Identification of a strategic brain network underlying processing speed deficits in vascular cognitive impairment
暂无分享,去创建一个
Martin Dichgans | Marco Duering | Dominique Hervé | Eric Jouvent | Hugues Chabriat | Rainer Malik | Andreas Gschwendtner | Mariya Gonik | Nikola Zieren | Sonia Reyes | Christian Opherk | M. Dichgans | H. Chabriat | C. Opherk | M. Duering | E. Jouvent | D. Hervé | R. Malik | M. Gonik | S. Reyes | A. Gschwendtner | Nikola Zieren
[1] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[2] D. Auer,et al. Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. , 2009, Brain : a journal of neurology.
[3] Jon Skranes,et al. White matter abnormalities and executive function in children with very low birth weight , 2009, Neuroreport.
[4] Marco Scutari,et al. Learning Bayesian Networks with the bnlearn R Package , 2009, 0908.3817.
[5] Stephen M. Smith,et al. Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.
[6] Victoria J. Williams,et al. Association between white matter microstructure, executive functions, and processing speed in older adults: The impact of vascular health , 2013, Human brain mapping.
[7] Peter Stoeter,et al. Correlation of Brain White Matter Diffusion Anisotropy and Mean Diffusivity with Reaction Time in an Oddball Task , 2009, Neuropsychobiology.
[8] T. Tombaugh. Trail Making Test A and B: normative data stratified by age and education. , 2004, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.
[9] Mark W. Woolrich,et al. Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.
[10] M. Dichgans,et al. Education modifies the relation of vascular pathology to cognitive function: cognitive reserve in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy , 2013, Neurobiology of Aging.
[11] Kevin B. Korb,et al. Bayesian Artificial Intelligence, Second Edition , 2010 .
[12] B. Tabachnick,et al. Using Multivariate Statistics , 1983 .
[13] L. Pantoni. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges , 2010, The Lancet Neurology.
[14] John Fox,et al. OpenMx: An Open Source Extended Structural Equation Modeling Framework , 2011, Psychometrika.
[15] Karen J. Ferguson,et al. Enlarged Perivascular Spaces on MRI Are a Feature of Cerebral Small Vessel Disease , 2010, Stroke.
[16] Kevin B. Korb,et al. Bayesian Artificial Intelligence , 2004, Computer science and data analysis series.
[17] Ricardo Tarrasch,et al. Structural correlates of cognitive domains in normal aging with diffusion tensor imaging , 2011, Brain Structure and Function.
[18] Joanna M. Wardlaw,et al. A General Factor of Brain White Matter Integrity Predicts Information Processing Speed in Healthy Older People , 2010, The Journal of Neuroscience.
[19] M. Dichgans,et al. White-matter lesions without lacunar infarcts in CADASIL. , 2012, Journal of Alzheimer's disease : JAD.
[20] B. Thompson,et al. EFFECTS OF SAMPLE SIZE, ESTIMATION METHODS, AND MODEL SPECIFICATION ON STRUCTURAL EQUATION MODELING FIT INDEXES , 1999 .
[21] F. Barkhof,et al. Diffusion tensor imaging in type 1 diabetes: decreased white matter integrity relates to cognitive functions , 2012, Diabetologia.
[22] J. Cummings,et al. Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. , 2002, Journal of psychosomatic research.
[23] Niels D Prins,et al. Cerebral small-vessel disease and decline in information processing speed, executive function and memory. , 2005, Brain : a journal of neurology.
[24] Anders M. Dale,et al. White matter tracts associated with set-shifting in healthy aging , 2009, Neuropsychologia.
[25] M. Dichgans. Cognition in CADASIL. , 2009, Stroke.
[26] P. Stoeter,et al. Association between cingulum bundle structure and cognitive performance: An observational study in major depression , 2010, European Psychiatry.
[27] Martin Dichgans,et al. Blood pressure and haemoglobin A1c are associated with microhaemorrhage in CADASIL: a two-centre cohort study. , 2006, Brain : a journal of neurology.
[28] Douglas M. Hawkins,et al. The Problem of Overfitting , 2004, J. Chem. Inf. Model..
[29] Qiang Shen,et al. Methods to accelerate the learning of bayesian network structures , 2007 .
[30] Peter A. Calabresi,et al. Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification , 2008, NeuroImage.
[31] H. Markus,et al. The cognitive profiles of CADASIL and sporadic small vessel disease , 2006, Neurology.
[32] S. Black,et al. Vascular Contributions to Cognitive Impairment and Dementia: A Statement for Healthcare Professionals From the American Heart Association/American Stroke Association , 2011, Stroke.
[33] Rong Chen,et al. Network analysis of mild cognitive impairment , 2006, NeuroImage.
[34] A. Danek,et al. The pattern of cognitive performance in CADASIL: a monogenic condition leading to subcortical ischemic vascular dementia. , 2005, The American journal of psychiatry.
[35] R. MacCallum,et al. Power analysis and determination of sample size for covariance structure modeling. , 1996 .
[36] Roland Bammer,et al. Cognitive processing speed and the structure of white matter pathways: Convergent evidence from normal variation and lesion studies , 2008, NeuroImage.
[37] J. Cummings. Anatomic and Behavioral Aspects of Frontal‐Subcortical Circuits a , 1995, Annals of the New York Academy of Sciences.
[38] G. Gold. Defining the neuropathological background of vascular and mixed dementia and comparison with magnetic resonance imaging findings. , 2009, Frontiers of neurology and neuroscience.
[39] Nir Friedman,et al. Data Analysis with Bayesian Networks: A Bootstrap Approach , 1999, UAI.
[40] Chris Rorden,et al. Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking , 2001, NeuroImage.
[41] C. Stein,et al. Structural equation modeling. , 2012, Methods in molecular biology.
[42] Yong He,et al. Discrete Neuroanatomical Networks Are Associated with Specific Cognitive Abilities in Old Age , 2011, The Journal of Neuroscience.
[43] Edward H Herskovits,et al. Application of a data-mining method based on Bayesian networks to lesion-deficit analysis , 2003, NeuroImage.
[44] Mark W. Woolrich,et al. Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.
[45] O Salonen,et al. Cognitive profile of subcortical ischaemic vascular disease , 2005, Journal of Neurology, Neurosurgery & Psychiatry.
[46] Yee Lee Shing,et al. Age differences in speed of processing are partially mediated by differences in axonal integrity , 2011, NeuroImage.
[47] M. Dichgans,et al. Strategic role of frontal white matter tracts in vascular cognitive impairment: a voxel-based lesion-symptom mapping study in CADASIL. , 2011, Brain : a journal of neurology.
[48] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[49] M. O’Sullivan,et al. Damage within a network of white matter regions underlies executive dysfunction in CADASIL , 2005, Neurology.
[50] P. Bentler,et al. Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .
[51] M. Moseley,et al. Atrophy and dysfunction of parahippocampal white matter in mild Alzheimer's disease , 2012, Neurobiology of Aging.
[52] C. Iadecola. The overlap between neurodegenerative and vascular factors in the pathogenesis of dementia , 2010, Acta Neuropathologica.
[53] J. Cummings. Frontal-subcortical circuits and human behavior. , 1998, Archives of neurology.
[54] M. O’Sullivan,et al. Impact of MRI markers in subcortical vascular dementia: A multi-modal analysis in CADASIL , 2010, Neurobiology of Aging.
[55] C. Jack,et al. Diffusion tensor imaging and cognitive function in older adults with no dementia , 2011, Neurology.
[56] Robert Leech,et al. White matter damage and cognitive impairment after traumatic brain injury , 2010, Brain : a journal of neurology.