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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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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 heterogeneit...

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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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

BACKGROUNdSkull-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. Exis...

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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 understo...

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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OS03.6.A UNSUPERVISEd CLUSTERING OF MORPHOLOGY PATTERNS ON WHOLE SLIdE IMAGES GUIdE PROGNOSTIC STRATIFICATION OF GLIOBLASTOMA PATIENTS

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii15-ii15页

作者： Baheti, B Innani, S 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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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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Genetic variants underlying the neuro-anatomical signatures of hypertension measured on structural MRI

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Alzheimer's & dementia 2023年第S10期19卷

作者： Sindhuja Tirumalai Govindarajan Elizabeth Mamourian Yuhan Cui Junhao Wen Guray Erus Ahmed Abdulkadir Randa Melhem R Nick Bryan Haochang Shou david A. Wolk Ilya M. Nasrallah Christos davatzikos Centre for Biomedical Image Computing and Analytics 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 University of Pennsylvania Health System Philadeplphia PA USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA

Background Hypertension (HTN) is associated with gray matter (GM) atrophy and increased white matter hyperintensity (WMH) burden, increasing their susceptibility to Alzheimer’s disease and related dementias. We devel...

Background Hypertension (HTN) is associated with gray matter (GM) atrophy and increased white matter hyperintensity (WMH) burden, increasing their susceptibility to Alzheimer’s disease and related dementias. We developed a machine learning based model to quantify spatial patterns of abnormality recognizing HTN-related brain changes (SPARE-HTN) from structural magnetic resonance images (sMRI). We performed a genome wide association study (GWAS) to identify the genetic variants and associated biological traits that underlie the “expression” of HTN-related brain changes, i.e., the individualized SPARE-HTN index. Method SPARE-HTN model was trained on the large multi-study iSTAGING dataset, with regional GM volumes from T1-weighted images and lobar WMH volumes from T2-weighted FLaiR images used as imaging features. GWAS was carried out in a subset of N = 7490 (46% Female, Age = 65.5±7 years) hypertensive participants from UK-Biobank. A GWAS of SPARE-HTN scores was run using linear regression with Plink, correcting for the top 40 genetic principal components, age, sex, and intracranial volume. Significant SNPs were identified and mapped to functional genes using the Functional Mapping and Annotation (FUMA) platform; and candidate SNPs were mapped to the GWAS catalog. Result SPARE-HTN was associated with regional atrophy most prominent in perisylvian and temporal cortex and increased WMH volumes, particularly in frontal white matter. SPARE-HTN scores showed better discriminative power between normal and stage 1 participants (Cohen’s-d effect size, d = 0.2) and stage 2 (d = 0.71) hypertensive participants when compared to total WMH volume (d = 0.04 and 0.38, respectively). GWAS identified N = 321 independently significant SNPs, of which a subset N = 46 candidate SNPs were mapped to 6 genes (ICA1L, CARF, WdR12, NBEAL1, KRT8P15, CYP20A1) in Chromosome 2 (Figure 2). These genes were previously reported to be associated with several cardiovascular conditions including coronary

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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 1-14页

作者： aisen, Paul Rafii, Michael S. Bai, Wenjia Walker, Keenan A. Ferrucci, Luigi Tian, Ye Ella Zalesky, Andrew Anagnostakis, Filippos Ko, Sarah Saadatinia, Mehrshad Wang, Jingyue davatzikos, Christos Wen, Junhao Laboratory of AI and Biomedical Science (LABS) Columbia University New York NY United States 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 United States Department of Radiology Columbia University New York NY United States New York Genome Center (NYGC) New York NY United States Department of Biomedical Engineering (BME) Columbia University New York NY United States Data Science Institute (DSI) Columbia University New York NY United States Zuckerman Institute Columbia University New York NY United States Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID) Department of Radiology Columbia University New York NY United States Systems Lab Department of Psychiatry Melbourne Medical School The University of Melbourne Melbourne VIC Australia National Institute on Aging National Institutes of Health Baltimore MD United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health Baltimore MD United States Department of Brain Sciences and Department of Computing Imperial College London London United Kingdom Alzheimer’s Therapeutic Research Institute Keck School of Medicine of the University of Southern California San Diego 92121 CA United States

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-speci...

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A multivariate MRI model of Alzheimer’s disease risk is associated with clinical diagnosis, PET imaging, and plasma biomarkers in a mixed dementia sample

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Alzheimer's & dementia 2023年第S10期19卷

作者： Jeffrey S Phillips Sindhuja Tirumalai Govindarajan Gyujoon Hwang Guray Erus Katheryn A Q Cousins Sandhitsu R. das david A. Wolk david J. Irwin Murray Grossman Ilya M. Nasrallah Christos davatzikos Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics 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 Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Frontotemporal Degeneration Center Department of Neurology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA

Background We sought to validate two structural MRI-based brain health models: Spatial Pattern of Atrophy for REcognition of Alzheimer’s disease (SPARE-Ad) and SPARE-Brain Age Gap (SPARE-BAG), which estimates the dis...

Background We sought to validate two structural MRI-based brain health models: Spatial Pattern of Atrophy for REcognition of Alzheimer’s disease (SPARE-Ad) and SPARE-Brain Age Gap (SPARE-BAG), which estimates the discrepancy between biological brain age and chronological age. These models' generalization to non-amnestic Ad (naAd) syndromes and associations with positron emission tomography (PET) and plasma biomarkers remain underinvestigated. We hypothesized that SPARE-Ad scores in naAd would be lower (less abnormal) than in amnestic Ad (aAd) but elevated (more abnormal) relative to controls; and that SPARE-Ad but not SPARE-BAG scores would be associated with PET and plasma biomarkers of Ad. Method SPARE-Ad and SPARE-BAG scores were estimated for each of 1607 MRI scans from 277 cognitively normal adults (62.8% female; mean age = 69.5 years) and 902 clinical participants (54.2% female; mean age = 73.1 years), including 585 with amnestic mild cognitive impairment (aMCI) or aAd; 56 with naAd syndromes; 28 with vascular disease; 28 with Lewy body disorders (LBd); 18 with suspected frontotemporal lobar degeneration (FTLd); 56 with non-amnestic MCI of uncertain etiology; and 131 with other clinical diagnoses. We used mixed effects models adjusting for age, sex, and global cognition to test group differences in SPARE-Ad and SPARE-BAG. Additionally, we assessed associations with tau (n = 122) and amyloid-beta (n = 199) PET imaging and plasma-based phosphorylated tau-181 (p-tau181) and glial fibrillary acidic protein (GFAP) (n = 358). Result SPARE-Ad scores were elevated vs. controls in aMCI/aAd, naAd, vascular disease, FTLd, and naMCI (all p<0.003) but not in LBd or other diagnoses (p>0.1). NaAd patients had lower SPARE-Ad (p<0.02) but higher SPARE-BAG scores (p<0.02) than aMCI/aAd patients. SPARE-Ad scores were positively associated with mean cortical tau PET uptake, amyloid PET positivity, and plasma p-tau181 and GFAP (all p<0.001). In contrast, SPARE-BAG scores were not

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