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1. Inhibitory interneurons in a brainstem circuit adjust their inhibitory motifs to process multimodal input

   https://doi.org/10.1113/JP280741  

   Wu, Calvin ; Shore, Susan E. ( November 2020 , The Journal of Physiology)

   Inhibitory‐interneuron networks, consisting of multiple forms of circuit motifs including reciprocal (inhibitory interneurons inhibiting other interneurons) and feedforward (inhibitory interneurons inhibiting principal neurons) connections, are crucial in processing sensory information.

   The present study applies a statistical method toin vivomultichannel spike trains of dorsal cochlear nucleus neurons to disentangle reciprocal and feedforward‐inhibitory motifs.

   After inducing input‐specific plasticity, reciprocal and feedforward inhibition are found to be differentially regulated, and the combined effect synergistically modulates circuit output.

   The findings highlight the interplay among different circuit motifs as a key element in neural computation.

   Inhibitory interneurons play an essential role in neural computations by utilizing a combination of reciprocal (interneurons inhibiting each other) and feedforward (interneuron inhibiting the principal neuron) inhibition to process information. To disentangle the interplay between the two inhibitory‐circuit motifs and understand their effects on the circuit output,in vivorecordings were made from the guinea pig dorsal cochlear nucleus, a cerebellar‐like brainstem circuit. Spikes from inhibitory interneurons (cartwheel cell) and principal output neurons (fusiform cell) were compared before and after manipulating their common multimodal input. Using a statistical model based on the Cox method of modulated renewal process of spike train influence, reciprocal‐ and feedforward‐inhibition motifs were quantified. In response to altered multimodal input, reciprocal inhibition was strengthened while feedforward inhibition was weakened, and the two motifs combined to modulate fusiform cell output and acoustic‐driven responses. These findings reveal the cartwheel cell's role in auditory and multimodal processing, as well as illustrated the balance between different inhibitory‐circuit motifs as a key element in neural computation.

    

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2. High‐definition transcranial direct current stimulation dissociates fronto‐visual theta lateralization during visual selective attention

   https://doi.org/10.1113/JP278788  

   Spooner, Rachel K. ; Eastman, Jacob A. ; Rezich, Michael T. ; Wilson, Tony W. ( February 2020 , The Journal of Physiology)

   Visual attention involves discrete multispectral oscillatory responses in visual and ‘higher‐order’ prefrontal cortices.

   Prefrontal cortex laterality effects during visual selective attention are poorly characterized.

   High‐definition transcranial direct current stimulation dynamically modulated right‐lateralized fronto‐visual theta oscillations compared to those observed in left fronto‐visual pathways.

   Increased connectivity in right fronto‐visual networks after stimulation of the left dorsolateral prefrontal cortex resulted in faster task performance in the context of distractors.

   Our findings show clear laterality effects in theta oscillatory activity along prefrontal–visual cortical pathways during visual selective attention.

   Studies of visual attention have implicated oscillatory activity in the recognition, protection and temporal organization of attended representations in visual cortices. These studies have also shown that higher‐order regions such as the prefrontal cortex are critical to attentional processing, but far less is understood regarding prefrontal laterality differences in attention processing. To examine this, we selectively applied high‐definition transcranial direct current stimulation (HD‐tDCS) to the left or right dorsolateral prefrontal cortex (DLPFC). We predicted that HD‐tDCS of the leftversusright prefrontal cortex would differentially modulate performance on a visual selective attention task, and alter the underlying oscillatory network dynamics. Our randomized crossover design included 27 healthy adults that underwent three separate sessions of HD‐tDCS (sham, left DLPFC and right DLPFC) for 20 min. Following stimulation, participants completed an attention protocol during magnetoencephalography. The resulting oscillatory dynamics were imaged using beamforming, and peak task‐related neural activity was subjected to dynamic functional connectivity analyses to evaluate the impact of stimulation site (i.e. left and right DLPFC) on neural interactions. Our results indicated that HD‐tDCS over the left DLPFC differentially modulated right fronto‐visual functional connectivity within the theta band compared to HD‐tDCS of the right DLPFC and further, specifically modulated the oscillatory response for detecting targets among an array of distractors. Importantly, these findings provide network‐specific insight into the complex oscillatory mechanisms serving visual selective attention.

    

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3. The connectome of an insect brain

   https://doi.org/10.1126/science.add9330  

   Winding, Michael ; Pedigo, Benjamin D. ; Barnes, Christopher L. ; Patsolic, Heather G. ; Park, Youngser ; Kazimiers, Tom ; Fushiki, Akira ; Andrade, Ingrid V. ; Khandelwal, Avinash ; Valdes-Aleman, Javier ; et al ( March 2023 , Science)

   INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function. 

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4. Orthogonal topography in the parallel input architecture of songbird HVC

   https://doi.org/10.1002/cne.24189  

   Elliott, Kevin C. ; Wu, Wei ; Bertram, Richard ; Hyson, Richard L. ; Johnson, Frank ( March 2017 , Journal of Comparative Neurology)

   Neural activity within the cortical premotor nucleus HVC (acronym is name) encodes the learned songs of adult male zebra finches (Taeniopygia guttata). HVC activity is driven and/or modulated by a group of five afferent nuclei (the Medial Magnocellular nucleus of the Anterior Nidopallium, MMAN; Nucleus Interface, NIf; nucleus Avalanche, Av; the Robust nucleus of the Arcopallium, RA; the Uvaeform nucleus, Uva). While earlier evidence suggested that HVC receives a uniformly distributed and nontopographic pattern of afferent input, recent evidence suggests this view is incorrect (Basista et al.,). Here, we used a double‐labeling strategy (varying both the distance between and the axial orientation of dual tracer injections into HVC) to reveal a massively parallel and in some cases topographic pattern of afferent input. Afferent neurons target only one rostral or caudal location within medial or lateral HVC, and each HVC location receives convergent input from each afferent nucleus in parallel. Quantifying the distributions of single‐labeled cells revealed an orthogonal topography in the organization of afferent input from MMAN and NIf, two cortical nuclei necessary for song learning. MMAN input is organized across the lateral‐medial axis whereas NIf input is organized across the rostral‐caudal axis. To the extent that HVC activity is influenced by afferent input during the learning, perception, or production of song, functional models of HVC activity may need revision to account for the parallel input architecture of HVC, along with the orthogonal input topography of MMAN and NIf.

    

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5. Stream-specific feedback inputs to the primate primary visual cortex

   https://doi.org/10.1038/s41467-020-20505-5  

   Federer, Frederick ; Ta’afua, Seminare ; Merlin, Sam ; Hassanpour, Mahlega S. ; Angelucci, Alessandra ( January 2021 , Nature Communications)

   The sensory neocortex consists of hierarchically-organized areas reciprocally connected via feedforward and feedback circuits. Feedforward connections shape the receptive field properties of neurons in higher areas within parallel streams specialized in processing specific stimulus attributes. Feedback connections have been implicated in top-down modulations, such as attention, prediction and sensory context. However, their computational role remains unknown, partly because we lack knowledge about rules of feedback connectivity to constrain models of feedback function. For example, it is unknown whether feedback connections maintain stream-specific segregation, or integrate information across parallel streams. Using viral-mediated labeling of feedback connections arising from specific cytochrome-oxidase stripes of macaque visual area V2, here we show that feedback to the primary visual cortex (V1) is organized into parallel streams resembling the reciprocal feedforward pathways. This suggests that functionally-specialized V2 feedback channels modulate V1 responses to specific stimulus attributes, an organizational principle potentially extending to feedback pathways in other sensory systems.

    

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