Brain PET in the diagnosis of Alzheimer's disease

Intrinsic aerobic capacity modulates Alzheimer's disease pathological hallmarks, brain mitochondrial function and proteome during aging

Low aerobic capacity is strongly associated with all-cause mortality and risk for Alzheimer's disease (AD). Individuals with early dementia and AD have lower aerobic capacity compared to age-matched controls. The mechanism by which aerobic capacity influences AD risk is unknown but is likely mediated by sexual dimorphism and tissue-level differences in mitochondrial energetics. Here, we used rats selectively bred for large differences in intrinsic aerobic exercise capacity. Brain tissue from 18-month and 24-month-old female and male low-capacity runner (LCR) and high-capacity runner (HCR) rats were analyzed for markers of mitochondrial function and AD-associated pathologies. LCR rats, irrespective of sex, exhibited a greater increase in brain amyloid beta (Aβ42) and tau hyperphosphorylation (pTauthr181/total tau) with aging. In female LCR rats, brain mitochondrial respiration at states 3, 4, and FCCP-induced uncoupling, when stimulated with pyruvate/malate, was reduced at 18 and 24 months, leading to lower ATP-linked mitochondrial respiration compared to mitochondria from HCR rats. Male LCR rats also showed reduced complex II-stimulated mitochondrial respiration (succinate + rotenone) at 24 months compared to HCR rats. Differences in mitochondrial respiration were associated with tau hyperphosphorylation and Aβ42 alterations in both HCR and LCR strains. Proteomic analysis unveiled a distinct difference in the mitochondrial proteome, wherein female LCR rats displayed diminished mitochondrial translation and oxidative phosphorylation (OXPHOS) proteins at 18 months compared to female HCR rats. Conversely, male LCR rats exhibited increased OXPHOS protein abundance but reduced tricarboxylic acid (TCA) cycle proteins compared to male HCR rats. These findings underscore a robust association between intrinsic aerobic exercise capacity, brain mitochondrial function, and AD pathologies during aging.

Use of lecanemab for the treatment of Alzheimer's disease: A systematic review

PURPOSE

The US Food and Drug Administration authorized lecanemab for the therapeutic use of Alzheimer's disease (AD) in January 2023. To assess the effectiveness and safety of lecanemab in treating AD, we thoroughly examined the studies that are currently accessible.

METHOD

Preferred Reporting Items for Systematic Reviews and Meta-Analysis recommendations were followed. In order to find relevant studies on lecanemab, we carried out a thorough literature search utilizing the electronic databases MEDLINE via PubMed, Cochrane, Web of Science, EBSCOhost, and Scopus. Excluding any research using experimental animals, we looked at lecanemab's effectiveness and side effects in treating AD in human clinical trials. Three randomized controlled studies were included.

FINDINGS

According to studies, lecanemab lessens clinical deterioration and reduces brain amyloid-beta plaques (difference,.45; 95% confidence interval,.67 to.23; p < .001). Participants who received lecanemab saw a greater frequency of amyloid-related imaging abnormalities (ARIA)-H (17.3% vs. 9.0%) and ARIA-E (12.6% vs. 1.7%), which is a significant adverse outcome.

CONCLUSION

Lecanemab has been shown to have an impact on the two primary pathophysiologic indicators of AD (Aβ and tau). There are still a lot of unresolved issues related to lecanemab. Future research on the effectiveness and safety of lecanemab is advised in order to determine that the advantages of this medication exceed the disadvantages.

Radiographers' perceptions of the experiences of patients with dementia attending the radiology department

BACKGROUND

Patients with dementia frequently present to the Radiology Department. However, stigmas have been recorded amongst radiographers surrounding imaging persons with dementia (PwD). This study aimed to investigate the impact of PwD attending the Radiology Department for imaging and the resultant effects to all patients, radiographers, and the Department from the perspectives of the examining radiographer.

METHODS

A paper-based questionnaire of radiographers' perceptions and experiences of individual examinations 'termed an interaction form' was created and made available in a public hospital in Ireland for a period of eight weeks. Radiographers completed the interaction form collecting data regarding individual imaging examinations of PwD. The form comprised sixteen closed and one open-ended question on the radiographers' individual perspectives of PwDs' abilities and distress levels, carers and comforters and their role in the examination, what the radiographer found helpful in the interaction, and any adverse events. Data were analysed using a combination of descriptive analysis and thematic content analysis.

RESULTS

Thirty-three interaction forms were completed by the participating radiographers. The modality most commonly represented in the survey was general X-ray (58%). Radiographers reported 84% of examinations for PwD required extra time, with 27% of examinations required repeat imaging and 69% of patients appeared distressed. A carer helped facilitate the completion of 77% of examinations. Qualitative data indicated that distractive and communicative techniques were used by radiographers to make the patient feel more comfortable and help with examination success.

CONCLUSION

PwD often require more time for radiological examinations, they often need repeat imaging and re-scheduling of an examination at a more appropriate time. These factors need to be considered when scheduling and performing radiological examinations. Patient distress was frequently encountered, this area may benefit from further research and dedicated practitioner training which could help drive improvements in patient experience.

Towards Early Diagnosis and Screening of Alzheimer's Disease Using Frequency Locked Whispering Gallery Mode Microtoroid Biosensors

Alzheimer's disease (AD) is a progressive form of dementia affecting almost 55 million people worldwide. It is characterized by the abnormal deposition of amyloid plaques and neurofibrillary tangles within the brain, leading to a pathological cascade of neuron degeneration and death as well as memory loss and cognitive decline. Amyloid beta (Aβ) is an AD biomarker present in cerebrospinal fluid and blood serum and correlates with the presence of amyloid plaques and tau tangles in the brain. Measuring the levels of Aβ can help with early diagnosis of AD, which is key for studying novel AD drugs and delaying the symptoms of dementia. However, this goal is difficult to achieve due to the low levels of AD biomarkers in biofluids. Here we demonstrate for the first time the use of FLOWER (frequency locked optical whispering evanescent resonator) for quantifying the levels of post-mortem cerebrospinal fluid (CSF) Aβ42 in clinicopathologically classified control, mild cognitive impairment (MCI), and AD participants. FLOWER is capable of measuring CSF Aβ42 (area under curve, AUC = 0.92) with higher diagnostic performance than standard ELISA (AUC = 0.82) and was also able to distinguish between control and MCI samples. Our results demonstrate the capability of FLOWER for screening CSF samples for early diagnosis of Alzheimer's pathology.

Protein Oxidation in Aging and Alzheimer's Disease Brain

Proteins are essential molecules that play crucial roles in maintaining cellular homeostasis and carrying out biological functions such as catalyzing biochemical reactions, structural proteins, immune response, etc. However, proteins also are highly susceptible to damage by reactive oxygen species (ROS) and reactive nitrogen species (RNS). In this review, we summarize the role of protein oxidation in normal aging and Alzheimer's disease (AD). The major emphasis of this review article is on the carbonylation and nitration of proteins in AD and mild cognitive impairment (MCI). The oxidatively modified proteins showed a strong correlation with the reported changes in brain structure, carbohydrate metabolism, synaptic transmission, cellular energetics, etc., of both MCI and AD brains compared to the controls. Some proteins were found to be common targets of oxidation and were observed during the early stages of AD, suggesting that those changes might be critical in the onset of symptoms and/or formation of the pathological hallmarks of AD. Further studies are required to fully elucidate the role of protein oxidation and nitration in the progression and pathogenesis of AD.

Deep Learning-Based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli

Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a continuous decline in cognitive functions and eventually results in death. An early AD diagnosis is important for taking active measures to slow its deterioration. Traditional diagnoses are usually based on clinical experience, which is limited by several realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as a typical eye-tracking behavior, is of great clinical value in detecting cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conducted a 3D comprehensive visual task on a noninvasive eye-tracking system to collect visual attention heatmaps. Then a multilayered comparison convolutional neural network (MC-CNN) is proposed to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multilayered feature representations of heatmaps were obtained by hierarchical residual blocks to better encode eye movement behaviors, which were further integrated into a distance vector to benefit the comprehensive visual task. From evaluation, MC-CNN can distinguish AD patients from normals with 0.84 accuracy, 0.86 recall, 0.82 precision, 0.83 F1-score and 0.90 area under the curve (AUC). The above results demonstrate the effectiveness of the proposed MC-CNN in AD diagnosis based on the comprehensive 3D visual task.

Cost-Effectiveness of Lecanemab for Individuals With Early-Stage Alzheimer Disease

BACKGROUND AND OBJECTIVES

Little is known regarding the cost-effectiveness of lecanemab (Leqembi), a monoclonal antibody approved by the US Food and Drug Administration in January 2023 for the treatment of mild cognitive impairment (MCI) or mild dementia due to Alzheimer disease (AD). This study aims to quantify the cost-effectiveness of lecanemab and how it varies based on the accuracy of AD testing and individuals' APOE ε4 status.

METHODS

Seven alternative test-treat-target strategies defined by combinations of testing approaches (PET, CSF, or plasma assay), treatment choices (standard of care [SoC] alone or lecanemab in addition to SoC), and targeting strategies (targeting APOE ε4 noncarriers or heterozygous patients or not) were compared. A hybrid decision tree-Markov cohort model was constructed with 5 states: (1) MCI (Clinical Dementia Rating-Sum of Boxes [CDR-SB] 0-4.5); (2) mild dementia (CDR-SB 4.6-9.5); (3) moderate dementia (CDR-SB 9.6-16); (4) severe dementia (CDR-SB >16); and (5) death. Effectiveness was measured by quality-adjusted life years and costs from third-party and societal perspectives were estimated in 2022 US dollars over a lifetime horizon.

RESULTS

Among the 7 test-treat-target strategies, SoC alone was the optimal strategy from a cost-effectiveness perspective. Neither targeted lecanemab treatment nor treatment unrestricted by APOE ε4 genotype was cost-effective vs SoC alone, regardless of the test used to diagnose patients with early-stage AD. However, CSF assay followed by targeted treatment would become cost-effective if lecanemab is priced below $5,100 per year. These results were robust to the accuracy of diagnostic testing and rates of lecanemab discontinuation and adverse events.

DISCUSSION

Neither targeted lecanemab treatment nor treatment unrestricted by APOE ε4 genotype is cost-effective vs SoC alone for patients with MCI or mild dementia due to AD. Lecanemab would be cost-effective in some settings if priced below $5,100 per year.

Astroglial activation: Current concepts and future directions

Astrocytes are abundantly and ubiquitously expressed cell types with diverse functions throughout the central nervous system. Astrocytes show remarkable plasticity and exhibit morphological, molecular, and functional remodeling in response to injury, disease, or infection of the central nervous system, as evident in neurodegenerative diseases. Astroglial mediated inflammation plays a prominent role in the pathogenesis of neurodegenerative diseases. This review focus on the role of astrocytes as essential players in neuroinflammation and discuss their morphological and functional heterogeneity in the normal central nervous system and explore the spatial and temporal variations in astroglial phenotypes observed under different disease conditions. This review discusses the intimate relationship of astrocytes to pathological hallmarks of neurodegenerative diseases. Finally, this review considers the putative therapeutic strategies that can be deployed to modulate the astroglial functions in neurodegenerative diseases. HIGHLIGHTS: Astroglia mediated neuroinflammation plays a key role in the pathogenesis of neurodegenerative diseases. Activated astrocytes exhibit diverse phenotypes in a region-specific manner in brain and interact with β-amyloid, tau, and α-synuclein species as well as with microglia and neuronal circuits. Activated astrocytes are likely to influence the trajectory of disease progression of neurodegenerative diseases, as determined by the stage of disease, individual susceptibility, and state of astroglial priming. Modulation of astroglial activation may be a therapeutic strategy at various stages in the trajectory of neurodegenerative diseases to modify the disease course.

Tracing synaptic loss in Alzheimer's brain with SV2A PET-tracer UCB-J

INTRODUCTION

Synaptic loss is an early prominent feature of Alzheimer's disease (AD). The recently developed novel synaptic vesicle 2A protein (SV2A) PET-tracer UCB-J has shown great promise in tracking synaptic loss in AD. However, there have been discrepancies between the findings and a lack of mechanistic insight.

METHODS

Here we report the first extensive pre-clinical validation studies for UCB-J in control (CN; n = 11) and AD (n = 11) brains using a multidimensional approach of post-mortem brain imaging techniques, radioligand binding, and biochemical studies.

RESULTS AND DISCUSSION

We demonstrate that UCB-J could target SV2A protein with high specificity and depict synaptic loss at synaptosome levels in AD brain regions compared to CNs. UCB-J showed highest synaptic loss in AD hippocampus followed in descending order by frontal cortex, temporal cortex, parietal cortex, and cerebellum. 3H-UCB-J large brain-section autoradiography and cellular/subcellular fractions binding studies indicated potential off-target interaction with phosphorylated tau (p-tau) species in AD brains, which could have subsequent clinical implications for imaging studies.

HIGHLIGHTS

Synaptic positron emission tomography (PET)-tracer UCB-J could target synaptic vesicle 2A protein (SV2A) with high specificity in Alzheimer's disease (AD) and control brains. Synaptic PET-tracer UCB-J could depict synaptic loss at synaptosome levels in AD brain regions compared to control. Potential off-target interaction of UCB-J with phosphorylated tau (p-tau) species at cellular/subcellular levels could have subsequent clinical implications for imaging studies, warranting further investigations.

It's a Trap! Aldolase-Prescribed C4 Deoxyradiofluorination Affords Intracellular Trapping and the Tracing of Fructose Metabolism by PET

Fructose metabolism has been implicated in various diseases, including metabolic disorders, neurodegenerative disorders, cardiac disorders, and cancer. However, the limited availability of a quantitative imaging radiotracer has hindered its exploration in pathology and diagnostic imaging. Methods: We adopted a molecular design strategy based on the catalytic mechanism of aldolase, a key enzyme in fructolysis. We successfully synthesized a radiodeoxyfluorinated fructose analog, [18F]4-fluoro-4-deoxyfructose ([18F]4-FDF), in high molar activity. Results: Through heavy isotope tracing by mass spectrometry, we demonstrated that C4-deoxyfluorination of fructose led to effective trapping as fluorodeoxysorbitol and fluorodeoxyfructose-1-phosphate in vitro, unlike C1- and C6-fluorinated analogs that resulted in fluorolactate accumulation. This observation was consistent in vivo, where [18F]6-fluoro-6-deoxyfructose displayed substantial bone uptake due to metabolic processing whereas [18F]4-FDF did not. Importantly, [18F]4-FDF exhibited low uptake in healthy brain and heart tissues, known for their high glycolytic activity and background levels of [18F]FDG uptake. [18F]4-FDF PET/CT allowed for sensitive mapping of neuro- and cardioinflammatory responses to systemic lipopolysaccharide administration. Conclusion: Our study highlights the significance of aldolase-guided C4 radiodeoxyfluorination of fructose in enabling effective radiotracer trapping, overcoming limitations of C1 and C6 radioanalogs toward a clinically viable tool for imaging fructolysis in highly glycolytic tissues.

Lost in space(s): Multimodal neuroimaging of disorientation along the Alzheimer's disease continuum

Orientation is a fundamental cognitive faculty and the bedrock of the neurologic examination. Orientation is defined as the alignment between an individual's internal representation and the external world in the spatial, temporal, and social domains. While spatial disorientation is a recognized hallmark of Alzheimer's disease (AD), little is known about disorientation beyond space in AD. This study aimed to explore disorientation in spatial, temporal, and social domains along the AD continuum. Fifty-one participants along the AD continuum performed an ecological orientation task in the spatial, temporal, and social domains while undergoing functional MRI. Disorientation in AD followed a three-way association between orientation domain, brain region, and disease stage. Specifically, patients with early amnestic mild cognitive impairment exhibited spatio-temporal disorientation and reduced brain activity in temporoparietal regions, while patients with AD dementia showed additional social disorientation and reduced brain activity in frontoparietal regions. Furthermore, patterns of hypoactivation overlapped different subnetworks of the default mode network, patterns of fluorodeoxyglucose hypometabolism, and cortical atrophy characteristic of AD. Our results suggest that AD may encompass a disorder of orientation, characterized by a biphasic process manifesting as early spatio-temporal and late social disorientation. As such, disorientation may offer a unique window into the clinicopathological progression of AD. SIGNIFICANCE STATEMENT: Despite extensive research into Alzheimer's disease (AD), its core cognitive deficit remains a matter of debate. In this study, we investigated whether orientation, defined as the ability to align internal representations with the external world in spatial, temporal, and social domains, constitutes a core cognitive deficit in AD. To do so, we used PET-fMRI imaging to collect behavioral, functional, and metabolic data from 51 participants along the AD continuum. Our findings suggest that AD may constitute a disorder of orientation, characterized by an early spatio-temporal disorientation and followed by late social disorientation, manifesting in task-evoked and neurodegenerative changes. We propose that a profile of disorientation across multiple domains offers a unique window into the progression of AD and as such could greatly benefit disease diagnosis, monitoring, and evaluation of treatment response.

Alzheimer's Disease Puzzle: Delving into Pathogenesis Hypotheses

Alzheimer's disease (AD) is a prevalent neurodegenerative disease characterized by both amnestic and non-amnestic clinical manifestations. It accounts for approximately 60-70% of all dementia cases worldwide. With the increasing number of AD patients, elucidating underlying mechanisms and developing corresponding interventional strategies are necessary. Hypotheses about AD such as amyloid cascade, Tau hyper-phosphorylation, neuroinflammation, oxidative stress, mitochondrial dysfunction, cholinergic, and vascular hypotheses are not mutually exclusive, and all of them play a certain role in the development of AD. The amyloid cascade hypothesis is currently the most widely studied; however, other hypotheses are also gaining support. This article summarizes the recent evidence regarding major pathological hypotheses of AD and their potential interplay, as well as the strengths and weaknesses of each hypothesis and their implications for the development of effective treatments. This could stimulate further studies and promote the development of more effective therapeutic strategies for AD.

Tracking the progression of Alzheimer's disease: Insights from metabolic patterns of SOMI stages

BACKGROUND

SOMI (Stages of Objective Memory Impairment) is a novel classification that identifies six stages of memory decline in Alzheimer's Disease (AD) using the Free and Cued Selective Reminding Test (FCSRT). However, the relationship between SOMI stages and brain metabolism remains unexplored. This study aims to investigate the metabolic correlates of SOMI stages using FDG-PET in Mild Cognitive Impairment due to AD (MCI-AD) and early AD patients.

METHODS

One hundred twenty-nine-patients (99 aMCI-AD and 30 AD), and 42 healthy controls (HCs) (MMSE = 29.2 ± .8; age:69.1 ± 8.6 years; education:10.7 ± 3.8 years) who underwent an extensive neuropsychological battery including FCSRT and brain FDG-PET were enrolled. According to their clinical relevance and available sample sizes, SOMI-4 (N = 24 subjects; MMSE score:26.6 ± 2.6: age:75.4 ± 3.2; education:9.9 ± 4.5) and SOMI-5 groups (N = 97; MMSE:25.3 ± 2.6; age:73.9 ± 5.8; education:9.4 ± 4.1) were investigated.

RESULTS

Compared to HCs, SOMI-4 showed hypometabolism in the precuneus, medial temporal gyrus bilaterally, right pecuneus and angular gyrus. SOMI-5 exhibited broader hypometabolism, extending to the left posterior cingulate and medial frontal gyrus bilaterally. The conjunction analysis revealed overlapping areas in the precuneus, medial temporal gyrus bilaterally, and in the right angular gyrus and cuneus. The disjunction analysis identified SOMI-5 specific hypometabolism encompassing left inferior temporal gyrus, uncus and parahippocampal gyrus, and medial frontal gyrus bilaterally (p < .001, p-value (FWE) < .05).

DISCUSSION

SOMI-4 relates to posterior hypometabolism, while SOMI-5 to more extensive hypometabolism further encompassing frontal cortices, suggesting SOMI as a biologically relevant classification system of memory decline.

CONCLUSION

Memory decline staged with SOMI is associated with hypometabolism spreading in amnesic MCI-AD/AD, suggesting its usefulness as a clinical marker of increasing neurodegeneration.

Over-expression of N-acetylaspartate synthase exacerbates pathological energetic deficit and accelerates cognitive decline in the 5xFAD mouse

N-acetylaspartate (NAA) is an abundant central nervous system amino acid derivative that is tightly coupled to mitochondria and energy metabolism in neurons. A reduced NAA signature is a prominent early pathological biomarker in multiple neurodegenerative diseases and becomes progressively more pronounced as disease advances. Because NAA synthesis requires aspartate drawn directly from mitochondria, we argued that this process is in direct competition with oxidative phosphorylation for substrate and that sustained high levels of NAA synthesis would be incompatible with pathological energy crisis. We show here that over-expression of the rate-limiting NAA synthetic enzyme in the hippocampus of the 5x familial Alzheimer's disease (5xFAD) mouse results in an exaggerated pathological ATP deficit and accelerated cognitive decline. Over-expression of NAA synthase did not increase amyloid burden or result in cell loss but did significantly deplete mitochondrial aspartate and impair the ability of mitochondria to oxidize glutamate for adenosine triphosphate (ATP) synthesis. These results define NAA as a sink for energetic substrate and suggest initial pathological reductions in NAA are part of a response to energetic crisis designed to preserve substrate bioavailability for mitochondrial ATP synthesis.

A multi-resolution TOF-DOI detector for human brain dedicated PET scanner

Objective. We propose a single-ended readout, multi-resolution detector design that can achieve high spatial, depth-of-interaction (DOI), and time-of-flight (TOF) resolutions, as well as high sensitivity for human brain-dedicated positron emission tomography (PET) scanners.Approach. The detector comprised two layers of LYSO crystal arrays and a lightguide in between. The top (gamma ray entrance) layer consisted of a 16 × 16 array of 1.53 × 1.53 × 6 mm3LYSO crystals for providing high spatial resolution. The bottom layer consisted of an 8 × 8 array of 3.0 × 3.0 × 15 mm3LYSO crystals that were one-to-one coupled to an 8 × 8 multipixel photon counter (MPPC) array for providing high TOF resolution. The 2 mm thick lightguide introduces inter-crystal light sharing that causes variations of the light distribution patterns for high DOI resolution. The detector was read out by a PETsys TOFPET2 application-specific integrated circuit.Main result. The top and bottom layers were distinguished by a convolutional neural network with 97% accuracy. All crystals in the top and bottom layers were resolved. The inter-crystal scatter (ICS) events in the bottom layer were identified, and the measured average DOI resolution of the bottom layer was 4.1 mm. The coincidence time resolution (CTR) for the top-top, top-bottom, and bottom-bottom coincidences was 476 ps, 405 ps, and 298 ps, respectively. When ICS events were excluded from the bottom layer, the CTR of the bottom-bottom coincidence was 277 ps.Significance. The top layer of the proposed two-layer detector achieved a high spatial resolution and the bottom layer achieved a high TOF resolution. Together with its high DOI resolution and detection efficiency, the proposed detector is well suited for next-generation high-performance brain-dedicated PET scanners.

PCA and logistic regression in 2-[18F]FDG PET neuroimaging as an interpretable and diagnostic tool for Alzheimer's disease

Objective.to develop an optimization and training pipeline for a classification model based on principal component analysis and logistic regression using neuroimages from PET with 2-[18F]fluoro-2-deoxy-D-glucose (FDG PET) for the diagnosis of Alzheimer's disease (AD).Approach.as training data, 200 FDG PET neuroimages were used, 100 from the group of patients with AD and 100 from the group of cognitively normal subjects (CN), downloaded from the repository of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Regularization methods L1 and L2 were tested and their respective strength varied by the hyperparameter C. Once the best combination of hyperparameters was determined, it was used to train the final classification model, which was then applied to test data, consisting of 192 FDG PET neuroimages, 100 from subjects with no evidence of AD (nAD) and 92 from the AD group, obtained at the Centro de Diagnóstico por Imagem (CDI).Main results.the best combination of hyperparameters was L1 regularization andC≈ 0.316. The final results on test data were accuracy = 88.54%, recall = 90.22%, precision = 86.46% and AUC = 94.75%, indicating that there was a good generalization to neuroimages outside the training set. Adjusting each principal component by its respective weight, an interpretable image was obtained that represents the regions of greater or lesser probability for AD given high voxel intensities. The resulting image matches what is expected by the pathophysiology of AD.Significance.our classification model was trained on publicly available and robust data and tested, with good results, on clinical routine data. Our study shows that it serves as a powerful and interpretable tool capable of assisting in the diagnosis of AD in the possession of FDG PET neuroimages. The relationship between classification model output scores and AD progression can and should be explored in future studies.

Biomarkers in Alzheimer's Disease: Are Olfactory Neuronal Precursors Useful for Antemortem Biomarker Research?

Alzheimer's disease (AD), as the main cause of dementia, affects millions of people around the world, whose diagnosis is based mainly on clinical criteria. Unfortunately, the diagnosis is obtained very late, when the neurodegenerative damage is significant for most patients. Therefore, the exhaustive study of biomarkers is indispensable for diagnostic, prognostic, and even follow-up support. AD is a multifactorial disease, and knowing its underlying pathological mechanisms is crucial to propose new and valuable biomarkers. In this review, we summarize some of the main biomarkers described in AD, which have been evaluated mainly by imaging studies in cerebrospinal fluid and blood samples. Furthermore, we describe and propose neuronal precursors derived from the olfactory neuroepithelium as a potential resource to evaluate some of the widely known biomarkers of AD and to gear toward searching for new biomarkers. These neuronal lineage cells, which can be obtained directly from patients through a non-invasive and outpatient procedure, display several characteristics that validate them as a surrogate model to study the central nervous system, allowing the analysis of AD pathophysiological processes. Moreover, the ease of obtaining and harvesting endows them as an accessible and powerful resource to evaluate biomarkers in clinical practice.

Using Neuroimaging to Study Cerebral Amyloid Angiopathy and Its Relationship to Alzheimer's Disease

Cerebral amyloid angiopathy (CAA) is characterized by amyloid-β aggregation in the media and adventitia of the leptomeningeal and cortical blood vessels. CAA is one of the strongest vascular contributors to Alzheimer's disease (AD). It frequently co-occurs in AD patients, but the relationship between CAA and AD is incompletely understood. CAA may drive AD risk through damage to the neurovascular unit and accelerate parenchymal amyloid and tau deposition. Conversely, early AD may also drive CAA through cerebrovascular remodeling that impairs blood vessels from clearing amyloid-β. Sole reliance on autopsy examination to study CAA limits researchers' ability to investigate CAA's natural disease course and the effect of CAA on cognitive decline. Neuroimaging allows for in vivo assessment of brain function and structure and can be leveraged to investigate CAA staging and explore its associations with AD. In this review, we will discuss neuroimaging modalities that can be used to investigate markers associated with CAA that may impact AD vulnerability including hemorrhages and microbleeds, blood-brain barrier permeability disruption, reduced cerebral blood flow, amyloid and tau accumulation, white matter tract disruption, reduced cerebrovascular reactivity, and lowered brain glucose metabolism. We present possible areas for research inquiry to advance biomarker discovery and improve diagnostics.

Harmonization of CSF and imaging biomarkers in Alzheimer's disease: Need and practical applications for genetics studies and preclinical classification

In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aβ), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers, leading to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS results using this approach with currently accepted methods. We also used a generalized mixture model to calculate the threshold for biomarker-positivity. Based on our findings, our normalization approach performed as well as meta-analysis and did not lead to any spurious results. In terms of dichotomization, cutoffs calculated with this approach were very similar to those reported previously. These findings show that the Z-score based harmonization approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.

Understanding the multifaceted role of miRNAs in Alzheimer's disease pathology

Small non-coding RNAs (miRNAs) regulate gene expression by binding to mRNA and mediating its degradation or inhibiting translation. Since miRNAs can regulate the expression of several genes, they have multiple roles to play in biological processes and human diseases. The majority of miRNAs are known to be expressed in the brain and are involved in synaptic functions, thus marking their presence and role in major neurodegenerative disorders, including Alzheimer's disease (AD). In AD, amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs) are known to be the major hallmarks. The clearance of Aβ and tau is known to be associated with miRNA dysregulation. In addition, the β-site APP cleaving enzyme (BACE 1), which cleaves APP to form Aβ, is also found to be regulated by miRNAs, thus directly affecting Aβ accumulation. Growing evidences suggest that neuroinflammation can be an initial event in AD pathology, and miRNAs have been linked with the regulation of neuroinflammation. Inflammatory disorders have also been associated with AD pathology, and exosomes associated with miRNAs are known to regulate brain inflammation, suggesting for the role of systemic miRNAs in AD pathology. Several miRNAs have been related in AD, years before the clinical symptoms appear, most of which are associated with regulating the cell cycle, immune system, stress responses, cellular senescence, nerve growth factor (NGF) signaling, and synaptic regulation. Phytochemicals, especially polyphenols, alter the expression of various miRNAs by binding to miRNAs or binding to the transcriptional activators of miRNAs, thus control/alter various metabolic pathways. Awing to the sundry biological processes being regulated by miRNAs in the brain and regulation of expression of miRNAs via phytochemicals, miRNAs and the regulatory bioactive phytochemicals can serve as therapeutic agents in the treatment and management of AD.

Impaired Glymphatic Flow on Diffusion Tensor MRI as a Marker of Neurodegeneration in Alzheimer's Disease: Correlation with Gray Matter Volume Loss and Cognitive Decline Independent of Cerebral Amyloid Deposition

Background

Impaired glymphatic flow on the Alzheimer's disease (AD) spectrum may be evaluated using diffusion tensor image analysis along the perivascular space (DTI-ALPS).

Objective

We aimed to validate impaired glymphatic flow and explore its association with gray matter volume, cognitive status, and cerebral amyloid deposition on the AD spectrum.

Methods

80 participants (mean age, 76.9±8.5 years; 57 women) with AD (n = 65) and cognitively normal (CN) (n = 15) who underwent 3T brain MRI including DTI and/or amyloid PET were included. After adjusting for age, sex, apolipoprotein E status, and burden of white matter hyperintensities, the ALPS-index was compared according to the AD spectrum. The association between the ALPS-index and gray matter volume, cognitive status, and quantitative amyloid from PET was assessed.

Results

The ALPS-index in the AD was significantly lower (mean, 1.476; 95% CI, 1.395-1.556) than in the CN (1.784;1.615-1.952; p = 0.026). Volumes of the entorhinal cortex, hippocampus, temporal pole, and primary motor cortex showed significant associations with the ALPS-index (all, p < 0.05). There was a positive correlation between the ALPS-index and MMSE score (partial r = 0.435; p < 0.001), but there was no significant correlation between the ALPS-index and amyloid SUVRs (all, p > 0.05).

Conclusions

Decreased glymphatic flow measured by DTI-ALPS in AD may serve as a marker of neurodegeneration correlating with structural atrophy and cognitive decline.

Sex modifies effects of imaging and CSF biomarkers on cognitive and functional outcomes: a study of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory and functional impairments. Two of 3 patients with AD are biologically female; therefore, the biological underpinnings of this diagnosis disparity may inform interventions slowing the AD progression. To bridge this gap, we conducted analyses of 1078 male and female participants from the Alzheimer's Disease Neuroimaging Initiative to examine associations between levels of cerebral spinal fluid (CSF)/neuroimaging biomarkers and cognitive/functional outcomes. The Chow test was used to quantify sex differences by determining if biological sex affects relationships between the studied biomarkers and outcomes. Multiple magnetic resonance imaging (whole brain, entorhinal cortex, middle temporal gyrus, fusiform gyrus, hippocampus), position emission tomography (AV45), and CSF (P-TAU, TAU) biomarkers were differentially associated with cognitive and functional outcomes. Post-hoc bootstrapped and association analyses confirmed these differential effects and emphasized the necessity of using separate, sex-stratified models. The studied imaging/CSF biomarkers may account for some of the sex-based variation in AD pathophysiology. The identified sex-varying relationships between CSF/imaging biomarkers and cognitive/functional outcomes warrant future biological investigation in independent cohorts.

Nonfluent Variant Primary Progressive Aphasia on FDG, 11 C-PIB, and 18 F-APN-1607 PET Imaging

ABSTRACT

A 61-year-old right-handed man presented with decreased cognitive function, short-term memory, fluent speech disorders, and grammatical errors for 1 year. The patient underwent PET imaging with 11 C-PIB, 18 F-FDG, and 18 F-APN-1607. The 11 C-PIB PET showed no amyloid accumulation; the 18 F-FDG PET showed hypometabolism in the bilateral frontal lobe, temporal lobe, and midbrain; and the 18 F-APN-1607 PET showed tau accumulation in the brainstem, basal ganglia, and left inferior frontal gyrus. These findings suggested a diagnosis of nonfluent variant primary progressive aphasia. This case emphasizes the value of combined imaging of glucose metabolism, Aβ, and tau PET in the diagnosis of nonfluent variant primary progressive aphasia.

Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis

INTRODUCTION

This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).

METHODS

This analysis consisted of 150 normal controls (CN), 257 MCI, and 205  AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.

RESULTS

Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.

DISCUSSION

Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.

The association of body mass index and body composition with plasma amyloid beta levels

Blood-based analysis of amyloid-β is increasingly applied to incrementally establish diagnostic tests for Alzheimer's disease. To this aim, it is necessary to determine factors that can alter blood-based concentrations of amyloid-β. We cross-sectionally analysed amyloid-β-40 and amyloid-β-42 concentrations and the 40/42 ratio in 440 community-dwelling adults and associations with body mass index, waist-to-height ratio and body composition assessed using bioelectrical impedance analysis. Body mass index and waist-to-height ratio were inversely associated with plasma amyloid-β-42 concentrations. Body fat mass, but not body cell mass and extracellular mass, was inversely associated with amyloid-β-42 levels. The results indicate that plasma concentrations of amyloid-β-42 are lower in those with increased body mass index and body fat, and associations with amyloid-β-40 did not reach significance after controlling for multiple testing. The findings support the use of body mass index as an easy-to-measure factor that should be accounted for in diagnostic models for plasma amyloid-β.

Early-stage mapping of macromolecular content in APPNL-F mouse model of Alzheimer's disease using nuclear Overhauser effect MRI

Non-invasive methods of detecting early-stage Alzheimer's disease (AD) can provide valuable insight into disease pathology, improving the diagnosis and treatment of AD. Nuclear Overhauser enhancement (NOE) MRI is a technique that provides image contrast sensitive to lipid and protein content in the brain. These macromolecules have been shown to be altered in Alzheimer's pathology, with early disruptions in cell membrane integrity and signaling pathways leading to the buildup of amyloid-beta plaques and neurofibrillary tangles. We used template-based analyzes of NOE MRI data and the characteristic Z-spectrum, with parameters optimized for increase specificity to NOE, to detect changes in lipids and proteins in an AD mouse model that recapitulates features of human AD. We find changes in NOE contrast in the hippocampus, hypothalamus, entorhinal cortex, and fimbria, with these changes likely attributed to disruptions in the phospholipid bilayer of cell membranes in both gray and white matter regions. This study suggests that NOE MRI may be a useful tool for monitoring early-stage changes in lipid-mediated metabolism in AD and other disorders with high spatial resolution.

Altered Brain Energy Metabolism Related to Astrocytes in Alzheimer's Disease

OBJECTIVE

Increasing evidence suggests that reactive astrocytes are associated with Alzheimer's disease (AD). However, its underlying pathogenesis remains unknown. Given the role of astrocytes in energy metabolism, reactive astrocytes may contribute to altered brain energy metabolism. Astrocytes are primarily considered glycolytic cells, suggesting a preference for lactate production. This study aimed to examine alterations in astrocytic activities and their association with brain lactate levels in AD.

METHODS

The study included 30 AD and 30 cognitively unimpaired participants. For AD participants, amyloid and tau depositions were confirmed by positron emission tomography using [11 C]PiB and [18 F]florzolotau, respectively. Myo-inositol, an astroglial marker, and lactate in the posterior cingulate cortex were quantified by magnetic resonance spectroscopy. These magnetic resonance spectroscopy metabolites were compared with plasma biomarkers, including glial fibrillary acidic protein as another astrocytic marker, and amyloid and tau positron emission tomography.

RESULTS

Myo-inositol and lactate levels were higher in AD patients than in cognitively unimpaired participants (p < 0.05). Myo-inositol levels correlated with lactate levels (r = 0.272, p = 0.047). Myo-inositol and lactate levels were positively associated with the Clinical Dementia Rating sum-of-boxes scores (p < 0.05). Significant correlations were noted between myo-inositol levels and plasma glial fibrillary acidic protein, tau phosphorylated at threonine 181 levels, and amyloid and tau positron emission tomography accumulation in the posterior cingulate cortex (p < 0.05).

INTERPRETATION

We found high myo-inositol levels accompanied by increased lactate levels in the posterior cingulate cortex in AD patients, indicating a link between reactive astrocytes and altered brain energy metabolism. Myo-inositol and plasma glial fibrillary acidic protein may reflect similar astrocytic changes as biomarkers of AD. ANN NEUROL 2023.

The role of A[Formula: see text] and Tau proteins in Alzheimer's disease: a mathematical model on graphs

In this Note we study a mathematical model for the progression of Alzheimer's Disease in the human brain. The novelty of our approach consists in the representation of the brain as two superposed graphs where toxic proteins diffuse, the connectivity graph which represents the neural network, and the proximity graph which takes into account the extracellular space. Toxic proteins such as [Formula: see text] amyloid and Tau play in fact a crucial role in the development of Alzheimer's disease and, separately, have been targets of medical treatments. Recent biomedical literature stresses the potential impact of the synergetic action of these proteins. We numerically test various modelling hypotheses which confirm the relevance of this synergy.

Endotype Characterization Reveals Mechanistic Differences Across Brain Regions in Sporadic Alzheimer's Disease

Background

While Alzheimer's disease (AD) pathology is associated with altered brain structure, it is not clear whether gene expression changes mirror the onset and evolution of pathology in distinct brain regions. Deciphering the mechanisms which cause the differential manifestation of the disease across different regions has the potential to help early diagnosis.

Objective

We aimed to identify common and unique endotypes and their regulation in tangle-free neurons in sporadic AD (SAD) across six brain regions: entorhinal cortex (EC), hippocampus (HC), medial temporal gyrus (MTG), posterior cingulate (PC), superior frontal gyrus (SFG), and visual cortex (VCX).

Methods

To decipher the states of tangle-free neurons across different brain regions in human subjects afflicted with AD, we performed analysis of the neural transcriptome. We explored changes in differential gene expression, functional and transcription factor target enrichment, and co-expression gene module detection analysis to discern disease-state transcriptomic variances and characterize endotypes. Additionally, we compared our results to tangled AD neuron microarray-based study and the Allen Brain Atlas.

Results

We identified impaired neuron function in EC, MTG, PC, and VCX resulting from REST activation and reversal of mature neurons to a precursor-like state in EC, MTG, and SFG linked to SOX2 activation. Additionally, decreased neuron function and increased dedifferentiation were linked to the activation of SUZ12. Energetic deficit connected to NRF1 inactivation was found in HC, PC, and VCX.

Conclusions

Our findings suggest that SAD manifestation varies in scale and severity in different brain regions. We identify endotypes, such as energetic shortfalls, impaired neuronal function, and dedifferentiation.

Correlation between cerebral hemodynamic functional near-infrared spectroscopy and positron emission tomography for assessing mild cognitive impairment and Alzheimer's disease: An exploratory study

This study was performed to investigate the usefulness of functional near-infrared spectroscopy (fNIRS) by conducting a comparative analysis of hemodynamic activation detected by fNIRS and positron emission tomography (PET) and magnetic resonance imaging (MRI) in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Participants were divided into four groups: the subjective memory impairment (SMI), amnestic MCI (aMCI), non-amnestic MCI (naMCI), and AD groups. We recorded the hemodynamic response during the semantic verbal fluency task (SVFT) using a commercial wireless continuous-wave NIRS system. The correlation between the parameters of the neuroimaging assessments among the groups was analyzed. Region of interest-based comparisons showed that the four groups had significantly different hemodynamic responses during SVFT in the bilateral dorsolateral prefrontal cortex (DLPFC). The linear mixed effect model result indicates that the mean ΔHbO2 from the bilateral DLPFC regions showed a significant positive correlation to the overall FDG-PET after controlling for age and group differences in the fNIRS signals. Amyloid PET signals tended to better differentiate the AD group from other groups, and fNIRS signals tended to better differentiate the SMI group from other groups. In addition, a comparison between the group pairs revealed a mirrored pattern between the hippocampal volume and hemodynamic response in the DLPFC. The hemodynamic response detected by fNIRS showed a significant correlation with metabolic and anatomical changes associated with disease progression. Therefore, fNIRS may be considered as a screening tool to predict the hemodynamic and metabolic statuses of the brain in patients with MCI and AD.

Improving Alzheimer's Disease Diagnosis With Multi-Modal PET Embedding Features by a 3D Multi-Task MLP-Mixer Neural Network

Positron emission tomography (PET) with fluorodeoxyglucose (FDG) or florbetapir (AV45) has been proved effective in the diagnosis of Alzheimer's disease. However, the expensive and radioactive nature of PET has limited its application. Here, employing multi-layer perceptron mixer architecture, we present a deep learning model, namely 3-dimensional multi-task multi-layer perceptron mixer, for simultaneously predicting the standardized uptake value ratios (SUVRs) for FDG-PET and AV45-PET from the cheap and widely used structural magnetic resonance imaging data, and the model can be further used for Alzheimer's disease diagnosis based on embedding features derived from SUVR prediction. Experiment results demonstrate the high prediction accuracy of the proposed method for FDG/AV45-PET SUVRs, where we achieved Pearson's correlation coefficients of 0.66 and 0.61 respectively between the estimated and actual SUVR and the estimated SUVRs also show high sensitivity and distinct longitudinal patterns for different disease status. By taking into account PET embedding features, the proposed method outperforms other competing methods on five independent datasets in the diagnosis of Alzheimer's disease and discriminating between stable and progressive mild cognitive impairments, achieving the area under receiver operating characteristic curves of 0.968 and 0.776 respectively on ADNI dataset, and generalizes better to other external datasets. Moreover, the top-weighted patches extracted from the trained model involve important brain regions related to Alzheimer's disease, suggesting good biological interpretability of our proposed method."

Getting ahead of Alzheimer's disease: early intervention with focused ultrasound

The amyloid-β (Aβ) hypothesis implicates Aβ protein accumulation in Alzheimer's disease (AD) onset and progression. However, therapies targeting Aβ have proven insufficient in achieving disease reversal, prompting a shift to focus on early intervention and alternative therapeutic targets. Focused ultrasound (FUS) paired with systemically-introduced microbubbles (μB) is a non-invasive technique for targeted and transient blood-brain barrier opening (BBBO), which has demonstrated Aβ and tau reduction, as well as memory improvement in models of late-stage AD. However, similar to drug treatments for AD, this approach is not sufficient for complete reversal of advanced, symptomatic AD. Here we aim to determine whether early intervention with FUS-BBBO in asymptomatic AD could delay disease onset. Thus, the objective of this study is to measure the protective effects of FUS-BBBO on anxiety, memory and AD-associated protein levels in female and male triple transgenic (3xTg) AD mice treated at an early age and disease state. Here we show that early, repeated intervention with FUS-BBBO decreased anxiety-associated behaviors in the open field test by 463.02 and 37.42% in male and female cohorts, respectively. FUS-BBBO preserved female aptitude for learning in the active place avoidance paradigm, reducing the shock quadrant time by 30.03 and 31.01% in the final long-term and reversal learning trials, respectively. Finally, FUS-BBBO reduced hippocampal accumulation of Aβ40, Aβ42, and total tau in females by 12.54, 13.05, and 3.57%, respectively, and reduced total tau in males by 18.98%. This demonstration of both cognitive and pathological protection could offer a solution for carriers of AD-associated mutations as a safe, non-invasive technique to delay the onset of the cognitive and pathological effects of AD.

Vertical Graphene-Based Printed Electrochemical Biosensor for Simultaneous Detection of Four Alzheimer's Disease Blood Biomarkers

Early detection and timely intervention play a vital role in the effective management of Alzheimer's disease. Currently, the diagnostic accuracy for Alzheimer's disease based on a single blood biomarker is relatively low, and the combined use of multiple blood biomarkers can greatly improve diagnostic accuracy. Herein, we report a printed electrochemical biosensor based on vertical graphene (VG) modified with gold nanoparticles (VG@nanoAu) for the simultaneous detection of four Alzheimer's disease blood biomarkers. The printed electrochemical electrode array was constructed by laser etching and inkjet printing. Then gold nanoparticles were modified onto the working electrode surface via electrodeposition to further improve the sensitivity of the sensor. In addition, the entire printed electrochemical sensing system incorporates an electrochemical micro-workstation and a smartphone. The customized electrochemical micro-workstation incorporates four electro-chemical control chips, enabling the sensor to simultaneously analyze four biomarkers. Consequently, the printed electrochemical sensing system exhibits excellent analytical performance due to the large surface area, biocompatibility, and good conductivity of VG@nanoAu. The detection limit of the sensing system for Aβ40, Aβ42, T-tau, and P-tau181 was 0.072, 0.089, 0.071, and 0.051 pg/mL, respectively, which meets the detection requirements of Alzheimer's disease blood biomarkers. The printed electrochemical sensing system also exhibits good specificity and stability. This work has great value and promising prospects for early Alzheimer's disease diagnosis using blood biomarkers.

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer's disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ([Formula: see text]). Moreover, our performance was equivalent to that of contemporary research.

Harmonization of CSF and imaging biomarkers for Alzheimer's disease biomarkers: need and practical applications for genetics studies and preclinical classification

INTRODUCTION

In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aβ), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers which leads to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values.

METHODS

We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS result using this method with currently accepted methods. We also used a generalized mixture modelling to calculate the threshold for biomarker-positivity.

RESULTS

Z-scores method performed as well as meta-analysis and did not lead to any spurious results. Cutoffs calculated with this approach were found to be very similar to those reported previously.

DISCUSSION

This approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.

Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning

Purpose

To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method.

Materials and Methods

This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity.

Results

The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes.

Conclusion

The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.

Levetiracetam Increases Hippocampal Blood Flow in Alzheimer's Disease as Measured by Arterial Spin Labelling MRI

BACKGROUND

Patients with Alzheimer's disease (AD) have an increased risk of developing epileptiform discharges, which is associated with a more rapid rate of progression. This suggests that suppression of epileptiform activity could have clinical benefit in patients with AD.

OBJECTIVE

In the current study, we tested whether acute, intravenous administration of levetiracetam led to changes in brain perfusion as measured with arterial spin labeling MRI (ASL-MRI) in AD.

METHODS

We conducted a double-blind, within-subject crossover design study in which participants with mild AD (n = 9) received placebo, 2.5 mg/kg, and 7.5 mg/kg of LEV intravenously in a random order in three sessions. Afterwards, the participants underwent ASL-MRI.

RESULTS

Analysis of relative cerebral blood flow (rCBF) between 2.5 mg of levetiracetam and placebo showed significant decreases in a cluster that included the posterior cingulate cortex, the precuneus, the posterior part of the cingulate gyrus, while increased cerebral blood flow was found in both temporal lobes involving the hippocampus.

CONCLUSION

Administration of 2.5 mg/kg of LEV in patients without any history of epilepsy leads to changes in rCBF in areas known to be affected in the early stages of AD. These areas may be the focus of the epileptiform activity. Larger studies are needed to confirm the current findings.

Regional spectral ratios as potential neural markers to identify mild cognitive impairment related to Alzheimer's disease

OBJECTIVE

Alzheimer's disease (AD) has prolonged asymptomatic or mild symptomatic periods. Given that there is an increase in treatment options and that early intervention could modify the disease course, it is desirable to devise biological indices that may differentiate AD and nonAD at mild cognitive impairment (MCI) stage.

METHODS

Based on two well-acknowledged observations of background slowing (attenuation in alpha power and enhancement in theta and delta powers) and early involvement of posterior cingulate cortex (PCC, a neural hub of default-mode network), this study devised novel neural markers, namely, spectral ratios of alpha1 to delta and alpha1 to theta in the PCC.

RESULTS

We analysed 46 MCI patients, with 22 ADMCI and 24 nonADMCI who were matched in age, education, and global cognitive capability. Concordant with the prediction, the regional spectral ratios were lower in the ADMCI group, suggesting its clinical application potential.

CONCLUSION

Previous research has verified that neural markers derived from clinical electroencephalography may be informative in differentiating AD from other neurological conditions. We believe that the spectral ratios in the neural hubs that show early pathological changes can enrich the instrumental assessment of brain dysfunctions at the MCI (or pre-clinical) stage.

The Key Role of Mitochondrial Function in Health and Disease

The role of mitochondrial function in health and disease has become increasingly recognized, particularly in the last two decades. Mitochondrial dysfunction as well as disruptions of cellular bioenergetics have been shown to be ubiquitous in some of the most prevalent diseases in our society, such as type 2 diabetes, cardiovascular disease, metabolic syndrome, cancer, and Alzheimer's disease. However, the etiology and pathogenesis of mitochondrial dysfunction in multiple diseases have yet to be elucidated, making it one of the most significant medical challenges in our history. However, the rapid advances in our knowledge of cellular metabolism coupled with the novel understanding at the molecular and genetic levels show tremendous promise to one day elucidate the mysteries of this ancient organelle in order to treat it therapeutically when needed. Mitochondrial DNA mutations, infections, aging, and a lack of physical activity have been identified to be major players in mitochondrial dysfunction in multiple diseases. This review examines the complexities of mitochondrial function, whose ancient incorporation into eukaryotic cells for energy purposes was key for the survival and creation of new species. Among these complexities, the tightly intertwined bioenergetics derived from the combustion of alimentary substrates and oxygen are necessary for cellular homeostasis, including the production of reactive oxygen species. This review discusses different etiological mechanisms by which mitochondria could become dysregulated, determining the fate of multiple tissues and organs and being a protagonist in the pathogenesis of many non-communicable diseases. Finally, physical activity is a canonical evolutionary characteristic of humans that remains embedded in our genes. The normalization of a lack of physical activity in our modern society has led to the perception that exercise is an "intervention". However, physical activity remains the modus vivendi engrained in our genes and being sedentary has been the real intervention and collateral effect of modern societies. It is well known that a lack of physical activity leads to mitochondrial dysfunction and, hence, it probably becomes a major etiological factor of many non-communicable diseases affecting modern societies. Since physical activity remains the only stimulus we know that can improve and maintain mitochondrial function, a significant emphasis on exercise promotion should be imperative in order to prevent multiple diseases. Finally, in populations with chronic diseases where mitochondrial dysfunction is involved, an individualized exercise prescription should be crucial for the "metabolic rehabilitation" of many patients. From lessons learned from elite athletes (the perfect human machines), it is possible to translate and apply multiple concepts to the betterment of populations with chronic diseases.

Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the development of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can effectively slow down deterioration. Fluorodeoxyglucose positron emission tomography (FDG-PET) can detect the metabolism of glucose in patients' brains, which can help to identify changes related to AD before brain damage occurs. Machine learning is useful for early diagnosis of patients with AD using FDG-PET, but it requires a sufficiently large dataset, and it is easy for overfitting to occur in small datasets. Previous studies using machine learning for early diagnosis with FDG-PET have either involved the extraction of elaborately handcrafted features or validation on a small dataset, and few studies have explored the refined classification of early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). This article presents a broad network-based model for early diagnosis of AD (BLADNet) through PET imaging of the brain; this method employs a novel broad neural network to enhance the features of FDG-PET extracted via 2D CNN. BLADNet can search for information over a broad space through the addition of new BLS blocks without retraining of the whole network, thus improving the accuracy of AD classification. Experiments conducted on a dataset containing 2,298 FDG-PET images of 1,045 subjects from the ADNI database demonstrate that our methods are superior to those used in previous studies on early diagnosis of AD with FDG-PET. In particular, our methods achieved state-of-the-art results in EMCI and LMCI classification with FDG-PET.

Localized White Matter Tract Integrity Measured by Diffusion Tensor Imaging Is Altered in People with Mild Cognitive Impairment and Associated with Dual-Task and Single-Task Gait Speed

BACKGROUND

Altered white matter (WM) tract integrity may contribute to mild cognitive impairment (MCI) and gait abnormalities.

OBJECTIVE

The purpose of this study was to determine whether diffusion tensor imaging (DTI) metrics were altered in specific portions of WM tracts in people with MCI and to determine whether gait speed variations were associated with the specific DTI metric changes.

METHODS

DTI was acquired in 44 people with MCI and 40 cognitively normal elderly controls (CNCs). Fractional anisotropy (FA) and radial diffusivity (RD) were measured along 18 major brain WM tracts using probabilistic tractography. The average FA and RD along the tracts were compared between the groups using MANCOVA and post-hoc tests. The tracts with FA or RD differences between the groups were examined using an along-tract exploratory analysis to identify locations that differed between the groups. Associations between FA and RD in whole tracts and in the segments of the tracts that differed between the groups and usual/dual-task gait velocities and gross cognition were examined.

RESULTS

Lower FA and higher RD was observed in right cingulum-cingulate gyrus endings (rh.ccg) of the MCI group compared to the CNC group. These changes were localized to the posterior portions of the rh.ccg and correlated with gait velocities.

CONCLUSION

Lower FA and higher RD in the posterior portion of the rh.ccg adjacent to the posterior cingulate suggests decreased microstructural integrity in the MCI group. The correlation of these metrics with gait velocities suggests an important role for this tract in maintaining normal cognitive-motor function.

Plasma-based diagnostic and screening platform using a combination of biosensing signals in Alzheimer's disease

Using biosensor to screen for Alzheimer's disease (AD) facilitates early detection of AD with high sensitivity and accuracy. This approach overcomes the limitations of conventional AD diagnostic methods, such as neuropsychological assessment and neuroimaging analysis. Here, we propose a simultaneous analysis of signal combinations generated by four crucial AD biomarkers (Amyloid beta 1-40 (Aβ₄₀), Aβ₄₂, total tau 441 (tTau₄₄₁), and phosphorylated tau 181 (pTau₁₈₁)) by inducing a dielectrophoretic (DEP) force on fabricated interdigitated microelectrode (IME) sensor. By applying an optimal DEP force, our biosensor selectively concentrates and filters the plasma-based AD biomarkers, exhibiting high sensitivity (limit of detection <100 fM) and selectivity in the plasma-based AD biomarkers detection (p < 0.0001). Consequently, it is demonstrated that a complex combined signal comprising four AD-specific biomarker signals (Aβ₄₀- Aβ₄₂+ tTau₄₄₁- pTau₁₈₁) can differentiate between patients with AD and healthy subjects with high accuracy (78.85%) and precision (80.95%) (p < 0.0001).

The Potential Roles of Exosomes Carrying APP and Tau Cleavage Products in Alzheimer's Disease

Alzheimer's disease (AD) is the leading cause of dementia throughout the world. It is characterized by major amyloid plaques and neurofibrillary tangles (NFTs), which are composed of amyloid-β (Aβ) peptide and hyperphosphorylated Tau (p-Tau), respectively. Exosomes, which are secreted by cells, are single-membrane lipid bilayer vesicles found in bodily fluids and they have a diameter of 30-150 nm. Recently, they have been considered as critical carriers and biomarkers in AD, as they facilitate communication between cells and tissues by delivering proteins, lipids, and nucleic acids. This review demonstrates that exosomes are natural nanocontainers that carry APP as well as Tau cleavage products secreted by neuronal cells and that their formation is associated with the endosomal-lysosomal pathway. Moreover, these exosomes can transfer AD pathological molecules and participate in the pathophysiological process of AD; therefore, they have potential diagnostic and therapeutic value for AD and might also provide novel insights for screening and prevention of the disease.

An Explainable Convolutional Neural Network for the Early Diagnosis of Alzheimer's Disease from 18F-FDG PET

Convolutional Neural Networks (CNN) which support the diagnosis of Alzheimer's Disease using 18F-FDG PET images are obtaining promising results; however, one of the main challenges in this domain is the fact that these models work as black-box systems. We developed a CNN that performs a multiclass classification task of volumetric 18F-FDG PET images, and we experimented two different post hoc explanation techniques developed in the field of Explainable Artificial Intelligence: Saliency Map (SM) and Layerwise Relevance Propagation (LRP). Finally, we quantitatively analyze the explanations returned and inspect their relationship with the PET signal. We collected 2552 scans from the Alzheimer's Disease Neuroimaging Initiative labeled as Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD) and we developed and tested a 3D CNN that classifies the 3D PET scans into its final clinical diagnosis. The model developed achieves, to the best of our knowledge, performances comparable with the relevant literature on the test set, with an average Area Under the Curve (AUC) for prediction of CN, MCI, and AD 0.81, 0.63, and 0.77 respectively. We registered the heatmaps with the Talairach Atlas to perform a regional quantitative analysis of the relationship between heatmaps and PET signals. With the quantitative analysis of the post hoc explanation techniques, we observed that LRP maps were more effective in mapping the importance metrics in the anatomic atlas. No clear relationship was found between the heatmap and the PET signal.

Spatial memory deficits in Alzheimer's disease and their connection to cognitive maps' formation by place cells and grid cells

Whenever we navigate through different contexts, we build a cognitive map: an internal representation of the territory. Spatial navigation is a complex skill that involves multiple types of information processing and integration. Place cells and grid cells, collectively with other hippocampal and medial entorhinal cortex neurons (MEC), form a neural network whose activity is critical for the representation of self-position and orientation along with spatial memory retrieval. Furthermore, this activity generates new representations adapting to changes in the environment. Though there is a normal decline in spatial memory related to aging, this is dramatically increased in pathological conditions such as Alzheimer's disease (AD). AD is a multi-factorial neurodegenerative disorder affecting mainly the hippocampus-entorhinal cortex (HP-EC) circuit. Consequently, the initial stages of the disease have disorientation and wandering behavior as two of its hallmarks. Recent electrophysiological studies have linked spatial memory deficits to difficulties in spatial information encoding. Here we will discuss map impairment and remapping disruption in the HP-EC network, as a possible circuit mechanism involved in the spatial memory and navigation deficits observed in AD, pointing out the benefits of virtual reality as a tool for early diagnosis and rehabilitation.

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer's disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.

Alzheimer's Disease and Neurosyphilis: Meaningful Commonalities and Differences of Clinical Phenotype and Pathophysiological Biomarkers

BACKGROUND

Neurosyphilis-associated cognitive and behavioral impairment- historically coined as "general paralysis of the insane"- share clinical and neuroradiological features with the neurodegenerative disease spectrum, in particular Alzheimer's disease (AD). Anatomopathological similarities have been extensively documented, i.e., neuronal loss, fibrillary alterations, and local amyloid-β deposition. Consequently, accurate classification and timely differential diagnosis may be challenging.

OBJECTIVE

To describe clinical, bio-humoral, brain MRI, FDG-PET, and amyloid-PET features in cases of neurosyphilis with an AD-like phenotypical presentation, as well as clinical outcome in terms of response to antibiotic therapy.

METHODS

We selected the studies comparing patients with AD and with neurosyphilis associated cognitive impairment, to investigate candidate biomarkers classifying the two neurological diseases.

RESULTS

The neuropsychological phenotype of general paralysis, characterized by episodic memory impairment and executive disfunction, substantially mimics clinical AD features. Neuroimaging often shows diffuse or medial temporal cortical atrophy, thus contributing to a high rate of misdiagnosis. Cerebrospinal fluid (CSF)-based analysis may provide supportive diagnostic value, since increased proteins or cells are often found in neurosyphilis, while published data on pathophysiological AD candidate biomarkers are controversial. Finally, psychometric testing using cross-domain cognitive tests, may highlight a wider range of compromised functions in neurosyphilis, involving language, attention, executive function, and spatial ability, which are atypical for AD.

CONCLUSION

Neurosyphilis should be considered a potential etiological differential diagnosis of cognitive impairment whenever imaging, neuropsychological or CSF features are atypical for AD, in order to promptly start antibiotic therapy and delay or halt cognitive decline and disease progression.

Seeing the Woods for the Trees? Applying Diagnostic Criteria for Dementia with Lewy Bodies to Patients Presenting with Posterior Cortical Atrophy

Posterior cortical atrophy (PCA) is a clinico-radiological syndrome characterised by progressive decline in visual processing and other posterior cognitive functions, relatively preserved memory and language in the early stages, and atrophy of posterior brain regions. Often considered a "visual variant" of Alzheimer's disease, a number of other pathological substrates are recognised. Dementia with Lewy Bodies is the second most common neurodegenerative dementia and there is increasing recognition of presentations with little or no parkinsonism, highlighting significant under-recognition of this condition. To complicate matters, some patients with PCA exhibit additional features consistent with other neurodegenerative conditions. We present a series of three such patients presenting with features satisfying the recent consensus criteria for "PCA-Plus (DLB)". We review the current classification of PCA and highlight the importance of deep clinico-radiological phenotyping in neurodegenerative disease to guide targeted interventions and establish future trial-ready cohorts.

Energy Crisis Links to Autophagy and Ferroptosis in Alzheimer's Disease: Current Evidence and Future Avenues

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases worldwide. The occult nature of the onset and the uncertainty of the etiology largely impede the development of therapeutic strategies for AD. Previous studies revealed that the disorder of energy metabolism in the brains of AD patients appears far earlier than the typical pathological features of AD, suggesting a tight association between energy crisis and the onset of AD. Energy crisis in the brain is known to be induced by the reductions in glucose uptake and utilization, which may be ascribed to the diminished expressions of cerebral glucose transporters (GLUTs), insulin resistance, mitochondrial dysfunctions, and lactate dysmetabolism. Notably, the energy sensors such as peroxisome proliferators-activated receptor (PPAR), transcription factor EB (TFEB), and AMP-activated protein kinase (AMPK) were shown to be the critical regulators of autophagy, which play important roles in regulating beta-amyloid (Aβ) metabolism, tau phosphorylation, neuroinflammation, iron dynamics, as well as ferroptosis. In this study, we summarized the current knowledge on the molecular mechanisms involved in the energy dysmetabolism of AD and discussed the interplays existing between energy crisis, autophagy, and ferroptosis. In addition, we highlighted the potential network in which autophagy may serve as a bridge between energy crisis and ferroptosis in the progression of AD. A deeper understanding of the relationship between energy dysmetabolism and AD may provide new insight into developing strategies for treating AD; meanwhile, the energy crisis in the progression of AD should gain more attention.

Biomarker clustering in autosomal dominant Alzheimer's disease

INTRODUCTION

As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others.

METHODS

We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation.

RESULTS

Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors.

DISCUSSION

These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.