Browsing by Author "Shou, Haochang"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression
    (American Medical Association, 2022) Wen, Junhao; Fu, Cynthia H. Y.; Tosun, Duygu; Veturi, Yogasudha; Yang, Zhijian; Abdulkadir, Ahmed; Mamourian, Elizabeth; Srinivasan, Dhivya; Skampardoni, Ioanna; Singh, Ashish; Nawani, Hema; Bao, Jingxuan; Erus, Guray; Shou, Haochang; Habes, Mohamad; Doshi, Jimit; Varol, Erdem; Mackin, R. Scott; Sotiras, Aristeidis; Fan, Yong; Saykin, Andrew J.; Sheline, Yvette I.; Shen, Li; Ritchie, Marylyn D.; Wolk, David A.; Albert, Marilyn; Resnick, Susan M.; Davatzikos, Christos; iSTAGING consortium; ADNI; BIOCARD; BLSA; Radiology and Imaging Sciences, School of Medicine
    Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. Design, setting, and participants: The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. Main outcomes and measures: Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. Results: A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). Conclusions and relevance: This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.
  • Thumbnail Image
    Item
    Early Cardiac Effects of Contemporary Radiation Therapy in Patients With Breast Cancer
    (Elsevier, 2020) Clasen, Suparna C.; Shou, Haochang; Freedman, Gary; Plastaras, John P.; Taunk, Neil K.; Teo, Boon-Keng Kevin; Smith, Amanda M.; Demissei, Biniyam G.; Ky, Bonnie; Medicine, School of Medicine
    Purpose To characterize the early changes in echocardiographically derived measures of cardiac function with contemporary radiation therapy (RT) in breast cancer and to determine the associations with radiation dose-volume metrics, including mean heart dose (MHD). Methods and Materials In a prospective longitudinal cohort study of 86 patients with breast cancer treated with photon or proton thoracic RT, clinical and echocardiographic data were assessed at 3 time points: within 4 weeks before RT initiation (T0), within 3 days before 6 weeks after the end of RT (T1), and 5 to 9 months after RT completion (T2). Associations between MHD and echocardiographically derived measures of cardiac function were assessed using generalized estimating equations to define the acute (T0 to T1) and subacute (T0 to T2) changes in cardiac function. Results The median estimates of MHD were 139 cGy (interquartile range, 99-249 cGy). In evaluating the acute changes in left ventricular ejection fraction (LVEF) from T0 to T1, and accounting for the time from RT, age, race, preexisting cardiovascular disease, and an interaction term with anthracycline or trastuzumab exposure and MHD, there was a modest decrease in LVEF of borderline significance (0.22%; 95% confidence interval [CI], –0.44% to 0.01%; P = .06) per 30-day interval for every 100 cGy increase of MHD. Similarly, there was a modest worsening in longitudinal strain (0.19%; 95% CI, –0.01% to 0.39%; P = .06) per 30-day interval for each 100 cGy increase in MHD. We did not find significant associations between MHD and changes in circumferential strain or diastolic function. Conclusions With modern radiation planning techniques, there are modest subclinical changes in measures of cardiac function in the short-term. Longer-term follow-up studies are needed to determine whether these early changes are associated with the development of overt cardiac disease.
  • Thumbnail Image
    Item
    Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer's disease continuum
    (Springer Nature, 2024-10-05) Wen, Junhao; Yang, Zhijian; Nasrallah, Ilya M.; Cui, Yuhan; Erus, Guray; Srinivasan, Dhivya; Abdulkadir, Ahmed; Mamourian, Elizabeth; Hwang, Gyujoon; Singh, Ashish; Bergman, Mark; Bao, Jingxuan; Varol, Erdem; Zhou, Zhen; Boquet-Pujadas, Aleix; Chen, Jiong; Toga, Arthur W.; Saykin, Andrew J.; Hohman, Timothy J.; Thompson, Paul M.; Villeneuve, Sylvia; Gollub, Randy; Sotiras, Aristeidis; Wittfeld, Katharina; Grabe, Hans J.; Tosun, Duygu; Bilgel, Murat; An, Yang; Marcus, Daniel S.; LaMontagne, Pamela; Benzinger, Tammie L.; Heckbert, Susan R.; Austin, Thomas R.; Launer, Lenore J.; Espeland, Mark; Masters, Colin L.; Maruff, Paul; Fripp, Jurgen; Johnson, Sterling C.; Morris, John C.; Albert, Marilyn S.; Bryan, R. Nick; Resnick, Susan M.; Ferrucci, Luigi; Fan, Yong; Habes, Mohamad; Wolk, David; Shen, Li; Shou, Haochang; Davatzikos, Christos; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction-driven by genes different from APOE-which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .
  • Thumbnail Image
    Item
    Genomic loci influence patterns of structural covariance in the human brain
    (National Academy of Science, 2023) Wen, Junhao; Nasrallah, Ilya M.; Abdulkadir, Ahmed; Satterthwaite, Theodore D.; Yang, Zhijian; Erus, Guray; Robert-Fitzgerald, Timothy; Singh, Ashish; Sotiras, Aristeidis; Boquet-Pujadas, Aleix; Mamourian, Elizabeth; Doshi, Jimit; Cui, Yuhan; Srinivasan, Dhivya; Skampardoni, Ioanna; Chen, Jiong; Hwang, Gyujoon; Bergman, Mark; Bao, Jingxuan; Veturi, Yogasudha; Zhou, Zhen; Yang, Shu; Dazzan, Paola; Kahn, Rene S.; Schnack, Hugo G.; Zanetti, Marcus V.; Meisenzahl, Eva; Busatto, Geraldo F.; Crespo-Facorro, Benedicto; Pantelis, Christos; Wood, Stephen J.; Zhuo, Chuanjun; Shinohara, Russell T.; Gur, Ruben C.; Gur, Raquel E.; Koutsouleris, Nikolaos; Wolf, Daniel H.; Saykin, Andrew J.; Ritchie, Marylyn D.; Shen, Li; Thompson, Paul M.; Colliot, Olivier; Wittfeld, Katharina; Grabe, Hans J.; Tosun, Duygu; Bilgel, Murat; An, Yang; Marcus, Daniel S.; LaMontagne, Pamela; Heckbert, Susan R.; Austin, Thomas R.; Launer, Lenore J.; Espeland, Mark; Masters, Colin L.; Maruff, Paul; Fripp, Jurgen; Johnson, Sterling C.; Morris, John C.; Albert, Marilyn S.; Bryan, R. Nick; Resnick, Susan M.; Fan, Yong; Habes, Mohamad; Wolk, David; Shou, Haochang; Davatzikos, Christos; Radiology and Imaging Sciences, School of Medicine
    Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
  • Thumbnail Image
    Item
    Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
    (Elsevier, 2023) Hu, Fengling; Chen, Andrew A.; Horng, Hannah; Bashyam, Vishnu; Davatzikos, Christos; Alexander-Bloch, Aaron; Li, Mingyao; Shou, Haochang; Satterthwaite, Theodore D.; Yu, Meichen; Shinohara, Russell T.; Radiology and Imaging Sciences, School of Medicine
    Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.