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Alzheimer’s disease (AD) is a progressive disease leading to cognitive decline, and to prevent it, researchers seek to diagnose mild cognitive impairment (MCI) early. Particularly, non-amnestic MCI (naMCI) is often mistaken for normal aging as the representative symptom of AD, memory decline, is absent. Subjective cognitive decline (SCD), an intermediate step between normal aging and MCI, is crucial for prediction or early detection of MCI, which determines the presence of AD spectrum pathology. We developed a computer-based cognitive task to classify the presence or absence of AD pathology and stage within the AD spectrum, and attempted to perform multi-stage classification through electroencephalography (EEG) during resting and memory encoding state. The resting and memory-encoding states of 58 patients (20 with SCD, 10 with naMCI, 18 with aMCI, and 10 with AD) were measured and classified into four groups. We extracted features that could reflect the phase, spectral, and temporal characteristics of the resting and memory-encoding states. For the classification, we compared nine machine learning models and three deep learning models using Leave-one-subject-out strategy. Significant correlations were found between the existing neurophysiological test scores and performance of our computer-based cognitive task for all cognitive domains. In all models used, the memory-encoding states realized a higher classification performance than resting states. The best model for the 4-class classification was cKNN. The highest accuracy using resting state data was 67.24%, while it was 93.10% using memory encoding state data. This study involving participants with SCD, naMCI, aMCI, and AD focused on early Alzheimer’s diagnosis. The research used EEG data during resting and memory encoding states to classify these groups, demonstrating the significance of cognitive process-related brain waves for diagnosis. The computer-based cognitive task introduced in the study offers a time-efficient alternative to traditional neuropsychological tests, showing a strong correlation with their results and serving as a valuable tool to assess cognitive impairment with reduced bias.

INTRODUCTION Identifying individuals at risk of developing dementia is crucial for early intervention. Mild cognitive impairment (MCI) and subjective memory complaints (SMCs) are considered its preceding stages. This study aimed to assess the utility of functional near‐infrared spectroscopy (fNIRS) in identifying individuals with MCI and SMC. METHODS One hundred fifty‐one participants were categorized into normal cognition (NC); amnestic MCI (aMCI); non‐amnestic MCI (naMCI); and mild, moderate, and severe SMC groups. Task‐related prefrontal hemodynamics were measured using fNIRS during a visual memory span task. RESULTS Results showed significantly lower oxyhemoglobin (HbO) levels in aMCI, but not in naMCI, compared to the NC. In addition, severe SMC had lower HbO levels than the NC, mild, and moderate SMC. Receiver operating characteristic analysis demonstrated 69.23% and 69.70% accuracy in differentiating aMCI and severe SMC from NC, respectively. DISCUSSION FNIRS may serve as a potential non‐invasive biomarker for early detection of dementia. Highlights Only amnestic mild cognitive impairment (aMCI), but not non‐amnestic MCI, showed lower oxyhemoglobin (HbO) than normal individuals. Reduced HbO was observed in those with severe subjective memory complaints (SMCs) compared to normal cognition (NC), mild, and moderate SMCs. Functional near‐infrared spectroscopy measures were associated with performance in memory assessments. Prefrontal hemodynamics could distinguish aMCI and severe SMC from NC.

Selenoprotein P, a selenium-transporter protein, has been hypothesized to play a role in the etiology of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and Alzheimer’s dementia (AD). However, data in humans are scarce and largely confined to autoptic samples. In this case–control study, we determined selenoprotein P concentrations in both the cerebrospinal fluid (CSF) and the serum of 50 individuals diagnosed with ALS, 30 with AD, 54 with mild cognitive impairment (MCI) and of 30 controls, using sandwich enzyme-linked immunosorbent assay (ELISA) methods. We found a positive and generally linear association between CSF and serum selenoprotein P concentrations in all groups. CSF selenoprotein P and biomarkers of neurodegeneration were positively associated in AD, while for MCI, we found an inverted-U-shaped relation. CSF selenoprotein P concentrations were higher in AD and MCI than in ALS and controls, while in serum, the highest concentrations were found in MCI and ALS. Logistic and cubic spline regression analyses showed an inverse association between CSF selenoprotein P levels and ALS risk, and a positive association for AD risk, while an inverted-U-shaped relation with MCI risk emerged. Conversely, serum selenoprotein P concentrations were positively associated with risk of all conditions but only in their lower range. Overall, these findings indicate some abnormalities of selenoprotein P concentrations in both the central nervous system and blood associated with ALS and neurocognitive disorders, though in different directions. These alterations may reflect either phenomena of etiologic relevance or disease-induced alterations of nutritional and metabolic status.

People who experience unwanted, intrusive, or frightening thoughts often suffer shamefully and struggle silently for fear of what the thoughts might mean about them. In this powerful book, two anxiety disorder experts offer powerful and proven-effective cognitive behavioral therapy (CBT) skills to help readers get unstuck from disturbing thoughts, overcome intense shame, and reduce anxiety.

Abstract Introduction: Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.

As the population of the world increases, there will be larger numbers of people with dementia and an emerging need for prompt diagnosis and treatment. Early dementia screening is the process by which a patient who might be in the prodromal phases of a dementing illness is determined as having, or not having, the hallmarks of a neurodegenerative condition. The concepts of mild cognitive impairment, or mild neurocognitive disorder, are useful in analyzing the patient in the prodromal phase of a dementing disease; however, the transformation to dementia may be as low as 10% per annum. The search for early dementia requires a comprehensive clinical evaluation, cognitive assessment, determination of functional status, corroborative history and imaging (including MRI, FDG-PET and maybe amyloid PET), cerebrospinal fluid (CSF) examination assaying Aβ1–42, T-τ and P-τ might also be helpful. Primary care physicians are fundamental in the screening process and are vital in initiating specialist investigation and treatment. Early dementia screening is especially important in an age where there is a search for disease modifying therapies, where there is mounting evidence that treatment, if given early, might influence the natural history—hence the need for cost-effective screening measures for early dementia.

At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.

Neuropsychological screening tools for mild cognitive impairment (MCI) have been widely used. However, to date, their sensitivity and specificity still remain unsatisfied. This study aims to investigate whether spatial memory can discriminate MCI better than neuropsychological screening tools. A total of 56 healthy older adults and 36 older adults with MCI participated in this study; they performed a spatial cognitive task based on virtual reality (SCT-VR), the Korean version of the Montreal Cognitive Assessment (MoCA-K), and the Wechsler Adult Intelligence Scale-Revised Block Design Test (WAIS-BDT). The discriminant power was compared between the SCT-VR and the MoCA-K, and the reliability and validity of the SCT-VR were analyzed. The spatial memory, assessed by the SCT-VR, showed better sensitivity and specificity (sensitivity: 0.944; specificity: 0.964) than the MoCA-K (sensitivity: 0.857; specificity: 0.746). The test-retest reliability of the SCT-VR was relatively high (ICCs: 0.982, p < 0.001) and the concurrent validity of the SCT-VR with the MoCA-K (r = −0.587, p < 0.001) and the WAIS-BDT (r = −0.594, p < 0.001) was statistically significant. These findings shed light on the importance of spatial memory as a behavioral marker of MCI. The ecologically validated spatial memory tasks based on VR need to be investigated by neuroscientific studies in the future.

The aim of this study was to validate the reliability of the Chinese version of Addenbrooke's Cognitive Examination III (ACE-III) for detecting mild cognitive impairment. Furthermore, the present study compares the diagnostic accuracy of ACE-III with that of Montreal Cognitive Assessment (MoCA). One hundred and twenty patients with MCI and 136 healthy controls were included in the study. All patients were evaluated by the Chinese version of ACE-III, MoCA and MMSE. Subjects in the control group showed better performance in ACE-III total score and its subdomain scores than those in the MCI group. There was a significantly positive correlation between ACE-III total score and MoCA score. Meanwhile, there was also a significantly positive correlation between ACE-III total score and MMSE score. For ACE-III total score, a cut-off point of 85 yielded a sensitivity of 97.3% and a specificity of 90.7%. The AUC for ACE-III total score was 0.978. For MoCA, a cut-off point of 23 yielded a sensitivity of 86.5% and a specificity of 97.7%. The AUC for MoCA was 0.961. There were no significant differences in diagnostic accuracy between ACE-III and MoCA. The present findings support that both ACE-III and MoCA are useful for detecting MCI in early stages.

Objective: To describe the neuropsychological characteristics of mild cognitive impairment (MCI) subgroups identified in the Cardiovascular Health Study (CHS) cognition study. Methods: MCI was classified as MCI-amnestic type (MCI-AT): patients with documented memory deficits but otherwise normal cognitive function; and MCI-multiple cognitive deficits type (MCI-MCDT): impairment of at least one cognitive domain (not including memory), or one abnormal test in at least two other domains, but who had not crossed the dementia threshold. The MCI subjects did not have systemic, neurological, or psychiatric disorders likely to affect cognition. Results: MCI-AT (n = 10) had worse verbal and non-verbal memory performance than MCI-MCDT (n = 28) or normal controls (n = 374). By contrast, MCI-MCDT had worse language, psychomotor speed, fine motor control, and visuoconstructional function than MCI-AT or normal controls. MCI-MCDT subjects had memory deficits, though they were less pronounced than in MCI-AT. Of the MCI-MCDT cases, 22 (78.5%) had memory deficits, and 6 (21.5%) did not. MCI-MCDT with memory disorders had more language deficits than MCI-MCDT without memory disorders. By contrast, MCI-MCDT without memory deficits had more fine motor control deficits than MCI-MCDT with memory deficits. Conclusions: The most frequent form of MCI was the MCI-MCDT with memory deficits. However, the identification of memory impaired MCI groups did not reflect the true prevalence of MCI in a population, as 16% of all MCI cases and 21.5% of the MCI-MCDT cases did not have memory impairment. Study of idiopathic amnestic and non-amnestic forms of MCI is essential for an understanding of the aetiology of MCI.