Browsing by Author "LaMontagne, Pamela"

Now showing 1 - 3 of 3
  • 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
    The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
    (ArXiv, 2023-06-01) Moawad, Ahmed W.; Janas, Anastasia; Baid, Ujjwal; Ramakrishnan, Divya; Jekel, Leon; Krantchev, Kiril; Moy, Harrison; Saluja, Rachit; Osenberg, Klara; Wilms, Klara; Kaur, Manpreet; Avesta, Arman; Cassinelli Pedersen, Gabriel; Maleki, Nazanin; Salimi, Mahdi; Merkaj, Sarah; von Reppert, Marc; Tillmans, Niklas; Lost, Jan; Bousabarah, Khaled; Holler, Wolfgang; Lin, MingDe; Westerhoff, Malte; Maresca, Ryan; Link, Katherine E.; Tahon, Nourel Hoda; Marcus, Daniel; Sotiras, Aristeidis; LaMontagne, Pamela; Chakrabarty, Strajit; Teytelboym, Oleg; Youssef, Ayda; Nada, Ayaman; Velichko, Yuri S.; Gennaro, Nicolo; Connectome Students; Group of Annotators; Cramer, Justin; Johnson, Derek R.; Kwan, Benjamin Y. M.; Petrovic, Boyan; Patro, Satya N.; Wu, Lei; So, Tiffany; Thompson, Gerry; Kam, Anthony; Guzman Perez-Carrillo, Gloria; Lall, Neil; Group of Approvers; Albrecht, Jake; Anazodo, Udunna; Lingaru, Marius George; Menze, Bjoern H.; Wiestler, Benedikt; Adewole, Maruf; Anwar, Syed Muhammad; Labella, Dominic; Li, Hongwei Bran; Iglesias, Juan Eugenio; Farahani, Keyvan; Eddy, James; Bergquist, Timothy; Chung, Verena; Shinohara, Russel Takeshi; Dako, Farouk; Wiggins, Walter; Reitman, Zachary; Wang, Chunhao; Liu, Xinyang; Jiang, Zhifan; Van Leemput, Koen; Piraud, Marie; Ezhov, Ivan; Johanson, Elaine; Meier, Zeke; Familiar, Ariana; Kazerooni, Anahita Fathi; Kofler, Florian; Calabrese, Evan; Aneja, Sanjay; Chiang, Veronica; Ikuta, Ichiro; Shafique, Umber; Memon, Fatima; Conte, Gian Marco; Bakas, Spyridon; Rudie, Jeffrey; Aboian, Mariam; Radiology and Imaging Sciences, School of Medicine
    Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.