References
- Altman DG, Bland JM. 1994. Statistics notes: diagnostic tests 2: predictive values. Bmj. 309(6947):102. doi:10.1136/bmj.309.6947.102.
- American Psychiatric Association. 2013. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub.
- Baron-Cohen S. 2000. Theory of mind and autism: a review. Int Rev Res Ment Retard. 23:169–184.
- Benesty J, Chen J, Huang Y. 2008. On the importance of the Pearson correlation coefficient in noise reduction. IEEE Trans Audio Speech Lang Processing. 16(4):757–765. doi:10.1109/TASL.2008.919072.
- Bishop CM. 2006. Pattern recognition and machine learning. New York: Springer.
- Boashash B. 1992. Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals. Proc IEEE. 80(4):520–538. doi:10.1109/5.135376.
- Chavez M, Besserve M, Adam C, Martinerie J. 2006. Towards a proper estimation of phase synchronization from time series. J Neurosci Methods. 154(1–2):149–160. doi:10.1016/j.jneumeth.2005.12.009.
- Chen Z, Caprihan A, Damaraju E, Rachakonda S, Calhoun V. 2018. Functional brain connectivity in resting-state fMRI using phase and magnitude data. J Neurosci Methods. 293:299–309. doi:10.1016/j.jneumeth.2017.10.016.
- Cressie NAC, Whitford HJ. 1986. How to use the two sample t‐test. Biom J. 28(2):131–148. doi:10.1002/bimj.4710280202.
- Dapretto M, Davies MS, Pfeifer JH, Scott AA, Sigman M, Bookheimer SY, Iacoboni M. 2006. Understanding emotions in others: mirror neuron dysfunction in children with autism spectrum disorders. Nat Neurosci. 9(1):28–30. doi:10.1038/nn1611.
- Duda RO, Hart PE. 1973. Pattern classification and scene analysis. Vol. 3, p. 731–739.New York (NY): Wiley.
- Ecker C, Bookheimer SY, Murphy DG. 2015. Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan. Lancet Neurol. 14(11):1121–1134. doi:10.1016/S1474-4422(15)00050-2.
- Guo X, Dominick KC, Minai AA, Li H, Erickson CA, Lu LJ. 2017. Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Front Neurosci. 11:460. doi:10.3389/fnins.2017.00460.
- Han J, Kamber M, Pei J. 2011. Data mining concepts and techniques third edition. Morgan Kaufmann Ser Data Manage Syst. 5(4):83–124.
- Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. 2018. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage. 17:16–23. doi:10.1016/j.nicl.2017.08.017.
- Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Comput. 9(8):1735–1780. doi:10.1162/neco.1997.9.8.1735.
- Hull JV, Dokovna LB, Jacokes ZJ, Torgerson CM, Irimia A, Van Horn JD. 2017. Resting-state functional connectivity in autism spectrum disorders: a review. Front Psychiatry. 7:205. doi:10.3389/fpsyt.2016.00205.
- Jain A, Nandakumar K, Ross A. 2005. Score normalization in multimodal biometric systems. Pattern Recognit. 38(12):2270–2285. doi:10.1016/j.patcog.2005.01.012.
- Javanray M, Yazdi M. 2019. Dynamic and static functional network connectivity analysis in autism: a resting state fMRI analysis. In: 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME); Nov; Tehran (Iran): IEEE. p. 31–36.
- Khosla M, Jamison K, Ngo GH, Kuceyeski A, Sabuncu MR. 2019. Machine learning in resting-state fMRI analysis. Magn Reson Imaging. 64:101–121. doi:10.1016/j.mri.2019.05.031.
- Lachaux JP, Rodriguez E, Martinerie J, Varela FJ. 1999. Measuring phase synchrony in brain signals. Hum Brain Mapp. 8(4):194–208. doi:10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C.
- Nicholas JS, Charles JM, Carpenter LA, King LB, Jenner W, Spratt EG. 2008. Prevalence and characteristics of children with autism-spectrum disorders. Ann Epidemiol. 18(2):130–136. doi:10.1016/j.annepidem.2007.10.013.
- Oppenheim AV, Buck JR, Schafer RW. 2001. Discrete-time signal processing. Vol. 2. Upper Saddle River (NJ): Prentice Hall.
- Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Calhoun VD. 2014. Deep learning for neuroimaging: a validation study. Front Neurosci. 8:229. doi:10.3389/fnins.2014.00229.
- Plitt M, Barnes KA, Martin A. 2015. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage. 7:359–366. doi:10.1016/j.nicl.2014.12.013.
- Pudil P, Novovičová J, Kittler J. 1994. Floating search methods in feature selection. Pattern Recognit Lett. 15(11):1119–1125. doi:10.1016/0167-8655(94)90127-9.
- Rashid B, Arbabshirani MR, Damaraju E, Cetin MS, Miller R, Pearlson GD, Calhoun VD. 2016. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage. 134:645–657. doi:10.1016/j.neuroimage.2016.04.051.
- Razek AA, Mazroa J, Baz H. 2014. Assessment of white matter integrity of autistic preschool children with diffusion weighted MR imaging. Brain Dev. 36(1):28–34. doi:10.1016/j.braindev.2013.01.003.
- Razek AAKA, Talaat M, El-Serougy L, Gaballa G, Abdelsalam M. 2019. Clinical applications of arterial spin labeling in brain tumors. J Comput Assist Tomogr. 43(4):525–532. doi:10.1097/RCT.0000000000000873.
- Rowe DB. 2005. Parameter estimation in the magnitude-only and complex-valued fMRI data models. NeuroImage. 25(4):1124–1132. doi:10.1016/j.neuroimage.2004.12.048.
- Rowe DB, Logan BR. 2004. A complex way to compute fMRI activation. Neuroimage. 23(3):1078–1092. doi:10.1016/j.neuroimage.2004.06.042.
- Saxe GN, Calderone D, Morales LJ. 2018. Brain entropy and human intelligence: a resting-state fMRI study. PloS One. 13(2):e0191582. doi:10.1371/journal.pone.0191582.
- Schölvinck ML, Howarth C, Attwell D. 2008. The cortical energy needed for conscious perception. Neuroimage. 40(4):1460–1468. doi:10.1016/j.neuroimage.2008.01.032.
- Seraj E, Mahalingam K. 2019. Essential motor cortex signal processing: an ERP and functional connectivity MATLAB toolbox–user guide. arXiv preprint arXiv:1907.02862.
- Seraj E, Sameni R. 2016. Cerebral signal phase analysis toolbox-user guide version 1.0. arXiv preprint arXiv:1610.02249.
- Seraj E, Sameni R. 2017. Robust electroencephalogram phase estimation with applications in brain-computer interface systems. Physiol Meas. 38(3):501. doi:10.1088/1361-6579/aa5bba.
- Seraj E, Yazdi M, Shahparian N. 2019. Instantaneous fMRI based cerebral parameters for automatic Alzheimer, mild cognitive impairment and healthy subject classification. J Integr Neurosci. 18(3):261–268.
- Van Den Heuvel MP, Pol HEH. 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 20(8):519–534. doi:10.1016/j.euroneuro.2010.03.008.
- Walenski M, Tager-Flusberg H, Ullman MT. 2006. Language in autism.
- Wang M, Zhang D, Huang J, Shen D, Liu M. 2018. Low-rank representation for multi-center autism spectrum disorder identification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention; Sept; Cham: Springer. p. 647–654.