Abstract
Endophenotype refers to a measurable and heritable component between genetics and diagnosis, and the same endophenotype is present in both individuals with a diagnosis and their unaffected siblings. Determination of the neural correlates of an endophenotype and diagnosis is important in autism spectrum disorder (ASD). However, prior studies enrolling individuals with ASD and their unaffected siblings have generally included only one group of typically developing (TD) subjects; they have not accounted for differences between TD siblings. Thus, they could not differentiate the neural correlates for endophenotype from the clinical diagnosis. In this context, we enrolled pairs of siblings with an ASD endophenotype (individuals with ASD and their unaffected siblings) and pairs of siblings without this endophenotype (pairs of TD siblings). Using resting-state functional MRI, we first aimed to identify an endophenotype pattern consisting of multiple functional connections (FCs) then examined the neural correlates of FCs for ASD diagnosis, controlling for differences between TD siblings. Sparse logistic regression successfully classified subjects as to the endophenotype (area under the curve = 0.78, classification accuracy = 75%). Then, a bootstrapping approach controlling for differences between TD siblings revealed that an FC between the right middle temporal gyrus and right anterior cingulate cortex was substantially different between individuals with ASD and their unaffected siblings, suggesting that this FC may be a neural correlate for the diagnosis, while the other FCs represent the endophenotype. The current findings suggest that an ASD endophenotype pattern exists in FCs, and a neural correlate for ASD diagnosis is dissociable from this endophenotype. (250 words).
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Adolphs, R. (2009). The social brain: Neural basis of social knowledge. Annual Review of Psychology, 60, 693–716.
Alcala-Lopez, D., Smallwood, J., Jefferies, E., Van Overwalle, F., Vogeley, K., Mars, R. B., et al. (2017). Computing the social brain connectome across systems and states. Cereb Cortex (1460-2199 (electronic)), 1-26.
Ameis, S. H., & Szatmari, P. (2012). Imaging-genetics in autism spectrum disorder: Advances, translational impact, and future directions. Frontiers in Psychiatry, 3, 46.
American Psychiatric, A. (2000). Diagnostic and statistical manual, 4th edn, text revision (DSM-IV-TR). Washington: American Psychiatric Association.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). Arlington, VA: American Psychiatric Association.
Aoki, Y., Kasai, K., & Yamasue, H. (2012). Age-related change in brain metabolite abnormalities in autism: A meta-analysis of proton magnetic resonance spectroscopy studies. Translational Psychiatry, 2, e69.
Aoki, Y., Cortese, S., & Tansella, M. (2015). Neural bases of atypical emotional face processing in autism: A meta-analysis of fMRI studies. The World Journal of Biological Psychiatry, 16(5), 291–300.
Aoki, Y., Yoncheva, Y. N., Chen, B., Nath, T., Sharp, D., Lazar, M., Velasco, P., Milham, M. P., & di Martino, A. (2017). Association of white matter structure with autism spectrum disorder and attention-deficit/hyperactivity disorder. JAMA Psychiatry, 74(11), 1120–1128.
Autism, & Developmental Disabilities Monitoring Network Surveillance Year Principal, I. (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. Surveillance Summaries, 63(2), 1–21.
Barnea-Goraly, N., Lotspeich, L. J., & Reiss, A. L. (2010). Similar white matter aberrations in children with autism and their unaffected siblings: A diffusion tensor imaging study using tract-based spatial statistics. Archives of General Psychiatry, 67(10), 1052–1060.
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(16), 1687–1690.
Colvert, E., Tick, B., McEwen, F., Stewart, C., Curran, S. R., Woodhouse, E., Gillan, N., Hallett, V., Lietz, S., Garnett, T., Ronald, A., Plomin, R., Rijsdijk, F., Happé, F., & Bolton, P. (2015). Heritability of autism spectrum disorder in a UK population-based twin sample. JAMA Psychiatry, 72(5), 415–423.
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., et al. (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.
Geschwind, D. H., & Levitt, P. (2007). Autism spectrum disorders: Developmental disconnection syndromes. Current Opinion in Neurobiology, 17(1), 103–111.
Glahn, D. C., Winkler, A. M., Kochunov, P., Almasy, L., Duggirala, R., Carless, M. A., Curran, J. C., Olvera, R. L., Laird, A. R., Smith, S. M., Beckmann, C. F., Fox, P. T., & Blangero, J. (2010). Genetic control over the resting brain. Proceedings of the National Academy of Sciences of the United States of America, 107(3), 1223–1228.
Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: Etymology and strategic intentions. American Journal of Psychiatry, 160(4), 636–645.
Hallmayer, J., Cleveland, S., Torres, A., Phillips, J., Cohen, B., Torigoe, T., Miller, J., Fedele, A., Collins, J., Smith, K., Lotspeich, L., Croen, L. A., Ozonoff, S., Lajonchere, C., Grether, J. K., & Risch, N. (2011). Genetic heritability and shared environmental factors among twin pairs with autism. Archives of General Psychiatry, 68(11), 1095–1102.
Hergueta, T., Baker, R., & Dunbar, G. C. (1998). The MINI-international neuropsychiatric interview (MINI): The development and validation of a structured diagnostic psychiatric interview for DSM-IVand ICD-10. Journal of Clinical Psychiatry, 59(Suppl 20), 2233.
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825–841.
Jou, R. J., Reed, H. E., Kaiser, M. D., Voos, A. C., Volkmar, F. R., & Pelphrey, K. A. (2016). White matter abnormalities in autism and unaffected siblings. Journal of Neuropsychiatry & Clinical Neurosciences, 28(1), 49–55.
Kana, R. K., Keller, T. A., Cherkassky, V. L., Minshew, N. J., & Just, M. A. (2009). Atypical frontal-posterior synchronization of theory of mind regions in autism during mental state attribution. Social Neuroscience, 4(2), 135–152.
Khadka, S., Meda, S. A., Stevens, M. C., Glahn, D. C., Calhoun, V. D., Sweeney, J. A., Tamminga, C. A., Keshavan, M. S., O’Neil, K., Schretlen, D., & Pearlson, G. D. (2013). Is aberrant functional connectivity a psychosis endophenotype? A resting state functional magnetic resonance imaging study. Biological Psychiatry, 74(6), 458–466.
Kleinhans, N. M., Richards, T., Greenson, J., Dawson, G., & Aylward, E. (2016). Altered dynamics of the fMRI response to faces in individuals with autism. Journal of Autism and Developmental Disorders, 46(1), 232–241.
Lai, M. C., Lerch, J. P., Floris, D. L., Ruigrok, A. N. V., Pohl, A., Lombardo, M. V., & Baron-Cohen, S. (2017). Imaging sex/gender and autism in the brain: Etiological implications. Journal of Neuroscience Research, 95(1–2), 380–397.
Lee, Y., Park, B. Y., James, O., Kim, S. G., & Park, H. (2017). Autism spectrum disorder related functional connectivity changes in the language network in children, adolescents and adults. Frontiers in Human Neuroscience, 11(1662–5161 (Print)), 418.
Lord, C., Rutter, M., DiLavore, P. C., Risi, S., Gotham, K., & Bishop, S. L. (2001). Autism diagnostic observation schedule (ADOS): Manual: WPS.
Meyer-Lindenberg, A., & Weinberger, D. R. (2006). Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nature Reviews Neuroscience, 7(10), 818–827.
Miles, J. H. (2011). Autism spectrum disorders—A genetics review. Genetics in Medicine, 13(4), 278–294.
Moseley, R. L., Ypma, R. J., Holt, R. J., Floris, D., Chura, L. R., Spencer, M. D., et al. (2015). Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents. Neuroimage Clinical, 9, 140–152.
Murphy, C. M., Christakou, A., Giampietro, V., Brammer, M., Daly, E. M., Ecker, C., Johnston, P., Spain, D., Robertson, D. M., MRC AIMS Consortium, Murphy, D. G., & Rubia, K. (2017). Abnormal functional activation and maturation of ventromedial prefrontal cortex and cerebellum during temporal discounting in autism spectrum disorder. Human Brain Mapping, 38(11), 5343–5355.
Okada, N., Kasai, K., Takahashi, T., Suzuki, M., Hashimoto, R., & Kawakami, N. (2014). Brief rating scale of socioeconomic status for biological psychiatry research among Japanese people: A scaling based on an educational history. Japanese Journal of Biological Psychiatry, 25, 115–117.
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9(1), 97–113.
Ozonoff, S., Young, G. S., Carter, A., Messinger, D., Yirmiya, N., Zwaigenbaum, L., Bryson, S., Carver, L. J., Constantino, J. N., Dobkins, K., Hutman, T., Iverson, J. M., Landa, R., Rogers, S. J., Sigman, M., & Stone, W. L. (2011). Recurrence risk for autism spectrum disorders: A baby siblings research consortium study. Pediatrics, 128(3), e488–e495.
Parkes, L., Fulcher, B., Yu Cel, M., & Fornitod, A. (2017). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage.
Pelphrey, K. A., Shultz, S., Hudac, C. M., & Vander Wyk, B. C. (2011). Research review: Constraining heterogeneity: The social brain and its development in autism spectrum disorder. Journal of Child Psychology and Psychiatry, 52(6), 631–644.
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 2142–2154.
Pruim, R. H., Mennes, M., Buitelaar, J. K., & Beckmann, C. F. (2015a). Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI. Neuroimage, 112, 278–287.
Pruim, R. H., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015b). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage, 112, 267–277.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B: Methodological, 267–288.
Toth, K., Dawson, G., Meltzoff, A. N., Greenson, J., & Fein, D. (2007). Early social, imitation, play, and language abilities of young non-autistic siblings of children with autism. Journal of Autism and Developmental Disorders, 37(1), 145–157.
Tsuchiya, K. J., Matsumoto, K., Yagi, A., Inada, N., Kuroda, M., Inokuchi, E., Koyama, T., Kamio, Y., Tsujii, M., Sakai, S., Mohri, I., Taniike, M., Iwanaga, R., Ogasahara, K., Miyachi, T., Nakajima, S., Tani, I., Ohnishi, M., Inoue, M., Nomura, K., Hagiwara, T., Uchiyama, T., Ichikawa, H., Kobayashi, S., Miyamoto, K., Nakamura, K., Suzuki, K., Mori, N., & Takei, N. (2013). Reliability and validity of autism diagnostic interview-revised, Japanese version. Journal of Autism and Developmental Disorders, 43(3), 643–662.
Uddin, L. Q., Supekar, K., & Menon, V. (2013). Reconceptualizing functional brain connectivity in autism from a developmental perspective. Frontiers in Human Neuroscience, 7, 458.
Wakabayashi, A., Baron-Cohen, S., Wheelwright, S., & Tojo, Y. (2006). The autism-Spectrum quotient (AQ) in Japan: A cross-cultural comparison. Journal of Autism and Developmental Disorders, 36(2), 263–270.
Wechsler, D. (1997). WAIS-III: Wechsler adult intelligence scale: Psychological corporation.
Wechsler, D., & De Lemos, M. M. (1981). Wechsler adult intelligence scale-revised: Harcourt brace Jovanovich.
Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara H., Kuroda M., Yamada T., Megumi F., Imamizu H., Náñez Sr J. E., Takahashi H., Okamoto Y., Kasai K., Kato N., Sasaki Y., Watanabe T., Kawato M. (2016). A small number of abnormal brain connections predicts adult autism spectrum disorder. Nature Communications, 7.
Yamagata, B., Itahashi, T., Nakamura, M., Mimura, M., Hashimoto, R. I., Kato, N., Mimura, M., Hashimoto, R. I., Kato, N., & Aoki, Y. (2018). White matter endophenotypes and correlates for the clinical diagnosis of autism spectrum disorder. Social Cognitive and Affective Neuroscience, 13, 765–773.
Yamashita, O., Sato, M. A., Yoshioka, T., Tong, F., & Kamitani, Y. (2008). Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. Neuroimage, 42(4), 1414–1429.
Zhan, Y., Paolicelli, R. C., Sforazzini, F., Weinhard, L., Bolasco, G., Pagani, F., Vyssotski, A. L., Bifone, A., Gozzi, A., Ragozzino, D., & Gross, C. T. (2014). Deficient neuron-microglia signaling results in impaired functional brain connectivity and social behavior. Nature Neuroscience, 17(3), 400–406.
Acknowledgements
This study is the result of “Development of BMI Technologies for Clinical Application” carried out under the Strategic Research Program for Brain Sciences by the Japan Agency for Medical Research and Development (AMED). This work is partly supported by a grant from The Japan Foundation for Pediatric Research (to YA).
Funding
This study is the result of “Development of BMI Technologies for Clinical Application” carried out under the Strategic Research Program for Brain Sciences by the Japan Agency for Medical Research and Development (AMED). This work is partly supported by a grant from The Japan Foundation for Pediatric Research (to YA).
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Yamagata, B., Itahashi, T., Fujino, J. et al. Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder. Brain Imaging and Behavior 13, 1689–1698 (2019). https://doi.org/10.1007/s11682-018-9973-2
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DOI: https://doi.org/10.1007/s11682-018-9973-2