Claustrum−cortical reciprocal connections orchestrate allostatic responses following stress

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Liu 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gaojie Shao 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 4 College of Forensic Medicine, Xi’an Jiaotong University , Xi’an 710061, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zijun Liu 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 4 College of Forensic Medicine, Xi’an Jiaotong University , Xi’an 710061, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jie Shao 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shuai Chen 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xirong Xu 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qian Xiao 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yu Chen 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 5 University of Chinese of Academy of Sciences , Beijing 100049, China 6 Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liping Wang 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 5 University of Chinese of Academy of Sciences , Beijing 100049, China 6 Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fan Yang 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 5 University of Chinese of Academy of Sciences , Beijing 100049, China 6 Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jie Tu 1 Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 2 Shenzhen Key Laboratory of Neuroimmunomodulation for Neurological Diseases, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 3 Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China 5 University of Chinese of Academy of Sciences , Beijing 100049, China 6 Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: jie.tu{at}siat.ac.cn Abstract Full Text Info/History Metrics Supplementary material Preview PDF SUMMARY Anxiety, whilst often viewed as a disorder, is an evolutionarily conserved mechanism that facilitates threat detection and survival. However, when stress regulation becomes maladaptive, this adaptive response can shift into pathology. Here, we identify the claustrum (CLA) as a key hub for allostasis following stress, integrating Gad2 (GABAergic)-vGluT1 (glutamatergic) microcircuits. We report that acute social defeat stress activated the CLA and induced hypervigilance and anxiety-like behaviors. Multimodal analyses revealed transcriptional plasticity in CLA neurons, and fiber photometry revealed anticipatory activation of Gad2 neurons and reactive activation of vGluT1 + neurons. We further delineated a reciprocal GABAergic-glutamatergic circuit between the CLA and the anterior cingulate cortex (ACC) that orchestrates allostasis following stress via opposing mechanisms: (1) glutamatergic CLA−ACC projections that amplify threat responses, and (2) two distinct GABAergic inhibitory pathways – intrinsic CLA Gad2+ activity and top-down ACC−CLA Gad2+ modulation. Chronic stress drives persistent hyperactivation of CLA Gad2+ neurons, suppressing CLA glutamatergic activity and leading to depression-like behaviors. Our results identify a dynamic CLA circuit that gates stress responses via CLA Gad2+ neurons acting as a brake under acute stress. Chronic stress amplifies this inhibition, thereby disturbing circuit balance and driving behavioral despair affective pathology. INTRODUCTION Anxiety disorders are strongly associated with chronic or excessive stress exposure, which disrupts brain circuits that are involved in emotional regulation. 1 , 2 Stress-induced neuronal hyperactivity, synaptic plasticity impairments, and neurochemical imbalances, are key contributors to the development of anxiety-like behaviors. 3 – 8 Yet, anxiety itself is not inherently pathological but is an evolutionarily conserved response that promotes threat detection and survival under acute stress. To prevent this adaptive response from developing into persistent anxiety, the brain recruits intrinsic protective mechanisms that dynamically modulate behavioral and physiological reactivity. Whilst significant progress has been made identifying the pathological outcomes of chronic stress, the underlying circuit-level mechanisms that regulate arousal and act to contain stress remain poorly understood. It is important to discover these circuits and to determine how they become dysregulated under chronic stress. Emerging evidence posits that the claustrum (CLA), a densely connected subcortical structure, is a potential integrative hub involved in coordinating affective and cognitive processes. 9 , 10 Experiments employing resting-state fMRI have demonstrated that acute stress robustly activates consciousness-regulating hubs such as the CLA, amygdala, and the thalamus. 11 The CLA plays a central role in cortical broadcasting networks, modulating vigilance states through its extensive bidirectional connectivity. 12 , 13 Studies using rodents have suggested a potential correlation between neuronal activity in the CLA and anxiety-like behaviors. 14 , 15 In addition, recent single-cell transcriptomic profiling in non-human primates has revealed notable cellular diversity within the CLA, including excitatory (vGluT1) and inhibitory (Gad2) neurons, and glial subtypes. 16 However, the cell-type-specific mechanisms by which the CLA regulates stress responses remain largely unknown. In this study, we employed a multimodal approach combining a 3D Behavior Atlas, 9.4T magnetic resonance imagery (MRI), fiber photometry, and single-nucleus RNA sequencing (snRNA-seq), to dissect the circuit and cellular architecture underlying stress regulation within the CLA. We identified a reciprocal GABAergic-glutamatergic circuit between the CLA and the anterior cingulate cortex (ACC) that orchestrates stress responses via opposing mechanisms. Specifically, glutamatergic projections from the CLA to the ACC amplify threat responses, whilst two distinct GABAergic pathways (intrinsic CLA Gad2+ activity and top-down modulation from the ACC) provide inhibitory control to restrain this excitatory drive. This push-pull dynamic enables fine-tuning of anxiety states, positioning CLA Gad2+ neurons as a central regulatory node. During acute stress, CLA Gad2+ neurons act as a “brake”, dampening excessive responses and maintaining appropriate vigilance. However, under chronic stress, overactivation of CLA Gad2+ neurons leads to prolonged suppression of CLA glutamatergic activity, contributing to the emergence of depressive-like behaviors, functioning, in effect, as an ‘accelerator’ for pathological emotional states. These findings reveal a dual role for CLA Gad2+ neurons in maintaining emotional homeostasis and underscore the CLA as a critical hub in the transition from adaptive to pathological stress responses. RESULTS Heightened vigilance in mice following acute social defeat stress To gain a more comprehensive understanding of the behavioral changes induced by acute social defeat stress (ASDS), we exposed mice to an established stress protocol and employed a 3D behavioral analysis to examine alterations in fine motor movement repertoire. After undergoing ASDS ( Fig.1A ), mice were placed individually in a cylindrical, transparent open field for 15 minutes and spontaneous behaviors were recorded (Fig.S1A). A multi-view 3D animal motion-capture system 17 was used to reconstruct body structures and trajectories, enabling detailed quantification of motor behaviors ( Fig.1B ). To characterize behavioral dynamics, we first applied t-SNE analysis to project high-dimensional motion features into a 3D space. This visualization revealed distinct clustering patterns between ASDS and control mice, suggesting altered movement profiles in the stressed animals ( Fig.1C ). Subsequent clustering analysis, based on a previously established protocol, 18 identified 13 discrete categories of fine motor behaviors ( Fig.1D and Fig.S1B). Quantitative comparison across these categories revealed that the ASDS group spent significantly less time than the control group engaged in trotting, stepping, turning left, head-down searching, rearing, falling, and climbing up, whilst exhibiting longer durations of grooming and pausing behaviors ( Fig.1E ). No significant differences were observed between groups in the time spent running, turning right, head-up searching, or sniffing (Fig.S1D). To better interpret the functional relevance of these movement categories, we further grouped them into four broader behavioral states: locomotion, exploration, maintenance, and inactivity ( Fig.1F and Fig.S1C). Compared to controls, the ASDS group exhibited a markedly lower locomotor activity and exploratory behavior, accompanied by longer engagement in maintenance behaviors (e.g., grooming) and longer periods of inactivity ( Fig.1G ). Together, these 3D behavioral analyses demonstrate that ASDS led to a shift in spontaneous behavior toward reduced exploration, increased inactivity, and heightened vigilance, consistent with anxiety-like phenotypes in stressed mice. Download figure Open in new tab Figure 1 Anxiety-like behavior induced by ASDS (A−B) Schematic showing the ASDS behavioral paradigm (A). 3D reconstruction of 16 key body parts and skeleton reconstruction (B). (C−G) 3D ethological test (n=14−15/group). Three-dimensional embedding of behavior fractions by t-SNE (C). Spatiotemporal feature space of 13 behavioral components are derived from video and 3D ethological skeleton plots and are used to define body positions and behaviors. (D). Relative proportions of spontaneous behaviors (9/13) in mice (E) and represent temporal alignment of raster movement (F). Movement categories and relative distribution of spontaneous behaviors (G) between Control and ASDS groups. (H−J) Startle reflex test (n=8/group). Representative trace of startle reflex text (H), max amplitude (I) and average startle amplitude (J) observed during the startle reflex test. (K−M) Serum stress markers (n=6/group). Schematic depicting the ELISA assay (K), concentration of N.E. (L) and Cort. (M). (N−O) Comparison of open-field behavior in ASDS and control groups (n=6/group). Representative trajectory heatmap during the OFT (N) and time spent in the central zone (O). (P−Q) Comparison of behavior on the EPM in ASDS and control groups (n=6/group). Heatmap of trajectories (N) and time spent in the open arms. Data are represented as mean ± SEM and analyzed by unpaired t test. * p <0.05, ** p <0.01, *** p <0.001. ASDS, acute social defeat stress. ELISA, enzyme linked immunosorbent assay. N.E., norepinephrine. Cort., corticosterone. EPM, elevated plus maze. OFT, open field test. See also Figure S1. Anxiety-like behavior, characterized by persistent hypervigilance and reduced exploration, may reflect dysregulated threat assessment under non-threatening conditions. To verify this behavioral phenotype, we first performed input-output curve analysis of the acoustic startle response in mice. We established that 110 dB white noise reliably evoked robust startle reflexes in mice (Fig. S1E−F). Using this stimulus, we found that ASDS mice exhibited significantly higher peak and mean startle amplitudes compared to controls ( Fig. 1H −J), indicating enhanced sensorimotor reactivity. To assess physiological stress responses, we conducted ELISA assays of serum stress biomarkers and found markedly elevated levels of norepinephrine and corticosterone relative in ASDS mice than in controls ( Fig. 1K −M), further supporting a stress-induced physiological state. We then evaluated exploratory behavior using the open-field test (OFT) and found that the ASDS group spent less time in the central zone ( Fig. 1N −O), made fewer entries into the center (Fig. S1H), and traveled shorter distances within the central area than the control group (Fig. S1I), whilst overall locomotor activity remained comparable between groups (Fig. S1G). These results suggest reduced center-directed exploration, independent of general mobility. Consistent findings were observed in the elevated plus maze (EPM) where, despite no difference in velocity in the open arms (Fig. S1J), the ASDS group spent less time in the open arms ( Fig. 1P −Q) than controls, had fewer entries to open arms (Fig. S1K), and shorter open-arm exploration distance traveled (Fig. S1L), indicating a shift toward risk-avoidance behavior. Together, these data demonstrate that the ASDS group had a robust anxiety-like phenotype, characterized by hyperresponsiveness to threat cues, reduced exploratory drive in low-risk environments, and elevated physiological markers of stress. Distinct neuronal subtypes in the claustrum respond to acute stress exposure After comprehensively characterizing post-stress anxiety-like states in mice using both novel 3D behavior analysis and classical behavioral assays, we next aimed to identify the brain regions involved in initiating anxiety processing following stress exposure. To this end, we performed high-resolution resting-state brain imaging using 9.4T MRI 19 in ASDS and control mice ( Fig. 2A ). Twenty differentially-activated brain regions were identified following analysis, including significant activation in the CLA, consistent with previous human work 11 ( Fig. 2B −C). Post-stress functional connectivity analysis of differentially activated brain regions revealed negative functional coupling between the Crus2 and M2 cortical regions, whereas most other regions showed positive coupling (Fig. S2A). Quantitative mapping of CLA-associated neural ensembles across the brain revealed enhanced connectivity between the CLA and the cerebellum, and between the CLA and the nucleus accumbens shell (AcbSh) (Fig. S2B). In contrast, connectivity between CLA−contralateral CLA, CLA−S1 cortex, and CLA−hippocampal CA3 were all significantly weaker in the ASDS group than in controls (Fig. S2B). Download figure Open in new tab Figure 2 Changes in cellular profiles in the CLA of mice following ASDS (A) Schematic of resting-state 9.4T fMRI acquisition under anesthesia following ASDS. (B) Merged coronal images from ASDS and Control groups comparing whole-brain BOLD signal alterations (n=10/group). (C) Statistical parametric mapping of BOLD signal T-values (left) and FC ratios (right). Threshold: p<0.01 with 50-voxel extent. (D) Cell-type-resolved DEGs in the CLA identified by snRNA-seq (n=10/group). (E) Inhibitory and excitatory neuron subtype-specific DEG quantification in the CLA. (F) Schematic showing the mFISH experiment (n=6/group). (G) vGluT1 (Slc17a7) mRNA expression in the CLA. (H) ASDS-induced changes in mRNA expression of neurochemical markers in the CLA. Data are represented as mean ± SEM and analyzed by unpaired t test, * p <0.05, ** p <0.01, *** p 0.05. FC, fold change. snRNA-seq, single-nucleus RNA sequencing. mFISH, multiplex fluorescent in situ hybridization. See also Figure S2. To further investigate the potential link between ASDS-induced anxiety-like phenotypes and the CLA, we performed snRNA-seq to uncover cell-type-specific transcriptional signatures within this region. Transcriptomic profiling revealed that stress-induced differentially-expressed genes (DEGs) in the CLA were predominantly enriched in excitatory neurons, inhibitory neurons, and astrocytes ( Fig. 2D ). Notably, although inhibitory neurons are relatively sparse in the CLA, 20 there was a comparable number of DEGs in both inhibitory and excitatory CLA neurons post ASDS ( Fig. 2E ), indicating disproportionate transcriptional plasticity in this subpopulation. KEGG pathway analysis of these DEGs revealed significant enrichment in excitatory neurons of pathways related to mitochondrial oxidative phosphorylation, Parkinson’s disease, and Huntington’s disease. Remarkably, inhibitory neurons also had highly similar enrichment patterns in these same pathways (Fig. S2C−D), suggesting that both neuronal types share metabolic and neurodegenerative stress-response mechanisms, despite their distinct functional roles. To further characterize neuronal alterations in the CLA, we employed multiplex fluorescent in situ hybridization (mul-FISH) to assess 15 common CLA markers ( Fig. 2F ), including excitatory neuronal markers (CaMKIIα, vGluT1, vGluT2, Gnb4, Nos, Sla, Slc30a3), inhibitory neuronal markers (Gad1 and Gad2), and additional markers identified through sequencing or previous reports (Drd1, Drd2, Crf, Chat, Dat, TH). There were no significant differences between the ASDS and control groups in DAPI counts within the CLA (Fig. S2E). Quantitative analysis revealed a notable reduction in vGluT1 mRNA expression following stress exposure ( Fig. 2G ). Parallel immunohistochemical analysis confirmed that the ASDS group had lower mRNA expression of CaMKIIα, vGluT1, Nos and Sla in excitatory neurons of the CLA compared to controls, in addition to lower Dat (dopaminergic) and TH (tyrosine hydroxylase) in inhibitory CLA populations ( Fig. 2H ). Notably, CaMKIIα-positive neurons in the CLA may not exclusively represent excitatory neurons, as they could encompass a broader range of neuronal subtypes. The observed reduction of these four excitatory markers-CaMKIIα, vGluT1, Nos and Sla-in the CLA following stress exposure suggests that excitatory neurons in this region play a critical role in stress-induced anxiety ( Fig. 2G , Fig. S2F−G). Temporal dynamics of excitatory and inhibitory neuronal responses in the CLA to acute stress To determine how CLA neuronal subpopulations encode aversive stimuli, we performed in vivo calcium imaging targeting excitatory neurons. Cre-dependent GCaMP6s (AAV-DIO-GCaMP6s) was administered into the CLA of vGluT1-Cre mice, and fiber photometry was used for population-level calcium signal recording ( Fig. 3A , Fig.S3A). Histological analysis confirmed that GCaMP6s expression was restricted to vGluT1 positive excitatory neurons in the CLA ( Fig. 3B ). During tail suspension (TS), CLA vGluT1+ neurons exhibited robust calcium transients that were temporally aligned with immobility episodes ( Fig. 3C −F, Fig.S3B), indicating engagement of this population during acute stress. Similar calcium dynamics were observed in vGluT1 positive neurons following ASDS exposure ( Fig. 3G −J, Fig.S3C), suggesting consistent recruitment of CLA excitatory neurons across distinct stress paradigms. These results support previous work that implicated CLA CaMKIIα-positive neurons in stress responses, 15 although this study did not assess inhibitory subpopulations. To address this gap, we next performed calcium imaging of Gad2-positive inhibitory neurons in the CLA. Download figure Open in new tab Figure 3 Distinct temporal dynamics of CLA excitatory neurons during acute stress (A) Schematic showing fiber photometry recording in CLA vGluT1⁺ neurons (n=3−4/group). Scale bar: 200 μm. (B) Viral expression: (Left) Representative histology of AAV-DIO-GCaMP6s⁺ neurons in the CLA. (Right) Quantification of GCaMP6s⁺/vGluT1⁺ colocalization. Scale bar: 50 μm. (C−F) Gcamp6s response to TS: (C) Peri-event ΔF/F trace, (D) Response heatmap, (E) AUC quantification, (F) Peak AUC amplitude. (G−H) Gcamp6s response to ASDS: (G) Peri-event ΔF/F trace, (H) Response heatmap, (I) AUC quantification, (J) Peak AUC amplitude. (K) Schematic: Fiber photometry in CLA Gad2⁺ neurons(n=3-4/group). (L) Cell-type confirmation: (Left) GCaMP6s⁺ (green) and GABA⁺ neurons in CLA. (Right) Quantification of colocalization. Scale bar: 50 μm. (M) Latency to peak GCaMP6s signal after TS onset (GABA⁺ vs. vGluT1⁺ neurons). (N−Q) Gcamp6s responses to TS in Gad2⁺ neurons: (N) Peri-event ΔF/F trace, (O) Heatmap, (P) AUC, (Q) Peak AUC amplitude. (R−U) Gcamp6s responses to ASDS in Gad2⁺ neurons: (R) Peri-event trace, (S) Heatmap, (T) AUC, (U) Peak AUC amplitude. Data are represented as mean ± SEM and analyzed by unpaired t test, *** p <0.001, **** p <0.0001. AUC, Area Under the Curve. TS, tail suspension. See also Figure S3. To examine how the sparse population of Gad2-positive neurons in the CLA responds to stress, we employed a parallel fiber photometry strategy in which Gad2-Cre mice received CLA-targeted injections of Cre-dependent GCaMP6s (AAV-DIO-GCaMP6s), followed by chronic optical fiber implantation ( Fig. 3K , Fig.S3D). Histological analysis confirmed selective GCaMP6s expression in CLA Gad2+ neurons ( Fig. 3L ). During both TS and ASDS paradigms, CLA Gad2+ neurons exhibited stress-responsive calcium transients ( Fig. 3N −U, Fig.S3E−F). Strikingly, CLA Gad2+ neurons exhibited peak activation during the anticipatory phase preceding stimulus onset, in sharp contrast to vGluT1 neurons, whose activity emerged after stimulus onset and gradually peaked toward the termination of the stressor ( Fig. 3M ). These findings suggest that CLA inhibitory and excitatory neurons engage in temporally distinct phases of stress processing, where Gad2 neurons potentially contribute to anticipatory threat monitoring and vGluT1 neurons may encode sustained responses to aversive stimuli. Bidirectional regulation of anxiety-like behaviors by two distinct claustral neuronal populations To investigate the potential functions of CLA Gad2+ neurons, we selectively targeted this population using Gad2-Cre mice and Cre-dependent AAV-DIO-hM3Dq-mCherry tools ( Fig.4A ). Following systemic administration of CNO (1 mg/kg, i.p.), elevated c-Fos expression in virus-labeled Gad2 neurons confirmed activation of these neurons ( Fig.4B −D). However, overall neuronal activity within the CLA was suppressed, suggesting that activation of Gad2 neurons exerts an inhibitory effect on the surrounding local network ( Fig.4B −D). We next assessed anxiety-like behaviors in Gad2-Cre mice following chemogenetic activation of CLA Gad2+ neurons using four behavioral paradigms. First, CNO administration in the hM3Dq group resulted in more time spent in and more entries to the open arms on the EPM than the mCherry group ( Fig.4E −G). Second, hM3Dq-expressing mice buried fewer marbles than the mCherry mice in a marble-burying test, suggestive of reduced defensive behavior ( Fig.4H −I). Third, CNO administration in the hM3Dq-mCherry group led to higher locomotor activity than the mCherry group during an OFT, in addition to more time spent in, and more entries to, the central zone, and shorter freezing duration ( Fig.4J −M, Fig.S4B−C). Finally, CNO administration in the hM3Dq-mCherry group led to a lower acoustic startle amplitude than the mCherry group ( Fig.4N ). Collectively, these behavioral changes indicate that activation of CLA Gad2+ neurons under conditions with no added stress is linked to lower anxiety-like responses. To test whether this anxiolytic effect extends to stress conditions, we repeated chemogenetic activation in mice subjected to acute social defeat stress. Under these conditions, we observed no significant anxiolytic effects in stressed mice across the OFT, EPM, or marble-burying tests (Fig. S4D−P), suggesting that the behavioral impact of CLA Gad2+ activation is context-dependent and may not reverse stress-induced anxiety-like phenotypes. These results raise the possibility that, under stress, enhanced excitatory drive or impaired inhibition within the CLA may override the regulatory influence of Gad2 neurons. Download figure Open in new tab Figure 4 Bidirectional modulation of anxiety-like behaviors by CLA neuronal populations (A) Schematic: Pharmacological activation of CLA Gad2 neurons in naïve Gad2-Cre mice. (B) Representative images of hM3Dq-mCherry or mCherry expression in CLA Gad2 neurons, c-Fos immunoreactivity (green). Scale bar: 100 µm. (C) Quantification of c-Fos⁺ cells in the CLA. (D) Percentage of mCherry⁺ cells colocalized with c-Fos in the CLA. (E−G) EPM analysis: (E) Heatmap of mouse trajectories, (F) time spent in the open arms, (G) number of entries to the open arms. (H−I) Buried bead test: (H) Schematic, (I) comparison of number of buried beads in the control vs. hM3Dq groups. (J−N) OFT analysis: (J) Heatmap, (K) mean speed, (L) time spent in the center, (M) number of entries to the central zone. (N) Startle reflex amplitude. (O) Schematic: Pharmacological activation and AAV targeting of CLA vGluT1 neurons in naïve vGluT1-Cre mice (n=8/group). Scale bar: 1 mm. (P−Q) Cell-type and c-Fos confirmation: (P) Representative confocal image of CLA vGluT1 neurons; (Q) c-Fos⁺ neurons in the CLA. Scale bar: 100 µm. (R−T) OFT analysis: (R) Schematic, (S) time spent in central zone, (T) number of entries to the central zone. (U−W) EPM analysis: (U) Schematic, (V) time spent in the open arms, (W) number of entries to the open arms. Data are represented as mean ± SEM and analyzed by unpaired t test, * p <0.05, ** p <0.01, **** p <0.0001. See also Figure S4-5. To test this possibility, we next investigated whether excitatory vGluT1 neurons within the CLA contribute to anxiety regulation. Using the same chemogenetic approach, we expressed hM3Dq in CLA vGluT1+ neurons in vGluT1-Cre mice ( Fig.4O ). Upon CNO administration, the hM3Dq group exhibited robust anxiety-like behaviors, evidenced by less time spent in, and fewer entries into, the open arms of the EPM than the mCherry group, more marble-burying behavior, less central zone exploration, and elevated startle responses ( Fig. 4P −W, Fig. S4Q−W). These results prompted us to examine whether inhibition of CLA vGluT1+ neurons could produce anxiolytic effects. We employed a chemogenetic inhibition strategy using AAV-DIO-hM4Di in vGluT1-Cre mice to selectively suppress CLA excitatory neurons (Fig. S5A). We found lower neuronal activity following CNO administration, as indicated by diminished c-Fos expression in hM4Di-expressing mice (Fig. S5B−C). Notably, following ASDS treatment, chemogenetic inhibition of CLA vGluT1+ neurons in the hM4Di-expressing group led to longer time spent in, and more entries to, the central zone during an OFT than the mCherry group, despite no group difference in locomotor activity (Fig. S5D-G). In addition, more open-arm exploration and less freezing behavior was noted in the hM4Di group than mCherry group on the EPM (Fig. S5H−K). In contrast, the same chemogenetic manipulation in non-stressed mice produced no significant behavioral changes (Fig. S5L−V), suggesting that the anxiolytic effect of vGluT1 inhibition is stress-dependent. These findings support a model in which stress promotes excessive excitatory drive within the CLA, and selective suppression of vGluT1 neurons can restore behavioral balance under these conditions. Gad2-vGluT1 interactions in the CLA orchestrate stress responses To directly test whether Gad2 neurons exert inhibitory control over local glutamatergic populations within the CLA, we conducted combined optogenetic activation and whole-cell patch-clamp recordings in acute brain slices. In Gad2-Cre mice, we selectively labeled Gad2- and CaMKIIα-positive neurons in the CLA by expressing ChR2-mCherry and GFP, respectively ( Fig. 5A −B). Partial overlap between these populations was observed (Fig. S6A−C), consistent with our snRNA-seq data, indicating that CaMKIIα expression only partially captures glutamatergic neurons in the CLA. We collected whole-cell patch-clamp recordings and found reliable light-evoked action potential firing, which confirms ChR2-mediated activation efficiency in Gad2-positive neurons ( Fig. 5C −D). Concurrent recordings from neighboring glutamatergic neurons, both unlabeled (red) and GFP-labeled (green), revealed a depolarizing shift in resting membrane potential (RMP) upon Gad2+ neuron optogenetic stimulation ( Fig. 5E −F). These glutamatergic neurons also displayed light-evoked inhibitory postsynaptic currents (IPSCs), which were almost abolished by the GABA A receptor antagonist Picrotoxin ( Fig. 5G −H), confirming that the inhibitory input was GABAergic. 21 Furthermore, current-clamp experiments further demonstrated that, following Gad2 neuronal activation, higher current injection amplitudes were required to elicit comparable action potential firing in glutamatergic neurons ( Fig. 5I −J), suggesting enhanced inhibitory tone. Collectively, these results identify a local GABAergic-glutamatergic (Gad2-vGluT1) inhibitory microcircuit within the CLA, in which GABAergic neurons exert direct synaptic suppression of excitatory activity through GABA A receptor-mediated transmission. Download figure Open in new tab Figure 5 Characterization of the CLA Gad2-vGluT1 microcircuit (A) Strategy for dual viral labeling of CLA Gad2⁺ (red) and vGluT1⁺ (green) neurons. (B) Representative confocal image showing Gad2⁺ (red) and vGluT1⁺ (green) neurons localization in CLA. Scale bar: 100 μm (C−D) Optogenetic activation (n=3−4/group): (C) Schematic showing ChR2 stimulation in Gad2⁺ neurons. (D) Representative action potential traces evoked by 470-nm light. Scale bar: 20 μm. (E−F) Whole-cell recording: (E) Photograph of non-Gad2⁺ neurons. (F) Resting membrane potential changes in non-Gad2⁺ neurons during Gad2⁺ neuron activation. Scale bar: 20 μm. (G−H) Representative and quantification IPSCs trace in vGluT1⁺ neurons during optical stimulation and Picrotoxin (100 μM) abolition of IPSCs. (I−J) Representative and quantification spike frequency of vGluT1⁺ neurons at varying current injections. (K−L) Representative and quantification amplitude of mIPSCs in vGluT1⁺ neurons. (M) Schematic diagram microcircuitry study on fiber photometry recording of vGluT1 neurons when optogenetic activation Gad2 neurons in vivo. (N−O) Peri-event plots representation of the Ca 2+ signals across trials and Quantification of the AUC of the average response during the laser activation. (P−S) Stress interactions: (P) Peri-TS ΔF/F traces. (Q) Heatmaps of GCamp6s signal in vGluT1 neurons. (R) Average Ca²⁺ transients. (S) AUC quantification. Data are represented as mean ± SEM and analyzed by unpaired t test, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, n=8−10 cells from 3−4 mice/group (A−L), n=3−4 mice/group (M−S). See also Figure S6. To investigate stress-induced plasticity within the Gad2-vGluT1 microcircuit, we assessed miniature inhibitory postsynaptic currents (mIPSCs) in vGluT1 neurons following ASDS. Compared to controls, stress-exposed mice exhibited significantly larger mIPSC amplitudes but no changes in frequency ( Fig. 5K −L, Fig. S6D−E), suggesting enhanced GABAergic synaptic strength rather than altered presynaptic release probability. To evaluate functional inhibition in vivo, we selectively expressed the red-shifted opsin ChrimsonR in Gad2 neurons and the calcium indicator GCaMP6s in vGluT1 neurons in Gad2-Cre mice ( Fig. 6M , Fig. S6F). Fiber photometry recordings revealed that optogenetic stimulation of Gad2 neurons at either 10 Hz or 40 Hz robustly suppressed calcium transients in vGluT1 neurons ( Fig. 5N −O, Fig. S6G−M), confirming functional inhibition in awake animals. Crucially, during tail suspension stress, real-time calcium signal recordings demonstrated that Gad2 neuronal activation mitigated stress-induced hyperactivity in vGluT1 neurons ( Fig. 5P −S), supporting a causal role for this microcircuit in modulating stress-related excitability. Together, these findings demonstrate that stress enhances inhibitory control within the CLA Gad2– vGluT1 microcircuit, which dynamically regulates excitatory output to support allostatic adaptation under stress conditions. Download figure Open in new tab Figure 6 Bidirectional control of anxiety by CLA−ACC Glutamate/GABA Circuits (A) Schematic diagram showing optogenetic activation of the CLA vGluT1 projections to the ACC. (B) Viral expression: (Left) CLA vGluT1⁺ soma; (Right) Terminals in the ACC. Scale bar: 100 μm. (C) Enhanced acoustic startle reflex amplitude during optical activation. (D−G) Comparison of open-field behavior of the GFP and ChR2 groups when optogenetic laser off/on: (D) Representative trajectory heatmap, (E) mean speed, (F) time spent in the central zone, (G) and the number of entries to the central zone. (H−K) Comparison of EPM behavior of the GFP and ChR2 groups when optogenetic laser off/on: (H) Representative path, (I) time spent in the open arms, (J) time spent in the closed arms, (K) freezing scores. (L) Strategy: Chemogenetic targeting of ACC→CLA Gad2⁺ projections using dual-retrograde Cre system. (M) Starter cells (yellow) co-labeling RV (red) and helper viruses (green) in the CLA Gad2 neuron, and RV-labeled neurons in the ACC colocalized with the hM3Dq or vehicle (green). Scale bar: 50 μm. (N−R) Comparison of open-field behavior in the GFP and hM3Dq groups. (N) Representative trajectory heatmap, (O) mean speed, (P) time spent in the central zone (Q), the number of entries to the central zone, and (R) freezing duration. Data are represented as mean ± SEM and analyzed by unpaired t test, * p < 0.05, ** p < 0.01, *** p 0.05, n=7−8 mice/group. See also Figure S7. Reciprocal CLA−ACC circuitry modulates anxiety through local Gad2-mediated inhibition Following the characterization of local microcircuits, we hypothesized that long-range projections originating from CLA excitatory neurons may contribute to stress-related anxiety. To test this, we performed anterograde tracing in vGluT1-Cre mice, targeting CLA glutamatergic neurons. Consistent with previous findings, 22 dense axonal projections were observed terminating in the ACC (Fig. S7A−D). Retrograde tracing from the ACC confirmed this robust CLA→ACC connectivity by labeling somata in the CLA (Fig. S7E−F). To functionally interrogate this circuit, Cre-dependent AAV encoding ChR2 was injected into the CLA of vGluT1-Cre mice, and optical fibers were implanted above the ACC to enable selective optogenetic stimulation of the CLA→ACC pathway ( Fig. 6A −B). Optogenetic activation of this projection induced pronounced anxiety-like behaviors, including larger startle amplitude ( Fig. 6C ) than the GFP control group, less time spent exploring the central area, fewer entries to the central area during an OFT ( Fig. 6D −G), in addition to less open-arm exploration ( Fig. 6H −J) and more freezing behavior in the EPM ( Fig. 6K ), confirming the anxiogenic role of the CLA→ACC circuit. To investigate potential feedback modulation, we next examined the ACC−CLA Gad2 inhibitory pathway. Using Gad2-Cre mice, a Cre-dependent DIO system was applied to express the RV system in Gad2 neurons, combined with a FLP-dependent fDIO system for the retrograde labeling 23 of ACC inputs to CLA Gad2+ neurons, which confirmed ACC→CLA Gad2 connectivity ( Fig. 6L −M). Following chemogenetic activation of this ACC→CLA Gad2+ circuit, there was no between-group difference in locomotor activity during an OFT, ( Fig. 6N −O) but compared to the control group, there was a significantly longer time spent in, and more entries into, the central zone, and less time spent freezing ( Fig. 6P −R), indicating anxiolytic effects. Complementary experiments in vGluT1-Cre mice showed that optogenetic activation of the ACC vGluT1→CLA pathway (Fig. S7G-H) also produced anxiolytic behaviors, including diminished startle reflex amplitude (Fig. S7I), elevated central zone exploration and entries into the central zone during an OFT (Fig. S7J−L), and enhanced open arm exploration in the EPM (Fig. S7M−O). These anxiolytic effects are likely mediated by GABAergic inhibition of downstream anxiogenic pathways, such as the CLA Gad2+ →CLA vGluT1+ circuit previously characterized ( Fig.4 and Fig.S4−5). Together, these data reveal a complex, reciprocal circuit organization, where excitatory CLA vGluT1+ →ACC projections promote anxiety-like behaviors, while the ACC→ CLA Gad2+ and ACC vGluT1+ →CLA pathways exert anxiolytic regulation, highlighting a dynamic bidirectional mechanism underpinning anxiety modulation. CLA GABAergic remodeling underlies circuit compensation during chronic stress To determine whether acute stress-induced hyperarousal is reversible, we measured startle response amplitudes in mice immediately after acute stress (ASDS) and following a 7-day recovery period. Following ASDS, startle amplitudes were higher post-ASDS than controls, and normalized after recovery ( Fig. 7A ). In contrast, there was no difference in startle responses post-stress between the 0-day recovery chronic social defeat stress (CSDS) group and controls, with significant reductions following 7-day recovery ( Fig. 7B ). This pattern suggests that CSDS mice develop depression-like phenotypes, supported by our finding of anhedonia in a sucrose preference test after 7-day recovery ( Fig. 7C ). Download figure Open in new tab Figure 7 Chronic stress-induced hyperexcitability of CLA GABAergic neurons disrupts emotional homeostasis (A) Acoustic startle amplitude following ASDS. (B) Startle amplitude following chronic stress (CSDS). (C) Comparison of sucrose preference between the ASDS and CSDS groups. (D−H) Excitatory synaptic transmission in CLA GABAergic neurons: (D) Representative sEPSC traces. (E) Cumulative probability plot (amplitude). (F) Cumulative probability (inter-event interval). (G) Mean sEPSC amplitude. (H) Frequency of sEPSCs. (I−J) Intrinsic excitability: (I) representative action potentials, (J) relationship of spike-frequency in Gad2⁺ neurons (ASDS vs. CSDS). (K−P) Action potential properties of CLA GABAergic neurons. Between group comparisons of (K) amplitude, (L) threshold, (M) half-width, (N) rise tau, (O) rheobase, and (P) decay tau. (Q-U) Gad2-Cre-dependent caspase-3 (Tacasp3) strategy in CLA (Q). Startle amplitude in the startle response test (R), open arm duration of the EPM (S), time spent immobile during the TST (T) and sucrose intake percentage in SPT (U) in GFP and Tacasp3 group after ASDS. Data are represented as mean ± SEM and analyzed by unpaired t test and One-way ANOVA, * p < 0.05, ** p < 0.01, *** p 0.05. n=8–10 mice/group. See also Figure S6-7. Whole-cell recordings revealed that CSDS led to elevation of both amplitude and frequency of spontaneous excitatory postsynaptic currents (sEPSCs) in CLA GABAergic neurons compared to ASDS controls ( Fig. 7D −H). Furthermore, CLA GABAergic neurons in CSDS mice had a current injection-dependent increase in action potential firing frequency, with significantly heightened excitability relative to ASDS ( Fig. 7I −J). Enhanced excitatory synaptic input to CLA GABAergic neurons may lead to hyperactivation, excessively suppressing downstream glutamatergic neurons via the Gad2-vGluT1 inhibitory microcircuit. Whilst this compensatory inhibition may protect against acute stress (ASDS), chronic hyperactivation (CSDS) likely triggers network cascade imbalance, a consequence of which is pathologically amplified inhibitory tone that impairs glutamatergic synaptic plasticity, ultimately manifesting as behavioral despair (e.g., reduced sucrose preference). Notably, CSDS led to a diminished action potential threshold and rheobase in CLA Gad2 neurons ( Fig. 7H ), indicating enhanced intrinsic excitability paradoxically coupled with reduced neuronal activation efficiency. In summary, our results indicate that allostatic overload arising from chronic hyperactivation of CLA Gad2-positive neurons disrupts emotional homeostasis. To investigate the necessity of CLA Gad2+ in regulation of allostatic overload, CLA Gad2+ neurons were ablated using Tacasp3 ( Fig. 7Q , Fig. S8A-B). We conducted a series of behavioral tests and found that, following ASDS, Tascap3 mice spent significantly more time in the open arms of the EPM ( Fig. 7S ), showed increased immobility in the TST ( Fig. 7T ), and consumed less sucrose solution in the SPT ( Fig. 7U ). At baseline, Tascap3 mice showed reduced startle amplitudes and increased time in the open arms of the EPM compared to the GFP control group (Fig. S8C−D), while no significant differences were observed in TST immobility or sucrose preference (Fig. S8E-F). These findings suggest that ablation of CLA Gad2+ neurons compromises defensive responses to potential threats and heightens sensitivity to stress, such that even a single episode of acute stress is sufficient to induce depressive-like behaviors. DISCUSSION Acute stress-induced hypervigilance is often considered a protective mechanism, enabling the brain to detect and respond to potential threats, thereby promoting survival. However, research has predominantly focused on anxiety as a pathological condition, in which extensive research has focused on understanding the underlying mechanisms. In contrast, this study aimed to explore the physiological basis of this heightened state of alertness under acute stress, and proposes that the body enters an allostatic state in response to such stimuli. This shift in perspective emphasizes the adaptive nature of emotional responses during acute stress, offering a new lens through which to understand how the brain maintains stability and resilience in the face of environmental challenges. The role of the claustrum (CLA) in stress regulation The CLA is a critical brain region that has long been recognized for its central role in coordinating sensory processing and higher-order cognitive functions. 24 , 25 Francis Crick even proposed that the CLA may be involved in the generation of consciousness. 26 From a developmental perspective, the CLA is highly interconnected with the amygdala, 9 , 27 which plays a key role in emotional regulation. However, unlike the amygdala, which has been extensively studied in the context of anxiety, the precise mechanisms by which the CLA contributes to stress responses and emotional regulation remain largely unexplored. Our findings reveal that the CLA exhibits pronounced hyperactivity in response to stress-inducing stimuli, as evidenced by high-resolution MRI. Stress-induced remodeling of CLA-centered circuits disrupts the balance between sensory processing, reward, and emotional regulation. Specifically, enhanced CLA connectivity with the AcbSh and cerebellum may reflect maladaptive reward processing and motor compensation, whereas reduced coupling with the hippocampus and cortex likely impairs memory integration and sensory processing. Notably, the observed negative correlation between ipsilateral and contralateral CLA activity points to a disruption of interhemispheric inhibitory balance, implicating the CLA in the maintenance of emotional homeostasis under stress. This interpretation is supported by activity-dependent viral retrograde labeling, which revealed co-activation of the CLA and the BLA during acute social defeat stress, 27 implicating both regions in a coordinated stress network. While the BLA’s role in stress processing has been extensively characterized, the functional involvement of the CLA in stress regulation remains largely unexplored. To the best of our knowledge, this study provides the first detailed cellular and circuit-level characterization of the CLA in stress modulation. Our optogenetic and chemogenetic manipulation of CLA neurons further highlighted the CLA’s bidirectional control over anxiety-like behaviors. Specifically, activating CLA excitatory neurons and their projections to the ACC led to an amplification of anxiety, whereas chemogenetic activation of Gad2 neurons within the CLA led to pronounced anxiolytic effects. These bidirectional effects were mirrored in behavioral paradigms that combined 3D motion capture with traditional assays, where stressed mice exhibited hypervigilance (e.g., increased startle, freezing and grooming), reduced exploration in the open arms of the elevated plus maze, and high-risk avoidance phenotypes tightly correlated with real-time calcium dynamics of CLA GABAergic neurons. These findings offer new insights into the CLA’s role in emotional responses and reveal a potential role in the regulation of stress-induced behaviors, paving the way for future research on therapeutic targets for stress-related disorders. The cellular and circuit mechanisms underlying CLA encoding of allostasis following stress This study builds upon prior clinical observations, such as the 2021 meta-analysis of human MRI data, which first linked CLA activation to acute stress. 11 However, the mechanistic underpinnings of this association have remained unclear due to the CLA’s deep anatomical location and its intricate connectivity. To dissect the mechanistic architecture of CLA microcircuits, we employed single-nucleus RNA sequencing and multiplex FISH, identifying stress-sensitive excitatory neurons enriched for vGluT1, Sla, and Nos transcripts, as well as Gad2-expressing inhibitory neurons. Electrophysiological analysis revealed increased mIPSC amplitude in vGluT1-excitatory neurons following acute stress, suggesting enhanced postsynaptic GABAergic input that may contribute to the amplification of stress-related signals. In contrast, Gad2 inhibitory neurons likely function to temper this response by modulating the network’s excitability and preventing overstimulation, highlighting a finely tuned excitatory–inhibitory balance within the CLA. Intriguingly, further analysis of our snRNA-seq dataset (data not shown) revealed transcriptional similarity between CLA Gad2+ neurons and Drd1 (dopamine receptor D1-expressing) neurons, suggesting a potential role in reward-related processing. Supporting this, in vivo calcium imaging during sucrose consumption revealed modest activation of CLA Gad2+ neurons during reward acquisition (Fig. S3G), implying that these neurons may contribute to hypervigilance through anticipatory threat monitoring rather than direct reward encoding. Together, these findings suggest that CLA Gad2+ neurons may serve as integrative nodes coordinating stress reactivity and motivational state. Future studies are warranted to dissect their circuit-level contributions to motivation and affective regulation. The inhibitory Gad2-vGluT1 microcircuit within the CLA functions as a local gain controller, dynamically adjusting network excitability based on stress duration and intensity. This microcircuit interfaces with long-range projections to form a hierarchical control system. Specifically, the CLA−ACC bidirectional axis operates as a threat appraisal loop: CLA−ACC vGluT1+ projections amplify anxiety through cortical disinhibition, while ACC−CLA feedback via Gad2 neurons restores equilibrium, effectively serving as a “brake” on stress escalation. This tripartite reciprocity (vGluT1-ACC-Gad2) mirrors principles of allostasis theory, 28 in which circuits engaged by acute stress can become maladaptive under chronic overload, reflecting the shift from adaptive responses to maladaptive states in stress regulation. Thus, local inhibition (Gad2-vGluT1) modulates regional gain, while reciprocal CLA−ACC connections regulate system-wide arousal state, together orchestrating a balanced response to stress. Aside from the reciprocal projections between the CLA and ACC, whether other circuits are involved in this process remains to be explored in future research. Non-adaptive responses and chronic stress Our data suggest that the CLA contributes to stress-related behavioral regulation through a dynamic balance between excitatory and inhibitory neuronal subtypes. Under baseline conditions, Gad2 neuronal activation is anxiolytic, whereas vGluT1 neuronal activation promotes anxiety. However, following acute stress, Gad2 activation no longer conferred anxiolysis, while inhibition of vGluT1 neurons alleviated anxiety-like phenotypes, implying a stress-induced shift toward excitatory dominance and a functional decoupling of local inhibitory control. This imbalance likely underlies the persistence of anxiety following stress. Under chronic stress, this disruption becomes more pronounced. Gad2 neurons, which normally respond slightly earlier than vGluT1 neurons and act as temporal gates, exhibit sustained hyperexcitability, contributing to anhedonia and depressive-like phenotypes. This aligns with previous reports of stress-induced reduction in claustral output to the anterior cortex via dynorphin/κ-opioid receptor signaling. 29 Calcium signaling recordings and optogenetic experiments further support the premise that Gad2 activation suppresses neighboring excitatory activity, highlighting its regulatory role. Distinct electrophysiological signatures between these neuronal populations support a multiplexed coding strategy, wherein acute stress sharpens inhibitory tone to enhance signal precision, but chronic stress drives excessive suppression, thereby impairing adaptive processing. Together, these findings show that the CLA is a dynamic regulator of stress adaptation, whose circuit-level reorganization contributes to the transition from adaptive vigilance to affective pathology. To further explore the functional relevance of CLA Gad2+ neurons in stress-related behaviors, we selectively ablated these neurons using Tascap3. Strikingly, even a single episode of acute stress was sufficient to induce depressive-like behaviors in these mice, highlighting a heightened stress sensitivity. At baseline, the same animals exhibited increased risk-taking behavior, suggesting impaired threat evaluation. Together, these findings support the notion that CLA Gad2+ neurons are key components in maintaining behavioral control under potentially threatening conditions. They appear to serve as a regulatory brake that constrains excessive emotional responses and preserves affective homeostasis in the face of stress. Our results from both chemogenetic activation and ablation experiments further suggest that Gad2 neurons within the CLA regulate stress resilience in a bidirectional and activity-dependent manner. Specifically, while acute stress adaptively enhances the excitability of these neurons, chronic stress drives pathological hyperactivation, which promotes maladaptive changes and depressive-like behaviors. Conversely, Gad2 + neuron deficiency reduces inhibitory tone and impairs emotional gating, as reflected by increased behavioral despair (increased tail suspension immobility). These results suggest that CLA Gad2+ neurons function as a tonic inhibitory brake”that must operate within an optimal excitability range: both excessive activation and loss of inhibition disrupt emotional homeostasis and increase susceptibility to stress-induced pathology. Future studies are needed to elucidate the detailed molecular mechanisms underlying the changes in excitability of CLA Gad2+ neurons induced by chronic stress require further investigation. Study limitations and translational potential Despite these advances, several key limitations must be considered. First, the lack of Sla/Nos-specific Cre lines hinders the functional dissection of crucial excitatory subpopulations, leaving their role in anxiety circuits ambiguous. Second, whilst we observed interhemispheric CLA balancing, the mechanisms underlying contralateral compensation, whether via callosal projections or subcortical relays, remain unexplored. Third, although we noted chronic stress-induced narrowing of action potential half-width in Gad2 neurons, suggesting potassium channel dysfunction, molecular validation is lacking. Fourth, although GABAergic projections from the CLA to distal targets such as the amygdala have been reported, 30 the specific contribution of local interneurons versus long-range projection GABAergic neurons in anxiolysis remains unresolved. These gaps highlight the need for intersectional genetic tools, in vivo patch-clamp recordings during stress challenges, and comparative transcriptomics across stress paradigms. The translational potential of CLA-centric mechanisms is twofold. First, given that CLA hyperactivity models replicate behavioral features of anxiety, such as hypervigilance and risk avoidance, biomarkers pertaining to CLA circuits can potentially be used for therapeutic targeting. Manipulation of ACC−CLA feedback can restore emotional set points disrupted in depression. Second, the conserved genetic architecture between CLA and BLA 9 suggests that there are developmental windows suitable for intervention. Enhancing SOX4/SOX11 signaling early in life could improve stress resilience by optimizing CLA−BLA circuit formation. A promising direction for future studies is investigation of the molecular pathology of Gad2 + neurons, focusing on epigenetic modifications, ion-channel isoform switching, and glial interactions, whilst also testing CLA circuit interventions in comorbid anxiety-depression models. By understanding how CLA microcircuits transition from adaptive plasticity to pathological rigidity, we may develop therapies that recalibrate allostasis following stress, rather than simply suppressing symptoms. Finally, under CSDS conditions, the observed differences in action potential (AP) half-width, threshold, and rheobase of CLA Gad2+ neurons are closely associated with the activity of specific potassium channels. In our single-nucleus sequencing experiments (data not shown), we also observed alterations in K + channel gene expression within inhibitory neurons after ASDS. Another promising future direction would to investigate potassium channels and related molecular targets for therapeutic intervention. STAR METHODS KEY RESOURCES TABLE View this table: View inline View popup Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled upon reasonable request by lead contact, Jie Tu, ( jie.tu{at}siat.ac.cn ). Materials availability The transgenic mouse lines vGluT1-Cre and Gad2-Cre mice are available upon reasonable request after signing a material transfer agreement with the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. Experimental model and subject details Animals All experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC Protocol #SIAT-IACUC-210201-NS-LD-A1539) of Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences. Male C57BL/6J and CD1/ICR mice were purchased from the Zhejiang Weitong Lihua Company. Transgenic mice expressing Cre recombinase under the control of the Slc17a7 (vGluT1-Cre, Stock #023527) or Gad2 (Gad2-Cre, Stock #010802) promoters were obtained from The Jackson Laboratory. All transgenic lines were maintained on a C57BL/6J background. Mice were group-housed (3−5 per cage) in a temperature and humidity-controlled environment under a 12-hour light/dark cycle (lights on at 07:00), with ad libitum access to food and water. Experimental cohorts consisted of age-matched littermates (8−12 weeks old) randomly assigned to experimental or control groups. To minimize circadian variability, all behavioral testing was conducted during the light phase. Efforts were made to minimize animal suffering, including environmental enrichment and post-surgical analgesia where applicable. Behavioral tests Acute social defeat stress (ASDS) ASDS was conducted using a modified version of the protocol established by Golden et al. 31 Male CD1 mice (8−12 weeks old) were pre-screened for consistent aggressive behavior, defined as an attack latency of less than 30 seconds and a minimum of 10 attacks per session for three consecutive days. Only animals meeting these criteria were used as aggressors. Experimental male C57BL/6J mice (8−10 weeks old) were individually introduced into the home cage of a screened aggressor and subjected to 10 physical attacks over a 10−15-minute period. Upon submissive posturing (defined as immobility or freezing within 3 seconds after each attack), the experimental mouse was immediately separated from the aggressor using a perforated transparent divider, allowing continued sensory contact (visual, olfactory, and auditory) while preventing further physical interaction and injury. Control mice were handled identically and exposed to the same housing conditions, including placement in the aggressor’s cage with a perforated divider, but without any physical contact or aggression. All behavioral assessments were conducted 30 minutes following the ASDS procedure under standardized conditions. Chronic social defeat stress (CSDS) Adult C57BL/6J male mice (8−12 weeks old) were subjected to a 10-day chronic social defeat paradigm. Each day, an experimental mouse was introduced into the home cage of an aggressive CD-1 resident mouse for 5 minutes of direct physical confrontation, followed by 24 hours of sensory contact separated by a perforated plexiglass divider. Control mice were housed in similar divided cages with another C57BL/6J mouse, without physical interaction. To ensure consistent stress exposure, experimental mice that exhibited a cumulative attack latency of less than 10 seconds per session were excluded from analysis. 3D behavioral analysis using multi-view motion capture Three-dimensional behavioral phenotyping was performed using a multi-view kinematic tracking system (BA-DC01, Shenzhen Bayone BioTech). Experimentally male mice (8−12 weeks old) underwent spontaneous exploration assays within a standardized open-field environment, which included: (1) a vertical cylindrical enclosure (height: 30 cm) made of translucent white acrylic polymer, allowing full 360° optical access; (2) an opaque white polycarbonate floor platform (diameter: 40 cm) with surface reflectivity conforming to SIAT behavioral standards; and (3) a modular stainless-steel framework (130 × 130 × 90 cm³) equipped with four Intel RealSense D435 depth cameras positioned orthogonally (90° angular separation; 30 Hz sampling rate), along with an overhead 56-inch LED lighting panel (6500K color temperature) for uniform illumination. Each mouse was introduced into the southeastern quadrant of the arena (Cartesian coordinate: X= +20 cm, Y= −15 cm) under controlled illumination (50 lux) and allowed to acclimate for 15 minutes. Multi-view video capture device and 3D motion-capture system tracked markerless pose estimations of 16 key body parts including the nose, left ear, right ear, neck, left front limb, right front limb, left hind limb, right hind limb, left front claw, right front claw, left hind claw, right hind claw, back, root tail, middle tail, and tip tail. Prior to each session, spatial calibration was performed using a 25-point reference grid (10-cm spacing), achieving sub-millimeter reconstruction accuracy (<0.5 mm RMS error) via triangulation algorithms. To ensure trial consistency, the arena was sterilized with 75% ethanol and background intensity was normalized between sessions. Raw kinematic data were processed through a series of steps including camera-to-world coordinate transformation, Kalman filter-based noise suppression, and velocity-thresholded event detection (movement onset defined as >2 cm/s). These procedures yielded high-resolution spatiotemporal locomotor trajectories and ethogram classifications, following validated analytical pipelines. 18 Elevated plus maze (EPM) testing Male mice (8−12 weeks old) were placed at the center of a plus-shaped maze comprising of two open arms (25×5 cm) and two closed arms (25×5×15 cm) under ambient lighting set at 50 lux. Each trial lasted either 3 or 5 minutes, during which video recordings of behavior were obtained and then analyzed using the ANY-maze automated tracking system. Quantified behavioral parameters included time spent in the open arms, the number of entries into open versus closed arms, and total locomotor activity. Open-field test (OFT) Male mice (8−12 weeks old) were individually placed in a square open-field arena (50×50×50 cm) under uniform illumination (50 lux). Each trial lasted either 3 or 5 minutes, during which video recordings of behavior were obtained and then analyzed using ANY-maze automated tracking software. Behavioral parameters included time spent in the central zone, total distance traveled, and the frequency of rearing behavior. Data were normalized where appropriate to account for variability in overall locomotor activity. Startle reflex testing Male mice (8−12 weeks old) were secured in a stabilized chamber inside a sound-attenuated startle reflex apparatus (VisuStartle Software XR-XZ208, Shanghai Xinruan, China). Following a 5-minute acclimation period, mice were exposed to randomized acoustic stimuli consisting of 40-ms bursts of 110-dB white noise, interspersed with prepulse tones (20 ms, 65 dB) delivered at variable intervals. Startle amplitude, latency, and prepulse inhibition were recorded and analyzed using VisuStartle software. Data were normalized to baseline startle responses, and statistical significance was assessed using Student’s t-test to evaluate trial effects. Marble-burying test The test was conducted in a polypropylene mouse cage (42 × 24 × 12 cm) with a metal wire mesh top. The cage floor was layered with 5 cm deep sawdust, on which 20 clean black glass marbles (diameter: 1.5 cm) were evenly placed at uniform intervals. The male mice (8−12 weeks old) was placed in this box for 30 minutes. After 30 minutes, the mouse was removed, and the number of marbles buried by the mouse was tallied (counting marbles with at least two-thirds of their surface area covered by bedding material). Tail suspension test (TST) For the TST, a commercial tail suspension chamber (Bioseb, US) was employed. Male mice (8−12 weeks old) were individually suspended via tail attachment to the force transducer, and their activity was monitored over a 6-minute session. Immobility time was assessed using Anymaze in conjunction with the Bioseb setup. Sucrose preference test (SPT) Male mice (8−12 weeks old) were single-housed and habituated to two identical drinking bottles (50 ml Falcon tubes with silicone stoppers) for 48 hours; one bottle contained tap water (Bottle A) and the other contained 1% sucrose solution (Bottle B; Sigma, S9378). Bottle positions were counterbalanced across cages to minimize side bias. After 24 hours of water deprivation, baseline fluid consumption was measured during a 4-hour habituation session (09:00–13:00) with both bottles available. Following 24 hours of free access to water, mice underwent a formal 24-hour preference test under identical conditions. Bottle weights (±0.01 g) were measured before and after each phase to calculate fluid consumption, with adjustments made for evaporation using parallel control bottles. Sucrose preference (%) was calculated as: [sucrose intake / (sucrose + water intake)] × 100. Mice exhibiting 0.5 g) were excluded in accordance with the criteria described by Golden et al. 31 Serum corticosterone and norepinephrine analysis Blood samples were collected from mice via retro-orbital puncture under isoflurane anesthesia. Serum was obtained by centrifugation at 3000 ×g for 15 minutes and stored at −80°C until further analysis. Circulating levels of corticosterone and norepinephrine were measured using commercial ELISA kits (Abcam, ab108821 and ab287789, respectively), following manufacturer’s instructions. Absorbance was read using a BioTek Synergy H1 microplate reader, and hormone concentrations were normalized to total serum protein content. 9.4T fMRI acquisition from mouse brains in vivo Data acquisition Mice were anesthetized with isoflurane and all MRI experiments were performed on a 9.4T scanner with a 30-cm diameter bore (uMR 9.4 T, United Imaging Life Science Instrument, Wuhan, China). An 86-mm diameter transmit volume coil and a Mouse Brain Surface Coil-3 (MBSC3) for RF reception was used. High-resolution structural images were acquired with a Fast Spin Echo T2-weighted (FSE-T2) sequence (TR = 2500 ms, TE = 41.3 ms, FOV = 20 × 20 mm², matrix = 208 × 208, slice thickness = 0.3 mm, no gap). Subsequently, Blood Oxygen Level Dependent (BOLD) functional data were obtained using a gradient-echo echo-planar imaging (GE-EPI) sequence optimized for higher signal-to-noise ratio (TR = 2700 ms, TE = 14.5 ms, FOV = 15 × 19 mm², matrix = 76 × 96, slice thickness = 0.3 mm, no gap). Data analysis All data processing was performed within MATLAB (The MathWorks, Inc.) using either the SPM12 (Wellcome Centre for Human Neuroimaging, London, UK) 32 toolbox or custom code. Analysis began by expanding voxels 10-fold, followed by slice timing correction based on slice number, order, and TR. Head motion correction was then applied, with motion parameters (translation < 1 voxel, rotation < 2° across all subjects) estimated for each scan session. Functional MRI (fMRI) data were spatially normalized to a standard template associated with the brain atlas 33 using the T2-weighted image. Gaussian smoothing was then applied using a smoothing kernel of [4 4 6]. Following this preprocessing pipeline, the DPABI v8.2 34 toolbox was used to calculate the Amplitude of Low-Frequency Fluctuation (ALFF) parameter maps from the fMRI data. Voxel-wise two-sample t-tests were performed on the ALFF parameter maps using the SPM toolbox. The statistical results were thresholded at a significance level of p 50 voxels. Single-nucleus RNA sequencing (snRNA-seq) Tissue Acquisition & Nuclear Isolation: Acquired CLA samples (n=5 mice/group) were flash-frozen on dry ice. For snRNA-seq, bilateral CLA tissues were homogenized in ice-cold buffer using a Dounce homogenizer (Wheaton, 357542). Homogenates were mixed 1:1 with OptiPrep™ and centrifuged (10,000×g, 20min, 4°C). Pelleted nuclei were washed, resuspended in DMEM/F12+10% FBS, and adjusted to 400 nuclei/μL (hemocytometer). All solutions contained RNase inhibitor (60U/mL). Library Construction & Sequencing: snRNA-seq libraries were generated with Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 (10x Genomics). Briefly, a 40-μL nuclei suspension was combined with RT reagents for droplet encapsulation. Barcoded cDNA was synthesized by PCR and used for library preparation. Library QC involved Qubit™ quantification and Fragment Analyzer™ sizing. Sequencing and Analysis: Using Seurat (v5.2.0), we performed differential gene expression analysis with the FindAllMarkers function, specifying MAST as the statistical test. DEGs were functionally annotated by clusterProfiler (version 4.4.4). Multiplex fluorescence in situ hybridization (mulFISH) We performed mulFISH following protocols adapted from Kunyu Biotech. 35 Probe design and hybridization : Spatially specific targeting probes were custom-designed by Spatial FISH Ltd. Tissue samples fixed in 4% paraformaldehyde were mounted in reaction chambers for subsequent processing. After methanol-mediated dehydration and denaturation, hybridization buffer containing the targeting probes was applied, and samples were incubated overnight at 37°C. Ligation and amplification : Following three washes in PBST, ligation was carried out using a ligation mix at 25°C for 3 hours. The samples were then washed and subjected to rolling circle amplification with Phi29 DNA polymerase at 30°C overnight.. Detection and imaging : Fluorescent detection probes complementary to the amplified sequences were hybridized in hybridization buffer. After final ethanol dehydration, samples were mounted for imaging. Spatial localization of RNA transcripts was decoded based on fluorescent puncta captured using a Leica THUNDER Imaging System (20× objective, NA = 0.80). Chemogenetic manipulation and stereotactic virus injection Adeno-associated viruses (AAVs), AAV-DIO-hM3Dq-mCherry, AAV-DIO-hM4Di-mCherry and AAV-DIO-mCherry were packaged by Brain Case (Shenzhen, China) at titers of 5×10 12 particles per ml. Adult male Gad2-Cre or vGluT1-Cre mice (8−12 weeks old) were deeply anesthetized with sodium pentobarbital (1%, 10 mL/kg body weight, i.p.; Sigma-Aldrich, #P3761) and positioned in a stereotaxic instrument (RWD Life Science Inc., Shenzhen, China) with their heads fixed. Virus was delivered using a microinjector pump (UMP3/Micro4, World Precision Instruments, USA) connected to a 10-μL Hamilton syringe, at an injection rate of 50 nL/min.. After infusion, the injection needle was left in place for 10 minutes to minimize reflux. For DREADD-based manipulations, 150 nL of virus was bilaterally injected into the Claustrum (CLA) using the following coordinates: anterior-posterior (AP) +1.00 mm, mediolateral (ML) ±2.80 mm, dorsoventral (DV) −3.70 mm. To activate hM3Dq-expressing neurons, clozapine-N-oxide (CNO; Med Chem Express, #HY-17366) was administered intraperitoneally (1 mg/kg). Behavioral testing was performed within a 60-minute window, starting 30 minutes post-injection. All behavioral experiments were conducted under ambient lighting (∼200 lux). In male Gad2-Cre (8−12 weeks old) mice used for circuit-specific manipulation and retrograde labeling of ACC-projecting CLA Gad 2 ⁺ neurons, a viral cocktail of AAV-DIO-EGFP-T2A-TVA and AAV-DIO-N2cG was injected into the CLA using the coordinates given above. Concurrently, AAV-fDIO-hM3Dq-GFP (or control AAV-fDIO-GFP) was delivered into the ACC (AP: +1 mm, ML: ±0.3 mm, DV: −1.8 mm). Three days prior to behavioral testing, EnvA-pseudotyped, glycoprotein-deleted rabies virus (RV-EnvA-Δ G-mCherry-Flp) was injected into the CLA for retrograde labeling of ACC→CLA Gad 2 ⁺ projections. Chemogenetic activation of this circuit was achieved by daily CNO administration (1 mg/kg, i.p.) over seven consecutive days, during which behavioral outcomes were quantitatively assessed. Optogenetic stimulation Stereotaxic surgery was performed on adult vGluT1-Cre mice (8−12 weeks, male) with bilateral injections of AAV-DIO-ChR2-GFP (150 nl/site; 5×10¹² vg/ml, Brain Case) into the CLA. Concurrently, 200-μm optical fibers (0.37 NA) were implanted above the ACC (coordinates: AP +1.0 mm, ML ±0.3 mm, DV −1.5 mm) to enable terminal stimulation of CLA-originating vGluT1 projections. Mice were given 4 weeks to recover and to ensure sufficient transgene expression. To minimize stress and facilitate behavioral testing, animals were habituated to fiber attachment in their home cages for 3 consecutive days (15 min/day in home cages). During behavioral testing, blue light stimulation (470 nm, 10 Hz, 40-ms pulse width, 5 mW) was delivered through the optic cannula during the designated “light-on” periods. A control group injected with AAV-DIO-mCherry underwent the same surgical procedures and received identical light stimulation. Immunohistochemistry Mice were euthanized via overdose of 1% sodium pentobarbital (15 ml/kg body weight, i.p.) and transcardially perfused with phosphate-buffered saline (PBS), followed by 4% paraformaldehyde (PFA; Aladdin, #C104188) in PBS. Brains were extracted, post-fixed overnight in 4% PFA at 4°C, and cryoprotected in 20% sucrose for 24 hours followed by 30% sucrose for 48 hours. Tissue was embedded in O.C.T. compound (Tissue-Tek® Optimal Cutting Temperature) and sectioned coronally at 40-μm thickness using a cryostat microtome (Leica CM1950, Germany). Sections were washed three times in PBS (3 min per wash, room temperature) to remove residual O.C.T., then blocked in PBS containing 0.3% Triton X-100 and 3% bovine serum albumin (BSA) for 1 hour at room temperature. Primary antibodies were incubated overnight at 4°C in dilutions of 1:200-1:300 in PBS containing 0.1% Triton X-100 and 3% BSA. Sections were subsequently incubated for 2 hour at room temperature with secondary antibodies: Alexa Fluor® 405-, 488-, 594-, or 647-conjugated goat anti-rabbit or anti-rat IgG (1:200-1:300, Invitrogen). After mounting with DAPI-containing anti-fade reagent (ProLong™ Gold, Life Technologies), sections were imaged using an Olympus VS120-S6-W slide scanner or Zeiss LSM 980 confocal microscope. Neuroanatomical regions were identified according to The Mouse Brain in Stereotaxic Coordinates (Franklin and Paxinos, 1997). Patch-clamp electrophysiology Combined patch-clamp and optogenetics We employed combined patch-clamp/optogenetics to record action potential dynamics in vGluT1 neurons during blue light (470 nm) activation of ChR2-expressing Gad2 neurons. Three weeks after stereotaxic injection of AAV-DIO-ChR2-GFP into the CLA of Gad2-Cre mice, coronal CLA slices (250 μm; bregma +1.4 to +0.6 mm) were prepared in ice-cold modified ACSF (mM: 205 Sucrose 2.5 KCl, 1.25 NaHPO₄, 25 NaHCO, 10 Glucose, 0.5 CaCl, 7.5 MgCl₂) using a Leica VT-1200S vibrotome. Slices recovered 30 min at 32−34°C in oxygenated standard ACSF (mM: 125 NaCl, 2.5 KCl, 1.3 NaH₂PO₄, 25 NaHCO₃, 25 Glucose, 2 CaCl₂, 1 MgCl₂; pH 7.3−7.4), then held at room temperature. All solutions: were between 300−320 mOsm/kg. All drugs used were from Sigma-Aldrich. Slices were visualized under Nikon FN1 microscope with IR optics. Recording used oxygenated ACSF perfusion (2 ml/min, RT). Pipettes (5−10 MΩ; Sutter P-97) contained (mM): 125 K-gluconate, 20 KCl, 0.5 EGTA, 10 HEPES, 10 Na 2 -creatine, 4 Mg-ATP, 0.3 Na-GTP. AP firing was evoked by 2-s depolarizing steps (current-clamp). Signals amplified via HEKA EPC-10, filtered at 2.9 kHz and 10 kHz, digitized at 20 kHz (Patchmaster). Series resistance (10-30 MΩ) was monitored; data discarded if >30% change occurred. Optical activation of Gad2 + neurons via blue light irradiation confirmed ChR2 functionality whilst electrophysiological responses of surrounding excitatory neurons were recorded. Patch-Clamp in Gad2 + neuron in CSDS mice Method guided by Jarvis Biological Pharmaceutical Co., Ltd (Wuhan, China). Mice were anesthetized with sodium pentobarbital (45 mg/kg) via intraperitoneal injection (i.p.) and intracardially perfused with ice-cold slicing solution (in mM, 209 sucrose, 3.1 sodium pyruvate, 22 glucose, 1.25 NaH₂PO₄, 12 sodium l-ascorbate, 4.9 MgSO₄-7H₂O, 26 NaHCO₃; 95% O₂/5% CO₂, pH 7.2–7.4). Coronal slices (300 μm) of the claustrum (CLA) were prepared using a vibratome (Leica VT1200 S, Leica Biosystems) and incubated in aCSF (in mM, 128 NaCl, 3 KCl, 24 NaHCO₃, 2 MgCl₂, 1.25 NaH₂PO₄, 10 d-glucose, 2 CaCl₂; oxygenated, pH 7.2–7.4, 295–305 mOsm) at 28°C for 1 hour, then equilibrated at room temperature for patch-clamp recordings. Neurons were voltage-clamped at −70 mV in voltage-clamp mode to record spontaneous excitatory postsynaptic currents (sEPSC). sEPSC pipettes (4–6 MΩ) were filled with an internal solution comprising (in mM, 122.5 Cs-gluconate, 17.5 CsCl, 0.2 EGTA, 10 HEPES, 1 MgCl₂, 4 Mg-ATP, 0.3 Na-GTP, 5 QX314, pH 7.25, 280–300 mOsm), with 100 μM PTX in the bath. For action potentials, current-clamp mode used pipette solution (in mM, 140 K-gluconate, 3 KCl, 2 MgCl₂, 10 HEPES, 0.2 EGTA, 2 Na₂ATP, 285–295 mOsm, pH 7.2). All data was obtained by pCLAMP 10 (Axon Instruments, Molecular Devices, San Jose, CA) and the MultiClamp 700B amplifier (Molecular Devices, Sunnyvale, CA). Recordings were low-pass filtered at 2–20 kHz and digitized at 5–50 kHz (Molecular Devices, Jarvisbio, Wuhan, China). Fiber photometry calcium signals recording Three weeks following bilateral injections of AAV-DIO-ChrimsonR-mCherry (150 nl/site; 5×10¹² vg/ml, BrainVTA) and AAV-vGluT1-GCaMP6s (150 nl/site; 5×10¹² vg/ml, BrainVTA) into the CLA, Gad2-Cre mice (8–12 weeks, male) were habituated to fiber patch cords attachment in their home cages (≥15 min/day for 3 days) prior to testing. Calcium signals were recorded using a dual-wavelength fiber photometry system (ThinkerTech, Nanjing) equipped with 470-nm and 405-nm LEDs for GCaMP6s excitation and motion artifact correction, respectively. Both excitation channels were coupled via a single objective to a 200-μm, 0.37 numerical aperture optical fiber (RWD Life Science). Simultaneously, a 590-nm laser (ThinkerTech) was used to activate ChrimsonR-expressing CLA Gad 2 ⁺ neurons while recording GCaMP6s signals from CLA vGluT 1 ⁺ neurons. Light intensity at the fiber tip was maintained at 25–30 μW. GCaMP6s fluorescence emission was collected through the same optical path, separated by a dichroic mirror, and detected by a photodetector. Calcium signal processing and analysis were performed using custom scripts in MATLAB R2017b. Statistical summary Unless otherwise specified, comparisons between two groups were performed using unpaired two-tailed Student’s t-tests. Comparisons involving four groups were analyzed using two-way analysis of variance (ANOVA). Where significant main effects or interactions were found by two-way ANOVA, post hoc pairwise comparisons between groups were performed using Tukey’s multiple comparisons test. All statistical analyses were conducted using GraphPad Prism version 9.0 (GraphPad Software, San Diego, CA). Data are presented as mean ± standard error of the mean (SEM). Statistical significance was defined as p < 0.05. Funding This study was supported by the National Key R&D Program of China (No.2024YFC3406700, 2024YFC3406701 J.T.), Shenzhen Medical Research Fund (B2402018 J.T.), the National Natural Science Foundation of China (32371070 J.T.), the Guangdong Provincial Key S&T Program (2018B030336001 J.T.), and Shenzhen Science and Technology Program (JCYJ20241202125015020 Q.X., JCYJ20220818101615033 D.L., JCYJ20210324101813035 D.L.). Author contributions J.T. conceived the study. D.L., G-J. S., Z-J.L., J.S. and X-R.X. performed the experiments. J.T., D.L., G-J. S., Z-J.L., Q.X. and S.C. analyzed the data. L-P. W., F.Y. and Y.C. provided suggestions on the manuscript. D.L. and J.T. wrote the manuscript. J.T. supervised the project. Declaration of interests The authors have declared that no conflict of interest exists. Acknowledgments We appreciate the very helpful suggestions and comments on earlier versions of the manuscript by Prof. Minmin Luo and Prof. Xiang Yu. We thank Prof. Teng Chen and Prof. Xinshe Liu for their support. We thank Yingying Du and Tong Ye for their contributions to the MRI data acquired and analysis. We thank Zili Liu for her technical support for the patch-clamp data acquired. The technical support for electrophysiology from Jarvis Biological Pharmaceutical Co., Ltd (Wuhan, China) is sincerely appreciated. The figures in this paper were created with BioRender.com. Funder Information Declared National Key R&D Program of China , 2024YFC3406700 , 2024YFC3406701 Shenzhen Medical Research Fund , B2402018 National Natural Science Foundation of China , 32371070 Guangdong Provincial Key S&T Program , 2018B030336001 Shenzhen Science and Technology Program , JCYJ20241202125015020 , JCYJ20220818101615033 , JCYJ20210324101813035 Reference 1. ↵ Adhikari , A . ( 2014 ). Distributed circuits underlying anxiety . Front Behav Neurosci 8 , 112 . doi: 10.3389/fnbeh.2014.00112 . OpenUrl CrossRef PubMed 2. ↵ Penninx , B.W. , Pine , D.S. , Holmes , E.A. , and Reif , A . ( 2021 ). Anxiety disorders . Lancet 397 , 914 – 927 . doi: 10.1016/S0140-6736(21)00359-7 . OpenUrl CrossRef PubMed 3. ↵ Christoffel , D.J. , Golden , S.A. , Walsh , J.J. , Guise , K.G. , Heshmati , M. , Friedman , A.K. , Dey , A. , Smith , M. , Rebusi , N. , Pfau , M. , et al. ( 2015 ). Excitatory transmission at thalamo-striatal synapses mediates susceptibility to social stress . 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Share Claustrum−cortical reciprocal connections orchestrate allostatic responses following stress Dan Liu , Gaojie Shao , Zijun Liu , Jie Shao , Shuai Chen , Xirong Xu , Qian Xiao , Yu Chen , Liping Wang , Fan Yang , Jie Tu bioRxiv 2025.09.03.673892; doi: https://doi.org/10.1101/2025.09.03.673892 Share This Article: Copy Citation Tools Claustrum−cortical reciprocal connections orchestrate allostatic responses following stress Dan Liu , Gaojie Shao , Zijun Liu , Jie Shao , Shuai Chen , Xirong Xu , Qian Xiao , Yu Chen , Liping Wang , Fan Yang , Jie Tu bioRxiv 2025.09.03.673892; doi: https://doi.org/10.1101/2025.09.03.673892 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7642) Biochemistry (17708) Bioengineering (13904) Bioinformatics (41992) Biophysics (21466) Cancer Biology (18618) Cell Biology (25531) Clinical Trials (138) Developmental Biology (13387) Ecology (19924) Epidemiology (2067) Evolutionary Biology (24337) Genetics (15615) Genomics (22521) Immunology (17749) Microbiology (40424) Molecular Biology (17194) Neuroscience (88673) Paleontology (667) Pathology (2839) Pharmacology and Toxicology (4827) Physiology (7650) Plant Biology (15160) Scientific Communication and Education (2046) Synthetic Biology (4302) Systems Biology (9826) Zoology (2271)