David Abbott, Ph.D.

Title: Mapping functional brain networks in epilepsy

Abstract:
Functional Magnetic Resonance Imaging (fMRI) has revolutionised our ability to image brain activity in vivo. The development of the ability to simultaneously record electroencephalography (EEG) inside the MR scanner has facilitated mapping functional brain networks associated with scalp recorded electrical events such as epileptic spikes. I will discuss our neuroimaging investigations of epilepsy, including functional connectivity, the fMRI response associated with epileptic spikes, and the evolution of fMRI activity in the minutes immediately prior to an epileptic seizure. I will discuss the use of both model-based and data-driven analysis methods.

Biography:
David Abbott is a Senior Research Fellow at the Brain Research Institute, located at the Austin Hospital in Melbourne, Australia. He is also an Honorary Senior Fellow in the Department of Medicine at The University of Melbourne. Dr Abbott completed a PhD in Physics in 1994 and has since specialised in the development and application of neuroimaging techniques for basic and clinical research. In addition to over seventy publications in well regarded international journals, Dr Abbott is the principal author of the iBrain^™ neuroimaging software package.

Dr Abbott’s work has spanned a wide range of research activities, providing new insights into the functional brain organisation of healthy individuals, and reorganisation after injury and disease. Studies have included patients with stroke, epilepsy, cerebral palsy, and sleep apnoea. His work has utilised technologies including Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Electroencephalography (EEG). A key achievement of his team has been development of novel methods for the simultaneous acquisition of EEG and functional MRI (EEG/fMRI) at a field strength of 3 tesla, including removal of magnetic field gradient and subject motion artefacts. This has permitted the mapping of functional brain networks associated with abnormal activity in patients with epilepsy.

Dr Abbott is a member of several international scientific organisations including the Organization for Human Brain Mapping (OHBM) and the International Society for Magnetic Resonance in Medicine (ISMRM). He is chairman of the Brain Research Institute Scientific Advisory Board and is a past chairman of the Victorian Branch of the Australian Institute of Physics.

Peter A. Bandettini, Ph.D.

Talk Title: There’s more there: Fluctuations and Patterns in fMRI

Abstract:
Since the inception of fMRI, a principle has become increasingly clear: The more carefully the data are examined, the more information will be extracted. As we go to higher field strengths and higher resolution fMRI, and as we look with more sophisticated tools at the ongoing fluctuations and the fine scale spatial patterns, we continually are rewarded with ever more detailed and information-rich information about neuronal and hemodynamic function. In this lecture, I will start with a quick recap of some of the more dramatic recent advancements in fMRI along this avenue, and will then discuss ongoing work by my research group towards more robustly and effectively extracting and interpreting spatial pattern information related to “decoding” and temporal fluctuation information related to functional connectivity.

Biography:
Dr. Bandettini is Chief of the Section on Functional Imaging Methods in the Laboratory of Brain and Cognition of the Intramural Research Program. He is also the director of the Functional MRI Core Facility, which maintains cutting edge fMRI capability (one 1.5T human scanner, three 3T human scanners and a planned 7T human scanner) to over 30 fMRI research teams within mostly NIMH and NINDS. Dr. Bandettini received his B.S. in Physics from Marquette University in 1989 and his Ph.D. in Biophysics from the Medical College of Wisconsin in 1994, where he worked on the early development of magnetic resonance imaging of human brain function using blood oxygenation contrast – known as functional MRI (fMRI). While in graduate school, he published one of the first three papers in fMRI in 1992. During his postdoctoral fellowship at the Massachusetts General Hospital, he continued his investigation of methods to increase the interpretability, resolution, and applicability of functional MRI techniques. In 1999, he joined NIMH. In 2001, he was awarded the Scientific Director’s Merit Award for his efforts in establishing the NIH FMRI core facility. In 2002, he was awarded the Wiley Young Investigator’s at the annual Organization for Human Brain Mapping Meeting. In 2005, he served as president of the Organization for Human Brain Mapping. He is currently the associate editor of the two primary journals in brain mapping: Human Brain Mapping and NeuroImage. Since the start of his career, he has published about 90 papers, 250 abstracts, 16 book chapters, and 1 book. He has also presented over 240 invited lectures to a wide range of audiences. His laboratory is currently developing MRI methods improve the spatial resolution, temporal resolution, sensitivity, interpretability, and applicability of functional MRI.

Danielle Bassett

Talk Title: Dynamic reconfiguration of human brain networks during learning

Abstract:
Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behaviour. These two attributes — flexibility and selection — must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.

Biography:
Danielle Bassett is a postdoctoral research associate in the Complex Systems Group of the Physics Department at the University of California Santa Barbara. She is affiliated with the Institute for Collaborative Biotechnologies, and performs interdisciplinary work in the area of complex network science. The majority of her work to date has focused on the characterization of large-scale functional and structural connectivity patterns in the human brain, in addition to examining the relationship between these patterns and cognitive ability, task and disease.

Linda Chang, M.D., M.S.

Talk Title: Multimodal Neuroimaging Studies: Applications to HIV-Associated Brain Injury

Abstract:
The prevalence of HIV-associated dementia (HAD) has declined significantly since the introduction of highly active antiretroviral therapy (HAART).  However, approximately 50% of HIV-infected individuals continue to suffer from milder forms of HIV-associated neurocognitive disorders (HAND).  Using multi-modal MR techniques, such as BOLD-fMRI (both during the activated state and at rest), diffusion tensor imaging, MR spectroscopy, MR morphometry, as well as PET imaging of dopamine transporters and receptors, we have documented various pathophysiological processes in the HIV-infected brain, including those without neurocognitive deficits.  The use of multiple techniques allows better understanding of the neuropathophysiology associated with the disease, which may provide insights regarding treatment targets.  Longitudinal follow-up also provide predictive values of these techniques as future biomarkers.  Future directions evaluating co-morbid disorders, such as drugs of abuse and genetic vulnerability, will also be discussed.

Biography:
Linda Chang is a Professor of Medicine (Neurology) at the University of Hawaii’s John A. Burns School of Medicine, and Co-Director of the Neuroscience and Imaging Research Program, located at the Queen’s Medical Center in Honolulu.

After completing her medical training at Georgetown University, Neurology Residency and Fellowships at UCLA, she was appointed as an Assistant Professor 1992 and promoted to Associate Professor with tenure in 1999 in the Department of Neurology at UCLA School of Medicine. In 2000, she was recruited as Chair of the Medical Department at Brookhaven National Laboratory, and in 2004, she relocated to Hawaii in order to continue her clinical research in the neurotoxic effects of methamphetamine. Together with Dr. Thomas Ernst, they obtained funding from the White House to start a Neuroscience & MR Research Center at the Queen’s Medical Center in Hawaii to study the effects of Drug Abuse and other co-morbid conditions, including neuro-HIV/AIDS.

She received several honors and awards, including the Richard E. Weitzman Award in Biochemical Research in 1998 and the Brookhaven Woman of Science Award in 2001, and this year the Joseph Wybran Research Award from the Society of NeuroImmune Pharmacology. In addition, she received four career development awards from NIH (NCRR & NIDA). She is active in various scientific organizations, serves on 5 Editorial Boards, and is dedicated to mentoring of junior clinician scientists.

She has many ongoing research studies, and collaborates extensively with other investigators, on Clinical and Translational Research in HIV and Drug Abuse. Her research is supported by the NIH (NIDA, NIMH, NINDS, NIBIB, NCRR, NCI), the ONDCP and the University of Hawaii at Manoa.

Mark D’Esposito, M.D.

Talk Title: Using resting state intrinsic connectivity networks to investigate human cognition

Abstract:
Numerous studies using resting state-fMRI have shown that neuronal activity is characterized by temporal correlations in blood oxygen level-dependent signal across disparate brain regions. These fluctuations seem highly consistent over time and reflect the presence of intrinsic functional and structural connectivity. Among these fluctuations, different networks can be distinguished, many of which show remarkable resemblance to task-related networks. In this talk, I will discuss methods for examining properties of these intrinsic networks in order to demonstrate their independence, whether they interact, and are modulated in a physiologically plausible fashion.

Two methods – the examination of perturbation of these networks by focal brain damage an pharmacological manipulations – will be the focus of my talk.

Biography:
Dr. D’Esposito is currently Professor of Neuroscience and Psychology, Director of the Henry H. Wheeler, Jr. Brain Imaging Center at the University of California, Berkeley and Chief of the NeuroRehabilitation Unit at the Northern California VA Medical Center.

Dr. D’Esposito’s laboratory investigates the neural basis of higher cognitive function using a wide range of methodological approaches (e.g. functional MRI, ERP, TMS, pharmacological) in healthy individuals as well as patients with neurological disorders. He publications include over 250 research articles and six books. He is currently the Editor-In-Chief of the Journal of Cognitive Neuroscience.

Gary Egan, Ph.D.

Talk Title: Challenges to integrating micro-structural and functional MR measures of brain connectivity

Abstract:
Neuroimaging is an indispensible research tool in the neurosciences with major advances in our understanding of the human brain resulting from imaging studies over the past 20 years. The continued development of novel Magnetic Resonance (MR) imaging techniques continues to provide new insights into brain function in neurological and psychiatric disease processes. MRI techniques including blood oxygenation level dependent (BOLD) functional MR, cerebral blood flow or perfusion, and diffusion weighted imaging and analytical techniques can provide integrative in vivo measures of brain function and microstructure.

Multi-modal MRI for the investigation and modeling of normal brain structural and functional connectivity, as well as connectivity changes in neurological and psychiatric diseases, are of great interest. The challenges to developing imaging analysis techniques for integrating brain measures of connectivity will be discussed, applications to structural and functional dis-connectivity in Freidreich’s ataxia and schizophrenia will be presented.

Biography:
Gary Egan is a Senior Principal Research Fellow at the Howard Florey Institute, University of Melbourne and Associate Director and Professor in the Centre for Neuroscience, University of Melbourne, and Deputy Director of the Australian National Imaging Facility. He has published over 150 papers and over 250 abstracts in peer reviewed journals. He leads the Neuroimaging and Neuroinformatics laboratory undertaking high resolution structural brain mapping research and clinical neuroimaging research in Multiple Sclerosis and Huntington’s diesase. He is also head of the small animal Magnetic Resonance (MR) imaging and spectroscopy laboratory where he leads a translational research program investigating small animal models of HD.

Alan Evans, Ph.D.

Talk Title: MRI analysis of neuronatomical networks

Abstract:
CBRAIN is high-bandwidth network linking brain imaging research centres to the Canadian high-performance computing grid, Compute Canada. Funded by CANARIE, a Canadian agency that supports advanced IT networking in all areas of science, CBRAIN middleware allows brain researchers to conduct remote pipeline analysis on any Compute Canada node where that pipeline has been configured. It also supports the installation of new pipelines from any participating site. As of November 20110, a total 10 brain imaging centers and 8 supercomputing facilities, corresponding to  approximately 30,000 processors participate with a total allocation of 1.5M compute-hours annually.  An international version, GBRAIN, also links CBRAIN with the JUROPA supercomputing facility in Germany and with the KISTI facility  in Korea. A parallel European nitiative, OutGRID, is examining inter-operability between CBRAIN, a similar European grid project (neuGRID) and the LONI facility at UCLA. This talk with review CBRAIN and associated tools and explore the potential for integration with similar initiaitives globally.

Biography:
Professor Alan Evans did his PhD in biophysics at Leeds University in the UK, studying 3D protein folding. He spent 5-year at Atomic Energy of Canada, working on the physics and analysis of PET images. In 1984, he moved to the Montreal Neurological Institute (MNI) at McGill where his research interests include multi-modal brain imaging with PET and MRI, image processing and large-scale brain databasing.

He has published 350+ peer-reviewed papers and has held numerous leadership roles, most notably as director of the McConnell Brain Imaging Centre (BIC) during the 1990’s. Dr. Evans is a founding member of the International Consortium for Brain Mapping (ICBM). He was one of the founders of the Organization for Human Brain Mapping (OHBM), serving in numerous positions on the OHBM Council since 1995. He chaired the 4th International Conference on Human Brain Mapping in 1998. In 2003 he received a CIHR Senior Scientist Award. He is P.I. of the Montreal Consortium for Brain Imaging Research (MCBIR), a $35M initiative to link the BIC with six institutions investigating brain development and aging, cognitive neuroscience and addiction. MCBIR employs using MRI/PET/MEG and large-scale data processing for human and animal studies.

Dr. Evans heads the data coordinating center for a large NIH-funded multi-center MRI study of normal pediatric development. This project provides a web-accessible reference database of normal maturation. The technologies developed here, notably (i) web-based imaging/behavioral database, (ii) automated MRI segmentation pipeline, (iii) brain-behavior correlation analysis for volume- or surface-based data, are now used in a series of network collaborations studying abnormal pediatric development (MAVAN, IBIS, NeuroDevNet, GUSTO) and two multi-centre European initiatives to investigate Alzheimer’s Disease (AddNeuroMed and NeuGrid). He leads network projects in grid-processing of large brain databases, nationally (CBRAIN) and internationally (GBRAIN).

Dr. Evans is Founder and Director of Biospective Inc., (www.biospective.com), a company that offers 3D image analysis for clinical/pre-clinical pharmaceutical studies.

Tianzi Jiang, Ph.D.

Talk Title: How Brain Networks Correlate with Intelligence?

Abstract:
Brain network consists of two basic elements: nodes and connections, which can be defined at different scales and levels. We can study brain networks at microscale by taking neurons as nodes of networks and their synaptic connections as the connections. However, the whole brain networks at this scale too large to study with current computing capacity because there are about one hundred billion neurons and one hundred trillion of their connections for human brain. Brain networks can be also studied at mesoscale by taking minicolumns as nodes of networks and their connections within them as the connections of the networks. Even at this scale, the whole brain networks are too large to study with current computing capacity because there are about two hundred millions of minicolumns for human brain. Therefore, all studies of brain networks conducted at macroscale, where the nodes and connections of brain networks are the anatomically distinct brain regions and inter-regional pathways.

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. Here, we present the advance on how functional brain networks correlate with Intelligence. We focus on the evidence obtained with functional magnetic resonance imaging (fMRI) in the rest state. After that, the issue on how individual differences in intelligence are associated with brain structural organization is addressed and we will demonstrate that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We focus on the evidence obtained with diffusion tensor imaging (DTI), a type of magnetic resonance imaging. In the fourth part, we discuss the genetic basis of intelligence-related brain networks. We try to address the issue on how intelligence-related genes influence intelligence-related neuronal systems. The evidence based on fMRI and DTI are presented. Finally, the future directions in this field will be presented.

Biography:
Dr. Tianzi Jiang is Professor of Brain Imaging and Cognitive Disorders, Institute Automation, Chinese Academy of Sciences, and Honorary Professor of Queensland Brain Institute, University of Queensland. He received his Ph.D. degree in computational mathematics from Zhejiang University in1994. After he graduated, he worked as a postdoctoral research fellow (1994-1996) and an Associate Professor (1996-1999), and full professor (1999-present) at his current institution. During that time, he worked as a Vice-Chancellor’s postdoctoral fellow at the University of New South Wales, a visiting scientist at Max Planck Institute for Human Cognitive and Brain Sciences, a research fellow at the Queen’s University of Belfast, and a research professor at University of Houston. He is the Chinese Director of the Sino-French Laboratory in Computer Science, Automation and Applied Mathematics (LIAMA), one National Center for International Research, since 2006. His research interests include anatomical and functional brain imaging, complex brain networks, imaging genetics, and their clinical applications in brain disorders and development. He is the author or co-author of over 130 reviewed journal papers in these fields and the co-editor of three issues of the Lecture Notes in Computer Sciences.

Dr. Jiang is an Associate Editor of both IEEE Transactions on Medical Imaging and IEEE Transactions on Autonomous Mental Development. He was/is also on editorial boards of several international journals, including NeuroImage, Cognitive Neurodynamics, International Journal of Computer mathematics, Genomics, Proteomics & Bioinformatics. He served and is serving as the Chairs and Program Committee members of a number of international conferences, including General Chair of MICCAI’2010. He was awarded the National Distinguished Youth Foundations by Chinese Government (2004), the Natural Science Award of China (2004), and the Natural Science Award of the Chinese Academy of Sciences (1996).

Ching-Po Lin

Talk Title:
Altered anatomical networks in neuropsychiatric disorders

Academic Appointments
2009-present: Associate Professor, Institute of Neuroscience, National Yang-Ming University, Taiwan
2004-2009: Assistant Professor, Institute of Neuroscience, National Yang-Ming University, Taiwan
1995-1996: Computer Engineer, Institute for Information Industry, Taiwan
1993-1995: Assistant Research Fellow, National Health Research Institute, Taiwan

Education
2002-2004: Postdoctoral Associate, Institute of Electrical-Engineering, National Taiwan University, Taiwan
1997-2002: Ph.D. Electrical Engineering, National Taiwan University, Taiwan
1991-1993: M.S. Biomedical Engineering, National Yang-Ming University, Taiwan
1987-1991: B.A. Physics, National Central University, Taiwan

Abstract:
Recent studies have suggested that structural networks of the human brain can be constructed from diffusion MRI tractography and further characterized by using graph theoretical approaches. Through these approaches, WM network of human brain has been found to have a “small-world” topology, characterized by a high degree of local interconnectivity and small path-lengths linking individual network nodes. This small-world property suggests that human brain WM network is optimally organized to not only support both specialized and integrated information processing but also maximize the efficiency of information transfer at a relatively low wiring cost. These results are compatible with the previous studies of functional brain networks. On neuropsychiatric disorders, such as Alzheimer’s disease and schizophrenia, several studies have demonstrated alternations of WM microstructure and functional connectivity. We therefore sought to determine whether the neuropsychiatric disorders would alter the small-world organization in WM networks and their link with behavioral/clinical variables.

John C. Mazziotta, M.D., Ph.D.

Talk Title: The Myth of the Normal, Average Human Brain—The ICBM Experience: Subject Screening and Eligibility

Abstract:
In the course of developing an atlas and reference system for the normal human brain throughout the human age span from structural and functional brain imaging data, the International Consortium for Brain Mapping (ICBM) developed a set of “normal” criteria for subject inclusion and the associated exclusion criteria. The approach was to minimize inclusion of subjects with any medical disorders that could affect brain structure or function. In the past two years, a group of 1,685 potential subjects responded to solicitation advertisements at one of the consortium sites (UCLA). Subjects were screened by a detailed telephone interview and then had in-person history and physical examination. Of those who responded to the advertisement and considered themselves to be normal, only 31.6% (532 subjects) passed the telephone screening process. Of the 348 individuals who submitted to in-person history and physical examinations, only 51.7% passed these screening procedures. Thus, only 10.7% of those individuals who responded to the original advertisement qualified for imaging. The most frequent cause for exclusion in the second phase of subject screening was hypertension followed by abnormal signs on neurological examination. It is concluded that the majority of individuals who consider themselves normal by self-report are found not to be so by detailed historical interviews about underlying medical conditions and by thorough medical and neurological examinations. Recommendations are made with regard to the inclusion of subjects in brain imaging studies and the criteria used to select them.

Biography:
Dr. John Mazziotta is Chair of the Department of Neurology, David Geffen School of Medicine at UCLA, and Director of the UCLA Ahmanson-Lovelace Brain Mapping Center. After receiving his B.A. and M.A. degrees from Columbia University in 1972, he obtained an M.D. and Ph.D. in Neuroanatomy and Computer Science from Georgetown University in 1977. Following an internship at Georgetown, he completed Neurology and Nuclear Medicine training at UCLA and joined the faculty here in 1983.

Dr. Mazziotta chairs one of the nation’s largest Neurology departments, which for the last seven years achieved the distinguished position of being first in National Institutes of Health (NIH) research funding. An expert in brain imaging, he established the Brain Mapping Center at UCLA that includes all of the methods available to study human brain structure and function. He was the principal investigator of the International Consortium for Brain Mapping, whose goal is to develop the first atlas of the human brain that will include behavioral, demographic, imaging, and genetic data from 7,000 subjects.

Since beginning this work, Dr. Mazziotta has published more than 255 research papers and eight texts. He has received numerous awards and honors, including the Oldendorf Award from the American Society of Neuroimaging, the S. Weir Mitchell Award and the Wartenberg Prize of the American Academy of Neurology, the Von Hevesy Prize from the International Society of Nuclear Medicine, the 1996 Medical Science Award from the UCLA Medical Alumni Association, election to the Institute of Medicine of the National Academy of Sciences, Honorary Doctorate from l’Université de Caen and membership in the Royal College of Physicians.

Randy McIntosh, Ph.D.

Talk Title: Network dynamics and brain noise: the formula for cognition

Abstract:
In relating brain signals to mental processes, the assumption is that engaging such processes will activate key regions of the brain. Much like a computer, the region is ‘on’ when the process it subserves is required and ‘off’ when it is not. Some critical features of brain organization suggest we need to rethink this mapping. First, the network architecture enables the pattern of information flow to change without appreciable activity changes. Second, as a nonlinear system, the brain relies on both signal and noise to ensure optimal function. Indeed, the noise may be vital for enabling a full exploration of the cognitive landscape. Considering these two features defines new principles of brain-behaviour linkages, which may also impact our conceptualization of the cognitive constructs.

Biography:
Director, The Rotman Research Institute, Vice President Research at Baycrest Professor, Department of Psychology, University of Toronto
Dr. McIntosh received his undergraduate training at the University of Calgary, and his Ph.D. in Psychology, with a specialization in neuroscience, in 1992 at the University of Texas at Austin.
Dr. McIntosh is a pioneer in the study of how different parts of the brain work together to bring about the wide range of human mental operations. He has combined modern functional neuroimaging methods with mathematical modeling to characterize the changes in brain network dynamics related to awareness and learning, and shown how these dynamics change in normal aging and different clinical conditions.

Michael Miller, Ph.D.

Talk Title: Variability and Reliability in fMRI

Abstract:
Whole brain activity during a particular cognitive task as measured by fMRI can vary significantly from individual to individual. While traditional group analyses are useful at examining commonalities across individuals, over-reliance on these methods can exclude important information available only at the individual level. In this talk I will discuss 1) the relationship between variability in brain activity across individuals and reliability of individual brain activity across time; 2) some of the common sources of variability across individuals; and 3) the potential contribution of individual differences in functional connectivity to variations in whole brain activity.

Biography:
Dr. Miller is an Associate Professor of Psychology at the University of California, Santa Barbara. He received his BA in Psychology from San Francisco State University in 1994. He then began his doctoral training in Neuroscience at the University of California, Davis where he won the Achievement Award for College Scientists. After joining the lab of Dr. Michael Gazzaniga, he transferred to Dartmouth College, where he received his Ph.D. in Cognitive Neuroscience in 1998. In 1999, he became an assistant professor at the Department of Psychology at the University of Massachusetts Boston. In 2002, he joined the faculty at UCSB. His publications studying the cognitive neuroscience of human memory, decision-making, and individual differences have utilized various techniques including functional magnetic resonance imaging, transcranial magnetic stimulation, patient testing, and signal detection analysis. Dr. Miller is the vice director of the Sage Center for the Study of the Mind and editor of The Year in Cognitive Neuroscience, an annual review published by the New York Academy of Sciences.

Susumu Mori, Ph.D.

Talk Title: Three-dimensional electronic atlas for quantitative image analysis of human white matter anatomy

Abstract:
Diffusion tensor imaging (DTI) is a revolutionary technique that can visualize macroscopic architectures of human brain white matter non-invasively. The anatomical information DTI carries is based on motion of water molecules, averaged over a large pixel size (2-2.5 mm). This neuronal information is new to us and, thus, the interpretation is not straightforward. Unlike histology-based methods, the information it provides has low specificity to specific cellular structures. On the other hand, it can provide electronic and quantitative information of the entire brain within 5 min. In this presentation, several techniques to quantify white matter structures revealed by DTI will be introduced. One of the promising approaches is the atlas-based anatomical analysis, in which a three-dimensional white matter atlas is used to parcellate the entire white matter into more than 100 sub-structures and quantify the properties of each segment. The advantages and disadvantages of the techniques will be discussed.

Biography:
1991-1996: Ph.D in Biophysics, Johns Hopkins University, School of Medicine
1997-2002: Assistant Professor in Radiology, Johns Hopkins University, School of Medicine
2002-2006: Associate Professor in Radiology, Johns Hopkins University, School of Medicine
2006- : Professor in Radiology, Johns Hopkins University, School of Medicine

Jorge Riera, Ph.D.

Talk Title: Stochastic scenery for spontaneous Ca2+ oscillations in astrocytes: Signatures in the resting states fMRI

Abstract:
It is well know that neurons send messages to the vasculature by both phasic and tonic pathways. The phasic pathway has been associated with the release of vasoactive substances directly from neurons, which may have fast desensitization dynamics and probably no cumulative effect. In contrast, the tonic pathway, that involves astrocytes as the major mediator, is thought to be sensitive to long lasting small increases in the excitability of the neuronal networks. In this study, we hypothesize that ongoing BOLD signal fluctuations may originate from the continuous accumulation of the neurotransmitters released by neurons to the extracellular space, in particular glutamate, through the tonic astrocytic pathway. We propose a mechanism by means of which astrocytes are able to spontaneously oscillate with their sensitivity determined by calcium influx and the levels of neurotransmitters in the extracellular pool. First, we discuss the validity of our biophysical model on the basis of in vivo/vitro two photon imaging data and LFP observed from the cortex of rodents. In particular, we found significant differences in the activity of the astrocytic networks, which was properly quantified by particular model parameters, as a function of age and the levels of amyloid beta deposition. Second, we used our model to interpret the temporal characteristics of the BOLD resting states in both APP and WT mice. We conclude that regional variations in the resting state BOLD signal may indirectly reflect steady state activity of the neuronal networks, but they are majorly determined by the local physiological states of the astrocytes.

Biography:
In 1988, I obtained a BS in Physics at the University of Havana. In the period 1995-1998, I was selected as “Junior Associate” of the International Centre for Theoretical Physics, Trieste (Italy), where I completed the required credits for a master degree in biophysics. In 1999, I received the PhD degree in Physics from the University of Havana with a dissertation on “Brain Electric Tomography: the Solution of EEG/MEG Forward and Inverse Problems based on a New Approach.” A part of my PhD thesis was done at the Pitie-Salpetriere Hospital in Paris. My first postdoc term was in the RIKEN Brain Science Institute (Japan) on the development of mathematical methods to study deep brain sources from MEG single trials. My second postdoc term was in Tohoku University (Japan) on the elucidation of the physiological foundations of fMRI and NIRs data. In 2004, I was appointed as associate professor in Tohoku University. My main scientific interest is to develop method for the integration of neuroimaging multimodalities based on modeling mesoscopic phenomena in the cerebral cortex. For that end, I focus the attention in the following issues:

• Signal integration by neurons and astrocytes
• Micro-circuitries and networks of cells in the cortex
• The dynamics of neuronal masses and the brain connectivity graphs
• The biophysics of the neuro-vascular/metabolic coupling
• The spatiotemporal inverse problems in neuroimaging

Based on neuroimaging methods, I have been working on the conception of novel prosthetic devices and original pharmacological tools to study some brain disorders, i.e. dementias and stroke. I have received three awards from the Cuban Academy of Science and Ministry of Education. I have completed three patents related to the fusion of different neuroimaging techniques and around 45 per-reviewed papers in related fields. My main contribution to Tohoku University started in 2007 with the creation of one of the most complete animal facility for neuroimaging and a multidisciplinary group, i.e. the Neuronal Mass Dynamics (NMD) group. NMD group belongs to the Functional Brain Imaging department (Prof. Ryuta Kawashima). This group is equipped with the most novel technology to imaging the brain of rodents in vivo (i.e. multi-photon laser microscopy, high field MRI, and high-resolution electrophysiological equipments). In 2009, we created a chronic facility with both wild type and transgenic rodents.

Greg Snider

Talk Title: Neuromorphic computing with memristive nanodevices

Abstract:
Building a brain with electronics is hard—we have only the faintest notions about how to structure such a thing, and attaining biological-scale power and volume (roughly 20 watts, stuffed in a shoebox), is beyond our current technological reach. One imposing problem for a brain-builder is implementing “synapses” in electronic circuits. Brains need synapses, and lots of them—roughly 10¹⁰ / cm² in human cortex. Biological synapses are tiny, consume miniscule power, have complex, nonlinear dynamics, and apparently maintain their memory for long periods of time. Memristive nanodevices are tiny electronic devices that behave like analog memories, forming an attractive substrate for building dense “synaptic memory.” The Cog ex Machina project is building a neuromorphic compute platform based on memristive memory that will allow researchers to build large brain models that interact with an environment, real or simulated, in real-time. This is joint work with Boston University and UCLA and is partly funded by the DARPA SyNAPSE program.

Andy Stenger, Ph.D.

Talk Title: Altered Resting State Activation in Methamphetamine Users

Biography:
Dr. Andy Stenger is an Associate Professor at the University of Hawaii in the Department of Medicine in the John A. Burns School of Medicine. He has been actively involved in physics research using magnetic resonance for almost 20 years, with the last 14 focusing on brain functional MRI (fMRI). He has continuing NIH funding for projects to develop new MR physics methodology for improved brain imaging at high field. These methods include spiral k-space trajectories, RF pulse design, susceptibility weighted imaging, and parallel transmission. He also collaborates with numerous clinical and basic science investigators as well as runs his own investigations with MRI to study the brain. These applications include fMRI studies to observe the effects of drugs of abuse on the human brain.

Talk Abstract:
Methamphetamine (MA) abuse is known to be associated with cognitive deficits and altered brain activation on prior blood-oxygenation-level-dependent functional MRI (BOLD-fMRI) studies. However, whether MA alters the resting state brain function is unknown. This work explores the use of resting state BOLD-fMRI to observe the resting state networks (RSNs) in MA users.

Methods:
Resting state BOLD-fMRI was performed in 20 healthy non-drug users and 20 abstinent (negative urine test) MA users. Each subject had one four-minute fMRI scan on a 3 Tesla Siemens MRI scanner. Images were acquired with a spiral in/out gradient-echo sequence to achieve a higher signal-to-noise ratio. The subjects were instructed to rest motionless with their eyes closed. The Melodic FSL software tool was used to perform Probabilistic Independent Component Analysis (ICA) on the entire group of 40 runs. A set of ICA spatial maps and time courses were obtained identifying the major sources of variation in the entire group. A dual-regression approach and a General Linear Model were used to assess the statistical significance of each ICA to each group.

Results:
The RSNs of interest were identified by visual inspection and corresponded to previously observed RSNs in the literature. Results of the dual-regression analysis found that the RSNs for executive function (prefrontal cortex) and inhibition (anterior cingulate cortex) appeared to be activated less in the MA users.

Conclusions:
The decreased activation in the executive and inhibitory RSNs in the MA users suggest that these areas are down regulated by MA use or MA-associated brain injury.

Stephen Strother, Ph.D.

Talk Title: The Structure of Functional Brain Connectivity Revealed by “Mind Reading” Prediction and Reproducibilty Metrics in the NPAIRS framework.

Abstract:
Analytic solutions using so called “mind reading” techniques with multivariate classifiers are typically reported as a single prediction or accuracy level and its associated spatial activation pattern (Periera et al., 2009).  The optimization criterion underlying most such techniques is to maximize the cross-validated prediction level of the reported solution. However, such solutions represent just one point on a continuum of solutions that may be traced out using Prediction vs. Reproducibility (p,r) curve plots in the NPAIRS framework (Strother et al., 2002, 2004, 2010). This continuum moves from the usual maximum prediction solutions that are typically statistically sparse and therefore have low reproducibility,  through solutions with intermediate levels of (p,r), to highly reproducible, nonpredictive solutions. I will show that relatively simple multivariate predictors in the NPAIRS framework provide superior signal detectors of brain networks in BOLD fMRI (Yourganov et al., Neuroimage, in press), and discuss what this continuum of (p,r) solutions tells us about the hierarchical structure of functional connectivity in the brain.

Biography:
Stephen C. Strother (sstrother@rotman-baycrest.on.ca) received the B.Sc. and M.Sc. in Physics and Mathematics from Auckland University, New Zealand in 1976 and 1979, respectively, and a PhD in Electrical Engineering from McGill University, Montreal in 1986. From 1985 he was a postdoctoral fellow at Memorial Sloan Kettering Cancer Center, New York, and in 1989 he joined the VA Medical Center, Minneapolis as senior PET Physicist, and the University of Minnesota where he became Professor of Radiology in 2002. In 2004 he moved to Toronto as a senior scientist at the Rotman Research Institute and Professor of Medical Biophysics at the University of Toronto where he is also a core member of the multi-institutional Centre for Stroke Recovery. His current research interests include neuroinformatics with a focus on machine and statistical learning techniques, particularly predictive modeling and network metrics, for optimizing PET and fMRI/MRI neuroimaging pipelines in research and clinical applications applied to databased-studies of the aging brain.  In 2001 he cofounded Predictek, Inc., in Chicago with Dr. Miles Wernick to provide state-of-the-art predictive modeling for industrial applications of medical imaging. From 2002-2007 he chaired the Data Format Working Group, an international standards committee that developed the NIfTI data format under the Neuroimaging Informatics Technology Initiative at NIH. He is an Associate Editor for Human Brain Mapping.