Marco Dal Maschio

Animals need to precisely perceive and integrate the environmental cues to orient and select the appropriate motor responses required for navigating. This is the case, for instance, of the optokinetic reflex (OKR) and the optomotor response (OMR) in Drosophila melanogaster , where optic flow stimulation modulates the head or the body and legs motor activity respectively. Despite large bodies of literature on both the OKR and the OMR, there is still a limited understanding, in flies, of the impact on these responses of concomitant, and potentially conflicting, sensory inputs. To investigate this aspect, we used fruit flies walking on a sphere, presented with optic flow stimulation leading to the OMR together with the simultaneous exposure to olfactory stimulation, either using established repellent or masking compounds. We analysed the effect of different substances, and of their concentration, on the dynamics of the flies’ response to moving gratings, evaluating the fly walking path as well as average speed and duration. This analysis revealed several alterations between the compounds tested, in agreement with reported data on the simpler OKR. In conclusion, we show that concomitant exposure to repellents and maskers may consistently affect fundamental processes (the OKR and OMR) available to insects for informing themselves while navigating through the environment.

Biological and living systems process information across spatiotemporal scales, exhibiting the hallmark ability to constantly modulate their behavior to ever-changing and complex environments. In the presence of repeated stimuli, a distinctive response is the progressive reduction of the activity at both sensory and molecular levels, known as habituation. In this work, we solve a minimal microscopic model devoid of biological details, where habituation to an external signal is driven by negative feedback provided by a slow storage mechanism. We show that our model recapitulates the main features of habituation, such as spontaneous recovery, potentiation, subliminal accumulation, and input sensitivity. Crucially, our approach enables a complete characterization of the stochastic dynamics, allowing us to compute how much information the system encodes on the input signal. We find that an intermediate level of habituation is associated with a steep increase in information. In particular, we are able to characterize this region of maximal information gain in terms of an optimal trade-off between information and energy consumption. We test our dynamical predictions against experimentally recorded neural responses in a zebrafish larva subjected to repeated looming stimulations, showing that our model captures the main components of the observed neural habituation. Our work makes a fundamental step towards uncovering the functional mechanisms that shape habituation in biological systems from an information-theoretic and thermodynamic perspective.

In an ever-changing environment, animals continuously implement decision-making processes. This mechanism primarily relies on gathering sensory information through evidence accumulation, which enables the optimization of strategies in uncertain conditions. According to this scheme, the higher the evidence level, the greater the level of accuracy. However, it is not yet clear which, apart from direct sensory inputs, are the other factors that potentially impact strategy selection and their corresponding temporal operating windows in invertebrates. Moreover, it is not guaranteed that maximal accuracy could be the only parameter dictating the decision-making process. To address these questions, we employed a visual stimulation paradigm based on the random dot motion kinetogram (RDMk) with varying coherence levels, applied to tethered Drosophila melanogaster . In this scenario, flies tend to turn their heads according to the optic flow’s strength and direction. We observed stereotyped head saccadic-like movements across all the RDMk coherence levels, with greater coherence leading to a higher total saccade number and an increased proportion of correct turns. Importantly, logistic regression analysis revealed that, besides sensory inputs, the behavioral outcome and, consequently, the maximal accuracy, are influenced by other factors, in particular the past motor activity. In conclusion, our data support a scenario where accuracy and reaction time are balanced to reach an optimal response condition.

Bessel beams (BBs) are propagation‐invariant optical fields that retain a narrow central intensity profile over longer propagation lengths than Gaussian beams (GBs). Due to this property, they have been adopted in fluorescence‐based light sheet microscopy (LSM) to obtain 2D longitudinally‐extended light‐sheets. Yet, current approaches for generating BB lattices in LSM focus on regular excitation patterns and involve complex and bulky optics, limiting integration capability and versatility. Here, a flexible method is presented to obtain BB‐arrays with arbitrary geometries by encoding on a single sub‐millimetric surface all the optical transformations required. This method is applied using a single metasurface to encode the generation of a linear array of BBs, avoiding the use of conjugation and focusing optics. With respect to the current strategies, this approach, allowing for the independent design of each beamlet of the array, increases the degrees of freedom while making optimal use of the available light with no rejection, thus facilitating its integration into optical systems. According to this method, we fabricated a metasurface‐based optical element for generating a linear BB‐array of excitation in an LSM configuration and recorded neuronal activity at cellular resolution from the zebrafish larva brain. Thus, the proposed approach greatly extends the BB‐array versatility and the application scenarios.

Animals’ ability to orient and navigate relies on selecting an appropriate motor response based on the perception and integration of the environmental information. This is the case, for instance, of the optokinetic response (OKR) in Drosophila melanogaster , where optic flow visual stimulation modulates the walking or flying patterns. Despite a large body of literature on the OKR, there’s still a limited understanding of the impact on OKR of concomitant, potentially conflicting, inputs. To evaluate the impact of this multimodal integration, we combined in D. melanogaster , while flying in a tethered condition, the optic flow stimulation leading to OKR with the simultaneous presentation of olfactory cues, based on repellent or masking compounds typically used against noxious insect species. First, this approach al lowed us to directly quantify the effect of several substances and their concentration, on the dynamics of the flies’ OKR in response to moving gratings by evaluating the number of saccades and the velocity of its slow phase. Subsequently, this analysis was capable of easily revealing the actual effect, i.e. masking vs repellent, of the compound tested. In conclusion, we show that D. melanogaster , a cost-affordable species, represents a viable option for studying the effects of several compounds on the navigational abilities of insects.

The effects of an early impoverished social or physical environment on vertebrate neural development and cognition has been known for decades. While existing studies have focused on the long-term effects, measuring adult cognitive phenotypes, studies on the effects of environmental complexity on the early stages of development are lacking. Zebrafish (Danio rerio) hatchlings are assumed to have minimal interaction with their environment and are routinely reared in small, bare containers. To investigate the effects of being raised under such conditions on development of behaviour and cognition, hatchlings housed for 10 days in either an enriched or a standard environment underwent two cognitive tasks. The results were mixed. Subjects of the two treatments did not differ in performance when required to discriminate two areas. Conversely, we found a significant effect in a number discrimination task, with subjects from impoverished condition performing significantly worse. In both experiments, larvae reared in impoverished environment showed a reduced locomotor activity. Given the effects that enrichment appears to exert on larvae, a third experiment explored whether hatchlings exhibit a spontaneous preference for more complex environments. When offered a choice between a bare setting and one with objects of different shapes and colors, larvae spent over 70% of time in the enriched sector. Deepening these effects of an early impoverished environment on cognitive development is crucial for the welfare of captive zebrafish populations and for enhancing the quality and reliability of studies on larval zebrafish.

Women are the main target of intimate partner violence (IPV), which is escalating worldwide. Mechanisms subtending IPV-related disorders, such as anxiety, depression and PTSD, remain unclear. We employed a mouse model molded on an IPV scenario (male vs. female prolonged violent interaction) to unearth the neuroendocrine alterations triggered by an aggressive male mouse on the female murine brain. Experimental IPV (EIPV) prompted marked anxiety-like behavior in young female mice, coincident with high circulating/cerebral corticosterone levels. The hippocampus of EIPV-inflicted female animals displayed neuronal loss, reduced BrdU-DCX-positive nuclei, decreased mature DCX-positive cells, and diminished dendritic arborization level in the dentate gyrus (DG), features denoting impaired neurogenesis and neuronal differentiation. These hallmarks were associated with marked down-regulation of estrogen receptor β (ERβ) density in the hippocampus, especially in the DG and dependent prosurvival ERK signaling. Conversely, ERα expression was unchanged. After EIPV, the DG harbored lowered local BDNF pools, diminished TrkB phosphorylation, and elevated glucocorticoid receptor phosphorylation. In unison, ERβ KO mice had heightened anxiety-like behavior and curtailed BDNF levels at baseline, despite enhanced circulating estradiol levels, while dying prematurely during EIPV. Thus, reiterated male-to-female violence jeopardizes hippocampal homeostasis in the female brain, perturbing ERβ/BDNF signaling, thus instigating anxiety and chronic stress.

Functional brain activity is supported by specific circuit wiring diagrams. When the synaptic annotation is available, the analysis of high-resolution connectomes allows for unraveling the circuit architectures. Despite the continuous technological and computational improvements, obtaining a whole-brain bauplan based on synaptic annotation remains a demanding effort. As an alternative, we present here an approach to extract an approximated brain connectome starting from libraries of single cell anatomical reconstructions aligned on the same anatomical reference brain. Our approach relies on the identification of neurite terminal nodes based on Strahler numbering of the cell morphology, and the adoption of a proximity range criterion, so as to infer, in the absence of synapse information, approximated brain network architectures. As an initial benchmark we used information theory metrics to confront our approach against a synaptically-annotated EM dataset of Drosophila melanogaster hemibrain. The comparison with our approach revealed a general agreement in the organization of the extracted connectivity modules measured in terms of Normalized Mutual Information, Adjusted Rand Index and Pearson correlation. Moreover, we show that the modules identified, along with their organization, can capture known circuit motives. We then applied this approach to a light microscopy dataset of the zebrafish larval brain composed of about 3000 neuronal skeletonizations. We show that the approximated connectome and the resulting modular organization is capable of capturing specific and topographically organized connection patterns as well as known functional circuit architectures. In conclusion, we present a scalable, from-circuit-to-brain range, approach to reveal approximated neuronal architectures supporting brain mechanisms, potentially suitable for hypothesis generation and for guiding the exploration of and integration with EM connectomes. Author Summary: Understanding how the brain works requires detailed maps of its neural connections, known as connectomes. While advanced techniques like electron microscopy (EM) can map these connections at the level of individual synapses, they are time consuming and resource intensive, limiting their use. As an alternative, we developed a computational method that approximates brain connectivity using existing datasets of neuron shapes (morphologies) without requiring synaptic annotations. Our approach identifies potential connections between neurons based on the proximity of their terminal branches regions where synapses are likely to form within a shared 3D reference brain. We validated our method using a high-resolution EM connectome of the fruit fly brain, demonstrating that it captures broad organizational patterns, such as clusters of densely interconnected neurons, despite moderate agreement at the synaptic level. Applying the method to a light microscopy dataset of the zebrafish larva brain (∼3,000 neurons), we successfully reconstructed large-scale networks that recapitulated known functional circuits. This approach offers a scalable way to extract brain-wide connectivity principles from existing datasets, bridging the gap between cellular anatomy and circuit function. It can guide targeted experiments and complement future EM studies, making neuroscience data more accessible for hypothesis generation.