From the connectome to brain function

Understanding the neurobiology of social behavior through exploring brain-wide dynamics of neural activity

Social behavior is highly complex and adaptable. It can be divided into multiple temporal stages: detection, approach, and consummatory behavior. Each stage can be further divided into several cognitive and behavioral processes, such as perceiving social cues, evaluating the social and non-social contexts, and recognizing the internal/emotional state of others. Recent studies have identified numerous brain-wide circuits implicated in social behavior and suggested the existence of partially overlapping functional brain networks underlying various types of social and non-social behavior. However, understanding the brain-wide dynamics underlying social behavior remains challenging, and several brain-scale dynamics (macro-, meso-, and micro-scale levels) need to be integrated. Here, we suggest leveraging new tools and concepts to explore social brain networks and integrate those different levels. These include studying the expression of immediate-early genes throughout the entire brain to impartially define the structure of the neuronal networks involved in a given social behavior. Then, network dynamics could be investigated using electrode arrays or multi-channel fiber photometry. Finally, tools like high-density silicon probes and miniscopes can probe neural activity in specific areas and across neuronal populations at the single-cell level.

A Drosophila computational brain model reveals sensorimotor processing

The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity3, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation4. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing5-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes6-10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.

Whole-brain annotation and multi-connectome cell typing of Drosophila

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

Mapping the developmental structure of stereotyped and individual-unique behavioral spaces in C. elegans

Developmental patterns of behavior are variably organized in time and among different individuals. However, long-term behavioral diversity was previously studied using pre-defined behavioral parameters, representing a limited fraction of the full individuality structure. Here, we continuously extract ∼1.2 billion body postures of ∼2,200 single C. elegans individuals throughout their full development time to create a complete developmental atlas of stereotyped and individual-unique behavioral spaces. Unsupervised inference of low-dimensional movement modes of each single individual identifies a dynamic developmental trajectory of stereotyped behavioral spaces and exposes unique behavioral trajectories of individuals that deviate from the stereotyped patterns. Moreover, classification of behavioral spaces within tens of neuromodulatory and environmentally perturbed populations shows plasticity in the temporal structures of stereotyped behavior and individuality. These results present a comprehensive atlas of continuous behavioral dynamics across development time and a general framework for unsupervised dissection of shared and unique developmental signatures of behavior.

Connectome-constrained networks predict neural activity across the fly visual system

We can now measure the connectivity of every neuron in a neural circuit1-9, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question10. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe1-5 but with unknown parameters for the single-neuron and single-synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning11, to allow the model network to detect visual motion12. Our mechanistic model makes detailed, experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 26 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected-a universally observed feature of biological neural networks across species and brain regions.

A line attractor encoding a persistent internal state requires neuropeptide signaling

Internal states drive survival behaviors, but their neural implementation is poorly understood. Recently, we identified a line attractor in the ventromedial hypothalamus (VMH) that represents a state of aggressiveness. Line attractors can be implemented by recurrent connectivity or neuromodulatory signaling, but evidence for the latter is scant. Here, we demonstrate that neuropeptidergic signaling is necessary for line attractor dynamics in this system by using cell-type-specific CRISPR-Cas9-based gene editing combined with single-cell calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1+ neurons that control aggression diminished attack, reduced persistent neural activity, and eliminated line attractor dynamics while only slightly reducing overall neural activity and sex- or behavior-specific tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor in mammals. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

Automated multimodal imaging of Caenorhabditis elegans behavior in multi-well plates

Assays of behavior in model organisms play an important role in genetic screens, drug testing, and the elucidation of gene-behavior relationships. We have developed an automated, high-throughput imaging and analysis method for assaying behaviors of the nematode C. elegans . We use high-resolution optical imaging to longitudinally record the behaviors of 96 animals at a time in multi-well plates, and computer vision software to quantify the animals' locomotor activity, behavioral states, and egg laying events. To demonstrate the capabilities of our system we used it to examine the role of serotonin in C. elegans behavior. We found that egg-laying events are preceded by a period of reduced locomotion, and that this decline in movement requires serotonin signaling. In addition, we identified novel roles of serotonin receptors SER-1 and SER-7 in regulating the effects of serotonin on egg laying across roaming, dwelling, and quiescent locomotor states. Our system will be useful for performing genetic or chemical screens for modulators of behavior.

A Markovian dynamics for Caenorhabditis elegans behavior across scales

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.

Heterogeneity in the projections and excitability of tyraminergic/octopaminergic neurons that innervate the Drosophila reproductive tract

Aminergic nuclei in mammals are generally composed of relatively small numbers of cells with broad projection patterns. Despite the gross similarity of many individual neurons, recent transcriptomic, anatomic and behavioral studies suggest previously unsuspected diversity. Smaller clusters of aminergic neurons in the model organism Drosophila melanogaster provide an opportunity to explore the ramifications of neuronal diversity at the level of individual cells. A group of approximately 10 tyraminergic/octopaminergic neurons innervates the female reproductive tract in flies and has been proposed to regulate multiple activities required for fertility. The projection patterns of individual neurons within the cluster are not known and it remains unclear whether they are functionally heterogenous. Using a single cell labeling technique, we show that each region of the reproductive tract is innervated by a distinct subset of tyraminergic/octopaminergic cells. Optogenetic activation of one subset stimulates oviduct contractions, indicating that the cluster as a whole is not required for this activity, and underscoring the potential for functional diversity across individual cells. Using whole cell patch clamp, we show that two adjacent and morphologically similar cells are tonically inhibited, but each responds differently to injection of current or activation of the inhibitory GluCl receptor. GluCl appears to be expressed at relatively low levels in tyraminergic/octopaminergic neurons within the cluster, suggesting that it may regulate their excitability via indirect pathways. Together, our data indicate that specific tyraminergic/octopaminergic cells within a relatively homogenous cluster have heterogenous properties and provide a platform for further studies to determine the function of each cell.

Resilience of circuits to environmental challenge

Animals of all kinds evolved to deal with anticipated and unanticipated changes in a variety of features in their environments. Consequently, all environmental perturbations, adaptations, and acclimation involve a myriad of factors that, together, contribute to environmental resilience. New work highlights the importance of neuromodulation in the control of environmental resilience, and illustrates that different components of the nervous system may be differentially resilient to environmental perturbations. Climate change is today pushing animals to deal with previously unanticipated environmental challenges, and therefore understanding the complex biology of adaptation and acclimation to various environmental conditions takes on new urgency.

Capillary connections between sensory circumventricular organs and adjacent parenchyma enable local volume transmission

Among contributors to diffusible signaling are portal systems which join two capillary beds through connecting veins (Dorland 2020). Portal systems allow diffusible signals to be transported in high concentrations directly from one capillary bed to the other without dilution in the systemic circulation. Two portal systems have been identified in the brain. The first was discovered almost a century ago and connects the median eminence to the anterior pituitary gland (Popa & Fielding 1930). The second was discovered a few years ago, and links the suprachiasmatic nucleus to the organum vasculosum of the lamina terminalis, a sensory circumventricular organ (CVO) (Yao et al. 2021). Sensory CVOs bear neuronal receptors for sensing signals in the fluid milieu (McKinley et al. 2003). They line the surface of brain ventricles and bear fenestrated capillaries, thereby lacking blood brain barriers. It is not known whether the other sensory CVOs, namely the subfornical organ (SFO), and area postrema (AP) form portal neurovascular connections with nearby parenchymal tissue. This has been difficult to establish as the structures lie at the midline and protrude into the ventricular space. To preserve the integrity of the vasculature of CVOs and their adjacent neuropil, we combined iDISCO clearing and light-sheet microscopy to acquire volumetric images of blood vessels. The results indicate that there is a portal pathway linking the capillary vessels of the SFO and the posterior septal nuclei, namely the septofimbrial nucleus and the triangular nucleus of the septum. Unlike the latter arrangement, the AP and the nucleus of the solitary tract share their capillary beds. Taken together, the results reveal that all three sensory circumventricular organs bear specialized capillary connections to adjacent neuropil, providing a direct route for diffusible signals to travel from their source to their targets.

A network control theory pipeline for studying the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Labeling of the serotonergic neuronal circuits emerging from the raphe nuclei via some retrograde tracers

Serotonin (5-hydroxytryptamine, 5-HT) is a very important neurotransmitter emerging from the raphe nuclei to several brain regions. Serotonergic neuronal connectivity has multiple functions in the brain. In this study, several techniques were used to trace serotonergic neurons in the dorsal raphe (DR) and median raphe (MnR) that project toward the arcuate nucleus of the hypothalamus (Arc), dorsomedial hypothalamic nucleus (DM), lateral hypothalamic area (LH), paraventricular hypothalamic nucleus (PVH), ventromedial hypothalamic nucleus (VMH), fasciola cinereum (FC), and medial habenular nucleus (MHb). Cholera toxin subunit B (CTB), retro-adeno-associated virus (rAAV-CMV-mCherry), glycoprotein-deleted rabies virus (RV-ΔG), and simultaneous microinjection of rAAV2-retro-Cre-tagBFP with AAV-dio-mCherry in C57BL/6 mice were used in this study. In addition, rAAV2-retro-Cre-tagBFP was microinjected into Ai9 mice. Serotonin immunohistochemistry was used for the detection of retrogradely traced serotonergic neurons in the raphe nuclei. The results indicated that rAAV2-retro-Cre-tagBFP microinjection in Ai9 mice was the best method for tracing serotonergic neuron circuits. All of the previously listed nuclei exhibited serotonergic neuronal projections from the DR and MnR, with the exception of the FC, which had very few projections from the DR. The serotonergic neuronal projections were directed toward the Arc by the subpeduncular tegmental (SPTg) nuclei. Moreover, the RV-ΔG tracer revealed monosynaptic non-serotonergic neuronal projections from the DR that were directed toward the Arc. Furthermore, rAAV tracers revealed monosynaptic serotonergic neuronal connections from the raphe nuclei toward Arc. These findings validate the variations in neurotropism among several retrograde tracers. The continued discovery of several novel serotonergic neural circuits is crucial for the future discovery of the functions of these circuits. RESEARCH HIGHLIGHTS: Various kinds of retrograde tracers were microinjected into C57BL/6 and Ai9 mice. The optimum method for characterizing serotonergic neuronal circuits is rAAV2-retro-Cre-tagBFP microinjection in Ai9 mice. The DR, MnR, and SPTg nuclei send monosynaptic serotonergic neuronal projections toward the arcuate nucleus of the hypothalamus. Whole-brain quantification analysis of retrograde-labeled neurons in different brain nuclei following rAAV2-retro-Cre-tagBFP microinjection in the Arc, DM, LH, and VMH is shown. Differential quantitative analysis of median and dorsal raphe serotonergic neurons emerging toward the PVH, DM, LH, Arc, VMH, MHb, and FC is shown.

Neuropeptide modulation of bidirectional internetwork synapses

Oscillatory networks underlying rhythmic motor behaviors, and sensory and complex neural processing, are flexible, even in their neuronal composition. Neuromodulatory inputs enable neurons to switch participation between networks or participate in multiple networks simultaneously. Neuromodulation of internetwork synapses can both recruit and coordinate a switching neuron in a second network. We previously identified an example in which a neuron is recruited into dual-network activity via peptidergic modulation of intrinsic properties. We now ask whether the same neuropeptide also modulates internetwork synapses for internetwork coordination. The crab (Cancer borealis) stomatogastric nervous system contains two well-defined feeding-related networks (pyloric, food filtering, ∼1 Hz; gastric mill, food chewing, ∼0.1 Hz). The projection neuron MCN5 uses the neuropeptide Gly1-SIFamide to recruit the pyloric-only lateral posterior gastric (LPG) neuron into dual pyloric- plus gastric mill-timed bursting via modulation of LPG's intrinsic properties. Descending input is not required for a coordinated rhythm, thus intranetwork synapses between LPG and its second network must underlie coordination among these neurons. However, synapses between LPG and gastric mill neurons have not been documented. Using two-electrode voltage-clamp recordings, we found that graded synaptic currents between LPG and gastric mill neurons (lateral gastric, inferior cardiac, and dorsal gastric) were primarily negligible in saline, but were enhanced by Gly1-SIFamide. Furthermore, LPG and gastric mill neurons entrain each other during Gly1-SIFamide application, indicating bidirectional, functional connectivity. Thus, a neuropeptide mediates neuronal switching through parallel actions, modulating intrinsic properties for recruitment into a second network and as shown here, also modulating bidirectional internetwork synapses for coordination.NEW & NOTEWORTHY Neuromodulation can enable neurons to simultaneously coordinate with separate networks. Both recruitment into, and coordination with, a second network can occur via modulation of internetwork synapses. Alternatively, recruitment can occur via modulation of intrinsic ionic currents. We find that the same neuropeptide previously determined to modulate intrinsic currents also modulates bidirectional internetwork synapses that are typically ineffective. Thus, complementary modulatory peptide actions enable recruitment and coordination of a neuron into a second network.

Connectomic reconstruction of a female Drosophila ventral nerve cord

A deep understanding of how the brain controls behaviour requires mapping neural circuits down to the muscles that they control. Here, we apply automated tools to segment neurons and identify synapses in an electron microscopy dataset of an adult female Drosophila melanogaster ventral nerve cord (VNC)1, which functions like the vertebrate spinal cord to sense and control the body. We find that the fly VNC contains roughly 45 million synapses and 14,600 neuronal cell bodies. To interpret the output of the connectome, we mapped the muscle targets of leg and wing motor neurons using genetic driver lines2 and X-ray holographic nanotomography3. With this motor neuron atlas, we identified neural circuits that coordinate leg and wing movements during take-off. We provide the reconstruction of VNC circuits, the motor neuron atlas and tools for programmatic and interactive access as resources to support experimental and theoretical studies of how the nervous system controls behaviour.

Norepinephrine changes behavioral state via astroglial purinergic signaling

Both neurons and glia communicate via diffusible neuromodulatory substances, but the substrates of computation in such neuromodulatory networks are unclear. During behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine drives fast excitation and delayed inhibition of behavior and circuit activity. We find that the inhibitory arm of this feedforward motif is implemented by astroglial purinergic signaling. Neuromodulator imaging, behavioral pharmacology, and perturbations of neurons and astroglia reveal that norepinephrine triggers astroglial release of adenosine triphosphate, extracellular conversion into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. This work, along with a companion piece by Lefton and colleagues demonstrating an analogous pathway mediating the effect of norepinephrine on synaptic connectivity in mice, identifies a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in norepinephrine-mediated behavioral and brain state transitions.

Zebrafish and nematodes as whole organism models to measure developmental neurotoxicity

Despite the growing epidemiological evidence of an association between toxin exposure and developmental neurotoxicity (DNT), systematic testing of DNT is not mandatory in international regulations for admission of pharmaceuticals or industrial chemicals. However, to date around 200 compounds, ranging from pesticides, pharmaceuticals and industrial chemicals, have been tested for DNT in the current OECD test guidelines (TG-443 or TG-426). There are calls for the development of new approach methodologies (NAMs) for DNT, which has resulted in a DNT testing battery using in vitro human cell-based assays. These assays provide a means to elucidate the molecular mechanisms of toxicity in humans which is lacking in animal-based toxicity tests. However, cell-based assays do not represent all steps of the complex process leading to DNT. Validated models with a multi-organ network of pathways that interact at the molecular, cellular and tissue level at very specific timepoints in a life cycle are currently missing. Consequently, whole model organisms are being developed to screen for, and causally link, new molecular targets of DNT compounds and how they affect whole brain development and neurobehavioral endpoints. Given the practical and ethical restraints associated with vertebrate testing, lower animal models that qualify as 3 R (reduce, refine and replace) models, including the nematode (Caenorhabditis elegans) and the zebrafish (Danio rerio) will prove particularly valuable for unravelling toxicity pathways leading to DNT. Although not as complex as the human brain, these 3 R-models develop a complete functioning brain with numerous neurodevelopmental processes overlapping with human brain development. Importantly, the main signalling pathways relating to (neuro)development, metabolism and growth are highly conserved in these models. We propose the use of whole model organisms specifically zebrafish and C. elegans for DNT relevant endpoints.

How neurobehavior and brain development in alternative whole-organism models can contribute to prediction of developmental neurotoxicity

Developmental neurotoxicity (DNT) is not routinely evaluated in chemical risk assessment because current test paradigms for DNT require the use of mammalian models which are ethically controversial, expensive, and resource demanding. Consequently, efforts have focused on revolutionizing DNT testing through affordable novel alternative methods for risk assessment. The goal is to develop a DNT in vitro test battery amenable to high-throughput screening (HTS). Currently, the DNT in vitro test battery consists primarily of human cell-based assays because of their immediate relevance to human health. However, such cell-based assays alone are unable to capture the complexity of a developing nervous system. Whole organismal systems that qualify as 3 R (Replace, Reduce and Refine) models are urgently needed to complement cell-based DNT testing. These models can provide the necessary organismal context and be used to explore the impact of chemicals on brain function by linking molecular and/or cellular changes to behavioural readouts. The nematode Caenorhabditis elegans, the planarian Dugesia japonica, and embryos of the zebrafish Danio rerio are all suited to low-cost HTS and each has unique strengths for DNT testing. Here, we review the strengths and the complementarity of these organisms in a novel, integrative context and highlight how they can augment current cell-based assays for more comprehensive and robust DNT screening of chemicals. Considering the limitations of all in vitro test systems, we discuss how a smart combinatory use of these systems will contribute to a better human relevant risk assessment of chemicals that considers the complexity of the developing brain.

Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images

Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.

Long-term neuropeptide modulation of female sexual drive via the TRP channel in Drosophila melanogaster

Connectomics research has made it more feasible to explore how neural circuits can generate multiple outputs. Female sexual drive provides a good model for understanding reversible, long-term functional changes in motivational circuits. After emerging, female flies avoid male courtship, but they become sexually receptive over 2 d. Mating causes females to reject further mating for several days. Here, we report that pC1 neurons, which process male courtship and regulate copulation behavior, exhibit increased CREB (cAMP response element binding protein) activity during sexual maturation and decreased CREB activity after mating. This increased CREB activity requires the neuropeptide Dh44 (Diuretic hormone 44) and its receptors. A subset of the pC1 neurons secretes Dh44, which stimulates CREB activity and increases expression of the TRP channel Pyrexia (Pyx) in more pC1 neurons. This, in turn, increases pC1 excitability and sexual drive. Mating suppresses pyx expression and pC1 excitability. Dh44 is orthologous to the conserved corticotrophin-releasing hormone family, suggesting similar roles in other species.

Genetic mapping identifies Homer1 as a developmental modifier of attention

Attention is required for most higher-order cognitive functions. Prior studies have revealed functional roles for the prefrontal cortex and its extended circuits to enabling attention, but the underlying molecular processes and their impacts on cellular and circuit function remain poorly understood. To develop insights, we here took an unbiased forward genetics approach to identify single genes of large effect on attention. We studied 200 genetically diverse mice on measures of pre-attentive processing and through genetic mapping identified a small locus on chromosome 13 (95%CI: 92.22-94.09 Mb) driving substantial variation (19%) in this trait. Further characterization of the locus revealed a causative gene, Homer1, encoding a synaptic protein, where down-regulation of its short isoforms in prefrontal cortex (PFC) during early postnatal development led to improvements in multiple measures of attention in the adult. Subsequent mechanistic studies revealed that prefrontal Homer1 down-regulation is associated with GABAergic receptor up-regulation in those same cells. This enhanced inhibitory influence, together with dynamic neuromodulatory coupling, led to strikingly low PFC activity at baseline periods of the task but targeted elevations at cue onset, predicting short-latency correct choices. Notably high-Homer1, low-attentional performers, exhibited uniformly elevated PFC activity throughout the task. We thus identify a single gene of large effect on attention - Homer1 - and find that it improves prefrontal inhibitory tone and signal-to-noise (SNR) to enhance attentional performance. A therapeutic strategy focused on reducing prefrontal activity and increasing SNR, rather than uniformly elevating PFC activity, may complement the use of stimulants to improve attention.

Fast and slow: Recording neuromodulator dynamics across both transient and chronic time scales

Neuromodulators transform animal behaviors. Recent research has demonstrated the importance of both sustained and transient change in neuromodulators, likely due to tonic and phasic neuromodulator release. However, no method could simultaneously record both types of dynamics. Fluorescence lifetime of optical reporters could offer a solution because it allows high temporal resolution and is impervious to sensor expression differences across chronic periods. Nevertheless, no fluorescence lifetime change across the entire classes of neuromodulator sensors was previously known. Unexpectedly, we find that several intensity-based neuromodulator sensors also exhibit fluorescence lifetime responses. Furthermore, we show that lifetime measures in vivo neuromodulator dynamics both with high temporal resolution and with consistency across animals and time. Thus, we report a method that can simultaneously measure neuromodulator change over transient and chronic time scales, promising to reveal the roles of multi-time scale neuromodulator dynamics in diseases, in response to therapies, and across development and aging.

Connectivity Matrix Seriation via Relaxation

Volume electron microscopy together with computer-based image analysis are yielding neural circuit diagrams of ever larger regions of the brain. These datasets are usually represented in a cell-to-cell connectivity matrix and contain important information about prevalent circuit motifs allowing to directly test various theories on the computation in that brain structure. Of particular interest are the detection of cell assemblies and the quantification of feedback, which can profoundly change circuit properties. While the ordering of cells along the rows and columns doesn't change the connectivity, it can make special connectivity patterns recognizable. For example, ordering the cells along the flow of information, feedback and feedforward connections are segregated above and below the main matrix diagonal, respectively. Different algorithms are used to renumber matrices such as to minimize a given cost function, but either their performance becomes unsatisfying at a given size of the circuit or the CPU time needed to compute them scales in an unfavorable way with increasing number of neurons. Based on previous ideas, I describe an algorithm which is effective in matrix reordering with respect to both its performance as well as to its scaling in computing time. Rather than trying to reorder the matrix in discrete steps, the algorithm transiently relaxes the integer program by assigning a real-valued parameter to each cell describing its location on a continuous axis ('smooth-index') and finds the parameter set that minimizes the cost. I find that the smooth-index algorithm outperforms all algorithms I compared it to, including those based on topological sorting.

Brain connectome correlates of short-term motor learning in healthy older subjects

The motor learning process entails plastic changes in the brain, especially in brain network reconfigurations. In the current study, we sought to characterize motor learning by determining changes in the coupling behaviour between the brain functional and structural connectomes on a short timescale. 39 older subjects (age: mean (SD) = 69.7 (4.7) years, men:women = 15:24) were trained on a visually guided sequential hand grip learning task. The brain structural and functional connectomes were constructed from diffusion-weighted MRI and resting-state functional MRI, respectively. The association of motor learning ability with changes in network topology of the brain functional connectome and changes in the correspondence between the brain structural and functional connectomes were assessed. Motor learning ability was related to decreased efficiency and increased modularity in the visual, somatomotor, and frontoparietal networks of the brain functional connectome. Between the brain structural and functional connectomes, reduced correspondence in the visual, ventral attention, and frontoparietal networks as well as the whole-brain network was related to motor learning ability. In addition, structure-function correspondence in the dorsal attention, ventral attention, and frontoparietal networks before motor learning was predictive of motor learning ability. These findings indicate that, in the view of brain connectome changes, short-term motor learning is represented by a detachment of the brain functional from the brain structural connectome. The structure-function uncoupling accompanied by the enhanced segregation into modular structures over the core functional networks involved in the learning process may suggest that facilitation of functional flexibility is associated with successful motor learning.

Spatiotemporal Data Processing with Memristor Crossbar-Array-Based Graph Reservoir

Memristor-based physical reservoir computing (RC) is a robust framework for processing complex spatiotemporal data parallelly. However, conventional memristor-based reservoirs cannot capture the spatial relationship between the time-varying inputs due to the specific mapping scheme assigning one input signal to one memristor conductance. Here, a physical "graph reservoir" is introduced using a metal cell at the diagonal-crossbar array (mCBA) with dynamic self-rectifying memristors. Input and inverted input signals are applied to the word and bit lines of the mCBA, respectively, storing the correlation information between input signals in the memristors. In this way, the mCBA graph reservoirs can map the spatiotemporal correlation of the input data in a high-dimensional feature space. The high-dimensional mapping characteristics of the graph reservoir achieve notable results, including a normalized root-mean-square error of 0.09 in Mackey-Glass time series prediction, a 97.21% accuracy in MNIST recognition, and an 80.0% diagnostic accuracy in human connectome classification.

Molecular organization of neuronal cell types and neuromodulatory systems in the zebrafish telencephalon

The function of neuronal networks is determined not only by synaptic connectivity but also by neuromodulatory systems that broadcast information via distributed connections and volume transmission. To understand the molecular constraints that organize neuromodulatory signaling in the telencephalon of adult zebrafish, we used transcriptomics and additional approaches to delineate cell types, to determine their phylogenetic conservation, and to map the expression of marker genes at high granularity. The combinatorial expression of GPCRs and cell-type markers indicates that all neuronal cell types are subject to modulation by multiple monoaminergic systems and distinct combinations of neuropeptides. Individual cell types were associated with multiple (typically >30) neuromodulatory signaling networks but expressed only a few diagnostic GPCRs at high levels, suggesting that different neuromodulatory systems act in combination, albeit with unequal weights. These results provide a detailed map of cell types and brain areas in the zebrafish telencephalon, identify core components of neuromodulatory networks, highlight the cell-type specificity of neuropeptides and GPCRs, and begin to decipher the logic of combinatorial neuromodulation.

Modulation by Neuropeptides with Overlapping Targets Results in Functional Overlap in Oscillatory Circuit Activation

Neuromodulation lends flexibility to neural circuit operation but the general notion that different neuromodulators sculpt neural circuit activity into distinct and characteristic patterns is complicated by interindividual variability. In addition, some neuromodulators converge onto the same signaling pathways, with similar effects on neurons and synapses. We compared the effects of three neuropeptides on the rhythmic pyloric circuit in the stomatogastric ganglion of male crabs, Cancer borealis Proctolin (PROC), crustacean cardioactive peptide (CCAP), and red pigment concentrating hormone (RPCH) activate the same modulatory inward current, I MI, and have convergent actions on synapses. However, while PROC targets all four neuron types in the core pyloric circuit, CCAP and RPCH target the same subset of only two neurons. After removal of spontaneous neuromodulator release, none of the neuropeptides restored the control cycle frequency, but all restored the relative timing between neuron types. Consequently, differences between neuropeptide effects were mainly found in the spiking activity of different neuron types. We performed statistical comparisons using the Euclidean distance in the multidimensional space of normalized output attributes to obtain a single measure of difference between modulatory states. Across preparations, the circuit output in PROC was distinguishable from CCAP and RPCH, but CCAP and RPCH were not distinguishable from each other. However, we argue that even between PROC and the other two neuropeptides, population data overlapped enough to prevent reliable identification of individual output patterns as characteristic for a specific neuropeptide. We confirmed this notion by showing that blind classifications by machine learning algorithms were only moderately successful.Significance Statement It is commonly assumed that distinct behaviors or circuit activities can be elicited by different neuromodulators. Yet it is unknown to what extent these characteristic actions remain distinct across individuals. We use a well-studied circuit model of neuromodulation to examine the effects of three neuropeptides, each known to produce a distinct activity pattern in controlled studies. We find that, when compared across individuals, the three peptides elicit activity patterns that are either statistically indistinguishable or show too much overlap to be labeled characteristic. We ascribe this to interindividual variability and overlapping subcellular actions of the modulators. Because both factors are common in all neural circuits, these findings have broad significance for understanding chemical neuromodulatory actions while considering interindividual variability.

Optimized design and in vivo application of optogenetically functionalized Drosophila dopamine receptors

Neuromodulatory signaling via G protein-coupled receptors (GPCRs) plays a pivotal role in regulating neural network function and animal behavior. The recent development of optogenetic tools to induce G protein-mediated signaling provides the promise of acute and cell type-specific manipulation of neuromodulatory signals. However, designing and deploying optogenetically functionalized GPCRs (optoXRs) with accurate specificity and activity to mimic endogenous signaling in vivo remains challenging. Here we optimize the design of optoXRs by considering evolutionary conserved GPCR-G protein interactions and demonstrate the feasibility of this approach using two Drosophila Dopamine receptors (optoDopRs). These optoDopRs exhibit high signaling specificity and light sensitivity in vitro. In vivo, we show receptor and cell type-specific effects of dopaminergic signaling in various behaviors, including the ability of optoDopRs to rescue the loss of the endogenous receptors. This work demonstrates that optoXRs can enable optical control of neuromodulatory receptor-specific signaling in functional and behavioral studies.

A cGAL-UAS bipartite expression toolkit for Caenorhabditis elegans sensory neurons

Animals integrate sensory information from the environment and display various behaviors in response to external stimuli. In Caenorhabditis elegans hermaphrodites, 33 types of sensory neurons are responsible for chemosensation, olfaction, and mechanosensation. However, the functional roles of all sensory neurons have not been systematically studied due to the lack of facile genetic accessibility. A bipartite cGAL-UAS system has been previously developed to study tissue- or cell-specific functions in C. elegans. Here, we report a toolkit of new cGAL drivers that can facilitate the analysis of a vast majority of the 60 sensory neurons in C. elegans hermaphrodites. We generated 37 sensory neuronal cGAL drivers that drive cGAL expression by cell-specific regulatory sequences or intersection of two distinct regulatory regions with overlapping expression (split cGAL). Most cGAL-drivers exhibit expression in single types of cells. We also constructed 28 UAS effectors that allow expression of proteins to perturb or interrogate sensory neurons of choice. This cGAL-UAS sensory neuron toolkit provides a genetic platform to systematically study the functions of C. elegans sensory neurons.

Whole-brain Optical Imaging: A Powerful Tool for Precise Brain Mapping at the Mesoscopic Level

The mammalian brain is a highly complex network that consists of millions to billions of densely-interconnected neurons. Precise dissection of neural circuits at the mesoscopic level can provide important structural information for understanding the brain. Optical approaches can achieve submicron lateral resolution and achieve "optical sectioning" by a variety of means, which has the natural advantage of allowing the observation of neural circuits at the mesoscopic level. Automated whole-brain optical imaging methods based on tissue clearing or histological sectioning surpass the limitation of optical imaging depth in biological tissues and can provide delicate structural information in a large volume of tissues. Combined with various fluorescent labeling techniques, whole-brain optical imaging methods have shown great potential in the brain-wide quantitative profiling of cells, circuits, and blood vessels. In this review, we summarize the principles and implementations of various whole-brain optical imaging methods and provide some concepts regarding their future development.

Neuropeptide Signaling is Required to Implement a Line Attractor Encoding a Persistent Internal Behavioral State

Internal states drive survival behaviors, but their neural implementation is not well understood. Recently we identified a line attractor in the ventromedial hypothalamus (VMH) that represents an internal state of aggressiveness. Line attractors can be implemented by recurrent connectivity and/or neuromodulatory signaling, but evidence for the latter is scant. Here we show that neuropeptidergic signaling is necessary for line attractor dynamics in this system, using a novel approach that integrates cell type-specific, anatomically restricted CRISPR/Cas9-based gene editing with microendoscopic calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1 + neurons that control aggression suppressed attack, reduced persistent neural activity and eliminated line attractor dynamics, while only modestly impacting neural activity and sex- or behavior-tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

Multilevel visual motion opponency in Drosophila

Inhibitory interactions between opponent neuronal pathways constitute a common circuit motif across brain areas and species. However, in most cases, synaptic wiring and biophysical, cellular and network mechanisms generating opponency are unknown. Here, we combine optogenetics, voltage and calcium imaging, connectomics, electrophysiology and modeling to reveal multilevel opponent inhibition in the fly visual system. We uncover a circuit architecture in which a single cell type implements direction-selective, motion-opponent inhibition at all three network levels. This inhibition, mediated by GluClα receptors, is balanced with excitation in strength, despite tenfold fewer synapses. The different opponent network levels constitute a nested, hierarchical structure operating at increasing spatiotemporal scales. Electrophysiology and modeling suggest that distributing this computation over consecutive network levels counteracts a reduction in gain, which would result from integrating large opposing conductances at a single instance. We propose that this neural architecture provides resilience to noise while enabling high selectivity for relevant sensory information.

Neural landscape diffusion resolves conflicts between needs across time

Animals perform flexible goal-directed behaviours to satisfy their basic physiological needs1-12. However, little is known about how unitary behaviours are chosen under conflicting needs. Here we reveal principles by which the brain resolves such conflicts between needs across time. We developed an experimental paradigm in which a hungry and thirsty mouse is given free choices between equidistant food and water. We found that mice collect need-appropriate rewards by structuring their choices into persistent bouts with stochastic transitions. High-density electrophysiological recordings during this behaviour revealed distributed single neuron and neuronal population correlates of a persistent internal goal state guiding future choices of the mouse. We captured these phenomena with a mathematical model describing a global need state that noisily diffuses across a shifting energy landscape. Model simulations successfully predicted behavioural and neural data, including population neural dynamics before choice transitions and in response to optogenetic thirst stimulation. These results provide a general framework for resolving conflicts between needs across time, rooted in the emergent properties of need-dependent state persistence and noise-driven shifts between behavioural goals.

Chemical cognition: chemoconnectomics and convergent evolution of integrative systems in animals

Neurons underpin cognition in animals. However, the roots of animal cognition are elusive from both mechanistic and evolutionary standpoints. Two conceptual frameworks both highlight and promise to address these challenges. First, we discuss evidence that animal neural and other integrative systems evolved more than once (convergent evolution) within basal metazoan lineages, giving us unique experiments by Nature for future studies. The most remarkable examples are neural systems in ctenophores and neuroid-like systems in placozoans and sponges. Second, in addition to classical synaptic wiring, a chemical connectome mediated by hundreds of signal molecules operates in tandem with neurons and is the most information-rich source of emerging properties and adaptability. The major gap-dynamic, multifunctional chemical micro-environments in nervous systems-is not understood well. Thus, novel tools and information are needed to establish mechanistic links between orchestrated, yet cell-specific, volume transmission and behaviors. Uniting what we call chemoconnectomics and analyses of the cellular bases of behavior in basal metazoan lineages arguably would form the foundation for deciphering the origins and early evolution of elementary cognition and intelligence.

Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images

Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By fine-tuning the final two layers of the neural network at a lower learning rate of the TrailMap model, we demonstrate an improved recall and an occasionally improved adjusted F1-score within our test dataset over using the originally trained TrailMap model.

Using neurons to maintain autonomy: Learning from C. elegans

Understanding how biological organisms are autonomous-maintain themselves far from equilibrium through their own activities-requires understanding how they regulate those activities. In multicellular animals, such control can be exercised either via endocrine signaling through the vasculature or via neurons. In C. elegans this control is exercised by a well-delineated relatively small but distributed nervous system that relies on both chemical and electric transmission of signals. This system provides resources to integrate information from multiple sources as needed to maintain the organism. Especially important for the exercise of neural control are neuromodulators, which we present as setting agendas for control through more traditional electrical signaling. To illustrate how the C. elegans nervous system integrates multiple sources of information in controlling activities important for autonomy, we focus on feeding behavior and responses to adverse conditions. We conclude by considering how a distributed nervous system without a centralized controller is nonetheless adequate for autonomy.

Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Using network control theory to study the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).

Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila

The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper 1 , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database 2 . Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the hemibrain connectome 3 . In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

Priorities for research on neuromodulatory subcortical systems in Alzheimer's disease: Position paper from the NSS PIA of ISTAART

The neuromodulatory subcortical system (NSS) nuclei are critical hubs for survival, hedonic tone, and homeostasis. Tau-associated NSS degeneration occurs early in Alzheimer's disease (AD) pathogenesis, long before the emergence of pathognomonic memory dysfunction and cortical lesions. Accumulating evidence supports the role of NSS dysfunction and degeneration in the behavioral and neuropsychiatric manifestations featured early in AD. Experimental studies even suggest that AD-associated NSS degeneration drives brain neuroinflammatory status and contributes to disease progression, including the exacerbation of cortical lesions. Given the important pathophysiologic and etiologic roles that involve the NSS in early AD stages, there is an urgent need to expand our understanding of the mechanisms underlying NSS vulnerability and more precisely detail the clinical progression of NSS changes in AD. Here, the NSS Professional Interest Area of the International Society to Advance Alzheimer's Research and Treatment highlights knowledge gaps about NSS within AD and provides recommendations for priorities specific to clinical research, biomarker development, modeling, and intervention. HIGHLIGHTS: Neuromodulatory nuclei degenerate in early Alzheimer's disease pathological stages. Alzheimer's pathophysiology is exacerbated by neuromodulatory nuclei degeneration. Neuromodulatory nuclei degeneration drives neuropsychiatric symptoms in dementia. Biomarkers of neuromodulatory integrity would be value-creating for dementia care. Neuromodulatory nuclei present strategic prospects for disease-modifying therapies.

A deep connectome learning network using graph convolution for connectome-disease association study

Multivariate analysis approaches provide insights into the identification of phenotype associations in brain connectome data. In recent years, deep learning methods including convolutional neural network (CNN) and graph neural network (GNN), have shifted the development of connectome-wide association studies (CWAS) and made breakthroughs for connectome representation learning by leveraging deep embedded features. However, most existing studies remain limited by potentially ignoring the exploration of region-specific features, which play a key role in distinguishing brain disorders with high intra-class variations, such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD). Here, we propose a multivariate distance-based connectome network (MDCN) that addresses the local specificity problem by efficient parcellation-wise learning, as well as associating population and parcellation dependencies to map individual differences. The approach incorporating an explainable method, parcellation-wise gradient and class activation map (p-GradCAM), is feasible for identifying individual patterns of interest and pinpointing connectome associations with diseases. We demonstrate the utility of our method on two largely aggregated multicenter public datasets by distinguishing ASD and ADHD from healthy controls and assessing their associations with underlying diseases. Extensive experiments have demonstrated the superiority of MDCN in classification and interpretation, where MDCN outperformed competitive state-of-the-art methods and achieved a high proportion of overlap with previous findings. As a CWAS-guided deep learning method, our proposed MDCN framework may narrow the bridge between deep learning and CWAS approaches, and provide new insights for connectome-wide association studies.

All-optical closed-loop voltage clamp for precise control of muscles and neurons in live animals

Excitable cells can be stimulated or inhibited by optogenetics. Since optogenetic actuation regimes are often static, neurons and circuits can quickly adapt, allowing perturbation, but not true control. Hence, we established an optogenetic voltage-clamp (OVC). The voltage-indicator QuasAr2 provides information for fast, closed-loop optical feedback to the bidirectional optogenetic actuator BiPOLES. Voltage-dependent fluorescence is held within tight margins, thus clamping the cell to distinct potentials. We established the OVC in muscles and neurons of Caenorhabditis elegans, and transferred it to rat hippocampal neurons in slice culture. Fluorescence signals were calibrated to electrically measured potentials, and wavelengths to currents, enabling to determine optical I/V-relationships. The OVC reports on homeostatically altered cellular physiology in mutants and on Ca²⁺-channel properties, and can dynamically clamp spiking in C. elegans. Combining non-invasive imaging with control capabilities of electrophysiology, the OVC facilitates high-throughput, contact-less electrophysiology in individual cells and paves the way for true optogenetic control in behaving animals.

The connectome of an insect brain

Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

Caenorhabditis elegans as a Model System to Study Human Neurodegenerative Disorders

In recent years, advances in science and technology have improved our quality of life, enabling us to tackle diseases and increase human life expectancy. However, longevity is accompanied by an accretion in the frequency of age-related neurodegenerative diseases, creating a growing burden, with pervasive social impact for human societies. The cost of managing such chronic disorders and the lack of effective treatments highlight the need to decipher their molecular and genetic underpinnings, in order to discover new therapeutic targets. In this effort, the nematode Caenorhabditis elegans serves as a powerful tool to recapitulate several disease-related phenotypes and provides a highly malleable genetic model that allows the implementation of multidisciplinary approaches, in addition to large-scale genetic and pharmacological screens. Its anatomical transparency allows the use of co-expressed fluorescent proteins to track the progress of neurodegeneration. Moreover, the functional conservation of neuronal processes, along with the high homology between nematode and human genomes, render C. elegans extremely suitable for the study of human neurodegenerative disorders. This review describes nematode models used to study neurodegeneration and underscores their contribution in the effort to dissect the molecular basis of human diseases and identify novel gene targets with therapeutic potential.

The REPAIR Project, a Deep-Underground Radiobiology Experiment Investigating the Biological Effects of Natural Background Radiation: The First 6 Years

In 2017, a special edition of Radiation Research was published [Oct; Vol. 188 4.2 (https://bioone.org/journals/radiation-research/volume-188/issue-4.2)] which focused on a recently established radiobiology project within SNOLAB, a unique deep-underground research facility. This special edition included original articles, reviews and commentaries relevant to the research goals of this new project which was titled Researching the Effects of the Presence and Absence of Ionizing Radiation (REPAIR). These research goals were founded in understanding the biological effects of terrestrial and cosmic natural background radiation (NBR). Since 2017, REPAIR has evolved into a sub-NBR radiobiology research program which investigates these effects using multiple model systems and various biological endpoints. This paper summarizes the evolution of the REPAIR project over the first 6-years including its experimental scope and capabilities as well as research accomplishments.

Advances in neurexin studies and the emerging role of neurexin-2 in autism spectrum disorder

Over the past 3 decades, the prevalence of autism spectrum disorder (ASD) has increased globally from 20 to 28 million cases making ASD the fastest-growing developmental disability in the world. Neurexins are a family of presynaptic cell adhesion molecules that have been increasingly implicated in ASD, as evidenced by genetic mutations in the clinical population. Neurexins function as context-dependent specifiers of synapse properties and critical modulators in maintaining the balance between excitatory and inhibitory transmission (E/I balance). Disrupted E/I balance has long been established as a hallmark of ASD making neurexins excellent starting points for understanding the etiology of ASD. Herein we review neurexin mutations that have been discovered in ASD patients. Further, we discuss distinct synaptic mechanisms underlying the aberrant neurotransmission and behavioral deficits observed in different neurexin mouse models, with focus on recent discoveries from the previously overlooked neurexin-2 gene (Nrxn2 in mice and NRXN2 in humans). Hence, the aim of this review is to provide a summary of new synaptic insights into the molecular underpinnings of ASD.