Alzheimer's disease neuroimaging

Evaluating serum CXCL12, sCD22, Lp-PLA2 levels and ratios as biomarkers for diagnosis of Alzheimer's disease

BACKGROUND

Grasping the underlying mechanisms of Alzheimer's disease (AD) is still a work in progress, and existing diagnostic techniques encounter various obstacles. Therefore, the discovery of dependable biomarkers is essential for early detection, tracking the disease's advancement, and steering treatment strategies.

AIM

To explore the diagnostic potential of serum CXCL12, sCD22, Lp-PLA2, and their ratios in AD, aiming to enhance early detection and inform targeted treatment strategies.

METHODS

The study was conducted in Dongying people's Hospital from January 2021 to December 2022. Participants included 60 AD patients (AD group) and 60 healthy people (control group). Using a prospective case-control design, the levels of CXCL12, sCD22 and Lp-PLA2 and their ratios were detected by enzyme-linked immunosorbent assay kit in the diagnosis of AD. The differences between the two groups were analyzed by statistical methods, and the corresponding ratio was constructed to improve the specificity and sensitivity of diagnosis.

RESULTS

Serum CXCL12 levels were higher in the AD group (47.2 ± 8.5 ng/mL) than the control group (32.8 ± 5.7 ng/mL, P < 0.001), while sCD22 levels were lower (14.3 ± 2.1 ng/mL vs 18.9 ± 3.4 ng/mL, P < 0.01). Lp-PLA2 levels were also higher in the AD group (112.5 ± 20.6 ng/mL vs 89.7 ± 15.2 ng/mL, P < 0.05). Significant differences were noted in CXCL12/sCD22 (3.3 vs 1.7, P < 0.001) and Lp-PLA2/sCD22 ratios (8.0 vs 5.2, P < 0.05) between the groups. Receiver operating characteristic analysis confirmed high sensitivity and specificity of these markers and their ratios in distinguishing AD, with area under the curves ranging from 0.568 to 0.787.

CONCLUSION

Serum CXCL12 and Lp-PLA2 levels were significantly increased, while sCD22 were significantly decreased, as well as increases in the ratios of CXCL12/sCD22 and Lp-PLA2/sCD22, are closely related to the onset of AD. These biomarkers and their ratios can be used as potential diagnostic indicators for AD, providing an important clinical reference for early intervention and treatment.

β-Amyloid peptide modulates peripheral immune responses and neuroinflammation in rats

Alzheimer's disease (AD) is characterized by immune system dysregulation, impacting both central and peripheral immune responses. The study aimed to investigate the mechanism behind the neurotoxic effects of β-amyloid (Aβ) peptide in the rat brain including the study of neuroinflammation, neurodegeneration, and alterations in peripheral immune responses (PIR). The neuroinflammation brought on by Aβ1-42 and is unknown to influence PIR. Animal models were prepared, after 28 days, control, sham, and treated rats were anaesthetized and inflammatory markers of hippocampus and serum levels (reactive oxygen species, nitrite, tumor necrosis factor-α, and interleukin-1β), and some markers of PIR (splenic mononuclear cells or MNC, cytotoxicity and phagocytic index of the white blood cells leukocyte adhesion inhibition index or LAI), as well as polymorphonuclear cells of the spleen, were assessed. In addition to changes in peripheral immune responses, the present study found that AD rats had higher blood levels of inflammatory markers. Based on the study, the immune system irregularities observed in AD rats in the peripheral regions might be connected to neuroinflammation, which is facilitated by a compromised blood-brain barrier. Hence, it is viable to propose that the neuroinflammatory condition in rats with Aβ-induced AD could modify immune responses in the peripheral areas with significantly higher levels of inflammatory cytokines markers in the hippocampal tissue in Aβ-injected AD rats.

The Major Hypotheses of Alzheimer's Disease: Related Nanotechnology-Based Approaches for Its Diagnosis and Treatment

Alzheimer's disease (AD) is a well-known chronic neurodegenerative disorder that leads to the progressive death of brain cells, resulting in memory loss and the loss of other critical body functions. In March 2019, one of the major pharmaceutical companies and its partners announced that currently, there is no drug to cure AD, and all clinical trials of the new ones have been cancelled, leaving many people without hope. However, despite the clear message and startling reality, the research continued. Finally, in the last two years, the Food and Drug Administration (FDA) approved the first-ever medications to treat Alzheimer's, aducanumab and lecanemab. Despite researchers' support of this decision, there are serious concerns about their effectiveness and safety. The validation of aducanumab by the Centers for Medicare and Medicaid Services is still pending, and lecanemab was authorized without considering data from the phase III trials. Furthermore, numerous reports suggest that patients have died when undergoing extended treatment. While there is evidence that aducanumab and lecanemab may provide some relief to those suffering from AD, their impact remains a topic of ongoing research and debate within the medical community. The fact is that even though there are considerable efforts regarding pharmacological treatment, no definitive cure for AD has been found yet. Nevertheless, it is strongly believed that modern nanotechnology holds promising solutions and effective clinical strategies for the development of diagnostic tools and treatments for AD. This review summarizes the major hallmarks of AD, its etiological mechanisms, and challenges. It explores existing diagnostic and therapeutic methods and the potential of nanotechnology-based approaches for recognizing and monitoring patients at risk of irreversible neuronal degeneration. Overall, it provides a broad overview for those interested in the evolving areas of clinical neuroscience, AD, and related nanotechnology. With further research and development, nanotechnology-based approaches may offer new solutions and hope for millions of people affected by this devastating disease.

Alterations of gut microbiota are associated with brain structural changes in the spectrum of Alzheimer's disease: the SILCODE study in Hainan cohort

Background

The correlation between gut microbiota and Alzheimer's disease (AD) is increasingly being recognized by clinicians. However, knowledge about the gut-brain-cognition interaction remains largely unknown.

Methods

One hundred and twenty-seven participants, including 35 normal controls (NCs), 62 with subjective cognitive decline (SCD), and 30 with cognitive impairment (CI), were included in this study. The participants underwent neuropsychological assessments and fecal microbiota analysis through 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. Structural MRI data were analyzed for cortical anatomical features, including thickness, sulcus depth, fractal dimension, and Toro's gyrification index using the SBM method. The association of altered gut microbiota among the three groups with structural MRI metrics and cognitive function was evaluated. Furthermore, co-expression network analysis was conducted to investigate the gut-brain-cognition interactions.

Results

The abundance of Lachnospiraceae, Lachnospiracea_incertae_sedis, Fusicatenibacter, and Anaerobutyricum decreased with cognitive ability. Rikenellaceae, Odoribacteraceae, and Alistipes were specifically enriched in the CI group. Mediterraneibacter abundance was correlated with changes in brain gray matter and cerebrospinal fluid volume (p = 0.0214, p = 0.0162) and significantly with changes in cortical structures in brain regions, such as the internal olfactory area and the parahippocampal gyrus. The three colonies enriched in the CI group were positively correlated with cognitive function and significantly associated with changes in cortical structure related to cognitive function, such as the precuneus and syrinx gyrus.

Conclusion

This study provided evidence that there was an inner relationship among the altered gut microbiota, brain atrophy, and cognitive decline. Targeting the gut microbiota may be a novel therapeutic strategy for early AD.

Early Identification of Different Behavioral Phenotypes in the Behavioral Variant of Frontotemporal Dementia with the Aid of the Mini-Frontal Behavioral Inventory (mini-FBI)

Background: The Frontal Behavioral Inventory (FBI) is a questionnaire designed to quantify behavioral changes in frontotemporal dementia (FTD). Literature showed heterogeneous FBI profiles in FTD versus Alzheimer’s disease (AD) with variable occurrence of positive and negative symptoms. Objective: In this study, we constructed a short FBI version (i.e., mini-FBI) with the aim to provide clinicians with a short tool for the identification of early behavioral changes in behavioral variant of FTD (bvFTD), also facilitating the differential diagnosis with AD. Methods: 40 bvFTD and 33 AD patients were enrolled. FBI items were selected based on internal consistency and exploratory factor analysis. Convergent validity of mini-FBI was also assessed. A behavioral index (i.e., B-index) representing the balance between positive and negative mini-FBI symptoms was computed in order to analyze its distribution in bvFTD through a cluster analysis and to compare performance among patient groups. Results: The final version of the mini-FBI included 12 items, showing a significant convergent validity with the Neuropsychiatric Inventory scores (rp = 0.61, p < 0.001). Cluster analysis split patients in four clusters. bvFTD were included in three different clusters characterized by prevalent positive symptoms, both positive and negative symptoms, or prevalent negative behavioral alterations, similar to a subset of AD patients. A fourth cluster included only AD patients showing no positive symptoms. Conclusion: The mini-FBI is a valuable easily administrable questionnaire able to early identify symptoms effectively contributing to the bvFTD behavioral syndrome, aiding clinician in diagnosis and management.

An Observation Study of Urinary Biomarkers Exploratory in Alzheimer's Disease using High Resolution Mass Spectrometry

Alzheimer's disease (AD) is regarded as a progressive neurodegenerative dementia, characterized by degeneration of distinct neuronal populations. A case-control study was carried out by using high resolution mass spectrometry to explore AD associated urinary metabolic biomarkers from 30 AD patients and 30 cognitively normal (CN) individuals, respectively. In total, 49 metabolites were determined and validated as known compounds by LC/MS analysis. With two sample t-test statistical analysis (p<0.05), 19 metabolites were shown to be significantly differed from AD to CN. A diagnostic model of receiver-operator characteristic (ROC) curve was constructed with a combination of 9 selected metabolites and yielded a separation with an area under the curve value of 0.976 between two groups. This study indicated urinary metabolites showed a significant expression between AD and CN. AD related metabolites enable to satisfy the diagnostic power of disease discrimination. Additionally, as a non-invasive approach, urine collection provides its convenience in clinical diagnosis of AD.

Novel Cyclopentaquinoline and Acridine Analogs as Multifunctional, Potent Drug Candidates in Alzheimer's Disease

A series of new cyclopentaquinoline derivatives with 9-acridinecarboxylic acid and a different alkyl chain length were synthesized, and their ability to inhibit cholinesterases was evaluated. All designed compounds, except derivative 3f, exhibited a selectivity for butyrylcholinesterase (BuChE) with IC₅₀ values ranging from 103 to 539 nM. The 3b derivative revealed the highest inhibitory activity towards BuChE (IC₅₀ = 103.73 nM) and a suitable activity against AChE (IC₅₀ = 272.33 nM). The 3f derivative was the most active compound to AChE (IC₅₀ = 113.34 nM) with satisfactory activity towards BuChE (IC₅₀ = 203.52 nM). The potential hepatotoxic effect was evaluated for both 3b and 3f compounds. The 3b and 3f potential antioxidant activity was measured using the ORAC-FL method. The 3b and 3f derivatives revealed a significantly higher antioxidant potency, respectively 35 and 25 higher than tacrine. Theoretical, physicochemical, and pharmacokinetic properties were calculated using ACD Labs Percepta software. Molecular modeling and kinetic study were used to reveal the mechanism of cholinesterase inhibition in the most potent compounds: 3b and 3f.

Personalized Management and Treatment of Alzheimer's Disease

Alzheimer’s disease (AD) is a priority health problem with a high cost to society and a large consumption of medical and social resources. The management of AD patients is complex and multidisciplinary. Over 90% of patients suffer from concomitant diseases and require personalized therapeutic regimens to reduce adverse drug reactions (ADRs), drug−drug interactions (DDIs), and unnecessary costs. Men and women show substantial differences in their AD-related phenotypes. Genomic, epigenetic, neuroimaging, and biochemical biomarkers are useful for predictive and differential diagnosis. The most frequent concomitant diseases include hypertension (>25%), obesity (>70%), diabetes mellitus type 2 (>25%), hypercholesterolemia (40%), hypertriglyceridemia (20%), metabolic syndrome (20%), hepatobiliary disorder (15%), endocrine/metabolic disorders (>20%), cardiovascular disorder (40%), cerebrovascular disorder (60−90%), neuropsychiatric disorders (60−90%), and cancer (10%). Over 90% of AD patients require multifactorial treatments with risk of ADRs and DDIs. The implementation of pharmacogenetics in clinical practice can help optimize the limited therapeutic resources available to treat AD and personalize the use of anti-dementia drugs, in combination with other medications, for the treatment of concomitant disorders.

Pharmacogenomics of Alzheimer's Disease: Novel Strategies for Drug Utilization and Development

Alzheimer's disease (AD) is a priority health problem in developed countries with a high cost to society. Approximately 20% of direct costs are associated with pharmacological treatment. Over 90% of patients require multifactorial treatments, with risk of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) for the treatment of concomitant diseases such as hypertension (>25%), obesity (>70%), diabetes mellitus type 2 (>25%), hypercholesterolemia (40%), hypertriglyceridemia (20%), metabolic syndrome (20%), hepatobiliary disorder (15%), endocrine/metabolic disorders (>20%), cardiovascular disorder (40%), cerebrovascular disorder (60-90%), neuropsychiatric disorders (60-90%), and cancer (10%).For the past decades, pharmacological studies in search of potential treatments for AD focused on the following categories: neurotransmitter enhancers (11.38%), multitarget drugs (2.45%), anti-amyloid agents (13.30%), anti-tau agents (2.03%), natural products and derivatives (25.58%), novel synthetic drugs (8.13%), novel targets (5.66%), repository drugs (11.77%), anti-inflammatory drugs (1.20%), neuroprotective peptides (1.25%), stem cell therapy (1.85%), nanocarriers/nanotherapeutics (1.52%), and other compounds (<1%).Pharmacogenetic studies have shown that the therapeutic response to drugs in AD is genotype-specific in close association with the gene clusters that constitute the pharmacogenetic machinery (pathogenic, mechanistic, metabolic, transporter, pleiotropic genes) under the regulatory control of epigenetic mechanisms (DNA methylation, histone/chromatin remodeling, microRNA regulation). Most AD patients (>60%) are carriers of over ten pathogenic genes. The genes that most frequently (>50%) accumulate pathogenic variants in the same AD case are A2M (54.38%), ACE (78.94%), BIN1 (57.89%), CLU (63.15%), CPZ (63.15%), LHFPL6 (52.63%), MS4A4E (50.87%), MS4A6A (63.15%), PICALM (54.38%), PRNP (80.7059), and PSEN1 (77.19%). There is also an accumulation of 15 to 26 defective pharmagenes in approximately 85% of AD patients. About 50% of AD patients are carriers of at least 20 mutant pharmagenes, and over 80% are deficient metabolizers for the most common drugs, which are metabolized via the CYP2D6, CYP2C9, CYP2C19, and CYP3A4/5 enzymes.The implementation of pharmacogenetics can help optimize drug development and the limited therapeutic resources available to treat AD, and personalize the use of anti-dementia drugs in combination with other medications for the treatment of concomitant disorders.

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease

The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.

Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer's Disease

OBJECTIVE

Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer's Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD.

METHODS

We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores.

RESULTS

In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores.

CONCLUSION

Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.

The cognitive dysfunction related to Alzheimer disease or cerebral small vessel disease: What's the differences

ABSTRACT

Alzheimer disease (AD) and sporadic cerebral small vessel disease (CSVD) are common cognitive disorders. Both AD and CSVD have mental symptoms including chronic progressive cognitive impairment, dysfunction, and behavioral abnormalities. However, the differences on the cognitive dysfunction of AD and CSVD remain unclear. It is necessary to elucidate the cognitive dysfunction differences of AD and CSVD, and to identify the potential risk factors.AD or sporadic CSVD patients treated in our hospital from December 1, 2018 to May 31, 2019 were included. And we selected healthy participants as controls. The mini-mental state examination and Montreal Cognitive Assessment Scale were used for neuropsychological assessment, and related medical information were collected and compared.A total of 190 patients were included. The total mini-mental state examination scores in AD, CSVD group were significantly less than that of control group, there were significant differences in the domains of directional ability, attention and computing ability, delayed recall, and visual perception (all P < .05); the total Montreal Cognitive Assessment Scale scores in AD, CSVD group were significantly less than that of control group. There were significant differences in the domains of visual space and execution, immediate remember, attention and computing ability, language, delayed recall, and directional ability (all P < .05); diabetes was a risk factor both for AD (hazard ratio = 1.63, 95% confidence interval: 1.35-1.97) and CSVD (hazard ratio = 1.15, 95% confidence interval: 1.08-1.27).The cognitive dysfunctions of AD are difference to that of CSVD patients, and diabetes is the risk factor both for AD and CSVD, future studies are needed to further identify the prevention and treatment of AD and CSVD.

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease

The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain tissue damage prevention. The study was intended to explore the segmentation effects of convolutional neural network (CNN) model on Magnetic Resonance (MR) imaging for Alzheimer's diagnosis and nursing. Specifically, 18 Alzheimer's patients admitted to Indira Gandhi Medical College (IGMC) hospital were selected as the experimental group, with 18 healthy volunteers in the Ctrl group. Furthermore, the CNN model was applied to segment the MR imaging of Alzheimer's patients, and its segmentation effects were compared with those of the fully convolutional neural network (FCNN) and support vector machine (SVM) algorithms. It was found that the CNN model demonstrated higher segmentation precision, and the experimental group showed a higher clinical dementia rating (CDR) score and a lower mini-mental state examination (MMSE) score (P < 0.05). The size of parahippocompalgyrus and putamen was bigger in the Ctrl (P < 0.05). In experimental group, the amplitude of low-frequency fluctuation (ALFF) was positively correlated with the MMSE score in areas of bilateral cingulum gyri (r = 0.65) and precuneus (r = 0.59). In conclusion, the grey matter structure is damaged in Alzheimer's patients, and hippocampus ALFF and regional homogeneity (ReHo) is involved in the neuronal compensation mechanism of hippocampal damage, and the caregivers should take an active nursing method.

In vivo Imaging With 18F-FDG- and 18F-Florbetaben-PET/MRI Detects Pathological Changes in the Brain of the Commonly Used 5XFAD Mouse Model of Alzheimer's Disease

Imaging biomarkers of Alzheimer's disease (AD) that are able to detect molecular changes in vivo and transgenic animal models mimicking AD pathologies are essential for the evaluation of new therapeutic strategies. Positron-emission tomography (PET) using either ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) or amyloid-tracers is a well-established, non-invasive tool in the clinical diagnostics of AD assessing two major pathological hallmarks. ¹⁸F-FDG-PET is able to detect early changes in cerebral glucose metabolism and amyloid-PET shows cerebral amyloid load. However, the suitability of ¹⁸F-FDG- and amyloid-PET in the widely used 5XFAD mouse model of AD is unclear as only a few studies on the use of PET biomarkers are available showing some conflicting results. The aim of this study was the evaluation of ¹⁸F-FDG-PET and amyloid-PET in 5XFAD mice in comparison to neurological deficits and neuropathological changes. Seven- and 12-month-old male 5XFAD mice showed a significant reduction in brain glucose metabolism in ¹⁸F-FDG-PET and amyloid-PET with ¹⁸F-Florbetaben demonstrated an increased cerebral amyloid deposition (n = 4-6 per group). Deficits in spatial reference memory were detected in 12-month-old 5XFAD mice in the Morris Water Maze (n = 10-12 per group). Furthermore, an increased plaque load and gliosis could be proven immunohistochemically in 5XFAD mice (n = 4-6 per group). PET biomarkers ¹⁸F-FDG and ¹⁸F-Florbetaben detected cerebral hypometabolism and increased plaque load even before the onset of severe memory deficits. Therefore, the 5XFAD mouse model of AD is well-suited for in vivo monitoring of AD pathologies and longitudinal testing of new therapeutic approaches.

Multifunctional Superparamagnetic Iron Oxide Nanoparticles Conjugated with Aβ Oligomer-Specific scFv Antibody and Class A Scavenger Receptor Activator Show Early Diagnostic Potentials for Alzheimer's Disease

Background

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia. Diagnosing AD before symptoms arise will facilitate earlier intervention. The early diagnostic approaches are thus urgently needed.

Methods

The multifunctional nanoparticles W20/XD4-SPIONs were constructed by the conjugation of oligomer-specific scFv antibody W20 and class A scavenger receptor (SR-A) activator XD4 onto superparamagnetic iron oxide nanoparticles (SPIONs). The SPIONs’ stability and uniformity in size were measured by dynamic light scattering and transmission electron microscopy. The ability of W20/XD4-SPIONs for recognizing Aβ oligomers (AβOs) and promoting AβOs phagocytosis was assessed by immunocytochemistry and flow cytometry analysis. The blood–brain barrier permeability of W20/XD4-SPIONs was determined by a co-culture transwell model. The in vivo probe distribution of W20/XD4-SPIONs in AD mouse brains was detected by magnetic resonance imaging (MRI).

Results

W20/XD4-SPIONs, as an AβOs-targeted molecular MRI contrast probe, readily reached pathological AβOs regions in brains and distinguished AD transgenic mice from WT controls. W20/XD4-SPIONs retained the property of XD4 for SR-A activation and significantly promoted microglial phagocytosis of AβOs. Moreover, W20/XD4-SPIONs exhibited the properties of good biocompatibility, high stability and low cytotoxicity.

Conclusion

Compared with W20-SPIONs or XD4-SPIONs, W20/XD4-SPIONs show the highest efficiency for AβOs-targeting and significantly enhance AβOs uptake by microglia. As a molecular probe, W20/XD4-SPIONs also specifically and sensitively bind to AβOs in AD brains to provide an MRI signal, demonstrating that W20/XD4-SPIONs are promising diagnostic agents for early-stage AD. Due to the beneficial effect of W20 and XD4 on neuropathology, W20/XD4-SPIONs may also have therapeutic potential for AD .

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-β positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.

In vivo synaptic density loss is related to tau deposition in amnestic mild cognitive impairment

OBJECTIVE

To investigate in vivo whether synaptic loss and neurofibrillary tangle load spatially overlap and correlate with clinical symptoms in patients with amnestic mild cognitive impairment (aMCI).

METHODS

In this cross-sectional study, 10 patients with aMCI and 10 healthy controls underwent triple PET-MRI with ¹¹C-UCB-J (synaptic vesicle protein 2A), ¹⁸F-MK-6240 (tau deposition), and ¹¹C-Pittsburgh compound B (β-amyloid) and neuropsychological assessment. Gray matter atrophy was assessed by voxel-based morphometry with T1-weighted MRIs. Voxel-wise and volume-of-interest analyses were conducted on PET data. The interrelationship of synaptic density and tau deposition was investigated. We also investigated correlations of ¹⁸F-MK-6240 and ¹¹C-UCB-J binding with cognitive performance.

RESULTS

Compared to controls, patients with aMCI showed a decreased ¹¹C-UCB-J binding mainly in substructures of the medial temporal lobe (MTL; 48%-51%, p _cluster = 0.02). Increased ¹⁸F-MK6240 binding in the same region was observed (42%-44%, p _cluster = 0.0003), spreading to association cortices. In the MTL, higher ¹⁸F-MK-6240 binding inversely related to lower ¹¹C-UCB-J binding (p = 0.02, r = -0.76). Decreased performance on cognitive tests was associated with both increased ¹⁸F-MK-6240 and decreased ¹¹C-UCB-J binding in the hippocampus (p < 0.01, r > 0.7), although in a multivariate analysis only ¹⁸F-MK-6240 binding was significantly related to cognitive performance.

CONCLUSIONS

Patients with aMCI have high tau deposition and synaptic density loss mainly in key regions known to be involved in early cognitive impairment, indicating that these are interrelated in the MTL, while tau binding had already spread toward association cortices. Longitudinal data are needed to provide further insight into the temporal aspects of this relationship.

Microglial activation and tau burden predict cognitive decline in Alzheimer's disease

Tau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer's disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by 18F-AV-1451 PET), neuroinflammation (measured by 11C-PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer's disease pathology. Twenty-six patients (n = 12 with clinically probable Alzheimer's dementia and n = 14 with amyloid-positive mild cognitive impairment) and 29 healthy control subjects underwent baseline assessment with 18F-AV-1451 PET, 11C-PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke's Cognitive Examination. Regional grey matter volumes, and regional binding of 18F-AV-1451 and 11C-PK11195 were derived from 15 temporo-parietal regions characteristically affected by Alzheimer's disease pathology. A principal component analysis was used on each imaging modality separately, to identify the main spatial distributions of pathology. A latent growth curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals' estimated rate of cognitive decline on the neuroimaging components and examined univariable predictive models with single-modality predictors, and a multi-modality predictive model, to identify the independent and combined prognostic value of the different neuroimaging markers. Principal component analysis identified a single component for the grey matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the rate of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of 18F-AV-1451 and the first (i.e. anterior temporal) component for 11C-PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer's disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer's disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer's disease.