CND

James Moeller, Ph.D. (Organizer/Moderator)

• Title: "Introductory Remarks" and "Closing Arguments Regarding the Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Human Brain Mapping: Applications to fMRI, PET and EEG"
• Bio: James Moeller received a BA in Mathematics from Stanford, a MA in mathematics and PhD in experimental psychology (1976) from the University of Michigan. In 1977, he joined the division of human visual psychophysics at the David Sarnoff Research Center/RCA in Princeton, New Jersey. He contributed to research on computational theories of visual psychophysics and neural modeling applied to image understanding. He subsequently joined the Department of Neurology, Sloan-Kettering Institute, Division of Neuroimaging (1984), and in 1989 moved to Columbia University, joining the Department of Psychiatry. He has authored and coauthored more than 90 refereed journal articles. At Columbia his initial research has included novel applications of multivariate analysis and pattern recognition methods to functional neuroimaging. His interests have included the development of neuroimaging biomarkers for use in the diagnosis of specific CNS disease, as well as assessments of disease progression and treatment efficacy. His original work in Parkinson's and Alzheimer's disease has been expanded to include hereditable disorders, thereby providing a window onto prodromal states of CNS disease. His research today is focused on developments in electromagnetic brain stimulation and computational methods of human brain mapping, with applications to H215O PET, functional MRI and topographic electroencephalography.
• References:
  - JR Moeller and CG Habeck. (in press) Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O- and FDG-PET, International Journal of Biomedical Imaging
  - C Habeck, JW Krakauer, C Ghez, HA Sackeim, D Eidelberg, Y Stern, JR Moeller. A new approach to spatial covariance modeling of functional brain imaging data: ordinal trend analysis. Neural Computation. 2005 Jul;17(7):1602-45.
  - B Luber, C Habeck, CT Trott, D Friedman, JR Moeller. A Ghost of Retrieval Past: A Functional Network of Alpha EEG Related to Source Memory in Elderly Humans. Brain Research Cognitive Brain Research, 2004; 20: 144-55.

Christian Habeck (Co-Organizer)

• Title: "Ordinal Trend Canonical Variates Analysis: using parametric designs and spatial covariance analysis to maximum effect"
• Bio: Christian Habeck originally trained as a Particle Physicist, and received his M.Sc degree from the University of Durham, UK, in 1994 and his DPhil from the University of Sussex, UK, in 1998. He then did a Postdoctoral fellowship at the Neurosciences Institute in La Jolla, CA, performing large-scale computer simulations of biophysically realistic neural networks. Since 2000 he has been in the Cognitive Neuroscience Division of the Taub Institute, Department Of Neurology, Columbia University Medical Center, specializing in multivariate approaches to neuroimaging analysis for EEG, PET and MRI data in close collaboration with James R. Moeller, co-organizer of the current symposium.
• References:
  - JR Moeller and C Habeck (in press) Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O- and FDG-PET, International Journal of Biomedical Imaging.
  - C Habeck, HJ Hilton, E Zarahn, T Brown, Y Stern. (2006) An event-related fMRI study of the neural networks underlying repetition suppression and reaction time priming in implicit visual memory. Brain Res 1075:133-41.
  - C Habeck, JW Krakauer, C Ghez, HA Sackeim, D Eidelberg, Y Stern, JR Moeller. (2005) A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis. Neural Computation 17:1602-45.

Gene Alexander, Ph.D.

• Title:"Applications of Regional Network Analysis to Translational Brain Imaging Studies of Aging"
• Bio:Gene E. Alexander, Ph.D. is Associate Professor and Director of the Neuroimage Analysis Lab, Department of Psychology, Arizona State University. He is also Director of the MRI Morphology Core of the Arizona Alzheimer's Research Center. Dr. Alexander obtained his Ph.D. in Clinical Psychology from Loyola University of Chicago and received his post-doctoral training in brain imaging and neuropsychology in the Brain Imaging Division, Department of Biological Psychiatry, New York State Psychiatric Institute and Columbia University Medical Center. Prior to joining the Arizona Alzheimer's Research Center, Dr. Alexander was Chief of the Neuropsychology Unit in the Laboratory of Neurosciences in the Intramural Research Program, National Institute on Aging, NIH. His research focuses on the use of neuroimaging methods, including PET, MRI, and fMRI to advance understanding of brain-behavior relationships in aging and neurodegenerative disease in humans and in animal models.
• References:
  - GE Alexander, K Chen, TL Merkley, EM Reiman, RJ Caselli, M Aschenbrenner, DJ Lewis, P Pietrini, SJ Teipel, H Hampel, SI Rapoport, JR Moeller (2006) Regional network of magnetic resonance imaging gray matter volume in healthy aging, NeuroReport, 17, 951-6
  - JF Smith, K Chen, SC Johnson, J Morrone-Strupinsky, EM Reiman, A Nelson, JR Moeller, GE Alexander (2006) Network analysis of single-subject fMRI during a finger opposition task. Neuroimage, 32, 325-32

David Eidelberg, M.D.

• Title: "Parkinson's disease: Brain Networks, Disease Progression and Treatment Responses"
• Bio: David Eidelberg received his BA from Columbia in 1977 and his MD from Harvard Medical School in 1981. After completing residency training in neurology at Harvard, he pursued post-doctoral training in functional brain imaging research in London and New York. In 1988, he joined North Shore University Hospital, where he established the Functional Brain Imaging Laboratory and the Movement Disorders Center. He is currently Director of the Center for Neurosciences at North Shore, and the Susan and Leonard Feinstein Professor of Neurology at New York University School of Medicine. Dr. Eidelberg's research focuses on the use of functional brain imaging and network modeling to study the alterations in brain circuitry that occur in Parkinson's disease and other movement disorders. He has been the recipient of numerous grants and awards from the National Institutes of Health and other funding sources including the prestigious American Parkinson's Disease Association Fred Springer award in November, 2005. He is the author of over 300 scientific publications and serves on the editorial boards of Journal of Nuclear Medicine and Current Opinion in Neurology. He is the Associate Editor of Clinical Neuroscience Research.
• References:
  - K Asanuma, C Tang, Y Ma, V Dhawan, P Mattis, C Edwards, MG Kaplitt, A Feigin, D Eidelberg (in press) Network modulation in the treatment of Parkinson's disease. Brain
  - Y Ma, C Tang, P Spetsieris, V Dhawan, D Eidelberg (in press) Abnormal metabolic network activity in Parkinson's disease: Test-retest reproducibility. Journal of Cerebral Blood Flow and Metabolism
  - C Huang, P Mattis, C Tang, K Perrine, M Carbon, D Eidelberg (in press) Metabolic brain networks associated with cognitive functioning in Parkinson's disease. NeuroImage

Lisa Mosconi, Ph.D.

• Title: "The Medial Temporal Lobe in the preclinical detection of Alzheimer's disease: hypometabolism, atrophy, and loss of connectivity"
• Bio: Lisa Mosconi received her M.S. degree in Neuroscience in 2001 from the University of Florence, Italy, and her Ph.D. degree in Neuroscience and Nuclear Medicine in 2005 from the University of Florence, Italy, in association with New York University (NYU) School of Medicine, New York, NY. She has been working at the Department of Psychiatry at NYU since 2003, where she was appointed as a Research Assistant Professor in 2005. She is a member of the Society for Nuclear Medicine and of the Italian Association for Nuclear Medicine. Her current research interests include Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) in the early diagnosis of Alzheimer's disease.
• References:
  - Mosconi L, De Santi S, Li J, Tsui WH, Li Y, Boppana M, Laska E, Rusinek H, de Leon MJ. Hippocampal hypometabolism predicts decline from normal aging to dementia. Neurobiology of Aging; in press.
  - Mosconi L, Tsui W-H, De Santi S, Rusinek H, Li J, Convit A, Li Y, de Leon MJ. Reduced Hippocampal metabolism in MCI and AD: Automated FDG-PET Image Analysis. Neurology 2005; 64:1860-1867.
  - Mosconi L. Brain metabolism as an early and specific marker in the diagnosis of Alzheimer disease [Review]. European Journal of Nuclear Medicine; 2005; 32:486-510.

Paul Sajda, Ph.D.

• Title: "Linear multivariate analysis of EEG for uncovering neural signatures of perceptual decision making"
• Bio: Paul Sajda received his B.S. in Electrical Engineering from MIT (1989) and his M.S. and Ph.D. in Bioengineering from the University of Pennsylvania (1992, 1994). In 1994 he joined the David Sarnoff Research Center where he went on to become the Head of the Adaptive Image and Signal Processing Group. He is currently an Associate Professor of Biomedical Engineering and Radiology at Columbia University where he is Director of the Laboratory for Intelligent Imaging and Neural Computing (LIINC). His research focuses on neural engineering, neuroimaging, computational neural modeling and machine learning applied to image understanding. He has received several awards for his research including an NSF CAREER Award (2002), the Sarnoff Technical Achievement Award (1996), The Pollack Award for outstanding dissertation research in Bioengineering (UPenn, 1994), The Flexner Award for outstanding thesis research in the Neurosciences (Upenn, 1993) and the Adler Award for outstanding undergraduate research in Electrical Engineering (MIT, 1989). He is a Senior Member of the IEEE and Associate Editor for IEEE Transactions on Biomedical Engineering. Currently he has over 80 publications and holds 6 US patents. website: liinc.bme.columbia.edu.
• References:
  - MG Philiastides, R Ratcliff and P Sajda (2006) Neural representation of task difficulty and decision making during perceptual categorization: a timing diagram, Journal of Neuroscience, 26 (35): 8965-8975, Aug. 30, 2006. (cover illustration)
  - AD Gerson, LC Parra, and P Sajda (2005) Cortical Origins of Response Time Variability During Rapid Discrimination of Visual Objects, NeuroImage, 28 (2) 326-341.
  - MG Philiastides and P Sajda (2005) Temporal Characterization of the Neural Correlates of Perceptual Decision Making in the Human Brain, Cerebral Cortex, 16 (4): 509-518, Apr. 2006. (cover illustration)
  - LC Parra, CD Spence, AD Gerson and P Sajda (2005) Recipes for the Linear Analysis of EEG, Neuroimage, 28 (2) 342-353.

Ramesh Srinivasan, Ph.D.

• Title: "From brain networks to brain resonances: spatial scales of functional networks in EEG and MEG"
• Bio: Ramesh Srinivasan trained in Electrical Engineering (B.S. U of Pennsylvania, 1989) and Biomedical Engineering (Ph.D., Tulane University, 1995) and received postdoctoral training in Psychology (Oregon, 1997) and Neurosciences (Neurosciences Institute, 1999). He is presently Associate Professor of Cognitive Sciences at the University of California, Irvine. He is author of 27 articles on EEG and MEG methodology, theory, and applications to analyze brain networks to studies of development, cognition, and consciousness. His contributions include a theoretical model of the influence of volume conduction of current through the head on measures of coherence in brain networks. He has also developed high-resolution EEG methods that largely remove volume conduction effects on EEG coherence. He has also developed the 'frequency-tagging' method to identify large-scale functional networks in EEG and MEG. This method has implied to disentangle co-active functional networks with distinct dynamic properties and sensitivity to cognitive processes and consciousness. He recently coauthored the book, Electric Fields of the Brain, 2nd ed. Oxford, New York, 2006 (see http://www.electricfieldsofthebrain.com).
• References:
  - J Ding, G Sperling, R Srinivasan (2006) Attentional modulation of SSVEP power depends on the network tagged by the flicker frequency. Cerebral Cortex, 16:1016-1029
  - R Srinivasan, PL Nunez, RB Silberstein (1998) Spatial filtering and neocortical dynamics: estimates of EEG coherence, IEEE Transactions on Biomedical Engineering, 45:814:826.

Tor Wager, Ph.D.

• Title: "Brain pathways in the cognitive generation and regulation of affect"
• Bio: Tor Wager received his Ph.D. in cognitive psychology from the University of Michigan in 2003. He joined the faculty of the Psychology Department at Columbia University in 2004. His primary research interest is the brain bases of cognition-emotion interaction. Of particular interest is how cognitive processes such as attention and expectation affect brain correlates of pain and emotion. Dr. Wager employs a variety of research methodologies in pursuit of this goal, including fMRI, PET, ERP/EEG, and detailed analysis of behavioral data. A focus is on developing new methods to identify functional brain pathways and circuits in humans, and in linking those pathways with behavioral, autonomic, and endocrine measures related to emotion and cognitive performance.
• References:
  -

Stephen Strother, Ph.D.

• Title: "fMRI Image Processing & Data Analysis Choices: Do They Matter?"
• Bio: Stephen C. Strother is a senior scientist at the Rotman Research Institute at Baycrest, a Toronto-based institute that studies the aging brain in health and disease. Dr. Strother is also a Professor of Medical Biophysics at the University of Toronto where he specializes in multivariate data analysis and approaches for optimizing PET and fMRI neuroimaging techniques for both research and clinical applications. He is best known for championing the use of multivariate analysis approaches, particularly statistical learning techniques, and using these to develop quantitative performance metrics in functional neuroimaging. In 2001 Dr. Strother co-founded Predictek, LLC, a Chicago-based consulting company that focuses on predictive modeling with neuroimages for the pharmaceutical and vision care industries. Since 2002 he has chaired the Data Format Working Group, a committee set up under the Neuroimaging Informatics Technology Initiative at NIH in the USA-the DFWG is tasked with solving the problem of interchanging fMRI data and metadata. Dr. Strother received his PhD in Electrical Engineering from McGill University in Montreal, was a postdoctoral fellow at Memorial Sloan Kettering Cancer Center, New York, and was then based at the VA Medical Center and the University of Minnesota in Minneapolis until he moved to Toronto in 2004.
• References:
  - Strother SC, LaConte S, Hansen LK, Anderson J, Zhang J, Pulapura S, Rottenberg D. Optimizing the fMRI Data-Processing Pipeline Using Prediction and Reproducibility Performance Metrics. I. A Preliminary Group Analysis. Neuroimage, 23S1:S196-S207, 2004.
  - Strother SC. Evaluating fMRI Preprocessing Pipelines. IEEE Eng. Med. Biol. Mag. 25(2):27-41, 2006.
  - Poline J-B, Strother SC, Dehaene-Lambertz G, Egan G, Lancaster J. Motivation and synthesis of the FIAC experiment: The reproducibility of fMRI results across expert analyses., Hum Brain Mapp 27(5):351-359, 2006.