to the clinical practice of AD pathology for diagnosis. To deal with this problem, in this paper, treating each task domain as a different one, we borrow the idea from transfer learning and propose a novel multi-auxiliary domain transfer learning (MaDTL) method, which explicitly utilizes the correlations between the target domain (task) and multi-auxiliary domains (tasks) according to the clinical practice. Specifically, the proposed MaDTL method incorporates two key modules. The first one is a multi-auxiliary domain transfer-based feature selection (MaDTFS) model, which can select a discriminative feature subset shared by the target domain and the multi-auxiliary domains. In the MaDTFS model, to combine more training data from multi-auxiliary domains and simultaneously suppress the negative effects resulting from the irrelevant parts of multi-auxiliary domains, we proposed a sparse group correlation Lasso that includes a proposed group correlation Lasso penalty (i.e., [Formula: see text]) and a proposed correlation Lasso penalty (i.e., [Formula: see text]). The second module in MaDTL is a multi-auxiliary domain transfer-based classification (MaDTC) model that improves the voting with linear weighting-based ensemble learning. This model extends the constraints of the linear weighting method so that it can simultaneously combine training data from multi-auxiliary domains and achieve a robust classifier by minimizing negative effects from the irrelevant part of multi-auxiliary domains. Experimental results on 409 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with the baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data validate the effectiveness of the proposed method by significantly improving the classification accuracy to 80.37% for the identification of MCI-to-AD conversion, outperforming the state-of-the-art methods.

[Predictive Diagnosis of Alzheimer's Dementia]

Expanding technologies of early disease detection allow to identify Alzheimer's disease (AD) long before symptom onset. Hence, patients are increasingly demanding for these diagnostic procedures. Biomarker-based early detection of AD is therefore increasingly important in the clinical work-up. This article gives an overview of predictive procedures in the field of Alzheimer's dementia.

Cerebral Blood Flow Predicts Conversion of Mild Cognitive Impairment into Alzheimer's Disease and Cognitive Decline: An Arterial Spin Labeling Follow-up Study

BACKGROUND

This is the first longitudinal study to assess regional cerebral blood flow (rCBF) changes during the progression from normal control (NC) through mild cognitive impairment (MCI) and Alzheimer's disease (AD).

OBJECTIVE

We aim to determine if perfusion MRI biomarkers, derived from our prior cross-sectional study, can predict the onset and cognitive decline of AD.

METHODS

Perfusion MRIs using arterial spin labeling (ASL) were acquired in 15 stable-NC, 14 NC-to-MCI, 16 stable-MCI, and 18 MCI/AD-to-AD participants from the Cardiovascular Health Study (CHS) cognition study. Group comparisons, predictions of AD conversion and time to conversion, and Modified Mini-Mental State Examination (3MSE) from rCBF were performed.

RESULTS

Compared to the stable-NC group: 1) the stable-MCI group exhibited rCBF decreases in the right temporoparietal (p = 0.00010) and right inferior frontal and insula (p = 0.0094) regions; and 2) the MCI/AD-to-AD group exhibited rCBF decreases in the bilateral temporoparietal regions (p = 0.00062 and 0.0035). Compared to the NC-to-MCI group, the stable-MCI group exhibited a rCBF decrease in the right hippocampus region (p = 0.0053). The baseline rCBF values in the posterior cingulate cortex (PCC) (p = 0.0043), bilateral superior medial frontal regions (BSMF) (p = 0.012), and left inferior frontal (p = 0.010) regions predicted the 3MSE scores for all the participants at follow-up. The baseline rCBF in the PCC and BSMF regions predicted the conversion and time to conversion from MCI to AD (p < 0.05; not significant after multiple corrections).

CONCLUSION

We demonstrated the feasibility of ASL in detecting rCBF changes in the typical AD-affected regions and the predictive value of baseline rCBF on AD conversion and cognitive decline.

Comparison of Diagnostic Performances Between Cerebrospinal Fluid Biomarkers and Amyloid PET in a Clinical Setting

The diagnostic performances of cerebrospinal fluid (CSF) biomarkers and amyloid positron emission tomography (PET) were compared by examining the association and concordance or discordance between CSF Aβ1-42 and amyloid PET, after determining our own cut-off values for CSF Alzheimer's disease (AD) biomarkers. Furthermore, we evaluated the ability of CSF biomarkers and amyloid PET to predict clinical progression. CSF Aβ1-42, t-tau, and p-tau levels were analyzed in 203 individuals [27 normal controls, 38 mild cognitive impairment (MCI), 62 AD dementia, and 76 patients with other neurodegenerative diseases] consecutively recruited from two dementia clinics. We used both visual and standardized uptake value ratio (SUVR)-based amyloid PET assessments for analyses. The association of CSF biomarkers with amyloid PET SUVR, hippocampal atrophy, and cognitive function were investigated by linear regression analysis, and the risk of conversion from MCI to AD dementia was assessed using a Cox proportional hazards model. CSF p-tau/Aβ1-42 and t-tau/Aβ1-42 exhibited the best diagnostic accuracies among the CSF AD biomarkers examined. Correlations were observed between CSF biomarkers and global SUVR, hippocampal volume, and cognitive function. Overall concordance and discordance between CSF Aβ1-42 and amyloid PET was 77% and 23%, respectively. Baseline positive CSF Aβ1-42 for MCI demonstrated a 5.6-fold greater conversion risk than negative CSF Aβ1-42 .  However, amyloid PET findings failed to exhibit significant prognostic value. Therefore, despite presence of a significant correlation between the CSF Aβ1-42 level and SUVR of amyloid PET, and a relevant concordance between CSF Aβ1-42 and amyloid PET, baseline CSF Aβ1-42 better predicted AD conversion.

A Review of Brain Atrophy Subtypes Definition and Analysis for Alzheimer’s Disease Heterogeneity Studies

Alzheimer’s disease (AD) is a heterogeneous disease with different subtypes. Studying AD subtypes from brain structure, neuropathology, and cognition are of great importance for AD heterogeneity research. Starting from the study of constructing AD subtypes based on the features of T1-weighted structural magnetic resonance imaging, this paper introduces the major connections between the subtype definition and analysis strategies, including brain region-based subtype definition, and their demographic, neuropathological, and neuropsychological characteristics. The advantages and existing problems are analyzed, and reasonable improvement schemes are prospected. Overall, this review offers a more comprehensive view in the field of atrophy subtype in AD, along with their advantages, challenges, and future prospects, and provide a basis for improving individualized AD diagnosis.

Quantitative Methods for the Detection of Tau Seeding Activity in Human Biofluids

The ability of tau aggregates to recruit and misfold monomeric tau and propagate across brain regions has been studied extensively and is now recognized as a critical pathological step in Alzheimer’s disease (AD) and other tauopathies. Recent evidence suggests that the detection of tau seeds in human samples may be relevant and correlate with clinical data. Here, we review the available methods for the measurement of such tau seeds, their limitations and their potential implementation for the development of the next-generation biomarkers.

Discordant Alzheimer's neurodegenerative biomarkers and their clinical outcomes

OBJECTIVE

In the 2018 ATN framework, Alzheimer's neurodegenerative biomarkers comprised cerebrospinal fluid (CSF) total tau, 18 F-fluorodeoxyglucose-positron emission tomography, and brain atrophy. We aimed to assess the clinical outcomes of having discordant Alzheimer's neurodegenerative biomarkers.

METHODS

A total of 721 non-demented individuals from the Alzheimer's Disease Neuroimaging Initiative database were included and then further categorized into concordant-negative, discordant, and concordant-positive groups. Demographic distributions of the groups were compared. Longitudinal changes in clinical outcomes and risk of conversion were assessed using linear mixed-effects models and multivariate Cox proportional hazard models, respectively.

RESULTS

Discordant group was intermediate to concordant-negative and concordant-positive groups in terms of APOE ε4 positivity, CSF amyloid-beta, and phosphorylated tau. Compared with concordant-negative group, discordant group deteriorated faster in cognitive scores (Mini-Mental State Examination, the Clinical Dementia Rating Scale-Sum of Boxes, and the Functional Activities Questionnaire) and demonstrated greater rates of atrophy in brain structures (hippocampus, entorhinal cortex, and whole brain), and concordant-positive group performed worse over time than discordant group. Moreover, the risk of cognitive decline increased from concordant-negative to discordant to concordant-positive. The results from longitudinal analyses were validated in A+T+, cognitively normal, and mild cognitive impairment individuals, and were also validated by applying different cutoffs and neurodegenerative biomarkers.

INTERPRETATION

Discordant neurodegenerative status denotes a stage of cognitive function which is intermediate between concordant-negative and concordant-positive. Identification of discordant cases would provide insights into intervention and new therapy approaches, particularly in A+T+ individuals. Moreover, this work may be a complement to the ATN scheme.

Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease

Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.

Effects of polygenic risk for Alzheimer's disease on rate of cognitive decline in normal aging

Most people's cognitive abilities decline with age, with significant and partly genetically driven, individual differences in rate of change. Although APOE ɛ4 and genetic scores for late-onset Alzheimer's disease (LOAD) have been related to cognitive decline during preclinical stages of dementia, there is limited knowledge concerning genetic factors implied in normal cognitive aging. In the present study, we examined three potential genetic predictors of age-related cognitive decline as follows: (1) the APOE ɛ4 allele, (2) a polygenic score for general cognitive ability (PGS-cog), and (3) a polygenic risk score for late-onset AD (PRS-LOAD). We examined up to six time points of cognitive measurements in the longitudinal population-based Betula study, covering a 25-year follow-up period. Only participants that remained alive and non-demented until the most recent dementia screening (1-3 years after the last test occasion) were included (n = 1087). Individual differences in rate of cognitive change (composite score) were predicted by the PRS-LOAD and APOE ɛ4, but not by PGS-cog. To control for the possibility that the results reflected a preclinical state of Alzheimer's disease in some participants, we re-ran the analyses excluding cognitive data from the last test occasion to model cognitive change up-until a minimum of 6 years before potential onset of clinical Alzheimers. Strikingly, the association of PRS-LOAD, but not APOE ɛ4, with cognitive change remained. The results indicate that PRS-LOAD predicts individual difference in rate of cognitive decline in normal aging, but it remains to be determined to what extent this reflects preclinical Alzheimer's disease brain pathophysiology and subsequent risk to develop the disease.

SWATH-MS analysis of cerebrospinal fluid to generate a robust battery of biomarkers for Alzheimer's disease

Cerebrospinal fluid (CSF) Aβ42 and tau protein levels are established diagnostic biomarkers of Alzheimer's disease (AD). However, their inadequacy to represent clinical efficacy in drug trials indicates the need for new biomarkers. Sequential window acquisition of all theoretical fragment ion spectra (SWATH)-based mass spectrometry (MS) is an advanced proteomic tool for large-scale, high-quality quantification. In this study, SWATH-MS showed that VGF, chromogranin-A, secretogranin-1, and opioid-binding protein/cell adhesion molecule were significantly decreased in 42 AD patients compared to 39 controls, whereas 14-3-3ζ was increased (FDR < 0.05). In addition, 16 other proteins showed substantial changes (FDR < 0.2). The expressions of the top 21 analytes were closely interconnected, but were poorly correlated with CSF Aβ42, tTau, and pTau181 levels. Logistic regression analysis and data mining were used to establish the best algorithm for AD, which created novel biomarker panels with high diagnostic value (AUC = 0.889 and 0.924) and a strong correlation with clinical severity (all p < 0.001). Targeted proteomics was used to validate their usefulness in a different cohort (n = 36) that included patients with other brain disorders (all p < 0.05). This study provides a list of proteins (and combinations thereof) that could serve as new AD biomarkers.

Neurofeedback Improves Memory and Peak Alpha Frequency in Individuals with Mild Cognitive Impairment

Mild cognitive impairment (MCI) is a syndrome characterized by a decrease in cognitive abilities, while daily function is maintained. This condition, which is associated with an increased risk for the development of Alzheimer's disease, has no known definitive treatment at present. In this open-label pilot study we explored the possible benefits of neurofeedback for subjects with MCI. Eleven participants diagnosed with MCI were trained to increase the power of their individual upper alpha band of the electroencephalogram (EEG) signal over the central parietal region. This was achieved using an EEG-based neurofeedback training protocol. Training comprised ten 30-min sessions delivered over 5 weeks. Cognitive and electroencephalographic assessments were conducted before and after training and at 30 days following the last training session. A dose-dependent increase in peak alpha frequency was observed throughout the period of training. Memory performance also improved significantly following training, and this improvement was maintained at 30-day follow-up, while peak alpha frequency returned to baseline at this evaluation. Our findings suggest that neurofeedback may improve memory performance in subjects with mild cognitive impairment, and this benefit may be maintained beyond the training period.

Cerebro-spinal fluid biomarker levels: phosphorylated tau (T) and total tau (N) as markers for rate of progression in Alzheimer's disease

Background

We investigated the potential associations between cerebro-spinal fluid (CSF) levels of phosphorylated tau (P-tau) and total tau (T-tau) with short-term response to cholinesterase inhibitor (ChEI) treatment, longitudinal outcome and progression rates in Alzheimer’s disease (AD).

Methods

This prospective, observational study included 129 participants clinically diagnosed with mild-to-moderate AD, who underwent a lumbar puncture. The CSF biomarkers amyloid-β_1–42 (Aβ₄₂), P-tau and T-tau were analysed with xMAP technology. Cognitive, global, instrumental and basic activities of daily living (ADL) capacities at the start of ChEI therapy and semi-annually over 3 years were evaluated.

Results

All patients had abnormal Aβ₄₂ (A+). Fifty-eight individuals (45%) exhibited normal P-tau and T-tau (A+ T– (N)–), 12 (9%) abnormal P-tau/normal T-tau (A+ T+ (N)–), 17 (13%) normal P-tau/abnormal T-tau (A+ T– (N)+) and 42 (33%) abnormal P-tau and T-tau (A+ T+ (N)+). The participants with A+ T+ (N)+ were younger than A+ T– (N)+ at the estimated onset of AD and the initiation of ChEIs. The proportion of 6-month responders to ChEI and deterioration/year after start of treatment did not differ between the AT(N) profiles in any scales. A higher percentage of globally improved/unchanged patients was exhibited in the A+ T– (N)– group after 12, 30 and 36 months of ChEI therapy but not at other assessments. In apolipoprotein E (APOE) ε4-carriers, linear relationships were found between greater cognitive decline/year and higher tau; Mini-Mental State Examination score – T-tau (r_s = − 0.257, p = 0.014) and Alzheimer’s Disease Assessment Scale–cognitive subscale – P-tau (r_s = − 0.242, p = 0.022). A correlation between faster progression in instrumental ADL (IADL) and higher T-tau was also detected (r_s = − 0.232, p = 0.028). These associations were not demonstrated in non-ε4-carriers.

Conclusions

Younger age and faster global deterioration were observed in AD patients with pathologic tau and neurodegeneration, whereas more rapid cognitive and IADL decline were related to higher P-tau or T-tau in APOE ε4-carriers only. The results might indicate an association between more pronounced tau pathology/neuronal injury and the APOE ε4-allele leading to a worse prognosis. Our findings showed that the AT(N) biomarker profiles have limited utility to predict AD progression rates and, thus, measure change and interpreting outcomes from clinical trials of future therapies.

Body mass index is associated with smaller medial temporal lobe volume in those at risk for Alzheimer's disease

Body mass index (BMI) has a complex relationship with Alzheimer's disease (AD); in midlife, high BMI is associated with increased risk for AD, whereas the relationship in late-life is still unclear. To clarify the relationship between late-life BMI and risk for AD, this study examined the extent to which genetic predisposition for AD moderates BMI and AD-related biomarker associations. Participants included 126 cognitively normal older adults at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Genetic risk for AD was assessed via polygenic hazard score. AD-related biomarkers assessed were medial temporal lobe volume and cerebrospinal fluid (CSF) biomarkers. Hierarchical linear regressions were implemented to examine the effects of BMI and polygenic hazard score on AD-related biomarkers. Results showed that BMI moderated the relationship between genetic risk for AD and medial temporal lobe volume, such that individuals with high BMI and high genetic risk for AD showed lower volume in the entorhinal cortex and hippocampus. In sex-stratified analyses, these results remained significant only in females. Finally, BMI and genetic risk for AD were independently associated with CSF biomarkers of AD. These results provide evidence that high BMI is associated with lower volume in AD-vulnerable brain regions in individuals at genetic risk for AD, particularly females. The genetic pathways of AD may be exacerbated by high BMI. Environmental and genetic risk factors rarely occur in isolation, which underscores the importance of looking at their synergistic effects, as they provide insight into early risk factors for AD that prevention methods could target.

Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers

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

Preoperative Phosphorylated Tau Concentration in the Cerebrospinal Fluid Can Predict Cognitive Function Three Years after Shunt Surgery in Patients with Idiopathic Normal Pressure Hydrocephalus

Background:

Idiopathic normal pressure hydrocephalus (iNPH) is commonly treated by cerebrospinal fluid (CSF) shunting. However, the long-term efficacy of shunt intervention in the presence of comorbid Alzheimer’s disease (AD) pathology is debated.

Objective:

To identify AD-associated CSF biomarkers predictive of shunting surgery outcomes in patients with iNPH.

Methods:

Preoperative levels of total and phosphorylated Tau (p-Tau) were measured in 40 patients with iNPH divided into low (<30 pg/mL) and high (≥30 pg/mL) p-Tau groups and followed up for three years after lumboperitoneal shunting. The modified Rankin Scale (mRS), Mini-Mental State Examination (MMSE), Frontal Assessment Battery, and iNPH Grading Scale scores were compared between the age-adjusted low (n = 24; mean age 75.7 years [SD 5.3]) and high (n = 11; mean age 76.0 years [SD 5.6]) p-Tau groups.

Results:

Cognitive function improved early in the low p-Tau group and was maintained thereafter (p = 0.005). In contrast, the high p-Tau group showed a gradual decline to baseline levels by the third postoperative year (p = 0.040). Although the p-Tau concentration did not correlate with the preoperative MMSE score, a negative correlation appeared and strengthened during follow-up (R² = 0.352, p < 0.001). Furthermore, the low p-Tau group showed rapid and sustained mRS grade improvement, whereas mRS performance gradually declined in the high p-Tau group.

Conclusions:

Preoperative CSF p-Tau concentration predicted some aspects of cognitive function after shunt intervention in patients with iNPH. The therapeutic effects of shunt treatment were shorter-lasting in patients with coexisting AD pathology.

Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease: A Predictive Study

Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g.: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows us to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e., the capacity of predicting biomarker values) is assessed in a cross-validation framework.

Subtle Cognitive Impairment and Alzheimer's Disease-Type Pathological Changes in Cerebrospinal Fluid are Common Among Neurologically Healthy Subjects

BACKGROUND

The neuropathology of Alzheimer's disease (AD) has previously been shown to be rather common among the elderly.

OBJECTIVE

The aim of this study was to inspect the associations between cerebrospinal fluid (CSF) AD biomarker concentrations, age, the APOEɛ4 allele, cardiovascular diseases, diabetes, and cognitive performance in a cohort of a neurologically healthy population.

METHODS

This study included 93 subjects (42 men, mean age 67 years) without previous neurological symptoms or subjective cognitive complaints. Their cognition was assessed, and CSF biomarkers and APOEɛ4 status were analyzed.

RESULTS

Of the studied subjects, 8.6% (n = 8) had a pathological CSF AD biomarker profile. An increase in age correlated positively with CSF tau pathology and negatively with global cognitive performance.

CONCLUSION

AD-type pathological changes in CSF and subtle cognitive impairment are common within a population with no previous memory complaints. Age was the main risk factor for the changes.

Robust multi-label transfer feature learning for early diagnosis of Alzheimer's disease

Transfer learning has been successfully used in the early diagnosis of Alzheimer's disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods.

Brain MR Radiomics to Differentiate Cognitive Disorders

OBJECTIVE

Subtle and gradual changes occur in the brain years before cognitive impairment due to age-related neurodegenerative disorders. The authors examined the utility of hippocampal texture analysis and volumetric features extracted from brain magnetic resonance (MR) data to differentiate between three cognitive groups (cognitively normal individuals, individuals with mild cognitive impairment, and individuals with Alzheimer's disease) and neuropsychological scores on the Clinical Dementia Rating (CDR) scale.

METHODS

Data from 173 unique patients with 3-T T₁-weighted MR images from the Alzheimer's Disease Neuroimaging Initiative database were analyzed. A variety of texture and volumetric features were extracted from bilateral hippocampal regions and were used to perform binary classification of cognitive groups and CDR scores. The authors used diagonal quadratic discriminant analysis in a leave-one-out cross-validation scheme. Sensitivity, specificity, and area under the receiver operating characteristic curve were used to assess the performance of models.

RESULTS

The results show promise for hippocampal texture analysis to distinguish between no impairment and early stages of impairment. Volumetric features were more successful at differentiating between no impairment and advanced stages of impairment.

CONCLUSIONS

MR radiomics may be a promising tool to classify various cognitive groups.

The 12-Word Philadelphia Verbal Learning Test Performances in Older Adults: Brain MRI and Cerebrospinal Fluid Correlates and Regression-Based Normative Data

Background/Aims

This study evaluated neuroimaging and biological correlates, psychometric properties, and regression-based normative data of the 12-word Philadelphia Verbal Learning Test (PVLT), a list-learning test.

Methods

Vanderbilt Memory and Aging Project participants free of clinical dementia and stroke (n = 230, aged 73 ± 7 years) completed a neuropsychological protocol and brain MRI. A subset (n = 111) underwent lumbar puncture for analysis of Alzheimer's disease (AD) and axonal integrity cerebrospinal fluid (CSF) biomarkers. Regression models related PVLT indices to MRI and CSF biomarkers adjusting for age, sex, race/ethnicity, education, APOE-ε4 carrier status, cognitive status, and intracranial volume (MRI models). Secondary analyses were restricted to participants with normal cognition (NC; n = 127), from which regression-based normative data were generated.

Results

Lower PVLT performances were associated with smaller medial temporal lobe volumes (p < 0.05) and higher CSF tau concentrations (p < 0.04). Among NC, PVLT indices were associated with white matter hyperintensities on MRI and an axonal injury biomarker (CSF neurofilament light; p < 0.03).

Conclusion

The PVLT appears sensitive to markers of neurodegeneration, including temporal regions affected by AD. Conversely, in cognitively normal older adults, PVLT performance seems to relate to white matter disease and axonal injury, perhaps reflecting non-AD pathways to cognitive change. Enhanced normative data enrich the clinical utility of this tool.

Predictors of neurodegeneration differ between cognitively normal and subsequently impaired older adults

Effects of Alzheimer's disease (AD) risk factors on brain volume changes may partly explain what happens during the preclinical AD stage in people who develop subsequent cognitive impairment (SI). We investigated predictors of neurodegeneration, measured by MRI-based volume loss, in older adults before diagnosis of cognitive impairment. There were 623 cognitively normal and 65 SI Baltimore Longitudinal Study of Aging participants (age 55-92 years) enrolled in the neuroimaging substudy from 1994 to 2015. Mixed-effects regression was used to assess the associations of AD risk factors (age, APOE e4 carrier status, diabetes, hypertension, obesity, current smoking, and elevated cholesterol) with brain regional volume change among the overall sample and by diagnostic status. Older age, APOE e4 carrier status, hypertension, and HDL cholesterol were predictors of volumetric change. Among SI participants only, hypertension, obesity, and APOE e4 carrier status were associated with greater declines in selected brain regions. SI individuals in the preclinical AD stage are vulnerable to risk factors that have either a protective or null effect in cognitively normal individuals.

The Combination of Functional and Structural MRI Is a Potential Screening Tool in Alzheimer's Disease

Introduction: This study aimed to survey the discrimination power of parameters from cerebrospinal fluid (CSF) biomarkers, fluorodeoxyglucose uptake on PET (FDG-PET), structural magnetic resonance imaging (MRI), and functional MRI in high- and low-risk subjects or in converters and stable subjects of normal and mild cognitive impairment (MCI) statuses. Methods: We used baseline resting-state functional MRI (rfMRI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to analyze functional networks and recorded subjects' characteristics and results of the CSF study, FDG-PET, and structural MRI from the ADNI website. All parameters were evaluated based on the between-group difference among normal (NC), MCI, and Alzheimer's disease (AD) groups. The parameters other than CSF results were included to study the difference between high- and low-AD-risk subjects in NC or MCI groups, based on CSF results. On the basis of two-year follow-up conditions, all parameters were compared between stable subjects and converters in NC and MCI. Results: CSF biomarkers, FDG-PET, structural MRI, and functional MRI are all able to differentiate AD from MCI or NC but not between MCI and NC. As compared with low-AD-risk subjects, high-risk subjects present decreased FDG-PET in both MCI and NC groups but structural MRI change only in MCI status and rfMRI alteration only in NC status. As compared with stable subjects, converters have decreased FDG-PET, functional network changes, and structural changes in both MCI and NC groups. Conclusion: The combination of functional and structural MRI is a safer screening tool but with similar power as FDG-PET to reflect CSF change in the AD pathological process and to identify high-risk subjects and converters in NC and MCI.

Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine

Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis method by integrating complementary information of different modalities. In this paper, we propose multi-modal sparse hierarchical extreme leaning machine (MSH-ELM). We used volume and mean intensity extracted from 93 regions of interest (ROIs) as features of MRI and FDG-PET, respectively, and used p-tau, t-tau, and A β 42 as CSF features. In detail, high-level representation was individually extracted from each of MRI, FDG-PET, and CSF using a stacked sparse extreme learning machine auto-encoder (sELM-AE). Then, another stacked sELM-AE was devised to acquire a joint hierarchical feature representation by fusing the high-level representations obtained from each modality. Finally, we classified joint hierarchical feature representation using a kernel-based extreme learning machine (KELM). The results of MSH-ELM were compared with those of conventional ELM, single kernel support vector machine (SK-SVM), multiple kernel support vector machine (MK-SVM) and stacked auto-encoder (SAE). Performance was evaluated through 10-fold cross-validation. In the classification of AD vs. HC and MCI vs. HC problem, the proposed MSH-ELM method showed mean balanced accuracies of 96.10% and 86.46%, respectively, which is much better than those of competing methods. In summary, the proposed algorithm exhibits consistently better performance than SK-SVM, ELM, MK-SVM and SAE in the two binary classification problems (AD vs. HC and MCI vs. HC).

A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease

Motivation

Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging.

Results

We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.

Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry

In the 12 years since the publication of the first Consensus Paper of the WFSBP on biomarkers of neurodegenerative dementias, enormous advancement has taken place in the field, and the Task Force takes now the opportunity to extend and update the original paper. New concepts of Alzheimer's disease (AD) and the conceptual interactions between AD and dementia due to AD were developed, resulting in two sets for diagnostic/research criteria. Procedures for pre-analytical sample handling, biobanking, analyses and post-analytical interpretation of the results were intensively studied and optimised. A global quality control project was introduced to evaluate and monitor the inter-centre variability in measurements with the goal of harmonisation of results. Contexts of use and how to approach candidate biomarkers in biological specimens other than cerebrospinal fluid (CSF), e.g. blood, were precisely defined. Important development was achieved in neuroimaging techniques, including studies comparing amyloid-β positron emission tomography results to fluid-based modalities. Similarly, development in research laboratory technologies, such as ultra-sensitive methods, raises our hopes to further improve analytical and diagnostic accuracy of classic and novel candidate biomarkers. Synergistically, advancement in clinical trials of anti-dementia therapies energises and motivates the efforts to find and optimise the most reliable early diagnostic modalities. Finally, the first studies were published addressing the potential of cost-effectiveness of the biomarkers-based diagnosis of neurodegenerative disorders.

Validity and Normative Data for the Biber Figure Learning Test: A Visual Supraspan Memory Measure

The Biber Figure Learning Test (BFLT), a visuospatial serial figure learning test, was evaluated for biological correlates and psychometric properties, and normative data were generated. Nondemented individuals (n = 332, 73 ± 7, 41% female) from the Vanderbilt Memory & Aging Project completed a comprehensive neuropsychological protocol. Adjusted regression models related BFLT indices to structural brain magnetic resonance imaging and cerebrospinal fluid (CSF) markers of brain health. Regression-based normative data were generated. Lower BFLT performances (Total Learning, Delayed Recall, Recognition) related to smaller medial temporal lobe volumes and higher CSF tau concentrations but not CSF amyloid. BFLT indices were most strongly correlated with other measures of verbal and nonverbal memory and visuospatial skills. The BFLT provides a comprehensive assessment of all aspects of visuospatial learning and memory and is sensitive to biomarkers of unhealthy brain aging. Enhanced normative data enriches the clinical utility of this visual serial figure learning test for use with older adults.

Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease

Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond APOE. Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months (p = 1.07e-5), and PHS was significantly more predictive than APOE alone (p = 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model p = 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both APOE and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.

Amyloid pathology in the progression to mild cognitive impairment

The objective of this study was to determine the cognitive and functional decline and development of brain injury in individuals progressing from preclinical (β-amyloid positive cognitively normal) to prodromal Alzheimer's disease (AD) (β-amyloid positive mild cognitive impairment [MCI]), and compare this with individuals who progress to MCI in the absence of significant amyloid pathology. Seventy-five cognitively healthy participants who progressed to MCI were followed for 4 years on average and up to 10 years. We tested effects of β-amyloid (Aβ) on measures of cognition, functional status, depressive symptoms, and brain structure and metabolism. Preclinical AD subjects showed greater cognitive decline in multiple domains and increased cerebrospinal fluid phosphorylated tau levels at baseline while Aβ-negative progressors showed increased rates of white matter hyperintensity accumulation and had a greater frequency of depressive symptoms at baseline. Aβ status did not influence patterns of brain atrophy, but preclinical AD subjects showed greater decline of brain metabolism than Aβ-negative progressors. Several unique features separate the transition from preclinical to prodromal AD from other causes of cognitive decline. These features may facilitate early diagnosis and treatment of AD, especially in clinical trials aimed at halting the progression from preclinical to prodromal AD.

Evaluating Alzheimer's disease biomarkers as mediators of age-related cognitive decline

Age-related changes in cognition are partially mediated by the presence of neuropathology and neurodegeneration. This manuscript evaluates the degree to which biomarkers of Alzheimer's disease, (AD) neuropathology and longitudinal changes in brain structure, account for age-related differences in cognition. Data from the AD Neuroimaging Initiative (n = 1012) were analyzed, including individuals with normal cognition and mild cognitive impairment. Parallel process mixed effects regression models characterized longitudinal trajectories of cognitive variables and time-varying changes in brain volumes. Baseline age was associated with both memory and executive function at baseline (p's < 0.001) and change in memory and executive function performances over time (p's < 0.05). After adjusting for clinical diagnosis, baseline, and longitudinal changes in brain volume, and baseline levels of cerebrospinal fluid biomarkers, age effects on change in episodic memory and executive function were fully attenuated, age effects on baseline memory were substantially attenuated, but an association remained between age and baseline executive function. Results support previous studies that show that age effects on cognitive decline are fully mediated by disease and neurodegeneration variables but also show domain-specific age effects on baseline cognition, specifically an age pathway to executive function that is independent of brain and disease pathways.

Overview and Current Status of Alzheimer's Disease in Bangladesh

Alzheimer’s disease (AD) is a complex neurological disorder with economic, social, and medical burdens which is acknowledged as leading cause of dementia marked by the accumulation and aggregation of amyloid-β peptide and phosphorylated tau (p-tau) protein and concomitant dementia, neuron loss and brain atrophy. AD is the most prevalent neurodegenerative brain disorder with sporadic etiology, except for a small fraction of cases with familial inheritance where familial forms of AD are correlated to mutations in three functionally related genes: the amyloid-β protein precursor and presenilins 1 and 2, two key γ-secretase components. The common clinical features of AD are memory impairment that interrupts daily life, difficulty in accomplishing usual tasks, confusion with time or place, trouble understanding visual images and spatial relationships. Age is the most significant risk factor for AD, whereas other risk factors correlated with AD are hypercholesterolemia, hypertension, atherosclerosis, coronary heart disease, smoking, obesity, and diabetes. Despite decades of research, there is no satisfying therapy which will terminate the advancement of AD by acting on the origin of the disease process, whereas currently available therapeutics only provide symptomatic relief but fail to attain a definite cure and prevention. This review also represents the current status of AD in Bangladesh.

A Quantitative Analysis of Brain Soluble Tau and the Tau Secretion Factor

Neurofibrillary tangles (NFTs) represent products of insoluble tau protein in the brains of patients with Alzheimer disease (AD). The cerebrospinal fluid (CSF) tau level is a biomarker in AD diagnosis. The soluble portion of tau protein in brain parenchyma is presumably the source for CSF tau but this has not previously been quantified. We measured CSF tau and soluble brain tau at autopsy in temporal and frontal brain tissue samples from 7 cognitive normal, 12 mild cognitively impaired, and 19 AD subjects. Based on the measured brain soluble tau, we calculated the whole brain tau load and estimated tau secretion factor. Our results suggest that the increase in NFT in AD is likely attributable to post-translational processes; the increase in CSF tau in AD patients is due to an accelerated carrier-based secretion. Moreover, cognitive dysfunction assessed by final Mini-Mental State Examination scores correlated with the secretion factor but not with the soluble tau.

Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative

Background: Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods: A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results: 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF Aβ1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD (χ2 = 38.23, df = 14, p < 0.001). Conclusions: The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI.

Cerebrospinal Fluid Levels of Amyloid Beta 1-43 Mirror 1-42 in Relation to Imaging Biomarkers of Alzheimer's Disease

Introduction: Amyloid beta 1-43 (Aβ43), with its additional C-terminal threonine residue, is hypothesized to play a role in early Alzheimer’s disease pathology possibly different from that of amyloid beta 1-42 (Aβ42). Cerebrospinal fluid (CSF) Aβ43 has been suggested as a potential novel biomarker for predicting conversion from mild cognitive impairment (MCI) to dementia in Alzheimer’s disease. However, the relationship between CSF Aβ43 and established imaging biomarkers of Alzheimer’s disease has never been assessed.

Materials and Methods: In this observational study, CSF Aβ43 was measured with ELISA in 89 subjects; 34 with subjective cognitive decline (SCD), 51 with MCI, and four with resolution of previous cognitive complaints. All subjects underwent structural MRI; 40 subjects on a 3T and 50 on a 1.5T scanner. Forty subjects, including 24 with SCD and 12 with MCI, underwent ¹⁸F-Flutemetamol PET. Seventy-eight subjects were assessed with ¹⁸F-fluorodeoxyglucose PET (21 SCD/7 MCI and 11 SCD/39 MCI on two different scanners). Ten subjects with SCD and 39 with MCI also underwent diffusion tensor imaging.

Results: Cerebrospinal fluid Aβ43 was both alone and together with p-tau a significant predictor of the distinction between SCD and MCI. There was a marked difference in CSF Aβ43 between subjects with ¹⁸F-Flutemetamol PET scans visually interpreted as negative (37 pg/ml, n = 27) and positive (15 pg/ml, n = 9), p < 0.001. Both CSF Aβ43 and Aβ42 were negatively correlated with standardized uptake value ratios for all analyzed regions; CSF Aβ43 average rho -0.73, Aβ42 -0.74. Both CSF Aβ peptides correlated significantly with hippocampal volume, inferior parietal and frontal cortical thickness and axial diffusivity in the corticospinal tract. There was a trend toward CSF Aβ42 being better correlated with cortical glucose metabolism. None of the studied correlations between CSF Aβ43/42 and imaging biomarkers were significantly different for the two Aβ peptides when controlling for multiple testing.

Conclusion: Cerebrospinal fluid Aβ43 appears to be strongly correlated with cerebral amyloid deposits in the same way as Aβ42, even in non-demented patients with only subjective cognitive complaints. Regarding imaging biomarkers, there is no evidence from the present study that CSF Aβ43 performs better than the classical CSF biomarker Aβ42 for distinguishing SCD and MCI.

Relation of Dysglycemia to Structural Brain Changes in a Multiethnic Elderly Cohort

OBJECTIVES

Abnormally high glucose levels (dysglycemia) increase with age. Epidemiological studies suggest that dysglycemia is a risk factor for cognitive impairment but the underlying pathophysiological mechanisms remain unclear. The objective of this study was to examine the relation of dysglycemia clinical categories (normal glucose tolerance (NGT), pre-diabetes, undiagnosed diabetes, known diabetes) with brain structure in older adults. We also assessed the relation between dysglycemia and cognitive performance.

DESIGN

Cross-sectional and longitudinal cohort study.

SETTING

Northern Manhattan (Washington Heights, Hamilton Heights, and Inwood).

PARTICIPANTS

Medicare recipients 65 years and older.

MEASUREMENTS

Dysglycemia categories were based on HBA1c or history of type 2 diabetes (diabetes). Brain structure (brain infarcts, white matter hyperintensities (WMH) volume, total gray matter volume, total white matter volume, total hippocampus volume) was assessed with brain magnetic resonance imaging; cognitive function (memory, language and visuospatial function, speed) was assessed with a validated neuropsychological battery.

RESULTS

Dysglycemia, defined with HbA1c as a continuous variable or categorically as pre-diabetes and diabetes, was associated with a higher number of brain infarcts, WMH volume and decreased total white matter, gray matter and hippocampus volumes cross-sectionally, and a significant decline in gray matter volume longitudinally. Dysglycemia was also associated with lower performance in language, speed and visuospatial function although these associations were attenuated when adjusted for education, APOE-ε4, ethnic group and vascular risk factors.

CONCLUSION

Our results suggest that dysglycemia affects brain structure and cognition even in elderly survivors, evidenced by higher cerebrovascular disease, lower white and gray matter volume, and worse language and visuospatial function and cognitive speed.

The Risk of Incident Mild Cognitive Impairment and Progression to Dementia Considering Mild Cognitive Impairment Subtypes

BACKGROUND

It remains unclear how demographic and clinical characteristics are related to the risk of incident mild cognitive impairment (MCI) by its subtypes. Moreover, the contribution of the subtypes of incident MCI to the progression to dementia remains puzzling.

METHODS

We used data collected by the National Alzheimer Coordinating Center. Our analysis sample included cognitively normal subjects at baseline. The associations were examined using competing-risks survival regression models and Cox proportional hazards models.

RESULTS

About 16.3% of subjects developed incident MCI of whom 15.8% progressed to Alz-heimer disease (overall mean follow-up of 4.3 years). The risk of incident amnestic MCI (aMCI) was greater in subjects with 1 copy (subhazard ratio [SHR]: 1.23; 95% CI: 1.00-1.50) or 2 copies (SHR: 2.14; 95% CI: 1.49-3.05) of the APOE ε4 allele than in those who had no ε4 allele. Multiple-domain aMCI patients were more likely to progress to dementia than single-domain aMCI patients (hazard ratio: 2.14; 95% CI: 1.28-3.58).

CONCLUSIONS

Cognitively normal subjects with an APOE ε4 allele had a higher likelihood of developing aMCI and the MCI subtype was associated with the dementia subtype. Our findings provide important information about practical indicators for the prediction of cognitive decline.

Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers

A variety of imaging, neuropsychological, and genetic biomarkers have been suggested as potential biomarkers for the identification of mild cognitive impairment (MCI) in patients who later develop Alzheimer's disease (AD). Here, we systematically evaluated the most promising combinations of these biomarkers regarding discrimination between stable and converter MCI and reflection of disease staging. Alzheimer's Disease Neuroimaging Initiative data of AD (n = 144), controls (n = 112), stable (n = 265) and converter (n = 177) MCI, for which apolipoprotein E status, neuropsychological evaluation, and structural, glucose, and amyloid imaging were available, were included in this study. Naïve Bayes classifiers were built on AD and controls data for all possible combinations of these biomarkers, with and without stratification by amyloid status. All classifiers were then applied to the MCI cohorts. We obtained an accuracy of 76% for discrimination between converter and stable MCI with glucose positron emission tomography as a single biomarker. This accuracy increased to about 87% when including further imaging modalities and genetic information. We also identified several biomarker combinations as strong predictors of time to conversion. Use of amyloid validated training data resulted in increased sensitivities and decreased specificities for differentiation between stable and converter MCI when amyloid was included as a biomarker but not for other classifier combinations. Our results indicate that fully independent classifiers built only on AD and controls data and combining imaging, genetic, and/or neuropsychological biomarkers can more reliably discriminate between stable and converter MCI than single modality classifiers. Several biomarker combinations are identified as strongly predictive for the time to conversion to AD.

Multimodal manifold-regularized transfer learning for MCI conversion prediction

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.