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Neuroanatomical Heterogeneity and Similarities Across Nine ai dimensions in Four Brain disorders in the General Population

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Biological Psychiatry 2025年第9期97卷 S40-S40页

作者： Filippos Anagnostakis Christos davatzikos Junhao Wen Laboratory of AI and Biomedical Science (LABS) University of Southern California Los Angeles Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for AI and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Columbia University

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Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential

arXiv

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arXiv 2025年

作者： Gao, Chenyu Xu, Kaiwen Kim, Michael E. Zuo, Lianrui Li, Zhiyuan Archer, derek B. Hohman, Timothy J. Moore, Ann Zenobia Ferrucci, Luigi Beason-Held, Lori L. Resnick, Susan M. davatzikos, Christos Prince, Jerry L. Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center NashvilleTN United States Department of Neurology Vanderbilt University Medical Center NashvilleTN United States Translational Gerontology Branch National Institute on Aging National Institutes of Health BaltimoreMD United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD United States AI2D Center for AI and Data Science University of Pennsylvania Philadelphia United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States

defacing is often applied to head magnetic resonance image (MRI) datasets prior to public release to address privacy concerns. The alteration of facial and nearby voxels has provoked discussions about the true capability of these techniques to ensure privacy as well as their impact on downstream tasks. With advancements in deep generative models, the extent to which defacing can protect privacy is uncertain. Additionally, while the altered voxels are known to contain valuable anatomical information, their potential to support research beyond the anatomical regions directly affected by defacing remains uncertain. To evaluate these considerations, we develop a refacing pipeline that recovers faces in defaced head MRIs using cascaded diffusion probabilistic models (dPMs). The dPMs are trained on images from 180 subjects and tested on images from 484 unseen subjects, 469 of whom are from a different dataset. To assess whether the altered voxels in defacing contain universally useful information, we also predict computed tomography (CT)-derived skeletal muscle radiodensity from facial voxels in both defaced and original MRIs. The results show that dPMs can generate high-fidelity faces that resemble the original faces from defaced images, with surface distances to the original faces significantly smaller than those of a population average face (p -4). For shin muscle, the correlation is statistically significant (p 0.05) when any defacing method is applied, suggesting that defacing might not only fail to protect privacy but also eliminate valuable information. We advocate two solutions for data sharing that comply with privacy: 1) share skull-stripped images along with measurements of facial and cranial features extracted before skull-stripping for public access, while acknowledging that this approach inherently compromises many research potentials;or 2) share the unaltered images with privacy enforced through policy restrictions. © 2025, CC BY-NC-Nd.

关键词： Magnetic resonance imaging

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Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk

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Nature communications 2025年第1期16卷 4871页

作者： Filippos Anagnostakis Sarah Ko Mehrshad Saadatinia Jingyue Wang Christos davatzikos Junhao Wen Laboratory of AI and Biomedical Science (LABS) Columbia University New York NY USA. Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for AI and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Laboratory of AI and Biomedical Science (LABS) Columbia University New York NY USA. junhao.wen89@***. Department of Radiology Columbia University New York NY USA. junhao.wen89@***. New York Genome Center (NYGC) New York NY USA. junhao.wen89@***. Department of Biomedical Engineering (BME) Columbia University New York NY USA. junhao.wen89@***. Data Science Institute (DSI) Columbia University New York NY USA. junhao.wen89@***. Zuckerman Institute Columbia University New York NY USA. junhao.wen89@***. Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID) Department of Radiology Columbia University New York NY USA. junhao.wen89@***.

Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.252). Crucially, including composite metabolites (e.g. sums or ratios of raw metabolites) results in poor generalizability to independent test data due to multicollinearity. Genome-wide associations identify 405 MetBAG-locus pairs (P < 5 × 10/5). Using SBayesS, we estimate the SNP-based heritability (0.09< < 0.18), negative selection signatures (-0.93 < S < -0.76), and polygenicity (0.001d Mendelian randomization analyses reveal potential causal links between the 5 MetBAGs and cardiometabolic conditions (e.g., metabolic disorders and hypertension). Integrating multi-organ and multi-omics features improves disease category and mortality predictions. The 5 MetBAGs extend existing biological aging clocks to study human aging and disease across multiple biological scales. All results are publicly available at https://***/medicine/ .

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Machine Learning in Chemoinformatics and Medicinal Chemistry

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Biomedical data science 1000年第1期5卷 43-65页

作者： Raquel Rodríguez-Pérez Filip Miljković Jürgen Bajorath 2Current affiliation: Novartis Institutes for Biomedical Research Novartis Campus Basel Switzerland 1Department of Life Science Informatics B-IT (Bonn-Aachen International Center for Information Technology) Chemical Biology and Medicinal Chemistry Program Unit LIMES (Life and Medical Sciences Institute) Rheinische Friedrich-Wilhelms-Universität Bonn Germany email: bajorath@bit.uni-bonn.de 3Current affiliation: Data Science and AI Imaging and Data Analytics Clinical Pharmacology and Safety Sciences R&D AstraZeneca Gothenburg Sweden

In chemoinformatics and medicinal chemistry, machine learning has evolved into an important approach. In recent years, increasing computational resources and new deep learning algorithms have put machine learning onto a new level, addressing previously unmet challenges in pharmaceutical research. In silico approaches for compound activity predictions, de novo design, and reaction modeling have been further advanced by new algorithmic developments and the emergence of big data in the field. Herein, novel applications of machine learning and deep learning in chemoinformatics and medicinal chemistry are reviewed. Opportunities and challenges for new methods and applications are discussed, placing emphasis on proper baseline comparisons, robust validation methodologies, and new applicability domains.

关键词： machine learning deep learning chemoinformatics medicinal chemistry data structures learning strategies

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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

arXiv

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arXiv 2024年

作者： Gao, Chenyu Kim, Michael E. Ramadass, Karthik Kanakaraj, Praitayini Krishnan, Aravind R. Saunders, Adam M. Newlin, Nancy R. Lee, Ho Hin Yang, Qi Taylor, Warren d. Boyd, Brian d. Beason-Held, Lori L. Resnick, Susan M. Barnes, Lisa L. Bennett, david A. Van Schaik, Katherine d. Archer, derek B. Hohman, Timothy J. Jefferson, Angela L. Išgum, Ivana Moyer, daniel Huo, Yuankai Schilling, Kurt G. Zuo, Lianrui Bao, Shunxing Khairi, Nazirah Mohd Li, Zhiyuan davatzikos, Christos Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37240 United States Department of Computer Science Vanderbilt University NashvilleTN37240 United States Vanderbilt Center for Cognitive Medicine Department of Psychiatry and Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Geriatric Research Education and Clinical Center Veterans Affairs Tennessee Valley Health System NashvilleTN United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD United States Rush Alzheimer’s Disease Center Rush University Medical Center ChicagoIL United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center NashvilleTN37240 United States Department of Neurology Vanderbilt University Medical Center NashvilleTN37240 United States Department of Medicine Vanderbilt University Medical Center NashvilleTN37240 United States Department of Biomedical Engineering and Physics Department of Radiology and Nuclear Medicine Amsterdam University Medical Center University of Amsterdam Amsterdam Netherlands AI2D Center for AI and Data Science University of Pennsylvania PhiladelphiaPA19104 United States

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies to slow disease progression and onset. diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and microstructural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that mitigates the model’s use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information mitigated, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two recent, popular, openly available T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI) (p-value = 0.023), but younger in participants already diagnosed with Alzheimer’s disease (Ad) (p-value © 2024, CC BY-NC-Nd.

关键词： Magnetic resonance imaging

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IMG-09. A dEEP LEARNING-BASEd APPROACH FOR BRaiN TISSUE EXTRACTION USING MULTI- ANd SINGLE-PARAMETRIC MRI IN PEdIATRICS

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Neuro-Oncology 2024年第SUPPLEMENT_4期26卷 0–0页

作者： deep B Gandhi Anurag Gottipati Wenxin Tu Ariana Familiar Shuvanjan Haldar Neda Khalili Paarth Jain Karthik Viswanathan Phillip B Storm Adam C Resnick Jeffrey B Ware Arastoo Vossough Ali Nabavizadeh Anahita Fathi Kazerooni Center for Data-Driven Discovery in Biomedicine (D3b) The Children’s Hospital of Philadelphia Philadelphia PA USA Department of Neurosurgery Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Neurosurgery The Children’s Hospital of Philadelphia Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Division of Radiology Children’s Hospital of Philadelphia Philadelphia PA USA AI2D Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania Philadelphia PA USA

BACKGROUNd Skull-stripping, the process of extracting brain tissue from MR images, is an important step for tumor segmentation and downstream imaging-based analytics such as ai-powered radiomic feature extraction. Existing skull-stripping models, designed for pediatric or adult patients, show limitations in accurately segmenting tumors in sellar/suprasellar regions. This limitation hinders their reliable application across different histologies of pediatric brain tumors. We propose a deep learning approach for fully automated skull-stripping, compatible with both single- or multi-parametric MRI sequences.

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NIMG-65. SYNTHETIC MRI GENERATION TO FACILITATE AUTOSEGMENTATION OF PEdIATRIC BRaiN TUMORS IN LIMITEd data SCENARIOS

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Neuro-Oncology 2024年第SUPPLEMENT_8期26卷 viii210–viii210页

作者： Chrysochoou, dimosthenis Gandhi, deep Familiar, Ariana Vossough, Arastoo Storm, Phillip Resnick, Adam davatzikos, Christos Nabavizadeh, Ali Kazerooni, Anahita Fathi Department of Bioengineering University of Pennsylvania Philadelphia USA Center for Data-Driven Discovery in Biomedicine (D3b) The Children’s Hospital of Philadelphia Philadelphia USA Division of Radiology Children’s Hospital of Philadelphia Philadelphia USA Department of Neurosurgery Perelman School of Medicine University of Pennsylvania Philadelphia USA Department of Neurosurgery The Children’s Hospital of Philadelphia Philadelphia USA AI2D Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania Philadelphia USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia USA

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deep Learning patterns of Aβ accumulation in Alzheimer’s disease

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Alzheimer's & dementia 2023年第S17期19卷

作者： dhivya Srinivasan Ilya M. Nasrallah Zhijian Yang Murat Bilgel Junhao Wen Guray Erus Yuhan Cui Elizabeth Mamourian Aristeidis Sotiras Tobey J Betthauser Susan M. Resnick duygu Tosun Marilyn S. Albert Christos davatzikos for the Alzheimer’s disease Neuroimaging Initiative,the iSTAGING consortium, & the Preclinical Ad consortium Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadephia PA USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore USA Baltimore MD USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory of AI and Biomedical Science Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Marina del Rey CA USA 4Department of Radiology Washington University in St. Louis St. Louis MO USA Department of Medicine University of Wisconsin-Madison Madison WI USA Wisconsin Alzheimer’s Disease Research Center University of Wisconsin-Madison School of Medicine and Public Health Madison WI USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco CA USA Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA

Background Accumulation of amyloid (Aß) plaques is an early pathologic change of Alzheimer’s disease (Ad). However, the mechanisms and pathways by which amyloid spreads across the cerebrum are not fully understood. deriving distinct dimensions of amyloid deposition and their associations with biomedical factors could be useful to understand how amyloid propagates. In this analysis, we identified two distinct subtypes of progression based on spatiotemporal variations using a recently developed data-driven, deep learning clustering method called Surreal-GAN 1 . Method We used data from 482 A+ and 801 A- subjects with 18 F-florbetapir PET from the Alzheimer’s disease Neuroimaging Initiative (AdNI; N = 832) and 11 C-PiB from the Preclinical Ad consortium (PAC; N = 451). Standardized uptake value ratio (SUVR) maps (cerebellar GM reference) were transformed to reference space. We derived amyloid status using Gaussian mixture modelling of mean total cortical SUVR for each of 4 sites. We then applied non-negative matrix factorization 2 on the SUVR maps to identify data-driven common patterns of deposition across the sample. Next, we applied Surreal-GAN on a total of 1283 subjects to determine the amyloid spatiotemporal subtypes. Correlations with clinical and cognitive variables were evaluated. Results Participants were 30%/22% female and had mean age 74.9 (±7.4)/ 67.9(±9.1) years for AdNI and PAC, respectively. We identified 24 components from NMF (Fig 1A) after excluding 6 as non-target features. Surreal-GAN showed optimal agreement indices for two patterns. While both patterns shared involvement of posterior cingulate/precuneus and inferior frontal cortex, pattern r1 shows relatively more occipital deposition and pattern r2 shows more frontal deposition (Fig 1B), as seen when comparing strongly r1 vs strongly r2 participants using voxel-based morphometry (Fig 2). Both were associated with worse global cognitive function and worsening neurodegeneration (Table 1). r1 corre

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P13.13.B INTERPRETABLE WHOLE SLIdE IMAGE PROGNOSTIC STRATIFICATION OF GLIOBLASTOMA PATIENTS FURTHERING dISEASE UNdERSTANdING

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii103–ii104页

作者： Baheti, B Innani, S Mehdiratta, G Nasrallah, M P Bakas, S Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Pathology and Laboratory Medicine Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States

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