Skip to main content

Advertisement

SpringerLink
Log in

Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets

  • Original Research
  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N = 930), Post-traumatic stress disorder (N = 87) and Alzheimer’s disease (N = 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (2019) The toolbox can also be found at the following URL: https://github.com/pradlanka/malini.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers used for processing this data as well as the files containing the functional connectivity features is available (Lanka, et al., 2019). The toolbox can also be found at the following URL: https://github.com/pradlanka/malini.

References

  • ADHD Consortium. (2012). The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience, 6, 62.

    Google Scholar 

  • Albert, M., DeKosky, S., Dickson, D., Dubois, B., Feldman, H., Fox, N., et al. (2011). 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. Alzheimer's & Dementia, 7(3), 270–279.

    Article  Google Scholar 

  • Allen, G., Barnard, H., McColl, R., Hester, A., Fields, J., Weiner, M., et al. (2007). Reduced hippocampal functional connectivity in Alzheimer disease. Archives of Neurology, 64(10), 1482–1487.

    Article  PubMed  Google Scholar 

  • American Psychiatric Association, D.-5. T. (2013). Diagnostic and statistical manual of mental disorders: DSM-5™ (5th ed.). Arlington: American Psychiatric Publishing Inc..

    Book  Google Scholar 

  • Anderson, J., Ferguson, M., Lopez-Larson, M., & Yurgelun-Todd, D. (2011a). Reproducibility of single-subject functional connectivity measurements. AJNR, 32, 548–555.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Anderson, J., Nielsen, J., Froehlich, A., DuBray, M., Druzgal, T., Cariello, A., et al. (2011b). Functional connectivity magnetic resonance imaging classification of autism. Brain, 134, 3742–3754.

    Article  PubMed  Google Scholar 

  • Arbabshirani, M., Plis, S., Sui, J., & Calhoun, V. (2017). Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. NeuroImage, 145, 137–165.

    Article  PubMed  Google Scholar 

  • Aron, A., & Poldrack, R. (2006). Cortical and subcortical contributions to stop signal response inhibition: Role of the subthalamic nucleus. Journal of Neuroscience, 26(9), 2424–2433.

    Article  CAS  PubMed  Google Scholar 

  • Assaf, M., Jagannathan, K., Calhoun, V., Miller, L., Stevens, M., Sahl, R., et al. (2010). Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. NeuroImage, 53(1), 247–256.

    Article  PubMed  Google Scholar 

  • Aylward, E., Reiss, A., Reader, M., Singer, H., Brown, J., & Denckla, M. (1996). Basal ganglia volumes in children with attention-deficit hyperactivity disorder. Journal of Child Neurology, 11(2), 112–115.

    Article  CAS  PubMed  Google Scholar 

  • Bai, F., Zhang, Z., Watson, D., Yu, H., Shi, Y., Yuan, Y., . . . Qian, Y. (2009). Abnormal functional connectivity of Hippocampus during episodic memory retrieval processing network in amnestic.

  • Birn, R., Molloy, E., Patriat, R., Parker, T., Meier, T., Kirk, G., et al. (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage, 83, 550–558.

    Article  PubMed  Google Scholar 

  • Biswal, B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S., et al. (2010). Toward discovery science of human brain function. PNAS, 107(10), 4734–4739.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Braun, U., Plichta, M., Esslinger, C., Sauer, C., Haddad, L., Grimm, O., et al. (2012). Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. NeuroImage, 59(2), 1404–1412.

    Article  PubMed  Google Scholar 

  • Bremner, J., Narayan, M., Staib, L., Southwick, S., McGlashan, T., & Charney, D. (1999). Neural correlates of memories of childhood sexual abuse in women with and without posttraumatic stress disorder. American Journal of Psychiatry, 156(11), 1787–1795.

    CAS  Google Scholar 

  • Bremner, J., Vermetten, E., Vythilingam, M., Afzal, N., Schmahl, C., Elzinga, B., & Charney, D. (2004). Neural correlates of the classic color and emotional stroop in women with abuse-related posttraumatic stress disorder. Biological Psychiatry, 55(6), 612–620.

    Article  PubMed  Google Scholar 

  • Brown, C., & Hamarneh, G. (2016). Machine learning on human connectome data from MRI. arXiv:1611.08699.

  • Brown, M., Sidhu, G., Greiner, R., Asgarian, N., Bastani, M., Silverstone, P., et al. (2012). ADHD-200 global competition: Diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements. Frontiers in Systems Neuroscience, 6, 69.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bush, G., Frazier, J., Rauch, S., Seidman, L., Whalen, P., Jenike, M., et al. (1999). Anterior cingulate cortex dysfunction in attention-deficit/hyperactivity disorder revealed by fMRI and the counting stroop. Biological Psychiatry, 45(12), 1542–1552.

    Article  CAS  PubMed  Google Scholar 

  • Cai, S., Chong, T., Zhang, Y., Li, J., von Deneen, K. M., Ren, J., et al. (2015). Altered functional connectivity of fusiform gyrus in subjects with amnestic mild cognitive impairment: a resting-state fMRI study. Frontiers in Human Neuroscience, 9, 471.

    Article  PubMed  PubMed Central  Google Scholar 

  • Carmona, S., Vilarroya, O., Bielsa, A., Trèmols, V., Soliva, J., Rovira, M., et al. (2005). Global and regional gray matter reductions in ADHD: A voxel-based morphometric study. Neuroscience Letters, 389(2), 88–93.

    Article  CAS  PubMed  Google Scholar 

  • Casey, B., Trainor, R., Giedd, J., Vauss, Y., Vaituzis, C., Hamburger, S., et al. (1997). The role of the anterior cingulate in automatic and controlled processes: A developmental neuroanatomical study. Developmental Psychobiology, 30, 61–69.

    Article  CAS  PubMed  Google Scholar 

  • Castellanos, F., & Aoki, Y. (2016). Intrinsic functional connectivity in attention-deficit/hyperactivity disorder: A science in development. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(3), 253–261.

    Google Scholar 

  • Castellanos, F., & Proal, E. (2012). Large-scale brain systems in ADHD: Beyond the prefrontal–striatal model. Trends in Cognitive Sciences, 16(1), 17–26.

    Article  PubMed  Google Scholar 

  • Castellanos, F., Di Martino, A., Craddock, R., Mehta, A., & Milham, M. (2013). Clinical applications of the functional connectome. NeuroImage, 80, 527–540.

    Article  CAS  PubMed  Google Scholar 

  • Cawley, G., & Talbot, N. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11, 2079–2107.

    Google Scholar 

  • CDC. (2014). Prevalence of autism Spectrum disorder among children aged 8 years — Autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report, 63(2), 1–21.

    Google Scholar 

  • Celone, K., Calhoun, V., Dickerson, B., Atri, A., Chua, E., Miller, S., et al. (2006). Alterations in memory networks in mild cognitive impairment and Alzheimer's disease: An independent component analysis. Journal of Neuroscience, 26(40), 10222–10231.

    Article  CAS  PubMed  Google Scholar 

  • Challis, E., Hurley, P., Serra, L., Bozzali, M., Oliver, S., & Cercignani, M. (2015). Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI. NeuroImage, 112, 232–243.

    Article  PubMed  Google Scholar 

  • Chao, L., Lenoci, M., & Neylan, T. (2012). Effects of post-traumatic stress disorder on occipital lobe function and structure. NeuroReport, 23(7), 412–419.

    Article  PubMed  Google Scholar 

  • Chen, C., Keown, C., Jahedi, A., Nair, A., Pflieger, M., Bailey, B., & Müller, R.-A. (2015). Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage: Clinical, 8, 238–245.

    Article  Google Scholar 

  • Chen, H., Duan, X., Liu, F., Lu, F., Ma, X., Zhang, Y., Uddin, L. Q., & Chen, H. (2016). Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity—A multi-center study. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 64, 1–9.

    Article  PubMed  Google Scholar 

  • Cheng, W., Rolls, E., Gu, H., Zhang, J., & Feng, J. (2015). Autism: Reduced connectivity between cortical areas involved in face expression, theory of mind, and the sense of self. Brain, 138, 1382–1393.

    Article  PubMed  PubMed Central  Google Scholar 

  • Choe, A., Jones, C., Joel, S., Muschelli, J., Belegu, V., Caffo, B., et al. (2015). Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years. PLoS ONE, 10(10), e0140134.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Chou, Y.-H., Panych, L., Dickey, C., Petrella, J., & Chen, N.-K. (2012). Investigation of Long-term reproducibility of intrinsic connectivity network mapping: A resting-state fMRI study. American Journal of Neuroradiology, 33, 833–838.

    Article  PubMed  PubMed Central  Google Scholar 

  • Christova, P., James, L., Engdahl, B., Lewis, S., & Georgopoulos, A. (2015). Diagnosis of posttraumatic stress disorder (PTSD) based on correlations of prewhitened fMRI data: Outcomes and areas involved. Experimental Brain Research, 233(9), 2695–2705.

    Article  PubMed  Google Scholar 

  • Clark, I. A., & Mackay, C. E. (2015). Mental imagery and post-traumatic stress disorder: A neuroimaging and experimental psychopathology approach to intrusive memories of trauma. Frontiers in Psychiatry, 6, 104.

    Article  PubMed  PubMed Central  Google Scholar 

  • Colby, J., Rudie, J., Brown, J., Douglas, P., Cohen, M., & Shehzad, Z. (2012). Insights into multimodal imaging classification of ADHD. Frontiers in Systems Neuroscience, 6, 59.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cortese, S., Kelly, C., Chabernaud, C., Proal, E., Di Martino, A., Milham, M., & Castellanos, F. (2012). Toward systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies. American Journal of Psychiatry, 169(10), 1038–1055.

    Article  Google Scholar 

  • Craddock, R., Holtzheimer, P., Hu, X., & Xiaoping, P. (2009). Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine, 62(6), 1619–1628.

    Article  PubMed  PubMed Central  Google Scholar 

  • Craddock, R. C., James, G., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8), 1914–1928.

    Article  PubMed  Google Scholar 

  • Craig, A. D. (2009). How do you feel — Now? The anterior insula and human awareness. Nature Reviews Neuroscience, 10, 59–70.

    Article  CAS  PubMed  Google Scholar 

  • Cubillo, A., Halari, R., Ecker, C., Giampietro, V., Taylor, E., & Rubia, K. (2010). Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood Attention-Deficit Hyperactivity Disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching. Journal of Psychiatric Research, 44(10), 629–639.

    Article  PubMed  Google Scholar 

  • Curatolo, P., D'Agati, E., & Moavero, R. (2010). The neurobiological basis of ADHD. Italian Journal of Pediatrics, 36, 79.

    Article  PubMed  PubMed Central  Google Scholar 

  • Demirci, O., Clark, V., Magnotta, V., Andreasen, N., Lauriello, J., Kiehl, K., et al. (2008). A review of challenges in the use of fMRI for disease classification / characterization and a projection pursuit application from a multi-site fMRI schizophrenia study. Brain Imaging and Behavior, 2(3), 207–226.

    Article  Google Scholar 

  • Deshpande, G., LaConte, S., James, G., Peltier, S., & Hu, X. (2009). Multivariate granger causality analysis of fMRI data. Human Brain Mapping, 30, 1361–1373.

    Article  PubMed  Google Scholar 

  • Deshpande, G., Li, Z., Santhanam, P., Coles, C., Lynch, M., Hamann, S., & Hu, X. (2010). Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity. PLoS One, 5(12), e14277.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Deshpande, G., Libero, L., Sreenivasan, K., Deshpande, H., & Kana, R. (2013). Identification of neural connectivity signatures of autism using machine learning. Frontiers in Human Neuroscience, 7, 670.

    Article  PubMed  PubMed Central  Google Scholar 

  • Deshpande, G., Wang, P., Rangaprakash, D., & Wilamowski, B. (2015). Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Transactions on Cybernetics, 45(12), 2668–2679.

    Article  PubMed  Google Scholar 

  • Devue, C., Collette, F., Balteau, E., Degueldre, C., Luxen, A., Maquet, P., & Brédart, S. (2007). Here I am: The cortical correlates of visual self-recognition. Brain Research, 1143, 169–182.

    Article  CAS  PubMed  Google Scholar 

  • Di Martino, A., Yan, C.-G., Li, Q., Li, Q., Denio, E., Castellanos, F., et al. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19, 659–667.

    Article  PubMed  Google Scholar 

  • Dickstein, S., Bannon, K., Xavier Castellanos, F., & Milham, M. (2006). The neural correlates of attention deficit hyperactivity disorder: An ALE meta-analysis. Journal of Child Psychology and Psychiatry, 47(10), 1051–1062.

    Article  PubMed  Google Scholar 

  • Dunkley, B., Doesburg, S., Sedge, P., Grodecki, R., Shek, P., Pang, E., & Taylor, M. (2014). Resting-state hippocampal connectivity correlates with symptom severity in post-traumatic stress disorder. NeuroImage: Clinical, 5, 377–384.

    Article  CAS  Google Scholar 

  • Dyrba, M., Grothe, M., Kirste, T., & Teipel, S. (2015). Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM. Human Brain Mapping, 36(6), 2118–2131.

    Article  PubMed  PubMed Central  Google Scholar 

  • Edgington, E. (1980). Randomization tests. New York: Marcel Dekker.

    Google Scholar 

  • Eloyan, A., Muschelli, J., Nebel, M. B., Liu, H., Han, F., Zhao, T., et al. (2012). Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging. Frontiers in Systems Neuroscience, 6, 61.

    Article  PubMed  PubMed Central  Google Scholar 

  • Elton, A., Alcauter, S., & Gao, W. (2014). Network connectivity abnormality profile supports a categorical-dimensional hybrid model of ADHD. Human Brain Mapping, 35(9), 4531–4543.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fair, D., Nigg, J., Iyer, S., Bathula, D., Mills, K., Dosenbach, N., et al. (2013). Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data. Frontiers in Systems Neuroscience, 6, 80.

    Article  PubMed  PubMed Central  Google Scholar 

  • Farràs-Permanyer, L., Guàrdia-Olmos, J., & Peró-Cebollero, M. (2015). Mild cognitive impairment and fMRI studies of brain functional connectivity: The state of the art. Frontiers in Psychology, 6, 1095.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fiecas, M., Ombao, H., Lunen, D., Baumgartner, R., Coimbra, A., & Feng, D. (2013). Quantifying temporal correlations: A test–retest evaluation of functional connectivity in resting-state fMRI. NeuroImage, 65, 231–241.

    Article  PubMed  Google Scholar 

  • Foster, K., Koprowski, R., & Skufca, J. (2014). Machine learning, medical diagnosis, and biomedical engineering research - commentary. Biomedical Engineering Online, 13(1), 94.

    Article  PubMed  PubMed Central  Google Scholar 

  • Foundas, A., Leonard, C., Mahoney, S., Agee, O., & Heilman, K. (1997). Atrophy of the Hippocampus, parietal cortex, and insula in Alzheimer's disease: A volumetric magnetic resonance imaging study. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 10(2), 81–89.

    CAS  PubMed  Google Scholar 

  • Galton, C., Gomez-Anson, B., Antounb, N., Scheltens, P., Patterson, K., Graves, M., et al. (2001). Temporal lobe rating scale: Application to Alzheimer's disease and frontotemporal dementia. Journal of Neurology, Neurosurgery, and Psychiatry, 70, 165–173.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gamberger, D., Ženko, B., Mitelpunkt, A., Shachar, N., & Lavrač, N. (2016). Clusters of male and female Alzheimer’s disease patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Brain Informatics, 3(3), 169–179.

    Article  PubMed  PubMed Central  Google Scholar 

  • Garrett, A., Penniman, L., Epstein, J., Casey, B., Hinshaw, S., Glover, G., et al. (2008). Neuroanatomical abnormalities in adolescents with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 47(11), 1321–1328.

    Article  Google Scholar 

  • Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R., Ritchie, K., Broich, K., et al. (2006). Mild cognitive impairment. The Lancet, 67(9518), 1262–1270.

    Article  Google Scholar 

  • Gentile, J., Atiq, R., & Gillig, P. (2006). Adult ADHD: Diagnosis, differential diagnosis, and medication management. Psychiatry (Edgmont), 3(8), 25–30.

    Google Scholar 

  • Gotts, S., Simmons, W., Milbury, L., Wallace, G., Cox, R., & Martin, A. (2012). Fractionation of social brain circuits in autism spectrum disorders. Brain, 135(9), 2711–2725.

    Article  PubMed  PubMed Central  Google Scholar 

  • Grove, A., & Schuurmans, D. (1998). Boosting in the limit: Maximizing the margin of learned ensembles. In Proc. of the Fifteenth National Conference on Artifical Intelligence.

  • Guo, C., Kurth, F., Zhou, J., Mayer, E., Eickhoff, S., Kramer, J., & Seeley, W. (2012). One-year test–retest reliability of intrinsic connectivity network fMRI in older adults. NeuroImage, 61(4), 1471–1483.

    Article  PubMed  Google Scholar 

  • Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 7(8), 1157–1182.

    Google Scholar 

  • Horwitz, B., & Rowe, J. (2011). Functional biomarkers for neurodegenerative disorders based on the network paradigm. Progress in Neurobiology, 95(4), 505–509.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huf, W., Kalcher, K., Boubela, R. N., Rath, G., Vecsei, A., Filzmoser, P., & Moser, E. (2014). On the generalizability of resting-state fMRI machine learning classifiers. Frontiers in Human Neuroscience, 8, 502.

    Article  PubMed  PubMed Central  Google Scholar 

  • Iidaka, T. (2015). Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex, 63, 55–67.

    Article  PubMed  Google Scholar 

  • Isaksson, A., Wallman, M., Göransson, H., & Gustafsson, M. (2008). Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recognition Letters, 29(14), 1960–1965.

    Article  Google Scholar 

  • Jie, B., Zhang, D., Gao, W., Wang, Q., Wee, C.-Y., & Shen, D. (2014a). Integration of network topological and connectivity properties for neuroimaging classification. IEEE Transactions on Biomedical Engineering, 61(2), 576–589.

    Article  PubMed  Google Scholar 

  • Jie, B., Zhang, D., Wee, C.-Y., & Shen, D. (2014b). Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Human Brain Mapping, 35(7), 2876–2897.

    Article  PubMed  Google Scholar 

  • Kang, H., Natelson, B., Mahan, C., Lee, K., & Murphy, F. (2003). Post-traumatic stress disorder and chronic fatigue syndrome-like illness among gulf war veterans: A population-based survey of 30,000 veterans. American Journal of Epidemiology, 157(2), 141–148.

    Article  PubMed  Google Scholar 

  • Kang, J., Caffo, B., & Liu, H. (2016). Editorial: Recent advances and challenges on big data analysis in neuroimaging. Frontiers in Neuroscience, 10, 505.

    Article  PubMed  PubMed Central  Google Scholar 

  • Karas, G., Scheltens, P., Rombouts, S., Visser, P., van Schijndel, R., Fox, N., & Barkhof, F. (2004). Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease. NeuroImage, 23(2), 708–716.

    Article  CAS  PubMed  Google Scholar 

  • Karnath, H.-O., Baier, B., & Nägele, T. (2005). Awareness of the functioning of One's own limbs mediated by the insular cortex? Journal of Neuroscience, 25(31), 7134–7138.

    Article  CAS  PubMed  Google Scholar 

  • Kelly, C., Biswal, B. B., Craddock, R. C., Castellanos, F., & Milham, M. (2012). Characterizing variation in the functional connectome: Promise and pitfalls. Trends in Cognitive Sciences, 16(3), 181–188.

    Article  PubMed  Google Scholar 

  • Kessler, R., Berglund, P., Demler, O., Jin, R., Merikangas, K., & Walters, E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6), 593–602.

    Article  PubMed  Google Scholar 

  • Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2015). Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clinical Neurophysiology, 126(11), 2132–2141.

    Article  PubMed  Google Scholar 

  • Kobel, M., Bechtel, N., Specht, K., Klarhöfer, M., Weber, P., Scheffler, K., et al. (2010). Structural and functional imaging approaches in attention deficit/hyperactivity disorder: Does the temporal lobe play a key role? Psychiatry Research: Neuroimaging, 183(3), 230–236.

    Article  PubMed  Google Scholar 

  • Koch, W., Teipel, S., Mueller, S., Benninghoff, J., Wagner, M., Bokde, A., et al. (2012). Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer's disease. Neurobiology of Aging, 33(3), 466–478.

    Article  PubMed  Google Scholar 

  • Konrad, K., & Eickhoff, S. (2010). Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Human Brain Mapping, 31(6), 904–916.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kriegeskorte, N., Simmons, W., Bellgowan, P., & Baker, C. (2009). Circular analysis in systems. Nature Neuroscience, 125–540.

  • Kroes, M., Rugg, M., Whalley, M., & Brewin, C. (2011). Structural brain abnormalities common to posttraumatic stress disorder and depression. Journal of Psychiatry & Neuroscience, 36(4), 256–265.

    Article  Google Scholar 

  • Lanius, R., Williamson, P., Bluhm, R., Densmore, M., Boksman, K., Neufeld, R., et al. (2005). Functional connectivity of dissociative responses in posttraumatic stress disorder: A functional magnetic resonance imaging investigation. Biological Psychiatry, 57(8), 873–884.

    Article  PubMed  Google Scholar 

  • Lanka, P., Rangaprakash, D., Roy Gotoor, S. S., Dretsch, M., Katz, J., Denney Jr., T., & Deshpande, G. (2019). Resting state functional connectivity data and a toolbox for automated disease diagnosis for neurological disorders. Data in Brief, Submitted.

  • Lei, D., Li, K., Li, L., Chen, F., Huang, X., Lui, S., Li, J., Bi, F., & Gong, Q. (2015). Disrupted functional brain connectome in patients with posttraumatic stress disorder. Radiology, 276(3), 818–827.

    Article  PubMed  Google Scholar 

  • Li, L., Lei, D., Li, L., Huang, X., Suo, X., Xiao, F., Kuang, W., Li, J., Bi, F., Lui, S., Kemp, G. J., Sweeney, J. A., & Gong, Q. (2016). White matter abnormalities in post-traumatic stress disorder following a specific traumatic event. EBioMedicine, 4, 176–183.

    Article  PubMed  PubMed Central  Google Scholar 

  • Liang, X., Wang, J., Yan, C., Shu, N., Xu, K., Gong, G., & He, Y. (2012). Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: A resting-state functional MRI study. PLoS One, 7(3), e32766.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Libero, L., DeRamus, T., Lahti, A., Deshpande, G., & Kana, R. (2015). Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates. Cortex, 66, 46–59.

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu, F., Xie, B., Wang, Y., Guo, W., Fouche, J.-P., Long, Z., Wang, W., Chen, H., Li, M., Duan, X., Zhang, J., Qiu, M., & Chen, H. (2015). Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach. Brain Topography, 28, 221–237.

    Article  PubMed  Google Scholar 

  • Lopez-Larson, M. P., King, J. B., Terry, J., McGlade, E. C., & Yurgelun-Todd, D. (2012). Reduced insular volume in attention deficit hyperactivity disorder. Psychiatry Research: Neuroimaging, 204(1), 32–39.

    Article  PubMed  Google Scholar 

  • Makris, N., Biederman, J., Monuteaux, M., & Seidman, L. (2009). Towards conceptualizing a neural systems-based anatomy of attention-deficit/hyperactivity disorder. Developmental Neuroscience, 31, 36–49.

    Article  CAS  PubMed  Google Scholar 

  • Marchitelli, R., Minati, L., Marizzoni, M., Bosch, B., Bartrés-Faz, D., Müller, B., et al. (2016). Test-retest reliability of the default mode network in a multi-centric fMRI study of healthy elderly: Effects of data-driven physiological noise correction techniques. Human Brain Mapping, 37(6), 2114–2132.

    Article  PubMed  PubMed Central  Google Scholar 

  • Maximo, J., Cadena, E., & Kana, R. (2014). The implications of brain connectivity in the neuropsychology of autism. Neuropsychology Review, 24(1), 16–31.

    Article  PubMed  PubMed Central  Google Scholar 

  • Meindl, T., Teipel, S., Elmouden, R., Mueller, S., Koch, W., Dietrich, O., et al. (2009). Test–retest reproducibility of the default-mode network in healthy individuals. Human Brain Mapping, 31(2), 237–246.

    PubMed Central  Google Scholar 

  • Mennes, M., Biswal, B., Castellanos, F., & Milham, M. (2013). Making data sharing work: The FCP/INDI experience. NeuroImage, 82, 683–691.

    Article  PubMed  Google Scholar 

  • Menon, V., & Uddin, L. (2010). Saliency, switching, attention and control: a network model of insula function. Brain Structure and Function, 214(5), 655–667.

    Article  PubMed  Google Scholar 

  • Miller, K., Alfaro-Almagro, F., Bangerter, N., Thomas, D., Yacoub, E., Xu, J., et al. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19, 1523–1536.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Monk, C., Peltier, S., Wiggin, J., Weng, S.-J., Carrasco, M., Risi, S., & Lord, C. (2009). Abnormalities of intrinsic functional connectivity in autism spectrum disorders. NeuroImage, 47(2), 764–772.

    Article  PubMed  Google Scholar 

  • Mostert, J., Shumskaya, E., Mennes, M., Onnink, A., Hoogman, M., Kan, C., et al. (2016). Characterising resting-state functional connectivity in a large sample of adults with ADHD. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 67, 82–91.

    Article  PubMed  Google Scholar 

  • Mueller, S., Weiner, M., Thal, L., Petersen, R., Jack, C., Jagust, W., et al. (2005). Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer's & Dementia, 1(1), 55–66.

    Article  Google Scholar 

  • Mundy, P. (2003). Annotation: The neural basis of social impairments in autism: The role of the dorsal medial-frontal cortex and anterior cingulate system. Journal of Child Psychology and Psychiatry, 44(6), 793–809.

    Article  PubMed  Google Scholar 

  • Mwangi, B., Tian, T., & Soares, J. (2014). A review of feature reduction techniques in neuroimaging. Neuroinformatics, 12(2), 229–244.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nielsen, J., Zielinski, B., Fletcher, P., Alexander, A., Lange, N., Bigler, E., et al. (2013). Multisite functional connectivity MRI classification of autism: ABIDE results. Frontiers in Human Neuroscience, 7, 599.

    Article  PubMed  PubMed Central  Google Scholar 

  • Orban, P., Madjar, C., Savard, M., Dansereau, C., Tam, A., Das, S., et al. (2015). Test-retest resting-state fMRI in healthy elderly persons with a family history of Alzheimer’s disease. Scientific Data, 2, 150043.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pereira, F., Mitchell, T., Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45(1), S199–S209.

  • Pinter, D., Beckmann, C., Koini, M., Pirker, E., Filippini, N., Pichler, A., Fuchs, S., Fazekas, F., & Enzinger, C. (2016). Reproducibility of resting state connectivity in patients with stable multiple sclerosis. PLoS One, 11(3), e0152158.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Plitt, M., Barnes, K., & Martin, A. (2015). Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage: Clinical, 7, 359–366.

    Article  Google Scholar 

  • Price, T., Wee, C.-Y., Gao, W., & Shen, D. (2014). Multiple-network classification of childhood autism using functional connectivity dynamics. In Golland P., Hata N., Barillot C., Hornegger J., Howe R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, 8675. Springer, Cham.

  • Qiu, M.-G., Ye, Z., Li, Q.-Y., Liu, G.-J., Xie, B., & Wang, J. (2011). Changes of brain structure and function in ADHD children. Brain Topography, 24(3), 243–252.

    Article  PubMed  Google Scholar 

  • Rao, R., Fung, G., & Rosales, R. (2008). On the dangers of cross-validation. An experimental evaluation. Proceedings of the 2008 SIAM International Conference on Data Mining (pp. 588-596). Society for Industrial and Applied Mathematics.

  • Rombouts, S., Barkhof, F., Veltman, D., Machielsen, W., Witter, M., Bierlaagha, M., et al. (2000). Functional MR imaging in Alzheimer's disease during memory encoding. AJNR, 21, 1869–1875.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Rooij, D., Hartman, C., Mennes, M., Oosterlaan, J., Franke, B., Rommelse, N., et al. (2016). Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings. NeuroImage: Clinical, 7, 325–335.

    Article  Google Scholar 

  • Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S., Simmons, A., & Bullmore, E. (1999). Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: A study with functional MRI. American Journal of Psychiatry, 156(6), 891–896.

    Article  CAS  Google Scholar 

  • Salmond, C., Ashburner, J., Connelly, A., Friston, K., Gadian, D., & Vargha-Khadem, F. (2005). The role of the medial temporal lobe in autistic spectrum disorders. European Journal of Neuroscience, 22(3), 762–772.

    Article  Google Scholar 

  • Sato, J., Hoexter, M., Fujita, A., & Luis, R. (2012). Evaluation of pattern recognition and feature extraction methods in ADHD prediction. Frontiers in Systems Neuroscience, 6, 68.

    Article  PubMed  PubMed Central  Google Scholar 

  • Schnack, H., & Kahn, R. (2016). Detecting neuroimaging biomarkers for psychiatric disorders: Sample size matters. Frontiers in Psychiatry, 7, 50.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shah, L., Cramer, J., Ferguson, M., Birn, R., & Anderson, J. (2016). Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state. Brain and Behavior, 6(5), 2162–3279.

    Article  Google Scholar 

  • Shehzad, Z., Kelly, A., Reiss, P., Gee, D., Gotimer, K., Uddin, L., et al. (2009). The resting brain: Unconstrained yet reliable. Cerebral Cortex, 19(10), 2209–2229.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shin, L., Orr, S., Carson, M., Rauch, S., Macklin, M., Lasko, N., et al. (2004). Regional cerebral blood flow in the amygdala and medial PrefrontalCortex during traumatic imagery in male and female Vietnam veterans with PTSD. Archives of General Psychiatry, 61(2), 168–176.

    Article  PubMed  Google Scholar 

  • Sidhu, G., Asgarian, N., Greiner, R., & Brown, M. (2012). Kernel principal component analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Frontiers in Systems Neuroscience, 6, 74.

    Article  PubMed  PubMed Central  Google Scholar 

  • Somandepalli, K., Kelly, C., Reiss, P., Zuo, X.-N., Craddock, R., Yan, C.-G., et al. (2015). Short-term test–retest reliability of resting state fMRI metrics in children with and without attention-deficit/hyperactivity disorder. Developmental Cognitive Neuroscience, 15, 83–93.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sowel, E., Thompson, P., Welcome, S., Henkenius, A., Toga, A., & Peterson, B. (2003). Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder. The Lancet, 362(9397), 1699–1707.

    Article  Google Scholar 

  • Tanielian, T., & Jaycox, L. (Eds.). (2008). Invisible wounds of war: Psychological and cognitive injuries, their consequences, and services to assist recovery. Santa Monica: RAND Corporation.

    Google Scholar 

  • Tian, L., Jiang, T., Wang, Y., Zang, Y., He, Y., Liang, M., Sui, M., Cao, Q., Hu, S., Peng, M., & Zhuo, Y. (2006). Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder. Neuroscience Letters, 400(1–2), 39–43.

    Article  CAS  PubMed  Google Scholar 

  • Tomasi, D., & Volkow, N. (2012). Abnormal functional connectivity in children with attention-deficit/hyperactivity disorder. Biological Psychiatry, 71(5), 443–450.

    Article  PubMed  Google Scholar 

  • Uddin, L., Supekar, K., & Menon, V. (2013a). Reconceptualizing functional brain connectivity in autism from a developmental perspective. Frontiers in Human Neuroscience, 7, 458.

    Article  PubMed  PubMed Central  Google Scholar 

  • Uddin, L., Supekar, K., Lynch, C., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., & Menon, V. (2013b). Salience network–based classification and prediction of symptom severity in children with autism. JAMA Psychiatry, 70(8), 869–879.

    Article  PubMed  PubMed Central  Google Scholar 

  • Varoquaux, G. (2018). Cross-validation failure: Small sample sizes lead to large error bars. NeuroImage, 180, 68–77.

    Article  PubMed  Google Scholar 

  • Varoquaux, G., Reddy Raamana, P., Engemann, D., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2017). Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. NeuroImage, 145, Part B, 166–179.

    Article  Google Scholar 

  • Venkataraman, A., Kubicki, M., Westin, C., & Golland, P. (2010). Robust feature selection in resting-state fMRI connectivity based on population studies. IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, (pp. 63–70). San Francisco, CA.

  • Vezhnevets, A., & Barinova, O. (2007). Avoiding boosting overfitting by removing confusing samples. In J. Kok, J. Koronacki, R. Mantaras, S. Matwin, D. Mladenič, & A. Skowron (Eds.), Machine learning: ECML 2007: 18th European Conference on machine learning, Warsaw, Poland, September 17–21, 2007. Proceedings (pp. 430–441). Berlin, Heidelberg: Springer Berlin Heidelberg.

    Google Scholar 

  • Vigneshwaran, S., Mahanand, B., Suresh, S., & Sundararajan, N. (2015). Using regional homogeneity from functional MRI for diagnosis of ASD among males. 2015 International Joint Conference on Neural Networks (IJCNN), (pp. 1-8). Killarney.

  • Visser, S., Danielson, M., Bitsko, R., Holbrook, J., Kogan, M., Ghandour, R., et al. (2014). Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/hyperactivity disorder: United States, 2003–2011. Journal of the American Academy of Child & Adolescent Psychiatry, 53(1), 34–46.

    Article  Google Scholar 

  • Wang, L., Zang, Y., He, Y., Liang, M., Zhang, X., Tian, L., Wu, T., Jiang, T., & Li, K. (2006). Changes in hippocampal connectivity in the early stages of Alzheimer's disease: Evidence from resting state fMRI. NeuroImage, 31(2), 496–504.

    Article  PubMed  Google Scholar 

  • Wang, J.-H., Zuo, X.-N., Gohel, S., Milham, M., Biswal, B., & He, Y. (2011). Graph theoretical analysis of functional brain networks: Test-retest evaluation on short- and Long-term resting-state functional MRI data. PLoS One, 6(7), e21976.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang, X., Jiao, Y., Tang, T., Wang, H., & Lu, Z. (2013). Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder. European Journal of Radiology, 82(9), 1552–1557.

    Article  PubMed  Google Scholar 

  • Wang, Y., Katwal, S., Rogers, B., Gore, J., & Deshpande, G. (2017). Experimental validation of dynamic granger causality for inferring stimulus-evoked sub-100ms timing differences from fMRI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(6), 539–546.

    Article  PubMed  CAS  Google Scholar 

  • Washington, S., Gordon, E., Brar, J., Warburton, S., Sawyer, A., Wolfe, A., et al. (2014). Dysmaturation of the default mode network in autism. Human Brain Mapping, 35(4), 1284–1296.

    Article  PubMed  Google Scholar 

  • Wee, C., Yap, P., Zhang, D., Wang, L., & Shen, D. (2012a). Constrained sparse functional connectivity networks for MCI classification. In N. Ayache, H. Delingette, P. Golland, & K. Mori (Eds.), Medical image computing and computer-assisted intervention – MICCAI 2012. MICCAI 2012. Lecture notes in computer science. 7511 (pp. 212–219). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Wee, C.-Y., Yap, P.-T., Zhang, D., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., Wang, L., & Shen, D. (2012b). Identification of MCI individuals using structural and functional connectivity networks. NeuroImage, 59(3), 2045–2056.

    Article  PubMed  Google Scholar 

  • Wu, G.-R., Liao, W., Stramaglia, S., Ding, J.-R., Chen, H., & Marinazzo, D. (2013). A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Medical Image Analysis, 17(3), 365–374.

    Article  PubMed  Google Scholar 

  • Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: A network visualization tool for human brain Connectomics. PLoS One, 8(7), e68910.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yan, C.-G., & Zang, Y.-F. (2010). DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.

    Google Scholar 

  • Yin, Y., Jin, C., Eyler, L., Jin, H., Hu, X., Duan, L., et al. (2012). Altered regional homogeneity in post-traumatic stress disorder: a restingstate functional magnetic resonance imaging study. Neuroscience Bulletin, 28(5), 541–549.

    Article  PubMed  PubMed Central  Google Scholar 

  • Yousef, M., Jung, S., Showe, L., & Showe, M. (2007). Recursive cluster elimination (RCE) for classification and feature selection from gene expression data. BMC Bioinformatics, 8(1), 144.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011). Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage, 55(3), 856–867.

    Article  PubMed  Google Scholar 

  • Zhang, Q., Wu, Q., Zhu, H., He, L., Huang, H., Zhang, J., & Zhang, W. (2016). Multimodal MRI-based classification of trauma survivors with and without post-traumatic stress disorder. Frontiers in Neuroscience, 10, 292.

    PubMed  PubMed Central  Google Scholar 

  • Zhao, X., Rangaprakash, D., Dutt, D., & Deshpande, G. (2016). Investigating the correspondence of clinical diagnostic grouping with underlying neurobiological and phenotypic clusters using unsupervised learning: An application to the Alzheimer’s spectrum. Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM, (p. 4034). Singapore.

  • Zhong, Y., Zhang, R., Li, K., Qi, R., Zhang, Z., Huang, Q., & Lu, G. (2015). Altered cortical and subcortical local coherence in PTSD: Evidence from resting-state fMRI. Acta Radiologica, 56(6), 746–753.

    Article  PubMed  Google Scholar 

  • Zhou, J., Greicius, M., Gennatas, E., Growdon, M., Jang, J., Rabinovici, G., et al. (2010). Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease. Brain, 133(5), 1352–1367.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhu, C.-Z., Zang, Y.-F., Cao, Q.-J., Yan, C.-G., He, Y., Jiang, T.-Z., Sui, M. Q., & Wang, Y.-F. (2008). Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. NeuroImage, 40, 110–120.

    Article  PubMed  Google Scholar 

  • Zhu, H., Zhang, J., Zhan, W., Qiu, C., Wu, R., Meng, Y., Cui, H., Huang, X., Li, T., Gong, Q., & Zhang, W. (2014). Altered spontaneous neuronal activity of visual cortex and medial anterior cingulate cortex in treatment-naïve posttraumatic stress disorder. Comprehensive Psychiatry, 55(7), 1688–1695.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Attention deficit hyperactivity disorder (ADHD) data acquisition was supported by NIMH (National Institute of Mental Health, Bethesda, MD, USA) grant # R03MH096321. Alzheimer’s disease neuroimaging initiative (ADNI) data acquisition was funded by multiple agencies and the list can be obtained from http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. Autism brain imaging data exchange (ABIDE) data acquisition was supported by NIMH grant # K23MH087770. The authors would also like to thank the personnel at the traumatic brain injury (TBI) clinic and behavioral health clinic, Fort Benning, GA, USA and the US Army Aeromedical Research Laboratory, Fort Rucker, AL, USA, and most of all, the Soldiers who participated in the study. The authors thank Julie Rodiek and Wayne Duggan for facilitating post-traumatic stress disorder (PTSD) data acquisition.

Funding

The authors acknowledge financial support for PTSD/PCS data acquisition from the U.S. Army Medical Research and Material Command (MRMC) (Grant # 00007218). The views, opinions, and/or findings from PTSD/PCS data contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Department of Defense (DoD) or the United States Government.

Author information

Authors and Affiliations

  1. AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA

    Pradyumna Lanka, D Rangaprakash, Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  2. Department of Psychological Sciences, University of California Merced, Merced, CA, USA

    Pradyumna Lanka

  3. Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, USA

    D Rangaprakash

  4. U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA

    Michael N. Dretsch

  5. US Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McCord, WA, USA

    Michael N. Dretsch

  6. Department of Psychology, Auburn University, Auburn, AL, USA

    Michael N. Dretsch, Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  7. Alabama Advanced Imaging Consortium, Birmingham, AL, USA

    Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  8. Center for Neuroscience, Auburn University, Auburn, AL, USA

    Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  9. Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA

    Gopikrishna Deshpande

  10. Department of Psychiatry, National Institute of Mental and Neurosciences, Bangalore, India

    Gopikrishna Deshpande

Authors
  1. Pradyumna Lanka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D Rangaprakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael N. Dretsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeffrey S. Katz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thomas S. Denney Jr
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gopikrishna Deshpande
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gopikrishna Deshpande.

Ethics declarations

Conflict of interest

The authors declare that the research was conducted in the absence of any competing interests.

Ethical approval

This paper uses subject data from the publicly available databases such as ABIDE, ADHD-200 and ADNI. The data collection procedures for the participant’s neuroimaging data present in these databases was approved by the local Institutional Review Boards of the respective data acquisition sites. The data for military veterans with PCS/PTSD and controls was acquired at Auburn University. The procedure and the protocols in this study were approved by the Auburn University Institutional Review Board (IRB) and the Headquarters U.S. Army Medical Research and Material Command, IRB (HQ USAMRMC IRB). Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 1002 kb)

ESM 2

(DOCX 6672 kb)

ESM 3

(DOCX 184 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lanka, P., Rangaprakash, D., Dretsch, M.N. et al. Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging and Behavior 14, 2378–2416 (2020). https://doi.org/10.1007/s11682-019-00191-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-019-00191-8

Keywords

Search

Navigation