<figure><img src="image_1.png" /></figure>

HAL
open science

# From mouse to man-a bridge too far?

Henry Kennedy, Colette Dehay

► To cite this version:

Henry Kennedy, Colette Dehay. From mouse to man-a bridge too far?. National Science Review, 2020, 7 (7), pp.1258 - 1259. 10.1093/nsr/nwz225. hal-04932494

HAL Id: hal-04932494

https://hal.science/hal-04932494v1

Submitted on 6 Feb 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
From mouse to man—a bridge too far?

The contemplation of our demise, particularly via the loss of cog-
nitive functions, is challenging for our species. While we seek to
understand our brain and its action as a particularly intriguing
manifestation of the physical universe, society also seeks solu-
tions to concrete problems and, here, understanding the brain
as an intellectual exercise must be extended to being able to
maintain and repair its function. Just how difficult this is be-
comes apparent when one considers the biological functions
of brains. The enormous capacity for adaptation of the mam-
mal class is reflected by an extraordinary range of brain sizes
from cortices of 8 kilograms to a mere fraction of a gram in cer-
tain rodents. Changes in brain size directly impact on the ar-
chitecture of the cortex [1]. This poses an insoluble problem;
a reductionist approach would lead us to assume that differ-
ent sorts of mammalian brains are all doing the same thing, but
the biological adaptation that they reflect argues against this
notion [2].

Thirty years ago, the model of choice for studying neuronal mechanisms underlying cognitive function was the visual system of the awake-behaving monkey. This allowed recoding of single neurons and the investigation of the feedforward pathway leading to a deep understanding of the representation of the world via the construction of receptive fields across the cortical hierarchy. However, the advent of genetic tools in the mouse and the ability of optic control of neuronal activity has ensured that, in 2020, the mouse has largely replaced the macaque as the model of choice. But then we must wonder how relevant the mouse brain is for understanding the human brain.

Rodent models exploiting mouse transgenics have made
brilliant progress in elucidating the cellular specifics of diverse
aspects of cortical cellular mechanisms involved in motor and
cognitive processes (e.g. [3,4]). Can we approach a similar
level of understanding in the non-human primate cortex?
Many cortical features are constant across species. This is
exemplified by the preservation of the areal layout reflecting
genetic regulation of conserved graded transcription pattern
expression during corticogenesis [5] and many aspects of
the specification, migration and cell-type differentiation to
form functional circuits proceed in a stereotypical fashion
across species [6]. However, key aspects of human perception,
cognition and behavior appear to depend on primate-specific
particularities of the cerebral cortex, and this may explain why
so many human neurological and neuropsychiatric diseases are
inadequately modeled in rodents [7]. Primate-specific special-
izations, we speculate, stem from the unique developmental

processes found in human and non-human primates [8–10]. These specializations generate numerous *qualitative* differences between rodent and primate cortices, of which we cite five examples. (i) There are dramatic changes in the dimensions of pyramidal dendritic arbors across the cortex in primates [11, 12] that are theorized [13] to shape the observed hierarchy of timescales [14]); these gradients in pyramidal cell dimensions are absent in rodents [15]. (ii) The mouse anterior cingulate cortex has significant output to area V1 that is known to drive experience-dependent spatial and motor [16] expectations as well as spatial attention [17]. There are no such connections in primates, meaning that the anterior cingulate cortex and area V1 are not homologous in primates and rodents. (iii) There is a dense inter-areal network linking the primary sensory and motor areas in the mouse [18], providing the anatomical substrate for a rodent-specific multimodal integration [18–20]; this network is absent in the macaque [21]. (iv) A dramatic shift in cortical layer-specific gene expression is observed, from the infragranular layers in rodents to the supragranular layers in primates, that constitutes further evidence of the expansion and changing role of the upper layers in primates [22]. (v) Finally, recent claims in rodents of non-canonical cortico-cortical feedback loops [23] and non-canonical cortical microcircuits [24] suggest further evidence of circuit-level species differences.

Presently, researchers are partly overcoming the gap between
mouse and primate by creating non-human primate (NHP)
models of disease [25]. This research, largely led by China,
goes towards solving the problem of a model organism for
understanding pathology in so far as it allows invasive inves-
tigation of a genetically modified macaque. But is this going
to allow sufficient progress in understanding the healthy pri-
mate brain? It could be argued that, until we know the neu-
ral mechanisms underlying perceptual and cognitive processes
in the normal healthy brain are, we are not going to fully un-
derstand the pathologies of the cardinal features of human
intelligence. The emerging model for understanding cortical
processing is Bayesian inference that plays out in hierarchi-
cal cortical processing known as predictive coding, which di-
rectly concerns the integration of ascending, feedforward and
descending feedback signals into the local circuit [26,27].
Predictive coding has been described as a strange inversion in
which the brain, instead of being thought of as a stimulus–
response link, is conceived as ensuring an inference providing
explanations of our sensorium [28]. Feedforward pathways are
thought to directly contribute to the elaboration of the receptive
field from sensory input and, here, 50 years in electrophysiolog-
ical experiments, first in cats, then monkeys and more recently
in mice, have allowed a very detailed understanding. Predictive
coding postulates that the feedback pathway relays predictions
and here it has been much harder to determine the underly-
ing neural mechanisms involved. Importantly, these feedback
pathways are implicated in human neurological disorders in-
cluding schizophrenia and autism [29]. We have shown that, in
primates, there are multiple routes taken by feedback connec-
tions in the primate [30] and one can speculate that they will
differently impact on the numerous functions subscribed to the
feedback pathway including those proposed by predictive cod-
ing theory. Disentangling these diverse feedback pathways and
determining the function of the circuits they support will cru-
cially require the use of optogenetic tools. Could this work sim-
ply be carried out in the mouse? In addition to the myriad
rodent-primate differences alluded to above, there are many
reasons why one might imagine that top-down processes are
quantitatively and perhaps qualitatively different across species
with different capacities of behavioral plasticity, such as ob-
served in mice versus monkeys. There is therefore an ethi-
cal argument that demands that this research is undertaken in
macaques. This would require the creation of monkey transgenic
lines expressing Cre in restricted layers and cell types to allow
targeted circuit manipulation.

The reductionist approach supports the notion of a model
organism. In addition to philosophical objections to excessive
reductionism, there are pitfalls in thinking of the brains of even
closely related species as being necessarily equivalent. For exam-
ple, SRGAP2 is a gene that is implicated in neocortical devel-
opment and undergoes human-specific duplications. Recently,
experiments in mice have suggested that, in humans, SRGAP2
induces neotenic spine maturation [31]. The maintenance of
juvenile features in the adult brain (neoteny) endows physio-
logical properties to the adult human, via unique dendritic spine
features that are presumably not found in non-human primates,
including apes. These findings suggest that we can expect clear
limitations in the concept of an NHP model for understanding
the totality of the integrative neurobiology of the human brain,
and therefore point to the need for humanization procedures,
allowing the function of a human gene of interest to be stud-
ied. Interestingly, the relevance of the neoteny hypothesis for
understanding the human brain has been elegantly explored by
the generation of transgenic monkeys carrying human copies of
the MCPH1 [32], revealing improved short-term memory and
reduced reaction times in delayed match-to-sample tasks. Over-
all, these findings point to the need for a complex triangulation
with investigation of different models but with a clear emphasis
on the non-human primate. They also urge us to address the eth-
ical issues raised by the nature of these experiments on our near
cousins, as well as the ethics of not doing these experiments.

Henry Kennedy <fnref n="1" />,<fnref n="2" />,* and Colette Dehay<fnref n="1" />

REFERENCES

1. Horvat S, Gamanut R and Ercsey-Ravasz M *et al.* *PLoS Biol* 2016; **14**: e1002512.

2. Nagel T. *Mind and Cosmos*. New York: Oxford University Press, 2012.

3. Li N, Daie K and Svoboda K *et al.* *Nature* 2016; **532**: 459–64.

4. Kim H, Ahrlund-Richter S and Wang X *et al.* *Cell* 2016; **164**: 208–18.

5. Krubitzer L. *Ann N Y Acad Sci* 2009; **1156**: 44–67.

6. Workman AD, Charvet CJ and Clancy B *et al.* *J Neurosci* 2013; **33**: 7368–83.

7. Izpisua Belmonte JC, Callaway EM and Caddick SJ *et al.* *Neuron* 2015; **86**: 617–31.

8. Smart IH, Dehay C and Giroud P *et al.* *Cereb Cortex* 2002; **12**: 37–53.

9. Betizeau M, Cortay V and Patti D *et al.* *Neuron* 2013; **80**: 442–57.

10. Geschwind DH and Rakic P. *Neuron* 2013; **80**: 633–47.

11. Elston GN. *J Neurosci* 2000; **20**: RC95.

12. Elston GN. Specialization of the neocortical pyramidal cell during primate evolution. In: Kaas JH and Preuss TM (eds). *Evolution of Nervous Systems: A Comprehensive Reference*. New York: Elsevier, 2007, 191–242.

13. Chaudhuri R, Knoblauch K and Gariel MA *et al.* *Neuron* 2015; **88**: 419–31.

14. Murray JD, Bernacchia A and Freedman DJ *et al.* *Nat Neurosci* 2014; **17**: 1661–3.

15. Hsu A, Luebke JI and Medalla M. *J Comp Neurol* 2017; **525**: 2175–91.

16. Leinweber M, Ward DR and Sobczak JM *et al.* *Neuron* 2017; **95**: 1420–32.

17. Zhang S, Xu M and Kamigaki T *et al.* *Science* 2014; **345**: 660–5.

18. Gamanut R, Kennedy H and Toroczkai Z *et al.* *Neuron* 2018; **97**: 698–715.

19. Lee HK and Whitt JL. *Curr Opin Neurobiol* 2015; **35**: 119–26.

20. Olcese U, Iurilli G and Medini P. *Neuron* 2013; **79**: 579–93.

21. Markov NT, Ercsey-Ravasz MM and Ribeiro Gomes AR *et al.* *Cereb Cortex* 2014; **24**: 17–36.

22. Zeng H, Shen EH and Hohmann JG *et al.* *Cell* 2012; **149**: 483–96.

23. Minamisawa G, Kwon SE and Chevee M *et al.* *Cell Rep* 2018; **23**: 2718–31 e6.

24. Pluta S, Naka A and Veit J *et al.* *Nat Neurosci* 2015; **18**: 1631–40.

25. Qiu P, Jiang J and Liu Z *et al.* *Nat Sci Rev* 2019; **6**: 87–100.

26. Rao RP and Ballard DH. *Nat Neurosci* 1999; **2**: 79–87.

27. Markov NT and Kennedy H. *Curr Opin Neurobiol* 2013; **23**: 187–94.

28. Friston K. *Nat Neurosci* 2018; **21**: 1019–21.

29. Friston KJ, Stephan KE and Montague R *et al.* *Lancet Psychiatry* 2014; **1**: 148–58.

30. Markov NT, Vezoli J and Chameau P *et al.* *J Comp Neurol* 2014; **522**: 225–59.

31. Charrier C, Joshi K and Coutinho-Budd J *et al.* *Cell* 2012; **149**: 923–35.

32. Shi L, Luo X and Jiang J *et al.* *Natl Sci Rev* 2019; **6**: 480–93.

<footnote n="1">¹Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, France</footnote>

<footnote n="2">²Institute of Neuroscience, Chinese Academy of Sciences, China</footnote>

<footnote>*Corresponding author. E-mail: henry.kennedy@inserm.fr</footnote>