scholarly journals Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Meiyan Huang ◽  
◽  
Wei Yang ◽  
Qianjin Feng ◽  
Wufan Chen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hierarchical Classification ◽  
Longitudinal Measurement ◽  
Classification Framework

scholarly journals Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease

2019 ◽  
Vol 23 ◽  
pp. 101811 ◽  
Author(s):  
Jun Pyo Kim ◽  
Jeonghun Kim ◽  
Yu Hyun Park ◽  
Seong Beom Park ◽  
Jin San Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Hierarchical Classification

Early detection of Alzheimer's disease using histograms in a dissimilarity-based classification framework

2014 ◽  
Author(s):  
Anne Luchtenberg ◽  
Rita Simões ◽  
Anne-Marie van Cappellen van Walsum ◽  
Cornelis H. Slump
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Classification Framework

296 Longitudinal measurement of serum NFL in early familial AD

2018 ◽  
Vol 89 (10) ◽  
pp. A47.1-A47
Author(s):  
Weston Philip ◽  
Poole Teresa ◽  
Ryan Natalie ◽  
Liang Yuying ◽  
Mead Simon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Symptom Onset ◽  
High Variability ◽  
Rate Of Change ◽  
Neurofilament Light ◽  
Sample Mean ◽  
Longitudinal Measurement ◽  
Mutation Carriers

A blood-based biomarker able to track early neurodegeneration in Alzheimer’s disease would be valuable. Serum neurofilament-light (NfL) is elevated in familial Alzheimer’s disease (FAD) mutation carriers prior to symptom onset, but exactly how early NfL becomes abnormal and whether it can track change within individuals is uncertain.We recruited 18 symptomatic carriers of autosomal dominant FAD mutations, 19 presymptomatic carriers, and 11 non-carriers. Blood was taken at baseline, and 26 participants also gave at least one follow-up sample (mean interval=2.5 years). Serum NfL was measured on the SIMOA platform. A longitudinal mixed effects framework was used to model change in NfL over time.Serum NfL was increased (p<0.05) in mutation carriers compared with non-carriers 11 years before the estimated time of symptom onset, with rate of change in NfL becoming significantly different 12 years before. However, there was high variability in the inter-individual rate of change in NfL between participants.Serum NfL concentration, and its rate of change, are sensitive, at the group level at least, to very early AD-neurodegenration. However, the high variability between individuals in NfL rate of change may make it difficult at present to use this measure to track early change in individual patients.

scholarly journals Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Zhe Xiao ◽  
Yi Ding ◽  
Tian Lan ◽  
Cong Zhang ◽  
Chuanji Luo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gray Matter Volume ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
Features Selection ◽  
Classification Framework ◽  
Single Feature ◽  
Gabor Feature ◽  
3D Information

We propose a novel classification framework to precisely identify individuals with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance.

O2-04-04: LONGITUDINAL MEASUREMENT OF SERUM NEUROFILAMENT LIGHT CONCENTRATION IN FAMILIAL ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_11) ◽  
pp. P623-P624
Author(s):  
Philip SJ. Weston ◽  
Teresa Poole ◽  
Natalie S. Ryan ◽  
Yuying Liang ◽  
Antoinette O'Connor ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Familial Alzheimer’S Disease ◽  
Neurofilament Light ◽  
Longitudinal Measurement ◽  
Light Concentration ◽  
Familial Alzheimer's Disease

scholarly journals Longitudinal measurement of serum neurofilament light in presymptomatic familial Alzheimer’s disease

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Philip S. J. Weston ◽  
Teresa Poole ◽  
Antoinette O’Connor ◽  
Amanda Heslegrave ◽  
Natalie S. Ryan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Familial Alzheimer’S Disease ◽  
Neurofilament Light ◽  
Longitudinal Measurement ◽  
Familial Alzheimer's Disease

scholarly journals Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer’s Disease

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 475 ◽  
Author(s):  
Eufemia Lella ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Angela Lombardi ◽  
Tommaso Maggipinto ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Brain Network ◽  
Data Set ◽  
Brain Connectome ◽  
Subcortical Brain ◽  
Classification Framework ◽  
Subcortical Regions ◽  
Cortical Regions

In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer’s disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 × 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity.

Cognitive control constrains memory attributions

2019 ◽  
Vol 42 ◽  
Author(s):  
Colleen M. Kelley ◽  
Larry L. Jacoby
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Cognitive Control

Abstract Cognitive control constrains retrieval processing and so restricts what comes to mind as input to the attribution system. We review evidence that older adults, patients with Alzheimer's disease, and people with traumatic brain injury exert less cognitive control during retrieval, and so are susceptible to memory misattributions in the form of dramatic levels of false remembering.

Formation of paired helical filaments in Alzheimer's disease

1984 ◽  
Vol 42 ◽  
pp. 302-303
Author(s):  
J. Metuzals ◽  
D. F. Clapin ◽  
V. Montpetit
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontal Lobe ◽  
Giant Axon ◽  
Paired Helical Filaments ◽  
Normal Brain ◽  
Protein Assemblies ◽  
Electron Microscopic ◽  
Helical Symmetry ◽  
Structural Levels

Information on the conformation of paired helical filaments (PHF) and the neurofilamentous (NF) network is essential for an understanding of the mechanisms involved in the formation of the primary lesions of Alzheimer's disease (AD): tangles and plaques. The structural and chemical relationships between the NF and the PHF have to be clarified in order to discover the etiological factors of this disease. We are investigating by stereo electron microscopic and biochemical techniques frontal lobe biopsies from patients with AD and squid giant axon preparations. The helical nature of the lesion in AD is related to pathological alterations of basic properties of the nervous system due to the helical symmetry that exists at all hierarchic structural levels in the normal brain. Because of this helical symmetry of NF protein assemblies and PHF, the employment of structure reconstruction techniques to determine the conformation, particularly the handedness of these structures, is most promising. Figs. 1-3 are frontal lobe biopsies.

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