Correlated signatures of social behavior in cerebellum and anterior cingulate cortex

  1. Sung Won Hur
  2. Karen Safaryan
  3. Long Yang
  4. Hugh T Blair
  5. Sotiris C Masmanidis
  6. Paul J Mathews
  7. Daniel Aharoni  Is a corresponding author
  8. Peyman Golshani  Is a corresponding author
  1. Department of Neurology, DGSOM, University of California Los Angeles, United States
  2. The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, United States
  3. Department of Neurobiology, University of California Los Angeles, United States
  4. Department of Psychology, University of California Los Angeles, United States
  5. Department of Neurology, Harbor-UCLA Medical Center, United States

Abstract

The cerebellum has been implicated in the regulation of social behavior. Its influence is thought to arise from communication, via the thalamus, to forebrain regions integral in the expression of social interactions, including the anterior cingulate cortex (ACC). However, the signals encoded or the nature of the communication between the cerebellum and these brain regions is poorly understood. Here, we describe an approach that overcomes technical challenges in exploring the coordination of distant brain regions at high temporal and spatial resolution during social behavior. We developed the E-Scope, an electrophysiology-integrated miniature microscope, to synchronously measure extracellular electrical activity in the cerebellum along with calcium imaging of the ACC. This single coaxial cable device combined these data streams to provide a powerful tool to monitor the activity of distant brain regions in freely behaving animals. During social behavior, we recorded the spike timing of multiple single units in cerebellar right Crus I (RCrus I) Purkinje cells (PCs) or dentate nucleus (DN) neurons while synchronously imaging calcium transients in contralateral ACC neurons. We found that during social interactions a significant subpopulation of cerebellar PCs were robustly inhibited, while most modulated neurons in the DN were activated, and their activity was correlated with positively modulated ACC neurons. These distinctions largely disappeared when only non-social epochs were analyzed suggesting that cerebellar-cortical interactions were behaviorally specific. Our work provides new insights into the complexity of cerebellar activation and co-modulation of the ACC during social behavior and a valuable open-source tool for simultaneous, multimodal recordings in freely behaving mice.

eLife assessment

Based on a technological advance which couples onboard calcium imaging with in vivo electrophysiology in freely behaving mice, this important work presents data about the modulation of some long range brain activity correlations during social interactions. Solid evidence shows that neural activity across cerebellum and cingulate cortex is more correlated during social behaviors than during non-social epochs. This study is of interest for a broad range of neurophysiologists.

https://doi.org/10.7554/eLife.88439.3.sa0

eLife digest

Social behaviour is important for many animals, especially humans. It governs interactions between individuals and groups. One of the regions involved in social behaviour is the cerebellum, a part of the brain commonly known for controlling movement. It is likely that the cerebellum connects and influences other socially important areas in the brain, such as the anterior cingulate cortex. How exactly these regions communicate during social interaction is not well understood.

One of the challenges studying communication between areas in the brain has been a lack of tools that can measure neural activity in multiple regions at once. To address this problem, Hur et al. developed a device called the E-Scope. The E-Scope can measure brain activity from two places in the brain at the same time. It can simultaneously record imaging and electrophysiological data of the different neurons. It is also small enough to be attached to animals without inhibiting their movements.

Hur et al. tested the E-Scope by studying neurons in two regions of the cerebellum, called the right Crus I and the dentate nucleus, and in the anterior cingulate cortex during social interactions in mice. The E-Scope recorded from the animals as they interacted with other mice and compared them with those in mice that interacted with objects.

During social interactions, Purkinje cells in the right Crus I were mostly less active, while neurons in the dentate nucleus and anterior cingulate cortex became overall more active. These results suggest that communication between the cerebellum and the anterior cingulate cortex is an important part of how the mouse brain coordinates social behaviour.

The study of Hur et al. deepens our understanding of the function of the cerebellum in social behaviour. The E-Scope is an openly available tool to allow researchers to record communication between remote brain areas in small animals. This could be important to researchers trying to understand conditions like autism, which can involve difficulties in social interaction, or injuries to the cerebellum resulting in personality changes.

Introduction

The cerebellum, a brain region traditionally associated with motor coordination, has been increasingly recognized as a region that participates in the coordination of social cognition and behavior. Early studies showed that cerebellar stroke or injury could result in personality changes, blunted affect, and even antisocial behavior (Schmahmann and Sherman, 1998). More recent studies using neuroimaging and behavioral assays indicate the cerebellum contributes to elements of social cognition including ‘mentalizing’ or ‘mirroring’ (Van Overwalle et al., 2020).

Studies in animal models have strengthened the link between the cerebellum and social behavior. Modulating the neural activity of cerebellar PCs through the insertion of disease related gene mutations or chemogenetic alterations of firing rate reduced social interactions (Badura et al., 2018; Kelly et al., 2020). Moreover, optogenetic suppression of cerebellar output to the forebrain or midbrain, especially cerebellar neurons projecting to the ventral tegmental area (VTA), decreased social interest (Carta et al., 2019). Among all cerebellar regions, modulation of the RCrus I of the cerebellar cortex had the most significant impact on the expression of social behavior (Stoodley et al., 2017).

However, it has yet to be established how the cerebellar output of the RCrus I region contributes to social behavior. It is likely that the cerebellum, through cerebellar communication with forebrain areas like the ACC, provides signals important in coordinating aspects of social interaction. These socially relevant brain regions have known multisynaptic connectivity, in which the RCrus I PC axons project to the DN, which in turn innervates the ventromedial thalamic neurons (Badura et al., 2018; Kelly et al., 2020). The cerebellum’s interactions with the ACC are of interest as targeted lesions to it in non-human primates and most recently, selective gene deletion in mice, decreased sociability and the recognition of social and emotional cues (Devinsky et al., 1995; Guo et al., 2019; Hadland et al., 2003). While both the cerebellum and ACC play important roles in the regulation of social behavior, how these regions interact at the neural and systems levels to drive social behavior is still poorly understood.

Here, we developed the E-Scope, a novel device that can perform simultaneous electrophysiological and calcium imaging recordings of cerebellum and ACC, respectively. We have, for the first time, recorded the activity of RCrus I PC, or DN neurons in synchrony with recordings from large populations of ACC neurons during social interaction. Our results demonstrate that PCs and DN neurons are mostly antagonistically modulated by social interaction with most PCs inhibited and DN neurons excited during interaction epochs. Furthermore, we find that there is a higher correlation between cerebellar and ACC neurons that are similarly excited or inhibited by social interaction than those modulated in an opposing manner, but that these differences disappear during non-social bouts. This highlights brain-state-specific modulation of social circuits across distant brain regions, critical for social interaction.

Results

E-Scope: A miniaturized calcium imaging microscope with integrated dense electrode technology for the synchronous acquisition of neural activity across distant regions of the brain

Limitations in the spatial and temporal resolution of current neural monitoring technologies have hampered our ability to systematically examine how interconnected brain regions communicate to control complex behaviors. To address these limitations, we have developed a novel device-based off the open-sourced UCLA Miniscope to synchronously measure single-cell activity at or near spike-time resolution across distant brain regions in freely behaving mice. We have integrated a miniatured microscope, which performs calcium imaging, with dense electrode electrophysiological recording, allowing simultaneous recordings from two remote brain regions in a freely behaving mouse (Figure 1A). This ‘E-Scope’ uses a single coaxial cable that minimally impacts animal movement to integrate both imaging and electrophysiological data streams along with the Miniscope’s power. The electrophysiological component of this dual neural activity monitoring system is especially advantageous for brain regions with high basal firing rate neurons, such as the cerebellum, where activity may be obscured or not amenable to calcium imaging.

Figure 1 with 1 supplement see all
E-Scope: An integrated device allowing synchronous calcium imaging of anterior cingulate cortex and electrophysiological recordings in cerebellum.

(A) Photograph of E-Scope hardware. The multichannel silicon probe (32 channels) connects to the custom Ephys PCB. The Ephys PCB is connected to the CMOS sensor printed circuit board (PCB) of the Miniscope via a 6-pin connector. The electrophysiology and image data streams are both conveyed through the coaxial cable. (B) Illustration of the process for implanting the E-Scope. (C) Illustrations and photomicrographs showing the location of AAV1-Syn-GCaMP6f virus injection in anterior cingulate cortex (ACC) (left, mid) and multichannel probe implant in the dentate nucleus of the cerebellum (left, right). (D) Pseudo-color (top left) and averaged activity heatmap from calcium imaging ACC neurons segmented using CNMF-E (bottom left). Calcium signals from neurons are shown on the left (right). (E) in vivo extracellular electrophysiology recording of Purkinje cells (PCs) in the cerebellum (Cb). (F) Average spike waveform of a dentate nucleus (DN) neuron. (G) Average simple spike (SS) waveform (left) and average complex spike (CS) waveform (mid) of a PC. Cross-correlogram of simple spikes and complex spikes shows the pause in simple spike activity after a complex spike (right).

The E-Scope builds off our previous open-source wired Version 3 miniaturized microscope (Miniscope V3). Using its modular capability, we incorporated 32 channel electrophysiology sampling capabilities with the ability to support various configurations and geometries of silicon probes and tetrodes. The entire assembly weighs less than 4.5 g. For performing electrophysiological recordings, we used a two-shank, 64 channel silicon probe (64 H with 32 channels activated) (Yang et al., 2020).

We used the E-Scope to investigate correlations in the activity of RCrus I cerebellar PCs or DN neurons with population activity of contralateral ACC neurons during social behavior. To monitor ACC population activity, we injected an AAV virus to express the genetically encoded calcium indicator GCaMP6f in the left ACC. After a one-week recovery period, a 1 mm diameter GRIN lens (Inscopix; PN 130–000143) was implanted at the site of injection at a 10° angle. After a subsequent 2 weeks, the V3 Miniscope baseplate was implanted over the ACC injection site. Animals were introduced to the 48 × 48 cm behavioral arena and gradually acclimated to the weight of the scope by using dummy scopes of increasing weight over a 7–10 day period. Silicon-based, dense-electrode probes (Yang et al., 2020) were implanted and affixed into the RCrus I PC layer or DN and secured into place with dental cement. We made the first set of recordings ~24 hr after implantation of the probes (Figure 1B and C). Prior to implantation, the probes were coated with DiI (#C7000, Thermo Fisher Scientific) to enable post hoc confirmation of the probe’s anatomical location (Figure 1C). For each mouse, we recorded approximately 200 ACC neurons using calcium imaging (Figure 1D) and 1–9 cerebellar neurons using dense electrode arrays (Figure 1E). The freeware analysis program Kilosort2/Phy2 was used to isolate single PC and DN units from recordings (Figure 1F and G). P-sort open-source software was used for PCs, which were identified by the presence of complex spikes (CS) along with the simple spikes (SS) in the recordings (Figure 1G; Sedaghat-Nejad et al., 2021).

E-Scope recordings of cerebellar activity during social and object interactions

Implanted subject animals were placed into the behavioral arena for 1 min before the introduction of either another novel male C57Bl/6 mouse or a 3D-printed object (cone or cube) for 7 min. Subsequently, the animal was removed from the behavior arena for 1 min while the arena was cleaned. The recorded animal was then reintroduced to the arena and introduced to a novel mouse or object for another 7 min in a counterbalanced manner. Proximity to each other’s body was used it as a measure of social interaction. Behaviors other than nose-to-nose, nose-to rear, or nose-to-body interactions were disregarded in the analysis. Recorded animals made more contact with the other mouse than with the object (Figure 2A), suggesting a normal preference for social contact with the E-Scope attached.

Figure 2 with 4 supplements see all
Purkinje cell and dentate nucleus neuron activity patterns during social behavior.

(A) Graph of the number of interaction bouts between the recorded subject mouse and a novel target mouse or object (two-sided Wilcoxon rank sum test; p=0.0245, Z=2.2485) (B) Illustration of probe location in the Purkinje cells (PC) layer (top). Average simple spike waveform of a PC (bottom). (C) Heatmap of normalized (Z-score) firing rates of a Soc– PC neuron aligned to the onset of social interaction shown for 10 interaction epochs (top). The mean normalized firing rate across all interaction sessions shown above (bottom). (D, E, F) Average activity of three Soc+ PCs (D), 10 Soc– PCs (E), and 17 ns PCs (F). The mean activity of each group is shown below each heat map. (G) Proportion of Soc+, Soc–, and ns PCs in the recorded population. (H) Trajectory of subject mouse (gray) with social interaction locations indicated in red within the social interaction arena (480 × 480 mm). (I) Illustration of probe location for dentate nucleus (DN) recordings (top). Average spike waveform of a DN neuron (bottom). (J) Normalized firing rates of a Soc+ DN neuron aligned to the onset of social interaction shown for 18 interaction epochs (top). The mean normalized firing rate across all interaction sessions shown above. (K, L, M) Average activity of 19 Soc+ DN neurons (K), 10 Soc– DN neurons (E), and 63 ns DN neurons (F). The mean activity of each group is shown below each heat map. (N) Proportion of Soc+, Soc–, and ns DNs in the recorded population.

We recorded SS from a total of 28 PCs during both social and object interaction (Figure 2B and G). We used receiver operating characteristics (ROC) analysis to measure the area under the curve (AUC) value which quantified the overlap of activity with binarized vectors characterizing whether the animal was socially interacting or not interacting with the other animal; these values were in turn compared to circularly shuffled controls to identify units significantly modulated by social or object interaction. Nearly one-third of the PCs (28.5%, n=8) were robustly inhibited by social interaction (Figure 2D, E and G) while only three PCs (10.7%) were excited during social interaction (Figure 2D). The remaining non-significant (nsPC) units were not modulated (Figure 2F). Neurons inhibited by social interaction were inhibited for 1–3 s (Figure 2C and E), which was far longer than the SS pause is typically induced by CSs (up to ~40 ms) (Figure 1G). The same procedure revealed only 2 significantly modulated cells (7.1%) in both object interaction excited and inhibited groups (Figure 2—figure supplement 1).

Conversely, most DN neurons significantly modulated by social interaction were excited. Out of 99 DN neurons recorded, 20 neurons (20.2%) were excited during social interaction (Soc + DN), 12 neurons (12.1%) were inhibited (Soc– DN) and the remaining 67 not significantly modulated (nsDN) (Figure 2I–N). The excitation was more robust than inhibition and firing rates were increased for 2–4 s post interaction onset. The DN cells recorded in the object interaction sessions (n=85) showed smaller responses compared to the social interaction with 10 (11.7%) and 6 (7.05%) excited and inhibited units, respectively (Figure 2—figure supplement 2).

Cerebellar PCs have been reported to have different modules that operate at different baseline SS frequencies (Zhou et al., 2014). These modules have also been shown to modulate bidirectionally during learning (De Zeeuw, 2021). We, therefore, analyzed the mean firing rate during non-social events for PCs and DN neurons. DN neurons did not show any difference (F=1.589, p=0.2172) in mean firing rate between Soc+ and Soc– groups and the bimodal rate distribution of the DN neurons were not related to the waveform properties (Figure 2—figure supplement 3A, B). However, PCs showed a clear difference (F=5.742, p=0.04) in mean firing rate between the two socially responding types (Figure 2—figure supplement 3C,D).

Social and object interactions are often coupled with locomotion and head rotational changes, which are especially relevant to cerebellar activity. Thus, we further investigated the possibility that PCs and DN neurons encode movement-related activity. First, we examined the body speed and angular head velocity at the onset of social interaction. In the sessions recorded, we found mice tended to reduce their speed and head angular velocity upon interaction (Figure 2—figure supplement 4A, B). Analysis of the Soc+ and Soc– PCs as well as DN neurons showed no significant change in activity upon onset (Figure 2—figure supplement 4C, D) or offset (Figure 2—figure supplement 4E, F) of locomotion. In addition, head rotation onset had no relationship to PC or DN activity in Soc+ or Soc– groups (Figure 2—figure supplement 4G, H). Therefore, the activity changes we observed in cerebellar neurons at the onset of social interactions are unlikely to be related to motor aspects of behavior during social interactions. Overall, the electrophysiological responses in the cerebellum during social interaction are predominantly composed of PC inhibition and DN excitation. These results are consistent with the cerebellar anatomical circuit, where simultaneously decreased PC activity would be predicted to disinhibit DNs, thus resulting in excitatory output from the cerebellum to downstream brain areas (Lee et al., 2015).

E-Scope calcium imaging of ACC neuron activity during social and object interaction

To determine how the cerebellar neurons communicate with ACC neurons during social interaction, we performed calcium imaging of ACC neurons (Figure 3A) synchronously with the electrophysiological recordings in the cerebellum discussed above using the E-Scope. We again used ROC analysis and circularly shuffled controls to determine whether ACC neurons were excited, inhibited, or not significantly modulated by social or object interactions (Figure 3B–F, Figure 3—figure supplement 1). We recorded 4868 and 3149 neurons during social and object interaction sessions, respectively. Of the recorded cells, 3.6% of ACC neurons were socially excited (Soc + ACC; Figure 3B, D and F), and 5.2% were socially inhibited (Soc– ACC; Figure 3C, E and F). These proportions were greater than what would be expected by chance given the criteria for AUC significance (p<0.025). Conversely, 4.1% and 8.4% of ACC neurons were excited or inhibited by object interactions, respectively (Figure 3G and H, Figure 3—figure supplement 1). The overlap of neurons significantly excited or inhibited by both social and object interactions was 0.1% and 0.4% of all neurons, respectively. Therefore, a significant proportion of non-overlapping ACC neurons were excited or inhibited by social and object interactions.

Figure 3 with 2 supplements see all
Anterior cingulate cortex (ACC) neuron activity patterns during social behavior.

(A) Illustration of GRIN lens implant location (top left). Raw image from ACC calcium imaging recording (mid left). Segmented and averaged activity heatmap from the recording shown above (bottom left). Example of raw behavioral trajectory of subject mouse (right). (B, C) Heatmap depicting the calcium activity of 10 Soc+ ACC neurons (B) and 15 Soc– ACC neurons (C) during a single social interaction session. Social interaction bouts are shown as gray bars. The average calcium activity is shown below each heatmap. (D, E) Top: Z-scored Soc+ (D) and Soc– (E) ACC neurons calcium activity for all neurons during all social interaction sessions. The onset of social interaction is marked as a dashed black line. Bottom: Mean of Z-scored activity shown for the heatmaps above for Soc+ (D) and Soc– (E) neurons. (F) Percentage of units showing significant modulation by social interaction. (G) Overlap of Soc+ and Obj + ACC neuronal populations. (H) Overlap of Soc– and Obj– ACC neuronal populations.

Finally, we assessed whether ACC neuron activity is correlated with locomotion or changes in head angular momentum. Similar to our observation in the cerebellum, ACC activity showed no significant correlation with movement at the onset or offset of locomotion or head rotation (Figure 3—figure supplement 2).

Correlated activity in the cerebellar-cortical circuit

To understand the relationship between cerebellum and ACC modulation during social interaction and non-social epochs, we performed correlation analyses between the different cell types in the two regions. Previous reports indicate the cerebellum indirectly connects to the ACC via the ventral medial thalamus (Figure 4A), but it is not clear whether this indirect connection drives ACC firing during social bouts (Badura et al., 2018; Kelly et al., 2020). We, therefore, calculated the distribution of correlation coefficients of activity during both the social interaction bouts as well as during the non-social (off-bouts) periods for different PC-ACC and DN-ACC cell pairs depending on their modulation by social interaction (Figure 4B and C). Overall, as expected, cerebellar PC and DN neurons and ACC neurons that were similarly modulated by social interaction showed higher correlation values compared to cell pairs that were modulated in opposite directions (Figure 4D–G). On the other hand, nsPC or nsDN activity correlation distributions with all ACC groups were similar (Figure 4—figure supplement 1I,J & 2). These patterns were largely consistent when the proportion of neurons significantly correlated was calculated between the differently modulated cell types in the cerebellum and ACC (Figure 4I–K). Interestingly, these differences almost entirely disappeared when only non-social epochs were analyzed (Figure 4D–G insets). Pairwise comparisons of correlation coefficients recorded in the same socially modulated cell pairs with social bouts included and excluded supported our findings (Figure 4—figure supplement 1 A-H). These findings suggest that correlated activity is largely driven by social interactions and not by the intrinsic connectivity of these neuronal groups.

Figure 4 with 2 supplements see all
Correlated activity in the cerebellar-cortical circuit during social behavior.

(A) Illustration of the cerebellar-cortical circuit. Purkinje cells (PCs) provide converging inhibition to dentate nucleus (DNs) that excite thalamic neurons. Thalamic neurons excite anterior cingulate cortex (ACC) neurons. (B) Simultaneously recorded calcium traces from nine ACC neurons and the electrophysiologically recorded firing rate of a single PC (red) during a social interaction epoch. Social interaction bouts are shown as gray bars. (C) Simultaneously recorded calcium traces from 10 ACC neurons (blue) and the electrophysiologically recorded firing rate of a single DN neuron during a social interaction epoch. Social interaction bouts are shown as gray bars. (D–G) Cumulative histogram of the distribution of the correlation coefficients for the activity of (D) Soc+ PCs, (E) Soc– PCs, (F) Soc+ DNs, or (G) Soc– DNs with Soc+ (dark blue), Soc– (light blue), and Socns (blue gray) ACCs. Insets: cumulative histogram of the activity of each set of neurons calculated during periods when the mouse was not engaged in social interaction. (H, I, J, K) Correlation matrix showing the percentage of cell pairs showing significant positive (H, J) or negative (I, K) correlations in activity between Soc+ and Soc– PCs (H, I) or Soc+ and Soc– DNs (J, K) with Soc+, Soc–, and Socns ACC neurons. The color of the squares represents the proportion of neurons correlated.

Discussion

Social interaction between animals involves a complex set of behaviors that involves the integration of multimodal information and communication between several brain areas. Here, we provide a new perspective on the physiology of the cerebellum during social interaction and how it communicates with downstream brain regions. To faithfully record this synchronized activity, we developed a new device (E-Scope) to record two remote but functionally connected regions of the brain via calcium imaging and electrophysiology. We show that the majority of RCrus I PCs modulated by social interaction decrease their SS firing activity. Conversely, most modulated DN neurons increased their firing rate during social interaction, consistent with disinhibition of DN firing by simultaneous decreases in PC SS firing rate. In vivo calcium imaging during social interaction showed that less than 10 % of ACC neurons were modulated by social interaction with the majority of neurons being inhibited. As expected, cerebellar and ACC neurons similarly modulated by social interaction showed higher correlations in their firing rate than cerebellar and ACC neurons that were modulated in opposite directions. These differences in correlated firing largely disappeared when only non-social bouts were analyzed, indicating that correlations were dependent on social behavior and not on the intrinsic indirect connectivity of the two brain regions.

The E-Scope: An integrated device for simultaneous calcium imaging and electrophysiology

The development of the miniature microscope (Ghosh et al., 2011) in conjunction with the invention of highly sensitive and robust genetically encoded calcium indicators (Chen et al., 2013; Nakai et al., 2001) has allowed the recording of activity in large populations of neurons in freely behaving mice. Open-source miniaturized microscopes (Aharoni et al., 2019a; Cai et al., 2016) have expanded access to this tool and encouraged new developments in Miniscope technology including the development of wire-free (Shuman et al., 2020) and large field of view miniaturized microscopes (Guo et al., 2021). In recent years, a miniaturized microscope called the NINscope was developed for multi-region recording of calcium signals (de Groot et al., 2020), allowing simultaneous calcium imaging complex spike activity in the cerebellum and motor cortical activity during free behavior. However, calcium imaging in PCs provides only a partial picture of their activity, as it reports only the rate of CSs, but not the dynamic and persistent SS firing that is characteristic of these fast-spiking neurons. Here, we introduce the E-Scope, which can synchronously acquire calcium imaging data from one area and electrophysiological data from another to allow simultaneous examination and integration of electrical, calcium, and behavioral data streams. The E-Scope also provides method flexibility, allowing simultaneous recordings with areas where implanting a lens may be too invasive, such as deep brain regions like the DN where lens implantation would require the removal of its input layer in the cerebellar cortex. Furthermore, the E-Scope can be used to record local field potentials from any brain region, regardless of depth, simultaneously with calcium imaging. This approach therefore, provides a unique tool to identify neurons activated during different oscillations critical for memory consolidation and cognition such as ripple and theta oscillations in the hippocampus or spindle oscillations in the cortex. Additionally, the integration of data streams and Miniscope power into a thin single coax cable is less limiting to the physical movement of the animal than multiple wires, enabling better interpretation of physiological correlates of behavior. Moreover, the E-Scope can be used with multiple different extracellular electrophysiological recording techniques including silicon microprobes as shown here, as well as tetrode arrays (Howe and Blair, 2022) or flexible electrode arrays (Chung et al., 2019). Several approaches now exist for linking different types of electrodes to GRIN lenses for simultaneous electrophysiological recordings and calcium imaging from the same brain region (Cobar et al., 2022; McCullough et al., 2022; Wu et al., 2021). The E-Scope would be able to couple with these electrode systems/imaging systems as well.

Electrophysiological recordings demonstrate reciprocal social modulation between RCrus I PCs and DNs

Lesion and imaging studies in humans have suggested that the cerebellum’s role extends beyond motor control and that the cerebellum may be important for social behavior (Schmahmann and Sherman, 1998; Sokolov et al., 2017). In fact, cerebellar abnormalities are one of the most replicated anatomical changes in brains of individuals with autism (Fatemi et al., 2012). A causal role for the cerebellum in social behavior was further solidified when specific genetic or activity modulations of RCrus I had dramatic effects on social behavior (Badura et al., 2018; Kelly et al., 2020). Yet, how distinct cerebellar cell types in this brain region are modulated by social interaction is not known as it requires direct electrophysiological recordings from cerebellar neurons in freely behaving animals. Our study is the first to demonstrate physiological cerebellar PC and DN neuron activity during social interaction in freely behaving animals. We find that most socially modulated PC neurons in RCrus I are inhibited. The mechanisms driving prolonged inhibition of PC firing during social interaction is still not clear. This inhibition lasts far longer than would be expected post-complex spike pauses in activity. One potential mechanism could be coordinated decreases in cerebellar granule cell activity during social interaction dynamically decreasing excitatory input to PCs and, therefore, reducing PC SS firing. Alternatively, inhibition of PC firing rate may be due to an increase in inhibitory versus excitatory input from molecular layer interneurons (MLIs) versus granule cells, respectively. A process that could be driven by LTP and LTD of the corresponding synapse during learning. Previous in vivo studies have shown that sensory stimulation can increase MLI activity (Jörntell and Ekerot, 2003) and, therefore, social sensory input such as olfactory stimuli could potentially increase MLI firing. This potential mechanism of PC inhibition is supported by experiments where suppression of Crus I MLIs causes changes in social preference (Badura et al., 2018), suggesting that MLI output may play a prominent role in modulating PC output and, therefore, DN output during social behavior. Future recordings from cerebellar granule neurons or MLI neurons during social interaction will be necessary to answer these questions.

Bidirectional modulation of activity in the cerebellum is a well-known phenomenon (De Zeeuw, 2021). We found bidirectional modulation of PC firing during social interaction with the majority of PCs inhibited by social interaction and a minority excited. Consistent with the inhibitory role of PCs onto DNs, we find this ratio to be inverted in DNs with the majority of modulated DNs excited and only minority inhibited. As PC’s receives inputs from parallel fibers, which convey proprioceptive, peripheral sensory and motor-related information (Gao et al., 2018), we performed analysis to demonstrate that our social activity modulations were not simply related to motor behavior. Movement of the animal at the onset, offset, or changes in locomotion direction could not explain the modulations of activity we recorded in the cerebellum, suggesting that activity modulations were driven specifically by social interaction. In addition, there was little overlap in the neurons modulated by social interaction and by object interactions. This indicates that RCrus I activity is responsive to elements of social interaction itself, although the specific information encoded is still to be ascertained.

Correlated firing between PCs, DNs, and ACC neurons

An indirect connection links RCrus I cerebellum to ACC via the thalamus (Badura et al., 2018). DN neurons project to the ventromedial thalamus, which in turn projects to the ACC (de Lima et al., 2022; Kuramoto et al., 2015). As expected, we find that correlations between PCs or DNs with ACC neurons are greater when these neurons are similarly modulated by social interaction. However, these differences in correlations disappear when only non-social epochs are analyzed, suggesting that spontaneous activity during non-social behavior and the intrinsic connectivity of the two regions is not sufficient to impose differences in correlations without social interaction. These results suggest that communication between the cerebellum and ACC is coordinating social behavior. However, future studies are necessary to understand more fully what the specific behaviorally relevant signals that are being output from the cerebellar circuit to the ventromedial thalamic neurons and then to the ACC.

Our findings support the theory that the cerebellum plays a role in social behavior. Moreover, the E-Scope provides an important tool for examining the activity of two distant brain regions during behavior. We have further uncovered how cerebellar activity indirectly impacts ACC neurons output during social behavior and non-social behavior. These physiological insights into cerebellar and ACC communication during social behavior may eventually provide ways to finely tune cerebellar activity in autism models and eventually in humans to augment and improve the quality of social interactions.

Methods

E-Scope parts and assembly

A two-shank, 64 channel silicon probe (64 H with 32 channels activated) was used that was wire bonded and epoxied onto a probe PCB (PCB: Hughes Circuits, assembly: IDAX Microelectronics). The probe PCB that incorporated the ground wire was connected to a slimstack connector (Molex) via a flat flex cable (Figure 1—figure supplement 1A). The male slimstack connector on the probe was then connected to the female slimstack connector Ephys PCB (Figure 1—figure supplement 1B). This Ephys PCB incorporated a 32-channel electrophysiology-integrated chip (Intan, RHD2132) and a 32-bit ARM-Cortex microcontroller unit (MCU) (Atmel; ATSAMS70N21A). This MCU configures the Intan electrophysiology chip and polls it using direct memory access (DMA) over a single-ended serial peripheral interface (SPI) at up to 20 kSps per channel for new electrophysiology data across its 32 channels. The MCU then repackages the serialized electrophysiology data into 512-bit-long packets, containing one 16-bit sample for each of the 32 channels. These data packets are then clocked out by the E-Scope’s image sensor pixel clock using the MCU’s synchronous serial controller (SSC) and DMA. The resulting timing and structure of the electrophysiology data allows for this data to be further serialized with the image sensor data due to all data now sharing the same acquisition clock, namely the image sensor pixel clock. The Ephys PCB was connected through a six-wire cable assembly soldered between the Ephys PCB and the V3 CMOS PCB (Figure 1, Figure 1A—figure supplement 1C), allowing integration and synchronization of electrophysiological and imaging data. The wires connecting the Ephys PCB to the CMOS PCB were fixed in place using hot melt adhesive to protect the wires during behavior. A single, flexible, 1.1 mm diameter, coaxial cable (Cooner Wire, CW2040-3650SR) connected the integrated E-Scope with all off-board power and DAQ hardware. This single cable supplies the E-Scope with power, carries low-bandwidth bidirectional communication data, and carries unidirectional high-bandwidth synchronized imaging and electrophysiology data to an open-source UCLA Miniscope DAQ connected by a coaxial SMA connector (Figure 1—figure supplement 1D). Miniscope imaging data acquired by the Miniscope DAQ was sent over USB using a USB video device (UVC) protocol while electrophysiology data carried over the coaxial cable was routed from the UCLA Miniscope DAQ to an Intan DAQ (RHD 2000 evaluation board) via an intermediary circuit used to mimic a standard 32 channel Intan headstage (Figure 1—figure supplement 1D–G). This intermediary circuit and PCB has the same MCU that is on the Ephys PCB and effectively undoes the timing and packeting steps implemented on the E-Scope as well as emulates the registers of the Intan electrophysiology amplifier chip and converts single-ended SPI to LVDS SPI (Figure 1—figure supplement 1E). These two acquisition systems were then synchronized together using the ‘frame sync’ output signal from the Miniscope DAQ as a digital input to the Intan DAQ. This served as a common low-voltage transistor-transistor logic (LVTTL) signal for each Miniscope frame acquired by the Miniscope DAQ and enabled alignment of electrophysiology and image data offline. The components necessary for building an E-Scope are listed in Table 1.

Table 1
List of components for E-Scope Assembly.
ComponentQuantityVendorPart #Weblink
Miniscope V3 Partshttps://miniscope.org
Body
Main body of scope. Black Delrin1N/AMS_MainBody v3.2https://github.com/daharoni/Miniscope_Machined_Parts
Filter cover of scope. Black Delrin1N/AMS_FilterCover v3
CMOS imaging sensor mount. Black Delrin1N/AMS_FocusSlider v3.2
Baseplate. Aluminum.1N/AMS_Baseplate v3
Cap to protect implanted GRIN lens. Black Delrin1N/AMS_Cap v3
Optics
5 mm Dia. × 20 mm FL, MgF2 Coated, Achromatic Doublet Lens1Edmund Optics45–408http://www.edmundoptics.com/optics/optical-lenses/achromatic-lenses/mgf2-coated-achromatic-lenses/45408/
5 mm Dia. × 15 mm FL, MgF2 Coated, Achromatic Doublet Lens1Edmund Optics45–207http://www.edmundoptics.com/optics/optical-lenses/achromatic-lenses/mgf2-coated-achromatic-lenses/45207/
5 mm Dia. × 12.5 mm FL, MgF2 Coated, Achromatic Doublet Lens1Edmund Optics49–923http://www.edmundoptics.com/optics/optical-lenses/achromatic-lenses/mgf2-coated-achromatic-lenses/49923/
5 mm Dia. × 10 mm FL, MgF2 Coated, Achromatic Doublet Lens1Edmund Optics45–206http://www.edmundoptics.com/optics/optical-lenses/achromatic-lenses/mgf2-coated-achromatic-lenses/45206/
5 mm Dia. × 7.5 mm FL, MgF2 Coated, Achromatic Doublet Lens1Edmund Optics45–407http://www.edmundoptics.com/optics/optical-lenses/achromatic-lenses/mgf2-coated-achromatic-lenses/45407/
3.0 mm Diameter, N-BK7 Half-Ball Lens1Edmund Optics47–269http://www.edmundoptics.com/optics/optical-lenses/ball-condenser-lenses/n-bk7-half-ball-lenses/47269/
Diced excitation filter, 3.5mm × 4 mm × 1 mm1ChromaET470/40 xhttps://www.chroma.com/products/parts/et470-40x
Diced dichroic mirror, 6mm × 4mm × 1 mm1ChromaT495lpxrhttps://www.chroma.com/products/parts/t495lpxr
Diced emission filter, 4mm × 4mm × 1 mm1ChromaET525/50 mhttps://www.chroma.com/products/parts/et525-50m
Electrical
Excitation LED, LED LUXEON REBEL BLUE SMDDigikeyLXML-PB01-0030http://www.digikey.com/product-detail/en/LXML-PB01-0030/1416-1028-1-ND/3961133
Coaxial Cable
50 ohm coax silicone rubber jacketed cableCooner WireCW2040-3650SRhttps://www.coonerwire.com/mini-coax/
Printed Circuit Boards (PCB)
4 layer, 0.031" CMOS imaging sensor PCB1N/AN/Ahttps://github.com/daharoni/Miniscope_CMOS_Imaging_Sensor_PCB
2 layer, 0.031" Excitation LED PCB1N/AN/Ahttps://github.com/daharoni/Miniscope_Machined_Parts/tree/master/Extra%20Components
2 layer, 0.062" Coax to SMA PCB1OSH ParkN/A
Electrophysiology Parts
Probe
64 channel silicon probe (with 32 channels wirebonded)1Sotiris Lab64 Hhttps://github.com/sotmasman/Silicon-microprobes
https://masmanidislab.neurobio.ucla.edu/technology.html
Printed Circuit Boards (PCB)
Ephys PBC1N/AN/Ahttps://github.com/Aharoni-Lab/Ephys-Miniscope
Miscellaneous Hardware
M1 thread-forming screws4McMaster-Carr96817a704https://www.mcmaster.com/tappingscrews/screw-size~m1/
Set Screw 18–8 Stainless Steel Cup Point Set Screw, 0–80 Thread, 3/16" Long2McMaster-Carr92311 A054https://www.mcmaster.com/92311A054/
(Black) 36 Gauge Ultra-Flexible Miniature High-Temperature Wire1McMaster-Carr9564T1https://www.mcmaster.com/wire/wire-gauge~36/
(Green) 36 Gauge Ultra-Flexible Miniature High-Temperature Wire1McMaster-Carr9564T1
(Red) 36 Gauge Ultra-Flexible Miniature High-Temperature Wire1McMaster-Carr9564T1
(White) 36 Gauge Ultra-Flexible Miniature High-Temperature Wire1McMaster-Carr9564T1
1/16" diameter × 1/32" thick. Axially Magnetized6K&J Magnetics, Inc.D101-N52https://www.kjmagnetics.com/proddetail.asp?prod=D101-N52
Data Acquisition Devices (DAQs)
UCLA Miniscope DAQ1LabmakerDAQ-Imaginghttps://www.labmaker.org/collections/miniscope-v3-2/products/data-aquistion-system-daq
SSC-2-Intan-LVDS PCB1N/AN/Ahttps://github.com/Aharoni-Lab/Ephys-Miniscope
Intan DAQ1Intan TechnologiesRHD 2000 evaluation boardhttps://intantech.com/RHD_USB_interface_board.html
Software
Aharoni-Lab Miniscope-DAQ-QTSoftware1N/AN/Ahttps://github.com/Aharoni-Lab/Miniscope-DAQ-Cypress-firmware (Aharoni and Klumpp, 2023)
https://github.com/daharoni/Miniscope_DAQ_Software (Aharoni et al., 2019b)
Intan RHX software1Intan TechnologiesRHXhttps://intantech.com/RHX_software.html (Intan Technologies, 2024)

Animals

6–8 week-old C57BL/6 J young adult male mice at the time of initial surgery were used for in vivo E-Scope calcium imaging and electrophysiology experiments. All mice were acquired from Jackson Laboratories and group-housed three per cage on a 12 hr light-dark cycle. For all behavioral experiments, aged-matched novel animals were used. All experimental protocols were approved by the Chancellor’s Animal Research Committee of the University of California, Los Angeles in accordance with the U.S. National Institutes of Health (NIH) guidelines.

Viral vectors

Stereotaxic injections for E-Scope experiments were done using a stereotaxic frame (David Kopf Instruments) and a Nanoject II microinjector (Drummond Scientific). For Miniscope calcium imaging experiments, AAV1. Syn.GCaMP6f.WPRE.SV40 virus (titer 4.5 × 1013 GC/mL; Penn Vector Core) was injected.

In vivo E-Scope surgeries

Mice were anesthetized with 1.5–2.0% isoflurane and placed into a stereotaxic apparatus (David Kopf Instruments) for all surgeries. Once the depth of anesthesia was confirmed by the absence of reflex, the mouse was moved to a heat pad (Harvard Apparatus) on the stereotaxic frame. The ear bars were firmly fixed onto the skull of the mouse. Eye ointment was applied to protect the eyes from drying. Hair was shaved off and incision sites were sterilized with multiple iterations of ethanol and beta-iodine before going into surgery.

The mouse skull was calibrated so that the bregma and lambda were aligned and on the same plane. AAV1. Syn.GCaMP6f.WPRE.SV40 virus was injected in the ACC (Anterioposterior: 0.9 mm, Mediolateral: 0.2 mm, Dorsoventral: –1.3 mm relative to bregma; 300 nL) using a Nanoject II microinjector (Drummond Scientific) at 60 nl∙min–1. The location of the DN (Anteroposterior: –5.8 mm, Mediolateral: –2.25 mm relative to bregma) or RCrus I (Anteroposterior: –6.5 mm, Mediolateral: –2.5 mm relative to bregma) was labeled for later access. The incision site was sutured using silk suture threads (#18020–00, Fine Science Tools). Mice were given carprofen analgesic (5 mg∙kg–1) for 3 days and amoxicillin antibiotic (0.25 mg∙ml-1) through ad libitum water supply for 3 days.

After a week of recovery, mice underwent relay GRIN lens (Inscopix; PN 130–000143) implant surgery. After sterilizing the scalp, an incision was made to expose the skull. A skull screw and a ground pin were fastened to the skull, followed by a craniotomy with a diameter of 1 mm was made above the virus injection site. The tissue above the targeted implantation site was carefully aspirated using a 30-gauge blunt needle. Buffered ACSF was constantly applied throughout the aspiration to prevent the tissue from drying. Aspiration ceased after the target depth (0.7 mm) had been reached and full termination of bleeding. Here, a 1 mm relay GRIN lens (1 mm diameter, 4 mm length, Inscopix) was stereotaxically lowered and implanted at a 10 degree angle to the target site (–0.7 mm dorsoventral from the skull surface relative to the most posterior point of the craniotomy). Cyanoacrylate adhesive (Vetbond, 3 M) and dental cement (Ortho-Jet, Lang Dental) were applied to fix the lens in place as well as to cover the exposed skull. Kwik-Sil was used to protect the protruding relay GRIN lens. Carprofen (5 mg∙kg–1) and dexamethasone (0.2 mg∙kg-1) were administered subcutaneously during and after surgery along with amoxicillin (0.25 mg∙ml-1) in the drinking water for 3 days.

Mice were anesthetized again 2 weeks subsequently, and a baseplate locked to a Miniscope containing an objective GRIN lens (2 mm diameter, 4.79 mm length, 0.25 pitch, 0.50 numerical aperture, Grintech) was placed above the relay lens to search for the optimal in-focus cells in the field of view. Once field of view was obtained, the baseplate was cemented in place, and the Miniscope was unlocked and detached from the baseplate. A plastic cap was attached on the top of the baseplate to prevent debris build-up.

Mice were anesthetized for the fourth time for silicon multichannel electrode probe implantation, which were manufactured using the same process previously reported by Yang et al., 2020. The 2-shank 32 channel silicon probe PCB was screwed to a holder and coated with DiI (#C7000, Thermo Fisher Scientific) prior to insertion. A 1 mm diameter craniotomy was made on the previously marked DN or RCrus I locations. A micro-incision was made in the dura for the probe to go in. The probe was slowly lowered to the target locations (DN: –2.0 mm or RCrus I: 200–250 μm) where optimal signal-to-noise ratio was obtained and waited for the probe to settle in the brain for more than 30 min. Once location was set, the probe was dental cemented into place. Mice were given carprofen and dexamethasone post-surgery for 3 days along with amoxicillin in drinking water.

Habituation

Subject mice were habituated on a dummy Miniscope which had the equal amount of weight as the E-Scope for 7–10 days prior to multichannel silicon probe implant. Weights (0.55 g/piece; DILB8P-223TLF, Digi-Key Electronics) were increased 0.55 g every 2 days. Habituation was halted once the mice were active enough to carry the weight of the dummy Miniscope (4.5 g). All novel target mice were pre-habituated 15 min each day, for 3 days to the arena prior to experiments. Before any of our social interaction behavioral experiments, aggressive or agitated mice were removed after assessing their behavior in the arena while habituation.

Social behavior and data analysis

Behavioral experiments were held in a low light (20–50 LUX) environment with white noise (50–3 kHz). Subject mice were placed in a 48 × 48 cm arena for 1 min prior to being introduced to either a novel object or novel target mouse. For all trials, all mice or objects were introduced for the first time. Social or object interactions were defined by the proximity between the subject mouse and novel target mouse or object (2 cm from the body, head, or base of tail). A novel object or a novel target mouse was initially placed in the middle of the arena and introduced for 7 min. Between all recording sessions, the arena was cleaned with 70% ethanol. Behavior sessions were recorded using a webcam (C920, Logitech) connected to the host computer using the Miniscope software.

Social interaction behavioral analyses were done by utilizing a custom Python script to trace the centroid of the body outline of both the subject and novel target mouse or object and tracking their proximity to each other. Either nose-to-nose, nose-to-rear, or nose-to-body were considered as social interactions. We traced the LED on the Miniscope CMOS PCB located on the head of the subject mouse to calculate the speed and head angular speed. Statistical analysis was done on Prism (GraphPad software).

In vivo E-Scope recording and data analysis

Recordings proceeded 2 days post probe implant. The body of the E-Scope was first connected to the Miniscope-DAQ (Figure 1—figure supplement 1). Ground wires were set in place before powering the DAQ. The Miniscope-DAQ as well as the Intan DAQ were connected to the host computer. The UCLA Miniscope and Intan electrophysiology data acquisition programs were opened and run to confirm the imaging region of interest and electrophysiological signal quality. Next, the electrophysiological amplifier PCB was connected to the Miniscope via 6-wire cable assembly. The Miniscope portion of the E-Scope was fixed onto the baseplate of the mouse’s head, followed by the connection of the electrophysiology amplifier PCB to the implanted probe in the mouse.

For cerebellar electrophysiological data analysis, spike sorting was performed by Kilosort 2.5 (Pachitariu et al., 2016) and P-sort (Sedaghat-Nejad et al., 2021). Isolated units were further manually curated in phy2 on other single unit activity criteria in addition to merging and splitting highly similar and mixture units, respectively. Cross-correlogram between simple and complex spikes were constructed for the spike trains of the putative Purkinje cell units that revealed signature complex spike induced simple spike pauses (Figure 1G). Further analyses were done using custom script in MATLAB (MathWorks,2018).

ACC calcium imaging analysis was done using the Miniscope Analysis pipeline (Etter et al., 2020). Videos went through NoRMCorre for motion correction (Pnevmatikakis and Giovannucci, 2017), then videos acquired during a session were concatenated to extract spatial components and calcium traces of individual neurons using CNMF-E (Zhou et al., 2018). Similar to electrophysiology analysis, the calcium traces were normalized, and Z-score values were used to test for the social or object interaction- and movement-initiated activity.

To synchronize electrophysiology and calcium imaging data, we binned unit’s spike trains using 33ms bins to match with calcium traces recorded at ~30 Hz. Spike trains were further convolved with the Gaussian kernel with sigma of 100 ms. The spike rate maps were Z-scored for further analysis. Correlation analysis was computed using Pearson correlation (Figure 4), which was tested for significance by comparing correlations to those obtained from 1000 randomly circularly shifted calcium trace data. Cross-correlation was computed between spike times and deconvolved signals.

Histology

Post-hoc histology was performed to confirm the location of the silicon probe. Both silicon probe shanks were coated with DiI (#C7000, Thermo Fisher Scientific). After the recordings, mice were deeply anesthetized with isoflurane then cardiac perfused with phosphate-buffered saline (PBS) followed by 4% Paraformaldehyde (PFA; Sigma-Aldrich). Whole brains were carefully dissected and post-fixed into 4% PFA overnight and sliced using a vibratome (Leica VT1200). Coronal serial sections were made at a thickness of 70 μm in a 4 °C PBS solution, mounted on a slide glass, and cover slipped with VECTASHIELD (H-1400, Vector Laboratories). We used the Paxinos Mouse Brain Atlas (Franklin and Paxinos, 2008) for nomenclature of brain regions. Images were taken using a widefield microscope (Apotome, Ziess). Contrast, brightness, and pseudocolor were adjusted in Image J (Schneider et al., 2012). Images were tilted to align to illustration based on the atlas.

Statistics

Statistical analyses were conducted using custom written scripts in MATLAB (MathWorks, 2018) and Prism (GraphPad software). No statistical methods were used to determine sample size. Sessions with no object interaction were excluded for electrophysiology and calcium imaging data analysis. Kolmogorov Smirnov normality test was applied before using Wilcoxon rank sum test for hypothesis testing. Non-parametric Kruskal-Wallis H test was used in some instances to cross validate significance for some of the figure supplements. Otherwise, the t-test was used. p-value indicates one-tailed values and significance level of 0.01 was used unless stated otherwise.

E-Scope availability

All design files, software, parts list, assembly details and tutorials to build the E-Scope are available at http://miniscope.org/index.php/Main_Page and https://github.com/Aharoni-Lab/Ephys-Miniscope.

Data availability

Data and code for analyzing the data for this study are available at: https://github.com/golshanilab/Escope_Social_Cerebellum_ACC (copy archived at Hur and Safaryan, 2024).

References

  1. Book
    1. Franklin K
    2. Paxinos G
    (2008)
    The Mouse Brain in Stereotaxic Coordinates, Compact (3rd Edition)
    In Academic Press.

Peer review

Reviewer #1 (Public Review):

In this manuscript, the authors describe an improved miniscope they name "E-scope" combining in vivo calcium imaging with electrophysiological recording and use it to examine neural correlates of social interactions with respect to cerebellar and cortical circuits. Through correlations between electrophysiological single units of Purkinje cells and dentate nucleus neurons as well as with calcium signals imaging of neurons from the anterior cingulate cortex, the authors provide correlative data supporting the view that intracerebellar circuits and cerebello-cortical communications take part in the modulation of social behavior. In particular, the electrophysiological dataset reflects the PC-DN connection and strongly suggests its involvement in social interactions. Cross-correlations analyses between PC / DN single units and ACC calcium signals suggest that the recorded cerebellar and cortical structures both take part in the brain networks at play in social behavior.

Comments on revised submission:

While the authors have, to some extent, replied to most of my comments, they seem to have chosen not to respond to the part concerning the different types of social interactions that are not addressed in the manuscript, as also pointed out by reviewer 3. However, I feel that given the scope of the paper, which aims at demonstrating the value of the E-scope new device, this should not preclude the current study from being published.

https://doi.org/10.7554/eLife.88439.3.sa1

Reviewer #2 (Public Review):

This report by Hur et al. examines simultaneous activity in the cerebellum and anterior cingulate cortex (ACC) to determine how activity in these regions is coordinated during social behavior. To accomplish this, the authors developed a recording device named the E-scope, which combines a head-mounted mini-scope for in vivo Ca2+ imaging with an extracellular recording probe (in the manuscript they use a 32-channel silicon probe). Using the E-scope, the authors find subpopulations of cerebellar neurons with social-interaction-related activity changes. The activity pattern is predominantly decreased firing in PCs and increases in DNs, which is the expected reciprocal relationship between these populations. They also find social-interaction-related activity in the ACC. The authors nicely show the absence of locomotion onset and offset activity in PCs and DNs ruling out that is movement driven. Analysis showed high correlations between cerebellar and ACC populations (namely, Soc+ACC and Soc+DN cells). The finding of correlated activity is interesting because non-motor functions of the cerebellum are relatively little explored. However, the causal relationship is far from established with the methods used, leaving it unclear if these two brain regions are similarly engaged by the behavior or if they form a pathway/loop. Overall, the data are presented clearly, and the manuscript is well written, however the biological insight gained is rather limited.

https://doi.org/10.7554/eLife.88439.3.sa2

Reviewer #3 (Public Review):

Complex behavior requires complex neural control involving multiple brain regions. The currently available tools to measure neural activity in multiple brain regions in small animals are limited and often involve obligatory head-fixation. The latter, obviously, impacts the behaviors under study. Hur and colleagues present a novel recording device, the E-Scope, that combines optical imaging of fluorescent calcium imaging in one brain region with high-density electrodes in another. Importantly, the E-Scope can be implanted and is, therefore, compatible with usage in freely moving mice. The authors used their new E-Scope to study neural activity during social interactions in mice. They demonstrate the presence of neural correlates of social interaction that happen simultaneously in the cerebellum and the anterior cingulate cortex.

The major accomplishment of this study is the development and introduction of the E-Scope. The evaluation of this part can be short: it works, so the authors succeeded.

The authors managed to reduce the weight of the implant to 4.5 g, which is - given all functionality - quite an accomplishment in my view. However, a mouse weighs between 20 and 40 g, so that an implant of 4.5 g is still quite considerable. It can be expected that this has an impact on the behavior and, possibly, the well-being of the animals. Whether this is the case or not, is not really addressed in this study. The authors suffice with the statement that "Recorded animals made more contact with the other mouse than with the object (Figure 2A), suggesting a normal preference for social contact with the E-Scope attached." A direct comparison between mice before and after implant, or between mice with and without an implant would provide more insight into the putative impact of the E-Scope on (social) behavior.

In Figure 1 D-G, the authors present raw data from the neurophysiological recordings. In panel D, we see events with vastly different amplitudes. It would be very insightful if the authors would describe which events they considered to be action potentials, and which not. Similarly, indicating the detected complex spikes in the raw traces of Figure 1E would provide more insight into the interpretation of the data. Although the authors mention to consider the occurrence of complex spikes and simple spikes, a clear definition of what is considered a single unit recording is lacking. As there is quite a wide range in reported firing rates in Figure 2 - figure supplement 3, more clarity on this aspect would be insightful. Furthermore, in their text, the authors state that the pause in simple spike firing following a complex spike normally lasts until around 40 ms, and for this statement they refer to Figure 1G that shows a pause of less than 10 ms.

The number of Purkinje cells recorded during social interactions is quite low: only 11 cells showed a modulation in their spiking activity (unclear whether in complex spikes, simple spikes or both). During object interaction, only 4 cells showed a significant modulation. Unclear is whether the latter 4 are a subset of the former 11, or whether "social cells" and "object cells" are different categories. Having so few cells, and with these having different types of modulation, the group of cells for each type of modulation is really small, going down to 2 cells/group. The small group sizes complicate the interpretation of the data - in particular also on the analysis of movement-related activity that is now very noisy (Figure 2 - figure supplement 4).

In conclusion, the authors present a novel method to record neural activity with single cell-resolution in two brain regions in freely moving mice. Given the challenges associated with understanding of complex behaviors, this approach can be useful for many neuroscientists. The authors demonstrate the potential of their approach by studying social interactions in mice. Clearly, there are correlations in activity of neurons in the anterior cingulate cortex and the cerebellum related to social interactions. To bring our understanding of these patterns to a higher level, more detailed analyses (and probably also larger group sizes of cerebellar neurons) are required, though.

https://doi.org/10.7554/eLife.88439.3.sa3

Author response

The following is the authors’ response to the original reviews.

Positive comments:

We appreciate the positive comments of the editor and reviewers. The editor noted that the paper presents a “technological advance” that has enabled “important insights about the brain circuits through which the cerebellum could participate in social interactions.” Reviewer 1 thought this was a “timely and important study with solid evidence for correlative conclusions” and that the experiments were “technically challenging” and “well-performed”. Reviewer 2 stated that the finding of correlated activity between the regions is “interesting as non-motor functions of the cerebellum are relatively little explored.” They also thought “that the data are presented clearly, and the manuscript is well-written”. Reviewer 3 mentioned that “this approach can be useful for many neuroscientists”. We thank all the positive comments from the editors and all the reviewers.

Reviewer #1 (Public Review)

While the novelty of the device is strongly emphasized, I find that its value is somewhat diminished by the wire-free device developed by the same group as it should thus be possible to perform calcium imaging wire-free and electrophysiological recording via a single conventional cable (or also via wireless headstages).

While it would be potentially possible to use a wire-free Miniscope in parallel with a wired electrophysiology recording system, this would result in a larger footprint on the animal’s head, more than a gram in increased weight due to an added LiPo battery, a larger electrophysiology head-stage, and limited recording length due to a battery capacity of around 20 minutes. Our main goal for the development of the E-scope platform was to develop an expandable electrophysiology recording board that would work with all previously built UCLA Miniscopes while also streamlining the integration of power and data into the coaxial cable connection already familiar to hundreds of labs using Miniscopes. The vast majority of Miniscope experiments are done using wired systems and we aimed to support the expansion of those systems instead of requiring a more substantial switch to using wire-free Miniscopes.

The role of the identified network activations in social interactions is not touched upon.

We agree with the reviewer that we have not discovered a causal role for the co-modulated activity patterns we have observed. As these causal experiments will require the development of real-time techniques for blocking socially evoked changes in firing rate in cerebellum and ACC, we are currently planning experiments to address causality. These results will be described in a future publication.

Reviewer #1 (Recommendations for the Authors):

Please provide the number of recorded mice.

The number is now provided in the revised manuscript.

If the recorded areas (cerebellar cortex, DN, and ACC) are part of the same circuit regulating social interactions, it would be nice to get insights into the directionality of the circuit. The authors favor the possibility that during social behavior, cerebellar efferences indirectly influence ACC activities (as in Figure 4A), however, no evidence is presented to support this interpretation. ACC activities might also indirectly influence PC firing. It may be possible to get insights into this by comparing the timing of neuronal activity in the different areas with respect to social onset.

For this study, we mainly focused on the output of the cerebellar circuit to the cortex as previous work shows that dentate nucleus projects to the thalamus, which in turn projects to ACC and other cortical regions. (Badura et al.,eLife, 2018; Kelly et al., Nat. Neurosci., 2020) The temporal resolution of calcium imaging is limited (with the rise time of calcium events with genetically-encoded indicators taking hundreds of milliseconds) such that the resolution is insufficient to precisely assess the relative onset timing of the two regions. Our work certainly does not rule out cortical influences on PC firing.

Reviewer #2 (Public Review)

However, the causal relationship is far from established with the methods used, leaving it unclear if these two brain regions are similarly engaged by the behavior or if they form a pathway/loop.

As indicated in our response to Reviewer #1’s similar critique, the goal of the presented study is to demonstrate the feasibility and capabilities of this novel device. This new tool will allow us to conduct a comprehensive and rigorous study to assess the causal role of the interactions between the cerebellum and ACC in social behavior (as well as other behaviors). These experiments are being designed now.

Reviewer #2 (Recommendations for the Authors):

It is unclear what is entirely unique about the E-scope. It seems that its advance is simply a common cable that allows interfacing with both devices (lighter weight than two cables is stated in the Discussion). Is this really an advance? What are its limitations? E.g., how close can the recording sites be to one another? How can it be configured for any other extracellular recording approach (tetrodes, 64-channel arrays, or Neuropixels)?

In our experience, multiple lines of wires tethered to different head-mounted devices on an animal significantly impacts their behavior. Therefore, one of the major advantages of the UCLA Miniscope Platform is the use of a single, flexible coaxial cable to minimize the impact on tethering on behavior. The E-Scope platform builds on top of this work by incorporating electrophysiology recording capabilities into this single, flexible coaxial cable. Additionally, the electrophysiology recording hardware is backwards compatible with all previously built UCLA Miniscopes and can run through open-source and commercial commutators already used in Miniscope experiments.

The available bandwidth within the shared single coaxial cable can handle megapixel Miniscope imaging along with the maximum data output of a 32 channel Intan Ephys IC. The E-Scope platform presented here does run the Intan Ephys IC at 20KSps for all 32 channels instead of the maximum 30KSps due to microcontroller speed limitations, but this could be overcome by using a fast microcontroller or clock, or slightly reducing the total number of electrodes samples. Finally, the E-Scope was designed to support any electrode types supported by the Intan Ephys IC. This includes up to 32 channels of passive probes such as single electrodes, tetrodes, silicon probes, and flexible multi-channel arrays but does not include Neuropixels as Neuropixels use custom active electronics on the probe to multiplex, sample, and serialize electrophysiology data.

The authors only analyzed simple spikes in PCs for social-related activity. What about complex spikes? Is this correlated with ACC activity?

Complex spikes were detectable to the extent that we were able to define that the recorded cell was a PC, but because these cells were recorded in freely behaving mice, accurate complex spike detection was not reliable enough to be used for further correlational analyses.

The data is sampled in the two regions (cerebellum and ACC) at very different rates (imaging is much slower than electrophysiology; ephys data was binned). How does this affect the correlation plots?

We generated firing rate maps for the cerebellar neural activity using a binning size that matched the sampling frequency of calcium imaging (see Methods). As mentioned in our methods, to study the relationship between the electrophysiology and calcium imaging data we binned the spike trains using 33 ms bins to match the calcium imaging sampling rate (~30 Hz). This limits the temporal resolution to calculate fine-scale correlations, but the correlations that we report are on a behaviorally relevant temporal scale. The fine temporal resolution of the electrophysiology data however can still be used to further examine at a higher temporal resolution the relationship between cerebellar output and specific social behavior epochs.

For the correlation analysis, over what time frame was the activity relationship examined? How was this duration determined?

The main criteria for the time frame used to study the correlation analysis was the behavioral timescale of social interaction (see Author response image 1 for the number of social [red] and object [blue] interaction bouts [a], their duration [b] and coefficient of variation [CV] [c]). Overall, the activity relationship time frame was based on the average duration of the social interactions (~3 sec). Periods of 3.8 before and 5.8 sec after interaction onset were used to study. Accordingly, the cross-correlograms were constructed using a maximum lag length of 5 sec. In the article we reported correlation at lag 0.

Author response image 1

The relationship between the cerebellum and ACC seems unconvincing. If two brain regions are similarly engaged by the behavior, wouldn't they have a high correlation? Is the activity in one region driving the other?

We reference studies showing an anatomical and functional indirect connection between the cerebellum and the ACC or prefrontal cortex (Badura et al., eLife, 2018). Also, as stated in the introduction, the ACC is a recognized brain area for social behavioral studies. In the results, we stated that correlations increase in groups of neurons that are similarly engaged during a specific epoch in the social interaction was an expected finding. What was not expected was that there would be no difference in the distribution's correlation when the social epochs were removed, suggesting that intrinsic connectivity does not drive a difference in correlations.

Although, since there is a cerebello-cortical loop, further study will be needed to understand which area initiates this type of activity during social behavior,

  • In the figures, the color-coded scale bars should be labeled as z-scores (confusing without them).

  • In Figure 4, the color differences for Soc-ACC, Soc+ACC and SocNS ACC should be more striking as it is hard to tell them apart because they are all similar shades of blue-gray.

We thank the reviewer for their suggestions for improving the figures. We have incorporated these changes in Figures 2, 3 and along with their figure supplements. Graphs in Figure 4D-G have been edited to make the lines more visible to the reader.

Reviewer #3 (Public Review)

However, a mouse weighs between 20 and 40 g, so that an implant of 4.5 g is still quite considerable. It can be expected that this has an impact on the behavior and, possibly, the well-being of the animals. Whether this is the case or not, is not really addressed in this study.

The weight of the E-Scope (4.5 g) is near the maximum that is tolerated by animals in our experience. We therefore acclimated the mouse to the weight with dummy scopes of increasing weights over a 7-10 day period. During this period, we observed the animal to have normal exploratory behavior. Specifically, there is no change in the sociability of the animals (Figure 2A) and animals cover the large arena (48x 48 cm, Figure 2H).

Overall, the description of animal behavior is rather sparse. The methods state only that stranger age-matched mice were used, but do not state their gender. The nature of the social interactions was not described? Was their aggressive behavior, sexual approach and/or intercourse? Did the stranger mice attack/damage the E-Scope? Were the interactions comparable (using which parameters?) with and without E-Scope attached? It is not even described what the authors define as an "interaction bout" (Figure 2A). The number of interaction bouts is counted per 7 minutes, I presume? This is not specified explicitly.

As mentioned in the methods section of the original version of our manuscript, all the target mice were age-matched “male” mice. As per the reviewer’s suggestion, we now have added in the manuscript that before any of our social interaction behavioral experiments, aggressive or agitated mice were removed after assessing their behavior in the arena during habituation. For all trials, all mice were introduced for the first time.

We also mention in the methods section of our manuscript, that social behaviors were evaluated by proximity between the subject mouse and novel target mouse (2 cm from the body, head, or base of tail). From our recordings, we did not observe any aggressive, mounting, nor any other dominance behavior over the E-Scope subject mouse during the 7 minutes of social interaction assessment. Social interaction bouts in Figure 2A show the average number of social interaction bouts during the recording time. This has now been expanded upon in our revised manuscript.

It would be very insightful if the authors would describe which events they considered to be action potentials, and which not. Similarly, the raw traces of Figure 1E are declared to be single-unit recordings of Purkinje cells. Partially due to the small size of the traces (invisible in print and pixelated in the digital version), I have a hard time recognizing complex spikes and simple spikes in these traces. This is a bit worrisome, as the authors declare the typical duration of the pause in simple spike firing after a complex spike to be 20-100 ms. In my experience, such long pauses are rare in this region, and definitely not typical. In the right panel of Figure 1A, an example of a complex spike-induced pause is shown. This pause is around 15 ms, so not typical according to the text, and starts only around 4 ms after the complex spike, which should not be the case and suggests either a misalignment of the figure or the detection of complex spike spikelets as simple spikes, while the abnormally long pause suggests that the authors fail to detect a lot of simple spikes. The authors could provide more confidence in their data by including more raw data, making explicit how they analyzed the signals, and by reporting basic statistics of firing properties (like rate, cv or cv2, pause duration). In this respect, Figure 2 - figure supplement 3 shows quite a large percentage of cells to have either a very low or a very high firing rate.

We now provide a better example of simple spikes and complex spikes in Fig 1E and corrected our comment in the body of the manuscript. Previous version of the SS x CS cross-correlation histogram in Figure 1G as the reviewer mentions, was not the best example, because of the detected CS spikelets. However, the detection of CS spikelets has little impact on the interpretation of the results. We have replaced this figure with a better example of the SS x CS cross-correlation histogram.

The number of Purkinje cells recorded during social interactions is quite low: only 11 cells showed a modulation in their spiking activity (unclear whether in complex spikes, simple spikes or both. During object interaction, only 4 cells showed a significant modulation. Unclear is whether the latter 4 are a subset of the former 11, or whether "social cells" and "object cells" are different categories. Having so few cells, and with these having different types of modulation, the group of cells for each type of modulation is really small, going down to 2 cells/group. It is doubtful whether meaningful interpretation is possible here.

While the number of neurons is not as high as those reported for other regions, the number presented depicts the full range of responses to social behavior. It is extremely difficult to obtain stable neurons in freely behaving socially interacting animals and only a handful of neurons could be recorded in each animal. Among these recorded neurons only a subset responds to social interactions further reducing the numbers. The results however are consistent among cell types and the direction of modulation fits with the inhibitory connectivity between PCs and DN neurons. To our knowledge, we are the first group to publish neuronal activity of PC and DN neurons from freely behaving mice during social behavior.

Neural activity patterns observed during social interaction do not necessarily relate specifically to social interaction, but can also occur in a non-social context. The authors control this by comparing social interactions with object interactions, but I miss a direct comparison between the two conditions, both in terms of behavior (now only the number of interactions is counted, not their duration or intensity), and in terms of neural activity. There is some analysis done on the interaction between movement and cerebellar activity (Figure 2 - figure supplement 4), but it is unclear to what extent social interactions and movements are separated here. It would already help to indicate in the plots with trajectories (e.g., Fig. 2H) indicate the social interactions (e.g., social interaction-related movements in red, the rest of the trajectories in black).

We have updated the social interaction plots in Figure 2H in the revised version of the manuscript.

Reviewer #3 (Recommendations for the Authors):

Increase the number of cerebellar neurons that are recorded.

Due to the difficulty of the experiment and the low yield which we get for cerebellar recordings, substantially increasing the number of neurons will require many more experiments which are not feasible at this time.

Include more raw data and make the analysis procedure more insightful with illustrations of intermediate steps.

We have included a more thorough description of the analysis in the methods section of the revised manuscript.

Provide a better description of the behavior.

We have increased the level of detail regarding the mouse behavior in the Results and Methods sections. This includes a more detailed description of the parameters we used to analyze the social interaction.

https://doi.org/10.7554/eLife.88439.3.sa4

Article and author information

Author details

  1. Sung Won Hur

    1. Department of Neurology, DGSOM, University of California Los Angeles, Los Angeles, United States
    2. The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Karen Safaryan
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2699-1386
  2. Karen Safaryan

    Department of Neurology, DGSOM, University of California Los Angeles, Los Angeles, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing – original draft
    Contributed equally with
    Sung Won Hur
    Competing interests
    No competing interests declared
  3. Long Yang

    Department of Neurobiology, University of California Los Angeles, Los Angeles, United States
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  4. Hugh T Blair

    Department of Psychology, University of California Los Angeles, Los Angeles, United States
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Sotiris C Masmanidis

    Department of Neurobiology, University of California Los Angeles, Los Angeles, United States
    Contribution
    Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8699-3335
  6. Paul J Mathews

    1. The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, United States
    2. Department of Neurology, Harbor-UCLA Medical Center, Torrance, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Investigation, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Daniel Aharoni

    Department of Neurology, DGSOM, University of California Los Angeles, Los Angeles, United States
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Investigation, Methodology, Writing – review and editing
    For correspondence
    daharoni@mednet.ucla.edu
    Competing interests
    No competing interests declared
  8. Peyman Golshani

    Department of Neurology, DGSOM, University of California Los Angeles, Los Angeles, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    PGolshani@mednet.ucla.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5406-7695

Funding

Veterans Affairs (Merit Award BX005202)

  • Peyman Golshani

National Science Foundation (NeuroNex Award 1707408)

  • Hugh T Blair

National Institutes of Health (P50HD103577)

  • Daniel Aharoni

National Institutes of Health (U01NS122124)

  • Daniel Aharoni

National Institutes of Health (R01NS090930)

  • Peyman Golshani

National Institutes of Health (1R61NS119708)

  • Paul J Mathews

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Dr. Tom Otis for supporting this project. We are very thankful to Ebrahim Feghhi, Alejandro Hipolito, Jason Kwon, and Marcus Min for handling animals and the technical support. This work was supported by the VA Merit Award BX005202 (to PG), NSF NeuroNex Award 1707408 (to PG, DA, SM, and TB), NIH P50HD103577 (to PG and DA), NIH U01NS122124 (to PG and DA), NIH R01NS090930 (to PG), and NIH 1R61NS119708 (to PM).

Ethics

This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#2006-066) of the University of California, Los Angeles.

Senior Editor

  1. Laura L Colgin, University of Texas at Austin, United States

Reviewing Editor

  1. Brice Bathellier, CNRS, France

Version history

  1. Preprint posted: April 5, 2023 (view preprint)
  2. Sent for peer review: April 28, 2023
  3. Preprint posted: July 17, 2023 (view preprint)
  4. Preprint posted: January 10, 2024 (view preprint)
  5. Version of Record published: February 12, 2024 (version 1)

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.88439. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2023, Hur, Safaryan et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 765
    Page views
  • 78
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Sung Won Hur
  2. Karen Safaryan
  3. Long Yang
  4. Hugh T Blair
  5. Sotiris C Masmanidis
  6. Paul J Mathews
  7. Daniel Aharoni
  8. Peyman Golshani
(2024)
Correlated signatures of social behavior in cerebellum and anterior cingulate cortex
eLife 12:RP88439.
https://doi.org/10.7554/eLife.88439.3

Share this article

https://doi.org/10.7554/eLife.88439

Further reading

    1. Medicine
    Shengjie Li, Jun Ren ... Wenjun Cao
    Research Article

    Background:

    Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness in Asia, and no reliable, effective diagnostic, and predictive biomarkers are used in clinical routines. A growing body of evidence shows metabolic alterations in patients with glaucoma. We aimed to develop and validate potential metabolite biomarkers to diagnose and predict the visual field progression of PACG.

    Methods:

    Here, we used a five-phase (discovery phase, validation phase 1, validation phase 2, supplementary phase, and cohort phase) multicenter (EENT hospital, Shanghai Xuhui Central Hospital), cross-sectional, prospective cohort study designed to perform widely targeted metabolomics and chemiluminescence immunoassay to determine candidate biomarkers. Five machine learning (random forest, support vector machine, lasso, K-nearest neighbor, and GaussianNaive Bayes [NB]) approaches were used to identify an optimal algorithm. The discrimination ability was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed by Hosmer-Lemeshow tests and calibration plots.

    Results:

    Studied serum samples were collected from 616 participants, and 1464 metabolites were identified. Machine learning algorithm determines that androstenedione exhibited excellent discrimination and acceptable calibration in discriminating PACG across the discovery phase (discovery set 1, AUCs=1.0 [95% CI, 1.00–1.00]; discovery set 2, AUCs = 0.85 [95% CI, 0.80–0.90]) and validation phases (internal validation, AUCs = 0.86 [95% CI, 0.81–0.91]; external validation, AUCs = 0.87 [95% CI, 0.80–0.95]). Androstenedione also exhibited a higher AUC (0.92–0.98) to discriminate the severity of PACG. In the supplemental phase, serum androstenedione levels were consistent with those in aqueous humor (r=0.82, p=0.038) and significantly (p=0.021) decreased after treatment. Further, cohort phase demonstrates that higher baseline androstenedione levels (hazard ratio = 2.71 [95% CI: 1.199–6.104], p=0.017) were associated with faster visual field progression.

    Conclusions:

    Our study identifies serum androstenedione as a potential biomarker for diagnosing PACG and indicating visual field progression.

    Funding:

    This work was supported by Youth Medical Talents – Clinical Laboratory Practitioner Program (2022-65), the National Natural Science Foundation of China (82302582), Shanghai Municipal Health Commission Project (20224Y0317), and Higher Education Industry-Academic-Research Innovation Fund of China (2023JQ006).

    1. Medicine
    Swee Sen Kwek, Eng Eong Ooi
    Insight

    Understanding the kinetics of dengue viruses in the bloodstream can provide insights into the clinical outcomes of the disease.