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Early diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) is crucial to prevent their progression. In this study, we proposed the analysis of magnetic resonance imaging (MRI) based on features including; hippocampus (HC) area size, HC grayscale statistics and texture features (mean, standard deviation, skewness, kurtosis, contrast, correlation, energy, homogeneity, entropy), lateral ventricle (LV) area size, gray matter area size, white matter area size, cerebrospinal fluid area size, patient age, weight, and cognitive score. Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI. Additionally, the correlation and dependence were calculated to examine the strength and direction of association between each extracted feature and each classification of the group. The average classification accuracies in 20 trials were 95% (SVM), 71.50% (RF), 82.58% (RF), 84.91% (SVM), 85.83% (RF), and 85.08% (RF), respectively, with the best accuracies being 100% (SVM, RF, and MLP), 83.33% (RF), 91.66% (RF), 95% (SVM, and MLP), 96.66% (RF), and 93.33% (DT). Cognitive scores, HC and LV area sizes, and HC texture features demonstrated significant potential for diagnosing AD and its subtypes for all groups. RF and SVM showed better performance in distinguishing between groups. These findings highlight the importance of using 2D-MRI to identify key features containing critical information for early diagnosis of AD.Article Highlights: Cognitive scores, brain structure sizes, and tissue features can assist in diagnosing Alzheimer’s and its early stages. Machine learning models classify Alzheimer’s stages using optimized brain MRI features. MRI scans show how brain features change as Alzheimer’s progresses. [ABSTRACT FROM AUTHOR]

The prevalence of Alzheimer's disease (AD) is on the rise globally, and everyone who develops AD eventually experiences mild cognitive impairment (MCI) first. Timely intervention at an early stage of the disease may mitigate disease progression. Recent studies indicate that BDNF and MMP-9 play a significant role in the pathogenesis of AD. Therefore, this study aims to ascertain whether there are differences in plasma BDNF and MMP-9 levels between individuals with mild cognitive impairment due to AD and those with normal cognition, and to analyze the factors influencing mild cognitive impairment.This case-control study included 102 individuals with mild cognitive impairment and 102 controls, matched by age and sex. Participants completed a series of questionnaires, neuropsychological assessments, and clinical examinations. Plasma concentrations of BDNF and MMP-9 of the participants were quantified using ELISA. Subsequently, the factors influencing MCI were analyzed using univariate and multivariate logistic regression. The differences in plasma BDNF levels, MOCA total scores, and scores in various cognitive domains (including visuospatial and executive abilities, abstract thinking, attention, language, naming, and delayed memory) between the MCI and the control groups showed statistically significant (p < 0.05). Logistic regression analysis revealed that plasma BDNF levels and years of formal education were significantly negatively associated with MCI. This study indicates that plasma BDNF and years of formal education are protective factors influencing cognitive function. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) and Alzheimer's Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline and is traditionally monitored through infrequent clinical tests. In this research, the Smart Driving System, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases, is presented. The system comprises a multi-modal biosensing array (MMS) and AI algorithms, including driving performance and driver's biometrics, offering insights into a driver's cognitive function. This publication is the first work reported towards the ultimate goal of developing the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies. [ABSTRACT FROM AUTHOR]

The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather than halt it entirely. Therefore, the early detection of MCI and the identification of MCI patients at high risk of progression to AD remain urgent unmet needs in the super-aged era. This study utilized transcriptomics data from cognitively unimpaired (CU) individuals, MCI, and AD patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and leveraged machine learning models to identify biomarkers that differentiate MCI from CU and also distinguish AD from MCI individuals. Furthermore, Cox proportional hazards analysis was conducted to identify biomarkers predictive of the progression from MCI to AD. Our machine learning models identified a unique set of gene expression profiles capable of achieving an area under the curve (AUC) of 0.98 in distinguishing those with MCI from CU individuals. A subset of these biomarkers was also found to be significantly associated with the risk of progression from MCI to AD. A linear mixed model demonstrated that plasma tau phosphorylated at threonine 181 (pTau181) and neurofilament light chain (NFL) exhibit the prognostic value in predicting cognitive decline longitudinally. These findings underscore the potential of integrating machine learning (ML) with transcriptomic profiling in the early detection and prognostication of AD. This integrated approach could facilitate the development of novel diagnostic tools and therapeutic strategies aimed at delaying or preventing the onset of AD in at-risk individuals. Future studies should focus on validating these biomarkers in larger, independent cohorts and further investigating their roles in AD pathogenesis. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We investigated blood DNA methylation patterns associated with 15 well‐established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration. METHODS: We assessed DNA methylation in 885 blood samples from the European Medical Information Framework for Alzheimer's Disease (EMIF‐AD) study using the EPIC array. RESULTS: We identified Bonferroni‐significant differential methylation associated with CSF YKL‐40 (five loci) and neurofilament light chain (NfL; seven loci) levels, with two of the loci associated with CSF YKL‐40 levels correlating with plasma YKL‐40 levels. A co‐localization analysis showed shared genetic variants underlying YKL‐40 DNA methylation and CSF protein levels, with evidence that DNA methylation mediates the association between genotype and protein levels. Weighted gene correlation network analysis identified two modules of co‐methylated loci correlated with several amyloid measures and enriched in pathways associated with lipoproteins and development. DISCUSSION: We conducted the most comprehensive epigenome‐wide association study (EWAS) of AD‐relevant CSF biomarkers to date. Future work should explore the relationship between YKL‐40 genotype, DNA methylation, and protein levels in the brain. Highlights: Blood DNA methylation was assessed in the EMIF‐AD MBD study.Epigenome‐wide association studies (EWASs) were performed for 15 Alzheimer's disease (AD)–relevant cerebrospinal fluid (CSF) biomarker measures.Five Bonferroni‐significant loci were associated with YKL‐40 levels and seven with neurofilament light chain (NfL).DNA methylation in YKL‐40 co‐localized with previously reported genetic variation.DNA methylation potentially mediates the effect of single‐nucleotide polymorphisms (SNPs) in YKL‐40 on CSF protein levels. [ABSTRACT FROM AUTHOR]

Objective: This study introduces a PET-based platform for brain radiomics analysis. We automatically identify key brain regions and features associated with Alzheimer's disease (AD), enabling more accurate diagnosis and staging compared to using predefined regions. Methods: To create an integrated platform that covers all the phases of radiomics, we obtained FDG-PET images of 549 individuals from the ADNI database. We used FastSurfer to segment the brain into 95 regions. We then obtained 120 features for each of the 95 ROIs. We employed eight feature selection methods to select and analyze the features. We finally utilized nine different classifiers on the 20 most significant features extracted. Results: For all three predictions AD vs. cognitively normal (CN), AD vs. mild cognitive impairments (MCI), and CN vs. MCI the Random Forest (RF) classifier with LASSO demonstrated the highest accuracy with an AUC of 0.976 for AD vs CN, AUC=0.917 for AD vs MCI, and AUC=0.877 for MCI vs CN. This is the highest performance that we encountered compared to the studies in the literature. Three subregions hippocampus, entorhinal, and amygdala could then be identified as critical. Conclusion: A brain radiomics platform can enable an efficient, standardized, and optimally accurate AD and MCI diagnosis from FDG PET images by using an automated pipeline. The three regions identified as having the highest discriminating power confirm the findings of previous clinical research results on AD. While the focus was AD in this study, the platform can potentially be used to address other brain conditions.

Abstract Background Due to the heterogeneity among patients with Mild Cognitive Impairment (MCI), it is critical to predict their risk of converting to Alzheimer’s disease (AD) early using routinely collected real-world data such as the electronic health record data or administrative claim data. Methods The study used MarketScan Multi-State Medicaid data to construct a cohort of MCI patients. Logistic regression with tree-guided lasso regularization (TGL) was proposed to select important features and predict the risk of converting to AD. A subsampling-based technique was used to extract robust groups of predictive features. Predictive models including logistic regression, generalized random forest, and artificial neural network were trained using the extracted features. Results The proposed TGL workflow selected feature groups that were robust, highly interpretable, and consistent with existing literature. The predictive models using TGL selected features demonstrated higher prediction accuracy than the models using all features or features selected using other methods. Conclusions The identified feature groups provide insights into the progression from MCI to AD and can potentially improve risk prediction in clinical practice and trial recruitment.

The clinical spectrum of Alzheimer’s disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been developed, current therapies can only delay disease progression rather than halt it entirely. Therefore, the early detection of MCI and the identification of MCI patients at high risk of progression to AD remain urgent unmet needs in the super-aged era. This study utilized transcriptomics data from cognitively unimpaired (CU) individuals, MCI, and AD patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and leveraged machine learning models to identify biomarkers that differentiate MCI from CU and also distinguish AD from MCI individuals. Furthermore, Cox proportional hazards analysis was conducted to identify biomarkers predictive of the progression from MCI to AD. Our machine learning models identified a unique set of gene expression profiles capable of achieving an area under the curve (AUC) of 0.98 in distinguishing those with MCI from CU individuals. A subset of these biomarkers was also found to be significantly associated with the risk of progression from MCI to AD. A linear mixed model demonstrated that plasma tau phosphorylated at threonine 181 (pTau181) and neurofilament light chain (NFL) exhibit the prognostic value in predicting cognitive decline longitudinally. These findings underscore the potential of integrating machine learning (ML) with transcriptomic profiling in the early detection and prognostication of AD. This integrated approach could facilitate the development of novel diagnostic tools and therapeutic strategies aimed at delaying or preventing the onset of AD in at-risk individuals. Future studies should focus on validating these biomarkers in larger, independent cohorts and further investigating their roles in AD pathogenesis.

Alzheimer's Disease (AD) is a common neurological brain disorder that causes the brain cells to die and shrink (Atrophy) gradually, resulting in a continuous decline in one's ability to function independently. Early diagnosis increases the possibility of preventing or delaying the advancement of this mental disorder. Magnetic Resonance Imaging (MRI) offers the potential of non-invasive longitudinal monitoring and plays a vital role as a biomarker of the disease progression. Structural Magnetic Resonance Imaging (sMRI) helps to measure Atrophy, which is considered to be the most dependable biomarker to assess the exact stage and severity of the neuro-degenerative aspect of AD pathology. There are five stages associated with AD, which include Normal Control (NC), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD). In this work, we have used the Alzheimer's Disease Neuroimaging Initiative (ADNI2) sMRI image dataset to measure and classify the stage of AD. In recent years, Convolutional Neural Networks (CNNs) are widely used for medical image analysis. This work focuses on applying different Deep Learning algorithms for the multi-class classification of AD MRI images and proposes the best pre-trained model that can accurately predict the patient's stage. It is observed that ResNet-50v2 gives the best accuracy of 91.84% and f1-score of 0.97 for AD class. Visualization techniques such as Grad-CAM and Saliency Map are applied on the model that gave the best accuracy to understand the region of focus in the image which led to predicting its class. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer’s disease (AD), with a high risk of converting to AD. We propose a classification framework with a data augment method to identify MCI converter (MCI-C) and MCI non-converter (MCI-NC). Resting-state functional magnetic resonance images (rs-fMRI) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) are processed as augmented resting-state functional connectivity by staggered sliding window (SSW) method proposed by us under Human Connectome Project (HCP) multi-modal parcellation. The HCP brain atlas provides a more detailed cortical parcellation of the brain, allowing for more precise localization of brain regions related to MCI and AD. Finally, the framework archive 88% accuracy in the task of identifying MCI-C. 46 brain regions are suggested as potential MCI-to-AD biomarkers.

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

In this study, we investigated the ability of commonly used neuropsychological tests to detect cognitive and functional decline across the Alzheimer's disease (AD) continuum. Moreover, as preclinical AD is a key area of investigation, we focused on the ability of neuropsychological tests to distinguish the early stages of the disease, such as individuals with Subjective Memory Complaints (SMC). This study included 595 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset who were cognitively normal (CN), SMC, mild cognitive impairment (MCI; early or late stage), or AD. Our cognitive measures included the Rey Auditory Verbal Learning Test (RAVLT), the Everyday Cognition Questionnaire (ECog), the Functional Abilities Questionnaire (FAQ), the Alzheimer's Disease Assessment Scale–Cognitive Subscale (ADAS-Cog), the Montreal Cognitive Assessment scale (MoCA), and the Trail Making test (TMT-B). Overall, our results indicated that the ADAS-13, RAVLT (learning), FAQ, ECog, and MoCA were all predictive of the AD progression continuum. However, TMT-B and the RAVLT (immediate and forgetting) were not significant predictors of the AD continuum. Indeed, contrary to our expectations ECog self-report (partner and patient) were the two strongest predictors in the model to detect the progression from CN to AD. Accordingly, we suggest using the ECog (both versions), RAVLT (learning), ADAS-13, and the MoCA to screen all stages of the AD continuum. In conclusion, we infer that these tests could help clinicians effectively detect the early stages of the disease (e.g., SMC) and distinguish the different stages of AD. [ABSTRACT FROM AUTHOR]

Introduction: Extracellular vesicles (EVs) may propagate and modulate Alzheimer's disease (AD) pathology. We aimed to comprehensively characterize the proteome of cerebrospinal fluid (CSF) EVs to identify proteins and pathways altered in AD. Methods: CSF EVs were isolated by ultracentrifugation (Cohort 1) or Vn96 peptide (Cohort 2) from non‐neurodegenerative controls (n = 15, 16) and AD patients (n = 22, 20, respectively). EVs were subjected to untargeted quantitative mass spectrometry‐based proteomics. Results were validated by enzyme‐linked immunosorbent assay (ELISA) in Cohorts 3 and 4, consisting of controls (n = 16, n = 43, (Cohort3, Cohort4)), and patients with AD (n = 24, n = 100). Results: We found > 30 differentially expressed proteins in AD CSF EVs involved in immune‐regulation. Increase of C1q levels in AD compared to non‐demented controls was validated by ELISA (∼ 1.5 fold, p (Cohort 3) = 0.03, p (Cohort 4) = 0.005). Discussion: EVs may be utilized as a potential biomarker and may play a so far unprecedented role in immune‐regulation in AD. [ABSTRACT FROM AUTHOR]

Introduction: Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI). Methods: Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow‐up: 75 who remained stable (s‐HC) and 22 who converted to MCI within 48 months (c‐HC). Anatomical magnetic resonance (MR) images were analyzed using a semi‐automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD‐related pathology, and the whole cortical thickness as a test of spatial specificity. Results: Compared with s‐HC individuals, c‐HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro‐structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48‐month follow‐up), suggesting that amygdala changes shape the cognitive progression to MCI. Discussion: Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD. Highlights: Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI).Amygdala's was observed within the basolateral and amygdaloid complexes.Macro‐structural alterations were associated with cognitive decline.No atrophy was found in the hippocampus and cortex. [ABSTRACT FROM AUTHOR]

Abstract Background Mild cognitive impairment (MCI) is not just a prodrome to dementia, but a very important intervention point to prevent dementia caused by Alzheimer's disease (AD). It has long been known that people with AD have a higher frequency of falls with some gait instability. Recent evidence suggests that vestibular impairment is disproportionately prevalent among individuals with MCI and dementia due to AD. Therefore, we hypothesized that the measurement of balance capability is helpful to identify individuals with MCI. Methods First, we developed a useful method to evaluate balance capability as well as vestibular function using Nintendo Wii balance board as a stabilometer and foam rubber on it. Then, 49 healthy volunteers aged from 56 to 75 with no clinically apparent cognitive impairment were recruited and the association between their balance capability and cognitive function was examined. Cognitive functions were assessed by MoCA, MMSE, CDR, and TMT-A and -B tests. Results The new balance capability indicator, termed visual dependency index of postural stability (VPS), was highly associated with cognitive impairment assessed by MoCA, and the area under the receiver operating characteristic (ROC) curve was more than 0.8, demonstrating high sensitivity and specificity (app. 80% and 60%, respectively). Conclusions Early evidence suggests that VPS measured using Nintendo Wii balance board as a stabilometer helps identify individuals with MCI at an early and preclinical stage with high sensitivity, establishing a useful method to screen MCI.

Abstract Introduction Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI). Methods Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow‐up: 75 who remained stable (s‐HC) and 22 who converted to MCI within 48 months (c‐HC). Anatomical magnetic resonance (MR) images were analyzed using a semi‐automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD‐related pathology, and the whole cortical thickness as a test of spatial specificity. Results Compared with s‐HC individuals, c‐HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro‐structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48‐month follow‐up), suggesting that amygdala changes shape the cognitive progression to MCI. Discussion Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD. Highlights Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI). Amygdala's was observed within the basolateral and amygdaloid complexes. Macro‐structural alterations were associated with cognitive decline. No atrophy was found in the hippocampus and cortex.

We have been developing a novel approach to identify cognitive impairment-related biomarkers by profiling brain-enriched and inflammation-associated microRNA (miRNA) in plasma specimens of cognitively unimpaired and cognitively impaired patients. Here, we present an analytical validation of the novel miRNA panel, CogniMIR®, using two competing quantitative PCR technologies for the expression analysis of 24 target miRNAs. Total RNA from the plasma specimens was isolated using the MagMAX mirVana Kit, and RT-qPCR was performed using stem-loop-based TaqMan and LNA-based qPCR assays. Evaluation of RNA dilution series for our target 24 miRNAs, performed by two operators on two different days, demonstrated that all CogniMIR® panel miRNAs can be reliably and consistently detected by both qPCR technologies, with sample input as low as 20 copies in a qPCR reaction. Intra-run and inter-run repeatability and reproducibility analyses using RNA specimens demonstrated that both operators generated repeatable and consistent Cts, with R2 values of 0.94 to 0.99 and 0.96 to 0.97, respectively. The study results clearly indicate the suitability of miRNA profiling of plasma specimens using either of the qPCR technologies. However, the LNA-based qPCR technology appears to be more operationally friendly and better suited for a CAP/CLIA-certified clinical laboratory. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) patients have a high risk of developing mesial temporal lobe epilepsy (MTLE) and subclinical epileptiform activity. MTLE in AD worsens outcomes. Therefore, we need to understand the overlap between these disease processes. We hypothesize that AD with MTLE represents a distinct subtype of AD, with the interplay between tau and epileptiform activity at its core. We discuss shared pathological features including histopathology, an initial mesial temporal lobe (MTL) hyperexcitability followed by MTL dysfunction and involvement of same networks in memory (AD) and seizures (MTLE). We provide evidence that tau accumulation linearly increases neuronal hyperexcitability, neuronal hyper‐excitability increases tau secretion, tau can provoke seizures, and tau reduction protects against seizures. We speculate that AD genetic mutations increase tau, which causes proportionate neuronal loss and/or hyperexcitability, leading to seizures. We discuss that tau burden in MTLE predicts cognitive deficits among (1) AD and (2) MTLE without AD. Finally, we explore the possibility that anti‐seizure medications improve cognition by reducing neuronal hyper‐excitability, which reduces seizures and tau accumulation and spread. Highlights: We hypothesize that patients with Alzheimer's disease (AD) and mesial temporal lobe epilepsy (MTLE) represents a distinct subtype of AD.AD and MTLE share histopathological features and involve overlapping neuronal and cortical networks.Hyper‐phosphorylated tau (pTau) increases neuronal excitability and provoke seizures, neuronal excitability increases pTau, and pTau reduction reduces neuronal excitability and protects against seizures.The pTau burden in MTL predicts cognitive deficits among (1) AD and (2) MTLE without AD.We speculate that anti‐seizure medications improve cognition by reducing neuronal excitability, which reduces seizures and pTau. [ABSTRACT FROM AUTHOR]

Abstract Background Alzheimer’s disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD. Methods To explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion. Results We detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B. Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains. Conclusions We revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology.

Background: Early screening of elderly individuals who are at risk of dementia allows timely medical interventions to prevent disease progression. The portable and low-cost electroencephalography (EEG) technique has the potential to serve it. Objective: We examined prefrontal EEG and event-related potential (ERP) variables in association with the predementia stages of Alzheimer’s disease (AD). Methods: One hundred elderly individuals were recruited from the GARD cohort. The participants were classified into four groups according to their amyloid beta deposition (A+ or A−) and neurodegeneration status (N+ or N−): cognitively normal (CN; A−N−, n = 27), asymptomatic AD (aAD; A + N−, n = 15), mild cognitive impairment (MCI) with AD pathology (pAD; A+N+, n = 16), and MCI with non-AD pathology (MCI(−); A−N+, n = 42). Prefrontal resting-state eyes-closed EEG measurements were recorded for five minutes and auditory ERP measurements were recorded for 8 min. Three variables of median frequency (MDF), spectrum triangular index (STI), and positive-peak latency (PPL) were employed to reflect EEG slowing, temporal synchrony, and ERP latency, respectively. Results: Decreasing prefrontal MDF and increasing PPL were observed in the MCI with AD pathology. Interestingly, after controlling for age, sex, and education, we found a significant negative association between MDF and the aAD and pAD stages with an odds ratio (OR) of 0.58. Similarly, PPL exhibited a significant positive association with these AD stages with an OR of 2.36. Additionally, compared with the MCI(-) group, significant negative associations were demonstrated by the aAD group with STI and those in the pAD group with MDF with ORs of 0.30 and 0.42, respectively. Conclusion: Slow intrinsic EEG oscillation is associated with MCI due to AD, and a delayed ERP peak latency is likely associated with general cognitive impairment. MCI individuals without AD pathology exhibited better cortical temporal synchronization and faster EEG oscillations than those with aAD or pAD. Significance: The EEG/ERP variables obtained from prefrontal EEG techniques are associated with early cognitive impairment due to AD and non-AD pathology. This result suggests that prefrontal EEG/ERP metrics may serve as useful indicators to screen elderly individuals’ early stages on the AD continuum as well as overall cognitive impairment [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the last decade, increasing evidence has emerged linking alterations in the brain-derived neurotrophic factor (BDNF) expression with the development of Alzheimer's disease (AD). Because of the important role of BDNF in cognition and its association with AD pathogenesis, the aim of this study was to evaluate the potential difference in plasma BDNF concentrations between subjects with mild cognitive impairment (MCI; N = 209) and AD patients (N = 295) and to determine the possible association between BDNF plasma levels and the degree of cognitive decline in these individuals. The results showed a significantly higher (p < 0.001) concentration of plasma BDNF in subjects with AD (1.16; 0.13–21.34) compared with individuals with MCI (0.68; 0.02–19.14). The results of the present study additionally indicated a negative correlation between cognitive functions and BDNF plasma concentrations, suggesting higher BDNF levels in subjects with more pronounced cognitive decline. The correlation analysis revealed a significant negative correlation between BDNF plasma levels and both Mini-Mental State Examination (p < 0.001) and Clock Drawing test (p < 0.001) scores. In conclusion, the results of our study point towards elevated plasma BDNF levels in AD patients compared with MCI subjects, which may be due to the body's attempt to counteract the early and middle stages of neurodegeneration. [ABSTRACT FROM AUTHOR]

Many clock drawing test (CDT) scoring systems focus on drawing results and lack drawing process assessments. This study created a CDT scoring procedure with drawing process assessment and explored its diagnostic value in screening for mild cognitive impairment (MCI) and early Alzheimer's disease (AD) from normal control (NC). We used logistic regression and receiver operating characteristic (ROC) curves to determine a new, sensitive scoring system for AD and MCI patients in a derivation cohort. The new scoring method was then compared to two common scoring systems and externally validated in a second cohort. We developed a new scoring system named CDT5, which contained one process assessment item: remember setting time without asking. Compared with two published scoring systems, CDT5 had better discriminatory power in distinguishing AD patients from NCs in derivation (area under the ROC curve [area under the curve, AUC] =.890) and validation (AUC =.867) cohorts. Three scoring systems had poor diagnostic accuracy at discriminating MCI patients from controls, with CDT5 being the most sensitive (78.57%). Adding the drawing process in CDT helps accurately detect patients with early AD, but its role in identifying patients with MCI needs to be further explored. [ABSTRACT FROM AUTHOR]

Background: Mild cognitive impairment (MCI) is not just a prodrome to dementia, but a very important intervention point to prevent dementia caused by Alzheimer's disease (AD). It has long been known that people with AD have a higher frequency of falls with some gait instability. Recent evidence suggests that vestibular impairment is disproportionately prevalent among individuals with MCI and dementia due to AD. Therefore, we hypothesized that the measurement of balance capability is helpful to identify individuals with MCI. Methods: First, we developed a useful method to evaluate balance capability as well as vestibular function using Nintendo Wii balance board as a stabilometer and foam rubber on it. Then, 49 healthy volunteers aged from 56 to 75 with no clinically apparent cognitive impairment were recruited and the association between their balance capability and cognitive function was examined. Cognitive functions were assessed by MoCA, MMSE, CDR, and TMT-A and -B tests. Results: The new balance capability indicator, termed visual dependency index of postural stability (VPS), was highly associated with cognitive impairment assessed by MoCA, and the area under the receiver operating characteristic (ROC) curve was more than 0.8, demonstrating high sensitivity and specificity (app. 80% and 60%, respectively). Conclusions: Early evidence suggests that VPS measured using Nintendo Wii balance board as a stabilometer helps identify individuals with MCI at an early and preclinical stage with high sensitivity, establishing a useful method to screen MCI. [ABSTRACT FROM AUTHOR]

Background: Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI) are preclinical stages of Alzheimer's Disease (AD), which is the most common entity of dementia. Homocysteine is an amino acid in the methionine cycle, and many studies revealed a significant association between elevated homocysteine serum levels and the progression of dementia. The primary objective of this retrospective study was to investigate whether elevated homocysteine serum levels could be associated with mortality and neuropsychological test results in individuals suffering from SCD, MCI or AD. Methods: This study is a single-center explorative retrospective data analysis with 976 data protocols from the Memory Outpatient's Clinic of the Medical University of Vienna included. All patients underwent a neurological examination, a laboratory blood test, and neuropsychological testing to establish a diagnosis of either SCD, MCI, or AD. Data was evaluated by Kaplan-Meier functions, factor analysis, and binary logistic regression models. Results: Patients with AD showed significantly higher mean homocysteine levels (SCD 12.15 ± 4.71, MCI 12.80 ± 4.81, AD 15.0 ± 6.44 μmol/L) compared to those with SCD and MCI (p ≤ .001). The mean age of patients with AD (75.2 ± 7.8) was significantly older at the time of testing than of patients with MCI (69.1 ± 9.6) or SCD (66.8 ± 9.3). Since homocysteine levels increase with age, this could be a possible explanation for the higher levels of AD patients. The age at death did not differ significantly between all diagnostic subgroups, resulting in the shortest survival times for AD patients. Homocysteine levels were negatively associated with in Mini-Mental State Examination (MMSE) and Neuropsyhcological Test Battery Vienna (NTBV) factors F1-F4 (F1 = attention, F2 = memory, F3 = executive functions, F4 = naming/verbal comprehension). Moreover, higher homocysteine levels significantly predicted shorter five-year survival in the logistic regression models, even after adjusting for age, diagnostic subgroups, sex, years of education and results of neuropsychological testing. Conclusion: The results of this study suggest that homocysteine levels are independently associated with impaired cognitive function and increased five-year mortality.

Alzheimer's disease (AD) is considered to be one of the most frequent neurogenerative dementia in the elderly population. In order to improve the quality of life span, early detection of AD has drawn significant attention to the researchers throughout the globe. To this aim, this paper makes a novel attempt to classify the brain MRI images into three classes viz. Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy control (HC) using the volumetric information of white matter (WM), grey matter (GM) and cerebro spinal fluid (CSF). This classification has been accomplished with the help of fuzzy logic based approach followed by a majority voter classifier. Our proposition is finally tested over several brain MRI images collected from ADNI dataset. Supremacy of our proposition has strongly been established by measuring its performance parameters such as accuracy, sensitivity and specificity and subsequently been compared with many of the state-of-the-art methods. Simulation results have shown an average improvement of approximately 6.5%, 6.9% and 4% over a number of existing works in terms of accuracy, sensitivity and specificity respectively. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer’s disease (AD) aects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the dierent AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess eective connectivity (EC) and investigate the changes that accompany AD progression. Methods: We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the eective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects’ cognitive scores. Results: The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores. Discussion: Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression. [ABSTRACT FROM AUTHOR]

Classification of Alzheimer's disease (AD) is one of the most challenging issues for neurologists. Manual methods are time consuming and may not be accurate all the time. Since, brain is the most affected region in AD, a proper classification framework using brain images may provide more accurate results. Deep Learning (DL) is a popular representation of machine learning techniques, that emulate the functionalities of a human brain to process information and creates patterns that help in making complex decisions. The ability to absorb information, even from the unstructured and unlabeled data, makes DL one of the first choices by researchers. In this paper, some of the most popular DL models are discussed along with their implementation results for AD classification. All brain Magnetic Resonance (MR) images are acquired from the online data-set, "Alzheimer's Disease Neuroimaging Initiative (ADNI)". From the performance comparison amongst all the discussed models, it is observed that the DenseNet-121 model achieves a convincing result with an average performance rate of 88.78%. But one limitation of the DenseNet model is that it uses lots of convolutional operations that make the model computationally slower than many of the discussed models. Depth-wise convolution is a popular way to make a convolutional operation faster and better. Hence, to improve the execution time, we have proposed replacing the convolution layers in the original DenseNet-121 architecture with depth-wise convolution layers. The new architecture also improved the performance of the model with an average rate of 90.22%. [ABSTRACT FROM AUTHOR]

Twin support vector machines (TSVMs) have been successfully employed for binary classification problems. With the advent of machine learning algorithms, data have proliferated and there is a need to handle or process large-scale data. TSVMs are not successful in handling large-scale data due to the following: 1) the optimization problem solved in the TSVM needs to calculate large matrix inverses, which makes it an ineffective choice for large-scale problems; 2) the empirical risk minimization principle is employed in the TSVM and, hence, may suffer due to overfitting; and 3) the Wolfe dual of TSVM formulation involves positive-semidefinite matrices, and hence, singularity issues need to be resolved manually. Keeping in view the aforementioned shortcomings, in this article, we propose a novel large-scale fuzzy least squares TSVM for class imbalance learning (LS-FLSTSVM-CIL). We formulate the LS-FLSTSVM-CIL such that the proposed optimization problem ensures that: 1) no matrix inversion is involved in the proposed LS-FLSTSVM-CIL formulation, which makes it an efficient choice for large-scale problems; 2) the structural risk minimization principle is implemented, which avoids the issues of overfitting and results in better performance; and 3) the Wolfe dual formulation of the proposed LS-FLSTSVM-CIL model involves positive-definite matrices. In addition, to resolve the issues of class imbalance, we assign fuzzy weights in the proposed LS-FLSTSVM-CIL to avoid bias in dominating the samples of class imbalance problems. To make it more feasible for large-scale problems, we use an iterative procedure known as the sequential minimization principle to solve the objective function of the proposed LS-FLSTSVM-CIL model. From the experimental results, one can see that the proposed LS-FLSTSVM-CIL demonstrates superior performance in comparison to baseline classifiers. To demonstrate the feasibility of the proposed LS-FLSTSVM-CIL on large-scale classification problems, we evaluate the classification models on the large-scale normally distributed clustered (NDC) dataset. To demonstrate the practical applications of the proposed LS-FLSTSVM-CIL model, we evaluate it for the diagnosis of Alzheimer’s disease and breast cancer disease. Evaluation on NDC datasets shows that the proposed LS-FLSTSVM-CIL has feasibility in large-scale problems as it is fast in comparison to the baseline classifiers. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a hazardous neurological disorder of people aged in the early 60s. The main symptoms of AD is significant memory loss. Mild Cognitive Impairment (MCI) is a state of dementia in which a patient exhibits the early symptoms of AD. Since brain is the most impacted region, the disorders can be classified by analyzing factors from brain tissues in different subjects. Machine Learning (ML) is a widely utilised concept that aids in the decision-making process. Deep Convolutional Neural Network (DNN) is a type of ML techniques that uses artificially connected neurons to mimic the human brain. In this work, we have proposed a novel DNN-based model for distinguishing AD and MCI patients from Cognitively Normal individuals. Inspired by the original VGG-19, we have created 19 deep layers in the network. In Back Propagation, deeper models suffer from the problem of vanishing gradient and information loss. As a solution, we borrowed the Dense-Block notion from the original DenseNet architecture, which provides a path of information exchange amongst all the layers. Furthermore, we have implemented depth-wise convolutional procedures to make the model computationally faster. Outcome of the proposed model is compared with some prominent DNN models and observed that, the proposed approach performs most convincingly with an average performance rate of 95.39%.

IntroductionAlzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression.MethodsWe included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects' cognitive scores.ResultsThe results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores.DiscussionOur results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression.

Background: Alzheimer's disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD.Methods: To explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion.Results: We detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B. Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains.Conclusions: We revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology. [ABSTRACT FROM AUTHOR]

Grey matter (GM) in human brain contains most of the important cells covering the regions involved in neurophysiological operations such as memory, emotions, decision making, etc. Alzheimer's disease (AD) is a neurological disease that kills the brain cells in regions which are mostly involved in the neurophysiological operations. Mild Cognitive Impairment (MCI) is a stage between Cognitively Normal (CN) and AD, where a significant cognitive declination can be observed. The destruction of brain cells causes a reduction in the size of GM. Evaluation of changes in GM, may help in studying the overall brain transformations and accurate classification of different stages of AD. In this work, firstly skull of brain images is stripped for 5 different slices, then segmentation of GM is performed. Finally, the average number of pixels in grey region and the average atrophy in grey pixels per year is calculated and compared amongst CN, MCI, and AD patients of various ages and genders. It is observed that, for some subjects (in some particular ages) from different dementia stages, pattern of GM changes is almost identical. To solve this issue, we have used the concept of fuzzy membership functions to classify the dementia stages more accurately. It is observed from the comparison that average difference in the number of pixels between CN and MCI= 10.01%, CN and AD= 19.63%, MCI and AD= 10.72%. It can be also observed from the comparison that, the average atrophy in grey matter per year in CN= 1.92%, MCI= 3.13%, and AD= 4.33%. [ABSTRACT FROM AUTHOR]

Abstract Background This study aimed to explore the level and changes in handgrip strength among preclinical Alzheimer’s disease (AD) and AD patients and to evaluate the association between handgrip strength and cognitive function. Methods A total of 1431 participants from the memory clinic of Shanghai JiaoTong University Affiliated Sixth People’s Hospital and community were enrolled in the final analysis, including 596 AD, 288 mild cognitive impairment (MCI), and 547 normal individuals (NC). All participants received a comprehensive neuropsychological assessment. Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment-Basic (MoCA-BC), and the Chinese version of Addenbrooke’s Cognitive Examination III (ACE-III-CV) were used as cognitive tests. The receiver operating characteristic curve (ROC) was plotted to assess the power of handgrip strength as a screening measure to discriminate AD and MCI. Results The results showed that handgrip strength in the MCI group was significantly lower than that of NC group, and the AD group had a further decline (both P

• Investigated use of deep learning and EEG signals for AD and MCI. • Applied PRISMA to search PubMed/WOS/IEEE/Scopus databases. • Selected 74 studies published between 2013 and 2024. • CNN model for AD, ResNet for MCI was found effective. • Limitations and future challenges are presented. Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impair the cognitive functions, memory, and daily activities. They affect millions of individuals worldwide, posing a significant challenge for its diagnosis and management, leading to detrimental impacts on patients' quality of lives and increased burden on caregivers. Hence, early detection of MCI and AD is crucial for timely intervention and effective disease management. This study presents a comprehensive systematic review focusing on the applications of deep learning in detecting MCI and AD using electroencephalogram (EEG) signals. Through a rigorous literature screening process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the research has investigated 74 different papers in detail to analyze the different approaches used to detect MCI and AD neurological disorders. The findings of this study stand out as the first to deal with the classification of dual MCI and AD (MCI+AD) using EEG signals. This unique approach has enabled us to highlight the state-of-the-art high-performing models, specifically focusing on deep learning while examining their strengths and limitations in detecting the MCI, AD, and the MCI+AD comorbidity situations. The present study has not only identified the current limitations in deep learning area for MCI and AD detection but also proposes specific future directions to address these neurological disorders by implement best practice deep learning approaches. Our main goal is to offer insights as references for future research encouraging the development of deep learning techniques in early detection and diagnosis of MCI and AD neurological disorders. By recommending the most effective deep learning tools, we have also provided a benchmark for future research, with clear implications for the practical use of these techniques in healthcare. [ABSTRACT FROM AUTHOR]

We have been developing a novel approach to identify cognitive impairment-related biomarkers by profiling brain-enriched and inflammation-associated microRNA (miRNA) in plasma specimens of cognitively unimpaired and cognitively impaired patients. Here, we present an analytical validation of the novel miRNA panel, CogniMIR®, using two competing quantitative PCR technologies for the expression analysis of 24 target miRNAs. Total RNA from the plasma specimens was isolated using the MagMAX mirVana Kit, and RT-qPCR was performed using stem-loop-based TaqMan and LNA-based qPCR assays. Evaluation of RNA dilution series for our target 24 miRNAs, performed by two operators on two different days, demonstrated that all CogniMIR® panel miRNAs can be reliably and consistently detected by both qPCR technologies, with sample input as low as 20 copies in a qPCR reaction. Intra-run and inter-run repeatability and reproducibility analyses using RNA specimens demonstrated that both operators generated repeatable and consistent Cts, with R2 values of 0.94 to 0.99 and 0.96 to 0.97, respectively. The study results clearly indicate the suitability of miRNA profiling of plasma specimens using either of the qPCR technologies. However, the LNA-based qPCR technology appears to be more operationally friendly and better suited for a CAP/CLIA-certified clinical laboratory.