KAIST workshop on Neuroimaging and Brain mappingneuroimage2010.kaist.ac.kr/abstracts.pdf · Jong Chul Ye, Department of Bio and Brain Engineering, KAIST Joon-Kyung Seong, School of

■ Date: Dec. 3 (Fri.) 09:00AM~6:40PM

■ Venue: Fusion hall, KI B/D (1F), KAIST, Daejeon, Korea

■ Host: WCU Neuro Systems Research GroupDepartment of Bio and Brain Engineering, KAISTDepartment of Computer Science, KAIST

■ Support: Korea Research Foundation, KAIST ICC, KORANET

KAIST workshop

on Neuroimaging and

Brain mapping

KAIST workshop on Neuroimaging and Brain mapping

Invitation Message …………………………………………………… 2

Organizing Committee ……………………………………………… 3

Program at a glance …………………………………………………… 4

Abstract and Curriculum vitae ………………………………………… 5

C ontents

1


I nvitationMessage

On behalf of the organizing committee, I would like to thank the speakers andaudience for attending. It is

truly a privilege to be able hold this workshopon Neuroimaging and Brain mapping.

The field of Neuroscience and Neuroimaging is truly an interdisciplinary approachwhich draws knowledge

from traditional sciences such as physics, chemistry,neurology, neurobiology, and from the cognitive sciences

such as psychiatry andpsychology.

Neuroimaging itself is a relatively new and emerging area of research in science, engineering and medicine.

With the development of X-ray CT (computed tomography) in the late 1900’s, the field of neuroimaging has

taken huge strides forward since, with the invention of MRI (magnetic resonance imaging), PET (positron

emission tomography), MEG (magnetoencephalography), and EEG (electroencephalography). These imaging

modalities allows for non-invasive in-vivo imaging of human brains allowing physicians to diagnose brain

disease and abnormalities early without the use of invasive surgery. It has also allowed scientists and

researchers to explore the inner structures as well as chemistry of the human brain in-vivo, extending the

research field of neuroscience from the study of mice and rats to the human brain. In addition analyzing

methods such as DTI, anatomical segmentation and fMRI allows us to gain in-depth knowledge to the

structural and functional features of individual human brains.

This workshop will address methods using neuroimaging to study mechanisms ofbrain function and how

these depend on structure and/or architecture of the brain. Itwill also address image analysis of these

neuroimaging modalities for brain mappingand clinical applications.

We invited ten distinguished speakers from abroad and Korea to provide a comprehensive overview of the

current trends and research being performed in this field. I hope you will all enjoy this workshop and that it

will provide invaluable knowledge and stimulate discussion and new ideas for future research.

Yong Jeong, MD, Ph.D

Chair of Organazing Committee

2


Host

WCU NeuroSystem Research Group

Department of Bio and Brain Engineering, KAIt

Department of Computer Science, KAIST

Organizing Committe

Yong Jeong, Department of Bio and Brain Engineering, KAIST, Chair

Sung Yong Shin, Department Computer Science, KAIST

Jinah Park, Department Computer Science, KAIST

Jong Chul Ye, Department of Bio and Brain Engineering, KAIST

Joon-Kyung Seong, School of Computer Science and Engineering, Soongsil University

Sponsors

Korea Research Foundation

KAIST ICC

KORANET

3


P rogram at a glance

Time Title

09:00 - 09:50 Registration

09:50 - 10:00Opening Remarks

Yong Jeong, KAIST, Korea

10:00 - 10:10

10:10 - 10:50

Welcome Address

Minho Kang, Vice President, KAIST, Korea

Session I

Chair: Jong Chul Ye, KAIST, Korea

From Structure to Function - Quantifying Connectivity in the Brain

Thomas Knosche, Max-Planck Institute for Human Cognitive and Brain Sciences, Germany

10:50 - 11:20A Spectral-based Method for Labeling Anatomical Structures using Expert-provided Examples

Joon-Kyung Seong, Soongsil University, Korea

11:20 - 12:00What can Cortical Morphology Tell about the Underlying Brain?

Jean-Francois Mangin, LNAO, Neurospin, CEA, France

12:00 - 13:00 Lunch

Special Session

Chair: Yong Jeong, KAIST, Korea

13:00 - 13:40From Neuro-Imaging to Neuro-Robotics

Dae-Shik Kim, KAIST, Korea

Session II

Chair: Hae-Jung Park, Yonsei University, Korea

13:40 - 14:10Clinical Impact of Small Vessel Disease MRI Markers

Sangwon Seo, Sungkyunkwan University, Korea

14:10 - 14:40Using the Functional MRI for Elucidating the Neural Underpinning of Social Dysfunctions

Jae-Jin Kim, Yonsei University, Korea

14:40 - 15:10Functional Network Changes in Alzheimer’s Disease Yong Jeong, KAIST, Korea

15:10 - 15:40 Discussion/Coffee Break

Session III

Chair: Jean-Francois Mangin, LNAO, Neurospin, CEA, France

15:40 - 16:10Diffusion Imaging and Anatomical Connectivity

Hae-Jung Park, Yonsei University, Korea

16:10 - 16:40Heat Kernel Soothing in Cortical Manifolds

Moo K. Chung, U of Wisconsin-Madison, USA

16:40 - 17:10Functional NIRS: Neuroimaging with the Speed of Light

Jong Chul Ye, KAIST, Korea

17:10 - 17:20 Workshop Concludes and Adjourn

17:20 - 18:40 Banquet

..

4

Session 1

Chair : Jong Chul Ye, Ph.D

Department of Bio and BrainEngineering, KAIST

KAIST workshop

on Neuroimaging and Brain mapping

6

In this presentation I will present the concept of anatomical connectivity and, in particular, how aspects of this connectivity can

be non-invasively estimated using diffusion MRI. I will also discuss the pitfalls and limits of the technique. Advanced algorithms

for the reconstruction of white matter fiber tracts, especially in the presence of complicated fiber configurations like crossings and

branching, will be presented. Finally, I will show how tractography algorithms can be used to perform connectivity based

parcellation of the cortex and to provide prior knowledge for functional models of the brain.

Speaker 1.

From Structure to Function - Quantifying Connectivity in the Brain

Max-Planck Institute for Human Cognitive andBrain Sciences, Germany

Thomas Reiner Knosche, Ph.D..

7

Thomas Reiner Knosche, Ph.D

Current Affiliation

Max Planck Institute for Human Cognitive and Brain Sciences

E-mail: [email protected]

Education and Professional Career

1984: Humboldt-Schule Potsdam

1987-1992: TU Ilmenau, General and Theoretical Electrical Engineering

1990-1991: Sheffield City Polytechnic (now Sheffield Hallam University) , Great Britain, Control Engineering

Diploma Thesis: 1992, TU Ilmenau, “Modelling of magnetocardiographic signals”PhD Thesis: 1997, TU Twente, Niederlande, “The neuroelectromagnetic inverse problem - an evaluation study ”, supervised by

Dr. M.J. Peters, Prof. Dr. H. Rogalla and Prof. Dr. Avan Oosterom

1992 -1993: PhD student, TU Ilmenau

1993 -1996: PhD student and scientific assistant, TU Twente, Enschede, Netherlands

1997 -1998: Postdoctoral researcher at MPI for Neuropsychological Research (now MPI for Human Cognitive and Brain

Sciences), Leipzig

1999 -2001: Research &Development manager of A.N.T. Software B.V., Enschede, Netherlands

2001- present: Researcher at MPI for Human Cognitive and Brain Sciences

2006 - present: Head of research group “Cortical Networks and Cognitive

..

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In this talk, I will introduce two of my recent research projects on labeling humananatomical structures based on spectral-based

matching algorithm. First project is todevelop a spectral-based method for automatically labeling and refining major sulcalcurves

of a human cerebral cortex. Given a set of input (unlabeled) sulcal curvesautomatically extracted from a cortical surface and a

collection of expert-providedexamples (labeled sulcal curves), our objective is to identify the input major sulcalcurves and assign

their neuroanatomical labels, and then refines these curves basedon the expert-provided example data, without employing any

atlas-based registrationscheme as preprocessing. Our method provides consistent labeling and refining resultseven under high

variability of cortical sulci across the subjects. Second project is todevelop a method for segmenting the white matter fibers into

anatomically meaningfulbundles using spectral-based algorithm based on example bundles labeled by experts.Unlike existing

segmentation methods, the example data are not registered to a singleatlas space in order to preserve their individual variability.

Instead, we use multipleatlases each of which consist of the labeled example fiber bundles for a single subject.To determine the

label of each input fiber, we could compare each input fiber with allfibers in the example data, which would result in excessive

computation time. To reducecomputation time, we adapt the spectral matching algorithm to our problem setting inorder to find the

best-matching bundle for each input fiber.

Speaker 2.

A Spectral-based Method for Labeling Anatomical Structures

using Expert-providedExamples

School of Computer Science and Engineering, Soongsil University, Korea

Joon-Kyung Seong, Ph.D

9

Joon-Kyung Seong, Ph.D

Current Affiliation

Assistant Professor

School of Computer Science and Engineering, Soongsil University



1996 -2000: B.S., Computer Science,Seoul National University, Seoul, Korea

2000 -2005: Ph.D., Computer Science Seoul National University, Seoul, Korea

Dissertation: A Problem Reduction Scheme for Solving Geometric Constraints and Its Applications. Advisor: Professor Myung-

Soo Kim

2005 -2008: Postdoctoral Fellow, School of Computing, University of Utah, US Research on computing Voronoi diagrams,

medial axis, and ridge curves on B-spline surfaces

2008 -2010: Assistant Research Professor, Department of Computer Science, KAIST, Korea Research on geometric problems in

computational neuroanatomy and computer graphics

2010 - present: Assistant Professor, School of Computer Science and Engineering, Soongsil University, Korea

Research Interests

Computational Neuroanatomy

Computer Visualization

Multi-Core Algorithms in Computational Biology

Geometric Problems in Computer Graphics

KAIST workshop


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Study of the variability of the cortical mantle thickness is now a key issue inneuroimaging. During this talk we will describe a

more recent trend aiming at the study of the variability of the cortical folding morphology.Computerized three-dimensional

versions of gyrification index and other morphometricfeatures dedicated to the folding patterns are modified in psychiatric

syndromes andneurologic disorders. These observations provide new insights into the mechanismsinvolved in abnormal

development or abnormal aging. Quantification of the folding morphology will contribute to the global endeavor aiming atbuilding

biomarkers from neuroimaging data, with a specific focus on developmentaldiseases.

Speaker 3.

What can Cortical Morphology Tell about the Underlying Brain?

LNAO, Neurospin, CEA, France

Jean-Francois Mangin, Ph.D

11

Jean-Francois Mangin, Ph.D

Current Affiliation

Head of the Computer Assisted Neuroimaging Lab, Neurospin, Biomedical Imaging Institute, CEA



Jean-Francois Mangin received the engineer degree from Ecole Centrale Paris in 1989, the M.Sc. degree in numerical analysis

from Pierre et Marie Curie University (Paris VI) in 1989, and the PhD degree in signal and image processing from Ecole Nationale

Superieure des Telecommunications of Paris in 1995.

Since 1991, he has been working with Service Hospitalier Frederic Joliot, Commissariat a l’Energie Atomique, Orsay, France,

on image analysis problems related to brain mapping. Since 1999, he has been leading a group, which project consists of the

development of a new bunch of brain mapping methods designed from a structural point of view.

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Special Session

Chair : Yong Jeong, MD, Ph.D

Department of Bio and Brain Engineering, KAIST

KAIST workshop


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With 50 million neurons (processing elements) and several hundred kilometers of axons (wires) terminating in almost one

trillion synapses (connections) for every (!) cubic centimeter, and consuming only about 12 watts energy for the entire cortex, the

brain is arguably one of the most complex and densely packed, yet highly efficient information processing systems known. It is

also the seat of sensory perception, motor coordination, memory, and creativity ? in short, what makes us humans to humans.

There are fascinating complementarities between human brains and artificial information processing systems created by humans:

what appears to be challenging for us, seems easy for computers, while highly demanding problems for computers and robots such

as face recognition and smooth locomotion are mastered without much difficulties by humans. I will argue in this talk that many of

the computationally hard problems of perception and action appear to be easy to humans precisely because they have been

successfully solved by the brain during the course of its evolution and ontogeny. The corollary of this claim is that the structure and

function of the brain may already contain solutions to many of the hard problems faced by artificial intelligence and cognitive

robotics systems today. To this end, I will first review the brain imaging studies I performed in the past 15 years using advanced

optical and magnetic resonance imaging technologies in order to elucidate the functional and connectivity architecture of the

mammalian brains. I will then try to convince you that time is ripe for communication engineers, circuit designers, and roboticists

to join with brain scientists to start reverse engineering the whole brain, which in turn will lay ground for new generations of truly

disruptive neuroengineering applications.

Speaker 4.

From Neuro-Imaging to Neuro-Robotics

Department of Electrical Engineering,KAIST, Korea

Dae-Shik Kim, Ph.D

15

Dae-Shik Kim,Ph.D

Current Affiliation

Professor

Department of Electrical Engineering, KAIST

Laboratory for Brain Reverse Engineering and Imaging

Adjunct Professor of Anatomy and Neurobiology, Boston University School of Medicine

Affiliated Faculty, Department of Cognitive and Neural Systems, Boston University

E-mail: [email protected] or [email protected]


1987-1992: Undergraduate training in Psychology and Computer Science,Darmstadt University of Technology, Germany

1991-1992: MS, Max-Planck-Institute for Brain Research, Germany

1992-1994: Ph.D., Max-Planck-Institute for Brain Research, Germany

1994-1996: Postdoctoral Associate, Massachusetts Institute of Technology(MIT),Cambridge

1996-1998: Frontier Researcher, The Institute of Physical and Chemical Research (RIKEN), Japan

1997-1998: Research Instructor, Institute for Cognitive and Computational Sciences, Georgetown University, USA

1998-2003: Research Associate then Assistant Professor, Center for Magnetic Resonance Research, University of Minnesota

Medical School, USA

2003-2009: Associate Professor and Director, Center for Biomedical Imaging, Boston University, USA

2009-present: Tenured Full Professor of Electrical Engineering, KAIST, Korea

Research Interests

Systems, developmental, and computational neurosciences, Functional andconnectivity mapping of the human brain, Brain

plasticity and development, BrainReading, Developmental Robotics, Diffusion Tensor Imaging, ComputationalNeuroanatomy,

MRI of neurodegeneration, Visual neuroscience, Development ofExtremely high-field (7T+, 14T) MRI

Session 2

Chair : Hae-Jeong Park, Ph.D

Department of Radiology and Psychiatry, Yonsei University

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Subcortical vascular cognitive impairment (SVCI), which is composed of vascular dementia (SVaD) and vascular mild cognitive

impairment (svMCI) of subcortical type, refers to cognitive impairment associated with small vessel disease. It is characterized by

extensive white matter intensities (WMH) and multiple lacunes. Previous studies also showed that small vessel disease can give

rise to microbleed (MB) as well as ischemia, which is associated with multiple cognitive impairments in SVCI. Although wmh and

lacunes were located in subcortical region, Prior MRI volumetric studies showed that cortical atrophy in SVaD occurred,

suggesting that it could be caused by concomitant AD pathology. However, they did not investigate the topography of cortical

atrophy. Recently, we reported that topography of cortical thinning was different between AD and SVaD. That is, when comparing

AD and SVaD, SVaD showed cortical thinning in frontal region while AD showed cortical thinning in medial temporal region.

Topography of cortical thinning largely overlapped between SVaD and svMCI, but their extent and severity were greater in SVaD

than in svMCI. Both these WMH and frontal thinning also had independent relationship with frontal executive dysfunction

suggesting that WMH are associated with frontal thinning, which is further associated with frontal executive dysfunction. Vascular

risk factors also associated with cortical thinning in frontal and perisylvian regions. Vascular risk factors affected cortical thinning

both with and without the mediation of WMH, where the effect without the mediation of WMH was greater than that with the

mediation of WMH. Diffusion tensor imaging is a sensitive tool for detecting microstructural changes in white matter. Patients

with small vessel disease showed that a specific distribution of fiber tract damage is more related with clinical deficits than is the

severity of the total ischemia. Pittsburgh compound-B (PIB) PET is a very sensitive method to detect the fibrillar form of β-

amyloid that is pathognomic marker of Alzheimer’s disease. Thus, it can differentiate mixed dementia from SVCI with amyloid.

Along with development with PIB-PET, studies on relationship between small vessel markers and amyloid increasingly occurred.

Also, 7T MRI can directly visualize the stenosis or obstruction of small vessel in patients with SVCI.

Speaker 5.

Clinical impact of small vessel disease MRI markers

Department of Neurology, Sungkyunkwan University, Samsung Medical Center

Sang Won Seo, MD, Ph.D

19

Current Affiliation

Assistant Professor




1997: M.D.Yonsei University, College of Medicine, Korea

2001: M.S.Yonsei University, College of Medicine, Korea

2008: Ph.DYonsei University, College of Medicine, Korea

1998-2002: ResidencyYonsei University, College of Medicine, Korea

2005-2007: Clinical fellowSamsung Medical Center, Korea

2007-2008: Research InstructorSamsung Medical Center, Korea

2008-2010: Clinical Assistant ProfessorSamsung Medical Center, Korea

2008-2010: Clinical Assistant Professor,Samsung Medical Center, Korea

2010-Present: Assistant Professor,Samsung Medical Center, Korea

Sang Won Seo, MD, Ph.D

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Appropriate usage of social skills by individual members ensures the survival of communities in the human society. Social skills

contain the cognitive abilities as well as verbal and nonverbal behaviors indispensable for interpersonal interactions. Social skills

encompass a set of cognitive abilities and interactive behaviors that facilitate efficient interaction among individuals. Social skills

cannot be claimed as a feature exclusive to human behavior, but they constitute an irreplaceable part of human interaction by

making social communication among one another more articulate and intelligible. Abnormal social skills have been reported in

patients with mental illnesses including schizophrenia. Various methods, including self-report, interviews, behavioral observation,

and clinical rating scales, have been used for the assessment of social skills, but their usefulness has been limited because of

subjective or observational biases. Recent development and technological advances have allowed the use of the virtual reality

system to present socio-affective stimuli to human subjects, thus enabling scholars to measure behavioral characteristics of

participants during social interaction with virtual avatars. Technological advancement in graphics and other human motion tracking

hardware should be able to promote pushing “virtual reality”closer to “reality,”and thus virtual reality can be used to assess social

cognition and behavior in real life-like situations. Given that a virtual reality system could provide viable environments for

individuals to interact with social avatars, it may be one of the most promising tools for assessing social skills without biases. One

promising future direction is to integrate the virtual reality system with neuroimaging methods such as fMRI. Through the

emergence of social neuroscience, there has been an explosion of neuroimaging research with much of its concentration on brain-

mapping of various social cognition abilities. Based on an on-going accumulation of extensive neuroimaging research in social

skills, integration of neuroimaging methods and virtual reality promises to help form a synergistic relationship between the two

fields whiling building on the past knowledge about the brain mechanisms involved in social skills. For example, an fMRI study

investigated brain activity evoked by mutual and averted gaze in a compelling and commonly experienced social encounter. In this

study, subjects wearing virtual-reality goggles viewed a man who walked toward them and shifted their neutral gaze either toward

or away, and the results showed that the superior temporal sulcus was involved in processing social information conveyed by shifts

in gaze within an overtly social context.

Using our accumulated knowledge to assess individuals’condition relating to the neurocognitive basis of social skills germane

to mental health problems provides new and exciting possibilities. For example, in order to evaluate attributional style which

means how people typically infer the causes of emotional behaviors, we developed a virtual reality attribution task, and patients

with schizophrenia and healthy controls underwent fMRI while performing three (happy, angry, and neutral) conditions of the task.

The results showed that the patients may have functional deficits in mirror neuron system when attributing positive behaviors,

which may be related to a lack of inner simulation and empathy and negative symptoms. In contrast, the patients may have

increased activation in the precuneus/posterior cingulate cortex related to self-representations while attributing negative behaviors,

which may be related to failures in self- and source-monitoring and positive symptoms. This experiment is a good example that

neuroimaging and human brain-mapping research can benefit from the improvement on the degree of realism depicted via

presentation of social stimuli using virtual reality. In summary, it may be expected that the neural basis of various social functions

and their deficits will be able to be elucidated by combined uses of virtual reality and functional neuroimaging techniques.

Speaker 6.

Using the Functional MRI for Elucidating the Neural Underpinning of

Social Dysfunctions

Department of Psychiatry, Yonsei University Gangnam Severance Hospital

Jae-Jin Kim, MD, Ph.D

21

Jae-Jin Kim, MD, Ph.D

Current Affiliation

Professor

Department of Psychiatry and Diagnostic Radiology, Yonsei University College of Medicine



1987: MD: Seoul National University Medical College, Seoul, Korea

1991: Residency: Department of Psychiatry, Seoul National University Hospital, Seoul, Korea

1990: M.S.Seoul National University Graduate School, Seoul, Korea

2002: Ph.D.Seoul National University Graduate School, Seoul, Korea

1997-1999: Visiting Research Scholar: Mental Health Clinical Research Center, University of Iowa Hospitals and Clinics, IA.

USA

2000-2002: Research professor: Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea

Research Interests

Neuroimaging , Social Neuroscience, Virtual Reality, Schizophrenia

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Alzheimer’s disease (AD) is the most common form of dementia which is characterized by a progressive decline of memory and

cognitive functions. Amnestic mild cognitive impairment (aMCI) is a risk factor for AD and is viewed as a prodromal stage of AD.

Patients with AD and aMCI show memory impairment in their early stages which reflects damage to the hippocampus and related

network, since it is a significant region associated with declarative memory. Functional magnetic resonance imaging (fMRI)

studies of the human brain have suggested that spontaneous low-frequency fluctuations in resting state blood oxygen level

dependent (BOLD) signal correspond to functionally relevant resting state networks (RSNs). Here we explore to see if the left and

right hippocampal networks exhibit different changing patterns of connectivity in patients as disease progresses from aMCI to AD.

When looking at the overall connectivity of both the left and right hippocampus we observed different dynamics when comparing

connectivity to disease progression. The left hippocampus of the brain shows greater correlation with the prefrontal cortex while

right shows showed decreased connectivity in these regions. Also the left hippocampus showed decrease connectivity to the

precuneus while the right increased connectivity. These differences may suggest that the left and right hemispheres are affected

differently with Alzheimer’s disease. Also these regions of increased connectivity in the left hippocampus occur in mainly the

frontal areas which may represent compensatory recruitment of brain networks.

Speaker 7.

Functional Network Changes in Alzheimer’s disease


Yong Jeong, MD, Ph.D

23

Yong Jeong, MD,Ph.D

Current Affiliation

Associate Professor


Laboratory for Cognitive Neuroscience and NeuroImage



URL: http://ibrain.kaist.ac.kr


1991: MD, Yonsei University College of Medicine, Seoul, Korea

1997: Ph.D (in Neurophysiology), Yonsei University, Seoul, Korea

2002: Boad of Neurology

2002 - 2003: Clinical and research Fellow, Department of Neurology, Samsung Mdical Center

2003 - 2005: Clinical and research Fellow, Department of Neurology, University of Florida

2005 - 2007: Visiting Professor, Department og Bio and Brain Engineering

2007 - present: Associate Professor Department og Bio and Brain Engineering and Adjunct Professor, Department of neurology,

Sunkyunkwan University, Samsung Medical Center

Research Interests

Yong Jeong’s research fields are Cognitive Neuroscience, Clinical Neurology (degenerative disease, vascular disease),

Functional Neuroimaging, and Bioengineering (biosignal). His interest is the fundamental architecture of cognitive function,

cerebral hemodynamics, and pathophysiology of neurodegenerative disease such as Alzheimer’s disease, Parkinson’s disease and

stroke.

Session 3

Chair: Jean-Francois Mangin, Ph.D

Neurospin, Biomedical Imaging Institute, CEA

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Recent brain research relies heavily on the connectionism since connection exists universally in the brain, for example, between

neuron and neurons, between region and regions, between brains and cultures. To examine various levels of connection within

brain, several neuroimaging techniques have been suggested to show invivo connectional information despite their own limitations.

The most widely used method for anatomic brain connection is provided by diffusion tensor imaging (DTI), which has rapidly

evolved as a new in vivo approach to the investigation of white-matter abnormalities or tissue damages: quantifying the diffusivity

of the water molecules in brain. The quantification of water diffusion in vivo is based on the characteristic movement of water

molecules, which varies depending upon the tissue. For example, in pure liquids, such as cerebrospinal fluid, the motion of

individual water molecules is random, meaning it has equal probability in all directions. However, the movement of water

molecules within myelinated fibers is substantially restricted along the direction perpendicular to the orientation of the axons.

Consequently, in white-matter fiber tracts, the principal direction of the water diffusion appears to represent the direction of the

fiber bundles. Thus, connecting points along the principal direction of the diffusion makes it possible to appreciate white-matter

tracts within the brain. Such fiber-tracking schemes, often referred to collectively as fiber tractography, provide important

information about the connectivity between brain regions. Therefore, DTI provides a quantitative assessment of the tissue-specific

diffusivity and also provides information on anatomical connection. This presentation will focus on the recent advances of DTI for

the quantification of white matter in terms of brain connectivity.

Speaker 8.

Diffusion imaging and anatomical connectivity

Department of Radiology and Psychiatry, Severance Biomedical Science Institute,

Yonsei University College of Medicine

Hae-Jeong Park, Ph.D

27

Hae-Jeong Park, Ph.D

Current Affiliation

Department of Radiology and Psychiatry, Yonsei University College of Medicine


URL: http://neuroimage.yonsei.ac.kr/


1993: B.S.Electrical Engineering Seoul National University

1995: M.S. Biomedical EngineeringSeoul National University

2000: Ph.D.Biomedical Engineering Seoul National University

2000-2001: Brain Korea 21 Postdoctoral fellow in the Medical Research Center, Seoul National University

2001-2004: Research Fellow, Clinical Neuroscience Division, Laboratory of Neuroscience, Boston VA Health Care System

Brockton Division, Department of Psychiatry, Harvard Medical School

2002- 2004: Research Fellow, Surgical Planning Laboratory, MRI Division, Department of Radiology, Brigham and Women’s

Hospital, Harvard Medical School

2004-present: Director, Laboratory of Molecular Neuroimaging Technology

2004-present: Assistant Professor, Department of Diagnostic Radiology, Yonsei University College of MedicineDivision of

Nuclear Medicine, Severance Hospital, Yonsei University Health System

2009-present: Associate Professor, Department of Diagnostic Radiology, Yonsei University College of Medicine, Division of

Nuclear Medicine, Severance Hospital, Yonsei University Health System Adjunct Professor, Department of

Psychiatry, Yonsei University College of Medicine

2010-present: Adjunct Professor, Severance Biomedical Science Institute, Yonsei University

Research Interests

Multimodal neuroimaging: functional MRI (fMRI), positron emission tomography (PET), structural MRI and diffusion tensor

imaging (DTI) Molecular neuroimaging for development and developmental disease Real-time fMRI, MRI-guided intervention

and neurosurgery Medical image processing, registration and segmentation for surgical planning and medical diagnosis.Advanced

brain imaging for social neuroscience?

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We present a novel kernel smoothing framework using the Laplace-Beltrami eigenfunctions. The Green’s function of an

isotropic diffusion equation on a manifold is analytically represented using the eigenfunctions of the Laplace-Beltraimi operator.

The Green’s function is then used in explicitly constructing heat kernel smoothing as a series expansion of the eigenfunctions.

Unlike many previous surface diffusion approaches, diffusion is analytically represented using the eigenfunctions substantially

improving numerical accuracy. Our numerical implementation is validated against the spherical harmonic representation of heat

kernel smoothing on a unit sphere. The proposed framework is illustrated with mandible, hippocampus and cortical surfaces, and is

compared to a widely used iterative kernel smoothing method in computational neuroanatomy.

Speaker 9.

Heat kernel smoothing in cortical manifolds

University of Wisconsin Madison

Moo K. Chung, Ph.D

29

Moo K. Chung, Ph.D

Current Affiliation

Associate Professor

Department of Biostatistics and Medical Informatics, University of Wisconsin Madison


URL: http://www.stat.wisc.edu/~mchung/


1995: B.Sc. Honors Applied Mathematics, McGill University. Graduating on the Dean’s Honor List and with the First Class

Honor

1997: M.Sc. Mathematics, University of Toronto

2001: Ph.D. Statistics, McGill University. Advisors: Keith J. Worsley and James Ramsay Thesis title: Statistical Morphometry

in Computational Neuroanatomy

1999-2001: Lecturer, Department of Mathematics, McGill University,Canada

2001-2007: Assistant Professor, Department of Statistics, University of Wisconsin-Madison

2002-2007: Assistant Professor, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison

2007-present: Associate Professor, Department of Statistics, Biostatistics and Medical Informatics, University of Wisconsin-

Madison. Also affiliated with the Waisman Laboratory for Brain Imaging and Behavior.

2009-2011: Visiting Associate Professor, World Class University Project, Department of Brainand Cognitive Science, Seoul

National University, South Korea

Research Interests

Medical Image Analysis, Human Brain Mapping, Computational Neuroanatomy, Shape Analysis, Partial Differential Equations,

Random Fields, Network Modeling, Functional Data Analysis, Computational Statistics

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Near infrared spectroscopy (NIRS) is a non- invasive method to measure brain activity via changes in the degree of hemoglobin

oxygenati on through the intact skull. As optically measured hemoglobin signals strongly correlate with BOLD signals,

simultaneous measurement using NIRS and fMRI promises a significant mutu al enhancement of temporal and spatial resolutions.

Although there exists a powerful statistical parametric mapping tool in fMRI, current public domain statistical tools for NIRS have

seve ral limitations related to the quantitative analysis of simultaneous recording studies with fMRI. In this talk, a new public

domain statistical toolbox known as NIRS-SPM is described, which enables the quantitative analysis of NIRS signal. We also

show that fMRI and near-infrared spectroscopy (NIRS) simultaneously recording provides multiple hemodynamic responses as

well as a robust estimation of the cerebral metabolic rate of oxygen (CMRO2), which can be used for to reveal various

hemodynamic and metabolic changes for an early detection or monitoring of SVD.

Speaker 10.

Functional NIRS: Neuroimaging with the Speed of Light


Jong Chul Ye, Ph.D

31

Jong Chul Ye, Ph.D

Current Affiliation

Associate Professor

Department of Bio and BrainEngineering, KAIST

E-mail:[email protected]


Jong Chul Ye received the B.Sc. and M.Sc. degrees with honors fromDept. of Control Engineering (now School of Electrical

Engineering),Seoul National University, Korea, in 1993 and 1995, respectively, andthe Ph.D. degree from the School of Electrical

and ComputerEngineering, Purdue University, West Lafayette, in 1999. Before hejoined KAIST in 2004, he worked as research

scientistat GE GlobalResearch Center, NY(2003 to 2004), Philips Research, NY(2001 to2003),University of Illinois at Urbana-

Champaign (1999-2001).

Research Interests

Hiscurrent research interest is developing signal processingalgorithmsfor various imaging modalities, such as MRI, f-MIRS,

PET, CT, andoptics. He received various awards from Korean academic societyincluding Guerbet Award from Korean Society for

Magnetic Resonance inMedicine (2010). His research group was the winner of 2009 ISMRMRecon Challenge at ISMRM

Workshop.

Documents

KAIST workshop on Neuroimaging and Brain mappingneuroimage2010.kaist.ac.kr/abstracts.pdf · Jong Chul Ye, Department of Bio and Brain Engineering, KAIST Joon-Kyung Seong, School of