An agent-based model clarifies the importance of functional and developmental integration in shaping brain evolution

Mosaic evolution of a learning and memory circuit in Heliconiini butterflies

How do neural circuits accommodate changes that produce cognitive variation? We explore this question by analyzing the evolutionary dynamics of an insect learning and memory circuit centered within the mushroom body. Mushroom bodies are composed of a conserved wiring logic, mainly consisting of Kenyon cells, dopaminergic neurons, and mushroom body output neurons. Despite this conserved makeup, there is huge diversity in mushroom body size and shape across insects. However, empirical data on how evolution modifies the function and architecture of this circuit are largely lacking. To address this, we leverage the recent radiation of a Neotropical tribe of butterflies, the Heliconiini (Nymphalidae), which show extensive variation in mushroom body size over comparatively short phylogenetic timescales, linked to specific changes in foraging ecology, life history, and cognition. To understand how such an extensive increase in size is accommodated through changes in lobe circuit architecture, we combined immunostainings of structural markers, neurotransmitters, and neural injections to generate new, quantitative anatomies of the Nymphalid mushroom body lobe. Our comparative analyses across Heliconiini demonstrate that some Kenyon cell sub-populations expanded at higher rates than others in Heliconius and identify an additional increase in GABA-ergic feedback neurons, which are essential for non-elemental learning and sparse coding. Taken together, our results demonstrate mosaic evolution of functionally related neural systems and cell types and identify that evolutionary malleability in an architecturally conserved parallel circuit guides adaptation in cognitive ability.

Embodied Computational Evolution: A Model for Investigating Randomness and the Evolution of Morphological Complexity

For an integrated understanding of how evolutionary dynamics operate in parallel on multiple levels, computational models can enable investigations that would be otherwise infeasible or impossible. We present one modeling framework, Embodied Computational Evolution (ECE), and employ it to investigate how two types of randomness-genetic and developmental-drive the evolution of morphological complexity. With these two types of randomness implemented as germline mutation and transcription error, with rates varied in an [Formula: see text] factorial experimental design, we tested two related hypotheses: ( H1 ) Randomness in the gene transcription process alters the direct impact of selection on populations; and ( H2 ) Selection on locomotor performance targets morphological complexity. The experiment consisted of 121 conditions; in each condition, nine starting phenotypic populations developed from different randomly generated genomic populations of 60 individuals. Each of the resulting 1089 phenotypic populations evolved over 100 generations, with the autonomous, self-propelled individuals under directional selection for enhanced locomotor performance. As encoded by their genome, individuals had heritable morphological traits, including the numbers of segments, sensors, neurons, and connections between sensors and motorized joints that they activated. An individual's morphological complexity was measured by three different metrics derived from counts of the body parts. In support of H1 , variations in the rate of randomness in the gene transcription process varied the dynamics of selection. In support of H2 , the morphological complexity of populations evolved adaptively.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.

Rapid expansion and visual specialisation of learning and memory centres in the brains of Heliconiini butterflies

Changes in the abundance and diversity of neural cell types, and their connectivity, shape brain composition and provide the substrate for behavioral evolution. Although investment in sensory brain regions is understood to be largely driven by the relative ecological importance of particular sensory modalities, how selective pressures impact the elaboration of integrative brain centers has been more difficult to pinpoint. Here, we provide evidence of extensive, mosaic expansion of an integration brain center among closely related species, which is not explained by changes in sites of primary sensory input. By building new datasets of neural traits among a tribe of diverse Neotropical butterflies, the Heliconiini, we detected several major evolutionary expansions of the mushroom bodies, central brain structures pivotal for insect learning and memory. The genus Heliconius, which exhibits a unique dietary innovation, pollen-feeding, and derived foraging behaviors reliant on spatial memory, shows the most extreme enlargement. This expansion is primarily associated with increased visual processing areas and coincides with increased precision of visual processing, and enhanced long term memory. These results demonstrate that selection for behavioral innovation and enhanced cognitive ability occurred through expansion and localized specialization in integrative brain centers.

Homo sapiens and Neanderthals share high cerebral cortex integration into adulthood

There is controversy around the mechanisms that guided the change in brain shape during the evolution of modern humans. It has long been held that different cortical areas evolved independently from each other to develop their unique functional specializations. However, some recent studies suggest that high integration between different cortical areas could facilitate the emergence of equally extreme, highly specialized brain functions. Here, we analyse the evolution of brain shape in primates using three-dimensional geometric morphometrics of endocasts. We aim to determine, firstly, whether modern humans present unique developmental patterns of covariation between brain cortical areas; and secondly, whether hominins experienced unusually high rates of evolution in brain covariation as compared to other primates. On the basis of analyses including modern humans and other extant great apes at different developmental stages, we first demonstrate that, unlike our closest living relatives, Homo sapiens retain high levels of covariation between cortical areas into adulthood. Among the other great apes, high levels of covariation are only found in immature individuals. Secondly, at the macro-evolutionary level, our analysis of 400 endocasts, representing 148 extant primate species and 6 fossil hominins, shows that strong covariation between different areas of the brain in H. sapiens and Homo neanderthalensis evolved under distinctly higher evolutionary rates than in any other primate, suggesting that natural selection favoured a greatly integrated brain in both species. These results hold when extinct species are excluded and allometric effects are accounted for. Our findings demonstrate that high covariation in the brain may have played a critical role in the evolution of unique cognitive capacities and complex behaviours in both modern humans and Neanderthals.

Convergent mosaic brain evolution is associated with the evolution of novel electrosensory systems in teleost fishes

Brain region size generally scales allometrically with brain size, but mosaic shifts in brain region size independent of brain size have been found in several lineages and may be related to the evolution of behavioral novelty. African weakly electric fishes (Mormyroidea) evolved a mosaically enlarged cerebellum and hindbrain, yet the relationship to their behaviorally novel electrosensory system remains unclear. We addressed this by studying South American weakly electric fishes (Gymnotiformes) and weakly electric catfishes (Synodontis spp.), which evolved varying aspects of electrosensory systems, independent of mormyroids. If the mormyroid mosaic increases are related to evolving an electrosensory system, we should find similar mosaic shifts in gymnotiforms and Synodontis. Using micro-computed tomography scans, we quantified brain region scaling for multiple electrogenic, electroreceptive, and non-electrosensing species. We found mosaic increases in cerebellum in all three electrogenic lineages relative to non-electric lineages and mosaic increases in torus semicircularis and hindbrain associated with the evolution of electrogenesis and electroreceptor type. These results show that evolving novel electrosensory systems is repeatedly and independently associated with changes in the sizes of individual major brain regions independent of brain size, suggesting that selection can impact structural brain composition to favor specific regions involved in novel behaviors.

Larger animals tend to have larger brains and smaller animals tend to have smaller ones. However, some species do not fit the pattern that would be expected based on their body size. This variation between species can also apply to individual brain regions. This may be due to evolutionary forces shaping the brain when favouring particular behaviours. However, it is difficult to directly link changes in species behaviour and variations in brain structure.

One way to understand the impact of evolutionary adaptations is to study species that have developed new behaviours and compare them to related ones that lack such a behaviour. An opportunity to do this lies in the ability of several species of fish to produce and sense electric fields in water. While this system is not found in most fish, it has evolved multiple times independently in distantly-related lineages. Schumacher and Carlson examined whether differences in the size of brains and individual regions between species were associated with the evolution of electric field generation and sensing.

Micro-computed tomography, or μCT, scans of the brains of multiple fish species revealed that the species that can produce electricity – also known as ‘electrogenic’ species’ – have more similar brain structures to each other than to their close relatives that lack this ability. The brain regions involved in producing and detecting electrical charges were larger in these electrogenic fish. This similarity was apparent despite variations in how total brain size has evolved with body size across species.

These results demonstrate how evolutionary forces acting on particular behaviours can lead to predictable changes in brain structure. Understanding how and why brains evolve will allow researchers to better predict how species’ brains and behaviours may adapt as human activities alter their environments.