Biomarker clusters are differentially associated with longitudinal cognitive decline in late midlife

The ability to detect preclinical Alzheimer’s disease is of great importance, as this stage of the Alzheimer’s continuum is believed to provide a key window for intervention and prevention. As Alzheimer’s disease is characterized by multiple pathological changes, a biomarker panel reflecting co-occurring pathology will likely be most useful for early detection. Towards this end, 175 late middle-aged participants (mean age 55.9 ± 5.7 years at first cognitive assessment, 70% female) were recruited from two longitudinally followed cohorts to undergo magnetic resonance imaging and lumbar puncture. Cluster analysis was used to group individuals based on biomarkers of amyloid pathology (cerebrospinal fluid amyloid-β42/amyloid-β40 assay levels), magnetic resonance imaging-derived measures of neurodegeneration/atrophy (cerebrospinal fluid-to-brain volume ratio, and hippocampal volume), neurofibrillary tangles (cerebrospinal fluid phosphorylated tau181 assay levels), and a brain-based marker of vascular risk (total white matter hyperintensity lesion volume). Four biomarker clusters emerged consistent with preclinical features of (i) Alzheimer’s disease; (ii) mixed Alzheimer’s disease and vascular aetiology; (iii) suspected non-Alzheimer’s disease aetiology; and (iv) healthy ageing. Cognitive decline was then analysed between clusters using longitudinal assessments of episodic memory, semantic memory, executive function, and global cognitive function with linear mixed effects modelling. Cluster 1 exhibited a higher intercept and greater rates of decline on tests of episodic memory. Cluster 2 had a lower intercept on a test of semantic memory and both Cluster 2 and Cluster 3 had steeper rates of decline on a test of global cognition. Additional analyses on Cluster 3, which had the smallest hippocampal volume, suggest that its biomarker profile is more likely due to hippocampal vulnerability and not to detectable specific volume loss exceeding the rate of normal ageing. Our results demonstrate that pathology, as indicated by biomarkers, in a preclinical timeframe is related to patterns of longitudinal cognitive decline. Such biomarker patterns may be useful for identifying at-risk populations to recruit for clinical trials

Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative

Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.Rationale: We evaluated K-means clustering to classify amyloid brain PETs as positive or negative. Patients and Methods: Sixty-six participants (31 men, 35 women; age range, 52–81 years) were recruited through a multicenter observational study: 19 cognitively normal, 25 mild cognitive impairment, and 22 demen- tia (11 Alzheimer disease, 3 subcortical vascular cognitive impairment, and 8 Parkinson–Lewy Body spectrum disorder). As part of the neurocognitive and imaging evaluation, each participant had an 18F-flutemetamol (Vizamyl, GE Healthcare) brain PET. All studies were processed using Cortex ID soft- ware (General Electric Company, Boston, MA) to calculate SUV ratios in 19 regions of interest and clinically interpreted by 2 dual-certified radiologists/ nuclear medicine physicians, using MIM software (MIM Software Inc, Cleveland, OH), blinded to the quantitative analysis, with final interpreta- tion based on consensus. K-means clustering was retrospectively used to classify the studies from the quantitative data. Results: Based on clinical interpretation, 46 brain PETs were negative and 20 were positive for amyloid deposition. Of 19 cognitively normal partici- pants, 1 (5%) had a positive 18F-flutemetamol brain PET. Of 25 participants with mild cognitive impairment, 9 (36%) had a positive 18F-flutemetamol brain PET. Of 22 participants with dementia, 10 (45%) had a positive 18F-flutemetamol brain PET; 7 of 11 participants with Alzheimer disease (64%), 1 of 3 participants with vascular cognitive impairment (33%), and 2 of 8 participants with Parkinson–Lewy Body spectrum disorder (25%) had a positive 18F-flutemetamol brain PET. Using clinical interpretation as the criterion standard, K-means clustering (K = 2) gave sensitivity of 95%, specificity of 98%, and accuracy of 97%. Conclusions: K-means clustering may be a powerful algorithm for classifying amyloid brain PET.This is a multisite project of the Toronto Dementia Research Alli- ance (www.tdra.utoronto.ca) partly funded by Brain Canada, The Edward Foundation, the Canadian Institutes of Health Research (FDN 159910), the LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, and the Dr Sandra Black Cen- tre for Brain Resilience and Recovery. M.F. received support from the Saul A. Silverman Family Foundation as a Canada Interna- tional Scientific Exchange Program and the Morris Kerzner Memo- rial Fund. We gratefully acknowledge GE Healthcare and the CAMH Brain Health Imaging Centre for manufacturing and sup- plying the ligand. We are also grateful to GE Healthcare for provid- ing the software to calculate the brain region of interest SUV ratios. The study protocol, Brain Eye Amyloid Memory study (BEAM), is registered at https://clinicaltrials.gov/ct2/show/NCT02524405? term=beam+sandra+black&rank=1

Symptom Experience and Influenza-Like Illness in a Military Population

AIMS: The primary objective of this study was to identify if symptom presentation expressed over the course of an influenza-like illness (ILI) can predict virus type by use of unsupervised machine learning. The secondary objective was to describe clinical characteristics of strain specific coronavirus. Finally, examine the psychometric properties of the Canadian Acute Respiratory Illness and Flu Scale (CARIFS). BACKGROUND: ILI outbreaks have been a significant source of non-battle injury among military personnel. Many different viruses cause ILI, and it is difficult to determine which virus is causing the illness. Recent studies have examined the etiology and epidemiology of ILIs. Other studies have examined influenza virus symptom severity either a dichotomous or liner-sum analysis. No studies to the researcher’s knowledge have examined ILI symptoms through an unsupervised learning analysis, and few studies have examined self-reported outpatient ILI reported symptoms over an extended time frame. METHODS: This is a secondary analysis of data collected over a four year period by the Acute Respiratory Infection Consortium (ARIC), from an otherwise healthy military population. The symptom data was captured on visit days and by a symptom diary patients filled out at home using a symptom severity instrument designed for this study. FINDINGS: Clustering by unsupervised machine learning was unable to predict virus type based on physical symptom presentation over the course of ILI. It did identify patient attributes, like sex and age that caused patients to experience symptoms differently. Additionally, clinical similarities and differences were noted between the four common human coronavirus strains. The strain HKU1 tended to have higher systemic symptom scores and higher gastrointestinal symptom severity score over the course of illness when compared to the other strains. Finally, the psychometric properties of CARIFS revealed many strengths and limitations for its use in research. The CARIFS should be reexamined using current knowledge of symptom management to increase the validity of the instrument. IMPLICATIONS: The results demonstrated how individuals experience physical symptoms differently making it difficult to predict the viral strain causing ILI. Future research should focus on the development of symptom instruments using the theoretical underpinnings of the symptom management theory

Using Unsupervised Learning Methods to Analyse Magnetic Resonance Imaging (MRI) Scans for the Detection of Alzheimer’s Disease

Background: Alzheimer’s disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. The manual diagnosis of AD by doctors is time-consuming and can be ineffective, so machine learning methods are increasingly being proposed to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. The aim of this thesis was therefore to use unsupervised learning methods to differentiate between MRI scans from people who were cognitively normal (CN), people with mild cognitive impairment (MCI), and people with AD. Objectives: This study applied a statistical method and unsupervised learning methods to discriminate scans from (1) people with CN and with AD; (2) people with stable mild cognitive impairment (sMCI) and with progressive mild cognitive impairment (pMCI); (3) people with CN and with pMCI, using a limited number of labelled structural MRI scans. Methods: Two-sample t-tests were used to detect the regions of interest (ROIs) between each of the two groups (CN vs. AD; sMCI vs. pMCI; CN vs. pMCI), and then an unsupervised learning neural network was employed to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between each of the two groups based on the extracted features. The approach was tested on baseline brain structural MRI scans from 715 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of which 231 were CN, 198 had AD, 152 had sMCI, and 134 were pMCI. The results were evaluated by calculating the overall accuracy, the sensitivity, specificity, and positive and negative predictive values. Results: The abnormal regions around the lower parts of the limbic system were indicated as AD-relevant regions based on the two-sample t-test (p<0.001), and the proposed method yielded an overall accuracy of 0.842 for discriminating between CN and AD, an overall accuracy of 0.672 for discriminating between sMCI and pMCI, and an overall accuracy of 0.776 for discriminating between CN and pMCI. Conclusion: The study combined statistical and unsupervised learning methods to identify scans of people with different stages of AD. This method can detect AD-relevant regions and could be used to accurately diagnose stages of AD; it has the advantage that it does not require large amounts of labelled MRI scans. The performances of the three discriminations were all comparable to those of previous state-of-the-art studies. The research in this thesis could be implemented in the future to help in the automatic diagnosis of AD and provide a basis for diagnosing sMCI and pMCI

Exploring the Diagnosis of Frontotemporal Dementia by Analyzing Neuropsychological Data With K-Means Clustering

Background: Differential diagnosis of dementia syndromes is difficult. Treatments are available for Alzheimer’s dementia (AD), but effects do not translate to other dementia syndromes. Therefore, early differential diagnosis is necessary to administer appropriate interventions and to boost drug development and research of therapeutic strategies that may lower patient and care-giver burden. The focus of the current study is to disentangle the clinical picture of frontotemporal dementia (FTD) from AD spectrum disorders. Findings could support an early, cheap, and more accurate diagnosis. Methods: K-means clustering, an unsupervised machine learning algorithm, was used on neuropsychological data from neurologic patients of the FTLD consortium databank. The analysis was performed twice, once including only neuropsychological test scores and a second time combining the neuropsychological variables with questionnaire scores assessing behavioral changes. In total n = 484 and n = 469 participants were included in the analysis with and without questionnaires, respectively. Participants included were either healthy controls with no family relation to patients in the dataset, or patients diagnosed with one of the following dementia disorders: AD, a behavioral variant of FTD (bvFTD), or one of three possible primary progressive aphasia (PPA) syndromes - a semantic variant (svPPA), a non-fluent variant (nfvPPA) or a logopenic variant (lvPPA). Results: Agreement of results from the various analyses performed was relatively high. Homogeneous clusters of diagnostic groups emerged. Homogeneity seemed higher for bvFTD and svPPA than for the other patient groups. NfvPPA and lvPPA patients were particularly likely to cluster together. Exploring neuropsychological patterns of cluster results demonstrated high variability between patients of the same diagnostic groups which could partly be explained by differences in disease severity. Tests that might prove particularly relevant to distinguish diagnostic subgroups are the FTLD-CDR sub-scores, the repeat and point task as well as questionnaires assessing apathy. Conclusion: K-means clustering proved to be a useful technique to explore various diagnostic syndromes that show overlapping clinical pictures. This study helped to formulate specific hypotheses based on the observation of patterns in multidimensional data. Disease severity showed to impact k-means clustering results considerably and should therefore be accounted for in future studies. Future studies will need to test the formulated hypotheses and inspect the meaning of impure clusters. Keywords: k-means clustering, frontotemporal dementia, primary progressive aphasia, Alzheimer’s dementia, differential diagnosis, neuropsychological testsBackground: Differential diagnosis of dementia syndromes is difficult. Treatments are available for Alzheimer’s dementia (AD), but effects do not translate to other dementia syndromes. Therefore, early differential diagnosis is necessary to administer appropriate interventions and to boost drug development and research of therapeutic strategies that may lower patient and care-giver burden. The focus of the current study is to disentangle the clinical picture of frontotemporal dementia (FTD) from AD spectrum disorders. Findings could support an early, cheap, and more accurate diagnosis. Methods: K-means clustering, an unsupervised machine learning algorithm, was used on neuropsychological data from neurologic patients of the FTLD consortium databank. The analysis was performed twice, once including only neuropsychological test scores and a second time combining the neuropsychological variables with questionnaire scores assessing behavioral changes. In total n = 484 and n = 469 participants were included in the analysis with and without questionnaires, respectively. Participants included were either healthy controls with no family relation to patients in the dataset, or patients diagnosed with one of the following dementia disorders: AD, a behavioral variant of FTD (bvFTD), or one of three possible primary progressive aphasia (PPA) syndromes - a semantic variant (svPPA), a non-fluent variant (nfvPPA) or a logopenic variant (lvPPA). Results: Agreement of results from the various analyses performed was relatively high. Homogeneous clusters of diagnostic groups emerged. Homogeneity seemed higher for bvFTD and svPPA than for the other patient groups. NfvPPA and lvPPA patients were particularly likely to cluster together. Exploring neuropsychological patterns of cluster results demonstrated high variability between patients of the same diagnostic groups which could partly be explained by differences in disease severity. Tests that might prove particularly relevant to distinguish diagnostic subgroups are the FTLD-CDR sub-scores, the repeat and point task as well as questionnaires assessing apathy. Conclusion: K-means clustering proved to be a useful technique to explore various diagnostic syndromes that show overlapping clinical pictures. This study helped to formulate specific hypotheses based on the observation of patterns in multidimensional data. Disease severity showed to impact k-means clustering results considerably and should therefore be accounted for in future studies. Future studies will need to test the formulated hypotheses and inspect the meaning of impure clusters. Keywords: k-means clustering, frontotemporal dementia, primary progressive aphasia, Alzheimer’s dementia, differential diagnosis, neuropsychological test

Early detection and characterization of Alzheimer's disease in clinical scenarios using Bioprofile concepts and K-means

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. There is a need for objective means to detect AD early to allow targeted interventions and to monitor response to treatment. To help clinicians in these tasks, we propose the creation of the Bioprofile of AD. A Bioprofile should reveal key patterns of a disease in the subject's biodata. We applied k-means clustering to data features taken from the ADNI database to divide the subjects into pathologic and non-pathologic groups in five clinical scenarios. The preliminary results confirm previous findings and show that there is an important AD pattern in the biodata of controls, AD, and Mild Cognitive Impairment (MCI) patients. Furthermore, the Bioprofile could help in the early detection of AD at the MCI stage since it divided the MCI subjects into groups with different rates of conversion to AD.</p