Cross-species neuroscience: closing the explanatory gap

Abstract

Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit level manipulations with exquisite spatiotemporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success.

Introduction: the explanatory gap

Measuring neural activity in the brain and relating it to complex behaviour remains a central challenge for contemporary neuroscience. In humans, this venture is limited by the noninvasive tools and techniques currently available. Magnetic Resonance Imaging (MRI), for example, is restricted to coarse measures of neural activity that aggregate the diverse responses of thousands of neurons over space and time. This approach allows macroscopic measures of cognitive processing that relate to human behaviour, but it fails to provide insight into neural activity at the microcircuit, cellular and synaptic levels. To investigate neural activity at the microscopic level, to reveal the division of labour across cell types in their host circuit and assess causality, we instead rely on invasive procedures in animal models. In recent years we have seen the development of new recording techniques that can simultaneously monitor activity from thousands of cells across numerous brain regions. Furthermore, the expansion in the use of genetic tools in rodents now permits manipulation of neural activity at unprecedented spatiotemporal resolution. Yet, in contrast to research carried out in humans, these approaches rarely characterise activity at the macroscopic level and interpreting animal behaviour is challenging. This makes it difficult to establish how neural mechanisms recorded and manipulated in animal models relate to higher-order cognition.

Due to the distinct training requirements for neuroscientists conducting research in humans or animal models, laboratories typically employ a species-specific approach where research is focused on only one species. By and large, this centres research on either the macroscopic or microscopic level, leaving an explanatory gap between genetic, (sub)cellular and circuit level mechanisms on the one hand, and higher-order cognition on the other. The adverse implications of this explanatory gap are made evident by high failure rates observed in clinical trials, where neuropsychiatric drugs have one of the highest failure rates at Phase III (1). With an aging global population, neuropsychiatric disease presents an increasing social and economic burden which the World Health Organization (WHO) describe as the major public health problem of all high-income countries (2). There is therefore an urgent need to develop a more integrated approach to neuroscientific research, one that seeks to close the explanatory gap between human and animal research.

Here, we explore the view that investing in an interdisciplinary, cross-species approach will provide a means to integrate different levels of neuroscientific description, paving the way for a comprehensive understanding of how the mammalian brain serves adaptive behaviour. We outline a three-armed approach for effective cross-species investigation. First, to provide appropriate interpretation of non-invasive methods, different tools (i.e. both non-invasive and invasive methods) need to be employed within the same species. Second, to provide a direct means to relate signals recorded across different species, the same tools need to be employed across multiple species. Third, to obtain complementary data sets that take advantage of the best tools available in each species, different tools should be employed across different species using a comparative approach. Thus, by complementing current approaches that provide detailed descriptions of neural processing within one species, or even within one brain region, a cross-species approach may uncover a set of general principles that describe the neural basis of cognition and behaviour in terms of cellular and circuit level mechanisms. Moreover, adopting a cross-species approach may harness the translational value of fundamental neuroscience to develop effective neuropsychiatric treatment.

What can we measure in humans?

Each tool used for measuring neural activity has its own advantages and limitations. Of the non-invasive techniques available for measuring brain function, electroencephalogram (EEG), Magnetoencephalography (MEG), and functional MRI (fMRI) all provide readouts of activity at the macroscopic level. The temporal resolution of EEG and MEG out-perform fMRI, but EEG has poor spatial resolution and, while MEG can match the spatial resolution of fMRI at the cortex, it has poorer subcortical coverage and is not as widely available as MRI. In the context of this special issue, here we focus on fMRI as a non-invasive method for imaging macroscopic signatures of human brain activity.

One advantage of fMRI is that it can be readily compared with other imaging modalities that provide insight into brain anatomy, connectivity and chemical composition, or combined with causal interventions such as non-invasive brain stimulation. Compared to invasive methods, fMRI has the advantage of allowing reliable whole-brain imaging, thus providing a readout of activity at the macroscopic level. However, its interpretation is not straightforward: the Blood Oxygen Level Dependent (BOLD) signal measured using fMRI provides only an indirect measure of neural activity and the relationship between neural activity and the BOLD signal is complex (3,4). Remarkably, despite multiple opportunities for non-linearity (for example, the relationship from stimulus to neural activity; and the relationship between neural activity and the BOLD signal) evidence suggests the relationship between neural firing rate and the BOLD signal is approximately linear, at least over a limited range (5–11). This approximately linear relationship underpins the use of fMRI as an effective tool to infer neural activity using a noninvasive method.

While fMRI boasts the highest spatial resolution of available non-invasive methods, even sub-millimetre ultra-high field fMRI includes tens of thousands of neurons per voxel. Researchers have therefore developed methodological approaches to map the coarse spatial organisation of neurons. For example, fMRI can be used to measure retinotopic (12–14), tonotopic (15,16) and somatotopic (14,17) maps which resemble topographic maps measured using invasive methods in animal models. Topographies that span connections (‘connectopies’) may also be used to decipher the overarching principles of organisation inherent to different brain regions in different individuals (18,19). Moreover, these methodological approaches have clinical relevance, where somatotopic mapping in primary somatosensory cortex can be used to measure the persistent digit topography of amputees’ missing hand (20), while retinotopic mapping in V1 can be used to characterise the relative plasticity and stability of visual cortex in patients with congenital visual pathway disorders (21,22).

The improved spatial resolution afforded by an increase in signal-to-noise ratio at high field strength has further opened up the possibility for columnar fMRI (23,24) and layer-specific (laminar) fMRI (25–28). In contrast to traditional fMRI, which captures the amalgamation of both feedforward and feedback responses (29), sub-millimetre resolution fMRI can begin to dissociate the functional role of feedforward and feedback projections that activate different cell layers within the cortex. For example, in human V1, consistent with the known anatomy (30,31), laminar fMRI shows that responses attributed to top-down feedback, such as representation of the occluded part of an object or an illusory shape, selectively activate deep cortical layers (32,33). However, despite providing a unique opportunity to measure cortical organisation in vivo at a resolution previously restricted to invasive methods in animals (34–39), laminar fMRI is affected by sequence-dependent and depth-dependent draining artefacts attributed to uneven vascular architecture (28,40). Reliable deployment of high field fMRI therefore requires a detailed understanding of neurovascular coupling.

Alongside improvements in spatial resolution, recent advances in fast scanning techniques have pushed the temporal resolution of fMRI. These include multiband and simultaneous multi-slice sequences that achieve sub-second sampling (41–43). The temporal resolution of fMRI, however, remains fundamentally limited by the slow nature of the haemodynamic response function (HRF) which peaks at approximately 5 seconds after stimulation, and is followed by an undershoot that lasts approximately 30 seconds (44). Overlap between successive events can be explicitly modelled under the assumption that the responses add in a linear fashion (45,46). However, when the inter-stimulus interval is below about 1.5 seconds, ‘saturation’ in the mapping from neural activity to the BOLD signal introduces nonlinearities (47,48) which cannot easily be accounted for using the standard analysis pipelines.

To measure neural events at a sub-second resolution requires alternative analytical approaches. Recent fMRI investigations demonstrate that relatively rapid neural sequences (on the order of a few hundred milliseconds) may be decoded using multivariate decoding techniques that assess subtle differences in the activity patterns across voxels, measured across consecutive repetition times (TRs) (49). Simulations further suggest this approach is, in principle, sensitive to sequential neural events that occur on the order of 100 ms (49). The ability to decode these relatively rapid neural sequences using fMRI can be understood as the consequence of temporal blurring of neural events by the HRF. Two neural events within the same multi-step sequence will affect the BOLD signal over several seconds, thus being represented by consecutive TRs. During periods of rest or sleep, this approach, along with recent developments using MEG (50,51), may be used to measure sequential activity patterns in the hippocampus, analogous to ‘replay’ spiking activity previously reported using invasive hippocampal electrophysiological recording in rodents (52–54). Hippocampal ‘replay’ involves accelerated reactivation of specific spiking activity patterns previously observed during the wake/active state and is thought to play a key role in memory consolidation and planning (55–57). Using non-invasive, whole-brain methods to measure relatively rapid activity patterns in humans may provide insight into how hippocampal ‘replay’ influences higher-order cognition and activity in other brain regions (49,51).

Multivariate decoding techniques used to measure relatively rapid sequences in the hippocampus (49) build on representational fMRI methods developed over the last couple of decades. These representational fMRI methods provide a set of tools that move beyond mere localisation, to quantify information processing at the level of neural representations. Two key approaches have been adopted. Firstly, repetition suppression has been used to index neuronal selectively to a particular stimulus or pair of stimuli (58,59). Repetition suppression relies on the fact that neurons show suppression in their response to repeated presentation of stimuli or information to which they are sensitive and is robustly observed across brain regions and species. Using fMRI, neural selectivity can therefore be assessed by comparing the BOLD signal between trials that include repeating and non-repeating stimuli. Secondly, techniques have been developed that build on population vector analyses applied to neural recordings in animal models where the relative similarity in the response to different cues is quantified. When applied to fMRI data, this approach relies upon the coarse distribution across voxels of neurons selective to a particular feature. The relative similarity in the response to different cues is then assessed across voxels using either a classifier (Multi-Voxel Pattern Analysis, MVPA) (60,61), or correlational or distance metrics with Representational Similarity Analysis (RSA) (62,63). By providing a means to decode sensory cues, memories, decision variables and more, these representational methods can provide insight into the neural codes that support computation underlying human cognition and behaviour.

But despite these improvements in spatiotemporal resolution and analytical approaches, fMRI continues to provide only limited insight into cellular and synaptic processes that characterise neural activity at the microcircuit level. Ongoing research is continuing to deepen our understanding of the relationship between specific neuronal subtypes and different vascular variables that affect the BOLD signal (64–66), but certain neurophysiological processes simply cannot be measured non-invasively. Even with a dramatic advance in the spatiotemporal resolution of non-invasive methods, in-vivo non-invasive recordings in the human brain (including ultra-high field fMRI) will at best provide an index or indirect measure for activity at the sub-voxel resolution, as demonstrated by innovative approaches showing insight into neural codes (67), temporal sequences (49), synaptic plasticity (20,68,69), and excitatory and inhibitory processes (69–71). The validity of these measures, the discovery of new principles of microcircuit organisation, and the precise contribution made by different cell types to neural computation will continue to rely on invasive recordings in animal models.

What can we measure in animal models?

Except in unusual circumstances, such as during electrocorticographic and depth recordings in epilepsy and deep brain stimulation patients (72,73), ethical restrictions limit the study of the human brain to non-invasive methods. Although this may change in the near future, with the advent of implantable bidirectional devices that piggy-back chronic neurophysiological recording capabilities on the delivery of chronic therapeutic stimulation, such opportunities will remain confined to selected conditions or disease states. To monitor and manipulate physiological neural activity at the cellular, synaptic and circuit level, we instead rely on invasive methods in animal models. Recent technological developments in invasive methods now permit large-scale and long-term recording, alongside manipulation of neural activity at unprecedented spatiotemporal resolution.

Methods available for recording neural activity during behaviour include in vivo electrophysiology that has temporal resolution sufficient to resolve individual action potentials, the fundamental currency of neural information. The micro-machined silicon probes developed in recent years, such as neuropixels (74), can be used to simultaneously record activity from thousands of neurons across numerous brain regions (75), thus representing an important advance from traditional recording techniques. The introduction of polymer electrode-based systems further support stable single-unit recording with longevity extending to 5 months or more (76). When coupled with automated spike sorting methods (77,78) and sophisticated analysis pipelines, large-scale electrophysiology can begin to reveal the organizing principles, distribution and character of neural activity supporting behaviourally relevant variables (79). Furthermore, the relationship between neuronal spiking and the local-field potential can be used to reveal how synchronised networks and particular oscillatory patterns support effective neuronal communication during well-defined behaviours (80,81).

While distinct cell types, including excitatory and inhibitory neurons, may be deduced from electrophysiological features, complementary methods must typically be employed to cross-validate identified neuronal types (e.g. 82–86). Notably, recent advances in genetic tools afford the necessary specificity and precision to relate the function of particular neuronal subtypes to well-defined behaviour in rodents (87). When combined with highly sensitive optical probes used for imaging intracellular calcium (a proxy for spiking activity) (88) or membrane voltage (89), genetic tools can also be employed to dissociate distinct interneuron subtypes within neural circuits (90,91). In the worm (92), zebrafish (92–94) and Drosophila (95), genetically encoded calcium indicators permit whole-brain imaging, a powerful approach for establishing the relationship between brain wide circuits and behaviour. Particularly in small animals, genetic tools further support causal manipulations, such as optogenetics where light is used to control neural activity with cell-type and millisecond precision (96,97). The specificity and breadth of optogenetic methods support both activation and inactivation experiments. When combined with well-defined behavioural tasks these methods provide a toolkit to relate physiological mechanisms to behaviour.

These readouts and manipulations of microcircuit-level activity go hand-in-hand with an understanding of structural neuroanatomy where axonal tracing in animal models still provide what is often termed the ‘gold-standard’. Such invasive tools are currently the only methods available for identifying the direction of a connection and the presence of synapses. While noninvasive anatomical methods, such as diffusion-weighted MRI-based tractography, have the advantage of providing in vivo reconstructions and visualization of the three-dimensional architecture of white matter tracts, they do not trace axons directly, and variables such as crossing fibres, fibre geometry, among others, influence the accuracy of the results. Therefore, results need to be carefully interpreted and often validated in animal models when possible.

Invasive methods in animal models are, however, not without their own limitations. Hubel and Wiesel, who pioneered some of the earliest use of electrophysiology in the late 1950s, recognised the drawbacks of their approach: “to attack such a three-dimensional problem with a one-dimensional weapon is a dismaying exercise in tedium, like trying to cut the back lawn with a pair of nail scissors” (98). Despite recent developments these criticisms do, in part, still ring true: electrophysiology can be biased towards the large spikes discharged by some neurons, leading to under sampling of smaller spikes discharged by other neuron types. Moreover, electrophysiology typically samples a subset of neurons at a restricted location, potentially overlooking the macroscopic structure of neural activity and system wide dynamics. And even when large numbers of neurons are recorded simultaneously, interpreting the neural activity is no mean feat, somewhat analogous to trying to decipher the “operation and function of an orchestra, without knowing much about the role of strings, woodwinds, brass or percussion instruments” (99).

The rapidly expanding use of optical and genetic tools available in rodents has also been met by growing recognition of the pros and cons associated with these methods. For instance, the slow kinetics of calcium imaging complicate interpretation of the signal (88), and voltage indicators currently have limited brightness and photostability to support in-vivo imaging during ongoing behaviour. Optogenetic stimulation risks driving neuronal responses outside their typical physiological range, causing bulk activation and the potential for unnatural plasticity; and the resulting behavioural effects may reflect the function of a manipulated circuit, as opposed to a loss- or gain-of-function manipulation.

However, perhaps the most pressing concern is simply that these contemporary invasive tools are predominantly employed in rodents. While non-human primates provide an opportunity to relate circuit and neuronal activity to higher order cognitive functions that are arguably more closely aligned with those in the human, this research will always be limited by numbers. Investigations in rodents are increasingly more widespread and provide an evolutionary related model organism that allows comprehensive measurement and manipulation. But this restricts investigations to the repertoire of rodent behaviours that are easy to interpret. This may in part be overcome by improved characterisation of rodent behaviour, where there is a growing need for precise and automated tools that permit behavioural quantification (100,101). However, difficulties in interpreting ethological behaviour and relating rodent behaviour to higher level cognition and behaviours observed in humans will persist, fundamentally limiting the scope of this approach. This has implications for fundamental neuroscientific research, but also for our ability to understand the cellular underpinnings of neuropsychiatric disorders. The stark consequence of these short comings is perhaps most evident in psychiatric research where the full complexity of disorders can rarely, if ever, be modelled (see Translational value of bridging the macroscopic and microscopic levels below).

Can a cross-species approach bridge the macroscopic-microscopic divide?

Having examined current state-of-the-art tools available for investigating neural activity in both humans and animals, the explanatory gap between non-invasive and invasive tools is evident and highlights the limitation of a species-specific approach. On the one hand, noninvasive methods available in humans can relate measures of macroscopic activity to complex cognition and behaviour. However, these non-invasive techniques are limited by poor spatial or temporal resolution, and, at least for fMRI, they provide an indirect measure of neural activity. On the other hand, invasive methods available in animal models can measure neural activity and synaptic changes at high spatiotemporal resolution, but often limit investigation to a single neural circuit or brain region and behaviours that are easy to interpret. Microscopic measures in animal models therefore fall short of providing insight into distributed computations that underlie the diverse and complex repertoire of human behaviour.

Can we use a cross-species approach to bridge the macroscopic-microscopic divide? After all, different species have different lifestyles, occupy and adapt to different ecological niches, and are exposed to different evolutionary pressures. While these different evolutionary pressures may in part account for differences between species (102,103), overall we see preserved structure and function of neural circuits and the encoded sequences within the human genome are highly overlapping with that of other mammals (e.g. 99% overlap between human and mouse) (104). More substantial differences are observed at the cellular-level expression of genes (79% overlap between humans and mice, for example), but species-specific expression differences appear to have discrete, non-widespread expression patterns that are considered to reflect subtle rather than global changes (105). Thus, despite important differences, the general organisation of neural circuits within the mammalian brain appears conserved.

At the structural level, early work by Brodmann and others revealed the cytoarchitectural organisation of cortex across species (106). Researchers have since shown that while some species-to-species variability in neuronal subtypes does exist (107–109), by and large the same neuronal sub-types, defined by molecular expression profiles and dendritic patterns, can be found in the same brain regions of humans and other mammals (110,111). For example, in both humans and rats, axo-axonic GABAergic cells show equivalent innervation patterns and initiate a stereotyped series of synaptic events in cortical networks (112). The interaction between different neuronal subtypes together form the basic microcircuit that appears to have been replicated several thousand times in larger mammalian brains (103). Therefore, despite 17,000-fold variability in brain volume leading to substantial differences in the number of brain areas across the mammalian order (113–115), the general principles of organisation, defined by neuronal subtypes and microcircuit structure, appear broadly conserved. Arguably, this means that even brain regions or neural circuits that are uniquely human may be understood using a set of general principles that derive from animal models (116).

Similarly at a functional level, resting-state fMRI in primates reveals a remarkably conserved profile for functional connectivity across large-scale networks such as the default mode network (macaque: (117); chimpanzee: (118)), with similar connectivity hubs across species (119). In both humans and non-human primates similar functional responses have also been observed during visual processing (120), tool use (121), sequence processing (122) and decision making (123). Furthermore, in the hippocampus, a brain region situated towards the apex of the visual processing hierarchy (124), neurons show equivalent functional significance across mammals. Indeed, ‘place cells’, neurons that are activated when animals pass through a specific location in the environment, have been identified in the hippocampus in rats (125), mice (126), chinchillas (127), bats (128), monkeys (129) and humans (72) (Fig. 1). In addition, in all tested species, place cells in the CA1 region of the hippocampus are reported to be pyramidal cells that have characteristic bursting activity with peak firing rates residing within a similar range (130). The significance of these cross-species comparisons is that place cells are reported to constitute a cognitive map that aids high-level cognitive function, including navigation, planning and memory (131).

Figure 1. Place cells in the hippocampus of different mammalian species.

Electrophysiology recordings in the hippocampus shows evidence for ‘place cells’ across different mammals. As animals/humans traverse an environment, place cells show increased firing at a specific location in the environment, in: a) rats (134); b) mice (126); c) chinchillas (127); d) bats (128); e) monkeys (129); f) and in humans navigating a virtual environment (72).

Thus, as we move to larger brains, compensatory mechanisms appear to preserve brain-size invariant neural dynamics and computation. Signal delay caused by increasing transmission distance is offset by increasing axon size and myelination which increase conduction velocity and reduce signal attenuation. A minority of disproportionately large axons further help preserve transmission time while minimising the cost of increasing brain volume (132,133). Across mammals these compensatory mechanisms appear to preserve neural codes, temporal dynamics and the core function neural circuits.

Developing a cross-species approach

Preserved homology of neural circuits across mammals underpins the rational for conducting investigations across multiple species. But even when investigating aspects of cognition that are considered to have uniquely human components, such as language, a comparative cross-species approach (e.g. between humans and non-human primates) can reveal structural and functional specialisation (135). Thus, a cross-species approach may be used to bridge the gap between human neuroimaging and invasive animal research. Here we outline three complementary approaches for efficacious cross-species investigation (Fig. 2).

Figure 2. A three-armed approach for efficacious cross-species research.

To bridge the explanatory gap between macro- and microcircuit measures of neural activity we propose a three-armed cross-species approach. First, different tools need to be simultaneously employed within the same species to aid appropriate interpretation of non-invasive methods (Approach 1). Second, the same tools need to be employed across different species to perform comparative investigations (Approach 2). Third, different tools should be employed in parallel across different species, to provide state-of-the-art measures of neural activity at both a macro- and microcircuit level, while employing methods to translate neural signatures across different recording modalities (Approach 3).

Approach 1

Different tools need to be simultaneously employed within the same species to provide appropriate interpretation of non-invasive methods. With regards to fMRI, the relationship between the BOLD signal and neural activity can be characterised in animal models by simultaneous fMRI and electrophysiological recordings (136–138), or by optical imaging of both neural activity and haemodynamics (139). By continuing to combine measures of the BOLD signal with invasive recording, Approach 1 will establish a deeper understanding of the relationship between the BOLD signal and the underlying neural activity. Since the relative merit of this approach and interpretation of the BOLD signal have been detailed elsewhere (3,4,140), in this opinion piece we will only consider Approach 1 in passing.

Approach 2

The same tools need to be employed across multiple species, to allow direct comparisons to be drawn between different species. For example, to reveal functional properties that generalise across species, MRI may be used to perform comparative investigations (see Cross-species MRI). Alternatively, electrophysiology may be employed across different animal models and compared with preoperative recordings in epilepsy patients. Functional comparisons can be established by matching behavioural assays (see Cross-species behavioural assays).

Approach 3

The third approach takes advantage of behavioural assays that can be implemented across species but uses the best tools available in each species to characterise the macroscopic and microscopic levels in tandem. To compare complementary data sets, this approach requires quantitative analytical approaches that translate different measures of neural activity into a common space (see Cross-species behavioural assays, Cross-species neural analyses and Cross-species computational modelling). In this manner, data obtained from different recording modalities can be directly compared. This third approach can thus facilitate an interplay between human and animal research that goes beyond the sum of its parts.

Cross-species MRI

The same tools need to be employed across multiple species (Approach 2). Cross-species MRI seeks to do exactly this, using non-invasive MRI to quantify neural structure and function in vivo across both animals and humans. Firstly, comparable signals can be obtained across species, allowing direct comparison by providing a means to assess structural and functional homology while also identifying brain regions and connections unique to a particular species. Secondly, cross-species MRI can be combined with invasive methods available in animal models. Therefore, histology, immunohistochemistry and other invasive methods can be carried out after or in combination with MRI assessments. In this manner, cross-species MRI can bridge the divide between aggregate measures of neural activity acquired with imaging and microcircuit-level activity measured with invasive methods.

At a structural level, diffusion-weighted MRI-based tractography can be used to provide direct anatomical comparisons across species, with validation using tracing studies and histology. For example, direct structural comparisons can be made between human and macaque cortex using surface-based registration to align a few known homologous cortical landmarks. Evolutionary expansion maps generated using this approach can reveal areas in the human brain that have disproportionally expanded (141). Alternatively, connectivity blueprints can be generated for each brain region (or grey matter vertex), and for each species. Within a common space, these connectivity profiles can then be compared to identify common principles and homologies between species, while also revealing unique specialisations (142). For example, when comparing the human brain with the macaque and chimpanzee brain, a large expansion can be observed in the arcuate fasciculus that mediates frontal-temporal connections, suggesting evolutionary divergence since our most recent common ancestor 6 million-years ago (114,143,144). Arguably, these comparative investigations reveal evolutionary relationships between species, while also delineating key differences that obviate the possibility for direct comparison (142).

Perhaps the real versatility of cross-species MRI becomes apparent when considering small-animal MRI. Small animal MRI, in mice and rats, is complicated by the small size of the rodent brain (~0.4g in mouse versus ~1.4kg in humans). Yet recent developments in cryo-coils (145), optimised imaging sequences and ultra-high field imaging ensure sufficient signal-to-noise for submillimetre spatial resolution. Small-animal MRI can therefore support reliable whole-brain fMRI in rodents and can be coupled with invasive methods that characterise neural circuits and establish causal specificity. Particularly in mice, this opens up an opportunity to take advantage of transgenic lines and genetically engineered mouse models. Small-animal MRI therefore provides a unique opportunity to characterise microcircuits while concomitantly acquiring whole-brain signatures of neural activity during behaviour.

To date, small-animal MRI has predominantly been carried out in anaesthetized or sedated animals, primarily due to the requirement to hold the head in the same position during imaging. This makes small-animal MRI highly suitable for studies investigating structural changes. For example, long lasting structural changes attributed to learning can be observed via regional changes in brain volume (146,147), or diffusion properties (148,149), even after only one day of learning (150). With the introduction of quantitative imaging and microstructural modelling approaches, structural imaging is moving closer to accurate estimates of neural morphometry (151–153).

Under anaesthesia, small-animal fMRI is also increasingly being used to probe whole-brain functional connectivity (‘resting-state fMRI’) (154). When applied across species this approach permits comparison of functional connectivity fingerprints between rodents, non-human primates, and humans (155). However, proper interpretation of small-animal fMRI relies upon an understanding of the effect of anaesthesia on the BOLD signal, which is complicated by variability in vasodilation caused by different levels of anaesthetic and the use of different anaesthetics across studies (156). Despite limitations, stimulus-evoked paradigms have been implemented in anaesthetised mice to successfully map layer-specific BOLD activation (157), whole-brain circuits (158) and monitor recovery and interventions following experimental stroke models (159). The full potential for small-animal fMRI may be realised when implementing imaging during awake behaviour, but minimising animal movement and potential distress presents a significant challenge. Despite these technical difficulties, fMRI in awake behaving rodents has recently been demonstrated in a Pavlovian fear conditioning paradigm in rats (160) and in an inhibitory control task in mice (161).

The versatility of small animal imaging has further led to wide-spread use of preclinical imaging as a test bed for pharmaceutical research. For example, preclinical imaging is now being used for high-throughput phenotyping of transgenic animals, profiling of new disease models, pharmacological and pharmacokinetic analysis for target identification, safety testing and evaluation of drug-effects on host anatomy, function, and metabolism (162,163). The noninvasive nature of pre-clinical imaging renders longitudinal studies possible, along with experimental designs that use each animal as their own control. As most preclinical imaging techniques are analogous to those available in the clinical setting, results have the potential to be translated into humans (164,165). Thus, this approach seeks to obtain non-invasive markers of neural activity that can be readily measured in human health and disease.

Cross-species behavioural assays

Although structural and functional homology across the mammalian brain broadly justifies adopting a cross-species approach, neural representations that support cognition cannot be measured and compared across species without comparable behavioural assays.

The systematic monitoring of overt behaviour in humans and animals began with the work of behaviourists in the early twentieth century. Work by Tolman, among others, further introduced the idea that overt behaviour may be considered the effect of a number of variables that include inputs from the environment (stimuli), but also motivational and emotional state, and internal representations of the environment stored within a ‘cognitive map’ (166). This nuanced perspective of behaviour accounts for the rich and flexible repertoire observed in humans and animals, but also highlights the challenges associated with modelling human behaviour in animals. In the absence of direct communication, animal behaviour is difficult to interpret. Furthermore, some behaviours are difficult to model or simply considered unique to humans. The high failure rates reported in clinical trials for neuropsychiatric drugs may, in part, be attributed to poor behavioural assays that fail to either simulate or quantify the full complexity of behaviour observed in patients (see Translational value of bridging the macroscopic and microscopic levels).

To take advantage of the potentially rich behavioural repertoire of animals, first we need to develop more advanced tools to quantify animal behaviour (100,101). Second, we need to develop behavioural assays that can be implemented in both humans and animal models. One approach involves using virtual reality (VR) to simulate three-dimensional (3D) environments. VR provides a means to deliver sensory stimulation within a dynamic, immersive and realistic environment, while ensuring tight control over experimental variables during physiological and behavioural monitoring. By carefully considering species-specific differences in the processing and response to stimuli, including their perceived saliency, near-equivalent VR environments can be employed across multiple species (167). In this manner, behavioural assays that employ VR can permit direct comparison of microscopic and macroscopic neural measures during the same cognitive task.

VR in humans has been used to compare performance on spatial mazes that are well characterised for investigating learning, memory and spatial navigation in rodents. For example, combining VR with fMRI in humans can be used to obtain a non-invasive measure of grid cells (67), previously reported using physiological recordings in rodents (168). A similar approach has been used to ask whether humans represent 3D space, where VR is combined with fMRI in humans (169,170), and may be directly compared with physiological recordings of rodents navigating similar environments in 3D (171).

These VR behavioural assays may further bridge preclinical and clinical research as they are easy to translate into clinical populations. For example, performance on VR environments designed to mimic well characterised spatial mazes previously investigated in rodents are sufficiently sensitive to detect clinical impairments observed in Alzheimer’s disease (172) and schizophrenia (173). Converting well established behavioural paradigms into VR may therefore provide a means to compare data across species (174) and within patient populations (175).

Additionally, more complex behaviours can be captured by continuous monitoring via microchips and radio-frequency antennas or cameras (176–178). In rodents, these measures can capture social hierarchies and exploration patterns, all in the ethologically valid - and potentially enriched - home environment, which in turn can be translated to equivalent human behaviours. For behaviours that cannot be readily modelled in rodents or other animal models, such as tool use, the complex behavioural repertoire of non-human primate provides a unique opportunity to model higher-order cognitive processes that are shared with humans.

Cross-species neural analyses

To integrate micro- and macroscopic levels of description, we must also take advantage of state-of-the-art tools available in different species (Approach 3). This necessitates cross-species comparison across different recording modalities. However, different measures of neural activity, such as single-cell recordings versus fMRI, do not have a common unit measure for neural activity. We therefore need quantitative analytical approaches that translate between these different recording modalities, to assess shared features and deviations in anatomical and functional organisation within a common space (160). One suggestion involves extracting the representational geometry of a given brain region or neural circuit (63). By building on mathematical literature on similarity analysis (179,180), this can be achieved using RSA as a data-analytical framework (Fig. 3).

Figure 3. Cross-species neural analyses: RSA.

RSA provides an analysis framework to compare data collected using multiple different recording methods. a-b) RSA involves assessing the activity patterns across voxels (MRI) or across neurons (electrophysiology or calcium imaging) in response to different cues. The relative similarity between pairs of cue-specific activity patterns is then assessed using either a correlation or distance metric. The resulting metrics are entered into a Representational Dissimilarity Matrix (RDM) to reveal the representational geometry of the data. c-d) RSA applied to data from human and macaque monkey inferotemporal cortex (area IT) reveals striking similarities in the overall structure of representational information across species, adapted from (183).

RSA involves estimating the relative similarity in multi-channel measures of neural activity between different conditions (e.g. stimuli or events). Therefore, for each pair of experimental conditions, the similarity in the response pattern elicited by the two conditions is assessed using a correlation or distance metric (181,182). The resulting similarity measures for all pairs of conditions are then entered into a similarity matrix, where each cell in the matrix represents the similarity in neural activity between a pair of experimental conditions. In this manner, the similarity matrix describes the representational content carried by a given brain region. This representational content can be quantified using the correlation distance between the similarity matrix and a theoretical model matrix, or by applying multi-dimensional scaling to the similarity matrix. RSA therefore provides a common framework to quantify the representational content of a given brain region across different recording modalities. Compared to other multivariate methods that aim to extract pattern information (such as MVPA), RSA is unique in abstracting the higher-order structure of representational information (‘second-order isomorphism’) (63).

RSA has been successfully used to compare neural responses to visual objects in humans and non-human primates. Using fMRI and electrophysiological recordings respectively, highly comparable representational structure can be observed in human and macaque inferotemporal cortex (area IT) (183) (Fig. 3). Similarly, RSA applied to fMRI data in humans and electrophysiological recordings in rodents reveals equivalent representational structure in the hippocampus on an inference task.

While this convergence between electrophysiology in animal models and multivariate human fMRI is encouraging, we must bear in mind the limitations of both fMRI and electrophysiology. As discussed above, for fMRI the relationship between neural activity and the BOLD signal measured from a given voxel is non-trivial. For electrophysiology, only a biased sub-sample of neuronal responses are monitored and RSA overlooks information in the precise timing of spikes. The limitation of these recording modalities and differences in methodological sensitivity to representational information may give rise to differences in RSA or other multivariate methods employed across species. For example, multivariate pattern analysis (MVPA) applied to both fMRI and electrophysiology data from the macaque reveals that fMRI MVPA is insensitive to some representational information that can otherwise be decoded from single-unit recordings (184). The accuracy of cross-species RSA will improve if we can account for the missing information inherent to each recording modality, which will be made apparent from investigations where multiple recording modalities are deployed in the same species (Approach 1).

Identifying spatial homologies between species as distant as the mouse and human presents a further challenge. The classic method of mapping like to like in anatomical ontologies, i.e. the mouse hippocampus is equal to the human hippocampus, remains the most employed method. Yet it is likely that homologies between rodent and human will not be best captured by this type of one-to-one mapping. Instead, it is plausible that, over the course of evolution, functions that are highly localized in one species might be more distributed in another. Using additional information, such as the expression patterns of homologous genes or connectivity mapped via resting state fMRI or diffusion MRI (185), could provide for more complex spatial transformations from one species to the other.

Cross-species computational modelling

In addition to analytical tools (see Cross-species neural analyses), computational models may be used to bridge the explanatory gap between neural recordings in humans and animal models (Approach 2 and 3). By mathematically formalising the complex interactions inherent to the brain, computational models can extract common quantitative descriptions for neural activity at both a micro- and macroscopic levels. The resulting models may further be used to simulate and predict the effect of biophysical activity at both a cellular and systems level.

Perhaps the most elegant example of a computational model that provides a common description for neural activity at both the microscopic and macroscopic level comes from reinforcement learning algorithms. Based on animal learning experiments of classical conditioning (186,187), the Rescorla-Wagner algorithm was devised to account for the fact that learning is dependent upon the degree of unpredictability of a reinforcer (188,189). The real-time extension of this algorithm, called temporal difference (TD) learning, incorporates a reward prediction error signal to learn a reward prediction signal. While this prediction error signal was initially hypothetical, researchers later discovered that it provides a good approximation for the temporal profile of activity in midbrain dopamine neurons, recorded using electrophysiology in the macaque (190,191) and in mice (84). At the macroscopic level, the TD learning algorithm can be fit to human behaviour. When combined with fMRI, this model-based approach reveals a reliable signature of reward prediction error signals in the human midbrain during classical conditioning paradigms (192).

While computational models of reinforcement learning provide a compelling case study, their ability to successfully explain cellular and macroscopic descriptions of neural activity, together with behaviour, may be the exception rather than the norm. Such close correspondence between neural activity and algorithms that describe behaviour may simply be a rare find. More commonly, computational models fall short of such parsimonious mathematical abstraction, but may nevertheless constrain interpretation of data to provide hypothetical insight into the underlying circuit mechanism or predict brain responses to a set of stimuli.

For example conceptual models, such as hippocampal models for pattern separation and completion, have explanatory power and constrain interpretation of data recorded at both a neural circuit level (193) and using human fMRI (70,194). Biophysically plausible models inspired by invasive recording in animal models (195–198) can provide mechanistic insight into aggregate neural activity measured using non-invasive methods in humans (69,199–201). More extensive network models, such as deep-neural networks trained using supervised learning, can account for visual representations in both the human and macaque brain (202). In addition to performing image classification, extracting the internal representations of these deep-neural networks may inform our understanding of the mammalian visual cortex, holding predictive power for data acquired across different species.

Meanwhile, across biology an alternative set of computational models are being developed to provide a means to directly translate findings across species. While avoiding the onerous task of biophysical realism, these models aim to explicitly translate findings from one species to another by describing a mapping between physiological parameters across species. Allometric scaling techniques can account for differences between species, where, for example, simple relationships between species are estimated using differences in body or brain weight. More accurate attempts to model physiological approaches have involved developing physiologically based pharmacokinetic (PBMK) modelling, where physiological and biochemical differences between species are used to translate mechanistic knowledge from one species into another (203–205). These biophysical models are playing an increasingly important role in assessing the effects of potential therapeutic intervention across the biomedical sciences. This is critical for translational work where different phases of drug development are necessarily conducted in different species, and attrition rates for first-in-human studies are above 30% (206). While currently used for translational work, these models may also provide the necessary tools for reliable cross-species extrapolation of basic research. Thus, by explicitly accounting for differences between species, computational models may formalise translation from microcircuit-level measures in animal models to macroscopic-level measures in humans.

Translational value of bridging the macroscopic and microscopic levels

Non-invasive measures of human brain activity are not routinely used as a tool for diagnosis, despite being readily available. As discussed above, this may be attributed to the explanatory gap between macroscopic measures of neural activity acquired using tools such as fMRI, and microcircuit mechanisms recorded in animal models.

Across medicine, this is perhaps most evident in modern psychiatry (207). Diagnosis in psychiatry is still dependent upon subjective behavioural tests that are not linked with physiological or histological abnormalities. This is further complicated by poor delineation between disease categories and heterogeneity across the current disease classification schemes. But without an understanding of the underlying pathophysiology or the full complexity of psychiatric disease, assumptions made when selecting an animal disease model are compromised. Consequently, animal disease models often show limited predictive power and fail to translate to humans. The majority of neuropsychiatric drugs have instead been discovered serendipitously and the molecular targets largely reverse engineered (208).

Even in cases where there is a single gene disorder promising results in animal models have at times failed to translate into drug development. A good example is the recent mGluR5 trials in Fragile X Syndrome. This high failure rate may in part be attributed to poor methodology. For example, animal studies appear to overestimate the likelihood of a treatment being effective, simply because negative results are often unpublished (209). For disorders of brain development or ageing a further challenge involves identifying common timepoints and stages of disease progression. Furthermore, despite highly conserved neuronal mechanisms via evolutionary descent, critical genetic, molecular, cellular and immunologic differences do occur between humans and animals. Therefore, animal models may provide a good model for a set of processes within a disease while failing to account for the full spectrum of physiological changes that occur in humans (210). Critically, current measures in pre-clinical trials are often poorly translated to human clinical trials, providing a further translational challenge.

In the current socioeconomic climate, the cost of developing new neuropsychiatric drugs and neurotechnologies is rising, and as a result, pharmaceutical companies will move away from neuroscience to shift resources to more profitable areas. By developing a cross-species approach within fundamental neuroscience, we propose a means to build a foundation from which to bridge the explanatory gap between a behavioural characterisation of neuropsychiatric disease and the underlying pathophysiology. This may be achieved by developing sensitive and effective tools for cross-species basic research that include imaging, behavioural assays, analytical methods and computational models, as outlined above.

Conclusions

Neuroscience has seen substantial development of non-invasive methods available for investigating the living human brain. Yet, due to ethical and practical difficulties, these methods rarely permit insight into microcircuit level mechanisms. To access the microcircuit, researchers instead rely on invasive recordings in animals, where recent advances in genetic tools now permit circuit level manipulations with exquisite spatiotemporal precision. However due to challenges associated with animal research, there has been limited progress in understanding how neural circuits interact or relate to complex behaviour. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of cognition and behaviour in humans, and microscopic descriptions of cellular and synaptic processes in animal models. To close this explanatory gap and establish a more holistic description of brain function, here we call for an integrative cross-species approach. This approach is broadly justified by evidence showing preserved homology of neural circuits across mammals.

To embark on effective cross-species investigation, first we highlight the need to establish a deeper understanding of the relationship between non-invasive methods, such as the BOLD signal, and underlying neural activity. This may be achieved by employing multiple different tools within the same species. Second, to promote comparative investigation across species, we need to employ the same tools across multiple species. Cross-species MRI provides a unique opportunity to achieve this, by obtaining non-invasive markers of neural activity in both humans and animals that can be directly related to invasive manipulations in animals. When combined with cross-species behavioural assays, as exemplified by studies using VR, this comparative approach has the potential to reveal non-invasive markers of microcircuit mechanisms. Third, by taking advantage of the best tools available in each species, cross-species analyses and computational modelling may provide a means to translate measures of neural activity into a common space, despite differences in species and recording modality. Together, these three approaches may bridge the explanatory gap between macroscopic and microscopic descriptions of neural activity in the living human brain. In the context of clinical translation, where we have seen minimal success in neuropsychiatric drug development, a cross-species approach has the potential to reveal pathophysiology mechanisms responsible for neuropsychiatric disease.