The diagnosis of young-onset dementia. Lancet Neurol 9:793-806

Dementia Research Centre, Department of Neurodegeneration, UCL Institute of Neurology, Queen Square, London, UK.
The Lancet Neurology (Impact Factor: 21.9). 08/2010; 9(8):793-806. DOI: 10.1016/S1474-4422(10)70159-9
Source: PubMed


A diagnosis of dementia is devastating at any age but diagnosis in younger patients presents a particular challenge. The differential diagnosis is broad as late presentation of metabolic disease is common and the burden of inherited dementia is higher in these patients than in patients with late-onset dementia. The presentation of the common degenerative diseases of late life, such as Alzheimer's disease, can be different when presenting in the fifth or sixth decade. Moreover, many of the young-onset dementias are treatable. The identification of causative genes for many of the inherited degenerative dementias has led to an understanding of the molecular pathology, which is also applicable to later-onset sporadic disease. This understanding offers the potential for future treatments to be tailored to a specific diagnosis of both young-onset and late-onset dementia.

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Available from: Cath Mummery, Dec 12, 2014
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    • "Recently there has been a growing interest for highdimensional feature selection and classification methods that can combine information from the whole brain measurement [4] and neuropsychological data [5] to discriminate between individual subjects. Moreover another study indicates that not only older population but also men and women under the age of 50 are affected by dementia [6]. There are several studies that have proved the effective utilization of neuropsychological test data [7] [8] [9] for earlier diagnosis of dementia and for conversion from Mild Cognitive Impairment to Dementia. "
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    ABSTRACT: The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures. A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques. Ensemble approach which is trained with features selected from multiple biomarkers facilitated accurate classification when compared with conventional classification techniques. Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5. Feature search is done with Particle Swarm Optimisation to retrieve the subset of features for further selection with the ensemble classifier. Features selected by the proposed C4.5 ensemble classifier with Particle Swarm Optimisation search, coupled with Merit Merge technique (CPEMM), outperformed bagging feature selection of SVM, NB, and Random forest classifiers. The proposed CPEMM feature selection found the best subset of features that efficiently discriminated normal individuals and patients affected with Mild Cognitive Impairment and Alzheimer’s Dementia with 98.7% accuracy.
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    • "All rights reserved This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License. type is clinically challenging because of difficulty in the assessment of memory and cognitive impairment (especially in the early stages) and incomplete knowledge of clinical phenotypes [2]. Even within the set of PSEN1 variants that cause early-onset Alzheimer's disease (EOAD), there is substantial clinical heterogeneity [3] [4] [5] [6]. "
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    Full-text · Article · Mar 2015 · Journal of Alzheimer's disease: JAD
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    • "The most common causes of dementia are Alzheimer's disease (AD), vascular disease (in several forms), dementia with Lewy bodies (DLB) and frontotemporal lobar degeneration (FTLD), but there are many others.10 11 Accurate and early diagnosis has considerable implications for the patient in terms of prognosis and management and will be increasingly important if and when disease modifying treatments become available. Currently, postmortem examination of brain tissue remains the only definitive means of establishing diagnosis in most cases. "
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    ABSTRACT: Accurate and timely diagnosis of dementia is important to guide management and provide appropriate information and support to patients and families. Currently, with the exception of individuals with genetic mutations, postmortem examination of brain tissue remains the only definitive means of establishing diagnosis in most cases, however, structural neuroimaging, in combination with clinical assessment, has value in improving diagnostic accuracy during life. Beyond the exclusion of surgical pathology, signal change and cerebral atrophy visible on structural MRI can be used to identify diagnostically relevant imaging features, which provide support for clinical diagnosis of neurodegenerative dementias. While no structural imaging feature has perfect sensitivity and specificity for a given diagnosis, there are a number of imaging characteristics which provide positive predictive value and help to narrow the differential diagnosis. While neuroradiological expertise is invaluable in accurate scan interpretation, there is much that a non-radiologist can gain from a focused and structured approach to scan analysis. In this article we describe the characteristic MRI findings of the various dementias and provide a structured algorithm with the aim of providing clinicians with a practical guide to assessing scans.
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