Development of a mesoscopic whole-brain scale circuit analysis pipeline

To analyze structural and functional BLA connectivity in individual mice, we unilaterally injected AAV expressing the ultrafast ChR variant oChIEF fused with the fluorescent protein Citrine at BLA of both Tbr1^+/– and wild-type (WT) mice (Fig 1A). Given that oChIEF-Citrine targets the plasma membrane of entire neurons, in addition to being activated by blue light, oChIEF-Citrine was also used to outline BLA neurons, including axons. The fluorescent signal of oChIEF-Critine outside of the BLA reflects the extent of axonal projections derived from unilateral BLA neurons. To analyze the functional connectivity of the BLA, Tbr1^+/– and WT mice were further separated into 2 groups. One group unilaterally received optical theta-burst stimulation (TBS) at the BLA of the AAV-injected side when they were freely moving in their home cages. The other control group was subjected to the same experimental procedure except for optical stimulation. Then, we analyzed neuronal activation by BLA stimulation based on C-FOS expression 2 h after stimulation (Fig 1A and 1B).

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Fig 1. Workflow of whole-brain-scale analysis of BLA-derived circuits.

There are 5 steps in the analysis pipeline. (A) Brain sample preparation, involving unilateral BLA infection of oChIEF-Citrine-expressing AAV, in vivo TBS of the AAV-infected BLA while the awake mice are in their home cage, and brain fixation by PFA 2 h after optogenetic stimulation. (B) High-content immunofluorescence imaging. At least 120 coronal cryosections (60 μm/section) of a single mouse brain were subjected to immunostaining to reveal C-FOS⁺ cells and oChIEF-Citrine signals and then imaged by a high-content imaging system. (C) Registration and transformation to the Allen brain atlas. Raw image series were registered to the templates of Allen CCFv3 by sequentially applying 12 global and 1 local transform parameters. (D) Image segmentation for oChIEF and C-FOS signals. The oChIEF signals were isolated by pixel classification and binarized. C-FOS⁺ cell locations were detected by the spot detection function of Imaris (also see S1 Fig). (E) Application of brain structure masks and quantification of oChIEF and C-FOS signals. Examples of choropleth maps show the quantification of oChIEF⁺ pixel number and C-FOS⁺ cell number of different brain regions in a single coronal section. Scale bar in (D), 1 mm. AAV, adeno-associated virus; BLA, basolateral amygdala; CCFv3, Common Coordinate Framework version 3; PFA, paraformaldehyde; TBS, theta-burst stimulation.

https://doi.org/10.1371/journal.pbio.3002646.g001

The fluorescence images of the serial whole-brain sections were then subjected to quantification at a whole-brain scale (Fig 1C–1E). Our entire pipeline basically comprises 5 elements, which have been outlined and detailed in Figs 1 and S1 and the Methods. The list of brain regions used in our study and their corresponding abbreviations can be found in S1 Data. Brain ontology is identical to CCFv3.

Structure-wise comparisons reveal rewiring in Tbr1^+/– mouse brains

First, we analyzed the whole-brain distribution pattern of oChIEF-Citrine. We found that oChIEF-Citrine signals were widely distributed ipsilaterally in WT brains and also extended contralaterally (Fig 2A and S1 Video, upper). However, oChIEF-Citrine signals barely extended contralaterally in Tbr1^+/– mouse brains (Fig 2A and S1 Video, lower).

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Fig 2. Rewiring of BLA-derived circuits.

(A) Coronal image series of binarized oChIEF-Citrine–labeled axonal signals (green) superimposed on a CCFv3 template (gray) of a WT (upper) and a Tbr1^+/– mouse brain (lower) (also see S1 Video). (B) Coronal (left) and horizontal (right) views of summarized 3D stacks with binarized and binned (10 μm³) oChIEF signals from WT (upper) and Tbr1^+/– (lower) mouse brains. Color map indicates different probabilities of oChIEF signal in a 10-μm³ cube (also see S2 Video). (C) Structure-wise comparison of binarized oChIEF signal distributions along the AP axis between WT and Tbr1^+/– mouse brains after slice-based quantification (also see S1 Data). Scatterplots of slice-based analysis show the summation of oChIEF differences along the AP axis (sum of ΔoChIEF) versus significance (Friedman test P value, determined by Friedman one-way repeated measure analysis) for each brain region. Vertical and horizontal dotted lines represent the sum of ΔoChIEF = 0 and Friedman test P value = 0.05, respectively. Open and filled circles represent ROI noncorrected and corrected brain regions, respectively. Colors represent brain region identities. The arrow points to the filled circle representing the contralateral BLA. (D) Coronal (left) and horizontal (right) views of 3D maps with binarized and binned (10 μm³) oChIEF pixels in the brain regions displaying significantly different BLA-circuit rewiring (P < 0.05) between WT (upper) and Tbr1^+/– mice (lower). Colors of oChIEF pixels in (D) represent brain regions as shown in (C) and the alpha levels (0–1) of the brain region color represent scaled oChIEF probabilities. Arrows point to the binned oChIEF pixels within the contralateral BLA. (E) Coronal (left) and horizontal (right) views of 3D maps showing the difference of binarized and binned (10 μm³) oChIEF pixels (Δ oChIEF) between WT and Tbr1^+/– mouse brains. Heatmap of the scaled difference indicates the differential oChIEF signal probabilities (ΔoChIEF of Tbr1^+/––WT). Arrow highlights that Tbr1^+/–mice display massive loss of contralateral axonal projections in the BLA. Scale bar in (B), 1 mm. Sample sizes of mice are the same for (B–E) (WT, n = 13; Tbr1^+/–, n = 14). The numerical value data for plotting (C) are available in S1 Data. AP, anterior-posterior; BLA, basolateral amygdala; CCFv3, Common Coordinate Framework version 3; ROI, region of interest; WT, wild-type.

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We compared brains from a total of 14 Tbr1^+/– mice and 13 WT littermates. Since the BLA consists of heterologous projections with varying patterns of long-range axonal projection [26], first we investigated the distributions of the virus injection sites between WT and Tbr1^+/– mouse brains and confirmed that they were indistinguishable and comparable (S2A–S2D Fig). Next, we binned and summarized the average percentage of oChIEF-Citrine signals in all brain regions of Tbr1^+/– mice and WT littermates. These results were subjected to 3D projection at both coronal and horizontal views, which indicated a reduction in contralateral projections of BLA neurons in Tbr1^+/– mice (Fig 2B and S2 Video).

To quantify the difference in BLA projections, we performed a slice-based analysis to determine the distribution of Citrine signal along the anterior-posterior (AP) axis of every brain region in each slice. A structure-wise comparison between Tbr1^+/– mice and WT littermates was then performed at the whole-brain scale. The Citrine signals in the ipsilateral, i.e., the virus injection site, and contralateral hemispheres were quantified separately (S3 Fig left, S1 Data). Consistent with our previous findings [17,23], we found that oChIEF-Citrine signals were greatly reduced in contralateral BLA-related regions (i.e., LA, BLAa, BLAp), and in the associated fiber tracts (act, amc), of Tbr1^+/– mice compared to those of WT littermates (Fig 2C–2E and S1 Data), supporting the reliability of our whole-brain analysis system.

Using our whole-brain structure-wise quantification, we found that a total of 104 brain regions exhibited differential oChIEF-Citrine expression at the contralateral site (Fig 2C–2E). Among them, 56 regions had much lower oChIEF-Citrine signals in Tbr1^+/– mice relative to WT, and these regions mainly reside in areas of the forebrain, such as the isocortex, olfactory area (OLF), cortical subplate (CTXsp), cerebral nuclei (CNU), and fiber tracts (fb) (Fig 2C–2E). We term these regions “affected contralateral projection regions (ACPRs)”. In WT mice, these regions receive topographically bilateral projections from the 2 amygdalae in the 2 brain hemispheres (Fig 2A and 2B, upper panel; also see Allen Mouse Brain Connectivity Atlas: connectivity.brain-map-org/projection/experiment/113144533 and connectivity.brain-map.org/projection/experiment/277710753). The other 48 regions had much higher oChIEF-Citrine signals in Tbr1^+/– mice compared to WT mice (Fig 2C–2E). These regions were mainly localized in the thalamus (TH), hypothalamus (HY), and midbrain (MB), which were mistargeted by contralateral BLA projection in Tbr1^+/– mice (Fig 2C–2E).

At the ipsilateral site, we uncovered twice as many brain regions displaying different oChIEF-Citrine levels. We found that 195 brain regions had increased oChIEF-Citrine signals in Tbr1^+/– mouse brains (Fig 2C–2E), which we denote “affected ipsilateral projection regions (AIPRs)”. Note that the majority of AIPRs are not the counterparts of the ACPRs, such as MOs, ORBl, ORBvl, ACAv, FRP, ILA, and PPN (ΔoChIEF (Tbr1^+/––WT) > 10,000) (Fig 2C–2E). We even found that 19 ipsilateral regions had decreased oChIEF-Citrine signals in Tbr1^+/– mouse brains (Fig 2C–2E). These findings indicate that Tbr1 haploinsufficiency impairs both contralateral and ipsilateral projections of BLA neurons and leads to mistargeting to ipsilateral brain regions and not necessarily limited to ACPR counterparts. We also noticed that AIPRs widely encompass several regions in the isocortex, TH, HY, MB, and fb (Fig 2C–2E), implying that Tbr1 haploinsufficiency may elicit stronger BLA ipsilateral connections with those regions and influence neuronal activity at the ipsilateral side.

Altered functional outcomes of rewired BLA-derived circuits in Tbr1^+/– mouse brains

In addition to structural connections, we further analyzed the whole-brain modulation of neuronal activities upon unilateral BLA activation, representing the net effect of direct or indirect BLA innervation. Modulation of neuronal activity was based on C-FOS⁺ cell numbers in the absence or presence of TBS treatment at BLA. We adapted our slice-based quantification to determine C-FOS⁺ cell numbers along the AP axis (with 100 μm binning) of both brain hemispheres (S3 Fig, right). Pairwise comparisons of our 4 experimental groups i.e., WT ctrl, Tbr1^+/– ctrl, WT TBS, and Tbr1^+/– TBS, were then conducted to establish which brain regions exhibited differential C-FOS expression (Fig 3 and S1 Data). These comparisons allowed us to analyze functional connections derived from the BLA at a whole-brain mesoscopic scale.

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Fig 3. Differential C-FOS expression in WT and Tbr1^+/– mouse brains with or without activation of BLA-derived circuits.

(A, C, E, G) Structure-wise comparison of C-FOS⁺ cell number distributions along the AP axis in the contralateral (left) and ipsilateral (right) brain hemispheres of (A) Tbr1^+/– ctrl vs. WT ctrl, (C) WT TBS vs. WT ctrl, (E) Tbr1^+/– TBS vs. Tbr1^+/– ctrl, and (G) Tbr1^+/– TBS vs. WT TBS groups. Each scatterplot shows the summed difference in C-FOS⁺ cell number long the AP axis (sum of ΔC-FOS) versus statistical significance (adjusted P value) for each brain region of the pairwise-compared groups (also see S1 Data). Friedman one-way repeated measure analyses, followed by post hoc pairwise tests with Bonferroni correction, were used for statistical analysis. Only brain regions with a significant difference as determined by Friedman one-way repeated measure analyses are shown. Vertical and horizontal dotted lines represent the sum of ΔC-FOS = 0 and adjusted P value = 0.05, respectively. Colors represent brain region identities (indicated in G). Open and filled circles represent ROI-noncorrected and ROI-corrected brain regions, respectively. The pie charts illustrate the proportions of brain regions that fit the condition annotated in the right panel of (G). (B, D, F, H) Horizontal views of 3D maps with binned (100 μm³) C-FOS cubes in the brain regions displaying a significant difference (adjusted P < 0.05) between compared groups. The left and middle panels are the results of individual conditions, as indicated. C-FOS⁺ cubes are shown in brain region color codes (indicated in H), with their sizes representing the relative normalized density of C-FOS⁺ cells within binned cubes. The right panels show the differences in the density of C-FOS⁺ cells (ΔC-FOS, 100 μm³ bins). The scale of the heatmap for ΔC-FOS is shown in (H). Sample sizes of mice are identical in (A–H) (WT ctrl, n = 6; Tbr1^+/– ctrl, n = 7; WT TBS, n = 7; Tbr1^+/– TBS, n = 7). The numerical value data for plotting (A, C, E, G) are available in S1 Data. AP, anterior-posterior; BLA, basolateral amygdala; ROI, region of interest; TBS, theta-burst stimulation; WT, wild-type.

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First, we compared the Tbr1^+/– ctrl versus WT ctrl groups. Although Tbr1^+/– mice had fewer BLA contralateral projections to many forebrain regions, we unexpectedly uncovered that only 5 regions displayed fewer C-FOS⁺ cells in the Tbr1^+/– ctrl group relative to WT ctrl. In contrast, 104 regions had more C-FOS⁺ cells in the contralateral side of Tbr1^+/– ctrl group brains (Fig 3A). On the ipsilateral side, we identified 94 regions having more C-FOS⁺ cells and only 5 regions having fewer C-FOS⁺ cells in the Tbr1^+/– ctrl group compared to the WT ctrl group (Fig 3A). Thus, generally speaking, the Tbr1^+/– ctrl group exhibited increased C-FOS⁺ cell numbers compared to the WT ctrl group (Fig 3B), although the mechanism causing this increased cell number is unclear. Nevertheless, these differentially activated regions are widely distributed across the brain, from the isocortex to MB, suggesting that the abnormally high brain activities of Tbr1^+/– mice during wakefulness (the state of our mice under experimental/control treatment) can be linked to circuits and areas distributed across the entire brain (Fig 3B).

Next, we analyzed the impact of BLA activation on whole-brain modulation of C-FOS expression. TBS at the BLA increased the number of C-FOS⁺ cells in 51 contralateral regions and 86 ipsilateral regions of WT mice. Only 4 regions in the contralateral side presented reduced C-FOS⁺ cell numbers in the WT TBS group (Fig 3C, WT TBS versus WT ctrl). Thus, unilateral activation of the BLA in WT mice mainly activates other brain regions, both contralaterally and ipsilaterally (Fig 3D). In contrast, Tbr1^+/– mice displayed fewer C-FOS⁺ cells at 61 contralateral and 48 ipsilateral regions upon TBS at BLA. Only 8 and 11 regions in the contralateral and ipsilateral sides, respectively, had increased C-FOS⁺ cell numbers in Tbr1^+/– mice (Fig 3E, Tbr1^+/– TBS versus Tbr1^+/– ctrl). These results suggest that Tbr1 deficiency generally represses neuronal activation of other brain regions upon TBS at BLA (Fig 3F), and the opposite is true for WT mice.

We also detected differing C-FOS⁺ cell distributions in many brain regions between the Tbr1^+/– TBS and WT TBS groups. The numbers of differentially activated/inhibited regions were fewer than for the other 3 comparisons described above. Moreover, we uncovered a reduced propensity for either increased or decreased C-FOS⁺ cell numbers in WT and Tbr1^+/– mice upon TBS at BLA (Fig 3G, Tbr1^+/– TBS versus WT TBS). We identified 19 regions with reduced and 34 with enhanced C-FOS⁺ cell numbers at the contralateral side of Tbr1^+/– mice. On the ipsilateral side, 34 and 13 regions presented reduced and increased C-FOS⁺ cell numbers, respectively (Fig 3H). Thus, this comparison indicates that TBS at BLA of Tbr1^+/– mice elicits a different outcome from that of WT mice.

Taken together, these results imply that Tbr1 deficiency alters functional connections between BLA and other brain regions and it plays a critical role in whole-brain responses to stimulation at the BLA.

Correlations between rewired BLA projection and altered C-FOS expression patterns

Several possibilities may explain the observed differences between structural and functional connections of WT and Tbr1^+/– mice described above. One is an indirect influence of targets directly innervated by BLA. Another is a dominant influence of local microcircuits (such as feed-forward or feedback inhibitions), regardless of receiving BLA axonal targeting. Significantly, we found that although many regions in the contralateral TH of Tbr1^+/– mice had more BLA axonal projections, the numbers of C-FOS⁺ cells were not increased but somehow decreased after unilateral TBS at BLA (Figs 2C and 3E). To dissect further the correlation between BLA axonal projection and neural activation of specific brain regions, we selected the brain regions exhibiting rewired axonal innervations and altered C-FOS expression patterns upon TBS of the BLA in either WT or Tbr1^+/– mice for further analysis.

First, we separately determined contralateral and ipsilateral ΔoChIEF traces by pairwise subtraction of oChIEF-Citrine signals along the AP axis (100 μm binning) between Tbr1^+/– and WT samples (Fig 4A and 4C, left, subtraction of coexisting 10 μm³ binning cubes between Tbr1^+/– and WT samples for visualization; Fig 4B and 4D, middle row, ΔoChIEF traces). Neural activation induced by TBS in Tbr1^+/– and WT mice, i.e., Tbr1^+/– ΔC-FOS traces and WT ΔC-FOS traces, respectively, was also determined. Pairwise subtraction between Tbr1^+/– ΔC-FOS and WT ΔC-FOS traces was then executed to obtain all possible differences, i.e., ΔΔC-FOS traces (Fig 4A and 4C, middle, subtractions of coexisting 100 μm³ binning cubes for visualization; Fig 4B and 4D, middle row, ΔΔC-FOS traces). A Pearson correlation coefficient (r) was calculated according to the correlation of the averaged ΔoChIEF trace and the averaged ΔΔC-FOS trace in each brain region. The brain regions exhibiting positive correlations between rewired BLA-derived circuits and altered C-FOS expression patterns reflect concurrent changes to BLA-derived axonal innervations and their neural activity upon BLA activation. In the contralateral hemisphere of Tbr1^+/– mice, the AUD regions (AUD, AUDp), AIp, PIR, BLA, and MBsen regions all exhibited impaired BLA contralateral axonal projections and consistently resulted in reduced boosting of BLA activation on C-FOS expression, suggesting that reduced BLA axonal projection likely contributes to a loss of intensification effect of contralateral BLA input to these regions in the Tbr1^+/– mice (Fig 4A and 4B, positive correlation). In contrast, the contralateral thalamus, DORsm regions (DORsm, VENT), and ILM each displayed higher oChIEF-Citrine signals in Tbr1^+/– mice. However, more BLA axonal innervation did not result in more C-FOS⁺ cells in these regions. Thus, they exhibited negative correlations between ΔoChIEF and ΔΔC-FOS (Fig 4A and 4B). Local inhibitory microcircuits triggered by mistargeting BLA inputs in these regions may mediate the net inhibitory effect. Alternatively, indirect innervation by direct BLA targets of other brain regions may also contribute to the inhibitory effect.

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Fig 4. Correlations of BLA axonal projection patterns and C-FOS expression.

(A) The 3D maps of rewired BLA-derived circuits (left, 10 μm³ binned cubes with binarized oChIEF signals) and differentially TBS-modulated C-FOS expression levels (middle, 100 μm³ binned C-FOS⁺ cell numbers) in affected brain regions (right) with the Allen brain region color code. Only the brain regions showing significant correlations between rewired BLA-derived circuits (indicated by ΔoChIEF) and altered C-FOS expression patterns upon TBS of BLA-derived circuits (indicated by ΔΔC-FOS) of the contralateral hemisphere are shown here. Heatmap of the scaled difference shows the differential oChIEF signal probabilities (left 3D map, ΔoChIEF of Tbr1^+/––WT) and the difference between the C-FOS modulatory effects of BLA-derived circuits in Tbr1^+/– (ΔC-FOS of Tbr1^+/– TBS–Tbr1^+/– ctrl) and WT (ΔC-FOS of WT TBS–WT ctrl) mouse brains (middle left 3D map, ΔΔC-FOS). Scatterplots in the right panels show the sum of ΔoChIEF (Tbr1^+/––WT) versus the correlation between ΔoChIEF and ΔΔC-FOS for the brain regions of the contralateral hemispheres. Colors indicate brain region identities. Open and filled circles represent ROI-noncorrected and ROI-corrected brain regions, respectively. (B) Examples of brain regions that have positive or negative correlations between rewired BLA-derived circuits of Tbr1^+/– mice and differential TBS-derived C-FOS expression levels. The oChIEF distributions (WT and Tbr1^+/–, upper panel), C-FOS⁺ cell distributions (WT ctrl, Tbr1^+/– ctrl, WT TBS, and Tbr1^+/– TBS, lower panel), and scaled differences (ΔoChIEF and ΔΔC-FOS, middle panel) along the AP axis are shown. Dashed lines in plots of scaled differences represent the average 99th percentile of scaled differences calculated from the pair-subtracted population data with 1,000 times AP axis reshuffling. (C) The 3D maps of the results of the ipsilateral hemisphere. (D) Examples of brain regions that exhibit correlations between ΔoChIEF and ΔΔC-FOS signals in the ipsilateral hemisphere. Data represent mean ± SEM. Contralateral (A, B) and ipsilateral (C, D) hemispheres were analyzed separately. Sample sizes are the same in (A–D) (oChIEF data: WT, n = 13; Tbr1^+/–, n = 14. C-FOS data: WT ctrl, n = 6; Tbr1^+/– ctrl, n = 7; WT TBS, n = 7; Tbr1^+/– TBS, n = 7). The numerical value data are available in S2 Data. AP, anterior-posterior; BLA, basolateral amygdala; ROI, region of interest; TBS, theta-burst stimulation; WT, wild-type.

https://doi.org/10.1371/journal.pbio.3002646.g004

In the ipsilateral hemisphere, the correlation results between ΔoChIEF and ΔΔC-FOS were the opposite of those for the contralateral hemisphere (Fig 4A and 4B versus Fig 4C and 4D). Only the posterior part (ventral) of the HIP and NOT had reduced ipsilateral BLA axonal projections in Tbr1^+/– mice, and it exhibited impaired boosting of BLA activation on C-FOS expression (Fig 4C and 4D, left panel). The reduction in direct innervation of the BLA likely contributed to the decreased neuronal activation of these 2 regions. A major group of regions at the ipsilateral side exhibited a higher level of oChIEF-Citrine in Tbr1^+/– mice but a negative correlation between ΔoChIEF and ΔΔC-FOS (Fig 4C and 4D). This group includes the isocortical area (MOp, AUDd, ACA, ACAv, TEa), as well as the CNU (MEA, PALv, SI, PALc, BST), TH (DORsm, DORpm, CL), HY (LZ), and MB (SCdg, VTA) areas. Again, local microcircuits are likely involved in the functional alterations among BLA and these regions in Tbr1^+/– mice. Mistargeting of BLA axons in Tbr1^+/– mice may lead to incorrect innervation and initiates abnormal local microcircuits to alter functional outcomes.

Overall, the results of this correlation analysis reveal that the structural rewiring of BLA-derived circuits caused by Tbr1 deficiency triggers complex alterations in the functional outcomes of neuronal activities at a whole-brain scale.

Alteration of whole-brain synchronization in Tbr1^+/– mice

In addition to the paired comparison of functional alterations to individual structures described above, we also assessed changes in neuronal activity at the network level. We evaluated functional connectivity, which is defined by the synchronization of C-FOS expression levels between 2 brain regions across all subjects [27,28]. To do so, we first determined the C-FOS⁺ cell density of individual brain regions (S4 Fig, volume-based quantification). The interregional correlation coefficient of C-FOS levels, r, was then calculated to reflect synchronization level and considered meaningful if its significant correlation level P was <0.05; otherwise, r was set to 0 to reflect no correlation. Accordingly, we identified large proportions of interregional pairs with positive correlations and comparable positive r values in all 4 experimental groups of WT ctrl, Tbr1^+/– ctrl, WT TBS, and Tbr1^+/– TBS, yet very few but variable interregional pairs exhibiting negative correlations in the WT ctrl (1.52%), Tbr1^+/– ctrl (2%), WT TBS (2.36%), and Tbr1^+/– TBS (0.03%) groups (Fig 5A). An interregional correlation matrix of C-FOS levels was then computed for each experimental group (Fig 5B).

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Fig 5. Distinct interregional correlations of C-FOS expression in WT and Tbr1^+/– mouse brains with or without BLA-derived activation.

(A) Cumulative plot showing the probability of all significant interregional correlations (P < 0.05, Kendall’s rank correlation) of C-FOS expression levels for the 4 experimental groups. (B) Matrices showing whole-brain interregional correlations of C-FOS expression in the WT ctrl, Tbr1^+/– ctrl, WT TBS, and Tbr1^+/– TBS groups (left to right). Brain regions are annotated with color codes (right panel). (C) Horizontal (top) and coronal (bottom) views of 3D maps showing simplified C-FOS correlation networks in the WT ctrl, Tbr1^+/– ctrl, WT TBS, and Tbr1^+/– TBS groups (left to right). Brain regions belonging to the same primary brain areas (colored spots, annotated in the lower right panel) have been combined. Mean correlations within and between primary brain areas were calculated and are expressed as spot size and edge thickness/color (annotated in the upper right panel), respectively. The sample sizes of mice are the same in (A–C) (WT ctrl, n = 7; Tbr1^+/– ctrl, n = 8, WT TBS, n = 7; Tbr1^+/– TBS, n = 7). The numerical value data are available in S3 Data. BLA, basolateral amygdala; TBS, theta-burst stimulation; WT, wild-type.

https://doi.org/10.1371/journal.pbio.3002646.g005

Upon examining the C-FOS correlation matrix, we observed that meaningful interregional correlations between brain regions in Tbr1^+/– mice in the awake state were noticeably reduced compared to WT mice (Fig 5B, WT ctrl versus Tbr1^+/– ctrl). After TBS, interregional correlations were noticeably reduced in the WT group (Fig 5B, WT ctrl versus WT TBS), whereas they were dramatically increased in Tbr1^+/– mice (Fig 5B, Tbr1^+/– ctrl versus Tbr1^+/– TBS). Therefore, the opposing consequences caused by BLA activation in WT and Tbr1^+/– mice are not only reflected in the neuronal recruitment of individual structures but also manifested by interregional activity correlation.

We further used network analysis for each C-FOS correlation matrix and quantified the degree centrality (DC) of each brain region based on the data of interregional C-FOS correlations to evaluate the differences among our 4 experimental groups (S5A Fig). A higher DC for a specific region indicates that the activity of that region is synchronous with more brain regions. Our results show that the WT ctrl group had a higher DC than the Tbr1^+/– ctrl group. However, TBS at the BLA reduced DC across the entire brain in WT mice but increased it in Tbr1^+/– mice (S5A Fig). Thus, the DC analysis also suggests that the WT ctrl group was more synchronous than the Tbr1^+/– ctrl group and that TBS at the BLA results in an opposing outcome in WT and Tbr1^+/– mice.

To confirm that the differences in DC were not caused by the particular correlation criteria we used, we evaluated the network stability of the 4 experimental groups by increasing the r (correlation coefficient) or P (correlation significance) threshold used for constructing the interregional correlation matrix (Fig 5B) and applying network analysis. Compared to the WT ctrl network, the reduced mean DC of the Tbr1^+/–ctrl network still existed across all the absolute r and a large range of P threshold conditions. Moreover, the contrasting modulatory effects of BLA-derived circuit activation on DC between the WT and Tbr1^+/– networks also existed when all the absolute r and large range of P value thresholds were used for network construction (S5B Fig). These results support that the differences in DC among the 4 experimental groups are unbiased.

To visualize the synchronization among major brain areas in a 3D map, we further calculated the mean values of the interregional C-FOS correlations within and between the major brain areas. The major brain areas we examined are listed in Fig 5C [29]. In the 3D maps of mouse brains, it is clear that the correlation networks between and within areas of the major brain regions, especially the isocortex areas, were much weaker in the Tbr1^+/– ctrl group compared to the WT ctrl group (Fig 5C, left 2 panels), even though C-FOS cell density was higher in Tbr1^+/– mice (Fig 3A). This outcome also implies dyssynchronization of whole brain activity attributable to Tbr1 deficiency. In contrast to the condition without TBS, unilateral TBS at the BLA of Tbr1^+/– mice generally reduced C-FOS⁺ cell density, but it resulted in better whole-brain synchronization compared to the Tbr1^+/– ctrl as well as WT TBS groups (Fig 5C). A majority of the brain areas of the Tbr1^+/– TBS group also exhibited stronger interregional correlations within areas (Fig 5C). Thus, our whole-brain C-FOS correlation analyses support that Tbr1 deficiency impairs whole-brain synchronization in mice and that TBS at the BLA ameliorates, at least partially, the dyssynchronization caused by Tbr1 deficiency.

Differential modular organization and reorganization upon BLA activation

To further understand which brain regions are more densely interconnected than others and achieve better modular integration, we used a Louvain community detection algorithm [30] to analyze modular organization. Optimal communities were defined by C-FOS levels of the brain regions being more correlated to each other within a community than between communities. To determine a spatial resolution parameter, R, for each network, we used the range from 0.4 to 0.9 in Louvain community detection and calculated the difference in the modularity metric (Q) for communities representing real networks or shuffled networks. The appropriate R-value for each network was determined according to the maximal difference between Q and Q_shuffled (S6A Fig) and used for Louvain community detection.

We found that the modular organizations of our experimental groups were distinct. The WT ctrl network contained 14 communities, with the majority of brain regions occupying the first 3 communities (598/618 brain regions). The minor communities of the WT ctrl network comprised a few intercorrelated regions (communities 4, 6, 8, 9) or single brain regions (communities 5, 7, 10, 11, 12, 13, 14) (Figs 6A, 6B, S6B, and S7 and S3 Data). In contrast, the Tbr1^+/– ctrl network only comprised 4 communities (Figs 6A, 6B, and S7). None of the communities identified in the WT ctrl group were identical to those of the Tbr1^+/– ctrl group, no matter whether brain regions or mean correlation within or between communities was being considered (Figs 6A, 6B, S6B, and S7 and S3 Data).

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Fig 6. Modular organization of C-FOS correlation networks.

(A) The same datasets of interregional correlations for C-FOS expression are shown in Fig 5A, except the interregional pair positions were sorted by their communities. The R values that generate the maximum difference in Q between real and shuffled networks for each experimental group are indicated, and they were used to analyze network communities (also see S6A Fig). (B) The 3D maps of binned and averaged C-FOS⁺ cubes for different communities (columns) of the 4 experimental groups (rows). C-FOS⁺ cubes are shown in brain region color codes, with their sizes representing the relative normalized density of C-FOS⁺ cells within binned cubes (100 μm³) (also see S7 Fig). For better visualization of minor communities of the WT ctrl (4–14) and WT TBS (4–7) groups, the C-FOS correlation matrices and C-FOS⁺ cube maps (only cube identities and locations) of those communities are also presented in S6B Fig. Mean correlations within (the bars at the top of 3D maps) and between communities (the lines under the 3D maps) are annotated according to differential extents (thickness) and polarity (color). Thicker bars or lines represent a higher degree of correlation, whether positive (red) or negative (blue). Sample sizes are the same as in Fig 5. The numerical value data are available in S3 Data. TBS, theta-burst stimulation; WT, wild-type.

https://doi.org/10.1371/journal.pbio.3002646.g006

Under BLA activation, the communities in the WT and Tbr1^+/– mice were substantially reorganized. All major communities of the WT TBS network (communities 1 to 3, total 613/618 regions) displayed weaker mean correlations within and between communities relative to the first 3 communities of the WT ctrl network (Figs 6A, 6B, and S7 and S3 Data, WT TBS communities 1 to 3 versus WT ctrl communities 1 to 3). For the Tbr1^+/– TBS network, the brain regions were highly concentrated in 2 communities, i.e., communities 1 and 3, with strong mean within and between community correlations (Fig 6A and 6B). Note that community 1 of the Tbr1^+/– TBS network is similar to community 2 of the WT ctrl network (Fig 6B), suggesting that amygdala activation may reorganize the neural network of Tbr1^+/– mice to be more comparable to that in WT mice.

Overall, the community analysis suggests that Tbr1 haploinsufficiency reorganizes how the mouse brain is networked and also leads to differential connectivity upon BLA activation. Importantly, BLA activation renders the neural network of Tbr1^+/– mice more similar (to a certain extent) to that of WT mice.

Tbr1 deficiency and BLA activation influence the default mode network

Although the above-described community analysis reveals the altered modular integration caused by Tbr1 deficiency and BLA activation, the physiological functions of the identified communities were unclear. Therefore, we applied our dataset to a physiologically relevant subnetwork analysis to explore any differences among experimental groups. Our control groups reflect a cognitive state where the mice were in their home cages during wakefulness and in the absence of any salient stimuli. Therefore, we adopted the default mode network (DMN) as appropriate for our analysis since it was initially identified by resting-state fMRI (functional magnetic resonance imaging) as a set of brain regions with correlated activities in the absence of goal-directed tasks and inactivation during task execution [31]. Given that the DMN has been implicated in social behaviors and ASD [32,33], we posited that our DMN analysis would likely reveal the physiological relevance of the deficits in Tbr1^+/– mice.

The core DMN regions in the mouse brain are located in the isocortical prefrontal (ACAd, ACAv, PL, ILA, ORBl, ORBm, ORBvl), somatomotor (SSp-tr, SSp-ll, MOs), and medial (VISa, VISam, RSPagl, RSPd, RSPv) areas of both brain hemispheres [34] (Fig 7A, right). We extracted our data on oChIEF patterns, C-FOS expression, and functional connectivity for these core DMN regions and compared them among our experimental groups. To aid visualization, the summarized signals in 10 and 100 mm³ cubes were used to highlight differences in oChIEF-Citrine and C-FOS expression, respectively.

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Fig 7. Comparison of DMNs in WT ctrl, Tbr1^+/– ctrl, WT TBS, and Tbr1^+/– TBS groups.

(A) Left, the differences of BLA axonal projections into DMN regions for Tbr1^+/– and WT mice, as revealed by ΔoChIEF for 10 μm³ binned cubes. Right, the colored spots indicate DMN brain region identities. Spot locations are derived from the locations of WT ctrl C-FOS cubes (100 μm³ bins) independent of information on C-FOS⁺ cell density. (B) The 3D maps showing differences in density of C-FOS⁺ cells (ΔC-FOS, 100 μm³ bins) for the DMN of experimental group pairs. The heatmaps indicate the scaled differences for ΔoChIEF (A) and ΔC-FOS (B). (C) Structure-wise comparison of DMN properties for 4 experimental group pairs. Each scatterplot shows the difference in DC versus the difference in PC in each contralateral (top) and ipsilateral (bottom) DMN brain region for each group pair. (D) Visualization in spring layout of the DMN networks for the 4 experimental groups. Brain regions (nodes) having many reciprocal C-FOS correlations (edges) are closer together. The color of each node represents brain region identity. The size of the node represents the normalized C-FOS density of the brain region. Labels “i_” and “c_” represent ipsilateral or contralateral brain regions, respectively. DMN region color codes are identical for (A), (C), and (D). In (C), the DMN regions indicated by arrows represent those regions showing a significant difference in BLA-derived circuits and C-FOS⁺ cell numbers between pairwise-compared groups (also see S8 Fig). The i_ACAv with blue font exhibited a negative correlation between the amount of BLA-derived axonal innervations and its modulatory effects on C-FOS expression patterns. Thus, the i_ACAv received enhanced BLA axonal projections that induce net inhibitory effects on neuronal activities in the Tbr1^+/– mouse brain. All DMN regions are ROI-corrected. Sample sizes for the DMN network analysis are identical to those described in Fig 5. Sample sizes for the DMN ΔoChIEF and DMN ΔC-FOS analyses are the same as those described in Fig 4. The numerical value data are available in S3 Data. BLA, basolateral amygdala; DC, degree centrality; DMN, default mode network; PC, participation coefficient; ROI, region of interest; TBS, theta-burst stimulation; WT, wild-type.

https://doi.org/10.1371/journal.pbio.3002646.g007

Compared to WT mice, Tbr1^+/– mice exhibited more ipsilateral BLA projections to the DMN regions, except RSPv, RSPd, ORBm, and VISa (Figs 7A, S8A, and S8B), implying a role for TBR1 in controlling BLA axonal projections into these regions. For C-FOS expression, we observed higher C-FOS⁺ cell numbers in bilateral DMN regions for the Tbr1^+/– ctrl versus WT ctrl and WT TBS versus WT ctrl comparisons (Figs 7B, S8C, and S8D). In contrast, there were more DMN regions with reduced numbers of C-FOS⁺ cells for the Tbr1^+/– TBS veresus Tbr1^+/– ctrl comparison (Figs 7B, S8C, S8D). Thus, DMN activity is indeed sensitive to Tbr1 deficiency and BLA activation.

Next, we determined the DC and participation coefficient (PC) of all DMN regions. Many regions of the Tbr1^+/– ctrl DMN exhibited reduced DC, and only the ipsilateral ACAv (i_ACAv) presented a slightly elevated DC (Fig 7C). PC reflects the relative distribution of a given brain region’s (node) correlations (edges) with other communities versus the correlations within its community. We found that in contrast to our observations for DC, Tbr1^+/– ctrl DMN regions showed elevated PC values for many regions relative to the WT ctrl DMN (Fig 7C). These analyses again evidence that the connectivity of the DMN in Tbr1^+/– ctrl mice is abnormal, thus resembling the aberrant DMN displayed by ASD patients [35,36].

We also observed that BLA activation elicited alterations to the DMN in both WT and Tbr1^+/– mice, albeit with different consequences (Fig 7C). In WT mice, BLA activation induced the same decreasing tendency for DC in many of the DMN regions, but various changes in PC (Fig 7C, WT TBS versus WT ctrl). In Tbr1^+/– mice, the same stimulation treatment elevated DC but reduced PC in many of the DMN regions (Fig 7C, Tbr1^+/– TBS versus Tbr1^+/– ctrl). These differences in the DMN between WT and Tbr1^+/– mice upon BLA activation demonstrate the differential responses of DMN regions to BLA activation.

Finally, we examined the reciprocal C-FOS correlation within different DMN regions to visualize the interconnected networks of the specific DMN regions (Fig 7D). Consistent with a previous study on the mesoscopic mouse DMN [34], our WT mice exhibited a strong interconnected network among DMN regions (Fig 7D). However, the DMN of awake Tbr1^+/– mice (Tbr1^+/– ctrl) had a much more loosely connected network, showing overall hypoconnectivity among DMN regions (Fig 7D). TBS at BLA again exerted opposing effects on Tbr1^+/– mice and their WT littermates, with the WT TBS group having a loosely connected network, whereas the interconnected network of the Tbr1^+/– TBS group became stronger than that of Tbr1^+/– ctrl mice (Fig 7D).

Thus, using DMN as an example, our analyses support that Tbr1 deficiency and BLA activation regulate the connectivity of a brain subnetwork relevant to social behaviors and ASD [32,33].

Alteration of Tbr1^+/– mouse social behaviors by BLA activation

Our previous study indicated that BLA deficiency is critical to the autism-linked behavioral deficits exhibited by Tbr1^+/– mice [17]. Our above-described results from the current study further indicate that TBS at the BLA enhances whole-brain synchronization in Tbr1^+/– mice so that it is more comparable to that of WT mice. If whole-brain synchronization is indeed a critical aspect of autism-linked deficits, we speculate that TBS at the BLA may improve to some extent the social behaviors of Tbr1^+/– mice. To investigate that possibility, we set up a paradigm in which the environmental conditions were close to those of our whole-brain circuit analysis to evaluate the effect of TBS at BLA on the social behaviors of Tbr1^+/– and WT mice (Fig 8A). After unilaterally injecting AAV into BLA, mice were first habituated to experimental handling for 3 days and then went through 2 sets of tests in 2 consecutive days. Each set of tests comprised 4 sessions, i.e., pretreatment, control treatment at Day 1 (D1) versus TBS at D2, posttreatment, and a reciprocal social interaction (RSI) test (Fig 8A). Therefore, we could compare the behavioral features of the same mice before and after TBS at BLA.

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Fig 8. Changes in behavior induced by TBS at BLA in WT and Tbr1^+/– mice.

(A) Experimental timeline. After AAV injection into the BLA, 3 days of habitation were first conducted, followed by 2 sets of tests on 2 consecutive days. Each set comprised 4 sessions: (1) pretreatment; (2) sham treatment (ctrl) or TBS at BLA; (3) posttreatment; and (4) RSI test. (B) Comparison of social behaviors before and after TBS at BLA (ctrl vs. TBS). Top panel: results for WT mice (blue circle; nose to nose, P = 0.9375, W = 13; side by side, P = 0.03125, W = 1; side reverse side, P = 0.296875, W = 7; nose to tail, P = 0.375, W = 8; nose to body, P = 0.578125, W = 10; following, P = 0.296875, W = 7). Bottom panel: results for Tbr1^+/– mice (red circle; nose to nose, P = 0.03125, W = 1; side by side, P = 0.8125, W = 12; side reverse side, P = 0.8125, W = 12; nose to tail, P = 0.015625, W = 0; nose to body, P = 0.6875, W = 11; following, P = 0.046875, W = 2). (C) Comparison of nonsocial behaviors during the RSI session before and after TBS at BLA (ctrl vs. TBS). Top panel: results for WT mice (blue circle; sniffing, P = 0.578125, W = 10; huddle, P = 0.6875, W = 11; looking around (lookaround), P = 0.078125, W = 3; climbing, P = 0.015625, W = 0). Bottom panel: results for Tbr1^+/– mice (red circle; sniffing, P = 0.21875, W = 6; huddle, P = 0.578125, W = 10; lookaround, P = 0.015625, W = 0; climbing, P = 0.578125, W = 10). Sample sizes of mice: WT, n = 7; Tbr1^+/–, n = 7. Two-tailed Wilcoxon signed-rank test was used for statistical analyses. *P < 0.05. The numerical value data are available in S4 Data. AAV, adeno-associated virus; BLA, basolateral amygdala; RSI, reciprocal social interaction; TBS, theta-burst stimulation; WT, wild-type.

https://doi.org/10.1371/journal.pbio.3002646.g008

We applied DeepLabCut together with DeepOF to characterize in detail behavioral features of the mice, including both social and nonsocial behaviors, in different sessions. We found that TBS at BLA noticeably increased the time of nose-to-nose interactions of Tbr1^+/– mice with unfamiliar mice in the RSI test. In contrast, the time of nose-to-tail and following behaviors of Tbr1^+/– mice were reduced after BLA activation (Fig 8B). Although nose-to-nose, nose-to-tail, and following behaviors all represent rodent social behaviors, their meanings are slightly different. Nose-to-tail and following behaviors are more relevant to mating behaviors. However, nose-to-nose interactions, involving social facial touching, are critical for mice to discriminate individual conspecifics [37]. Whisker-dependent somatosensory responses, olfaction, vision, and hippocampus-dependent memory are all involved in the process of social recognition and memory inherent to nose-to-nose interactions. Consistently, our analyses revealed that connections between the BLA and visual and somatosensory cortices and hippocampus were affected by Tbr1 deficiency and TBS at BLA (Fig 3 and S1 Data). Thus, TBS at BLA alters both functional connectivity and patterns of social behavior of Tbr1^+/– mice.

In contrast to the effect observed for Tbr1^+/– mice, TBS at the BLA did not influence nose-to-nose, nose-to-tail, and following behaviors in WT mice, although it did reduce the time spent in side-by-side contact (Fig 8B). Thus, the effect of TBS at BLA specifically impacted the behaviors of Tbr1^+/– mice. In terms of nonsocial behaviors, only “looking around” was consistently found to differ in our comparisons (Figs 8C, S9, and S10). In particular, Tbr1^+/– mice spent more time looking around than WT during the posttreatment period (S10 Fig, lowest panel) and the RSI test on D2 (Fig 8C). Perhaps BLA activation in Tbr1^+/– mice also increases their awareness of the environment, as well as alters their social behaviors.

In conclusion, our detailed behavioral analyses are consistent with the conclusion from our connectivity studies that TBS at BLA alters whole-brain synchronization and social behavior patterns of Tbr1^+/– mice.

Ethics statement

All animal experiments were performed with the approval of the Academia Sinica Institutional Animal Care and Utilization Committee (Protocol No. 14-11-759 and 18-10-1234) and in strict accordance with its guidelines and those of the Council of Agriculture Guidebook for the Care and Use of Laboratory Animals. Human subject study is not included in the current work.

Mice

The Tbr1^+/− mice were originally provided by Drs. J. L. Rubenstein (Department of Psychiatry, University of California, San Francisco) and R. F. Hevner (Department of Neurological Surgery, University of Washington, Seattle) and had been backcrossed to WT C57BL/6 for more than 40 generations. Tbr1^+/– mice and WT littermates were housed in the animal facility of the Institute of Molecular Biology, Academia Sinica, under controlled temperature and humidity and a 12-h light/12-h dark cycle. All animal experiments were performed at 3 to 5 mo of age with the approval of the Academia Sinica Institutional Animal Care and Utilization Committee and in strict accordance with its guidelines (Protocol No. 14-11-759 and 18-10-1234).

Virus production

For axon tracing and in vivo stimulation of BLA-derived circuits, we packaged an AAV that expresses the ultrafast ChR variant oChIEF under the control of the human Synapsin promoter (AAV-Syn-oChIEF-Citrine and AAV-Syn-oChIEF-tdTomato) as described in our previous study [23]. Briefly, a transgenic plasmid encoding oChIEF-Citrine, AAV helper plasmid serotype AAV8 capsid and AAV2 replication proteins, together with an adenovirus helper plasmid encoding essential proteins for AAV packaging, were transfected into adenovirus E1-expressing cells. Crude virus soup was obtained from transfected E1-expressing cells and purified by iodixanol gradient centrifugation. The physical vector titers were quantified by measuring the number of packaged vector genomes by real-time PCR using SYBR Green reaction mix (Roche Diagnostics, Mannheim, Germany).

Experimental procedure of the mesoscopic whole-brain scale circuit analysis pipeline

In order to analyze the structural and functional connectivity of BLA-derived circuits, we developed a mesoscopic whole-brain scale circuit analysis pipeline (Figs 1 and S1). Detailed methods are described in the following sections.

k-nearest neighbors (KNN) algorithm-based classification

To examine the spatial separability of virus injection sites, we conducted a KNN-based classification with 5-fold cross-validation using the scikit-learn Python package. A series of numbers of nearest neighbors was used to identify the one achieving the best KNN classification accuracy. Furthermore, a randomized dataset of virus-injecting locations or PC locations, shuffled 1,000 times, between experimental groups was used to validate the significance of spatial separability. The spatial separability was considered significant when the KNN classification accuracy of the real dataset deviated from the same metric distribution calculated for the randomized dataset (P < 0.05).

Slice-based quantification and related analysis

Slice-based quantifications of oChIEF and C-FOS signals involved calculating the oChIEF pixel and C-FOS⁺ cell distributions along the AP axis of each brain region, respectively. To achieve fair comparisons, we carried out 100 μm binning of oChIEF pixel and C-FOS⁺ cell distributions along the AP axis. The binned oChIEF pixel and C-FOS⁺ cell distributions were then used for structure-wise comparisons and correlation analysis between rewired BLA-derived circuits and their C-FOS modulatory ability.

Network analysis of functional connectivity based on interregional C-FOS correlations

Functional connectivity, as measured in our study, was based on the synchronization of C-FOS expression levels between 2 brain regions in individual subjects. To determine functional connectivity, we first conducted volume-based quantification of the C-FOS⁺ cell density of each brain region (S4 Fig). To calculate the C-FOS density, total C-FOS⁺ cells within a brain region were calculated by summing over all C-FOS⁺ cells in individual slices and then dividing by the volume of the brain region (converted to mm³). We excluded brain regions with low C-FOS density (summation of C-FOS density from all samples <800 cells/mm³) for subsequent analytical steps. The relative C-FOS density for a given brain region of each sample was obtained by normalizing it to the averaged value calculated from all WT ctrl samples. Therefore, we used relative C-FOS density as a single metric to represent the C-FOS level of a given brain region of one sample. To measure all possible synchronizations, we evaluated interregional C-FOS correlations between all possible brain region pairs by means of two-sided Kendall’s rank correlation (using scipy.stats) across all the samples in the same experimental group. A network of interregional C-FOS correlations was constructed using the significant C-FOS correlations (edges, significance level P < 0.05) between brain region pairs (nodes) by using the NetworkX Python package. Self-to-self correlations were removed. The DC and PC of individual nodes (brain regions) were calculated using NetworkX.

To dissect the modular organization of networks, we adopted a Louvain community detection algorithm and the modularity metric (Q) calculation provided by NetworkX. To identify the optimal resolution parameter R value used for Louvain community detection, we generated a shuffled network by maintaining the same correlation weights (edges) while randomizing the source and target regions (nodes). An R value series (0.4 to 0.9 in steps of 0.1) was used for Louvain community detection of real and shuffled networks. For each R value, the difference in Q between the modularity of the real network and the shuffled network was calculated 1,000 times. The most appropriate R value that generated the maximal mean difference of Q (Q–Q_shuffled) was selected for Louvain community detection. These computations were achieved using Numpy, Pandas, and NetworkX.

ROI corrections of brain region masks

We selected 44 contralateral and 52 ipsilateral brain regions for manual correction of the mismatched ROI in every single brain slice image of each brain sample. To do this, the original coronal indicator masks of each brain region were separated into contralateral and ipsilateral masks and converted to ImageJ ROIs by sequentially applying the ImageJ Create Selection function and adding it to ImageJ ROI Manager. Using the C-FOS or oChIEF raw images (registered to CCFv3) as references, ROIs were verified and corrected by manually adjusting their shapes and locations. After ROI corrections, the ROIs of each brain region in both hemispheres were converted back to image masks by applying the ImageJ Create Mask function. The new coronal indicator masks were then used for requantifications.

3D visualization

Behavior paradigm for examining the effect of TBS at BLA on reciprocal social interaction

To manipulate BLA-derived circuits and examine its functional impacts in terms of behavioral changes, we expressed AAV-Syn-oChIEF-Citrine or AAV-oChIEF-tdTomato in mice and inserted optic fibers in their BLA. Mice were single-housed after surgery. On days 19 to 21 after surgery, we conducted the same habituation sessions as conducted for the mesoscopic connectome experiments (once per day for 3 d). RSI was carried out on the day following the last habituation session. The behavior paradigm comprised 4 sessions. The first session was a pretreatment, which allowed the test mouse with a connected patch cable to freely explore the lid-opened homecage for 10 min. The second session differed between Day 1 (D1) and D2. For D2, the same TBS protocol (473 nm laser with 10 mW light intensity) as applied for the mesoscopic connectome experiment was applied to activate the BLA. For D1, mice underwent sham stimulation (ctrl, no light). The third session, i.e., posttreatment, was for recovery from treatment. The patch cable was removed and the mouse was put back in the lid-opened homecage for an additional 10 min. Finally, RSI was carried out by introducing an unfamiliar and younger (at least 30 d younger) mouse into the homecage. The social interactions of the test mouse with the unfamiliar mouse were recorded for 10 min. Mouse behaviors were continuously recorded from a top-down perspective by a digital camera (30 frames/s) during the entire behavior paradigm.

Deep learning–based analyses of behavior videos

Single videos of the entire behavior paradigm were cropped, trimmed, and separated into 10 min pretreatment, posttreatment, and RSI videos (each video contains 18,000 frames), respectively, using the FFMPEG ImageJ plugin and OpenCV python package. To achieve more objective and comprehensive behavioral analysis, we used DeepLabCut [55,56] for pose estimation and then applied DeepOF [57] to annotate the behaviors in video frames. Single-animal and multi-animal modes of DeepLabCut 2.3.7 were used for pose estimation of pretreatment/posttreatment videos and RSI videos, respectively. According to the DeepOF analysis pipeline, 11 body parts per mouse were annotated, including the nose, right and left ears, 4 limbs, and 3 points along the spine and tail base. DeepLabCut models for single- and 2-animals were trained separately according to the DeepLabCut workflow, which includes extracting frames for labeling, creating a training dataset, training the network, evaluating the trained network, applying the trained model for videos, and relabeling outlier frames for repeated training of models. After repeating the DeepLabCut workflow 3 times and achieving suitable pose estimation results, DeepLabCut models were applied to the videos to obtain the tracklet datasets of all body parts. In addition, tracklets associated with RSI videos were manually verified and corrected for identity shift and incorrectly annotated body parts before subsequent behavioral annotation.

For behavioral annotations, we used the supervised pipeline of DeepOF v0.6 to label social and nonsocial behaviors in each frame. All the predefined rules and classifier from DeepOF were directly used for analysis with one modification, i.e., the threshold distance of the nose to other body parts was restricted to 15 mm. For each social and nonsocial behavior, total behavior time was calculated as the sum of all labeled behavior frames and converted to seconds. Pairwise comparisons of behavior time between groups were conducted (ctrl versus TBS, pretreatment versus posttreatment). Two-tailed Wilcoxon signed-rank test (using Pingouin) was used for statistical analysis.

Quantification and statistical analysis

Sample size n represents the number of brain samples from individual animals or number of animals in the behavior assay for each experimental group. We did not use statistical methods to predetermine sample sizes, but our sample size is similar to that of a previously published brain connectome study [58]. KNN-based classification of virus injection sites, slice-based quantifications of oChIEF and C-FOS signals, and the ΔoChIEF/ΔΔC-FOS correlation analysis are based on n = 13 WT (6 from WT ctrl and 7 from WT TBS groups) and n = 14 Tbr1^+/– brain samples (7 from Tbr1^+/– ctrl and 7 from Tbr1^+/– TBS groups). Volume-based quantification of relative C-FOS⁺ cell density and interregional C-FOS correlation analysis was based on samples largely overlapping with our slice-based quantification of C-FOS signals. For both genotypes, we included 1 additional brain sample for each that had mistargeted virus expression in the control condition. Behavior assays are based on n = 7 WT and n = 7 Tbr1^+/– mice with the correct virus expression and optic fiber insertion in the BLA. The exact n numbers of experimental groups for every analysis can be found in the figure legends. For the data distribution, we did not use statistical methods to test data normality but we assumed most of the quantified data is not normally distributed. Therefore, nonparametric tests were used for statistical analysis. Statistical details can be found in the method sections describing each analysis. Data representing statistical parameters such as mean ± SEM are described in the figure legends. The test statistics (e.g., exact P value, Friedman chi-squared statistic, and degrees of freedom) of structure-wise comparisons can be found in S1 Data, and the test statistics of behavior analyses can be found in the figure legends (Figs 8, S9, and S10).