Dana Tudorascu, PhD

  • Associate Professor of Psychiatry, Biostatistics and Intelligent Systems

Dr. Tudorascu received her PhD in Biostatistics from University of Pittsburgh, a MS in Computational Mathematics from Duquesne University and a BS in Mathematics from University of Craiova in Romania. She is currently an Associate Professor of Psychiatry and Biostatistics in the Department of Psychiatry at University of Pittsburgh where she Co-Leads the Study Design and Data Analysis Group. She is the Leader of the Biostatistics Core for the “The Role of Astrogliosis in Aging and the Pathological and Clinical Progression of Alzheimer’s Disease” program, Co-Leads the Data Management and Statistics Core for ADRC at Pitt as well as the Biostatistics and Data Management Core for Alzheimer’s Disease Biomarker’s Consortium-Down Syndrome (ABC-DS), an international multisite study.

Dr. Tudorascu’s research focuses on developing methods for data harmonization for multi scanner, multi center neuroimaging studies as well as on improving brain tissue classification in presence of white matter lesions and atrophy in neuroimaging studies of Alzheimer’s disease. She is currently principal investigator of a National Institute on Aging-funded R01 grant focused on statistical methods to improve reproducibility and reduce technical variability in multimodal imaging studies of Alzheimer’s disease.

Representative Publications

  1. Eshaghzadeh Torbati M, Minhas DS, Ahmad G, O’Connor EE, Muschelli J, Laymon CM, Yang Z, Cohen AD, Aizenstein HJ, Klunk WE, Christian BT, Hwang SJ, Crainiceanu CM, Tudorascu DL. A multi-scanner neuroimaging data harmonization using RAVEL and ComBat. Neuroimage. 2021 Dec 15;245:118703. doi: 10.1016/j.neuroimage.2021.118703. Epub 2021 Nov 1. PMID: 34736996; PMCID: PMC8820090.
  2. Minhas D.S, Yang Z, Muschelli J, Laymon CM, Mettenburg JM, Zammit MD, Johnson S, Mathis CA, Cohen AD, Handen BL, Klunk WE, Crainiceanu CM, Christian BT, Tudorascu DL. Statistical Methods for Processing Neuroimaging Data from Two Different Sites with a Down Syndrome Population Application”. In: Lesot MJ. et al. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. https://doi.org/10.1007/978-3-030-50153-2_28 Communications in Computer and Information Science, vol 1239. Springer, Cham.
  3. Tudorascu DL, Minhas DS, Lao PJ, Betthauser TJ, Yu Z, Laymon CM, Lopresti BJ, Mathis CA, Klunk WE, Handen BL, Christian BT, Cohen AD. The use of Centiloids for applying [11C]PiB classification cutoffs across region-of-interest delineation methods. Alzheimers Dement (Amst). 2018 Apr 21;10:332-339. doi: 10.1016/j.dadm.2018.03.006. PMID: 30014032; PMCID: PMC6024172.
  4. Tudorascu DL, Anderson SJ, Minhas DS, Yu Z, Comer D, Lao P, Hartley S, Laymon CM, Snitz BE, Lopresti BJ, Johnson S, Price JC, Mathis CA, Aizenstein HJ, Klunk WE, Handen BL, Christian BT, Cohen AD. Comparison of longitudinal Aβ in nondemented elderly and Down syndrome Neurobiol Aging. 2019 Jan;73:171-176. doi: 10.1016/j.neurobiolaging.2018.09.030. Epub 2018 Sep 27. PubMed PMID:30359879.
  5. Tudorascu DL, Karim HT, Maronge JM, Alhilali L, Fakhran S, Aizenstein HJ, Muschelli J, Crainiceanu CM. Reproducibility and bias in healthy brain segmentation: Comparison of two popular neuroimaging platforms. Front Neurosci. 2016 Nov 9;10:503. PubMed PMID: 27881948 PubMed Central PMCID: PMC5101202.

Research Interests

  • Data harmonization for multimodal neuroimaging studies
  • Reproducibility in Neuroimaging studies of Alzheimer’s disease
  • Brain segmentation improvement

Research Grants

PI: Ro1, “Statistical methods to improve reproducibility and reduce technical variability in multimodal imaging studies of Alzheimer’s disease” (https://grantome.com/grant/NIH/R01-AG063752-01)