bioRxiv Subject Collection: Neuroscience's Journal

Effects of sex, mating status, and genetic background on circadian behavior in Drosophila
Circadian rhythms play a crucial role in regulating behavior, physiology, and health. Sexual dimorphism, a widespread phenomenon across species, influences circadian behaviors. Additionally, post-mating physiological changes in females are known to modulate various behaviors, yet their effects on circadian rhythms remain underexplored. Here, using Drosophila melanogaster, a powerful model for studying circadian mechanisms, we systematically assessed the impact of sex and mating status on circadian behavior. We measured circadian period length and rhythm strength in virgin and mated males and females, including females mated to males lacking Sex Peptide (SP), a key mediator of post-mating changes. Across four wild-type and control strains, we found that males consistently exhibited shorter circadian periods than females, regardless of mating status, suggesting that circadian period length is a robust sexually dimorphic trait. In contrast, rhythm strength was influenced by the interaction between sex and mating status, with female mating generally reducing rhythm strength in the presence of SP signaling. Notably, genetic background significantly modulated these effects on rhythm strength. Our findings demonstrate that while circadian period length is a stable sex-specific trait, rhythm strength is shaped by a complex interplay between sex, mating status, and genetic background. This study advances our understanding of how sex and mating influence circadian rhythms in Drosophila and provides a foundation for future research into sexually dimorphic mechanisms underlying human diseases associated with circadian disruptions.

Higher-Order Interaction Analysis via Hypergraph Models for Studying Multidimensional Neuroscience Data.
Higher-Order Interaction (HOI) theory offers a powerful framework for capturing complex, non-linear relationships within multidimensional systems, moving beyond traditional pairwise graph methods to encompass multi-way interactions. This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data.

Efficient pheromone navigation via antagonistic detectors
Chemotaxis to a potential mate who is moving and emitting a volatile sex pheromone poses a navigation challenge that requires rapid, precise responses to maximize reproductive success. We demonstrate that Caenorhabditis elegans males address this challenge by utilizing two pheromone detectors located in head and tail sensory neurons. Despite sharing a receptor SRD-1, AWA head neurons promote forward movement and acceleration, while tail PHD neurons induce reversals and deceleration. In increasing pheromone gradients AWA dominates; whereas weakening gradients inactivate AWAs, allowing PHDs to fine-tune the response and correct the path. Head AWAs are essential for mate-searching, while tail PHDs are crucial for complex tasks. The navigation mode and velocity adapt as males climb a pheromone gradient. A minimal-parameter computational model recapitulates key findings and illuminates the interplay between head and tail signals in adaptive navigation.

Decoding Motivational States and Craving through Electrical Markers for Neural 'Mind Reading'
The aim of this electroencephalogram (EEG) study was to identify electrical neuro-markers of 12 different motivational and physiological states such as visceral craves, affective and somatosensory states, and secondary needs. Event-related potentials (ERPs) were recorded in 30 right-handed participants while recalling a specific state upon the presentation of an auditory verbal command incorporating an evocative sound background consistent with that state (e.g. the chirping of cicadas associated with the verbal complaint about feeling hot). ERP data showed larger amplitude N400 responses in the affective and somatosensory states, while the P400 component displayed greater amplitudes for the secondary and visceral states. Furthermore, the two components were also discernibly responsive to the 12 micro-categories (e.g., joy vs. pain or hunger), by providing a distinctive electric pattern for mostly all microstates. The reconstruction of the intracranial generators of surface signals revealed common imagery-related activations, including the middle and superior frontal gyri, the fusiform and lingual gyri, supramarginal, and middle occipital regions, as well as the middle temporal region. Additionally, specific regions were identified that were active for distinct mentally represented content, such as that visceral needs were associated with activations in the medial and inferior frontal gyri, uncus, precuneus, and cingulate gyrus. Affective states were associated with activations in the medial frontal, superior temporal, and middle temporal gyri. Somatosensory states (e.g., pain or cold) activated regions in the parietal cortex and the crave for music was linked to activations in the auditory and motor regions. These findings support the use of ERP markers for BCI applications.

Transcallosal generation of phase aligned beta-bursts underlies TMS-induced interhemispheric inhibition
Background The excitability of the sensorimotor (SM1) cortices is reflected in the bilateral ~20 Hz beta oscillations. The extent to which these oscillations subtend the interhemispheric inhibition captured by the Transcranial Magnetic Stimulation (TMS) ipsilateral Silent Period (iSP) protocol is unknown. Objectives We investigated the relationship between movement-induced beta suppression, iSP, and manual dexterity. Methods Forty adults underwent an Electroencephalography assessment of beta oscillations during volitional left hand movement and a TMS assessment of iSP recorded from the right hand. In both cases, left SM1 beta oscillations (contralateral to the activated right SM1), were monitored through a proxy signal - the Electromyography of the contracted right hand. Manual dexterity was assessed with the Purdue Pegboard Test. Results Volitional movement caused significant bilateral SM1 beta suppression in nearly all participants (85 %). ISPs were observed in every participant. In the proxy signal for the left SM1, the iSP coincides with TMS-induced high-amplitude beta bursts. These bursts showed significant phase alignment across participants 10-70 ms after the TMS pulse. There was no significant association between the left-/right-hemisphere beta suppression, iSP, and manual dexterity. Conclusion Our results highlight the distinct nature of beta oscillation changes during voluntary movement compared to TMS-iSP and show that TMS induces IHI via transcallosal induction of phase aligned beta bursts. Furthermore, our data suggests that only the initial phase of a beta burst carries an inhibitory effect. It also highlights the possibility of evoking a beta burst with the iSP protocol, opening perspectives for future modeling studies.

Visual and auditory object representations in ventral visual cortex after restoring sight in humans
Visual category-selective representations in human ventral occipital temporal cortex (VOTC) seem to emerge early in infancy. Surprisingly, the VOTC of congenitally blind humans features category-selectivity for auditory and haptic objects. Yet it has been unknown whether VOTC would show category-selective visual responses if sight were restored in congenitally blind humans. Assuming competition for synaptic space during development, cross-modal activation of VOTC as a consequence of congenital blindness might interfere with visual processing in sight-recovery individuals. To test this hypothesis, we investigated adults who had suffered a transient phase of congenital blindness due to bilateral dense cataracts before their sight was restored by cataract-removal surgery. In a functional magnetic resonance imaging (fMRI) study, participants watched movies of faces, scenes, body parts and other objects in the visual condition, while in the auditory condition they listened to the corresponding sounds. The most prominent group difference was the reduced face-selectivity in individuals with reversed congenital cataracts compared to age- and sex-matched normally-sighted individuals. In addition, a double dissociation was found: only sight-recovery individuals demonstrated significant decoding accuracy of visual categories based on auditory category representations in VOTC, while only normally-sighted individuals' VOTC decoded auditory categories based on visual category representations. The present results uncovered the neural mechanisms of previously observed face processing impairments in individuals with reversed congenital blindness. We suggest that lower face-selectivity in the sight recovery group might arise from selective deficits in the cortical representation of the central visual field in lower-tier visual areas. Additionally, we speculate that in higher-order visual areas cross-modal activity might facilitate - rather than interfere - with visual functional recovery after congenital blindness.

Electrical synapse molecular diversity revealed by proximity-based proteomic discovery
Neuronal circuits are composed of synapses that are either chemical, where signals are transmitted via neurotransmitter release and reception, or electrical, where signals pass directly through interneuronal gap junction channels. While the molecular complexity that controls chemical synapse structure and function is well appreciated, the proteins of electrical synapses beyond the gap-junction-forming Connexins are not well defined. Yet, electrical synapses are expected to be molecularly complex beyond the gap junctions. Connexins are integral membrane proteins requiring vesicular transport and membrane insertion/retrieval to achieve function, homeostasis, and plasticity. Additionally, electron microscopy of neuronal gap junctions reveals neighboring electron dense regions termed the electrical synapse density (ESD). To reveal the molecular complexity of the electrical synapse proteome, we used proximity-dependent biotinylation (TurboID) linked to neural Connexins in zebrafish. Proteomic analysis of developing and mature nervous systems identifies hundreds of Connexin-associated proteins, with overlapping and distinct representation during development and adulthood. The identified protein classes span cell adhesion molecules, cytoplasmic scaffolds, vesicular trafficking, and proteins usually associated with the post synaptic density (PSD) of chemical synapses. Using circuits with stereotyped electrical and chemical synapses, we define molecular sub-synaptic compartments of ESD localizing proteins, we find molecular heterogeneity amongst electrical synapse populations, and we examine the synaptic intermingling of electrical and chemical synapse proteins. Taken together, these results reveal a new complexity of electrical synapse molecular diversity and highlight a novel overlap between chemical and electrical synapse proteomes. Moreover, human homologs of the electrical synapse proteins are associated with autism, epilepsy, and other neurological disorders, providing a novel framework towards understanding neuro-atypical states.

Sex-specific perturbations of neuronal development caused by mutations in the autism risk gene DDX3X
DDX3X is an X-linked RNA helicases that escapes X chromosome inactivation and is expressed at higher levels in female brains. Mutations in DDX3X are associated with intellectual disability (ID) and autism spectrum disorder (ASD) and are predominantly identified in females. Using cellular and mouse models, we show that Ddx3x mediates sexual dimorphisms in brain development at a molecular, cellular, and behavioral level. During cortical neuronal development, Ddx3x sustains a female-biased signature of enhanced ribosomal biogenesis and mRNA translation. Female neurons display higher levels of ribosomal proteins and larger nucleoli, and these sex dimorphisms are obliterated by Ddx3x loss. Ddx3x regulates dendritic outgrowth in a sex- and dose-dependent manner in both female and male neurons, and dendritic spine development only in female neurons. Further, ablating Ddx3x conditionally in forebrain neurons is sufficient to yield sex-specific changes in developmental outcomes and motor function. Together, these findings pose Ddx3x as a mediator of sexual differentiation during neurodevelopment and open new avenues to understand sex differences in health and disease.

Assessing changes in whole-brain structural connectivity in the unilateral 6-hydroxydopamine rat model of Parkinson's Disease using diffusion imaging and tractography
Parkinson's disease (PD) is a multifactorial, progressive neurodegenerative disease that has a profound impact on those it afflicts. Its hallmark pathophysiology is characterized by degeneration of dopaminergic neurons in the midbrain which trigger a host of motor and non-motor symptoms. Many preclinical research efforts utilize unilateral lesion models to assess the neural mechanisms of PD and explore new therapeutic approaches because these models produce similar motor symptoms to those of PD patients. The goal of this work is to examine changes in brain structure resulting from a unilateral lesion both within the nigrostriatal system, where dopaminergic neurons are lost, and throughout the brain. Using multi-shell diffusion magnetic resonance imaging and correlational tractography, we assessed microstructural changes throughout the brain resulting from unilateral injection of 6-hydroxydopamine (6-OHDA) in the median forebrain bundle (MFB). Following lesioning, the PD phenotype was confirmed using behavioral and histological assessment. Correlational tractography found networks of fiber tracts that were either positively or negatively correlated with lesion status throughout the brain. Analyzing patterns of intra- and inter-hemispheric connectivity between the positively and negatively correlated fiber tracts revealed two separate neural networks. The first contained only negatively correlated fibers in the lesioned hemisphere consistent with the local effects of the lesion (i.e. dopaminergic depletion in the nigrostriatal system). The second contained systematically overlapping fiber tracts in the lesioned and non-lesioned hemispheres including the olfactory system and cerebellum, which we suggest are indicative of adaptive mechanisms to compensate for the lesion. Taken together, these results suggest that correlational tractography is a reasonable tool to examine whole brain structural changes in rodent models of neurodegenerative disease, and may have future translational value as a diagnostic tool for patients with PD.

Neuronal heterogeneity of normalization strength in a circuit model
The size of a neuron's receptive field increases along the visual hierarchy. Neurons in higher-order visual areas integrate information through a canonical computation called normalization, where neurons respond sublinearly to multiple stimuli in the receptive field. Neurons in the visual cortex exhibit highly heterogeneous degrees of normalization. Recent population recordings from visual cortex find that the interactions between neurons, measured by spike count correlations, depend on their normalization strengths. However, the circuit mechanism underlying the heterogeneity of normalization is unclear. In this work, we study normalization in a spiking neuron network model of visual cortex. The model produces a range of neuronal heterogeneity of normalization strength and the heterogeneity is highly correlated with the inhibitory current each neuron receives. Our model reproduces the dependence of spike count correlations on normalization as observed in experimental data, which is explained by the covariance with the inhibitory current. We find that neurons with stronger normalization are more sensitive to contrast differences in images and encode information more efficiently. In addition, networks with more heterogeneity in normalization encode more information about visual stimuli. Together, our model provides a mechanistic explanation of heterogeneous normalization strengths in the visual cortex, and sheds new light on the computational benefits of neuronal heterogeneity.

Ethanol alters mechanosensory habituation in C. elegans by way of the BK potassium channel through a novel mechanism
In this research, we investigated how alcohol modulates the simplest form of learning, habituation, in Caenorhabditis elegans. We used our high throughput Multi-Worm Tracker to conduct a large scale study of more than 21,000 wild-type worms to assess the effects of different doses of alcohol on habituation of the well-characterized tap withdrawal response. We found that the effect of alcohol on habituation of the reversal response to repeated mechanosensory stimuli (taps) differed depending on the component of the reversal response assessed. Furthermore, we discovered that alcohol shifted the dominant response to tap from a backward reversal to a brief forward movement. Because the large conductance potassium (BK) channel has been shown to be important for alcohol's effects on behaviour in a variety of organisms, including C. elegans, we investigated whether the C. elegans BK channel ortholog, SLO-1, mediated the effects of alcohol on habituation. We tested several different strains of worms with mutations in slo-1 along with wild-type controls; null mutations in slo-1 made animals resistant to alcohol induced changes in learning. However, a mutation in the putative ethanol binding site on SLO-1 did not disrupt ethanol's impact on habituation. Finally, by degrading SLO-1 in different parts of the nervous system we found that SLO-1s function in ethanol's impact on habituation is likely distributed throughout the neural circuit that responds to tap. Based on these results, our main conclusions are 1) ethanol is not a general facilitator or inhibitor of habituation but rather a complex modulator, 2) SLO-1 is required for ethanol's effect on habituation, 3) ethanol is interacting (directly or indirectly) with SLO-1 through a novel unidentified mechanism to influence response plasticity.

Neural Encoding of Semantic StructuresDuring Sentence Production
The neural representations for compositional processing have so far been mostly studied during sentence comprehension. In an fMRI study of sentence production, we investigated the brain representations for compositional processing during speaking. We used a rapid serial visual presentation sentence recall paradigm to elicit sentence production from the conceptual memory of an event. With voxel-wise encoding models, we probed the specificity of the compositional structure built during the production of each sentence, comparing an unstructured model of word meaning without relational information with a model that encodes abstract thematic relations and a model encoding event-specific relational structure. Whole-brain analyses revealed that sentence meaning at different levels of specificity was encoded in a large left fronto-parieto-temporal network. A comparison with semantic structures composed during the comprehension of the same sentences showed similarly distributed brain activity patterns. An ROI analysis over left fronto-temporal language parcels showed that event-specific relational structure above word-specific information was encoded in the left inferior frontal gyrus (LIFG). Overall, we found evidence for the encoding of semantic meaning during sentence production in a distributed brain network and for the encoding event-specific semantic structures in the LIFG.

Large-scale Signal Propagation Modes in the Human Brain
The dynamic allocation of neural resources throughout brain-wide networks is essential for optimal brain operation. However, the neural principles underlying the facilitation of such allocation remain inadequately understood. In this work, we address this gap by uncovering brain-wide coherent causal structures in resting-state fMRI timeseries data. We identified five distinct and interpretable modes of signal propagation that most effectively predict future blood-oxygen-level-dependent (BOLD) signals. Each identified mode uniquely represents specific aspects of neural resource allocation among established networks: (1) signal propagation along cortical hierarchy; (2) the salience network's modulation between default mode and the central executive networks; (3) the mapping between visual and sensorimotor areas with frontal modulation; and (4) interhemispheric interaction. Notably, four of these modes were associated with the default mode network, highlighting its central role as a functional hub. Collectively, these modes account for a broad spectrum of well-known dynamical properties in the brain. Importantly, individual variability in these modes correlates with general cognitive abilities, is influenced by genetic factors, and exhibits stability across different cognitive tasks. Together, these modes not only integrate previously disparate phenomena observed in fMRI data but also offer a concise framework for characterizing individual functional fingerprints within BOLD signals.

Divergent Visuomotor Strategies in Teleosts: Neural Circuit Mechanisms in Zebrafish and Danionella cerebrum
Many animals respond to sensory cues with species-specific coordinated movements to successfully navigate their environment. However, the neural mechanisms that support diverse sensorimotor transformations across species with distinct navigational strategies remain largely unexplored. By comparing related teleost species, zebrafish (Danio rerio, ZF) and Danionella cerebrum (DC), we investigated behavioral patterns and neural architectures during the visually guided optomotor response (OMR). Closed-loop behavioral tracking during visual stimulation revealed that larval ZF employ burst-and-glide locomotion, while larval DC display continuous, smooth swimming punctuated with sharp directional turns. Although DC achieve higher average speeds, they lack the direction-dependent velocity modulation observed in ZF. Whole-brain two-photon calcium imaging and tail tracking in head-fixed fish reveals that both species exhibit direction-selective motion encoding in homologous regions, including the retinorecipient pretectum, with DC exhibiting fewer binocular, direction-selective neurons overall. Kinematic analysis of head-fixed behavior reveals that DC sustain significantly longer directed swim events across all stimuli than ZF, highlighting the divergent visuomotor strategies, with ZF reducing tail movement duration in response to oblique, turn-inducing stimuli. Lateralized motor-associated neural activity in the medial and anterior hindbrain of both species suggests a shared circuit motif, with distinct neural circuits that independently control movement vigor and direction. These findings highlight the diversity in visuomotor strategies among teleost species, underscored by shared sensorimotor neural circuit motifs, and establish a robust framework for unraveling the neural mechanisms driving continuous and discrete visually guided locomotion, paving the way for deeper insights into vertebrate sensorimotor functions.

Task-induced internal bodily rather than brain states regulate human self-perception
A fundamental ability for us is to identify and distinguish with others, known as the self, whose neural substrate is hard to identify by external self-related stimuli because of possible interference from internal self-related states of both body and brain. Using the same stimuli, we ruled out this dilemma by manipulating human subjects performing own- and celebrity-face discrimination tasks to induce self-related and non-self-related states, respectively. Results showed that stimulus-driven sensory sensory differences among own-, celebrity-, and stranger-faces were independent of task-induced internal states, whereas their perceptual differences were strongly modulated by these states. Intriguingly, we further found that subjects' bodily (indexed by heartbeat-evoked potentials) rather than brain (indexed by pre-stimulus -powers) states not only predicated their self-perception but also moderated the relationship between external stimuli and self-perception. Our results reveal for the first time an adaptive self-perception, shaped by not only external stimuli but also internal states, especially the interoception.

Optogenetic stimulation of a cortical biohybrid implant guides goal directed behavior
Brain-computer interfaces (BCIs) hold exciting therapeutic potential, but tissue damage caused by probe insertion limits channel count. Biohybrid devices, in which the cell-device interface is crafted in the laboratory, hold promise to address this limitation, but these devices have lacked a demonstration of their applicability for BCI. We developed a biohybrid approach to engraft optogenetically-enabled neurons on the cortical surface housed in a 2D-scaffold of circular microwells. The engrafted neurons survived, exhibited spontaneous activity, and integrated with the host brain several weeks after implantation. We then trained mice with biohybrid implants to perform an optical stimulation task and showed that they could effectively report optogenetic stimulation of their neural graft. This demonstration shows that a cortical biohybrid implant can be used to transmit information to the brain of an implanted animal.

Innervation density governs crosstalk of GPCR-based norepinephrine and dopamine sensors
GPCR-based fluorescent sensors are widely used to correlate neuromodulatory signaling with brain function. While experiments in transfected cells often reveal selectivity for individual neurotransmitters, sensor specificity in the brain frequently remains uncertain. Pursuing experiments in brain slices and in vivo, we find that norepinephrine and dopamine cross-activate the respective sensors. Non-specific activation occurred when innervation of the cross-reacting transmitter was high, and silencing of specific innervation was indispensable for interpreting sensor fluorescence.

Unbiased data-driven analysis of five amyloid-beta peptides for biomarker investigations in familial Alzheimer's disease
INTRODUCTION: Changes to the relative abundance of amyloid-beta (Abeta) peptides are hallmarks of Alzheimer's disease (AD). iPSC-derived neurons offer a physiological model of Abeta production. We employed unbiased, data-driven analyses to investigate combinations of Abeta peptides as AD biomarkers and the relative contribution of peptides to AD pathogenesis. METHODS: We measured Abeta;37, Abeta;38, Abeta;40, Abeta;42 and Abeta;43 in ten iPSC-neuronal cultures from PSEN1 mutation carriers. We combined these data with published cell model data and used linear weighted combinations to 1) distinguish AD from controls, and 2) predict age-at-onset for PSEN1 mutations. RESULTS: Data-driven approaches distinguished Abeta;42 and Abeta;43 from shorter peptides, providing unbiased evidence for their contribution to disease. Weighted linear combinations of Abeta peptides outperform Abeta;42/40 and provide insights into relative peptide contribution as biomarkers; the optimal ratio for all data is represented as 21 . Abeta37 + 10. Abeta38+ 69 . Abeta40/(94 . Abeta42 + 6 . Abeta43). DISCUSSION: The algorithm discovered herein can be further refined to improve biomarkers for AD.

Transcriptional Regulation of Neuropeptide Receptors Decodes Complexity of Peptidergic Modulation of Behavior and Physiology.
The modulation of complex behaviors in response to environmental and physiological contexts is a fundamental aspect of animal biology, with neuropeptides (NPs) playing a crucial role in this process. This study investigates the transcriptional regulation of neuropeptide receptors (NPRs) as a mechanism for context-dependent neuropeptidergic modulation of physiology and behavior. We hypothesize that the transcriptional control of NPR genes, rather than the NPs themselves, is a critical determinant in the context-dependent modulation of behavior and physiology. Using a multi-faceted approach, including comparative genomics, transcription factor network analysis, and empirical validation in model organisms such as Drosophila melanogaster, we reveal a complex regulatory landscape where NPR expression is tightly controlled. Our findings demonstrate that NPR genes exhibit a higher number of enhancers, CTCF-binding sites, and open chromatin regions compared to NP genes, suggesting a greater susceptibility to transcriptional modulation. This regulatory architecture allows for precise control over neuropeptidergic signaling, enabling dynamic and context-specific behavioral and physiological responses. Our results highlight the importance of NPR-expressing cells by transcriptional regulation in mediating the effects of NPs on behavior and physiology. We show that this regulation is conserved across species, indicating an evolutionarily significant mechanism for fine-tuning neuropeptidergic signaling. Furthermore, our study provides insights into the distinct regulatory mechanisms underlying the multifunctionality of NPs and their receptors, offering a novel perspective on the transcriptional control of complex behaviors. In conclusion, this study advances our understanding of neuropeptidergic signaling by focusing on the transcriptional regulation of NPRs. Our findings have broad implications for the development of therapeutic strategies targeting neuropeptidergic systems in various neurological and behavioral disorders.