Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging

Multiple Early Biomarkers to Predict Cognitive Decline in Dementia-Free Older Adults

INTRODUCTION

Baseline olfactory impairment, poor performance on cognitive test, and medial temporal lobe atrophy are considered biomarkers for predicting future cognitive decline in dementia-free older adults. However, the combined effect of these predictors has not been fully investigated.

METHODS

A group of 110 participants without dementia were continuously recruited into this study, and underwent olfactory, cognitive tests and MRI scanning at baseline and 5-year follow-up. Olfactory function was assessed using the University of Pennsylvania Smell Identification Test (UPSIT). Participants were divided into the cognitive decliners and non-decliners.

RESULTS

Among 87 participants who completed the 5-year follow-up, cognitive decline was present in 32 cases and 55 remained stable. Compared with non-decliners, cognitive decliners presented lower scores on both the UPSIT and the Montreal Cognitive Assessment (MoCA), and smaller hippocampal volume at baseline (all P < .001). The logistic regression analysis revealed that lower scores on UPSIT and MoCA, and smaller hippocampal volume were strongly associated with subsequent cognitive decline, respectively (all P < .001). For the prediction of cognitive decline, lower score on UPSIT performed the sensitivity of 63.6% and specificity of 81.2%, lower score on MoCA with the sensitivity of 74.5% and specificity of 65.6%, smaller hippocampal volume with the sensitivity of 70.9% and specificity of 78.1%, respectively. Combining three predictors resulted in the sensitivity of 83.6% and specificity of 93.7%.

CONCLUSIONS

The combination of olfactory test, cognitive test with structural MRI may enhance the predictive ability for future cognitive decline for dementia-free older adults.

Predicting conversion from mild cognitive impairment to Alzheimer's disease: a multimodal approach

Successively predicting whether mild cognitive impairment patients will progress to Alzheimer's disease is of significant clinical relevance. This ability may provide information that can be leveraged by emerging intervention approaches and thus mitigate some of the negative effects of the disease. Neuroimaging biomarkers have gained some attention in recent years and may be useful in predicting the conversion of mild cognitive impairment to Alzheimer's disease. We implemented a novel multi-modal approach that allowed us to evaluate the potential of different imaging modalities, both alone and in different degrees of combinations, in predicting the conversion to Alzheimer's disease of mild cognitive impairment patients. We applied this approach to the imaging data from the Alzheimer's Disease Neuroimaging Initiative that is a multi-modal imaging dataset comprised of MRI, Fluorodeoxyglucose PET, Florbetapir PET and diffusion tensor imaging. We included a total of 480 mild cognitive impairment patients that were split into two groups: converted and stable. Imaging data were segmented into atlas-based regions of interest, from which relevant features were extracted for the different imaging modalities and used to construct machine-learning models to classify mild cognitive impairment patients into converted or stable, using each of the different imaging modalities independently. The models were then combined, using a simple weight fusion ensemble strategy, to evaluate the complementarity of different imaging modalities and their contribution to the prediction accuracy of the models. The single-modality findings revealed that the model, utilizing features extracted from Florbetapir PET, demonstrated the highest performance with a balanced accuracy of 83.51%. Concerning multi-modality models, not all combinations enhanced mild cognitive impairment conversion prediction. Notably, the combination of MRI with Fluorodeoxyglucose PET emerged as the most promising, exhibiting an overall improvement in predictive capabilities, achieving a balanced accuracy of 78.43%. This indicates synergy and complementarity between the two imaging modalities in predicting mild cognitive impairment conversion. These findings suggest that β-amyloid accumulation provides robust predictive capabilities, while the combination of multiple imaging modalities has the potential to surpass certain single-modality approaches. Exploring modality-specific biomarkers, we identified the brainstem as a sensitive biomarker for both MRI and Fluorodeoxyglucose PET modalities, implicating its involvement in early Alzheimer's pathology. Notably, the corpus callosum and adjacent cortical regions emerged as potential biomarkers, warranting further study into their role in the early stages of Alzheimer's disease.

A Two-Year Observational Study to Evaluate Conversion Rates from High- and Low-Risk Patients with Amnestic Mild Cognitive Impairment to Probable Alzheimer's Disease in a Real-World Setting

Background

Predicting conversion to probable Alzheimer&s disease (AD) from amnestic mild cognitive impairment (aMCI) is difficult but important. A nomogram was developed previously for determining the risk of 3-year probable AD conversion in aMCI.

Objective

To compare the probable AD conversion rates with cognitive and neurodegenerative changes for 2 years from high- and low risk aMCI groups classified using the nomogram.

Methods

This prospective, multicenter, observational study was conducted in Korea. A total of patients were classified as high- or low-risk aMCI according to the nomogram and followed-up for 2 years to compare the annual conversion rate to probable AD and brain structure changes between the two groups.

Results

In total, 176 (high-risk, 85; low-risk, 91) and 160 (high-risk, 77; low-risk, 83) patients completed the 1-year and 2-year follow-up, respectively. The probable AD conversion rate was significantly higher in the high-risk (Year 1, 28.9%; Year 2, 46.1%) versus low-risk group (Year 1, 0.0%; Year 2, 4.9%, both p < 0.0001). Mean changes from baseline in Seoul Neuropsychological Screening Battery-Dementia Version, Clinical Dementia Rating-Sum of Box, and Korean version of the Instrumental Activities of Daily Living scores and cortical atrophy index at Years 1 and 2 were significantly greater in the high-risk group (p < 0.0001).

Conclusions

The high-risk aMCI group, as determined by the nomogram, had a higher conversion rate to probable AD and faster cognitive decline and neurodegeneration change than the low-risk group. These real-world results have clinical implications that help clinicians in accurately predicting patient outcomes and facilitating early decision-making.Trial Registration: ClinicalTrials.gov (NCT03448445).

Precise prediction of cerebrospinal fluid amyloid beta protein for early Alzheimer's disease detection using multimodal data

Alzheimer's disease (AD) constitutes a neurodegenerative disorder marked by a progressive decline in cognitive function and memory capacity. The accurate diagnosis of this condition predominantly relies on cerebrospinal fluid (CSF) markers, notwithstanding the associated burdens of pain and substantial financial costs endured by patients. This study encompasses subjects exhibiting varying degrees of cognitive impairment, encompassing individuals with subjective cognitive decline, mild cognitive impairment, and dementia, constituting a total sample size of 82 participants. The primary objective of this investigation is to explore the relationships among brain atrophy measurements derived from magnetic resonance imaging, atypical electroencephalography (EEG) patterns, behavioral assessment scales, and amyloid β-protein (Aβ) indicators. The findings of this research reveal that individuals displaying reduced Aβ1-42/Aβ-40 levels exhibit significant atrophy in the frontotemporal lobe, alongside irregularities in various parameters related to EEG frequency characteristics, signal complexity, inter-regional information exchange, and microstates. The study additionally endeavors to estimate Aβ1-42/Aβ-40 content through the application of a random forest algorithm, amalgamating structural data, electrophysiological features, and clinical scales, achieving a remarkable predictive precision of 91.6%. In summary, this study proposes a cost-effective methodology for acquiring CSF markers, thereby offering a valuable tool for the early detection of AD.

Towards validation in clinical routine: a comparative analysis of visual MTA ratings versus the automated ratio between inferior lateral ventricle and hippocampal volumes in Alzheimer's disease diagnosis

PURPOSE

To assess the performance of the inferior lateral ventricle (ILV) to hippocampal (Hip) volume ratio on brain MRI, for Alzheimer's disease (AD) diagnostics, comparing it to individual automated ILV and hippocampal volumes, and visual medial temporal lobe atrophy (MTA) consensus ratings.

METHODS

One-hundred-twelve subjects (mean age ± SD, 66.85 ± 13.64 years) with varying degrees of cognitive decline underwent MRI using a Philips Ingenia 3T. The MTA scale by Scheltens, rated on coronal 3D T1-weighted images, was determined by three experienced radiologists, blinded to diagnosis and sex. Automated volumetry was computed by icobrain dm (v. 5.10) for total, left, right hippocampal, and ILV volumes. The ILV/Hip ratio, defined as the percentage ratio between ILV and hippocampal volumes, was calculated and compared against a normative reference population (n = 1903). Inter-rater agreement, association, classification accuracy, and clinical interpretability on patient level were reported.

RESULTS

Visual MTA scores showed excellent inter-rater agreement. Ordinal logistic regression and correlation analyses demonstrated robust associations between automated brain segmentations and visual MTA ratings, with the ILV/Hip ratio consistently outperforming individual hippocampal and ILV volumes. Pairwise classification accuracy showed good performance without statistically significant differences between the ILV/Hip ratio and visual MTA across disease stages, indicating potential interchangeability. Comparison to the normative population and clinical interpretability assessments showed commensurability in classifying MTA "severity" between visual MTA and ILV/Hip ratio measurements.

CONCLUSION

The ILV/Hip ratio shows the highest correlation to visual MTA, in comparison to automated individual ILV and hippocampal volumes, offering standardized measures for diagnostic support in different stages of cognitive decline.

The Associations of Phosphorylated Tau 181 and Tau 231 Levels in Plasma and Cerebrospinal Fluid with Cognitive Function in Alzheimer's Disease: A Systematic Review and Meta-Analysis

Background

Cerebrospinal fluid (CSF) or blood biomarkers like phosphorylated tau proteins (p-tau) are used to detect Alzheimer's disease (AD) early. Increasing studies on cognitive function and blood or CSF p-tau levels are controversial.

Objective

Our study examined the potential of p-tau as a biomarker of cognitive status in normal control (NC), mild cognitive impairment (MCI), and AD patients.

Methods

We searched PubMed, Cochrane, Embase, and Web of Science for relevant material through 12 January 2023. 5,017 participants from 20 studies-1,033 AD, 2,077 MCI, and 1,907 NC-were evaluated. Quantitative analysis provided continuous outcomes as SMDs with 95% CIs. Begg tested publication bias.

Results

MCI patients had lower CSF p-tau181 levels than AD patients (SMD =-0.60, 95% CI (-0.85, -0.36)) but higher than healthy controls (SMD = 0.67). AD/MCI patients had greater plasma p-tau181 levels than healthy people (SMD =-0.73, 95% CI (-1.04, -0.43)). MCI patients had significantly lower p-tau231 levels than AD patients in plasma and CSF (SMD =-0.90, 95% CI (-0.82, -0.45)). MCI patients showed greater CSF and plasma p-tau231 than healthy controls (SMD = 1.34, 95% CI (0.89, 1.79) and 0.43, (0.23, 0.64)). Plasma p-tau181/231 levels also distinguished the three categories. MCI patients had higher levels than healthy people, while AD patients had higher levels than MCI patients.

Conclusions

CSF p-tau181 and p-tau231 biomarkers distinguished AD, MCI, and healthy populations. Plasma-based p-tau181 and p-tau231 biomarkers for AD and MCI need further study.

Structural and Functional Retinal Changes in Patients with Mild Cognitive Impairment with and without Diabetes

Our objective is to analyze retinal changes using optical coherence tomography angiography (OCT-A) in patients with mild cognitive impairment (MCI) to characterize structural and vascular alterations. This cross-sectional study involved 117 eyes: 39 eyes from patients with MCI plus diabetes (DM-MCI), 39 eyes from patients with MCI but no diabetes (MCI); and 39 healthy control eyes (C). All patients underwent a visual acuity measurement, a structural OCT, an OCT-A, and a neuropsychological examination. Our study showed a thinning of retinal nerve fiber layer thickness (RNFL) and a decrease in macular thickness when comparing the MCI-DM group to the C group (p = 0.008 and p = 0.016, respectively). In addition, an increase in arteriolar thickness (p = 0.016), a reduction in superficial capillary plexus density (p = 0.002), and a decrease in ganglion cell thickness (p = 0.027) were found when comparing the MCI-DM group with the MCI group. Diabetes may exacerbate retinal vascular changes when combined with mild cognitive impairment.

Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer's Disease Progression Using 18F-FDG PET

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early and accurate prediction of AD progression is crucial for early intervention and personalized treatment planning. Although AD does not yet have a reliable therapy, several medications help slow down the disease's progression. However, more study is still needed to develop reliable methods for detecting AD and its phases. In the recent past, biomarkers associated with AD have been identified using neuroimaging methods. To uncover biomarkers, deep learning techniques have quickly emerged as a crucial methodology. A functional molecular imaging technique known as fluorodeoxyglucose positron emission tomography (18F-FDG-PET) has been shown to be effective in assisting researchers in understanding the morphological and neurological alterations to the brain associated with AD. Convolutional neural networks (CNNs) have also long dominated the field of AD progression and have been the subject of substantial research, while more recent approaches like vision transformers (ViT) have not yet been fully investigated. In this paper, we present a self-supervised learning (SSL) method to automatically acquire meaningful AD characteristics using the ViT architecture by pretraining the feature extractor using the self-distillation with no labels (DINO) and extreme learning machine (ELM) as classifier models. In this work, we examined a technique for predicting mild cognitive impairment (MCI) to AD utilizing an SSL model which learns powerful representations from unlabeled 18F-FDG PET images, thus reducing the need for large-labeled datasets. In comparison to several earlier approaches, our strategy showed state-of-the-art classification performance in terms of accuracy (92.31%), specificity (90.21%), and sensitivity (95.50%). Then, to make the suggested model easier to understand, we highlighted the brain regions that significantly influence the prediction of MCI development. Our methods offer a precise and efficient strategy for predicting the transition from MCI to AD. In conclusion, this research presents a novel Explainable SSL-ViT model that can accurately predict AD progress based on 18F-FDG PET scans. SSL, attention, and ELM mechanisms are integrated into the model to make it more predictive and interpretable. Future research will enable the development of viable treatments for neurodegenerative disorders by combining brain areas contributing to projection with observed anatomical traits.

The Memory Alteration Test Is Correlated with Clinical, Cerebrospinal Fluid, and Brain Imaging Markers of Alzheimer Disease in Lima, Peru

INTRODUCTION

As disease-modifying therapies become available for Alzheimer's disease (AD), detection of AD in early stages of illness (mild cognitive impairment [MCI], early dementia) becomes increasingly important. Biomarkers for AD in low- and middle-income countries (LMICs) are costly and not widely available; hence, it is important to identify cognitive tests that correlate well with AD biomarker status. In this study, we evaluated the memory alteration test (M@T) to detect biomarker-proven AD and quantify its correlation with neurodegeneration and cerebrospinal fluid (CSF) AD biomarkers in a cohort of participants from Lima, Peru.

METHODS

This is a secondary analysis of a cohort of 185 participants: 63 controls, 53 with amnestic MCI (aMCI), and 69 with dementia due to AD. Participants underwent testing with M@T and a gold standard neuropsychological battery. We measured total tau (t-tau), phosphorylated tau (p-tau), and beta-amyloid (β-amyloid) in CSF, and evaluated neurodegeneration via medial temporal atrophy score in MRI. We used receiver-operator curves to determine the discriminative capacity of the total M@T score and its subdomains. We used the Pearson coefficient to correlate M@T score and CSF biomarkers.

RESULTS

The M@T had an area under the curve (AUC) of 0.994 to discriminate between controls and cognitively impaired (aMCI or AD) patients, and an AUC of 0.98 to differentiate between aMCI and AD patients. Free-recall and cued recall had the highest AUCs of all subdomains. Total score was strongly correlated with t-tau (-0.77) and p-tau (-0.72), and moderately correlated with β-amyloid (0.66). The AUC for discrimination of neurodegeneration was 0.87.

CONCLUSION

The M@T had excellent discrimination of aMCI and dementia due to AD. It was strongly correlated with CSF biomarkers and had good discrimination of neurodegeneration. In LMICs, the M@T may be a cost-effective screening tool for aMCI and dementia caused by AD.

A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive illness with a slow start that lasts many years; the disease's consequences are devastating to the patient and the patient's family. If detected early, the disease's impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer's disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices' raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.

Sex and APOE genotype differences in amyloid deposition and cognitive performance along the Alzheimer's Continuum

Conflicting findings exist regarding the differences in amyloid burden and cognitive performance based on sex and apolipoprotein E (APOE) genotype. This study aimed to investigate the brain amyloid-β (Aβ) burden and cognitive performances by sex and APOE genotype in a cohort of Aβ-positron emission tomography (PET)-positive participants. Brain Aβ burden was assessed using 18F-florbetapir PET standard uptake value ratios. Cognitive performance was evaluated using standardized neuropsychological tests. In the cognitively normal participants, females had a higher Aβ burden than males in APOE ε4 noncarriers, whereas APOE ε4 carriers had a higher Aβ burden than noncarriers in males. In the cognitively impaired participants, APOE ε4 carriers were more likely to have a higher Aβ burden than noncarriers in the brain regions of the lateral parietal gyrus, frontal gyrus, and precuneus. In addition, females were more likely to have poorer language and visuospatial performance compared to males, while the APOE genotype did not significantly impact cognitive performance. These findings further elucidate the impact of sex and APOE genotype on brain Aβ burden and sex-related cognitive performance should be considered in the Alzheimer's Continuum.

CSF proteome profiling reveals biomarkers to discriminate dementia with Lewy bodies from Alzheimer´s disease

Diagnosis of dementia with Lewy bodies (DLB) is challenging and specific biofluid biomarkers are highly needed. We employed proximity extension-based assays to measure 665 proteins in the cerebrospinal fluid (CSF) from patients with DLB (n = 109), Alzheimer´s disease (AD, n = 235) and cognitively unimpaired controls (n = 190). We identified over 50 CSF proteins dysregulated in DLB, enriched in myelination processes among others. The dopamine biosynthesis enzyme DDC was the strongest dysregulated protein, and could efficiently discriminate DLB from controls and AD (AUC:0.91 and 0.81 respectively). Classification modeling unveiled a 7-CSF biomarker panel that better discriminate DLB from AD (AUC:0.93). A custom multiplex panel for six of these markers (DDC, CRH, MMP-3, ABL1, MMP-10, THOP1) was developed and validated in independent cohorts, including an AD and DLB autopsy cohort. This DLB CSF proteome study identifies DLB-specific protein changes and translates these findings to a practicable biomarker panel that accurately identifies DLB patients, providing promising diagnostic and clinical trial testing opportunities.

Recall and recognition subtests of the repeatable battery for the assessment of neuropsychological status and their relationship to biomarkers of Alzheimer's disease

Recently, two new recognition subtests for the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) were developed and initially validated in a cohort of older adults who were cognitively intact or classified as amnestic Mild Cognitive Impairment (MCI) or mild Alzheimer's disease (AD). The current paper extends that validation by comparing the recall and recognition subtests of the RBANS, including the existing and recently developed scores, to three commonly used biomarkers in AD in an expanded sample from the initial validation. One hundred fifty-four older adults (65 intact, 46 MCI, 43 AD) were administered the RBANS, which included the recently developed subtests for Story Recognition and Figure Recognition (hits, false positives, total correct), as part of a study on memory and biomarkers. Participants also completed magnetic resonance imaging to obtain hippocampal volumes, positron emission tomography to obtain amyloid plaque deposition, and a blood draw to obtain APOE ε4 status. Whereas correlations between recall scores and biomarkers tended to be moderate (average r = ±0.48), these correlations were comparable across the three recognition total scores (average r = ±0.42), but tended to be lower for recognition hits (average r = ±0.28) and false positives (average r = ±0.38). These results further validate the existing and recently developed recognition scores on the RBANS as providing useful information about brain and genetic pathology in older adults with intact and impaired cognitive functioning.

Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review

BACKGROUND

This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia.

METHODS

We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines.

RESULTS

Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia.

CONCLUSION

Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.

Decreased GABA levels of the anterior and posterior cingulate cortex are associated with executive dysfunction in mild cognitive impairment

Background and purpose

Executive function impairment, a slight but noticeable cognitive deficit in mild cognitive impairment (MCI) patients, is influenced by gamma-aminobutyric acid (GABA) levels. Reduced cognitive function is accompanied by thinning of the cerebral cortex, which has higher GABA levels than white matter. However, the relationships among GABA levels, cortical thickness, and executive function in MCI patients have not yet been elucidated. We investigated the relationships among GABA levels, cortical thickness, and executive function in MCI patients.

Methods

In this study, a total of 36 MCI patients and 36 sex-, age-, and education-matched healthy controls (HC) were recruited. But 33 MCI patients and 35 HC were included because of head motion or poor data quality for three MCI patients and one HC. The levels of gamma-aminobutyric acid plus relative to creatine (GABA+/Cr) and glutamate-glutamine relative to creatine (Glx/Cr) in the anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC) were measured using the Meshcher-Garwood point resolved spectroscopy (MEGA-PRESS) sequence. Metabolite ratios, cortical thickness, and executive function and their interrelationships were determined in the MCI and HC groups.

Results

Patients with MCI showed lower GABA+/Cr levels in the ACC and PCC. Combined levels of GABA+ and Glx in the ACC and GABA+ in the PCC showed good diagnostic efficacy for MCI (AUC: 0.82). But no differences in cortical thickness were found between the two groups. In the MCI group, lower GABA+/Cr level was correlated to worse performance on the digit span test backward, and the shape trail test-B. The cortical thickness was not associated with GABA+ levels and executive function in patients.

Conclusion

These results implied that decreased GABA levels in the ACC and PCC had a critical role in the early diagnosis of impaired executive function of MCI. Therefore, GABA in the ACC and PCC could be a potential diagnostic marker of the executive function decline of MCI.

Advances in diagnosing mild cognitive impairment and Alzheimer's disease using 11C-PIB- PET/CT and common neuropsychological tests

Alzheimer's disease (AD) is a critical health issue worldwide that has a negative impact on patients' quality of life, as well as on caregivers, society, and the environment. Positron emission tomography (PET)/computed tomography (CT) and neuropsychological scales can be used to identify AD and mild cognitive impairment (MCI) early, provide a differential diagnosis, and offer early therapies to impede the course of the illness. However, there are few reports of large-scale ¹¹C-PIB-PET/CT investigations that focus on the pathology of AD and MCI. Therefore, further research is needed to determine how neuropsychological test scales and PET/CT measurements of disease progression interact.

Challenges associated with using extracellular vesicles as biomarkers in neurodegenerative disease

INTRODUCTION

The hunt for new biomarkers - for the diagnosis of subcategories of disease, or for the monitoring of the efficacy of novel therapeutics - is an increasingly relevant challenge in the current era of precision medicine. In neurodegenerative research, the aim is to look for simple tools which can predict cognitive or motor decline early, and to determine whether these can also be used to test the efficacy of new interventions. Extracellular vesicles (EVs) are thought to play an important role in intercellular communication and have been shown to play a vital role in a number of diseases.

AREAS COVERED

The aim of this review is to examine what we know about EVs in neurodegeneration and to discuss their potential to be diagnostic and prognostic biomarkers in the future. It will cover the techniques used to isolate and study EVs and what is currently known about their presence in neurodegenerative diseases. In particular, we will discuss what is required for standardization in biomarker research, and the challenges associated with using EVs within this framework.

EXPERT OPINION

The technical challenges associated with isolating EVs consistently, combined with the complex techniques required for their efficient analysis, might preclude 'pure' EV populations from being used as effective biomarkers. Whilst biomarker discovery is important for more effective diagnosis, monitoring, prediction and prognosis in neurodegenerative disease, reproducibility and ease-of-use should be the priorities.

Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)

Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.

Compensatory Mechanisms in Early Alzheimer's Disease and Clinical Setting: The Need for Novel Neuropsychological Strategies

Despite advances in the detection of biomarkers and in the design of drugs that can slow the progression of Alzheimer's disease (AD), the underlying primary mechanisms have not been elucidated. The diagnosis of AD has notably improved with the development of neuroimaging techniques and cerebrospinal fluid biomarkers which have provided new information not available in the past. Although the diagnosis has advanced, there is a consensus among experts that, when making the diagnosis in a specific patient, many years have probably passed since the onset of the underlying processes, and it is very likely that the biomarkers in use and their cutoffs do not reflect the true critical points for establishing the precise stage of the ongoing disease. In this context, frequent disparities between current biomarkers and cognitive and functional performance in clinical practice constitute a major drawback in translational neurology. To our knowledge, the In-Out-test is the only neuropsychological test developed with the idea that compensatory brain mechanisms exist in the early stages of AD, and whose positive effects on conventional tests performance can be reduced in assessing episodic memory in the context of a dual-task, through which the executive auxiliary networks are 'distracted', thus uncover the real memory deficit. Furthermore, as additional traits, age and formal education have no impact on the performance of the In-Out-test.

A biomarker-validated time scale in years of disease progression has identified early- and late-onset subgroups in sporadic Alzheimer's disease

BACKGROUND

It is possible to calculate the number of years to the expected clinical onset (YECO) of autosomal-dominant Alzheimer's disease (adAD). A similar time scale is lacking for sporadic Alzheimer's disease (sAD). The purpose was to design and validate a time scale in YECO for patients with sAD in relation to CSF and PET biomarkers.

METHODS

Patients diagnosed with Alzheimer's disease (AD, n = 48) or mild cognitive impairment (MCI, n = 46) participated in the study. They underwent a standardized clinical examination at the Memory clinic, Karolinska University Hospital, Stockholm, Sweden, which included present and previous medical history, laboratory screening, cognitive assessment, CSF biomarkers (Aβ₄₂, total-tau, and p-tau), and an MRI of the brain. They were also assessed with two PET tracers, ¹¹C-Pittsburgh compound B and ¹⁸F-fluorodeoxyglucose. Assuming concordance of cognitive decline in sAD and adAD, YECO for these patients was calculated using equations for the relationship between cognitive performance, YECO, and years of education in adAD (Almkvist et al. J Int Neuropsychol Soc 23:195-203, 2017).

RESULTS

The mean current point of disease progression was 3.2 years after the estimated clinical onset in patients with sAD and 3.4 years prior to the estimated clinical onset in patients with MCI, as indicated by the median YECO from five cognitive tests. The associations between YECO and biomarkers were significant, while those between chronological age and biomarkers were nonsignificant. The estimated disease onset (chronological age minus YECO) followed a bimodal distribution with frequency maxima before (early-onset) and after (late-onset) 65 years of age. The early- and late-onset subgroups differed significantly in biomarkers and cognition, but after control for YECO, this difference disappeared for all except the APOE e4 gene (more frequent in early- than in late-onset).

CONCLUSIONS

A novel time scale in years of disease progression based on cognition was designed and validated in patients with AD using CSF and PET biomarkers. Two early- and late-disease onset subgroups were identified differing with respect to APOE e4.

Utility of Diffusion Modeling of Cogstate Brief Battery Test Performance in Detecting Mild Cognitive Impairment

Cognitive testing data are essential to the diagnosis of mild cognitive impairment (MCI), and computerized cognitive testing, such as the Cogstate Brief Battery, has proven helpful in efficiently identifying harbingers of dementia. This study provides a side-by-side comparison of traditional Cogstate outcomes and diffusion modeling of these outcomes in predicting MCI diagnosis. Participants included 257 older adults (160 = normal cognition; 97 = MCI). Results showed that both traditional Cogstate and diffusion modeling analyses predicted MCI diagnosis with acceptable accuracy. Cogstate measures of recognition learning and working memory accuracy and diffusion modeling variable of decision-making efficiency (drift rate) and nondecisional time were most predictive of MCI. While participants with normal cognition demonstrated a change in response caution (boundary separation) when transitioning tasks, participants with MCI did not evidence this change.

In Alzheimer's disease, amyloid beta accumulation is a protective mechanism that ultimately fails

HYPOTHESIS AND PREDICTIONS

Here, we claim that amyloid beta (Aβ) accumulation is a protective mechanism that ultimately fails. We predict that more Aβ accumulates in regions with higher rates of glucose metabolism, reaching a maximum followed by progression of pathology.

BACKGROUND

Aβ accumulation is characteristic of Alzheimer's disease (AD) but the accumulation does not correlate with cognitive decline, unlike the rates of glucose metabolism.

STRATEGY

We compared averaged and individual estimates of regional binding potentials of [11C]Pittsburgh compound B to regionally averaged and individual values of metabolism of [18F]fluorodeoxyglucose in brain regions of volunteers with AD.

SIGNIFICANCE

The claim explains the cognitive decline in some patients at a significantly lower level of Aβ deposition than in other patients, as well as the presence of cognitively healthy individuals with high Aβ accumulation. With further support of the hypothesis, the significance of Aβ accumulation in brains of patients with AD may require revision.

Change on the Repeatable Battery for the Assessment of Neuropsychological Status and its relationship to brain amyloid

BACKGROUND

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has been associated with commonly used biomarkers of Alzheimer's disease (AD), including brain amyloid plaque density. However, less is known about if changes in the RBANS across time are also related to brain amyloid deposition. The current study sought to expand on prior work by examining the relationship between changes over time on the RBANS and amyloid deposition via positron emission tomography (PET).

METHOD

One-hundred twenty-six older adults with intact or impaired cognition and daily functioning underwent repeat assessment with the RBANS across nearly 16 months, as well as had a baseline amyloid PET scan.

RESULTS

In the entire sample, amyloid deposition was significantly related to change on all five Indexes and the Total Scale score of the RBANS, with greater amyloid being associated with worsening cognition. This pattern was also observed in 11 of 12 subtests.

CONCLUSIONS

Whereas prior studies have identified a relationship between baseline RBANS and amyloid status, the current findings support that changes in the RBANS are also indicative of AD brain pathology, even if these findings are mediated by cognitive status. Although replication in a more diverse sample is needed, these results continue to support the use of the RBANS in AD clinical trials.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models

Background

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.

Objective

To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.

Materials and methods

We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.

Results

In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.

Conclusion

The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

Cognitive Trajectories and Associated Biomarkers in Patients with Mild Cognitive Impairment

BACKGROUND

To diagnose mild cognitive impairment (MCI) patients at risk of progression to dementia is clinically important but challenging.

OBJECTIVE

We classified MCI patients based on cognitive trajectories and compared biomarkers among groups.

METHODS

This study analyzed amnestic MCI patients with at least three Clinical Dementia Rating (CDR) scores available over a minimum of 36 months from the Alzheimer's Disease Neuroimaging Initiative database. Patients were classified based on their progression using trajectory modeling with the CDR-sum of box scores. We compared clinical and neuroimaging biomarkers across groups.

RESULTS

Of 569 eligible MCI patients (age 72.7±7.4 years, women n = 223), three trajectory groups were identified: stable (58.2%), slow decliners (24.6%), and fast decliners (17.2%). In the fifth year after diagnosis, the CDR-sum of box scores increased by 1.2, 5.4, and 11.8 points for the stable, slow, and fast decliners, respectively. Biomarkers associated with cognitive decline were amyloid-β 42, total tau, and phosphorylated tau protein in cerebrospinal fluid, hippocampal volume, cortical metabolism, and amount of cortical and subcortical amyloid deposits. Cortical metabolism and the amount of amyloid deposits were associated with the rate of cognitive decline.

CONCLUSION

Data-driven trajectory analysis provides new insights into the various cognitive trajectories of MCI. Baseline brain metabolism, and the amount of cortical and subcortical amyloid burden can provide additional information on the rate of cognitive decline.

Employing nanoparticle tracking analysis of salivary neuronal exosomes for early detection of neurodegenerative diseases

Neurodegenerative diseases are a set of progressive and currently incurable diseases that are primarily caused by neuron degeneration. Neurodegenerative diseases often lead to cognitive impairment and dyskinesias. It is now well recognized that molecular events precede the onset of clinical symptoms by years. Over the past decade, intensive research attempts have been aimed at the early diagnosis of these diseases. Recently, exosomes have been shown to play a pivotal role in the occurrence and progression of many diseases including cancer and neurodegenerative diseases. Additionally, because exosomes can cross the blood-brain barrier, they may serve as a diagnostic tool for neural dysfunction. In this review, we detail the mechanisms and current challenges of these diseases, briefly review the role of exosomes in the progression of neurodegenerative diseases, and propose a novel strategy based on salivary neuronal exosomes and nanoparticle tracking analysis that could be employed for screening the early onset of neurodegenerative diseases.

Partial Least Squares Regression Analysis of Alzheimer's Disease Biomarkers, Modifiable Health Variables, and Cognitive Change in Older Adults with Mild Cognitive Impairment

BACKGROUND

Prior work has shown that certain modifiable health, Alzheimer's disease (AD) biomarker, and demographic variables are associated with cognitive performance. However, less is known about the relative importance of these different domains of variables in predicting longitudinal change in cognition.

OBJECTIVE

Identify novel relationships between modifiable physical and health variables, AD biomarkers, and slope of cognitive change over two years in a cohort of older adults with mild cognitive impairment (MCI).

METHODS

Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, and AD pathology were assessed in 123 older adults with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (mean age = 73.9; SD = 7.6; mean education = 16.0; SD = 3.0). Partial least squares regression (PLSR)-a multivariate method which creates components that best predict an outcome-was used to identify whether these physiological variables were important in predicting slope of change in episodic memory or executive function over two years.

RESULTS

At two-year follow-up, the two PLSR models predicted, respectively, 20.0% and 19.6% of the variance in change in episodic memory and executive function. Baseline levels of AD biomarkers were important in predicting change in both episodic memory and executive function. Baseline education and neurotrophic/growth factors were important in predicting change in episodic memory, whereas cardiometabolic variables such as blood pressure and cholesterol were important in predicting change in executive function.

CONCLUSION

These data-driven analyses highlight the impact of AD biomarkers on cognitive change and further clarify potential domain specific relationships with predictors of cognitive change.

Repeatable battery for the assessment of neuropsychological status and its relationship to biomarkers of Alzheimer's disease

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has been associated with commonly used biomarkers of Alzheimer's disease (AD). However, prior studies have typically utilized small and poorly characterized samples, and they have not analyzed the subtests of the RBANS. The current study sought to expand on prior work by examining the relationship between the Indexes and subtest scores of the RBANS and three AD biomarkers: amyloid deposition via positron emission tomography, hippocampal volume via magnetic resonance imaging, and APOE ε4 status.

One-hundred twenty-one older adults across the AD continuum (intact, amnestic Mild Cognitive Impairment, mild AD), who were mostly Caucasian and well-educated, underwent assessment with the RBANS and collection of the three biomarkers.

Greater amyloid deposition was significantly related to lower scores on all five Indexes and the Total Scale score of the RBANS, as well as 11 of 12 subtests. For bilateral hippocampal volume, significant correlations were observed for 4 of the 5 Indexes, Total Scale score, and 9 of 12 subtests, with smaller hippocampi being related to lower RBANS scores. Participants with at least one APOE ε4 allele had significantly lower scores on 3 of the 5 Indexes, Total Scale score, and 8 of the 12 subtests.

In this sample of participants across the dementia spectrum, most RBANS Indexes and subtests showed relationships with the amyloid deposition, hippocampal volumes, and APOE status, with poorer performance on the RBANS being associated with biomarker positivity. Although memory scores on the RBANS have traditionally been linked to biomarkers in AD, other Index and subtest scores also hold promise as indicators of AD. Replication in a more diverse sample is needed.

Early-onset Alzheimer's disease with depression as the first symptom: a case report with literature review

Background

Alzheimer's disease is a common neurodegenerative disease, and patients with early-onset Alzheimer's disease (onset age < 65 years) often have atypical symptoms, which are easily misdiagnosed and missed. Multimodality neuroimaging has become an important diagnostic and follow-up method for AD with its non-invasive and quantitative advantages.

Case presentation

We report a case of a 59-year-old female with a diagnosis of depression at the age of 50 after a 46-year-old onset and a 9-year follow-up observation, who developed cognitive dysfunction manifested by memory loss and disorientation at the age of 53, and eventually developed dementia. Combined with neuropsychological scales (MMSE and MOCA scores decreased year by year and finally reached the dementia criteria) and the application of multimodal imaging. MRI showed that the hippocampus atrophied year by year and the cerebral cortex was extensively atrophied. 18F-FDG PET image showed hypometabolism in right parietal lobes, bilateral frontal lobes, bilateral joint parieto-temporal areas, and bilateral posterior cingulate glucose metabolism. The 18F-AV45 PET image showed the diagnosis of early-onset Alzheimer's disease was confirmed by the presence of Aβ deposits in the cerebral cortex.

Conclusion

Early-onset Alzheimer's disease, which starts with depression, often has atypical symptoms and is prone to misdiagnosis. The combination of neuropsychological scales and neuroimaging examinations are good screening tools that can better assist in the early diagnosis of Alzheimer's disease. Graphical Abstract.

Amyloid-β PET in Alzheimer's disease: A systematic review and Bayesian meta-analysis

BACKGROUND

In recent years, longitudinal studies of Alzheimer's disease (AD) have been successively concluded. Our aim is to determine the efficacy of amyloid-β (Aβ) PET in diagnosing AD and early prediction of mild cognitive impairment (MCI) converting to AD. By pooling studies from different centers to explore in-depth whether diagnostic performance varies by population type, radiotracer type, and diagnostic approach, thus providing a more comprehensive theoretical basis for the subsequent widespread application of Aβ PET in the clinical setting.

METHODS

Relevant studies were searched through PubMed. The pooled sensitivities, specificities, DOR, and the summary ROC curve were obtained based on a Bayesian random-effects model.

RESULTS

Forty-eight studies, including 5967 patients, were included. Overall, the pooled sensitivity, specificity, DOR, and AUC of Aβ PET for diagnosing AD were 0.90, 0.80, 35.68, and 0.91, respectively. Subgroup analysis showed that Aβ PET had high sensitivity (0.91) and specificity (0.81) for differentiating AD from normal controls but very poor specificity (0.49) for determining AD from MCI. The pooled sensitivity and specificity were 0.84 and 0.62, respectively, for predicting the conversion of MCI to AD. The differences in diagnostic efficacy between visual assessment and quantitative analysis and between ¹¹ C-PIB PET and ¹⁸ F-florbetapir PET were insignificant.

CONCLUSIONS

The overall performance of Aβ PET in diagnosing AD is favorable, but the differentiation between MCI and AD patients should consider that some MCI may be at risk of conversion to AD and may be misdiagnosed. A multimodal diagnostic approach and machine learning analysis may be effective in improving diagnostic accuracy.

Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI

BACKGROUND

Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM.

OBJECTIVE

In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls.

METHODS

By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons.

RESULTS

Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus.

CONCLUSION

This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.

Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis

INTRODUCTION

Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence.

METHODS

A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods.

RESULTS

Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers.

DISCUSSION

On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.

Exploration of salient risk factors involved in mild cognitive impairment

Mild cognitive impairment (MCI) is a prevalent and complex condition among older adults that often progresses into Alzheimer's disease (AD). Although MCI affects individuals differently, there are specific indicators of risk commonly associated with the development of MCI. The present study explored the prevalence of seven established MCI risk categories within a large sample of older adults with and without MCI. We explored trends across the different diagnostic groups and extracted the most salient risk factors related to MCI using partial least squares. Neuropsychological risk categories showed the largest differences across groups, with the cognitively unimpaired groups outperforming the MCI groups on all measures. Apolipoprotein E4 (ApoE4) carriers were significantly more common among the more severe MCI group, whereas ApoE4 non-carriers were more common in the healthy controls. Participants with subjective and objective cognitive impairment were trending towards AD-like cerebral spinal fluid (CSF) biomarker levels. Increased age, being male and having fewer years of education were identified as important risk factors of MCI. Higher CSF tau levels were correlated with ApoE4 carrier status, age and a decrease in the ability to carry out daily activities across all diagnostic groups. Amyloid beta_1-42 CSF concentration was positively correlated with cognitive and memory performance and non-ApoE4 carrier status regardless of diagnostic status. Unlike previous research, poor cardiovascular health or being female had no relation to MCI. Altogether, the results highlighted risk factors that were specific to persons with MCI, findings that will inform future research in healthy aging, MCI and AD.

Associations between cerebrospinal fluid markers and cognition in ageing and dementia: A systematic review

A biomarker associated with cognition in neurodegenerative dementias would aid in the early detection of disease progression, complement clinical staging and act as a surrogate endpoint in clinical trials. The current systematic review evaluates the association between cerebrospinal fluid protein markers of synapse loss and neuronal injury and cognition. We performed a systematic search which revealed 67 studies reporting an association between cerebrospinal fluid markers of interest and neuropsychological performance. Despite the substantial heterogeneity between studies, we found some evidence for an association between neurofilament-light and worse cognition in Alzheimer's diseases, frontotemporal dementia and typical cognitive ageing. Moreover, there was an association between cerebrospinal fluid neurogranin and cognition in those with an Alzheimer's-like cerebrospinal fluid biomarker profile. Some evidence was found for cerebrospinal fluid neuronal pentraxin-2 as a correlate of cognition across dementia syndromes. Due to the substantial heterogeneity of the field, no firm conclusions can be drawn from this review. Future research should focus on improving standardization and reporting as well as establishing the importance of novel markers such as neuronal pentraxin-2 and whether such markers can predict longitudinal cognitive decline.

Differential Diagnosis of Alzheimer Disease vs. Mild Cognitive Impairment Based on Left Temporal Lateral Lobe Hypomethabolism on 18F-FDG PET/CT and Automated Classifiers

Purpose: We evaluate the ability of Artificial Intelligence with automatic classification methods applied to semi-quantitative data from brain ¹⁸F-FDG PET/CT to improve the differential diagnosis between Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). Procedures: We retrospectively analyzed a total of 150 consecutive patients who underwent diagnostic evaluation for suspected AD (n = 67) or MCI (n = 83). All patients received brain 18F-FDG PET/CT according to the international guidelines, and images were analyzed both Qualitatively (QL) and Quantitatively (QN), the latter by a fully automated post-processing software that produced a z score metabolic map of 25 anatomically different cortical regions. A subset of n = 122 cases with a confirmed diagnosis of AD (n = 53) or MDI (n = 69) by 18–24-month clinical follow-up was finally included in the study. Univariate analysis and three automated classification models (classification tree –ClT-, ridge classifier –RC- and linear Support Vector Machine –lSVM-) were considered to estimate the ability of the z scores to discriminate between AD and MCI cases in. Results: The univariate analysis returned 14 areas where the z scores were significantly different between AD and MCI groups, and the classification accuracy ranged between 74.59% and 76.23%, with ClT and RC providing the best results. The best classification strategy consisted of one single split with a cut-off value of ≈ −2.0 on the z score from temporal lateral left area: cases below this threshold were classified as AD and those above the threshold as MCI. Conclusions: Our findings confirm the usefulness of brain 18F-FDG PET/CT QL and QN analyses in differentiating AD from MCI. Moreover, the combined use of automated classifications models can improve the diagnostic process since its use allows identification of a specific hypometabolic area involved in AD cases in respect to MCI. This data improves the traditional 18F-FDG PET/CT image interpretation and the diagnostic assessment of cognitive disorders.

The Key Role of Magnetic Resonance Imaging in the Detection of Neurodegenerative Diseases-Associated Biomarkers: A Review

Neurodegenerative diseases (NDs), including chronic disease such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis, and acute diseases like traumatic brain injury and ischemic stroke are characterized by progressive degeneration, brain tissue damage and loss of neurons, accompanied by behavioral and cognitive dysfunctions. So far, there are no complete cures for NDs; thus, early and timely diagnoses are essential and beneficial to patients' treatment. Magnetic resonance imaging (MRI) has become one of the advanced medical imaging techniques widely used in the clinical examination of NDs due to its non-invasive diagnostic value. In this review, research published in English in current decade from PubMed electronic database on the use of MRI to detect specific biomarkers of NDs was collected, summarized, and discussed, which provides valuable suggestions for the early diagnosis, prevention, and treatment of NDs in the clinic.

Vascular burden and cognition: Mediating roles of neurodegeneration and amyloid PET

It remains unclear to what extent cerebrovascular burden relates to amyloid beta (Aβ) deposition, neurodegeneration, and cognitive dysfunction in mixed disease populations with small vessel disease and Alzheimer's disease (AD) pathology. In 120 subjects, we investigated the association of vascular burden (white matter hyperintensity [WMH] volumes) with cognition. Using mediation analyses, we tested the indirect effects of WMH on cognition via Aβ deposition (¹⁸ F-AV45 positron emission tomography [PET]) and neurodegeneration (cortical thickness or ¹⁸ F fluorodeoxyglucose PET) in AD signature regions. We observed that increased total WMH volume was associated with poorer performance in all tested cognitive domains, with the strongest effects observed for semantic fluency. These relationships were mediated mainly via cortical thinning, particularly of the temporal lobe, and to a lesser extent serially mediated via Aβ and cortical thinning of AD signature regions. WMH volumes differentially impacted cognition depending on lobar location and Aβ status. In summary, our study suggests mainly an amyloid-independent pathway in which vascular burden affects cognitive function via localized neurodegeneration. HIGHLIGHTS: Alzheimer's disease often co-exists with vascular pathology. We studied a unique cohort enriched for high white matter hyperintensities (WMH). High WMH related to cognitive impairment of semantic fluency and executive function. This relationship was mediated via temporo-parietal atrophy rather than metabolism. This relationship was, to lesser extent, serially mediated via amyloid beta and atrophy.

Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.

Discriminating Aging Cognitive Decline Spectrum Using PET and Magnetic Resonance Image Features

Background: The population aging increased the prevalence of brain diseases, like Alzheimer’s disease (AD), and early identification of individuals with higher odds of cognitive decline is essential to maintain quality of life. Imaging evaluation of individuals at risk of cognitive decline includes biomarkers extracted from brain positron emission tomography (PET) and structural magnetic resonance imaging (MRI). Objective: We propose investigating ensemble models to classify groups in the aging cognitive decline spectrum by combining features extracted from single imaging modalities and combinations of imaging modalities (FDG+AMY+MRI, and a PET ensemble). Methods: We group imaging data of 131 individuals into four classes related to the individuals’ cognitive assessment in baseline and follow-up: stable cognitive non-impaired; individuals converting to mild cognitive impairment (MCI) syndrome; stable MCI; and Alzheimer’s clinical syndrome. We assess the performance of four algorithms using leave-one-out cross-validation: decision tree classifier, random forest (RF), light gradient boosting machine (LGBM), and categorical boosting (CAT). The performance analysis of models is evaluated using balanced accuracy before and after using Shapley Additive exPlanations with recursive feature elimination (SHAP-RFECV) method. Results: Our results show that feature selection with CAT or RF algorithms have the best overall performance in discriminating early cognitive decline spectrum mainly using MRI imaging features. Conclusion: Use of CAT or RF algorithms with SHAP-RFECV shows good discrimination of early stages of aging cognitive decline, mainly using MRI image features. Further work is required to analyze the impact of selected brain regions and their correlation with cognitive decline spectrum.

Effects of biophilic virtual reality on cognitive function of patients undergoing laparoscopic surgery: study protocol for a sham randomised controlled trial

INTRODUCTION

Virtual reality (VR) is already being used for cognitive or emotional rehabilitation. However, its role in postoperative cognitive dysfunction (POCD) has not been fully recognised. Due to the lack of an effective postoperative follow-up system, the incidence of POCD in China is not clear, and although many drugs have been proposed to improve POCD in the animal study, their clinical applications are limited, while VR provides an innovative method to provide non-pharmacological management.

METHODS AND DESIGN

This is a single-centre, randomised, double-blind, sham-controlled clinical trial. In this study, 600 patients over 55 years old undergoing laparoscopic surgery will be recruited. Participants will be randomly assigned to receive biophilic VR or sham VR (1:1 ratio), all patients have 20 min of exposure per day during the hospital stay. The primary outcome is the impact of VR on the incidence of POCD. Secondary outcomes include perioperative anxiety and instrumental activities of daily living. Changes in the performance of the neurocognitive batteries are measured by a local resident doctor. Serum samples will be collected on the day before surgery and 7 days after surgery.

ETHICS AND DISSEMINATION

This trial has ethical approval from the Medical Ethics Committee of the Affiliated Hospital of Medical School of Ningbo University (KY20210302). The study is sponsored by Ningbo University and Ningbo Science and Technology Bureau.

CONTACT

Dr. Mao Haijiao, Chair of the hospital medical Ethics committee (ndfylunli@126.com). Trial results will be submitted for publication in peer-reviewed journals, patient recruitment began in April 2021. Written informed consent is obtained for all participants. All information acquired will be disseminated via national or international conferences and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ChiCTR2000040919.

A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers

Alzheimer’s disease (AD) is a neurodegenerative disorder with an insidious onset and irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of converting to AD. Early diagnosis of unstable MCI patients is therefore vital for slowing the progression to AD. However, current diagnostic methods are either highly invasive or expensive, preventing their wide applications. Developing low-invasive and cost-efficient screening methods is desirable as the first-tier approach for identifying unstable MCI patients or excluding stable MCI patients. This study developed feature selection and machine learning algorithms to identify blood-sample gene biomarkers for predicting stable MCI patients. Two datasets obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were utilized to conclude 29 genes biomarkers (31 probes) for predicting stable MCI patients. A random forest-based classifier performed well with area under the receiver operating characteristic curve (AUC) values of 0.841 and 0.775 for cross-validation and test datasets, respectively. For patients with a prediction score greater than 0.9, an excellent concordance of 97% was obtained, showing the usefulness of the proposed method for identifying stable MCI patients. In the context of precision medicine, the proposed prediction model is expected to be useful for identifying stable MCI patients and providing medical doctors and patients with new first-tier diagnosis options.

The Laterality of Early Age-Related Hearing Loss and Brain β-Amyloid

OBJECTIVE

Worse hearing was linked to higher brain β-amyloid, a pathologic hallmark of Alzheimer's disease, in a recent study. We analyze the associations between β-amyloid and early age-related hearing loss in the right versus left ear to explore the laterality of this relationship.

STUDY DESIGN

Cross-sectional analysis of a prospective cohort study.

SETTING

Tertiary referral center.

PARTICIPANTS

Ninety-eight late middle-age adults.

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

The primary outcome was whole brain and regional β-amyloid standardized uptake value ratio (SUVR) on positron emission tomography. The exposure was hearing in the right and left ear, measured by pure tone average (PTA) and word recognition score (WRS). Linear regression analyzed the association between β-amyloid and hearing in each ear, adjusting for potential confounders, including age, gender, education, cardiovascular disease, and hearing aid use.

RESULTS

Mean age ± standard deviation was 64.3 ± 3.5 years. Mean PTA was 20.4 ± 8.8 dB. Multivariable regression adjusting for covariates demonstrated that a 10 dB worsening in PTA in the left ear was associated with significantly higher β-amyloid (SUVR) in the bilateral cingulate gyri (right coefficient: 0.029 [95% confidence interval: 0.003-0.054]; left: 0.029 [0.003-0.055]), bilateral frontal lobes (right: 0.024 [0.002-0.047]; left: 0.028 [0.006-0.049]), and the right temporal lobe (0.019 [0.002-0.037]). Consistent results were observed when WRS served as the exposure. No associations were observed between β-amyloid and PTA or WRS in the right ear.

CONCLUSIONS

Worse hearing in the left ear, but not the right ear, was associated with higher β-amyloid. This might relate to asymmetric central auditory processing.

Association of CSF, Plasma, and Imaging Markers of Neurodegeneration With Clinical Progression in People With Subjective Cognitive Decline

Background and Objectives

Multiple biomarkers have been suggested to measure neurodegeneration (N) in the AT(N) framework, leading to inconsistencies between studies. We investigated the association of 5 N biomarkers with clinical progression and cognitive decline in individuals with subjective cognitive decline (SCD).

Methods

We included individuals with SCD from the Amsterdam Dementia Cohort and SCIENCe project, a longitudinal cohort study (follow-up 4±3 years). We used the following N biomarkers: CSF total tau (t-tau), medial temporal atrophy visual rating on MRI, hippocampal volume (HV), serum neurofilament light (NfL), and serum glial fibrillary acidic protein (GFAP). We determined correlations between biomarkers. We assessed associations between N biomarkers and clinical progression to mild cognitive impairment or dementia (Cox regression) and Mini-Mental State Examination (MMSE) over time (linear mixed models). Models included age, sex, CSF β-amyloid (Aβ) (A), and CSF p-tau (T) as covariates, in addition to the N biomarker.

Result

We included 401 individuals (61±9 years, 42% female, MMSE 28 ± 2, vascular comorbidities 8%–19%). N biomarkers were modestly to moderately correlated (range r −0.28 – 0.58). Serum NfL and GFAP correlated most strongly (r 0.58, p < 0.01). T-tau was strongly correlated with p-tau (r 0.89, p < 0.01), although these biomarkers supposedly represent separate biomarker groups. All N biomarkers individually predicted clinical progression, but only HV, NfL, and GFAP added predictive value beyond Aβ and p-tau (hazard ratio 1.52 [95% CI 1.11–2.09]; 1.51 [1.05–2.17]; 1.50 [1.04–2.15]). T-tau, HV, and GFAP individually predicted MMSE slope (range β −0.17 to −0.11, p < 0.05), but only HV remained associated beyond Aβ and p-tau (β −0.13 [SE 0.04]; p < 0.05).

Discussion

In cognitively unimpaired older adults, correlations between different N biomarkers were only moderate, indicating they reflect different aspects of neurodegeneration and should not be used interchangeably. T-tau was strongly associated with p-tau (T), which makes it less desirable to use as a measure for N. HV, NfL, and GFAP predicted clinical progression beyond A and T. Our results do not allow to choose one most suitable biomarker for N, but illustrate the added prognostic value of N beyond A and T.

Classification of Evidence

This study provides Class II evidence that HV, NfL, and GFAP predicted clinical progression beyond A and T in individuals with SCD.

Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.

Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks

Background: In recent years, predicting and modeling the progression of Alzheimer's disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data. Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer's disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers. Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD. Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

Neurodegenerative Disorders of Alzheimer, Parkinsonism, Amyotrophic Lateral Sclerosis and Multiple Sclerosis: An Early Diagnostic Approach for Precision Treatment

Neurodegenerative diseases (NDs) are characterised by progressive dysfunction of synapses, neurons, glial cells and their networks. Neurodegenerative diseases can be classified according to primary clinical features (e.g., dementia, parkinsonism, or motor neuron disease), anatomic distribution of neurodegeneration (e.g., frontotemporal degenerations, extrapyramidal disorders, or spinocerebellar degenerations), or principal molecular abnormalities. The most common neurodegenerative disorders are amyloidosis, tauopathies, a-synucleinopathy, and TAR DNA-binding protein 43 (TDP-43) proteopathy. The protein abnormalities in these disorders have abnormal conformational properties along with altered cellular mechanisms, and they exhibit motor deficit, mitochondrial malfunction, dysfunctions in autophagic-lysosomal pathways, synaptic toxicity, and more emerging mechanisms such as the roles of stress granule pathways and liquid-phase transitions. Finally, for each ND, microglial cells have been reported to be implicated in neurodegeneration, in particular, because the microglial responses can shift from neuroprotective to a deleterious role. Growing experimental evidence suggests that abnormal protein conformers act as seed material for oligomerization, spreading from cell to cell through anatomically connected neuronal pathways, which may in part explain the specific anatomical patterns observed in brain autopsy sample. In this review, we mention the human pathology of select neurodegenerative disorders, focusing on how neurodegenerative disorders (i.e., Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis) represent a great healthcare problem worldwide and are becoming prevalent because of the increasing aged population. Despite many studies have focused on their etiopathology, the exact cause of these diseases is still largely unknown and until now with the only available option of symptomatic treatments. In this review, we aim to report the systematic and clinically correlated potential biomarker candidates. Although future studies are necessary for their use in early detection and progression in humans affected by NDs, the promising results obtained by several groups leads us to this idea that biomarkers could be used to design a potential therapeutic approach and preclinical clinical trials for the treatments of NDs.

Changes in Brain Volume Resulting from Cognitive Intervention by Means of the Feuerstein Instrumental Enrichment Program in Older Adults with Mild Cognitive Impairment (MCI): A Pilot Study

There is increasing interest in identifying biological and imaging markers for the early detection of neurocognitive decline. In addition, non-pharmacological strategies, including physical exercise and cognitive interventions, may be beneficial for those developing cognitive impairment. The Feuerstein Instrumental Enrichment (FIE) Program is a cognitive intervention based on structural cognitive modifiability and the mediated learning experience (MLE) and aims to promote problem-solving strategies and metacognitive abilities. The FIE program uses a variety of instruments to enhance the cognitive capacity of the individual as a result of mediation. A specific version of the FIE program was developed for the cognitive enhancement of older adults, focusing on strengthening orientation skills, categorization skills, deductive reasoning, and memory. We performed a prospective interventional pilot observational study on older subjects with MCI who participated in 30 mediated FIE sessions (two sessions weekly for 15 weeks). Of the 23 subjects who completed the study, there was a significant improvement in memory on the NeuroTrax cognitive assessment battery. Complete sets of anatomical MRI data for voxel-based morphometry, taken at the beginning and the end of the study, were obtained from 16 participants (mean age 83.5 years). Voxel-based morphometry showed an interesting and unexpected increase in grey matter (GM) in the anterolateral occipital border and the middle cingulate cortex. These initial findings of our pilot study support the design of randomized trials to evaluate the effect of cognitive training using the FIE program on brain volumes and cognitive function.

Psychological Stress as a Risk Factor for Accelerated Cellular Aging and Cognitive Decline: The Involvement of Microglia-Neuron Crosstalk

The relationship between the central nervous system (CNS) and microglia is lifelong. Microglia originate in the embryonic yolk sac during development and populate the CNS before the blood-brain barrier forms. In the CNS, they constitute a self-renewing population. Although they represent up to 10% of all brain cells, we are only beginning to understand how much brain homeostasis relies on their physiological functions. Often compared to a double-edged sword, microglia hold the potential to exert neuroprotective roles that can also exacerbate neurodegeneration once compromised. Microglia can promote synaptic growth in addition to eliminating synapses that are less active. Synaptic loss, which is considered one of the best pathological correlates of cognitive decline, is a distinctive feature of major depressive disorder (MDD) and cognitive aging. Long-term psychological stress accelerates cellular aging and predisposes to various diseases, including MDD, and cognitive decline. Among the underlying mechanisms, stress-induced neuroinflammation alters microglial interactions with the surrounding parenchymal cells and exacerbates oxidative burden and cellular damage, hence inducing changes in microglia and neurons typical of cognitive aging. Focusing on microglial interactions with neurons and their synapses, this review discusses the disrupted communication between these cells, notably involving fractalkine signaling and the triggering receptor expressed on myeloid cells (TREM). Overall, chronic stress emerges as a key player in cellular aging by altering the microglial sensome, notably via fractalkine signaling deficiency. To study cellular aging, novel positron emission tomography radiotracers for TREM and the purinergic family of receptors show interest for human study.

Prodromal frontotemporal dementia: clinical features and predictors of progression

BACKGROUND

The prodromal phase of frontotemporal dementia (FTD) is still not well characterized, and conversion rates to dementia and predictors of progression at 1-year follow-up are currently unknown.

METHODS

In this retrospective study, disease severity was assessed using the global CDR plus NACC FTLD. Prodromal FTD was defined to reflect mild cognitive or behavioural impairment with relatively preserved functional independence (global CDR plus NACC = 0.5) as well as mild, moderate and severe dementia (classified as global CDR plus NACC = 1, 2, 3, respectively). Disease progression at 1-year follow-up and serum NfL measurements were acquired in a subgroup of patients.

RESULTS

Of 563 participants, 138 were classified as prodromal FTD, 130 as mild, 175 as moderate and 120 as severe FTD. In the prodromal and mild phases, we observed an early increase in serum NfL levels followed by behavioural disturbances and deficits in executive functions. Negative symptoms, such as apathy, inflexibility and loss of insight, predominated in the prodromal phase. Serum NfL levels were significantly increased in the prodromal phase compared with healthy controls (average difference 14.5, 95% CI 2.9 to 26.1 pg/mL), but lower than in patients with mild FTD (average difference -15.5, 95% CI -28.4 to -2.7 pg/mL). At 1-year follow-up, 51.2% of patients in the prodromal phase had converted to dementia. Serum NfL measurements at baseline were the strongest predictors of disease progression at 1-year follow-up (OR 1.07, 95% CI 1.03 to 1.11, p < 0.001).

CONCLUSIONS

Prodromal FTD is a mutable stage with high rate of progression to fully symptomatic disease at 1-year follow-up. High serum NfL levels may support prodromal FTD diagnosis and represent a helpful marker to assess disease progression.