Abstract
Atypical sensory processing is a core characteristic in autism spectrum disorders1 that negatively impacts virtually all activities of daily living. Sensory symptoms are predictive of the subsequent appearance of impaired social behavior and other autistic traits2, 3. Thus, a better understanding of the changes in neural circuitry that disrupt perceptual learning in autism could shed light into the mechanistic basis and potential therapeutic avenues for a range of autistic symptoms2. Likewise, the lack of directly comparable behavioral paradigms in both humans and animal models currently limits the translational potential of discoveries in the latter. We adopted a symptom-to-circuit approach to uncover the circuit-level alterations in the Fmr1-/- mouse model of Fragile X syndrome (FXS) that underlie atypical visual discrimination in this disorder4, 5. Using a go/no-go task and in vivo 2-photon calcium imaging in primary visual cortex (V1), we find that impaired discrimination in Fmr1-/- mice correlates with marked deficits in orientation tuning of principal neurons, and a decrease in the activity of parvalbumin (PV) interneurons in V1. Restoring visually evoked activity in PV cells in Fmr1-/- mice with a chemogenetic (DREADD) strategy was sufficient to rescue their behavioral performance. Finally, we found that human subjects with FXS exhibit strikingly similar impairments in visual discrimination as Fmr1-/- mice. We conclude that manipulating orientation tuning in autism could improve visually guided behaviors that are critical for playing sports, driving or judging emotions.
For these studies, we focused on FXS because it is the leading inherited cause of autism5, because there are no associated major neuroanatomical defects, and because a single, well-characterized animal model, the Fmr1-/- mouse, is widely used. Although many human psychophysical studies have demonstrated deficits in visual perception in individuals with autism, including those with FXS6, 7, whether animal models of autism also exhibit impaired visual processing is not known. Thus, we first sought to determine whether Fmr1-/- mice manifest perceptual learning deficits associated with abnormal visual sensory discrimination. We trained male and female Fmr1 knockout (Fmr1-/-; n= 21) and wild-type (WT; n= 19) mice (FVB strain) on a go/no-go visual discrimination task8, 9. Following water deprivation, awake head-restrained young adult mice (2-4 months old) were allowed to run on an air-suspended polystyrene ball while they performed the task (Fig. 1a, b; see Materials and Methods). Mice were presented with sinusoidal gratings drifting in two orthogonal directions, 45° (preferred, ‘go’) vs. 135° (non-preferred, ‘no-go’) at 100% contrast. Incorrect behavioral responses resulted in a 6.5 s ‘time-out’ period (Fig.1c). Task performance, as determined by the discriminability index statistic d’ (Materials and methods), was dependent on primary visual cortex (V1), because pharmacological silencing of V1 with bilateral infusions of muscimol, a GABA-A receptor agonist, reversibly disrupted perceptual learning in WT mice (Fig. 1d).
WT mice learned quickly (3-4 sessions) to lick in response to the preferred orientation for a water reward and withhold licking when presented with the non-preferred orientation (Fig. 1e). In contrast, Fmr1-/- mice exhibited a significantly delayed learning curve, compared to the WT mice (Fig. 1e and Suppl. Fig. 1; F1,29= 1.76, p= 0.0002, two-way ANOVA with repeated measures on training). Fmr1-/- mice exhibited a significantly higher percentage of false alarm (FA) responses compared to WT mice at session #4 (Fig. 1f and Suppl. Fig. 2; 11.3 ± 2.2% in WT vs. 22.3 ± 3.1% in Fmr1-/-; p= 0.009, Students t-test), which likely contributed to their poor performance during early training sessions. This increase in FA rates in Fmr1-/- mice was not caused by hyperactivity or abnormal locomotion, as the average running speed was similar between mice of both genotypes (not shown) and there was no change in running speed in mice of either genotype during the course of each trial on session 1 (Suppl. Fig. 3c). However, by session 4, we observed significantly more slowing down in WT than in Fmr1-/- mice, towards the end of each trial (Suppl. Fig. 3c). The delay of Fmr1-/- mice in learning the visual discrimination task was evident in female and male mice alike (Suppl. Fig. 4). Importantly, both WT and Fmr1-/- mice exhibited significant improvements in task performance throughout training (F(4,116)= 4.63, p< 10-14, two-way ANOVA with repeated measures on training session). Even though Fmr1-/- mice took, on average, 2.5 sessions longer to achieve a d’ > 2 (Fig. 1e; 3.5 ± 0.2 for WT vs. 6.0 ± 0.4 for Fmr1-/-; p= 4.3 x 10-6, t-test), there was no significant difference in the final d’ values between WT and Fmr1-/- mice (Fig.1 g). Thus, Fmr1-/- mice eventually achieve the same level of performance in this visual discrimination task as WT controls. Notably, when we reduced the contrast of gratings, Fmr1-/- mice did not exhibit obvious impairments in visual perception, at least down to 10% contrast (Suppl. Fig. 5), suggesting that their delayed learning was not due to a primary visual deficit.
Fmr1-/- mice are known to exhibit a broadening of receptive fields in somatosensory cortex10-12. Similar broader tuning in V1, if it exists, could affect the discrimination of visual stimuli with very similar orientations. Therefore, we next tested whether Fmr1-/- mice would be particularly challenged by a reduced angle task, in which the difference in angle between the preferred and non-preferred orientation was gradually reduced to 7.5o, after the animals had learned the basic 90o task (Fig. 1h). A difference in orientation angle of 15o did not impair the performance of either WT or Fmr1-/- mice (n= 10 for each); however, a further reduction down to 10o resulted in a significant reduction in d’ values of Fmr1-/- mice, but not in WT controls (Fig. 1i; interaction effect, F(1,18)= 7.42, p= 0.01, ANOVA; d’ at 10o was 2.3 ± 0.1 for WT vs. 1.2 ± 0.2 for Fmr1-/-; post-hoc t-test, p= 0.0007). A reduction in the angle difference to 7.5o further impaired the performance of Fmr1-/- mice, but also led to a decrease in d’ in some of the WT mice (Suppl. Fig. 6). To further probe the extent to which mice were challenged by this reduced angle task, we assessed their response times and observed a significant delay in the distribution of licking onset in Fmr1-/- mice, compared to WT mice, for the reduced angle task, but not for the normal (90o) task (Fig. 1j, K-S test, p= 0.009). This suggests that Fmr1-/- mice take longer to make a decision only in the face of ambiguous sensory information.
Having established a defect in perceptual learning in the fragile X mouse model that is relevant to the human disease, we next adopted a reverse engineering approach to identify the circuit- and neuronal-level alterations that might underlie the impaired visual discrimination. In light of various reports of cortical hyperexcitability and network hypersynchrony in Fmr1-/- mice13-15, we first investigated whether the perceptual learning deficit we observed in Fmr1-/- mice, was caused by abnormal orientation tuning of pyramidal cells in V1. To test this, we performed in vivo 2-photon calcium imaging in layer (L) 2/3 neurons in awake mice running on a floating polystyrene ball (Fig. 2a-c; Materials and methods). A rAAV to express GCaMP6s16 was injected in V1 following stereotaxic coordinates, and successful targeting was confirmed using intrinsic signal imaging (Fig. 2b). We recorded both spontaneous and visually evoked activity in L2/3 neurons (Fig. 2c). For the latter, WT and Fmr1-/- mice (n= 9 and 10, respectively) were presented with 4 sets of sinusoidal gratings drifting in 8 different directions, at random (Fig. 2d; Materials and methods). Although previous studies have reported hyperexcitable cortical circuits in Fmr1-/- mice (reviewed in 13), we did not observe a significant increase in either spontaneous or visually evoked activity in Fmr1-/- mice (Fig. 2e and Suppl. Fig. 7a, b). We did find a trend toward higher population coupling in Fmr1-/- mice (Suppl. Fig. 7c), which is consistent with published results showing local circuit hypersynchrony in these mice14.
Despite the seemingly normal frequency of visually evoked activity in Fmr1-/- mice, mutant mice had a significantly lower percentage of orientation selective (OS) cells in L2/3 (Fig. 2f; 49.5 ± 2.1% in WT vs. 31.2 ± 4.6% in Fmr1-/-; p= 0.003, t-test). In other words, on average, pyramidal neurons in Fmr1-/- mice were tuned to multiple orientations. Importantly, when we trained these mice on the visual discrimination task, we found a significant inverse correlation between the percentage of OS cells and the number of days it took animals to reach a d’ > 2 (Fig. 2g; r= -0.605, p= 0.006). This implies that, with fewer available OS cells in V1, Fmr1-/- mice had more difficulty discriminating between two different orientations, particularly when the difference was small (Fig 1 h-j). In addition, in vivo calcium imaging revealed that L2/3 neurons in V1 of Fmr1-/- mice had a significantly broader tuning compared to those in WT mice (Fig. 2h; 36.7 ± 1.0o in WT vs. 43.3 ± 1.4o in Fmr1-/-; p= 0.002, t-test). This 6.6o difference in the mean tuning width of pyramidal neurons in V1 between WT and Fmr1-/- mice, though slight, might be sufficient to explain why Fmr1-/- mice can discriminate at 15o but not at 10o. Critically, we also found a significant correlation between the tuning width of L2/3 cells and the number of days it took the animals to reach a d’ > 2 (Suppl. Fig. 8; r= 0.48, p= 0.041).
Abnormal V1 network dynamics pertaining to orientation selectivity and tuning width could be the result of dysfunction in parvalbumin (PV) interneurons, the most prevalent inhibitory neuron in V117. PV cells exhibit very broad orientation tuning by simply responding to all orientations, since they receive local input from a wide range of orientation tuned pyramidal cells18-20. Furthermore, selective stimulation of PV cells in V1 with channelrhodopsin-2 leads to improved feature selectivity and visual discrimination9. For these reasons, we tested the hypothesis that PV cells were hypoactive in fragile X mice. We used in vivo calcium imaging to record the activity of PV neurons in V1 of WT and Fmr1-/- mice (n= 6 and 7, respectively) that expressed Td-Tomato in PV neurons (PV-Cre mice x ai9 mice; see Materials and methods). At the time of the cranial window surgery, we injected a Cre-dependent virus into V1, to selectively express GCaMP6s in PV cells (Fig. 3a, b). Our calcium imaging recordings revealed stark differences in the activity of PV cells between WT and Fmr1-/- mice; whereas traces of PV cell activity in WT mice showed the expected broadly tuned, non-selective responses to visual stimuli, traces of PV cells in Fmr1-/- mice exhibited little visually evoked activity (Fig. 3c). While there was no significant difference in the amplitudes of calcium transients in PV cells in Fmr1-/- mice (Fig. 3d), we found a significantly lower frequency of events triggered by visual stimuli (Fig. 3e; 1.8 ± 0.5 for WT vs. 0.3 ± 0.1 for Fmr1-/-; p= 0.02, t-test). One of our criteria for selecting PV cells for analysis in both WT and Fmr1-/- mice was that they exhibit at least one calcium transient in the recordings (Materials and methods), and neither the proportion of active PV cells (Fig. 3f; p= 0.5, t-test), nor the amplitude and frequency of spontaneous calcium transients in PV cells, were significantly different between WT and Fmr1-/- mice (Suppl. Fig. 9). We also found a significantly lower fraction of stimulus-responsive PV cells in Fmr1-/- mice (Fig. 3g; 0.7 ± 0.02 for WT vs. 0.4 ± 0.03 for Fmr1-/-; p < 10-5, t-test), which would also ultimately be expected to affect the functional output of V1.
Based on the finding that PV cells were indeed hypoactive in Fmr1-/- mice, we hypothesized that a successful manipulation of PV cell activity that would restore their output in these animals, might also improve their performance on the visual discrimination task. Hence, we used a Designer Receptors Exclusively Activated by Designer Drugs (DREADD)21 approach (see Materials and methods) to selectively express the excitatory hM3Dq receptor in PV cells of Fmr1-/- mice (n= 6; Fig. 3h). We then used the hM3Dq ligand, clozapine-N-oxide (CNO, 5 mg/kg, i.p.), to excite PV cells and increase their output in these Fmr1-/-, hM3Dq mice. Overexpressing hM3Dq in PV cells alone (before administering CNO) did not affect visually evoked activity of PV cells in Fmr1-/-, hM3Dq mice (Suppl. Fig. 10a-d). In contrast, 30 min after a single CNO injection, we observed a robust increase in visually evoked PV cell output in these Fmr1-/-, hM3Dq mice (Fig. 3i-k, Suppl. Fig. 10e). Specifically, we observed a significant increase in both the z-score of the amplitude of visually evoked calcium transients in PV cells of Fmr1-/-, hM3Dq mice (Fig. 3i; 7.9 ± 0.3 before CNO vs. 8.9 ± 0.3 after CNO; p= 0.03, t-test), and in the frequency of those events (Fig. 3j; 0.3 ± 0.04 before CNO vs. 0.6 ± 0.1 after CNO; p= 0.03, t-test). The fraction of stimulus responsive PV cells in Fmr1-/-, hM3Dq mice was also significantly increased by CNO, restoring it to WT levels (Fig. 3k; 0.4 ± 0.06 before CNO vs. 0.7 ± 0.04 after CNO; p= 0.001, t-test). Importantly, the fact that we could increase the activity of PV cells with DREADDs supports the notion that PV cells were not silent in Fmr1-/- mice due to poor health. Also, the proportion of PV cells that was active did not change after CNO administration (not shown), suggesting that the DREADD effect on the fraction of visually responsive PV neurons was not due to simply making previously silent cells more active.
Having restored visually evoked PV cell activity in Fmr1-/-/, hM3Dq mice to near normal WT levels, we hypothesized that we might be able to reverse the delay in learning the visual discrimination task. A subset of the DREADD-expressing Fmr1-/- mice were therefore trained on the standard visual discrimination task (90o angle) and injected with CNO, ~30 min prior to each training session. This chemogenetic manipulation resulted in a leftward shift in the learning curve (i.e., faster learning) of CNO-treated Fmr1-/-, hM3Dq mice (Fig. 3l), indicating that we were able to rescue the learning impairment by acutely elevating the PV cell output. CNO led to a significant reduction in the number of days required to reach expert level (d’ > 2) on the visual discrimination task compared to Fmr1-/- mice (Fig. 3m; 6.0 ± 0.4 d for Fmr1-/- vs. 3.7 ± 0.3 d Fmr1-/-, hM3Dq with CNO; F2,43= 1.7, p< 10-5, one-way ANOVA), which was comparable to the WT mice (3.5 ± 0.2; p= 0.53). To come full circle back to OS cells in V1, we also tested whether the DREADD manipulation on PV cells would be sufficient to affect the properties of pyramidal neurons in the circuit. Calcium imaging with rAAV-GCaMP6s in a group of Fmr1-/-, hM3Dq mice revealed that CNO administration significantly raised the proportion of orientation selective pyramidal cells and showed a trend towards sharper tuning (Fig. 3n; p= 0.03 and 0.07, respectively; t-test). Notably, the relationship between PV cell output and behavior was apparent from the negative correlation between the fraction of stimulus responsive PV cells and the number of days needed to reach a d’ > 2 (Fig. 3o; r -0.753, p= 0.0007). This relationship showed clearly how Fmr1-/-, hM3Dq mice treated with CNO were not distinguishable from WT mice.
It was recently argued that the absence of directly comparable behavior paradigms between human and animal studies is a real impediment to progress in translational research for autism2. It might even explain, in part, the failure of clinical trials in FXS22. In order to assess the translational potential of our findings of impaired visual discrimination (and, by extension, the associated circuit dysfunction) in a mouse model of FXS, we next asked whether the same perceptual learning task could be applied to humans with FXS. We implemented the same paradigm as in mice with relatively minor modifications, to make it suitable for individuals with FXS (Fig. 4a, b; Materials and methods). Healthy control human participants and FXS participants (n= 8 each; see Suppl. Table 1) were administered the task. Healthy controls learned the basic 90o task with high discriminability very quickly (within the first ten trials) in a single training session (Fig. 4c). Performance declined slightly in some healthy control participants at reduced angles, but on average, this was not significant. In contrast, FXS participants showed significantly lower d’ at the 15o task compared with 90 o or 45 o tasks (p= 0.002; repeated measures ANOVA). Thus, FXS participants and Fmr1-/- mice exhibit strikingly similar visual perception deficits for ambiguous stimuli with similar orientations. This suggests that a discrimination task like the one we used could eventually be used as biologically-based measure of sensory processing in human clinical trials.
Progress in autism research is limited by the lack of clearly identified circuit-level alterations that can explain the neuropsychiatric phenotype that characterizes the disorder. Though circuit activity in monogenetic murine models of ASD can be readily interrogated and manipulated, there is increasing interest to demonstrate both face validity and predictive validity for these translational approaches to be used in clinical trials2. To bridge this gap, we implemented a fully translatable behavioral assay of sensory processing in both Fmr1-/- mice and FXS patients, and followed a symptom-to-circuit approach to delineate specific circuit-level defects using calcium imaging in V1. Our discovery that Fmr1-/- mice have a reduced proportion of orientation selective neurons with abnormally broad tuning in V1, as well as extremely hypoactive PV cells, provides a mechanistic understanding of their visual discrimination deficits. The fact that we could rescue their perceptual deficits in mice by restoring activity in PV cells with DREADDs and that humans with FXS exhibit analogous deficits in visual discrimination, provides a realistic path for novel translational clinical trials.
Our data implicates a role for PV cells in circuit dysfunction in FXS through converging evidence of their hypoactivity from experiments in two different groups of mice (Fmr1-/- and Fmr1-/-, hM3Dq), and from the DREADD approach, which not only restored PV cell activity, but also raised the percentage of orientation selective pyramidal cells in V1. It is exciting to consider that PV cell dysfunction could be involved in other aspects of FXS, such as impaired neuronal adaptation in tactile defensiveness23. Additionally, by rapidly translating this paradigm into a clinical population, we have substantially reduced potential barriers to further study whether PV cell dysfunction represents an important aspect (or even the principal one) of a canonical micro-circuitry in autism2.
METHODS SUMMARY
Head-fixed, water-deprived, adult male and female WT and Fmr1-/- mice (2-4 months; FVB strain) were trained on a go/no-no task wherein they learned to discriminate between sinusoidal gratings drifting at two different orientations, 90o apart. After learning the basic task, mice were advanced to a reduced angle task, in which the angle separating the orientations of preferred and non-preferred stimuli was reduced to 15o, 10 o and 7.5 o. A subset of these mice received an injection of rAAV-GCaMP6s and a cranial window surgery, and then underwent two-photon calcium imaging at 15 Hz using a custom resonant scanning microscope. For PV cell imaging, we used PV-Cre x ai9 mice (Td-Tomato) and injected rAAV-syn-GCaMP6s or rAAV-fl-STOP-fl-GCaMP6s into V1. Analysis of calcium signals was performed with custom routines written in MATLAB. For DREADD experiments, we injected rAAV-EF1a-DIO-hM3D(Gq)-mCherry into PV-Cre x Fmr1-/- mice at the time of the cranial window surgery. We also trained these mice on the basic visual discrimination task. For human studies, we administered an analogous discrimination task to adolescent and adult participants with FXS and to age-matched healthy controls.
See below for a full description of methods.
Author contributions
A.G. and C.P-C. conceived the project and designed the experiments with help from J.G., L.S. and C.E. for the human studies. A.G. developed the behavioral paradigm for mice and humans. A.G. and D.A.C. wrote the MATLAB code for analysis. A.G, G.C., A.N., L.S. and J.G. conducted the experiments and analyzed the data. A.G., C.E. and C.P-C. interpreted the data and wrote the paper with input from other authors.
Author information
The authors declare no competing financial interests. Correspondence and requests for materials should be addressed to C.P.-C. (cpcailliau{at}mednet.ucla.edu).
Acknowledgements
The authors thank Kaela Cohan, Steve Cohen and Michael Hong for help with early behavioral experiments, Peyman Golshani and Michael Einstein for advice on mouse behavior, the Janelia GENIE project (GCaMP6s), Peter Yu for building custom lickports, as well as Dean Buonomano, Alcino Silva, Anis Contractor and John Sweeney for feedback on the manuscript. Kim Battista created the illustration in Fig. 1b. This work was supported by the following grants: W81XWH-14-1-0433 (USAMRMC, DOD), Developmental Disabilities Translational Research Program #20160969 (John Merck), SFARI Award 295438 (Simons Foundation) and 5R01HD054453 (NICHD/NIH) awarded to CP-C; K23 MH112936 (NIMH/NIH) to EP; a grant from the Fragile X Alliance of Ohio to CAE; U01 DD001185 (NCBDDD/NIH), U54 HD082008 (NICHD/NIH) and a grant from the Cincinnati Children’s Hospital Research Foundation to EP and CAE.