Predicting individual clinical trajectories of depression with generative embedding
Tài liệu tham khảo
Ai, 2019, Longitudinal brain changes in MDD during emotional encoding: effects of presence and persistence of symptomatology, Psychol. Med., 1
Almeida, 2009, Abnormal amygdala-prefrontal effective connectivity to happy faces differentiates bipolar from major depression, Biol. Psychiatry, 66, 451, 10.1016/j.biopsych.2009.03.024
Anand, 2007, Reciprocal effects of antidepressant treatment on activity and connectivity of the mood regulating circuit: an FMRI study, J. Neuropsychiatry Clin. Neurosci., 19, 274, 10.1176/jnp.2007.19.3.274
Andrade, 2003, The epidemiology of major depressive episodes: results from the international consortium of psychiatric epidemiology (ICPE) surveys, Int. J. Methods Psychiatr. Res., 12, 3, 10.1002/mpr.138
American Psychiatric Association, 2013. Diagnostic and statistical manual of mental disorders (DSM-5 R). Am. Psychiatric Publ.
Benjamini, 2001, The control of the false discovery rate in multiple testing under dependency, Ann. Stat., 29, 1165, 10.1214/aos/1013699998
Bishop, 2006, 12, 105
Breiter, 1996, Response and habituation of the human amygdala during visual processing of facial expression, Neuron, 17, 875, 10.1016/S0896-6273(00)80219-6
Brodersen, 2014, Dissecting psychiatric spectrum disorders by generative embedding, Neuroimage Clin., 4, 98, 10.1016/j.nicl.2013.11.002
Brodersen, 2011, Generative embedding for model-based classification of fMRI data, PLoS Comput. Biol., 7, 10.1371/journal.pcbi.1002079
Buxton, 1998, Dynamics of blood flow and oxygenation changes during brain activation: the balloon model, Magn. Reson. Med., 39, 855, 10.1002/mrm.1910390602
Catani, 2008, A diffusion tensor imaging tractography atlas for virtual in vivo dissections, Cortex, 44, 1105, 10.1016/j.cortex.2008.05.004
Cawley, 2010, On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res., 11, 2079
Clarke, 1990, Occipital cortex in man - Organization of callosal connections, related myeloarchitecture and cytoarchitecture, and putative boundaries of functional visual areas, J. Comp. Neurol., 298, 188, 10.1002/cne.902980205
Crowther, 2015, Resting-State connectivity predictors of response to psychotherapy in major depressive disorder, Neuropsychopharmacology, 40, 1659, 10.1038/npp.2015.12
Cuthbert, 2013, Toward the future of psychiatric diagnosis: the seven pillars of RDOC, BMC Med, 11, 126, 10.1186/1741-7015-11-126
Daunizeau, 2011, Dynamic causal modelling: a critical review of the biophysical and statistical foundations, Neuroimage, 58, 312, 10.1016/j.neuroimage.2009.11.062
de Graaf, 2012, Prevalence of mental disorders and trends from 1996 to 2009. results from the Netherlands mental health survey and incidence study-2, Soc. Psychiatry Psychiatr. Epidemiol., 47, 203, 10.1007/s00127-010-0334-8
Demenescu, 2011, Neural correlates of perception of emotional facial expressions in out-patients with mild-to-moderate depression and anxiety. A multicenter fMRI study, Psychol. Med., 41, 2253, 10.1017/S0033291711000596
Dietterich, 1998, Approximate statistical tests for comparing supervised classification learning algorithms, Neural. Comput., 10, 1895, 10.1162/089976698300017197
Dinga, 2018, Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach, Transl. Psychiatry, 8, 241, 10.1038/s41398-018-0289-1
Dunlop, 2017, Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive-behavioral therapy or antidepressant medication for major depressive disorder, Am. J. Psychiatry, 174, 533, 10.1176/appi.ajp.2016.16050518
Fairhall, 2007, Effective connectivity within the distributed cortical network for face perception, Cereb. Cortex, 17, 2400, 10.1093/cercor/bhl148
Fan, 2005, Working set selection using second order information for training support vector machines, J. Mach. Learning Res., 6, 1889
Fortin, 2017, Harmonization of multi-site diffusion tensor imaging data, Neuroimage, 161, 149, 10.1016/j.neuroimage.2017.08.047
Frässle, 2018, A generative model of whole-brain effective connectivity, Neuroimage, 179, 505, 10.1016/j.neuroimage.2018.05.058
Frässle, 2017, Regression DCM for fMRI, Neuroimage, 155, 406, 10.1016/j.neuroimage.2017.02.090
Frässle, 2016, Mechanisms of hemispheric lateralization: asymmetric interhemispheric recruitment in the face perception network, Neuroimage, 124, 977, 10.1016/j.neuroimage.2015.09.055
Frässle, 2018, Generative models for clinical applications in computational psychiatry, Wiley Interdiscip. Rev. Cogn. Sci., 9, e1460, 10.1002/wcs.1460
Frazier, P.I., 2018. A tutorial on bayesian optimization. arXiv e-prints.
Friston, 2003, Dynamic causal modelling, Neuroimage, 19, 1273, 10.1016/S1053-8119(03)00202-7
Friston, 2007, Variational free energy and the Laplace approximation, Neuroimage, 34, 220, 10.1016/j.neuroimage.2006.08.035
Friston, 1998, Event-related fMRI: characterizing differential responses, Neuroimage, 7, 30, 10.1006/nimg.1997.0306
Friston, 2000, Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics, Neuroimage, 12, 466, 10.1006/nimg.2000.0630
Fu, 2007, Neural responses to happy facial expressions in major depression following antidepressant treatment, Am J Psychiatry, 164, 599, 10.1176/ajp.2007.164.4.599
Fu, 2004, Attenuation of the neural response to sad faces in major depression by antidepressant treatment: a prospective, event-related functional magnetic resonance imaging study, Arch. Gen. Psychiatry, 61, 877, 10.1001/archpsyc.61.9.877
Fu, 2008, Pattern classification of sad facial processing: toward the development of neurobiological markers in depression, Biol. Psychiatry, 63, 656, 10.1016/j.biopsych.2007.08.020
Godlewska, 2012, Short-term SSRI treatment normalises amygdala hyperactivity in depressed patients, Psychol. Med., 42, 2609, 10.1017/S0033291712000591
Good, 2000
Greicius, 2007, Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus, Biol. Psychiatry, 62, 429, 10.1016/j.biopsych.2006.09.020
Groenewold, 2013, Emotional valence modulates brain functional abnormalities in depression: evidence from a meta-analysis of fMRI studies, Neurosci. Biobehav. Rev., 37, 152, 10.1016/j.neubiorev.2012.11.015
Gueorguieva, 2017, Trajectories of relapse in randomised, placebo-controlled trials of treatment discontinuation in major depressive disorder: an individual patient-level data meta-analysis, Lancet Psychiatry, 4, 230, 10.1016/S2215-0366(17)30038-X
Gueorguieva, 2011, Trajectories of depression severity in clinical trials of duloxetine: insights into antidepressant and placebo responses, Arch. Gen. Psychiatry, 68, 1227, 10.1001/archgenpsychiatry.2011.132
Harmer, 2009, Why do antidepressants take so long to work? A cognitive neuropsychological model of antidepressant drug action, Br. J. Psychiatry, 195, 102, 10.1192/bjp.bp.108.051193
Haufe, 2014, On the interpretation of weight vectors of linear models in multivariate neuroimaging, Neuroimage, 87, 96, 10.1016/j.neuroimage.2013.10.067
Haxby, 2000, The distributed human neural system for face perception, Trends Cogn. Sci., 4, 223, 10.1016/S1364-6613(00)01482-0
Hofer, 2006, Topography of the human corpus callosum revisited - Comprehensive fiber tractography using diffusion tensor magnetic resonance imaging, Neuroimage, 32, 989, 10.1016/j.neuroimage.2006.05.044
Horesh, 2008, Stressful life events and major depressive disorders, Psychiatry Res., 160, 192, 10.1016/j.psychres.2007.06.008
Itani, 2019, Towards interpretable machine learning models for diagnosis aid: a case study on attention deficit/hyperactivity disorder, PLoS ONE, 14, 10.1371/journal.pone.0215720
Kanwisher, 1997, The fusiform face area: a module in human extrastriate cortex specialized for face perception, J. Neurosci., 17, 4302, 10.1523/JNEUROSCI.17-11-04302.1997
Kapur, 2012, Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?, Mol. Psychiatry, 17, 1174, 10.1038/mp.2012.105
Kessler, 2016, Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports, Mol. Psychiatry, 21, 1366, 10.1038/mp.2015.198
Kohler, 2019, Differences between chronic and nonchronic depression: systematic review and implications for treatment, Depress.Anxiety, 36, 18, 10.1002/da.22835
Lyketsos, 1994, The life chart interview - A Standardized method to describe the course of psychopathology, Int. J. Methods Psychiatr. Res., 4, 143
MacQueen, 2009, Magnetic resonance imaging and prediction of outcome in patients with major depressive disorder, J. Psychiatry Neurosci., 34, 343
Mayberg, 1997, Limbic-cortical dysregulation: a proposed model of depression, J. Neuropsychiatry Clin. Neurosci., 9, 471, 10.1176/jnp.9.3.471
Mayberg, 1997, Cingulate function in depression: a potential predictor of treatment response, Neuroreport, 8, 1057, 10.1097/00001756-199703030-00048
McFadyen, 2019, An afferent white matter pathway from the pulvinar to the amygdala facilitates fear recognition, Elife, 8, 10.7554/eLife.40766
McMahon, 2012, Pharmacogenomics and personalized medicine in neuropsychiatry, Neuron, 74, 773, 10.1016/j.neuron.2012.05.004
Muller, 2017, Altered brain activity in unipolar depression revisited: meta-analyses of neuroimaging studies, JAMA Psychiatry, 74, 47, 10.1001/jamapsychiatry.2016.2783
Murphy, 2009, Effect of a single dose of citalopram on amygdala response to emotional faces, Br. J. Psychiatry, 194, 535, 10.1192/bjp.bp.108.056093
Musliner, 2016, Heterogeneity in 10-Year course trajectories of moderate to severe major depressive disorder: a danish national register-based study, JAMA Psychiatry, 73, 346, 10.1001/jamapsychiatry.2015.3365
Muthen, 2011, Growth modeling with nonignorable dropout: alternative analyses of the star*d antidepressant trial, Psychol. Methods, 16, 17, 10.1037/a0022634
Nadeem, 2010, Accuracy-Rejection curves (ARCs) for comparing classification methods with a reject option, Proc. Third Int. Workshop Mach Learn Syst. Biol., 8, 65
Naselaris, 2011, Encoding and decoding in fMRI, Neuroimage, 56, 400, 10.1016/j.neuroimage.2010.07.073
Nord, 2019, Neural predictors of treatment response to brain stimulation and psychological therapy in depression: a double-blind randomized controlled trial, Neuropsychopharmacology, 44, 1613, 10.1038/s41386-019-0401-0
Ojala, 2010, Permutation tests for studying classifier performance, J. Mach. Learn. Res., 11, 1833
Pan, 2017, Ventral striatum functional connectivity as a predictor of adolescent depressive disorder in a longitudinal community-based sample, Am. J. Psychiatry, 174, 1112, 10.1176/appi.ajp.2017.17040430
Paulus, 2015, Bayesian neural adjustment of inhibitory control predicts emergence of problem stimulant use, Neuropsychopharmacology, 40, S32
Penninx, 2011, Two-year course of depressive and anxiety disorders: results from the Netherlands study of depression and anxiety (NESDA), J. Affect. Disord., 133, 76, 10.1016/j.jad.2011.03.027
Penninx, 2008, The Netherlands study of depression and anxiety (NESDA): rationale, objectives and methods, Int. J. Methods Psychiatr. Res., 17, 121, 10.1002/mpr.256
Penny, 2010, Comparing families of dynamic causal models, PLoS Comput. Biol., 6, 10.1371/journal.pcbi.1000709
Phillips, 2015, Identifying predictors, moderators, and mediators of antidepressant response in major depressive disorder: neuroimaging approaches, Am. J. Psychiatry, 172, 124, 10.1176/appi.ajp.2014.14010076
Pitcher, 2011, The role of the occipital face area in the cortical face perception network, Exp. Brain Res., 209, 481, 10.1007/s00221-011-2579-1
Power, 2012, Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, Neuroimage, 59, 2142, 10.1016/j.neuroimage.2011.10.018
Puce, 1996, Differential sensitivity of human visual cortex to faces, letterstrings, and textures: a functional magnetic resonance imaging study, J. Neurosci., 16, 5205, 10.1523/JNEUROSCI.16-16-05205.1996
Rasmussen, C.E., Williams, C.K.I., 2005. Gaussian processes for machine learning. Gaussian Processes for Machine Learning, 1–247.
Rhebergen, 2012, Course trajectories of unipolar depressive disorders identified by latent class growth analysis, Psychol. Med., 42, 1383, 10.1017/S0033291711002509
Rigoux, 2014, Bayesian model selection for group studies - revisited, Neuroimage, 84, 971, 10.1016/j.neuroimage.2013.08.065
Rive, 2013, Neural correlates of dysfunctional emotion regulation in major depressive disorder. A systematic review of neuroimaging studies, Neurosci. Biobehav. Rev., 37, 2529, 10.1016/j.neubiorev.2013.07.018
Robertson, 2007, Effect of bupropion extended release on negative emotion processing in major depressive disorder: a pilot functional magnetic resonance imaging study, J. Clin. Psychiatry, 68, 261, 10.4088/JCP.v68n0212
Robins, 1988, The composite international diagnostic interview - An epidemiological instrument suitable for use in conjunction with different diagnostic systems and in different cultures, Arch. Gen. Psychiatry, 45, 1069, 10.1001/archpsyc.1988.01800360017003
Rush, 2006, Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a star*d report, Am. J. Psychiatry, 163, 1905, 10.1176/ajp.2006.163.11.1905
Schmaal, 2015, Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: a multivariate pattern recognition study, Biol. Psychiatry, 78, 278, 10.1016/j.biopsych.2014.11.018
Shawe-Taylor, J., Cristianini, N., 2004. Kernel methods for pattern analysis. cambridge University Press.
Sheline, 2001, Increased amygdala response to masked emotional faces in depressed subjects resolves with antidepressant treatment: an fMRI study, Biol. Psychiatry, 50, 651, 10.1016/S0006-3223(01)01263-X
Shen, 2015, Sub-hubs of baseline functional brain networks are related to early improvement following two-week pharmacological therapy for major depressive disorder, Hum. Brain Mapp., 36, 2915, 10.1002/hbm.22817
Siegle, 2012, Toward clinically useful neuroimaging in depression treatment: prognostic utility of subgenual cingulate activity for determining depression outcome in cognitive therapy across studies, scanners, and patient characteristics, Arch. Gen. Psychiatry, 69, 913, 10.1001/archgenpsychiatry.2012.65
Stephan, 2017, Computational neuroimaging strategies for single patient predictions, Neuroimage, 145, 180, 10.1016/j.neuroimage.2016.06.038
Stephan, 2007, Comparing hemodynamic models with DCM, Neuroimage, 38, 387, 10.1016/j.neuroimage.2007.07.040
Stone, 1974, Cross-Validatory choice and assessment of statistical predictions, J. R. Stat. Soc. Ser. B-Stat. Methodology, 36, 111
Stuhrmann, 2013, Mood-congruent amygdala responses to subliminally presented facial expressions in major depression: associations with anhedonia, J. Psychiatry Neurosci., 38, 249, 10.1503/jpn.120060
Symmonds, 2018, Ion channels in EEG: isolating channel dysfunction in NMDA receptor antibody encephalitis, Brain, 141, 1691, 10.1093/brain/awy107
Van Essen, 1982, The pattern of interhemispheric connections and its relationship to extrastriate visual areas in the macaque monkey, J. Neurosci., 2, 265, 10.1523/JNEUROSCI.02-03-00265.1982
van Tol, 2010, Regional brain volume in depression and anxiety disorders, Arch. Gen. Psychiatry, 67, 1002, 10.1001/archgenpsychiatry.2010.121
Velligan, 2010, Strategies for addressing adherence problems in patients with serious and persistent mental illness: recommendations from the expert consensus guidelines, J. Psychiatr. Pract., 16, 306, 10.1097/01.pra.0000388626.98662.a0
Vogelzangs, 2014, Inflammatory and metabolic dysregulation and the 2-year course of depressive disorders in antidepressant users, Neuropsychopharmacology, 39, 1624, 10.1038/npp.2014.9
Vreeburg, 2013, Salivary cortisol levels and the 2-year course of depressive and anxiety disorders, Psychoneuroendocrinology, 38, 1494, 10.1016/j.psyneuen.2012.12.017
Walsh, 2017, Attenuation of frontostriatal connectivity during reward processing predicts response to psychotherapy in major depressive disorder, Neuropsychopharmacology, 42, 831, 10.1038/npp.2016.179
Wang, 2012, A systematic review of resting-state functional-MRI studies in major depression, J. Affect. Disord., 142, 6, 10.1016/j.jad.2012.04.013
Wiecki, 2016, A computational cognitive biomarker for early-stage huntington's disease, PLoS ONE, 11, 10.1371/journal.pone.0148409
Woo, 2017, Building better biomarkers: brain models in translational neuroimaging, Nat. Neurosci., 20, 365, 10.1038/nn.4478
Yarkoni, 2011, Large-scale automated synthesis of human functional neuroimaging data, Nat. Methods, 8, 665, 10.1038/nmeth.1635
Yu, 2018, Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data, Hum. Brain Mapp., 39, 4213, 10.1002/hbm.24241
Zeki, 1970, Interhemispheric connections of prestriate cortex in monkey, Brain Res., 19, 10.1016/0006-8993(70)90237-4
Zilles, 1997, Architecture, connectivity and transmitter receptors of human extrastriate cortex, 673, 10.1007/978-1-4757-9625-4_15