100 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of PennsylvaniaUS2 papers
- Columbia UniversityUS1 paper
- Columbia University Irving Medical CenterUS1 paper
- Erasmus MCNL1 paper
- InsermFR1 paper
- Islamic University of IndonesiaID1 paper
- Lund UniversitySE1 paper
- Netherlands Institute for Health Services ResearchNL1 paper
- Penn Center for AIDS ResearchUS1 paper
- Radboud University Medical CenterNL1 paper
- Radboud University NijmegenNL1 paper
- Seoul National UniversityKR1 paper
48 papers
Cross-scale spatially-aware generative modeling of transcriptomic programs underlying neurodegenerative brain organization
Krishnakumar Vaithianathan
Neurodegenerative disorders such as Alzheimer's disease exhibit highly organized patterns of regional brain vulnerability, yet the biological mechanisms underlying this spatial sel…
Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast
Mehmet Yigit Avci, Akshit Achara, Andrew King +1
Demographic attributes can be predicted from medical images, raising concerns about bias in clinical AI systems. In X-ray imaging, acquisition characteristics have been shown to co…
Improving operating characteristics of clinical trials by augmenting control arm using propensity score-weighted borrowing-by-parts power prior
Apu Chandra Das, Sakib Salam, Aninda Roy +3
Borrowing external data can improve estimation efficiency but may introduce bias when populations differ in covariate distributions or outcome variability. A proper balance needs t…
MethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection
Gang Qu, Guanghao Li, Zhongming Zhao
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive decline and widespread epigenetic dysregulation in the brain. DNA met…
R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression
Kun Zhao, Siyuan Dai, Yingying Zhang +9
Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility, yet existing mul…
Alzheimer's Clinical Research Data via R Packages: the alzverse
Michael C. Donohue, Kedir Hussen, Oliver Langford +3
Sharing clinical research data is essential for advancing research in Alzheimer's disease (AD) and other therapeutic areas. However, challenges in data accessibility, standardizati…
TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
Yuxin Gong, Se-in Jang, Wei Shao +2
Accurate quantification of tau pathology via tau positron emission tomography (PET) scan is crucial for diagnosing and monitoring Alzheimer's disease (AD). However, the high cost a…
LV-Net: Anatomy-aware lateral ventricle shape modeling with a case study on Alzheimer's disease
Wonjung Park, Suhyun Ahn, Jinah Park
Lateral ventricle (LV) shape analysis holds promise as a biomarker for neurological diseases; however, challenges remain due to substantial shape variability across individuals and…
LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data
Ali Zolnour, Hossein Azadmaleki, Yasaman Haghbin +13
Alzheimer's disease and related dementias(ADRD) affect nearly five million older adults in the United States, yet more than half remain undiagnosed. Speech-based natural language p…
AdapDISCOM: An Adaptive Sparse Regression Method for High-Dimensional Multimodal Data With Block-Wise Missingness and Measurement Errors
Maimouna Baldé, Abdoul O. Diakité, Claudia Moreau +6
Multimodal high-dimensional data are increasingly prevalent in biomedical research, yet they are often compromised by block-wise missingness and measurement errors, posing signific…
Learning to segment anatomy and lesions from disparately labeled sources in brain MRI
Meva Himmetoglu, Ilja Ciernik, Ender Konukoglu
Segmenting healthy tissue structures alongside lesions in brain Magnetic Resonance Images (MRI) remains a challenge for today's algorithms due to lesion-caused disruption of the an…
Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks
Oktay Agcaoglu, Rogers F. Silva, Deniz Alacam +3
Different brain imaging modalities offer unique insights into brain function and structure. Combining them enhances our understanding of neural mechanisms. Prior multimodal studies…
Flexi-Fuzz least squares SVM for Alzheimer's diagnosis: Tackling noise, outliers, and class imbalance
Mushir Akhtar, A. Quadir, M. Tanveer +1
Alzheimer's disease (AD) is a leading neurodegenerative condition and the primary cause of dementia, characterized by progressive cognitive decline and memory loss. Its progression…
MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysis
Wenqi Zhu, Yinghua Fu, Ze Wang
Alzheimer's Disease (AD) is a currently incurable neurodegeneartive disease. Accurately detecting AD, especially in the early stage, represents a high research priority. AD is char…
Adaptive Critical Subgraph Mining for Cognitive Impairment Conversion Prediction with T1-MRI-based Brain Network
Yilin Leng, Wenju Cui, Bai Chen +3
Prediction the conversion to early-stage dementia is critical for mitigating its progression but remains challenging due to subtle cognitive impairments and structural brain change…
Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models
Sami Hamdan, Shammi More, Leonard Sasse +3
The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In n…
Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI
Mengjin Dong, Long Xie, Sandhitsu R. Das +5
Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical…
Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling
Selena Wang, Yiting Wang, Frederick H. Xu +2
Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical…
A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry
Jianfeng Wu, Yi Su, Yanxi Chen +7
Background: Alzheimer's Disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed tha…
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models
Umang Gupta, Tamoghna Chattopadhyay, Nikhil Dhinagar +3
Transfer learning has remarkably improved computer vision. These advances also promise improvements in neuroimaging, where training set sizes are often small. However, various diff…
HIBMatch: Hypergraph Information Bottleneck for Semi-supervised Alzheimer's Progression
Zhongying Deng, Shujun Wang, Angelica I Aviles-Rivero +2
Alzheimer's disease progression prediction is critical for patients with early Mild Cognitive Impairment (MCI) to enable timely intervention and improve their quality of life. Whil…
DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification
Lemuel Puglisi, Frederik Barkhof, Daniel C. Alexander +3
Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within the…
Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers
Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev +2
Healthcare datasets often contain groups of highly correlated features, such as features from the same biological system. When feature selection is applied to these datasets to ide…
Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery
Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev +2
Healthcare datasets present many challenges to both machine learning and statistics as their data are typically heterogeneous, censored, high-dimensional and have missing informati…