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      KCI등재 SCOPUS

      Spatial and Temporal Characteristics of Visual Field Progression in Glaucoma Assessed by Parallel Factor Analysis

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      https://www.riss.kr/link?id=A106326877

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      다국어 초록 (Multilingual Abstract)

      Purpose: To explore spatial and temporal characteristics of glaucomatous visual field (VF) progression throughmulti-way decomposition of data.
      Methods: Six serial VF exams with intervals of 6.0 ± 1.0 months in 121 pre-perimetric glaucoma eyes and 80perimetric glaucoma eyes were arranged into a three-dimensional cube. The data were decomposed usingparallel factor analysis.
      Results: Three tri-linear components (i.e., spatial scores, temporal loadings, and subject-specific loadings) wereidentified. Component 1 clearly showed differences between superior and inferior hemispheres, linear trendsover time, and wide variability in perimetric glaucoma. Findings were compatible with well-known characteristicsof glaucomatous VF defects. Component 2 showed nasal and central areas in contrast with superior, inferior,and temporal peripheral locations, whereas component 3 showed a contrast between nasal and temporalhemispheres. Both components 2 and 3 failed to show clear temporal trends.
      Conclusions: Identification of spatio-temporal patterns shows new possibilities for a multi-way decompositionmethodology for earlier diagnosis and prediction of glaucomatous VF progression.
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      Purpose: To explore spatial and temporal characteristics of glaucomatous visual field (VF) progression throughmulti-way decomposition of data. Methods: Six serial VF exams with intervals of 6.0 ± 1.0 months in 121 pre-perimetric glaucoma eyes and 80p...

      Purpose: To explore spatial and temporal characteristics of glaucomatous visual field (VF) progression throughmulti-way decomposition of data.
      Methods: Six serial VF exams with intervals of 6.0 ± 1.0 months in 121 pre-perimetric glaucoma eyes and 80perimetric glaucoma eyes were arranged into a three-dimensional cube. The data were decomposed usingparallel factor analysis.
      Results: Three tri-linear components (i.e., spatial scores, temporal loadings, and subject-specific loadings) wereidentified. Component 1 clearly showed differences between superior and inferior hemispheres, linear trendsover time, and wide variability in perimetric glaucoma. Findings were compatible with well-known characteristicsof glaucomatous VF defects. Component 2 showed nasal and central areas in contrast with superior, inferior,and temporal peripheral locations, whereas component 3 showed a contrast between nasal and temporalhemispheres. Both components 2 and 3 failed to show clear temporal trends.
      Conclusions: Identification of spatio-temporal patterns shows new possibilities for a multi-way decompositionmethodology for earlier diagnosis and prediction of glaucomatous VF progression.

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      참고문헌 (Reference)

      1 Bresson-Dumont H, "Visual field progression in glaucoma : cluster analysis" 35 : 735-741, 2012

      2 Iester M, "Visual field loss morphology in high-and normal-tension glaucoma" 2012 : 327326-, 2012

      3 Goldbaum MH, "Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects" 103 : 270-280, 2005

      4 Del Ferraro MA, "ThreeWay: threeway component analysis (version 1.1.3)"

      5 Kruskal JB, "Three-way arrays : rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics" 18 : 95-138, 1977

      6 Paolo Giordani, "Three-Way Component Analysis Using the R Package ThreeWay" Foundation for Open Access Statistic 57 (57): 2014

      7 Beckmann CF, "Tensorial extensions of independent component analysis for multisubject FMRI analysis" 25 : 294-311, 2005

      8 Strouthidis NG, "Structure and function in glaucoma : the relationship between a functional visual field map and an anatomic retinal map" 47 : 5356-5362, 2006

      9 Pascual JP, "Spatial characteristics of visual field progression determined by Monte Carlo simulation : diagnostic innovations in glaucoma study" 48 : 1642-1650, 2007

      10 Quigley HA, "Retinal ganglion cell atrophy correlated with automated perimetry in human eyes with glaucoma" 107 : 453-464, 1989

      1 Bresson-Dumont H, "Visual field progression in glaucoma : cluster analysis" 35 : 735-741, 2012

      2 Iester M, "Visual field loss morphology in high-and normal-tension glaucoma" 2012 : 327326-, 2012

      3 Goldbaum MH, "Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects" 103 : 270-280, 2005

      4 Del Ferraro MA, "ThreeWay: threeway component analysis (version 1.1.3)"

      5 Kruskal JB, "Three-way arrays : rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics" 18 : 95-138, 1977

      6 Paolo Giordani, "Three-Way Component Analysis Using the R Package ThreeWay" Foundation for Open Access Statistic 57 (57): 2014

      7 Beckmann CF, "Tensorial extensions of independent component analysis for multisubject FMRI analysis" 25 : 294-311, 2005

      8 Strouthidis NG, "Structure and function in glaucoma : the relationship between a functional visual field map and an anatomic retinal map" 47 : 5356-5362, 2006

      9 Pascual JP, "Spatial characteristics of visual field progression determined by Monte Carlo simulation : diagnostic innovations in glaucoma study" 48 : 1642-1650, 2007

      10 Quigley HA, "Retinal ganglion cell atrophy correlated with automated perimetry in human eyes with glaucoma" 107 : 453-464, 1989

      11 Na JH, "Rates a nd patterns of macular and circumpapillary retinal nerve fiber layer thinning in preperimetric and perimetric glaucomatous eyes" 24 : 278-285, 2015

      12 Cho JW, "Progression detection in different stages of glaucoma : mean deviation versus visual field index" 56 : 128-133, 2012

      13 Nouri-Mahdavi K, "Prediction of visual field progression in glaucoma" 45 : 4346-4351, 2004

      14 Bengtsson B, "Prediction of glaucomatous visual field loss by extrapolation of linear trends" 127 : 1610-1615, 2009

      15 Nouri-Mahdavi K, "Pointwise rates of visual field progression cluster according to retinal nerve fiber layer bundles" 53 : 2390-2394, 2012

      16 Wilkins MR, "Pointwise linear progression criteria and the detection of visual field change in a glaucoma trial" 20 : 98-106, 2006

      17 Bro R, "PARAFAC : tutorial and applications" 38 : 149-171, 1997

      18 Deburchgraeve W, "Neonatal seizure localization using PARAFAC decomposition" 120 : 1787-1796, 2009

      19 He B, "Multimodal functional neuroimaging : integrating functional MRI and EEG/MEG" 1 : 23-40, 2008

      20 Weis M, "Multi-dimensional PARAFAC2 component analysis of multi-channel EEG data including temporal tracking" 2010 : 5375-5378, 2010

      21 Yousefi S, "Learning from data : recognizing glaucomatous defect patterns and detecting progression from visual field measurements" 61 : 2112-2124, 2014

      22 Robinson SD, "ICA of f MRI studies : new approaches and cutting edge applications" 7 : 724-, 2013

      23 Asman P, "Glaucoma hemifield test : automated visual field evaluation" 110 : 812-819, 1992

      24 Harshman RA, "Foundations of the PARAFAC procedure:models and conditions for an” explanatory” multimodal factor analysis" University of California at Los Angeles 1-84, 1970

      25 De Moraes CG, "Examination of the performance of different pointwise linear regression progression criteria to detect glaucomatous visual field change" 40 : e190-e196, 2012

      26 De Moraes CG, "Effect of treatment on the rate of visual field change in the ocular hypertension treatment study observation group" 53 : 1704-1709, 2012

      27 Viswanathan AC, "Early detection of visual field progression in glaucoma : a comparison of PROGRESSOR and STATPAC 2" 81 : 1037-1042, 1997

      28 Sui J, "Discriminating schizophrenia and bipolar disorder by fusing f MRI and DTI in a multimodal CCA+ joint ICA model" 57 : 839-855, 2011

      29 Lee JR, "Discrepancy between optic disc and nerve fiber layer assessment and optical coherence tomography in detecting glaucomatous progression" 57 : 546-552, 2013

      30 Miwakeichi F, "Decomposing EEG data into space-time-frequency components using Parallel Factor Analysis" 22 : 1035-1045, 2004

      31 Martinez-Montes E, "Concurrent EEG/fMRI analysis by multiway Partial Least Squares" 22 : 1023-1034, 2004

      32 Carroll JD, "Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition" 35 : 283-319, 1970

      33 Bengtsson B, "A visual field index for calculation of glaucoma rate of progression" 145 : 343-353, 2008

      34 Marin-Franch I, "A novel strategy for the estimation of the general height of the visual field in patients with glaucoma" 252 : 801-809, 2014

      35 Caprioli J, "A method to measure and predict rates of regional visual field decay in glaucoma" 52 : 4765-4773, 2011

      36 Varoquaux G, "A group model for stable multi-subject ICA on fMRI datasets" 51 : 288-299, 2010

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2024 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2021-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2020-01-01 평가 등재학술지 선정 (재인증) KCI등재
      2019-12-01 평가 등재후보로 하락 (계속평가) KCI등재후보
      2010-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2009-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2007-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.11 0.11 0.12
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.1 0.13 0.482 0.03
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