Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Arbabshirani, Mohammad R.; Plis, Sergey; Sui, Jing; Calhoun, Vince D.

doi:10.1016/j.neuroimage.2016.02.079

Cited by 912Dementia and Cognitive Impairment ResearchFunctional Brain Connectivity StudiesArtificial Intelligence in Healthcare

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

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Arbabshirani, Mohammad R., Plis, Sergey, Sui, Jing, Calhoun, Vince D.·2017·NeuroImage

DOI 10.1016/j.neuroimage.2016.02.079PMC PMC5031516

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Abstract

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

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Almost all studies that have reported high classification accuracies had sample sizes smaller than 100.
“A recent review highlights these issues, pointing out that almost all studies that have reported high classification accuracies had sample sizes smaller than 100.3”
From Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder (2017) · cites this paper as [3]
From the homogeneity assumption, the comorbidity and heterogeneity problems emerge and limit psychiatric research.
“From the homo- geneity assumption, the comorbidity (17) and heterogeneity (18,19) problems emerge and limit psychiatric research (20–26).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [25]

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Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
Lifetime and 12-Month Prevalence of DSM-III-R Psychiatric Disorders in the United States: Results From the National Comorbidity Survey
Kessler, R. C. (1994) · Archives of General Psychiatry · doi:10.1001/archpsyc.1994.03950010008002 · Cited by 12,370 · see citing works
Diagnostic and Statistical Manual of Mental Disorders(DSM)
Leong, F. T. L. (2008) · Sage eBooks · doi:10.4135/9781412963978.n182 · Cited by 10,858 · see citing works
The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · see citing works
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954 · see citing works
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,940 · see citing works
Untitled work
(2006) · Springer eBooks · Cited by 8,123 · see citing works
The Epidemiology of Major Depressive Disorder: Results From the National Comorbidity Survey Replication (NCS-R)
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., Walters, E. E., & Wang, P. S. (2003) · JAMA · doi:10.1001/jama.289.23.3095 · Cited by 7,988 · see citing works
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,843 · see citing works
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429 · see citing works
Brain magnetic resonance imaging with contrast dependent on blood oxygenation.
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.24.9868 · Cited by 6,483 · see citing works
Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014
Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., Kurzius-Spencer, M., Zahorodny, W., Robinson, C., Rosenberg, et al. (2018) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss6706a1 · Cited by 6,078 · see citing works
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651 · see citing works
Differential Privacy
Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11787006_1 · Cited by 5,362 · see citing works
Untitled work
(2010) · Cited by 4,411 · see citing works
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347 · see citing works
Mild cognitive impairment
Gauthier, S. G., Reisberg, ‌., Zaudig, M., Petersen, R. C., Ritchie, K. A., Broich, K., Belleville, S., Brodaty, H., Bennett, D. A., Chertkow, H. M., et al. (2006) · The Lancet · doi:10.1016/s0140-6736(06)68542-5 · Cited by 3,920 · see citing works
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748 · see citing works
Diffusion tensor imaging: Concepts and applications
Le Bihan, D., Mangin, J., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1076 · Cited by 3,547 · see citing works
Forecasting the global burden of Alzheimer's disease
Brookmeyer, R., Johnson, E., Ziegler‐Graham, K., & Arrighi, H. M. (2007) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2007.04.381 · Cited by 3,370 · see citing works
Selection of relevant features and examples in machine learning
Blum, A. L., & Pat Langley, B. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00063-5 · Cited by 3,294 · see citing works
Toward discovery science of human brain function
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911855107 · Cited by 2,970 · see citing works
Neurocognitive deficit in schizophrenia: A quantitative review of the evidence.
Heinrichs, R. W., & Zakzanis, K. K. (1998) · Neuropsychology · doi:10.1037/0894-4105.12.3.426 · Cited by 2,798 · see citing works
Neurocognitive deficit in schizophrenia: A quantitative review of the evidence.
Heinrichs, R. W., & Zakzanis, K. K. (1998) · Neuropsychology · doi:10.1037//0894-4105.12.3.426 · Cited by 2,791 · see citing works
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780 · see citing works
The Diagnosis of Mild Cognitive Impairment due to Alzheimer’s Disease: Recommendations from the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease
Albert, M., DeKosky, S. T., Dickson, D. W., Dubois, B., Feldman, H., Fox, N. C., Gamst, A. C., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2013) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.11.1.96 · Cited by 2,630 · see citing works
Prevalence of autism spectrum disorders--autism and developmental disabilities monitoring network, 14 sites, United States, 2002.
(2007) · PubMed · Cited by 2,018 · see citing works
Machine learning classifiers and fMRI: A tutorial overview
Pereira, F., Mitchell, T., & Botvinick, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.007 · Cited by 1,624 · see citing works
Feature selection, L 1 vs. L 2 regularization, and rotational invariance
Ng, A. Y. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015435 · Cited by 1,572 · see citing works
Attention-deficit/hyperactivity disorder: a selective overview.
Biederman, J. (2005) · Biological Psychiatry · doi:10.1016/j.biopsych.2004.10.020 · Cited by 1,439 · see citing works
Multimodal classification of Alzheimer's disease and mild cognitive impairment
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.008 · Cited by 1,312 · see citing works
Automatic classification of MR scans in Alzheimer’s disease.
Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2008) · Brain · doi:10.1093/brain/awm319 · Cited by 1,170 · see citing works
Deep neural networks: a new framework for modelling biological vision and brain information processingPreprint
Kriegeskorte, N. (2015) · bioRxiv · doi:10.1101/029876 · Cited by 1,141 · see citing works
Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease
Jack, C. R., Petersen, R. C., Xu, Y. C., Waring, S. C., O'Brien, P. C., Tangalos, E. G., Smith, G. E., Ivnik, R. J., & Kokmen, E. (1997) · Neurology · doi:10.1212/wnl.49.3.786 · Cited by 1,128 · see citing works
Deep neural networks: a new framework for modeling biological vision and brain information processing
Kriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093 · see citing works
Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection
Gavin Brown, B., Pocock, A., Zhao, M., & Luján, M. (2012) · Research Explorer (The University of Manchester) · Cited by 1,031 · see citing works
Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease
Zhou, J., Greicius, M. D., Gennatas, E. D., Growdon, M. E., Jang, J. Y., Rabinovici, G. D., Kramer, J. H., Weiner, M., Miller, B. L., & Seeley, W. W. (2010) · Brain · doi:10.1093/brain/awq075 · Cited by 1,016 · see citing works
Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review
Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.01.004 · Cited by 1,012 · see citing works
Proceedings of the twenty-first international conference on Machine learning
Brodley, C. E. (2004) · Cited by 999 · see citing works
Untitled work
(2011) · PubMed · Cited by 990 · see citing works
A method for functional network connectivity among spatially independent resting-state components in schizophrenia
Jafri, M. J., Pearlson, G. D., Stevens, M. C. G., & Calhoun, V. D. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.001 · Cited by 976 · see citing works
Self-citation
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database
Cuingnet, R., Gérardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M., Marie Chupin, A., Benali, H., & Colliot, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.013 · Cited by 960 · see citing works
Recent progress and outstanding issues in motion correction in resting state fMRI
Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.044 · Cited by 957 · see citing works
Deep Learning of Representations for Unsupervised and Transfer Learning.
Bengio, Y. (2011) · Cited by 885 · see citing works
L 1-Regularization Path Algorithm for Generalized Linear Models
Park, M. Y., & Hastie, T. J. (2007) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00607.x · Cited by 859 · see citing works
Prevalence of autism spectrum disorders--Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2008.
(2012) · PubMed · Cited by 852 · see citing works
Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters
Franke, K., Ziegler, G., Klöppel, S., & Christian Gaser, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.005 · Cited by 799 · see citing works
Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects
Moradi, E., Pepe, A., Christian Gaser, B., Huttunen, H. J., & Tohka, J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.002 · Cited by 720 · see citing works
Permutation, Parametric and Bootstrap Tests of Hypotheses
(2005) · Springer Series in Statistics · doi:10.1007/b138696 · Cited by 707 · see citing works
Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease
Zhang, D., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.069 · Cited by 679 · see citing works
Mild Cognitive Impairment: An Overview
Petersen, R. C., & Negash, S. (2008) · CNS Spectrums · doi:10.1017/s1092852900016151 · Cited by 669 · see citing works
Hippocampus and entorhinal cortex in mild cognitive impairment and early AD
Pennanen, C., Kivipelto, M., Tuomainen, S., Hartikainen, P. H., Hänninen, T., Laakso, M. P., Hallikainen, M., Vanhanen, M., Nissinen, A. M., Helkala, E., et al. (2003) · Neurobiology of Aging · doi:10.1016/s0197-4580(03)00084-8 · Cited by 646 · see citing works
Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep
Tagliazucchi, E., & Laufs, H. (2014) · Neuron · doi:10.1016/j.neuron.2014.03.020 · Cited by 637 · see citing works
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637 · see citing works
Introduction to machine learning for brain imaging
Lemm, S., Blankertz, B., Dickhaus, T., & Müller, K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.004 · Cited by 626 · see citing works
Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism
Uddin, L. Q., Supekar, K., Lynch, C. J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., & Menon, V. (2013) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.104 · Cited by 621 · see citing works
Deep learning for neuroimaging: a validation study
Plis, S., Hjelm, D., Salakhutdinov, R., Allen, E. A., Bockholt, H. J., Long, J. D., Johnson, H. J., Paulsen, J. S., Turner, J. A., & Calhoun, V. D. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00229 · Cited by 617 · see citing works
Self-citation
The Entire Regularization Path for the Support Vector Machine
Hastie, T. J., Rosset, S., Tibshirani, R. J., & Zhu, J. (2004) · Cited by 617 · see citing works
The US economic and social costs of Alzheimer's disease revisited.
Ernst, R. L., & Hay, J. W. (1994) · American Journal of Public Health · doi:10.2105/ajph.84.8.1261 · Cited by 593 · see citing works
Structural brain abnormalities in major depressive disorder: a selective review of recent MRI studies
Lorenzetti, V., Allen, N. B., Fornito, A., & Yücel, M. (2009) · Journal of Affective Disorders · doi:10.1016/j.jad.2008.11.021 · Cited by 553 · see citing works
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
Initiative, T. A. D. N., Suk, H., Lee, S., & Shen, D. G. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0687-3 · Cited by 523 · see citing works
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Fadlallah, B. H., Brockmeier, A. J., Seth, S., Li, L., Keil, A., & Príncipe, J. C. (2012) · CiteSeer X (The Pennsylvania State University) · Cited by 520 · see citing works
The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience
Milham, M. P., Fair, D. A., Mennes, M., & Mostofsky, S. H. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00062 · Cited by 519 · see citing works
Practical feature subset selection for machine learning
Hall, M. A., & Smith, L. A. (1998) · Research Commons (University of Waikato) · Cited by 500 · see citing works
Characterizing Thalamo-Cortical Disturbances in Schizophrenia and Bipolar Illness
Anticevic, A., Cole, M. W., Repovš, G., Murray, J. D., Brumbaugh, M. S., Winkler, A. M., Savić, A., Krystal, J. H., Pearlson, G. D., & Glahn, D. C. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht165 · Cited by 494 · see citing works
Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism
McAlonan, G. M. (2004) · Brain · doi:10.1093/brain/awh332 · Cited by 491 · see citing works
A sensorimotor paradigm for Bayesian model selection
Genewein, T., & Braun, D. A. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00291 · Cited by 487 · see citing works
Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI
Magnin, B., Mesrob, L., Kinkingnéhun, S., Pélégrini‐Issac, M., Colliot, O., Sarazin, M., Dubois, B., Lehéricy, S., & Benali, H. (2008) · Neuroradiology · doi:10.1007/s00234-008-0463-x · Cited by 474 · see citing works
Annual Incidence of Alzheimer Disease in the United States Projected to the Years 2000 Through 2050
Hebert, L. E., Beckett, L. A., Scherr, P. A., & Evans, D. A. (2001) · Alzheimer Disease and Associated Disorders · doi:10.1097/00002093-200110000-00002 · Cited by 467 · see citing works
Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data
Fair, D. A., Nigg, J. T., Iyer, S., Bathula, D., Mills, K. L., Dosenbach, N. U. F., Schlaggar, B. L., Mennes, M., Gutman, D., Bangaru, S., et al. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00080 · Cited by 463 · see citing works
The extensible neuroimaging archive toolkit
Marcus, D. S., Olsen, T. R., Ramaratnam, M., & Buckner, R. L. (2007) · Neuroinformatics · doi:10.1385/ni:5:1:11 · Cited by 462 · see citing works
Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
Vemuri, P., Gunter, J. L., Senjem, M. L., Whitwell, J. L., Kantarci, K., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Jack, C. R. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.073 · Cited by 455 · see citing works
Random forest-based similarity measures for multi-modal classification of Alzheimer's disease
Gray, K. R., Aljabar, P., Heckemann, R. A., Hammers, A., & Rueckert, D. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.065 · Cited by 455 · see citing works
Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness
Calhoun, V. D., & Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2015.12.005 · Cited by 451 · see citing works
Self-citation
Permutation, Parametric, and Bootstrap Tests of Hypotheses
(2005) · Technometrics · doi:10.1198/tech.2005.s333 · Cited by 420 · see citing works
Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach
Ecker, C., Marquand, A., Mourão-Miranda, J., Johnston, P., Daly, E. M., Brammer, M. J., Maltezos, S., Murphy, C. M., Robertson, D., Williams, S. C., et al. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5413-09.2010 · Cited by 415 · see citing works
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging
Gérardin, E., Chételat, G., Marie Chupin, A., Cuingnet, R., Desgranges, B., Kim, H., Niethammer, M., Dubois, B., Lehéricy, S., Garnero, L., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.036 · Cited by 415 · see citing works
Principles of Magnetic Resonance Imaging
Liang, Z., & Lauterbur, P. C. (1999) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470545652 · Cited by 412 · see citing works
Altered resting state complexity in schizophrenia
Bassett, D. S., Nelson, B. G., Mueller, B. A., Camchong, J., & Lim, K. O. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.002 · Cited by 412 · see citing works
Functional connectivity magnetic resonance imaging classification of autism
Anderson, J. S., Nielsen, J. A., Froehlich, A. L., DuBray, M. B., Druzgal, T. J., Cariello, A. N., Cooperrider, J. R., Zielinski, B. A., Ravichandran, C., Fletcher, P. T., et al. (2011) · Brain · doi:10.1093/brain/awr263 · Cited by 405 · see citing works
Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population
Chris Hinrichs, A., Singh, V., Xu, G., & Johnson, S. C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.081 · Cited by 405 · see citing works
Functional magnetic resonance imaging (FMRI) of the human brain
DeYoe, E. A., Bandettini, P. A., Jay Neitz, A., Miller, D. A., & Winans, P. (1994) · Journal of Neuroscience Methods · doi:10.1016/0165-0270(94)90191-0 · Cited by 395 · see citing works
Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach
Ecker, C., Rocha-Rego, V., Johnston, P., Mourão-Miranda, J., Marquand, A. F., Daly, E., Brammer, M. J., Murphy, C. M., & Murphy, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.024 · Cited by 387 · see citing works
The design of experiments
Gasparini, M., & Rogantin, M. P. (2008) · PORTO Publications Open Repository TOrino (Politecnico di Torino) · Cited by 383 · see citing works
Identification of MCI individuals using structural and functional connectivity networks
Chong-yaw Wee, A., Yap, P., Zhang, D., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., Wang, L., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.015 · Cited by 378 · see citing works
COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements
Yong Fan, A., Shen, D. G., Gur, R. C., Gur, R. E., & Davatzikos, C. A. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.886812 · Cited by 366 · see citing works
Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer's disease
Mueller, S. G., Schuff, N., Yaffe, K., Madison, C., Miller, B., & Weiner, M. W. (2010) · Human Brain Mapping · doi:10.1002/hbm.20934 · Cited by 361 · see citing works
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia
Kim, J., Calhoun, V. D., Shim, E., & Lee, J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.018 · Cited by 354 · see citing works
Self-citation
Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI
Shen, H., Wang, L., Liu, Y., & Hu, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.011 · Cited by 352 · see citing works
Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder
Calhoun, V. D., Maciejewski, P. K., Pearlson, G. D., & Kiehl, K. A. (2007) · Human Brain Mapping · doi:10.1002/hbm.20463 · Cited by 344 · see citing works
Self-citation
Toward open sharing of task-based fMRI data: the OpenfMRI project
Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, S., & Milham, M. P. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00012 · Cited by 341 · see citing works
Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease
Plant, C., Teipel, S. J., Oswald, A., Böhm, C., Meindl, T., Mourão-Miranda, J., Bokde, A. L. W., Hampel, H. J., & Ewers, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.046 · Cited by 341 · see citing works
Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards
Plitt, M., Barnes, K. A., & Martin, A. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.12.013 · Cited by 335 · see citing works
The autistic spectrum
Wing, L. (1997) · The Lancet · doi:10.1016/s0140-6736(97)09218-0 · Cited by 332 · see citing works
Alzheimer Disease: Quantitative Structural Neuroimaging for Detection and Prediction of Clinical and Structural Changes in Mild Cognitive Impairment
McEvoy, L. K., Fennema-Notestine, C., Roddey, J. C., Hagler, D. J., Holland, D., Karow, D. S., Pung, C. J., Brewer, J. B., & Dale, A. M. (2009) · Radiology · doi:10.1148/radiol.2511080924 · Cited by 330 · see citing works
Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images
Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.066 · Cited by 319 · see citing works
Self-diffusion NMR imaging using stimulated echoes
Merboldt, K., Hänicke, W., & Frahm, J. (1985) · Journal of Magnetic Resonance (1969) · doi:10.1016/0022-2364(85)90111-8 · Cited by 307 · see citing works
Functional MRI of the vocalization-processing network in the macaque brain
Ortiz-Rios, M., KuÅ›mierek, P., DeWitt, I., Archakov, D., Azevedo, F. A. C., Sams, M. E., Jääskeläinen, I. P., Keliris, G. A., & Rauschecker, J. P. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00113 · Cited by 304 · see citing works
The use of “overall accuracy” to evaluate the validity of screening or diagnostic tests
Alberg, A. J., Park, J. W., Hager, B. W., Brock, M. V., & Diener‐West, M. (2004) · Journal of General Internal Medicine · doi:10.1111/j.1525-1497.2004.30091.x · Cited by 295 · see citing works
Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
Zhang, D., Shen, D. G., & Initiative, A. D. N. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033182 · Cited by 293 · see citing works
Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3)
Dai, Z., Yan, C., Wang, Z., Wang, J., Xia, M., Li, K., & Yong He, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.003 · Cited by 291 · see citing works
From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics
Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.001 · Cited by 290 · see citing works
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study
Fan, Y., Resnick, S. M., Wu, X., & Davatzikos, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.043 · Cited by 288 · see citing works
The Global Prevalence of Schizophrenia
Bhugra, D. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020151 · Cited by 284 · see citing works
Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
Wolz, R., Julkunen, V., Koikkalainen, J. R., Niskanen, E. I., Zhang, D. P., Rueckert, D., Soininen, H. S., Lötjönen, J. M. P., & Initiative, T. A. D. N. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025446 · Cited by 282 · see citing works
Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls
Lerch, J. P., Pruessner, J. C., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H. J., & Evans, A. C. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.09.013 · Cited by 281 · see citing works
Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning
Zhang, Y., Dong, Z., Phillips, P., Wang‎, S., Ji, G., Yang, J., & Yuan, T. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00066 · Cited by 279 · see citing works
Diagnostic neuroimaging across diseases
Klöppel, S., Abdulkadir, A., Jack, C. R., Koutsouleris, N., Mourão-Miranda, J., & Vemuri, P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.002 · Cited by 279 · see citing works
Whole-Brain Morphometric Study of Schizophrenia Revealing a Spatially Complex Set of Focal Abnormalities
Davatzikos, C. A., Shen, D. G., Gur, R. C., Wu, X., Liu, D., Yong Fan, A., Hughett, P., Turetsky, B. I., & Gur, R. E. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.11.1218 · Cited by 270 · see citing works
Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning
Eskildsen, S. F., Pierrick Coupé, A., García-Lorenzo, D., Fonov, V., Pruessner, J. C., & Collins, D. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.058 · Cited by 269 · see citing works
Subtyping schizophrenia: implications for genetic research
Jablensky, A. V. (2006) · Molecular Psychiatry · doi:10.1038/sj.mp.4001857 · Cited by 267 · see citing works
Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model
Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pearlson, G. D., Caprihan, A., Adalı, T., Kiehl, K. A., Liu, J., Yamamoto, J., & Calhoun, V. D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.055 · Cited by 260 · see citing works
Self-citation
Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach
Salvatore, C., Cerasa, A., Battista, P., Gilardi, M. C., Quattrone, A., & Castiglioni, I. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00307 · Cited by 259 · see citing works
Method for multimodal analysis of independent source differences in schizophrenia: Combining gray matter structural and auditory oddball functional data
Calhoun, V. D., Adalı, T., Giuliani, N. R., Pekar, J. J., Kiehl, K. A., & Pearlson, G. D. (2005) · Human Brain Mapping · doi:10.1002/hbm.20166 · Cited by 259 · see citing works
Self-citation
Predicting clinical scores from magnetic resonance scans in Alzheimer's disease
Stonnington, C. M., Chu, C., Klöppel, S., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.051 · Cited by 256 · see citing works
Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns
Chong-yaw Wee, A., Yap, P., Shen, D. G., & Initiative, A. D. N. (2012) · Human Brain Mapping · doi:10.1002/hbm.22156 · Cited by 255 · see citing works
Predictive models of autism spectrum disorder based on brain regional cortical thickness
Jiao, Y., Chen, R., Ke, X., Chu, K., Lu, Z., & Herskovits, E. H. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.047 · Cited by 250 · see citing works
Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment
Young, J., Modat, M., Cardoso, M. J., Mendelson, A., Cash, D., & Ourselin, S. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.05.004 · Cited by 249 · see citing works
Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification
Liu, F., Chong-yaw Wee, A., Chen, H., & Shen, D. G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.015 · Cited by 239 · see citing works
Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors
Cui, Y. J., Liu, B., Luo, S., Zhen, X., Fan, M., Liu, T., Zhu, W., Park, M., Jiang, T., Jin, J. S., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021896 · Cited by 237 · see citing works
Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder
Zhu, C.-Z., Zang, Y.-F., Cao, Q.-J., Yan, C.-G., He, Y., Jiang, T.-Z., Sui, M.-Q., & Wang, Y.-F. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.029 · Cited by 236 · see citing works
Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory
Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.02.060 · Cited by 234 · see citing works
Multivariate Data Analysis and Machine Learning in Alzheimer's Disease with a Focus on Structural Magnetic Resonance Imaging
Falahati, F., Westman, E., & Simmons, A. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-131928 · Cited by 229 · see citing works
Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects
Schnack, H. G., Nieuwenhuis, M., van Haren, N. E. M., Abramovic, L., Scheewe, T. W., Brouwer, R. M., Pol, H. E. H., & Kahn, R. S. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.053 · Cited by 229 · see citing works
Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset
Chris Hinrichs, A., Singh, V., Mukherjee, L., Xu, G., Chung, M. K., & Johnson, S. C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.056 · Cited by 226 · see citing works
Feature-Based Fusion of Medical Imaging Data
Calhoun, V. D., & Adalı, T. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.923773 · Cited by 222 · see citing works
Self-citation
Detecting Neuroimaging Biomarkers for Schizophrenia: A Meta-Analysis of Multivariate Pattern Recognition Studies
Kambeitz, J. P., Kambeitz‐Ilankovic, L., Leucht, S., Wood, S. J., Davatzikos, C. A., Malchow, B., Falkai, P., & Koutsouleris, N. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.22 · Cited by 217 · see citing works
Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment
Costafreda, S. G., Dinov, I. D., Tu, Z., Shi, Y., Liu, C.-Y., Kłoszewska, I., Mecocci, P., Soininen, H. S., Tsolaki, M., Vellas, B. J., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.050 · Cited by 213 · see citing works
Multiple instance learning for classification of dementia in brain MRI
Tong, T., Wolz, R., Gao, Q., Guerrero, R., Hajnal, J. V., & Rueckert, D. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.04.006 · Cited by 213 · see citing works
The MCIC Collection: A Shared Repository of Multi-Modal, Multi-Site Brain Image Data from a Clinical Investigation of Schizophrenia
Gollub, R. L., Shoemaker, J. M., King, M. D., White, T. J. H., Ehrlich, S., Sponheim, S. R., Clark, V. P., Turner, J. A., Mueller, B. A., Magnotta, V. A., et al. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9184-3 · Cited by 212 · see citing works
Self-citation
Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder
Mwangi, B., Ebmeier, K. P., Matthews, K., & Steele, J. D. (2012) · Brain · doi:10.1093/brain/aws084 · Cited by 207 · see citing works
Prognostic prediction of therapeutic response in depression using high-field MR imaging
Gong, Q., Wu, Q., Scarpazza, C., Lui, S., Jia, Z., Marquand, A. F., Huang, X., McGuire, P. K., & Mechelli, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.079 · Cited by 202 · see citing works
Enriched white matter connectivity networks for accurate identification of MCI patients
Chong-yaw Wee, A., Yap, P., Li, W., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., Wang, L. V., & Shen, D. G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.026 · Cited by 199 · see citing works
Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features
Li, Y., Wang, Y., Guorong Wu, A., Shi, F., Zhou, L., Lin, W., & Shen, D. G. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.11.008 · Cited by 197 · see citing works
Extreme Learning Machine-Based Classification of ADHD Using Brain Structural MRI Data
Peng, X., Lin, P., Zhang, T., & Wang, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079476 · Cited by 194 · see citing works
A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia
Yang, H., Liu, J., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pearlson, G. D., & Calhoun, V. D. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00192 · Cited by 193 · see citing works
Self-citation
MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease
Freeborough, P. A., & Fox, N. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.712137 · Cited by 193 · see citing works
Domain Transfer Learning for MCI Conversion Prediction
Cheng, B., Liu, M., Zhang, D., Munsell, B. C., & Shen, D. G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2404809 · Cited by 192 · see citing works
Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease
Chincarini, A., Bosco, P., Calvini, P., Gemme, G., Esposito, M. P., Olivieri, C., Rei, L., SQUARCIA, S., Rodriguez, G., Bellotti, R., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.083 · Cited by 190 · see citing works
The Cost of Brain Diseases: A Burden or a Challenge?
Luca, M. D., & Olesen, J. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.044 · Cited by 189 · see citing works
Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder
Costafreda, S. G., Fu, C. H. Y., Picchioni, M., Toulopoulou, T., McDonald, C., Kravariti, E., Walshe, M., Prata, D. P., Murray, R. M., & McGuire, P. K. (2011) · BMC Psychiatry · doi:10.1186/1471-244x-11-18 · Cited by 188 · see citing works
Multivariate Searchlight Classification of Structural Magnetic Resonance Imaging in Children and Adolescents with Autism
Uddin, L. Q., Menon, V., Young, C. B., Ryali, S., Chen, T., Khouzam, A., Minshew, N. J., & Hardan, A. Y. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.07.014 · Cited by 187 · see citing works
Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples
Nieuwenhuis, M., van Haren, N. E. M., Pol, H. E. H., Cahn, W., Kahn, R. S., & Schnack, H. G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.079 · Cited by 186 · see citing works
Multimodal analysis of functional and structural disconnection in A lzheimer's disease using multiple kernel SVM
Dyrba, M., Grothe, M. J., Kirste, T., & Teipel, S. J. (2015) · Human Brain Mapping · doi:10.1002/hbm.22759 · Cited by 185 · see citing works
ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements
Brown, M. R. G., Sidhu, G. S., Greiner, R., Asgarian, N., Bastani, M., Silverstone, P. H., Greenshaw, A. J., & Dursun, S. M. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00069 · Cited by 185 · see citing works
Unsupervised classification of major depression using functional connectivity MRI
Zeng, L., Shen, H., Liu, L., & Hu, D. (2013) · Human Brain Mapping · doi:10.1002/hbm.22278 · Cited by 183 · see citing works
The economic impact of schizophrenia.
Rice, D. P. (1999) · PubMed · Cited by 183 · see citing works
Identification of neural connectivity signatures of autism using machine learning
Deshpande, G., Libero, L. E., Sreenivasan, K. R., Deshpande, H., & Kana, R. K. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00670 · Cited by 183 · see citing works
Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism
Chen, C. P., Keown, C. L., Jahedi, A., Nair, A., Pflieger, M. E., Bailey, B. A., & Müller, R.-A. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.04.002 · Cited by 183 · see citing works
Disintegration of Sensorimotor Brain Networks in Schizophrenia
Kaufmann, T., Skåtun, K. C., Alnæs, D., Doan, N. T., Duff, E. P., Tønnesen, S., Roussos, E., Ueland, T., Aminoff, S. R., Lagerberg, T. V., et al. (2015) · Schizophrenia Bulletin · doi:10.1093/schbul/sbv060 · Cited by 181 · see citing works
In Search of Multimodal Neuroimaging Biomarkers of Cognitive Deficits in Schizophrenia
Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pearlson, G. D., Du, Y., Yu, Q., Jones, T. R., Chen, J., Jiang, T., Bustillo, J., & Calhoun, V. D. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.02.017 · Cited by 181 · see citing works
Self-citation
Classification of schizophrenia patients based on resting-state functional network connectivity
Arbabshirani, M. R., Kiehl, K. A., Pearlson, G. D., & Calhoun, V. D. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00133 · Cited by 181 · see citing works
Self-citation
High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables
Wang, Y., Yong Fan, A., Bhatt, P., & Davatzikos, C. A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.092 · Cited by 180 · see citing works
Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls
Kawasaki, Y., Suzuki, M., Kherif, F., Takahashi, T., Zhou, S.-Y., Nakamura, K., Matsui, M., Sumiyoshi, T., Seto, H., & Kurachi, M. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.018 · Cited by 180 · see citing works
Large deformation diffeomorphic metric mapping of vector fields
Cao, Y., Miller, M. I., Winslow, R. L., & Laurent Younes, B. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.853923 · Cited by 180 · see citing works
Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI
Challis, E., Hurley, P. D., Serra, L., Bozzali, M., Oliver, S., & Cercignani, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.037 · Cited by 179 · see citing works
COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets
Scott, A., Courtney, W., Wood, D., de la Garza, R., Lane, S., King, M. D., Wang, R., Roberts, J., Turner, J. A., & Calhoun, V. D. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00033 · Cited by 178 · see citing works
Self-citation
Anatomical Brain Images Alone Can Accurately Diagnose Chronic Neuropsychiatric Illnesses
Bansal, R., Staib, L. H., Laine, A. F., Hao, X., Xu, D., Liu, J., Weissman, M. M., & Peterson, B. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050698 · Cited by 176 · see citing works
Resting state functional magnetic resonance imaging and neural network classified autism and control
Iidaka, T. (2014) · Cortex · doi:10.1016/j.cortex.2014.08.011 · Cited by 176 · see citing works
Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression
Lord, A., Horn, D., Breakspear, M., & Walter, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041282 · Cited by 174 · see citing works
Sparse network-based models for patient classification using fMRI
Rosa, M. J., Portugal, L. C. L., Hahn, T., Fallgatter, A. J., Garrido, M. I., Shawe-Taylor, J. S., & Mourão-Miranda, J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.021 · Cited by 173 · see citing works
Machine learning approaches for integrating clinical and imaging features in late‐life depression classification and response prediction
Patel, M. J., Andreescu, C., Price, J. C., Edelman, K. L., Reynolds, C. F., & Aizenstein, H. J. (2015) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.4262 · Cited by 169 · see citing works
Locally linear embedding (LLE) for MRI based Alzheimer's disease classification
Liu, X., Tosun, D., Weiner, M. W., & Schuff, N. W. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.033 · Cited by 166 · see citing works
Abnormalities of Thalamic Volume and Shape in Schizophrenia
Csernansky, J. G., Schindler, M. K., Splinter, N. R., Wang, L., Gado, M. H., Selemon, L. D., Rastogi-Cruz, D., Posener, J. A., Thompson, P. A., & Miller, M. I. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.5.896 · Cited by 166 · see citing works
Volumetric MRI Measurements Can Differentiate Alzheimer’s Disease, Mild Cognitive Impairment, and Normal Aging
Bottino, C. M. C., Marchetti, R. L., Louzã, M. R., de Castro, C. C., Gomes, R. L. E., & Buchpiguel, C. A. (2002) · International Psychogeriatrics · doi:10.1017/s1041610202008281 · Cited by 164 · see citing works
Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data
Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2379621 · Cited by 161 · see citing works
Large Deformation Diffeomorphism and Momentum Based Hippocampal Shape Discrimination in Dementia of the Alzheimer type
Wang, L., Beg, M. F., Ratnanather, J. T., Ceritoglu, C., Laurent Younes, B., Morris, J. C., Csernansky, J. G., & Miller, M. I. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.887380 · Cited by 161 · see citing works
Classification of ADHD children through multimodal magnetic resonance imaging
Dai, D., Wang, J., Hua, J., & He, H. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00063 · Cited by 160 · see citing works
A method for multitask fMRI data fusion applied to schizophrenia
Calhoun, V. D., Adalı, T., Kiehl, K. A., Astur, R. S., Pekar, J. J., & Pearlson, G. D. (2005) · Human Brain Mapping · doi:10.1002/hbm.20204 · Cited by 159 · see citing works
Self-citation
Hippocampal volume discriminates between normal cognition; questionable and mild dementia in the elderly
Wolf, H., Grunwald, M., Kruggel, F., Riedel-Heller, S. G., Angerhöfer, S., Hojjatoleslami, A., Hensel, A., Arendt, T., & Gertz, H.-J. (2001) · Neurobiology of Aging · doi:10.1016/s0197-4580(00)00238-4 · Cited by 159 · see citing works
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Koutsouleris, N., Meisenzahl, E. M., Borgwardt, S., Riecher‐Rössler, A., Frodl, T., Kambeitz, J. P., Köhler, Y., Falkai, P., Möller, H., Reiser, M. F., et al. (2015) · Brain · doi:10.1093/brain/awv111 · Cited by 156 · see citing works
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Self-citation
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Yong Fan, A., Shen, D. G., & Davatzikos, C. A. (2005) · Lecture notes in computer science · doi:10.1007/11566465_1 · Cited by 148 · see citing works
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Tang, X., Holland, D., Dale, A. M., Laurent Younes, B., Miller, M. I., & Initiative, A. D. N. (2014) · Human Brain Mapping · doi:10.1002/hbm.22431 · Cited by 148 · see citing works
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Akshoomoff, N. A., Lord, C., Lincoln, A. J., Yeung-Courchesne, R., Carper, R. A., Townsend, J., & Courchesne, E. (2004) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/00004583-200403000-00018 · Cited by 130 · see citing works
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Research works printed but not linked: 5

Arbabshirani MR Calhoun VD Functional network connectivity during rest and task: Comparison of healthy controls and schizophrenic patients. Conf Proc IEEE Eng Med Biol Soc 2011 2011 4418 4421 doi:10.1109/IEMBS.2011.6091096 22255319 · DOI
Baker B Silva R Calhoun VD Sarwate AD Plis S Large scale collabortion with automony: decentralized data ICA IEEE Machine Learning for Signal Processing Workshop 2015 Boston, MA.
Castro E Ulloa A Plis SM Turner JA Calhoun VD Simulation of Structural Magnetic Resonance Images for Deep Learning Pre-Training IEEE International Symposium on Biomedical Imaging 2015 New York, NY, USA.
Retico A Tosetti M Muratori F Calderoni S Neuroimaging-based methods for autism identification: a possible translational application? Funct. Neurol 2013 29 231 239 25764253 PMC4370436
Silva RF Castro E Gupta CN Cetin M Arbabshirani M Potluru VK Plis SM Calhoun VD THE TENTH ANNUAL MLSP COMPETITION: SCHIZOPHRENIA CLASSIFICATION CHALLENGE The Mind Research Network, 1101 Yale Blvd., Albuquerque, New Mexico 87106 2014 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING Remis, France

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Hett, K., Ta, V.-T., Catheline, G., Tourdias, T., Manjón, J. V., Coupé, P., Initiative, A. D. N., Weiner, M. W., Aisen, P., Petersen, R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49970-9 · Cited by 49 · see citing works
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Sundermann, B., Bode, J., Lueken, U., Westphal, D., Gerlach, A. L., Straube, B., Wittchen, H.-U., Ströhle, A., Wittmann, A., Konrad, C., et al. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00099 · Cited by 46 · see citing works
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Chen, Z., Liu, X., Yang, Q., Wang, Y.-J., Miao, K., Gong, Z., Yu, Y., Leonov, A., Liu, C., Feng, Z., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.1671 · Cited by 43 · see citing works
Predictors of survival and progression in behavioural variant frontotemporal dementia
Agarwal, S., Ahmed, R. M., D'Mello, M., Foxe, D., Kaizik, C., Kiernan, M. C., Halliday, G. M., Piguet, O., & Hodges, J. R. (2019) · European Journal of Neurology · doi:10.1111/ene.13887 · Cited by 31 · see citing works
Individual prediction of behavioral variant frontotemporal dementia development using multivariate pattern analysis of magnetic resonance imaging data
Zhutovsky, P., Vijverberg, E. G. B., Bruin, W. B., Thomas, R. M., Wattjes, M. P., Pijnenburg, Y. A. L., van Wingen, G. A., & Dols, A. (2019) · Journal of Alzheimer's Disease · doi:10.3233/jad-181004 · Cited by 20 · see citing works
Cognitive performance classification of older patients using machine learning and electronic medical records
Richter-Laskowska, M., Sobotnicka, E., & Bednorz, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-90460-y · Cited by 16 · see citing works
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Sharif, H., & Khan, R. A. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2004655 · Cited by 10 · see citing works
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Liloia, D., Rocca, P., Brasso, C., Tanaka, M., Manuello, J., Crocetta, A., Duca, S., Costa, T., & Cauda, F. (2026) · Psychological Medicine · doi:10.1017/s0033291726103511 · Cited by 4 · see citing works

Pediatric Neuroimaging (Contemporary Neuroimaging, Vol. 1)
Afshani, E. (1990) · Neurology · doi:10.1212/wnl.40.5.870
Predictors of Abnormal Neuroimaging of the Brain in Children With Epilepsy Aged 1 Month to 2 Years
Sanmaneechai, O., Danchaivijitr, N., & Likasitwattanakul, S. (2015) · Journal of Child Neurology · doi:10.1177/0883073815574199 · Cited by 3 · see citing works
[Genetic neuroimaging].
van der Wee, N. J. A., van Noorden, M. S., & Veltman, D. J. (2009) · PubMed
Neuroimaging методологијата и студиите на развој на мозокот кај адолесцентите
Miloseva, L., & Milosev, V. (2013) · Goce Delchev University Repository (Goce Delčev University of Štip)
Can Structural Neuroimaging be Used to Define Phenotypes and Course of Schizophrenia?
Kerns, J. G., & Lauriello, J. (2012) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2012.06.005 · Cited by 6 · see citing works

Authors: Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D.
Affiliations: Mind Research Network (US)
Corresponding author: Arbabshirani, Mohammad R.
ORCID: Arbabshirani, Mohammad R.
Plis, Sergey
Calhoun, Vince D.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2017-01
Volume: 145
Issue: Pt B
Pages: 137-165
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2016.02.079
PMC: PMC5031516
OpenAlex: W2310177520
Cited by: 912
References: 392
Citations by year: 2026: 62 · 2025: 78 · 2024: 90 · 2023: 88 · 2022: 103 · 2021: 103 · 2020: 113 · 2019: 115 · 2018: 92 · 2017: 54 · 2016: 13
FWCI: 13.04
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 354 d)
Locations: NeuroImage (acceptedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National High-tech Research and Development Program (2015AA020513)
National Institutes of Health (1R01DA040487, P20GM103472, R01EB005846, R01EB020407)
Kansas NSF EPSCoR (1539067)
National Natural Science Foundation of China (81471367)
Chinese Academy of Sciences
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: neuroimaging, brain disorders, schizophrenia, Alzheimer's disease, mild cognitive impairment, depressive disorders, autism spectrum disorder, attention deficit hyperactivity disorder, neurodegenerative diseases, machine learning, sample size limitations, data sharing, multimodal neuroimaging, differential diagnosis, disease subtype classification, deep learning, generalizability
MeSH terms: Machine Learning, Brain Diseases, Humans, Neuroimaging

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Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

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