Abnormal medial prefrontal cortex functional connectivity and its association with clinical symptoms in chronic low back pain.
暂无分享,去创建一个
Ishtiaq Mawla | Jessica Gerber | Yiheng Tu | Ana Ortiz | Jian Kong | Vitaly Napadow | Bruce Rosen | Suk-Tak Chan | B. Rosen | V. Napadow | Suk-tak Chan | R. Gollub | J. Kong | T. Kaptchuk | C. Lang | R. Edwards | I. Mawla | A. Wasan | M. Jung | Y. Tu | Wei Shen | A. Ortiz | J. Gerber | Randy L Gollub | Robert R Edwards | Ajay D Wasan | Ted J Kaptchuk | Minyoung Jung | Courtney Lang | Wei Shen | R. Edwards
[1] Aapo Hyvärinen,et al. Icasso: software for investigating the reliability of ICA estimates by clustering and visualization , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).
[2] J. Pekar,et al. A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.
[3] Karl J. Friston,et al. Unified segmentation , 2005, NeuroImage.
[4] Keith A. Johnson,et al. Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.
[5] J. Kong,et al. Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder. , 2018, The British journal of psychiatry : the journal of mental science.
[6] Tor D Wager,et al. Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience , 2011, The Journal of Neuroscience.
[7] D. Chialvo,et al. Beyond Feeling: Chronic Pain Hurts the Brain, Disrupting the Default-Mode Network Dynamics , 2008, The Journal of Neuroscience.
[8] Vince D. Calhoun,et al. Automatic Bayesian Classification of Healthy Controls, Bipolar Disorder, and Schizophrenia Using Intrinsic Connectivity Maps From fMRI Data , 2010, IEEE Transactions on Biomedical Engineering.
[9] Li Hu,et al. Mesocorticolimbic Pathways Encode Cue-Based Expectancy Effects on Pain , 2019, Journal of Neuroscience.
[10] M. Baliki,et al. The Cortical Rhythms of Chronic Back Pain , 2011, The Journal of Neuroscience.
[11] M. V. Centeno,et al. Brain activity for tactile allodynia: a longitudinal awake rat functional magnetic resonance imaging study tracking emergence of neuropathic pain , 2017, Pain.
[12] D. Hu,et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. , 2012, Brain : a journal of neurology.
[13] Yeung Sam Hung,et al. Decoding Subjective Intensity of Nociceptive Pain from Pre-stimulus and Post-stimulus Brain Activities , 2016, Front. Comput. Neurosci..
[14] P. Sandroni,et al. International association for the study of pain , 1986, Pain.
[15] Kyungmo Park,et al. Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. , 2010, Arthritis and rheumatism.
[16] V. Calhoun,et al. EEG Signatures of Dynamic Functional Network Connectivity States , 2017, Brain Topography.
[17] Sylvain Houle,et al. Abnormal intrinsic brain functional network dynamics in Parkinson’s disease , 2017, Brain : a journal of neurology.
[18] Dante R. Chialvo,et al. Chronic pain patients are impaired on an emotional decision-making task , 2004, Pain.
[19] Kevin A. Johnson,et al. Multivariate classification of structural MRI data detects chronic low back pain. , 2014, Cerebral cortex.
[20] Reinder Vos de Wael,et al. Effects of Tissue-Specific Functional Magnetic Resonance Imaging Signal Regression on Resting-State Functional Connectivity , 2017, Brain Connect..
[21] K. Davis,et al. The Neural Code for Pain: From Single-Cell Electrophysiology to the Dynamic Pain Connectome , 2017, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[22] Tom M. Mitchell,et al. Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.
[23] Ajay D. Wasan,et al. Default mode network connectivity encodes clinical pain: An arterial spin labeling study , 2013, PAIN®.
[24] Abraham Z. Snyder,et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.
[25] K. Davis,et al. The dynamic pain connectome , 2015, Trends in Neurosciences.
[26] Xiaoqi Huang,et al. Disrupted Brain Connectivity Networks in Drug-Naive, First-Episode Major Depressive Disorder , 2011, Biological Psychiatry.
[27] Jessica Gerber,et al. Enhancing treatment of osteoarthritis knee pain by boosting expectancy: A functional neuroimaging study , 2018, NeuroImage: Clinical.
[28] Brandon Galarita,et al. Chronic , 2020, Definitions.
[29] Andrew Simmons,et al. Pattern classification of response inhibition in ADHD: Toward the development of neurobiological markers for ADHD , 2013, Human brain mapping.
[30] K. Davis. Neuroimaging of pain. , 2004, Supplements to Clinical neurophysiology.
[31] Martin A. Lindquist,et al. Group-regularized individual prediction: theory and application to pain , 2017, NeuroImage.
[32] Sara E. Berger,et al. The indirect pathway of the nucleus accumbens shell amplifies neuropathic pain , 2015, Nature Neuroscience.
[33] Jianren Mao,et al. Current challenges in translational pain research. , 2012, Trends in pharmacological sciences.
[34] Kevin Murphy,et al. The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.
[35] Li Hu,et al. Evaluating Cortical Alterations in Patients With Chronic Back Pain Using Neuroimaging Techniques: Recent Advances and Perspectives , 2019, Front. Psychol..
[36] M. Bushnell,et al. How neuroimaging studies have challenged us to rethink: is chronic pain a disease? , 2009, The journal of pain : official journal of the American Pain Society.
[37] D. Cherkin,et al. Developing methods for acupuncture research: rationale for and design of a pilot study evaluating the efficacy of acupuncture for chronic low back pain. , 2003, Alternative therapies in health and medicine.
[38] Thomas J. Schnitzer,et al. Corticostriatal functional connectivity predicts transition to chronic back pain , 2012, Nature Neuroscience.
[39] Chris C. Tang,et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis , 2010, The Lancet Neurology.
[40] M. Vangel,et al. Analgesic Effects Evoked by Real and Imagined Acupuncture: A Neuroimaging Study. , 2018, Cerebral cortex.
[41] Jian Kong,et al. S1 is associated with chronic low back pain: a functional and structural MRI study , 2013, Molecular pain.
[42] V. Napadow,et al. Disrupted functional connectivity of the periaqueductal gray in chronic low back pain , 2014, NeuroImage: Clinical.
[43] David A. Williams,et al. Executive function in chronic pain patients and healthy controls: different cortical activation during response inhibition in fibromyalgia. , 2011, The journal of pain : official journal of the American Pain Society.
[44] Bernadette A. Thomas,et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.
[45] Howard C. Tenenbaum,et al. Enhanced Medial Prefrontal-Default Mode Network Functional Connectivity in Chronic Pain and Its Association with Pain Rumination , 2014, The Journal of Neuroscience.
[46] A. Abi-Dargham,et al. The search for imaging biomarkers in psychiatric disorders , 2016, Nature Medicine.
[47] M. Baliki,et al. A dynamic network perspective of chronic pain , 2012, Neuroscience Letters.
[48] Daniel Paul Kerr,et al. Effectiveness of Acupuncture for Low Back Pain: A Systematic Review , 2008, Spine.
[49] Assia Jaillard,et al. Reliability of graph analysis of resting state fMRI using test-retest dataset from the Human Connectome Project , 2016, NeuroImage.
[50] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[51] Olaf Sporns,et al. Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.
[52] Daniel J Buysse,et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. , 2010, Journal of clinical epidemiology.
[53] C. Keown,et al. Network organization is globally atypical in autism: A graph theory study of intrinsic functional connectivity. , 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[54] L. Becerra,et al. Pain and analgesia: The value of salience circuits , 2013, Progress in Neurobiology.
[55] Enzo Tagliazucchi,et al. Automatic sleep staging using fMRI functional connectivity data , 2012, NeuroImage.
[56] K. Davis,et al. Abnormal cross-network functional connectivity in chronic pain and its association with clinical symptoms , 2016, Brain Structure and Function.
[57] Gian Domenico Iannetti,et al. Alpha and gamma oscillation amplitudes synergistically predict the perception of forthcoming nociceptive stimuli , 2015, Human brain mapping.
[58] M. Lindquist,et al. An fMRI-based neurologic signature of physical pain. , 2013, The New England journal of medicine.
[59] Jonathan P. McNulty,et al. The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM , 2014, NeuroImage.
[60] S. Mackey,et al. Neuroimaging of Pain: Human Evidence and Clinical Relevance of Central Nervous System Processes and Modulation. , 2018, Anesthesiology.
[61] R. Deyo,et al. Physician Office Visits for Low Back Pain: Frequency, Clinical Evaluation, and Treatment Patterns From a U.S. National Survey , 1995, Spine.
[62] G. Coppola,et al. Increased neural connectivity between the hypothalamus and cortical resting-state functional networks in chronic migraine , 2019, Journal of Neurology.
[63] Zengjian Wang,et al. Regional Homogeneity and Multivariate Pattern Analysis of Cervical Spondylosis Neck Pain and the Modulation Effect of Treatment , 2018, Front. Neurosci..
[64] Dewen Hu,et al. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI , 2010, NeuroImage.
[65] J. Kippenhan,et al. Evaluation of a neural-network classifier for PET scans of normal and Alzheimer's disease subjects. , 1992, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[66] A. Mechelli,et al. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.
[67] Yong He,et al. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics , 2015, Front. Hum. Neurosci..
[68] Christos Davatzikos,et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.
[69] T. Kocak,et al. Evaluation criteria for the assessment of occupational diseases of the lumbar spine - how reliable are they? - , 2019, BMC Musculoskeletal Disorders.
[70] A. Vania Apkarian,et al. Functional Reorganization of the Default Mode Network across Chronic Pain Conditions , 2014, PloS one.
[71] Yeung Sam Hung,et al. A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction , 2018, Neurocomputing.
[72] L. Uddin. Salience processing and insular cortical function and dysfunction , 2014, Nature Reviews Neuroscience.
[73] Jonathan D. Power,et al. Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.
[74] Chen Su,et al. Activation of Corticostriatal Circuitry Relieves Chronic Neuropathic Pain , 2015, The Journal of Neuroscience.
[75] Zening Fu,et al. An fMRI-based neural marker for migraine without aura , 2020, Neurology.
[76] Luke J. Chang,et al. Building better biomarkers: brain models in translational neuroimaging , 2017, Nature Neuroscience.
[77] J. Fries,et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): Progress of an NIH Roadmap Cooperative Group During its First Two Years , 2007, Medical care.
[78] M. Baliki,et al. Towards a theory of chronic pain , 2009, Progress in Neurobiology.
[79] Dimitris Samaras,et al. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example , 2016, NeuroImage.
[80] S. McDonough,et al. Effectiveness of Acupuncture for Low Back Pain , 2008 .