Mild cognitive impairment: a systematic review

MCI is a nosological entity proposed as an intermediate state between normal aging and dementia. The syndrome can be divided into two broad subtypes: amnestic MCI ( aMCI) characterized by reduced memory, and non- amnestic MCI ( naMCI) in which other cognitive functions rather than memory are mostly impaired. aMCI seems to represent an early stage of AD, while the outcomes of the naMCI subtypes appear more heterogeneous - including vascular dementia, frontotemporal dementia or dementia with Lewy bodies- but this aspect is still under debate. MCI in fact represents a condition with multiple sources of heterogeneity, including clinical presentation, etiology, and prognosis. To improve classification and prognosis, there is a need for more sensitive instruments specifically developed for MCI as well as for more reliable methods to determine its progression or improvement. Current clinical criteria for MCI should be updated to include restriction in complex ADL; also the diagnostic and prognostic role of behavioral symptoms and motor dysfunctions should be better defined. A multidisciplinary diagnostic approach including biological and neuroimaging techniques may probably represent the best option to predict the conversion from MCI to dementia. In this review we discuss the most recent aspects related to the epidemiological, clinical, neuropathological, neuroimaging, biochemical and therapeutic aspects of MCI, with specific attention to possible markers of conversion to dementia

Nutritional cognitive neuroscience of aging: Focus on carotenoids and cognitive frailty

The term „nutritional cognitive neuroscience” was recently established to define a research field focusing on the impact of nutrition on cognition and brain health across the life span. In this overview, we summarize the robust evidence on the role of carotenoids as micronutrients with different biological properties in persons with cognitive (pre)frailty. As neurodegenerative processes during aging occur in a continuum from brain aging to dementia, we propose the name „nutritional cognitive neuroscience of aging“ to define research on the role of nutrition and micronutrients in cognitive frailty. Further studies are warranted which integrate carotenoid interventions in multidomain, personalized lifestyle strategies

Leukocyte telomere shortening in Huntington's disease

Huntington's disease (HD) is an autosomal dominant neurodegenerative disease caused by an expanded CAG repeat. Though symptom onset commonly occurs at midlife and inversely correlates with the CAG repeat expansion, age at clinical onset and progression rate are variable. In the present study we investigated the relationship between leukocyte telomere length (LTL) and HD development. LTL was measured by real-time PCR in manifest HD patients (HD, n = 62), pre-manifest HD patients (pre-HD, n = 38), and age-matched controls (n = 76). Significant LTL differences were observed between the three groups (p < .0001), with LTL values in the order: HD < pre-HD < controls. The relationship between LTL and age was different in the three groups. An inverse relationship between mean LTL and CAG repeat number was found in the pre-HD (p = .03). The overall data seem to indicate that after age 30 years, LT begins to shorten markedly in pre-HD patients according to CAG number and increasing age, up to the values observed in HD. This very suggestive picture allowed us to hypothesize that in pre-manifest HD, LTL could be a measure of time to clinical HD onset. The possible use of LTL as a reliable biomarker to track HD development and progression was evaluated and discussed

Alzheimer's Disease Prediction Using Longitudinal and Heterogeneous Magnetic Resonance Imaging

Recent evidence has shown that structural magnetic resonance imaging (MRI) is an effective tool for Alzheimer's disease (AD) prediction and diagnosis. While traditional MRI-based diagnosis uses images acquired at a single time point, a longitudinal study is more sensitive and accurate in detecting early pathological changes of the AD. Two main difficulties arise in longitudinal MRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects (i.e., different scanning time and different total number of scans); (2) the heterogeneous progressions of high-dimensional regions of interest (ROIs) in MRI. In this work, we propose a novel feature selection and estimation method which can be applied to extract features from the heterogeneous longitudinal MRI. A key ingredient of our method is the combination of smoothing splines and the $l_1$-penalty. We perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results corroborate the advantages of the proposed method for AD prediction in longitudinal studies

The advanced activities of daily living: a tool allowing the evaluation of subtle functional decline in mild cognitive impairment

Objectives: Assessment of advanced activities of daily living (a-ADL) can be of interest in establishing the diagnosis of Alzheimer's disease (AD) in an earlier stage, since these activities demand high cognitive functioning and are more responsive to subtle changes. In this study we tested a new a-ADL tool, developed according to the International Classification of Functioning, Disability and Health (ICF). The a-ADL tool is based on the total number of activities performed (TNA) by a person and takes each subject as his own reference. It distinguishes a total Disability Index (a-ADL-DI), a Cognitive Disability Index (a-ADL-CDI), and a Physical Disability Index (a-ADL-PDI), with lower score representing more independency. We explored whether these indices allow distinction between cognitively healthy persons, patients with Mild Cognitive Impairment (MCI) and patients with mild AD. Methods: Participants were on average 80 years old (SD 4.6; 66-90), were community dwelling, and were diagnosed as (1) cognitively healthy subjects (n=26); (2) patients with MCI (n = 17), or (3) mild AD (n = 25), based upon extensive clinical evaluation and a set of global, cognitive, mood and functional assessments. The a-ADL-tool was not part of the clinical evaluation. Results: The a-ADL-CDI was significantly different between the three groups (p<.01). The a-ADL-DI was significantly different between MCI and AD (p<.001). The tool had good psychometrical properties (inter-rater reliability; agreement between patient and proxy; correlations with cognitive tests). Although the sample size was relatively small, ROC curves were computed for the a-ADL-DI and a-ADL-CDI with satisfactory and promising results. Conclusion: The a-ADL-CDI and a-ADL-DI might offer a useful contribution to the identification and follow up of patients with mild cognitive disorders in an older population

Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective

Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinician

Oxidant/Antioxidant Imbalance in Alzheimer’s Disease: Therapeutic and Diagnostic Prospects

Alzheimer’s disease (AD) is the most common cause of dementia and a great socioeconomic burden in the aging society. Compelling evidence demonstrates that molecular change characteristics for AD, such as oxidative stress and amyloid β (Aβ) oligomerization, precede by decades the onset of clinical dementia and that the disease represents a biological and clinical continuum of stages, from asymptomatic to severely impaired. Nevertheless, the sequence of the early molecular alterations and the interplay between them are incompletely understood. This review presents current knowledge about the oxidative stress-induced impairments and compromised oxidative stress defense mechanisms in AD brain and the cross-talk between various pathophysiological insults, with the focus on excessive reactive oxygen species (ROS) generation and Aβ overproduction at the early stages of the disease. Prospects for AD therapies targeting oxidant/antioxidant imbalance are being discussed, as well as for the development of novel oxidative stress-related, blood-based biomarkers for early, noninvasive AD diagnostics