Test-retest variability of resting-state networks in healthy aging and prodromal Alzheimer's disease

[1]  Yufeng Zang,et al.  DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging , 2016, Neuroinformatics.

[2]  Ludovico Minati,et al.  Test‐retest reliability of the default mode network in a multi‐centric fMRI study of healthy elderly: Effects of data‐driven physiological noise correction techniques , 2016, Human brain mapping.

[3]  Giovanni Giulietti,et al.  Longitudinal Changes in Functional Brain Connectivity Predicts Conversion to Alzheimer's Disease. , 2016, Journal of Alzheimer's disease : JAD.

[4]  C. Jack,et al.  Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria , 2016, Alzheimer's & Dementia.

[5]  Hyun Kook Lim,et al.  Three Large-Scale Functional Brain Networks from Resting-State Functional MRI in Subjects with Different Levels of Cognitive Impairment , 2015, Psychiatry investigation.

[6]  P. Scheltens,et al.  Diagnostic impact of CSF biomarkers for Alzheimer's disease in a tertiary memory clinic , 2015, Alzheimer's & Dementia.

[7]  Ching-Po Lin,et al.  Age‐Related Changes in Resting‐State Networks of A Large Sample Size of Healthy Elderly , 2015, CNS neuroscience & therapeutics.

[8]  Jonathan D. Power,et al.  Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.

[9]  W. Klunk,et al.  Regional amyloid burden and intrinsic connectivity networks in cognitively normal elderly subjects. , 2014, Brain : a journal of neurology.

[10]  Abraham Z Snyder,et al.  Unrecognized preclinical Alzheimer disease confounds rs-fcMRI studies of normal aging , 2014, Neurology.

[11]  Mark Patterson,et al.  Advancing research , 2014, eLife.

[12]  S. Rombouts,et al.  Resting-state functional MR imaging: a new window to the brain. , 2014, Radiology.

[13]  Nick C Fox,et al.  Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria , 2014, The Lancet Neurology.

[14]  Jinglong Wu,et al.  Network-Based Biomarkers in Alzheimer’s Disease: Review and Future Directions , 2014, Front. Aging Neurosci..

[15]  Fenna M. Krienen,et al.  Opportunities and limitations of intrinsic functional connectivity MRI , 2013, Nature Neuroscience.

[16]  H. Jo,et al.  Functional alteration patterns of default mode networks: comparisons of normal aging, amnestic mild cognitive impairment and Alzheimer's disease , 2013, The European journal of neuroscience.

[17]  Valerie Kirsch,et al.  Long-term test-retest reliability of resting-state networks in healthy elderly subjects and with amnestic mild cognitive impairment patients. , 2013, Journal of Alzheimer's disease : JAD.

[18]  Masaki Ishihara,et al.  Decreased Functional Connectivity by Aging Is Associated with Cognitive Decline , 2012, Journal of Cognitive Neuroscience.

[19]  Maja A. A. Binnewijzend,et al.  Resting-state fMRI changes in Alzheimer's disease and mild cognitive impairment , 2012, Neurobiology of Aging.

[20]  G. Frisoni,et al.  Resting state fMRI in Alzheimer's disease: beyond the default mode network , 2012, Neurobiology of Aging.

[21]  Simon B. Eickhoff,et al.  One-year test–retest reliability of intrinsic connectivity network fMRI in older adults , 2012, NeuroImage.

[22]  C. Dickey,et al.  Investigation of Long-Term Reproducibility of Intrinsic Connectivity Network Mapping: A Resting-State fMRI Study , 2012, American Journal of Neuroradiology.

[23]  Filippo Caraci,et al.  New pharmacological strategies for treatment of Alzheimer's disease: focus on disease modifying drugs. , 2012, British journal of clinical pharmacology.

[24]  Jonas Persson,et al.  Local brain atrophy accounts for functional activity differences in normal aging , 2012, Neurobiology of Aging.

[25]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[26]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[27]  Bharat B. Biswal,et al.  Empirical Evaluations of Slice-Timing, Smoothing, and Normalization Effects in Seed-Based, Resting-State Functional Magnetic Resonance Imaging Analyses , 2011, Brain Connect..

[28]  R. Sperling.  The potential of functional MRI as a biomarker in early Alzheimer's disease , 2011, Neurobiology of Aging.

[29]  L. Schneider,et al.  Prevention trials in Alzheimer's disease: An EU-US task force report , 2011, Progress in Neurobiology.

[30]  Tülay Adali,et al.  Comparison of multi‐subject ICA methods for analysis of fMRI data , 2010, Human brain mapping.

[31]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[32]  Marcus E. Raichle,et al.  The Restless Brain , 2011, Brain Connect..

[33]  Bradford C. Dickerson,et al.  Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer’s disease , 2005, NeuroRX.

[34]  A. Snyder,et al.  Longitudinal analysis of neural network development in preterm infants. , 2010, Cerebral cortex.

[35]  Zhi-jun Zhang,et al.  Resting brain connectivity: changes during the progress of Alzheimer disease. , 2010, Radiology.

[36]  V. Menon,et al.  Saliency, switching, attention and control: a network model of insula function , 2010, Brain Structure and Function.

[37]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[38]  Stephen M. Smith,et al.  Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data , 2010, Front. Syst. Neurosci..

[39]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[40]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[41]  O. Dietrich,et al.  Test–retest reproducibility of the default‐mode network in healthy individuals , 2009, Human brain mapping.

[42]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[43]  A. Dale,et al.  High consistency of regional cortical thinning in aging across multiple samples. , 2009, Cerebral cortex.

[44]  V. Calhoun,et al.  Interrater and intermethod reliability of default mode network selection , 2009, Human brain mapping.

[45]  Michael D. Greicius,et al.  Distinct Cerebellar Contributions to Intrinsic Connectivity Networks , 2009, NeuroImage.

[46]  A. Mitchell,et al.  Rate of progression of mild cognitive impairment to dementia – meta‐analysis of 41 robust inception cohort studies , 2009, Acta psychiatrica Scandinavica.

[47]  Wang Zhan,et al.  Group independent component analysis reveals consistent resting-state networks across multiple sessions , 2008, Brain Research.

[48]  S. Rombouts,et al.  Reduced resting-state brain activity in the "default network" in normal aging. , 2008, Cerebral cortex.

[49]  Zhijun Zhang,et al.  Default-mode network activity distinguishes amnestic type mild cognitive impairment from healthy aging: A combined structural and resting-state functional MRI study , 2008, Neuroscience Letters.

[50]  V. Calhoun,et al.  Selective changes of resting-state networks in individuals at risk for Alzheimer's disease , 2007, Proceedings of the National Academy of Sciences.

[51]  Tianzi Jiang,et al.  Regional coherence changes in the early stages of Alzheimer’s disease: A combined structural and resting-state functional MRI study , 2007, NeuroImage.

[52]  V. Calhoun,et al.  Aberrant "default mode" functional connectivity in schizophrenia. , 2007, The American journal of psychiatry.

[53]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[54]  Philip D. Harvey,et al.  Administration and interpretation of the Trail Making Test , 2006, Nature Protocols.

[55]  Nikolaus Weiskopf,et al.  Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: A whole-brain analysis at 3 T and 1.5 T , 2006, NeuroImage.

[56]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[57]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[58]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[59]  Frederik Barkhof,et al.  Challenging the cholinergic system in mild cognitive impairment: a pharmacological fMRI study , 2004, NeuroImage.

[60]  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.

[61]  C. Jack,et al.  Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. , 2004, Archives of neurology.

[62]  R Turner,et al.  Optimized EPI for fMRI studies of the orbitofrontal cortex , 2003, NeuroImage.

[63]  P. Skudlarski,et al.  Detection of functional connectivity using temporal correlations in MR images , 2002, Human brain mapping.

[64]  J. Morris,et al.  Current concepts in mild cognitive impairment. , 2001, Archives of neurology.

[65]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[66]  Karl J. Friston,et al.  A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains , 2001, NeuroImage.

[67]  Silke Lux,et al.  Verbaler Lern- und Merkfähigkeitstest , 2001 .

[68]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[69]  Andrew J. Saykin,et al.  The Relationship between fMRI Activation and Cerebral Atrophy: Comparison of Normal Aging and Alzheimer Disease , 2000, NeuroImage.

[70]  E. Tangalos,et al.  Mild Cognitive Impairment Clinical Characterization and Outcome , 1999 .

[71]  H. Lehfeld,et al.  The Bayer Activities of Daily Living Scale (B-ADL) , 1998, Dementia and Geriatric Cognitive Disorders.

[72]  P. Vargha,et al.  A critical discussion of intraclass correlation coefficients. , 1997, Statistics in medicine.

[73]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[74]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.

[75]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[76]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[77]  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.

[78]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[79]  M. Hamilton.  A RATING SCALE FOR DEPRESSION , 1960, Journal of neurology, neurosurgery, and psychiatry.

[80]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .