Neuropsychological assessment in the diagnosis of Alzheimer's disease

An ontology-based approach for modelling and querying Alzheimer's disease data

BACKGROUND

The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained.

METHODS

Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.

RESULTS

We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease.

CONCLUSIONS

The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

Rationale for a Multi-Factorial Approach for the Reversal of Cognitive Decline in Alzheimer's Disease and MCI: A Review

Alzheimer's disease (AD) is a multifactorial, progressive, neurodegenerative disease typically characterized by memory loss, personality changes, and a decline in overall cognitive function. Usually manifesting in individuals over the age of 60, this is the most prevalent type of dementia and remains the fifth leading cause of death among Americans aged 65 and older. While the development of effective treatment and prevention for AD is a major healthcare goal, unfortunately, therapeutic approaches to date have yet to find a treatment plan that produces long-term cognitive improvement. Drugs that may be able to slow down the progression rate of AD are being introduced to the market; however, there has been no previous solution for preventing or reversing the disease-associated cognitive decline. Recent studies have identified several factors that contribute to the progression and severity of the disease: diet, lifestyle, stress, sleep, nutrient deficiencies, mental health, socialization, and toxins. Thus, increasing evidence supports dietary and other lifestyle changes as potentially effective ways to prevent, slow, or reverse AD progression. Studies also have demonstrated that a personalized, multi-therapeutic approach is needed to improve metabolic abnormalities and AD-associated cognitive decline. These studies suggest the effects of abnormalities, such as insulin resistance, chronic inflammation, hypovitaminosis D, hormonal deficiencies, and hyperhomocysteinemia, in the AD process. Therefore a personalized, multi-therapeutic program based on an individual's genetics and biochemistry may be preferable over a single-drug/mono-therapeutic approach. This article reviews these multi-therapeutic strategies that identify and attenuate all the risk factors specific to each affected individual. This article systematically reviews studies that have incorporated multiple strategies that target numerous factors simultaneously to reverse or treat cognitive decline. We included high-quality clinical trials and observational studies that focused on the cognitive effects of programs comprising lifestyle, physical, and mental activity, as well as nutritional aspects. Articles from PubMed Central, Scopus, and Google Scholar databases were collected, and abstracts were reviewed for relevance to the subject matter. Epidemiological, pathological, toxicological, genetic, and biochemical studies have all concluded that AD represents a complex network insufficiency. The research studies explored in this manuscript confirm the need for a multifactorial approach to target the various risk factors of AD. A single-drug approach may delay the progression of memory loss but, to date, has not prevented or reversed it. Diet, physical activity, sleep, stress, and environment all contribute to the progression of the disease, and, therefore, a multi-factorial optimization of network support and function offers a rational therapeutic strategy. Thus, a multi-therapeutic program that simultaneously targets multiple factors underlying the AD network may be more effective than a mono-therapeutic approach.

The cognitive performance in the Phototest is predictor of biological markers of Alzheimer's disease

BACKGROUND

The abnormal cerebrospinal fluid levels of biomarkers, such as β-amyloid and phosphorylated tau (pTau), support the biological diagnosis of Alzheimer Disease (AD) independently of its clinical stage. However, this invasive exam cannot be extensively applied and requires previous sound clinical screen that can be based on brief, well validated cognitive tests, such as the Phototest.

OBJECTIVE

To evaluate the association of partial (naming [NA], total recall [TR], free recall [FR], and verbal fluency) and total scores of the Phototest with the biological diagnosis of AD and the potential use of this test as a screening tool in the clinical work up.

DESIGN

Retrospective study of Individuals attending a Memory Clinic who were applied the Phototest and classified, according to cerebrospinal fluid biomarkers (β-amyloid1-42 and pTau), in the biological AD continuum stage (ContAD) as "no AD" (A-), "AD changes" (A+T-) or "AD" (A+T+). Multivariate analyses were conducted with one fixed factor, ContAD, and partial and total Phototest scores. The area under the receiver operating characteristics curve (AUC) was calculated to estimate the capacity of Phototest scores to predict amyloidosis (A+) and AD.

RESULTS

The study included 170 individuals (92 A-, 23 A+T- and 55 A+T+). FR (7.9, 0.01 [F,p]) and TR (8.1, 0.001) scores were associated with ContAD and had a moderate ability (AUC 0.71-0.74) to detect the presence of "A+" or "AD".

CONCLUSIONS

Partial memory scores of Phototest are associated with ContAD. They predict acceptably the presence of abnormal levels of β-amyloid and AD signature in CSF and can be useful to support further biological diagnostic tests.

Longitudinal Speech Biomarkers for Automated Alzheimer's Detection

We introduce a novel audio processing architecture, the Open Voice Brain Model (OVBM), improving detection accuracy for Alzheimer's (AD) longitudinal discrimination from spontaneous speech. We also outline the OVBM design methodology leading us to such architecture, which in general can incorporate multimodal biomarkers and target simultaneously several diseases and other AI tasks. Key in our methodology is the use of multiple biomarkers complementing each other, and when two of them uniquely identify different subjects in a target disease we say they are orthogonal. We illustrate the OBVM design methodology by introducing sixteen biomarkers, three of which are orthogonal, demonstrating simultaneous above state-of-the-art discrimination for two apparently unrelated diseases such as AD and COVID-19. Depending on the context, throughout the paper we use OVBM indistinctly to refer to the specific architecture or to the broader design methodology. Inspired by research conducted at the MIT Center for Brain Minds and Machines (CBMM), OVBM combines biomarker implementations of the four modules of intelligence: The brain OS chunks and overlaps audio samples and aggregates biomarker features from the sensory stream and cognitive core creating a multi-modal graph neural network of symbolic compositional models for the target task. In this paper we apply the OVBM design methodology to the automated diagnostic of Alzheimer's Dementia (AD) patients, achieving above state-of-the-art accuracy of 93.8% using only raw audio, while extracting a personalized subject saliency map designed to longitudinally track relative disease progression using multiple biomarkers, 16 in the reported AD task. The ultimate aim is to help medical practice by detecting onset and treatment impact so that intervention options can be longitudinally tested. Using the OBVM design methodology, we introduce a novel lung and respiratory tract biomarker created using 200,000+ cough samples to pre-train a model discriminating cough cultural origin. Transfer Learning is subsequently used to incorporate features from this model into various other biomarker-based OVBM architectures. This biomarker yields consistent improvements in AD detection in all the starting OBVM biomarker architecture combinations we tried. This cough dataset sets a new benchmark as the largest audio health dataset with 30,000+ subjects participating in April 2020, demonstrating for the first time cough cultural bias.

Association Between Cardiovascular Health and Cognitive Performance: A Twins Study

BACKGROUND/OBJECTIVE

The 2020 Strategic Impact Goal introduced by the American Heart Association (AHA) aims at improving cardiovascular health (CVH) of all Americans by 20%. AHA defined ideal CVH across seven established modifiable risk factors for cardiovascular diseases. Prior studies have indicated that ideal CVH also benefits brain health and cognitive aging, but it is possible that this association is explained by familial factors.

METHODS

We examined 272 male monozygotic and dizygotic twin pairs (total 544 subjects) free of overt cardiovascular disease and dementia from the Vietnam Era Twin Registry. Memory and learning were measured by Trail Making tests and Wechsler Memory Scale (Immediate and Delayed Memory tests and Visual Reproductive Test). Each of the seven CVH components (smoking, body mass index, physical activity, diet, total cholesterol, blood pressure, and blood glucose) was scored per established criterion.

RESULTS

The mean age of the twins was 55 years, 96% were whites, and 61% monozygotic. When considering twins as individuals, for every unit increase in CVH score (indicating better cardiovascular health), twins demonstrated faster cognitive processing speed (Trail B: - 5.6 s, 95% CI - 10.3, - 0.9; p = 0.03) and better story recall, both immediate (0.35, 95% CI 0.06, 0.62; p = 0.02) and delayed (0.39, 95% CI 0.08, 0.70; p = 0.01).

CONCLUSIONS

Better CVH is associated with better cognitive health in several domains. As suggested by within-pair analysis, this association is largely explained by familial factors, implying that early life exposures are shared determinants of both brain health and cardiovascular health.

Dementia medical screening using mobile applications: A systematic review with a new mapping model

Early detection is the key to successfully tackling dementia, a neurocognitive condition common among the elderly. Therefore, screening using technological platforms such as mobile applications (apps) may provide an important opportunity to speed up the diagnosis process and improve accessibility. Due to the lack of research into dementia diagnosis and screening tools based on mobile apps, this systematic review aims to identify the available mobile-based dementia and mild cognitive impairment (MCI) apps using specific inclusion and exclusion criteria. More importantly, we critically analyse these tools in terms of their comprehensiveness, validity, performance, and the use of artificial intelligence (AI) techniques. The research findings suggest diagnosticians in a clinical setting use dementia screening apps such as ALZ and CognitiveExams since they cover most of the domains for the diagnosis of neurocognitive disorders. Further, apps such as Cognity and ACE-Mobile have great potential as they use machine learning (ML) and AI techniques, thus improving the accuracy of the outcome and the efficiency of the screening process. Lastly, there was overlapping among the dementia screening apps in terms of activities and questions they contain therefore mapping these apps to the designated cognitive domains is a challenging task, which has been done in this research.

The fornix in mild cognitive impairment and Alzheimer's disease

The fornix is an integral white matter bundle located in the medial diencephalon and is part of the limbic structures. It serves a vital role in memory functions and as such has become the subject of recent research emphasis in Alzheimer's disease (AD) and mild cognitive impairment (MCI). As the characteristic pathological processes of AD progress, structural and functional changes to the medial temporal lobes and other regions become evident years before clinical symptoms are present. Though gray matter atrophy has been the most studied, degradation of white matter structures especially the fornix may precede these and has become detectable with use of diffusion tensor imaging (DTI) and other complimentary imaging techniques. Recent research utilizing DTI measurement of the fornix has shown good discriminability of diagnostic groups, particularly early and preclinical, as well as predictive power for incident MCI and AD. Stimulating and modulating fornix function by the way of DBS has been an exciting new area as pharmacological therapeutics has been slow to develop.

Epidemiology, assessment, and treatment of dementia

The risk of developing dementia is associated with increasing age, lifestyle, and cardiovascular health. Alzheimer dementia is characterized by progressive cognitive deficits and decline in functional ability. Using history, examination, and laboratory testing, the clinician can evaluate the patient with dementia. Specific to these conditions are assessments of cognition, neuropsychiatric symptoms, and level of functioning. Managing neuropsychiatric symptoms is challenging and requires a team approach in which nonpharmacological strategies are preferred before medications are considered. Various diagnostic methods are being developed to discriminate disease from nondisease and track progression. Drug discovery is identifying novel molecules that target underlying disease mechanisms.

The effects of cholesterol on learning and memory

Cholesterol is vital to normal brain function including learning and memory but that involvement is as complex as the synthesis, metabolism and excretion of cholesterol itself. Dietary cholesterol influences learning tasks from water maze to fear conditioning even though cholesterol does not cross the blood brain barrier. Excess cholesterol has many consequences including peripheral pathology that can signal brain via cholesterol metabolites, pro-inflammatory mediators and antioxidant processes. Manipulations of cholesterol within the central nervous system through genetic, pharmacological, or metabolic means circumvent the blood brain barrier and affect learning and memory but often in animals already otherwise compromised. The human literature is no less complex. Cholesterol reduction using statins improves memory in some cases but not others. There is also controversy over statin use to alleviate memory problems in Alzheimer's disease. Correlations of cholesterol and cognitive function are mixed and association studies find some genetic polymorphisms are related to cognitive function but others are not. In sum, the field is in flux with a number of seemingly contradictory results and many complexities. Nevertheless, understanding cholesterol effects on learning and memory is too important to ignore.