Diagnosis of neurodegenerative dementia: where do we stand, now?

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Textural properties of microglial activation in Alzheimer's disease as measured by (R)-[11C]PK11195 PET

Alzheimer's disease is the most common form of dementia worldwide, accounting for 60-70% of diagnosed cases. According to the current understanding of molecular pathogenesis, the main hallmarks of this disease are the abnormal accumulation of amyloid plaques and neurofibrillary tangles. Therefore, biomarkers reflecting these underlying biological mechanisms are recognized as valid tools for an early diagnosis of Alzheimer's disease. Inflammatory mechanisms, such as microglial activation, are known to be involved in Alzheimer's disease onset and progression. This activated state of the microglia is associated with increased expression of the translocator protein 18 kDa. On that account, PET tracers capable of measuring this signature, such as (R)-[¹¹C]PK11195, might be instrumental in assessing the state and evolution of Alzheimer's disease. This study aims to investigate the potential of Gray Level Co-occurrence Matrix-based textural parameters as an alternative to conventional quantification using kinetic models in (R)-[¹¹C]PK11195 PET images. To achieve this goal, kinetic and textural parameters were computed on (R)-[¹¹C]PK11195 PET images of 19 patients with an early diagnosis of Alzheimer's disease and 21 healthy controls and submitted separately to classification using a linear support vector machine. The classifier built using the textural parameters showed no inferior performance compared to the classical kinetic approach, yielding a slightly larger classification accuracy (accuracy of 0.7000, sensitivity of 0.6957, specificity of 0.7059 and balanced accuracy of 0.6967). In conclusion, our results support the notion that textural parameters may be an alternative to conventional quantification using kinetic models in (R)-[¹¹C]PK11195 PET images. The proposed quantification method makes it possible to use simpler scanning procedures, which increase patient comfort and convenience. We further speculate that textural parameters may also provide an alternative to kinetic analysis in (R)-[¹¹C]PK11195 PET neuroimaging studies involving other neurodegenerative disorders. Finally, we recognize that the potential role of this tracer is not in diagnosis but rather in the assessment and progression of the diffuse and dynamic distribution of inflammatory cell density in this disorder as a promising therapeutic target.

Meta-analysis of published cerebrospinal fluid proteomics data identifies and validates metabolic enzyme panel as Alzheimer's disease biomarkers

To develop therapies for Alzheimer's disease, we need accurate in vivo diagnostics. Multiple proteomic studies mapping biomarker candidates in cerebrospinal fluid (CSF) resulted in little overlap. To overcome this shortcoming, we apply the rarely used concept of proteomics meta-analysis to identify an effective biomarker panel. We combine ten independent datasets for biomarker identification: seven datasets from 150 patients/controls for discovery, one dataset with 20 patients/controls for down-selection, and two datasets with 494 patients/controls for validation. The discovery results in 21 biomarker candidates and down-selection in three, to be validated in the two additional large-scale proteomics datasets with 228 diseased and 266 control samples. This resulting 3-protein biomarker panel differentiates Alzheimer's disease (AD) from controls in the two validation cohorts with areas under the receiver operating characteristic curve (AUROCs) of 0.83 and 0.87, respectively. This study highlights the value of systematically re-analyzing previously published proteomics data and the need for more stringent data deposition.

Biobanking for Neurodegenerative Diseases: Challenge for Translational Research and Data Privacy

Biobanking has emerged as a strategic challenge to promote knowledge on neurological diseases, by the application of translational research. Due to the inaccessibility of the central nervous system, the advent of biobanks, as structure collecting biospecimens and associated data, are essential to turn experimental results into clinical practice. Findings from basic research, omics sciences, and in silico studies, definitely require validation in clinically well-defined cohorts of patients, even more valuable when longitudinal, or including preclinical and asymptomatic individuals. Finally, collecting biological samples requires a great effort to guarantee respect for transparency and protection of sensitive data of patients and donors. Since the European General Data Protection Regulation 2016/679 has been approved, concerns about the use of data in biomedical research have emerged. In this narrative review, we focus on the essential role of biobanking for translational research on neurodegenerative diseases. Moreover, we address considerations for biological samples and data collection, the importance of standardization in the preanalytical phase, data protection (ethical and legal) and the role of donors in improving research in this field.

Recent advances in pre-clinical diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is the most common dementia with currently no known cures or disease modifying treatments (DMTs), despite much time and effort from the field. Diagnosis and intervention of AD during the early pre-symptomatic phase of the disease is thought to be a more effective strategy. Therefore, the detection of biomarkers has emerged as a critical tool for monitoring the effect of new AD therapies, as well as identifying patients most likely to respond to treatment. The establishment of the amyloid/tau/neurodegeneration (A/T/N) framework in 2018 has codified the contexts of use of AD biomarkers in neuroimaging and bodily fluids for research and diagnostic purposes. Furthermore, a renewed drive for novel AD biomarkers and innovative methods of detection has emerged with the goals of adding additional insight to disease progression and discovery of new therapeutic targets. The use of biomarkers has accelerated the development of AD drugs and will bring new therapies to patients in need. This review highlights recent methods utilized to diagnose antemortem AD.

Alteration of miRNAs in Small Neuron-Derived Extracellular Vesicles of Alzheimer's Disease Patients and the Effect of Extracellular Vesicles on Microglial Immune Responses

Alzheimer's disease (AD) is one of the most severe neurodegenerative diseases observed in the elderly population. Although the hallmarks of AD have been identified, the methods for its definitive diagnosis and treatment are still lacking. Extracellular vesicles (EVs) have become a promising source for biomarkers since the identification of their content. EVs are released from multiple cell types and, when released from neurons, they pass from the brain to the blood with their cargo molecules. Hence, neuron-specific EV-resident microRNAs (miRNAs) are promising biomarkers for diagnosis of AD. This study aimed to identify altered miRNA content in small neuron-derived extracellular vesicles (sNDEVs) isolated from AD patients and healthy individuals. Furthermore, we examined the role of sNDEV-resident miRNAs in neuron-glia cellular interaction to understand their role in AD propagation. We identified 10 differentially expressed miRNAs in the sNDEVs of patients via next-generation sequencing and validated the most dysregulated miRNA, let-7e, with qRT-PCR. Let-7e was significantly increased in the sNDEVs of AD patients compared with those of healthy controls in a larger cohort. First, we evaluated the diagnostic utility of let-7e via ROC curve analysis, which revealed an AUC value of 0.9214. We found that IL-6 gene expression was increased in human microglia after treatment with sNDEVs of AD patients with a high amount of let-7e. Our study suggests that sNDEV-resident let-7e is a potential biomarker for AD diagnosis, and that AD patient-derived sNDEVs induce a neuroinflammatory response in microglia.

Fas Apoptosis Inhibitory Molecule Blocks and Dissolves Pathological Amyloid-β Species

A number of neurodegenerative diseases are associated with the accumulation of misfolded proteins, including Alzheimer’s disease (AD). In AD, misfolded proteins such as tau and amyloid-β (Aβ) form pathological insoluble deposits. It is hypothesized that molecules capable of dissolving such protein aggregates might reverse disease progression and improve the lives of afflicted AD patients. Here we report new functions of the highly conserved mammalian protein, Fas Apoptosis Inhibitory Molecule (FAIM). We found that FAIM-deficient Neuro 2A cells accumulate Aβ oligomers/fibrils. We further found that recombinant human FAIM prevents the generation of pathologic Aβ oligomers and fibrils in a cell-free system, suggesting that FAIM functions without any additional cellular components. More importantly, recombinant human FAIM disaggregates and solubilizes established Aβ fibrils. Our results identify a previously unknown, completely novel candidate for understanding and treating irremediable, irreversible, and unrelenting neurodegenerative diseases.

Association of Superficial White Matter Alterations with Cerebrospinal Fluid Biomarkers and Cognitive Decline in Neurodegenerative Dementia

BACKGROUND

Superficial white matter (SWM) alterations correlated with cognitive decline have been described in Alzheimer's disease (AD).

OBJECTIVE

The study aims to extend the investigation of the SWM alterations to AD and non-AD neurodegenerative dementia (ND) and explore the relationship with cerebrospinal fluid (CSF) biomarkers and clinical data.

METHODS

From a database of 323 suspected dementia cases, we retrospectively recruited 55 ND with abnormal amyloid-β42 (AD) and 38 ND with normal amyloid-β42 (non-AD) and collected clinical data, CSF biomarkers, and magnetic resonance images. Ten healthy controls (HC) were recruited for imaging and Mini-Mental State Examination (MMSE). Diffusion tensor imaging (DTI) measurements were performed in the lobar SWM regions and Kruskal Wallis tests were used for among-group comparison. Spearman's correlation tests were performed between DTI measures, CSF biomarkers, and clinical data.

RESULTS

AD and non-AD showed significant differences in the DTI measures across the SWM compared to HC. Significant differences between AD and non-AD were detected in the left parietal lobe. DTI measures correlated with amyloid-β42 and MMSE diffusely in the SWM, less extensively with total-tau and phosphorylated tau, and with disease duration in the parietal lobe bilaterally.

CONCLUSION

Widespread SWM alterations occur in both AD and non-AD ND and AD shows appreciably more severe alterations in the parietal SWM. Notably, the alterations in the SWM are strongly linked not only to the cognitive decline but also to the diagnostic CSF biomarkers. Further studies are encouraged to evaluate the DTI measures in the SWM as in vivo non-invasive biomarkers in the preclinical phase.

Tau-Atrophy Variability Reveals Phenotypic Heterogeneity in Alzheimer's Disease

OBJECTIVE

Tau neurofibrillary tangles (T) are the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in Alzheimer's disease (AD). However, there is substantial variability in the T-N relationship - manifested in higher or lower atrophy than expected for level of tau in a given brain region. The goal of this study was to determine if region-based quantitation of this variability allows for identification of underlying modulatory factors, including polypathology.

METHODS

Cortical thickness (N) and 18 F-Flortaucipir SUVR (T) were computed in 104 gray matter regions from a cohort of cognitively-impaired, amyloid-positive (A+) individuals. Region-specific residuals from a robust linear fit between SUVR and cortical thickness were computed as a surrogate for T-N mismatch. A summary T-N mismatch metric defined using residuals were correlated with demographic and imaging-based modulatory factors, and to partition the cohort into data-driven subgroups.

RESULTS

The summary T-N mismatch metric correlated with underlying factors such as age and burden of white matter hyperintensity lesions. Data-driven subgroups based on clustering of residuals appear to represent different biologically relevant phenotypes, with groups showing distinct spatial patterns of higher or lower atrophy than expected.

INTERPRETATION

These data support the notion that a measure of deviation from a normative relationship between tau burden and neurodegeneration across brain regions in individuals on the AD continuum captures variability due to multiple underlying factors, and can reveal phenotypes, which if validated, may help identify possible contributors to neurodegeneration in addition to tau, which may ultimately be useful for cohort selection in clinical trials. ANN NEUROL 2021;90:751-762.

Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.

The Roles of Flavonols/Flavonoids in Neurodegeneration and Neuroinflammation

The inflammatory process in the human body is a physiological response involving many cellular types and mediators. It results in scar formation to separate the damaged area from the surrounding healthy tissue. Because of increased blood-brain barrier permeability following inflammation, leukocytes infiltrate the CNS and are also supplemented by proinflammatory mediators. However, an acute inflammatory process after cerebral trauma or stroke may also result in a prolonged lesion formation, leading to a severe neuronal loss. The prolonged inflammatory process in the CNS may cause serious damage to the neuronal system. It may lead to CNS damage in such a way that endangers functional integration and proinflammatory system balance. Effects of different flavonoid species on ischemia-reperfusion injury and cognition and function have also been shown in experimental studies. Flavonoids are presented broadly in plants and diets. They are believed to have various bioactive effects including anti-viral, anti-inflammatory, cardioprotective, anti-diabetic, anti-cancer, anti-aging, etc. Quercetine is the predominant dietary flavonoid. Main sources are tea, onion, and apple. It is demonstrated that the frequently consumed food like soybean, peanut, mustard, rice, sesame, olive, potatoes, onion, and oats contain flavonoids. Catechin and its derivates which are isolated from tea leaves have antioxidant activity but in low doses, their prooxidant effects are also reported. Ipriflavone which is a synthetic flavonoid may increase total calcium in bone. In this review, the effects of flavonoids species on the inflammatory process in the neurodegenerative process were examined as general.

Detection of memory- and learning-related brain connectivity changes following trace eyeblink-conditioning using resting-state functional magnetic resonance imaging in the awake rabbit

Animal imaging studies have the potential to further establish resting-state fMRI (rs-fMRI) and enable its validation for clinical use. The rabbit subjects used in this work are an ideal model system for studying learning and behavior and are also an excellent established test subject for awake scanning given their natural tolerance for restraint. We found that analysis of rs-fMRI conducted on a cohort of rabbits undergoing eyeblink conditioning can reveal functional brain connectivity changes associated with learning, and that rs-fMRI can be used to capture differences between subjects with different levels of cognitive performance. rs-fMRI sessions were conducted on a cohort of rabbits before and after trace eyeblink conditioning. MRI results were analyzed using independent component analysis (ICA) and network analysis. Behavioral data were collected with standard methods using an infrared reflective sensor aimed at the cornea to detect blinks. Behavioral results were analyzed, and a median split was used to create two groups of rabbits based on their performance. The cohort of rabbits undergoing eyeblink conditioning exhibited increased functional connectivity in the cingulate cortex, retrosplenial cortex, and thalamus consistent with brain reorganization associated with increased learning. Differences in the striatum and right cerebellum were also identified between rabbits in the top or bottom halves of the group as measured by the behavioral assay. Thus, rs-fMRI can provide not only a tool to detect and monitor functional brain changes associated with learning, but also to discriminate between different levels of cognitive performance.

Role of triggering receptor expressed on myeloid cells 2 (TREM2) in neurodegenerative dementias

Neurodegeneration is a debilitating condition that causes nerve cell degeneration or death. Neurodegenerative diseases (NDDs) such as Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and Lewy body dementia (LBD) are posing a larger population burden of dementia worldwide. Neurodegenerative dementia is one of the main challenges in public health with its main characteristics being permanent loss of memory, impairment in cognition, and impaired daily functions. The published literature about genetic studies of these disorders suggests genetic underpinning in the pathogenesis of neurodegenerative dementia. In the process of underlining the pathogenesis of NDD, growing evidence has related genetic variations in the triggering receptor expressed on myeloid cells 2 (TREM2). This review paper aims to provide a detailed information regarding the association of TREM2 and NDDs leading to dementia. A central consideration is AD that accounts for almost 50%-70% of all late-life dementias alone or in combination with other neurological disorders. Other prevalent neurodegenerative conditions that lead to dementia are also discussed. Such studies are important as they can give a comprehensive knowledge of TREM2's role in various NDDs, in order to maximize the potential for developing new therapeutic approaches.

Effects of Chinese herbal medicines on dementia risk in patients with sleep disorders in Taiwan

ETHNOPHARMACOLOGICAL RELEVANCE

Sleep disorders affect an estimated 150 million people worldwide and result in adverse health, safety, and work performance-related outcomes that have important economic consequences. In Taiwan, Chinese herbal medicine (CHM) is a complementary natural medicine and has been widely used as an adjunctive therapy.

AIM OF THE STUDY

This study aimed to investigate the effect of CHM on dementia risk in patients with sleep disorders in Taiwan.

MATERIALS AND METHODS

We identified 124,605 patients with sleep disorders between the ages of 20 and 60 years. Of these, 5876 CHM users and 5876 non-CHM users were matched according to age and gender. The chi-squared test, Cox proportional hazard model, Kaplan-Meier method, and log-rank test were used for the comparisons. Association rule mining and network analysis were applied to determine a CHM pattern specialized for sleep disorders.

RESULTS

More CHM users did not use sleeping pills than non-CHM users. CHM users had a lower risk of dementia than non-CHM users after adjusting for age, gender, and sleeping pill use (hazard ratio (HR): 0.469, 95% CI = 0.289-0.760; p-value = 0.002). The cumulative incidence of dementia was lower among CHM users (long-rank test, p-value < 0.001). Association rule mining and network analysis showed that Ye-Jiao-Teng (YJT; Caulis Polygoni Multiflori; Polygonum multiflorum Thunb), Suan-Zao-Ren-Tang (SZRT), Jia-Wei-Xiao-Yao-San (JWXYS), He-Huan-Pi (HHP; Cortex Albizziae; Albizia julibrissin Durazz.), and Suan-Zao-Ren (SZR; Semen Zizyphi Spinosae; Ziziphus jujuba Mill.) were important CHMs for patients with sleep disorders in Taiwan.

CONCLUSIONS

A comprehensive list of herbal medicines may be useful for the clinical treatment of patients with sleep disorders, and for future scientific investigations into the prevention of dementia in these patients.

Detecting and targeting neurodegenerative disorders using electrospun nanofibrous matrices: current status and applications

Neurodegeneration is defined as the progressive atrophy and loss of function of neurons; it is present in neurodegenerative disorders such as Multiple Sclerosis, Alzheimer's, Huntington's, and Parkinson's diseases. The detection of such disorders is performed by various imaging modalities while their therapeutic management is quite challenging. Besides, the pathogenesis of neurodegenerative disorders is still under ongoing research due to complex and multi-factorial mechanisms. Currently, targeting the specific proteins responsible for neurodegeneration is of great interest to many researchers. Furthermore, nanotechnology-based approaches for targeting the affected neurons became an emerging field of interest. Nanostructures of various forms have been developed aiming to act as therapeutics for neurodegeneration, in which electrospun nanofibers seem to play an important role as biomedical products for both detection and management of the diseases. Electrospinning is an intriguing method able to produce nanofibers with a wide range of sizes and morphological characteristics. Such nanofibrous matrices can be delivered through different administration routes to target various diseases. In this review, the most recent advancements in electrospun nanofibrous systems that target or detect multiple neurodegenerative diseases have been enlightened and an introduction to the general aspects of neurodegenerative diseases and the electrospinning process has been made. Finally, future perspectives of neurodegeneration targeting were also discussed.

A Versatile Workflow for Cerebrospinal Fluid Proteomic Analysis with Mass Spectrometry: A Matter of Choice between Deep Coverage and Sample Throughput

Human cerebrospinal fluid (CSF) is a sample of choice in the study of brain disorders. This biological fluid circulates in the brain and the spinal cord and contains tissue-specific proteins, indicative of health and disease conditions. Despite its potential as a valid source of biological markers, CSF remains largely understudied as compared to blood, in particular due to its more invasive way of sampling.Challenges remain when performing proteomic analysis in clinical research studies. State-of-the-art mass spectrometry (MS) enables deep characterization of the human proteome. But some technical limitations are cardinal to be addressed, such as the capacity to routinely analyze large cohorts of samples. Importantly, a trade-off still needs to be made between the proteome coverage depth and the number of measured samples. In this context, we developed a scalable automated proteomic pipeline for the analysis of CSF. Because of its versatility, this workflow can be adapted to accommodate proteome coverage and/or sample throughput. It allows us to prepare and quantitatively analyze hundreds to thousands of CSF samples; it can also allow identification of more than 3000 proteins in a CSF sample when coupled with isoelectric focusing fractionation.In this chapter, we describe an end-to-end pipeline for the proteomic analysis of CSF. The main steps of the sample preparation comprise spiking of a standard, protein digestion, isobaric labeling, and purification; these are performed in a 96-well plate format enabling automation. Depending on the targeted depth of the CSF proteome, optional analytical steps can be included, such as the removal of abundant proteins and sample pre-fractionation. Liquid chromatography tandem MS as well as data processing and analysis complete the pipeline.

Aging and Neuroinflammatory Disorders: New Biomarkers and Therapeutic Targets

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Chronic neuroinflammation is a common feature of the pathogenic mechanisms involved in various neurodegenerative age-associated disorders, such as Alzheimer's disease, multiple sclerosis, Parkinson’s disease, and dementia.

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In particular, persistent low-grade inflammation may disrupt the brain endothelial barrier and cause a significant increase of pro-inflammatory cytokines and immune cells into the cerebral tissue that, in turn, leads to microglia dysfunction and loss of neuroprotective properties.

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Nowadays, growing evidence highlights a strong association between persistent peripheral inflammation, as well as metabolic alterations, and neurodegenerative disorder susceptibility. The identification of common pathways involved in the development of these diseases, which modulate the signalling and immune response, is an important goal of ongoing research.

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The aim of this review is to elucidate which inflammation-related molecules are robustly associated with the risk of neurodegenerative diseases. Of note, peripheral biomarkers may represent direct measures of pathophysiologic processes common of aging and neuroinflammatory processes. In addition, molecular changes associated with the neurodegenerative process might be present many decades before the disease onset. Therefore, the identification of a comprehensive markers panel, closely related to neuroinflammation, could be helpful for the early diagnosis, and the identification of therapeutic targets to counteract the underlying chronic inflammatory processes.

Alzheimer's disease and other neurodegenerative dementias in comorbidity: A clinical and neuropathological overview

Neuropathological diagnostic criteria of neurodegenerative disorders are based on the presence of specific inclusions in a specific area of brain tissue that correlate with clinical manifestations. Concomitant neurodegenerative disorders correspond to a combination of two (or more) different fully developed diseases in the same patient. Concomitant neurodegenerative pathology represents the presence of definite neurodegeneration and deposits of pathological proteins specific for another disease, which is not, however, fully developed. Very frequent overlaps include Alzheimer's disease and alpha-synuclein inclusions. Nevertheless, careful neuropathological investigations reveal an increasing frequency of different co-pathologies in examined brains. In Alzheimer's disease, protein TDP-43 may co-aggregate, but it is not clear whether this is atypical isolated Alzheimer's disease or overlap of Alzheimer's disease with early frontotemporal lobar degeneration. Comorbidities of Alzheimer's disease and tauopathies are relatively rare. A combination of vascular pathology with primary neurodegeneration (mostly Alzheimer's disease or dementia with Lewy bodies) is historically called mixed dementia. Overlap of different neuropathologically confirmed neurodegenerations could lead to atypical and unusual clinical presentations and may be responsible for faster disease progression. Several CSF biomarkers have been evaluated for their utility in diagnostic processes in different neurodegenerative dementias; however, evidence regarding their role in neurodegenerative overlaps is still limited.