Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION: Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression. METHODS: We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD. RESULTS: We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions. DISCUSSION: Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD

Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer's disease

Genetic variation in cis-regulatory elements related to splicing machinery and splicing regulatory elements (SREs) results in exon skipping and undesired protein products. We developed a splicing decision model to identify actionable loci among common SNPs for gene regulation. The splicing decision model identified SNPs affecting exon skipping by analyzing sequence-driven alternative splicing (AS) models and by scanning the genome for the regions with putative SRE motifs. We used non-Hispanic Caucasians with neuroimaging, and fluid biomarkers for Alzheimer's disease (AD) and identified 17,088 common exonic SNPs affecting exon skipping. GWAS identified one SNP (rs1140317) in HLA-DQB1 as significantly associated with entorhinal cortical thickness, AD neuroimaging biomarker, after controlling for multiple testing. Further analysis revealed that rs1140317 was significantly associated with brain amyloid-f deposition (PET and CSF). HLA-DQB1 is an essential immune gene and may regulate AS, thereby contributing to AD pathology. SRE may hold potential as novel therapeutic targets for AD

Prediction of Cognitive Decline in Healthy Older Adults using fMRI

Few studies have examined the extent to which structural and functional MRI, alone and in combination with genetic biomarkers, can predict future cognitive decline in asymptomatic elders. This prospective study evaluated individual and combined contributions of demographic information, genetic risk, hippocampal volume, and fMRI activation for predicting cognitive decline after an 18-month retest interval. Standardized neuropsychological testing, an fMRI semantic memory task (famous name discrimination), and structural MRI (sMRI) were performed on 78 healthy elders (73% female; mean age = 73 years, range = 65 to 88 years). Positive family history of dementia and presence of one or both apolipoprotein E (APOE) ε4 alleles occurred in 51.3% and 33.3% of the sample, respectively. Hippocampal volumes were traced from sMRI scans. At follow-up, all participants underwent a repeat neuropsychological examination. At 18 months, 27 participants (34.6%) declined by at least 1 SD on one of three neuropsychological measures. Using logistic regression, demographic variables (age, years of education, gender) and family history of dementia did not predict future cognitive decline. Greater fMRI activity, absence of an APOE ε4 allele, and larger hippocampal volume were associated with reduced likelihood of cognitive decline. The most effective combination of predictors involved fMRI brain activity and APOE ε4 status. Brain activity measured from task-activated fMRI, in combination with APOE ε4 status, was successful in identifying cognitively intact individuals at greatest risk for developing cognitive decline over a relatively brief time period. These results have implications for enriching prevention clinical trials designed to slow AD progression

Identification of Novel Fluid Biomarkers for Alzheimer\u27s Disease

Clinicopathological studies suggest that Alzheimer\u27s disease: AD) pathology begins to appear ~10-20 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset and progression would, therefore, be invaluable for patient care and efficient clinical trial design. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid: CSF) using an unbiased proteomics approach: two-dimensional difference gel electrophoresis with liquid chromatography tandem mass spectrometry). From this, we identified 47 proteins that differed in abundance between cognitively normal: Clinical Dementia Rating [CDR] 0) and mildly demented: CDR 1) subjects. To validate these findings, we measured a subset of the identified candidate biomarkers by enzyme linked immunosorbent assay: ELISA); promising candidates in this discovery cohort: N=47) were further evaluated by ELISA in a larger validation CSF cohort: N=292) that contained an additional very mildly demented: CDR 0.5) group. Levels of four novel biomarkers were significantly altered in AD, and Receiver-operating characteristic: ROC) analyses using a stepwise logistic regression model identified optimal panels containing these markers that distinguished CDR 0 from CDR\u3e0: tau, YKL-40, NCAM) and CDR 1 from CDR\u3c1: tau, chromogranin-A, carnosinase-I). Plasma levels of the most promising marker, YKL-40, were also found to be increased in CDR 0.5 and 1 groups and to correlate with CSF levels. Importantly, the CSF YKL-40/Aâ42 ratio predicted risk of developing cognitive impairment: CDR 0 to CDR\u3e0 conversion) as well as the best CSF biomarkers identified to date, tau/Aâ42 and p-tau181/Aâ42. Additionally, YKL-40 immunoreactivity was observed within astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Aâ deposition. Utilizing an alternative, targeted proteomics approach to identify novel biomarkers, 333 CSF samples were evaluated for levels of 190 analytes using a multiplexed Luminex platform. The mean concentrations of 37 analytes were found to differ between CDR 0 and CDR\u3e0 participants. ROC and statistical machine learning algorithms identified novel biomarker panels that improved upon the ability of the current best biomarkers to discriminate very mildly demented from cognitively normal participants, and identified a novel biomarker, Calbindin, with significant prognostic potential

Neural Dedifferentiation in Relation to Risk for Alzheimer\u27s Disease

Functional magnetic resonance imaging (fMRI) research indicates that as an individual\u27s age increases, the task-related spatial extent of neural activation increases. This decrease in neural specificity, or dedifferentiation, is often demonstrated by older adults during challenging cognitive tasks. Cognitively intact individuals at-risk for Alzheimer\u27s disease (AD), as deemed by having an apolipoprotein-E ε4 allele or a family history of AD, demonstrate increased fMRI activation as compared to individuals at lower risk. Using a low effort, high accuracy event-related semantic memory task involving the presentation of famous and non-famous names, we examined spatial neural specificity through a measure of dedifferentiation using fMRI. In particular, the goal was to look at degree of dedifferentiation between older healthy subjects with or without risk factors for AD. Our results indicated that while there was not a significant difference between the two groups on the total amount of neural dedifferentiation, there was a significant interaction between stimulus type and risk group. Individuals at-risk for AD displayed greater dedifferentiation for non-famous names yet greater differentiation (i.e., less dedifferentiation) for famous names as compared to the low-risk group. These findings may reflect disturbances in memory formation for individuals at-risk for AD

A comparison of magnetic resonance imaging and neuropsychological examination in the diagnostic distinction of Alzheimer’s disease and behavioral variant frontotemporal dementia

The clinical distinction between Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) remains challenging and largely dependent on the experience of the clinician. This study investigates whether objective machine learning algorithms using supportive neuroimaging and neuropsychological clinical features can aid the distinction between both diseases. Retrospective neuroimaging and neuropsychological data of 166 participants (54 AD; 55 bvFTD; 57 healthy controls) was analyzed via a Naïve Bayes classification model. A subgroup of patients (n = 22) had pathologically-confirmed diagnoses. Results show that a combination of gray matter atrophy and neuropsychological features allowed a correct classification of 61.47% of cases at clinical presentation. More importantly, there was a clear dissociation between imaging and neuropsychological features, with the latter having the greater diagnostic accuracy (respectively 51.38 vs. 62.39%). These findings indicate that, at presentation, machine learning classification of bvFTD and AD is mostly based on cognitive and not imaging features. This clearly highlights the urgent need to develop better biomarkers for both diseases, but also emphasizes the value of machine learning in determining the predictive diagnostic features in neurodegeneration

Identifying patterns in signs and symptoms preceding the clinical diagnosis of Alzheimer’s disease

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyPrevious research indicates that there is a major challenge caused by the late diagnosis of Alzheimer’s disease (AD), with no suitable diagnostic tool available for use in primary care. Aim: This research is aimed at identifying patterns in the early signs and symptoms of AD to suggest the development of a predictive model for the early detection of AD. Objectives: To; a) map, synthesise and appraise the quality of existing literature on the signs and symptoms preceding the diagnosis of AD via the systematic scoping review of the literature; b) identify patterns in signs and symptoms preceding the clinical diagnosis of AD in general practices via a retrospective medical record review study (RMRRS); c) explore the clinicians perspectives regarding the early signs and symptoms, issues surrounding the late diagnosis and collect recommendations for overcoming barriers to timely detection of AD via a semi-structured interview. Methods: This was a mixed method research comprising a systematic scoping review of literature from 1937-2016, undertaken using the descriptive analysis on the sequence and the timing of signs and symptoms preceding the diagnosis of AD. Methodological quality of studies was assessed with the QUADAS-2 tool as well as PRISMA guidelines and descriptive analysis followed. A RMRRS followed using the logistic regression analysis and a semi-structured interview of general practitioners (GPs) in Milton Keynes (MK) and Luton, using the framework analysis. Results: The findings from the review suggest that neurological and depressive behaviours are an early occurrence in early-onset AD with depressive and cognitive symptoms in the measure of semantic memory and conceptual formation in late-onset AD. It appears that there is a big variation in the patterns of signs and symptoms with cases of misdiagnosis. However, there was limited evidence due to the limited number of studies of this kind. The nested case control design of 109 samples indicates that auditory disturbances could have diagnostic value, with a range of signs and symptoms that appears at different time. While the interviews highlight and confirm areas for consideration in the primary care and NHS. Additionally, the study reports practices in relation to the early diagnosis of AD. However, the result is not an overall representation of the views of GPs. Conclusion: Findings suggest that individuals with auditory disturbances have increased odds of AD. This was more striking in the white female population, with borderline significance due to limited data that is too small to detect such an uncommon symptom(s)