Publications

Last updated Oct 2018. See Google Scholar for a complete listing

2018

Wolfers, T., Doan, N. T., Kaufmann, T., Alnæs, D., Moberget, T., Agartz, I., … & Andreassen, O. A. (2018). Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA psychiatry.
Kia S.M., Marquand A. (2018) Normative Modeling of Neuroimaging Data Using Scalable Multi-task Gaussian Processes. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11072. Springer, Cham
Andrews, D. S., Marquand, A., Ecker, C., & McAlonan, G. (2018). Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder.

2017

Huertas, I., Oldehinkel, M., van Oort, E., Garcia-Solis, D., Mir, P., Beckmann, C. F., Marquand, A. F. (2017) A Bayesian spatial model for neuroimaging data based on biologically informed basis functions. Neuroimage 161, 134-148
Marquand, A, Haak, K., Beckmann, C. F. (2017) Functional corticostriatal connection topographies predict goal-directed behaviour in humans Nature Human Behaviour, 1
Wolfers, T., Llera Arenas, A., Onnink, A., M., H. Dammers, J., Hoogman, M., Zwiers, M., Buitelaar, J. Franke, B. Marquand, A. F., Beckmann, C. F. (2017) Refinement by integration: aggregated effects of multimodal imaging markers on adult ADHD Journal of Psychiatry & Neuroscience, 42, 386
Ecker, C., Andrews, D., Gudbrandsen, C., Marquand, A. F. et al (2017) Association between the probability of autism spectrum disorder and normative sex-related phenotypic diversity in brain structure 754 (4), 329-338
Andrews, D. et al (2017) In Vivo Evidence of Reduced Integrity of the Gray–White Matter Boundary in Autism Spectrum Disorder Cerebral Cortex 27(2), 877-887
Hibar D. et al (2017) Novel genetic loci associated with hippocampal volume Nature Communications 8, 13624
Guadeloupe, T et al (2017) Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex Brain imaging and Behaviour
Schoffelen, J. M. Hulten, A., Lam, N. Marquand, A. F. Udden, J. Hagoort, P. Frequency-specific directed interactions in the human brain network for language Proceedings of the National Academy of Sciences 114 (30), 8083-8088
Haak, K., Marquand, A., F., Beckman C., F. Connectopic mapping with resting-state fMRI. Neuroimage (In press)
Kavaklioglu, T. et al (2017) Structural asymmetries of the human cerebellum in relation to cerebral cortical asymmetries and handedness Brain Structure and Function 222, 1611-1623

2016

Marquand A. et al (2016) Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies Biological Psychiatry, 80 (7), 552-561
Marquand A., et al (2016) Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 1 (5) 433-447
Aksman L., Lythgoe, D., Williams, S., Jokisch, M., Mönninghoff, C., Streffer, J., Jöckel, K-H, Weimar, C., Marquand, A. (2016)Making use of longitudinal information in pattern recognition. Human Brain Mapping 37(12), 4385-4404
Wolfers, T., van Rooij, D., Oosterlaan, J., Heslenfeld, D., Hartman, C., Hoekstra, P., Beckmann, C., Franke, B., Buitelaar, J., Marquand A. (2016) Quantifying patterns of brain activity: Distinguishing unaffected siblings from participants with ADHD and healthy individuals. Neuroimage: Clinical 12, 227 – 233
Adams H., et al (2016) Novel genetic loci underlying human intracranial volume identified through genome-wide association Nature Neuroscience 19(12), 1569-1582
Valli, I, Marquand A et al (2016) Identifying individuals at high risk of psychosis: predictive utility of Support Vector Machine using structural and functional MRI data . Frontiers in Psychiatry, 7, 52

2015

Wolfers, T., Buitelaar, J., Beckmann C., Franke, B. Marquand, A. (2015) From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neuroscience and Biobehavioral Reviews 57, 328 – 349
Schmaal, L., Marquand A.*, et al. ( 2015 ) Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: a multivariate pattern recognition study. Biological Psychiatry
O’Muircheartaigh, J., Marquand A. et al. ( 2015 ) Multivariate decoding of cerebral blood flow measures in a clinical model of on – going postsurgical pain. Hum Brain Mapping 36, 633 – 642
Mansson K., Frick A., Boraxbekk C., Marquand A. et al (2015) Predicting long – term outcome of internet – delive red cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning. Translational Psychiatry 5, e530
Rosa, M., Mehta, M., Pich, E., Risterucci, C., Zelaya, F. Reinders, A., Williams, S., Dazz an, P. Doyle, O. Marquand, A. (2015) Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imag ing. Frontiers in Neuroscience 9

2014

Rondina, J., Hahn, T., Marquand A., et al ( 2014 ) SCoRS – a method based on stability for feature selection and mapping in neuroimaging. IEEE Transactions on Medical Imaging 33, 85 – 98
Hart, H., Chantiluke, K., Cubillo, A., Smith, A., Simmonds, A., Brammer, M., Marquand A., Rubia K. (2014) Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD. Hum Brain Mapp, 35, 3083 – 94
Marquand A., Brammer, M., Williams, S., Doyle O (2014) Bayesian multi – task learning for decoding multi – subject neuroimaging data Neuroimage , 92, 298 – 311
Marquand A. et al (2014) Full Bayesian multi – task learning for multi – output brain decoding and accommodating missing data, International Workshop in Pattern Recognition in Neuroimaging , Tuebingen, Germany
O’Harney, A., Marquand, A. et al. (2014) Pseudo – Marginal Bayesian Multiple – Class Mul tiple – Kernel Learning for Neuroimaging Data . International Conference on Pattern Recognition , Stockholm, Sweden
Hart H., Marquand, A., et al (2014). Predictive neurofunctional markers of ADHD based on pattern classification of temporal processing J Child and Ad ol Psych 53, 569 – 78
Rocha – Rego V., Jogia, J., Marquand A. et al (20 1 4) Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach. Psych Medicine 44, 519 – 32
Gong, Q., Tognin, S., Pettersson – Yeo, W., Marquand, A. et al (2014) Multivariate analysis of structural MRI identifies trauma survivors with and without Post – Traumatic Stress Disorder with high accuracy Psychological Medicine 44, 1 95 – 203
Doyle O., Westman E, Marquand A., et al. (2014) Predicting progression of Alzheimer’s disease using ordinal regression. PLOS ONE . 9 e105542
Frick A., Gingnell, M. Marquand A. et al (2014) Classifyin g social anxiety disorder using multivoxel pattern analysis of brain function and structure. Behav Brain Res 259, 330 – 35
Pettersson – Yeo, W., Benetti, S., Marquand, A. et al (2014) A n empirical comparison of different approaches f or combining multimodal neuroimaging data with support vector machine. Front Neurosci 8, 189

2013

Almeida, J., Mourao – Miranda, J., Aizenstein, H., Versace, A., Kozel, F., Lu, H., Marquand, A. et al ( 2013 ) Pattern recognition analysis of anterior cingul ate cortex blood flow to classify depression polarity. Br J Psych 203, 310 – 1.
Marquand, A., Filippone, M. et al (2013) Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach. PLOS ONE 8, e69237
Doyle O., Ashburner, J., Zelaya, F, Williams, S., Mehta, M, Marquand, A. (2013) Multivariate decoding of brain images using ordinal regression. Neuroimage 81, 347 – 57
Hahn, T., Marquand, A . * , Plichta, M. et al. (2013) A nov el approach to probabilistic biomarker – based classification using functional Near – Infrared Spectroscopy, Human Brain Mapping 34, 1102 – 14
Lim, L., Marquand A. et al (2013) Disorder – specific predictive classification of adolescents wi th attention – deficit hyperactivity disorder relative to autism using structural magnetic resonance imaging PLOS ONE 8, e63660
Schrouff, J., Rosa, M., Rondina, J., Marquand A. et al (2013). Pronto: Pattern Recognition for Neuroimagin g Toolbox. Neuroinformatics 11, 319 – 37
Deeley, Q., Oakley, D., Toone, B., Bell V., Walsh, E., Marquand, A. et al ( 2013 ). The functional anatomy of suggested limb paralysis. Cortex 49, 411 – 22
De Simoni , S . , Schwarz , A . , O’Daly , O . , Marquand , A . et al (2013) Test – retest reliability of the BOLD pharmacological MRI response to ketamine in healthy volunteers. Neuroimage 64, 75 – 90
Marquand, A., Rosa, M. J., Doyle, O. (2013) Conditional Gaussian graphical models for multi – output regression of neuroimaging data. Internationa l Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines , Leuven Belgium
Schrouff, J., Rosa, M., Rondina, J., Marquand, A., Chu C., Ashburner, J., Richiardi, J., Phillips C., Mourão – Miranda, J. (20 13) Pattern recognition for neuroimaging toolbox. International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines , Leuven Belgium
Schrouff, J., Rosa, M., Rondina, J., Marquand, A., Chu C., Ashburner, J., Phil lips C., Richiardi, J., Mourão – Miranda, J. (2013) Multivariate pattern interpretation using PRoNTo Pattern Recognition in Neuroimaging, Pittsburg, U.S.A.
Pettersson – Yeo, W., Benetti, S., Marquand A. (2013) et al Using genetic, cognitive and multi – modal neuroimaging data to identify ultra – high – risk and first – episode psychosis at the individual level Psychological Medicine 14, 1 – 16

2012

Marquand A., O’Daly, O., De Simoni S., Allsop, D., Maguir e, R. P., Williams, S., Zelaya, F., Mehta, M. (2012). Dissociable effects of methylphenidate, atomoxetine and placebo on regional cerebral blood flow in healthy volunteers at rest: a multi – class pattern recognition approach. NeuroImage 36, 1237 – 47
Filippone, M., Marquand A., et al (2012). Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities. Annals of Applied Statistics 6, 1883 – 1905
Mourao – Miranda J ., Almeida, J. Hassel, S., De Oliveira L., Versace, A., Marquand A. et al. (2012). Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression. Bipolar Disorders 14, 451 – 60
Mourao – Miranda, J., Olivera, L., Ladoucer, C., Marquand, A., et al ( 2012 ). Machine learning and neuroimaging predict future mental illness in at – risk adolescents, PLOS ONE 7, e29482
Orrù, G., Pettersson – Yeo W., Marquand A., Sarto ri G., Mechelli. A. ( 2012 ) Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review. Neuroscience and Biobehavioural Reviews 36, 1140 – 52

2011

Marquand A., De Simoni S., O’D aly, O., Williams, S., Mourao – Miranda, J., Mehta, M. (2011) Pattern classification of working memory networks reveals differential effects of methylphenidate, atomoxetine and placebo in healthy volunteers. Neuropsychopharmacology 36, 1237 – 47
Doyle, O., Mehta, M., Brammer, M., Schwarz, A., Marquand, A. (2011) Data – driven modeling of BOLD drug response curves using Gaussian process learning. Workshop on Machine Learning and Interpretability in Neuroimaging, Neural Information Processing Systems , Granada, Spain
Hahn, T., Marquand, A., Ehlis, A., Dresler, T., Kittel – Schneider, S., Jarczok, T., et al. (2011) Integrating neurobiological markers of depression. Archives of General Psychiatry 68, 361 – 8 [IF = 14.4, rank = 2/136]
Mourao – Miranda, J., Hardoon, D ., Hahn, T., Marquand A., et al ( 2011 ). Patient classification as an outlier detection problem: an application of the one – class support vector machine. NeuroImage 58,793 – 804
Gong, Q., Lui, S., Jiaa, Z., Marquand, A., Scarpazza C. , M cGuire , P. Mechelli , A. ( 2011 ). Predicting therapeutic response in depression with MRI: a support vector machine study. Neuroimage 55, 1497 – 503

2010

Marquand, A., Howard M., Brammer, M., Chu, C., et al. (2010). Quantitative prediction of subjective pain intensity from whole – brain fMRI data using Gaussian processes. NeuroImage 126, 272 – 7.
Ecker, C., Marquand , A., Mourão – Miranda, J., Johnston, P., Daly E. et al. (2010). Describing the brain in autism in five dimensio ns – magnetic resonance imaging – assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. Journal of Neuroscience 30, 10612 – 23
Ecker, C., Rocha – Rego, V., Mourão – Miranda, J., Marquand, A., et al . (2010) Investigating the predictive value of whole – brain structural MR scans in autism: a pattern classification approach. NeuroImage 49 , 44 – 56
Cole, J., Toga A., Hojatkashani C., Thompson P., Costafreda S., Cleare A., Williams S., Bullmore E., Scott J., Mitterschiffthaler M., Walsh N., Donaldson C., Mirza M., Marquand A. et al (2010) Subregional hippocampal deformations in major depressive disorder J Affect Disord 126, 272.
Marquand, A., De Simoni, S, O’Daly, O., Mourao – Miranda, J., et al. (2010). Quantifying the information content of brain voxels using target information, Gaussian processes and recursive feature elimination. Workshop on Brain Decoding, International Conference on Pattern Recognition , Istanbul, Turkey
Chu, C., Bandettini, P., Ashburner, J., Marquand, A., Klo eppel, S. (2010). Classification of neurodegenerative diseases using Gaussian process classification with automatic feature determination., International Conference on Pattern Recognition , Istanbul, Turkey

2008

Marquand, A . , M ourão – Miranda, J., Brammer, M ., Clea re, A., Fu, C . (2008). Neuroanatomy of verbal working memory as a diagnostic biomarker fo r depression. Neuroreport 19 , 1507 – 11. [IF = 1.8, rank = 196/252]
Fu, C ., Mourão – Miranda, J., Costafred a, S., Khanna, A., Marquand, A. et al., (2008 ). Pattern classification of sad facial processing: towards the development of neurobiological markers in depression. Biol Psych 63 , 656 – 62