Application of modern neuroimaging technology in the diagnosis and study of Alzheimer's disease

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Evaluating frontoparietal network topography for diagnostic markers of Alzheimer's disease

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer's disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

Correlation between abnormal cellular immune and changes of magnetic resonance spectroscopy in patients with Alzheimer's disease

BACKGROUND

Evidence from previous studies indicates that neuroinflammation contributes to the onset of Alzheimer's Disease (AD). Moreover, cellular dysfunction is induced by impaired signaling of neurotransmitters. This study aimed to explore the correlation between cellular immune dysfunction and neurotransmitter changes through cranial Magnetic Resonance Spectroscopy (MRS) in AD patients.

METHODS

Here, 32 AD, 40 Vascular Dementia (VD), and 35 Non-Dementia Elderly Control (NDE) cases were enrolled. Flow cytometry was performed to characterize lymphocyte subsets in plasma samples. The IL-1β and Caspase-1 levels were detected by ELISA. The NLRP3 expression level was measured by Western Blot (WB). The equivalence of N-acetylaspartate (NAA), Creatine (Cr), Choline (Cho), and Inositol (MI) in bilateral hippocampi of patients was examined by MRS. The association of NAA/Cr or MI/Cr ratios with the proportion of T lymphocyte subsets or NK cell subsets was determined through single-factor correlation analysis.

RESULTS

The proportion of T lymphocyte subsets was significantly lower in the AD group than in the NDE group (P < 0.01). On the other hand, the Caspase-1, NLRP3, and IL-1β protein expression levels were significantly higher in the AD group than in the other groups. Further analysis showed that the NAA/Cr ratio was lower in the AD group than in the NDE group. Additionally, a significant positive correlation was found between the NAA/Cr ratio and the MMSE score (r = 0.81, P < 0.01). Moreover, a significant positive correlation was observed between the NAA/Cr and T lymphocyte ratios. The NAA/Cr ratio was significantly negatively correlated with the proportion of NK cells in the blood (r = －0.83, P < 0.01). A significant negative correlation was also recorded between the MI/Cr and T cell ratios in blood samples.

CONCLUSIONS

Impaired cellular immune dysfunction in AD patients was significantly correlated with abnormal MRS. Neuroimmune dysfunction may contribute to the pathogenesis of AD and alter the metabolism of neurotransmitters such as aspartic acid and MI in the brains of AD patients.

TRIAL REGISTRATION

Not applicable.

A new approach towards highly sensitive detection of endogenous N-acetylaspartic acid, N-acetylglutamic acid, and N-acetylaspartylglutamic acid in brain tissues based on strong anion exchange monolith microextraction coupled with UHPLC-MS/MS

A novel in-tube solid-phase microextraction coupled with an ultra-high performance liquid chromatography-mass spectrometry method has been established for simultaneous quantification of three crucial brain biomarkers N-acetylaspartic acid (NAA), N-acetylglutamic acid (NAG), and N-acetylaspartylglutamic acid (NAAG). A polymer monolith with quaternary ammonium as the functional group was designed and exhibited efficient enrichment of target analytes through strong anion exchange interaction. Under the optimized conditions, the proposed method displayed wide linear ranges (0.1-80 nM for NAA and NAG, 0.2-160 nM for NAAG) with good precision (RSDs were lower than 15%) and low limits of detection (0.019-0.052 nM), which is by far the most sensitive approach for NAA, NAG, and NAAG determination. Furthermore, this approach has been applied to measure the target analytes in mouse brain samples, and endogenous NAA, NAG, and NAAG were successfully detected and quantified from only around 5 mg of cerebral cortex, cerebellum, and hippocampus. Compared with existing methods, the newly developed method in the current study provides highest sensitivity and lowest sample consumption for NAA, NAG, and NAAG measurements, which would potentially be utilized in determining and tracking these meaningful brain biomarkers in diseases or treatment processes, benefiting the investigations of pathophysiology and treatment of brain disorders.

Hippocampal region metabolites and cognitive impairment in patients with general paresis: based on 1H-proton magnetic resonance spectroscopy

Background

This study utilizes Hydrogen proton magnetic resonance spectroscopy (1H-MRS) to investigate metabolite concentrations in the bilateral hippocampus of general paresis (GP) patients.

Methods

A total of 80 GP patients and 57 normal controls (NCs) were enrolled. Metabolite ratios in the bilateral hippocampus were measured using 1H-MRS. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Based on MMSE scores, participants were categorized into normal control, mild cognitive impairment, and moderate-severe dementia groups. Metabolite ratios (N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/creatine (Cr), N-acetylaspartate (NAA)/choline (Cho), myoinositol (MI)/creatine (Cr), choline (Cho)/N-acetylaspartate (NAA)) were compared between groups, and correlations between metabolite ratios and cognitive performance were examined.

Results

MMSE scores progressively decreased in the normal, mild cognitive impairment, and moderate-severe dementia groups (p < 0.001). The moderate-severe dementia group showed significantly lower NAA/Cr ratios in the left hippocampus region (L-NAA/Cr ratios) (p < 0.001) and higher Cho/NAA ratios in the left hippocampus region (L-Cho/NAA ratios) (p < 0.05) compared to the other groups. However, differences in L-NAA/Cr and L-Cho/NAA ratios between the mild cognitive impairment group and the NC group were not significant in the hippocampus region (p > 0.05). NAA/Cho and NAA/Cr ratios in the right hippocampus region (R-NAA/Cho and R-NAA/Cr ratios) in the moderate-severe dementia group were lower than those in the control group (p < 0.05). No correlation was found between metabolite ratios and MMSE scores in bilateral hippocampus regions.

Conclusion

There are distinctive metabolic characteristics in the hippocampus of GP patients. GP patients exhibited lower NAA/Cr and NAA/Cho ratios in the bilateral hippocampus, indicating neuron loss in these areas, which may become more pronounced as the disease progresses.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers

AIMS

The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.

METHODS

We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau.

RESULTS

The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia.

CONCLUSION

We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Background

The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too.

Background

Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.

Methods

Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.

Results

The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.

Conclusions

The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

Developing a Two-Photon "AND" Logic Probe and Its Application in Alzheimer's Disease Differentiation

In Alzheimer's disease, hypochlorous acid involved in the clearance of invading bacteria or pathogens and butyrylcholinesterase engaged in the hydrolysis of the neurotransmitter acetylcholine are relatively significantly altered. However, there are few dual detection probes for hypochlorous acid and butyrylcholinesterase. In addition, single-response probes suffer from serious off-target effects and near-infrared probes do not easily penetrate the blood-brain barrier due to their excessive molecular weight. In this work, we constructed a two-photon fluorescent probe that recognizes hypochlorous acid and butyrylcholinesterase based on a dual-lock strategy. The thiocarbonyl group is oxidized in the presence of hypochlorous acid, and the hydrolysis occurs at the 7-position ester bond in the existence of butyrylcholinesterase, releasing a strongly fluorescent fluorophore, 4-methylumbelliferone. Excellent imaging was performed in PC12 cells using this probe, and deep two-photon imaging was observed in the brains of AD mice after tail vein injection with this probe. It indicates that the probe can provide a promising tool for the more precise diagnosis of Alzheimer's disease.

Hippocampal morphology in Huntington's disease, implications for plasticity and pathogenesis: The IMAGE-HD study

While striatal changes in Huntington's Disease (HD) are well established, few studies have investigated changes in the hippocampus, a key neuronal hub. Using MRI scans obtained from the IMAGE-HD study, hippocampi were manually traced and then analysed with the Spherical Harmonic Point Distribution Method (SPHARM-PDM) in 36 individuals with presymptomatic-HD, 37 with early symptomatic-HD, and 36 healthy matched controls. There were no significant differences in overall hippocampal volume between groups. Interestingly we found decreased bilateral hippocampal volume in people with symptomatic-HD who took selective serotonin reuptake inhibitors compared to those who did not, despite no significant differences in anxiety, depressive symptoms, or motor incapacity between the two groups. In symptomatic-HD, there was also significant shape deflation in the right hippocampal head, showing the utility of using manual tracing and SPHARM-PDM to characterise subtle shape changes which may be missed by other methods. This study confirms previous findings of the lack of hippocampal volumetric differentiation in presymptomatic-HD and symptomatic-HD compared to controls. We also find novel shape and volume findings in those with symptomatic-HD, especially in relation to decreased hippocampal volume in those treated with SSRIs.

Application of Pt@ZIF-8 nanocomposite-based electrochemical biosensor for sensitive diagnosis of tau protein in Alzheimer's disease patients

Alzheimer's disease (AD) is a progressive brain disorder characterized by the ongoing decline of brain functions. Studies have revealed the detrimental effects of hyperphosphorylated tau (p-tau) protein fibrils in AD pathogenesis, highlighting the importance of this factor in the early-stage detection of AD conditions. We designed an electrochemical immunosensor for quantitative detection of the cis conformation of the p-tau protein (cis-p-tau) employing platinum nanoparticles (Pt NPs) supported on zeolitic imidazolate frameworks (ZIF) for modifying the glassy carbon electrode (GCE) surface. Under optimum conditions, the immunosensor selectively and sensitively detected cis-p-tau within the broad linear range of 1 fg mL-1 to 10 ng mL-1 and the low limit of detection (LOD) of 1 fg mL-1 with desired reproducibility and stability. Furthermore, the fabricated immunosensor's performance was examined for the cis-p-tau analysis in the serum of AD patients, indicating its accuracy and feasibility for real-sample analysis. Notably, this is the first application of Pt@ZIF-8 nanocomposite in fabricating a valid immunosensor for selective cis-p-tau detection, even in the presence of trans-p-tau. It is worth mentioning that the enzyme-linked immunosorbent assay (ELISA) reference technique is not able to evaluate pico- or femtomolar concentrations of cis-p-tau, making the fabricated immunosensor superior for early-stage measurement and screening of AD.

Association between dietary potassium intake and severe headache or migraine in US adults: a population-based analysis

Background

Migraine is a prevalent neurovascular headache disorder. The link between dietary potassium and blood pressure has been established. We sought to delineate the relationship between dietary potassium intake and the prevalence of migraines.

Methods

We conducted a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999-2004, comprising 10,254 participants aged ≥20 years. Participants who reported severe headaches or migraine in the self-report questionnaire were identified as migraineurs. A 24-h dietary recall methodology was used to assess dietary potassium intake. Multivariate regression analysis and restricted cubic spline (RCS) modeling were utilized to elucidate the relationship between dietary potassium and migraines.

Results

Among the 10,254 participants, 20.1% were identified with migraine or severe headaches. The adjusted odds ratio (OR) for migraine occurrence in the Q2 dietary potassium intake (1771-2,476 mg/d) was 0.84 (95% CI: 0.73-0.97, p = 0.021) compared to the lowest quartile (Q1, ≤ 1771 mg/d). The relationship between dietary potassium and migraine exhibited an L-shaped pattern (non-linear, p = 0.016) with an inflection at approximately 1439.3 mg/d. In the subgroup analysis, when compared to Q1, who had the lowest dietary potassium intake, the adjusted OR for Q2 in females, those in the medium-high household income group, and with a Body Mass Index (BMI) ≥ 25 kg/m2 were as follows: (OR, 0.82; 95% CI, 0.69-0.98), (OR, 0.79; 95% CI, 0.66-0.95), and (OR, 0.78; 95% CI, 0.66-0.93), respectively. No significant interaction was observed across groups after adjusting for all possible covariates.

Conclusion

The relationship between dietary potassium intake and migraine prevalence among US adults appears to follow an L-shaped curve.

An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease

The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.

Chiral Amino Acid Profiling in Serum Reveals Potential Biomarkers for Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disease, and increasing evidence has linked dysregulation of amino acids to AD pathogenesis. However, the existing studies often ignore the chirality of amino acids, and some results are inconsistent and controversial. The changes of amino acid profiles in AD from the perspective of enantiomers remain elusive.

OBJECTIVE

The purpose of this study is to investigate whether the levels of amino acids, especially D-amino acids, are deregulated in the peripheral serum of AD patients, with the ultimate goal of discovering novel biomarkers for AD.

METHODS

The chiral amino acid profiles were determined by HPLC-MS/MS with a pre-column derivatization method. Experimental data obtained from 37 AD patients and 34 healthy controls (HC) were statistically analyzed.

RESULTS

Among the 35 amino acids detected, D-proline, D/total-proline ratio, D-aspartate, and D/total-aspartate ratio were decreased, while D-phenylalanine was elevated in AD compared to HC. Significant age-dependent increases in D-proline, D/total-proline ratio, and D-phenylalanine were observed in HC, but not in AD. Receiver operator characteristic analyses of the combination of D-proline, D-aspartate, D-phenylalanine, and age for discriminating AD from HC provided satisfactory area under the curve (0.87), specificity (97.0%), and sensitivity (83.8%). Furthermore, the D-aspartate level was significantly decreased with the progression of AD, as assessed by the Clinical Dementia Rating Scale and Mini-Mental State Examination.

CONCLUSION

The panels of D-proline, D-phenylalanine, and D-aspartate in peripheral serum may serve as novel biomarker candidates for AD. The latter parameter is further associated with the severity of AD.

The Utility of Arterial Spin Labeling MRI in Medial Temporal Lobe as a Vascular Biomarker in Alzheimer's Disease Spectrum: A Systematic Review and Meta-Analysis

We sought to systematically review and meta-analy the role of cerebral blood flow (CBF) in the medial temporal lobe (MTL) using arterial spin labeling magnetic resonance imaging (ASL-MRI) and compare this in patients with Alzheimer's disease (AD), individuals with mild cognitive impairment (MCI), and cognitively normal adults (CN). The prevalence of AD is increasing and leading to high healthcare costs. A potential biomarker that can identify people at risk of developing AD, whilst cognition is normal or only mildly affected, will enable risk-stratification and potential therapeutic interventions in the future. All studies investigated the role of CBF in the MTL and compared this among AD, MCI, and CN participants. A total of 26 studies were included in the systematic review and 11 in the meta-analysis. Three separate meta-analyses were conducted. Four studies compared CBF in the hippocampus of AD compared with the CN group and showed that AD participants had 2.8 mL/min/100 g lower perfusion compared with the CN group. Eight studies compared perfusion in the hippocampus of MCI vs. CN group, which showed no difference. Three studies compared perfusion in the MTL of MCI vs. CN participants and showed no statistically significant differences. CBF measured via ASL-MRI showed impairment in AD compared with the CN group in subregions of the MTL. CBF difference was significant in hippocampus between the AD and CN groups. However, MCI and CN group showed no significant difference in subregions of MTL.

Classification of Alzheimer's Disease Based on Deep Learning of Brain Structural and Metabolic Data

To improve the diagnosis and classification of Alzheimer's disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The t-test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD (r = 0.627), and the F statistics were largest (F = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD.

High Resolution Magic Angle Spinning Proton NMR Study of Alzheimer's Disease with Mouse Models

Alzheimer's disease (AD) is a crippling condition that affects millions of elderly adults each year, yet there remains a serious need for improved methods of diagnosis. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease; however, studies of human brain tissue metabolomics are challenging, due to sample limitations and ethical considerations. Comprehensive comparisons of imaging measurements in animal models to identify similarities and differences between aging- and AD-associated metabolic changes should thus be tested and validated for future human non-invasive studies. In this paper, we present the results of our highresolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) studies of AD and wild-type (WT) mouse models, based on animal age, brain regions, including cortex vs. hippocampus, and disease status. Our findings suggest the ability of HRMAS NMR to differentiate between AD and WT mice using brain metabolomics, which potentially can be implemented in in vivo evaluations.

Emotion Processing Dysfunction in Alzheimer's Disease: An Overview of Behavioral Findings, Systems Neural Correlates, and Underlying Neural Biology

We described behavioral studies to highlight emotional processing deficits in Alzheimer's disease (AD). The findings suggest prominent deficit in recognizing negative emotions, pronounced effect of positive emotion on enhancing memory, and a critical role of cognitive deficits in manifesting emotional processing dysfunction in AD. We reviewed imaging studies to highlight morphometric and functional markers of hippocampal circuit dysfunction in emotional processing deficits. Despite amygdala reactivity to emotional stimuli, hippocampal dysfunction conduces to deficits in emotional memory. Finally, the reviewed studies implicating major neurotransmitter systems in anxiety and depression in AD supported altered cholinergic and noradrenergic signaling in AD emotional disorders. Overall, the studies showed altered emotions early in the course of illness and suggest the need of multimodal imaging for further investigations. Particularly, longitudinal studies with multiple behavioral paradigms translatable between preclinical and clinical models would provide data to elucidate the time course and underlying neurobiology of emotion processing dysfunction in AD.

Application value of molecular imaging technology in epilepsy

Epilepsy is a common neurological disease with various seizure types, complicated etiologies, and unclear mechanisms. Its diagnosis mainly relies on clinical history, but an electroencephalogram is also a crucial auxiliary examination. Recently, brain imaging technology has gained increasing attention in the diagnosis of epilepsy, and conventional magnetic resonance imaging can detect epileptic foci in some patients with epilepsy. However, the results of brain magnetic resonance imaging are normal in some patients. New molecular imaging has gradually developed in recent years and has been applied in the diagnosis of epilepsy, leading to enhanced lesion detection rates. However, the application of these technologies in epilepsy patients with negative brain magnetic resonance must be clarified. Thus, we reviewed the relevant literature and summarized the information to improve the understanding of the molecular imaging application value of epilepsy.

Optical Imaging of Beta-Amyloid Plaques in Alzheimer's Disease

Alzheimer's disease (AD) is a multifactorial, irreversible, and incurable neurodegenerative disease. The main pathological feature of AD is the deposition of misfolded β-amyloid protein (Aβ) plaques in the brain. The abnormal accumulation of Aβ plaques leads to the loss of some neuron functions, further causing the neuron entanglement and the corresponding functional damage, which has a great impact on memory and cognitive functions. Hence, studying the accumulation mechanism of Aβ in the brain and its effect on other tissues is of great significance for the early diagnosis of AD. The current clinical studies of Aβ accumulation mainly rely on medical imaging techniques, which have some deficiencies in sensitivity and specificity. Optical imaging has recently become a research hotspot in the medical field and clinical applications, manifesting noninvasiveness, high sensitivity, absence of ionizing radiation, high contrast, and spatial resolution. Moreover, it is now emerging as a promising tool for the diagnosis and study of Aβ buildup. This review focuses on the application of the optical imaging technique for the determination of Aβ plaques in AD research. In addition, recent advances and key operational applications are discussed.

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease

Background: Alzheimer’s disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) levels of amyloid-β1-42 (Aβ42), total tau protein and hyperphosphorylated tau (p-tau). However, the available methods are expensive and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis. Methods: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews for reviews and trials that investigated the machine learning and novel biomarkers in diagnosis of AD. Results: In additional to Aβ and tau-related biomarkers, biomarkers according to other mechanisms of AD pathology have been investigated. Neuronal injury biomarker includes neurofiliament light (NFL). Biomarkers about synaptic dysfunction and/or loss includes neurogranin, BACE1, synaptotagmin, SNAP-25, GAP-43, synaptophysin. Biomarkers about neuroinflammation includes sTREM2, and YKL-40. Besides, d-glutamate is one of coagonists at the NMDARs. Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy controls. Conclusions: Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing AD in outpatient clinics.

History in perspective: How Alzheimer's Disease came to be where it is?

Treatment of Alzheimer's Disease (AD) remains an unsolved issue despite the pronounced global attention it has received from researchers over the last four decades. Determining the primary cause of the disease is challenging due to its long prodromal phase and multifactorial etiology. Regardless, academic disagreements amongst the scientific community have helped in making significant advancements in underpinning the molecular basis of disease pathogenesis. Substantial development in fluid and imaging biomarkers for AD led to a sharp turn in defining the disease as a molecular construct, dispensing its clinical definition. With conceptual progress, revisions in the diagnostic criteria of AD were made, culminating into the research framework proposed by National Institute on Aging and Alzheimer's Association in 2018 which unified different stages of the disease continuum, giving a common language of AT(N)¹ classification to researchers. With realization that dementia is the final stage of AD spectrum, its early diagnosis by means of cerebrospinal fluid biomarkers, Positron Emission Tomography and Magnetic Resonance Imaging of the brain holds crucial importance in discovering ways of halting the disease progression. This article maps the insights into the pathogenesis as well as the diagnostic criteria and tests for AD as these have evolved over time. A contextualized timeline of how the understanding of AD has matured with advancing knowledge allows future research to be directed and unexplored avenues to be prioritized.