Browsing by Author "Marcus, Daniel"

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    Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease
    (Nature Research, 2019-02) Preische, Oliver; Schultz, Stephanie A.; Apel, Anja; Kuhle, Jens; Kaeser, Stephan A.; Barro, Christian; Gräber, Susanne; Kuder-Buletta, Elke; LaFougere, Christian; Laske, Christoph; Vöglein, Jonathan; Levin, Johannes; Masters, Colin L.; Martins, Ralph; Schofield, Peter R.; Rossor, Martin N.; Graff-Radford, Neill R.; Salloway, Stephen; Ghetti, Bernardino; Ringman, John M.; Noble, James M.; Chhatwal, Jasmeer; Goate, Alison M.; Benzinger, Tammie L. S.; Morris, John C.; Bateman, Randall J.; Wang, Guoqiao; Fagan, Anne M.; McDade, Eric M.; Gordon, Brian A.; Jucker, Mathias; Alzheimer Network; Allegri, Ricardo; Amtashar, Fatima; Bateman, Randall; Benzinger, Tammie; Berman, Sarah; Bodge, Courtney; Brandon, Susan; Brooks, William; Buck, Jill; Buckles, Virginia; Chea, Sochenda; Chhatwal, Jasmeer; Chrem, Patricio; Chui, Helena; Cinco, Jake; Clifford, Jack; Cruchaga, Carlos; D’Mello, Mirelle; Donahue, Tamara; Douglas, Jane; Edigo, Noelia; Erekin-Taner, Nilufer; Fagan, Anne; Farlow, Marty; Farrar, Angela; Feldman, Howard; Flynn, Gigi; Fox, Nick; Franklin, Erin; Fujii, Hisako; Gant, Cortaiga; Gardener, Samantha; Ghetti, Bernardino; Goate, Alison; Goldman, Jill; Gordon, Brian; Graff-Radford, Neill; Gray, Julia; Gurney, Jenny; Hassenstab, Jason; Hirohara, Mie; Holtzman, David; Hornbeck, Russ; DiBari, Siri Houeland; Ikeuchi, Takeshi; Ikonomovic, Snezana; Jerome, Gina; Jucker, Mathias; Karch, Celeste; Kasuga, Kensaku; Kawarabayashi, Takeshi; Klunk, William; Koeppe, Robert; Kuder-Buletta, Elke; Laske, Christoph; Lee, Jae-Hong; Levin, Johannes; Marcus, Daniel; Martins, Ralph; Mason, Neal Scott; Masters, Colin; Maue-Dreyfus, Denise; McDade, Eric; Montoya, Lucy; Mori, Hiroshi; Morris, John; Nagamatsu, Akem; Neimeyer, Katie; Noble, James; Norton, Joanne; Perrin, Richard; Raichle, Marc; Ringman, John; Roh, Jee Hoon; Salloway, Stephen; Schofield, Peter; Shimada, Hiroyuki; Shiroto, Tomoyo; Shoji, Mikio; Sigurdson, Wendy; Sohrabi, Hamid; Sparks, Paige; Suzuki, Kazushi; Swisher, Laura; Taddei, Kevin; Wang, Jen; Wang, Peter; Weiner, Mike; Wolfsberger, Mary; Xiong, Chengjie; Xu, Xiong; Pathology and Laboratory Medicine, School of Medicine
    Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker.
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    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.