Meta-analysis of CSF and MRI biomarkers for detecting preclinical Alzheimer's disease

Combined cognitive assessment and automated MRI volumetry improves the diagnostic accuracy of detecting MCI due to Alzheimer's disease

BACKGROUND

Mild cognitive impairment (MCI) confers a high annual risk of 10-15 % of conversion to Alzheimer's disease (AD) dementia. MRI atrophy patterns derived from automated ROI analysis, particularly hippocampal subfield volumes, were reported to be useful in diagnosing early clinical stages of Alzheimer's disease.

OBJECTIVE

The aim of the present study was to combine automated ROI MRI morphometry of hippocampal subfield volumes and cortical thickness estimates using FreeSurfer 6.0 with cognitive measures to predict disease progression and time to conversion from MCI to AD dementia.

METHODS

Baseline (Neuropsychology, MRI) and clinical follow-up data from 62 MCI patients were analysed retrospectively. Individual cortical thickness and volumetric measures were obtained from T1-weighted MRI. Linear discriminant analysis (LDA) of both, cognitive measures and MRI measures (hippocampal subfields, temporal and parietal lobe volumes), were performed to differentiate MCI converters from stable MCI patients.

RESULTS

Out of 62 MCI patients 21 (34 %) converted to AD dementia within a mean follow-up time of 74.7 ± 36.8 months (mean ± SD, range 12 to 130 months). LDA identified temporal lobe atrophy and hippocampal subfield volumes in combination with cognitive measures of verbal memory, verbal fluency and executive functions to correctly classify 71.4.% of MCI subjects converting to AD dementia and 92.7 % with stable MCI. Lower baseline GM volume of the subiculum and the superior temporal gyrus was associated with faster disease progression of MCI converters.

CONCLUSION

Combining cognitive assessment with automated ROI MRI morphometry is superior to using a single test in order to distinguish MCI due to AD from non converting MCI patients.

"Guttmann Cognitest®," a digital solution for assessing cognitive performance in adult population: A feasibility and usability pilot study

Background

As the world population continues to age, the prevalence of neurological diseases, such as dementia, poses a significant challenge to society. Detecting cognitive impairment at an early stage is vital in preserving and enhancing cognitive function. Digital tools, particularly mHealth, offer a practical solution for large-scale population screening and prompt follow-up assessments of cognitive function, thus overcoming economic and time limitations.

Objective

In this work, two versions of a digital solution called Guttmann Cognitest® were tested.

Methods

Two hundred and one middle-aged adults used the first version (Group A), while 132 used the second one, which included improved tutorials and practice screens (Group B). This second version was also validated in an older age group (Group C).

Results

This digital solution was found to be highly satisfactory in terms of usability and feasibility, with good acceptability among all three groups. Specifically for Group B, the system usability scale score obtained classifies the solution as the best imaginable in terms of usability.

Conclusions

Guttmann Cognitest® has been shown to be effective and well-perceived, with a high potential for sustained engagement in tracking changes in cognitive function.

CERAD-NAB and flexible battery based neuropsychological differentiation of Alzheimer's dementia and depression using machine learning approaches

Depression (DEP) and dementia of the Alzheimer's type (DAT) represent the most common neuropsychiatric disorders in elderly patients. Accurate differential diagnosis is indispensable to ensure appropriate treatment. However, DEP can yet mimic cognitive symptoms of DAT and patients with DAT often also present with depressive symptoms, impeding correct diagnosis. Machine learning (ML) approaches could eventually improve this discrimination using neuropsychological test data, but evidence is still missing. We therefore employed Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) and conventional Logistic Regression (LR) to retrospectively predict the diagnoses of 189 elderly patients (68 DEP and 121 DAT) based on either the well-established Consortium to Establish a Registry for Alzheimer's Disease neuropsychological assessment battery (CERAD-NAB) or a flexible battery approach (FLEXBAT). The best performing combination consisted of FLEXBAT and NB, correctly classifying 87.0% of patients as either DAT or DEP. However, all accuracies were similar across algorithms and test batteries (83.0% - 87.0%). Accordingly, our study is the first to show that common ML algorithms with their default parameters can accurately differentiate between patients clinically diagnosed with DAT or DEP using neuropsychological test data, but do not necessarily outperform conventional LR.

Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks

Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.

Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative

Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

Systematic Review of Different Neuroimaging Correlates in Mild Cognitive Impairment and Alzheimer's Disease

Alzheimer's disease (AD) is a heterogeneous progressive neurocognitive disorder. Although different neuroimaging modalities have been used for the identification of early diagnostic and prognostic factors of AD, there is no consolidated view of the findings from the literature. Here, we aim to provide a comprehensive account of different neural correlates of cognitive dysfunction via magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI) (resting-state and task-related), positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) modalities across the cognitive groups i.e., normal cognition, mild cognitive impairment (MCI), and AD. A total of 46 meta-analyses met the inclusion criteria, including relevance to MCI, and/or AD along with neuroimaging modality used with quantitative and/or functional data. Volumetric MRI identified early anatomical changes involving transentorhinal cortex, Brodmann area 28, followed by the hippocampus, which differentiated early AD from healthy subjects. A consistent pattern of disruption in the bilateral precuneus along with the medial temporal lobe and limbic system was observed in fMRI, while DTI substantiated the observed atrophic alterations in the corpus callosum among MCI and AD cases. Default mode network hypoconnectivity in bilateral precuneus (PCu)/posterior cingulate cortices (PCC) and hypometabolism/hypoperfusion in inferior parietal lobules and left PCC/PCu was evident. Molecular imaging revealed variable metabolite concentrations in PCC. In conclusion, the use of different neuroimaging modalities together may lead to identification of an early diagnostic and/or prognostic biomarker for AD.

Cross-sectional associations of tau protein biomarkers with semantic and episodic memory in older adults without dementia: A systematic review and meta-analysis

Pathological tau is suggested to play a role in cognitive deterioration in the preclinical phase of Alzheimer's disease. We investigated cross-sectional associations of tau burden with episodic and semantic memory performance in older adults without dementia. A systematic search in MEDLINE (via PubMed), PsychINFO, and Embase resulted in 24 eligible studies for meta-analysis. Tau burden was assessed using CSF, PET, or histopathological measures. All studies evaluated associations of tau with episodic memory: weighted effect sizes were -0.46 (95 % CI [-0.73; -0.20], p < .001) for episodic composite scores, -0.19 ([-0.36; -0.03], p = .024) for delayed word list recall, and -0.05 ([-0.14; 0.04], p = .257) for logical memory. Fourteen studies evaluated associations of tau with semantic memory: weighted effect sizes were -0.28 ([-0.52; -0.04], p = .023) for semantic composite scores, -0.06 ([-0.16; 0.03], p = .194) for semantic fluency, and 0.06 ([-0.06; 0.18], p = .319) for picture naming. Our findings indicate that tau burden related to both episodic and semantic memory impairment in older individuals without a diagnosis of mild cognitive impairment or manifest dementia, with episodic composite scores showing the strongest association with tau burden. Future potential lies in developing more sensitive scores to detect this subtle cognitive impairment, which could contribute to early identification of individuals in the preclinical phase of Alzheimer's disease, thereby improving early diagnosis and timely intervention.

Amyloid and tau positive mild cognitive impairment: clinical and biomarker characteristics of dementia progression

Background:

According to the amyloid, tau, neurodegeneration research framework classification, amyloid and tau positive (A+T+) mild cognitive impairment (MCI) individuals are defined as prodromal Alzheimer disease. This study was designed to compare the clinical and biomarker features between A+T+MCI individuals who progressed to progressive MCI (pMCI) and those who remained stable MCI (sMCI), and to identify relevant baseline clinical biomarker and features that could be used to predict progression to dementia within 2 years.

Methods:

We stratified 197 A+T+MCI individuals into pMCI (n = 64) and sMCI (n = 133) over 2 years. Demographics and cognitive assessment scores, cerebrospinal fluid (CSF), and neuroimaging biomarkers (¹⁸F-florbetapir positron emission tomography mean standardized uptake value ratios [SUVR] and structural magnetic resonance imaging [MRI]) were compared between pMCI and sMCI at baseline, 12- and 24-month follow-up. Logistic regression models then were used to evaluate clinical baseline and biomarker features that predicted dementia progression in A+T+MCI.

Results:

pMCI individuals had higher mean ¹⁸F-florbetapir SUVR, CSF total-tau (t-tau), and p-tau_181P than those in sMCI individuals. pMCI individuals performed poorer in cognitive assessments, both global and domain specific (memory, executive, language, attention, and visuospatial skills) than sMCI. At baseline, there were significant differences in regions of interest of structural MRI between the two groups, including bilateral amygdala, hippocampus and entorhinal, bilateral inferior lateral ventricle, left superior and middle temporal, left posterior and caudal anterior cingulate (P < 0.05). Baseline CSF t-tau levels and cognitive scores of Montreal cognitive assessment, functional assessment questionnaire, and everyday cognition by the patient's study partner language domain could predict progression to dementia in A+T+MCI within 2 years.

Conclusions:

In future clinical trials, specific CSF and cognitive measures that predict dementia progression in A+T+MCI might be useful risk factors for assessing the risk of dementia progression.

The efficacy of the picture version of the free and cued selective reminding test to detect significant neurocognitive deficits in a Chilean population

The picture version of the Free and cued selective reminding test with immediate recall is a test adept at measuring the memory encoding and the effect of semantic cues. Furthermore, it is sensitive to detect early dementia stages. This study aimed to obtain psychometric properties of visual Buschke and Grober The Free and Cued Selective Reminding Test (FCSRT) in healthy older adults, mild neurocognitive disorders, and major neurocognitive subjects on a Chilean population.

METHOD

226 participants were included, 113 healthy older adults (HOA), 65 mild neurocognitive disorder (NCD) subjects, and 48 major NCD. Each individual was assessed with the same protocol.

RESULTS

The observed area under the curve (AUC) was higher than .90 in all the FCSRT measures in the major cognitive disorders and healthy older people.

CONCLUSION

according to the AUCs, it was shown that Free Recall, Sensitivity to Cueing Index, and Delay Recall of the FCSRT are suitable to detect major neurocognitive disorders.

To Explore the Predictive Power of Visuomotor Network Dysfunctions in Mild Cognitive Impairment and Alzheimer's Disease

Background

Research into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.

Method

Here, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.

Results

Fair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).

Comparison with Existing Method(s)

The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.

Conclusion

The data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

The Effects of Amyloid and Tau on Functional Network Connectivity in Older Populations

Background: Neuroimaging studies suggest that aged brains show altered connectivity within and across functional networks. Similar changes in functional network integrity are also linked to the accumulation of pathological proteins in the brain, such as amyloid-beta plaques and neurofibrillary tau tangles seen in Alzheimer's disease. However, less is known about the specific impacts of amyloid and tau on functional network connectivity in cognitively normal older adults who harbor these proteins. Methods: We briefly summarize recent neuroimaging studies of aging and then thoroughly review positron emission tomography and functional magnetic resonance imaging studies measuring the relationship between amyloid-tau pathology and functional connectivity in cognitively normal older individuals. Results: The literature overall suggests that amyloid-positive older individuals show minor cognitive dysfunction and aberrant default mode network connectivity compared with amyloid-negative individuals. Tau, however, is more closely associated with network hypoconnectivity and poorer cognition. Those with substantial amyloid and tau experience even greater cognitive decline compared with those with primarily amyloid or tau, suggesting a potential interaction. Multimodal neuroimaging studies suggest that older adults with pathological protein deposits show amyloid-related hyperconnectivity and tau-related hypoconnectivity in multiple functional networks, including the default mode and frontoparietal networks. Discussion: We propose an updated model considering the effects of amyloid and tau on functional connectivity in older individuals. Large, longitudinal neuroimaging studies with multiple levels of analysis are required to obtain a deeper understanding of the dynamic relationship between pathological protein accumulation and functional connectivity changes, as amyloid- and tau-induced connectivity alterations may have critical and time-varying effects on neurodegeneration and cognitive decline. Impact statement Amyloid and tau accumulation have been linked with altered functional connectivity in cognitively normal older adults. This review synthesized recent functional imaging literatures in a discussion of how amyloid and tau can interactively affect functional connectivity in nonlinear ways, which can explain previous conflicting findings. Changes in connectivity strength may depend on the accumulation of both amyloid and tau, and their integrative effects seem to have critical consequences on cognition. Elucidating the effects of these pathological proteins on brain functioning is paramount to understand the etiology of Alzheimer's disease and the aging process overall.

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

Cerebrospinal Fluid Biomarkers of Alzheimer's Disease: Current Evidence and Future Perspectives

Alzheimer's disease is a progressive, clinically heterogeneous, and particularly complex neurodegenerative disease characterized by a decline in cognition. Over the last two decades, there has been significant growth in the investigation of cerebrospinal fluid (CSF) biomarkers for Alzheimer's disease. This review presents current evidence from many clinical neurochemical studies, with findings that attest to the efficacy of existing core CSF biomarkers such as total tau, phosphorylated tau, and amyloid-β (Aβ42), which diagnose Alzheimer's disease in the early and dementia stages of the disorder. The heterogeneity of the pathophysiology of the late-onset disease warrants the growth of the Alzheimer's disease CSF biomarker toolbox; more biomarkers showing other aspects of the disease mechanism are needed. This review focuses on new biomarkers that track Alzheimer's disease pathology, such as those that assess neuronal injury (VILIP-1 and neurofilament light), neuroinflammation (sTREM2, YKL-40, osteopontin, GFAP, progranulin, and MCP-1), synaptic dysfunction (SNAP-25 and GAP-43), vascular dysregulation (hFABP), as well as CSF α-synuclein levels and TDP-43 pathology. Some of these biomarkers are promising candidates as they are specific and predict future rates of cognitive decline. Findings from the combinations of subclasses of new Alzheimer's disease biomarkers that improve their diagnostic efficacy in detecting associated pathological changes are also presented.

Plasma phosphatidylcholines and vitamin B12/folate levels are possible prognostic biomarkers for progression of Alzheimer's disease

OBJECTIVES

In clinical practice it is important to identify patients suffering from mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD). The purpose of this study is to investigate whether lipid metabolites and vitamin B12 and folate levels are effective biomarker for an accurate prediction of MCI-to-AD conversion.

METHODS

During the standard diagnostic assessment at our memory clinic 48 cognitively healthy subjects and MCI patients were recruited. These participants were followed up after 7-9 years. Blood was collected, various biochemical markers (including vitamin B12 and folate) analysed and plasma lipids were measured using the AbsoluteIDQ p150 Kit.

RESULTS

There was no significant change in lipid levels in controls converting to MCI. However, we found significant changes in five lipids in converters from controls to AD. Interestingly, also two lipids were altered when MCI re-converted to controls. Vitamin B12 levels were not affected by conversion but folate levels significantly decreased in MCI-AD conversion.

CONCLUSIONS

Taken together, our study provides evidence that some plasma lipids are significantly altered in subjects converting to AD. Future studies will investigate whether the peripheral lipid changes correspond with changes in the brain during the course of the disease. Although this is a small study, there are indications that lipids may be suitable as prognostic markers.

Plasma Tau Variants Detected by a Novel Anti-Tau Monoclonal Antibody: A Potential Biomarker for Alzheimer's Disease

BACKGROUND

A major drawback in Alzheimer's disease (AD) is the lack of validated biomarkers for routine clinical diagnostic. We have reported earlier a novel blood biomarker, named Alz-tau®, based on variants of platelet tau. This marker evaluates the ratio of high molecular weight tau (HMWtau) and the low molecular weight (LMWtau) tau.

OBJECTIVE

To analyze a potential novel source of antigen for Alz-tau®, plasma tau, detected by immunoreactivity with the novel monoclonal antibody, tau51.

METHODS

We evaluated tau variants in plasma precipitated with ammonium sulfate from 36 AD patients and 15 control subjects by western blot with this novel monoclonal antibody.

RESULTS

The HMW/LMWtau ratio was statistically different between AD patients and controls.

CONCLUSIONS

Plasma tau variants are suitable to be considered as a novel antigen source for the Alz-tau® biomarker for AD.

Long-term cognitive trajectory and activities of daily living in healthy aging

Objective: The purpose of the present study was to investigate if participants in NANOK study (National Normative Study of Cognitive Determinants of Healthy Ageing) who show no cognitive decline throughout five years (successful healthy agers; SHA) will show less age-related differences in instrumental activities of daily living (IADL) based on Functional Activities Questionnaire in comparison to participants who show subtle cognitive decline (Decliners) over time.Method: We used two different classifications of SHA: Rogalski (N = 25 SHA and N = 15 Decliners) based on cross-sectional neuropsychology measures and linear mixed model (LMEM; 20 SHA and 20 Decliners) based on the Montreal Cognitive Assessment longitudinal 5-years follow-up. Whole-brain T1- and T2-weighted images were corrected for distortions and segmented using Freesurfer. Whole-brain volumetry was performed using FSL's voxel-based morphometry tool.Results: The cognitive decline after four years follow-up but not age predicts subtle impairment in IADL in healthy ageing participants. We found brain volumetric differences between SHA and Decliners based on Rogalski but not LMEM classification especially in bilateral insular cortices and ventrolateral frontal cortex. The logistic regression model achieved an accuracy of 75% for the Rogalski in comparison to 67.5% for the LMEM classification.Conclusions: Slight restrictions in IADL seem to be a useful tool for screening healthy ageing participants at risk of developing subtle cognitive decline over a period of five years and the cross-sectional Rogalski criteria based on standardized neuropsychological measures were superior for tapping age-related brain changes to longitudinal LMEM classification based on screening (Montreal Cognitive Assessment).

Age-variant and age-invariant features of functional brain organization in middle-aged and older autistic adults

Background

The majority of research effort into autism has been dedicated to understanding mechanisms during early development. As a consequence, research on the broader life course of an autism spectrum condition (ASC) has largely been neglected and almost nothing is known about ASC beyond middle age. Differences in brain connectivity that arise during early development may be maintained across the lifespan and may play protective or detrimental roles in older age.

Method

This study explored age-related differences in functional connectivity across middle and older age in clinically diagnosed autistic adults (n = 44, 30-73 years) and in an age-matched typical comparison group (n = 45).

Results

The results indicated parallel age-related associations in ASC and typical aging for the local efficiency and connection strength of the default mode network and for the segregation of the frontoparietal control network. In contrast, group differences in visual network connectivity are compatible with a safeguarding interpretation of less age-related decline in brain function in ASC. This divergence was mirrored in different associations between visual network connectivity and reaction time variability in the ASC and comparison group.

Limitations

The study is cross-sectional and may be affected by cohort effects. As all participants received their autism diagnosis in adulthood, this might hinder generalizability.

Conclusion

These results highlight the complexity of aging in ASC with both parallel and divergent trajectories across different aspects of functional network organization.

Advanced diffusion magnetic resonance imaging in patients with Alzheimer's and Parkinson's diseases

The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings; however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer’s and Parkinson’s diseases, the first and second most common neurodegenerative diseases, respectively.

A multipredictor model to predict the conversion of mild cognitive impairment to Alzheimer's disease by using a predictive nomogram

Predicting the probability of converting from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is still a challenging task. This study aims at providing a personalized MCI-to-AD conversion estimation by using a multipredictor nomogram that integrates neuroimaging features, cerebrospinal fluid (CSF) biomarker, and clinical assessments. To do so, 290 MCI patients were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI), of whom 76 has converted to AD and 214 remained with MCI. All subjects were randomly divided into a primary and validation cohort. Radiomics signature (Rad-sig) was obtained based on 17 cerebral cortex features selected by using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Clinical factors and amyloid-beta peptide (Aβ) concentration were selected by using Spearman correlation between the converted and not-converted patients. Then, a nomogram that combines image features, clinical factor, and Aβ concentration was constructed and validated. Furthermore, we explored the associations between various predictors from the macro- to the microperspective by assessing gene expression patterns. Our results showed that the multipredictor nomogram (C-index 0.978 and 0.956 in both cohorts, respectively) outperformed the nomogram using either Rad-sig or Aβ concentration as individual predictors. Significant associations were found between neuropsychological scores, cerebral cortex features, Aβ levels, and underlying gene pathways. Our study may have a clinical impact as a powerful predictive tool for predicting the conversion probability of MCI and providing associations between cognitive impairment, structural changes, Aβ levels, and underlying biological patterns from the macro- to the microperspective.

MR Biomarkers of Degenerative Brain Disorders Derived From Diffusion Imaging

The incidence of neurodegenerative diseases has shown an increasing trend. These conditions typically cause progressive functional disability. Identification of robust biomarkers of neurodegenerative diseases is a key imperative to facilitate early identification of the pathological features and to foster a better understanding of the pathogenetic mechanisms of individual diseases. Diffusion tensor imaging (DTI) is the most widely used diffusion MRI technique for assessment of neurodegenerative diseases. The DTI parameters are promising biomarkers for evaluation of microstructural changes; however, some limitations of DTI restrict its wider clinical use. New diffusion MRI techniques, such as diffusion kurtosis imaging (DKI), bi-tensor DTI, and neurite orientation density and dispersion imaging (NODDI) have been demonstrated to provide value addition to DTI for evaluation of neurodegenerative diseases. In this review article, we summarize the key technical aspects and provide an overview of the current state of knowledge regarding the role of DKI, bi-tensor DTI, and NODDI as biomarkers of microstructural changes in representative neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1620-1636.

Combining Cognitive, Genetic, and Structural Neuroimaging Markers to Identify Individuals with Increased Dementia Risk

Background:

Cognitive and biological markers have shown varying degrees of success in identifying persons who will develop dementia.

Objective: To evaluate different combinations of cognitive and biological markers and identify prediction models with the highest accuracy for identifying persons with increased dementia risk.

Methods:

Neuropsychological assessment, genetic testing (apolipoprotein E –APOE), and structural magnetic resonance imaging (MRI) were performed for 418 older individuals without dementia (60–97 years) from a population-based study (SNAC-K). Participants were followed for six years.

Results:

Cognitive, genetic, and MRI markers were systematically combined to create prediction models for dementia at six years. The most predictive individual markers were perceptual speed or carrying at least one APOEɛ4 allele (AUC = 0.875). The most predictive model (AUC = 0.924) included variables from all three modalities (category fluency, general knowledge, any ɛ4 allele, hippocampal volume, white matter-hyperintensity volume).

Conclusion:

This study shows that combining markers within and between modalities leads to increased predictivity for future dementia. However, minor increases in predictive value should be weighed against the cost of additional tests in larger-scale screening.

A 'Framingham-like' Algorithm for Predicting 4-Year Risk of Progression to Amnestic Mild Cognitive Impairment or Alzheimer's Disease Using Multidomain Information

BACKGROUND

There are no agreed-upon variables for predicting progression from unimpaired cognition to amnestic mild cognitive impairment (aMCI), or from aMCI to Alzheimer's disease (AD).

OBJECTIVE

Use ADNI data to develop a 'Framingham-like' prediction model for a 4-year period.

METHODS

We developed models using the strongest baseline predictors from six domains (demographics, neuroimaging, CSF biomarkers, genetics, cognitive tests, and functional ability). We chose the best predictor from each domain, which was dichotomized into more versus less harmful.

RESULTS

There were 224 unimpaired individuals and 424 aMCI subjects with baseline data on all predictors, of whom 37 (17% ) and 150 (35% ) converted to aMCI and AD, respectively, during 4 years of follow-up. For the unimpaired, CSF tau/Aβ ratio, hippocampal volume, and a memory score predicted progression. For those aMCI at baseline, the same predictors plus APOE4 status and functional ability predicted progression. Demographics and family history were not important predictors for progression for either group. The fit statistic was good for the unimpaired-aMCI model (C-statistic 0.80) and very good for the aMCI-AD model (C-statistic 0.91). Among the unimpaired, those with no harmful risk factors had a 4-year predicted 2% risk of progression, while those with the most harmful risk factors had a predicted 35% risk. The aMCI subjects with no harmful risk factors had a predicted 1% risk of progression those with all six harmful risk factors had a predicted 90% risk.

CONCLUSION

Our parsimonious model accurately predicted progression from unimpaired to aMCI with three variables, and from aMCI to AD with five variables.

The Alz-tau Biomarker for Alzheimer's Disease: Study in a Caucasian Population

The establishment of a molecular biomarker for early detection of Alzheimer's disease (AD) is critical for diagnosis and follow up of patients, and as a quantitative parameter in the evaluation of potential new drugs to control AD. A list of blood biomarkers has been reported but none has been validated for the Alzheimer's clinic. The changes in hyperphosphorylated tau and amyloid peptide in the cerebrospinal fluid is currently used as a tool in the clinics and for research purposes, but this method is highly invasive. Recently, we reported a non-invasive and reliable blood biomarker that correlates the increase in the ratio of heavy tau (HMWtau) and the low molecular weight tau (LMWtau) in human platelets and the decrease in the brain volume as measured by structural MRI. This molecular marker has been named Alz-tau®. Beyond the clinical trials developed with a Latin American population, the present study focuses on an evaluation of this biomarker in a Caucasian population. We examined 36 AD patients and 15 cognitively normal subjects recruited in Barcelona, Spain. Tau levels in platelets were determined by immunoreactivity and the cognitive status by using GDS and MMSE neuropsychological tests. The HMW/LMW tau ratio was statistically different between controls and AD patients. A high correlation was found between the increase in MMSE scores and HMW/LMW tau ratio. This study showed that this ratio is significantly higher in AD patients than controls. Moreover, this study on a peripheral marker of AD is valuable to understanding the AD pathogenesis.

Context Memory in Alzheimer's Disease: The "Who, Where, and When"

Objective

Context memory, a component of episodic system, refers to the ability to retrieve conditions under which an event has occurred, such as who was present during that event and where and when it occurred. Context memory has been found to be compromised in older adults, an issue that we investigated in Alzheimer's disease (AD).

Methods

Thirty-one participants with AD and 35 older adults were asked to generate three autobiographical events. Afterward, they were asked to remember the names of all people who were evoked during the events, and the names for any location that was mentioned during the events. Participants were also asked to remember the year, season, month and day of the week when the events occurred.

Results

Compared to older adults, participants with AD showed lower memory for "who" (p < .001), "where" (p < .05), and "when" (p < .01). Compared to "who" and "where", both participants with AD and older adults showed pronounced difficulties in remembering the "when".

Conclusion

these findings highlight difficulties in remembering temporal information as an indication of context memory decline in AD. The difficulties in retrieving temporal information are discussed in terms of timing failures and hippocampal degenerations in AD.

Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data

A multivariate functional joint model framework is proposed which enables the repeatedly measured functional outcomes, scalar outcomes, and survival process to be modeled simultaneously while accounting for association among the multiple (functional and scalar) longitudinal and survival processes. This data structure is increasingly common across medical studies of neurodegenerative diseases and is exemplified by the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study, in which serial brain imaging, clinical and neuropsychological assessments are collected to measure the progression of Alzheimer's disease (AD). The proposed functional joint model consists of a longitudinal function-on-scalar submodel, a regular longitudinal submodel, and a survival submodel which allows time-dependent functional and scalar covariates. A Bayesian approach is adopted for parameter estimation and a dynamic prediction framework is introduced for predicting the subjects' future health outcomes and risk of AD conversion. The proposed model is evaluated by a simulation study and is applied to the motivating ADNI study.

Visual Evaluation of Medial Temporal Lobe Atrophy as a Clinical Marker of Conversion from Mild Cognitive Impairment to Dementia and for Predicting Progression in Patients with Mild Cognitive Impairment and Mild Alzheimer's Disease

BACKGROUND/AIMS

To evaluate whether visual assessment of medial temporal lobe atrophy (vaMTA) can predict 2-year conversion from mild cognitive impairment (MCI) to dementia and progression of MCI and Alzheimer's disease dementia as measured by the Clinical Dementia Rating Scale Sum of Boxes score (CDR-SB).

METHODS

vaMTA was performed in 94 patients with MCI according to the Winblad criteria and in 124 patients with AD according to ICD-10 and NINCDS-ADRDA criteria. Demographic data, the Consortium to Establish a Registry for Alzheimer's Disease 10-word delayed recall, APOE ɛ4 status, Cornell Scale for Depression in Dementia, and comorbid hypertension were used as covariates.

RESULTS

vaMTA was associated with MCI conversion in an unadjusted model but not in an adjusted model (p = 0.075), where delayed recall and APOE ɛ4 status were significant predictors. With CDR-SB change as the outcome, an interaction between vaMTA and diagnosis was found, but in the adjusted model only delayed recall and age were significant predictors. For vaMTA below 2, the association between vaMTA and CDR-SB change differed between diagnostic groups. Similar results were found based on a trajectory analysis.

CONCLUSION

In adjusted models, memory function, APOE ɛ4 status and age were significant predictors of disease progression, not vaMTA. The association between vaMTA and CDR-SB change was different in patients with MCI and Alzheimer's disease dementia.

Evaluation of Metabolic and Synaptic Dysfunction Hypotheses of Alzheimer's Disease (AD): A Meta-Analysis of CSF Markers

BACKGROUND

Alzheimer's disease (AD) is currently incurable and a majority of investigational drugs have failed clinical trials. One explanation for this failure may be the invalidity of hypotheses focusing on amyloid to explain AD pathogenesis. Recently, hypotheses which are centered on synaptic and metabolic dysfunction are increasingly implicated in AD.

OBJECTIVE

Evaluate AD hypotheses by comparing neurotransmitter and metabolite marker concentrations in normal versus AD CSF.

METHODS

Meta-analysis allows for statistical comparison of pooled, existing cerebrospinal fluid (CSF) marker data extracted from multiple publications, to obtain a more reliable estimate of concentrations. This method also provides a unique opportunity to rapidly validate AD hypotheses using the resulting CSF concentration data. Hubmed, Pubmed and Google Scholar were comprehensively searched for published English articles, without date restrictions, for the keywords "AD", "CSF", and "human" plus markers selected for synaptic and metabolic pathways. Synaptic markers were acetylcholine, gamma-aminobutyric acid (GABA), glutamine, and glycine. Metabolic markers were glutathione, glucose, lactate, pyruvate, and 8 other amino acids. Only studies that measured markers in AD and controls (Ctl), provided means, standard errors/deviation, and subject numbers were included. Data were extracted by six authors and reviewed by two others for accuracy. Data were pooled using ratio of means (RoM of AD/Ctl) and random effects meta-analysis using Cochrane Collaboration's Review Manager software.

RESULTS

Of the 435 identified publications, after exclusion and removal of duplicates, 35 articles were included comprising a total of 605 AD patients and 585 controls. The following markers of synaptic and metabolic pathways were significantly changed in AD/controls: acetylcholine (RoM 0.36, 95% CI 0.24-0.53, p<0.00001), GABA (0.74, 0.58-0.94, p<0.01), pyruvate (0.48, 0.24-0.94, p=0.03), glutathione (1.11, 1.01- 1.21, p=0.03), alanine (1.10, 0.98-1.23, p=0.09), and lower levels of significance for lactate (1.2, 1.00-1.47, p=0.05). Of note, CSF glucose and glutamate levels in AD were not significantly different than that of the controls.

CONCLUSION

This study provides proof of concept for the use of meta-analysis validation of AD hypotheses, specifically via robust evidence for the cholinergic hypothesis of AD. Our data disagree with the other synaptic hypotheses of glutamate excitotoxicity and GABAergic resistance to neurodegeneration, given observed unchanged glutamate levels and decreased GABA levels. With regards to metabolic hypotheses, the data supported upregulation of anaerobic glycolysis, pentose phosphate pathway (glutathione), and anaplerosis of the tricarboxylic acid cycle using glutamate. Future applications of meta-analysis indicate the possibility of further in silico evaluation and generation of novel hypotheses in the AD field.

Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia

Background

The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta_1–42 (Aβ42), amyloid-beta_1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia.

Methods

We used 115 complete datasets from MCI patients of the “Dementia Competence Network”, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%.

Results

Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone.

Conclusion

A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.

Neuropsychological Testing and Machine Learning Distinguish Alzheimer's Disease from Other Causes for Cognitive Impairment

With promising results in recent treatment trials for Alzheimer’s disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aβ(1–42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer’s Disease—Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease.

Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data

BACKGROUND

Identifying predictors of conversion to Alzheimer's disease (AD) is critically important for AD prevention and targeted treatment.

OBJECTIVE

To compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD.

METHODS

Participants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic and genetic factors. We used joint modelling of longitudinal and survival data to examine the association between changes of measures and disease progression. We also employed time-dependent receiver operating characteristic method to assess the discriminating capability of the measures.

RESULTS

23 of 33 longitudinal clinical and imaging measures are significant predictors of AD conversion beyond demographic and genetic factors. The strong phenotypic and biological predictors are in the cognitive domain (ADAS-Cog; RAVLT), functional domain (FAQ), and neuroimaging domain (middle temporal gyrus and hippocampal volume). The strongest predictor is ADAS-Cog 13 with an increase of one SD in ADAS-Cog 13 increased the risk of AD conversion by 2.92 times.

CONCLUSION

Prediction of AD conversion can be improved by incorporating longitudinal change information, in addition to baseline characteristics. Cognitive measures are consistently significant and generally stronger predictors than imaging measures.

Walking ability to predict future cognitive decline in old adults: A scoping review

Early identification of individuals at risk for cognitive decline may facilitate the selection of those who benefit most from interventions. Current models predicting cognitive decline include neuropsychological and/or biological markers. Additional markers based on walking ability might improve accuracy and specificity of these models because motor and cognitive functions share neuroanatomical structures and psychological processes. We reviewed the relationship between walking ability at one point of (mid) life and cognitive decline at follow-up. A systematic literature search identified 20 longitudinal studies. The average follow-up time was 4.5 years. Gait speed quantified walking ability in most studies (n=18). Additional gait measures (n=4) were step frequency, variability and step-length. Despite methodological weaknesses, results revealed that gait slowing (0.68-1.1 m/sec) preceded cognitive decline and the presence of dementia syndromes (maximal odds and hazard ratios of 10.4 and 11.1, respectively). The results indicate that measures of walking ability could serve as additional markers to predict cognitive decline. However, gait speed alone might lack specificity. We recommend gait analysis, including dynamic gait parameters, in clinical evaluations of patients with suspected cognitive decline. Future studies should focus on examining the specificity and accuracy of various gait characteristics to predict future cognitive decline.

Update on the core and developing cerebrospinal fluid biomarkers for Alzheimer disease

Alzheimer disease (AD) is a complex neurodegenerative disorder, whose prevalence will dramatically rise by 2050. Despite numerous clinical trials investigating this disease, there is still no effective treatment. Many trials showed negative or inconclusive results, possibly because they recruited only patients with severe disease, who had not undergone disease-modifying therapies in preclinical stages of AD before severe degeneration occurred. Detection of AD in asymptomatic at risk individuals (and a few presymptomatic individuals who carry an autosomal dominant monogenic AD mutation) remains impractical in many of clinical situations and is possible only with reliable biomarkers. In addition to early diagnosis of AD, biomarkers should serve for monitoring disease progression and response to therapy. To date, the most promising biomarkers are cerebrospinal fluid (CSF) and neuroimaging biomarkers. Core CSF biomarkers (amyloid β_1-42, total tau, and phosphorylated tau) showed a high diagnostic accuracy but were still unreliable for preclinical detection of AD. Hence, there is an urgent need for detection and validation of novel CSF biomarkers that would enable early diagnosis of AD in asymptomatic individuals. This article reviews recent research advances on biomarkers for AD, focusing mainly on the CSF biomarkers. In addition to core CSF biomarkers, the potential usefulness of novel CSF biomarkers is discussed.

Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease

BACKGROUND

Regulatory qualification of a biomarker for a defined context of use provides scientifically robust assurances to sponsors and regulators that accelerate appropriate adoption of biomarkers into drug development.

METHODS

The Coalition Against Major Diseases submitted a dossier to the Scientific Advice Working Party of the European Medicines Agency requesting a qualification opinion on the use of hippocampal volume as a biomarker for enriching clinical trials in subjects with mild cognitive impairment, incorporating a scientific rationale, a literature review and a de novo analysis of Alzheimer's Disease Neuroimaging Initiative data.

RESULTS

The literature review and de novo analysis were consistent with the proposed context of use, and the Committee for Medicinal Products for Human Use released an opinion in November 2011.

CONCLUSIONS

We summarize the scientific rationale and the data that supported the first qualification of an imaging biomarker by the European Medicines Agency.

Mild cognitive impairment

Mild cognitive impairment is the term applied to the cognitive state that lies between normal aging and dementia. There has been significant controversy around describing, defining and characterizing mild cognitive impairment. This review will cover current understanding of the condition and discuss clinical features, research strategies and future directions.

Effect of APOE genotype status on targeted clinical trials outcomes and efficiency in dementia and mild cognitive impairment resulting from Alzheimer's disease

BACKGROUND

The apolipoprotein E (APOE) ε4 genotype has been recommended as a potential inclusion or exclusion criterion in targeted clinical trials for Alzheimer's disease (AD) and mild cognitive impairment (MCI) resulting from AD, and has been implemented in trials of immunotherapeutic agents.

METHODS

We tested this recommendation with clinical trial simulations using participants from a meta-database of 19 studies to create trial samples with APOE ε4 proportions ranging from 0% (all noncarriers) to 100% (all carriers). For each percentage of APOE ε4 carriers, we resampled the database randomly for 1000 trials for each trial scenario, planning for 18- or 24-month trials with samples from 50 to 400 patients per treatment or placebo group, up to 40% dropouts, and outcomes on the Alzheimer's Disease Assessment Scale, cognitive subscale (ADAS-cog) with effect sizes from 0.15 to 0.75, and calculated statistical power.

RESULTS

Enrichment of clinical trial participants based on APOE ε4 carrier status resulted in minimal increases in power compared with enrolling participants with the APOE ε3 genotype only or enrolling patients without regard to APOE genotype. Increased screening requirements to enhance the sample would offset gains in power.

CONCLUSIONS

Although samples enriched for APOE ε4 carriers in AD or MCI clinical trials showed slightly more cognitive impairment and greater decline using the number APOE ε4 alleles as an inclusion criterion most likely would not result in more efficient trials, and trials would take longer because fewer patients would be available. The APOE ε4/εX (where X = 2, 3 or 4) genotype could be useful, however, as an explanatory variable or covariate if warranted by a drug's action.

Meta-Review of CSF Core Biomarkers in Alzheimer's Disease: The State-of-the-Art after the New Revised Diagnostic Criteria

BACKGROUND

Current research criteria for Alzheimer's disease (AD) include cerebrospinal fluid (CSF) biomarkers into the diagnostic algorithm. However, spreading their use to the clinical routine is still questionable.

OBJECTIVE

To provide an updated, systematic and critical review on the diagnostic utility of the CSF core biomarkers for AD.

DATA SOURCES

MEDLINE, PreMedline, EMBASE, PsycInfo, CINAHL, Cochrane Library, and CRD.

ELIGIBILITY CRITERIA

(1a) Systematic reviews with meta-analysis; (1b) Primary studies published after the new revised diagnostic criteria; (2) Evaluation of the diagnostic performance of at least one CSF core biomarker.

RESULTS

The diagnostic performance of CSF biomarkers is generally satisfactory. They are optimal for discriminating AD patients from healthy controls. Their combination may also be suitable for mild cognitive impairment (MCI) prognosis. However, CSF biomarkers fail to distinguish AD from other forms of dementia.

LIMITATIONS

(1) Use of clinical diagnosis as standard instead of pathological postmortem confirmation; (2) variability of methodological aspects; (3) insufficiently long follow-up periods in MCI studies; and (4) lower diagnostic accuracy in primary care compared with memory clinics.

CONCLUSION

Additional work needs to be done to validate the application of CSF core biomarkers as they are proposed in the new revised diagnostic criteria. The use of CSF core biomarkers in clinical routine is more likely if these limitations are overcome. Early diagnosis is going to be of utmost importance when effective pharmacological treatment will be available and the CSF core biomarkers can also be implemented in clinical trials for drug development.

Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers

The global prevalence of dementia is as high as 24 million, and has been predicted to quadruple by the year 2050. In the US alone, Alzheimer disease (AD) - the most frequent cause of dementia characterized by a progressive decline in cognitive function in particular the memory domain - causes estimated health-care costs of $ 172 billion per year. Key neuropathological hallmarks of the AD brain are diffuse and neuritic extracellular amyloid plaques - often surrounded by dystrophic neurites - and intracellular neurofibrillary tangles. These pathological changes are frequently accompanied by reactive microgliosis and loss of neurons, white matter and synapses. The etiological mechanisms underlying these neuropathological changes remain unclear, but are probably caused by both environmental and genetic factors. In this review article, we provide an overview of the epidemiology of AD, review the biomarkers that may be used for risk assessment and in diagnosis, and give suggestions for future research.

Context memory in Alzheimer's disease

Background

Alzheimer's disease (AD) is a neurodegenerative disease characterized by a gradual loss of memory. Specifically, context aspects of memory are impaired in AD. Our review sheds light on the neurocognitive mechanisms of this memory component that forms the core of episodic memory function.

Summary

Context recall, an element of episodic memory, refers to remembering the context in which an event has occurred, such as from whom or to whom information has been transmitted.

Key Messages

Our review raises crucial questions. For example, (1) which context element is more prone to being forgotten in the disease? (2) How do AD patients fail to bind context features together? (3) May distinctiveness heuristic or decisions based on metacognitive expectations improve context retrieval in these patients? (4) How does cueing at retrieval enhance reinstating of encoding context in AD? By addressing these questions, our work contributes to the understanding of the memory deficits in AD.

Using Voxel-Based Morphometry to Examine the Relationship between Regional Brain Volumes and Memory Performance in Amnestic Mild Cognitive Impairment

Alzheimer’s disease (AD) is a slowly progressive neurodegenerative disorder, in which morphological alterations of brain tissue develop many years before the first neuropsychological and clinical changes occur. Among the first and most prominent symptoms are deficiencies of declarative memory functions. This stage of precursory symptoms to AD has been described as amnestic mild cognitive impairment (aMCI) and is discussed as a potential AD prodrome. As therapy in the later stages of AD has been shown to be of limited impact, aMCI would be the key target for early intervention. For that purpose a comprehensive neuropsychological and anatomical characterization of this group is necessary. Previous neuropsychological investigations identified tests which are highly sensitive in diagnosing aMCI and very early AD. However, the sensitivity of those neuropsychological tests to the particular structural neuropathology in aMCI remains to be specified. To this end, we investigated 25 patients with single-domain aMCI. All participants underwent extensive neuropsychological testing and anatomical scanning with structural magnetic resonance imaging. Voxel-based morphometry (VBM) was performed to identify brain regions that show a significant correlation between regional brain volume and behavioral measures of memory and executive functioning. We found that performance in a variety of mnemonic tests was directly related to the integrity of the medial temporal lobe cortex (MTLC). Moreover, impairment of memory sub-functions in aMCI might be detected earlier than overt structural damage. By this, these findings contribute to the identification of cerebral structures associated with memory deficits in aMCI.

MRI and cerebrospinal fluid biomarkers for predicting progression to Alzheimer's disease in patients with mild cognitive impairment: a diagnostic accuracy study

OBJECTIVES

To assess the incremental value of MRI and cerebrospinal fluid (CSF) analysis after a short memory test for predicting progression to Alzheimer's disease from a pragmatic clinical perspective.

DESIGN

Diagnostic accuracy study in a multicentre prospective cohort study.

SETTING

Alzheimer Disease Neuroimaging Initiative participants with complete data on neuropsychological assessment, MRI of the brain and CSF analysis.

PARTICIPANTS

Patients with mild cognitive impairment (MCI; n=181) were included. Mean follow-up was 38.9 months (range 5.5-75.9).

MAIN OUTCOME MEASURES

Diagnostic accuracy of individual instruments and incremental value of entorhinal cortex volume on MRI and p-τ/Aβ ration in CSF after administration of Rey's Auditory Verbal Learning Memory Test are calculated and expressed as the 'Net Reclassification Improvement' (NRI), which is the change in the percentage of individuals that are correctly diagnosed as Alzheimer or non-Alzheimer case.

RESULTS

Tested in isolation, a short memory test, MRI and CSF all substantially contribute to the differentiation of those MCI patients who remain stable during follow-up from those who progress to develop Alzheimer's disease. The memory test, MRI and CSF improved the diagnostic classification by 21% (95% CI 15.1 to 26.9), 22.1% (95% CI 16.1 to 28.1) and 18.8% (95% CI 13.1 to 24.5), respectively. After administration of a short memory test, however, the NRI of MRI is +1.1% (95% CI 0.1 to 3.9) and of CSF is -2.2% (95% CI -5.6 to -0.6).

CONCLUSIONS

After administration of a brief test of memory, MRI or CSF do not substantially affect diagnostic accuracy for predicting progression to Alzheimer's disease in patients with MCI. The NRI is an intuitive and easy to interpret measure for evaluation of potential added value of new diagnostic instruments in daily clinical practice.

A prediction model to calculate probability of Alzheimer's disease using cerebrospinal fluid biomarkers

BACKGROUND

We aimed to develop a prediction model based on cerebrospinal fluid (CSF) biomarkers, that would yield a single estimate representing the probability that dementia in a memory clinic patient is due to Alzheimer's disease (AD).

METHODS

All patients suspected of dementia in whom the CSF biomarkers had been analyzed were selected from a memory clinic database. Clinical diagnosis was AD (n = 272) or non-AD (n = 289). The prediction model was developed with logistic regression analysis and included CSF amyloid β42, CSF phosphorylated tau181, and sex. Validation was performed on an independent data set from another memory clinic, containing 334 AD and 157 non-AD patients.

RESULTS

The prediction model estimated the probability that AD is present as follows: p(AD) = 1/(1 + e (- [-0.3315 + score])), where score is calculated from -1.9486 × ln(amyloid β42) + 2.7915 × ln(phosphorylated tau181) + 0.9178 × sex (male = 0, female = 1). When applied to the validation data set, the discriminative ability of the model was very good (area under the receiver operating characteristic curve: 0.85). The agreement between the probability of AD predicted by the model and the observed frequency of AD diagnoses was very good after taking into account the difference in AD prevalence between the two memory clinics.

CONCLUSIONS

We developed a prediction model that can accurately predict the probability of AD in a memory clinic population suspected of dementia based on CSF amyloid β42, CSF phosphorylated tau181, and sex.

Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance

The current study tested the accuracy of primary MRI and cerebrospinal fluid (CSF) biomarker candidates and neuropsychological tests for predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia. In a cross-validation paradigm, predictor models were estimated in the training set of AD (N = 81) and elderly control subjects (N = 101). A combination of CSF t-tau/Aβ(1-4) ratio and MRI biomarkers or neuropsychological tests (free recall and trail making test B (TMT-B)) showed the best statistical fit in the AD vs. HC comparison, reaching a classification accuracy of up to 64% when applied to the prediction of MCI conversion (3.3-year observation interval, mean = 2.3 years). However, several single-predictor models showed a predictive accuracy of MCI conversion comparable to that of any multipredictor model. The best single predictors were right entorhinal cortex (prediction accuracy = 68.5% (95% CI (59.5, 77.4))) and TMT-B test (prediction accuracy 64.6% (95% CI (55.5, 73.4%))). In conclusion, short-term conversion to AD is predicted by single marker models to a comparable degree as by multimarker models in amnestic MCI subjects.