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Cross-scale functional connectivity patterns of the aging brain learned from the multi-cohort iSTAGing study

Alzheimer's & dementia 2023年第S17期19卷

作者： Zhen Zhou Hongming Li Chau B Tran Yuncong Ma dhivya Srinivasan Ahmed Abdulkadir Junhao Wen Guray Erus Elizabeth Mamourian Ilya M. Nasrallah Nick Bryan david A. Wolk Lori L Beason-Held Susan M. Resnick Haochang Shou Christos davatzikos Yong Fan University of Pennsylvania philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics 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 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 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 Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Texas at Austin Austin TX USA Department of Pathology and Laboratory Medicine Alzheimer’s Disease Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA National Institute on Aging Baltimore MD USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA Correspondece

Background Brain functional connectivity (FC) measures derived from resting-state fMRI (rsfMRI) data have advanced our understanding of the brain organization. However, most existing studies investigate the brain functional networks (FNs) at a specific scale, ignoring potentially complementary FC information at different levels of spatial granularity as well as the cross-scale information. Given that the brain is a multi-scale, hierarchical system, FCs between FNs at different spatial scales may provide complementary information to those measured within-scales for characterizing the aging brain. Method We computed multiscale FNs to capture their FC measures across multiple scales and investigated the associations between cross-scale FCs and the brain aging based on rsfMRI data of a diverse cohort (n = 4186, aged 22 to 97 years) from seven sites of the iSTAGING study [1]. For each subject, we computed the FNs at seven scales, yielding seven sets of k (k = 17, 25, 50, 75, 100, 125 and 150) FNs, same as our previous study [2-3]. We estimated FC measures between the 542 FNs using Pearson’s correlation, yielding the cross-scale FC matrices that were harmonized across sites in the tangent space using ComBat-GAM [4] with sex and age as covariates. The harmonized FC measures were used as features to build an age prediction model using Ridge regression as illustrated in Figure 1. The brain age prediction model was compared with that built on harmonized FC measures between FNs of the same scales, with the performance estimated with a nested five-fold cross-validation. Result The cross-scale FC measures performed significantly better than the within-scale FC measures for the brain age prediction, as summarized in Table 1. Figure 2 shows the most informative FC measures for the brain age prediction. Specifically, FC measures computed both cross- and within-scales between the visual, somatomotor, and frontoparietal networks provided relatively more useful information for the bra

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discovering Alzheimer’s disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

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Alzheimer's & dementia 2023年第S17期19卷

作者： Zhijian Yang Junhao Wen Ahmed Abdulkadir Yuhan Cui Guray Erus Elizabeth Mamourian Randa Melhem dhivya Srinivasan Sindhuja Tirumalai Govindarajan Jiong Chen Mohamad Habes Colin L Masters Paul Maruff Jurgen Fripp Luigi Ferrucci Marilyn S. Albert Sterling C Johnson John C Morris Pamela LaMontagne daniel S. Marcus Tammie L.S. Benzinger david A. Wolk Li Shen Jingxuan Bao Susan M. Resnick duygu Tosun Haochang Shou Ilya M. Nasrallah Christos davatzikos the Alzheimer’s disease Neuroimaging Initiative, the Preclinical Ad Consortium, the Baltimore Longitudinal Study of Aging, and the iSTAGING study 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 Correspondece 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 Bern Bern Switzerland 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 Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of Texas Health San Antonio San Antonio TX USA Florey Institute of Neuroscience and Mental Health Parkville VIC Australia The Florey Institute of Neuroscience and Mental Health University of Melbourne Parkville VIC Australia CSIRO Health and Biosecurity Australian E-Health Research Centre Brisbane QLD Australia National Institute on Aging NIH Baltimore MD USA Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA Wisconsin Alzheimer’s Institute University of Wisconsin-Madison School of Medicine and Public Health Madison WI USA Knight Alzheimer Disease Research Center St. Louis MO USA Washington University School of Medicine Saint Louis MO USA Washington University School of Medicine St. Louis MO USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA Institute for Biomedical Informatics University of Pennsylvania ATHENS PA USA University of Pennsylvania Philadelphia PA USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA Department of Radiology and Biomedical Imaging University of Cal

Background The heterogeneity of neurodegenerative diseases, including Alzheimer’s disease (Ad), has hampered precision diagnosis and treatment. Machine learning methods enable the identification of genetically-explained disease subtypes with distinct brain phenotypes. Method Gene-SGAN is a novel, multi-view, weakly-supervised deep clustering method that jointly considers phenotypic and genetic data, thereby deriving subtypes of disease-related brain changes with genetic associations. We apply the method to derive subtypes related to Ad. Analyses used structural MRIs and whole-genome sequencing data from the Alzheimer’s disease Neuroimaging Initiative (1,533 participants, including controls and patients with Mild Cognitive Impairment (MCI) and Ad). Result Gene-SGAN identified four distinct subtypes within MCI/Ad: A1, normal anatomy; A2, focal medial temporal lobe (MTL) atrophy; A3, severe atrophy in neocortex and MTL; A4, severe cortical but relatively modest MTL atrophy (Figure 1). The four subtypes reveal significant differences in seven known Ad-related genetic variants (Figure 2). The identified subtype-associated SNPs not only resemble previous findings, but also suggest genetic protective effects contributing to the observed resilience (A1), potential inflammatory mechanisms underlying subtypes (A2&A3), and the effect of SNPs on atypical atrophy patterns of Ad (A4). Clinically, A1 and A3 participants showed the best and the worst cognitive performances (Figure 3). A3 participants had the most abnormal CSF Aβ (p<0.001 vs. A1&A4 and p = 0.039 vs. A2), and exhibited the highest white matter hyperintensity (WMH) volumes (p<0.001 vs. A1&A4 and p = 0.017 vs. A2). However, A2 participants showed significantly higher CSF phospho-tau than A3 participants (p = 0.019). A4 participants were the youngest group (p<0.001 vs. all other groups), suggesting the inclusion of early onset Ad participants, who have a higher prevalence of hippocampal-sparing variants such as posterio

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Novel genomic loci shape two neuroanatomical dimensions of Ad in non-demented populations

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Alzheimer's & dementia 2023年第S10期19卷

作者： Junhao Wen Zhijian Yang Ioanna Skampardoni Marilena de Pian Guray Erus Elizabeth Mamourian Yuhan Cui Bingxin Zhao Ilya M. Nasrallah Arthur W. Toga Haochang Shou Li Shen Christos davatzikos 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 Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of Pennsylvania philadelphia PA USA Upenn Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Laboratory of Neuro Imaging (LONI) University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA Greece

Background Artificial intelligence has been in burgeoning demand to be applied to magnetic imaging resonance (MRI) data to parse the neuroanatomical heterogeneity of Alzheimer’s disease (Ad) and/or mild cognitive impairment (MCI) (Wen et al., 2021a, 2021b; Yang et al., 2021). However, the genetic heterogeneity leading to this neuroanatomical heterogeneity, especially at the early asymptomatic stages, is not fully elucidated. Method A deep semi-supervised representation learning method, called Surreal-GAN (Yang et al., 2022), was applied to T1-weighted MRI from Ad/MCI patients from AdNI to define the two neuroanatomical dimensions of Ad at the symptomatic stages. The R1 dimension was characterized by widespread brain atrophy without an exclusive focus on the medial temporal lobe, whereas the R2 dimension showed focal medial temporal lobe atrophy (Wen et al., 2022). We then applied the trained model to 33,540 non-demented individuals from UK Biobank. Imputed genotyping data were analyzed in genome-wide association studies (GWAS). We performed multiple linear regressions controlling for confounding factors (e.g., age) via Plink (Purcell et al., 2007). We defined a genomic locus (represented by the top lead SNP) considering linkage disequilibrium. Finally, we manually queried all leading SNPs on GWAS Catalog (Buniello et al., 2019) to determine if the genomic loci were novel, provided they were not previously associated with any clinical traits. Result GWAS discovered 24 genomic loci, of which 7 were novel for R1 and R2, respectively (Fig. 1). For R1, 8 mapped genes, based on physical positions, eQTL, and chromatin interactions, were associated with Ad-related traits in the literature. For example, the KdM2B gene from the genomic locus (rs9795600, P-value = 1.13e-08) was previously associated with Ad (Gouveia et al., 2022). For R2, 13 mapped genes were associated with Ad-related traits. For example, the TESC gene from the genomic locus (rs77956314, P-value = 9.99e-29) w

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. d. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. daum M. de Bruijne A. depeursinge R. dorent J. Egger d. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner d. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia d. Zimmerer d. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira d. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi d. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany 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 USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCai 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Assessing the Global Impact of Brain Small Vessel disease on Cognition: The Multi-Ethnic Study of Atherosclerosis

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Alzheimer's and dementia 2025年第6期21卷 e70326页

作者： Charisis, Sokratis Rashid, Tanweer dintica, Christina Gonzales, Mitzi Liu, Hangfan Ware, Jeffrey B. Austin, Thomas R. Jensen, Paul N. Fohner, Alison E. Tanley, Jordan E. ding, Jingzhong Luchsinger, José A. Sachs, Bonnie Nasrallah, Ilya M. Bryan, R. Nick Hayden, Kathleen M. Wolk, david Rascovsky, Katya davatzikos, Christos Longstreth, William T. Yaffe, Kristine Seshadri, Sudha Heckbert, Susan R. Hughes, Timothy Habes, Mohamad Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases University of Texas Health Science Center San Antonio San Antonio TX United States Department of Neurology University of Texas Health Science Center at San Antonio San Antonio TX United States Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco CA United States Department of Neurology Cedars Sinai Medical Center Los Angeles CA United States Center for Advanced Imaging Research (CAIR) University of Maryland School of Medicine Baltimore MD United States Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Epidemiology University of Washington Seattle WA United States Cardiovascular Health Research Unit University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Department of Internal Medicine Wake Forest School of Medicine Winston-Salem NC United States Departments of Medicine and Epidemiology Columbia University Medical Center New York NY United States Department of Neurology Wake Forest University School of Medicine Winston-Salem NC United States AI2D Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States Department of Social Sciences and Health Policy Division of Public Health Sciences Wake Forest University School of Medicine Winston-Salem NC United States Alzheimer's Disease Research Center University of Pennsylvania Philadelphia PA United States Department of Neurology University of Pennsylvania Philadelphia PA United States Department of Neurology University of Washington Seattle WA United States

INTROdUCTION: We aimed to examine the global impact of brain small vessel disease (SVd) on cognitive performance. METHOdS: In 892 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we derived perivascular spaces (PVS), white matter hyperintensities (WMH), microbleeds (MB), and white matter fractional anisotropy (FA) and trace (TR). Cognitive function was assessed with a comprehensive neuropsychological battery. RESULTS: A composite SVd measure was constructed as a linear combination of basal ganglia PVS, thalamus PVS, periventricular WMH, subcortical WMH, and white matter FA and TR, and exhibited associations with worse global and domain-specific cognitive performance. Additionally, SVd mediated the effect of age and cardiovascular disease risk on global cognitive function, both directly and through smaller gray matter (GM) volume. dISCUSSION: Integrating multiple individual SVd endophenotypes may more accurately reflect the neurobiology of SVd and capture its global impact on cognition. SVd mediates the effects of age and cardiovascular disease risk on cognition through both atrophy-related and non–atrophy-related pathways. Highlights: Associations between individual magnetic resonance imaging (MRI) markers of brain small vessel disease and cognitive outcomes might not fully capture the global impact of small vessel disease on cognition. We modeled small vessel disease as a latent construct, integrating multiple MRI endophenotypes in strategic brain regions. The small vessel disease construct was associated with worse global and domain-specific cognitive performance. The small vessel disease construct exhibited mediating effects in the relationships of aging and cardiovascular disease risk with cognition through pathways that both involve and are independent of brain atrophy. Integrating information from multiple relevant imaging endophenotypes could open new avenues in small vessel disease research, broadening our understanding of its risk factors

关键词： aging atrophy attention cardiovascular disease risk cerebral small vessel disease cognition cognitive performance deep learning delayed memory diffusion tensor imaging enlarged perivascular spaces executive function factor analysis fractional anisotropy Framingham risk score global cognition gray matter volume immediate memory language latent variable magnetic resonance imaging mediation microbleeds phonemic fluency processing speed semantic fluency structural equation modeling trace visuospatial functioning white matter hyperintensities

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