A human electrophysiological signature of Fragile X pathophysiology is shared in V1 of Fmr1^-/y mice

Domain: neuroscience
Published: 2026-02-09
Editorial outcome: major_revision

Abstract
Predicting clinical therapeutic outcomes from animal studies using conserved electrophysiological phenotypes could facilitate developing treatments for neuropsychiatric disorders. Alpha oscillations in human resting-state electroencephalogram recordings are altered in many disorders, but whether these disruptions exist in mouse models is unknown. Here, we employed a uniform analytical method to show in males with fragile X syndrome (FXS) that alpha oscillations in humans and alpha-like oscillations in the visual cortex of Fmr1-/y mice are slowed, with a stronger phenotype in adults than juveniles and a juvenile-specific power phenotype in both species. We find that alpha-like oscillations are disrupted by deletion of Fmr1 in cortical excitatory neurons and glia, reflect differential activity of two classes of GABAergic interneurons, and are more sensitive to activation of GABAB receptors by Arbaclofen in wild-type than Fmr1-/y mice. Our framework reveals evolutionary conservation of alpha oscillation disruptions, enables a deeper understanding of FXS pathophysiology, and narrows the gap between treatment promise and practice.

Introduction
Fragile X syndrome (FXS) is the leading inherited cause of intellectual disability (ID) and autism spectrum disorder (ASD)1,2. In most cases, FXS is caused by transcriptional silencing of the FMR1 gene and loss of the protein product, fragile X messenger ribonucleoprotein (FMRP)3–5. Because it is monogenic, genetically engineered animal models of FXS are available for preclinical research5–7. Studies in Fmr1-/y mice have revealed key aspects of neural pathophysiology and provided an opportunity to test candidate therapeutics8–10. To date, however, the molecules nominated to treat FXS have failed to meet prospectively defined clinical trial endpoints in humans11. A mismatch between preclinical promise and clinical outcomes is certainly not unique to FXS11,12. Given the wide differences in animal and human behavior, a bridge between the preclinical and clinical domains that measures shared alterations in circuit function is needed to better predict clinical trial outcomes11–14.

Conserved electrophysiological phenotypes captured through field potential recordings of brain activity could be this bridge, as they are quantitative measures of pathophysiology and therapeutic target engagement that can be readily measured and consistently analyzed across species, a foundational requirement for translational biomarkers14–17. In children and adults with FXS, and in Fmr1-/y rodent models, resting-state electroencephalogram (rsEEG) recordings reveal elevated power in the gamma (30–60 Hz) frequency band (gamma phenotype)18–29. Adults with FXS also have slower alpha (8–13 Hz) oscillations18,19,21,30, and, critically, alpha band disruptions demonstrate clinical correlations19 and are more reliable across individuals and testing sessions than gamma power disruptions31–33. Although less well characterized, there are also reports of slower background activity (i.e., alpha oscillations) in children with FXS34,35. However, this alpha phenotype has yet to be characterized in genetically engineered rodent models of FXS.

A more reliable, lower frequency translational measure of FXS pathophysiology and treatment response would be practically important given the challenges in isolating higher frequency signals from human EEG, particularly in infants. Moreover, since alpha oscillations regulate sensory processing36,37 and are the most prominent rsEEG signal in humans, their disruption also fundamentally contributes to the pathology of ID38,39, ASD40–42, schizophrenia43–46, and other neuropsychiatric disorders47,48. However, as is the case for FXS, these disruptions have not yet been identified in preclinical models of any of these disorders. Thus, identifying the FXS alpha phenotype in mice could provide a framework for future study of the mechanistic bases of these alpha oscillation disruptions and perhaps give insight into the pathophysiology of other conditions if the shared phenotype is a broader marker of neural dysfunction.

In the current study, we characterized the cross-sectional developmental trajectory of rsEEG phenotypes in male humans with FXS using spectral analysis methods that forgo traditional frequency band delineations. This approach facilitated the study of oscillations across development, and, crucially, enabled us to identify and characterize a correlate of the alpha phenotype in primary visual cortex (V1) of male Fmr1-/y mice. Intracortical field potential (LFP) recordings in layer (L) 4 of V1 of juvenile and adult mice revealed similar age-related changes in the alpha phenotype as for humans with FXS, a phenotype in the power of the alpha-like oscillations specific to juveniles in both species, and additional alterations in the power and temporal dynamics of the oscillations not detectable from the surface. Using mouse genetic tools, we found that knocking out FMRP in cortical excitatory neurons and glia was sufficient for all alpha-like oscillation disruptions, including impairing their coupling to higher frequency oscillations coordinated by gamma-aminobutyric acid (GABAergic) interneurons. We discovered that the power of alpha-like oscillations reflects differential activity of two genetically defined classes of cortical GABAergic interneurons, parvalbumin-positive (PV+) and somatostatin-positive (SOM+) cells. Moreover, we found that the power and center frequency of alpha-like oscillations (but not gamma oscillations) were more sensitive to enhancing GABAergic inhibition through administration of the GABAB receptor agonist Arbaclofen in wild-type than Fmr1-/y mice. These results clarify the relationship between alpha oscillations and GABAergic inhibition, inform the cellular basis for the FXS alpha phenotype, and guide future therapeutic approaches for FXS. Furthermore, these findings bridge the preclinical and clinical worlds by showing for the first time how alpha oscillation disruptions can be studied in preclinical models and used to objectively measure pathophysiology and treatment response to better understand therapeutic outcomes.

Results
To characterize oscillatory phenotypes cross-sectionally in children and adults, rsEEG was collected using 128-channel caps in male subjects with FXS and age-matched typically developing (TD) controls across two laboratories, one which recorded from children ages 2–7 and the other which recorded from adults ages 19–44 (Fig. 1a, k). We sampled from the same electrodes across the cortex for all subjects to generate an absolute power spectrum per group (FXS and TD) within each age range (Fig. 1b, l). Across all ages, we observed significantly elevated absolute power for FXS subjects below 8 Hz and above 23.5 Hz (non-parametric bootstrapping with 95% confidence interval, n=17 per group in children, n=20 per group in adults).

To further analyze these spectra, we improved on existing methodology to decompose a power spectrum into aperiodic (underlying 1/f slope) and periodic (oscillatory) components (Supplementary Fig. 1a, b)49,50. Isolating periodic spectra improved comparisons across ages over calculating absolute or relative power in different frequency bands, as the alpha rhythm sits in the theta band (3–9 Hz) in children less than 7 years old50–52. We found elevated aperiodic power in FXS subjects across age groups, accounting for the increased power in the absolute spectrum (Fig. 1c, m). Aperiodic offset (the power at 3 Hz, the edge of the fitting range) was significantly elevated in children and adults with FXS, while aperiodic slope was not significantly different between groups in either age bin (Fig. 1d, e, n, o). FXS aperiodic disruptions were consistent across age groups.

Removing the aperiodic component from the absolute spectra revealed that oscillatory (periodic) differences between FXS and TD subjects were not the same for the two age groups (Fig. 1f, p). We found two periodic peaks across all groups and ages: a narrow peak one (Pk1, 4–14 Hz) that was differentially altered in children and adults with FXS, and a broad, more variable peak two (Pk2, 15–40 Hz) that was strikingly aberrant only in children with FXS. Accordingly, the FXS gamma phenotype was due to elevated aperiodic power in adults, but due to elevated aperiodic and periodic (oscillatory) power in children.

We quantified the maximum power and center frequency of each local peak for each subject (Supplementary Fig. 1c, h). The most pronounced EEG phenotype in children with FXS was an increase in the power and center frequency of Pk2 (Fig. 1g, Supplementary Fig. 1d, f). The center frequency of Pk2 in children with FXS younger than 60 months old clustered around the median (31.3 Hz), while the distributions for all TD and older FXS children were more variable (Fig. 1g, Supplementary Fig. 1e)50. There was no phenotype in periodic Pk2 in adults, although adult FXS subjects displayed a reduced correlation between the maximum power of Pk1 and Pk2 relative to TD subjects (Fig. 1q, Supplementary Fig. 1i-j).

As expected, the most pronounced phenotype in adults with FXS was a reduction in center frequency of Pk1 (median 8 Hz in FXS vs. 10.5 Hz in TD adults, Fig. 1r). Children with FXS had a modest reduction in center frequency (median 8.3 Hz in FXS vs 9 Hz in TD children; Fig. 1h), as well as a reduced maximum power of Pk1 relative to TD children (Fig. 1i). FXS children with the lowest Pk1 center frequency values also had the lowest Pk1 power (Fig. 1j), meaning alpha oscillations (periodic Pk1) were slower and weaker in FXS children. Unlike with Pk2, this Pk1 power phenotype persisted across all ages tested in FXS children (Supplementary Fig. 1f, g). However, Pk1 maximum power was not significantly different between FXS and TD adult subjects (Fig. 1s, Supplementary Fig. 1k), meaning alpha oscillations were much slower but not weaker in FXS adults. Therefore, the shift in Pk1 center frequency (the alpha phenotype) was the only oscillatory phenotype present across all ages, and was stronger in adults, while the Pk1 power phenotype was juvenile-specific.

Although the alpha phenotype in FXS children and adults was detectable across all cortical regions (Supplementary Figs. 2, 3), we found that the source of absolute 4–14 Hz power (i.e., the range of periodic Pk1) localized to occipital cortex in adult TD subjects (Fig. 1t). Although enlarged head size made source localization more difficult to estimate in FXS subjects, we also observed a posterior origin of 4–14 Hz power (Supplementary Fig. 1l). If a correlate of the alpha phenotype exists in rodent models, we expected it might be easiest to detect in visual cortex.

Based on these human findings, we used our analyses to investigate if mice also have a periodic Pk1, and if so, whether its center frequency is reduced in V1 of Fmr1-/y mice. Adult Fmr1-/y and littermate wild-type (WT) mice (postnatal day (p) 70–115, n=11 per group) were implanted with screw-type electrodes in the skull over V1 and primary somatosensory cortex (S1) (Fig. 2a, Supplementary Fig. 4a). One week later, the awake, freely moving mice were sequentially placed in a video-monitored chamber while EEG from the cortical surface (electrocorticography data) were continuously recorded. Behavioral scoring using video recordings facilitated extraction of 100 seconds of resting-state (i.e., awake but stationary) data from each animal.

We observed significantly elevated absolute power from the S1 electrode in Fmr1-/y mice between 45.6–55 Hz (non-parametric bootstrapping with 99% confidence interval) and a similar trend in V1 (Fig. 2b, Supplementary Fig. 4b), replicating reports of elevated gamma power in Fmr1-/y mice23–25,28,29. We next analyzed the aperiodic and periodic components. Unlike in humans, there was no difference in aperiodic power or offset between the two groups (Fig. 2c, Supplementary Fig. 4c). Instead, both genotypes had a broad increase in periodic power at high frequencies which was significantly larger in Fmr1-/y mice (Fig. 2d, Supplementary Fig. 4d). There was a robust periodic Pk1 between 2–13 Hz in both genotypes and no significant difference between genotypes in the maximum power of Pk1 in either V1 or S1 (Fig. 2e, Supplementary Fig. 4e). Using the electrode over V1, we found a significant reduction in the center frequency of Pk1 in Fmr1-/y mice (median 4.6 Hz) relative to WT mice (median 6.2 Hz; Fig. 2f). Pk1 center frequency in S1 of Fmr1-/y mice had a broader interquartile range, so no significant difference between genotypes pulled out (Supplementary Fig. 4f) and the disruption was indeed easiest to detect in murine visual cortex. This shift in center frequency of Pk1 in V1 of adult Fmr1-/y mice was consistent with the shift in Pk1 seen in adult humans with FXS. We therefore identified a correlate of the alpha phenotype in mice.

Our discovery of the Pk1 phenotype in Fmr1-/y mice supports work positing a commonality between the theta oscillations in V1 of mice and human alpha oscillations53,54. 3–6 Hz alpha-like oscillations in mouse V1 are present in all cortical layers but strongest in L453. Therefore, we further characterized the alpha phenotype by implanting adult Fmr1-/y and littermate WT mice (p70–150, n=22 per group) with LFP microelectrodes targeted to L4 of V1. After recovery, the mice were habituated to the recording setup (head-fixation in front of a monitor displaying a static, iso-luminant gray screen; Fig. 3a inset). Head-fixation permitted control of visual input and limited motion artifacts in high frequencies of the LFP. After two days of habituation, we characterized 150 seconds of data while the mice passively viewed the same, static gray screen on the monitor (i.e., resting-state data).

Only modest genotype differences pulled out of the absolute spectrum (significant difference 4.6–5.2 Hz, non-parametric bootstrapping with 99% confidence interval; Fig. 3a). Of note, the gray screen stimulus induced a narrow band periodic signal around 60 Hz in both genotypes55; this intracortical luminance signal was not present when the monitor was turned off and the animals were in the dark (Supplementary Fig. 5a). Consistent with the EEG recordings, we found no difference in the aperiodic fit between genotypes (Fig. 3b) even though we used an additional knee parameter to accommodate the large fitting range (Supplementary Fig. 5b–d). Removing the aperiodic component from the absolute spectra revealed the full broad shape of the periodic high-frequency signal in both genotypes (with the narrow band luminance signal sitting atop) and a significant elevation in this periodic power in Fmr1-/y mice between 21.2–127.6 Hz (Fig. 3c).

Intriguingly, analyzing with a higher spectral resolution revealed that periodic Pk1 (2–10 Hz) was composed of two sub-peaks (Pk1a and Pk1b). There was a significant difference in periodic power between genotypes from 3.8–5.4 Hz, within Pk1a (Fig. 3c). Both the center frequency and maximum power of Pk1a (but not Pk1b) were reduced in adult Fmr1-/y mice (Fig. 3d–g). The alpha phenotype was contained within Pk1a and was accompanied by a power reduction that was not detectable from the surface.

After characterizing the alpha phenotype in adult mice with intracortical recordings, we next analyzed 150 seconds of L4 LFP resting-state data from head-fixed juvenile Fmr1-/y and WT littermate mice (p30–40, n=67 per group) passively viewing the same gray screen to test if, like humans, oscillatory phenotypes differed across age groups. We observed a significant reduction in absolute power in juvenile Fmr1-/y mice relative to WT between 4.2–6 Hz and a significant elevation between 29–61 Hz and 63.6–85 Hz (Fig. 3h), and, as with adult mice, no difference in the aperiodic fit between genotypes (Fig. 3i). Subtracting out the aperiodic component revealed the same elevation in the broad high frequency periodic signal seen in adult Fmr1-/y mice (significant genotype difference between 21–132.8 Hz for juveniles; Fig. 3j). Additionally, the same two-subpeak structure was observed for periodic Pk1, but for juvenile Fmr1-/y mice, power was significantly reduced between 3.6–7.8 Hz and there was a significant genotype difference in the maximum power of both Pk1a and Pk1b (Fig. 3j, l, n). In adult mice, only Pk1a was different, so the power difference in Pk1b was specific to juvenile mice, just like the Pk1 power difference was specific to human children. Furthermore, the center frequency of Pk1a in juvenile Fmr1-/y mice was reduced relative to WT littermates (Fig. 3k), but the phenotype was much less strong than in adult Fmr1-/y mice. Excitingly, this recapitulated the difference in the alpha phenotype seen between human children and adults.

To ensure that our cross-sectional approach to studying developmental trajectories was not misleading, we ran a longitudinal study in a subset of mice (n=7–8 per group). We analyzed 120 seconds of resting-state, head-fixed LFP data in the same cohort of Fmr1-/y and WT littermate mice viewing the static gray screen at postnatal 4 and 11 weeks and replicated the developmental changes in periodic Pk1 phenotypes. At ~p30, there was a significant difference in Pk1 between 2.4–6.2 Hz and 7.2–9 Hz (Pk1a and Pk1b), while at ~p80 the difference was between 3.8–5 Hz (Pk1a, with a clear shift in center frequency in Fmr1-/y mice; Supplementary Fig. 6a). Pk1b maximum power increased significantly over development in Fmr1-/y but not WT mice (Supplementary Fig. 6b). Pk1a center frequency decreased over development in both genotypes, but more significantly in Fmr1-/y than WT mice (Supplementary Fig. 6c), which matched our cross-sectional findings (Supplementary Fig. 6d). This validated our cross-sectional approaches and our reports of a juvenile-specific power phenotype and a stronger alpha phenotype in adults than juveniles in both species.

In WT mice, alpha-like oscillations are strongly driven by a flash of light53, so we investigated if genotype differences in the resting-state LFP persisted in the absence of luminance. In a cohort of n=44 mice per group (p30–150), we characterized 150 seconds of head-fixed LFP data while the monitor was turned off (i.e., resting-state data while the mice were in the dark). As before, we observed significantly elevated periodic power between 22–133.4 Hz and a significant reduction in Pk1a center frequency (i.e., the alpha phenotype) in Fmr1-/y mice (Supplementary Fig. 5e, f). There was also a modest reduction in Pk1a power, but not Pk1b power (Supplementary Fig. 5g). Even in juvenile mice (n=38 per group) there was no significant difference in Pk1b power, unlike during the gray screen stimulus in the same mice (Supplementary Fig. 5h). Thus, the developmental differences in Pk1b power were specific to luminance conditions. Across all animals, luminance increased aperiodic power below 60 Hz and significantly increased aperiodic offset and slope (Supplementary Fig. 5i). In the juvenile mice, luminance enhanced the maximum power of Pk1a in WT, but not in Fmr1-/y mice, and it suppressed the maximum power of Pk1b in Fmr1-/y, but not WT mice (Supplementary Fig. 5j), thereby exacerbating genotype power differences. Consistent with the age-dependence of the Pk1b power phenotype, luminance did not have the same effect on Pk1b in adult Fmr1-/y mice (Supplementary Fig. 5k).

In humans, alpha oscillations occur in bursts and burst dynamics are altered in FXS56. To test if dynamics are altered in Fmr1-/y mice, we bandpass-filtered 100 seconds of continuous resting-state LFP data between 2–10 Hz (periodic Pk1) and used thresholding to identify microbursts in the analytical signal (Supplementary Fig. 7a). In both juvenile and adult Fmr1-/y mice, microbursts occurred more frequently but lasted for shorter durations across both ‘gray screen’ and ‘monitor off’ data (Supplementary Fig. 7b, d). Microbursts tended to occur during periods of behavioral quiescence, but there was no evidence of hyperactivity in Fmr1-/y mice (Supplementary Fig. 7c, e)23,57, in contrast to FXS human subjects. Altered microburst dynamics were detectable in L4 but not at the cortical surface (Supplementary Fig. 7f). Similarly, we could not detect these effects in our rsEEG data from adult humans, largely due to immense variability in the TD data (Supplementary Fig. 7h). Intriguingly, L4 burst distributions in WT mice also had larger interquartile ranges than in Fmr1-/y mice (Supplementary Fig. 7b, d). Previous work identified elevated burst counts in FXS male subjects in inter-stimulus intervals of a task (not resting-state data)56, consistent with our results in Fmr1-/y mice. For resting-state data, intracortical recordings revealed differences in temporal dynamics not detectable from the surface. Studying these altered temporal dynamics might help generate hypotheses of mechanistic disruptions in FXS, but from a translational perspective remains less useful than the alpha phenotype, which we detected across species and both inside and outside mouse cortex.

Thus far, we have characterized the alpha phenotype in both children and adults with FXS and established a correlate of the phenotype and its age-related changes in V1 of juvenile and adult Fmr1-/y mice through surface and intracortical recordings, bolstering its potential translational utility for testing therapeutic outcomes. Importantly, we can interrogate the cellular basis of the phenotype in the mouse model in ways not possible in humans to inform therapeutic avenues. For example, it was previously shown that deletion of FMRP in excitatory neurons is sufficient for the gamma phenotype in Fmr1-/y mice58. To test if it is also sufficient for the alpha phenotype, we employed a genetic strategy to selectively knockout Fmr1 from excitatory neurons and glia in the cortex (Emx1-Fmr1 KO), sparing its expression in cortical inhibitory interneurons and subcortically59. We used juvenile mice (p30–40) and implanted electrodes in L4 of V1 to record head-fixed resting-state LFP while the monitor was off or displaying the gray screen. WT mice in this triple transgenic line (n=20) showed no difference from WT mice used previously (n=9) (Supplementary Fig. 8a–e). As shown in Fig. 4 and Supplementary Fig. 8f–m, all the resting-state periodic LFP phenotypes in both luminance and darkness replicated in Emx1-Fmr1 KO mice (n=15). Loss of FMRP in excitatory neurons and glia is sufficient for the alpha phenotype.

Previous studies have shown a coupling between the phase of mouse alpha-like oscillations in L4 of V1 and the amplitude of higher frequency oscillations in the beta and gamma frequency bands (15–40 Hz)53,60,61. We have reproduced that finding in littermate WT mice and shown that this phase-amplitude coupling is impaired and phase-shifted in Fmr1-/y and Emx1-Fmr1 KO mice (Fig. 5). Since the higher frequency oscillations are within the range of human Pk2, this echoes the reduced correlation between the power of periodic Pk1 and Pk2 in adult humans with FXS (Supplementary Fig. 1j). In mice, these higher frequency oscillations are thought to be coordinated by PV+ and SOM+ interneurons61–65, suggesting a link between the activity of these inhibitory (GABAergic) interneurons and periodic Pk1.

We next investigated this hypothesized link between periodic Pk1 and the activity of PV+ and SOM+ interneurons. Alpha oscillations are broadly tied to cortical inhibition37,66–68 and reflect activity in the thalamocortical loop36,54,69, but whether they also reflect activity of specific classes of cortical inhibitory (GABAergic) interneurons is unclear. In mice, previous reports suggest that either PV+ or SOM+ interneurons coordinate cortical or visually-evoked theta oscillations, but these reports have been conflicting60,61,70,71. Here, we test the effect of temporarily inactivating PV+ and SOM+ neurons in WT mice on the resting-state LFP, particularly alpha-like oscillations.

We began by investigating the effect of inhibiting PV+ neurons in V1 of WT mice on the L4 LFP, particularly within periodic Pk1a and Pk1b. We re-analyzed previously published data wherein chemogenetic methods were used to temporarily inactivate PV+ cells in V172. We analyzed 150 seconds of resting-state LFP data where head-fixed mice (n=16) expressed the chemogenetic actuator hM4Di in PV+ neurons of V1 and passively viewed a static, iso-luminant gray screen before and after systemic injection of Clozapine N-Oxide (CNO) to trigger the hM4Di receptor. Inactivating PV+ cells increased absolute power between 1.6–110.4 Hz (non-parametric bootstrapping with 99% confidence interval; Fig. 6a). This effect resulted from an increase in aperiodic power between 1.6–106.2 Hz and an increase in periodic power between 9.6–115.8 Hz (Fig. 6b, f). The paradoxical increase in aperiodic and periodic gamma power is presumably through disinhibition of reciprocally connected excitatory cells73–76. Indeed, aperiodic slope, offset, and knee frequency values were all significantly affected by the manipulation, indicating a large effect on network excitability (Fig. 6c–e, Supplementary Fig. 9a, b). The manipulation also increased the frequency and shortened the duration of Pk1 microbursts without changing overall movement (Supplementary Fig. 9c–f), but this analysis was affected by the large changes in aperiodic power and was thus not further utilized in the manuscript. Removing aperiodic power crucially revealed that PV+ neuronal inactivation eliminated periodic Pk1b power (significant difference 6.8–8.2 Hz) but did not affect Pk1a power or center frequency (Fig. 6f–j).

Next, we used optogenetics to temporarily inactivate SOM+ activity in V1 while recording resting-state LFP from head-fixed mice passively viewing a static, iso-luminant gray screen (Fig. 6k). Optogenetic inactivation reduced absolute power between 4.6–6.2 Hz in mice expressing the inhibitory actuator Halorhodopsin (NpHR3) in SOM+ neurons of V1 (n=8), relative to control mice (n=7; Fig. 6k). The manipulation had no effect on the aperiodic spectrum (Fig. 6l–o), revealing a specific effect on periodic Pk1, with a significant reduction in periodic power in NpHR3-expressing mice between 4.4–6.4 Hz, specifically within Pk1a (Fig. 6p). Comparing Pk1a maximum power across genotypes revealed a strong trend supporting the bootstrapped spectrum results (Fig. 6r; uncorrected p-value=0.02), with no apparent difference in Pk1b maximum power (Fig. 6t) nor the center frequency of either subpeak (Fig. 6q, s). The power of Pk1a and Pk1b are therefore separately affected by inhibition of SOM+ and PV+ cells, respectively.

Since shutting off PV+ and SOM+ interneurons affected alpha-like oscillations in WT mice, the oscillations might also be sensitive to drugs which enhance GABAergic inhibition. Pharmacologically boosting inhibition is a well-trodden therapeutic avenue for FXS77–79, wherein GABAergic activity is impaired9,77,78. Indeed, hypoactivity of SOM+ and/or PV+ interneurons has been documented for FXS80–85, even when deletion of Fmr1 is restricted to excitatory neurons58, so deficient activity of these classes of GABAergic interneurons might contribute to the electrophysiological differences we have observed in the LFP of Fmr1-/y and Emx1-Fmr1 KO mice. To test if boosting GABAergic inhibition might affect alpha-like oscillations in WT mice and even be therapeutic to periodic Pk1 phenotypes in Fmr1-/y mice, we used Arbaclofen, a GABAB receptor agonist. Arbaclofen was previously shown to reverse several intracellular and behavioral phenotypes in Fmr1-/y mice86,87 and has produced clinically meaningful improvements in several secondary measures in a phase-3 clinical trial with children with FXS88. We administered Arbaclofen intraperitoneally 60–90 minutes before characterizing 150 seconds of resting-state L4 LFP data in V1 while juvenile head-fixed mice (p30–40) passively viewed a static gray screen.

Arbaclofen was administered in WT mice at two doses, 0.5 mg/kg (n=10) and 1 mg/kg (n=15), and we analyzed the periodic and aperiodic spectra to probe for process engagement. Our analytical method more precisely read out dose response differences in the LFP than could be realized through analysis of the absolute spectra (Fig. 7). In the absolute spectra, there was a significant increase in low frequency power from 3.4–5 Hz for 0.5 mg/kg and from 1.6–16.2 Hz for 1 mg/kg relative to saline treatment (non-parametric bootstrapping with 99% confidence interval; Fig. 7a, k). 1 mg/kg of Arbaclofen significantly increased aperiodic power below 39 Hz and decreased it above 172 Hz (Fig. 7l), increased aperiodic slope and offset but not knee frequency (Fig. 7m–o), and decreased high frequency periodic power between 21–52.8 Hz and 56.2–76.6 Hz (Fig. 7p). At the 0.5 mg/kg dose, only increased low frequency aperiodic power and offset were observed (Fig. 7b–f). At both doses, Arbaclofen increased periodic power from 1.8–4.6 Hz (Fig. 7f, p) and increased the maximum power of Pk1a but not Pk1b (Fig. 7h, j, r, t). At 1 mg/kg, Arbaclofen also significantly reduced the center frequency of Pk1a (Fig. 7q). It is noteworthy that optogenetically stimulating SOM+ interneurons had the same effect on Pk1a center frequency (Supplementary Fig. 10c, d).

Significantly, these findings in WT mice demonstrate that Pk1a center frequency and power are sensitive to pharmacological intervention, bolstering the utility of alpha oscillation disruptions, including the alpha phenotype, as translatable measures of pathophysiology and therapeutic target engagement. Moreover, since both the 0.5 and 1 mg/kg doses increased Pk1a power, it is possible that Arbaclofen might improve the Pk1a power phenotype in Fmr1-/y mice.

We next tested the same two doses (0.5 mg/kg, n=10, and 1 mg/kg, n=14) on the resting-state L4 LFP in head-fixed juvenile Fmr1-/y mice (p30–40) to investigate if Arbaclofen has a therapeutic effect on periodic Pk1a power. Relative to saline controls, we observed a significant increase in the absolute spectra below 9 Hz and a significant decrease between 72.4–74.6 Hz for the 1 mg/kg dose but not 0.5 mg/kg dose (Fig. 8a, f). As with WT mice, the 1 mg/kg dose significantly increased aperiodic power below 30.6 Hz and decreased it above 174 Hz (Fig. 8g), increased aperiodic slope and offset (Fig. 8g inset), and decreased high frequency periodic power between 21–125.6 Hz (Fig. 8h), while the 0.5 mg/kg dose only affected the aperiodic spectrum (Fig. 8b, c). The decrease in high-frequency periodic power recapitulated the therapeutic effect of Arbaclofen and racemic baclofen on the gamma phenotype in Fmr1-/y mice25,89,90. Unlike WT mice, Arbaclofen had no effect on Pk1a power at the lower dose (0.5 mg/kg; Fig. 8c–e). At 1 mg/kg, there was a significant periodic power difference from 2.6–4.4 Hz (Fig. 8h), so we compared Pk1a properties to saline-treated WT littermates (from Fig. 7q, r) as well as between treatment groups in Fmr1-/y mice. Between the latter, we observed a tighter interquartile range but no significant shift in Pk1a center frequency and a significant improvement in Pk1a maximum power with Arbaclofen treatment (Fig. 8h-j). Relative to saline-treated WT, the genotype difference in Pk1a power was no longer significant while the genotype difference in Pk1a frequency was enhanced in Fmr1-/y mice treated with Arbaclofen.

Since the minimum effective dose for Arbaclofen to shift Pk1a power was higher in Fmr1-/y mice, we also tested a 3 mg/kg dose in a separate, small cohort of head-fixed juvenile Fmr1-/y mice (n=3, p30–40) passively viewing a static, iso-luminant gray screen. This dose increased aperiodic offset and slope relative to saline-treated WT littermates and essentially eradicated periodic power other than in Pk1a (Supplementary Fig. 11b-c). Our analytical methodology clarified the effective therapeutic dose range, as the negating effect of the 3 mg/kg dose on periodic power is not clear from the absolute spectrum (Supplementary Fig. 11a). This high dose also had a noticeable sedative effect on the mice, as movement over 100 seconds of the 150-second recording was essentially abolished for the mice treated with 3 mg/kg, unlike for Fmr1-/y mice treated with 1 mg/kg, where forepaw movement was comparable to saline-treated WT littermates (Supplementary Fig. 11d-e).

Finally, to assess if the changes observed in Pk1a were unique to the mechanism of Arbaclofen, we tested the effect of acutely administering CTEP (2-chloro-4-[2-[2,5-dimethyl-1-[4-(trifluoromethoxy)phenyl]imidazol-4-yl]ethynyl]pyridine), a negative allosteric modulator (NAM) of metabotropic glutamate receptor 5 (mGluR5). These NAMs showed immense preclinical promise but poor performance in clinical trials, possibly due to acquired treatment resistance91–94. 2 mg/kg of CTEP was administered intraperitoneally 90 minutes before resting-state L4 LFP data were collected while head-fixed adult Fmr1-/y mice and WT littermates (p70–90) passively viewed a static gray screen (Supplementary Fig. 12a, g). In both WT and Fmr1-/y mice, CTEP significantly elevated aperiodic offset relative to saline treatment and had a similar flattening effect on high-frequency periodic power as 3 mg/kg Arbaclofen (Supplementary Fig. 12b, c, h, i), revealing target engagement. However, this dose had no effect on Pk1a center frequency or power (Supplementary Fig. 12d, e, j, k), so the effects of Arbaclofen on periodic Pk1a are specific to its mechanism of action (boosting GABAergic inhibition).

Methods
Our studies in humans subjects complied with all relevant ethical regulations, as approval was obtained prior to starting each study from the Institutional Review Board (IRB) at Boston Children’s Hospital or the Institutional Review Board at Cincinnati Children’s Hospital and Medical Center, depending on the site where the study was conducted. Written informed consent was obtained from either the participant or from the participant’s parent/guardian, with assent from the participant. For the pediatric study, written informed consent was obtained from all parents or guardians prior to their children’s participation in the study.

Our studies in murine subjects also complied with all relevant ethical regulations, as all experimental techniques were approved by The Institutional Animal Care and Use Committees and Department of Comparative Medicine at MIT (Bear protocol: 2403000639) and conformed to the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health, or were approved by the Ethical Committee for Animal Use of King’s College London (Home Office License: PF82DF031-5) and were in accordance with the United Kingdom Home Office Animals (Scientific Procedures) Act 1986, depending on the site where the data were collected.

Participants for Pediatric FXS study. Resting-state EEG (rsEEG) data were collected from 17 males (27−78 months old) with full mutation of FMR1 and 17 age-matched (27–80 months) typically-developing males. Participants were recruited as part of two studies (IRB-P00034676 and IRB-P00025493) conducted at Boston Children’s Hospital/Harvard Medical School. An additional secondary analysis IRB was approved for combining and analyzing data from both studies. FXS participants all had documented full mutation of the FMR1 gene, however methylation status was not known for many participants, and participants were not excluded for size mosaicism (mixture of full and premutation). Additional exclusion criteria included history of prematurity (<35 weeks gestational age), low birth weight (<2000 gms), known birth trauma, known genetic disorders (other than FXS), unstable seizure disorder, current use of anticonvulsant medication, and uncorrected hearing or vision problems.

Participants for Adult FXS study. rsEEG data were collected from 20 males (19–43 years old; mean=31.09, standard deviation=7.67) with a full mutation of FMR1 and 20 age-matched (20–44 years old; mean=29.42, standard deviation=7.12) typically-developed males. Participants were recruited as part of a study (IRB#2013-7327) conducted at Cincinnati Children’s Hospital and Medical Center. Adult FXS participants all had full FMR1 mutations (>200 CGG repeats), confirmed via past testing results made available in a participant’s medical record or via Southern Blot and/or PCR conducted in collaboration with the Molecular Diagnostic Laboratory at Rush University. Participants were not excluded for size mosaicism (mixture of full and premutation). Additional exclusion criteria for adult participants included known syndromic conditions associated with intellectual disability (other than FXS; e.g., Down Syndrome), being on benzodiazepines or anticonvulsant medications, or being on any novel potential treatment (e.g., minocycline) known to affect EEG measures within 4 weeks of testing. Typically developed controls were excluded if they scored ≥8 on the Social Communication Questionnaire (SCQ ® Print Kit from Rutter, Bailey, & Lord, 2003, W-381 WPS Publishing, Torrence, CA), had a history of psychiatric or neurologic disorders, or had a first or second-degree relative with autism spectrum disorder or a serious psychiatric illness.

We note that our study focused on male subjects (both humans and mice). The human FMR1 and mouse Fmr1 genes are on the X-chromosome, FXS diagnoses are most prevalent in males, and the electrophysiological phenotypes are most prominent in the male subjects19. However, we recognize that there are sexually dimorphic phenotypes in FXS, including in alpha oscillations18,56, and encourage future study of the alpha phenotype in human females with FXS and in female Fmr1-/y mice.

Pediatric study. rsEEG data were collected while the child either sat in their caregiver’s lap or independently in a chair situated in a dimly lit, sound-attenuated, electrically shielded room. At times, room luminance levels were increased for the comfort of the child. Continuous EEG was recorded while the participant was shown a silent screensaver of abstract, colorful moving images for 2–5 minutes depending on the child’s compliance. In some cases, participants watched a silent video of their choosing to improve behavioral cooperation.

rsEEG data were collected using a 128-channel Hydrocel Geodesic Sensor Net (Version 1, EGI Inc, Eugene, OR) connected to a DC-coupled amplifier (NetAmps 300, EGI Inc, Eugene, OR). Data were sampled at 1000 Hz and referenced to a single vertex electrode (Cz). Raw NetStation (NetStation version 4.5, EGI Inc, Eugene, OR) files were exported to MATLAB (version R2017a) for pre-processing using the Batch EEG Automated Processing Platform (BEAPP)108 with integrated Harvard Automated Preprocessing Pipeline for EEG (HAPPE)109. Preprocessing has previously been described in detail for similar data110. Briefly, data were 1 Hz high-pass and 100 Hz low-pass filtered, downsampled to 250 Hz, and then run through the HAPPE module for 60 Hz line noise removal, bad channel rejection and artifact removal using combined wavelet-enhanced independent component analysis (ICA) and Multiple Artifact Rejection Algorithm (MARA)111. Given the short length of EEG recording, 39 of the 128 channels were selected for ICA/MARA (Standard 10-20 electrodes: 22, 9, 33, 24, 11, 124, 122, 45, 36, 104, 108, 58, 52, 62, 92, 96, 70, 83; additional electrodes: 23, 28, 19, 4, 3, 117, 13, 112, 37, 55, 87, 41, 47, 46, 103, 98, 102, 75, 67, 77, 72, 71, 76). These electrodes were selected based on their spatial location, covering frontal, temporal, central, and posterior regions of interest (see Fig. 1). After artifact removal, channels removed during bad channel rejection were interpolated, data were re-referenced to an average reference, detrended using the signal mean, and segmented into 2-second segments. Any segments with retained artifact were rejected using HAPPE’s amplitude and joint probability criteria. EEG signals were rejected for data quality if they had fewer than 10 segments (20 seconds total) or did not meet the following HAPPE data quality output parameters: percent good channels >80%, mean and median retained artifact probability <0.3, percent of independent components rejected <80%, and percent variance after artifact removal <32%.

Adult study. rsEEG data were collected while the subject sat independently in a chair situated in either a dimly lit, electrically shielded booth or a more brightly lit clinic room within the research center where care was taken to reduce electrical noise in the environment. At times, luminance levels in the rooms were adjusted for the comfort of the subject. Five minutes of continuous EEG were recorded while the participant was seated comfortably watching a silent video of their choosing on a battery-powered iPad, consistent with previous studies19,20.

rsEEG was continuously recorded and digitized at 1000 Hz, filtered 0.01-200 Hz, referenced to Cz, and amplified 10,000x using a 128-channel saline Electrical Geodesics system (EGI, MagStim, Minnesota) with sensors placed approximately according to the International 10/10 system. Data were preprocessed using MATLAB (R2021b) and the Cincinnati Very High Throughput Pipeline (VHTP), which utilized EEGLAB 2021b112.

rsEEG data used for the spectral power analysis were digitally filtered from 0.5 to 100 Hz with a 60 Hz notch (57–63 Hz; harmonics were removed up to the Nyquist frequency of the original sampling rate), channels with poor quality data were interpolated (no more than 5% of channels interpolated), segments of poor-quality data were manually rejected, and then remaining data were submitted to independent components analysis via EEGLAB for artifact removal. Artifacts (e.g., muscle tension, ocular-related events, etc.) were removed as components and then data were average referenced and segmented into 2-second segments. During the creation of segments, data were submitted to an automatic amplitude rejection threshold where segments containing artifact exceeding +/- 120 µV were automatically rejected.

For analysis of burst dynamics, data were also digitally filtered from 0.5 to 100 Hz with a 60 Hz notch (57–63 Hz; harmonics were removed up to the Nyquist frequency of the original sampling rate), with no more than 5% of channels interpolated for bad data, but data did not undergo manual segment rejection. Instead, a section of continuous data was isolated starting from approximately 60 seconds into the recording through approximately 230 seconds into the recording, depending on the degree of artifact present. The continuous segment was then submitted to ICA for artifact correction, where removal of low-frequency artifacts (e.g., heart rate, ocular-related events, etc.) was prioritized due to the planned low-frequency filtering for burst analyses (i.e., high frequency noise was filtered out). Data were average referenced and then reduced to 90-second-long segments for analysis, as 90 seconds was approximately the longest segment of clean, continuous data available for analysis.

Fmr1-/y mice were obtained from Jackson Laboratories, Maine, USA (stock #003025) and backcrossed onto a C57BL/6J background for at least six generations at Massachusetts Institute of Technology or King’s College London. Fmr1 cKO mice were also obtained from Jackson Labs (stock #035184) and crossed with Emx1-Cre mice (Jackson stock #005628). As previously reported, for hM4D(Gi) experiments, mice were Parvalbumin-Cre recombinase knock-in mice (B6;129P2-Pvalbtm1(cre)Arbr/J, PV-Cre) on a C57BL/6 background (Jackson stock # 017320)71. For optogenetic inhibition experiments, SOM-Cre mice were used (B6J.Cg-Ssttm2.1(cre)Zjh/MwarJ, Jackson stock # 028864). Experimental cohorts consisted of male littermates that were weaned at p21 and were p30–P150 at the time of experiments (n=215 Fmr1-/y mice and littermate controls, p30–40 and p70–150, n=44 Emx1-Fmr1 KO mice and littermate controls, p30–40, n=16 PV-Cre mice, p50–80, and n=31 Som-Cre mice and littermate controls, p50–70). Mice were maintained on a 12-hour light-dark cycle (7 am–7 pm) in a temperature- and humidity-controlled animal care facility (68–72° F and 30–70% humidity) with ad lib access to food and water and housed with 1–4 other littermates. All experiments were performed blind to genotype using age-matched littermate controls within the 12-hour light phase.

L4 LFP electrodes. Methods for LFP surgeries were followed as previously reported65. Mice (p30–150) were administered with pre-operative analgesia (0.1 mg/kg Buprenex subcutaneously) and anesthetized with isoflurane (3% in oxygen at induction, 1.5% in oxygen through operation). The head was shaved and disinfected with povidone–iodine (10% w/v) and ethanol (70% v/v), the scalp was incised, and the skull surface was scored. A steel screw for head-fixation was bound to the front of the skull with cyanoacrylate glue, the skull was levelled, and a reference electrode (silver wire, A-M systems) was placed in the right frontal cortex. LFP tungsten recording electrodes (FHC), 75 µm in diameter at their widest point, were implanted in the binocular visual cortex (-3.5 mm bregma, +/- 3 mm midline, 450 µm depth). All electrodes were secured using cyanoacrylate and the skull was covered in dental cement. A nonsteroidal anti-inflammatory drug (meloxicam, 1.5 mg/kg) was delivered subcutaneously post-operatively for 3 consecutive days and mice were monitored daily for discomfort. Mice were given 48–96 hours to recover before being habituated to head-fixation.

Cortical surface electrodes. Using similar pre-operative and surgical techniques (except use of carprofen as the pre-operative analgesia), Fmr1-/y and littermate WT mice (p70–115) were chronically implanted with screw-type electrodes (1.6 mm, Protech International) in the skull to measure EEG from the cortical surface (electrocorticography) in V1 and S1, and with the same type of LFP electrode as described above in V1. Screw electrodes were placed in burr holes over S1 (-2.06 mm bregma, 3.20 mm midline) and V1 (-3.50 mm bregma, -3 mm midline) with a reference electrode and a ground over the left and right olfactory bulb. The coordinate for the V1 LFP electrode was the same as the skull screw (i.e., the same hemisphere) except the wire was inserted into the cortex tissue at a 10-degree angle and advanced in the dorsoventral axis to a depth of 0.52 mm from the cortical surface. Electrodes were connected to head-mounts and secured with dental cement and mice were given at least one week to recover before cortical surface EEG/depth LFP recordings.

Mice were habituated to the recording set-ups for at least two days. For habituation of head-fixed mice, the monitor was positioned 20 cm away from the animals and displayed a static, iso-luminant gray screen (80 lux luminance) generated using previously published code (https://github.com/jeffgavornik/VEPStimulusSuite) written in C++ for interaction with a VSG2/2 card (Cambridge Research Systems) or MATLAB (MathWorks, R2012b) using the PsychToolbox extension (version 3.0.12, http://psychtoolbox.org). For resting-state LFP recordings in head-fixed mice, five minutes of data while the same monitor was turned off were collected in some of the mice, and three to five minutes of data while the monitor displayed the iso-luminant gray screen were collected in all mice. Data from head-fixed mice were collected either in a Faraday cage on a Plexon Recorder 64 system, with a PBX-2112003 pre-amplifier (Plexon Inc, Dallas TX, USA) at MIT or on a Plexon OmniPlex® Neural Recording System, with Plexon’s DigiAmp™ acquisition system (Plexon Inc, Dallas, TX, USA) at King’s College London. At both institutions, LFP data were collected from the electrode placed in V1 (left or right hemisphere) and forepaw movement data were collected from a piezoelectric disk (C.B. Gitty) placed under the animals’ forepaws; data in total were collected from three channels (the V1 channel, the ground in frontal cortex, and the piezo). LFP voltage data were sampled at 1 kHz. At the UK site, data were high-pass filtered at 0.5 Hz and low-pass filtered at 500 Hz. At the US site, data were band-passed through analog filtering via a low cut, 2-pole 3 Hz filter and a high-cut, 4-pole, 8 kHz filter, and frequencies down to 0.4 Hz were digitally recovered using the inverse transfer function for the Linkwitz-Riley analog filter obtained from Plexon. This and all other preprocessing were conducted in MATLAB (R2018b). Data from the V1 LFP channel were loaded into MATLAB, unfiltered, converted to microvolts, and detrended using the signal mean. A second-order IIR notch filter was applied with a bandwidth of 0.01 (for gray screen data from juvenile animals, collected at MIT), 0.05 (for gray screen data from adult animals, collected at King’s College and MIT), or 0.025 (for black screen data from all animals, collected at MIT) to remove 50 or 60 Hz line noise while preserving underlying biological (i.e., luminance processing) signals around 60 Hz. Data from the Piezo channel were also loaded into MATLAB and rectified.

For cortical surface EEG recordings (electrocorticography) in freely-moving mice, the animals were tethered to four-channel EEG/EMG recording systems (Pinnacle Technology Inc.) and housed individually and sequentially in a sound-proof and light-controlled cabinet (standard lighting conditions, 200 lux luminance) equipped with a video camera. Voltage data were acquired continuously for a 10–12-hour period with a sampling rate of 1 kHz, amplified 100x, and band-passed filtered through analog filtering via a two-series, single pole simple RC filter at 1 Hz and an eighth-order elliptical filter at 100 Hz. Frequencies down to 0.6 Hz were recovered using the inverse-transfer function for the passive RC filter obtained from Pinnacle and implemented in MATLAB (R2018b). 100-second-long resting state time series from S1 and V1 electrodes in each mouse were prepared using the synchronous video to identify when mice were in a quiet wake (resting) vigilance state (i.e., sitting at the corner of the recording cage, moving the head but not the whole body, no running). These time-series data were loaded into MATLAB, unfiltered, converted to microvolts, and detrended using the signal mean. A second-order IIR notch filter was applied with a bandwidth of 0.15 was applied to remove 50 Hz line noise while preserving underlying biological signals.

As described in Kaplan et al. (2016) for hM4D(Gi) experiments, in addition to implanting LFP electrodes, V1 of p30–60 PV-Cre mice was infected with an AAV9-hSyn-DIO-HA-hM4D(Gi)-IRES-mCitrine virus (UNC viral core – generated by Dr. Brian Roth’s laboratory)72. Using a glass pipette and Nanoject system (Drummond Scientific, Broomall, PA, US), 81 nL of virus was delivered at each of 3 cortical depths: 600, 450, and 300 µm from the cortical surface, with 5 minutes between repositioning for each depth. Mice were allowed 3–4 weeks of recovery for virus expression to peak before experiments. Then, after two days of habituation, 5 minutes of resting-state LFP data with mice head-fixed in front of a static, iso-luminant gray screen were collected before and 30 minutes after Clozapine-N-oxide (CNO, Enzo Life Sciences) was diluted in saline and injected intraperitoneally at a dosage of 5 mg/kg. For these data, an aggressive 60 Hz notch filter was incorporated into the Plexon recording software, so no further digital notch filtering was applied during data preprocessing and frequencies between 45-75 Hz were not analyzed.

For optogenetic experiments, we infected V1 of p30–50 SOM-Cre mice with either pAAV-Ef1a-DIO eNpHR3.0-EYFP (AAV5) virus (for inactivation), pAAV-EF1a-double floxed-hChR2(H134R)-EYFP-WPRE-HFHpA (AAV5) virus (for activation), or the empty viral vector pAAV-Ef1a-DIO EYFP (AAV5). Using the aforementioned Nanoject system, we delivered 100 nL of virus at 2 nL/s at 4 cortical depths: 600, 450, 300 and 150 µm from the cortical surface, with 6 minutes between re-positioning for each depth. In addition to implanting LFP electrodes and infusing the virus, ready-made optic fibers (200 µm girth) mounted in stainless steel optic cannulas (1.25 mm diameter, 2 mm fiber projection, Thorlabs, CFMLC12L02, Newton, NJ, US) were then implanted lateral to the recording site and at a 22° angle to the recording electrode, 0.1 mm below surface in each hemisphere. 3 weeks later, mice were habituated for 2 days. For resting-state LFP data collection during SOM+ inactivation, while mice were head-fixed in front of a static gray screen, we delivered five 10-second long continuous pulses of green light (550 nm, 6–7 mW) into V1 using a digitally-controlled LED driver (PlexBright® Optogenetic Stimulation System). Each pulse was separated by 60 seconds, resulting in a recording session that lasted about 7 minutes. For SOM+ activation, we delivered three 100-second long continuous pulses of blue light (465 nm, 1.5 mW) with each pulse separated by 30 seconds, resulting in a recording session that also lasted about 7 minutes. For these data, notch filtering was not activated in the Plexon software, so a second-order IIR notch filter with a bandwidth of 0.2 was applied in MATLAB (R2018b) during data preprocessing to remove 50 Hz line noise.

Following established protocols, Arbaclofen (R−4-amino-3-(4-chlorophenyl)butanoic acid) was dissolved in 0.9% saline (NaCl) and administered via an intraperitoneal injection at 0.5, 1, or 3 mg/kg in an injection volume of 0.1 mg/mL87. The vehicle was saline. Injections occurred 60–90 minutes before gray screen presentation and LFP data collection. Vehicle and Arbaclofen were tested on the same day, with the vehicle administered first and then Arbaclofen several hours later. Thus, the experimenter was blind to genotype but not treatment.

CTEP (2-chloro-4-((2,5-dimethyl-1-(4-(trifluoromethoxy)phenyl)-1H-imidazol-4-yl)ethynyl)pyridine) solution was prepared according to established protocols91. Briefly, CTEP was synthesized at Roche and micro-suspended in vehicle (0.9% NaCl, 0.3% Tween-80) and administered via an intraperitoneal injection at 2 mg/kg in an injection volume of 0.2 mg/mL. The vehicle was a sterile solution of 10% dimethyl sulfoxide (DMSO), 45% polyethyleneglycol (PEG-400), and 45% artificial cerebrospinal fluid (aCSF). Injections occurred 90 minutes before gray screen presentation and LFP data collection. A crossover design was used and both Fmr1-/y and wild type littermates in a pair received matching treatments on the same recording sessions, with 3–7 days between treatments. Thus, the experimenter was blind to genotype and treatment.

Spectral analyses were performed in MATLAB (R2018b) using the Chronux toolbox (version 2.12)113. For all human subjects, the power spectral density (PSD) at each electrode, for each 2 second segment, was calculated with a multi-taper spectral analysis, using 3 orthogonal tapers and a time-bandwidth product (TW) of 2. PSDs had interpolated 0.1 Hz bin sizes for pediatric subjects and non-interpolated (i.e., equivalent to the frequency resolution) 0.5 Hz bin sizes for adult subjects. PSDs were averaged across segments and then averaged across electrodes. The following electrodes were averaged for each region of interest (ROI): frontal−3, 4, 11, 19, 23, 24, 28, 117, 124; central−13, 36, 37, 55, 87, 104, 112; temporal−41, 45, 47, 46, 52, 58, 96, 98, 102, 103, 108; posterior−67, 70, 71, 72, 75, 76, 77, 83. All listed electrodes from each ROI were averaged for the full brain absolute power spectrum.

For source modeling in human subjects, absolute power within the range of periodic pk1 (4–14 Hz) was averaged within group and submitted to Brainstorm for source localization in an approximation of previously published methods19. A head model was generated where EGI 128 channel locations were co-registered with the Montreal Neurological Institute (MNI) averaged ICBM152 common brain template (default anatomy), OpenM/EEG was used to compute a lead-field mesh with 15,002 vertices, and noise covariance was set to an identity matrix. Sources were then computed where a minimum norm estimate model was generated via current source density with dipole orientation constrained as normal to cortex.

For freely-moving murine subjects, the 100-second resting-state time series was segmented into 5 second segments, resulting in 20 segments per subject. Longer segments were needed for the mice as the periodic signals of interest were at lower frequencies. This yielded PSDs with 0.2 Hz bin sizes, equivalent to the frequency resolution. The appropriate time-bandwidth product (TW) was calculated as TW=(FR*segment length)/2114 to achieve a consistent spectral resolution (FR) with human data (2 Hz). Multi-taper spectra were calculated with 9 tapers and a time-bandwidth product of 5, with no zero padding. For head-fixed murine subjects with L4 LFP electrodes, 150-seconds of resting-state and piezo data while the monitor was off or displaying a static gray screen were extracted for spectral analysis (except for SOM-Cre mice, where there were only 50 seconds of ‘laser on’ data, and for mice in the longitudinal experiment, where only 120 seconds of resting-state data were collected). 5-second segments were again used, but due to occasional contamination of the piezo signal into the LFP signal during certain movement bouts, a very small fraction of segments (usually 0–2 per mouse) were eliminated if they contained both a large movement artifact and a huge spike in low-frequency power (less than 1.5 Hz). As a result, 28–30 segments per animal per condition were usually analyzed (8–10 segments for SOM-Cre mice and 22–24 segments for mice in the longitudinal study). The multi-taper absolute spectra were calculated using 9 tapers and a time-bandwidth product of 5, as before. For additional analysis of 2–10 Hz data (periodic Pk1 subpeak parameters), multi-taper absolute spectra were recalculated with a higher spectral resolution (1 Hz) by using 4 tapers and a time-bandwidth product of 2.5.

An effective way to model the 1/f signal of the absolute power spectrum across a variety of datasets is to use a Lorentzian function49. Published code (SpecParam, https://fooof-tools.github.io/fooof/) allows users to fit a Lorentzian to a spectrum, optimized for selection of a fitting range with no periodic power at the edges of the range49. Realistically, selecting such a fitting range is challenging with biological data, and indeed, the implementation of the Lorentzian fit through SpecParam does not work well for pediatric rsEEG data50. The algorithm cut through the absolute spectrum of the pediatric data in the middle of the fitting range, generating negative periodic power between 10–20 Hz, which is not theoretically possible. This issue was resolved previously by shifting the fit downwards (changing the y-intercept but not the slope) until there was no more negative periodic power50. In the present study, we encountered multiple issues of overestimated aperiodic power when using SpecParam to fit the 1/f component of both human EEG data of all ages and mouse LFP data, especially at the bottom edge of the fitting ranges and in the 10–20 Hz range (Supplementary Fig. 1b, 5c). We ran SpecParam (version 1.1.1) in MATLAB R2018b using the MATLAB wrapper (https://github.com/fooof-tools/fooof_mat) and used the following settings: peak width = [0.5 18], number of peaks = 7, peak threshold = 1, and fixed mode for EEG data and knee mode for LFP data. Since our L4 LFP data in mice did not have a linear aperiodic component (Supplementary Fig. 5b, c), the fits could not be uniformly adjusted in the same way as before. Therefore, we wrote custom code in MATLAB (R2018b) to fit Lorentzian functions to the 1/f signals effectively and consistently across all our datasets. Our implementation required the user to have confidence that only one, not both edges of their selected fitting range be without periodic power, which is more realistic given limitations of experimental datasets (such as artifacts, filters, etc.). The first step, as outlined previously, was to plot the average absolute power spectrum per group in log-log space49. This allowed us to identify a good fitting range and to decide if the 1/f fit would be linear or nonlinear (which affected the fitting procedure but maintained consistent application of math and methodology). Our fitting is performed in log-log space on the average absolute spectrum for each subject. Our approach is more data-driven than SpecParam, but this minimized negative periodic power in a consistent way across all datasets.

For data requiring a linear fit in log-log space, which were all data recorded outside of cortex (human and mouse EEG), the bottom edge of the fitting range was determined by the lowest frequency unaffected by high pass filtering (3 Hz for humans, 2 Hz for mice). The [x,y] coordinates for the first point of the fit were [edge of fitting range, average power of the lowest 0.6 Hz of the fitting range]. Next, the algorithm identified the frequency value above 8 Hz that yielded the largest slope magnitude for the fit, as shallower slopes would cut through the absolute spectrum and generate negative periodic power. The slope and intercept were calculated using this frequency value (x-coordinate) and the average power at that frequency and at the frequencies one either side of it (y-coordinate). The offset was calculated as the aperiodic power at the edge of the fitting range (2 or 3 Hz). The linear Lorentzian was then subtracted from the data, revealing the periodic spectrum. To analyze the periodic peaks for human data, we found the maximum values of the periodic spectra less than (Pk1) and greater than (Pk2) 15 Hz. The x-coordinate of these maximum values was the center frequency, and the y-coordinate was the maximum power of the peak. For mouse cortical surface EEG data, there was only one periodic peak, which was identified as the maximum value below 10 Hz through a locally-weighted smoothing linear regression filter.

For data with a non-linear 1/f signal in log-log space, which was all the L4 LFP electrode data, an additional knee parameter was needed in the Lorentzian function. Our general approach for fitting the knee was consistent with SpecParam, where we first fit a linear aperiodic component within the frequency range above the knee and then fit the knee to the low-frequency values below the inflection point49. Thus, we needed to fit our initial linear 1/f estimate based on the upper edge of the frequency range, which meant it was critical to select an upper point with no periodic power. This required fitting out to 175 Hz, as there was still periodic power (especially in Fmr1-/y mice) through about 160 Hz. The frequency value (x) with the lowest absolute power (ignoring the 45–75 Hz range if there was notch filtering) and the average power of it and its neighboring two frequency values (y) were used to form the first coordinate pair for the linear aperiodic estimate. Then, the algorithm identified the frequency value between 9 and 25 Hz (one of the problematic ranges for SpecParam) that yielded the smallest magnitude slope of the linear fit, as steeper slope values would cut through the absolute spectrum and generate negative power. The second coordinate pair for linear estimate was determined from this frequency value (x2) and the average power at that frequency and at the frequencies one either side of it (y2). The slope and intercept values of the Lorentzian were ascertained through this linear estimate. To determine the knee, we found the local minima power values at frequencies between 1.5 Hz (the edge of the fitting range) and 2.24 Hz (below the first periodic peak), and, assuming these values were less than the linear 1/f estimate at this frequency, used them to calculate multiple knee value estimates according to the Lorentzian function: knee = 10^(b-y-x-m), where (x,y) are the (frequency, power) coordinates of the spectrum, m is the slope, and b is the intercept. The final knee parameter was calculated as the average of these estimates, and the offset was calculated as the aperiodic power at the edge of the fitting range (1.5 Hz). With the full equation for the non-linear 1/f, we then subtracted the fit from the absolute spectrum to analyze the periodic spectrum. To analyze the subpeaks of periodic Pk1 (2–10 Hz), we found the largest local minimum of the data in the middle of this range using a locally-weighted smoothing linear regression filter. Pk1a was either the maximum value of the periodic spectrum below the local minimum or the maximum value below 6 Hz, and Pk1b was the maximum value above the local minimum or 6 Hz. This automated selection was confirmed manually for each subpeak, with manual selection used in cases of poor automated detection.

Following previously published methods, modulation index (MI) for head-fixed murine LFP data was calculated from the probability distribution of the amplitude signal across 18 bins of phases60,115. Signals were band-passed using the eegfilt function in MATLAB (R2018b) designed by Scott Makeig (https://sccn.ucsd.edu/~arno/eeglab/auto/eegfilt.html, copyright 1997) and then the Hilbert transform was calculated to find the amplitude or phase values from the analytical signals. To generate comodulograms, the phase for each 5-second epoch was analyzed between 2–9 Hz in step sizes of one and with a bandpass bandwidth of two, while the amplitude was analyzed between 10–60 Hz with a step size of two and a bandpass bandwidth of four, resulting in 6 low-frequency phase signals and 26 high-frequency amplitude signals. For each combination of low-frequency phase and high-frequency amplitude signals, the average amplitude across all phases within each of the 18 bins was normalized to the sum of all mean amplitudes across all bins, yielding the phase-amplitude distribution, PA. The MI was calculated as follows: MI = (log(N) - sum(PAj * log(PAj))) / log(N), where N = the number of phase bins (18). Noise comodulograms were generated by shuffling trials, correlating the low-frequency phase signal of one 5-second epoch with the high-frequency amplitude signal of a different 5-second epoch. MIs were noise-subtracted using a bootstrapping procedure described in the Statistics section below. For PA plots, the phase was calculated from the 4–6 Hz analytical signal and the amplitude was calculated from the 15–40 Hz analytical signal for each 5-second epoch.

Murine subjects. To analyze the temporal dynamics of periodic Pk1, the same bandpass filter and Hilbert transform methodology were used, filtering 100 seconds of continuous head-fixed LFP data between 2–10 Hz and freely-moving EEG/LFP data between 3–8 Hz (the steeper aperiodic slope required a tighter range). If more than 100 seconds of continuous head-fixed LFP data were available, the starting point of the 100-second chunk was randomly generated. Head-fixed mice recorded on the piezo disk that did not have at least 100 seconds of continuous data free of large motion artifacts that spiked low frequency power (see Spectral analysis section) were excluded from this analysis. Following published methods, a burst threshold was set at the 90th percentile of the amplitude of the analytical signal for each animal56. Burst count was quantified as the number of threshold crossings, and burst duration was the length of time spent above the threshold in one crossing; a threshold crossing had to last for more than 3 milliseconds to count as a burst56. For animals with piezo data, movement bouts were identified where the piezoelectric signal exceeded a voltage of 2 mV (standard threshold across all animals). To compensate for voltage drops below 2 mV in the middle of movement bouts, a buffer threshold of 750 milliseconds was used. The piezo voltage signal was subsequently converted to binary movement state categorization (1=moving, 0=still) and aligned to the analytical signal to determine if bursts occurred during intervals of movement or quiescence. The sum of this binary signal, normalized to the length of the analytical signal (100 seconds) times the sampling frequency, was used to determine total % time spent moving. For both murine and human data, spectrograms were generated using the Chronux toolbox with 2 tapers, no zero-padding, 3-second moving windows, and 750-millisecond step sizes.

Human subjects. Burst counts and lengths were calculated from the same selection of occipital channels used for the spectral power analysis, but using the 90 seconds of continuous (non-segmented) data. Each individual channel was filtered according to a frequency range determined in a data-driven manner from the analysis of Pk1 in the periodic spectrum. Given the shift in center frequency of Pk1 for FXS subjects, data were filtered from 5–10 Hz for FXS and 7–12 Hz for TD. Filtered data were Hilbert-transformed, and the amplitude of the analytical signal was squared and log-transformed to express in units of power. As with the mice, the 90th percentile of the power for each channel was used as the threshold to calculate endogenous burst counts and burst lengths. Individual channel burst metrics were averaged across channels to provide the average burst dynamics over the occipital region of the head.

Statistics were conducted in MATLAB (R2022b). Initial statistical tests using the one-sample, two-sided Kolmogorov-Smirnov test (MATLAB function kstest) confirmed non-normal distributions of the various datasets. Thus, statistical tests were conducted using methods that did not assume normality, including the two-sample, two-sided Wilcoxon Rank-Sum test (also known as the Mann-Whitney U-test, MATLAB function ranksum), the two-sample, two-sided Wilcoxon Signed-Rank test (for paired comparisons pre/post drug within genotype, MATLAB function signrank), and non-parametric hierarchical bootstrapping to test for differences for metrics that could be well-summarized by a single (or average) value per animal/subject (i.e., aperiodic parameters, periodic peak parameters, microburst count), while bootstrapping was used for more complicated datasets where a single-value summary failed to capture the variability in the data (i.e., power spectra, comodulograms, phase-amplitude distributions).

The output of the MATLAB ranksum and signrank functions yielded a rank-sum or sign-rank statistic, a z-statistic for larger sample sizes (greater than 10 for ranksum, and greater than 15 for signrank), and a p-value. For all Wilcoxon tests, the p-values were corrected using the False Discovery Rate (FDR) Benjamini-Hochberg correction when multiple comparisons were made per plot. These non-parametric Wilcoxon tests do not have degrees of freedom. Effect sizes were determined for sample sizes of 10 or more using Cliff’s Delta (MATLAB function meanEffectsize, with either paired or unpaired comparisons based on the type of Wilcoxon test used), yielding a value between -1 and 1 (where values >= 0.3 or <= -0.3 indicated a medium to large effect size) and confidence intervals (CIs).

CIs for power spectra were assessed through non-parametric hierarchical bootstrapping. Code for bootstrapping was written following published methods65,116. For bootstrapping absolute power spectra in human adults and for all sub-strains of mice, a random subset of subjects within each group or genotype equal to the total sample size was selected with replacement, and for each subject, a random subset of epochs equal to the total number of epochs for that subject was selected with replacement. The average power spectrum was tabulated for each genotype from these random subsets of subjects and epochs, and the difference between genotypes (FX or KO – TD or WT) was stored as the first bootstrapped difference spectrum. The process was repeated 1000 times, and for each frequency in the spectrum, the values were rank ordered and the 5th and 995th values (for 99% CIs, used for murine data) or 25th and 975th values (for 95% CIs, used for human data) were stored along with the median. For each frequency, if the lowest (5th or 25th) value of the CI of the bootstrapped difference was greater than 0, or if the largest (995th or 975th) value of the CI was less than 0, the CI did not include zero and we report a statistically significant difference between genotypes with a small dot below the corresponding frequency on the power spectra plot. For bootstrapping absolute power spectra in human children and aperiodic and periodic spectra for all species, there was only one interpolated or fitted spectrum per subject, so only one level of hierarchical bootstrapping was performed (a random subset of subjects within each genotype equal to the total sample size was selected with replacement). For all power spectra, to utilize littermate pair comparisons, the random selection of subjects (and epochs, if relevant) was matched between the distributions of TD and FXS human subjects and between WT and Fmr1-/y mice when their sample sizes (and epoch counts, if relevant) were equal. This matching was always used for within-subject matched-pair comparisons of periodic and aperiodic spectra before and after a drug.

For phase-amplitude coupling statistical comparisons in WT and Fmr1-/y mice, the generation of bootstrapped 99% CIs for PA plots across the 18 phase bins followed the same procedure as described above for bootstrapping murine absolute power spectra across the frequency bins. The procedure for generating noise-subtracted comodulograms differed slightly. As before, a random subset of subjects within each genotype equal to the total sample size was selected with replacement, and for each subject, a random subset of epochs equal to the total number of epochs for that subject was selected with replacement to generate each of 1000 average comodulograms per genotype. Additionally, a second random subset of epochs equal to the total number of epochs for that subject was selected with replacement (with each epoch value in the second list never matching the corresponding epoch value in the first list) to generate each of 1000 average noise comodulograms per genotype. The difference between the 1000 comodulograms and noise comodulograms was tabulated for each genotype, and the resulting noise-subtracted comodulograms values were rank ordered, with the median, 5th, and 995th values stored (for 99% CIs). Median noise-subtracted comodulograms were plotted (Fig. 5a, b left). MI values in the noise-subtracted comodulograms where the 99% CI included zero (i.e., the MI was not significantly greater than noise) were set to 0 in the median noise-subtracted comodulograms and the plots were replotted (Fig. 5a, b right). Finally, for measurement of the linear correlations in human EEG data, Pearson’s r2 (which also does not assume normality) was determined by fitting a line to the data (MATLAB function polyfit) and then dividing the sum of the squared residuals by the total sum of squares (i.e., the variance times the sample size minus one) and subtracting that value from one. The effect size (Pearson’s r) was determined by taking the square root of r2, yielding a value between 0 and 1, where values <= 0.1 were considered a small effect size.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript by Kornfeld-Sylla and colleagues reports neurophysiological results obtained from human FXS subjects and the Fmr1-/y mouse model. The authors compare EEG recordings in human subjects and mice, aiming to find common altered patterns to be used as biomarkers for the disease. The authors reason that this would allow for circuit dissection in the Fmr1-/y mouse model. The authors find some EEG and LFP alterations in mouse V1, and they correlate them with human EEG alterations. They go on and attempt to define the role of PV and SOM interneurons and excitatory pyramidal neurons in the emergence of LFP alterations in FXS mice. Finally, the authors analyze alterations in VEPs and visual plasticity in control and Fmr1-/y mice.\n\nThe topic is of high interest, and the manuscript contains high-quality data. However I feel that in the present form, the paper includes a series of orthogonal observations and falls short in providing a cohesive narrative and a clear solid message. The authors provide data from EEG from mice and humans, LFP analysis in ‘resting state’ and in response to visual stimulations in L4 of V1, analysis of visual evoked potentials and their plasticity only in mice. This is intermingled with an ambiguous and inconclusive comparison of different cell types that raises more questions than answers. In addition, even the individual sections of the paper often lack a clear conclusion and, when present, the interpretation requires some additional evidence to be fully convincing. The overall impression I had is that the end of the paper does not relate with the initial case that was built in the introduction.\n\nMajor issues:\n1. If the authors wish to provide an overarching comparison between human FXS subjects and the mouse model of this condition, they should compare most spontaneous and visually evoked alterations in both organisms, when this is feasible.\n2. The experiments on cell-type dissection are confusing. Silencing PV and SOM cells can lead to non-controllable network alterations that preclude the interpretation of the results. Moreover, the authors do not perform this analysis on wt and Fmr1-/y mice, so their conclusions are correlative at best. Importantly the silencing methods (pharmaco- vs. optogenetic silencing) present profound temporal controls, adding more confusing method-dependent alterations. I wonder whether one can compare pharmacogenetic silencing of PV cells, optogenetic silencing of SOM cells and genetic removal of FRMP in glutamatergic neurons and glial cells.\n3. The authors should be cautious in comparing burst activity in humans and mice, as it seems that enhanced bursts occur in FXS patients only during tasks, whereas here the authors analyze bursts in mice only during resting states. Are these micro-bursts the same? The authors do not record burst activity during resting state in their human samples, raising a potential problem in comparing the two conditions in the two organisms.\n4. The experiments of Fig. 8 are difficult to follow. Why are only KO mice shown in panels B and F? KO data is usually compared to their wt littermates, otherwise it becomes anecdotal. In panel C, why are VEPs lacking the P1 peak? What is the interpretation of the results illustrated in panel K? Overall, the SRP results are not even discussed. These results seem preliminary, and they do not relate with the alterations of the alpha phenotype described in Figs. 1-5, which represents the only associated element with the human disease.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "This manuscript reports a translational electrophysiological biomarker linking alpha-frequency slowing in humans with homologous Pk1a deceleration in Fmr1 KO mice. The multimodal convergence—human EEG, mouse VEPs, and intracortical LFPs—is compelling and methodologically sophisticated. The study also attempts to assign mechanistic specificity to cortical excitatory neurons using Emx1-Cre conditional knockouts.\n\nHowever, the central causal claim—that this biomarker is caused by loss of FMRP in excitatory neurons (as seen in the title)—goes beyond what the current data can justify. The Emx1-Cre model demonstrates sufficiency but not necessity, as no selective rescue experiment is performed. Developmental confounds and indirect network effects remain plausible explanations. Without demonstrating that restoring FMRP in excitatory neurons abolishes the biomarker, the causal inference is incomplete and should be reflected within the manuscript and title.\n\nBehavioral state discrepancies (e.g., arousal, visual input) between humans and mice are not explicitly controlled or covaried and are known to be strongly associated with alpha states. This should be more clearly documented and raised as limitation.\n\nCross-species translation is thoughtfully approached but weakened by anatomical and behavioral mismatch. Human alpha is recorded at the scalp without source localization, whereas mouse data come from layer 4 V1. Human EEG reflects a blend of signals from various cortical areas, not just V1 in contrast to the murine recordings. To a degree, the strong translational claim of functional homology between V1 and human occipital cortex is hinged on this argument. A source-reconstruction analysis (e.g., beamforming or CSD) is needed to confirm anatomical homology. At minimum, if source analysis is not performed than interpretation should be tempered on circuit level homology and speak more broadly to contributions from parietal, frontal, and non-visual regions.\n\nStatistical methodology is rigorous but under-documented in places. Multiple spectral comparisons per figure require formal correction (e.g., FDR). Even if bootstrapping is used, it is not typically a reason to avoid multiple comparison correction since that is determined by the number of tests performed. Additional reporting on experimental blinding, randomization, and replication strategies—especially for opto- and chemogenetic conditions.\n\nThe biomarker’s specificity is unproven, as similar alpha-frequency slowing has been documented in other neurodevelopmental and psychiatric disorders, including autism spectrum disorder (ASD), Down syndrome, and first-episode psychosis. This raises the possibility that alpha slowing is a non-specific marker of global neural dysfunction rather than a signature unique to Fragile X Syndrome. Without comparative data or test–retest reliability in FXS, its translational utility remains uncertain, claims of a biomarker should be tempered and discussed in the limitations.\n\nDespite these critiques, your manuscript represents an ambitious and highly promising contribution. With careful revision addressing these points, your work can set a new standard for rigor in translational biomarker research. I look forward to seeing a revised version that realizes the full impact and potential of your findings.", "reviewer_4": "This study identifies an electrophysiological biomarker of Fragile X Syndrome (FXS)—a shift in alpha oscillations—that is present in both human patients and a mouse model (Fmr1-/y mice). Specifically, the researchers found that children and adults with FXS exhibit a consistent slowing of alpha rhythms in resting-state EEG, and this same pattern is mirrored by a corresponding shift in low-frequency oscillations in the visual cortex (V1) of Fmr1 knockout mice. The authors show that although both PV+ and SOM+ interneurons modulate different aspects of the affected oscillatory activity in mice (although not the frequency shift), it originates from a loss of the FMRP protein in cortical excitatory neurons, not inhibitory neurons. The study further links these oscillatory disruptions to altered sensory responses. Overall, this work provides a useful cross-species EEG analysis and some mechanistic insight into the physiopathology of Fmr1-/y mice.\n\nMajor comments:\n1. The developmental trajectory of oscillatory changes differs between mice and humans – i.e., the alpha frequency decreases in adulthood in mice, whereas in humans it stagnates. This weakens the model’s predictive power for developmental timing in translational research. Could the authors please address this? Also, could the authors please comment on the developmental trajectory of this alpha frequency phenotype in mice in their data, from juvenile to adulthood? Are the developmental differences only observed under luminescence conditions?\n2. The alpha frequency changes in humans were consistent across different neocortical regions, whereas in the mouse model, they were observed in V1 but not S1. Do the authors have an explanation for this? How would this affect their interpretation as a translational biomarker? Unless the authors have a common function related to alpha frequency oscillations in V1, it will be difficult to reconcile these findings in a translational manner. Can the alpha phenotype be reversed or normalized by therapeutics? To be useful in clinical trials, the biomarker must respond to treatment. Does pharmacological treatment or re-expression of FMRP, or targeted V1 circuit manipulation, reverse the phenotype? And could this correct, for instance, the N1 component of the VEP? I think it would be important to demonstrate the link between alpha and VEP or another physiological readout to strengthen the relevance of alpha oscillations in V1 as a biomarker.\n3. What is the exact mechanism driving the alpha frequency shift in FXS? While PV+ and SOM+ interneurons were shown to modulate different subcomponents of the alpha-like oscillations, neither accounted for the frequency shift – the core ‘alpha phenotype.’ This suggests the proposed mechanism is incomplete and may involve other mechanisms or circuits, such as the thalamocortical loop. In light of this, the interneuron modulation experiments seem insufficient to address the mechanism underlying the alpha frequency shift.\n4. Although alpha oscillations are proposed as a translational biomarker, spectral power and frequency measures are influenced by many biological and technical variables, such as brain region, recording setup, and luminance, amongst others. It may be difficult to generalize the biomarker for clinical use or across labs without standardization. Could the authors please clarify whether, and if so, how they standardized the recordings across labs? Do they have data from either lab to support the developmental trajectory of their alpha wave findings?\n5. The authors go into some detail to describe the neurobiological underpinnings of alpha frequency wave alterations in mice by focusing on layer 4. Some critical findings (e.g., burst dynamics, two-subpeak structure) were only observable in intracortical recordings and not surface EEG. This limits how well human EEG studies can capture the same level of detail as mouse intracortical recordings. Can they please address this and explain the usefulness of investigating the burst dynamics and two-peak structure?\n6. The center frequency in Fmr1-/y mice is ~3-4 Hz within V1. Can the authors please discuss how this can be considered alpha-like instead of theta?\n\nMinor:\n- is this particular alpha oscillation phenotype (frequency shift) also reported in other developmental disorders? Is it unique, or could it serve as a broader biomarker for neural circuit dysfunction?\n- can the authors discuss in more detail how the particular alpha wave alterations might contribute to the sensory symptoms in FXS?"}, "author_response": "We thank the reviewer for the positive assessment of the data quality and for the helpful comments. We understand that the narrative was difficult to follow and have worked to streamline the story and provide a cohesive thread between sections with clear conclusions for each section. We believe the manuscript is much stronger now, thanks to the feedback of this reviewer.\n\nSome of our major changes to the structure of the manuscript include:\n1. We have removed data related to visual stimuli and plasticity, to center the paper around measurements that can be compared between humans and mice. Thus, we just focus on resting-state LFP recordings in mice and tie this to EEG data in mice and humans.\n2. We have clarified that the manipulations of PV+ and SOM+ cells are not meant to explain the emergence of LFP alterations in FXS mice, but rather to test the hypothesis that the activity of inhibitory interneurons is linked to alpha-like oscillations (in mice, in general, see text lines 319-66). Importantly, these results validate the two subpeak structure of periodic Pk1 (this is a novel finding in our work) and set us up to show that pharmacologically boosting inhibition affects these alpha-like oscillations.\n3. To this end, we have also added experiments using Arbaclofen, a GABA agonist, B to address reviewer 4’s interest in studying the effects of pharmacological treatment on the phenotypes (see text lines 368-434 and new figures 7 and 8 and supplementary fig. 11). We first examined the effect of Arbaclofen on WT mice, showing that it increases Pk1a maximum power and decreases Pk1a center frequency. In KO mice, the drug had the same general effect, although their alpha-like oscillations were less sensitive to Arbaclofen, while the sensitivity of their gamma oscillations to the drug was consistent between the two genotypes (see text lines 542-71). This reveals how measuring alpha oscillation disruptions refines our understanding of treatment response and relates to the initial goal of the paper to establish a translatable measure of pathophysiology and treatment response.\n\nMajor issues:\n1. If the authors wish to provide an overarching comparison between human FXS subjects and the mouse model of this condition, they should compare most spontaneous and visually evoked alterations in both organisms, when this is feasible.\nWe thank the reviewer for this insight and agree wholeheartedly. The human and mouse datasets were not designed around each other but rather were collected separately and then brought together afterwards for the paper and analyzed uniformly. We see that the VEP data in mice are confusing because they are not coordinated with any human data, and thus we have removed this data to focus on the cross-species comparisons of spontaneous/resting-state data. We recognize that we do not have parallel human data for our Arbaclofen results but feel this is reasonable given the observed results for Pk1a center frequency in mice and the complexity of collecting such a dataset in humans.\n\n2. The experiments on cell-type dissection are confusing. Silencing PV and SOM cells can lead to non-controllable network alterations that preclude the interpretation of the results. Moreover, the authors do not perform this analysis on wt and Fmr1-/y mice, so their conclusions are correlative at best. Importantly the silencing methods (pharmaco- vs. optogenetic silencing) present profound temporal controls, adding more confusing method-dependent alterations. I wonder whether one can compare pharmacogenetic silencing of PV cells, optogenetic silencing of SOM cells and genetic removal of FRMP in glutamatergic neurons and glial cells.\nThe reviewer raises a valid concern about relating these interneuron manipulations in WT mice to our experiments in Fmr1-KO and EMX1-Cre Fmr1-KO mice. First, we have changed the way we present the data (see text lines 301-448). We now begin with the removal of FMRP in glutamatergic neurons and glial cells, to show that this is sufficient for the “alpha” phenotype, as it is for the “gamma” phenotype. We then replicate that the alpha-like oscillations are coupled to higher frequency (beta and gamma, 15-40 Hz) oscillations in WT mice and show that this coupling is impaired in KO and EMX1-KO mice. Since these higher frequency oscillations are known to be coordinated by PV+ and SOM+ interneurons, this suggests there might be a link between these interneurons and the alpha-like oscillations in all mice. And, if such a link does exist, then perhaps pharmacologically boosting inhibition might affect alpha-like oscillations in WT mice, and potentially improve FXS periodic Pk1 phenotypes, since inhibition is known to be deficient in FMR1 KO mice (and in mice where FMRP is only knocked out of excitatory neurons). Thus, we test if there is a link between these inhibitory interneurons and alpha-like oscillations in WT mice and then test the effect of boosting inhibition in both WT and KO mice with Arbaclofen. By setting the narrative up this way, we believe we are doing a better job of relating the interneuron manipulations to the alpha-like oscillations in general, rather than specifically comparing these manipulations to FXS. Any written inferences suggesting, for example, that the effect of a given interneuron manipulation looks similar to an FXS LFP phenotype, are correlative – not a direct comparison - and noted as such with clarifying language in the discussion (see text lines 531-40).\nRegarding the methods of these manipulations - although such experiments might cause broad network alterations (which we acknowledge in the text lines 345-54), it is well established in the literature to use these methods to relate interneurons to different oscillations (see Cardin et al, 2009; Sohal et al, 2009; Chen et al, 2017; and Viet et al, 2017). Since we have clarified that our goal is to relate the interneurons to the oscillation, rather than to relate these various manipulations to the mechanistic basis of FXS phenotypes, we believe we are now well constrained within the precedents set by previous literature and can conclude that the alpha-like oscillations are differentially sensitive to manipulation of different classes of interneurons. Importantly, both the PV+ and SOM+ manipulations used viral instead of purely transgenic methods, limiting the manipulations to V1 and thus controlling for some non-specific cortical network effects (see methods lines 781-810).\nRegarding the difference in pharmo- and opto-genetic approaches, we initially tried pharmacological inhibition of SOM+ interneurons but found this method was not successful, which is why we switched to an optogenetic approach. The timescales are of course different, but we have carefully selected time-series data where the effect of the manipulation will be maximized (i.e., during the laser on periods for the optogenetic manipulation, and 30 minutes after injection of CNO for the chemogenetic manipulation). We do not feel this temporal/methodological difference precludes demonstrating a relationship between the different classes of interneurons and periodic Pk1.\n\n3. The authors should be cautious in comparing burst activity in humans and mice, as it seems that enhanced bursts occur in FXS patients only during tasks, whereas here the authors analyze bursts in mice only during resting states. Are these micro-bursts the same? The authors do not record burst activity during resting state in their human samples, raising a potential problem in comparing the two conditions in the two organisms.\nIn fact, we did study burst activity in our resting-state data from humans in our original submission and plotted the results in a supplemental figure, but we recognize that presenting the mouse and human data split up between main and supplemental figures made the data more challenging to locate and parse. Thus, so everything is together, we have moved the mouse burst analysis to the supplement as well and everything can now be seen together in Supplementary Fig. 7 (see also text lines 281-99).\nWe note did not observe a significant difference in burst dynamics between human TD and FXS subjects for our resting-state data due to large variability in the TD dataset. At this time, it is unclear whether this differing result from what was previously reported in the inter-stimulus period between tasks (Norris et al, 2022) is due to greater internal noise during rsEEG, differing analytical approaches and time-frames measured, the general engagement in periodic sensory stimuli vs. rest, or the task itself. It is intriguing that altered resting-state burst dynamics in Fmr1 KO mice were only detectable in layer 4 and not at the cortical surface. However, from a translational perspective these altered temporal dynamics are not as useful as the “alpha” phenotype, which we detected across species and both inside and outside mouse cortex, which is why we have combined all burst analysis into a supplementary figure.\n\n4. The experiments of Fig. 8 are difficult to follow. Why are only KO mice shown in panels B and F? KO data is usually compared to their wt littermates, otherwise it becomes anecdotal. In panel C, why are VEPs lacking the P1 peak? What is the interpretation of the results illustrated in panel K? Overall, the SRP results are not even discussed. These results seem preliminary, and they do not relate with the alterations of the alpha phenotype described in Figs. 1-5, which represents the only associated element with the human disease.\nWe have removed all mouse VEP data from this paper, including the SRP experiments."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This manuscript is a much-revised version of the previous submission. The authors removed some results (VEPs) and introduced new results on the effects produced by GABAbR agonist Arbaclofen. Although improved, I found it still quite dislocated, disorganized and I struggle to grasp a cohesive conclusion.\n\nThe authors find some modest effects in alpha-band oscillations in mice (albeit EEG effects are present only in V1 but not in S1). Based on the robust alpha phenotype in humans, they claim that alterations of alpha-oscillations represent a general phenotype of FRX.\n\nThe rest of the paper is still very confusing. I am not sure what the main goal of the paper is (finding a biomarker? Studying the mechanisms of alpha activity dysfunctions?).\n\nCell-type-specific manipulations: Genetic removal of frmp from glutamatergic neurons and glial cells of the forebrain results in effects on alpha activity. However, results from PV and SOM interneurons do not bear any causality with these experiments on EMX-cre mice nor, in general, with FRX. Silencing PV cells might result in massive (non-specific?) downstream effects on pyramidal neurons. Also, silencing SOM cells with NpHR3 does not produce any significant results (Fig. 6q-t), although the authors claim a reduction of peak 1a and peak1b based on Fig. 6p (inset). In this context, the results of SOM activation with ChR2 are confusing. Overall, what do the authors conclude from these experiments on different cell types? How can they compare genetic removal of frmp from pyramidal neurons with broad pharmacogenetic silencing of PV cells, optogenetic silencing of SOM cells and optogenetic activation only of SOM cells? I am profoundly confused by the approach used by the authors.\n\nRole of glial cells in LFP generation (EMX1 line): The conclusion that “loss of FMRP in excitatory neurons is sufficient for the alpha phenotype” underplays the established role of glia in shaping local field potentials. Acknowledging this limitation would strengthen interpretation.\n\nArbaclofen results: this effect might indeed be interesting, but I have a few perplexities. First, what is the rationale for using GABAbR agonists in the context of fast inhibition provided by PV and SOM cells? To my knowledge, GABABRs are activated in pyramidal neurons mainly by neurogliaform interneurons. Importantly, these pharmacological results, as illustrated, are very difficult to understand. I struggle to judge whether there is any effect. Perhaps a better way to analyze/illustrate these results would be to plot alpha-oscillation effects for WT (saline + drug) and Frmp+/y mice (saline + drug) with an overall ANOVA analysis to test for differences, based on treatment and/or genotype. Results illustrated in different graphs and figures are highly misleading (and not statistically sound).\n\nDefinition and use of “resting-state”: The manuscript refers to both a gray-screen condition and a screen-off condition as “resting-state.” Given that your own prior work (Hayden et al., 2021) demonstrated increased high-frequency power during gray-screen presentation, these conditions cannot be considered equivalent. The authors should clarify this terminology, as “resting-state” is a central concept in the manuscript.\n\nThe manuscript reports peak center frequencies with sub-Hz precision (e.g., 4.6 Hz), yet the underlying spectral resolution in the mouse datasets is 1 Hz at best. This suggests that the extra decimal precision is generated by the FOOOF/SpecParam model fits rather than directly measured from the PSD. While model fitting is appropriate for estimating peak positions, the manuscript should clarify that these values are interpolated estimates based on a fitted function, not actual measured frequencies. Explicitly stating the true frequency resolution (delta_f) alongside the modeled estimates would prevent readers from overinterpreting the apparent precision.\n\nIn many occasions, results are moved from main figures to supplementary figures, although they are described as full independent datasets (see the analyses on microbursts, for example). This makes reading the paper quite a painful task. In my view, supplementary data should be non-essential extended information of data illustrated in the main figures (as is the case for the human data).", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "We appreciate the authors' thorough and timely response to our reviewer queries. I am satisfied with the breadth of the responses and the modifications. I think this will be a fantastic contribution to the field.", "reviewer_4": "The revised manuscript addresses my prior concerns comprehensively. The additional analyses/clarifications improve the paper’s rigor and readability. I have no substantive remaining issues."}, "author_response": "We thank the reviewer for the positive assessment of our methods and approach.\n\nHowever, the central causal claim—that this biomarker is caused by loss of FMRP in excitatory neurons (as seen in the title)—goes beyond what the current data can justify. The Emx1-Cre model demonstrates sufficiency but not necessity, as no selective rescue experiment is performed. Developmental confounds and indirect network effects remain plausible explanations. Without demonstrating that restoring FMRP in excitatory neurons abolishes the biomarker, the causal inference is incomplete and should be reflected within the manuscript and title.\nWe thank the reviewer for this insight and have changed all language related to the Emx1-Cre results from describing causality to describing sufficiency. We have removed this clause from the title entirely, which was a bit too long anyway!\n\nBehavioral state discrepancies (e.g., arousal, visual input) between humans and mice are not explicitly controlled or covaried and are known to be strongly associated with alpha states. This should be more clearly documented and raised as limitation.\nThe reviewer raises an important point that while visual input was controlled for mouse LFP recordings and movement was measured with a piezo disk, revealing no significant difference in total movement between KO and WT groups (see Supplementary Fig. 7), this was not the case for human EEG recordings. We have now explicitly noted in the text that FXS children and adults move more and Fmr1 KO mice do not (see lines 287-89). As a point of consistency, for both mouse LFP and human EEG recordings, epochs containing large motion artifacts were excluded (while for mouse EEG recordings, epochs with no movement were selected through video-monitoring). For humans we recognize that there are differences between the experimental groups: for example, FXS subjects, especially the children, sometimes required different room lighting or visual input (i.e., a cartoon instead of the standard screen saver on the screen) to help them sit still. This has been more explicitly stated in the discussion (lines 511-514) and methods (lines 637-42 and 668-73) and raised as a limitation.\n\nCross-species translation is thoughtfully approached but weakened by anatomical and behavioral mismatch. Human alpha is recorded at the scalp without source localization, whereas mouse data come from layer 4 V1. Human EEG reflects a blend of signals from various cortical areas, not just V1 in contrast to the murine recordings. To a degree, the strong translational claim of functional homology between V1 and human occipital cortex is hinged on this argument. A source-reconstruction analysis (e.g., beamforming or CSD) is needed to confirm anatomical homology. At minimum, if source analysis is not performed than interpretation should be tempered on circuit level homology and speak more broadly to contributions from parietal, frontal, and non-visual regions.\nWe agree whole-heartedly with this comment and have performed source localization to show that 4-14 Hz absolute power (the range of periodic Pk1, i.e., alpha oscillations) is localized to occipital cortex in TD. We have included this analysis in Figure 1t. Despite the enlarged head size of FXS patients, which presents technical differences in interpreting source localization results, the power still localized to the back of the head, though slightly more superior and anterior. We have included this analysis in Supplementary Fig. 1l). See also text lines 166-172.\n\nStatistical methodology is rigorous but under-documented in places. Multiple spectral comparisons per figure require formal correction (e.g., FDR). Even if bootstrapping is used, it is not typically a reason to avoid multiple comparison correction since that is determined by the number of tests performed. Additional reporting on experimental blinding, randomization, and replication strategies—especially for opto- and chemogenetic conditions.\nWe thank the reviewer for the helpful feedback regarding statistics and have added the FDR multiple comparison correction for all Mann-Whitney tests from aperiodic and periodic spectra. We now report FDR-corrected p-values in the figures and uncorrected p-values in the figure legends. We have updated our statistical methods section to reflect our corrections of multiple comparisons (see text lines 990-92).\nWe have also added additional details to the Nature Reporting Summary about experimental blinding, randomization and replication strategies for experiments with drug treatments (including the new Arbaclofen experiments and the opto/chemogenetic experiments). Further details on chemogenetic methods can be found in Kaplan et al, 2016, as we reanalyzed data from this manuscript.\n\nThe biomarker’s specificity is unproven, as similar alpha-frequency slowing has been documented in other neurodevelopmental and psychiatric disorders, including autism spectrum disorder (ASD), Down syndrome, and first-episode psychosis. This raises the possibility that alpha slowing is a non-specific marker of global neural dysfunction rather than a signature unique to Fragile X Syndrome. Without comparative data or test–retest reliability in FXS, its translational utility remains uncertain, claims of a biomarker should be tempered and discussed in the limitations.\nWe have removed the term “biomarker” when describing our results and have used “signature,” “phenotype,” or “translational measure of pathophysiology and treatment response” to offer a clearer, more limited description. By this definition, the measure could be shared by other disorders (see text lines 82-89), so long as it is not observed in the control/typically-developing groups, as it is not intended to be a diagnostic measure (since EEG is not needed to diagnose FXS). Instead, the measure offers a way to parse patients and monitor treatment responses across a variety of neuropsychiatric conditions.\n\nDespite these critiques, your manuscript represents an ambitious and highly promising contribution. With careful revision addressing these points, your work can set a new standard for rigor in translational biomarker research. I look forward to seeing a revised version that realizes the full impact and potential of your findings.\nWe thank the reviewer for the positive assessment of the research and really appreciate the helpful feedback."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors responded to my previous criticisms with an almost identical manuscript, and a rebuttal letter that expresses understandable frustration but provides quite unconvincing arguments. All the major issues that I raised remain, despite the authors claim factual errors from my side.\n\nOverall, I am persuaded that this manuscript does not make the level of Nature Communications. I still think that the paper does not provide a clear conclusion nor a specific ‘alpha’ electrophysiological signature of FXS pathophysiology that spans species. In addition, I think that there are serious concerns about signal processing, thereby hindering the interpretation of the results.\n\nHere’s a tentative response to the authors’ rebuttal.\n\nNarrative and conclusions:\nThe authors claim that they find an alpha phenotype in humans and mice. However, the EEG/LFP is strongly affected in all frequency ranges. So, in my opinion, in both humans and mice there is not an alpha phenotype, but a general EEG/LFP phenotype. Just seeing widespread changes that include alpha frequencies is not generally sufficient to declare an alpha-specific phenotype. This suggests a broader electrophysiological pattern involving multiple frequency bands. This broader spectral change may reflect a state of cortical hyperexcitability or altered network dynamics rather than a purely alpha-specific phenotype. Therefore, it is more accurate to describe such findings as an altered spectral phenotype or a disrupted oscillatory profile rather than solely an alpha phenotype. The authors cite published literature justifying their alpha phenotype case. However, in these papers (e.g. Sohal et al., 2009; Cardin et al., 2009; Chen et al., 2017; Veit et al., 2017; Huang 2020) conclusions are drawn because specific manipulations induced effects on restricted frequency bands.\n\nThe authors then ask: Can the FXS “alpha” phenotype be reproduced by loss of FMRP restricted to cortical excitatory neurons and glia? Again, the evidence points towards a general effect, and not really an ‘alpha’ phenotype. A broad spectral effect resulting from FRMP deletion in pyramidal neurons and glial cells is unsurprising, as glutamatergic neurons and glial cells make the vast majority of the cell population constituting the cortical network from which they record.\n\nThey then look whether brute-force silencing of PV or SOM interneurons alters the LFP of the mouse V1. Silencing PV cells does indeed alter the LFP of V1. This observation is, however, orthogonal to the main topic of the paper. If the authors do not mean to provide a mechanism involving PV-cell role in FXS, the relationship between PV-cell activity and alpha oscillations is merely descriptive and correlative. But again, the main problem, in my opinion, is that silencing PV cells does not affect the b-peak of the alpha-frequency band of the LFP, but it affects many frequencies.\n\nRegarding SOM cells, I am not convinced that there is a statistically significant effect following optogenetic silencing. In their response, the authors state: “Although the p-value for the by-animal comparison of peak 1a maximum power was 0.08 following multiple comparisons corrections (Fig. 6r), the genotype difference is still strongly apparent in the separation of the datasets and uncorrected p-value of 0.02.” I find it confusing. Why did they use multiple comparisons to analyze Peak 1a power in two conditions? Why do they claim significant changes from uncorrected p-values? When do they use corrected-vs. uncorrected p-values?\n\nRegardless of whether silencing SOM cells does indeed change the LFP power spectrum, and similarly to the case of PV cells, I really don’t see how this experiment fits with a pathophysiological investigation of FXS.\n\nSupplementary information, in the present form, is not extended data to results illustrated in the main figures. Instead, they are stand-alone findings that make the paper confusing. The authors’ response is quite underwhelming, as this organization does very little to “connect[ing] worlds that are often not in sufficient dialogue with each other”. I strongly encourage the authors to de-clutter the paper, focusing on a simple straightforward message.\n\nRegarding the criticisms of signal processing and statistics:\nSpectral resolution. In their response they state: “we used a different definition of frequency resolution based on the tapers (smoothing) used in the multitaper method, which sets the minimum resolvable distance between spectral peak”. Therefore, spectral resolution is determined by the taper bandwidth, and not by the actual (numerical) frequency resolution. The Fourier transform has bins spacing at 1/T = 0.2 Hz. However, the taper window (TW) = 5 so the actual resolution (W) is 1 Hz. Hence, peak separation must follow W and not 1/T. Whereas they can obtain a numerical sub-Hz resolution, this would be biologically misleading as sub-Hz values are extrapolated after multi-tapering. This makes sub-Hz precision claims biologically meaningless and thus decimals should not be compared. The separation in peaks 1a and 1b seems very aleatory (I don’t see clear peaks in Fig. 3c, inset, black trace; Fig. 3j inset (both black and red trace); Fig. 4b, inset (both black and brown traces); Fig. 7f,p insets (both black and magenta traces); Fig. 8c, inset (both magenta and purple traces).", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "I appreciate the authors thoughtful responses. Though my initial queries were satisfactory, I was confused on this recent revision (though this issue may have existed in previous versions).\n\nThe methods clearly state that a 60 Hz notch filter was used throughout for murine preprocessing. However, across nearly every figure in the main manuscript and supplementary figures there prominant 60 Hz features. In addition, it appears as though statistics were performed showing significance bars under 60 Hz peaks.\n\nI tried to come up with an alternative reason why this might be the case, but if the notch was performed properly it would significantly affect multiple analyses. I would appreciate if the authors could comment on this curosity, as typically a line noise filter greatly attenuate these frequencies.", "reviewer_4": "Overall, the authors have addressed Reviewer 1’s comments appropriately. I have one additional remark regarding a specific point (see below).\n\nReviewer #1 comment:\nAlso, silencing SOM cells with NpHR3 does not produce any significant results (Fig. 6q-t), although the authors claim a reduction of peak 1a and peak1b based on Fig. 6p (inset). In this context, the results of SOM activation with ChR2 are confusing. Overall, what do the authors conclude from these experiments on different cell types?"}, "author_response": "[Review submitted as PDF attachment - text not found in document]"}]

Decision letter