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• W2968403417 abstract "•Binocular rivalry is slower in the autistic brain•Potential marker of E/I balance in visual cortex•Predicts clinical symptoms and classifies diagnostic status (autism versus controls)•Non-verbal measure, which may be suitable for infant and cross-species research Autism has traditionally been regarded as a disorder of the social brain. Recent reports of differences in visual perception have challenged this notion, but little evidence for altered visual processing in the autistic brain exists. We have previously observed slower behaviorally reported rates of a basic visual phenomenon, binocular rivalry, in autism [1Robertson C.E. Kravitz D.J. Freyberg J. Baron-Cohen S. Baker C.I. Slower rate of binocular rivalry in autism.J. Neurosci. 2013; 33: 16983-16991Crossref PubMed Scopus (95) Google Scholar, 2Robertson C.E. Ratai E.-M. Kanwisher N. Reduced GABAergic action in the autistic brain.Curr. Biol. 2016; 26: 80-85Abstract Full Text Full Text PDF PubMed Scopus (188) Google Scholar]. During rivalry, two images—one presented to each eye—vie for awareness, alternating back and forth in perception. This competition is modeled to rely, in part, on the balance of excitation and inhibition in visual cortex [3Laing C.R. Chow C.C. A spiking neuron model for binocular rivalry.J. Comput. Neurosci. 2002; 12: 39-53Crossref PubMed Scopus (247) Google Scholar, 4Seely J. Chow C.C. Role of mutual inhibition in binocular rivalry.J. Neurophysiol. 2011; 106: 2136-2150Crossref PubMed Scopus (67) Google Scholar, 5Said C.P. Heeger D.J. A model of binocular rivalry and cross-orientation suppression.PLoS Comput. Biol. 2013; 9: e1002991Crossref PubMed Scopus (73) Google Scholar, 6van Loon A.M. Knapen T. Scholte H.S. St John-Saaltink E. Donner T.H. Lamme V.A. GABA shapes the dynamics of bistable perception.Curr. Biol. 2013; 23: 823-827Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar, 7Noest A.J. van Ee R. Nijs M.M. van Wezel R.J.A. Percept-choice sequences driven by interrupted ambiguous stimuli: a low-level neural model.J. Vis. 2007; 7: 10Crossref PubMed Scopus (178) Google Scholar, 8Li H.-H. Rankin J. Rinzel J. Carrasco M. Heeger D.J. Attention model of binocular rivalry.Proc. Natl. Acad. Sci. USA. 2017; 114: E6192-E6201Crossref PubMed Scopus (40) Google Scholar], which may be altered in autism [2Robertson C.E. Ratai E.-M. Kanwisher N. Reduced GABAergic action in the autistic brain.Curr. Biol. 2016; 26: 80-85Abstract Full Text Full Text PDF PubMed Scopus (188) Google Scholar, 9Ma D.Q. Whitehead P.L. Menold M.M. Martin E.R. Ashley-Koch A.E. Mei H. Ritchie M.D. Delong G.R. Abramson R.K. Wright H.H. et al.Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism.Am. J. Hum. Genet. 2005; 77: 377-388Abstract Full Text Full Text PDF PubMed Scopus (258) Google Scholar, 10Sanders S.J. Ercan-Sencicek A.G. Hus V. Luo R. Murtha M.T. Moreno-De-Luca D. Chu S.H. Moreau M.P. Gupta A.R. Thomson S.A. et al.Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism.Neuron. 2011; 70: 863-885Abstract Full Text Full Text PDF PubMed Scopus (945) Google Scholar, 11Fatemi S.H. Reutiman T.J. Folsom T.D. Thuras P. GABA(A) receptor downregulation in brains of subjects with autism.J. Autism Dev. Disord. 2009; 39: 223-230Crossref PubMed Scopus (315) Google Scholar, 12Gogolla N. Takesian A.E. Feng G. Fagiolini M. Hensch T.K. Sensory integration in mouse insular cortex reflects GABA circuit maturation.Neuron. 2014; 83: 894-905Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar, 13Orefice L.L. Zimmerman A.L. Chirila A.M. Sleboda S.J. Head J.P. Ginty D.D. Peripheral mechanosensory neuron dysfunction underlies tactile and behavioral deficits in mouse models of ASDs.Cell. 2016; 166: 299-313Abstract Full Text Full Text PDF PubMed Scopus (185) Google Scholar, 14Marín O. Interneuron dysfunction in psychiatric disorders.Nat. Rev. Neurosci. 2012; 13: 107-120Crossref PubMed Scopus (740) Google Scholar]. Yet direct neural evidence for this potential marker of excitation/inhibition (E/I) balance in autism is lacking. Here, we report a striking alteration in the neural dynamics of binocular rivalry in individuals with autism. Participants viewed true and simulated frequency-tagged binocular rivalry displays while steady-state visually evoked potentials (SSVEPs) were measured over occipital cortex using electroencephalography (EEG). First, we replicate our prior behavioral findings of slower rivalry and reduced perceptual suppression in individuals with autism compared with controls. Second, we provide direct neural evidence for slower rivalry in autism compared with controls, which strongly predicted individuals’ behavioral switch rates. Finally, using neural data alone, we were able to predict autism symptom severity (ADOS) and correctly classify individuals’ diagnostic status (autistic versus control; 87% accuracy). These findings clearly implicate atypical visual processing in the neurobiology of autism. Down the road, this paradigm may serve as a non-verbal marker of autism for developmental and cross-species research. Autism has traditionally been regarded as a disorder of the social brain. Recent reports of differences in visual perception have challenged this notion, but little evidence for altered visual processing in the autistic brain exists. We have previously observed slower behaviorally reported rates of a basic visual phenomenon, binocular rivalry, in autism [1Robertson C.E. Kravitz D.J. Freyberg J. Baron-Cohen S. Baker C.I. Slower rate of binocular rivalry in autism.J. Neurosci. 2013; 33: 16983-16991Crossref PubMed Scopus (95) Google Scholar, 2Robertson C.E. Ratai E.-M. Kanwisher N. Reduced GABAergic action in the autistic brain.Curr. Biol. 2016; 26: 80-85Abstract Full Text Full Text PDF PubMed Scopus (188) Google Scholar]. During rivalry, two images—one presented to each eye—vie for awareness, alternating back and forth in perception. This competition is modeled to rely, in part, on the balance of excitation and inhibition in visual cortex [3Laing C.R. Chow C.C. A spiking neuron model for binocular rivalry.J. Comput. Neurosci. 2002; 12: 39-53Crossref PubMed Scopus (247) Google Scholar, 4Seely J. Chow C.C. Role of mutual inhibition in binocular rivalry.J. Neurophysiol. 2011; 106: 2136-2150Crossref PubMed Scopus (67) Google Scholar, 5Said C.P. Heeger D.J. A model of binocular rivalry and cross-orientation suppression.PLoS Comput. Biol. 2013; 9: e1002991Crossref PubMed Scopus (73) Google Scholar, 6van Loon A.M. Knapen T. Scholte H.S. St John-Saaltink E. Donner T.H. Lamme V.A. GABA shapes the dynamics of bistable perception.Curr. Biol. 2013; 23: 823-827Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar, 7Noest A.J. van Ee R. Nijs M.M. van Wezel R.J.A. Percept-choice sequences driven by interrupted ambiguous stimuli: a low-level neural model.J. Vis. 2007; 7: 10Crossref PubMed Scopus (178) Google Scholar, 8Li H.-H. Rankin J. Rinzel J. Carrasco M. Heeger D.J. Attention model of binocular rivalry.Proc. Natl. Acad. Sci. USA. 2017; 114: E6192-E6201Crossref PubMed Scopus (40) Google Scholar], which may be altered in autism [2Robertson C.E. Ratai E.-M. Kanwisher N. Reduced GABAergic action in the autistic brain.Curr. Biol. 2016; 26: 80-85Abstract Full Text Full Text PDF PubMed Scopus (188) Google Scholar, 9Ma D.Q. Whitehead P.L. Menold M.M. Martin E.R. Ashley-Koch A.E. Mei H. Ritchie M.D. Delong G.R. Abramson R.K. Wright H.H. et al.Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism.Am. J. Hum. Genet. 2005; 77: 377-388Abstract Full Text Full Text PDF PubMed Scopus (258) Google Scholar, 10Sanders S.J. Ercan-Sencicek A.G. Hus V. Luo R. Murtha M.T. Moreno-De-Luca D. Chu S.H. Moreau M.P. Gupta A.R. Thomson S.A. et al.Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism.Neuron. 2011; 70: 863-885Abstract Full Text Full Text PDF PubMed Scopus (945) Google Scholar, 11Fatemi S.H. Reutiman T.J. Folsom T.D. Thuras P. GABA(A) receptor downregulation in brains of subjects with autism.J. Autism Dev. Disord. 2009; 39: 223-230Crossref PubMed Scopus (315) Google Scholar, 12Gogolla N. Takesian A.E. Feng G. Fagiolini M. Hensch T.K. Sensory integration in mouse insular cortex reflects GABA circuit maturation.Neuron. 2014; 83: 894-905Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar, 13Orefice L.L. Zimmerman A.L. Chirila A.M. Sleboda S.J. Head J.P. Ginty D.D. Peripheral mechanosensory neuron dysfunction underlies tactile and behavioral deficits in mouse models of ASDs.Cell. 2016; 166: 299-313Abstract Full Text Full Text PDF PubMed Scopus (185) Google Scholar, 14Marín O. Interneuron dysfunction in psychiatric disorders.Nat. Rev. Neurosci. 2012; 13: 107-120Crossref PubMed Scopus (740) Google Scholar]. Yet direct neural evidence for this potential marker of excitation/inhibition (E/I) balance in autism is lacking. Here, we report a striking alteration in the neural dynamics of binocular rivalry in individuals with autism. Participants viewed true and simulated frequency-tagged binocular rivalry displays while steady-state visually evoked potentials (SSVEPs) were measured over occipital cortex using electroencephalography (EEG). First, we replicate our prior behavioral findings of slower rivalry and reduced perceptual suppression in individuals with autism compared with controls. Second, we provide direct neural evidence for slower rivalry in autism compared with controls, which strongly predicted individuals’ behavioral switch rates. Finally, using neural data alone, we were able to predict autism symptom severity (ADOS) and correctly classify individuals’ diagnostic status (autistic versus control; 87% accuracy). These findings clearly implicate atypical visual processing in the neurobiology of autism. Down the road, this paradigm may serve as a non-verbal marker of autism for developmental and cross-species research. Thirty-seven adult participants (18 autism and 19 age- and IQ-matched controls; Table S1) viewed true and simulated frequency-tagged binocular rivalry displays while steady-state visually evoked potentials (SSVEPs) were measured over occipital cortex using electroencephalography (EEG) (Figure 1A). During rivalry, activity levels in neuronal populations coding for left- and right-eye percepts rise and fall in alternation as the two images fluctuate in perceptual awareness [15Xu H. Han C. Chen M. Li P. Zhu S. Fang Y. Hu J. Ma H. Lu H.D. Rivalry-like neural activity in primary visual cortex in anesthetized monkeys.J. Neurosci. 2016; 36: 3231-3242Crossref PubMed Scopus (30) Google Scholar]. We first sought to identify this counterphase neural activity associated with rivalry in the human brain using EEG. To independently track the ebb and flow of neural activity corresponding to each eye during rivalry, we tagged the two images presented to each eye with a signature frequency (5.67 or 8.5 Hz) and measured activity in the two corresponding frequency bands over time (Figures 1A and S1) [16Zhang P. Jamison K. Engel S. He B. He S. Binocular rivalry requires visual attention.Neuron. 2011; 71: 362-369Abstract Full Text Full Text PDF PubMed Scopus (134) Google Scholar, 17Brown R.J. Norcia A.M. A method for investigating binocular rivalry in real-time with the steady-state VEP.Vision Res. 1997; 37: 2401-2408Crossref PubMed Scopus (99) Google Scholar]. As predicted, left- and right-eye signals fluctuated in counterphase during both rivalry trials (Figures 1B, 2A, and 2B) and control rivalry simulation trials (Figures 3A and 3B ): as one eye’s signal increased, the other eye’s signal decreased.Figure 2Slower Binocular Rivalry in the Autistic BrainShow full caption(A) We observed rivalry-like alternations in neural activity recorded from visual cortex in both individuals with and without autism: as one eye’s SSVEP signal increased, the other eye’s signal decreased. To quantify this antiphase relationship, we calculated the mean phase-locking values (PLVs) between the power in the left- and right-eye frequency bands (where a 0-degree PLV indicates perfectly in-phase signals and a 180-degree PLV indicates perfectly antiphase signals). Left- and right-eye signals were significantly antiphase during rivalry trials for both groups (both p < 0.001), and PLVs were significantly greater than mean PLV magnitudes derived from noise simulations (both p < 0.002).(B) For illustration purposes, left- and right-eye data are averaged ±5 s before and after each reported left-to-right eye perceptual switch for both groups. As can be seen, left- and right-eye signals alternated around the point of a perceptual switch for both groups: left-eye signals (solid lines) rose and fell around each perceptual switch, and right-eye signals (dotted lines) showed the opposite response pattern. Importantly, the average duration of one epoch of binocular rivalry was slower for individuals with autism, as compared with controls, as can be seen by comparing the zero crossings of the two left-eye signals (quantified in C). Shaded region represents ±1 SEM.(C) To quantify the rate of individual neural rivalry alternations, we calculated the characteristic frequency of rivalry measured from visual cortex (the Neural Rivalry Index [NRI]). The rate of both neural (left) and behavioral (right) binocular rivalry alternations for individuals with autism as compared with controls (both p < 0.01) is shown.In all plots, error bars represent 1 SEM. ∗∗p < 0.01; ∗∗∗p < 0.001 difference between the two groups. See also Figures S1 and S2 and Table S1.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure 3Comparable Visual Responses to Rivalry Simulation Control TrialsShow full caption(A) During rivalry simulation control trials, both eyes viewed the same frequency-tagged checkerboards at any given time. The two checkerboards, either red (e.g., 8.5 Hz) or green (e.g., 5.67 Hz), alternated back and forth on the screen throughout the duration of the trial. Similar to rivalry, during rivalry simulations, the power in left- and right-eye frequency bands was significantly antiphase throughout simulation trials for both groups (both p < 0.001) and PLVs were significantly greater than mean PLV magnitudes derived from noise simulations (both p < 0.001).(B) Data from the two frequency bands are averaged ±5 s before the time when participants’ reported the stimulus on the screen changing from a red (8.5 Hz) to a green (5.67 Hz) image, for illustration purposes. For both groups, power in the two frequency bands alternated around the point of a reported switch for both groups, as expected. Shaded region represent ±1 SEM.(C) Crucially (left), the rate of neural alternations (NRIs) during rivalry simulation control trials were comparable between individuals with and without autism (controls: 0.43 ± 0.01 Hz STE; autism: 0.42 ± 0.01 Hz STE; group difference: F(1,35) = 0.555; ηp2 = 0.016; p = 0.461). Similarly (right), behaviorally reported image changes were comparable between the two groups during rivalry simulation control trials (both p > 0.89).In all plots, error bars represent 1 SEM; n.s., p > 0.05; ∗∗∗p < 0.001. See also Figure S1.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) We observed rivalry-like alternations in neural activity recorded from visual cortex in both individuals with and without autism: as one eye’s SSVEP signal increased, the other eye’s signal decreased. To quantify this antiphase relationship, we calculated the mean phase-locking values (PLVs) between the power in the left- and right-eye frequency bands (where a 0-degree PLV indicates perfectly in-phase signals and a 180-degree PLV indicates perfectly antiphase signals). Left- and right-eye signals were significantly antiphase during rivalry trials for both groups (both p < 0.001), and PLVs were significantly greater than mean PLV magnitudes derived from noise simulations (both p < 0.002). (B) For illustration purposes, left- and right-eye data are averaged ±5 s before and after each reported left-to-right eye perceptual switch for both groups. As can be seen, left- and right-eye signals alternated around the point of a perceptual switch for both groups: left-eye signals (solid lines) rose and fell around each perceptual switch, and right-eye signals (dotted lines) showed the opposite response pattern. Importantly, the average duration of one epoch of binocular rivalry was slower for individuals with autism, as compared with controls, as can be seen by comparing the zero crossings of the two left-eye signals (quantified in C). Shaded region represents ±1 SEM. (C) To quantify the rate of individual neural rivalry alternations, we calculated the characteristic frequency of rivalry measured from visual cortex (the Neural Rivalry Index [NRI]). The rate of both neural (left) and behavioral (right) binocular rivalry alternations for individuals with autism as compared with controls (both p < 0.01) is shown. In all plots, error bars represent 1 SEM. ∗∗p < 0.01; ∗∗∗p < 0.001 difference between the two groups. See also Figures S1 and S2 and Table S1. (A) During rivalry simulation control trials, both eyes viewed the same frequency-tagged checkerboards at any given time. The two checkerboards, either red (e.g., 8.5 Hz) or green (e.g., 5.67 Hz), alternated back and forth on the screen throughout the duration of the trial. Similar to rivalry, during rivalry simulations, the power in left- and right-eye frequency bands was significantly antiphase throughout simulation trials for both groups (both p < 0.001) and PLVs were significantly greater than mean PLV magnitudes derived from noise simulations (both p < 0.001). (B) Data from the two frequency bands are averaged ±5 s before the time when participants’ reported the stimulus on the screen changing from a red (8.5 Hz) to a green (5.67 Hz) image, for illustration purposes. For both groups, power in the two frequency bands alternated around the point of a reported switch for both groups, as expected. Shaded region represent ±1 SEM. (C) Crucially (left), the rate of neural alternations (NRIs) during rivalry simulation control trials were comparable between individuals with and without autism (controls: 0.43 ± 0.01 Hz STE; autism: 0.42 ± 0.01 Hz STE; group difference: F(1,35) = 0.555; ηp2 = 0.016; p = 0.461). Similarly (right), behaviorally reported image changes were comparable between the two groups during rivalry simulation control trials (both p > 0.89). In all plots, error bars represent 1 SEM; n.s., p > 0.05; ∗∗∗p < 0.001. See also Figure S1. To quantify this counterphase relationship between left- and right-eye signals, we calculated the mean phase-locking values (PLVs) between the power in the left- and right-eye frequency bands, where a 0-degree PLV indicates perfectly in-phase signals and a 180-degree PLV indicates perfectly antiphase signals (Figure 2A). Left- and right-eye signals were significantly antiphase during rivalry trials (controls, top: 207.97° ± 9.18° STE; autism, bottom: 208.74° ± 14.48° STE; difference from 0 degrees: both p < 0.001; difference from 180 degrees: both p > 0.170; group difference: p = 0.50). Rivalry PLVs were also significantly “peaky,” or non-uniformly distributed around 180 degrees (Rayleigh test of non-uniformity, controls: Z = 5.06, p < 0.001; autism: Z = 9.07, p < 0.001). Further, PLVs during rivalry trials were comparable to those observed during rivalry simulation trials, where two frequency-tagged images were displayed in temporal alternation on the screen and therefore drove known, stimulus-locked antiphase responses (controls, top: 200.48° ± 19.99° STE; autism, bottom: 199.84° ± 13.84° STE; difference from rivalry PLVs: both p > 0.153; Figure 3A). In contrast, rivalry PLVs and vector magnitudes were significantly greater than those derived from noise simulations in both groups (both p < 0.002), emphasizing that the significant antiphase modulations we observed during rivalry trials are unlikely to occur by chance. These results demonstrate that robust, rivalry-like alternations in left- and right-eye signals were recorded over occipital cortex during our binocular rivalry experiment. We next developed a metric to quantify individual differences in rivalry alternations from these neurally derived signals. In brief, this Neural Rivalry Index (NRI) determines the characteristic frequency of the alternation in power between left- and right-eye signals during rivalry for each participant (STAR Methods). To validate the NRI metric, we compared individuals’ NRIs to behaviorally reported switch rates as well as to known image changes during rivalry simulation trials. The NRI strongly predicted participants’ perceptual switch rates in both groups (controls: Pearson’s R = 0.76, p < 0.001; autism: Pearson’s R = 0.54, p = 0.020; group difference p = 0.27; Figure 4A) and matched the rate of controlled image changes during rivalry simulation trials (controls: 0.43 ± 0.01 Hz STE; autism: 0.42 ± 0.01 Hz STE; ground truth: 0.5 Hz). NRIs during control trials were slightly slower than the true rate of controlled image changes (controls: t(18) = −6.97, p < 0.01; autism: t(17) = −10.72, p < 0.01). However, this loss was equal for the two groups (group difference in simulation NRIs: F(1,35) = 0.56; ηp2 = 0.016; p = 0.461) and therefore unlikely to mediate group differences during rivalry trials. Although rivalry-like alternations have been previously observed in humans using SSVEP [16Zhang P. Jamison K. Engel S. He B. He S. Binocular rivalry requires visual attention.Neuron. 2011; 71: 362-369Abstract Full Text Full Text PDF PubMed Scopus (134) Google Scholar, 17Brown R.J. Norcia A.M. A method for investigating binocular rivalry in real-time with the steady-state VEP.Vision Res. 1997; 37: 2401-2408Crossref PubMed Scopus (99) Google Scholar, 18Katyal S. Vergeer M. He S. He B. Engel S.A. Conflict-sensitive neurons gate interocular suppression in human visual cortex.Sci. Rep. 2018; 8: 1239Crossref PubMed Scopus (14) Google Scholar], to our knowledge, these results provide the first neural metric to quantify individual differences in perceptual alternation rates during rivalry. We next compared the rates of neural rivalry alternations (measured using NRIs) in individuals with and without autism. We observed markedly slower neural binocular rivalry alternations for individuals with autism as compared with controls (controls: 0.40 ± 0.01 Hz STE; autism: 0.35 ± 0.01 Hz STE; group difference: F(1,35) = 8.399; ηp2 = 0.194; p = 0.006; Figures 2B and 2C). This slower rate of rivalry in the autistic brain was directly mirrored in each group’s behaviorally reported switch rates, replicating our previous behavioral results of slower rivalry in autism [1Robertson C.E. Kravitz D.J. Freyberg J. Baron-Cohen S. Baker C.I. Slower rate of binocular rivalry in autism.J. Neurosci. 2013; 33: 16983-16991Crossref PubMed Scopus (95) Google Scholar, 2Robertson C.E. Ratai E.-M. Kanwisher N. Reduced GABAergic action in the autistic brain.Curr. Biol. 2016; 26: 80-85Abstract Full Text Full Text PDF PubMed Scopus (188) Google Scholar, 19Freyberg J. Robertson C.E. Baron-Cohen S. Reduced perceptual exclusivity during object and grating rivalry in autism.J. Vis. 2015; 15: 11Crossref PubMed Scopus (31) Google Scholar]. Specifically, although control individuals reported perceptual switches at 0.35 (switches per second) ± 0.02 Hz STE, switch rates for individuals with autism were nearly 60% of this speed at 0.21 ± 0.02 Hz STE (F(1,35) = 21.8; ηp2 = 0.384; p < 0.001; Figure 2C). Additionally, this slower rate of binocular rivalry in behavior was marked by a reduced proportion of perceptual suppression in individuals with autism (controls: 0.84 ± 0.02 STE; autism: 0.68 ± 0.04 STE; group difference: F(1,35) = 11.51; ηp2 = 0.248; p = 0.002; Figure S2). In contrast, comparable behavioral switch rates were observed during rivalry simulation control trials, indicating comparable task understanding between our two groups (p = 0.889; Figure 3C). These results provide a direct neural readout of slower binocular rivalry in individuals with autism, obtained without any need for participant reporting. This neural marker of slower binocular rivalry dynamics in autism predicted clinical measures of autistic symptomatology. Individuals with slower rivalry dynamics in the brain showed higher autistic symptoms (ADOS social subscale; Rho = −0.48, p = 0.045; ADOS total; Rho = −0.44, p = 0.064; Figure 4B), although a self-report scale of autistic traits (AQ) did not predict rivalry dynamics in either group (both p > 0.34). No relationship was observed between IQ and either neural (both p > 0.176) or behavioral switch rates (both p > 0.127) in either group, indicating that these effects were independent of individual differences in general intelligence, on which the groups were matched (Table S1). Consistent with our previous findings [1Robertson C.E. Kravitz D.J. Freyberg J. Baron-Cohen S. Baker C.I. Slower rate of binocular rivalry in autism.J. Neurosci. 2013; 33: 16983-16991Crossref PubMed Scopus (95) Google Scholar, 2Robertson C.E. Ratai E.-M. Kanwisher N. Reduced GABAergic action in the autistic brain.Curr. Biol. 2016; 26: 80-85Abstract Full Text Full Text PDF PubMed Scopus (188) Google Scholar], these results indicate that this relatively low-level perceptual marker of autism is associated with clinically measured autistic traits defined at much more complex levels of behavior. Crucially, these results could not be explained by group differences in SSVEP signal quality or the duration of general (non-rivalrous) evoked visual responses. First, for both groups, signal was high and significantly greater than noise throughout the experiment for both frequencies (autism 5.67 Hz: t(17) = 11.03, p < 0.001; autism 8.5 Hz: t(17) = 10.92, p < 0.001; controls 5.67 Hz: t(18) = 8.48, p < 0.001; controls 8.5 Hz: t(18) = 10.14, p < 0.001; 5.67 Hz group difference: F(1,35) = 0.11, ηp2 = 0.003, p = 0.742; 8.5 Hz group difference: F(1,35) = 3.428, ηp2 = 0.089, p = 0.073; Figure S1). Second, the rate of neural alternations (NRIs) during rivalry simulation control trials, where binocularly viewed images were displayed in temporal alternation on the screen, were comparable between individuals with and without autism (controls: 0.43 ± 0.01 Hz STE; autism: 0.42 Hz ± 0.01 Hz STE; group difference: F(1,35) = 0.555; ηp2 = 0.016; p = 0.461; Figures 3B and 3C). Finally, group differences in rivalry NRIs could not reflect group differences in neural signals associated with motor responses, rather than visually evoked responses, as NRIs specifically compare power in the two visually evoked frequency bands (8.5 Hz and 5.67 Hz) rather than in the much slower frequency bands associated with button-press responses (controls: 0.35 Hz and autism: 0.21 Hz). These results indicate that the slower rate of binocular rivalry we observed in the autistic brain cannot be accounted for by differences in rivalry signal quality, the duration of evoked visual responses to non-rivalrous stimuli, or motor responses between the groups. Finally, we asked whether our measurements of binocular rivalry dynamics in the autistic brain could accurately classify an individual’s diagnostic status using a linear support vector machine classifier. Using a leave-one-out cross-validation procedure with individuals’ trial-averaged NRIs and frequency-tagged amplitudes as features, we were able to classify an individual’s diagnostic status (autism versus control) with 86.5% accuracy (±0.06 STE; sensitivity = 0.83; specificity = 0.89; p < 0.001; Figure 4C). Thus, this basic alteration in the autistic visual cortex is not only correlated with higher-order autistic symptoms in social cognition but also predictive of diagnostic status (autism versus controls). Notably, this accuracy level is comparable to the results of classification analyses using hallmark autistic traits in social behavior, such as eye-to-mouth gaze preferences in toddlers with autism (classification accuracy = 86%) [20Constantino J.N. Kennon-McGill S. Weichselbaum C. Marrus N. Haider A. Glowinski A.L. Gillespie S. Klaiman C. Klin A. Jones W. Infant viewing of social scenes is under genetic control and is atypical in autism.Nature. 2017; 547: 340-344Crossref PubMed Scopus (153) Google Scholar]. Our findings demonstrate a slower rate of binocular rivalry in the autistic brain. Importantly, this finding was specific to dichoptic, rivalrous displays, where two images compete for perceptual awareness. In contrast, neural alternations elicited by rivalry simulation trials, where binocularly viewed images were displayed in temporal alternation on the screen, were comparable between groups. This pattern of results underscores that visual processing is not generally altered in autism but perhaps specifically altered by visual processes that tax competitive interactions in visual cortex, such as rivalry [3Laing C.R. Chow C.C. A spiking neuron model for binocular rivalry.J. Comput. Neurosci. 2002; 12: 39-53Crossref PubMed Scopus (247) Google Scholar, 4Seely J. Chow C.C. Role of mutual inhibition in binocular rivalry.J. Neurophysiol. 2011; 106: 2136-2150Crossref PubMed Scopus (67) Google Scholar, 5Said C.P. Heeger D.J. A model of binocular rivalry and cross-orientation suppression.PLoS Comput. Biol." @default.
• W2968403417 created "2019-08-22" @default.
• W2968403417 creator A5008937403 @default.
• W2968403417 creator A5050643352 @default.
• W2968403417 creator A5050844896 @default.
• W2968403417 creator A5069283078 @default.
• W2968403417 date "2019-09-01" @default.
• W2968403417 modified "2023-10-16" @default.
• W2968403417 title "Slower Binocular Rivalry in the Autistic Brain" @default.
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