Translational Advances in Neurodegenerative Dementias

The usefulness of Contingent Negative Variation (CNV) potential as a biomarker of neurocognitive disorders due to possible Alzheimer’s disease, is based on its possible physiological correlates. However, its application in the diagnostic evaluation of these disorders is still incipient. The aim of this study is to characterize the patterns of cognitive processing of information in the domain of nonspecific global attention, by recording potential CNV in a group of patients with neurocognitive disorders due to possible Alzheimer’s disease. An experimental study of cases and controls was carried out. The sample included 39 patients classified according to DSM-5 with a neurocognitive disorder subtype possibly due Alzheimer’s disease, and a Control Group of 53 subjects with normal cognitive functions. CNV potential was registered using standard protocol. The analysis of variance obtained significant differences in mean values and confidence intervals of total CNV amplitude between the three study groups. The late CNV segment amplitudes makes it possible to discriminate between the level of mild and major dysfunction in the group of patients. The CNV total amplitudes of potential allows for effective discrimination between normal cognitive functioning and neurocognitive disorders due to possible Alzheimer’s disease. Full article

Intracranial compliance (ICC) holds significant potential in neuromonitoring, serving as a diagnostic tool and contributing to the evaluation of treatment outcomes. Despite its comprehensive concept, which allows consideration of changes in both volume and intracranial pressure (ICP), ICC monitoring has not yet established itself as a standard component of medical care, unlike ICP monitoring. This review highlighted that the first challenge is the assessment of ICC values, because of the invasive nature of direct measurement, the time-consuming aspect of non-invasive calculation through computer simulations, and the inability to quantify ICC values in estimation methods. Addressing these challenges is crucial, and the development of a rapid, non-invasive computer simulation method could alleviate obstacles in quantifying ICC. Additionally, this review indicated the second challenge in the clinical application of ICC, which involves the dynamic and time-dependent nature of ICC. This was considered by introducing the concept of time elapsed (TE) in measuring the changes in volume or ICP in the ICC equation (volume change/ICP change). The choice of TE, whether short or long, directly influences the ICC values that must be considered in the clinical application of the ICC. Compensatory responses of the brain exhibit non-monotonic and variable changes in long TE assessments for certain disorders, contrasting with the mono-exponential pattern observed in short TE assessments. Furthermore, the recovery behavior of the brain undergoes changes during the treatment process of various brain disorders when exposed to short and long TE conditions. The review also highlighted differences in ICC values across brain disorders with various strain rates and loading durations on the brain, further emphasizing the dynamic nature of ICC for clinical application. The insight provided in this review may prove valuable to professionals in neurocritical care, neurology, and neurosurgery for standardizing ICC monitoring in practical application related to the diagnosis and evaluation of treatment outcomes in brain disorders. Full article

Background: Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model with multimodal features to predict the conversion of MCI to AD during different follow-up periods while providing interpretability. Methods: This retrospective case-control study is conducted with data obtained from the ADNI database, with records of 1042 MCI patients from 2006 to 2022 included. The exposures included in this study were MRI biomarkers, cognitive scores, demographics, and clinical features. The main outcome was AD conversion from aMCI during follow-up. The EBM model was utilized to predict aMCI converting to AD based on three feature combinations, obtaining interpretability while ensuring accuracy. Meanwhile, the interaction effect was considered in the model. The three feature combinations were compared in different follow-up periods with accuracy, sensitivity, specificity, and AUC-ROC. The global and local explanations are displayed by importance ranking and feature interpretability plots. Results: The five-years prediction accuracy reached 85% (AUC = 0.92) using both cognitive scores and MRI markers. Apart from accuracies, we obtained features’ importance in different follow-up periods. In early stage of AD, the MRI markers play a major role, while for middle-term, the cognitive scores are more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected factors and outcome. In one-year prediction, lower right inferior temporal volume (<9000) is significantly associated with AD conversion. For two-year prediction, low left inferior temporal thickness (<2) is most critical. For three-year prediction, higher FAQ scores (>4) is the most important. During four-year prediction, APOE4 is the most critical. For five-year prediction, lower right entorhinal volume (<1000) is the most critical feature. Conclusions: The established glass-box model EBMs with multimodal features demonstrated a superior ability with detailed interpretability in predicting AD conversion from MCI. Multi features with significant importance were identified. Further study may be of significance to determine whether the established prediction tool would improve clinical management for AD patients. Full article

Type 2 diabetes mellitus (T2D) is a metabolic disease reaching pandemic levels worldwide. In parallel, Alzheimer’s disease (AD) and vascular dementia (VaD) are the two leading causes of dementia in an increasingly long-living Western society. Numerous epidemiological studies support the role of T2D as a risk factor for the development of dementia. However, few basic science studies have focused on the possible mechanisms involved in this relationship. On the other hand, this review of the literature also aims to explore the relationship between T2D, AD and VaD. The data found show that there are several alterations in the central nervous system that may be promoting the development of T2D. In addition, there are some mechanisms by which T2D may contribute to the development of neurodegenerative diseases such as AD or VaD. Full article

Background: Patients with end-stage renal disease (ESRD) undergoing hemodialysis are at an elevated risk of developing dementia, potentially linked to the high prevalence of vitamin D deficiency in this population, which may contribute to cognitive impairment. Nevertheless, the impact of vitamin D supplementation on the risk of dementia in hemodialysis patients remains uncertain, necessitating further investigation to elucidate the potential benefits of vitamin D intervention in this vulnerable group. Methods: In this propensity-score-matched comparative cohort study, we sought to assess the impact of vitamin D supplementation on the occurrence of dementia in patients with end-stage renal disease (ESRD) undergoing hemodialysis. A total of 1424 patients were included and matched 1:1 using propensity scores. The study population was divided into two groups: those receiving vitamin D supplementation at a dose of ≥70 μg/week and those without any supplementation. The primary outcome of interest was the incidence of dementia. We calculated adjusted hazard ratios (aHRs) to examine the association between vitamin D supplementation and the risk of dementia while controlling for relevant covariates. Results: The adjusted hazard ratio (aHR) comparing vitamin D supplementation to no supplementation was 0.44 (95% CI 0.29–0.69; p < 0.0001), demonstrating a significant decrease in the risk of dementia associated with vitamin D supplementation. The aHRs for vitamin D supplementation at different dose ranges (70–105, 106–350, 351–1000, and >1000 μg/week) were 0.51, 0.49, 0.43, and 0.41, respectively (p for trend < 0.0001). These findings suggest a potential dose-dependent relationship between vitamin D supplementation and the reduction of dementia risk. Conclusions: In our study, we found that vitamin D supplementation at doses of ≥70 μg/week significantly reduced the risk of dementia in patients with end-stage renal disease (ESRD) undergoing hemodialysis. Furthermore, our results indicated a dose-dependent effect, with higher doses of supplementation correlating with a greater reduction in dementia risk. These findings underscore the potential of vitamin D supplementation as a preventive approach for cognitive impairment in this high-risk population. Full article

Sleep–wake cycle disorders most often accompany the elderly and are frequently associated with the development of neurodegenerative processes, primarily Alzheimer’s disease. Sleep disturbances can be diagnosed in patients with AD even before the onset of memory and cognitive impairment, and become more pronounced as the disease progresses. Therefore, the expansion of our knowledge of how sleep relates to AD pathogenesis needs to be addressed as soon as possible. Here, we investigated the influence of chronic sleep deprivation on the motor and orienting–exploratory activity of 5xFAD mice, as well as their spatial learning ability and long-term memory retention. The studies carried out revealed that chronic sleep deprivation negatively affects the processes of spatial memory reconsolidation in 5xFAD mice. This leads to the development of stress-related behavioral responses, including aggressive behavior. In addition, the morphological changes in the cerebral cortex, including changes in the nuclear–cytoplasmic ratio and degradation of neuronal processes are observed. Moreover, we found an increase in the level of total DNA methylation in the blood of the sleep-deprived mice, which may be one of the mechanisms of the two-way relationship between sleep and neurodegeneration. Full article

Treatments to restore the balance of the temporomandibular joint (TMJ) are performed in the field of complementary and alternative medicine; however, evidence supporting this approach remains weak. Therefore, this study attempted to establish such evidence. Bilateral common carotid artery stenosis (BCAS) operation, which is commonly used for the establishment of a mouse model of vascular dementia, was performed, followed by tooth cutting (TEX) for maxillary malocclusion to promote the imbalance of the TMJ. Behavioural changes, changes in nerve cells and changes in gene expression were assessed in these mice. The TEX-induced imbalance of the TMJ caused a more severe cognitive deficit in mice with BCAS, as indicated by behavioural changes in the Y-maze test and novel object recognition test. Moreover, inflammatory responses were induced via astrocyte activation in the hippocampal region of the brain, and the proteins involved in inflammatory responses were found to be involved in these changes. These results indirectly show that therapies that restore the balance of the TMJ can be effectively used for the management of cognitive-deficit-related brain diseases associated with inflammation. Full article

Numerous factors can contribute to the development of neurodegenerative disorders (NDs), such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and multiple sclerosis. Oxidative stress (OS), a fairly common ND symptom, can be caused by more reactive oxygen species being made. In addition, the pathological state of NDs, which includes a high number of protein aggregates, could make chronic inflammation worse by activating microglia. Carotenoids, often known as “CTs”, are pigments that exist naturally and play a vital role in the prevention of several brain illnesses. CTs are organic pigments with major significance in ND prevention. More than 600 CTs have been discovered in nature, and they may be found in a wide variety of creatures. Different forms of CTs are responsible for the red, yellow, and orange pigments seen in many animals and plants. Because of their unique structure, CTs exhibit a wide range of bioactive effects, such as anti-inflammatory and antioxidant effects. The preventive effects of CTs have led researchers to find a strong correlation between CT levels in the body and the avoidance and treatment of several ailments, including NDs. To further understand the connection between OS, neuroinflammation, and NDs, a literature review has been compiled. In addition, we have focused on the anti-inflammatory and antioxidant properties of CTs for the treatment and management of NDs. Full article

Deep learning has shown impressive diagnostic abilities in Alzheimer’s disease (AD) research in recent years. However, although neuropsychological tests play a crucial role in screening AD and mild cognitive impairment (MCI), there is still a lack of deep learning algorithms only using such basic diagnostic methods. This paper proposes a novel semi-supervised method using neuropsychological test scores and scarce labeled data, which introduces difference regularization and consistency regularization with pseudo-labeling. A total of 188 AD, 402 MCI, and 229 normal controls (NC) were enrolled in the study from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We first chose the 15 features most associated with the diagnostic outcome by feature selection among the seven neuropsychological tests. Next, we proposed a dual semi-supervised learning (DSSL) framework that uses two encoders to learn two different feature vectors. The diagnosed 60 and 120 subjects were randomly selected as training labels for the model. The experimental results show that DSSL achieves the best accuracy and stability in classifying AD, MCI, and NC (85.47% accuracy for 60 labels and 88.40% accuracy for 120 labels) compared to other semi-supervised methods. DSSL is an excellent semi-supervised method to provide clinical insight for physicians to diagnose AD and MCI. Full article