Reproducing macaque lateral grasping and oculomotor networks using resting state functional connectivity and diffusion tractography

One largely accepted current view in neuroscience is that cortical functions result from the integration of different, reciprocally connected areas working together as large-scale functionally specialised networks (for example, see Bressler and Menon 2010; Catani et al. 2012; Borra and Luppino 2019). Accordingly, to be able to fully understand cortical functions, definition of the underlying neural circuits connecting different structures and their functional interplay is necessarily required. To achieve this, experimental approaches are often combined in order to identify which cortical areas are involved in a given function and their possible reciprocal connectivity. By integrating these data, comprehensive models can be constructed of specialised circuits to identify the possible flows of information processing, and the neural mechanisms from which a given function can emerge.

These large-scale, functionally specialized networks have been carefully defined, in detail, for skilled motor control in the animal model (e.g., non-human primates) using the architectonic approach for the definition of cortical areas, single unit recording for their functional characterisation, and neural tracers to define their connectivity. For example, using this direct approach, different cortical sectors involved in distinct aspects of skilled hand actions have been identified and the circuits that connect them, recently formulated as the ‘lateral grasping network’ (Borra et al. 2017). The development of non-invasive functional and connectional imaging techniques has allowed for the construction of similar definitions of motor control pathways also in the human brain (Turella and Lingnau 2014), although these are still less clearly defined compared to evidence obtained in the macaque. Functionally distinct cortical sectors have been identified for highly skilled movement using intraoperative stimulation (Viganò et al. 2019; Fornia et al. 2020), and the connections have been studied using diffusion MRI tractography (dMRI) (Budisavljevic et al. 2016; Howells et al. 2018). Approaches measuring the spontaneous organized fluctuations of brain activity at rest (resting state functional connectivity; rs-FC) may also provide further evidence for large-scale, functionally specialized networks in the human brain (Simone et al. 2020), although the interpretation of these networks with respect to structural connections is still not clearly defined.

Diffusion MRI uses estimations of the orientation of diffusion of water molecules in tissue to reconstruct likely trajectories of white matter pathways (i.e., tractography), which are commonly used to construct whole brain structural ‘connectomes’ or visualisations of specific sets of connections using a ‘virtual dissection’ approach. This technique has rapidly become the most important tool for investigating connectional anatomy, however, comparisons with data from anatomical tracing in the macaque have questioned the technique’s validity for precise in vivo reproduction of sector-to-sector connectivity (Thomas et al. 2014; Reveley et al. 2015; Knösche et al. 2015; Jbabdi et al. 2015).

Resting state FC (rs-FC) measures temporal associations between brain regions based on spontaneous fluctuations of brain activity, measured with blood-oxygen-level-dependent (BOLD) signal. The definition of a ‘functional connection’ between two areas is, therefore, divergent between the rs-FC community and those that use electrophysiological approaches. Functional connectivity is related to, but distinct from, anatomical connectivity, as it may be subserved by both polysynaptic and monosynaptic anatomical circuits, and can be influenced by several factors in contrast to structural connectivity (Biswal et al. 2010; Buckner et al. 2013). Despite some limitations, these techniques provide a unique opportunity to acquire whole brain datasets that can be compared with human data, as well as between hemispheres in a single monkey (Croxson et al. 2018; Balezeau et al. 2020). Digital data also enable comparison of large groups of macaques within a single space (Thiebaut de Schotten et al. 2019). Furthermore, the use of non-invasive techniques in living monkeys provides more ethical, efficient data acquisition and enables longitudinal studies to be performed. As the field of comparative MRI grows, it is relevant to identify the false positives and negatives of different structural and functional connectivity neuroimaging techniques. The extent to which they provide us with comparable information to that provided by single unit recording, architectonics and tracing techniques still remains to be fully assessed.

In this context, one possible way for addressing this issue is to test the efficacy of these techniques in identifying large-scale motor control networks of the macaque brain that have been well defined based on neural tracers and electrophysiology. Non-human primate (NHP) imaging is still in its infancy in comparison with human imaging, as it requires custom-built equipment and sequences with high field strengths that require more complex processing pipelines. A valuable resource, PRIME-DE, has recently been created to pool international NHP neuroimaging data, with the intention of facilitating advances in this field and fostering collaborations (Milham et al. 2018; 2020). This resource provides structural, resting state and diffusion MRI data from over twenty sites worldwide as well as quality assessment of the provided datasets. In the present study, we used two macaque in vivo datasets provided by PRIME-DE to trace two, well-established large-scale networks of the macaque, each including specific sectors of the temporal, parietal, and frontal cortex, involved in controlling purposeful hand actions (“lateral grasping network” LGNet) and explorative oculomotor behaviour (“explorative oculomotor network” EONet) respectively (see Borra and Luppino 2019). To this aim, cortical sectors of these two networks were defined anatomically on structural images in every monkey and used as seeds for dMRI and rs-FC.

Magnetic resonance techniques such as dMRI and resting state FC were first introduced in the late 1990s to investigate the structural and functional architecture of the central nervous system in human subjects (see Pierpaoli et al. 1996; Biswal 2012). By providing information that can be collected non-invasively in a digital format, these approaches have great utility for clinical and research purposes. Using these data to construct maps of connections, whether as tractography or functional connectivity, provides considerably opportunities for studying brain architecture and its functional role. In the animal model, especially in the macaque, detailed knowledge of cortical connectivity and functional organization of motor control has been collected based on neural tracer and electrophysiological experiments, which enables some validation to be made of these MR-based approaches and, thus, assessment of how accurate they may be in representing genuine anatomical and functional organisation. A growing number of studies have compared data on the cortical connectivity of the macaque brain with dMRI data (e.g., Markov et al. 2014; Thomas et al. 2014; van den Heuvel et al. 2015; Azadbakht et al. 2015; Donahue et al. 2016), arriving at different conclusions on the accuracy of dMRI in tracing neural connections. Other studies have used rs-FC to highlight functional correlations between cortical areas of the macaque brain which may indicate some similarity in the functional architecture of the macaque and the human brain (Hutchison et al. 2011, 2015). In the present study, we test the efficacy and accuracy of different dMRI and rs-FC techniques in identifying two large-scale cortical motor control networks (i.e., the lateral grasping network and the exploratory oculomotor network) in the living macaque brain, for which there is detailed, solid knowledge in terms of involved areas and interconnections based on neural tracing and electrophysiological studies. In contrast with other studies, the cortical sectors used as seeds have been defined in every monkey in order to include the core of each cortical area of the networks under study.

This investigation showed that dMRI and rs-FC produce different results in terms of estimating connections. Tractography was able to detect connections with higher specificity than resting state techniques, but less sensitivity. Importantly, both deterministic and probabilistic methods provided false positives and negatives and were not always in accordance as to the connections identified. Tractography has limited utility for studying short-range connections within directly adjacent cortex, although it can be used to show U-shaped fibres between adjacent gyri (Catani et al. 2012, 2017; Guevara et al. 2017). Long range fibre bundles are more reliably reproduced with tractography (e.g., Rojkova et al. 2016; Warrington et al. 2020). As such, it is noteworthy that neural tracing studies show that connections of a given cortical area typically involve, qualitatively and quantitatively, mostly adjacent cortex and neighbouring areas and that long-distance connections, although important from a functional point of view, generally represent a minor component of the total labelled cells. For example, after neural tracer injections in F5p, about 60–70% of the labelled neurons are located within the primary motor/premotor cortex, whereas only about 4–5% of the labelled cells are located in IPL areas AIP and PFG, which are sources of visual information crucial for selecting and controlling object-oriented hand actions (Gerbella et al. 2011). Thus, comparing long-distance, point-to-point cortical connectivity is somewhat challenging when comparing the accuracy of dMRI in identifying connections between different sectors. For this reason, we instead compared the presence of connections between techniques, however, this may be an area for future study. On the other hand, resting state techniques were better able to identify local over long-range functional connectivity, but their interpretation is less clear. As discussed below, the present data, in agreement with some previous studies (Thomas et al. 2014; Reveley et al. 2015), indicate some expedients that may be helpful in improving the quality of these neuroimaging techniques in representing the underlying anatomy.

In summary, the present study strongly indicates that invasive anatomical data acquired in the macaque using axonal tracing and electrophysiological approaches may provide a useful starting point for studying motor control networks using structural and functional comparative MR approaches. The two methods for mapping macaque connectomes are complementary, in that it is possible to directly validate neuroimaging data with the ‘gold standard’ showing axonal trajectories, and once these are established, to use imaging data to evaluate interindividual similarities and differences. The detailed study of the architectonics and connectional anatomy of specific circuits, such as those involved in hand and eye movements, is a basis from which it is possible to expand to study whole brain networks with comparative MR. Once these systems can be reliably reproduced, it may be possible to evaluate the emergence of uniquely human features of motor behaviour such as population-level right hand preference (Howells et al. 2018). Comparative MR is a promising and rapidly growing area of research, and we here emphasise that validation of non-invasive approaches with genuine anatomy is a priority, which will also be of great benefit to the human neuroimaging community.