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Anterior Cingulate Cortex Projections to the Amygdala in Primates: Topographic and Layer-Specific Organization Underlying Emotion and Mood Regulation

  1. Ken-Ichiro Tsutsui1
  1. 1Laboratory of Systems Neuroscience, Tohoku University Graduate School of Life Sciences, Sendai, Miyagi 980-8577, Japan
  2. 2Institute for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi 484-8506, Japan
  3. 3Department of Integrative Anatomy, Graduate School of Medical Sciences and Medical School, Nagoya City University, Nagoya, Aichi 467-8601, Japan
  4. 4Department of Neurology, The University of Osaka Graduate School of Medicine, Suita, Osaka 565-0871, Japan
  1. Correspondence should be addressed to Masahiko Takada at primasa4208{at}gmail.com or Ken-Ichiro Tsutsui at tsutsui{at}tohoku.ac.jp.

  1. Author contributions: M.T. and K-I.T. designed research; K.K., Y.S., R.Y., A.Z., S.N., G.Y., S.T., S.N., S.O., and K-i.I. performed research; K.K., Y.S., and R.Y. analyzed data; K.K., Y.S., R.Y., M.T., and K-I.T. wrote the paper.

 
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Abstract

Emotion and mood regulation critically depends on interactions between the anterior cingulate cortex (ACC) and the amygdala. However, the detailed architecture of ACC projections to their major targets, the basal (BA) and accessory (AcBA) basal nuclei of the amygdala, remains unclear. To address this issue, a combined retrograde and anterograde tracing with viral vectors was performed in macaques of either sex to map the projection patterns from the pregenual (pgACC), subgenual (sgACC), and dorsal (dACC) subareas. Data revealed that ACC neurons projecting to the BA arose predominantly from the superficial layers (II/III) of all subareas and the deep layers (V/VI) of the sgACC, whereas ACC neurons projecting to the AcBA originated mainly in the deep layers of the sgACC and dACC. The present study defines the topographic and layer-specific organization of ACC–amygdala connectivity in primates and subserves to provide an anatomical basis for future causal and translational approaches, such as targeted interventions against ACC-related mood disorders.

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Significance Statement

Emotion and mood regulation critically depends on interactions between the anterior cingulate cortex (ACC) and the amygdala, and their dysfunctions have been implicated in mood disorders such as depression. However, the detailed organization of primate ACC–amygdala circuitry remains to be fully understood. Notably, the existence of projections from the pregenual ACC (pgACC) to the basal nucleus of the amygdala (BA) has been controversial. Using viral vector-based neural tracings in macaques, we have identified a distinct pgACC–BA pathway and shown that this pathway arises predominantly from the superficial layers of the cortex. Such an unexpected laminar origin, atypical for cortico–subcortical projections, reveals a previously unrecognized circuit architecture and challenges prevailing models of cortico–amygdala linkage involved in mood disorders.

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Introduction

Emotion and mood regulation is crucial for adaptive behavior, and its disruption causes serious mental illness, such as depression and anxiety disorders (for reviews, see Vogt et al., 1992; Devinsky et al., 1995; Rolls, 2015). The regulatory mechanism underlying emotion and mood fundamentally relies on interactions between cortical and subcortical limbic structures. As widely recognized, the amygdala is largely involved in encoding negative emotion (Klüver and Bucy, 1939; Stefanacci and Amaral, 2000; Ghashghaei and Barbas, 2002; Zikopoulos et al., 2016). While the amygdala is known to have neuronal connections with various cortical areas, the projections from the anterior cingulate cortex (ACC) to the amygdala are particularly important for the regulation of emotion and mood (Kunishio and Haber, 1994; Barbas, 1995; Ghashghaei and Barbas, 2002). To know about how emotion and mood are regulated, it is thus essential to elucidate the architecture of the neural pathway linking these two structures.

The ACC has long been considered a critical “hub” for the information flow around the limbic system, including the amygdala (Barbas, 1995; Bachevalier et al., 1997; Y. T. Cho et al., 2013; Kelly et al., 2021). In humans and nonhuman primates (NHPs), the ACC is generally divided into structurally and functionally heterogeneous subareas, such as the pregenual, subgenual, and dorsal ACC (pgACC, sgACC, and dACC), which correspond respectively to Brodmann areas 32/24b, 25/14c, and 24a/b/c (Vogt et al., 2005 ; Drevets et al., 2008; Myers-Schulz and Koenigs, 2012). Meta-analyses of numerous human neuroimaging studies indicate that these subareas of the ACC are involved in various aspects of emotion and mood regulation (Vogt et al., 2005; Fujiwara et al., 2009). The importance of the ACC in the regulation of mood has also been highlighted in NHPs (Wallis et al., 2017; Nakamura et al., 2022). Using repetitive transcranial magnetic stimulation (rTMS), we have recently found that inhibitory interventions mainly targeting the pgACC and sgACC induce a typical depressive state in macaques (Nakamura et al., 2022).

There is a consensus that the ACC projections in NHPs are mainly directed toward the basal (BA) and accessory basal (AcBA) nuclei of the amygdala (Carmichael and Price, 1995; Ghashghaei and Barbas, 2002; Stefanacci and Amaral, 2002; Kelly et al., 2021). However, the origins of these projections within the ACC remain controversial. An early study (Ghashghaei et al., 2007) demonstrated that the ACC–amygdala projections arise from the sgACC (area 25) and dACC (dorsal area 24), whereas a more recent work (Kelly et al., 2021) reported the existence of massive projections not only from the sgACC and dACC, but also from the pgACC (areas 32/24b). Therefore, a precise examination is needed for understanding the architecture of the ACC–amygdala pathway.

In the present study, we utilized retrograde/anterograde tracing approaches with adeno-associated virus (AAV) vectors to investigate the detailed neural connectivity of the ACC to the amygdala in macaques. For retrograde labeling from the amygdala, we injected a retrogradely transported AAV variant (so-called AAV2retro; Tervo et al., 2016) into the BA and AcBA. To confirm the retrograde labeling data, we further conducted multicolor anterograde tract-tracing from the ACC by using a mosaic AAV vector (so-called AAV2.1; Kimura et al., 2023) that achieves high levels of transgene expression and neuron specificity. In a single animal, separate injections of AAV2.1 vectors expressing three different fluorescent proteins were made into the dACC, pgACC, and sgACC, respectively. These approaches indeed allowed us to define the topographic and layer-specific organization of distinct ACC projections to the BA and AcBA.

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Materials and Methods

Animals

Five adult macaques were used for this study (Monkeys A–E in Tables 1, 2): four rhesus monkeys (Macaca mulatta), 5.7–19.5 years old, two males and two females, 5.0–8.1 kg, and one Japanese monkey (Macaca fuscata), 7.2 years old, male, 9.8 kg. They were housed in individual cages (approximately W750 × D900 × H900 mm) in a room with controlled temperature (23–26°C) and light (12 h on–off cycle) conditions. Every effort was made to minimize animal suffering.

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Table 1.

Summary of subjects

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Table 2.

Summary of experiments

In Monkeys A and B, AAV2retro was injected unilaterally into the AcBA, with two tracks delivered in each monkey (Table 2). In Monkeys C and D, AAV2retro injections were also made unilaterally into the BA, with one track delivered in Monkey C and two tracks delivered in Monkey D (Table 2). In Monkey E, on the other hand, three AAV2.1 vectors expressing distinct fluorescent proteins were injected separately into the pgACC, sgACC, and dACC of the same hemisphere (Table 2). According to preceding studies (Vogt et al., 2005; Drevets et al., 2008; Myers-Schulz and Koenigs, 2012), we cytoarchitectonically defined the pgACC as Brodmann areas 32 and 24b, the sgACC as areas 25 and 14c, and the dACC as areas 24a, 24b, and 24c.

The entire research project was approved by the Tohoku University Animal Care and Use Committee (Tohoku University) and the Animal Welfare and Animal Care Committee of the Center for the Evolutionary Origins of Human Behavior (Kyoto University). All experiments were conducted in accordance with the Regulations for Animal Experiments and Related Activities at Tohoku University (Version 18, 2025), the Guidelines for Care and Use of Nonhuman Primates established by the Primate Research Institute, Kyoto University (2010), and the Guide for the Care and Use of Nonhuman Primates in Neuroscience Research (Japan Neuroscience Society).

Surgery

The monkeys were initially sedated with ketamine hydrochloride (5 mg/kg, i.m.) and xylazine hydrochloride (0.5 mg/kg, i.m.) and then anesthetized with sodium pentobarbital (20 mg/kg, i.v.) or propofol (5 mg/kg/h). An antibiotic (ceftazidime; 25 mg/kg, i.v.) was administered at the initial anesthesia. During the surgery, the monkeys were kept hydrated with a lactated Ringer's solution (intravenous) and also applied with the same antibiotic (cefazolin; 250–500 mg/kg, i.m.) and an analgesic (buprenorphine hydrochloride; 0.02 mg/kg, i.m.). On the day of surgery, magnetic resonance images (MRIs) were acquired using an IRIS Vento (Fujifilm Healthcare) while the monkey's head was fixed with an MRI-compatible head holder made of polyether ether ketone (PEEK) and acrylic resin. The same head holder was kept attached during transfer to a stereotaxic apparatus, thereby minimizing positional errors caused by repositioning from MRI acquisition to surgery. The MRI data were imported into the Brainsight Primate MRI-guided stereotaxic navigation system (Rogue Research), and the injection targets and trajectories were planned on the individual MRI images. After removal of a skull portion over the frontal lobe, stereotaxic injections of AAV vectors were made with the aid of an MRI-guided navigation system (Brainsight Primate). The AAV2retro expressing red fluorescent protein (RFP) (AAV2retro-hSyn-mKO2) was injected unilaterally into the AcBA or BA, while the AAV2.1 vectors expressing three different fluorescent proteins (AAV2.1-TetOff-CaMKII-pal-mClover/mKO2/mRaspberry) were injected, respectively, into the pgACC, sgACC, and dACC of the same hemisphere (Table 2). The AAV2.1 vector is a mosaic capsid vector that exhibits high neuronal specificity and robust transgene expression in the primate brain (Kimura et al., 2023). In the present study, we incorporated a tetracycline transactivator (tTA) gene downstream of the mouse CaMKIIα promoter and a TRE-tight promoter upstream of the fluorescent genes to enhance transgene expression, thereby facilitating the visualization of fine axon terminals (Sohn et al., 2017; Sakaguchi et al., 2018; Kojima et al., 2024). The details of production for the AAV vector were mentioned previously (Kimura et al., 2023). All injections were performed through a 10 µl Hamilton Neuros Syringe (Hamilton) at the rate of 0.1–0.2 µl/min. The injection volume of AAV2retro in Monkeys A–D was 2.0–2.5 µl/site (one site per track; one or two tracks), and that of AAV2.1 vectors in Monkey E was 0.5 µl/site (two sites per track; two tracks). The injection titer for each vector is provided in Table 2. After the injections, the monkeys were monitored until full recovery from the anesthesia. All experimental procedures were carried out in a special laboratory designated for in vivo animal infectious experiments (biosafety level 2 at Kyoto University and level 1 at Tohoku University).

Tissue preparation

Three to four weeks after the vector injections, the monkeys underwent perfusion fixation. Each animal was anesthetized deeply with an overdose of sodium pentobarbital (50 mg/kg, i.v.), sodium secobarbital (50 mg/kg, i.v.), or sodium thiopental (100 mg/kg, i.v.) and perfused transcardially with 0.1 M phosphate-buffered saline (PBS), pH 7.4, followed by 10% formalin in 0.1 M phosphate buffer, pH 7.4 (Table 1). The removed brain was postfixed in the same fresh fixative overnight at 4°C and then saturated with 30% sucrose in 0.1 M PBS at 4°C. Coronal sections were cut serially at the 50 µm thickness on a freezing microtome and divided into 10 series.

Histological analyses

Every 10th section 500 µm apart was mounted onto glass slides and observed under a fluorescence microscope (AxioScan Z1; Carl Zeiss). For cytoarchitectonic delineation of cortical areas/layers and amygdala nuclei, the adjacent series of sections were mounted onto gelatin-coated glass slides and Nissl-stained with 1% Cresyl violet. To confirm the distinction of injection sites, the fluorescence histochemistry for mClover, mKO2, mRaspberry, and NeuN was performed for Monkey E. First, Coronal sections were rinsed three times in 0.1 M PBS and immersed in 1% skim milk for 1 h. These sections were then incubated for 2 d at 4°C with a cocktail of rabbit monoclonal antibody against mClover (1:2,000; Invitrogen), mouse monoclonal antibody against mKO2 (1:750; Medical & Biological Laboratories), rat monoclonal antibody against mRaspberry (1:500; ChromoTek), and guinea pig polyclonal antibody against NeuN (1:1,000; Millipore) in 0.1 M PBS containing 2% normal donkey serum and 0.1% Triton X-100. Subsequently, the sections were incubated with a cocktail of Alexa Fluor 488-conjugated donkey anti-rabbit IgG antibody (1:200; Jackson ImmunoResearch Laboratories), Alexa Fluor 555-conjugated donkey anti-mouse IgG antibody (1:200; Invitrogen), Alexa Fluor 647-conjugated donkey anti-rat IgG antibody (1:200; Jackson ImmunoResearch Laboratories), and DyLight 405-conjugated donkey anti-guinea pig IgG antibody (1:200; Jackson ImmunoResearch Laboratories) in the same fresh medium for 2 h at room temperature. In addition, NeuN immunohistochemistry was performed for Monkeys A–D using the same procedure. All histological sections were mounted onto gelatin-coated glass slides. NeuN immunofluorescence images were not included in the figures but were used solely to identify the regional boundaries during histological analysis.

In Monkeys A–D, the number of RFP-positive neurons was exhaustively counted on digital fluorescence images acquired with an AxioScan Z1 slide scanner (Carl Zeiss) using a 10× objective, and neurons located within the manually delineated medial frontal cortex (MFC) ROIs were used for the subsequent analysis. Neither random sampling nor stereological estimation was used. Based on these data, unfolded maps of the MFC were constructed from coronal sections 1 mm apart (Figs. 2, 3). To quantify the density of neurons in individual areas of the MFC, we performed an unfolded map analysis, in which cortical sulci were virtually unfolded and the cortical sheet was flattened into a two-dimensional plane to visualize the distribution of labeled neurons (Van Essen and Maunsell, 1980). Coronal section images obtained at 1 mm intervals were imported into Adobe Illustrator (Adobe). Regions of interest (ROIs) were manually delineated along the medial cortical surface, extending from the pial surface to the gray–white matter boundary, based on the adjacent Nissl-stained sections and NeuN-immunostained images. The cytoarchitectonic boundaries of cortical areas and layers were determined by reference to these Nissl and NeuN images. For binning, we first traced the border between the superficial and the deep cortical layer on each coronal section, corresponding to the transition from layers II/III to layer V. Along this layer boundary, anchor points were placed at 1,000 µm intervals. Each ROI was then subdivided into columnar bins by drawing bin boundaries through these anchor points from the pial surface to the gray–white matter boundary. Because of cortical curvature, the resulting bins were trapezoidal or fan-like in shape. For the whole-cell-count analysis without layer distinction, the number of labeled neurons within each bin was quantified from dorsal to ventral. For the cell-count analysis with layer distinction, labeled neurons were counted separately in superficial-layer bins (layers I–IV) and deep-layer bins (layers V and VI). Cell counts from each bin were entered into a matrix, with each column representing one coronal section arranged from anterior to posterior. The resulting matrix was then converted into a heatmap, in which color values were assigned based on normalization to the average number of labeled neurons across all bins. In addition, we assessed the layer-specific distribution patterns of labeled neurons. For this purpose, the superficial (layers II and III) and deep (layers V and VI) layers were separately subdivided into columnar bins (1,000 µm width), and the number of labeled neurons in each bin was counted. The bin-wise counts were then normalized to the total number of labeled neurons in the MFC, and the normalized values were plotted onto the unfolded maps to visualize the layer distribution patterns. To prepare Figure 2, the values for the superficial and deep layers were combined into single maps.

Statistical analyses

For quantitative analysis of retrogradely labeled neurons, all statistical analyses were performed in each animal. Labeled neurons in the ACC were assigned to the three major ACC subareas: pgACC, sgACC, and dACC. For the analysis of areal distribution, labeled neurons were first pooled across cortical layers, and the proportion of labeled neurons in each ACC subarea was calculated relative to the total number of labeled ACC neurons in each animal. To examine whether labeled neurons were uniformly distributed through the three ACC subareas, χ2 goodness-of-fit tests were performed with the expected distribution set to equal proportions through them (Table S1). Pairwise comparisons between the ACC subareas were then carried out using two-sided exact binomial tests, with a null hypothesis that labeled neurons were equally distributed between each pair of the subareas (Table S2).

For the analysis of laminar distribution, labeled neurons were classified as belonging to either the superficial or the deep layers. The superficial/deep laminar distribution was summarized separately for each ACC subarea. To test whether the superficial/deep laminar distribution differed among the ACC subareas, χ2 tests of independence were performed using 3 × 2 contingency tables, with the ACC subarea as one factor and the cortical layer as the other (Table S3). Pairwise comparisons of laminar distribution between the ACC subareas were done using 2 × 2 χ2 tests. In addition, within each ACC subarea, deviations from an equal superficial/deep laminar distribution were assessed using two-sided exact binomial tests (Table S4).

For pairwise comparisons, p values were adjusted for multiple comparisons using the Holm–Bonferroni’s method. Statistical significance was defined as p < 0.05 after correction where applicable. Statistical analyses were performed using MATLAB 2025b (MathWorks).

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Results

Sites of AAV2retro injections in the AcBA and BA

To examine the distribution of neurons in the MFC giving rise to the ACC–amygdala pathway, we injected AAV2retro into the AcBA or BA in four monkeys (Monkeys A–D; Fig. 1). In each case, the extent of the injection site was reconstructed in coronal sections at five anteroposterior levels selected to cover the whole region of the amygdala (Fig. 1a). In Monkeys A and B, AAV2retro injections were made into the AcBA (Fig. 1b). The injection site in each case was almost limited to the AcBA as involving its magnocellular (AcBAmc) and parvicellular (AcBApc) divisions, with minimal diffusion into the anterior hypothalamic area (Fig. 1a). In Monkeys C and D, on the other hand, AAV2retro was injected into the BA (Fig. 1b). The injection site in each case largely involved the magnocellular (BAmc) and intermediate (BAi) divisions, but not the parvicellular division of the BA, though slightly encroached on the lateral nucleus of the amygdala (Fig. 1a).

Figure 1.
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Figure 1.

Sites of AAV2retro injections in the amygdala. a, Extent of the injection sites in the AcBA (Monkeys A and B) and BA (Monkeys C and D). Five representative coronal planes are arranged anteroposteriorly from (1) to (5), and the four injection cases are overlaid thereon. Regions in yellow and green show the spread of the injected vector within the AcBA in Monkeys A and B, respectively, and those in red and blue depict the spread of the injected vector within the BA in Monkeys C and D, respectively. b, Fluorescent images of the injection sites (left) and Nissl-stained cytoarchitecture (right) in Monkeys A–D. mKO2 native fluorescence is shown in white, and NeuN immunofluorescence is shown in blue. AcBA, accessory basal nucleus of the amygdala; AHA, anterior hypothalamic area; BA, basal nucleus of the amygdala; D, dorsal; i, intermediate division; L, lateral; LA, lateral nucleus of the amygdala; mc, magnocellular division; MeA, medial amygdaloid area; pc, parvocellular division; PLA, paralaminar nucleus of the amygdala; SB, subiculum.

Cells of origin of ACC projections to the AcBA and BA

To compare the distribution of cells of origin of ACC projections to the AcBA and BA, we plotted retrogradely labeled neurons in the MFC on coronal sections and prepared unfolded maps (see Materials and Methods). Consistent with previous findings (Ghashghaei et al., 2007), retrograde neuron labeling prominently occurred not only in the MFC but also in the orbitofrontal cortex in all cases (Figs. S1, S2). Within the MFC, more intense labeling was seen more posteriorly, especially in areas 25 and 14c corresponding to the sgACC in the four cases (Fig. 2, Figs. S1, S2). However, the distribution patterns of labeled neurons in other MFC areas varied depending on the site of AAV2retro injection, either the AcBA or the BA. To quantify these distributions, we calculated the proportion of labeled neurons in each of the major ACC subareas (i.e., pgACC, sgACC, and dACC) to a total of labeled ACC neurons. We then statistically tested a null hypothesis that labeled neurons were uniformly distributed among pgACC, sgACC, and dACC within each animal (Table S1). Pairwise comparisons between ACC subareas are summarized in Table S2.

Figure 2.
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Figure 2.

Unfolded maps of the MFC showing the distribution patterns of retrograde labeling from the AcBA in Monkeys A (a) and B (b) and from the BA in Monkeys C (c) and D (d). For each case, cell counts were performed at 1 mm intervals and normalized to the mean of all cell number within each section. In each 1 mm bin, the density of labeled cortical neurons is expressed as a colormap (red, high; yellow, intermediate; blue, low). CMAr, rostral cingulate motor area; cs, cingulate sulcus; D, dorsal; P, posterior; preSMA, presupplementary motor area; rs, rhinal sulcus.

In Monkeys A and B where the AAV2retro injections were made into the AcBA, dense clusters of labeled neurons were located in the sgACC (i.e., areas 25 and 14c), as described above (Fig. 2, Fig. S1). The sgACC contained the largest fraction of labeled ACC neurons in both cases, accounting for 62.2% in Monkey A and 58.3% in Monkey B. The dACC accounted for 21.7 and 33.0%, whereas the pgACC accounted for 16.2 and 8.7%, respectively. The distribution of labeled neurons through the ACC subareas significantly deviated from a uniform distribution in both animals (χ2 goodness-of-fit tests; Monkey A, χ2(2) = 1,866.92; p < 1.0 × 10−16; Monkey B, χ2(2) = 1,437.22; p < 1.0 × 10−16; Table S1). Moreover, large numbers of labeled neurons were found in the posterior and dorsal part of the MFC, such as posterior areas 24b/c, the rostral cingulate motor area, and the presupplementary motor area (Fig. 2, Fig. S1). A small number of neurons were also labeled in area 24a. Similar patterns of MFC labeling were observed in the two cases, although neurons in the posterior and dorsal MFC were more numerously labeled in Monkey A (Fig. 2).

In Monkeys C and D where the AAV2retro injections were performed into the BA, a large sector over the pgACC and dACC, including posterior area 32 and anterior areas 24a/b/c, contained clusters of labeled neurons (Fig. 2, Fig. S2). Dense neuron labeling was seen in the sgACC (i.e., areas 25 and 14c) like the AcBA-injection cases. The sgACC again contained the largest fraction of labeled ACC neurons in both BA-injection cases, accounting for 54.3% in Monkey C and 52.7% in Monkey D. In contrast to the AcBA-injection cases, the pgACC contributed more strongly than the dACC in both BA-injection cases, accounting for 25.4 and 26.1%, compared with 20.3 and 21.2% for the dACC, respectively. The distribution of labeled neurons through the ACC subareas also significantly deviated from a uniform distribution in both animals (χ2 goodness-of-fit tests; Monkey C, χ2(2) = 1,233.46; p < 1.0 × 10−16; Monkey D, χ2(2) = 159.80; p < 1.0 × 10−16; Table S1). Unlike the AcBA-injection cases, only a few neurons were retrogradely labeled in the posterior and dorsal MFC (Fig. 2, Fig. S2). Substantially the same patterns of MFC labeling were obtained in the two cases, though the labeled neurons in the pgACC and sgACC were more frequently found in Monkey C where the injection volume of AAV2retro was twice as much as in Monkey D (Figs. 2, 4).

Differential layer distributions of ACC neurons projecting to the AcBA versus BA

Furthermore, we analyzed the layer distribution of ACC neurons projecting to the AcBA and BA. Unfolded maps prepared based on the ratio of superficial versus deep layers neurons (see Materials and Methods) revealed that ACC neurons retrogradely labeled from the AcBA were primarily distributed within the deep layers (i.e., layers V and VI) throughout the MFC (Figs. 3a,b, 4a,b, 5a), which is consistent with previous findings (Ghashghaei et al., 2007). In the AcBA-injection cases, the proportions of superficial-layer neurons in the pgACC, sgACC, and dACC were 39.4, 21.6, and 15.9% in Monkey A and 44.6, 20.7, and 17.0% in Monkey B, respectively. The superficial/deep laminar distribution differed significantly among the ACC subareas in both animals (χ2 tests of independence; Monkey A, χ2(2) = 153.10; p < 1.0 × 10−16; Monkey B, χ2(2) = 122.92; p < 1.0 × 10−16; Table S3). Pairwise comparisons further confirmed significant differences in the laminar distribution between the ACC subareas in both animals (Table S4).

Figure 3.
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Figure 3.

Unfolded maps of the MFC showing the layer distribution patterns of retrograde labeling from the AcBA in Monkeys A (a) and B (b) and from the BA in Monkeys C (c) and D (d). For each case, cell counts were performed at 1 mm intervals and normalized to the mean cell count. Each 1 mm bin is indicated with a bivariate colormap encoding superficial (layers II/III) versus deep (layers V/VI) layer densities: Bins enriched in superficial-layer labeling are denoted in green; those enriched in deep-layer labeling in magenta; those with high densities in both layers in white; and those with low densities in both layers in black. Deep, deep layers; Sup, superficial layers. Other abbreviations are as in Figure 4.

Figure 4.
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Figure 4.

Quantification of ACC neurons projecting from the superficial versus deep layers to the AcBA and BA. a, b, Number of labeled neurons projecting from the pgACC, sgACC, and dACC to the AcBA in Monkeys A (a) and B (b). c, d, Number of labeled neurons projecting from the pgACC, sgACC, and dACC to the BA in Monkeys C (c) and D (d). Green dots represent neurons projecting from the superficial layers, while magenta lines represent neurons projecting from the deep layers. The number of coronal sections used for cell counts is indicated in parentheses under each cortical area number.

Figure 5.
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Figure 5.

Fluorescent images showing the layer distribution patterns of retrograde labeling from the AcBA in Monkeys A and B (b) and from the BA in Monkeys C and D (c). a, Schematics indicating the locations of six regions within the ACC subareas in b and c. D, dorsal; L, lateral. b, c, Fluorescent photomicrographs taken from the pgACC (areas 32 and 24b), sgACC (areas 25 and 14c), and dACC (areas 24a and 24c). The layer assignment was cytoarchitectonically determined based on adjacent Nissl-stained sections. Note that the projections from the pgACC and dACC to the BA arise predominantly from the superficial layers (II/III), whereas the other projections arise mainly from the deep layers (V/VI).

In contrast, many neurons labeled from the BA were located within the superficial layers (i.e., layers II and III). Notably, the vast majority of labeled neurons in the pgACC (i.e., posterior area 32 and anterior area 24b) were observed in the superficial layers (Figs. 3c,d, 4c,d, 5b). In addition, at least part of the sgACC (i.e., areas 25 and 14c) and dACC (i.e., anterior areas 24a/b/c) often contained labeled neurons in the superficial layers (Figs. 3c,d, 4c,d, 5b). In the BA-injection cases, the proportions of superficial-layer neurons in the pgACC, sgACC, and dACC were 73.7, 42.2, and 63.1% in Monkey C and 72.3, 43.8, and 63.5% in Monkey D, respectively. Again, the superficial/deep laminar distribution differed significantly among the ACC subareas in both animals (χ2 tests of independence; Monkey C, χ2(2) = 471.88; p < 1.0 × 10−16; Monkey D, χ2(2) = 59.99; p < 1.0 × 10−13; Table S3).

Topographic distributions of ACC-derived terminals in the AcBA and BA

To confirm the data obtained from the retrograde labeling experiments, multicolor anterograde tract-tracing was carried out by using three AAV2.1 vectors expressing three different fluorescent proteins. In Monkey E, these vectors were injected separately into the pgACC, sgACC, and dACC (Fig. 6a,b). The pgACC injection was placed in area 32 and also in rostral area 24b and produced dense terminal labeling in the BA, especially in the BAi (Fig. 6, Fig. S3). Some labeled terminals were further detected in the BAmc and AcBAmc. The sgACC injection was placed in areas 25 and 14c, with slight diffusion into ventral area 32 and area 24a. After the sgACC injection, anterogradely labeled terminals were distributed widely in both the BA and the AcBA, with a particular predominance in the BAi and AcBAmc (Fig. 6, Fig. S3). Following the dACC injection covering anterior areas 24a/b/c, terminal labeling was seen sparsely but extensively over the BA (including both the BAmc and the BAi) and AcBA (including both the AcBAmc and the AcBApc; Fig. 6, Fig. S3).

Figure 6.
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Figure 6.

Anterograde labeling in the amygdala after AAV2.1 injections into the pgACC, sgACC, and dACC in Monkey E. (a), Low-power image of the injection sites of three vectors (AAV2.1-CaMKII-mClover, AAV2.1-CaMKII-mKO2, and AAV2.1-CaMKII-mRaspberry) in the pgACC (green), sgACC (orange), and dACC (magenta). (b), Representative coronal planes through the injection sites in the pgACC (green), sgACC (orange), and dACC (magenta) in conjunction with the Nissl-stained cytoarchitecture. Schematics on the lower right indicate the regions cropped from the corresponding coronal planes: i, pgACC; ii, sgACC; iii, dACC. D, dorsal; L, lateral. (c), Arranged anteroposteriorly are three representative coronal planes through the amygdala, separated by 1.0 mm intervals along the rostrocaudal axis, following the pgACC injection (top, i-i”), sgACC injection (middle, ii-ii”), and dACC injection (bottom, iii-iii”). Anterogradely labeled terminals from the pgACC, sgACC, and dACC are plotted in green, orange, and magenta, respectively. cc, corpus callosum. Other abbreviations are as in Figure 1.

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Discussion

In the present study, AAV vector systems permitting sensitive retrograde and anterograde neural tracing were employed to investigate precisely the architecture of ACC–amygdala projections in macaques. We have identified that the three distinct areas of the ACC, the pgACC, sgACC, and dACC, exhibit different projection patterns to the BA and AcBA (Fig. 7). As indicated by the results on our retrograde viral tracing, although the pgACC, sgACC, and dACC each possess widespread projections to the BA, projections to the AcBA arise mainly from the sgACC, with only sparse input originating in the pgACC. This projection pattern to the AcBA is consistent with the previous report (Ghashghaei et al., 2007), and our findings further support the existence of a novel projection from the pgACC to the BA, as also described in the prior work (Kelly et al., 2021). Because the BA has a limited extent along the anteroposterior and mediolateral axes, achieving a highly localized tracer injection to the BA is technically challenging. Consequently, many previous studies that targeted the BA with retrograde tracers likely failed to confine their injections to the BA and included some degree of leakage into the AcBA, potentially leading to an underestimation of pgACC-derived projections relative to those from the sgACC and dACC.

Figure 7.
View larger version:
Figure 7.

Schematic diagram showing the topographic and layer-specific projections from the ACC to the amygdala. Projections from the pgACC (a; green), sgACC (b; orange), and dACC (c; magenta) to the BA and AcBA. Superficial and deep-layer projections are depicted by dotted and solid lines, respectively. Varying arrow thickness represents the relative intensity of projections. All abbreviations are as in Figure 1.

These NHP data provide an important clue to considering the functional parcellation within the MFC and encourage reconsideration of a prevailing rodent model. In rodents, it has been shown that MFC subregions exhibit the target- and layer-dependent differences in their projections to the amygdala and other limbic and autonomic centers (Vertes, 2004; Gabbott et al., 2005). More recent works have also demonstrated that the prelimbic cortex (PL) projects primarily to the basolateral amygdala (Alemán-Andrade et al., 2022), whereas the infralimbic cortex (IL) projects mainly to the basomedial amygdala (Adhikari et al., 2015; Alemán-Andrade et al., 2022). Based on such an anatomical framework, several early studies in rodents proposed a relatively simple functional dissociation within the MFC, in which the PL facilitates fear expression whereas the IL promotes the consolidation and recall of fear extinction (Milad and Quirk, 2002; Heidbreder and Groenewegen, 2003; Vidal-Gonzalez et al., 2006; Quirk and Mueller, 2008; Sierra-Mercado et al., 2011). However, accumulated evidence to date suggests that this dichotomy may be simplistic. Notably, neuronal activity in both the PL and the IL increases during the recall of fear extinction, i.e., the memory of safety (Holmes et al., 2012; J-H. Cho et al., 2013), indicating that these regions may contribute jointly, and in a task-dependent manner, to extinction-related processes rather than supporting strictly opposing functions. These complexities become even more apparent when considering findings from human studies. Structural and functional imaging studies in individuals with major depressive disorder consistently report gray matter reduction and hyperactivity in the sgACC (Drevets et al., 1997, 2008; Mayberg et al., 1999). fMRI works have further demonstrated dissociable contributions of the pgACC and sgACC to affective processing (Fujiwara et al., 2009; Etkin et al., 2011): the pgACC is implicated in fear extinction and stress resilience (Milad et al., 2007; Ressler, 2010; Kalisch et al., 2024), whereas the sgACC is associated with negative affective processing and depressive symptomatology (Vogt et al., 2005; Etkin et al., 2011; Schmaal et al., 2017; Brandl et al., 2022). These observations in humans do not clearly align with the functional distinctions proposed in early rodent studies, resulting in a fragmented and often inconsistent understanding of how MFC–amygdala circuits contribute to affective regulation across species. However, human neuroimaging studies alone cannot establish the circuit-level mechanisms underlying these affective functions. The lack of cross-species correspondence highlights the need for studies in NHPs whose MFC–amygdala organization more closely resembles that of humans. Recent causal manipulations in NHPs have begun to address this gap. We have recently reported that low-frequency rTMS targeting the monkey MFC, which was centered on the pgACC and sgACC, induces depressive-like phenotypes (Nakamura et al., 2022). Complementing this approach, deep brain stimulation near the subcallosal ACC has likewise produced large-scale network alterations in macaques (Fujimoto et al., 2026), reinforcing the value of primate models for probing the causal mechanisms of affective dysfunction. NHP models of mood disorders often recapitulate key clinical features observed in humans, underscoring their value for bridging mechanistic gaps that remain unresolved in rodent studies. Consequently, detailed investigations of limbic circuit architecture in monkeys are increasingly needed to advance our understanding of the neural mechanisms underlying psychiatric disorders, which remain largely enigmatic.

Importantly, our cortical laminar distribution analysis has revealed a marked contrast between the ACC projections to the BA and the AcBA: whereas the inputs to the AcBA arise almost exclusively from the deep layers, those to the BA exhibit the substantial involvement of the superficial layers (Figs. 3–5, 7). Notably, among the ACC subareas, only the pgACC showed a predominance of the superficial-layer projections over the deep-layer projections in its input to the BA. Cortical outputs to subcortical structures typically arise from neurons in the deep layers, whereas superficial-layer neurons primarily contribute to corticocortical pathways (Felleman and Van Essen, 1991). Thus, superficial-layer projections to subcortical targets are considered highly exceptional. Interestingly, however, similar superficial-layer projections from the MFC to the amygdala have also been reported in rodents (Gabbott et al., 2005; Little and Carter, 2013; Ferreira et al., 2015), suggesting that this laminar feature may represent a conserved but underappreciated aspect of the MFC–amygdala connectivity across species. The ACC is further notable for exhibiting marked developmental transitions in cellular and circuit organization. In rodents, microglial morphology and activity in the MFC change dynamically throughout the developmental stages (Hui et al., 2019; Komori et al., 2024). Consistent with this, our previous work has shown coordinated activation of astrocytes, microglia, and oligodendrocyte-lineage cells in the macaque ACC during late infancy and puberty (Yamanaka et al., 2022). Moreover, it has recently been demonstrated that some developmental changes occur in the functional impact of MFC projections to the basolateral amygdala in rodents (Klune et al., 2025). In addition, transcriptomic analyses in primates have revealed substantial shifts of gene expression in the ACC during adolescence (Law et al., 2009; Shimogori et al., 2024). Together, these findings raise the possibility that the superficial-layer ACC projections to the BA emerge as part of broader developmental reorganization of the limbic-related circuitry. Although the precise function of the pgACC superficial-layer projections to the amygdala remains to be determined, the ACC receives strong cognitive inputs from the prefrontal cortical areas (Vogt and Pandya, 1987; Arikuni et al., 1994; Morecraft et al., 2012). One speculative possibility is that superficial-layer pgACC neurons contribute to the maturation of cognitive control over emotion (including resilience) by providing a developmentally refined route through which higher-order cognitive signals can modulate information processing in the amygdala. A recent theoretical work also highlights the contribution of the ACC to predictive coding integrating cognitive and emotional information, suggesting that the ACC–amygdala pathway may support predictions that guide affective responses (Ito et al., 2025). In this context, the superficial-layer-derived pathway may be responsible for more rapid or efficient transfer of cognitive information from the prefrontal cortex to the amygdala, thereby subserving the effective integration of cognitive and affective processes in the mature brain.

To further advance our understanding of the ACC–amygdala circuitry, pathway-selective causal manipulations should be called for. In particular, separately manipulating the activity of amygdala-projecting neurons arising from the superficial versus deep layers of the ACC subareas will help to dissociate their specific functions. Prior studies on the monkey ACC indicate its extensive roles not only in affective responses but also in decision-making and social cognition (Kennerley et al., 2006; Rushworth et al., 2007; Fujimoto and Takahashi, 2016; Monosov et al., 2020; Fujimoto et al., 2021; Murray and Fellows, 2022; Simon IV and Rich, 2024). Therefore, layer-selective manipulations of the ACC–amygdala pathways in macaques by means of optogenetic/chemogenetic techniques may enable us to define individual pathways critical for these cognitive processes (Myers et al., 2025). Considering the developmental changes highlighted above, future works should take into account the possibility that these pathways undergo substantial reorganization during postnatal maturation. Developmentally informed circuit analyses, including comparisons of the laminar origins, synaptic targets, and functional roles of the ACC–amygdala projections throughout the juvenile, adolescent, and adult stages, will be essential for determining whether the superficial- and deep-layer pathways acquire differential functions as the ACC matures. In addition to layer- and pathway-level distinctions, the ACC itself exhibits pronounced neurochemical heterogeneity across its subareas. Multireceptor autoradiography in the human cingulate cortex has demonstrated that the ACC subareas such as the pgACC and sgACC differ markedly in their receptor fingerprints, including AMPA, kainate, GABAB, and 5-HT1A receptor densities (Palomero-Gallagher et al., 2009). These findings suggest that the ACC subareas are configured with distinct neurochemical architectures that may interact with their layer-specific projection patterns to shape affective and cognitive functions. Incorporating such receptor-level information into future circuit analyses will be important for understanding how developmental or pathological perturbations selectively impact the ACC–amygdala pathways. Parsing the ACC circuitry at this finer spatial and temporal granularity may ultimately provide critical insights into the mechanisms by which developmental perturbations contribute to mood disorders and may help to guide the design of targeted interventions against the ACC-related dysfunctions.

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Data Availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the supporting information. No custom code was used in this study.

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Footnotes

  • This work was supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT)/Japan Society for the Promotion of Science (JSPS) KAKENHI under grant numbers 24K23229, 25K18578, 26K00009 (to K.K.), 22H04922 (AdAMS; to K.K. and K-i.I.), 22H05157 (to K-i.I.), 24K02338 (to M.T.), 24223004, 26112009, 26560455, 17H01014, 19H05725, 20H00104, 23H00073, and 24H00700 (to K-I.T.); the Moonshot Research and Development Program (Goal 9) of the Japan Science and Technology Agency (JST) under grant numbers JPMJMS2295-12 (to K-i.I.) and JPMJMS2292 (to K-I.T.); JST K Program Grant Number JPMJKP25Y8 (to K-I.T.); Suzuken Memorial Foundation under grant number 24-069 (to K.K.). We are grateful to M. Fujiwara, M. Nakano, E. Sumiya, E. Tanaka, W. Lu, X. Zhao, H. Minegishi, and T. Moritani for their technical assistance. Generative AI and AI-assisted technologies were used in the writing process for correcting and improving upon grammatical and spelling errors to improve readability. The authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

  • ↵*K.K., Y.S., and R.Y. are co-first authors.

  • The authors declare no competing financial interests.

  • This paper contains supplemental material available at: https://doi.org/10.1523/JNEUROSCI.0671-26.2026

  • Received April 22, 2026.
  • Revision received July 15, 2026.
  • Accepted July 31, 2026.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

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  1. Published online before print 14 August 2026, doi: 10.1523/JNEUROSCI.0671-26.2026 The Journal of Neuroscience, 46(37): e0671262026
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