Skip to main content
SpringerLink
Log in

Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

The advances in neuroimaging methods reveal that resting-state functional fMRI (rs-fMRI) connectivity measures can be potential diagnostic biomarkers for autism spectrum disorder (ASD). Recent data sharing projects help us replicating the robustness of these biomarkers in different acquisition conditions or preprocessing steps across larger numbers of individuals or sites. It is necessary to validate the previous results by using data from multiple sites by diminishing the site variations. We investigated partial least square regression (PLS), a domain adaptive method to adjust the effects of multicenter acquisition. A sparse Multivariate Pattern Analysis (MVVPA) framework in a leave one site out cross validation (LOSOCV) setting has been proposed to discriminate ASD from healthy controls using data from six sites in the Autism Brain Imaging Data Exchange (ABIDE). Classification features were obtained using 42 bilateral Brodmann areas without presupposing any prior hypothesis. Our results showed that using PLS, SVM showed poorer accuracies with highest accuracy achieved (62%) than without PLS but not significantly. The regions occurred in two or more informative connections are Dorsolateral Prefrontal Cortex, Somatosensory Association Cortex, Primary Auditory Cortex, Inferior Temporal Gyrus and Temporopolar area. These interrupted regions are involved in executive function, speech, visual perception, sense and language which are associated with ASD. Our findings may support early clinical diagnosis or risk determination by identifying neurobiological markers to distinguish between ASD and healthy controls.

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

Similar content being viewed by others

References

  • American Psychiatric Association (2013) Diagnostic and statistical manual of mental dis- orders, fourth edn., Washington, DC: American Psychiatric Association.

  • Anderson, J. S., Druzgal, T. J., Froehlich, A., Dubray, M. B., Lange, N., Alexander, A. L., Abildskov, T., Nielsen, J. A., Cariello, A. N., Cooperrider, J. R., et al. (2011a). Decreased interhemispheric functional connectivity in autism. Cereb Cortex, 21, 1134–1146.

    Article  PubMed  Google Scholar 

  • 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., Alexander, A. L., Bigler, E. D., Lange, N., & Lainhart, J. E. (2011b). Functional connectivity magnetic resonance imaging classification of autism. Brain, 134(12), 3742–3754. https://doi.org/10.1093/brain/awr26322006979.

    Article  PubMed  Google Scholar 

  • Benjamini Y, Hochberg Y. , (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing., J Roy Statist Soc Ser B (Methodological) 57:289-300.

  • Bertone, A., Mottron, L., Jelenic, P., & Faubert, J. (2005). Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity. Brain, 128(Pt 10), 2430–2441.

    Article  PubMed  Google Scholar 

  • Bhaumik, R., Jenkins, L. M., Gowins, J. R., Jacobs, R. H., Barba, A., Bhaumik, D. K., & Langenecker, S. A. (2016). Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity. NeuroImage: Clinical. https://doi.org/10.1016/j.nicl.2016.02.018.

    Book  Google Scholar 

  • Castrillon, J. G., Ahmadi, A., Navab, N., & Richiardi, J. (2014). Learning with multi-site fmri graph data. In 2014 48th Asilomar conference on signals, systems and computers, IEEE (pp. 608–612).

    Chapter  Google Scholar 

  • Chen, C. P., Keown, C. L., Jahedi, A., Nair, A., Pflieger, M. E., Bailey, B. A., & Müller, R. (2015). Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage: Clinical, 8, 238–245. https://doi.org/10.1016/j.nicl.2015.04.002.

    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 

  • Cherkassky, V. L., Kana, R. K., Keller, T. A., & Just, M. A. (2006). Functional connectivity in a baseline resting-state network in autism. Neuroreport, 17, 1687–1690. https://doi.org/10.1097/01.wnr.0000239956.45448.4c.

    Article  PubMed  Google Scholar 

  • Chevallier, C., Kohls, G., Troiani, V., Brodkin, E. S., & Schultz, R. T. (2012). The social motivation theory of autism. Trends in Cognitive Sciences, 16, 231–239.

    Article  PubMed  PubMed Central  Google Scholar 

  • Dakin, S., & Frith, U. (2005). Vagaries of visual perception in autism. Neuron, 48, 497–507.

    Article  PubMed  CAS  Google Scholar 

  • Damiano, C. R., Aloi, J., Teadway, M., Bodfish, J. W., & Dichter, G. S. (2012). Adults with autism spectrum disorders exhibity decreased sensitivity to reward parameters when making effort-based decisions. Journal of Neurodevelopmental Disorders, 4, 13.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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., Deen, B., Delmonte, S., Dinstein, I., Ertl-Wagner, B., Fair, D. A., Gallagher, L., Kennedy, D. P., Keown, C. L., Keysers, C., Lainhart, J. E., Lord, C., Luna, B., Menon, V., Minshew, N. J., Monk, C. S., Mueller, S., Müller, R. A., Nebel, M. B., Nigg, J. T., O’Hearn, K., Pelphrey, K. A., Peltier, S. J., Rudie, J. D., Sunaert, S., Thioux, M., Tyszka, J. M., Uddin, L. Q., Verhoeven, J. S., Wenderoth, N., Wiggins, J. L., Mostofsky, S. H., & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large- scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667. https://doi.org/10.1038/mp.2013.7823774715.

    Article  PubMed  Google Scholar 

  • DiMartino, A., Kelly, C., Grzadzinski, R., Zuo, X. N., Mennes, M., Mairena, M. A., et al. (2011). Aberrant striatal functional con- nectivity in children with autism. Biological Psychiatry, 69, 847–856. https://doi.org/10.1016/j.biopsych.2010.10.029.

    Article  Google Scholar 

  • Dinstein, I., Heeger, D. J., Lorenzi, L., Minshew, N. J., Malach, R., & Behrmann, M. (2012). Unreliable evoked responses in autism. Neuron, 75, 981–991.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Dubac, B. (2014). The brain from top to bottom. McGill, 2002. Web.

  • Ecker, C., Marquand, A., Mourao-Miranda, J., Johnston, P., Daly, E. M., Brammer, M. J., et al. (2010). Describing the brain in autism in five dimensions—Magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multi parameter classification approach. The Journal of Neuroscience, 30, 10612–10623. https://doi.org/10.1523/JNEUROSCI.5413-09.2010.

    Article  PubMed  CAS  Google Scholar 

  • Fair D. A., Bathula D., Nikolas M. A., Nigg J. T., (2012) Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proceedings of the National Academy of Sciences 109 (17):6769-6774

    Article  Google Scholar 

  • Gotts, S. J., Simmons, W. K., Milbury, L. A., Wallace, G. L., Cox, R. W., & Martin, A. (2012). Fractionation of social brain circuits in autism spectrum disor- ders. Brain, 135, 2711–2725. https://doi.org/10.1093/brain/aws160.

    Article  PubMed  PubMed Central  Google Scholar 

  • Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100, 253–258.

    Article  PubMed  CAS  Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. (2001). New York: Springer-Verlag.

  • Jiang, J. (2008). A literature survey on domain adaptation of statistical classifiers. Technical report, Computer Science Department at University of Illinois at Urbana-Champaign. Available at URL <http://sifaka.cs.uiuc.edu/jiang4/domainadaptation/survey>.

  • Keehn, B., Shih, P., Brenner, L. A., Townsend, J., & Muller, R. A. (2013). Functional connectivity for an “island of sparing” in autism spectrum disorder: An fMRI study of visual search. Human Brain Mapping, 34, 2524–2537. https://doi.org/10.1002/hbm.22084.

    Article  PubMed  Google Scholar 

  • Lin, A., Tsai, K., Rangel, A., & Adolphs, R. (2012). Reduced social preferences in autism: Evidence from charitable donations. Journal of Neurodevelopmental Disorders, 4, 8.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lombardo, M. V., Barnes, J. L., Wheelwright, S. J., & Baron-Cohen, S. (2007). Self-referential cognition and empathy in autism. PLoS ONE, 2, e883.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lynch, C. J., Uddin, L. Q., Supekar, K., Khouzam, A., Phillips, J., & Menon, V. (2013). Default mode network in childhood autism: Pos- teromedial cortex heterogeneity and relationship with social deficits. Biological Psychiatry, 74, 212–219. https://doi.org/10.1016/j.biopsych.2012.12.013.

    Article  PubMed  PubMed Central  Google Scholar 

  • Minshew, N. J., & Goldstein, G. (1993). Is autism an amnesic disorder? Evidence from the California verbal learning test. Neuropsychology, 7, 209–216.

    Article  Google Scholar 

  • Müller, R.-A., Shih, P., Keehn, B., Deyoe, J. R., Leyden, K. M., & Shukla, D. K. (2011). Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cerebral Cortex, 21(10), 2233–2243. https://doi.org/10.1093/cercor/bhq29621378114.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nair, A., Keown, C. L., Datko, M., Shih, P., Keehn, B., & Müller, R. A. (2014). Impact of methodolog- ical variables on functional connectivity findings in autism spectrum disorders. Human Brain Mapping, 35(8), 4035–4048. https://doi.org/10.1002/hbm.2245624452854.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nielsen, J. A., Zielinski, B. A., Fletcher, P. T., Alexander, A. L., Lange, N., Bigler, E. D., Lainhart, J. E., & Anderson, J. S. (2013). Multisite functional connectivity MRI classification of au- tism: ABIDE results. Frontiers in Human Neuroscience, 7, 599. https://doi.org/10.3389/fnhum.2013.0059924093016.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ozonoff, S., Pennington, B., & Rogers, S. (1991). Executive function deficits in high-functioning autistic individuals: Relationship to theory-of-mind. Journal of Child Psychology and Psychiatry, 32, 1081–1105.

    Article  PubMed  CAS  Google Scholar 

  • Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191.

    Article  Google Scholar 

  • Perry, W., Minassian, A., Lopez, B., Maron, L., & Lincoln, A. (2007). Sensorimotor gating deficits in adults with autism. Biological Psychiatry, 61, 482–486.

    Article  PubMed  Google Scholar 

  • Plitt, M., Barnes, K. A., & Martin, A. (2015). Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage: Clinical, 7, 359–366. https://doi.org/10.1016/j.nicl.2014.12.013.

    Article  Google Scholar 

  • Power J. D., Mitra A., Laumann T. O., Snyder A. Z., Schlaggar B. L., Petersen S. E., (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage 84:320–341

    Article  PubMed  Google Scholar 

  • Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proc Natl Acad Sci U S A, 98, 676–682.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Rogers, S., & Pennington, B. (1991). A theoretical approach to the deficits in infantile autism. Dev Psychopathol, 3, 137–162.

    Article  Google Scholar 

  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (methodological), 58(1). Wiley: 267-88.

  • Tommerdahl, M., Tannan, V., Holden, J. K., & Baranek, G. T. (2008). Absence of stimulus-driven synchronization effects on sensory perception in autism: Evidence for local underconnectivity? Behavioral and Brain Functions, 4, 19.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tyszka, J. M., Kennedy, D. P., Paul, L. K., & Adolphs, R. (2013). Largely typical patterns of resting-state functional connectivity in high- functioning adults with autism. Cerebral Cortex https://doi.org/10.1093/cercor/bht040.

  • Uddin, L. Q. (2011). The self in autism: An emerging view from neuroimaging. Neurocase, 17, 201–208.

    Article  PubMed  PubMed Central  Google Scholar 

  • Uddin L. Q., Menon V., Young C. B., Ryali S., Chen T., Khouzam A., Minshew N. J., Hardan A. Y., (2011). Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biological Psychiatry, 70(9): 833-841.

  • Uddin, L.Q., Supekar, K., Lynch, C.J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., & Menon, V. (2013). Salience network-based classification and prediction of symptom severity in children with autism. J.A.M.A. Psychiatry 70, 869–879. 10.1001/ jamapsychiatry.2013.10423803651.

  • Van Dijk, K. R., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010). Intrinsic functional connectivity as a tool for human connectomics: Theory, proper- ties, and optimization. Journal of Neurophysiology, 103(1), 297–321. https://doi.org/10.1152/jn.00783.200919889849.

    Article  PubMed  Google Scholar 

  • Vapnik, V. (1995). The natures of statistical learning theory. New York: Springer-Verlag.

    Book  Google Scholar 

  • Von dem Hagen, E. A., Stoyanova, R. S., Baron-Cohen, S., & Calder, A. J. (2013). Reduced func- tional connectivity within and between ‘social’ resting state networks in autism spec- trum conditions. Social Cognitive and Affective Neuroscience, 8, 694–701. https://doi.org/10.1093/scan/nss05322563003.

    Article  Google Scholar 

  • Wiggins, J. L., Peltier, S. J., Ashinoff, S., Weng, S. J., Carrasco, M., Welsh, R. C., et al. (2011). Using a self-organizing map algorithm to detect age-related changes in functional connectivity during rest in autism spectrum disorders. Brain Res, 1380, 187–197. https://doi.org/10.1016/j.brainres.2010.10.102.

    Article  PubMed  CAS  Google Scholar 

  • Zhou, Y., Shi, L., Cui, X., Wang, S., & Lou, X. (2016). *Functional connectivity of the caudal anterior cingulate cortex is decreased in autism. PLoS One, 11(3), e0151879.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67, 301–320.

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to acknowledge Dr. Heide Klumpp, Assistant Professor of Psychiatry at University of Illinois at Chicago and Dr. Cameron Craddock from Child Mind Institute in New York for their valuable comments and suggestions.

Author information

Authors and Affiliations

  1. Department of Psychiatry, Bio-Statistical Research Center, The University of Illinois at Chicago, Chicago, IL, USA

    Runa Bhaumik, Ashish Pradhan, Soptik Das & Dulal K. Bhaumik

Authors
  1. Runa Bhaumik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashish Pradhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Soptik Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dulal K. Bhaumik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Runa Bhaumik.

Electronic supplementary material

ESM 1

(DOCX 22 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhaumik, R., Pradhan, A., Das, S. et al. Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation. Neuroinform 16, 197–205 (2018). https://doi.org/10.1007/s12021-018-9366-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-018-9366-0

Keywords

Search

Navigation