Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
problem Differentiating Alzheimer Disease from Mild Cognitive Impairment.
method Hybrid deep learning ensemble framework using MRI slices, pretrained models, and stacked ensemble learning.
result State-of-the-art accuracy (99.21%) for Alzheimer vs. Mild Cognitive Impairment classification.
Enhances prediction credibility for Alzheimer's conversion risk.
problem Lack of prediction credibility in machine learning for medical applications.
method Combines Ensemble learning with Conformal Predictors.
result Proposed approach outperforms standard ensemble methods.
Paper proposes RL for efficient MCI diagnosis from dialogue data.
problem Efficiently diagnose MCI from conversational data with minimal interaction.
method Reinforcement learning framework trained on clinical trial transcripts.
result Significantly outperforms supervised learning approaches with minimal conversation turns.
Deep learning predicts cognitive decline in MCI patients using brain imaging.
problem Identifying subjects at risk of rapid cognitive decline in mild cognitive impairment.
method Developed a deep convolutional neural network framework trained on baseline PET studies of AD and normal subjects.
result CNN-based approach accurately predicts conversion to Alzheimer's disease in MCI patients with high accuracy.
Global feature model improves early AD diagnosis accuracy.
problem Early diagnosis of mild cognitive impairment (MCI) using structural MRI images.
method Gaussian discriminant analysis (GDA) with dual high-dimensional decision spaces for global feature extraction.
result Achieved F1 score of 91.06% for MCI vs. CN group.
Deep learning predicts Alzheimer's Disease progression with high accuracy.
problem Predicting multiple aspects of Alzheimer's Disease progression.
method Unsupervised deep learning on 1908 patients' 18-month clinical data.
result Model accurately predicts ADAS-Cog scores and identifies word recall as a predictor.
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
problem Identifying MCI early and accurately.
method Scoping review with co-occurrence analysis and PAGER framework.
result Main research themes identified: ERP/EEG, QEEG, and EEG-based machine learning.
DIM shows promise in predicting Alzheimer's progression.
problem Predicting Alzheimer's disease progression from brain imaging.
method Used variants of Deep InfoMax (DIM) for brain imaging analysis.
result DIM outperforms supervised AlexNet and ResNet in Alzheimer's progression prediction.
New method predicts Alzheimer's risk with individual uncertainty estimates.
problem Predicting conversion from mild cognitive impairment to Alzheimer's disease.
method Persistent homology of clinical trajectories combined with stacking ensemble.
result Pipeline achieves high accuracy and individual-level uncertainty quantification.
A new method learns brain dynamics from limited rs-fMRI data.
problem Limited training data for brain connectivity dynamics.
method Residual D-net with recurrent multi-scale representation.
result Significantly higher classification accuracy for MCI vs NC.
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
New method uses SPHARM coefficients to classify AD, MCI, and controls.
problem Discriminating between AD, MCI, and normal aging.
method Spherical harmonics (SPHARM) coefficients for hippocampal shape modeling, SVM classification, feature selection.
result High accuracy in classifying AD vs controls (94%) and MCI vs controls (83%).
A CNN on semi-regular meshes classifies brain diseases from MRI scans.
problem Classifying brain diseases from MRI scans.
method Developed a vertex-based graph CNN for semi-regular triangulated meshes.
result Vertex-based graph CNN outperformed spectral graph CNN in classifying MCI and AD.
New method handles missing data in multimodal brain imaging.
problem Missing data in multimodal brain imaging.
method Full Information Linked ICA (FI-LICA) algorithm.
result FI-LICA outperforms current practices in classification and prediction.
NEURO-DRAM improves neuroimaging classification accuracy.
problem Improper use of traditional computer vision models in neuroimaging.
method 3D recurrent visual attention model trained with reinforcement learning.
result NEURO-DRAM achieves state-of-the-art accuracy in Alzheimer's disease prediction.
THS-GAN uses tensorizing and high-order pooling for AD diagnosis.
problem Early diagnosis of Alzheimer's Disease (AD) using MRI images.
method Tensorizing a three-player cooperative game framework with high-order pooling for MRI images.
result THS-GAN achieves superior performance in AD diagnosis compared to existing methods.
Wide and deep neural network predicts Alzheimer's progression from shape and clinical data.
problem Predicting Alzheimer's disease progression from shape and clinical data.
method Fused anatomical shape and tabular clinical data in a neural network, employing survival analysis loss.
result The model outperforms shape and clinical models individually.
Deep learning models outperform MRI-based methods for MCI to AD conversion prediction.
problem Predicting the conversion of mild cognitive impairments to Alzheimer's Disease.
method Applied deep learning and machine learning algorithms on neuroimaging and clinical data.
result XGBoost algorithm trained on clinical and embedding data provided the best results with an accuracy of 0.76 and AUC of 0.86.
Bayesian model detects altered neural circuits in MCI patients.
problem Detecting altered neural circuits in Mild Cognitive Impairment patients.
method Hierarchical Bayesian recurrent state space model.
result Model discovers latent states predominantly observed in MCI patients.
Deep learning improves AD diagnosis and prognosis from neuroimaging data.
problem Early detection and accurate classification of Alzheimer's disease.
method Deep learning models applied to neuroimaging data for AD diagnosis and prognosis.
result Deep learning models can achieve high accuracy in AD diagnosis and prognosis.
Predict and classify brain image evolution trajectories from a single MRI timepoint.
problem Diagnosing early mild cognitive impairment (eMCI) from a single MRI scan.
method Supervised and unsupervised learning frameworks that predict and label intensity patch evolution trajectories from a baseline MRI.
result Classification accuracy increased by up to 10% points compared to single timepoint-based methods.
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
problem Cognitive load's effect on financial market information processing.
method Developed a theoretical framework and tested it with exogenous disclosure complexity variation.
result Cognitive load significantly impairs price discovery, particularly for less sophisticated investors.
Improved CNNs detect Alzheimer's with 14% accuracy boost.
problem Early detection of Alzheimer's Disease using MRI scans.
method Optimized 3D CNNs with instance normalization, spatial downsampling, model widening, and age information.
result 14% increase in test accuracy distinguishing AD, MCI, and controls.
Traditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, whic…
Ensemble model predicts AD progression from CN status with high accuracy.
problem Early prediction of clinical progression from cognitively normal to mild cognitive impairment or Alzheimer's disease.
method Ensemble survival analysis combining penalized Cox regression, advanced survival models, and aggregation techniques.
result Ensemble model achieved peak C-index of 0.907 and integrated time-dependent AUC of 0.904, outperforming baseline models.
Paper characterizes early-stage dementia signatures from sensor data.
problem Detecting early-stage dementia from sensor data.
method Developed bespoke behavioural models from longitudinal sensor data.
result Found subtle differences in sleep quality and wandering between dementia patients and controls.
Develops a new feature theory for robust machine learning.
problem Creating robust machine learning features from training data.
method Stochastic tensor space feature theory with Karhunen-Loeve expansion and hierarchical subspaces.
result Dramatic increases in accuracy for predicting Alzheimer's disease stages.
A brain signature predicts future Alzheimer's dementia in MCI patients.
problem Hard to predict which MCI patients will progress to Alzheimer's dementia.
method Identified a brain signature using machine learning in dementia patients, validated in MCI.
result 90% of MCI individuals with the signature progressed to dementia within 3 years.
Project predicts Alzheimer's progression using neural networks and novel data processing.
problem Difficulty in early identification of Alzheimer's patients.
method Used machine learning, specifically neural networks, and a novel pre-processing technique.
result Neural network model accurately predicts AD progression with high accuracy.
New method predicts AD progression using MEG brain networks.
problem Early diagnosis and prediction of Alzheimer's disease progression.
method MG2G, a deep learning method that maps brain networks into a latent space.
result MG2G detects subtle brain connectivity patterns and predicts AD progression.
A new neural network model improves fMRI classification.
problem Classifying brain states using fMRI data.
method Developed a connectome-convolutional neural network (CCNN) for fMRI functional connectivity classification.
result CCNN outperforms single metric classifiers and can adapt to various connectivity descriptors.
Study identifies five AD subtypes using graph diffusion and similarity learning.
problem Identifying homogeneous AD subtypes to improve diagnosis and treatment.
method Unsupervised clustering with graph diffusion and similarity learning.
result Five distinct AD subtypes identified with significant differences in biomarkers and clinical features.
In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer's disease using neuroimaging data. Specifically, we may wish to e…
Study identifies key MRI features for predicting cognitive performance after mTBI.
problem Identify relevant diffusion MRI metrics for cognitive functions in mTBI patients.
method Proposes a novel feature selection method combining best-first search with genetic algorithm crossover.
result Achieves significantly more accurate predictions than other feature selection algorithms.
Deep CNN models simulate cognitive deficits from neurodegenerative diseases and TBI.
problem Limited ability to assess damaged neurons in vivo for accurate diagnosis and prognosis.
method Used convolutional neural networks (CNNs) to damage simulated brain connections based on biophysically relevant data on FAS.
result Damage to simulated brain connections leads to human-like cognitive mistakes and quantifiable accuracy reductions.
Study examines APOE's impact on AD progression using a novel DEBM approach.
problem Understanding APOE's role in AD progression and developing targeted clinical trials.
method Developed a discriminative event-based model (DEBM) and proposed a stratified approach to improve model accuracy.
result Identified APOE carriers' impact on AD progression timeline, aiding clinical trial selection.
The Giroux correspondence and the notion of a near force-free magnetic field are used to topologically characterize near force-free magnetic fields which describe a variety of physical processes, including plasma equilibrium. As a byproduct, the topological characterization of force-free magnetic fields associated with…
Unsupervised framework captures acquisition variability in structural connectomes.
problem Acquisition differences across sites, scanners, and protocols complicate structural connectome analysis.
method An unsupervised framework using architectural annealing to balance discrete and continuous latent variables.
result Architectural annealing produces stronger site learning than baseline models.
The study explains autism through minimizing surprise and uncertainty using information theory.
problem Insistence on sameness in autism.
method Information theory framework to quantify cognitive limitations and behaviors.
result Insistence on sameness is a manifestation of minimizing uncertainty.
Develops variational Bayesian neural network for complex biomedical applications.
problem High computational cost of Markov Chain Monte Carlo in BNN.
method Variational Bayes inference for posterior consistency and classification accuracy.
result Developed statistical theory for posterior consistency and prediction accuracy.
Develops an inverse particle filter for cognitive systems.
problem Tracking cognitive adversaries in counter-adversarial applications.
method Global filtering approach using Monte Carlo methods and differentiable I-PF.
result Demonstrates convergence to optimal inverse filter and improved estimation performance.
This research uses machine learning to identify Alzheimer's disease subtypes and predict progression.
problem Heterogeneity in Alzheimer's disease clinical manifestations and progression rate limit personalized care and treatment planning.
method Unsupervised and supervised machine learning approaches applied to ADNI data.
result Identification of patient subtypes and prediction of disease progression zones.
The study compares feature learning techniques for predicting Alzheimer's disease from MRI.
problem Predicting cognitive impairment from MRI data.
method Review and comparison of feature learning and selection techniques.
result Stacked auto-encoders outperformed other methods in MRI-based Alzheimer's disease prediction.
3D CNN accurately classifies infant neurodevelopmental age from MRI scans.
problem Estimating neurodevelopmental age in infants from MRI data.
method 3D Convolutional Neural Network (3D CNN) trained on MRI images of 112 infants.
result 3D CNN achieves 99% sensitivity and 98.3% specificity in age classification.
New algorithms mitigate impairment effect in stochastic bandits.
problem Impairment effect in stochastic multi-armed bandits.
method Developed two novel algorithms with bucketing to address impairment and temporal constraints.
result Achieved sublinear regret, explicitly capturing impairment cost.
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
problem Difficult prediction of medium-horizon Alzheimer's disease progression due to tied clinical scores and irregular biomarker observations.
method Developed a residual gap-aware transformer that combines statistical reference with transformer-based residual learning.
result The proposed model reduces mean error and improves prediction-observation correlation compared to baseline models.
Paper learns latent and hierarchical structures in CDMs from data.
problem Jointly learning latent and hierarchical structures in CDMs from observed data.
method Penalized likelihood approach for selecting attributes and estimating structures; EM and latent structure recovery algorithms.
result Good performance demonstrated by simulation and real data applications.
QC-SPHARM detects Alzheimer's Disease early using hippocampal surface geometry.
problem Early detection of Alzheimer's Disease (AD) using hippocampal surface geometry.
method Spherical harmonics registration, conformality and curvature distortions quantification, t-test feature selection, SVM classification.
result 85.2% testing accuracy on ADNI data, 81.2% on aMCI progression data.