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Home › Research › Topics › deep

deep

205 studies tagged deep, dated 2006-12-06 to 2026-10-30. Bibliography: BibTeX (.bib) · RIS (.ris)

Newest in this topic

  1. Tau-CTFP: A connectome-informed continuous-time first-passage model for forecasting the onset of regional tau PET abnormality 2026-10-30
  2. A Wasserstein Weisfeiler-Lehman Kernel Framework for Multimodal Brain Connectome Analysis 2026-10-29
  3. Current gaps in downstream applications of fMRI foundation models: lessons learned from BrainLM & BrainHarmonix on Prevent-AD 2026-10-26

Studies

205 studies carry this tag.

Date range

2006 to 2026

Top journals

  • Research Square 22
  • medRxiv 22
  • NeuroImage 14
  • bioRxiv 14
  • Preprints.org 7

87 journals in all.

Newest first, then by address. Each study has a usefulness score out of 100 (rubric version 4.0); tap a score to see what makes it up. Tags were assigned automatically from each study's title and summary. The same studies as a filter in the library.

  1. Tau-CTFP: A connectome-informed continuous-time first-passage model for forecasting the onset of regional tau PET abnormality

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-10-30 Brain scans and imaging

    Tau PET burden is associated with subsequent cognitive decline in Alzheimer's disease, motivating forecasts for monitoring and studies of spatially targeted therapies.

  2. A Wasserstein Weisfeiler-Lehman Kernel Framework for Multimodal Brain Connectome Analysis

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-29 Brain scans and imaging

    Under nested cross-validation, the WWL kernel achieves a balanced accuracy of $0.651\pm0.028$, significantly outperforming both a raw-feature SVM and a histogram-based WL-kernel baseline.

  3. Current gaps in downstream applications of fMRI foundation models: lessons learned from BrainLM & BrainHarmonix on Prevent-AD

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-10-26 Brain scans and imaging

    Foundation models have driven major advancements in the language and vision domains, as seen in the popularity of Artificial Intelligence (AI) tools, and a growing number of studies are bringing the approach to human brain imaging.

  4. MVMGNN: Multi-View Masked Graph Neural Network for Alzheimer’s Disease Diagnosis using Structural MRI

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-23 Brain scans and imaging

    Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder, and early diagnosis is essential for timely intervention.

  5. A Dual-Stream Spatio-Temporal Network with Bidirectional Rectification for Differential Diagnosis of Dementia using EEG

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-22 Brain scans and imaging

    Accurate differential diagnosis between Alzheimer’s disease (AD) and frontotemporal dementia (FTD) is of critical importance for the early screening of neurodegenerative diseases.

  6. Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-10-22 Lewy body dementia

    A bstract In this study, we build EEG based graph model that uses frequency-resolved connectivity features, with Coherence, phase locking value (PLV) and phase lag index (PLI) in five frequency bands as multi-dimensional edge features.

  7. Speaker-Wise Dementia Screening fromNaturalistic Speech with Lexical Entropy, Semantic Drift, and Multimodal Fusion

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-21 Digital health tools

    Although advances have been made in dementia symptom management, early detection during stages when interventions are most effective remains a significant challenge.

  8. SpineTool 2.0: A Deep Learning Platform with an Interactive User Interface for Dendritic Spine Segmentation and Morphological Analysis

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-21 Disease mechanisms

    Quantitative analysis of dendritic spine morphology is central to understanding synaptic function and its disruption in neurological disease, yet reliable measurement depends on accurate segmentation of spines from fluorescence microscopy images — a task still limited by faint or discontinuous spine necks and by tools that require extensive manual correction.

  9. Integrating Multisource Target Evidence with Machine-Learning Potency Prediction for Alzheimer’s Disease Drug Discovery

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-10-20 Digital health tools

    Alzheimer’s disease (AD) target evidence is distributed across biological, chemical, structural, regulatory, and trial-registry sources, complicating reproducible target evaluation and modeling.

  10. Interpretable Phenotype Discovery Using Temporal Risk-Aware Clustering

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-10-20 Long-term cohort studies

    Longitudinal clinical cohorts contain heterogeneous progression patterns that are obscured by coarse diagnostic labels.

  11. Predictive EEG Signatures of Alzheimer's Disease: A Cross-Validated, Cross-Method Analysis Using ANCOVA Filtering and Sparse Logistic Regression

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-20 Brain scans and imaging

    Alzheimer’s Disease (AD) changes how the brain’s electrical activity behaves, and these changes can, in principle, be picked up directly from a resting-state EEG recording without any invasive testing.

  12. Understanding Performance Plateaus and Representation Collapse in 3D MRI Transformers Trained to Classify Brain Disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-10-16 Digital health tools

    To distinguish these cases, we trained three types of supervised 3D transformers (ViT3D, MINiT, NIT) and, as a non-collapsing alternative, a frozen self-supervised encoder (DINO), to classify people with dementia or mild cognitive impairment (MCI) versus cognitively normal (CN) individuals from 3D brain MRI, using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

  13. Neuro-JEPA: Building and Evaluating Health-System Scale Neuroimaging Foundation Models with Mixture of Experts

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-15 Mild cognitive impairment

    Magnetic resonance imaging (MRI) of the brain routinely combines complementary contrasts—including T1-weighted (T1w), T2-weighted (T2w) and FLAIR sequences—to capture anatomy and pathology.

  14. PaliGemX: An Interpretable Multimodal Transformer for Early Alzheimer’s Detection

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-14 Digital health tools

    Alzheimer disease (AD) is a disorder of the nervous system that mostly impacts the brain cells and causes the slow destruction of the brain.

  15. Information-Theoretic Geometry HCFIM features for data-limited EEG screening of Alzheimer’s disease and Frontotemporal Dementia

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-13 Brain scans and imaging

    Resting-state EEG is a scalable modality for neurophysiological screening, but conventional spectral descriptors capture only part of the signal organization altered in neurodegenerative disease.

  16. Interpretable Frequency-Aware Deep Learning for Multi-Class EEG Classification of Neurological Disorders

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Preprints.org 2026-10-12 Brain scans and imaging

    Electroencephalography (EEG) records cortical activity with high temporal resolution without radiation and at low cost, which makes it a practical signal for automated screening of neurological disorders.

  17. Continuous amyloid staging complements global amyloid burden in early Alzheimer’s disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-10-11 Brain scans and imaging

    A continuous amyloid staging score complements global amyloid burden for early AD staging and cognitive risk stratification.

  18. EEG Pathology Detection via Clinical Concept Bottleneck Models

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-10-11 Digital health tools

    A bstract Deep learning models for electroencephalography (EEG) pathology detection achieve competitive accuracy but offer limited interpretability.

  19. Identifying Individualized Multiple Chronic Condition Pathways Associated with Dementia

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Preprints.org 2026-10-10 Digital health tools

    Unlike traditional eager decision trees (eagerDT), which lacked sufficient training data for 17%-33% of patient profiles, lazyDT-MCCPath successfully modeled all profiles and achieved a 0.17 improvement in AUROC (0.66 vs. 0.83). lazyDT-MCCPath effectively translates pre-existing MCC profiles into personalized dementia risk probabilities for emerging conditions, which may help clinicians prioritize MCCs to monitor more closely.

  20. A Machine Learning-Based Framework for differential screening of Neurodegenerative Dementias using routine clinical data

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-07 Getting a diagnosis

    Symptom chronology emerged as a robust diagnostic discriminator, underscoring the clinical value of temporal disease trajectories.

  21. Forecasting Alzheimer’s disease progression using non-imaging clinical features and XGBoost

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-10-07 Digital health tools

    Machine learning (ML) methods have shown promise in modeling clinical trajectories associated with neurodegenerative disorders such as Alzheimer’s disease (AD).

  22. Generalizing Amyloid β Pathology from Postmortem Human Brains with Vision Foundation Models for Lesion Quantification

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-10-07 Vascular contributions

    The ViTamyloid classifier achieved strong feature-separation metrics, yielding area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) values of 0.994/0.958 for CCP, 0.989/0.945 for CGP, 0.992/0.977 for CAA Type 1, and 0.997/0.978 for CAA Type 2.

  23. Explainable machine learning identifies predictors for postural instability and cognitive impairment in early Parkinson’s Disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-10-05 Mild cognitive impairment

    Parkinson’s disease (PD) exhibits considerable variability in symptom onset, severity, and clinical trajectories, making prediction of individual disease progression a complex challenge.

  24. Imputation-Based Harmonization Mitigates Site Effects Without Data Leakage in Machine Learning Studies

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-10-05 Digital health tools

    Neuroimaging studies that pool data across clinical sites often suffer from site effects — variability in imaging measures that arises from technical heterogeneity across sites as opposed to true biological signal.

  25. An explainable multimodal framework for exploring depression-associated speech representations in Alzheimer’s disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-10-01 Risk factors

    By comparing these unsupervised representations to a depression-labelled source corpus, one cluster demonstrated mathematical proximity (measured via cosine similarity in a shared latent space) to the source depression centroid.

  26. Evaluating Patient Representation Strategies for Early Prediction of Alzheimer’s Disease and Related Dementias from Longitudinal EHRs

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-10-01 Mild cognitive impairment

    The neurodegenerative processes underlying Alzheimer’s disease and related dementias (ADRD) begin years before clinical diagnosis, motivating scalable approaches for earlier identification.

  27. Examining the Clinical Utility of Deep Learning Models for Perivascular Space Segmentation in Alzheimer’s Disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-09-28 Digital health tools

    Open-source deep learning models promise scalable quantification of MRI-visible perivascular spaces (PVS), an emerging marker of glymphatic dysfunction in Alzheimer’s disease (AD).

  28. AT(N)-Anchored Multimodal Deep Learning for Alzheimer’s Staging: A Taxonomy and Continuous Burden Index for Low-Resource Settings

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Preprints.org 2026-09-25 Tau biomarkers

    The global burden of dementia is heavily concentrated in low- and middle-income countries (LMICs), yet biological diagnosis of Alzheimer’s Disease (AD) relies on positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis that are operationally inaccessible in these regions.

  29. Evaluating biomarkers and prediction models: predictive utility analysis with E2P Simulator

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. PsyArXiv 2026-09-23 Risk factors

    Precision psychiatry aims to identify biomarkers and develop prediction models to improve diagnosis, prognosis, and treatment selection.

  30. Artificial Intelligence for Alzheimer’s Disease Detection Across Neuroimaging and Speech: A Reproducible Cross-Modal Secondary Analysis

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-09-22 Digital health tools

    Artificial-intelligence (AI) systems for Alzheimer’s disease (AD) increasingly use neuroimaging or spontaneous speech, but reported performance is difficult to interpret across studies because cohorts, endpoints, languages, preprocessing, and validation protocols differ.

  31. Diagnostic Labels and Measurement Timing Drive Systematic Inconsistency in ADNI Neuroimaging Data

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-09-22 Digital health tools

    The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is widely used to train machine learning models for Alzheimer’s disease, yet whether it provides consistent ground truth for predictive modeling has not been systematically tested.

  32. Machine learning on subcortical brain features: A study of sample size efficiency for neurodegenerative disease classification

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-09-21 Digital health tools

    Subcortical brain alterations are a key feature of dementia disease progression.

  33. A Multimodal Mathematical and Computational Framework for Personalized Medication-Adherence and Cognitive-Risk Modeling in Alzheimer's Disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-09-20 Digital health tools

    Digital medication-management tools for Alzheimer's disease (AD) typically record only whether a dose was taken, leaving the temporal and behavioral structure of adherence largely unused as a predictive signal.

  34. MedBackdoorGuard: Detecting and Mitigating Backdoor Attacks in Deep Learning Models for MRI-Based Alzheimer's Disease and Mild Cognitive Impairment Diagnosis

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Preprints.org 2026-09-20 Digital health tools

    Deep learning classifiers trained on structural magnetic resonance imaging (MRI) are increasingly proposed for early detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), yet the pipelines that produce these models depend on shared datasets, pre-trained weights, and federated updates that an adversary can poison.

  35. Hippocampal surface analysis using discrete Ricci flow and Riemannian metrics for the diagnosis of Alzheimer's disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-09-18 Getting a diagnosis

    Alzheimer’s disease (AD) is a neurodegenerative disorder that is characterized by structural changes of the hippocampus, thus hippocampal morphology becomes an important biomarker for early diagnosis.

  36. Divergent amyloid trajectories distinguish normal aging from Alzheimer's disease progression

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-09-14 Brain scans and imaging

    Aging is the greatest risk factor for Alzheimer's disease (AD), yet how and when AD-related pathological progression diverges from aging remains poorly understood.

  37. Gender bias in the diagnosis of Alzheimer's Disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-09-11 Mild cognitive impairment

    Fairness in clinical machine-learning (ML) remains a critical challenge, particularly in neurodegenerative disease where demographic factors may confound disease-related patterns.

  38. nterpretable Alzheimer’s Disease Detection from CHAT Transcripts Using Criterion-Guided LLM Prompting and In-Context Learning

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-09-10 Digital health tools

    Early detection of Alzheimer’s disease (AD) from spontaneous speech remains a clinically significant challenge.

  39. How Reliable and Explainable Are Machine Learning Models for Dementia Detection? A Leakage-Aware Evaluation

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-09-09 Cognitive tests and assessment

    Reported dementia-classification performance depends on how the label is defined, which clinical information the model sees, and whether participants are separated between training and test data.

  40. A 2-Level Stacking Ensemble Framework for Multi-Class Alzheimer’s Disease Diagnosis Using CNN Feature Extractors and SVM-XGBoost Classifiers

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-09-07 Long-term cohort studies

    Alzheimer's disease classification by means of Magnetic Resonance Imaging demands strong models that are able to generalise to different stages of dementia.

  41. Unlocking Sensitive Data with SPHERE in the Age of AI

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-09-05 Long-term cohort studies

    Sensitive human data underpin discoveries across medicine, biology and the social sciences, yet privacy regulation often prevents sharing them with collaborators or artificial intelligence (AI) systems.

  42. Alzheimer's Disease Detection via Large Pre-Trained Speech and Language Models: Taxonomy, Progress, and Open Challenges

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. PsyArXiv 2026-09-03 Digital health tools

    Alzheimer's Disease (AD) detection from spoken language has advanced considerably with the rise of large pre-trained speech and language models, including wav2vec 2.0, BERT, generative LLMs, and emerging audio-LLMs.

  43. AI on Alzheimer Disease: Large-Scale Reproduction Shows the Field’s Hypotheses Cannot Be Distinguished on Common Data

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-08-27 End-of-life care

    After more than three decades of intensive research, Alzheimer disease care remains largely palliative, and the field has drawn sustained criticism, both for that reason and for pursuing some directions of inquiry over others.

  44. Intelligence Breaks the System It Controls

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-08-27 Long-term cohort studies

    Using two large-scale longitudinal datasets covering more than 75,000 patients or stays and 7.8 million clinical observations—spanning emergency vasopressor titration in sepsis (MIMIC- IV; 6,243 ICU admissions drawn from 73,141 encounters) and serial cognitive assessment in Alzheimer’s disease (ADNI; 2,262 longitudinal participants)—we train a 13-family computa- tional model zoo and perform a novel experiment: auditing whether clinician and AI treatment decisions jointly or individually match the subsequent patient trajectory, and testing whether cooperation between the two actors resolves their disagreements.

  45. Biomarker fidelity score: A quantitative framework for individual-level validation of explainability methods in 3D Alzheimer’s disease MRI classification

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. bioRxiv 2026-08-20 Long-term cohort studies

    Explainability methods applied to deep learning models for Alzheimer’s disease neuroimaging produce attribution maps that vary substantially across methods and architectures, yet no validated quantitative framework exists for determining which method most faithfully localises attribution signal within established AD neuroimaging biomarker anatomy at the individual subject level.

  46. Multimodal Transformer-Based Fusion of Blood-Based and Digital Biomarkers for Explainable (XAI) Ultra-Early Alzheimer's Disease Detection

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Research Square 2026-08-19 Brain scans and imaging

    Alzheimer’s disease (AD) pathology develops years in advance prior to its clinical presentation.

  47. A Deep Learning-Derived Insulin Resistance Index for Cardiovascular Risk Prediction: A Prospective Cohort Study with External Validation in Chinese and US Populations

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-08-12 How common it is

    The MoE-derived DNN-IR index demonstrated robust and stable performance in predicting atherosclerosis, incident CVD, cardiovascular mortality, and all-cause mortality in the general population.

  48. A Biologically Constrained Continuous-Time Framework for Long-Horizon Cognition Forecasting in Alzheimer’s Disease

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-08-10 Cognitive tests and assessment

    Accurate long-term forecasting of cognitive trajectories across the Alzheimer’s disease continuum is essential for early intervention, personalized prognosis, patient stratification, and clinical trial enrichment.

  49. Conditional Latent Diffusion for Synthetic Brain MRI in Alzheimer’s Disease: A Preprocessing-Focused Pipeline

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. Preprints.org 2026-08-07 Digital health tools

    Alzheimer’s disease (AD) detection from structural magnetic resonance imaging (MRI) using deep learning depends on large, labelled datasets.

  50. Predicting Cognitive Function Using Transformer-Derived Speech Representations and Longitudinal Coherence Features

    ⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv 2026-08-06 Cognitive tests and assessment

    Dementia affects more than 55 million people worldwide, and its progressive decline is difficult to track using infrequent in-person assessments, which can miss subtle changes between visits and add to clinician burden.

From across this tag

9 of the 205 studies in this tag, picked by a fixed rule (a hash of each study's address), not by score or date; each page of this list shows a different 9. The same 9 appear here every time.

All topic tags with their study counts