Sex Differences in Resting-State Functional Connectivity of the Cerebellum in Autism Spectrum Disorder

Rachel E W Smith, Jason A Avery, Gregory L Wallace, Lauren Kenworthy, Stephen J Gotts, Alex Martin, Rachel E W Smith, Jason A Avery, Gregory L Wallace, Lauren Kenworthy, Stephen J Gotts, Alex Martin

Abstract

Autism spectrum disorder (ASD) is more prevalent in males than females, but the underlying neurobiology of this sex bias remains unclear. Given its involvement in ASD, its role in sensorimotor, cognitive, and socio-affective processes, and its developmental sensitivity to sex hormones, the cerebellum is a candidate for understanding this sex difference. The current study used resting-state functional magnetic resonance imaging (fMRI) to investigate sex-dependent differences in cortico-cerebellar organization in ASD. We collected resting-state fMRI scans from 47 females (23 ASD, 24 controls) and 120 males (56 ASD, 65 controls). Using a measure of global functional connectivity (FC), we ran a linear mixed effects analysis to determine whether there was a sex-by-diagnosis interaction in resting-state FC. Subsequent seed-based analyses from the resulting clusters were run to clarify the global connectivity effects. Two clusters in the bilateral cerebellum exhibited a diagnosis-by-sex interaction in global connectivity. These cerebellar clusters further showed a pattern of interaction with regions in the cortex, including bilateral fusiform, middle occipital, middle frontal, and precentral gyri, cingulate cortex, and precuneus. Post hoc tests revealed a pattern of cortico-cerebellar hyperconnectivity in ASD females and a pattern of hypoconnectivity in ASD males. Furthermore, cortico-cerebellar FC in females more closely resembled that of control males than that of control females. These results shed light on the sex-specific pathophysiology of ASD and are indicative of potentially divergent neurodevelopmental trajectories for each sex. This sex-dependent, aberrant cerebellar connectivity in ASD might also underlie some of the motor and/or socio-affective difficulties experienced by members of this population, but the symptomatic correlate(s) of these brain findings remain unknown. Clinical Trial Registration: www.ClinicalTrials.gov, NIH Clinical Study Protocol 10-M-0027 (ZIA MH002920-09) identifier #NCT01031407.

Keywords: autism; cerebellum; connectivity; fMRI; resting state; sex differences.

Figures

Figure 1
Figure 1
Cerebellar clusters exhibiting a diagnosis-by-sex interaction. Shown in standard Talairach Tournoux space. Left is shown on the left. Mode contrast = autism spectrum disorder (ASD males—ASD Females) × typically developing (TD males—TD females). Clusters shown were significant at p < 0.05 with small volume correction for the cerebellum.
Figure 2
Figure 2
Mean global connectedness values for left and right cerebellar clusters. The y-axis displays the average z-transformed correlation coefficient of the cluster’s mask with every other voxel in the brain. White bars denote standard error.
Figure 3
Figure 3
Regions resulting from seed-based analysis showing diagnosis-by-sex interaction. Cerebellar seeds are pictured in the lower right panel. Left is shown on the left, and results are in standard Talairach-Tournoux space. Refer to Table 2 for key of numeric region labels.
Figure 4
Figure 4
(A–C) Cerebellar-cortical region correlation matrices. (A)F-values for diagnosis-by-sex interaction (left), and surviving pairs at P < 0.0069 after false discovery rate (FDR) correction to q < 0.05 shown in white (right). (B) Z-values for ASD—TD contrast in females (left), and surviving pairs at P < 0.0216 (FDR-corrected to q < 0.05 within those combinations already FDR-corrected in the diagnosis-by-sex contrast) shown in red (right). (C) Z-values for ASD—TD contrast in males (left), and surviving pairs at P < 0.0216 (FDR-corrected to q < 0.05 within those combinations already FDR-corrected in the diagnosis-by-sex contrast) shown in blue (right).

References

    1. Alaerts K., Swinnen S. P., Wenderoth N. (2016). Sex differences in autism: a resting-state fMRI investigation of functional brain connectivity in males and females. Soc. Cogn. Affect. Neurosci. 11, 1002–1016. 10.1093/scan/nsw027
    1. American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders. 5th Edn. Arlington, VA: American Psychiatric Publishing.
    1. Baron-Cohen S. (2002). The extreme male brain theory of autism. Trends Cogn. Sci. 6, 248–254. 10.1016/s1364-6613(02)01904-6
    1. Baron-Cohen S., Auyeung B., Nørgaard-Pedersen B., Hougaard D. M., Abdallah M. W., Melgaard L., et al. . (2015). Elevated fetal steroidogenic activity in autism. Mol. Psychiatry 20, 369–376. 10.1038/mp.2014.48
    1. Baumann O., Borra R. J., Bower J. M., Cullen K. E., Habas C., Ivry R., et al. . (2015). Consensus paper: the role of the cerebellum in perceptual processes. Cerebellum 14, 197–220. 10.1007/s12311-014-0627-7
    1. Baumann O., Mattingley J. B. (2010). Scaling of neural responses to visual and auditory motion in the human cerebellum. J. Neurosci. 30, 4489–4495. 10.1523/JNEUROSCI.5661-09.2010
    1. Bejerot S., Eriksson J. M., Bonde S., Carlström K., Humble M. B., Eriksson E. (2012). The extreme male brain revisited: gender coherence in adults with autism spectrum disorder. Br. J. Psychiatry 201, 116–123. 10.1192/bjp.bp.111.097899
    1. Berman R. A., Gotts S. J., McAdams H. M., Greenstein D., Lalonde F., Clasen L., et al. . (2016). Disrupted sensorimotor and social-cognitive networks underlie symptoms in childhood-onset schizophrenia. Brain 139, 276–291. 10.1093/brain/awv306
    1. Biamonte F., Assenza G., Marino R., D’Amelio M., Panteri R., Caruso D., et al. . (2009). Interactions between neuroactive steroids and reelin haploinsufficiency in Purkinje cell survival. Neurobiol. Dis. 36, 103–105. 10.1016/j.nbd.2009.07.001
    1. Birn R. M., Smith M. A., Jones T. B., Bandettini P. A. (2008). The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40, 644–654. 10.1016/j.neuroimage.2007.11.059
    1. Bloss C. S., Courchesne E. (2007). MRI neuroanatomy in young girls with autism: a preliminary study. J. Am. Acad. Child Adolesc. Psychiatry 46, 515–523. 10.1097/chi.0b013e318030e28b
    1. Buckner R. L. (2013). The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron 80, 803–815. 10.1016/j.neuron.2013.10.044
    1. Buckner R. L., Krienen F. M., Castellanos A., Diaz J. C., Yeo B. T. (2011). The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurphysiol. 106, 2322–2345. 10.1152/jn.00339.2011
    1. Centers for Disease Control and Prevention (2017). Autism spectrum disorder (ASD). Data and Statistics. CDC Available online at: (Accessed 2014)
    1. Cole M. W., Pathak S., Schneider W. (2010). Identifying the brain’s most globally connected regions. Neuroimage 49, 3132–3148. 10.1016/j.neuroimage.2009.11.001
    1. Courchesne E., Yeung-Courchesne R., Press G. A., Hesselink J. R., Jernigan T. L. (1988). Hypoplasia of cerebellar vermal lobules VI and VII in autism. N. Engl. J. Med. 318, 1349–1354. 10.1056/nejm198805263182102
    1. Cox R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173. 10.1006/cbmr.1996.0014
    1. Cox R. W., Chen G., Glen D. R., Reynolds R. C., Taylor P. A. (2017). fMRI clustering and false-positive rates. Proc. Natl. Acad. Sci. U S A 114, E3370–E3371. 10.1073/pnas.1614961114
    1. D’Mello A. M., Stoodley C. J. (2015). Cerebro-cerebellar circuits in autism spectrum disorder. Front. Neurosci. 9:408. 10.3389/fnins.2015.00408
    1. Daniels A. M., Mandell D. S. (2014). Explaining differences in age at autism spectrum disorder diagnosis: a critical review. Autism 18, 583–597. 10.1177/1362361313480277
    1. Dean S. L., McCarthy M. M. (2008). Steroids, sex and the cerebellar cortex: implications for human disease. Cerebellum 7, 38–47. 10.1007/s12311-008-0003-6
    1. Dworzynski K., Ronald A., Bolton P., Happé F. (2012). How different are girls and boys above and below the diagnostic threshold for autism spectrum disorders? J. Am. Acad. Child Adolesc. Psychiatry 51, 788–797. 10.1016/j.jaac.2012.05.018
    1. Fischl B., Salat D. H., Busa E., Albert M., Dieterich M., Haselgrove C., et al. . (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355. 10.1016/S0896-6273(02)00569-X
    1. Floris D. L., Lai M.-C., Nath T., Milham M. P., Di Martino A. (2018). Network-specific sex differentiation of intrinsic brain function in males with autism. Mol. Autism 9:17. 10.1186/s13229-018-0192-x
    1. Glover G. H., Li T. Q., Ress D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167. 10.1002/1522-2594(200007)44:1<162::aid-mrm23>;2-e
    1. 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 disorders. Brain 135, 2711–2725. 10.1093/brain/aws160
    1. Halladay A. K., Bishop S., Constantino J. N., Daniels A. M., Koenig K., Palmer K., et al. . (2015). Sex and gender differences in autism spectrum disorder: summarizing evidence gaps and identifying emerging areas of priority. Mol. Autism 6:36. 10.1186/s13229-015-0019-y
    1. Hampson D. R., Blatt G. J. (2015). Autism spectrum disorders and neuropathology of the cerebellum. Front. Neurosci. 9:420. 10.3389/fnins.2015.00420
    1. Hogan M. J., Staff R. T., Bunting B. P., Murray A. D., Ahearn T. S., Deary I. J., et al. . (2011). Cerebellar brain volume accounts for variance in cognitive performance in older adults. Cortex 47, 441–450. 10.1016/j.cortex.2010.01.001
    1. Hull J. V., Jacokes Z. J., Togerson C. M., Irimia A., Van Horn J. D. (2017). Resting state functional connectivity in autism spectrum disorders: a review. Front. Psychiatry 7:205. 10.3389/fpsyt.2016.00205
    1. Ingudomnukul E., Baron-Cohen S., Wheelwright S., Knickmeyer R. (2007). Elevated rates of testosterone-related disorders in women with autism spectrum conditions. Horm. Behav. 51, 597–604. 10.1016/j.yhbeh.2007.02.001
    1. Iossifov I., O’Roak B. J., Sanders S. J., Ronemus M., Krumm N., Levy D., et al. . (2014). The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221. 10.1038/nature13908
    1. Jack A., Keifer C., Pelphrey K. A. (2017). Cerebellar contributions to biological motion perception in autism and typical development. Hum. Brain Mapp. 38, 1914–1932. 10.1002/hbm.23493
    1. Jacquemont S., Coe B. P., Hersch M., Duyzend M. H., Krumm N., Bergmann S., et al. . (2014). A higher mutational burden in females supports a “female protective model” in neurodevelopmental disorders. Am. J. Hum. Genet. 94, 415–425. 10.1016/j.ajhg.2014.02.001
    1. Jamison R., Bishop S. L., Huerta M., Halladay A. K. (2017). The clinician perspective on sex differences in autism spectrum disorders. Autism 21, 772–784. 10.1177/1362361316681481
    1. Jo H. J., Gotts S. J., Reynolds R. C., Bandettini P. A., Martin A., Cox R. W., et al. . (2013). Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. J. Appl. Math. 2013:935154. 10.1155/2013/935154
    1. Jo H. J., Saad Z. S., Simmons W. K., Milbury L. A., Cox R. W. (2010). Mapping sources of correlation in resting state fMRI, with artifact detection and removal. Neuroimage 52, 571–582. 10.1016/j.neuroimage.2010.04.246
    1. Khan A. J., Nair A., Keown C. L., Datko M. C., Lincoln A. J., Müller R. A. (2015). Cerebro-cerebellar resting-state functional connectivity in children and adolescents with autism spectrum disorder. Biol. Psychiatry 78, 625–634. 10.1016/j.biopsych.2015.03.024
    1. Koibuchi N., Ikeda Y. (2013). “Hormones and cerebellar development,” in Handbook of the Cerebellum and Cerebellar Disorders., eds Manto M., Schmahmann J. D., Rossi F., Gruol D. L., Koibuchi N. (Dordrecht: Springer; ), 319–339.
    1. Lai M. C., Baron-Cohen S., Buxbaum J. D. (2015a). Understanding Autism in the light of sex/gender. Mol. Autism 13:24. 10.1186/s13229-015-0021-4
    1. Lai M. C., Lombardo M. V., Auyeung B., Chakrabarti B., Baron-Cohen S. (2015b). Sex/gender differences and autism: setting the scene for future research. J. Am. Acad. Child Adolesc. Psychiatry 54, 11–24. 10.1016/j.jaac.2014.10.003
    1. Lai M. C., Lerch J. P., Floris D. L., Ruigork A. N., Pohl A., Lombardo M. V., et al. . (2017). Imaging sex/gender and autism in the brain: etiological implications. Neurosci. Res. 95, 380–397. 10.1002/jnr.23948
    1. Lainhart J. E., Bigler E. D., Bocian M., Coon H., Dinh E., Dawson G., et al. . (2006). Head circumference and height in autism: a study by the collaborative program of excellence in autism. Am. J. Med. Genet. A 140, 2257–2274. 10.1002/ajmg.a.31465
    1. Le Couteur A. L., Rutter M., Lord C., Rios P., Robertson S., Holdgrafer M., et al. . (1989). Autism diagnostic interview: a standardized investigator-based instrument. J. Autism Dev. Disord. 19, 363–387. 10.1007/bf02212936
    1. Lord C., Risi S., Lambrecht L., Cook E. H., Jr., Leventhal B. L., DiLavore P. C., et al. . (2000). The autism diagnostic observation schedule—generic: a standard measure of social and communication deficits associated with the spectrum of autism. J. Autism Dev. Disord. 30, 205–223. 10.1023/A:1005592401947
    1. Lord C., Rutter M., Le Couteur A. (1994). Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J. Autism Dev. Disord. 24, 659–685. 10.1007/bf02172145
    1. Mash L. E., Reiter M. A., Linke A. C., Townsend J., Müller R. A. (2018). Multimodal approaches to functional connectivity in autism spectrum disorders: an integrative perspective. Dev. Neurobiol. 78, 456–473. 10.1002/dneu.22570
    1. McCarthy M. M., Wright C. L. (2017). Convergence of sex differences and the neuroimmune system in autism spectrum disorder. Biol. Psychiatry 81, 402–410. 10.1016/j.biopsych.2016.10.004
    1. Menache I., Grange P., Larsen E. C., Banerjee-Basu S., Mitra P. P. (2013). Co-expression profiling of autism genes in the mouse brain. PLoS Comput. Biol. 7:e1003128. 10.1371/journal.pcbi.1003128
    1. Nguon K., Ladd B., Baxter M. G., Sajdel-Sulkowska E. M. (2005). Sexual dimorphism in cerebellar structure, function, and response to environmental perturbations. Prog. Brain Res. 148, 341–351. 10.1016/s0079-6123(04)48027-3
    1. Noonan S. K., Haist F., Müller R. A. (2009). Aberrant functional connectivity in autism: evidence from low-frequency BOLD signal fluctuations. Brain Res. 1262, 48–63. 10.1016/j.brainres.2008.12.076
    1. Olivito G., Lupo M., Laghi F., Clausi S., Baiocco R., Cercignani M., et al. . (2018). Lobular patterns of cerebellar resting state connectivity in adults with autism spectrum disorder. Eur. J. Neurosci. 47, 729–735. 10.1111/ejn.13752
    1. Pezoulas V. C., Zervakis M., Michelogiannis S., Klados M. A. (2017). Resting-state functional connectivity and network analysis of cerebellum with respect to crystallized IQ and gender. Front. Hum. Neurosci. 11:189. 10.3389/fnhum.2017.00189
    1. Picci G., Gotts S. J., Scherf K. S. (2016). A theoretical rut: revisiting and critically evaluating the generalized under/over-connectivity hypothesis of autism. Dev. Sci. 19, 524–549. 10.1111/desc.12467
    1. Riva D., Annunziata S., Contarino V., Erbetta A., Aquino D., Bulgheroni S. (2013). Gray matter reduction in the vermis and CRUS-II is associated with social and interaction deficits in low-functioning children with Autistic Spectrum Disorders: a VBM-DARTEL study. Cerebellum 12, 676–685. 10.1007/s12311-013-0469-8
    1. Robinson E. B., Lichtenstein P., Anckarsäter H., Happé F., Ronald A. (2013). Examining and interpreting the female protective effect against autistic behavior. Proc. Natl. Acad. Sci. U S A 110, 5258–5262. 10.1073/pnas.1211070110
    1. Saad Z., Reynolds R. C., Jo H. J., Gotts S. J., Chen G., Martin A., et al. . (2013). Correcting brain-wide correlation differences in resting-state fMRI. Brain Connect. 3, 339–352. 10.1089/brain.2013.0156
    1. Salomon R., Bleich-Cohen M., Hahamy-Dubossarsky A., Dinstein I., Weizman R., Poyurovsky M., et al. . (2011). Global functional connectivity deficits in schizophrenia depend on behavioral state. J. Neurosci. 31, 12972–12981. 10.1523/JNEUROSCI.2987-11.2011
    1. Sanders S. J., He X., Willsey A. J., Ercan-Sencicek A. G., Samocha K. E., Cicek E., et al. . (2015). Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233. 10.1016/j.neuron.2015.09.016
    1. Schmahmann J. D. (2010). The role of the cerebellum in cognition and emotion: personal reflections since 1982 on the dysmetria of thought hypothesis and its historical evolution from theory to therapy. Neuropsychol. Rev. 20, 236–260. 10.1007/s11065-010-9142-x
    1. Stoddard J., Gotts S. J., Brotman M. A., Lever S., Hsu D., Zarate C., et al. . (2016). Aberrant intrinsic functional connectivity within and between corticostriatal and temporal-parietal networks in adults and youth with bipolar disorder. Psychol. Med. 46, 1509–1522. 10.1017/s0033291716000143
    1. Talairach J., Tournoux P. (1988). Co-planar Stereotaxic Atlas of the Human Brain. New York, NY: Thieme.
    1. Tan D. W., Gilani S. Z., Mayberry M. T., Mian A., Hunt A., Walters M., et al. . (2017). Hypermasculinised facial morphology in boys and girls with autism spectrum disorder and its association with symptomatology. Sci. Rep. 7:9348. 10.1038/s41598-017-09939-y
    1. Taniai H., Nishiyama T., Miyachi T., Imaeda M., Sumi S. (2008). Genetic influences on the broad spectrum of autism: study of proband-ascertained twins. Am. J. Med. Genet. B Neuropsychiatr. Genet. 147B, 844–849. 10.1002/ajmg.b.30740
    1. Wang S. S. H., Kloth A. D., Badura A. (2014). The cerebellum, sensitive periods, and autism. Neuron 83, 518–532. 10.1016/j.neuron.2014.07.016
    1. White E. I., Wallace G. L., Bascom J., Armour A. C., Register-Brown K., Popal H. S., et al. . (2017). Sex differences in parent-reported executive functioning and adaptive behavior in children and young adults with autism spectrum disorder. Autism Res. 10, 1653–1662. 10.1002/aur.1811
    1. Willsey A. J., Sanders S. J., Li M., Dong S., Tebbenkamp A. T., Muhle R. A., et al. . (2013). Coexpression networks implicate human midfetal deep cortical projection in the pathogenesis of autism. Cell 155, 997–1007. 10.1016/j.cell.2013.10.020

Source: PubMed

3
購読する