Large-Scale Microarray Studies of Gene Expression in Multiple Regions of the Brain in Schizophrenia and Alzheimer's Disease

2005 ◽  
pp. 41-82 ◽  
Author(s):  
Pavel L. Katsel ◽  
Kenneth L. Davis ◽  
Vahram Haroutunian
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Multiple Regions ◽  
Microarray Studies ◽  
The Brain

scholarly journals Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Angela M. Crist ◽  
Kelly M. Hinkle ◽  
Xue Wang ◽  
Christina M. Moloney ◽  
Billie J. Matchett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Digital Pathology ◽  
Selective Vulnerability ◽  
Brain Regions ◽  
Biochemical Analyses ◽  
The Brain ◽  
Relevant Gene ◽  
Tau Expression

AbstractSelective vulnerability of different brain regions is seen in many neurodegenerative disorders. The hippocampus and cortex are selectively vulnerable in Alzheimer’s disease (AD), however the degree of involvement of the different brain regions differs among patients. We classified corticolimbic patterns of neurofibrillary tangles in postmortem tissue to capture extreme and representative phenotypes. We combined bulk RNA sequencing with digital pathology to examine hippocampal vulnerability in AD. We identified hippocampal gene expression changes associated with hippocampal vulnerability and used machine learning to identify genes that were associated with AD neuropathology, including SERPINA5, RYBP, SLC38A2, FEM1B, and PYDC1. Further histologic and biochemical analyses suggested SERPINA5 expression is associated with tau expression in the brain. Our study highlights the importance of embracing heterogeneity of the human brain in disease to identify disease-relevant gene expression.

scholarly journals A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Mechanisms ◽  
The Brain

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).MethodsA total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.ResultsWhile HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.ConclusionOur results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

scholarly journals The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

2020 ◽  
Author(s):  
Jingyan Qiu ◽  
Linjian Li ◽  
Yida Liu ◽  
Yingjun Ou ◽  
Yubei Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Early Stage ◽  
Training Transfer ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Model ◽  
The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

scholarly journals The solution of large-scale Minimum Cost SAT Problem as a tool for data analysis in bioinformatics

2016 ◽  
Author(s):  
Giovanni Felici ◽  
Daniele Ferone ◽  
Paola Festa ◽  
Antonio Napoletano ◽  
Tommaso Pastore
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Large Scale ◽  
Large Body ◽  
Minimum Cost ◽  
Iterative Solution ◽  
Gene Expression Measurement ◽  
Species Classification

Data mining is one of the main activities in bioinformatics, specifically to extract knowledge from massive data sets related with gene expression measurement, CNV, DNA strings, and others. A long array of methods are used to perform such task, ranging from the more established parametric statistical analysis to non parametric techniques, to classification methods that have been developed in knowledge engineering and artificial intelligence. In this paper, we consider a method for extracting logic formulas from data that relies on a large body of literature in integer and logic optimization, originally presented in [1], that has been largely and successfully applied to different problems in bioinformatics ([2], [3], [4], [5], [6]). Such method is based on the iterative solution of Minimum Cost SAT Problems and is able to extract logic formulas in DNF form that possess interesting features for their interpretation. While leaving the discussion of the main features and motivations of this approach to the related literature, in this talk we focus on the problem of solving efficiently very large scale instances of this well known logic programming problem and propose a new GRASP approach that, being able to exploit the specific structure of the problem, largely outperforms other established solvers for the same problem. References [1] G. Felici, K. Truemper. A Minsat Approach for Learning in Logic Domains, INFORMS Journal on Computing 14(1): 20-36 (2002). [2] P. Bertolazzi, G. Felici, E. Weitschek. Learning to classify species with barcodes, BMC Bioinformatics, 10:1-12 (2009). [3] M. Arisi, R. D’Onofrio, A. Brandi, S. Felsani, G. Capsoni, G. Drovandi, G. Felici, E. Weitschek, P. Bertolazzi, A. Cattaneo. Gene Expression Biomarkers in the Brain of a Mouse Model for Alzheimer’s Disease: Mining of Microarray Data by Logic Classification and Feature Selection. Journal of Alzheimer's Disease, 24(4) 721-738 (2011). [4] E. Weitschek, A. Lo Presti, G. Drovandi, G. Felici, M. Ciccozzi, M. Ciotti, P. Bertolazzi. Human polyomaviruses identification by logic mining techniques. BMC Virology Journal, 9:58 (2012). [5] E. Weitschek, G. Fiscon, G. Felici. Supervised DNA Barcodes species classification: analysis, comparisons and results, BMC BioData Mining, 7:4 (2014). [6] P. Bertolazzi, G. Felici, P. Festa, G. Fiscon, E. Weitschek. Integer Programming models for Feature Selection: new extensions and a randomized solution algorithm, European Journal of Operational Research, 250-389–399, 250 (2016).

A SOFTWARE APPLICATION FOR PREDICTING ALZHEIMER'S DISEASE BASED ON THE LEVEL OF GENE EXPRESSION

2017 ◽  
Author(s):  
Sofiia Yefremova ◽  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Data ◽  
Middle Temporal Gyrus ◽  
Expression Data ◽  
Middle Temporal ◽  
Software Application ◽  
The Brain

This article discusses the process of creating a software application that predicts Alzheimer's disease based on gene expression data in healthy and sick patients. The object of the study is the expression samples of genes taken from the study, which used the side of the middle temporal gyrus of the brain of frozen samples.

scholarly journals Conserved Architecture of Brain Transcriptome Changes between Alzheimer’s Disease and Progressive Supranuclear Palsy in Pathologically Affected and Unaffected Regions

2021 ◽  
Author(s):  
Xue Wang ◽  
Mariet Allen ◽  
Joseph S. Reddy ◽  
Minerva M. Carrasquillo ◽  
Yan W. Asmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Progressive Supranuclear Palsy ◽  
Neurodegenerative Disorders ◽  
Transcriptomic Data ◽  
Brain Transcriptome ◽  
Transcriptional Changes ◽  
The Brain

AbstractWe identify a striking correlation in the directionality and magnitude of gene expression changes in brain transcriptomes between Alzheimer’s disease (AD) and Progressive Supranuclear Palsy (PSP). Further, the transcriptome architecture in AD and PSP is highly conserved between the temporal and cerebellar cortices, indicating highly similar transcriptional changes occur in pathologically affected and “unaffected” areas of the brain. These data have broad implications for interpreting transcriptomic data in neurodegenerative disorders.

scholarly journals Impairments of neural circuit function in Alzheimer's disease

2016 ◽  
Vol 371 (1700) ◽  
pp. 20150429 ◽  
Author(s):  
Marc Aurel Busche ◽  
Arthur Konnerth
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hippocampal Neurons ◽  
Large Scale ◽  
Neural Circuit ◽  
Essential Feature ◽  
Amyloid Β ◽  
Amyloid Plaque ◽  
Aβ Oligomers ◽  
The Brain

An essential feature of Alzheimer's disease (AD) is the accumulation of amyloid-β (Aβ) peptides in the brain, many years to decades before the onset of overt cognitive symptoms. We suggest that during this very extended early phase of the disease, soluble Aβ oligomers and amyloid plaques alter the function of local neuronal circuits and large-scale networks by disrupting the balance of synaptic excitation and inhibition ( E / I balance) in the brain. The analysis of mouse models of AD revealed that an Aβ-induced change of the E / I balance caused hyperactivity in cortical and hippocampal neurons, a breakdown of slow-wave oscillations, as well as network hypersynchrony. Remarkably, hyperactivity of hippocampal neurons precedes amyloid plaque formation, suggesting that hyperactivity is one of the earliest dysfunctions in the pathophysiological cascade initiated by abnormal Aβ accumulation. Therapeutics that correct the E / I balance in early AD may prevent neuronal dysfunction, widespread cell loss and cognitive impairments associated with later stages of the disease. This article is part of the themed issue ‘Evolution brings Ca 2+ and ATP together to control life and death’.

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