The neural circuits centered on the amygdala and temporal lobe on the one hand and the prefrontal cortex on the other are crucial in a variety of complex behaviors, including reward-based learning and decision-making (De Martino et al., 2006; Hunt and Hayden, 2017; Morrison and Salzman, 2010; Murray and Wise, 2010; Rudebeck and Murray, 2014; Rushworth et al., 2011), and emotional and social behavior (Klüver and Bucy, 1937; Weiskrantz, 1956; Noonan et al., 2014; Volman et al., 2011; Whalen et al., 1998). Recent models increasingly emphasize how the amygdala, a complex subcortical area located in the medial bank of the anterior temporal lobe, and the prefrontal cortex (PFC) do not support isolated computations but instead have complementary roles in these processes (Munuera et al., 2018; Saez et al., 2015; Murray and Wise, 2010). Similar computational divisions of labor have been proposed for the anterior temporal association cortex and the prefrontal cortex (Freedman et al., 2003).

Understanding these computational roles requires an understanding of the anatomy of the brain systems that are involved (Marr, 1982). Specifically, we want to understand the principles of connectivity between amygdala/anterior temporal systems and prefrontal systems, in order to better comprehend how information can flow between the various nodes of these networks. Such principles, for instance, are evident in early models by Carmichael and Price (1995) that argue for a mostly dichotomous organization of circuits stretching across temporal and frontal lobes. This line of work, however, has mostly been based on invasive tracer studies that measure area-to-area connections with high spatial accuracy. These approaches are not available for in-vivo human studies. Recent work on diffusion MRI tractography, the only available method to establish structural connectivity in-vivo, has demonstrated that using this approach to reconstruct area-to-area connections has many pitfalls (Maier-Hein et al., 2017; Reveley et al., 2017). A more feasible strategy is to reconstruct fiber bundles (Mars et al., 2016a; Thiebaut de Schotten et al., 2011). By seeding in the reliably identifiable white matter, it becomes possible to isolate the core of fiber bundles and then follow their trajectory towards the gray matter in both directions. Although this technique has the potential to elucidate principles of connectivity between systems, it relies on a different philosophy than the invasive tracer studies, making it difficult to draw firm comparative conclusion between results obtained using the two techniques. Therefore, we here apply this technique first in the macaque monkey, for which tracer results are available and thus comparison of the results of the two techniques is possible, and then to the human.

White matter dissections and tract-tracing studies in non-human primates have shown that anterior temporal regions, including amygdala, and the frontal lobe have widespread reciprocal connections (Aggleton et al., 2015; Barbas et al., 2011; Barbas and De Olmos, 1990; Ghashghaei et al., 2007; Ghashghaei and Barbas, 2002; Price, 2003; Timbie and Barbas, 2014). Two primary pathways are known to connect anterior temporal regions including the amygdala and the frontal lobe: a major association fiber pathway, the uncinate fascicle (UF) (Amaral and Price, 1984; Déjerine, 1895; Klingler and Gloor, 1960; Kuypers et al., 1965; Nauta, 1961; Thiebaut de Schotten et al., 2012; Yeterian et al., 2012), and a limbic-cortical ventral amygdalofugal pathway (from here on also referred simply to as the amygdalofugal pathway, AmF) (Aggleton et al., 1980; Bachevalier et al., 1985; Lehman et al., 2011; Nauta, 1961; Nieuwenhuys et al., 2008; Russchen et al., 1985). To date, the AmF has not been described in great detail in the primate brain in-vivo (Carmichael and Price, 1995; Croxson et al., 2005) and is often omitted from human studies of circuits for social-emotional behavior and decision making (Alm et al., 2015; Ameis and Catani, 2015), even though its role in cognitive and emotional processes in animals is critical (Bachevalier et al., 1985; de Guglielmo et al., 2019). Therefore, we focus our investigation on the translatability of knowledge of macaque amygdala/anterior temporal-prefrontal fiber bundles to the human brain along these two tracts.

We used high-resolution diffusion MRI data in the macaque to ascertain the degree to which the diffusion MRI tractography approach identified aspects of amygdala/anterior temporal-prefrontal connectivity known from previous macaque studies. This demonstrated that the core bundles of the AmF perforate the porous substantia innominata in the basal forebrain. In this inhomogeneous tissue, the fractional anisotropy index is not fully informative of tract integrity. This could lead to false negatives in deterministic tractography, but the probabilistic tractography approach that we employ here demonstrates a distribution of streamlines running through this area. Accordingly, here we have adopted a probabilistic approach to tractography that is strongly informed by prior anatomical knowledge to reconstruct this tract in a manner similar to that used in previous tract tracing studies.

Using this approach, we were able to robustlydefine and to dissociate a medial amygdalofugal pathway and an orbital uncinate tract in both the macaque and human brain. Together, these pathways form a larger constellation of amygdala/anterior temporal-prefrontal circuits, but each with a distinct connectional profile interfacing with a unique set of brain regions. While the amygdalofugal pathway predominantly ran in the white matter exntending between the amygdala, nucleus accumbens, and subgenual cingulate, ventromedial, and frontopolar regions in ventral PFC, the uncinate pathway primarily coursed in the white matter adjacent to anterior temporal regions, including the amygdala, lateral orbital areas and frontopolar cortex. The relationship and structure of these tracts is preserved across primate species, supporting the translation of insights from non-human primate anatomy to inform our understanding of the human brain.

Our goal was to reconstruct the anatomical organization of the amgydalofugal and uncinate bundles in white matter (WM) within amygdala/anterior temporal regions (ATR) and prefrontal territory. In particular, we were interested in assessing the course of these pathways towards the prefrontal cortex and their interaction with a wide range of prefrontal regions of interest. Given the difficulty of reconstructing these pathways, we employed the following strategy. First, we reconstructed the tracts in the macaque monkey brain, so that they can be compared to known tracer results (Figure 1A–C, left and middle panels). This demonstrated whether our tractography techniques are valid for these pathways. Then, we applied the same tracking strategy to the human, using the seeding and masking procedures applied successfully in the macaque (Figure 1A–C, right panels). Finally, we compared the tracts’ projections directly, across species and with respect to a number of control tracts.

Figure 1

Download asset Open asset

Amygdalofugal pathway in the macaque monkey brain

The frontal body of the AmF was represented by a thin bundle of fibers running in the WM adjacent to medial subcortical and cortical brain structures (Figure 2A–E and Figure 2—figure supplement 1A–C). In a posterior section of the macaque brain, fibers running in the medially projecting limb of the AmF pathway coursed in the WM dorsal to the amygdala, lying between the amygdala and the medial longitudinal fissure at the level of the posterior anterior commissure (AC) and the sublenticular extended amygdala area (SLEA) (Figure 1A–B, Figure 2A and Figure 2—figure supplement 1A–B). A first bundle of AmF fibers ran in the WM territory in between the nucleus basalis of Meynert, the lentiform nucleus, the ventral pallidum, and the piriform cortex (Figure 2—figure supplement 1A–B). The medial AmF fibers are known to separate into different sets of projections (Lehman et al., 2011; Nauta, 1961; Novotny, 1977; Oler et al., 2017) that extended ventrally to innervate the anterior hypothalamic areas at the level of the paraventricular-supraoptic nuclei and dorsally to connect the amygdala with the thalamus, the BNST, and the septum. All of these fibers in our data visibly left the seed location, but the frontal waypoint mask means that the pathways to the prefrontal cortex, which was the focus of the current investigation, were the most prominent (Figure 2A and Figure 2—figure supplement 1A–B). A second bundle of the AmF ran in the WM surrounding the substantia innominata, ventral pallidum, and ventral bank of the AC, and more rostrally in the region ventral to the ventral striatum/nucleus accumbens (VS/NAc; Figure 1C and Figure 2A–E). From tract tracing, we know that some fibers connecting ventral PFC and amygdala are intermingled with the much more prominent anterior commissure (Lehman et al., 2011). The AC is very prominent in the principal fiber direction of diffusion MRI data, but nevertheless some weak AmF fibers overlapping with AC were evident in our tractography data (Figure 1B, Figure 2 and Figure 2—figure supplement 1B).

Figure 2 with 1 supplement see all

Download asset Open asset

It must be noted that over its course in the basal forebrain and ventral to the striatum, the AmF is likely to interdigitate with other connectional systems, including the rostral fibers of the thalamic projections, the medial forebrain bundle, the diagonal band of Broca and the dopaminergic fibers connecting the ventral tegmental area with prefrontal cortex. Eventually, the anterior fibers of the AmF extended into the orbital periallocortex on the medial wall of the hemisphere and into the PFC by coursing in WM adjacent to the subgenual anterior cingulate cortex (sACC or Brodmann area 25; Figure 2A,B,D and Figure 2—figure supplement 1C).

Near the sACC, the AmF split again into two sub-sections. The first sub-section ran dorsally alongside the cingulum bundle (CB) in anterior cingulate sulcus and perigenual WM. The second branch instead stretched between the sACC and the frontopolar cortex, passing subjacent to medial orbitofrontal cortex (OFC) between the gyrus rectus and the medial orbital gyrus (Figure 2B and Figure 2D). Because diffusion MRI cannot discern between monosynaptic and polysynaptic connections, the identified fibers could represent either type of projections and could run in either direction.

Comparison of the two fiber bundles

The main bodies of the human AmF and UF were anatomically segregated and constituted two anatomically distinct connectional systems in ATR. Alongside the dorsolateral amygdala, AmF interdigitated with more lateral temporo-frontal UF axons. At this level, it was possible to identify in both species a medial-lateral organization of the two bundles, with the AmF leaving the amygdalar WM more caudally and the UF fibers projecting to and from this subcortical structure in a more anterior position (Figure 2).

More anteriorly, the two tracts maintained a clear and evolutionarily preserved organization along a medial-lateral gradient, but also started to develop a dorsal-ventral relationship (Figure 2A–E). As previously described, in this region the AmF coursed medial to the ventral striatum, just underneath the anterior limb of the internal capsule and medial to the ventromedial striatum, specifically inferiorly to the internal and external globus pallidus, the ventral head of the caudate and the medial bank of the putamen (Figure 2A–E and Figure 2—figure supplement 1A–B). Conversely, the UF approached the basal ganglia more laterally. A medial portion of UF fibers interdigitated with lateral AmF axons and courses ventral to the AmF in the WM surrounding the putamen. In contrast to the more ventromedially positioned course, UF fibers run alongside the full ventrolateral extent of the basal ganglia. In the WM lateral to the putamen and ventral to the claustrum, UF fibers interlaced with the ventral axons of the EmC (Figure 2A–E and Figure 2—figure supplement 1E–F).

The relative mediolateral positioning of the AmF and UF extended more rostrally within the WM adjacent to the sACC (Figure 2A and Figure 2D–E). A major component of the AmF lay adjacent to sACC (Figure 2—figure supplement 1C), whereas only a medial bundle from the UF lay adjacent to sACC (Figure 2A and Figure 2—figure supplement 1F). The relative positioning of the two pathways then began to rotate partially, with the UF moving to a location ventral to the AmF as the anatomical distance between the two fiber bundles reduced in the subgenual WM. In the WM adjacent to infralimbic cortex, the segregation of the two tracts began to fade as the dorsomedial UF fibers approached the ventrolateral AmF axons (Figure 2A,F and G).

Although the AmF and UF were clearly distinct entities in more posterior parts of the brain, their clear separation disappeared in the posterior OFC. Here, the UF and AmF merged into a single connectional system. This anatomical organization was present and conserved across both human and macaques in relation to several homologous landmarks in both species (Figure 2B–E; Figure 2—figure supplement 1C and F).

These results describe a preserved dichotomous organization of the AmF and UF along a predominantly medial-lateral axis in both species. As described in the introduction, the choice of these two fiber bundles was guided by tract-tracing results that suggested their connectivity of prefrontal regions with predominantly ATR fibers. As argued, tractography is not suitable for an investigation of full area-to-area connectivity, so instead we sought to investigate the contributions of connectivity with these gray matter areas by means of a connectivity-gradient analysis. Specifically, we were interested to see whether we could observe a similar medial-lateral gradient of connectivity within the fiber bundles, driven by amygdalar and anterior temporal polar connectivity. In different analyses, we quantified the relative probabilities that streamlines coursing through the core of the AmF and UF originating in the amygdala or in the temporal pole (see Materials and methods). The resultant connectivity-gradient ratio is represented in Figure 3, separately for AmF (left panel) and for UF (right panel). These analyses showed that a strong medial-lateral organization in the human brain is present not only across but also within tracts. This is particularly evident for the UF that contained streamlines originating from both the anterior temporal lobe and the amygdala, but with clear prevalence in the lateral and medial directions, respectively. A similar pattern is visible in the AmF, although it contained only very few streamlines originating in the temporal pole.

Figure 3

Download asset Open asset

Prefrontal connectivity and control tracts

We have described the distinct courses of the AmF and UF between the vicinity of the ATR and the prefrontal cortex. To quantify the streamlines of the tracts to prefrontal areas, we can describe their connectivity fingerprints with prefrontal regions of interest (Mars et al., 2018). This allows a quantitative comparison of the areal connectivity of AmF and UF. To examine the specificity of the results, we also compared the connectivity fingerprints of AmF and UF to those of three control tracts connecting nearby systems: the anterior limb of the internal capsule (ICa), the extreme capsule (EmC), and the cingulum bundle (CB) (Figure 4).

Figure 4 with 2 supplements see all

Download asset Open asset

Comparing the connectivity fingerprints of AmF and UF, a medial-lateral organization in the density of streamlines between the two tracts was evident in both species (Figure 4A–B). Although both bundles primarily reached ventral prefrontal cortex in both species, AmF was more likely to reach medial areas such as 25, 14 m, 11 and 10 m (Figure 4A), whereas UF primarily reached more lateral areas such as the inferior frontal gyrus, insular cortex, frontal operculum, area 47 (or the macaque homolog 47/12), and area 45 (Figure 4B). However, UF fibers in the rostral ventral PFC were also in contact with more medial OFC (area 11) and FPC (10 m). Some species-specific differences were also evident. Human AmF fibers were denser in areas 11 and 10 m, whereas macaque AmF showed this pattern in area 32 and caudal vmPFC area 14 m (Figure 4A). Inter-species differences in cingulate fibers may partly be the result of species-specific idiosyncrasies in neuroanatomical geometries, as can be seen especially when diffusion MRI is used to reconstruct tracts running in the WM adjacent to brain structures presenting different degrees of curvatures in different species. UF also exhibited some contrasting inter-species differences. In the human brain, the numbers of streamlines reaching lateral orbital, ventrolateral, insular, and opercular areas were higher than the corresponding numbers in the macaque brain, suggesting a more widespread pattern of connectivity in human prefrontal cortex (Figure 4B). This may reflect prefrontal cortical expansion in humans compared to that in other Old World primates (Smaers et al., 2017). As discussed above, in the most anterior part of the prefrontal cortex, AmF and UF tend to intermingle, and it is therefore not surprising to see that both reached the frontal pole (area 10 m), including the human lateral frontal pole (area 10 l) for which the homology in the macaque monkey brain is debated (Neubert et al., 2015).

The connectivity fingerprints of the control tracts were markedly distinct from those of both AmF and UF (Figure 4C–E). The anterior limb of the internal capsule carries fibers originating and terminating in the thalamus and brainstem (Lehman et al., 2011). Frontally, its streamlines predominantly reached more dorsomedial regions than AmF and UF, including areas 32, 8 m, and 9 m (Figure 4C). The connectivity pattern of EmC was even more widespread in relation to areas of lateral and dorsal prefrontal cortex, including areas 45, 8 m, 9 m, and 32 (Figure 4D). The cingulum bundle showed high streamline count in medial areas (Figure 4E). Interestingly, similarly to the UF, some branches of these control tracts exhibited a more widespread pattern of connectivity in the human brain compared to the macaque brain. Indeed, human EmC streamline counts were higher in more caudal areas of dorsal PFC such as the preSMA (Figure 4D). CB fibers, instead, showed a trend in frontopolar, orbital, subgenual and midcingulate cortex that was similar to that in the macaque (Figure 4E).

The connectivity fingerprints can be used to compare the connectivity profiles across species quantitatively. We can create a dissimilarity matrix between species by calculating the Manhattan distance for each possible pair of tracts. This measure indicates how different two connectivity fingerprints are: the best matching pairs are defined as the set of fingerprints that have the smallest distance values (Mars et al., 2016b). When comparing the fingerprints (averaged across the two hemispheres) between species, we saw that the smallest differences for each tract were on the diagonal of the distance matrix (Figure 5B), indicating that each tract had a connectivity fingerprint most similar to its counterpart in the other species. AmF and UF are each other’s second best choice, but matched best with their counterpart. This similarity is not unexpected given the close proximity in ventral PFC and considering the strong interconnection and reciprocal horizontal projections of the OFC regions. Yet despite these correspondences, the dissimilarity matrix confirms that the two pathways are distinguishable.

Figure 5 with 1 supplement see all

Download asset Open asset

These results suggest that the AmF and UF streamlines reach different parts of prefrontal cortex and, moreover, that their connectivity profiles are distinct from those of nearby fiber bundles. This dissimilarity with respect to the control tracts was also reflected in their course through the white matter (Figure 5A). To provide a more complete picture of the organization of AmF and UF in the human brain, a clear topographic organization of these two fiber bundles relative to the three tracts described above in the human brain was observed. Indeed, the course of both tracts could be dissociated from ICa, EmC and CB but also with respect to two other tracts, namely the stria terminalis (ST) and fornix (Fx), that lie and course in temporal portions of WM near to AmF and UF (Figure 5A). Because prefrontally ST and Fx primarily innervate basal forebrain and limbic regions and have poor or absent projections to more rostral PFC areas (Aggleton, 2008; Catani et al., 2002; Kitt et al., 1987; Oler et al., 2017), with only some ST fibers reaching the posterior vmPFC (Lehman et al., 2011), these tracts were not included in the dissimilarity matrix analysis (Figure 5B).

In this study we use diffusion MRI tractography in primates to identify and define two closely related but distinct connectional systems linking ATR and PFC. The structure, course, and projections of these tracts in the macaque monkey closely match those observed in previous tracer- injection studies (Fudge et al., 2002; Fudge and Haber, 2001; Kunishio and Haber, 1994; Lehman et al., 2011). Moreover, we showed that the connectional profiles of these two tracts are preserved across humans and monkeys. We demonstrated that both the AmF and UF run separately and in parallel when exiting the ATR but then merge to form a single fiber complex in the ventral PFC.

Tract tracing studies provide important information about where specific axons in fiber bundles terminate or originate in the brain (Petrides and Pandya, 2007). However, the technique is invasive and requires the sacrifice of the subject, limiting its use in human anatomy. Diffusion MRI tractography excels when tracing in homogenous white matter and can reconstruct many of the major white matter tracts. However, it can fail in areas in which fiber density is low or where different fibers cross. These properties are exhibited by the porous heterogenous tissue in the basal forebrain that the amygdalofugal pathway traces through, as evidenced by invasive tracer injection and histology studies. It would be especially difficult to track in these circumstances using algorithms that do not utilize the full probability distribution of the fiber directions. Perhaps because of these difficulties, previous studies using tractography to investigate the limbic system might have overlooked or undervalued the prefrontal projections of the AmF (Catani et al., 2013; Croxson et al., 2005; Kamali et al., 2016). To overcome these challenges, we employed a strategy of first performing probabilistic tractography based on crossing-fiber resolved diffusion models in the macaque monkey to develop a protocol that was able to reconstruct the AmF fibers as known from previous invasive tracer studies in that species (Oler et al., 2017). This strategy was successful. We were able to reconstruct the course of the tract and confirmed the AmF as primarily a subcortical limbic tract, but with prefrontal fibers mostly extending along medial areas such as 25 and 14 m. This pattern was similar in the human brain.

The division between AmF and UF bundles maps onto the tracer-based division of the ventral PFC into a medial region and a more lateral/orbital region, as originally suggested by Price and colleagues (Carmichael and Price, 1995; Ongür et al., 2003). Following the logic that the connections of an area are a crucial determinant of its function, this suggested a different functional role of these subdivisions with the medial network strongly connected with visceromotor areas and the lateral orbital network interconnected with multi-modal sensory inputs (Ongür and Price, 2000). This was confirmed by dissociations apparent from lesion studies with medial frontal lesions showing an impairment in the capacity to maintain sustained arousal when anticipating rewards (Rudebeck et al., 2014), but temporal and ventral lateral frontal lesions leading to an impairment in learning arbitrary associations between stimuli and outcomes or stimuli and actions (Browning and Gaffan, 2008; Eacott and Gaffan, 1992; Gutnikov et al., 1997). Our results show divergences across AmF and UF in ventral PFC that supports this dissociation and demonstrate that it generalizes to the human brain. Fibers in AmF and UF may constitute the primary white matter scaffolding from which these two networks may arise.

The importance of understanding a dissociation between AmF and UF may be further underlined by considering the directionality of influence of these two bundles as described by more invasive investigations: the UF is a bidirectional tract with many projections from prefrontal cortex terminating in medial and lateral basal nuclei of the amygdala, whereas the AmF predominantly carries efferent projections from the amygdala to subcortical and prefrontal targets (Bickart et al., 2012). Potentially, this dissociation can help us to understand whether abnormal neural processes that are central to certain psychiatric disorders are more related to efferent or afferent projections of the amygdala (Bramson et al., 2019; Murray and Rudebeck, 2018; Volman et al., 2013). Unfortunately, one of the limitations of probabilistic tractography is that it cannot infer the directionality of the reconstructed bundles and is unable to differentiate monosynaptic from polysynaptic connections. For this reason, the combination of tracer and imaging studies to translate results from macaque to human brains is an essential approach that allows us to better understand human neuroanatomy in a non-invasive fashion.

It is important to note, however, that the dissociation between the medial and lateral circuits is not complete, with the circuits joining up in anterior OFC and rostral prefrontal cortex. An integration between the two processing streams has been suggested both on anatomical and functional grounds. Anatomically, the more rostral parts of the prefrontal cortex are suggested to be more interconnected (Carmichael and Price, 1995). Functionally, such an integration would subserve learning and decision making computations by allowing the integration of learned stimulus-outcome contingencies with the internal state of the organism. This is evident in the proposal of a ‘common currency’ representation comprising the value of different objects irrespective of the physical properties (Montague and Berns, 2002; Padoa-Schioppa and Assad, 2008). The rostral interactions of AmF and UF in areas 11, 13 and 14 m may therefore represent anatomical support for such computations.

It is important to point out that our use of diffusion-weighted MRI tractography necessitated some different protocol choices than would be made for a conventional tract tracing study. A number of recent studies have criticized tractography for its perceived lack of reliability in areal-to-areal connections (Reveley et al., 2017; Donahue et al., 2016). Therefore, rather than replicating the tracer approach of seeding in circumscribed areas and assessing which gray matter regions are reached, we choose to seed in the core of the white matter bundles. Here, the tractography algorithm is less impaired and not affected by gyral bias or superficial white matter. Only in follow-up analyses did we investigate whether tractography seeded in these bundles reached gray matter targets or whether gray-matter-seeded tractography reaches a certain bundle. By placing the bundle at the core of the analyses, our results become more reliable. This approach also allows us to create more easily tractography recipes for the fiber bundles of interest that can then be shared with other researchers (e.g., Bramson et al., 2019).

The importance of considering neural circuitry, extending beyond individual brain regions, is increasingly evident when studying the contributions of ventral PFC in emotion, learning, and decision making. Combining controlled focal lesion and transient perturbation studies with whole-brain neuroimaging highlights the critical role of remote connections to the impact of local perturbations (Folloni et al., 2019; Froudist-Walsh et al., 2018; Verhagen et al., 2019). Moreover, it has recently been demonstrated that some lesion effects might be better explained by damage to connections as opposed to damage to single regions (Rudebeck et al., 2013; Thiebaut de Schotten and Foulon, 2018), and differences in connectivity have already been argued to play a crucial role in regional specialization in other brain areas (Krubitzer, 2007). Given the inverse problem in which changes in brain activity can have multiple causes (O'Reilly et al., 2013), an understanding of connectional anatomy is essential to understand these network effects. As such, this work can help us to move from a view of ventral PFC as a constellation of isolated, ‘functionally specialized’ regions to a more network-based approach, thereby helping us to describe the underlying neurobiology of psychiatric disorders more appropriately and to promote treatment options more effectively.

Conventionally, surgical interventions, including deep brain stimulation for depression, addiction, and obsessive-compulsive disorders were primarily targeted at gray matter tissue. For instance, aspects of area 25 anatomy are associated with the presence of psychiatric disorders (Drevets et al., 1997; Murray et al., 2011). Indeed, chronic deep brain stimulation in this area improves chronic dysphoric conditions (Mayberg et al., 2005). However, with evidence accumulating that the most effective electrodes in deep brain stimulation are situated in or close to white matter fiber tracts, rather than in gray matter proper, recent deep brain stimulation approaches are explicitly targeting white matter fiber structures (Baldermann et al., 2019; Lozano et al., 2019). Area 25 is innervated and visited by the nearby AmF, suggesting the hypothesis that the physiological effects of effective electrical stimulation of this region (Johansen-Berg et al., 2008) may be ascribed to the AmF pathway and the regions it interconnects.

For this study, we used high- quality diffusion-weighted MRI data, obtained by scanning ex-vivo macaque samples for multiple hours, and by using in-vivo human diffusion MRI data with an acquisition duration of one hour. Both of these datasets relied on unconventionally strong diffusion gradients, high number of diffusion directions, and high spatial resolution. However, the tractography recipes developed as part of this study have already been shown to be applicable and successful in human diffusion MRI data of more conventional quality. In fact, the approach and results achieved here, including the division between these two amygdala-prefrontal tracts, are behaviorally relevant in the context of social-emotional actions, advancing our understanding of the neurobiology of emotion regulation (Bramson et al., 2019).

To conclude, two principles underlie the organization of amygdala–PFC connectivity in the human brain. They are both evolutionarily conserved and shared with the macaque monkey. First a ‘connectional segregation principle’ means that AmF and UF occupy a relative mediolateral position with respect to one another. Second, a ‘connectional integration principle’ means that the tracts merge together as they enter and progress through the WM in the PFC to create an intricate network of projections from both cortical and subcortical regions. The two connectional systems described here may support distinct but synergic brain functions in PFC and this hypothesis should be investigated in future to understand how regions in the temporal lobe and PFC interact during affective, social, and cognitive tasks, how they could be disturbed in psychiatric disorders, and how this can be alleviated using targeted brain-stimulation approaches.

The tractograms created as part of this study will be made available from the lab's website (www.neuroecologylab.org) and the Wellcome Centre for Integrative Neuroimaging Git (https://git.fmrib.ox.ac.uk/davidef/amygtemp_pfc), as will tractography recipes to identify the described tracts in other datasets. Original human data are available from the Human Connectome Project (www.humanconnectome.org). Raw and preprocessed human data from the Q2 and other public data releases are available from the Human Connectome Project (https://www.humanconnectome.org/study/hcp-young-adult/data-releases/). Macaque data is made available through the PRIMatE Data Exchange (PRIME-DE; http://fcon_1000.projects.nitrc.org/indi/indiPRIME.html).

1. 1. Davide Folloni
   2. Jerome Sallet
   3. Alexandre A Khrapitchev
   4. Nicola Sibson
   5. Lennart Verhagen
   6. Rogier B Mars

   (2019) PRIMatE Data Exchange

   Oxford WIN macaque PM.

   http://fcon_1000.projects.nitrc.org/indi/PRIME/oxford2.html

1. 1. Aggleton JP
   2. Burton MJ
   3. Passingham RE

   (1980) Cortical and subcortical afferents to the amygdala of the rhesus monkey (Macaca mulatta)

   Brain Research 190:347–368.

   https://doi.org/10.1016/0006-8993(80)90279-6

   • PubMed
   • Google Scholar
2. 1. Aggleton JP

   (2008) EPS Mid-Career award 2006: understanding anterograde amnesia: disconnections and hidden lesions

   Quarterly Journal of Experimental Psychology 61:1441–1471.

   https://doi.org/10.1080/17470210802215335

   • Google Scholar
3. 1. Aggleton JP
   2. Wright NF
   3. Rosene DL
   4. Saunders RC

   (2015) Complementary patterns of direct amygdala and hippocampal projections to the macaque prefrontal cortex

   Cerebral Cortex 25:4351–4373.

   https://doi.org/10.1093/cercor/bhv019

   • Google Scholar
4. 1. Alm KH
   2. Rolheiser T
   3. Mohamed FB
   4. Olson IR

   (2015) Fronto-temporal white matter connectivity predicts reversal learning errors

   Frontiers in Human Neuroscience 9:343.

   https://doi.org/10.3389/fnhum.2015.00343

   • PubMed
   • Google Scholar
5. 1. Amaral DG
   2. Price JL

   (1984) Amygdalo-cortical projections in the monkey (Macaca fascicularis)

   The Journal of Comparative Neurology 230:465–496.

   https://doi.org/10.1002/cne.902300402

   • PubMed
   • Google Scholar
6. 1. Ameis SH
   2. Catani M

   (2015) Altered white matter connectivity as a neural substrate for social impairment in autism spectrum disorder

   Cortex 62:158–181.

   https://doi.org/10.1016/j.cortex.2014.10.014

   • PubMed
   • Google Scholar
7. 1. Andersson JL
   2. Skare S
   3. Ashburner J

   (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

   NeuroImage 20:870–888.

   https://doi.org/10.1016/S1053-8119(03)00336-7

   • PubMed
   • Google Scholar
8. 1. Bachevalier J
   2. Parkinson JK
   3. Mishkin M

   (1985) Visual recognition in monkeys: effects of separate vs. combined transection of fornix and amygdalofugal pathways

   Experimental Brain Research 57:554–561.

   https://doi.org/10.1007/BF00237842

   • Google Scholar
9. 1. Baldermann JC
   2. Melzer C
   3. Zapf A
   4. Kohl S
   5. Timmermann L
   6. Tittgemeyer M
   7. Huys D
   8. Visser-Vandewalle V
   9. Kühn AA
   10. Horn A
   11. Kuhn J

   (2019) Connectivity profile predictive of effective deep brain stimulation in Obsessive-Compulsive disorder

   Biological Psychiatry 85:735–743.

   https://doi.org/10.1016/j.biopsych.2018.12.019

   • PubMed
   • Google Scholar
10. 1. Barbas H
    2. Zikopoulos B
    3. Timbie C

    (2011) Sensory pathways and emotional context for action in primate prefrontal cortex

    Biological Psychiatry 69:1133–1139.

    https://doi.org/10.1016/j.biopsych.2010.08.008

    • Google Scholar
11. 1. Barbas H
    2. De Olmos J

    (1990) Projections from the amygdala to basoventral and mediodorsal prefrontal regions in the rhesus monkey

    The Journal of Comparative Neurology 300:549–571.

    https://doi.org/10.1002/cne.903000409

    • PubMed
    • Google Scholar
12. 1. Behrens TE
    2. Woolrich MW
    3. Jenkinson M
    4. Johansen-Berg H
    5. Nunes RG
    6. Clare S
    7. Matthews PM
    8. Brady JM
    9. Smith SM

    (2003) Characterization and propagation of uncertainty in diffusion-weighted MR imaging

    Magnetic Resonance in Medicine 50:1077–1088.

    https://doi.org/10.1002/mrm.10609

    • PubMed
    • Google Scholar
13. 1. Behrens TE
    2. Berg HJ
    3. Jbabdi S
    4. Rushworth MF
    5. Woolrich MW

    (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?

    NeuroImage 34:144–155.

    https://doi.org/10.1016/j.neuroimage.2006.09.018

    • PubMed
    • Google Scholar
14. 1. Bickart KC
    2. Hollenbeck MC
    3. Barrett LF
    4. Dickerson BC

    (2012) Intrinsic amygdala-cortical functional connectivity predicts social network size in humans

    Journal of Neuroscience 32:14729–14741.

    https://doi.org/10.1523/JNEUROSCI.1599-12.2012

    • PubMed
    • Google Scholar
15. Preprint

    1. Bramson B
    2. Folloni D
    3. Verhagen L
    4. Hartogsveld B
    5. Mars RB
    6. Toni I
    7. Roelofs K

    (2019) Human lateral frontal pole contributes to control over social-emotional behaviour

    bioRxiv.

    https://doi.org/10.1101/584896

    • Google Scholar
16. 1. Browning PG
    2. Gaffan D

    (2008) Prefrontal cortex function in the representation of temporally complex events

    Journal of Neuroscience 28:3934–3940.

    https://doi.org/10.1523/JNEUROSCI.0633-08.2008

    • PubMed
    • Google Scholar
17. 1. Carmichael ST
    2. Price JL

    (1995) Limbic connections of the orbital and medial prefrontal cortex in macaque monkeys

    The Journal of Comparative Neurology 363:615–641.

    https://doi.org/10.1002/cne.903630408

    • PubMed
    • Google Scholar
18. 1. Catani M
    2. Howard RJ
    3. Pajevic S
    4. Jones DK

    (2002) Virtual in vivo interactive dissection of white matter fasciculi in the human brain

    NeuroImage 17:77–94.

    https://doi.org/10.1006/nimg.2002.1136

    • PubMed
    • Google Scholar
19. 1. Catani M
    2. Dell'acqua F
    3. Thiebaut de Schotten M

    (2013) A revised limbic system model for memory, emotion and behaviour

    Neuroscience & Biobehavioral Reviews 37:1724–1737.

    https://doi.org/10.1016/j.neubiorev.2013.07.001

    • PubMed
    • Google Scholar
20. 1. Catani M
    2. Thiebaut de Schotten M

    (2008) A diffusion tensor imaging tractography atlas for virtual in vivo dissections

    Cortex 44:1105–1132.

    https://doi.org/10.1016/j.cortex.2008.05.004

    • PubMed
    • Google Scholar
21. 1. Croxson PL
    2. Johansen-Berg H
    3. Behrens TE
    4. Robson MD
    5. Pinsk MA
    6. Gross CG
    7. Richter W
    8. Richter MC
    9. Kastner S
    10. Rushworth MF

    (2005) Quantitative investigation of connections of the prefrontal cortex in the human and macaque using probabilistic diffusion tractography

    Journal of Neuroscience 25:8854–8866.

    https://doi.org/10.1523/JNEUROSCI.1311-05.2005

    • PubMed
    • Google Scholar
22. 1. D'Arceuil H
    2. de Crespigny A

    (2007) The effects of brain tissue decomposition on diffusion tensor imaging and tractography

    NeuroImage 36:64–68.

    https://doi.org/10.1016/j.neuroimage.2007.02.039

    • PubMed
    • Google Scholar
23. 1. de Guglielmo G
    2. Kallupi M
    3. Pomrenze MB
    4. Crawford E
    5. Simpson S
    6. Schweitzer P
    7. Koob GF
    8. Messing RO
    9. George O

    (2019) Inactivation of a CRF-dependent amygdalofugal pathway reverses addiction-like behaviors in alcohol-dependent rats

    Nature Communications 10:1238.

    https://doi.org/10.1038/s41467-019-09183-0

    • PubMed
    • Google Scholar
24. 1. De Martino B
    2. Kumaran D
    3. Seymour B
    4. Dolan RJ

    (2006) Frames, biases, and rational decision-making in the human brain

    Science 313:684–687.

    https://doi.org/10.1126/science.1128356

    • PubMed
    • Google Scholar
25. 1. deCampo DM
    2. Fudge JL

    (2013) Amygdala projections to the lateral bed nucleus of the stria terminalis in the macaque: comparison with ventral striatal afferents

    Journal of Comparative Neurology 521:3191–3216.

    https://doi.org/10.1002/cne.23340

    • PubMed
    • Google Scholar
26. Book

    1. Déjerine JJ

    (1895)

    Anatomie Des Centres Nerveux

    Paris: Rueff.

    • Google Scholar
27. 1. Donahue CJ
    2. Sotiropoulos SN
    3. Jbabdi S
    4. Hernandez-Fernandez M
    5. Behrens TE
    6. Dyrby TB
    7. Coalson T
    8. Kennedy H
    9. Knoblauch K
    10. Van Essen DC
    11. Glasser MF

    (2016) Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey

    The Journal of Neuroscience 36:6758–6770.

    https://doi.org/10.1523/JNEUROSCI.0493-16.2016

    • PubMed
    • Google Scholar
28. 1. Drevets WC
    2. Price JL
    3. Simpson JR
    4. Todd RD
    5. Reich T
    6. Vannier M
    7. Raichle ME

    (1997) Subgenual prefrontal cortex abnormalities in mood disorders

    Nature 386:824–827.

    https://doi.org/10.1038/386824a0

    • PubMed
    • Google Scholar
29. 1. Eacott MJ
    2. Gaffan D

    (1992) Inferotemporal-frontal disconnection: the uncinate fascicle and visual associative learning in monkeys

    European Journal of Neuroscience 4:1320–1332.

    https://doi.org/10.1111/j.1460-9568.1992.tb00157.x

    • PubMed
    • Google Scholar
30. 1. Folloni D
    2. Verhagen L
    3. Mars RB
    4. Fouragnan E
    5. Constans C
    6. Aubry J-F
    7. Rushworth MFS
    8. Sallet J

    (2019) Manipulation of subcortical and deep cortical activity in the primate brain using transcranial focused ultrasound stimulation

    Neuron 101:1109–1116.

    https://doi.org/10.1016/j.neuron.2019.01.019

    • Google Scholar
31. 1. Freedman DJ
    2. Riesenhuber M
    3. Poggio T
    4. Miller EK

    (2003) A comparison of primate prefrontal and inferior temporal cortices during visual categorization

    The Journal of Neuroscience 23:5235–5246.

    https://doi.org/10.1523/JNEUROSCI.23-12-05235.2003

    • PubMed
    • Google Scholar
32. 1. Froudist-Walsh S
    2. Browning PG
    3. Young JJ
    4. Murphy KL
    5. Mars RB
    6. Fleysher L
    7. Croxson PL

    (2018) Macro-connectomics and microstructure predict dynamic plasticity patterns in the non-human primate brain

    eLife 7:e34354.

    https://doi.org/10.7554/eLife.34354

    • PubMed
    • Google Scholar
33. 1. Fudge JL
    2. Kunishio K
    3. Walsh P
    4. Richard C
    5. Haber SN

    (2002) Amygdaloid projections to ventromedial striatal subterritories in the primate

    Neuroscience 110:257–275.

    https://doi.org/10.1016/S0306-4522(01)00546-2

    • PubMed
    • Google Scholar
34. 1. Fudge JL
    2. Haber SN

    (2001) Bed nucleus of the stria terminalis and extended amygdala inputs to dopamine subpopulations in primates

    Neuroscience 104:807–827.

    https://doi.org/10.1016/S0306-4522(01)00112-9

    • PubMed
    • Google Scholar
35. 1. Ghashghaei HT
    2. Hilgetag CC
    3. Barbas H

    (2007) Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala

    NeuroImage 34:905–923.

    https://doi.org/10.1016/j.neuroimage.2006.09.046

    • PubMed
    • Google Scholar
36. 1. Ghashghaei HT
    2. Barbas H

    (2002) Pathways for emotion: interactions of prefrontal and anterior temporal pathways in the amygdala of the rhesus monkey

    Neuroscience 115:1261–1279.

    https://doi.org/10.1016/S0306-4522(02)00446-3

    • PubMed
    • Google Scholar
37. 1. Glasser MF
    2. Sotiropoulos SN
    3. Wilson JA
    4. Coalson TS
    5. Fischl B
    6. Andersson JL
    7. Xu J
    8. Jbabdi S
    9. Webster M
    10. Polimeni JR
    11. Van Essen DC
    12. Jenkinson M
    13. WU-Minn HCP Consortium

    (2013) The minimal preprocessing pipelines for the human connectome project

    NeuroImage 80:105–124.

    https://doi.org/10.1016/j.neuroimage.2013.04.127

    • PubMed
    • Google Scholar
38. 1. Gutnikov SA
    2. Ma Y-Y
    3. Gaffan D

    (1997) Temporo-frontal Disconnection impairs visual-visual paired association learning but not configural learning in Macaca Monkeys

    European Journal of Neuroscience 9:1524–1529.

    https://doi.org/10.1111/j.1460-9568.1997.tb01507.x

    • Google Scholar
39. 1. Hunt LT
    2. Hayden BY

    (2017) A distributed, hierarchical and recurrent framework for reward-based choice

    Nature Reviews Neuroscience 18:172–182.

    https://doi.org/10.1038/nrn.2017.7

    • PubMed
    • Google Scholar
40. 1. Jbabdi S
    2. Sotiropoulos SN
    3. Savio AM
    4. Graña M
    5. Behrens TEJ

    (2012) Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems

    Magnetic Resonance in Medicine 68:1846–1855.

    https://doi.org/10.1002/mrm.24204

    • Google Scholar
41. 1. Jbabdi S
    2. Lehman JF
    3. Haber SN
    4. Behrens TE

    (2013) Human and monkey ventral prefrontal fibers use the same organizational principles to reach their targets: tracing versus tractography

    Journal of Neuroscience 33:3190–3201.

    https://doi.org/10.1523/JNEUROSCI.2457-12.2013

    • PubMed
    • Google Scholar
42. 1. Johansen-Berg H
    2. Gutman DA
    3. Behrens TE
    4. Matthews PM
    5. Rushworth MF
    6. Katz E
    7. Lozano AM
    8. Mayberg HS

    (2008) Anatomical connectivity of the subgenual cingulate region targeted with deep brain stimulation for treatment-resistant depression

    Cerebral Cortex 18:1374–1383.

    https://doi.org/10.1093/cercor/bhm167

    • PubMed
    • Google Scholar
43. 1. Kamali A
    2. Sair HI
    3. Blitz AM
    4. Riascos RF
    5. Mirbagheri S
    6. Keser Z
    7. Hasan KM

    (2016) Revealing the ventral amygdalofugal pathway of the human limbic system using high spatial resolution diffusion tensor tractography

    Brain Structure and Function 221:3561–3569.

    https://doi.org/10.1007/s00429-015-1119-3

    • PubMed
    • Google Scholar
44. 1. Kitt CA
    2. Mitchell SJ
    3. DeLong MR
    4. Wainer BH
    5. Price DL

    (1987) Fiber pathways of basal forebrain cholinergic neurons in monkeys

    Brain Research 406:192–206.

    https://doi.org/10.1016/0006-8993(87)90783-9

    • PubMed
    • Google Scholar
45. 1. Klingler J
    2. Gloor P

    (1960) The connections of the amygdala and of the anterior temporal cortex in the human brain

    Journal of Comparative Neurology 115:333–369.

    https://doi.org/10.1002/cne.901150305

    • PubMed
    • Google Scholar
46. 1. Klüver H
    2. Bucy PC

    (1937)

    “Psychic blindness” and other symptoms following bilateral temporal lobectomy in Rhesus monkeys

    American Journal of Physiology 119:352–353.

    • Google Scholar
47. 1. Krubitzer L

    (2007) The magnificent compromise: cortical field evolution in mammals

    Neuron 56:201–208.

    https://doi.org/10.1016/j.neuron.2007.10.002

    • PubMed
    • Google Scholar
48. 1. Kunishio K
    2. Haber SN

    (1994) Primate cingulostriatal projection: limbic striatal versus sensorimotor striatal input

    The Journal of Comparative Neurology 350:337–356.

    https://doi.org/10.1002/cne.903500302

    • PubMed
    • Google Scholar
49. 1. Kuypers H
    2. Szwarcbart MK
    3. Mishkin M
    4. Rosvold HE

    (1965) Occipitotemporal corticocortical connections in the rhesus monkey

    Experimental Neurology 11:245–262.

    https://doi.org/10.1016/0014-4886(65)90016-6

    • PubMed
    • Google Scholar
50. 1. Lehman JF
    2. Greenberg BD
    3. McIntyre CC
    4. Rasmussen SA
    5. Haber SN

    (2011) Rules ventral prefrontal cortical axons use to reach their targets: implications for diffusion tensor imaging tractography and deep brain stimulation for psychiatric illness

    Journal of Neuroscience 31:10392–10402.

    https://doi.org/10.1523/JNEUROSCI.0595-11.2011

    • PubMed
    • Google Scholar
51. 1. Lozano AM
    2. Lipsman N
    3. Bergman H
    4. Brown P
    5. Chabardes S
    6. Chang JW
    7. Matthews K
    8. McIntyre CC
    9. Schlaepfer TE
    10. Schulder M
    11. Temel Y
    12. Volkmann J
    13. Krauss JK

    (2019) Deep brain stimulation: current challenges and future directions

    Nature Reviews Neurology 15:148–160.

    https://doi.org/10.1038/s41582-018-0128-2

    • PubMed
    • Google Scholar
52. Book

    1. Mai JK
    2. Majtanik M
    3. Nhmrc GPA

    (2015)

    Atlas of the Human Brain (4 edition)

    Amsterdam: Academic Press.

    • Google Scholar
53. 1. Maier-Hein KH
    2. Neher PF
    3. Houde JC
    4. Côté MA
    5. Garyfallidis E
    6. Zhong J
    7. Chamberland M
    8. Yeh FC
    9. Lin YC
    10. Ji Q
    11. Reddick WE
    12. Glass JO
    13. Chen DQ
    14. Feng Y
    15. Gao C
    16. Wu Y
    17. Ma J
    18. Renjie H
    19. Li Q
    20. Westin CF
    21. Deslauriers-Gauthier S
    22. González JOO
    23. Paquette M
    24. St-Jean S
    25. Girard G
    26. Rheault F
    27. Sidhu J
    28. Tax CMW
    29. Guo F
    30. Mesri HY
    31. Dávid S
    32. Froeling M
    33. Heemskerk AM
    34. Leemans A
    35. Boré A
    36. Pinsard B
    37. Bedetti C
    38. Desrosiers M
    39. Brambati S
    40. Doyon J
    41. Sarica A
    42. Vasta R
    43. Cerasa A
    44. Quattrone A
    45. Yeatman J
    46. Khan AR
    47. Hodges W
    48. Alexander S
    49. Romascano D
    50. Barakovic M
    51. Auría A
    52. Esteban O
    53. Lemkaddem A
    54. Thiran JP
    55. Cetingul HE
    56. Odry BL
    57. Mailhe B
    58. Nadar MS
    59. Pizzagalli F
    60. Prasad G
    61. Villalon-Reina JE
    62. Galvis J
    63. Thompson PM
    64. Requejo FS
    65. Laguna PL
    66. Lacerda LM
    67. Barrett R
    68. Dell'Acqua F
    69. Catani M
    70. Petit L
    71. Caruyer E
    72. Daducci A
    73. Dyrby TB
    74. Holland-Letz T
    75. Hilgetag CC
    76. Stieltjes B
    77. Descoteaux M

    (2017) The challenge of mapping the human connectome based on diffusion tractography

    Nature Communications 8:1349.

    https://doi.org/10.1038/s41467-017-01285-x

    • PubMed
    • Google Scholar
54. Book

    1. Marr D

    (1982)

    Vision: A Computational Investigation Into the Human Representation and Processing of Visual Information

    San Francisco: W.H Freeman.

    • Google Scholar
55. 1. Mars RB
    2. Foxley S
    3. Verhagen L
    4. Jbabdi S
    5. Sallet J
    6. Noonan MP
    7. Neubert FX
    8. Andersson JL
    9. Croxson PL
    10. Dunbar RI
    11. Khrapitchev AA
    12. Sibson NR
    13. Miller KL
    14. Rushworth MF

    (2016a) The extreme capsule fiber complex in humans and macaque monkeys: a comparative diffusion MRI tractography study

    Brain Structure and Function 221:4059–4071.

    https://doi.org/10.1007/s00429-015-1146-0

    • PubMed
    • Google Scholar
56. 1. Mars RB
    2. Verhagen L
    3. Gladwin TE
    4. Neubert FX
    5. Sallet J
    6. Rushworth MF

    (2016b) Comparing brains by matching connectivity profiles

    Neuroscience & Biobehavioral Reviews 60:90–97.

    https://doi.org/10.1016/j.neubiorev.2015.10.008

    • PubMed
    • Google Scholar
57. 1. Mars RB
    2. Passingham RE
    3. Jbabdi S

    (2018) Connectivity fingerprints: from areal descriptions to abstract spaces

    Trends in Cognitive Sciences 22:1026–1037.

    https://doi.org/10.1016/j.tics.2018.08.009

    • PubMed
    • Google Scholar
58. 1. Mayberg HS
    2. Lozano AM
    3. Voon V
    4. McNeely HE
    5. Seminowicz D
    6. Hamani C
    7. Schwalb JM
    8. Kennedy SH

    (2005) Deep brain stimulation for treatment-resistant depression

    Neuron 45:651–660.

    https://doi.org/10.1016/j.neuron.2005.02.014

    • PubMed
    • Google Scholar
59. 1. Montague PR
    2. Berns GS

    (2002) Neural economics and the biological substrates of valuation

    Neuron 36:265–284.

    https://doi.org/10.1016/S0896-6273(02)00974-1

    • PubMed
    • Google Scholar
60. 1. Morrison SE
    2. Salzman CD

    (2010) Re-valuing the amygdala

    Current Opinion in Neurobiology 20:221–230.

    https://doi.org/10.1016/j.conb.2010.02.007

    • PubMed
    • Google Scholar
61. 1. Munuera J
    2. Rigotti M
    3. Salzman CD

    (2018) Shared neural coding for social hierarchy and reward value in primate amygdala

    Nature Neuroscience 21:415–423.

    https://doi.org/10.1038/s41593-018-0082-8

    • PubMed
    • Google Scholar
62. 1. Murray EA
    2. Wise SP
    3. Drevets WC

    (2011) Localization of dysfunction in major depressive disorder: prefrontal cortex and amygdala

    Biological Psychiatry 69:e43–e54.

    https://doi.org/10.1016/j.biopsych.2010.09.041

    • PubMed
    • Google Scholar
63. 1. Murray EA
    2. Rudebeck PH

    (2018) Specializations for reward-guided decision-making in the primate ventral prefrontal cortex

    Nature Reviews Neuroscience 19:404–417.

    https://doi.org/10.1038/s41583-018-0013-4

    • PubMed
    • Google Scholar
64. 1. Murray EA
    2. Wise SP

    (2010) Interactions between orbital prefrontal cortex and amygdala: advanced cognition, learned responses and instinctive behaviors

    Current Opinion in Neurobiology 20:212–220.

    https://doi.org/10.1016/j.conb.2010.02.001

    • PubMed
    • Google Scholar
65. 1. Nauta WJ

    (1961)

    Fibre degeneration following lesions of the amygdaloid complex in the monkey

    Journal of Anatomy 95:515–531.

    • PubMed
    • Google Scholar
66. 1. Neubert FX
    2. Mars RB
    3. Thomas AG
    4. Sallet J
    5. Rushworth MF

    (2014) Comparison of human ventral frontal cortex Areas for cognitive control and language with Areas in monkey frontal cortex

    Neuron 81:700–713.

    https://doi.org/10.1016/j.neuron.2013.11.012

    • PubMed
    • Google Scholar
67. 1. Neubert FX
    2. Mars RB
    3. Sallet J
    4. Rushworth MF

    (2015) Connectivity reveals relationship of brain areas for reward-guided learning and decision making in human and monkey frontal cortex

    PNAS 112:E2695–E2704.

    https://doi.org/10.1073/pnas.1410767112

    • PubMed
    • Google Scholar
68. Book

    1. Nieuwenhuys R
    2. Voogd J
    3. Huijzen C

    (2008) The Human Central Nervous System: A Synopsis and Atlas (4th ed)

    Steinkopff-Verlag Heidelberg.

    https://doi.org/10.1007/978-3-540-34686-9

    • Google Scholar
69. 1. Noonan MP
    2. Sallet J
    3. Mars RB
    4. Neubert FX
    5. O'Reilly JX
    6. Andersson JL
    7. Mitchell AS
    8. Bell AH
    9. Miller KL
    10. Rushworth MF

    (2014) A neural circuit covarying with social hierarchy in macaques

    PLOS Biology 12:e1001940.

    https://doi.org/10.1371/journal.pbio.1001940

    • PubMed
    • Google Scholar
70. 1. Novotny GE

    (1977)

    A ventral tract from the bed nucleus of the stria terminalis to the amygdaloid complex in the monkey (Macaca fascicularis): possible pathway in the regulation of ovulation [proceedings]

    The Journal of Physiology 266:13P–14.

    • PubMed
    • Google Scholar
71. 1. O'Reilly JX
    2. Croxson PL
    3. Jbabdi S
    4. Sallet J
    5. Noonan MP
    6. Mars RB
    7. Browning PG
    8. Wilson CR
    9. Mitchell AS
    10. Miller KL
    11. Rushworth MF
    12. Baxter MG

    (2013) Causal effect of disconnection lesions on interhemispheric functional connectivity in rhesus monkeys

    PNAS 110:13982–13987.

    https://doi.org/10.1073/pnas.1305062110

    • PubMed
    • Google Scholar
72. 1. Oler JA
    2. Tromp DP
    3. Fox AS
    4. Kovner R
    5. Davidson RJ
    6. Alexander AL
    7. McFarlin DR
    8. Birn RM
    9. E Berg B
    10. deCampo DM
    11. Kalin NH
    12. Fudge JL

    (2017) Connectivity between the central nucleus of the amygdala and the bed nucleus of the stria terminalis in the non-human primate: neuronal tract tracing and developmental neuroimaging studies

    Brain Structure and Function 222:21–39.

    https://doi.org/10.1007/s00429-016-1198-9

    • PubMed
    • Google Scholar
73. 1. Ongür D
    2. Ferry AT
    3. Price JL

    (2003) Architectonic subdivision of the human orbital and medial prefrontal cortex

    The Journal of Comparative Neurology 460:425–449.

    https://doi.org/10.1002/cne.10609

    • PubMed
    • Google Scholar
74. 1. Ongür D
    2. Price JL

    (2000) The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans

    Cerebral Cortex 10:206–219.

    https://doi.org/10.1093/cercor/10.3.206

    • PubMed
    • Google Scholar
75. 1. Padoa-Schioppa C
    2. Assad JA

    (2008) The representation of economic value in the orbitofrontal cortex is invariant for changes of menu

    Nature Neuroscience 11:95–102.

    https://doi.org/10.1038/nn2020

    • PubMed
    • Google Scholar
76. 1. Passingham RE
    2. Stephan KE
    3. Kötter R

    (2002) The anatomical basis of functional localization in the cortex

    Nature Reviews Neuroscience 3:606–616.

    https://doi.org/10.1038/nrn893

    • PubMed
    • Google Scholar
77. Book

    1. Paxinos G
    2. Huang XF
    3. Toga AW

    (2000)

    The Rhesus Monkey Brain in Stereotaxic Coordinates

    Academic Press.

    • Google Scholar
78. 1. Petrides M
    2. Tomaiuolo F
    3. Yeterian EH
    4. Pandya DN

    (2012) The prefrontal cortex: comparative architectonic organization in the human and the macaque monkey brains

    Cortex 48:46–57.

    https://doi.org/10.1016/j.cortex.2011.07.002

    • PubMed
    • Google Scholar
79. 1. Petrides M
    2. Pandya DN

    (2007) Efferent association pathways from the rostral prefrontal cortex in the macaque monkey

    Journal of Neuroscience 27:11573–11586.

    https://doi.org/10.1523/JNEUROSCI.2419-07.2007

    • PubMed
    • Google Scholar
80. 1. Price JL

    (2003) Comparative aspects of amygdala connectivity

    Annals of the New York Academy of Sciences 985:50–58.

    https://doi.org/10.1111/j.1749-6632.2003.tb07070.x

    • PubMed
    • Google Scholar
81. 1. Reveley C
    2. Gruslys A
    3. Ye FQ
    4. Glen D
    5. Samaha J
    6. E Russ B
    7. Saad Z
    8. K Seth A
    9. Leopold DA
    10. Saleem KS

    (2017) Three-Dimensional digital template atlas of the macaque brain

    Cerebral Cortex 27:4463–4477.

    https://doi.org/10.1093/cercor/bhw248

    • PubMed
    • Google Scholar
82. 1. Rudebeck PH
    2. Saunders RC
    3. Prescott AT
    4. Chau LS
    5. Murray EA

    (2013) Prefrontal mechanisms of behavioral flexibility, emotion regulation and value updating

    Nature Neuroscience 16:1140–1145.

    https://doi.org/10.1038/nn.3440

    • PubMed
    • Google Scholar
83. 1. Rudebeck PH
    2. Putnam PT
    3. Daniels TE
    4. Yang T
    5. Mitz AR
    6. Rhodes SE
    7. Murray EA

    (2014) A role for primate subgenual cingulate cortex in sustaining autonomic arousal

    PNAS 111:5391–5396.

    https://doi.org/10.1073/pnas.1317695111

    • PubMed
    • Google Scholar
84. 1. Rudebeck PH
    2. Murray EA

    (2014) The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes

    Neuron 84:1143–1156.

    https://doi.org/10.1016/j.neuron.2014.10.049

    • PubMed
    • Google Scholar
85. 1. Rushworth MF
    2. Noonan MP
    3. Boorman ED
    4. Walton ME
    5. Behrens TE

    (2011) Frontal cortex and reward-guided learning and decision-making

    Neuron 70:1054–1069.

    https://doi.org/10.1016/j.neuron.2011.05.014

    • PubMed
    • Google Scholar
86. 1. Russchen FT
    2. Amaral DG
    3. Price JL

    (1985) The afferent connections of the substantia innominata in the monkey, Macaca fascicularis

    The Journal of Comparative Neurology 242:1–27.

    https://doi.org/10.1002/cne.902420102

    • PubMed
    • Google Scholar
87. 1. Saez A
    2. Rigotti M
    3. Ostojic S
    4. Fusi S
    5. Salzman CD

    (2015) Abstract context representations in primate amygdala and prefrontal cortex

    Neuron 87:869–881.

    https://doi.org/10.1016/j.neuron.2015.07.024

    • PubMed
    • Google Scholar
88. 1. Sallet J
    2. Mars RB
    3. Noonan MP
    4. Neubert FX
    5. Jbabdi S
    6. O'Reilly JX
    7. Filippini N
    8. Thomas AG
    9. Rushworth MF

    (2013) The organization of dorsal frontal cortex in humans and macaques

    Journal of Neuroscience 33:12255–12274.

    https://doi.org/10.1523/JNEUROSCI.5108-12.2013

    • PubMed
    • Google Scholar
89. Book

    1. Schmahmann JD
    2. Pandya DN

    (2006) Fiber Pathways of the Brain

    Oxford University Press.

    https://doi.org/10.1093/acprof:oso/9780195104233.001.0001

    • Google Scholar
90. 1. Schoenemann PT
    2. Sheehan MJ
    3. Glotzer LD

    (2005) Prefrontal white matter volume is disproportionately larger in humans than in other primates

    Nature Neuroscience 8:242–252.

    https://doi.org/10.1038/nn1394

    • PubMed
    • Google Scholar
91. 1. Smaers JB
    2. Gómez-Robles A
    3. Parks AN
    4. Sherwood CC

    (2017) Exceptional evolutionary expansion of prefrontal cortex in great apes and humans

    Current Biology 27:714–720.

    https://doi.org/10.1016/j.cub.2017.01.020

    • PubMed
    • Google Scholar
92. 1. Smith SM
    2. Jenkinson M
    3. Woolrich MW
    4. Beckmann CF
    5. Behrens TE
    6. Johansen-Berg H
    7. Bannister PR
    8. De Luca M
    9. Drobnjak I
    10. Flitney DE
    11. Niazy RK
    12. Saunders J
    13. Vickers J
    14. Zhang Y
    15. De Stefano N
    16. Brady JM
    17. Matthews PM

    (2004) Advances in functional and structural MR image analysis and implementation as FSL

    NeuroImage 23 Suppl 1:S208–S219.

    https://doi.org/10.1016/j.neuroimage.2004.07.051

    • PubMed
    • Google Scholar
93. 1. Sotiropoulos SN
    2. Jbabdi S
    3. Xu J
    4. Andersson JL
    5. Moeller S
    6. Auerbach EJ
    7. Glasser MF
    8. Hernandez M
    9. Sapiro G
    10. Jenkinson M
    11. Feinberg DA
    12. Yacoub E
    13. Lenglet C
    14. Van Essen DC
    15. Ugurbil K
    16. Behrens TE
    17. WU-Minn HCP Consortium

    (2013) Advances in diffusion MRI acquisition and processing in the human connectome project

    NeuroImage 80:125–143.

    https://doi.org/10.1016/j.neuroimage.2013.05.057

    • PubMed
    • Google Scholar
94. 1. Stejskal EO
    2. Tanner JE

    (1965) Spin diffusion measurements: spin echoes in the presence of a time‐dependent field gradient

    The Journal of Chemical Physics 42:288–292.

    https://doi.org/10.1063/1.1695690

    • Google Scholar
95. 1. Thiebaut de Schotten M
    2. Dell'Acqua F
    3. Forkel SJ
    4. Simmons A
    5. Vergani F
    6. Murphy DG
    7. Catani M

    (2011) A lateralized brain network for visuospatial attention

    Nature Neuroscience 14:1245–1246.

    https://doi.org/10.1038/nn.2905

    • PubMed
    • Google Scholar
96. 1. Thiebaut de Schotten M
    2. Dell'Acqua F
    3. Valabregue R
    4. Catani M

    (2012) Monkey to human comparative anatomy of the frontal lobe association tracts

    Cortex 48:82–96.

    https://doi.org/10.1016/j.cortex.2011.10.001

    • PubMed
    • Google Scholar
97. 1. Thiebaut de Schotten M
    2. Foulon C

    (2018) The rise of a new associationist school for lesion-symptom mapping

    Brain 141:2–4.

    https://doi.org/10.1093/brain/awx332

    • PubMed
    • Google Scholar
98. 1. Timbie C
    2. Barbas H

    (2014) Specialized pathways from the primate amygdala to posterior orbitofrontal cortex

    Journal of Neuroscience 34:8106–8118.

    https://doi.org/10.1523/JNEUROSCI.5014-13.2014

    • PubMed
    • Google Scholar
99. 1. Uğurbil K
    2. Xu J
    3. Auerbach EJ
    4. Moeller S
    5. Vu AT
    6. Duarte-Carvajalino JM
    7. Lenglet C
    8. Wu X
    9. Schmitter S
    10. Van de Moortele PF
    11. Strupp J
    12. Sapiro G
    13. De Martino F
    14. Wang D
    15. Harel N
    16. Garwood M
    17. Chen L
    18. Feinberg DA
    19. Smith SM
    20. Miller KL
    21. Sotiropoulos SN
    22. Jbabdi S
    23. Andersson JL
    24. Behrens TE
    25. Glasser MF
    26. Van Essen DC
    27. Yacoub E
    28. WU-Minn HCP Consortium

    (2013) Pushing spatial and temporal resolution for functional and diffusion MRI in the human connectome project

    NeuroImage 80:80–104.

    https://doi.org/10.1016/j.neuroimage.2013.05.012

    • PubMed
    • Google Scholar
100. 1. Van Essen DC
     2. Smith SM
     3. Barch DM
     4. Behrens TE
     5. Yacoub E
     6. Ugurbil K
     7. WU-Minn HCP Consortium

     (2013) The WU-Minn human connectome project: an overview

     NeuroImage 80:62–79.

     https://doi.org/10.1016/j.neuroimage.2013.05.041

     • PubMed
     • Google Scholar
101. 1. Van Essen DC
     2. Dierker DL

     (2007) Surface-based and probabilistic atlases of primate cerebral cortex

     Neuron 56:209–225.

     https://doi.org/10.1016/j.neuron.2007.10.015

     • PubMed
     • Google Scholar
102. 1. Verhagen L
     2. Gallea C
     3. Folloni D
     4. Constans C
     5. Jensen DE
     6. Ahnine H
     7. Roumazeilles L
     8. Santin M
     9. Ahmed B
     10. Lehericy S
     11. Klein-Flügge MC
     12. Krug K
     13. Mars RB
     14. Rushworth MF
     15. Pouget P
     16. Aubry JF
     17. Sallet J

     (2019) Offline impact of transcranial focused ultrasound on cortical activation in primates

     eLife 8:e40541.

     https://doi.org/10.7554/eLife.40541

     • PubMed
     • Google Scholar
103. 1. Volman I
     2. Roelofs K
     3. Koch S
     4. Verhagen L
     5. Toni I

     (2011) Anterior prefrontal cortex inhibition impairs control over social emotional actions

     Current Biology 21:1766–1770.

     https://doi.org/10.1016/j.cub.2011.08.050

     • PubMed
     • Google Scholar
104. 1. Volman I
     2. Verhagen L
     3. den Ouden HE
     4. Fernández G
     5. Rijpkema M
     6. Franke B
     7. Toni I
     8. Roelofs K

     (2013) Reduced serotonin transporter availability decreases prefrontal control of the amygdala

     Journal of Neuroscience 33:8974–8979.

     https://doi.org/10.1523/JNEUROSCI.5518-12.2013

     • PubMed
     • Google Scholar
105. 1. Weiskrantz L

     (1956) Behavioral changes associated with ablation of the amygdaloid complex in monkeys

     Journal of Comparative and Physiological Psychology 49:381–391.

     https://doi.org/10.1037/h0088009

     • PubMed
     • Google Scholar
106. 1. Whalen PJ
     2. Rauch SL
     3. Etcoff NL
     4. McInerney SC
     5. Lee MB
     6. Jenike MA

     (1998) Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge

     The Journal of Neuroscience 18:411–418.

     https://doi.org/10.1523/JNEUROSCI.18-01-00411.1998

     • PubMed
     • Google Scholar
107. 1. Yeterian EH
     2. Pandya DN
     3. Tomaiuolo F
     4. Petrides M

     (2012) The cortical connectivity of the prefrontal cortex in the monkey brain

     Cortex 48:58–81.

     https://doi.org/10.1016/j.cortex.2011.03.004

     • PubMed
     • Google Scholar

Author details

1. Davide Folloni

   1. Wellcome Centre for Integrative Neuroimaging (WIN),Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
   2. Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain (FMRIB),Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   davide.folloni@psy.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1653-5969

2. Jerome Sallet

   1. Wellcome Centre for Integrative Neuroimaging (WIN),Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
   2. Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain (FMRIB),Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7878-0209

3. Alexandre A Khrapitchev

   Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7616-6635

4. Nicola Sibson

   Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Funding acquisition, Methodology

   Competing interests

   No competing interests declared

5. Lennart Verhagen

   1. Wellcome Centre for Integrative Neuroimaging (WIN),Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
   2. Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain (FMRIB),Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
   3. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Contributed equally with

   Rogier B Mars

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3207-7929

6. Rogier B Mars

   1. Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional MRI of the Brain (FMRIB),Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
   2. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Contributed equally with

   Lennart Verhagen

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6302-8631

• 3,571

  views

• 446

  downloads

• 67

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.