Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information …
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arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,291 papers · 148 categories
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48 results for “neurological disorders”
PR-GNN identifies salient brain regions for ASD biomarkers.
problem Identifying brain regions associated with neurological disorders.
method Pooling Regularized Graph Neural Network (PR-GNN) with novel salient region selection.
result PR-GNN outperforms baseline methods in ASD classification accuracy.
Study uses GMM-UBM and i-vectors to assess Parkinson's patients via speech, handwriting, and gait.
problem Assessing neurological state of Parkinson's disease patients using speech, handwriting, and gait signals.
method GMM-UBM and i-vectors applied to speech, handwriting, and gait signals.
result Different feature sets from each signal are crucial for assessing Parkinson's patients.
Structured and sparse partial least squares coherence for multivariate cortico-muscular analysisstat.AP
New algorithm extracts shared latent space for cortico-muscular interactions.
problem Challenges of high dimensionality and limited sample sizes in multivariate cortico-muscular analysis.
method Structured and sparse partial least squares coherence (ssPLSC) algorithm.
result ssPLSC achieves competitive or better performance in scenarios with limited sample sizes and high noise levels.
Paper proposes a new method for population-wise matching of sulcal graphs.
problem Challenges in matching cortical fold variations across individuals.
method Population-wise multi-graph matching of sulcal graphs.
result Effectiveness of multi-graph matching in obtaining consistent labeling of sulcal basins.
Simple 1-D CNNs classify Autism from rsfMRI.
problem Classifying Autism from high-dimensional rsfMRI data.
method Sub-sampled 1-D convolutional network.
result 1-D CNNs perform as well as state-of-the-art methods.
EEG measures brain activity to diagnose ASD more efficiently.
problem Lack of objective measures for early ASD diagnosis.
method Use EEG to classify ASD using machine learning.
result EEG can be a biomarker for ASD.
Unified framework for human-like decision making in various sequential tasks.
problem Real-life decision-making involves diverse strategies leading to similar outcomes.
method Two-stream reward processing mechanism for flexible and unified models.
result Framework unified MAB, CB, and RL with comparable performance.
Sparse GFA identifies disease factors in FTD subgroups.
problem Heterogeneity in neurological disorders hinders understanding and treatment.
method Sparse Group Factor Analysis (GFA) with regularised horseshoe priors.
result Identified latent disease factors differentially expressed in FTD subgroups.
Neurology-as-a-Service uses deep neural networks to assist non-specialist doctors in EEG analysis.
problem TeleEEG requires expensive infrastructure and expertise, limiting its use in developing countries.
method Cloud-based deep neural network approach for non-specialist EEG analysis.
result Deep neural network achieves 63.4% accuracy in classifying EEG activity, significantly higher than shallow approaches.
Framework learns structural and functional brain network embeddings while preserving their properties.
problem Joint learning of structural and functional brain networks while preserving their intrinsic properties.
method Siamese community-preserving graph convolutional network (SCP-GCN) that learns from both structural and functional connectivity.
result Superior performance in neurological disorder analysis compared to existing methods.
Machine learning applied to MRI connectome data for disease prediction and subnetwork analysis.
problem Predicting clinical outcomes and analyzing brain subnetworks from MRI connectome data.
method Review of machine learning approaches adapted to connectome data's unique properties.
result Machine learning models can improve disease prediction and subnetwork analysis in connectome data.
QuPWM detects epileptic spikes in MEG signals with high accuracy.
problem Manual detection of epileptic spikes in MEG signals is time-consuming and subjective.
method QuPWM combines PWM and SVM for feature extraction and classification.
result Average accuracy of 98% achieved on a balanced dataset of 3104 samples.
Generative model predicts multiple brain graphs from one, preserving topology.
problem Predicting multiple brain graphs from a single one, preserving topology.
method MultiGraphGAN architecture, graph adversarial auto-encoder, cluster-specific decoders, topological loss.
result Significantly outperformed variants in multi-view brain graph generation.
NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classificationcs.LG
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.
CNN predicts epileptic seizures from iEEG signals.
problem Accurately forecasting epileptic seizures to reduce patient uncertainty.
method Used a CNN for seizure prediction without hand-crafted features.
result CNN models outperformed previous methods on public datasets.
Improved QSM maps from MRI using deep learning.
problem Inaccurate susceptibility maps due to ill-posed dipole inversion.
method 3D GAN with increased receptive field and WGAN with gradient penalty.
result Significantly better QSM maps from single orientation phase maps.
A new reinforcement learning framework for multi-reward processing.
problem Understanding and modeling multi-reward interactions in complex systems.
method Two-stream reward processing with biological associations.
result Agents can react differently to different types of rewards.
Deep learning detects sleep events in EEG, reducing expert dependency.
problem Manual EEG event annotation by sleep experts is time-consuming and variable.
method Convolutional neural network for joint event detection in EEG signals.
result Deep learning method outperforms existing event-specific algorithms.
R-PLS improves analysis of brain functional connectivity matrices.
problem Improving analysis of functional connectivity matrices in brain imaging.
method Introducing R-PLS, a generalization of PLS for symmetric positive definite matrices.
result R-PLS identifies key functional connections in brain imaging datasets.
This work uses Sylvester normalizing flows for more accurate metabolite quantification in MRS.
problem Challenges in accurate metabolite quantification in MRS due to spectral overlap, low SNR, and artifacts.
method Bayesian inference framework with physics-informed Sylvester normalizing flows.
result Accurate metabolite quantification, well-calibrated uncertainties, and insights into parameter correlations and multi-modal distributions.
A new algorithm speeds up EEG source localization using regularization.
problem Challenging inverse problem in mapping EEG readings to brain activity.
method Formulated as a graphical generalized elastic net inverse problem, solved with a variable projected algorithm (VPAL).
result VPAL provides faster and more accurate EEG source localization compared to existing methods.
Machine learning for ASD diagnosis using morphological MRI networks.
problem Challenging to diagnose ASD using MRI due to heterogeneity and incomplete network neuroscience.
method Crowdsourced Kaggle competition to develop and benchmark ML pipelines.
result First-ranked team achieved 70% accuracy, 72.5% sensitivity, and 67.5% specificity.
Binary and multiclass epilepsy detection methods using EEG features.
problem Epilepsy diagnosis from EEG data.
method Feature extraction from power spectrum, spectrogram, and bispectrogram; eight machine learning algorithms used.
result Random forest and backpropagation algorithms achieved highest accuracy for binary and multiclass classification.
Sparse symmetric tensor regression reduces brain connectivity complexity.
problem Complex brain connectivity analysis in neuroimaging.
method Sparse symmetric tensor regression model for functional connectivity.
result Superior performance in Alzheimer's disease detection.
Study builds models to predict post-cardiac arrest outcomes using patient data.
problem Lack of accurate prognostication methods for patients resuscitated from cardiac arrest.
method Integrated electronic health records (EHR) and physiological time series (PTS) data to train machine learning classifiers.
result Combined EHR-PTS24 models outperformed models using either EHR or PTS24 alone in predicting survival and neurological outcomes.
WISDoM uses the Wishart distribution to analyze neurological data like EEG and brain connectivity.
problem Characterizing deviations of covariance or correlation matrices from expected values.
method WISDoM framework for quantifying deviations from the Wishart distribution.
result Validated on EEG feature ranking and classification of autism subjects.
Machine learning detects epilepsy development from EEG before seizures.
problem Early detection of epilepsy development (epileptogenesis) before seizures.
method Deep CNN combined with prediction aggregation for EEG data analysis.
result Deep learning achieves 99% AUC for EPG detection from EEG recordings.
A signature-based machine learning model for bipolar disorder and borderline personality disorderstat.ML
Machine learning identifies distinctive mood patterns in bipolar and borderline personality disorders.
problem Challenges in diagnosing bipolar and borderline personality disorders using retrospective mood recall.
method Signature-based machine learning model using daily mood ratings from smartphone apps.
result The model effectively separates participants into three groups with high accuracy.
Cheap model diagnoses vocal disorders accurately.
problem Diagnosing vocal disorders without expensive equipment.
method Used Mel-Cepstrum vectors and Support Vector Machine.
result Accurately diagnosed three vocal disorders.
Paper proposes a neural network for estimating brain conductivity without segmentation.
problem Accurate head model generation for personalized TMS with realistic conductivity.
method Convolutional neural network estimating conductivity from MRI data.
result Smooth electric field results similar to conventional methods without segmentation.
Study uses interviews to automatically detect BD and BPD with good accuracy.
problem Challenges in distinguishing BD and BPD from clinical interviews.
method Developed a multi-modal dataset and used a linear classifier with selected features from interviews.
result Different sets of features characterize BD and BPD, providing insights into their differences.
LSTM model detects voice disorders with high accuracy.
problem Automated detection of voice disorders is challenging due to continuous audio data.
method Used Long Short Term Memory (LSTM) model for feature extraction and classification of voice disorders.
result 22% sensitivity, 97% specificity, 56% unweighted average recall.
Deep model diagnoses psychiatric disorders from fMRI data.
problem Diagnosing psychiatric disorders from brain imaging data.
method Deep neural generative model using Bayes' rule.
result Improved diagnostic accuracy over competitive models.
ED-Filter improves eating disorder classification on Twitter.
problem High dimensionality and extensive feature sets in Twitter data for ED classification.
method Informed branch and bound search technique with hybrid greedy-based deep learning.
result Significant improvements in classification accuracy and efficiency.
Study on a pinning model with random walk increments, showing convergence to a critical disordered pinning measure.
problem Understanding the critical behavior of a disordered pinning model.
method Analyzing a disordered pinning model induced by a random walk with specific moment conditions, showing convergence to a limiting measure.
result Convergence of point-to-point partition functions to the critical disordered pinning measure in the critical window.
New minimal surfaces show stacking disorder in periodic structures.
problem Reproducing experimental twinning defects in periodic minimal surfaces.
method Constructing non-periodic minimal surfaces that lift to disordered stacking in 3D.
result Reproduced twinning defects in periodic minimal surfaces as stacking disorder.
Glottal Closure Instants Detection From Pathological Acoustic Speech Signal Using Deep Learningcs.SD
Proposes deep learning method for GCI detection from pathological speech.
problem Detecting glottal closure instants (GCI) in pathological acoustic speech.
method Convolutional neural network with fused deep acoustic speech and linear prediction residual features.
result Significantly better than state-of-the-art methods in GCI detection.
Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Trainingcs.LG
Develops a semi-supervised learning method to generate missing neuroimaging modalities.
problem Lack of paired neuroimaging data for training and inference.
method Semi-Supervised Adversarial CycleGAN (SSA-CGAN) using adversarial and cycle losses.
result Improves reconstruction error and robustness to noise.
RED detects sleep EEG events using deep neural networks, outperforming previous methods.
problem Manual detection of sleep EEG events is time-consuming and variable.
method Deep Recurrent Neural Networks (RNNs) with convolutional and recurrent components.
result RED outperforms state-of-the-art methods in sleep spindle and K-complex detection.
Deep learning predicts mental disorders from audio and text samples.
problem Predicting mental disorders from speech samples.
method Multimodal deep learning structure using various pre-trained models for audio and text embeddings, transfer learning, and auxiliary corpora.
result Acceptable accuracy in predicting mental disorders through multimodal analysis.
Paper presents a method to diagnose schizophrenia using fMRI dynamics from healthy controls.
problem Early diagnosis of mental disorders like schizophrenia using fMRI.
method Self-supervised pre-training on fMRI dynamics of healthy controls for transfer learning.
result Effective classification of schizophrenia using fMRI dynamics with small datasets.
Study identifies diverse health states of opioid users to improve policy.
problem Diverse health states of opioid users lead to ineffective policy interventions.
method Probabilistic topic modeling of medical histories.
result Learned phenotypes predict future opioid use and prescription variability.
Whole MILC learns brain disorder dynamics from unlabeled data.
problem Learning spatio-temporal brain disorder dynamics from unlabeled data.
method Self-supervised pre-training of whole MILC on unlabeled healthy control data.
result Whole MILC outperforms existing methods and provides diagnostic insights.
The study predicts how discussions in mental disorder Reddit communities affect users' emotional states.
problem Improving mental health conditions through social support analysis.
method Text embedding techniques and RNNs for predicting emotional tone shifts.
result Users' emotional states can improve due to social support, as evidenced by positive comments following negative posts.
Towards automated symptoms assessment in mental healthphysics.med-ph
Research uses activity analysis to identify mental health symptoms.
problem Identifying mental health symptoms using objective activity metrics.
method Proposes a framework for mHealth monitoring of psychiatric patients based on physical activity time series.
result Identifies distinct behavioural phenotypes and measures for mood assessment.
Matrix Reordering for Noisy Disordered Matrices: Optimality and Computationally Efficient Algorithmsmath.ST
New algorithm optimizes matrix reordering for noisy disordered matrices.
problem Optimizing matrix reordering for noisy disordered matrices in single-cell biology and metagenomics.
method Proposed a polynomial-time adaptive sorting algorithm to improve upon spectral seriation.
result Our algorithm achieves superior performance compared to existing methods in real datasets.
Study uses machine learning to detect sleep disorders by identifying brain patterns.
problem Detecting sleep disorders through EEG patterns in NREM sleep cycles.
method Feature engineering and machine learning model for predicting Cyclic Alternating Patterns (CAP).
result The model accurately predicts CAP sequences associated with sleep disorders.