Abstract
Functional magnetic resonance imaging (fMRI) has helped characterize the pathophysiology of autism spectrum disorders (ASD) and carries promise for producing objective biomarkers for ASD. Recent work has focused on deriving ASD biomarkers from resting-state functional connectivity measures. However, current efforts that have identified ASD with high accuracy were limited to homogeneous, small datasets, while classification results for heterogeneous, multi-site data have shown much lower accuracy. In this paper, we propose the use of recurrent neural networks with long short-term memory (LSTMs) for classification of individuals with ASD and typical controls directly from the resting-state fMRI time-series. We used the entire large, multi-site Autism Brain Imaging Data Exchange (ABIDE) I dataset for training and testing the LSTM models. Under a cross-validation framework, we achieved classification accuracy of 68.5%, which is 9% higher than previously reported methods that used fMRI data from the whole ABIDE cohort. Finally, we presented interpretation of the trained LSTM weights, which highlight potential functional networks and regions that are known to be implicated in ASD.
This work was supported in part by T32 MH18268 and R01 NS035193.
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References
Abraham, A., Milham, M.P., Martino, A.D., Craddock, R.C., Samaras, D., Thirion, B., Varoquaux, G.: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. Neuroimage 147, 736–745 (2017)
Baron-Cohen, S., Abraham, A., Leslie, M., Frith, U.: Does the autistic child have a “theory of mind”. Cognition 21, 37–46 (1985)
Chen, C.P., Keown, C.L., Jahedi, A., Nair, A., Pflieger, M.E., Bailey, B.A., Müller, R.A.: Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. Neuroimage: Clin. 8, 238–245 (2015)
Chollet, F.: Keras (2015). https://github.com/fchollet/keras
Craddock, C., Benhajali, Y., Chu, C., Chouinard, F., Evans, A., Jakab, A., Khundrakpam, B.S., Lewis, J.D., Li, Q., Milham, M., Yan, C., Bellec, P.: The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. In: Neuroinformatics (2013)
Craddock, R.C., James, G.A., Holtzheimer, P.E., Hu, X.P., Mayberg, H.S.: A whole brain fMRI atlas generated via spatially constrained spectral clustering, human brain mapping. Hum. Brain Mapp. 33, 1914–1928 (2012)
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.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659–667 (2014)
Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: NIPS (2016)
Ghiassian, S., Greiner, R., Jin, P., Brown, M.R.G.: Using functional or structural magnetic resonance images and personal characteristic data to identify adhd and autism. PLOS One 11(12) (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Lombardo, M.V., Barnes, J.L., Wheelwright, S.J., Baron-Cohen, S.: Self-referential cognition and empathy in autism. PLoS One 2 (2007)
Nielsen, J.A., Zielinski, B.A., Fletcher, P.T., Alexander, A.L., Lange, N., Bigler, E.D., Lainhart, J.E., Anderson, J.S.: Multisite functional connectivity MRI classification of autism: abide results. Front. Hum. Neurosci. 7, 599 (2013)
Plitt, M., Barnes, K.A., Martin, A.: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. Neuroimage: Clin. 7, 359–366 (2015)
Preprocessed Connectomes Project: ABIDE Preprocessed. http://preprocessed-connectomes-project.org/abide/
Uddin, L.Q., Supekar, K., Lynch, C.J., Khouzam, A., Phillips, J., Feinstein, C., Menon, V.: Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 70(8), 869–879 (2014)
Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C., Wager, T.D.: Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods (2011). www.neurosynth.org
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Dvornek, N.C., Ventola, P., Pelphrey, K.A., Duncan, J.S. (2017). Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_42
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DOI: https://doi.org/10.1007/978-3-319-67389-9_42
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