RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      KCI등재 SCOPUS

      Clinical Personal Connectomics Using Hybrid PET/MRI

      한글로보기

      https://www.riss.kr/link?id=A106835506

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual Abstract)

      Brain connectivity can now be studied with topological analysis using persistent homology. It overcame the arbitrariness of thresholding to make binary graphs for comparison between disease and normal control groups. Resting-state fMRI can yield personal interregional brain connectivity based on perfusion signal on MRI on individual subject bases and FDG PET produces the topography of glucose metabolism. Assuming metabolism perfusion coupling and disregarding the slight difference of representing time of metabolism (before image acquisition) and representing time of perfusion (during image acquisition), topography of brain metabolism on FDG PET and topologically analyzed brain connectivity on resting-state fMRI might be related to yield personal connectomics of individual subjects and even individual patients. The work of association of FDG PET/ resting-state fMRI is yet to be warranted; however, the statistics behind the group comparison of connectivity on FDG PET or resting-state MRI was already developed. Before going further into the connectomics construction using directed weighted brain graphs of FDG PET or resting-state fMRI, I detailed in this review the plausibility of using hybrid PET/MRI to enable the interpretation of personal connectomics which can lead to the clinical use of brain connectivity in the near future.
      번역하기

      Brain connectivity can now be studied with topological analysis using persistent homology. It overcame the arbitrariness of thresholding to make binary graphs for comparison between disease and normal control groups. Resting-state fMRI can yield perso...

      Brain connectivity can now be studied with topological analysis using persistent homology. It overcame the arbitrariness of thresholding to make binary graphs for comparison between disease and normal control groups. Resting-state fMRI can yield personal interregional brain connectivity based on perfusion signal on MRI on individual subject bases and FDG PET produces the topography of glucose metabolism. Assuming metabolism perfusion coupling and disregarding the slight difference of representing time of metabolism (before image acquisition) and representing time of perfusion (during image acquisition), topography of brain metabolism on FDG PET and topologically analyzed brain connectivity on resting-state fMRI might be related to yield personal connectomics of individual subjects and even individual patients. The work of association of FDG PET/ resting-state fMRI is yet to be warranted; however, the statistics behind the group comparison of connectivity on FDG PET or resting-state MRI was already developed. Before going further into the connectomics construction using directed weighted brain graphs of FDG PET or resting-state fMRI, I detailed in this review the plausibility of using hybrid PET/MRI to enable the interpretation of personal connectomics which can lead to the clinical use of brain connectivity in the near future.

      더보기

      참고문헌 (Reference)

      1 Logothetis NK, "What we can do and what we cannot do with fMRI" 453 : 869-, 2008

      2 Lee H, "Weighted functional brain network modeling via network filtration" Citeseer 3 : 2012

      3 Matsuda H, "Voxel-based morphometry of brain MRI in normal aging and Alzheimer’s disease" 4 : 29-37, 2013

      4 Lee H, "Volume entropy for modeling information flow in a brain graph" 2019

      5 Vicente R, "Transfer entropy—a model-free measure of effective connectivity for the neurosciences" 30 : 45-67, 2011

      6 Lee H, "Topological distances between networks and its application to brain imaging"

      7 ChungMK, "Topological brain network distances"

      8 Allard A, "The geometric nature of weights in real complex networks" 8 : 14103-, 2017

      9 Lindner M, "TRENTOOL : a Matlab open source toolbox to analyse information flow in time series data with transfer entropy" 12 : 119-, 2011

      10 Caron F, "Sparse graphs using exchangeable random measures" 79 : 1295-1366, 2017

      1 Logothetis NK, "What we can do and what we cannot do with fMRI" 453 : 869-, 2008

      2 Lee H, "Weighted functional brain network modeling via network filtration" Citeseer 3 : 2012

      3 Matsuda H, "Voxel-based morphometry of brain MRI in normal aging and Alzheimer’s disease" 4 : 29-37, 2013

      4 Lee H, "Volume entropy for modeling information flow in a brain graph" 2019

      5 Vicente R, "Transfer entropy—a model-free measure of effective connectivity for the neurosciences" 30 : 45-67, 2011

      6 Lee H, "Topological distances between networks and its application to brain imaging"

      7 ChungMK, "Topological brain network distances"

      8 Allard A, "The geometric nature of weights in real complex networks" 8 : 14103-, 2017

      9 Lindner M, "TRENTOOL : a Matlab open source toolbox to analyse information flow in time series data with transfer entropy" 12 : 119-, 2011

      10 Caron F, "Sparse graphs using exchangeable random measures" 79 : 1295-1366, 2017

      11 Choe AS, "Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years" 10 : e0140134-, 2015

      12 Santoro A, "Relational recurrent neural networks"

      13 Lee H, "Persistent brain network homology from the perspective of dendrogram" 31 : 2267-2277, 2012

      14 Tadić B, "Origin of hyperbolicity in brain-to-brain coordination networks" 6 : 7-, 2018

      15 Lee D, "Optimal likelihood-ratio multiple testing with application to Alzheimer’s disease and questionable dementia" 15 : 9-, 2015

      16 Nichols TE, "Nonparametric permutation tests for functional neuroimaging : a primer with examples" 15 : 1-25, 2002

      17 Kipf T, "Neural relational inference for interacting systems"

      18 Kim E, "Morphological brain network assessed using graph theory and network filtration in deaf adults" 315 : 88-98, 2014

      19 Lee DS, "Metabolic connectivity by interregional correlation analysis using statistical parametric mapping(SPM)and FDG brain PET; methodological development and patterns of metabolic connectivity in adults" 35 : 1681-1691, 2008

      20 Lee H, "Med Image Comput Comput Assist Interv" Springer 297-304, 2014

      21 Lee H, "Med Image Comput Comput Assist Interv" Springer 302-309, 2011

      22 Choi H, "Maturation of metabolic connectivity of the adolescent rat brain" 4 : e11571-, 2015

      23 Silver D, "Mastering the game of Go without human knowledge" 550 : 354-, 2017

      24 Muscoloni A, "Machine learning meets complex networks via coalescent embedding in the hyperbolic space" 8 : 1615-, 2017

      25 Sheth SA, "Linear and nonlinear relationships between neuronal activity, oxygen metabolism, and hemodynamic responses" 42 : 347-355, 2004

      26 Kaiser A, "Information transfer in continuous processes" 166 : 43-62, 2002

      27 Hwang D, "Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning" 59 : 1624-1629, 2018

      28 Krioukov D, "Hyperbolic geometry of complex networks" 82 : 036106-, 2010

      29 Yu M, "Horizontal visibility graph transfer entropy(HVG-TE) : a novel metric to characterize directed connectivity in large-scale brain networks" 156 : 249-264, 2017

      30 Deco G, "Great expectations : using whole-brain computational connectomics for understanding neuropsychiatric disorders" 84 : 892-905, 2014

      31 Hallquist MN, "Graph theory approaches to functional network organization in brain disorders : a critique for a brave new small-world" 3 : 1-26, 2018

      32 Ying R, "Graph convolutional neural networks for web-scale recommender systems"

      33 Hahm J, "Gating of memory encoding of time-delayed cross-frequency MEG networks revealed by graph filtration based on persistent homology" 7 : 41592-, 2017

      34 Smith SM, "Functional connectomics from resting-state fMRI" 17 : 666-682, 2013

      35 Friston KJ, "Functional and effective connectivity : a review" 1 : 13-36, 2011

      36 Vafaee MS, "Frequency-dependent changes in cerebral metabolic rate of oxygen during activation of human visual cortex" 19 : 272-277, 1999

      37 Lee D, "Extended likelihood approach to multiple testing with directional error control under a hidden Markov random field model" 151 : 1-3, 2016

      38 Lee Y, "Extended likelihood approach to large-scale multiple testing" 75 : 553-575, 2013

      39 Latora V, "Efficient behavior of small-world networks" 87 : 198701-, 2001

      40 Im HJ, "Disrupted brain metabolic connectivity in a 6-OHDA-induced mouse model of Parkinson’s disease examined using persistent homology-based analysis" 6 : 33875-, 2016

      41 Lee DS, "Disparity of perfusion and glucose metabolism of epileptogenic zones in temporal lobe epilepsy demonstrated by SPM/SPAManalysis on 15O water PET, [18F] FDG-PET, and [99mTc]-HMPAO SPECT" 42 : 1515-1522, 2001

      42 Bielczyk NZ, "Disentangling casual webs in the brain using functional magnetic resonance imaging: a review of current approaches" 1-37, 2018

      43 Yue T, "Deep learning for genomics: a concise overview"

      44 Arganda-Carreras I, "Crowdsourcing the creation of image segmentation algorithms for connectomics" 9 : 142-, 2015

      45 Nielsen AN, "Coupling and uncoupling of activitydependent increases of neuronal activity and blood flow in rat somatosensory cortex" 533 : 773-785, 2001

      46 Buchholz HG, "Construction and comparative evaluation of different activity detection methods in brain FDG-PET" 14 : 79-, 2015

      47 Park J, "Computed tomography super-resolution using deep convolutional neural network" 63 : 145011-, 2018

      48 Weber M, "Characterizing complex networks with Forman-Ricci curvature and associated geometric flows" 5 : 527-550, 2017

      49 Kim H, "Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration" 5 : 245-258, 2015

      50 Berthelot D, "BEGAN : boundary equilibrium generative adversarial networks"

      51 Choi H, "Alzheimer’s Disease Neuroimaging Initiative. Predicting aging of brain metabolic topography using variational autoencoder" 10 : 212-, 2018

      52 Choi H, "Alzheimer’s Disease Neuroimaging Initiative. Generation of structuralMRimages from amyloid PET: application to MR-less quantification" 59 : 1111-1117, 2018

      53 Choi H, "Alzheimer’s Disease Neuroimaging Initiative. Deep learning only by normal brain PET identify unheralded brain anomalies"

      54 Seung Kwan Kang, "Adaptive template generation for amyloid PET using a deep learning approach" Wiley 39 (39): 3769-3778, 2018

      55 Lee H, "Abnormal hole detection in brain connectivity by kernel density of persistence diagramand Hodge Laplacian" IEEE 20-23, 2018

      56 Santoro A, "A simple neural network module for relational reasoning" 30 : 4967-4976, 2017

      57 Frässle S, "A generative model of whole-brain effective connectivity" 179 : 505-529, 2018

      더보기

      동일학술지(권/호) 다른 논문

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      인용정보 인용지수 설명보기

      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2016-12-26 학술지명변경 한글명 : Nuclear Medicine and Molecular Imaging -> Nuclear Medicine and Molecular Imaging
      외국어명 : 미등록 -> Nuclear Medicine and Molecular Imaging
      KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-03-12 학술지명변경 한글명 : 핵의학 분자영상 -> Nuclear Medicine and Molecular Imaging KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2002-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      더보기

      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.06 0.06 0.06
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.09 0.08 0.275 0
      더보기

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼