Different Stages Of Alzheimer's Disease Research Articles

Tau-related grey matter network breakdown across the Alzheimer\u2019s disease continuum

BackgroundChanges in grey matter covariance networks have been reported in preclinical and clinical stages of Alzheimer’s disease (AD) and have been associated with amyloid-β (Aβ) deposition and cognitive decline. However, the role of tau pathology on grey matter networks remains unclear. Based on previously reported associations between tau pathology, synaptic density and brain structural measures, tau-related connectivity changes across different stages of AD might be expected. We aimed to assess the relationship between tau aggregation and grey matter network alterations across the AD continuum.MethodsWe included 533 individuals (178 Aβ-negative cognitively unimpaired (CU) subjects, 105 Aβ-positive CU subjects, 122 Aβ-positive patients with mild cognitive impairment, and 128 patients with AD dementia) from the BioFINDER-2 study. Single-subject grey matter networks were extracted from T1-weighted images and graph theory properties including degree, clustering coefficient, path length, and small world topology were calculated. Associations between tau positron emission tomography (PET) values and global and regional network measures were examined using linear regression models adjusted for age, sex, and total intracranial volume. Finally, we tested whether the association of tau pathology with cognitive performance was mediated by grey matter network disruptions.ResultsAcross the whole sample, we found that higher tau load in the temporal meta-ROI was associated with significant changes in degree, clustering, path length, and small world values (all p < 0.001), indicative of a less optimal network organisation. Already in CU Aβ-positive individuals associations between tau burden and lower clustering and path length were observed, whereas in advanced disease stages elevated tau pathology was progressively associated with more brain network abnormalities. Moreover, the association between higher tau load and lower cognitive performance was only partly mediated (9.3 to 9.5%) through small world topology.ConclusionsOur data suggest a close relationship between grey matter network disruptions and tau pathology in individuals with abnormal amyloid. This might reflect a reduced communication between neighbouring brain areas and an altered ability to integrate information from distributed brain regions with tau pathology, indicative of a more random network topology across different AD stages.

Dynamic analysis of synaptic loss and synaptic compensation in the process of associative memory ability decline in Alzheimer’s disease

The cognitive decline caused by Alzheimer’s disease (AD) has a great impact on the life of patients and their families. Modern medicine has shown that loss of synaptic function is one of the causes of AD, and synaptic compensation compensates for cognitive abilities of the human brain. However, there are no studies on the internal mechanism of synaptic loss and synaptic compensation affecting human cognitive ability. In order to solve this problem, we propose here a three-layer neural network with multiple associative memory abilities, which is one of the main cognitive abilities. Based on synaptic plasticity, models of synaptic loss and synaptic compensation are established to study the pathogenesis of the degeneration of associative memory and explore feasible treatment approaches by setting different degrees of loss and compensation. Our simulation results show that the model can describe the associative memory ability at different stages of AD, which is of great significance for paramedics to determine the stage of disease and develop effective treatment strategies.

Sleep-Based Interventions in Alzheimer's Disease: Promising Approaches from Prevention to Treatment along the Disease Trajectory.

The multifactorial nature of Alzheimer’s disease (AD) has led scientific researchers to focus on the modifiable and treatable risk factors of AD. Sleep fits into this context, given the bidirectional relationship with AD confirmed by several studies over the last years. Sleep disorders appear at an early stage of AD and continue throughout the entire course of the pathology. Specifically, sleep abnormalities, such as more fragmented sleep, increase in time of awakenings, worsening of sleep quality and primary sleep disorders raise with the severity and progression of AD. Intervening on sleep, therefore, means acting both with prevention strategies in the pre-clinical phase and with treatments during the course of the disease. This review explores sleep disturbances in the different stages of AD, starting from the pre-clinical stage. Particular attention is given to the empirical evidence investigating obstructive sleep apnea (OSA) disorder and the mechanisms overlapping and sharing with AD. Next, we discuss sleep-based intervention strategies in the healthy elderly population, mild cognitive impairment (MCI) and AD patients. We mention interventions related to behavioral strategies, combination therapies, and bright light therapy, leaving extensive space for new and raising evidence on continuous positive air pressure (CPAP) treatment effectiveness. Finally, we clarify the role of NREM sleep across the AD trajectory and consider the most recent studies based on the promising results of NREM sleep enhancement, which use innovative experimental designs and techniques.

Synopses of Cochrane Reviews from Cochrane Library Issue 12 2020 Through Issue 3 2021.

Synopses of Cochrane Reviews from Cochrane Library Issue 12 2020 Through Issue 3 2021.

Dynamic Change of Intracellular Metabolism of Microglia Evaluated by Transcriptomics in an Alzheimer's Mouse Model.

Microglia play diverse roles in Alzheimer's disease (AD). Intracellular metabolism has been indicated an important factor in modulating the function of microglia. However, it is not clear whether the intracellular metabolism of microglia changes dynamically in different stages of AD. To determine whether microglia intracellular metabolism changes dynamically in different stages of AD. Microglia were extracted from APPSwe/PS1dE9 (APP/PS1) mice and wild-type littermates at 2, 4, and 8 months old by fluorescence-activated cell sorting and used for RNA-sequencing analysis and quantitative PCR. Morphologies of amyloid plaques and microglia were detected by immunofluorescence staining. Compared with control littermates, the microglia of APP/PS1 mice exhibited significant transcriptional changes at 2-month-old before microglia morphological alterations and the plaque formation. The changes continued drastically following age with defined morphological shift of microglia and amyloid plaque enhancement in brains. Further analysis of those genotype and age dependent transcriptomic changes revealed that differentially expressed genes were enriched in pathways related to energy metabolism. Compared with wild-type mice, there were changes of some vital genes related to glucose metabolism and lipid metabolism pathways in APP/PS1 mice at different ages. Glucose metabolism may play a major role in early activation of microglia, and lipid metabolism may be more important in later activation period. Our results showed that microglia actively participate in the pathological progress of AD. The intracellular metabolism of microglia changed significantly in different stages of AD, even preceding amyloid-β deposition.

Early diagnosis of Alzheimer's disease based on three-dimensional convolutional neural networks ensemble model combined with genetic algorithm

The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD vs. normal control (NC), 88.1% for MCI patients who will convert to AD (MCIc) vs. NC, and 71.3% for MCI patients who will not convert to AD (MCInc) vs. MCIc. In addition, with the statistical analysis of the behavioral domains corresponding to ROIs (i.e. brain regions), besides left hippocampus, medial and lateral amygdala, and left para-hippocampal gyrus, anterior superior temporal sulcus of middle temporal gyrus and dorsal area 23 of cingulate gyrus were also found with GA. It is concluded that the functions of the selected brain regions mainly are relevant to emotions, memory, cognition and the like, which is basically consistent with the symptoms of indifference, memory losses, mobility decreases and cognitive declines in AD patients. All of these show that the proposed method is effective.

Toxic Amyloid-β42 Conformer May Accelerate the Onset of Alzheimer's Disease in the Preclinical Stage.

Toxic amyloid-β protein (Aβ) conformers play an important role in the progression of Alzheimer's disease (AD). The ratio of toxic conformer to total Aβ42 in cerebrospinal fluid (CSF) was significantly high in AD and mild cognitive impairment (MCI) due to AD using an enzyme-linked immunosorbent assay kit with a 24B3 antibody. We compared the toxic Aβ42, conformer at different stages of AD to identify its contribution to AD pathogenesis. We compared 5 patients with preclinical AD, 11 patients with MCI due to AD, 21 patients with AD, and 5 healthy controls to measure CSF levels of total Aβ42, total tau, tau phosphorylated at threonine 181 (p-tau), and toxic Aβ conformers. All were classified using the Clinical Dementia Rating. Cognitive function was assessed using the Japanese version of the Mini-Mental State Examination (MMSE-J). Toxic Aβ conformer level was insignificant between groups, but its ratio to Aβ42 was significantly higher in AD than in preclinical AD (p < 0.05). Toxic Aβ42 conformer correlated positively with p-tau (r = 0.67, p < 0.01) and p-tau correlated negatively with MMSE-J (r = -0.38, p < 0.05). Toxic Aβ conformer triggers tau accumulation leading to neuronal impairment in AD pathogenesis.

Hyperconnectivity of Self-Referential Network as a Predictive Biomarker of the Progression of Alzheimer's Disease.

Self-referential processing is associated with the progression of Alzheimer's disease (AD), and cerebrospinal fluid (CSF) proteins have become accepted biomarkers of AD. Our objective in this study was to focus on the relationships between the self-referential network (SRN) and CSF pathology in AD-spectrum patients. A total of 80 participants, including 20 cognitively normal, 20 early mild cognitive impairment (EMCI), 20 late MCI (LMCI), and 20 AD, were recruited for this study. Independent component analysis was used to explore the topological SRN patterns, and the abnormalities of this network were identified at different stages of AD. Finally, CSF pathological characteristics (i.e., CSF Aβ, t-tau, and p-tau) that affected the abnormalities of the SRN were further determined during the progression of AD. Compared to cognitively normal subjects, AD-spectrum patients (i.e., EMCI, LMCI, and AD) showed a reversing trend toward an association between CSF pathological markers and the abnormal SRN occurring during the progression of AD. However, a certain disease state (i.e., the present LMCI) with a low concentration of CSF tau could evoke more hyperconnectivity of the SRN than other patients with progressively increasing concentrations of CSF tau (i.e., EMCI and AD), and this fluctuation of CSF tau was more sensitive to the hyperconnectivity of the SRN than the dynamic changes of CSF Aβ. The integrity of the SRN was closely associated with CSF pathological characteristics, and these findings support the view that the hyperconnectivity of the SRN will play an important role in monitoring the progression of the pre-dementia state to AD.

TREM2 Mediates Microglial Anti-Inflammatory Activations in Alzheimer's Disease: Lessons Learned from Transcriptomics.

Alzheimer’s disease (AD) is a lethal neurodegenerative disorder primarily affecting the aged population. The etiopathogenesis of AD, especially that of the sporadic type, remains elusive. The triggering receptor expressed on myeloid cells 2 (TREM2), a member of TREM immunoglobulin superfamily, plays a critical role in microglial physiology. Missense mutations in human TREM2 are determined as genetic risk factors associated with the development of sporadic AD. However, the roles of TREM2 in the pathogenesis of AD are still to be established. In this review, we outlined the influence of Trem2 on balance of pro- and anti-inflammatory microglial activations from a perspective of AD mouse model transcriptomics. On this basis, we further speculated the roles of TREM2 in different stages of AD, which may shed light to the development of TREM2-targeted strategy for the prevention and treatment of this neurodegenerative disorder.

Accelerated inflammatory aging in Alzheimer\u2019s disease and its relation to amyloid, tau, and cognition

It is unclear how pathological aging of the inflammatory system relates to Alzheimer’s disease (AD). We tested whether age-related inflammatory changes in cerebrospinal fluid (CSF) and plasma exist across different stages of AD, and whether such changes related to AD pathology. Linear regression was first used model chronological age in amyloid-β negative, cognitively unimpaired individuals (Aβ− CU; n = 312) based on a collection of 73 inflammatory proteins measured in both CSF and plasma. Fitted models were then applied on protein levels from Aβ+ individuals with mild cognitive impairment (Aβ+ MCI; n = 150) or Alzheimer’s disease dementia (Aβ+ AD; n = 139) to test whether the age predicted from proteins alone (“inflammatory age”) differed significantly from true chronological age. Aβ− individuals with subjective cognitive decline (Aβ− SCD; n = 125) or MCI (Aβ− MCI; n = 104) were used as an independent contrast group. The difference between inflammatory age and chronological age (InflammAGE score) was then assessed in relation to core AD biomarkers of amyloid, tau, and cognition. Both CSF and plasma inflammatory proteins were significantly associated with age in Aβ− CU individuals, with CSF-based proteins predicting chronological age better than plasma-based counterparts. Meanwhile, the Aβ− SCD and validation Aβ− CU groups were not characterized by significant inflammatory aging, while there was increased inflammatory aging in Aβ− MCI patients for CSF but not plasma inflammatory markers. Both CSF and plasma inflammatory changes were seen in the Aβ+ MCI and Aβ+ AD groups, with varying degrees of change compared to Aβ− CU and Aβ− SCD groups. Finally, CSF inflammatory changes were highly correlated with amyloid, tau, general neurodegeneration, and cognition, while plasma changes were mostly associated with amyloid and cognition. Inflammatory pathways change during aging and are specifically altered in AD, tracking closely with pathological hallmarks. These results have implications for tracking AD progression and for suggesting possible pathways for drug targeting.

Clinical and biomarker trajectories in sporadic Alzheimer's disease: A longitudinal study

AbstractBackgroundIt has been documented that β‐amyloid (Aβ) deposition, tau pathology and neuronal degeneration precedes clinical symptoms in autosomal dominant Alzheimer's disease (AD). Since the divergence on pathogenesis between sporadic and familial AD limits the extension of these findings to sporadic AD, where and magnitude of pathologic processes in sporadic AD are of significant importance to clinical management.MethodIn this longitudinal study, we analyzed data from 982 ADNI participants with abnormal amyloid accumulations who underwent cerebrospinal fluid (CSF) test, brain imaging and cognitive assessments. We compared the levels of clinical outcomes and biomarkers at baseline and the rates of change in follow‐up between different stages of AD. The slope of the regression model over years was applied to estimate rates of change for Aβ deposition, tau alteration, hypometabolism, brain atrophy, and cognitive decline.ResultIn total 167 healthy controls, 133 patients with preclinical AD, 451 patients with MCI due to AD, and 231 patients with dementia due to AD were studied. The levels of clinical and biomarkers at baseline and their change rates at follow‐up significantly differed among the four groups. In terms of average years, Aβ deposition was observed over a range of 45·6 years for CSF Aβ, and 31·1 years with amyloid imaging, although Aβ markers began to be abnormal at 27·8 years for CSF Aβ and 26·3 years for neuroimaging. When Aβ markers exceeded the threshold, CSF tau and other non‐ Aβ markers started to change, and patients were estimated to take 29·1 years (CSF tau),12·7 years (memory), 12·2 years (hippocampus atrophy), 10·1years (hypometabolism) to transition from clinically normal to dementia. Finally, CSF p‐tau significantly increased as dementia onset approached.ConclusionOur results reveal the trajectory of biomarkers in sporadic Alzheimer's disease is led by Aβ accumulation, followed by CSF tau change, memory deficits, brain atrophy, hypometabolism, cognitive decline, and lastly CSF p‐tau increase.

Characterizing chromogranin and secretogranin proteoforms in dementia pathophysiology

AbstractBackgroundChromogranins and secretogranins are neurotransmitter‐like molecules (neuropeptides) that are processed into functional peptides (proteoforms) by proteases. Full‐length chromogranins and secretogranins and their peptide proteoforms are involved in diverse neuronal and synaptic functions, but their specific processing pathways and contribution to dementia remain largely unknown. This project aims to investigate chromogranin and secretogranin proteolytic processing and validate if specific proteoforms may be useful cerebrospinal fluid (CSF) biomarkers of synaptic health in dementia.MethodMass spectrometry (MS) of intact processed peptides was used to produce a detailed map of identified proteoforms arising from chromogranins and secretogranins in the CSF of 5 control and 5 Alzheimer’s disease (AD) patients. This CSF profile was compared against known processed peptides and those used in CSF‐targeted MS assays. Proteasix was used to identify potential proteases involved in the production of identified proteoforms and the human cell atlas was used to identify whether they were expressed in the brain. The cleavage specificity was assayed in vitro using recombinant proteins, analysed by gel electrophoresis and western blotting with chromogranin and secretogranin antibodies.ResultUsing tryptic versus non‐tryptic MS techniques, we detected distinct chromogranin and secretogranin proteoform profiles, suggesting that tryptic digestion underestimates the complexity of proteoform profiles. We are now developing a targeted MS assay to characterize the profile of chromogranin and secretogranin proteoforms in CSF samples spanning controls, different stages of AD and other neurodegenerative diseases. Using Proteasix; members of the calpain, cathepsin, caspase and MMP families were identified to cleave chromogranins and secretogranins. These protease families are known to be expressed in the brain as shown by the human cell atlas and have been previously identified to be dysregulated in dementia. Recombinant assays identified that these proteases can cleave chromogranins and secretogranins to produce different proteoforms.ConclusionOur results show that (i) standard tryptic digestion methods underestimate the proteoform complexity of chromogranins and secretogranins, and (ii) that chromogranins and secretogranins are proteolytically processed by proteases relevant to dementia pathophysiology. This work improves our mechanistic understanding of chromogranin and secretogranin processing and further establishes them as a potential synaptic health biomarker in dementia pathophysiology.

Neurogranin and VILIP-1 as Molecular Indicators of Neurodegeneration in Alzheimer's Disease: A Systematic Review and Meta-Analysis.

Neurogranin (Ng) and visinin-like protein 1 (VILIP-1) are promising candidates for Alzheimer’s Disease (AD) biomarkers closely related to synaptic and neuronal degeneration. Both proteins are involved in calcium-mediated pathways. The meta-analysis was performed in random effects based on the ratio of means (RoM) with calculated pooled effect size. The diagnostic utility of these proteins was examined in cerebrospinal fluid (CSF) of patients in different stages of AD compared to control (CTRL). Ng concentration was also checked in various groups with positive (+) and negative (-) amyloid beta (Aβ). Ng highest levels of RoM were observed in the AD (n = 1894) compared to CTRL (n = 2051) group (RoM: 1.62). Similarly, the VILIP-1 highest values of RoM were detected in the AD (n = 706) compared to CTRL (n = 862) group (RoM: 1.34). Concentrations of both proteins increased in more advanced stages of AD. However, Ng seems to be an earlier biomarker for the assessment of cognitive impairment. Ng appears to be related with amyloid beta, and the highest levels of Ng in CSF was observed in the group with pathological Aβ+ status. Our meta-analysis confirms that Ng and VILIP-1 can be useful CSF biomarkers in differential diagnosis and monitoring progression of cognitive decline. Although, an additional advantage of the protein concentration Ng is the possibility of using it to predict the risk of developing cognitive impairment in normal controls with pathological levels of Aβ1-42. Analyses in larger cohorts are needed, particularly concerning Aβ status.

Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data.

The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy.

Association of serum Apolipoprotein B with cerebrospinal fluid biomarkers of Alzheimer’s pathology

ObjectiveTo examine whether apolipoprotein B (ApoB), apolipoprotein A‐1 (ApoA1), or their ratio (ApoB/A1) were associated with early changes in cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD) pathology in elderly adults with subjective cognitive decline (SCD).MethodsThis study included 507 objective cognitive normal participants from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database including 288 cognitive normal participants (CN) and 219 SCD. Multiple linear regression models were used to examine the associations of apolipoproteins with CSF AD biomarkers.ResultsCompared with control group, SCD participants with significant AD biological characteristics had lower ApoB levels (P = 0.0461). In total participants, lower level of serum ApoB was associated with decreases in CSF Aβ42 (P = 0.0015) and Aβ42/40 (P = 0.0081) as well as increases in CSF p‐tau/Aβ42 (P < 0.0001) and t‐tau/Aβ42 (P = 0.0013), independent of APOEɛ4 status. In further subgroup analysis, these associations were more significant in SCD participants (ApoB × Diagnose: P < 0.05). In addition, lower levels of ApoB were also found associated with increases in p‐tau in the SCD subgroup (P = 0.0263). Furthermore, these protective associations were more significant in the overweight participants (ApoB × weight: P < 0.05). Results showed no association between ApoA1 and CSF biomarkers.InterpretationThis study is the first to find protective associations of serum ApoB with CSF AD core biomarkers, especially in SCD individuals. It indicated that ApoB may be a potential biomarker for preclinical AD and may play different roles in different stages of AD.

Non-Invasive RF Technique for Detecting Different Stages of Alzheimer's Disease and Imaging Beta-Amyloid Plaques and Tau Tangles in the Brain.

This paper describes a novel approach of detecting different stages of Alzheimer's disease (AD) and imaging beta-amyloid plaques and tau tangles in the brain using RF sensors. Dielectric measurements were obtained from grey matter and white matter regions of brain tissues with severe AD pathology at a frequency range of 200 MHz to 3 GHz using a vector network analyzer and dielectric probe. Computational models were created on CST Microwave Suite using a realistic head model and the measured dielectric properties to represent affected brain regions at different stages of AD. Simulations were carried out to test the performance of the RF sensors. Experiments were performed using textile-based RF sensors on fabricated phantoms, representing a human brain with different volumes of AD-affected brain tissues. Experimental data was collected from the sensors and processed in an imaging algorithm to reconstruct images of the affected areas in the brain. Measured dielectric properties in brain tissues with AD pathology were found to be different from healthy human brain tissues. Simulation and experimental results indicated a correlated shift in the captured reflection coefficient data from RF sensors as the amount of affected brain regions increased. Finally, images reconstructed from the imaging algorithm successfully highlighted areas of the brain affected by plaques and tangles as a result of AD. The results from this study show that RF sensing can be used to identify areas of the brain affected by AD pathology. This provides a promising new non-invasive technique for monitoring the progression of AD.

Staging of Dementia Severity With the Hong Kong Version of the Montreal Cognitive Assessment (HK-MoCA)'s.

The Hong Kong version of Montreal Cognitive Assessment (HK-MoCA) has been used to screen for dementia, but it has not been validated to delineate the stages of Alzheimer disease (AD). This study aimed to determine the cut-off score ranges for mild, moderate, and severe AD. The HK-MoCA score was matched against the Clinical Dementia Rating on 155 patients with AD. Investigators performing the HK-MoCA and Clinical Dementia Rating were blinded to each other. Receiver-operating characteristic analysis was used to determine the cut-off scores between different stages of AD (mild, moderate, and severe stage). A secondary analysis with adjustments for age and education received were also performed. The cut-off score in HK-MoCA was ≤4 for those with severe AD (sensitivity 84.4%, specificity 91.9%, area under curve=0.92, P<0.001) and 5 to 9 for those with moderate AD (sensitivity 86.3%, specificity of 93.3%, area under curve=0.953, P<0.001). With adjustments for age and education, the cut-off score for moderate AD was adjusted to 5 to 8, whereas the cut-off score for severe AD remained unchanged. The severity of AD could be delineated using the HK-MoCA for the Cantonese-speaking population in Hong Kong, and the effect of education on the cut-off score needs further investigation.

Gaussian discriminative component analysis for early detection of Alzheimer’s disease: A supervised dimensionality reduction algorithm

BackgroundUsing multiple modalities of biomarkers, several machine leaning-based approaches have been proposed to characterize patterns of structural, functional and metabolic differences discernible from multimodal neuroimaging data for Alzheimer’s disease (AD). Current investigations report several studies using binary classification often augmented with local feature selection methods, while fewer other studies address the challenging problem of multiclass classification. New methodTo assess the merits of each of these research directions, this study introduces a supervised Gaussian discriminative component analysis (GDCA) algorithm, which can effectively delineate subtle changes of early mild cognitive impairment (EMCI) group in relation to the cognitively normal control (CN) group. Using 251 CN, 297 EMCI, 196 late MCI (LMCI), and 162 AD subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and considering both structural and functional (metabolic) information from magnetic resonance imaging (MRI) and positron emission tomography (PET) modalities as input, the proposed method conducts a dimensionality reduction algorithm taking into consideration the interclass information to define an optimal eigenspace that maximizes the discriminability of selected eigenvectors. ResultsThe proposed algorithm achieves an accuracy of 79.25 % for delineating EMCI from CN using 38.97 % of Gaussian discriminative components (i.e., dimensionality reduction). Moreover, for detecting the different stages of AD, a multiclass classification experiment attained an overall accuracy of 67.69 %, and more notably, discriminates MCI and AD groups from the CN group with an accuracy of 75.28 % using 48.90 % of the Gaussian discriminative components. Comparison with existing method(s)The classification results of the proposed GDCA method outperform the more recently published state-of-the-art methods in AD-related multiclass classification tasks, and seems to be the most stable and reliable in terms of relating the most relevant features to the optimal classification performance. ConclusionThe proposed GDCA model with its high prospects for multiclass classification has a high potential for deployment as a computer aided clinical diagnosis system for AD.

Amyloid beta 42 oligomers induce neuronal and synaptic receptor dysfunctions.

Amyloid beta 42 oligomers induce neuronal and synaptic receptor dysfunctions.

Neurogranin as a cognitive biomarker in cerebrospinal fluid and blood exosomes for Alzheimer\u2019s disease and mild cognitive impairment

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with clinical, biological, and pathological features occurring along a continuum from normal to end-stage disease. Currently, the diagnosis of AD depends on clinical assessments and post-mortem neuropathology, which is unbenefited early diagnosis and progressive monitoring. In recent years, clinical studies have reported that the level of cerebrospinal fluid (CSF) and blood neurogranin (Ng) are closely related to the occurrence and subsequent progression of AD. Therefore, the study used meta-analysis to identify the CSF and blood Ng levels for the development of diagnosis biomarker of patients with AD and mild cognitive impairment (MCI). We searched the Pubmed, Embase, Cochrane Library, and Web of Science databases. A total of 24 articles eligible for inclusion and exclusion criteria were assessed, including 4661 individuals, consisting of 1518 AD patients, 1501 MCI patients, and 1642 healthy control subjects. The level of CSF Ng significantly increased in patients with AD and MCI compared with healthy control subjects (SMD: 0.84 [95% CI: 0.70–0.98], P < 0.001; SMD: 0.53 [95% CI: 0.40–0.66], P = 0.008), and higher in AD patients than in MCI patients (SMD: 0.18 [95% CI: 0.07–0.30], P = 0.002), and CSF Ng level of patients with MCI-AD who progressed from MCI to AD was significantly higher than that of patients with stable MCI (sMCI) (SMD: 0.71 [95% CI: 0.25–1.16], P = 0.002). Moreover, the concentration of Ng in blood plasma exosomes of patients with AD and MCI was lower than that of healthy control subjects (SMD: −6.657 [95% CI: −10.558 to −2.755], P = 0.001; and SMD: −3.64 [95% CI: −6.50 to −0.78], P = 0.013), and which in patients with AD and MCI-AD were also lower than those in patients with sMCI (P < 0.001). Furthermore, regression analysis showed a negative relationship between MMSE scores and CSF Ng levels in MCI patients (slope = −0.249 [95% CI: −0.003 to −0.495], P = 0.047). Therefore, the Ng levels increased in CSF, but decreased in blood plasma exosomes of patients with AD and MCI-AD, and highly associated with cognitive declines. These findings provide the clinical evidence that CSF and blood exosomes Ng can be used as a cognitive biomarker for AD and MCI-AD, and further studies are needed to define the specific range of Ng values for diagnosis at the different stages of AD.