Multiscale regulation of experience-dependent plasticity by a Pannexin1 homolog in a developing vertebrate brain

Zebrafish, Habituation, Non-associative learning, Synaptic plasticity, Pannexin1 27 28 29 30 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted April 23, 2026. ; https://doi.org/10.64898/2026.04.22.720230doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted April 23, 2026. ; https://doi.org/10.64898/2026.04.22.720230doi: bioRxiv preprint .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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31 Experience-dependent plasticity enables the developing brain to adapt to repeated sensory input 32 while maintaining stability. However, how such plasticity is coordinated across behavior, 33 transcription, and network dynamics remains unclear. Here, using larval zebrafish, we identify 34 the pannexin channel homolog, Panx1a as a regulator of multiscale adaptations during visual 35 habituation. Panx1a loss selectively impairs long-term habituation without affecting baseline 36 sensorimotor responses. This behavioral deficit is accompanied by disrupted activity-dependent 37 transcription across distributed brain regions and altered excitation-inhibition balance. At the 38 network level, Panx1a deficiency attenuates experience-dependent modulation of gamma 39 activity, cross-frequency coupling, and inter-regional coherence. We further show that sharp 40 wave-ripple-like events are present in vivo at an early developmental stage and exhibit selective 41 refinement of their sharp-wave component following experience, while ripple features remain 42 largely unchanged. This refinement is reduced in panx1a mutants, indicating a role for Panx1a in 43 shaping network dynamics rather than event generation. Together, these findings identify Panx1a 44 as a mediator linking extracellular signaling to coordinated behavioral, molecular, and circuit-45 level plasticity during early brain development. 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted April 23, 2026. ; https://doi.org/10.64898/2026.04.22.720230doi: bioRxiv preprint

61 Habituation, the progressive attenuation of a behavioral response to repeated, inconsequential 62 stimuli, is among the most phylogenetically conserved forms of learning (Rankin et al., 2009; 63 Thompson & Spencer, 1966). Far from a passive process of neural fatigue, habituation requires 64 active synaptic modification, including postsynaptic receptor engagement, and at longer 65 timescales, de novo gene expression and protein synthesis (Bailey et al., 2015; Cooke et al., 66 2015; Esdin et al., 2010; Ezzeddine & Glanzman, 2003; Ma et al., 2023). Its defining properties, 67 stimulus specificity, spontaneous recovery, and reversibility by dishabituation, make it a 68 powerful model for studying experience-dependent plasticity (Rankin et al., 2009). Recent work 69 further suggests that habituation reflects an optimization principle, balancing information gain 70 with the metabolic cost of continued responding, implying that neural systems must integrate 71 sensory input with internal energy constraints (Adibi et al., 2013; Attwell & Laughlin, 2001; 72 Nicoletti et al., 2025). 73 Larval zebrafish provide a tractable vertebrate model for investigating these mechanisms. At 6 74 days post-fertilization, larvae exhibit robust visually evoked locomotor responses mediated by 75 reticulospinal circuits downstream of the optic tectum (Gahtan et al., 2005; Burgess & Granato, 76 2007; Randlett et al., 2019; Lamiré et al., 2023). Repeated light stimulation produces progressive 77 response attenuation, and long-term habituation requires NMDA receptor activation and de novo 78 transcription (Roberts et al., 2011, 2016), findings originally established in acoustic and tactile 79 paradigms that parallel molecular requirements identified across other vertebrate systems 80 (Malkani & Rosen, 2000; Mayford et al., 1996; Pellicano et al., 1993; Shimizu et al., 2000). In 81 larval zebrafish, dark-flash habituation, characterized by sudden reductions in illumination, 82 persists for multiple hours and depends on GABAergic inhibition, with distributed plasticity 83 engaging tectal and pallial circuits (Lamiré et al., 2023; Randlett et al., 2019), Despite these 84 advantages, the upstream signaling pathways linking neural activity to transcriptional responses 85 during habituation remain poorly understood. 86 Pannexin 1 (Panx1) is a membrane channel widely expressed in the vertebrate brain (Ray et al., 87 2005; Vogt et al., 2005; G. Zoidl et al., 2007) and enriched at postsynaptic sites of excitatory 88 synapses (G. Zoidl et al., 2007). Its primary function is the release of ATP (Bao et al., 2004; 89 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted April 23, 2026. ; https://doi.org/10.64898/2026.04.22.720230doi: bioRxiv preprint Dahl, 2015), which initiates purinergic signaling through P2X, P2Y, and adenosine (P1) 90 receptors (Khakh & North, 2012; Latini & Pedata, 2001; North & Verkhratsky, 2006). This 91 signaling cascade modulates neurotransmission, intracellular calcium, and gene expression 92 across neurons and glia (Mayhew et al., 2018; Pankratov et al., 2009; Shigetomi et al., 2024). At 93 the synaptic level, Panx1 interacts with NMDA receptor signaling and in turn modulates 94 NMDAR-dependent currents through purinergic feedback (Patil et al., 2022; Rangel-Sandoval et 95 al., 2024a; Weilinger et al., 2016). Loss of Panx1 alters GABAergic transmission, modifies 96 synaptic plasticity thresholds (Ardiles et al., 2014; Gajardo et al., 2018; García-Rojas et al., 97 2023), and affects dendritic structure (Flores-Muñoz et al., 2022; Sanchez-Arias et al., 2019, 98 2020) and network synchronization (Sanchez-Arias et al., 2019, p. 20). Consistent with these 99 roles, Panx1 deficiency has been linked to impairments in learning and memory across model 100 systems (Casillas Martinez et al., n.d.; Gajardo et al., 2018; Illanes-González et al., 2025; Obot et 101 al., 2023, 2024; Prochnow et al., 2012). However, how Panx1 influences brain-wide 102 transcriptional programs and network-level dynamics during learning remains unknown. 103 Neural oscillations provide a systems-level framework for understanding coordinated circuit 104 activity during learning. Theta and gamma oscillations, and their coupling, are closely associated 105 with memory processes (Kendrick et al., 2011; Tort et al., 2009; Wulff et al., 2009), while 106 gamma activity reflects interactions between excitatory neurons and inhibitory interneurons 107 (Amilhon et al., 2015; Buzsáki & Wang, 2012; Fries, 2015). Disruptions in these dynamics often 108 indicate altered excitation-inhibition balance. Sharp wave-ripple (SWR) complexes, 109 characterized by large-amplitude slow waves accompanied by transient high-frequency 110 oscillations, are a hallmark of offline hippocampal activity associated with memory 111 consolidation (Buzsáki, 2015; Colgin, 2016; Koniaris et al., 2011). In zebrafish, recent work has 112 identified sharp wave-ripple-like events in hippocampal homologs, suggesting conserved 113 mechanisms of network coordination (Blanco et al., 2024). Whether such dynamics are present 114 during early development in vivo, and how they are modulated by experience, remains unclear. 115 Here, we use a panx1a loss-of-function zebrafish model (panx1a-/-, TALEN-generated premature 116 stop) to examine the role of Pannexin1 channel in visual habituation. We show that panx1a is 117 broadly expressed across the larval brain and that its loss produces selective neuroanatomical 118 alterations. Behaviorally, panx1a-/- larvae exhibit impaired long-term habituation while 119 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted April 23, 2026. ; https://doi.org/10.64898/2026.04.22.720230doi: bioRxiv preprint maintaining baseline sensorimotor responses, indicating a specific deficit in experience-120 dependent plasticity. This impairment is accompanied by disrupted brain-wide transcriptional 121 responses and altered excitation-inhibition balance, as revealed by pharmacological 122 manipulations. At the network level, Panx1a loss reduces gamma power, disrupts theta-gamma 123 coupling, and alters inter-regional coordination. We further demonstrate that sharp wave-ripple-124 like events are present in vivo at larval stages, are modulated by experience, and depend on 125 Panx1a. Together, these findings identify Panx1a as a multiscale regulator linking extracellular 126 purinergic signaling to transcriptional and network-level plasticity during learning. 127 128

129 Panx1a is broadly expressed in the larval zebrafish brain and is required for visual 130 habituation. 131 We first characterized the spatial distribution of panx1a transcript in 6-dpf WT zebrafish larvae 132 using Hybridization Chain Reaction RNA fluorescence in situ hybridization (HCR RNA-FISH). 133 Panx1a mRNA was widely expressed throughout the brain, with prominent signal in the optic 134 tectum (TeO), telencephalon, and hindbrain (Fig. 1A–C). Quantification of fluorescence 135 intensity across annotated brain regions revealed region-specific differences in expression, 136 including a rostro–caudal gradient along the anterior–posterior axis (Fig. 1B, C). Panx1a signal 137 was observed in proximity to postsynaptic density (PSD) markers, indicating localization within 138 synaptic regions (Fig. 1D, top panel). In addition, panx1a transcript expression was distributed 139 across regions corresponding to both glutamatergic (slc-defined) and GABAergic (gad-defined) 140 domains in the MapZebrain atlas, suggesting broad positioning across excitatory and inhibitory 141 systems (Fig 1D, bottom panel). 142 To assess whether loss of Panx1a loss alters brain size, we performed DAPI-based morphometric 143 analysis in WT and panx1a-/- larvae at 6-dpf. Panx1a-deficient larvae exhibited selective 144 reductions in forebrain width (WT: 183.2 µm; panx1a-/- : 161.1 µm; p < 0.0001) and optic tectum 145 dimensions, including decreased length (WT: 416.4 µm; panx1a-/-: 386.8 µm; p = 0.0023) and 146 width (WT: 198.5 µm; panx1a-/-: 178.5 µm; p = 0.0097) (Fig. 1E, F; Supplementary Table 1). 147 In contrast, hindbrain measurements were unchanged, indicating that Panx1a loss results in 148 region-specific anatomical alterations rather than global developmental defects. 149 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted April 23, 2026. ; https://doi.org/10.64898/2026.04.22.720230doi: bioRxiv preprint We next examined the functional consequences of Panx1a loss using a light-flash habituation 150 paradigm (Fig. 1G), in which repeated visual stimuli elicit progressively reduced behavioral 151 responses. WT larvae exhibited robust within-session habituation across repeated stimuli, with 152 response probability progressively decreasing over successive stimuli and across training blocks, 153 and remaining suppressed at the 2 h test time point (Fig. 1H–K). At the block level, WT larvae 154 showed a marked reduction in the proportion of responders after the first stimulus across 155 successive blocks, consistent with effective habituation (Fig. 1H). 156 In contrast, panx1a-/- larvae showed impaired habituation, with weaker response attenuation 157 during training and elevated response probability at 2 h (Fig. 1H–K; Supplementary Table 1). 158 This impairment was evident across blocks, where mutants maintained a higher proportion of 159 responders following repeated stimulation (Fig. 1H). Notably, baseline responses to the initial 160 stimulus were comparable between genotypes, indicating intact sensory detection and motor 161 output. 162 To determine whether transcription is required for habituation, we inhibited transcription using 163 Actinomycin D. In WT larvae, transcriptional blockade significantly reduced habituation (Fig. 164 1J, K). In panx1a-/- larvae, Actinomycin D further disrupted habituation relative to their already 165 impaired baseline (Fig. 1J, K; Supplementary Table 1). Given that WT larvae exhibit stable 166 retention at 2 h while mutants already display pronounced deficits, subsequent analyses focused 167 on this time point to probe the molecular and circuit mechanisms underlying Panx1a-dependent 168 plasticity. 169 .CC-BY-NC-ND 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted April 23, 2026. ; https://doi.org/10.64898/2026.04.22.720230doi: bioRxiv preprint 170 Figure 1. panx1a expression, neuroanatomical alterations, and impaired visual habituation in 171 larval zebrafish. (A–C) Whole-brain mapping of panx1a mRNA expression in 6-dpf WT larvae 172 using HCR RNA-FISH. (A) Dorsal view showing widespread expression across major brain regions, 173 including telencephalon, optic tectum (TeO), and hindbrain. (B) Quantification of mean fluorescence 174 intensity across annotated brain regions, grouped by major anatomical divisions, revealing region-175 specific enrichment. (C) Distribution of panx1a expression along the anterior–posterior axis, showing 176 a rostro–caudal gradient. (D) High-resolution confocal images showing panx1a signal (magenta) with 177 neuronal marker co-labeling (green), indicating expression within neuronal populations. Insets 178 highlight regional localization across z-planes. (E–F) Neuroanatomical analysis in WT and panx1a-/- 179 -100 -50 0 50 100Habituation percent (%) ✱✱✱✱✱✱✱✱ ✱✱✱✱ ✱✱ 123450 60 70 80 90 100 110 % of larvae responding to first stimuli for each block WTPanx1a-/- ✱✱✱✱✱✱✱✱✱✱✱✱ Block A G WT panx1a-/- DAPI 1 2 4 3 7 5 6 E F a 3 Panx1a Telencephalon Optic tectumHindbrain Eye 6dpf WT anteriorposterior3 BA H panx1a Expression Mapping Neuroanatomical Differences Behavioral Assessment C 3 40 100 200 300 400 500µm ✱✱✱✱✱ 1 20 100 200 300 400 500µm ns✱✱✱✱ 5 6 70 100 200 300 400 500µm ns ns ns Response Probability WT panx1a-/- ForebrainMidbrain Hindbrain D panx1aExcitatory neuronsInhibitory neurons z = 40 z = 48 z = 56 z = 64 panx1aPSD Visual Habituation Assay I J Actinomycin D Response ProbabilityWT panx1a-/- K panx1a-/-WT No drug+ActinomycinStimuli Stimuli Actinomycin D WT panx1a-/- WT panx1a-/- larvae. (E) Representative DAPI-stained brains with standardized measurement axes. (F) 180 Quantification of regional brain dimensions reveals reduced forebrain width and optic tectum size in 181 panx1a-/- larvae, with no changes in hindbrain measurements. (G) Schematic of the visual habituation 182 paradigm consisting of repeated light stimuli across training blocks followed by retention testing. 183 (H–I) Habituation dynamics across training blocks. Percentage of responding larvae to initial and 184 repeated stimuli (stimulus number shown on the y-axis) shows progressive response reduction in WT 185 and impaired habituation in panx1a-/- larvae. (J) Effect of transcriptional inhibition (Actinomycin D) 186 on habituation dynamics. (K) Quantification of habituation percentage in WT and panx1a-/- after 187 adding ActinomycinD. Data are presented as mean ± SD. Statistical significance was determined 188 using two-way ANOVA with Šídák’s multiple comparisons test and is indicated (*p < 0.05, **p < 189 0.01, ****p < 0.0001; ns, not significant). 190 191 Habituation engages distributed transcriptional programs that require Panx1a. 192 To determine how habituation modulates transcription, we first assessed activity-dependent gene 193 expression using targeted analysis of IEGs. In WT larvae, habituation induced robust expression 194 of IEGs, including fosab, consistent with activation of transcriptional programs following 195 sensory experience (Fig. 2B). In contrast, panx1a-/- larvae showed reduced or altered expression 196 across multiple IEGs, indicating impaired activity-dependent transcriptional responses. 197 We next quantified de novo RNA synthesis using 5-ethynyl uridine (5-EU) incorporation across 198 anatomically defined regions. We focused our analysis on regions that showed robust signal and 199 consistent changes across both transcriptional and activity-dependent readouts (Supplementary 200 Tables 2–3). 201 In WT larvae, habituation was associated with coordinated transcriptional activity across 202 multiple brain regions, including the optic tectum (TeO), cerebellum (Cb), habenula (dHb, vHb), 203 and epithalamus (Ep) (Fig. 2C–E). These regions are associated with sensory processing, 204 integration, and behavioral adaptation. Across these areas, transcriptional responses were largely 205 maintained following training, suggesting stable engagement of distributed brain networks. 206 In contrast, panx1a-/- larvae showed elevated baseline transcription across many of these same 207 regions that was not sustained after habituation. Instead, transcriptional signal decreased 208 following training, notably in optic tectum, cerebellum, epiphysis, dorsal habenula (Fig. 2C–E; 209 Supplementary Table 3). Similar patterns were observed in hindbrain sensorimotor nuclei and 210 tectal neuropil layers, pointing to a broad disruption of activity-dependent transcription. 211 To assess activity-dependent gene expression, we quantified fosab using HCR RNA-FISH. WT 212 larvae showed robust induction of fosab following habituation across forebrain, midbrain, and 213 hindbrain regions, including the optic tectum, habenula, dorsal telencephalon, and epiphysis 214 (Fig. 2F–H; Supplementary Table 4). In contrast, panx1a-/- larvae showed weaker or absent 215 induction, and in some regions, reduced signal after training (e.g., area postrema: 1624.0 → 216 1000.4; superior raphe: 906.1 → 806.1). Regions that showed strong induction in WT, including 217 epiphysis and dorsal telencephalon, displayed only partial responses in mutants. 218 Across regions, the patterns observed with 5-EU and fosab were consistent, indicating a failure 219 to sustain activity-transcription coupling in the absence of a functional Panx1a. Together, these 220 results show that habituation engages coordinated, brain-wide transcriptional responses, and that 221 Panx1a is required to maintain these responses across functionally relevant regions. 222 223 APSRLCAF8LTaCholGlPhGFMNT egintD−MO_s23AF3TNaT rgMNintD−MO_s1PTMT_esD−MO_s1eMBMesintD−MOsD−MO_s1rsD−MO_s23MT_rT eOT eTAF9intD−MO_s4sD−MOPVLPreTpreTh_vvHbPreT_aEmT_rHbdT eldThProdHbPoRsProTLT elvT elRetEmTvTh_aOEAF5vENTOBEp WT_Naive WT_T rained KO_Naive KO_T rained SOSMiD−MOiD−MO_s4iD−MO_s23CbSFGSiD−MO_s5MONintD−MO_s4T eOEpPVLtectal_neuropilAF10T eTSFGS/SGCMBMesAF7iD−MO_s1HbvHbdHbintD−MOsD−MO_s4intD−MO_s5SGCintD−MO_s1sD−MO_s5TLintD−MO_s23OBsac/spvSACsD−MOdT elPreT_aAF8AF9PreTsD−MO_s23FMNaT rgMNsD−MO_s1rsD−MO_s1eEmT_rT elMT_rRh WT_Naive WT_T rained KO_Naive KO_T rained 0 0.2 0.4 0.6 0.8 1 fosab 100µm 5-EU WT + trained panx1a-/- panx1a-/- trained BA C D E H G Activity induced fosabtranscription post-habituation test Global de novo transcriptionpost-habituation test Global de novo transcription labelling using click chemistry Real-time PCR for IEGs Activity-induced fosab labelling using HCR RNA-FISH IEG expressionanalysis Brain activity mapping SOSMiD−MOiD−MO_s4iD−MO_s23CbSFGSiD−MO_s5MONintD−MO_s4T eOEpPVLtectal_neuropilAF10T eTSFGS/SGCMBMesAF7iD−MO_s1HbvHbdHbintD−MOsD−MO_s4intD−MO_s5SGCintD−MO_s1sD−MO_s5TLintD−MO_s23OBsac/spvSACsD−MOdT elPreT_aAF8AF9PreTsD−MO_s23FMNaT rgMNsD−MO_s1rsD−MO_s1eEmT_rT elMT_rRh WT_Naive WT_T rained KO_Naive KO_T rained 0 0.2 0.4 0.6 0.8 1 N = 6/group N = 6/group F WTnaive WTtrained panx1a-/-trainedpanx1a-/-naive WTnaive WTtrained panx1a-/-trainedpanx1a-/-naive 100µm Figure 2. Panx1a-dependent transcriptional responses to visual habituation. (A) 224 Experimental workflow linking behavioral habituation to transcriptional and activity-dependent 225 readouts, including de novo RNA synthesis (5-EU labeling) and fosab expression. Values in red 226 text are non-significant. (B) Heatmap of IEG expression across knockout and trained conditions, 227 highlighting differential regulation following habituation. All fold change values are normalized 228 to naïve WT. (C) Representative whole-brain 5-EU labeling in WT and panx1a-/- larvae under 229 naïve and trained conditions. Scale bar, 100μm (D) Quantification of regional 5-EU signal 230 intensity across anatomically defined brain regions. (E) 3D mapping of transcriptional changes 231 across brain regions, color-coded by training-induced effect size. (F) Whole-brain HCR RNA-232 FISH detection of fosab expression in WT and panx1a-/- larvae under naïve and trained 233 conditions. Scale bar, 100μm. (G) Quantification of fosab signal intensity across brain regions, 234 showing reduced activity-dependent induction in panx1a-/- larvae. (H) 3D reconstruction of 235 fosab-positive signal across brain regions, color-coded by effect strength. Data are presented as 236 mean values (N = 6 larva per group). Statistical details are provided in Supplementary Tables 3-237 4. 238 Panx1a regulates visual habituation through modulation of excitatory and inhibitory 239 signaling. 240 To test whether Panx1a influences circuit excitability during habituation, we pharmacologically 241 manipulated glutamatergic and GABAergic signaling during the visual habituation assay (Fig. 242 3A). Under control conditions, panx1a-/- larvae exhibited impaired habituation relative to WT, as 243 indicated by reduced attenuation of responses across repeated stimuli (Fig. 3B–F; 244 Supplementary Table 1). 245 We systematically probed excitatory and inhibitory signaling to determine whether Panx1a-246 dependent habituation deficits arise from altered synaptic transmission, plasticity, or inhibitory 247 control. We first examined glutamatergic signaling, targeting AMPA receptors to assess fast 248 excitatory transmission and NMDA receptors to probe activity-dependent plasticity. We then 249 manipulated GABAergic signaling to test whether inhibitory control of neural activity 250 contributes to habituation and the observed mutant phenotype. Blockade of AMPA receptors 251 with DNQX reduced overall habituation in WT larvae and diminished genotype-dependent 252 differences (Fig. 3B), indicating that fast glutamatergic transmission contributes to habituation 253 dynamics. Similarly, NMDA receptor antagonism with MK-801 markedly reduced habituation in 254 WT larvae while producing minimal additional effects in panx1a-/- mutants, thereby reducing 255 genotype-dependent differences (Fig. 3C). These results are consistent with a role for NMDAR-256 dependent plasticity in habituation. 257 To further probe NMDA receptor involvement, we enhanced NMDAR function using glycine. 258 Glycine reduced habituation in WT larvae but had limited effects in panx1a-/- mutants, revealing 259 differential sensitivity to NMDAR modulation (Fig. 3D). 260 We next examined inhibitory signaling. Blockade of GABAA receptors with gabazine disrupted 261 habituation in both genotypes and attenuated genotype-dependent differences (Fig. 3E), 262 indicating that inhibitory transmission is required for normal habituation. In contrast, 263 potentiation of GABAA receptors with diazepam did not significantly change habituation in 264 either genotype and did not rescue the deficit observed in panx1a⁻/⁻ larvae (Fig. 3F) suggesting 265 that increasing inhibitory tone alone is insufficient to restore Panx1a-dependent learning deficits. 266 Across manipulations, excitatory and inhibitory perturbations produced complementary effects 267 on habituation. Notably, increasing inhibitory tone alone was insufficient to restore the mutant 268 phenotype, indicating that the observed deficits do not arise from a simple reduction in inhibition 269 but reflect a broader imbalance in circuit dynamics. Similar effects were observed across 270 additional pharmacological manipulations targeting the same pathways (Supplementary Fig. 2), 271 indicating that these results are robust across independent compounds. Together, these findings 272 indicate that Panx1a regulates habituation by modulating circuit-level excitation–inhibition 273 balance. 274 Figure 3. Panx1a-dependent modulation of excitation–inhibition balance during visual 275 habituation. (A) Schematic of pharmacological manipulations during the visual habituation 276 assay targeting glutamatergic and GABAergic signaling pathways. (B) Effects of AMPA 277 receptor blockade (DNQX) on habituation. Left: trial-by-trial response probability across 278 repeated stimuli. Right: quantification of habituation percentage under control and DNQX 279 conditions. (C) Effects of NMDA receptor antagonism (MK-801) on habituation dynamics and 280 overall response attenuation. (D) Effects of NMDA receptor potentiation with glycine on 281 habituation across genotypes. (E) Effects of GABAA receptor inhibition (gabazine) on 282 habituation. (F) Effects of GABAA receptor potentiation (diazepam), showing no rescue of 283 habituation deficits in panx1a-/- larvae. Across panels, line plots represent mean response 284 probability across repeated stimuli, and bar graphs summarize habituation percentage. Data are 285 presented as mean ± SD. Statistical significance was determined using two-way ANOVA with 286 Šídák’s multiple comparisons test and is indicated (*p < 0.05, **p < 0.01, ****p < 0.0001; ns, 287 not significant). 288 289 290 291 MK801 Glycine WT Panx1a-/- WT Panx1a-/- Stimuli StimuliNo drug +MK801 No drug +Glycine Response probability Response probability C D WT Panx1a-/- WT Panx1a-/--100 -50 0 50 100Habituation percent (%) ✱✱✱✱✱✱✱✱ ns ✱✱✱✱ -100 -50 0 50 100Habituation percent (%) ✱✱✱✱✱✱ ns ✱✱✱✱ Gabazine Diazepam Stimuli Stimuli WT Panx1a-/- WT Panx1a-/- No drug +Gabazine No drug +Diazepam Response probability Response probability E F WT Panx1a-/- WT Panx1a-/--100 -50 0 50 100Habituation percent (%) ✱✱✱✱✱ ns ✱✱✱✱ -100 -50 0 50 100Habituation percent (%) ✱✱✱✱✱✱✱✱ ns ns Stimuli DNQX WT Panx1a-/- Response probability A B No drug +DNQX WT Panx1a-/--100 -50 0 50 100Habituation percent (%) ✱✱✱✱ns ns ✱✱ Panx1a regulates experience-dependent oscillatory dynamics and network coordination 292 during habituation. 293 Given the behavioral and transcriptional abnormalities observed in panx1a-/- larvae, we next 294 examined whether network-level activity is altered during habituation. Local field potentials 295 (LFPs) were recorded simultaneously from the antero-dorsolateral pallium (ADL), a higher-order 296 associative region, and the optic tectum (TeO), a primary visual processing center, in 6-dpf WT 297 and panx1a-/- larvae in vivo (Fig. 4A). Representative traces illustrate theta (3-7 Hz) and gamma 298 (30-45 Hz) activity across conditions (Fig. 4B). 299 Theta-band power did not show consistent training-dependent changes across genotypes or 300 regions (Fig. 4C, D), indicating that low-frequency activity is largely preserved. In contrast, 301 gamma-band activity was selectively modulated by experience. WT larvae exhibited training-302 dependent reductions in gamma power, particularly in the ADL, whereas panx1a-/- larvae 303 showed blunted modulation in both ADL and TeO (Fig. 4E, F), indicating impaired regulation of 304 higher-frequency oscillations. 305 We next assessed theta-gamma phase-amplitude coupling (PAC), a measure of cross-frequency 306 coordination. In WT larvae, habituation decreased PAC in the ADL, as reflected in both 307 comodulograms and quantification (Fig. 4G, H). This decrease was absent in panx1a-/- larvae. In 308 the TeO, PAC was significantly increased by training across genotypes (Fig. 4I, J), indicating 309 region-specific effects. 310 Finally, we examined inter-regional coherence as the TeO and ADL are part of the ascending 311 visual pathway (Fig. 4K). WT larvae showed increased coherence in the theta-band following 312 habituation, whereas panx1a-/- larvae exhibited reduced or disrupted coherence compared with 313 trained WT (Fig. 4L). In contrast, coherence in the gamma-band remained largely unaffected by 314 training across conditions (Fig. 4M), suggesting that Panx1a preferentially supports network 315 coordination in slower-wave coherence. Collectively, these results show that Panx1a is required 316 for experience-dependent modulation of gamma activity, cross-frequency coupling, and network 317 coherence. Rather than reflecting a global loss of oscillatory activity, Panx1a deficiency 318 selectively disrupts higher-frequency and integrative network dynamics that support learning. 319 Figure 4. Panx1a regulates oscillatory dynamics and cross-frequency coupling during visual 320 habituation. (A–B) Electrophysiology overview. (A) Schematic of the electrophysiology setup 321 indicating dual electrode placement for recording brain activity in vivo. (B) Representative traces 322 showing theta-band activity (sensory encoding) and gamma-band activity (memory processing). 323 (C–F) Region-specific oscillatory power changes. (C–D) Theta power in the ADL and TeO in WT 324 and panx1a-/- larvae under naïve and trained conditions. Training modestly increased the theta-band 325 power of WT larvae. (E–F) Gamma power in ADL and TeO. WT larvae show decreased gamma 326 power following habituation, whereas panx1a-/- larvae exhibit increased gamma responses across 327 0.0 0.5 1.0 1.5Theta power (%) ✱✱ ns ✱ ns WT Trained panx1a-/- NaiveG PAC strength 0 0.1 (Sensory encoding) (Memory processing) A C Theta Gamma I WTpanx1a-/- TrainedNaive PAC strength 0 0.1 WT panx1a-/- TrainedNaive H 0.00 0.02 0.04 0.06 0.08 0.10PAC Strength ✱✱✱✱ ✱✱✱✱ ns ✱✱✱✱ TrainedNaive J 0.00 0.02 0.04 0.06 0.08 0.10PAC Strength ✱✱✱✱ ✱✱✱✱ ✱✱✱✱ ✱✱✱✱ TrainedNaive ADL EWT panx1a-/- 0.0 0.2 0.4 0.6 0.8 1.0Gamma power (%) ✱✱✱ ✱✱ ✱✱✱ ✱✱✱✱ TrainedNaive TrainedNaive0.0 0.5 1.0 1.5Theta power (%) nsns ns ns TeO D TrainedNaive0.0 0.1 0.2 0.3 0.4Gamma power (%) ✱✱ ✱✱ ✱✱ ✱✱F Theta-Gamma Phase Amplitude Coupling Coherence B K TrainedNaive0.0 0.2 0.4 0.6 0.8 1.0Coherence ✱✱✱✱ns ✱ ns L Theta TrainedNaive0.0 0.2 0.4 0.6 0.8 1.0Coherence nsns ns ns M Gamma WT panx1a-/- WT panx1a-/- both brain regions. (G–J) Theta-gamma PAC. (G, I) Comodulograms illustrating PAC strength 328 across frequency pairs in ADL and TeO for naïve and trained conditions. WT larvae show enhanced 329 PAC following training, especially in the TeO. (H, J) Quantification of theta-gamma PAC strength 330 confirms training-induced decreases in the ADL of WT larvae, while significant training-induced 331 increases in WT that are diminished in panx1a-/- larvae are observed in the TeO. (K–M) Coherence 332 analysis. (K) Schematic illustrating dual-site LFP recording schematic illustrating inter-regional 333 coherence between ADL and TeO. Electrodes (#1, #2) capture local activity (concentric rings), 334 while synchronized oscillations (dashed lines) indicate functional coupling across regions. (L–M) 335 Coherence measurements between the ADL and TeO show frequency-specific alterations following 336 habituation. WT larvae exhibit increased theta-band coherence after training, whereas panx1a-/- 337 larvae show reduced or disrupted coherence compared to the WT; no significant changes are 338 observed in the gamma-band. Data are presented as individual data points with mean ± SD. Number 339 of animals: WT, WT Trained, panx1a-/- trained N = 8; panx1a-/- N = 9. Outliers were removed using 340 the ROUT test (Q = 1%). Data were analyzed using Welch’s t-test (PSD and coherence) and a two-341 way ANOVA with Tukey’s multiple comparisons test (PAC). Statistical significance is indicated as 342 *p < 0.05, **p < 0.01, ***p<0.001, ****p < 0.0001, ns, not significant. 343 344 Experience-dependent modulation of sharp wave-ripple dynamics in the zebrafish pallium 345 requires Panx1a 346 To determine whether experience-dependent plasticity extends to transient network events, we 347 analyzed sharp wave-ripple (SWR)-like complexes recorded from the dorsolateral pallium 348 (ADL) of 6-dpf larvae in vivo (Fig. 5A, B). These events consisted of a large-amplitude sharp-349 wave (SW) deflection temporally coupled to a brief burst of high-frequency ripple activity, 350 consistent with canonical SWR signatures. Time-frequency analysis aligned to ripple peaks 351 confirmed a transient increase in ripple-band power, supporting the identification of these events 352 as SWR-like complexes. 353 Visual habituation was associated with a selective modification of SW structure in the ADL of 354 WT larvae. Specifically, the SW duration was reduced following training, indicating experience-355 dependent refinement of sharp-wave temporal dynamics (Fig. 5C). In contrast, this shortening 356 was attenuated in panx1a-/- larvae, in which SW waveforms exhibited increased variability and 357 lacked consistent training-induced modulation. These observations were confirmed using a two-358 factor linear model, which revealed a significant effect of training on SW duration in WT 359 animals (P<0.01), but not in Panx1a⁻/⁻ mutants, resulting in a genotype-dependent divergence 360 after training. 361 In contrast to these changes in sharp-wave structure, other SWR features were largely preserved 362 (Fig. 5D-F). The total number of SW events did not differ significantly across genotype or 363 training conditions, nor did ripple duration or total ripple count. These results indicate that the 364 generation and frequency of SWR-like events remain intact in the absence of Panx1a, whereas 365 the temporal structure of the sharp-wave component is selectively sensitive to experience. 366 Together, these findings show that SWR-like activity is present in the developing zebrafish 367 pallium and is modulated by sensory experience. Panx1a is not required for the occurrence of 368 these events per se but is necessary for their experience-dependent temporal refinement, 369 suggesting a role in shaping network-level dynamics associated with early forms of learning. 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 Figure 5. Panx1a regulates experience-dependent SWR duration in the dorsolateral pallium. 387 (A) Representative SWR-like complex recorded in vivo from the ADL of 6-dpf larvae. The LFP 388 shows a large-amplitude SW coincident with a transient high-frequency ripple (R) burst. Time-389 frequency spectrogram aligned to ripple peak (dashed line) confirms a temporally restricted increase 390 in ripple-band power. (B) Representative SW waveforms for WT and panx1a⁻/⁻ larvae under naïve 391 and trained conditions (gray, individual events; black, mean), illustrating experience-dependent 392 shortening in WT and increased variability in mutants. (C–F) Quantification of SWR features 393 (points represent individual larvae; mean ± SD). (C) SW duration is reduced following training in 394 WT larvae; an effect attenuated in panx1a-/-. (D) Total number of SW events shows no significant 395 training or genotype effect under mixed-model analysis. (E) Ripple duration is not significantly 396 altered across conditions. (F) Total ripple count remains unchanged across genotype and training. 397 Outliers were removed using the ROUT test (Q = 1%). Statistical comparisons were performed 398 using a two-factor linear model testing the effects of genotype, treatment, and their interaction, with 399 Tukey-adjusted post hoc comparisons of estimated marginal means. Number of animals: WT naïve 400 N = 18; WT Trained N = 14; panx1a-/- naïve N = 16; panx1a-/- trained N = 13. Significance is 401 indicated as **P < 0.01, ns, not significant. 402 WTpanx1a-/- WT –Naïve WT –Trained panx1a-/-–naïve panx1a-/-–Trained Representative SWR complexes 403 Discussion 404 Pannexin 1 channels have been implicated in synaptic signaling, circuit development, and 405 purinergic communication in the central nervous system. Through ATP release and downstream 406 receptor activation, Panx1 is positioned to influence both neuronal excitability and activity-407 dependent plasticity. However, how these functions contribute to learning-related processes 408 across behavioral, molecular, and network levels remains incompletely understood. Here, we 409 identify Panx1a as a regulator of experience-dependent plasticity in larval zebrafish, linking 410 habituation behavior to coordinated transcriptional and circuit-level dynamics. 411 412 Panx1a loss selectively disrupts experience-dependent plasticity 413 Panx1a is broadly expressed across the larval zebrafish brain, with enrichment in sensory and 414 integrative regions such as the optic tectum (TeO) and telencephalon. In mammals, PANX1 has 415 been shown to regulate neural progenitor proliferation and differentiation through ATP-mediated 416 purinergic signaling, suggesting that its loss can produce region-specific developmental and 417 circuit-level effects rather than global disruption (Wicki-Stordeur & Swayne, 2013). Consistent 418 with this, panx1a⁻/⁻ larvae exhibit selective reductions in forebrain and tectal dimensions, 419 pointing to localized vulnerability in circuits that process and integrate sensory information. 420 The reduction in tectal structure is notable given that our HCR RNA-FISH data place panx1a 421 transcript near a synaptic marker. Previous work has localized Panx1 protein to postsynaptic 422 compartments and demonstrated its association with PSD-95 in mammalian neurons (G. Zoidl et 423 al., 2007), supporting a role in synaptic signaling. In this context, Panx1a-mediated ATP release 424 may contribute to the stabilization or modulation of active synapses, particularly in circuits that 425 undergo repeated sensory engagement. Its absence may therefore compromise the structural or 426 functional integrity of high-demand networks, contributing to the regional anatomical differences 427 observed in mutants. 428 At the behavioral level, panx1a⁻/⁻ larvae display a selective impairment in habituation despite 429 preserved baseline locomotor capacity (Figure. 1I, Supp. Figure. S1). In a no-stimulus condition, 430 mutants exhibit activity levels comparable to WT, indicating that the observed phenotype is 431 unlikely to arise from gross motor deficits. While mutants show some response decrement during 432 initial stimulation, they fail to maintain suppression at the 2-hour test point. This pattern is 433 consistent with a disruption in the stabilization or consolidation of habituation rather than in 434 sensory detection or motor output. 435 Interestingly, independent observations from our lab indicate that panx1a⁻/⁻ larvae exhibit 436 reduced and intermittent locomotor activity during the dark phase, characterized by bouts of 437 slow- and medium-speed swimming (Safarian et al., 2020). Together, these findings suggest that 438 Panx1a does not regulate baseline locomotion per se but may instead contribute to the context-439 dependent modulation of behavioral state. In this framework, the habituation deficit reflects a 440 failure to appropriately adjust behavior in response to repeated sensory input, rather than an 441 inability to generate or sustain movement. In zebrafish and other systems, long-term habituation 442 depends on spaced training and requires new gene expression and protein synthesis (Esdin et al., 443 2010; Ezzeddine & Glanzman, 2003; Rankin et al., 2009; Roberts et al., 2016). Supporting this, 444 transcriptional blockade in WT larvae phenocopies aspects of the panx1a⁻/⁻ deficit, suggesting 445 that Panx1a contributes to processes that sustain experience-dependent behavioral change. 446 447 Purinergic signaling and inhibitory regulation during habituation 448 One mechanism that may underlie the failure to maintain habituation is impaired purinergic 449 feedback. Panx1 channels are a major pathway for ATP release, and extracellular ATP can be 450 converted to adenosine, which generally suppresses neuronal excitability through adenosine 451 receptor signaling (Burnstock et al., 2010; Dahl, 2015). During repeated stimulation, such 452 signaling may contribute to activity-dependent dampening of circuit output. In the absence of 453 Panx1a, this feedback loop may be weakened, allowing circuits to recover responsiveness more 454 rapidly and preventing stable suppression. This interpretation is consistent with broader 455 principles of homeostatic plasticity, in which neural circuits adjust excitability to maintain stable 456 function (Pozo & Goda, 2010; Turrigiano, 2008). In panx1a⁻/⁻ larvae, the failure to sustain 457 response suppression may reflect a disruption in excitation–inhibition balance. Panx1 regulates 458 GABAergic transmission, and its loss reduces inhibitory efficacy while shifting circuits toward 459 excitation (García-Rojas et al., 2023), a change that could impair activity-dependent dampening 460 during repeated stimulation. 461 462 463 Disruption of activity-dependent transcriptional programs 464 At the molecular level, Panx1a loss disrupts activity-dependent transcriptional responses, 465 effectively decoupling sensory experience from sustained gene expression changes. In WT 466 larvae, habituation is associated with coordinated increases in de novo RNA synthesis and 467 immediate early gene (IEG) expression across multiple brain regions. In contrast, panx1a⁻/⁻ 468 larvae show a blunted and less organized transcriptional response following training. 469 Activity-dependent transcription in neurons is typically driven by calcium-dependent signaling 470 pathways that link synaptic activity to transcription factor activation and IEG induction (Greer & 471 Greenberg, 2008; Lyons & West, 2011). Panx1-mediated ATP release has been shown to 472 influence intracellular calcium dynamics through purinergic receptor activation (Dahl, 2015; 473 Locovei et al., 2006; Swayne & Boyce, 2017), suggesting a potential mechanism by which 474 Panx1a could contribute to activity-transcription coupling. Through this pathway, Panx1-475 mediated signaling has the potential to influence calcium-dependent transcriptional responses. In 476 this framework, Panx1a may contribute to activity–transcription coupling by facilitating the 477 translation of synaptic activity into sustained gene expression programs. Interestingly, panx1a⁻/⁻ 478 larvae exhibit elevated baseline transcription in several regions. This may reflect compensatory 479 circuit regulation, as neural systems are known to adjust baseline excitability in response to 480 perturbations to maintain functional stability (Pozo & Goda, 2010; Turrigiano, 2008). An 481 elevated baseline state could reduce the dynamic range available for further activity-dependent 482 induction, effectively imposing a ceiling on transcriptional responses. Activity-dependent gene 483 expression is known to depend on stimulus intensity and intracellular signaling thresholds (Greer 484 & Greenberg, 2008; Tyssowski et al., 2018), suggesting that elevated baseline activity may 485 constrain the ability of circuits to mount coordinated transcriptional responses during 486 habituation. 487 Panx1a and excitation–inhibition balance 488 Our pharmacological data further suggest that Panx1a contributes to the regulation of excitation–489 inhibition (E/I) balance during habituation. NMDA receptor antagonism reduces habituation in 490 WT larvae but produces minimal additional effects in panx1a⁻/⁻ mutants, consistent with partial 491 convergence between Panx1a-dependent signaling and NMDAR-linked plasticity mechanisms. 492 Panx1 and NMDAR signaling are functionally coupled in mammalian neurons, with NMDAR-493 mediated calcium influx triggering Panx1 channel activation and Panx1-dependent ATP release 494 further modulating excitability and synaptic signaling (Li et al., 2018; Rangel-Sandoval et al., 495 2024b; Weilinger et al., 2016). In this framework, loss of Panx1 disrupts this feedback loop, 496 which may alter NMDAR-dependent activity and impair the regulation of circuit excitability. 497 Perturbation of GABAergic signaling strongly affects habituation, yet enhancement of GABAA 498 receptor function does not rescue the mutant phenotype. This argues against a simple reduction 499 in inhibitory tone and instead suggests a defect in the activity-dependent recruitment or 500 regulation of inhibition during repeated stimulation. This interpretation is consistent with 501 evidence that visual habituation in larval zebrafish is sensitive to GABAA/GABAC receptor 502 blockade, indicating that inhibitory signaling contributes to habituation learning, and with 503 broader work showing that habituation and experience-dependent sensory filtering can arise from 504 plastic changes in inhibitory circuits rather than static inhibition alone (Das et al., 2011; Kato et 505 al., 2015; Lamiré et al., 2023). Panx1 is also implicated in the regulation of inhibitory synaptic 506 transmission and excitation–inhibition balance in hippocampal circuits, where disruption of 507 Panx1 signaling alters GABAergic transmission and network excitability (Ardiles et al., 2014; 508 Flores-Muñoz et al., 2022; Illanes-González et al., 2025), providing a potential mechanism for 509 the impaired stabilization of habituation observed in panx1a⁻/⁻ larvae. 510 Network-level coordination across oscillations and transient events 511 At the network level, experience-dependent plasticity is reflected in coordinated changes across 512 oscillatory activity and transient population events. In wild-type larvae, habituation was 513 associated with a reduction in gamma-band power in the ADL, a region functionally analogous 514 to the mammalian hippocampus. Gamma oscillations arise from interactions between excitatory 515 neurons and interneurons and are widely interpreted as a marker of local circuit synchronization 516 (Colgin, 2016). The observed reduction in gamma activity is therefore consistent with a 517 reorganization of local network dynamics following repeated stimulation. The absence of 518 comparable modulation in panx1a⁻/⁻ larvae indicate impaired adjustment of these coordinated 519 activity patterns. 520 Changes were also evident at the level of cross-frequency interactions. In wild-type larvae, 521 habituation reduced theta–gamma phase–amplitude coupling (PAC) in the ADL, consistent with 522 a shift in network state. PAC reflects structured interactions between low-frequency phase and 523 high-frequency amplitude and is thought to support the coordination of neural activity across 524 temporal and spatial scales (Soulat et al., 2022). Recent work demonstrates that hippocampal 525 theta–gamma PAC coordinates interactions between frontal and medial temporal lobe circuits 526 and tracks working memory demand and behavioral performance (Daume et al., 2024). In this 527 context, the reduction in PAC observed here is consistent with a transition from a highly 528 coordinated, novelty-responsive state toward a more stable processing regime. This adaptation 529 was absent in panx1a⁻/⁻ larvae, suggesting reduced flexibility in experience-dependent network 530 reconfiguration. 531 At the inter-regional level, coherence between the optic tectum and ADL increased in the theta 532 band following habituation in wild-type larvae but was disrupted in mutants. This indicates that 533 Panx1a contributes not only to local circuit dynamics but also to coordination across regions 534 within the visual processing pathway. Together with the PAC findings, these results point to a 535 role for Panx1a in supporting to organize activity across multiple temporal and spatial scales. 536 Experience-dependent effects were also evident in transient network events. SWR-like 537 complexes were present in the developing pallium in vivo and exhibited selective modulation 538 following habituation. Specifically, the duration of the SW component was reduced in wild-type 539 larvae, whereas ripple-associated features, including duration and event frequency, remained 540 unchanged. This dissociation indicates that experience-dependent plasticity preferentially affects 541 the low-frequency component of these events at this developmental stage. In panx1a⁻/⁻ larvae, 542 this refinement of SW duration was attenuated, while overall event occurrence and ripple 543 properties were preserved. These findings indicate that Panx1a is not required for the generation 544 of SWR-like events but contributes to their temporal structuring. 545 Altogether, these observations suggest that experience-dependent plasticity in the developing 546 brain operates through coordinated adjustments of both ongoing oscillatory activity and transient 547 population events. Across measures, Panx1a loss is associated with reduced modulation of 548 gamma activity, impaired PAC adaptation, disrupted inter-regional coherence, and diminished 549 refinement of sharp-wave dynamics. Rather than reflecting a global disruption of activity, this 550 pattern is consistent with a reduced capacity to reorganize network dynamics in response to 551 experience. 552 A unifying framework across scales 553 Taken together, our findings support a model in which Panx1a functions as a mediator of 554 activity-dependent signaling across multiple levels of organization. Through ATP release and 555 purinergic signaling, Panx1a may couple neural activity to intracellular pathways that regulate 556 transcription, synaptic plasticity, and network coordination. Loss of Panx1a disrupts this 557 coupling, leading to deficits that manifest at the behavioral, molecular, and electrophysiological 558 levels. Rather than acting within a single pathway, Panx1a appears to shape how neural activity 559 is translated into stable adaptations. In this framework, the impaired habituation observed in 560 panx1a⁻/⁻ larvae reflects a broader failure to stabilize experience-dependent changes over time. 561 562 Energy and efficiency in habituation 563 Although we did not directly measure metabolic activity, the dual role of Panx1 in ATP release 564 and purinergic signaling raises the possibility that it contributes to coupling neural activity with 565 energetic regulation (G. S. O. Zoidl et al., 2025). Habituation has been proposed to reflect an 566 optimization process in which neural responses are reduced as stimuli become predictable, 567 balancing information processing with energetic cost. Our results are consistent with this 568 framework, suggesting that Panx1a may participate in signaling pathways that constrain circuit 569 activity during repeated stimulation, although direct tests of this hypothesis will be required. 570 571 Limitations and future directions 572 General limitations should be considered. Transcriptional and co-activation measures remain 573 correlational and do not establish causal relationships between brain regions or processes. 574 Pharmacological manipulations may have indirect effects on circuit excitability, and 575 developmental compensation in panx1a⁻/⁻ larvae cannot be excluded. Additionally, the temporal 576 dynamics of transcriptional responses may differ between genotypes, and our sampling window 577 may not capture all relevant changes. Despite these limitations, the convergence of behavioral, 578 molecular, pharmacological, and electrophysiological findings supports a model in which Panx1a 579 is a key regulator of experience-dependent plasticity. By facilitating the coupling of neural 580 activity to transcriptional and network-level responses, Panx1a contributes to the brain’s ability 581 to filter repetitive stimuli and adapt to a changing sensory environment. 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597

598 Zebrafish Husbandry 599 Tupfel long fin strains of zebrafish were used as wild-type (WT) (Tupfel-longfin - wildtype) 600 larvae. The panx1a-/- mutant lines were generated from these Tupfel long-fin strains via TALEN 601 technology (Safarian et al., 2020). The knockout lines were bred to at least the 3rd generation 602 before testing on their larvae. The fishes were maintained at 28°C on a 14-hour light/10-hour 603 dark cycle in a recirculation system (Aquaneering Inc., San Diego, CA). 604 Behavioral testing of 6-dpf larvae 605 Set up - 6 days post fertilization (dpf) larvae were tested using a Zebrabox® behaviour recording 606 system (ViewPoint Life Technology, Lyon, France; http://www.viewpoint.fr), and the Zebralab 607 software (ViewPoint Life Technology, Lyon, France; http://www.viewpoint.fr). Activity 608 quantization was used to record the response of larvae to the light/dark flashes during the 609 training and testing blocks. For each block, larvae were subjected to 60 light flashes lasting 5 610 seconds, occurring at every 30 seconds. The larvae were trained for three training blocks 611 separated by 30-minutes, and their response was tested after 2hrs and 24hrs to the same stimuli. 612 Light controls were adjusted using a lightbox and were set to 30% visible light for the light-ON 613 segment and 0% for the light-OFF segment. All experiments were performed at 28°C and were 614 performed simultaneously at consistent times of the day to minimize the confounding factors that 615 affect the larval behaviour, such as the circadian clock. 616 Pharmacological assays 617 For pharmacological manipulations, 6-dpf larvae were incubated in E3 medium containing the 618 indicated drug or vehicle control for 1 hr prior to behavioral training. The following compounds 619 were used: AP5 (50 µM) (Wolman et al., 2011), MK801 (20 µM) (Zoodsma et al., 2020), 620 Memantine (30 µM) (Best et al., 2008), DNQX (20 µM) (Bhandiwad et al., 2018), Diazepam 621 (0.75 µM), Gabazine (10 µM) (Jadhav et al., 2025), Bicuculline (5 µM) (Lamiré et al., 2023), 622 Glycine (1 mM), and Actinomycin D (10 µM). Drug concentrations were adapted from 623 published literature for the duration of the visual habituation assay and were selected to avoid 624 effects on kinematic performance or spontaneous initiation of light-evoked bends. Larvae were 625 maintained in drug solution throughout training and testing, unless otherwise noted. 626 For behavioural assays, control and treated groups underwent the standard training protocol, 627 followed by testing at 2 hr post-training. Habituation percent was calculated relative to baseline 628 performance as described above. For each drug condition, both WT and panx1a⁻/⁻ larvae were 629 tested in parallel to enable direct genotype comparisons. 630 Click chemistry in 6-pf larvae: 631 Zebrafish larvae (6-dpf, WT) were incubated with 2.5mM 5-Ethydyl uridine (5-EU) in egg water 632 and tested for habituation paradigm (3 training blocks, 2hr testing block). After testing 2hr 633 response, the larvae were washed 5 times for 2-3 minutes with ice-cold egg water to anesthetize 634 them and were fixed in cold PFA-fixative (4% PFA, 4% sucrose). The larvae were incubated 635 overnight at 4°C. Next day, larvae were digested with 1 mg/mL Collagenase for 1 hr at room 636 temperature and were then washed briefly in PBS-Tween, pH 7.4. They were immediately post-637 fixed for 20 minutes in PFA-sucrose at room temperature. The click-reaction was adjusted to 638 500uL in individual wells and contained 100mM CuSO4, 100mM BTTAA, 100mM sodium 639 phosphate buffer and 10mM Alexa 594-azide. Larvae were stained for DAPI in PBS buffer 640 overnight before mounting in 0.6% low-melt agarose. 641 Hybridization chain reaction RNA-fluorescence in situ hybridization (HCR RNA-FISH). 642 Larvae were raised under standard conditions in egg water. When embryos reached 12 643 hours post-fertilization (hpf), egg water was replaced with egg water containing 0.003% of 1- 644 phenyl 2-thiourea (PTU). Fresh egg water containing 0.003% PTU was replaced every 24 hours 645 until the larvae reached 5 dpf. At 6-dpf, the larvae were treated for behavioural assay or left in 646 dark; and fixed in 4% ice-cold paraformaldehyde (PFA). After 24 hours, the PFA was washed 647 out with 1× PBS, and the samples were gradually dehydrated, permeabilized with methanol, and 648 stored at −20°C for several days until HCR in-situ labelling was performed. Staining was 649 performed according to the manufacturer's protocol for whole-mount zebrafish larvae (Choi et 650 al., 2018). Samples were separated into 5 larvae per well in a 24-well. plate. Rehydration steps 651 were performed by washing for 5 minutes (mins) each in 75% methanol/PBST (1× PBS + 0.1% 652 Tween-20), 50% methanol/PBST, 25% methanol/PBST, and 5 times with 100% PBST. The 653 samples were permeabilized with 30 µg/ml proteinase K for 45 mins at room temperature (RT), 654 followed by post-fixation with 4% PFA for 20 mins at RT, and 5 washes in PBST for 5 mins 655 each. The samples were prehybridized in 500 µl of probe hybridization buffer (Molecular 656 Instruments) for 30 mins at 37°C. Hybridization was performed by adding 2 pmol of each probe 657 set to the hybridization buffer and incubating for 16 hours at37°C. 658 Probe sets for panx1a and fosab were purchased from and designed by Molecular Instruments 659 using proprietary HCR methodology to detect and fluorescently label target RNA transcripts. To 660 maximize targeting, the probe was designed against shared regions of known variants found on 661 NCBI Gene and the Ensembl database. Each probe set consisted of 20 split-initiator probe pairs 662 per target and utilized a B1 amplifier with a 546 nm fluorophore label. To remove excess probes, 663 the samples were washed 4 times for 15 mins each with a wash buffer (Molecular Instruments) at 664 37°C, followed by 2 washes of 5 mins each with 5× SSCT (5× SSC + 0.1% Tween-20) at RT. 665 Pre-amplification was performed by incubating the samples for 30 mins in an amplification 666 buffer (Molecular Instruments) at RT. The fluorescently labelled hairpins (B2-488 for fosab and 667 B1-594 for panx1a) were prepared by snap cooling: heating at 95°C for 90 seconds and then 668 cooling to RT for 30 mins. The hairpin solution was prepared by adding 10 µl of the snap-cooled 669 hairpins (3 µM stock concentration) to 500 µl of amplification buffer. The pre-amplification 670 buffer was removed, and the samples were incubated in the hairpin solution for 16 hours at RT. 671 Excess hairpins were washed three times with 5× SSCT for 20 mins each. Following HCR RNA-672 FISH, larvae were stained with DAPI (1:12,000) overnight at 4°C, followed by three 10-minute 673 washes in 5× SSCT. The samples were then stored in 5× SSCT in the dark at 4°C until imaging. 674 A negative control was included, where no probes were added but fluorescent hairpins were 675 used. The larvae were raised under standard conditions, euthanized at 6 dpf, depigmented in 676 peroxide and potassium hydroxide treatment, and processed for HCR in-situ labeling. 677 678 Confocal image acquisition and analysis. 679 A z-mold or an 8-teeth mold for zebrafish larvae was 3D-printed using 2% low melting 680 agarose on glass-bottom culture dishes (MatTek, P35G-0-10C) (Geng and Peterson, 2021). The 681 larvae were positioned in larva-shaped slots and embedded dorsal side down in 0.5% low 682 melting agarose. Images were acquired on the Nikon A1R confocal system with 10x air or 20x 683 water objectives using the 546 nm, 488 nm and 468 nm lasers. Image analysis was performed 684 using FIJI (v2.9.0). The acquired z-stacks were mapped against the mapZebrain atlas (Kunst et 685 al., 2019; Shainer et al., 2023) using the DAPI stain channel as a bridge (see reference brain 686 mapping in materials and methods). Mean raw fluorescence values were then extracted from 687 well-defined anatomical mapZebrain atlas regions. 688 689 Reference brain mapping. 690 691 We used the ANTs brain registration library (Avants et al., 2011) to map volumes against 692 the mapZebrain atlas (Kunst et al., 2019; Shainer et al., 2023) following a previously established 693 approach applied to another pannexin channel, Pannexin2 (Shanbhag et al., 2025). We first used 694 Fiji to manually convert each acquired confocal volume into separate nrrd files, one for the DAPI 695 and one for the panx1a channel/fosab channel. To then generate a warp transformation file 696 across coordinate systems, we mapped DAPI volumes against the T_AVG_DAPI reference 697 volume (downloaded from the mapZebrain atlas platform). We then applied the resulting 698 transform to the panx1a probe/fosab probe channel. We used brain region masks (downloaded 699 from the mapZebrain platform) to slice out the fluorescence from the mapped image stacks for 700 further analysis of the panx1a or fosab expression levels within well-defined separate brain 701 areas. 702 For all mappings, we used the following ANTs commands: 703 antsRegistration -v 1 -d 3 --float 1 --winsorize-image-intensities [0.005, 0.995] --use-histogram- 704 matching 0 -o --initial-moving-transform 705 [,,1] -t Rigid[0.1] -m 706 MI[,,1,32,Regular,0.5] -c 707 [1000x500x250x300,1e-8,10] -s 3x2x1x0 -f 8x4x2x1 -t Affine[0.1] -m 708 MI[,,1,32,Regular,0.5] -c 709 [200x200x200x100,1e-8,10] -s 3x2x1x0 -f 8x4x2x1 -t SyN[0.1,6,0] -m 710 CC[,,1,2] -c [200x100,1e-7,10] -s 4x3 -f 711 12x8 antsApplyTransforms -d 3 -v 0 -- float -n linear -i -r 712 -o -t -t 714 Fluorescence signal analysis post registration 715 Mean fosab signal intensities were quantified across brain regions in larval zebrafish under 716 different training conditions and genotypes (WT and panx1a-/-). The dataset was filtered to 717 include only those brain regions in WT fish that had signal data from both Naive and Trained 718 groups. For these regions, a Wilcoxon rank-sum test (Mann-Whitney U test) was performed to 719 identify significantly modulated regions (p < 0.05) between WT Naive and WT Trained groups. 720 The heatmap displays the mean fosab signal intensity for each group (WT Naive, WT Trained, 721 KO Naive, KO Trained) across these significant brain regions. The brain regions (columns) are 722 ordered based on the effect size of training in KO larvae, calculated as the difference in mean 723 signal between KO Trained and KO Naive groups. Each row in the heatmap corresponds to a 724 group, and each column to a brain region. Signal intensity is represented using a custom 725 diverging color palette ranging from dark green to bright red. 726 Mean fluorescence signal intensity from click-chemistry labeling was calculated across brain 727 regions in WT and panx1a-/- larval zebrafish under Naive and Trained conditions. For each brain 728 region, the mean signal in KO_Trained and KO_Naive groups was compared, and the top 50 729 brain regions were selected based on the largest training-related change (KO_Trained – 730 KO_Naive). These regions were then plotted in a horizontal heatmap showing mean signals for 731 all four experimental groups: WT_Naive, WT_Trained, KO_Naive, and KO_Trained. Rows in 732 the heatmap represent groups, and columns represent individual brain regions. The brain regions 733 are ordered from left to right by increasing training-related effect size in KO animals. A custom 734 diverging color palette ranging from dark green to red was used to represent signal strength. 735 In vivo electrophysiology 736 Published procedures were used to anesthetize 6-dpf zebrafish larvae for in vivo 737 electrophysiology (Baraban, 2013; Safarian et al., 2020; Whyte-Fagundes et al., 2022). Zebrafish 738 larvae at 6-dpf with or without 4hrs MPTP treatment were anesthetized with 0.3 mM 739 Pancuronium bromide (Sigma‒Aldrich, Oakville, ON, Canada) for 2‒3 min until the touch 740 response stopped. Anesthetized larvae were immobilized in freshly prepared 2% low-melting 741 temperature agarose. A Leica S9E dissecting microscope (Leica Microsystems, Richmond Hill, 742 ON, Canada) was used to orient the dorsal aspect of the larvae to the gel surface. The embedded 743 larvae were placed on the upright stage of an Olympus BX51 fluorescence microscope 744 (Olympus, Richmond Hill, ON, Canada). Larvae were submerged in 1 ml of egg mixture (E3; 745 pH 7.2-7.4) applied topically to the agar. Under direct visual guidance, two glass microelectrodes 746 (1.2 mM OD, approximately 1 µM tip diameter, 2–7 MΩ), backloaded with 2 M NaCl, were 747 placed into the right optic tectum (OT) and the dorsolateral (ADL) region of the pallium. Both 748 microelectrodes recorded local field potentials simultaneously via a Multiclamp 700B amplifier 749 (Axon Instruments, San Jose, CA, USA). The voltage recordings were low-pass filtered at 1 kHz 750 (-3 dB; eight-pole Bessel), high-pass filtered at 0.1 Hz, digitized at 10 kHz via a Digidata 1550A 751 A/D interface, and stored on a PC running pClamp11 software (all from Axon Instruments). The 752 basal activity was recorded for 10 minutes under Light-ON conditions (1000 lux), during which 753 images of the electrode placement were taken for reference. For each fish, brain activity was 754 normalized to its baseline activity to account for the biological variability of individual brains. In 755 the treatment group, larvae were habituated and exposed to the short-term recall, followed by 756 immediate electrophysiological recording. A temperature of 28°C was maintained during the 757 experiments. 758 Power spectral density and coherence detection analysis from continuous LFP recording 759 Power spectral density (PSD) and coherence estimation were performed using NeuroExplorer 760 Version 4 (Nex Technologies, Colorado Springs, CO, 80906, USA). To calculate PSD the 761 original data were split into 65536 data segments. Each interval in a rate histogram was 762 detrended by linear regression, and subjected to Fourier transform (FFT) using Welch’s method 763 of windowing (1, 6, 7). A raw power spectrum was generated from the FFTs. Normalized 764 spectral densities for continuous variables were determined for the frequency bands theta (3–7 765 Hz) and gamma (35–45 Hz) and used for statistical analysis in GraphPad Prism VS10. All data 766 within their respective frequency bands were averaged per animal for visualization and statistical 767 analysis. To measure Coherence, as a function of frequency, between two time series with 768 continuous variables digitized at the same frequency, the FFTs were calculated after detrending 769 and applying Welch’s method of tapering. Confidence levels were calculated as described in 770 Kattla and Lowery (Kattla & Lowery, 2010). Changes in both PSD and coherence between 771 genotypes and training were analyzed for significance via Welch’s t-test in GraphPad Prism. 772 Phase-Amplitude Coupling Analysis 773 The phase amplitude coupling (PAC) was calculated using the pactools code (Tour et al., 2017). 774 The code provides calculations for several PAC methods. Here, a PAC was performed based on 775 the Hilbert transformation and the generalized linear models (GLM) (Penny et al., 2008). The 776 raw LFP recordings were downsampled to 1000Hz. A custom data loader was created to load in 777 the “.abf” file format. The phase from one to 12 hertz (Hz) was calculated to determine how it 778 modulates the amplitudes of beta and gamma oscillations (13 - 100Hz) for one-second 779 timeframes. Following the PAC analysis using the GLM model, the results were averaged and 780 visualized using comodulograms. The unitless beta-coefficients (PAC strength) were extracted 781 from the comodulograms and compared between the different treatments and genotypes. 782 Changes in PAC strength between baseline, treatment, and genotype were determined using a 783 two-way ANOVA with Tukey’s multiple comparisons test. 784 Sharp Wave Ripple Complex Analysis 785 LFP recordings were analyzed using a custom Python pipeline. Signals were imported, and 786 processed channel-wise. Line noise at 60 Hz and its harmonics was removed with notch filters, 787 after which the signal was bandpass-filtered from 1-1,000 Hz using a zero-phase Gaussian-788 windowed finite impulse response filter. Sharp-wave and ripple components were then isolated 789 using zero-phase bandpass filters at 1-30 Hz and 120-220 Hz, respectively. 790 Event detection was performed on the root mean square (RMS) envelopes of the filtered signals, 791 calculated in sliding windows of 30ms for sharp waves and 5ms for ripples. Baseline mean and 792 standard deviation were estimated for each RMS trace using a two-component Gaussian mixture 793 model fitted to the full recording; the lower-mean component was taken as baseline. Sharp waves 794 were detected when SW RMS exceeded 6 standard deviations (SDs) above baseline, with start 795 and end times defined at 4 (SDs). Ripple events were detected when ripple RMS exceeded 4 796 (SDs) above baseline, with start and end times defined at 3 (SDs). Events shorter than 25ms were 797 discarded, and events separated by less than 150ms were merged. SWR complexes were defined 798 as sharp-wave events overlapping in time with at least one ripple. Where multiple ripples 799 overlapped a single sharp wave, the ripple with the largest RMS peak was assigned. SWR 800 parameters were extracted and averaged per animal. Outliers were removed using a ROUT test 801 (Q = 1%). Significance was calculated using a two-factor linear model testing the effects of 802 genotype, treatment, and their interaction, with Tukey-adjusted post hoc comparisons of 803 estimated marginal means. R packages ‘car’ and ‘emmeans’ were used for statistical analysis. 804 Statistics and data reproducibility 805 806 Statistical analyses were performed using GraphPad Prism (v10.2.3) and R. Data are presented as 807 mean ± SD. Brain morphometric measurements were compared using the Kolmogorov–Smirnov 808 test. Behavioral datasets were analyzed using two-way ANOVA followed by Šídák’s multiple 809 comparisons tests. Electrophysiological measures (PSD, coherence) were analyzed using 810 Welch’s t-tests, while phase–amplitude coupling (PAC) was assessed using two-way ANOVA 811 with Tukey’s post hoc comparisons. fosab signal differences between groups were evaluated 812 using the Wilcoxon rank-sum test. For SWR analyses, significance was determined using a two-813 factor linear model followed by Tukey-adjusted post hoc comparisons. Normality and 814 homogeneity of variance were assessed using Shapiro-Wilk and Levene’s tests where applicable. 815 A P value < 0.05 was considered statistically significant. Full statistical details, including sample 816 sizes and exact P values, are provided in the figure legends and Supplementary Table 1. 817 818 819 820 821 822 823 824 825 826 827

828 We thank two members of York University Zebrafish Vivarium, Janet Fleites-Medina, and 829 Veronica Scavo for outstanding zebrafish husbandry. 830 831 Funding 832 This research was supported by the Natural Sciences and Engineering Research Council 781 833 (NSERC) discovery grants RGPIN-2022-04605 (NT), and RGPIN-2019-06378 (GRZ). AB 834 received funding through the Emmy Noether Program (BA 5923/1-1), the Excellence Strategy 835 783 (EXC 2117 422037984), and the Zukunftskolleg Konstanz. 836 837 Author contributions 838 Conceptualization, FN, GRZ; data analysis, FN, GSZ, AB; investigation, all authors; writing – 839 original draft preparation, FN, GRZ; writing - review and editing, all authors; visualization, FN, 840 GSZ; supervision, GRZ; project administration, GRZ; funding acquisition, GRZ, AB. 841 842 Consent for publication 843 All authors have read and agreed to the published version of the manuscript. 844 845 Ethics approval 846 All animal work was performed at York University’s zebrafish vivarium and in an S2 biosafety 847 laboratory following the Canadian Council for Animal Care guidelines after approval of the 848 study 797 protocol by the York University Animal Care Committee (GZ#2019-7-R2). 849 850 Competing interests 851 The authors declare no competing interests. 852 853 Data availability 854 All data supporting the findings of this study are available from the corresponding author upon 855 request. 856 857 Materials & Correspondence 858 Correspondence and material requests should be addressed to Fatema Nakhuda, Georg R. Zoidl 859 Table. Region Acronyms in Figures 1 and 2. 860 861 Acronym Full region name AbdMN abducens_motor_nucleus aTrgMN anterior_(dorsal)_trigeminal_motor_nucleus aChol anterior_cholinergic_domain aLLG anterior_lateral_line_ganglion AP area_postrema cHyp caudal_hypothalamus Cb cerebellum dNIL diffuse_nucleus_of_the_inferior_lobe dHb dorsal_habenula dTel dorsal_telencephalon_(pallium) dTh dorsal_thalamus_proper EmT eminentia_thalami EmT_r eminentia_thalami_(remaining) Ep epiphysis FMN facial_motor_nucleus GABA gabaergic_domain GlPhG glossopharyngeal_ganglion Glu glutamatergic_domain Hb habenula Hyp hypothalamus iD-MO inferior_dorsal_medulla_oblongata iD-MO_s1 inferior_dorsal_medulla_oblongata_stripe_1 iD-MO_s23 inferior_dorsal_medulla_oblongata_stripe_2&3 iD-MO_s4 inferior_dorsal_medulla_oblongata_stripe_4 iD-MO_s5 inferior_dorsal_medulla_oblongata_stripe_5 iMO inferior_medulla_oblongata IO inferior_olive IR inferior_raphe iV-MO_e inferior_ventral_medulla_oblongata_(entire) iV-MO_r inferior_ventral_medulla_oblongata_(remaining) intD-MO intermediate_dorsal_medulla_oblongata intD-MO_s1 intermediate_dorsal_medulla_oblongata_stripe_1 intD-MO_s23 intermediate_dorsal_medulla_oblongata_stripe_2&3 intD-MO_s4 intermediate_dorsal_medulla_oblongata_stripe_4 intD-MO_s5 intermediate_dorsal_medulla_oblongata_stripe_5 intHyp_e intermediate_hypothalamus_(entire) intHyp_r intermediate_hypothalamus_(remaining) intMO intermediate_medulla_oblongata intV-MO_e intermediate_ventral_medulla_oblongata_(entire) intV-MO_r intermediate_ventral_medulla_oblongata_(remaining) IPN interpeduncular_nucleus LRN lateral_reticular_nucleus LT lateral_tegmentum LC locus_coeruleus MO medulla_oblongata MON medial_octavolateralis_nucleus MT_e medial_tegmentum_(entire) MT_r medial_tegmentum_(remaining) Mes mesencephalon_(midbrain) MB midbrain NI nucleus_isthmi nMLF nucleus_of_the_medial_longitudinal_fascicle_(pretectum,_basal_part) OG octaval_ganglion OMN oculomotor_nucleus OB olfactory_bulb OE olfactory_epithelium PNS peripheral_nervous_system PVL periventricular_layer Pit pituitary pTrgMN posterior_(ventral)_trigeminal_motor_nucleus pChol posterior_cholinergic_domain pLLG posterior_lateral_line_ganglion PT posterior_tuberculum_(basal_part_of_prethalamus_and_thalamus) PoR preoptic_region PreT pretectum PreT_a pretectum__alar_part PreTh_v prethalamus_(ventral_thalamus) vTh ventral_thalamus_(subpallium) Pro prosencephalon_(forebrain) Ret retina AF1 retinal_arborization_field_1 AF2 retinal_arborization_field_2 AF3 retinal_arborization_field_3 AF4 retinal_arborization_field_4 AF5 retinal_arborization_field_5 AF6 retinal_arborization_field_6 AF7 retinal_arborization_field_7 AF8 retinal_arborization_field_8 AF9 retinal_arborization_field_9 AF10 retinal_arborization_field_10 Rh rhombencephalon_(hindbrain) rHyp rostral_hypothalamus sPro secondary_prosencephalon SAC stratum_album_centrale SFGS stratum_fibrosum_et_griseum_superficiale SGC stratum_griseum_centrale SM stratum_marginale SO stratum_opticum sD-MO superior_dorsal_medulla_oblongata sD-MO_s1e superior_dorsal_medulla_oblongata_stripe_1_(entire) sD-MO_s1r superior_dorsal_medulla_oblongata_stripe_1_(remaining) sD-MO_s23 superior_dorsal_medulla_oblongata_stripe_2&3 sD-MO_s4 superior_dorsal_medulla_oblongata_stripe_4 sD-MO_s5 superior_dorsal_medulla_oblongata_stripe_5 sMO superior_medulla_oblongata SR superior_raphe sV-MO_e superior_ventral_medulla_oblongata_(entire) sV-MO_r superior_ventral_medulla_oblongata_(remaining) TN trochlear_motor_nucleus TeO tectueom TeT tectum_&_tori Teg tegmentum Tel telencephalon TL torus_longitudinalis TS torus_semicircularis TrG trigeminal_ganglion TrMN trigeminal_motor_nucleus VSL vagal_sensory_lobe VgMN vagus_motor_nucleus vENT ventral_entopeduncular_nucleus vHb ventral_habenula vTel ventral_telencephalon_(subpallium) vTh_a ventral_thalamus__alar_part 862 863 864 865 866 867 868

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