Research
Research Topics
Brain Connectome
The recent perspective on the brain can be best described by the term ‘brain connectome’, which describes the brain as a large complex network of neurons linked by local and inter-regional connections. Accordingly, a number of neuroimaging studies have investigated the anatomical and functional connectivity of the brain. Anatomical brain connectivity can be researched using diffusion tensor imaging (DTI), which enables us to explore axonal fiber distributions in the brain.
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Functional brain connectivity can be defined as temporal associations between brain regions using EEG/MEG or functional MRI (fMRI). Among methods for studying functional connectivity, the resting-state fMRI has vitalized the investigation of functional brain networks, such as the default mode network (DMN) and the global network.
Fig. 1. Functional connectivity in Parkinson disease
Fig. 2. Fiber bundles extracted using DTI
Especially, the global brain network approach based on the graph theory regards human brain as a small world network, which is locally well-segregated and globally integrated by a few wiring connectivities. We are aiming to research the mechanism of brain networks using multimodal neuroimaging tools and to develop methods for clinical application. For this purpose, we use DTI for anatomical connectivity, fMRI for functional and effective connectivity, EEG/MEG for temporal synchronization and TMS for causal network. To understand brain, perturbed brains will be of great importance. As a part of clinical unit, we are exploring networks in the lesioned brains before and after surgical operation. Sleep is another example of perturbed dynamic networks, which is studied using EEG-fMRI.
Relevant Publications and Conference Proceedings
Functional connectivity
Bumhee Park, Joong Il Kim, Dongha Lee, Seok-Oh Jeong, Jong Doo Lee, Hae-Jeong Park*, Are brain networks stable during a 24-Hour period?, NeuroImage, 2012, 59, 456-466.Short abstract
Despite the widespread view of the brain as a large complex network, the stability of the brain network over the course of a day has not been explored. In this study, resting state functional MRIs acquired from 12 young adults at three-hour intervals over 24 consecutive hours were analyzed by evaluating the intra-class correlation coefficients (ICC) from independent component analysis (ICA), seed correlation analysis, and graph-theoretical analysis. It was found that while some default mode networks were stable (ICC>0.5), others were unstable throughout the day. These results suggest that dynamic network properties may be an inherent property of the resting state brain network.
Fig. 2. Stability of Resting State Networks. High ICC denotes network stability.
Dae-Jin Kim, Bumhee Park, Hae-Jeong Park*, Functional connectivity-based identification of subdivisions of the basal ganglia and thalamus using multi-level independent component analysis of resting state fMRI, Hum Brain Mapp, accepted.
Anatomical connectivity
Kim, D. J., Kim, I. Y., Jeong, S. O., Park, H. J.*, 2009. Evaluation of bayesian tensor estimation using tensor coherence. Phys Med Biol 54, 3785-3802.
Kim, D. J., Kim, J. J., Park, J. Y., Lee, S. Y., Kim, J., Kim, I. Y., Kim, S. I., Park, H. J.*, 2008. Quantification of thalamocortical tracts in schizophrenia on probabilistic maps. Neuroreport 19, 399-403.
Park, H. J., Kim, J. J., Lee, S. K., Seok, J. H., Chun, J., Kim, D. I., Lee, J. D., 2008. Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI. Hum Brain Mapp 29, 503-516. Short abstract
Population maps of the corpus callosum (CC) and cortical lobe connections were generated by combining cortical gray matter parcellation with the diffusion tensor fiber tractography of individual subjects, using T1-weighted structural MRIs and diffusion tensor MRIs (DT-MRI) of 22 righthanded, healthy subjects. Forty-seven cortical parcellations were derived from structural MRIs, registered to DT-MRI, and used to identify callosal fibers. The probablistic connects to each cortex were mapped on entire mid-sagittal CC voxels that had anatomical homology between subjects as determined by spatial registration. The probabilistic subdivision of the CC by connecting to the cortical gray matter provides a more precise understanding of the CC.
Fig. 5. Statistical callosal topography based on the projection from cortical lobes.
Park, H. J.*, Jeong, S. O., Kim, E. Y., Kim, J. I., Park, H., Oh, M. K., Kim, D. J., Kim, S. Y., Lee, S. C., Lee, J. D., 2007. Reorganization of neural circuits in the blind on diffusion direction analysis. Neuroreport 18, 1757-1760.
Park, H. J., 2005. Quantification of white matter using diffusion-tensor imaging. Int Rev Neurobiol 66, 167-212.
Park, H. J., Kubicki, M., Westin, C. F., Talos, I. F., Brun, A., Peiper, S., Kikinis, R., Jolesz, F. A., McCarley, R. W., Shenton, M. E., 2004c. Method for combining information from white matter fiber tracking and gray matter parcellation. Am J Neuroradiol 25, 1318-1324.
Park, H. J., Kubicki, M., Shenton, M. E., Guimond, A., McCarley, R. W., Maier, S. E., Kikinis, R., Jolesz, F. A., Westin, C. F., 2003. Spatial normalization of diffusion tensor MRI using multiple channels. NeuroImage 20, 1995-2009. Short abstract
Diffusion Tensor MRI (DT-MRI) can provide information for detection of brain abnormalities in diseases characterized by compromised neural connectivity. But for this, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration.In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance.
Fig. 6. Maps and histograms of dispersion index for linear registration and nonlinear registration.
Dispersion maps at three different coronal slices derived from linear registration (Linear) using TC are displayed in the upper row of (a) and those of nonlinear registration (Warp) using TC are displayed in the lower row of (a).
엄민희, 박범희, 박해정, 한국 아동 집단의 구조 뇌 연결지도 (Anatomical brain connectivity map of Korean children), 대한자기공명의과학회지 (J. Korean Soc. Magn. Reson. Med.), 15:1-10, 2011.
Research Funds
2008. 7 - 2012. 6. Ministry of Education, Science and Technology, 교육과학기술부, 바이오기술개발사업,
M10862020005-08N6202-00410, Neuroimage analysis of brain connectivity in the movement
disorder (복합 뇌영상을 이용한 운동장애질환의 뇌신경 연결 기전 연구)
Invited speakers at 2010 NIMS Hot Topics Workshop on
The Human Connectome: Views from MRI and microscopy
March 28~31, 2010 at Ewha Womans University, Seoul, Korea
Multimodal Neuroimaging Methods
(fMRI, DTI, sMRI, TMS-EEG, TMS-fMRI, PET, EEG, and MEG)
Brain research can be characterized as both multi-scale and multi-modal, in that it covers behavioral, functional, structural, and biomolecular information. The recent advancement of neuroimaging techniques has greatly influenced the current field of brain research. Among numerous imaging methods, structural data analysis using high resolution MRI has advanced to provide accurate information on volume, shape and cortical thickness of gray matter in an automated fashion.
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Diffusion tensor imaging (DTI) opened ways to investigate white matter structures. Biological and molecular information can be acquired from positron emission tomography (PET) while information processing of the brain is investigated using electro/magnetoencephalogram (EEG/MEG). Recently, functional neuroimaging is led by the functional magnetic resonance imaging (fMRI). Although TMS is not an imaging tool, it provides a variety of chances to explore brain networks and localized brain functions when combined with fMRI or EEG. TMS-EEG and TMS-fMRI provides causal information which is not provided by neuroimaging tools.
Fig. 1. Multimodal neuroimaging modalities
These imaging modalities are sensitive to specific aspects of information and have their own limitation. For example, fMRI and PET have high spatial resolution but cannot reveal sub-millimeter scale temporal processes. In contrast, EEG/MEG provides information with high temporal but low spatial resolution. Limited information from a single imaging modality can be compensated by complementary data from the different imaging modalities. Data uncertainty of each imaging modality can also be reduced by supplementing with additional information.
Fig. 2. Multimodal neuroimaging tools in MoNET
Fig. 3. Combinations of imaging modalities
Various types of combinations of different imaging modalities have been researched (e.g., combination of spatial and temporal, anatomical and functional, and anatomical and biological data). The most common approach is to combine anatomy and function. Anatomic information such as volume, shape and cortical thickness has been used for better understanding anatomy-function relationship as well as atlasing brain functions. Research on the anatomy-function relationship has been extended to functional and anatomic interconnection between brain regions. Localized brain activities detected by fMRI have been interpreted in relation to the anatomic connection defined by fiber tractography of DTI. Combination of anatomical and functional connections such as dynamic causal modeling and default mode network is also a new topic of recent multimodal imaging techniques. Molecular imaging such as receptor ligand PET can be an exceptional tool in understanding the link from biology to behavior as a whole when it is combined with functional or anatomical data. The spatiotemporal processing of the brain can be better understood with synchronous EEG/fMRI acquisition, which can yield complementary high temporal resolution information from EEG with high spatial resolution information from fMRI.
The importance and the rationale of multimodal imaging are based on the understanding that the whole is greater than the sum of its parts. However, it is important to understand the advantages and limitations of each modality. As the analysis techniques for each imaging modality become more sophisticated, the complexity of the imaging method increases. The assumptions and limitations of these procedures have rarely been addressed by clinician and researchers, although they may be critical for reliability and interpretation of the results. Complexity also increases as the number of modalities increases, for example, image registration errors. Integrated interpretation as well as integrated knowledge of each process is becoming more important for multimodal data analysis.
Fig. 4. Multimodal neuroimaging researches in MoNET
We are aiming to develop methods for multimodal image combination for clinical application in the surgical planning and methods for research of developmental disease such as cerebral palsy and psychiatric disease including schizophrenia.
Relevant Publications and Conference Proceedings
fMRI Analysis
HW Nam, DH Lee, JD Lee, H Park*, A method for anisotropic spatial smoothing of functional magnetic resonance images using distance transformation of structural image, Phys. Med. Biol., 2011, 56, 5063-5077.
DTI Analysis
Haewon Nam, Hae-Jeong Park*, Distortion correction of high b-valued and high angular resolution diffusion images using iterative simulated images, NeuroImage, 2011, 57, 968-978. Short abstract
High b-valued diffusion-weighted images (DWI) are susceptible to many artifacts and distortions. The mutual information-based affine registration is not adequate for precise correction of distortions in these DWIs. To overcome this problem, we proposed an iterative image registration through which simulated DWIs are generated, driven from a diffusion tensor estimate, as targets for measured DWIs in the registration. Since simulated DWIs have intensity profiles similar to those of the measured DWIs and the same geometric profiles as b0-images, the iterative procedure enables intensity-based nonlinear registration. Performance evaluation for high angular resolution diffusion imaging and diffusion kurtosis imaging showed that the performance of the proposed method was superior compared to the conventional registration technique.
Fig. 7. Results of artifact correction in four cases.
Results after affine registration to target b0-image (AFFb0) and after nonlinear registration to target sDWI (NLsDWI).
Kim, D. J., Park, H. J., Kang, K. W., Shin, Y. W., Kim, J. J., Moon, W. J., Chung, E. C., Kim, I. Y., Kwon, J. S., Kim, S. I., 2006a. How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study. Magn Reson Imaging 24, 1369-1376.
PET Analysis
Park, H. J., Lee, J. D., Chun, J. W., Seok, J. H., Yun, M., Oh, M. K., Kim, J. J., 2006. Cortical surface-based analysis of 18F-FDG PET: measured metabolic abnormalities in schizophrenia are affected by cortical structural abnormalities. NeuroImage 31, 1434-1444. Short abstract
We proposed a new framework for surface-based statistical parametric mapping of PET images using MRI-based cortical surface analysis. After surface-based spatial normalization, a statistical evaluation of both cortical thickness and cortical metabolic activity was conducted on the normalized surfaces of schizophrenic patients and healthy controls. The schizophrenic patients were found to have significant cortical thinning. Accordingly, their PET imaging was significantly affected by the partial volume effect. After the correction of the partial volume effects, the patients showed hyperactivity in the temporal cortex and hypoactivity in the prefrontal cortex. Our results demonstrate that anatomical factors affect analysis of functional data from the PET, thereby emphasizing the importance of combining anatomy and function in the analysis of imaging data for schizophrenia.
Fig. 5. Insensitivity to the PVE (iPVE) between the patients with schizophrenia and the normal controls.
Schizophrenia (SZ) and normal control (NC). (A) The average iPVE maps of NC and SZ derived from smoothing the virtual glucose metabolic image with a FWHM of 6 mm (Fig. 2). (B) Surface-based statistical parametric mapping (S-SPM) of iPVE maps (FWHM 6 mm) of 16 NC and 16 SZ after surface smoothing with 250 iterations (approximately 29 mm in FWHM). (C) S-SPM of iPVE derived by smoothing the virtual glucose metabolic image using an isotropic Gaussian kernel with 10 mm and 16 mm in FWHM. (D) S-SPM difference between NC and SZ after surface smoothing of iPVE with iterations 120 and 1000, which are equivalent to 20 mm and 58 mm in FWHM. In all evaluations (B–D), NC have significantly higher iPVE regions at the inferior frontal cortex and temporal cortex (P < 0.01) but do not have significantly lower iPVE regions than SZ. n = 16(NC)/16(SZ).
Park, H. J., Kim, J. J., Youn, T., Lee, D. S., Lee, M. C., Kwon, J. S., 2003. Independent component model for cognitive functions of multiple subjects using [15O]H2O PET images. Hum Brain Mapp 18, 284-295. Short abstract
An independent component model of PET images from multiple subjects is proposed to explore the overall functional components involved in a task and to explain subject-specific variations of metabolic activities under altered experimental conditions. The variation of hemodynamic activities depending on subjects and tasks can be explained by the variation in the usage weight of the functional components. We evaluated the model using serial cognitive experiments of simple object perception, object recognition, two-back working memory, and divided attention of a syntactic process. We found that the independent component model satisfactorily explained the functional components involved in the task and discuss here the application of ICA in PET images of multiple subjects to explore the functional association of brain activations.
Fig. 2. ICA concept in PET decompositions.
The basic assumption of ICM is a linear mixture of spatially independent functional components and ICA is an effective tool for unmixing these components. The assumption that all subjects share basic common functional components with subtle variation enables the usage of ICA for finding basic functional components.
MRI Analysis in General
Park, H. J., Youn, T., Jeong, S. O., Oh, M. K., Kim, S. Y., Kim, E. Y., 2008. SENSE factors for reliable cortical thickness measurement. NeuroImage 40, 187-196. Short abstract
The effect of sensitivity encoding (SENSE) factors on cortical thickness measurements in 3.0 T and 1.5 T T1-weighted MRI images was examined. The 3D T1- Turbo field-echo (TFE) images were acquired from 11 healthy volunteers. Cortical thickness was calculated for the entire cortical surface. Repeated measures multivariate ANOVA revealed that the main effect of SENSE factors was a significant underestimation of cortical thickness at SENSE 5.0 (p=0.022) and 6.0 (p=0.011) for 3.0 T and at SENSE 4.0 (p < 0.000) for 1.5 T. The result showed that thickness measurements at the insula, superior temporal sulcus, the medial part of the superior frontal lobe, and cingulate cortex are highly affected by SENSE factors. SENSE factors affect thickness estimation more significantly at 1.5 T. Faster imaging can be done without too much loss of reliability using a high SENSE factor at 3.0 T with acquisition time being inversely proportional to the SENSE factor.
Fig. 4. Significant effect of SENSE factors on cortical thickness in 1.5 T (a–d) and 3.0 T (e–h).
p-values of the repeated measures ANOVA that met the threshold set by pb0.05 are displayed on inflated cortical surfaces.
EEG Analysis
Park, H. J., Kwon, J. S., Youn, T., Pae, J. S., Kim, J. J., Kim, M. S., Ha, K. S., 2002. Statistical parametric mapping of LORETA using high density EEG and individual MRI: application to mismatch negativities in schizophrenia. Hum Brain Mapp 17, 168-178. Short abstract
Statistical parametric mapping of low resolution electromagnetic tomography (LORETA) using high-density electroencephalography (EEG) and individual MRI was used to investigate the characteristics of the mismatch negativity (MMN) generators in schizophrenia. LORETA estimated the current density maps from the 128-channel EEG measurements. Volumetric current density images were reconstructed from the current density maps. Afterwards, statistical parametric mapping of the normalized current density images was performed. This method was applied to the source localization of MMN in schizophrenia. We found that the schizophrenic group exhibited significant current density reductions of MMN in the left superior temporal gyrus and the left inferior parietal gyrus (P < 0.0005). This study is the first voxel-by-voxel statistical mapping of current density using individual MRI and high-density EEG.
Fig. 2. Statistical parametric map of t statistic (SPM{t}) of MMN in control subjects (a) and schizophrenia (b).
Park, H. J.*, Jeong, D. U., Park, K. S., 2002b. Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method. IEEE Trans Biomed Eng 49, 1526-1533.
Park, H. J., Oh, J. S., Jeong, D. U., Park, K. S., 2000. Automated sleep stage scoring using hybrid rule- and case-based reasoning. Comput Biomed Res 33, 330-349.
Brain Research using EEG
BK Min, SJ Kim, JY Park, H.J. Park*, Prestimulus top-down reflection of obsessive-compulsive disorder in EEG frontal theta and occipital alpha oscillations, Neuroscience Letters, 2011, 496(3) 181-185.
Min, B.K., Park, H.J.*, 2010. Task-related modulation of anterior theta and posterior alpha EEG reflects top-down preparation. BMC Neurosci 11, 79.
Min, B. K., Park, J. Y., Kim, E. J., Kim, J. I., Kim, J. J., Park, H. J.*, 2008. Prestimulus EEG alpha activity reflects temporal expectancy. Neurosci Lett. 438, 270-274.
Brain Research using TMS
Imm, J. H., Kang, E., Youn, T., Park, H., Kim, J. I., Kang, J. I., Kim, S. J., Lee, J. D., Park, H. J.*, 2008. Different hemispheric specializations for pitch and audioverbal working memory. Neuroreport 19, 99-103.
Clinical Research using Multimodal Imaging
General
Kim, E. Y., Kim, D. H., Chang, J. H., Yoo, E., Lee, J. W., Park, H. J., 2009. Triple-layer appearance of brodmann area 4 at thin-section double inversion-recovery MR imaging. Radiology 250, 515-522.
Kim, E., Park, H., Kim, D., Lee, S., Kim, J., 2008b. Measuring fractional anisotropy of the corpus callosum using diffusion tensor imaging: mid-sagittal versus axial imaging planes. Korean J Radiol. 9, 391-395.
Cerebral palsy
Jong Doo Lee, Hae-Jeong Park+, Eun Sook Park, Maeng-Keun Oh, Bumhee Park, Dong-Wook Rha, Sung-Rae Cho, Eung Yeop Kim, Jun Young Park, Chul Hoon Kim, Dong Goo Kim, and Chang Il Park, (2011). Motor pathway injury in patients with periventricular leukomalacia and spastic diplegia, Brain 134, 1199-1210.
Lee, J. D., Park, H. J.+, Park, E.S., Kim, D.G., Rha, D.W., Kim, E.Y., Kim, D.I., Kim, J.J., Yun, M., Ryu, Y.H., Lee, J., Jeong, J.M., Lee, D.S., Lee, M.C., & Park, C.I. (2007). Assessment of regional GABA(A) receptor binding using 18F-fluoroflumazenil positron emission tomography in spastic type cerebral palsy. NeuroImage, 34(1), 19-25. Short abstract
Statistical parametric mapping (SPM) analysis of cerebral gamma-aminobutyric acid (GABA) receptor PET imaging using [18F]-fluoroflumazenil showed increased GABA_A receptor binding in the bilateral motor and visual cortices in spastic diplegia (SD) type CP patients (n=20) compared with normal controls (n=10). As GABA_A receptor signaling modulates biological perception and production of movement, complex motor skills and use-dependent plasticity in the motor cortex, increased GABA_A receptor binding in the motor cortex might play an important role in poor motor control. Decreased GABA_A receptor binding was seen in the brain stem in SD CP patients, which appears to be related to spastic symptom.
Fig. 2. Increased [18F]-FFMZ binding in SD CP patients compared with normal controls.
Blind and deaf
Park, H.J., Chun, J.W., Park, B., Park, H., Kim, J.I., Lee, J. D., Kim, J.J. (2011). Activation of the occipital cortex and deactivation of the default mode network during working memory in the early blind, J Int Neuropsycho Soc, 17(3), 407-422.
Park, H. J., Lee, J. D., Kim, E. Y., Park, B., Oh, M. K., Lee, S., Kim, J. J., 2009. Morphological Alterations in the Congenital Blind Based on the Analysis of Cortical Thickness and Surface Area. NeuroImage 47, 98-106. Short abstract
We analyzed the regional cortical thickness and cortical surface area in the congenitally blind (CB, n=21), the late-onset blind (LB, n=12), and sighted control (SC, n=35) subjects. Cortical thickness was calculated from T1 MRI images. ANCOVA of cortical layer thickness with global thickness, age, and gender as covariates was performed. We found increased cortical thickness in the regions involved in vision and eye movement, but cortical thinning in the left somatosensory cortex and right auditory cortex of CB compared to SC. CB had significantly reduced surface extent in the primary and associated visual areas, which explains volumetric atrophies in the visual cortex of CB despite increased cortical thickness. Conversely, LB tended to have cortical thinning in the primary visual cortex with a slight or no significant reduction in the surface extent. These morphological alterations in CB suggest cortical reorganization at the visual cortex in connection with other sensory cortices.
Fig. 1. Cortical thickness differences between congenital blind and sighted subjects.
Colour scale indicates −log10(p). For example, 2 in the colour bar corresponds to p=0.01 and 3 corresponds to p=0.001. In (A) and (B), blue colours show where the sighted have thicker cortices than the blind while hot colours show where the blind have thicker cortices than the sighted. White lines indicate where the significance level is below 0.001.
Kim, D. J., Park, S. Y., Kim, J., Lee, D. H., Park, H. J.*, 2009. Alterations of white matter diffusion anisotropy in early deafness. Neuroreport 20, 1032-1036.
Schizophrenia
Park, H. J., Westin, C. F., Kubicki, M., Maier, S. E., Niznikiewicz, M., Baer, A., Frumin, M., Kikinis, R., Jolesz, F. A., McCarley, R. W., Shenton, M. E., 2004. White matter hemisphere asymmetries in healthy subjects and in schizophrenia: a diffusion tensor MRI study. NeuroImage 23, 213-223. Short abstract
Hemisphere asymmetry was explored in normal healthy subjects and in schizophrenic patients using a novel voxel-based tensor analysis applied to fractional anisotropy (FA) of the diffusion tensor. A symmetrical group average template of the diffusion tensor was generated by applying nonlinear elastic warping of the demons algorithm. Next, all diffusion tensor MRIs were normalized to the symmetrical average template. A statistical evaluation of white matter asymmetry was conducted on the normalized FA images and their flipped images. In controls, we found significant anisotropic asymmetry. In patients, the asymmetry was lower in some parts and not found at all in the anterior limb of the internal capsule, the uncinate fasciculus, and the superior cerebellar peduncle compared to healthy subjects. These anisotropic asymmetry pattern differences between healthy controls and patients with schizophrenia are likely related to neurodevelopmental abnormalities in schizophrenia.
Fig. 6. Asymmetry of fractional anisotropy (FA) in normal controls (NC) and patients with schizophrenia (SZ) superimposed on the three-dimensional tractography.
The portion of the figure above the yellow line illustrates left-greater-than-right FA asymmetry, while the portion below shows FA asymmetry in opposite direction (right-greater-than-left). A indicates anterior; P, posterior.
Park, H. J., Levitt, J., Shenton, M. E., Salisbury, D. F., Kubicki, M., Kikinis, R., Jolesz, F. A., McCarley, R. W., 2004. An MRI study of spatial probability brain map differences between first-episode schizophrenia and normal controls. NeuroImage 22, 1231-1246. Short abstract
A spatial probability atlas of schizophrenia was created to examine the neuroanatomic variability of brain regions of schizophrenic patients. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects’ MRIs and linearly transforming them into Talairach space using the MNI template. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls.
Fig. 4. Probability atlas map of the superior temporal gyrus (STG).
(a) shows the location of left STG in the full brain view, (b) and (c) show the probability maps created using linear normalization (b) and nonlinear normalization (c) of SPM99. The black lines in the maps indicate 50% probability.
Kubicki, M., Park, H. J., Westin, C. F., Nestor, P. G., Mulkern, R. V., Maier, S. E., Niznikiewicz, M., Connor, E. E., Levitt, J. J., Frumin, M., Kikinis, R., Jolesz, F. A., McCarley, R. W., Shenton, M. E., 2005. DTI and MTR abnormalities in schizophrenia: analysis of white matter integrity. NeuroImage 26, 1109-1118. Short abstract
DTI abnormalities in schizophrenia was localized and specified by combining DTI with magnetization transfer imaging (MTI). 21 chronic schizophrenics and 26 controls were scanned. Diffusion information was used to normalize co-registered maps of fractional anisotropy (FA) and magnetization transfer ratio (MTR) to a study-specific template. Diffusion anisotropy was decreased in schizophrenia in several brain regions. MTR maps demonstrated changes in only some of the regions indicated by FA changes. In addition, the right posterior cingulum bundle showed MTR but not FA changes in schizophrenia. These findings suggest that while some of the diffusion abnormalities in schizophrenia are likely due to abnormal coherence or organization of the fiber tracts, others may be attributed to myelin/axonal disruption.
Fig. 5. Specific locations of MTR group differences.
Kim, J. J., Kim, D. J., Kim, T. G., Seok, J. H., Chun, J. W., Oh, M. K., Park, H. J.*, 2007. Volumetric abnormalities in connectivity-based subregions of the thalamus in patients with chronic schizophrenia. Schizophr Res 97, 226-235.
Koo, M. S., Dickey, C. C., Park, H. J., Kubicki, M., Ji, N. Y., Bouix, S., Pohl, K. M., Levitt, J. J., Nakamura, M., Shenton, M. E., McCarley, R. W., 2006. Smaller neocortical gray matter and larger sulcal cerebrospinal fluid volumes in neuroleptic-naive women with schizotypal personality disorder. Arch Gen Psychiatry 63, 1090-1100.
Shin, Y. W., Kwon, J. S., Ha, T. H., Park, H. J., Kim, D. J., Hong, S. B., Moon, W. J., Lee, J. M., Kim, I. Y., Kim, S. I., Chung, E. C., 2006. Increased water diffusivity in the frontal and temporal cortices of schizophrenic patients. NeuroImage 30, 1285-1291. Short abstract
The apparent diffusion coefficient (ADC) reflects the degree of diffusion barriers and heterosynaptic communication for the brain neurotransmitter. DTI of 19 patients with DSM-IV schizophrenia and 21 control subjects were measured and the severity of the patients’ symptoms was evaluated according to the Positive and Negative Syndrome Scale (PANSS). The ADC values were determined and compared between patients and control subjects via voxel-based morphometry. The results show an increased ADC in the bilateral fronto-temporal regions of the schizophrenic patients. In addition, the ADC values in the area of the right insular were correlated with the negative syndromes from the PANSS. Our findings indicate that damaged brain microcircuitry might contribute to the pathophysiology of schizophrenia.
Fig. 1. Statistical parametric maps showing regions with increased ADC value in schizophrenic patients.
Z scores are shown by the color map.
Obsessive-compulsive disorder
BK Min, SJ Kim, JY Park, H.J. Park*, Prestimulus top-down reflection of obsessive-compulsive disorder in EEG frontal theta and occipital alpha oscillations, Neuroscience Letters, 2011, 496(3) 181-185.
Yoo, S. Y., Jang, J. H., Shin, Y. W., Kim, D. J., Park, H. J., Moon, W. J., Chung, E. C., Lee, J. M., Kim, I. Y., Kim, S. I., Kwon, J. S., 2007. White Matter Abnormalities in drug-naive patients with obsessive-compulsive cisorder: a diffusion tensor study before and after citalopram treatment. Acta Psychiatr Scand 116, 211-219.
Parkinson's Disease
Lee, J. E., Park, H. J., Park, B., Song, S. K., Sohn, Y. H., Lee, J. D., Lee, P. H., A comparative analysis of cognitive profiles and white matter alterations using voxel-based diffusion tensor imaging between patients with Parkinson's disease dementia and dementia with Lewy bodies. J Neurol Neurosurg Psychiatry. 2010; 81(3):320-6.
Epilepsy
Kim, J. T., Bai, S. J., Choi, K. O., Lee, Y. J., Park, H. J., Kim, D. S., Kim, H. D., Lee, J. S., 2009. Comparison of various imaging modalities in localization of epileptogenic lesion using epilepsy surgery outcome in pediatric patients. Seizure. 18(7), 504-510.
Kim, M. A., Heo, K., Choo, M. K., Cho, J. H., Park, S. C., Lee, J. D., Yun, M., Park, H. J., Lee, B. I., 2006b. Relationship between bilateral temporal hypometabolism and EEG findings for mesial temporal lobe epilepsy: analysis of 18F-FDG PET using SPM. Seizure 15, 56-63.
Brain Development
Kim, E. Y., Kim, D. H., Yoo, E., Park, H. J., Golay, X., Lee, S. K., Kim, D. J., Kim, J., Kim, D. I., 2007. Visualization of maturation of the corpus callosum during childhood and adolescence using T2 relaxometry. Int J Dev Neurosci 25, 409-414.
Yoo, S. S., Park, H. J., Soul, J. S., Mamata, H., Park, H., Westin, C. F., Bassan, H., Du Plessis, A. J., Robertson, R. L., Jr., Maier, S. E., Ringer, S. A., Volpe, J. J., Zientara, G. P., 2005. In vivo visualization of white matter fiber tracts of preterm- and term-infant brains with diffusion tensor magnetic resonance imaging. Invest Radiol 40, 110-115.
Reading Mind using Real-time fMRI and Brain Decoding
The brain decoding using fMRI infers mental states associated with BOLD signal changes. This association is a converse operation to the traditional inference, i.e., depiction of brain activations elicited by stimuli. Although the fMRI-based brain decoding approach has recently received growing attention, the brain decoding itself is not a new concept. Numerous studies were performed to read one’s mind noninvasively by measuring brain activities, using mostly electroencephalogram (EEG).
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This research area, which is often called brain computer interface (BCI), or brain machine interface (BMI), is successfully performed in various applications using EEG. However, fMRI is rarely recommended for conventional BCI applications since it is very expensive and lacks portability. Nonetheless, fMRI has a high spatial resolution covering the whole brain, not simply the surface. Therefore, fMRI has advantages that cannot be replaced by any other brain decoding techniques. Certainly, fMRI also has been used to detect human intentions for BCI. The goal of using fMRI for BCI is to present the usefulness of fMRI images as an index reflecting the various brain states as well as understanding how the neural system behaves or spatially represents information.
Fig. 1. Real-time classification of brain function using fMRI
By decoding the brain using fMRI, we can expand our knowledge about the mechanism of encoding one’s mind. The advancement of brain decoding using fMRI can be attributed to the increased brain research. Again, brain decoding can improve knowledge of the brain itself. We expect a great impact of brain decoding on science and the public as shown in the evaluation of patients with the vegetative state. However, we have many challenges to overcome, including the gaps between the mind’s intention and brain neural activity, and between the neural activity and the measured BOLD signals.
Fig. 2. Classification of brain states using fMRI
In combination with brain decoding, fMRI processing in real time is a promising tool for assessing dynamic changes in brain states. A system that provides schemes to detect activated brain regions in real time is called real-time fMRI (rt-fMRI). Since rt-fMRI allows data analysis simultaneously with image acquisition, we have more freedom in expanding the brain decoding applications, which were not possible previously. The application area of rt-fMRI includes BCI, brain state monitoring, and neurofeedback. When compared to EEG-based BCI methods, rt-fMRI can allow prediction of brain status with superior reliability and a higher degree of freedom due to its high spatial resolution covering the whole brain. We are working on developing rt-fMRI system with individualized brain decoding schemes.
Fig. 3. Real-time fMRI processing
Relevant Publications and Conference Proceedings
Dongha Lee, Bumhee Park, Changwon Jang and Hae-Jeong Park*, Decoding Brain States Using Functional Magnetic Resonance Imaging, Biomedical Engineering Letters. 2011(1), 82-88.
Park, H. J., Park, B., Kim, D. J., 2009. Real-Time Functional MRI for Patient Monitoring During a Language Task. Conf Proc IEEE Eng Med Biol Soc 1, 5389-5392.
Changwon Jang, Sunghyon Kyeong, Dongha Lee, Hae-Jeong Park, Development of real-time mapping system for interpersonal synchrony during hyperscan fMRI, 17th Annual Meeting of the Organization for Human Brain Mapping, June 26-30, 2011 in Quebec, Canada (Trainee Abstract Travel Award, selected as an 'Interactive Poster').
Dongha Lee, Joongil Kim, Hae-Jeong Park, The classification of motor imagery and motor intention using multivariate pattern analysis, 17th Annual Meeting of the Organization for Human Brain Mapping, June 26-30, 2011 in Quebec, Canada.
Bumhee Park, Seok-Oh Jeong, Hae-Jeong Park, Iterative calculation of the Granger causality for real-time imaging, 16th Annual Meeting of the Organization for Human Brain Mapping, June 6-10, 2010 in Barcelona, Spain.
B. Park, HJ Park, Evaluation of Recursive Least Squares for the detrending of real-time fMRI, 15th Annual Meeting of the Organization for Human Brain Mapping, June 18-23, 2009, San Francisco, CA.
Research Funds
2010. 5 - 2015. 4. Ministry of Education, Science and Technology(교육기술과학부), Development of real-time
neuroimage analysis techniques for the communicative intention based on neural model of the
human intention 뇌신경 모델에 기초한 상호작용 의도의 실시간 뇌신경영상정보 분석 기술 개발
Communicative, Motivative and Aesthetic brains
For the past few years, neuroscientific approach to topics in traditional humanities and social sciences has been rapidly developing. This can mainly be contributed to the development in measurement technology such as fMRI and MEG and analytical methods.
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Fig. 1. Intention generation and reading other's intention using non-verbal signals
As a multidisciplinary team, we are interested in answering various topics discussed in traditional humanities and social sciences (ego, compensation, decision making, communication, sympathy, reading intention of others, motivation, etc.) using neuroscientific methods. We have several questions regarding these issues.
How is interpersonal communication processed? What is the mechanism of social rewards? What is the source of the motivation? What makes some people intrinsically more motivated than other people? Why are some humans more autonomous than others? What is the influence of autonomy supportive vs. controlling environments on students? How do various social contexts influence adolescents’ motivation?
To address these and related issues, our lab examines two broad areas of social neuroscience: (1) neural basis of interpersonal communication and (2) neural correlates of human motivation and social rewards within various social contexts. Our lab employs functional neuroimaging (fMRI), eye tracking and behavioral methods to understand how such processes may impact educational and corporate environments.
Currently our experiments have addressed questions including neural correlates of two types of social rewards (likeability vs. competence perception), the perception of the others’ achievement in two different contexts (cooperative vs. competitive), perception of relevance of “polite” utterances in various social contexts and the cross-cultural comparison, the autonomy supportive vs. controlling context, and the cross-cultural comparison of autonomy. Anatomical and functional correlates of higher vs. lower communicative competence are also being investigated.
We are also interested in the mechanism of the phonological perception in terms of embodied cognition, and nonverbal elements in human communication.
To answer these questions, technologies appropriate for social neuroscience such as real-time fMRI or hyperscanning are introduced to study various topics including intention reading and communication without being bound by frames. Such brain science research in humanities and social sciences overcomes the limitations in existing research that involve subjective judgment and has the advantage of providing a more scientific and objective methodology. We are also working for neural correlates of social intelligence and traits.
Fig. 2. Hyperscanned and real-time fMRI.
Synchronous and interactive fMRI scanning for multiple brains at different locations
This research is conducted in conjunction with the Department of Journalism and the Department of Education. We have performed basic research on perception of aesthetics and sociological framework, and participated in video arts in cooperation with the Graduate School of Film and Digital Media at Yonsei University.
Relevant Publications and Conference Proceedings
Haeil Park, Gregory K Iverson, Hae-Jeong Park*, Neural correlates in the processing of phoneme-level complexity in vowel production, Brain and Language, 2011, 119, 158-166.
Park, J.Y., Park, H., Kim, J.I., Park, H. J.* (2011). Consonant chords stimulate higher EEG gamma activity than dissonant chords. Neurosci Lett, 488(1), 101-105.
Haeil Park, Hae-Jeong Park*, Gregory K. Iverson, 2010, The frontal and temporal lobe in the identification of laryngeal contrasts. Neuroreport, 21, 474-478.
Changwon Jang, Sunghyon Kyeong, Dongha Lee, Hae-Jeong Park*, Development of real-time mapping system for interpersonal synchrony during hyperscan fMRI, 17th Annual Meeting of the Organization for Human Brain Mapping, June 26-30, 2011 in Quebec, Canada (Trainee Abstract Travel Award, selected as an 'Interactive Poster').
Eun Joo Kim, Joohan Kim, Shin-ae Yoon, Sunghyon Kyeong, Wooyeol Shin, Mina Choi, Jiwon Chun, Hae-Jeong Park*, Do Human Brains Distinguish Between Different Types of Social Rewards? Likability vs. Respectability, submitted.
Eun-Joo Kim, Hae-Jeong Park, Joo Han Kim 김은주, 박해정, 김주환, 교육에서의 긍정적 감성의 역할 (The role of positive emotion in education), Korean Journal of the Science of Emotion and Sensibility 감성과학, 2010, 13(1), 225-234.
Youn, T., Lyoo, I. K., Kim, J. K., Park, H. J., Ha, K. S., Lee, D. S., Abrams, K. Y., Lee, M. C., Kwon, J. S., 2002. Relationship between Personality Trait and Regional Cerebral Glucose Metabolism Assessed with Positron Emission Tomography. Biol Psychol 60, 109-120.
J. KIM, S.-A. YOON, I.-Y. SHIN, E.-B. LIM, Y.-J. SHIN, E.-J. KIM, H.-J. PARK, I am happier when my success contributes to our team: Comparing the neural correlates of the rewards under the cooperative and the competitive contexts, Annual Meeting of the Society for Neuroscience, Washington, DC, USA. 2011.
Ji-Won Chun, Sung-Hyon Kyeong, Jae-Jin Kim, Hae-Jeong Park, The influence of stimulus duration on brain activations during the emotional discrimination task, 17th Annual Meeting of the Organization for Human Brain Mapping, June 26-30, 2011 in Quebec City, Canada.
Sung-en Lee, Shin-ae Yoon, Eun Joo Kim, Joohan Kim, Hae-Jeong Park, Neural Correlates of Inferential Processing in the Comprehension of Utterances, 17th Annual Meeting of the Organization for Human Brain Mapping, June 26-30 , 2011 in Quebec, Canada.
Shin-ae Yoon, Eun Bi, Lim, In Young Shin, Eun Joo Kim, Joohan Kim, Hae-Jeong Park, Different Neural Activity within Cooperative vs. Competitive context, 17th Annual Meeting of the Organization for Human Brain Mapping, June 26-30, 2011 in Quebec City, Canada.
Ji-Won Chun, Jae-Jin Kim, Hae-Jeong Park, The brain activity of cognitive control in the Simon task, The 40th Annual Meeting of the Society for Neuroscience, San Diego, USA. 2010
Eun Joo Kim, Wooyeol Shin, Mina Park, Jung In Choi, Jisun Yeo, Joohan Kim, Hae-Jeong Park, Brain Activations during Judgments of Competence and Likability on Self-presentation: A fMRI Study, 16th Annual Meeting of the Organization for Human Brain Mapping, June 6-10, 2010 in Barcelona, Spain
Joohan Kim, Eun Joo Kim, Mina Choi, Wooyeol Shin, Mina Park, Jung In Choi, Hae-Jeong Park, Effects of Emotion on Interpretive Process of Other’s Facial Expression, 16th Annual Meeting of the Organization for Human Brain Mapping, June 6-10, 2010 in Barcelona, Spain.
Neuroart participation
Co-director of Neuroimage Modeling (뉴로이미지 모델링), “춤을 추며 산을 오르다”, 김형수 예술감독, 예술의전당 자유 소극장 (Seoul Arts Center, Jayu Theater), 2008. 3. 19
Co-director of Neuroimage Modeling (뉴로이미지 모델링), “카마수투라, 꿈”, 김형수 예술감독, 한국예술종합학교 (Korea National University of Arts), 2008. 6. 11
Co-director of Neuroimage Modeling (뉴로이미지 모델링), “산에서 꿈을 꾸다”, 김형수 예술감독, 국립현대미술관, National Museum of Contemporary Art, KOREA 2008. 7
Co-director of Neuroimage Modeling (뉴로이미지 모델링), “문화원형미디어아트”, 김형수 예술감독, The 1st Korea Contents Affairs 제1회 대한민국콘텐츠 어페어전, 2008. 9
Co-director of Neuroimage Modeling (뉴로이미지 모델링), “봄의제전 (Le sacre du printempts) III”, 김형수 예술감독, 예술의전당 자유 소극장 (Seoul Arts Center, Jayu Theater), 2009. 3. 31
Research Funds
2010. 5 - 2013. 4. Ministry of Education, Science and Technology(교육기술과학부), Neural Correlates of Communication
Intelligence Quotient (CQ) and Developing Educational Programs for Enhancing Communication
Intelligence 뇌과학에 기반한 소통지능 지수 개발 및 소통지능 향상을 위한 교육 프로그램 개발
Medical Image Analysis
(image processing, registration, and segmentation for surgical planning and medical diagnosis)
The image processing analysis technology for extracting information from measured medical image is increasing in importance recently. Our laboratory is developing brain image processing and analysis technologies for various medical applications.
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Segmentation and registration techniques are researched for estimating epilepsy lesions for application in clinic and surgery. We are also researching technology for real-time registration of intra-op MRI and high resolution multiple images. Visualization of the technology is implemented through the NeuroAN software. Especially, CUDA-based parallel processing technology is applied for high-speed computation. We are attempting to create attenuation image required for PET-MR and are performing OCT segmentation.
Fig. 1. NeuroAN for multimodal neuroimaging analysis being developed in MoNET
Fig. 2. OCT (optical coherence tomography) segmentation
Relevant Publications and Conference Proceedings
Joong Il Kim, Joon Soo Lee, Hae-Jeong Park, Surface-based iaSPM analysis for FDG PET imaging, 517 MT-AM, 16th Annual Meeting of the Organization for Human Brain Mapping, June 6-10, 2010 in Barcelona, Spain.
Seong-yong Park, Maeng-Keun Oh, Jong Hee Chang, Hae-Jeong Park, Co-registration of high resolution pre-operative brain images and intra-operative MR mage, 16th Annual Meeting of the Organization for Human Brain Mapping, June 6-10, 2010 in Barcelona, Spain.
Dongha Lee, Seong-yong Park, Hae-Jeong Park, A new registration method of EPI to T1-weighted image using B0 fieldmap and B-spline registration, 16th Annual Meeting of the Organization for Human Brain Mapping, June 6-10, 2010 in Barcelona, Spain.
Research Funds
2008. 9. - 2011. 8. Korea Science and Engineering Foundation (한국과학재단), R01-2008-000-20545-0 (2008),
Real-time imaging and fusion techniques for MRI guided neurointervention and intraoperative
MRI-based neurosurgery (MRI 유도 뉴로인터벤션과 뇌수술을 위한 실시간 MRI 제어 및 영상 융합
기술 개발)
2010. 5 - 2015. 4. Korean Health Technology R&D Project, Ministry for Health, Welfare and Family Affairs(보건진흥원),
OCT segmentation and quantification (OCT 영상 세그멘테이션을 이용한 관심영역 정량화 기법 개발)
