Association of brain network dynamics with plasma biomarkers in subjective memory complainers

[1]  R. Buckner,et al.  The brain’s default network: updated anatomy, physiology and evolving insights , 2019, Nature Reviews Neuroscience.

[2]  F. Edwards A Unifying Hypothesis for Alzheimer’s Disease: From Plaques to Neurodegeneration , 2019, Trends in Neurosciences.

[3]  Amaia M. Arranz,et al.  The role of astroglia in Alzheimer's disease: pathophysiology and clinical implications , 2019, The Lancet Neurology.

[4]  C. Torres,et al.  Astrocyte senescence: Evidence and significance , 2019, Aging cell.

[5]  O. Witte,et al.  Phenotypic and functional differences between senescent and aged murine microglia , 2019, Neurobiology of Aging.

[6]  L. Tan,et al.  Inflammatory markers in Alzheimer’s disease and mild cognitive impairment: a meta-analysis and systematic review of 170 studies , 2019, Journal of Neurology, Neurosurgery, and Psychiatry.

[7]  José Luis Molinuevo,et al.  Current state of Alzheimer’s fluid biomarkers , 2018, Acta Neuropathologica.

[8]  Simone Lista,et al.  Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic , 2018, Nature Reviews Neurology.

[9]  Olaf Sporns,et al.  Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. , 2018, Journal of Alzheimer's disease : JAD.

[10]  Alzheimer's Disease Neuroimaging Initiative,et al.  Hypermetabolism in the hippocampal formation of cognitively impaired patients indicates detrimental maladaptation , 2018, Neurobiology of Aging.

[11]  Prashanthi Vemuri,et al.  Resistance vs resilience to Alzheimer disease , 2018, Neurology.

[12]  N. Toschi,et al.  Alzheimer's disease biomarker-guided diagnostic workflow using the added value of six combined cerebrospinal fluid candidates: Aβ1–42, total-tau, phosphorylated-tau, NFL, neurogranin, and YKL-40 , 2018, Alzheimer's & Dementia.

[13]  Norbert Benda,et al.  Precision pharmacology for Alzheimer's disease. , 2018, Pharmacological research.

[14]  H. Benali,et al.  Cognitive and neuroimaging features and brain β -amyloidosis in individuals at risk of Alzheimer’s disease (INSIGHT-preAD): a longitudinal observational study , 2019 .

[15]  J. Cummings,et al.  Clinical Trials for Disease-Modifying Therapies in Alzheimer’s Disease: A Primer, Lessons Learned, and a Blueprint for the Future , 2018, Journal of Alzheimer's disease : JAD.

[16]  Funda Meric-Bernstam,et al.  Efficacy of Larotrectinib in TRK Fusion–Positive Cancers in Adults and Children , 2018, The New England journal of medicine.

[17]  Clifford R. Jack,et al.  Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum , 2017, Cortex.

[18]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[19]  W. Jagust,et al.  Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity , 2017, Nature Communications.

[20]  N. Toschi,et al.  Two-level diagnostic classification using cerebrospinal fluid YKL-40 in Alzheimer's disease , 2017, Alzheimer's & Dementia.

[21]  N. Toschi,et al.  Diagnostic accuracy of CSF neurofilament light chain protein in the biomarker-guided classification system for Alzheimer's disease , 2017, Neurochemistry International.

[22]  Paul M. Thompson,et al.  Revolution of Resting-State Functional Neuroimaging Genetics in Alzheimer’s Disease , 2017, Trends in Neurosciences.

[23]  Alzheimer's Disease Neuroimaging Initiative Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease , 2017 .

[24]  Henrik Zetterberg,et al.  Association of Plasma Neurofilament Light With Neurodegeneration in Patients With Alzheimer Disease , 2017, JAMA neurology.

[25]  H. Hampel,et al.  Diagnostic function of the neuroinflammatory biomarker YKL-40 in Alzheimer’s disease and other neurodegenerative diseases , 2017, Expert review of proteomics.

[26]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

[27]  H. Hampel,et al.  Synaptic degeneration and neurogranin in the pathophysiology of Alzheimer’s disease , 2017, Expert review of neurotherapeutics.

[28]  P. Snyder,et al.  Blood-based biomarkers in Alzheimer disease: Current state of the science and a novel collaborative paradigm for advancing from discovery to clinic , 2017, Alzheimer's & Dementia.

[29]  C. Duyckaerts,et al.  The Cerebrospinal Fluid Neurogranin/BACE1 Ratio is a Potential Correlate of Cognitive Decline in Alzheimer’s Disease , 2016, Journal of Alzheimer's disease : JAD.

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

[31]  K. Blennow,et al.  CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis , 2016, The Lancet Neurology.

[32]  K. Lapidus,et al.  The Significance of the Default Mode Network (DMN) in Neurological and Neuropsychiatric Disorders: A Review , 2016, The Yale journal of biology and medicine.

[33]  Eric Karran,et al.  The Cellular Phase of Alzheimer’s Disease , 2016, Cell.

[34]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.

[35]  M. Raichle The brain's default mode network. , 2015, Annual review of neuroscience.

[36]  M. Breakspear,et al.  The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.

[37]  L. Uddin Salience processing and insular cortical function and dysfunction , 2014, Nature Reviews Neuroscience.

[38]  Jonathan D. Power,et al.  Studying Brain Organization via Spontaneous fMRI Signal , 2014, Neuron.

[39]  S. Cappa,et al.  Brain connectivity in neurodegenerative diseases—from phenotype to proteinopathy , 2014, Nature Reviews Neurology.

[40]  Cindee M. Madison,et al.  Neural compensation in older people with brain β-amyloid deposition , 2014, Nature Neuroscience.

[41]  Bradley T. Hyman,et al.  The Intersection of Amyloid Beta and Tau at Synapses in Alzheimer’s Disease , 2014, Neuron.

[42]  A. Fagan,et al.  Functional connectivity and graph theory in preclinical Alzheimer's disease , 2014, Neurobiology of Aging.

[43]  P. Thompson,et al.  Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease , 2014, Neuropsychology Review.

[44]  M. Ramanathan,et al.  Network‐Based Approaches in Drug Discovery and Early Development , 2013, Clinical pharmacology and therapeutics.

[45]  A. Fagan,et al.  Cerebrospinal fluid Aβ42, phosphorylated Tau181, and resting-state functional connectivity. , 2013, JAMA neurology.

[46]  Val Lowe,et al.  Dissecting phenotypic traits linked to human resilience to Alzheimer's pathology. , 2013, Brain : a journal of neurology.

[47]  H. Geerts,et al.  Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development , 2013, Journal of Pharmacokinetics and Pharmacodynamics.

[48]  Stephan G. Boehm,et al.  Emotional faces and the default mode network , 2012, Neuroscience Letters.

[49]  R. Iyengar,et al.  Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. , 2012, Annual review of pharmacology and toxicology.

[50]  G. Frisoni,et al.  Functional network disruption in the degenerative dementias , 2011, The Lancet Neurology.

[51]  Anthony Randal McIntosh,et al.  Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review , 2011, NeuroImage.

[52]  Keith A. Johnson,et al.  Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden , 2011, Alzheimer's & Dementia.

[53]  Michael D. Greicius,et al.  Relationships between β-amyloid and functional connectivity in different components of the default mode network in aging. , 2011, Cerebral cortex.

[54]  J. Callicott,et al.  Age-related alterations in default mode network: Impact on working memory performance , 2010, Neurobiology of Aging.

[55]  Efstathios D. Gennatas,et al.  Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. , 2010, Brain : a journal of neurology.

[56]  D. Head,et al.  Amyloid Plaques Disrupt Resting State Default Mode Network Connectivity in Cognitively Normal Elderly , 2010, Biological Psychiatry.

[57]  Keith A. Johnson,et al.  Disruption of Functional Connectivity in Clinically Normal Older Adults Harboring Amyloid Burden , 2009, The Journal of Neuroscience.

[58]  A. Bokde,et al.  Assessing neuronal networks: Understanding Alzheimer's disease , 2009, Progress in Neurobiology.

[59]  M. Weiner,et al.  Language in Alzheimer's disease. , 2008, The Journal of clinical psychiatry.

[60]  Scott J. Russo,et al.  Molecular Adaptations Underlying Susceptibility and Resistance to Social Defeat in Brain Reward Regions , 2007, Cell.

[61]  Maria Luisa Gorno-Tempini,et al.  Structural anatomy of empathy in neurodegenerative disease. , 2006, Brain : a journal of neurology.

[62]  Benjamin J. Shannon,et al.  Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory , 2005, The Journal of Neuroscience.

[63]  E. Sausville,et al.  Preclinical models for defining efficacy of drug combinations: mapping the road to the clinic. , 2003, Molecular cancer therapeutics.

[64]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[65]  D. Selkoe Alzheimer's Disease Is a Synaptic Failure , 2002, Science.

[66]  J R Moeller,et al.  Activate your online subscription , 2000, Neurology.

[67]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[68]  M. Storandt,et al.  Visuospatial deficit in dementia of the Alzheimer type. , 1995, Archives of neurology.

[69]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[70]  Nicola Toschi,et al.  Cerebrospinal Fluid Neurogranin as a Biomarker of Neurodegenerative Diseases: A Cross-Sectional Study. , 2017, Journal of Alzheimer's disease : JAD.

[71]  P. Dupont,et al.  Functional Changes in the Language Network in Response to Increased Amyloid β Deposition in Cognitively Intact Older Adults. , 2016, Cerebral cortex.

[72]  Hervé Abdi,et al.  Partial least squares methods: partial least squares correlation and partial least square regression. , 2013, Methods in molecular biology.

[73]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

[74]  Sumiti Saharan,et al.  Visuospatial perception: an emerging biomarker for Alzheimer's disease. , 2012, Journal of Alzheimer's disease : JAD.

[75]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[76]  D. Raskin On understanding Alzheimer's disease. , 1985, The American journal of psychiatry.