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Publications

Our team has published hundreds of papers in academic peer-reviewed journals. The following is a sampling of our publications in key areas.


Neuroimaging and fMRI

  • Yourganov G, Chen X, Lukic A, Grady C, Small S, Wernick M, Strother SC. Dimensionality Estimation for Optimal Detection of Functional Networks in BOLD fMRI Data. Neuroimage 56, 531-543 2011. [PubMed]
  • Strother SC, Oder A, Spring R, Grady C. The NPAIRS Computational Statistics Framework for Data Analysis in Neuroimaging. Proc. 19th Int. Conf. on Computational Statistics: Refereed Keynote, Invited and Contributed Papers, Lechevallier, Yves; Saporta, Gilbert (Eds.), pp. 111-120, Physica-Verlag, Berlin, 2010
  • Schmah T, Yourganov G, Zemel RS, Hinton GE, Small SL, Strother SC. Comparing classification methods for longitudinal fMRI studies. Neural Computation, 22, 2729-2762 2010. [PubMed]
  • Spreng RN, Rosen, HJ, Strother SC, Chow TW, Diehl-Schmid J, Freedman M, Graff-Radford NR, Hodges JR, Lipton AM, Mendez MF, Morelli SA, Black SE, Miller BL, Levine B. Occupation attributes relate to location of atrophy in frontotemporal lobar degeneration. Neuropsychologia 48, 3634-3641 2010. [PubMed]
  • Wernick MN, Yang Y, Brankov JG, Yourganov G, Strother SC. Machine Learning In Medical Imaging. Invited Review Paper: IEEE Sig Proc Mag 27:25-38, 2010. [IEEE Xplore]
  • Sidtis JJ, Strother SC, Groshong A, Rottenberg DA, Gomez C. Longitudinal cerebral blood flow changes during syllable repetition in hereditary ataxia. Brain Lang 114, 43-51 2010. [PubMed]
  • Carter CS, Heckers S, Nichols T, Pine DS, Strother SC. Optimizing the Design and Analysis of Clinical fMRI Research Studies. Biol Psychiatry 64(10):842-9, 2008. [PubMed]
  • Schmah T, Hinton G, Zemel RS, Small SL, Strother SC. Generative versus discriminative training of RBMs for classification of fMRI images. Proc. Neural Information Processing Systems, 1409-1416, 2008
  • Lukic AS, Wernick M N , Strother SC. (2007) Evaluation of methods for detection of brain activations from functional neuroimages,”Artificial Intelligence in Medicine (invited for special issue) 25: 69-88. [PubMed]
  • Lukic AS, Wernick MN, Yang Y, Hansen LK, Arfanakis K, Strother SC. Effect of spatial alignment transformations in PCA and ICA of functional neuroimages. IEEE Trans Med Imaging 26(8):1058-68 2007 [PubMed]
  • Lukic AS, Wernick MN, Tzikas DG, Chen X, Likas A, Galatanos NP, Yang Y, Zhao F, Strother SC. (2007): Kernel methods for analysis of functional neuroimages. IEEE Trans Med Imaging 26(12):1613-1624. [PubMed]
  • Strother SC. (2006): Evaluating fMRI preprocessing pipelines. IEEE Eng Med Biol Mag 25(2):27-41. [PubMed]
  • LaConte S, Strother SC, Cherkassky V, Anderson J, Hu X. Support Vector Machines for Temporal Classification of fMRI Data. Neuroimage, 26:317-329, 2005. [PubMed]
  • Strother SC, La Conte S, Kai Hansen L, Anderson J, Zhang J, Pulapura S, Rottenberg D. (2004): Optimizing the fMRI data-processing pipeline using prediction and reproducibility performance metrics: I. A preliminary group analysis. Neuroimage 23 Suppl 1:S196-207. [PubMed]
  • Shaw ME, Strother SC, Gavrilescu M, Podzebenko K, Waites A, Watson J, Anderson J, Jackson G, Egan G. (2003): Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics. Neuroimage 19(3):988-1001. [PubMed]
  • LaConte S, Anderson J, Muley S, Ashe J, Frutiger S, Rehm K, Hansen LK, Yacoub E, Hu X, Rottenberg D and others. (2003): The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics. Neuroimage 18(1):10-27. [PubMed]
  • Strother SC, Anderson J, Hansen LK, Kjems U, Kustra R, Siditis J, Frutiger S, Muley S, LaConte S, Rottenberg D. The quantitative evaluation of functional neuroimaging experiments: The NPAIRS data analysis framework. Neuroimage 15:747-771, 2002. [PubMed]
  • Kjems U, Hansen LK, Strother SC. Generalizable singular value decomposition for ill-posed datasets. In: TK Leen, TG Dietterich, V Tresp, Eds: Advances in Neural Information Processing Systems, Vol. 13, MIT Press, 549-555, 2001
  • Kustra R, Strother SC. (2001): Penalized discriminant analysis of [15O]-water PET brain images with prediction error selection of smoothness and regularization hyperparameters. IEEE Trans Med Imaging 20(5):376-87. [PubMed]
  • Liow JS, Rehm K, Strother SC, Anderson JR, Morch N, Hansen LK, Schaper KA, Rottenberg DA. Voxel based covariance analysis for optimal discrimination of groups of FDG PET scans between normal and HIV-1 seropositive subjects. J Nucl Med, 41:612-621, 2000.
  • Rottenberg DA, Sidtis JJ, Strother SC, Schaper KA, Anderson JR, Nelson MJ, Price RW. Abnormal cerebral glucose metabolism in HIV-1 seropositives with and without dementia. J Nuc Med, 37:1133-1141, 1996. [PubMed]
  • Moeller JR, Strother SC. A regional covariance approach to the analysis of functional patterns in positron emission tomographic data. J Cereb Blood Flow Metab. 11:A121-A135, 1991.
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PET

  • Miles N. Wernick and John N. Aarsvold, eds., Emission Tomography: The Engineering and Physics of PET and SPECT, San Diego: Academic Press, 2004, pp. 596.
  • Kustra R, Strother SC. (2001): Penalized discriminant analysis of [15O]-water PET brain images with prediction error selection of smoothness and regularization hyperparameters. IEEE Trans Med Imaging 20(5):376-87. [PubMed]
  • M.N. Wernick, E.J. Infusino, and M. Milosevic, “Fast spatio-temporal image reconstruction for dynamic PET,” IEEE Transactions on Medical Imaging, vol. 18, pp. 185-195, March 1999.
  • C.-M. Kao, J.T. Yap, J. Mukherjee, and M.N. Wernick, “Image reconstruction for dynamic PET based on low-order approximation and restoration of the sinogram,” IEEE Transactions on Medical Imaging, vol. 16, pp. 738-749, 1997.
  • Berti V, Pupi A, Mosconi L. PET/CT in diagnosis of movement disorders. Ann N Y Acad Sci. 2011 Jun;1228:93-108. doi: 10.1111/j.1749-6632.2011.06025.x. Review.
  • Schmidt ME, Andrews RD, van der Ark P et al. Dose-dependent effects of the CRF(1) receptor antagonist R317573 on regional brain activity in healthy male subjects. Psychopharmacology (Berl) 2010;208:109-119
  • J. Mukherjee, Z.-Y. Yang, T. Brown, R. Lew, M. Wernick, X. Ouyang, N. Yasillo, C.-T. Chen, R. Mintzer, and M. Cooper, “Preliminary assessment of extrastriatal dopamine D-2 receptor binding in the rodent and non-human primate brains using the high affinity radioligand, 18F-fallypride,” Nuclear Medicine and Biology, vol. 26, pp. 519-527, 1999.
  • J.G. Brankov, N.P. Galatsanos, Y. Yang, and M.N. Wernick, “Segmentation of dynamic PET or fMRI images based on a similarity metric,” IEEE Transactions on Nuclear Science, vol. 50, pp. 1410-1414, 2003.

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SPECT

  • Xiaofeng Niu, Yongyi Yang, Michael A. King, and Miles N. Wernick, “Detectability of perfusion defect in five-dimensional gated-dynamic cardiac SPECT images,” Medical Physics, vol. 37, pp. 5102-5112, 2010.
  • Xiaofeng Niu, Mingwu Jin, Yongyi Yang, Michael A. King, and Miles N. Wernick, “Regularized fully 5D reconstruction of cardiac gated dynamic SPECT images,”IEEE Transactions on Nuclear Science, vol. 57, no. 3, pp. 1085-1095, 2010.
  • Mingwu Jin, Yongyi Yang, Xiaofeng Niu, Thibault Marin, Jovan G. Brankov, Bing Feng, P. Hendrik Pretorius, Michael A. King, and Miles Wernick, “Quantitative evaluation study of four-dimensional gated cardiac
SPECT reconstruction,”Physics in Medicine and Biology, vol. 54, pp. 5643-5659, 2009.
  • Bing Feng, P. Hendrik Pretorius, Troy Farncombe, Seth. T. Dahlberg, Manoj V. Narayanan, Anna M. Celler, Jeffrey A. Leppo and Michael A. King, “Simultaneous assessment of cardiac perfusion and function using 5-dimensional imaging with Tc-99m teboroxime,” Journal of Nuclear Cardiology, vol. 13, pp. 354-361, 2006.

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Image analysis, Machine learning, and Computer-aided diagnosis

  • Miles N. Wernick, Yongyi Yang, Jovan G. Brankov, Grigori Yourganov, and Stephen C. Strother, “Machine learning in medical imaging,” IEEE Signal Processing Magazine, vol. 27, no. 4, pp. 25-38, 2010.
  • Yusuf Artan, Masoom A. Haider, Deanna L. Langer, Andrew J. Evans, Yongyi Yang, Miles N. Wernick, and Imam Samil Yetik, “Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields,” IEEE Transactions on Image Processing, vol. 19, no. 9, pp. 2444-2455, 2010.
  • Sedat Ozer, Deanna L. Langer, Xin Liu, Masoom A. Haider, Theodorus H. van der Kwast, Andrew J. Evans, Yongyi Yang, Miles N. Wernick, and Imam S. Yetik, “Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI,” Medical Physics, vol. 27, no. 4, pp. 1873-1872, 2010.
  • Liyang Wei, Yongyi Yang, Miles N. Wernick, and Robert M. Nishikawa, “Learning of perceptual similarity from expert readers for mammogram retrieval,” IEEE Journal of Selected Topics in Signal Processing, vol. 1, pp. 53-61, 2009.
  • Jovan G. Brankov, Yongyi Yang, Liyang Wei, Issam El-Naqa, and Miles N. Wernick, “Learning a nonlinear channelized observer for image quality assessment,” IEEE Transactions on Medical Imaging, vol. 28, pp. 991-999, 2009.
  • Xin Liu, Imam Samil Yetik, Deanna L. Langer, Masoom A. Haider, Yongyi Yang and Miles N. Wernick, “Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and classes,” IEEE Transactions on Medical Imaging, vol. 28, pp. 906-915, 2009.
  • Ahmad Abu Naser, Nikolas P. Galatsanos, and Miles N. Wernick, “Methods of detecting objects in photon-limited images,” Journal of the Optical Society of America A, in press, 2005.
  • Liyang Wei, Yongyi Yang, Robert M. Nishikawa, and Miles N. Wernick, “Relevance vector machine for automatic detection of clustered microcalcifications,” IEEE Transactions on Medical Imaging, in press, 2005.
  • Jovan G. Brankov, Nikolas P. Galatsanos, Yongyi Yang, and Miles N. Wernick, “Segmentation of dynamic PET or fMRI images based on a similarity metric,” IEEE Transactions on Nuclear Science, vol. 50, pp. 1410-1414, 2003.
  • Issam El-Naqa, Yongyi Yang, and Miles N. Wernick, “A support vector machine approach for detection of microcalcifications,” IEEE Transactions on Medical Imaging, vol. 21, no. 12, pp. 1552-1563, 2002.
  • hmad Abu-Naser, Nikolas P. Galatsanos, Miles N. Wernick, and Dan Schonfeld, “Object recognition based on impulse restoration using an expectation-maximization algorithm,” Journal of the Optical Society of America A, vol. 15, pp. 2327-2340, 1998.
  • Miles N. Wernick, “Pattern classification by convex analysis,” Journal of the Optical Society of America A, vol. 8, pp. 1874-1880, 1991.
    - Miles N. Wernick and G. Michael Morris, “Image classification at low light levels,” Journal of the Optical Society of America A, vol. 3, pp. 2179-2187, 1986.
  • G. Michael Morris, Miles N. Wernick, and Thomas A. Isberg, “Image correlation at low light levels,” Optics Letters, vol. 10, pp. 315-317, 1985.

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Imaging biomarkers for Alzheimer’s Disease

  • Mosconi L, Tsui W, Murray J, McHugh P, Li Y, Williams S, Pirraglia E, Glodzik L, De Santi S, Vallabhajosula S, de Leon MJ. Maternal age affects brain metabolism in adult children of mothers affected by Alzheimer’s disease. Neurobiol Aging. 2012 Mar;33(3):624.e1-9.
  • Berti V, Mosconi L, Glodzik L, Li Y, Murray J, De Santi S, Pupi A, Tsui W, De Leon MJ. Structural brain changes in normal individuals with a maternal history of Alzheimer’s. Neurobiol Aging. 2011 Dec;32(12):2325.e17-26.
  • Mosconi L, de Leon M, Murray J, E L, Lu J, Javier E, McHugh P, Swerdlow RH. Reduced mitochondria cytochrome oxidase activity in adult children of mothers with Alzheimer’s disease. J Alzheimers Dis. 2011;27(3):483-90.
  • During EH, Osorio RS, Elahi FM, Mosconi L, de Leon MJ. The concept of FDG-PET endophenotype in Alzheimer’s disease. Neurol Sci. 2011 Aug;32(4):559-69. Review.
  • Mosconi L, McHugh PF. FDG- and amyloid-PET in Alzheimer’s disease: is the whole greater than the sum of the parts? Q J Nucl Med Mol Imaging. 2011 Jun;55(3):250-64. Review.
  • Glodzik L, Mosconi L, Tsui W, de Santi S, Zinkowski R, Pirraglia E, Rich KE, McHugh P, Li Y, Williams S, Ali F, Zetterberg H, Blennow K, Mehta P, de Leon MJ. Alzheimer’s disease markers, hypertension, and gray matter damage in normal elderly. Neurobiol Aging. 2011 Apr 27. [Epub ahead of print]
  • Glodzik L, Rusinek H, Brys M, Tsui WH, Switalski R, Mosconi L, Mistur R, Pirraglia E, de Santi S, Li Y, Goldowsky A, de Leon MJ. Framingham cardiovascular risk profile correlates with impaired hippocampal and cortical vasoreactivity to hypercapnia.  J Cereb Blood Flow Metab. 2011 Feb;31(2):671-9. Epub 2010 Sep 15.
  • Mosconi L, Glodzik L, Mistur R, McHugh P, Rich KE, Javier E, Williams S, Pirraglia E, De Santi S, Mehta PD, Zinkowski R, Blennow K, Pratico D, de Leon MJ. Oxidative stress and amyloid-beta pathology in normal individuals with a maternal history of Alzheimer’s.  Biol Psychiatry. 2010 Nov 15;68(10):913-21.
  • Mosconi L, Berti V, Swerdlow RH, Pupi A, Duara R, de Leon M. Maternal transmission of Alzheimer’s disease: prodromal metabolic phenotype and the search for genes.  Hum Genomics. 2010 Feb;4(3):170-93. Review.
  • Mosconi L, Rinne JO, Tsui WH, Berti V, Li Y, Wang H, Murray J, Scheinin N, Någren K, Williams S, Glodzik L, De Santi S, Vallabhajosula S, de Leon MJ. Increased fibrillar amyloid-{beta} burden in normal individuals with a family history of late-onset Alzheimer’s.  Proc Natl Acad Sci U S A. 2010 Mar 30;107(13):5949-54. Epub 2010 Mar 15.
  • Berti V, Osorio RS, Mosconi L, Li Y, De Santi S, de Leon MJ. Early detection of Alzheimer’s disease with PET imaging.  Neurodegener Dis. 2010;7(1-3):131-5. Epub 2010 Mar 3. Review.
  • Mosconi L, Berti V, Glodzik L, Pupi A, De Santi S, de Leon MJ. Pre-clinical detection of Alzheimer’s disease using FDG-PET, with or without amyloid imaging.  J Alzheimers Dis. 2010;20(3):843-54. Review.
  • Glodzik L, de Santi S, Tsui WH, Mosconi L, Zinkowski R, Pirraglia E, Wang HY, Li Y, Rich KE, Zetterberg H, Blennow K, Mehta P, de Leon MJ. Phosphorylated tau 231, memory decline and medial temporal atrophy in normal elders.  Neurobiol Aging. 2011 Dec;32(12):2131-41.
  • Mistur R, Mosconi L, Santi SD, Guzman M, Li Y, Tsui W, de Leon MJ. Current Challenges for the Early Detection of Alzheimer’s Disease: Brain Imaging and CSF Studies. J Clin Neurol. 2009 Dec;5(4):153-66. Epub 2009 Dec 31.
  • Clerici F, Del Sole A, Chiti A, Maggiore L, Lecchi M, Pomati S, Mosconi L, Lucignani G, Mariani C. Differences in hippocampal metabolism between amnestic and non-amnestic MCI subjects: automated FDG-PET image analysis. Q J Nucl Med Mol Imaging. 2009 Dec;53(6):646-57. Epub 2009 Oct 7.
  • Glodzik L, De Santi S, Rich KE, Brys M, Pirraglia E, Mistur R, Switalski R, Mosconi L, Sadowski M, Zetterberg H, Blennow K, de Leon MJ. Effects of memantine on cerebrospinal fluid biomarkers of neurofibrillary pathology. J Alzheimers Dis. 2009;18(3):509-13.
  • Matthews D, Mosconi M, Andrews R, Brown T, Rickert K, Tsui WH, Li Yi, Mistur R, de Leon M, and the Alzheimer’s Disease Neuroimaging Initiative. Combining Amyloid Imaging and Measurement of  Regional Cerebral Glucose Metabolism to Evaluate Dementia State and Progression.  Presented at Human Amyloid Imaging Meeting, April 2009.
  • Matthews D, Mosconi M, Andrews R, Rickert K, Tsui WH, Li Yi, de Leon M, and the Alzheimer’s Disease  Neuroimaging Initiative.  Hippocampal Glucose Metabolism Predicts Cognitive Decline and Correlates to  Disease Progression in the ADNI Population.  Presented at the International Conference on  Alzheimer’s Disease (ICAD), July 2009.
  • Mosconi L, Mistur R, Switalski R, Tsui WH, Glodzik L, Li Y, Pirraglia E, De Santi S, Reisberg B,  Wisniewski T, de Leon MJ. FDG-PET changes in brain glucose metabolism from normal cognition to  pathologically verified Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2009 May;36(5):811-22.
  • Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, Reiman EM, Holthoff V, Kalbe E, Sorbi S, Diehl-Schmid J, Perneczky R, Clerici F, Caselli R, Beuthien-Baumann B, Kurz A, Minoshima S, de Leon MJ.  Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s  disease, and other dementias. J Nucl Med. 2008 Mar;49(3):390-8.
  • Mosconi L, De Santi S, Li J, Tsui WH, Li Y, Boppana M, Laska E, Rusinek H, de Leon MJ. Hippocampal hypometabolism predicts cognitive decline from normal aging. Neurobiol Aging. 2008 May; 29(5):676-92.
  • Mosconi L, De Santi S, Brys M, Tsui WH, Pirraglia E, Glodzik-Sobanska L, Rich KE, Switalski R, Mehta  PD, Pratico D, Zinkowski R, Blennow K, de Leon MJ.   Hypometabolism and Altered Cerebrospinal Fluid  Markers in Normal Apolipoprotein E E4 Carriers with Subjective Memory Complaints.  Biol Psychiatry.  2007 Aug 24; [Epub ahead of print]
  • Mosconi L, Tsui WH, Pupi A, De Santi S, Drzezga A, Minoshima S, de Leon MJ. (18)F-FDG PET database of longitudinally confirmed healthy elderly individuals improves detection of mild cognitive impairment  and Alzheimer’s disease.  J Nucl Med. 2007 Jul;48(7):1129-34.
  • Mosconi L, Tsui WH, Rusinek H, De Santi S, Li Y, Wang GJ, Pupi A, Fowler J, de Leon MJ.   Quantitation,  regional vulnerability, and kinetic modeling of brain glucose metabolism in mild Alzheimer’s disease.   Eur J Nucl Med Mol Imaging. 2007 Sep;34(9):1467-79.
  • Mosconi L et al.  Reduced hippocampal metabolism in MCI and AD: automated FDG-PET image analysis.  Neurology. 2005, Jun 14:64(11):1860-7.

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