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Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease

Younghoon Seo, Hyemin Jang, Hyejoo Lee
2022 Life  
eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization.  ...  Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues  ...  The hippocampus was segmented into seven subfields using an atlas-based automatic algorithm based on Markov random fields in FreeSurfer.  ... 
doi:10.3390/life12020275 pmid:35207561 pmcid:PMC8879055 fatcat:46ccay3fhvhtrhxlq2g734sqzy

Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning

Simon F. Eskildsen, Pierrick Coupé, Daniel García-Lorenzo, Vladimir Fonov, Jens C. Pruessner, D. Louis Collins
2013 NeuroImage  
The selected regions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages.  ...  We used a novel technique for identifying cortical regions potentially discriminative for separating individuals with MCI who progress to probable AD, from individuals with MCI who do not progress to probable  ...  Using baseline data, Chupin et al. (2009) automatically segmented the hippocampus and used the volume in a k-means classifier to predict MCI conversion to AD within 18 months.  ... 
doi:10.1016/j.neuroimage.2012.09.058 pmid:23036450 pmcid:PMC4237400 fatcat:mnw3debmyvczffpkrlmkhnce6a

A Survey on Classification algorithms of Brain Images in Alzheimer's disease based on Feature Extraction techniques

Ruhul Amin Hazarika, Arnab Kumar Maji, Samarendra Nath Sur, Babu Sena Paul, Debdatta Kandar
2021 IEEE Access  
For selecting the maximum likelihood features, the Expectation Maximization (EM) algorithm is used.  ...  algorithm is used for building the classifier.  ... 
doi:10.1109/access.2021.3072559 fatcat:cc4ffd325naozaxs63geaut76i

Machine Learning for the Classification of Alzheimer's Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Lucia Billeci, Asia Badolato, Lorenzo Bachi, Alessandro Tonacci
2020 Processes  
However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their  ...  Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and  ...  T1 sMRI images of the participants were segmented with a two-level diffeomorphic multi-atlas likelihood-fusion algorithm and the help of an expert neuroanatomist, in order to calculate the volume of hippocampus  ... 
doi:10.3390/pr8091071 fatcat:p22wqxbulvf3xpv6jognlj3hxu

A Robust Machine Learning Approach for Multiclass Alzheimer's Disease Detection using 3D Brain Magnetic Resonance Images

G Nagarjuna Reddy, Research Scholar, Department of ECE, JNTUA, Ananthapuramu, India., Nagireddy K, Professor, Department of ECE, NBKRIST, Vidyanagar, India.
2021 Maǧallaẗ al-abḥāṯ al-handasiyyaẗ  
The proposed algorithm is validated using Open Access Series of Imaging Studies (OASIS) datasets to classify the subjects into AD, Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) categories using  ...  Moreover, this approach is also evaluated and compared with the state-of-the-art approaches. 87.84% diagnosis accuracy is achieved with Ensemble classifier using hybrid features to diagnose the severity  ...  Here the generated samples X are alike principal components which can be retrieved from maximum likelihood estimation.  ... 
doi:10.36909/jer.10511 fatcat:fjuhdwodf5efplm7duvi2qbxpm

Alzheimer's Disease Diagnosis Based on Cortical and Subcortical Features

Yubraj Gupta, Kun Ho Lee, Kyu Yeong Choi, Jang Jae Lee, Byeong Chae Kim, Goo-Rak Kwon
2019 Journal of Healthcare Engineering  
Datasets were divided in a 70/30 ratio, and later, 70% were used for training and the remaining 30% were used to get an unbiased estimation performance of the suggested methods.  ...  In this study, a new approach is proposed using cortical thickness and subcortical volume for distinguishing binary and tertiary classification of the National Research Center for Dementia dataset (NRCD  ...  [21] used an ensemble of a classifier for the classification of AD vs HC and achieved a classification accuracy of 93.75%, a sensitivity of 100%, and a specificity of 87.5%.  ... 
doi:10.1155/2019/2492719 pmid:30944718 pmcid:PMC6421724 fatcat:3kppc3u22jb7vahl7gxblcdehu

Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach

Ignacio A. Illan, Juan M. Górriz, Javier Ramírez, Anke Meyer-Base
2014 Frontiers in Computational Neuroscience  
This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images.  ...  In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD.  ...  Once the topology is fixed, a maximum likelihood algorithm estimates the values of the network parameters, and the network is finally used for inference.  ... 
doi:10.3389/fncom.2014.00156 pmid:25505408 pmcid:PMC4244642 fatcat:nenlk7s35bf5biqgkyh4jtv5gu

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

Benjamin H. Brinkmann, Joost Wagenaar, Drew Abbot, Phillip Adkins, Simone C. Bosshard, Min Chen, Quang M. Tieng, Jialune He, F. J. Muñoz-Almaraz, Paloma Botella-Rocamora, Juan Pardo, Francisco Zamora-Martinez (+8 others)
2016 Brain  
The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted  ...  The model using open access data and algorithms generated reproducible research that advanced seizure forecasting.  ...  The canine data was recorded using devices developed by NeuroVista Inc., and we acknowledge the contributions of NeuroVista's former management and employees.  ... 
doi:10.1093/brain/aww045 pmid:27034258 pmcid:PMC5022671 fatcat:qqsoxoelynaaxgdwmw6zsct34u

Efficient Morphometric Techniques in Alzheimer's Disease Detection: Survey and Tools

Vinutha N., P. Deepa Shenoy, P. Deepa Shenoy, K.R. Venugopal
2016 Neuroscience International  
The different types of segmentation techniques such as Tissue Segmentation, Atlas based Segmentation, Hippocampus Segmentation and other segmentation techniques have been discussed.  ...  Further the data is classified into healthy normal and AD by supervised, unsupervised or probabilistic methods.  ...  This is followed by Linear Discriminant Analysis (LDA) (Li and Yuan, 2005) a feature subset selection algorithm, which is used as the evaluation classifier.  ... 
doi:10.3844/amjnsp.2016.19.44 fatcat:3zeb2s5pjzfv7mptqi7cy2a3au

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects

G. Ziegler, G.R. Ridgway, R. Dahnke, C. Gaser
2014 NeuroImage  
Using Gaussian process models as 26 a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in 27 terms of Normative Probability Maps (NPM) and global  ...  We here propose an individualized Gaussian process-based inference scheme for clinical decision sup-19 port in healthy and pathological aging elderly subjects using MRI.  ...  pattern 680 across voxels, and thus we applied a voxel specific Box-Cox transforma-681 tion using a maximum likelihood method.  ... 
doi:10.1016/j.neuroimage.2014.04.018 pmid:24742919 pmcid:PMC4077633 fatcat:mqtqmlxlijee3nvms2w6eczybi

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

Andrés Ortiz, Juan M. Górriz, Javier Ramírez, Francisco J. Martinez-Murcia, Olaf Sporns
2014 PLoS ONE  
The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD).  ...  The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM).  ...  Acknowledgments Data used in preparation of this article were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (  ... 
doi:10.1371/journal.pone.0093851 pmid:24728041 pmcid:PMC3984096 fatcat:rrfsnh47ojffvp47ccmrxi6vn4

Noise-Robust Modes of the Retinal Population Code have the Geometry of "Ridges" and Correspond with Neuronal Communities [article]

Adrianna R. Loback, Jason S. Prentice, Mark L. Ioffe, Michael J. Berry II
2017 arXiv   pre-print
We start by defining a soft local maximum, which is a local probability maximum when constrained to a fixed spike count.  ...  We argue that a neuronal community shares many of the properties of Donald Hebb's classic cell assembly, and show that a simple, biologically plausible decoding algorithm can recognize the presence of  ...  can converge to a stable result.  ... 
arXiv:1610.06886v2 fatcat:nkvaion4w5a5rmpsw7hshzm64u

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Nitsa J. Herzog, George D. Magoulas
2021 Sensors  
Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer's Disease (AD)), and can help  ...  The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the  ...  Another popular method for the improvement of classification performance is the implementation of classifier ensembles [29] .  ... 
doi:10.3390/s21030778 pmid:33498908 fatcat:7wvbs7voovcytnexkezntwr3lm

Entorhinal cortex receptive fields are modulated by spatial attention, even without movement [article]

Niklas Wilming, Peter König, Seth König, Elizabeth A. Buffalo
2017 bioRxiv   pre-print
Notably, these results support the notion that grid cells may be capable of serving a variety of different cognitive functions and suggest that grid cells are a versatile component of many neural algorithms  ...  This contrast with our recordings obtained in the hippocampus, where grid-like representations were not observed.  ...  Red lines indicate ellipses selected by a peak shift algorithm whose maximum gridness score was assigned to this unit.  ... 
doi:10.1101/183327 fatcat:47pdxsgzfnbdnansxkde4t3q5u

In vivo calcium imaging of CA3 pyramidal neuron populations in adult mouse hippocampus [article]

Gwendolin Schoenfeld, Stefano Carta, Peter Rupprecht, Asli Ayaz, Fritjof Helmchen
2021 bioRxiv   pre-print
Most of the calcium transients were consistent with a high incidence of bursts of action potentials, based on calibration measurements using simultaneous juxtacellular recordings and calcium imaging.  ...  Their abundance in particular subsets of neurons was relatively stable across days.  ...  We excised 3-s segments around detected calcium transient events (-1 s to +2 s relative to the peak) and deconvolved the calcium transient using a supervised algorithm based on neural networks (Rupprecht  ... 
doi:10.1101/2021.01.21.427642 fatcat:kjhmgm5rpffrdglzwarrhux4cq
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