Early-Stage White Matter Lesions Detected by Multispectral MRI Segmentation Predict Progressive Cognitive Decline
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Ricardo Vigário | Frederik Barkhof | Reinhold Schmidt | Nicolau Gonçalves | Jari Lipsanen | Hanna Jokinen | Domenico Inzitari | Leonardo Pantoni | Franz Fazekas | Timo Erkinjuntti | Ana Verdelho | R. Vigário | F. Barkhof | F. Fazekas | L. Pantoni | D. Inzitari | T. Erkinjuntti | R. Schmidt | J. Lipsanen | S. Madureira | A. Verdelho | H. Jokinen | Sofia Madureira | Nicolau Gonçalves | R. Schmidt
[1] Colin M. Macleod. Half a century of research on the Stroop effect: an integrative review. , 1991, Psychological bulletin.
[2] Koenraad Van Leemput,et al. Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.
[3] Anke Meyer-Bäse,et al. Fully automated biomedical image segmentation by self-organized model adaptation , 2004, Neural Networks.
[4] Koen L. Vincken,et al. Brain atrophy and cognition: Interaction with cerebrovascular pathology? , 2011, Neurobiology of Aging.
[5] José V. Manjón,et al. Improved estimates of partial volume coefficients from noisy brain MRI using spatial context , 2010, NeuroImage.
[6] M. Lawton,et al. Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living , 1969 .
[7] Ricardo Vigário,et al. Self-Supervised MRI Tissue Segmentation by Discriminative Clustering , 2014, Int. J. Neural Syst..
[8] B. Ginneken,et al. 3D Segmentation in the Clinic: A Grand Challenge , 2007 .
[9] P. Scheltens,et al. Confirmatory factor analysis of the Neuropsychological Assessment Battery of the LADIS study: A longitudinal analysis , 2013, Journal of clinical and experimental neuropsychology.
[10] S. Ferris. General Measures of Cognition , 2003, International Psychogeriatrics.
[11] Frederik Barkhof,et al. Progression of White Matter Hyperintensities and Incidence of New Lacunes Over a 3-Year Period: The Leukoaraiosis and Disability Study , 2008, Stroke.
[12] Jerry L. Prince,et al. Partial volume estimation and the fuzzy C-means algorithm [brain MRI application] , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).
[13] H E GRIFFITHS,et al. Analysis of Function , 1947, Occupational therapy and rehabilitation.
[14] R. Reitan. Validity of the Trail Making Test as an Indicator of Organic Brain Damage , 1958 .
[15] Alan C. Evans,et al. Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.
[16] Owen Carmichael,et al. White Matter Hyperintensity Penumbra , 2011, Stroke.
[17] M. O’Sullivan,et al. Activate your online subscription , 2001, Neurology.
[18] M. Lawton,et al. Assessment of older people: self-maintaining and instrumental activities of daily living. , 1969, The Gerontologist.
[19] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[20] P. Scheltens,et al. Impact of Age-Related Cerebral White Matter Changes on the Transition to Disability – The LADIS Study: Rationale, Design and Methodology , 2004, Neuroepidemiology.
[21] P. Scheltens,et al. 2001–2011: A Decade of the LADIS (Leukoaraiosis And DISability) Study: What Have We Learned about White Matter Changes and Small-Vessel Disease? , 2011, Cerebrovascular Diseases.
[22] Alfredo Vellido,et al. Semi-Supervised Analysis of Human Brain Tumours from Partially Labeled MRS Information, Using Manifold Learning Models , 2011, Int. J. Neural Syst..
[23] John A. D. Aston,et al. MR Image Segmentation Using a Power Transformation Approach , 2009, IEEE Transactions on Medical Imaging.
[24] Owen Carmichael,et al. FLAIR and Diffusion MRI Signals Are Independent Predictors of White Matter Hyperintensities , 2013, American Journal of Neuroradiology.
[25] Koenraad Van Leemput,et al. A unifying framework for partial volume segmentation of brain MR images , 2003, IEEE Transactions on Medical Imaging.
[26] Mark W. Woolrich,et al. Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.
[27] Koenraad Van Leemput,et al. Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.
[28] Rainer Goebel,et al. Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single‐subject to cortically aligned group general linear model analysis and self‐organizing group independent component analysis , 2006, Human brain mapping.
[29] J. Ferro,et al. Brain atrophy accelerates cognitive decline in cerebral small vessel disease , 2012, Neurology.
[30] A. Hofman,et al. Cerebral microbleeds are associated with worse cognitive function , 2012, Neurology.
[31] Karl J. Friston,et al. Unified segmentation , 2005, NeuroImage.
[32] W. M. van der Flier,et al. Incident lacunes influence cognitive decline , 2011, Neurology.
[33] P. Scheltens,et al. Diffusion changes predict cognitive and functional outcome: The LADIS study , 2013, Annals of neurology.
[34] S. Folstein,et al. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.
[35] W. Mali,et al. Vascular brain lesions, brain atrophy, and cognitive decline. The Second Manifestations of ARTerial disease—Magnetic Resonance (SMART-MR) study , 2014, Neurobiology of Aging.
[36] P. Scheltens,et al. Impact of White Matter Hyperintensities Scoring Method on Correlations With Clinical Data: The LADIS Study , 2006, Stroke.
[37] W. M. van der Flier,et al. Diffusion-Weighted Imaging and Cognition in the Leukoariosis and Disability in the Elderly Study , 2010, Stroke.
[38] D. Harvey,et al. Longitudinal Changes in Memory and Executive Functioning are Associated with longitudinal change in instrumental activities of daily living in older Adults , 2009, The Clinical neuropsychologist.
[39] K. Jellinger,et al. Heterogeneity in age-related white matter changes , 2011, Acta Neuropathologica.