Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study
暂无分享,去创建一个
Ruiwang Huang | Huawang Wu | Shufei Zhang | Xiaoyan Wu | S. She | Huiqing Hu | Yidan Qiu | W. Zheng | Zezhi Li | Shenglin She
[1] S. Eickhoff,et al. Altered brain activity in unipolar depression unveiled using connectomics , 2023, Nature Mental Health.
[2] M. Breeuwer,et al. Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects , 2022, Journal of neuroimaging : official journal of the American Society of Neuroimaging.
[3] C. Boutry,et al. Resting-state functional connectivity correlates of anxiety co-morbidity in major depressive disorder , 2022, Neuroscience & Biobehavioral Reviews.
[4] Zengcai V. Guo,et al. An entorhinal-visual cortical circuit regulates depression-like behaviors , 2022, Molecular Psychiatry.
[5] Sultan S. Alshamrani,et al. Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions , 2022, Electronics.
[6] J. Qiu,et al. Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes , 2022, Molecular Psychiatry.
[7] Carol A. Seger,et al. Multimodal MRI reveals alterations of the anterior insula and posterior cingulate cortex in bipolar II disorders: A surface-based approach , 2022, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[8] Q. Gong,et al. Cortical thickness abnormalities in patients with bipolar disorder: a systematic review and meta-analysis. , 2021, Journal of affective disorders.
[9] J. P. Hamilton,et al. Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium , 2021, Translational Psychiatry.
[10] P. Klivényi,et al. Voxel-based asymmetry of the regional gray matter over the inferior temporal gyrus correlates with depressive symptoms in medicated patients with major depressive disorder , 2021, Psychiatry Research: Neuroimaging.
[11] Jiajia Zhu,et al. Functional stability predicts depressive and cognitive improvement in major depressive disorder: A longitudinal functional MRI study , 2021, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[12] Ying Wang,et al. Common and distinct patterns of intrinsic brain activity alterations in major depression and bipolar disorder: voxel-based meta-analysis , 2020, Translational Psychiatry.
[13] R. Canbeyli. Sensory stimulation via the visual, auditory, olfactory and gustatory systems can modulate mood and depression , 2021, The European journal of neuroscience.
[14] Zhiliang Long,et al. Prediction on treatment improvement in depression with resting state connectivity: A coordinate-based meta-analysis. , 2020, Journal of affective disorders.
[15] Guanmao Chen,et al. Shared and specific patterns of dynamic functional connectivity variability of striato-cortical circuitry in unmedicated bipolar and major depressive disorders , 2020, Psychological Medicine.
[16] G. V. van Wingen,et al. Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis , 2020, Translational Psychiatry.
[17] Zhijun Zhang,et al. Task-related functional magnetic resonance imaging-based neuronavigation for the treatment of depression by individualized repetitive transcranial magnetic stimulation of the visual cortex , 2020, Science China Life Sciences.
[18] O. Witte,et al. Surface-based analysis increases the specificity of cortical activation patterns and connectivity results , 2020, Scientific Reports.
[19] W. Rief,et al. Distorted Cognitive Processes in Major Depression: A Predictive Processing Perspective , 2020, Biological Psychiatry.
[20] Simon B Eickhoff,et al. Multimodal Abnormalities of Brain Structure and Function in Major Depressive Disorder: A Meta-Analysis of Neuroimaging Studies. , 2020, The American journal of psychiatry.
[21] V. Calhoun,et al. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises , 2020, Biological Psychiatry.
[22] Huafu Chen,et al. Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder , 2020, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[23] Camarin E. Rolle,et al. Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial. , 2020, JAMA psychiatry.
[24] J. Grange,et al. Computational modelling of attentional selectivity in depression reveals perceptual deficits , 2020, Psychological Medicine.
[25] Yong Xu,et al. The rise and fall of MRI studies in major depressive disorder , 2019, Translational Psychiatry.
[26] Alishia D. Williams,et al. Prospective biomarkers of major depressive disorder: a systematic review and meta-analysis , 2019, Molecular Psychiatry.
[27] S. Lui,et al. Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder , 2019, Neuropsychopharmacology.
[28] B. Turetsky,et al. Fronto‐parietal and temporal brain dysfunction in depression: A fMRI investigation of auditory mismatch processing , 2019, Human brain mapping.
[29] Ben D. Fulcher,et al. Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem , 2019, NeuroImage.
[30] Tianzi Jiang,et al. Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population , 2019, Schizophrenia bulletin.
[31] Timothy C.Y. Chan,et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. , 2018, Journal of affective disorders.
[32] J. Mann,et al. Depression , 2018, The Lancet.
[33] M. Milham,et al. Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder , 2018, Cerebral cortex.
[34] Jing Sui,et al. Machine learning in major depression: From classification to treatment outcome prediction , 2018, CNS neuroscience & therapeutics.
[35] Adrian Preda,et al. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion , 2018, Nature Communications.
[36] David C Van Essen,et al. The impact of traditional neuroimaging methods on the spatial localization of cortical areas , 2018, Proceedings of the National Academy of Sciences.
[37] Daniel Rueckert,et al. Multimodal surface matching with higher-order smoothness constraints , 2017, NeuroImage.
[38] Kevin Murphy,et al. Towards a consensus regarding global signal regression for resting state functional connectivity MRI , 2017, NeuroImage.
[39] V. Arolt,et al. Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample , 2017, Journal of Neural Transmission.
[40] Timothy O. Laumann,et al. Sources and implications of whole-brain fMRI signals in humans , 2017, NeuroImage.
[41] Vince D. Calhoun,et al. Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data , 2017, NeuroImage.
[42] Ruiwang Huang,et al. Common and Specific Abnormalities in Cortical Thickness in Patients with Major Depressive and Bipolar Disorders , 2017, EBioMedicine.
[43] Thang M. Le,et al. Alterations in visual cortical activation and connectivity with prefrontal cortex during working memory updating in major depressive disorder , 2017, NeuroImage: Clinical.
[44] E. Rolls,et al. Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. , 2016, Brain : a journal of neurology.
[45] Jun Cao,et al. Resting-state functional MRI of abnormal baseline brain activity in young depressed patients with and without suicidal behavior. , 2016, Journal of affective disorders.
[46] Huafu Chen,et al. Frequency-specific alterations in functional connectivity in treatment-resistant and -sensitive major depressive disorder. , 2016, Journal of psychiatric research.
[47] S. Jeon,et al. Neuroinflammation and cytokine abnormality in major depression: Cause or consequence in that illness? , 2016, World journal of psychiatry.
[48] Neda Bernasconi,et al. The superficial white matter in temporal lobe epilepsy: a key link between structural and functional network disruptions. , 2016, Brain : a journal of neurology.
[49] Steen Moeller,et al. The Human Connectome Project's neuroimaging approach , 2016, Nature Neuroscience.
[50] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[51] Xi-Nian Zuo,et al. Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability , 2016, bioRxiv.
[52] V. Calhoun,et al. Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[53] Thomas E. Nichols,et al. Non‐parametric combination and related permutation tests for neuroimaging , 2016, Human brain mapping.
[54] Q. Gong,et al. Brain gray matter alterations in first episodes of depression: A meta-analysis of whole-brain studies , 2016, Neuroscience & Biobehavioral Reviews.
[55] Andrew H. Miller,et al. The role of inflammation in depression: from evolutionary imperative to modern treatment target , 2015, Nature Reviews Immunology.
[56] V. Calhoun,et al. In Search of Multimodal Neuroimaging Biomarkers of Cognitive Deficits in Schizophrenia , 2015, Biological Psychiatry.
[57] C. Beckmann,et al. Resting-state functional connectivity in major depressive disorder: A review , 2015, Neuroscience & Biobehavioral Reviews.
[58] S. Lawrie,et al. Cortical Thickness in Individuals at High Familial Risk of Mood Disorders as They Develop Major Depressive Disorder , 2015, Biological Psychiatry.
[59] P. Blier,et al. A Prospective, Longitudinal Study of the Effect of Remission on Cortical Thickness and Hippocampal Volume in Patients with Treatment-Resistant Depression , 2015, The international journal of neuropsychopharmacology.
[60] M. Phillips,et al. Right superior temporal gyrus volume in adolescents with a history of suicide attempt , 2015, British Journal of Psychiatry.
[61] Dinggang Shen,et al. Surface Vulnerability of Cerebral Cortex to Major Depressive Disorder , 2015, PloS one.
[62] G. Dichter,et al. A systematic review of relations between resting-state functional-MRI and treatment response in major depressive disorder. , 2015, Journal of affective disorders.
[63] S. Lui,et al. Brain grey matter abnormalities in medication-free patients with major depressive disorder: a meta-analysis , 2014, Psychological Medicine.
[64] P. Fitzgerald. Gray colored glasses: is major depression partially a sensory perceptual disorder? , 2013, Journal of affective disorders.
[65] Peter Hagoort,et al. Stimulating the Brain's Language Network: Syntactic Ambiguity Resolution after TMS to the Inferior Frontal Gyrus and Middle Temporal Gyrus , 2013, Journal of Cognitive Neuroscience.
[66] Mark Jenkinson,et al. The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.
[67] R. Canbeyli. Sensorimotor Modulation of Mood and Depression: In Search of an Optimal Mode of Stimulation , 2013, Front. Hum. Neurosci..
[68] M. Furey,et al. Potential of pretreatment neural activity in the visual cortex during emotional processing to predict treatment response to scopolamine in major depressive disorder. , 2013, JAMA psychiatry.
[69] Jianpin Liu,et al. Impaired pre-attentive change detection in major depressive disorder patients revealed by auditory mismatch negativity , 2013, Psychiatry Research: Neuroimaging.
[70] I. Hickie,et al. A systematic review of resting-state functional-MRI studies in major depression. , 2012, Journal of affective disorders.
[71] M. Breakspear,et al. Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression , 2012, PloS one.
[72] M. Bach,et al. Effect of antidepressive therapy on retinal contrast processing in depressive disorder , 2012, British Journal of Psychiatry.
[73] D. Hu,et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. , 2012, Brain : a journal of neurology.
[74] Klaus Mathiak,et al. Impaired pitch identification as a potential marker for depression , 2012, BMC Psychiatry.
[75] E. Bora,et al. Gray matter abnormalities in Major Depressive Disorder: a meta-analysis of voxel based morphometry studies. , 2012, Journal of affective disorders.
[76] Ellen Frank,et al. Major depressive disorder: new clinical, neurobiological, and treatment perspectives , 2012, The Lancet.
[77] E. Mohammadi,et al. Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.
[78] J. Price,et al. Neural circuits underlying the pathophysiology of mood disorders , 2012, Trends in Cognitive Sciences.
[79] Klaus P. Ebmeier,et al. Magnetic resonance imaging studies in unipolar depression: Systematic review and meta-regression analyses , 2012, European Neuropsychopharmacology.
[80] Andrea Mechelli,et al. Voxelwise meta-analysis of gray matter reduction in major depressive disorder , 2012, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[81] Adam K. Anderson,et al. Mood-Linked Responses in Medial Prefrontal Cortex Predict Relapse in Patients with Recurrent Unipolar Depression , 2011, Biological Psychiatry.
[82] C. Beevers,et al. Neural mechanisms of the cognitive model of depression , 2011, Nature Reviews Neuroscience.
[83] Marisa O. Hollinshead,et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.
[84] M. Bach,et al. Seeing Gray When Feeling Blue? Depression Can Be Measured in the Eye of the Diseased , 2010, Biological Psychiatry.
[85] S. Rombouts,et al. Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .
[86] Jiing-Feng Lirng,et al. Cortical and Subcortical Abnormalities in Late-Onset Depression With History of Suicide Attempts Investigated With MRI and Voxel-Based Morphometry , 2010, Journal of geriatric psychiatry and neurology.
[87] R. Canbeyli. Sensorimotor modulation of mood and depression: An integrative review , 2010, Behavioural Brain Research.
[88] Jen-Chuen Hsieh,et al. Structural and cognitive deficits in remitting and non-remitting recurrent depression: A voxel-based morphometric study , 2010, NeuroImage.
[89] M. Yücel,et al. An MRI study of the superior temporal subregions in patients with current and past major depression , 2010, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[90] Gerty J. L. M. Lensvelt-Mulders,et al. Brain volume abnormalities in major depressive disorder: A meta‐analysis of magnetic resonance imaging studies , 2009, Human brain mapping.
[91] Paul B. Fitzgerald,et al. A magnetic resonance imaging study of the entorhinal cortex in treatment-resistant depression , 2008, Psychiatry Research: Neuroimaging.
[92] Yasuhisa Sakurai,et al. Agraphia for Kanji Resulting From a Left Posterior Middle Temporal Gyrus Lesion , 2008, Behavioural neurology.
[93] Angela R Laird,et al. A meta‐analytic study of changes in brain activation in depression , 2008, Human brain mapping.
[94] Anders M. Dale,et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.
[95] David C. Van Essen,et al. Surface-based approaches to spatial localization and registration in primate cerebral cortex , 2004, NeuroImage.
[96] Martin Styner,et al. Statistical surface-based morphometry using a nonparametric approach , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).
[97] J. Haxby,et al. Human neural systems for face recognition and social communication , 2002, Biological Psychiatry.
[98] Karl J. Friston,et al. A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains , 2001, NeuroImage.
[99] Leslie G. Ungerleider,et al. Distributed representation of objects in the human ventral visual pathway. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[100] Janet B W Williams,et al. A structured interview guide for the Hamilton Depression Rating Scale. , 1988, Archives of general psychiatry.
[101] R. Rubini-Costa,et al. 产前诊断和干预改善结节性硬化症儿童的发育和癫痫结局 , 2022, Developmental Medicine & Child Neurology.
[102] Ulrike Kübler. Structured Clinical Interview for DSM-IV (SCID) , 2020, Encyclopedia of Behavioral Medicine.
[103] P. Fox,et al. Altered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies , 2017, JAMA psychiatry.
[104] D. V. van Essen,et al. Surface-based approaches to spatial localization and registration in primate cerebral cortex. , 2004, NeuroImage.
[105] J. Haxby,et al. Distinct representations of eye gaze and identity in the distributed human neural system for face perception , 2000, Nature Neuroscience.