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Introduction: Behaviors often involve a sequence of events, and learning and reproducing it is essential for sequential memory. Brain loop structures refer to loop-shaped inter-regional connection structures in the brain such as cortico-basal ganglia-thalamic and cortico-cerebellar loops. They are thought to play a crucial role in supporting sequential memory, but it is unclear what properties of the loop structure are important and why. Methods: In this study, we investigated conditions necessary for the learning of sequential memory in brain loop structures via computational modeling. We assumed that sequential memory emerges due to delayed information transmission in loop structures and presented a basic neural activity model and validated our theoretical considerations with spiking neural network simulations. Results: Based on this model, we described the factors for the learning of sequential memory: first, the information transmission delay should decrease as the size of the loop structure increases; and second, the likelihood of the learning of sequential memory increases as the size of the loop structure increases and soon saturates. Combining these factors, we showed that moderate-sized brain loop structures are advantageous for the learning of sequential memory due to the physiological restrictions of information transmission delay. Discussion: Our results will help us better understand the relationship between sequential memory and brain loop structures.

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Diverse neuron classes in hippocampal CA1 have been identified through the heterogeneity of their cellular/molecular composition. How these classes relate to hippocampal function and the network dynamics that support cognition in primates remains unclear. Here, we report inhibitory functional cell groups in CA1 of freely moving macaques whose diverse response profiles to network states and each other suggest distinct and specific roles in the functional microcircuit of CA1. In addition, pyramidal cells that were grouped by their superficial or deep layer position differed in firing rate, burstiness, and sharp-wave ripple-associated firing. They also showed strata-specific spike-timing interactions with inhibitory cell groups, suggestive of segregated neural populations. Furthermore, ensemble recordings revealed that cell assemblies were preferentially organized according to these strata. These results suggest that hippocampal CA1 in freely moving macaques bears a sublayer-specific circuit organization that may shape its role in cognition.

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Brain oscillations are crucial for perception, memory, and behavior. Parvalbumin-expressing (PV) interneurons are critical for these oscillations, but their population dynamics remain unclear. Using voltage imaging, we simultaneously recorded membrane potentials in up to 26 PV interneurons in vivo during hippocampal ripple oscillations in mice. We found that PV cells generate ripple-frequency rhythms by forming highly dynamic cell assemblies. These assemblies exhibit rapid and significant changes from cycle to cycle, varying greatly in both size and membership. Importantly, this variability is not just random spiking failures of individual neurons. Rather, the activities of other PV cells contain significant information about whether a PV cell spikes or not in a given cycle. This coordination persists without network oscillations, and it exists in subthreshold potentials even when the cells are not spiking. Dynamic assemblies of interneurons may provide a new mechanism to modulate postsynaptic dynamics and impact cognitive functions flexibly and rapidly.

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Under normal conditions the principal cells of the striatum, medium spiny neurons (MSNs), show structured cell assembly activity patterns which alternate sequentially over exceedingly long timescales of many minutes. It is important to understand this activity since it is characteristically disrupted in multiple pathologies, such as Parkinson's disease and dyskinesia, and thought to be caused by alterations in the MSN to MSN lateral inhibitory connections and in the strength and distribution of cortical excitation to MSNs. To understand how these long timescales arise we extended a previous network model of MSN cells to include synapses with short-term plasticity, with parameters taken from a recent detailed striatal connectome study. We first confirmed the presence of sequentially switching cell clusters using the non-linear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP). We found that the network could generate non-stationary activity patterns varying extremely slowly on the order of minutes under biologically realistic conditions. Next we used Simulation Based Inference (SBI) to train a deep net to map features of the MSN network generated cell assembly activity to MSN network parameters. We used the trained SBI model to estimate MSN network parameters from ex-vivo brain slice calcium imaging data. We found that best fit network parameters were very close to their physiologically observed values. On the other hand network parameters estimated from Parkinsonian, decorticated and dyskinetic ex-vivo slice preparations were different. Our work may provide a pipeline for diagnosis of basal ganglia pathology from spiking data as well as for the design pharmacological treatments.

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Tissue engineering can provide in vitro models for drug testing, disease modeling, and perhaps someday, tissue/organ replacements. For building 3D heart tissue, the alignment of cardiac cells or cardiomyocytes (CMs) is important in generating a synchronously contracting tissue. To that end, researchers have generated several fabrication methods for building heart tissue, but direct comparisons of pros and cons using the same cell source is lacking. Here, we derived cardiomyocytes (CMs) from human induced pluripotent stem cells (hiPSCs) and compare the assembly of these cells using three fabrication methods: cardiospheres, muscle rings, and muscle strips. All three protocols successfully generated compacted tissue comprised of hiPSC-derived CMs stable for at least 2 weeks. The percentage of aligned cells was greatest in the muscle strip (55%) and the muscle ring (50%) compared with the relatively unaligned cardiospheres (35%). The iPSC-derived CMs within the muscle strip also exhibited the greatest elongation, with elongation factor at 2.0 compared with 1.5 for the muscle ring and 1.2 for the cardiospheres. This is the first direct comparison of various fabrication techniques using the same cell source.

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We make decisions based on currently perceivable information or an internal model of the environment. The medial prefrontal cortex (mPFC) and its interaction with the hippocampus have been implicated in the latter, model-based decision-making; however, the underlying computational properties remain incompletely understood. We have examined mPFC spiking and hippocampal oscillatory activity while rats flexibly select new actions using a known associative structure of environmental cues and outcomes. During action selection, the mPFC reinstates representations of the associative structure. These awake reactivation events are accompanied by synchronous firings among neurons coding the associative structure and those coding actions. Moreover, their functional coupling is strengthened upon the reactivation events leading to adaptive actions. In contrast, only cue-coding neurons improve functional coupling during hippocampal sharp wave ripples. Thus, the lack of direct experience disconnects the mPFC from the hippocampus to independently form self-organized neuronal ensemble dynamics linking prior knowledge with novel actions.

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Abnormal mechanical loading often leads to the progressive degradation of cartilage and causes osteoarthritis (OA). Although multiple mechanoresponsive strategies based on biomaterials have been designed to restore healthy cartilage microenvironments, methods to remotely control the on-demand mechanical forces for cartilage repair pose significant challenges. Here, a magneto-mechanically controlled mesenchymal stem cell (MSC) platform, based on the integration of intercellular mechanical communication and intracellular mechanosignaling processes, is developed for OA treatment. MSCs loaded with antioxidative melanin@Fe3O4 magnetic nanoparticles (Magcells) rapidly assemble into highly ordered cell clusters with enhanced cell-cell communication under a time-varying magnetic field, which enables long-term retention and differentiation of Magcells in the articular cavity. Subsequently, via mimicking the gait cycle, chondrogenesis can be further enhanced by the dynamic activation of mechanical signaling processes in Magcells. This sophisticated magneto-mechanical actuation strategy provides a paradigm for developing mechano-therapeutics to repair cartilage in OA treatment.

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Acoustic cell assembly devices are applied in cell spheroid fabrication attributed to their rapid, label-free and low-cell damage production of size-uniform spheroids. However, the spheroids yield and production efficiency are still insufficient to meet the requirements of several biomedical applications, especially those that require large quantities of cell spheroids, such as high-throughput screening, macro-scale tissue fabrication, and tissue repair. Here, we developed a novel 3D acoustic cell assembly device combined with a gelatin methacrylamide (GelMA) hydrogels for the high-throughput fabrication of cell spheroids. The acoustic device employs three orthogonal piezoelectric transducers that can generate three orthogonal standing bulk acoustic waves to create a 3D dot-array (25 × 25 × 22) of levitated acoustic nodes, enabling large-scale fabrication of cell aggregates (>13,000 per operation). The GelMA hydrogel serves as a supporting scaffold to preserve the structure of cell aggregates after the withdrawal of acoustic fields. As a result, mostly cell aggregates (>90%) mature into spheroids maintaining good cell viability. We further applied these acoustically assembled spheroids to drug testing to explore their potency in drug response. In conclusion, this 3D acoustic cell assembly device may pave the way for the scale-up fabrication of cell spheroids or even organoids, to enable flexible application in various biomedical applications, such as high-throughput screening, disease modeling, tissue engineering, and regenerative medicine.

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Tissue engineering raised a high requirement to control cell distribution in defined materials and structures. In "ink"-based bioprintings, such as 3D printing and photolithography, cells were associated with inks for spatial orientation; the conditions suitable for one ink are hard to apply on other inks, which increases the obstacle in their universalization. The Magneto-Archimedes effect based (Mag-Arch) strategy can modulate cell locomotion directly without impelling inks. In a paramagnetic medium, cells were repelled from high magnetic strength zones due to their innate diamagnetism, which is independent of substrate properties. However, Mag-Arch has not been developed into a powerful bioprinting strategy as its precision, complexity, and throughput are limited by magnetic field distribution. By controlling the paramagnetic reagent concentration in the medium and the gaps between magnets, which decide the cell repelling scope of magnets, we created simultaneously more than a hundred micrometer scale identical assemblies into designed patterns (such as alphabets) with single/multiple cell types. Cell patterning models for cell migration and immune cell adhesion studies were conveniently created by Mag-Arch. As a proof of concept, we patterned a tumor/endothelial coculture model within a covered microfluidic channel to mimic epithelial-mesenchymal transition (EMT) under shear stress in a cancer pathological environment, which gave a potential solution to pattern multiple cell types in a confined space without any premodification. Overall, our Mag-Arch patterning presents an alternative strategy for the biofabrication and biohybrid assembly of cells with biomaterials featured in controlled distribution and organization, which can be broadly employed in tissue engineering, regenerative medicine, and cell biology research.

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BACKGROUND: The brain-machine interface is a technology that has been used for improving the quality of life of individuals with physical disabilities and also healthy individuals. It is important to improve the methods used for decoding the brain-machine interface data as the accuracy and speed of movements achieved using the existing technology are not comparable to the normal body. COMPARISON WITH THE EXISTING METHOD: Decoding of brain-machine interface data using the proposed method resulted in improved decoding accuracy compared to the existing method. CONCLUSIONS: The results demonstrated the usefulness of cell assembly state estimation method for decoding the brain-machine interface data. NEW METHOD: We incorporated a novel method of estimating cell assembly states using spike trains with the existing decoding method that used only firing rate data. Synaptic connectivity pattern was used as feature values in addition to firing rate. Publicly available monkey brain-machine interface datasets were used in the study. RESULTS: As long as the decoding was successful, the root mean square error of the proposed method was significantly smaller than the existing method. Artificial neural netowork-based decoding method resulted in more stable decoding, and also improved the decoding accuracy due to incorporation of synaptic connectivity pattern.

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Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the "synchrony through synaptic plasticity" hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

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The hippocampus plays a critical role in spatial navigation and episodic memory. However, research on in vivo hippocampal activity dynamics mostly relies on single modalities, such as electrical recordings or optical imaging, with respectively limited spatial and temporal resolution. Here, we develop the E-Cannula, integrating fully transparent graphene microelectrodes with imaging cannula, which enables simultaneous electrical recording and two-photon calcium imaging from the exact same neural populations across an anatomically extended region of the mouse hippocampal CA1 stably across several days. The large-scale multimodal recordings show that sharp wave ripples (SWRs) exhibit spatiotemporal wave patterns along multiple axes in two-dimensional (2D) space with different spatial extents and temporal propagation modes. Notably, distinct SWR wave patterns are associated with the selective recruitment of orthogonal CA1 cell assemblies. These results demonstrate the utility of the E-Cannula as a versatile neurotechnology with the potential for future integration with other optical components.

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Three-dimensional (3D) cell arrays provide an in vitro platform for clinical drug screening, but the bulky culture devices limit their application scenarios. Here, we demonstrate an integrated portable device that can realize contact-free construction of 3D cell spheroids. The interaction between the ultrasound generated by the portable device and the capillary results in periodic pressure nodes or anti-nodes, which lead to form a 3D cell array for cell culture. Such a 3D cell array pattern can be constructed in seconds and requires only 1 µL of cell samples. We further assessed the spheroids formed by the portable device and the impact of the acoustic field on spheroids and demonstrated the drug screening with assembled spheroids. More importantly, the integrated acoustic device can be further integrated with other components for more complex cell culture and all-round analysis. This portable and effective integrated device provides a new avenue for clinical biomedicine.

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Precise and flexible three-dimensional (3D) cell construct assembly using external forces or fields can produce micro-scale cellular architectures with intercellular connections, which is an important prerequisite to reproducing the structures and functions of biological systems. Currently, it is also a substantial challenge in the bioengineering field. Here, we propose a smart acoustic 3D cell assembly strategy that utilizes a 3D printed module and hydrogel sheets. Digitally controlled six wave beams offer a high degree of freedom (including wave vector combination, frequency, phase, and amplitude) that enables versatile biomimetic micro cellular patterns in hydrogel sheets. Further, replaceable frames can be used to fix the acoustic-built micro-scale cellular structures in these sheets, enabling user-defined hierarchical or heterogeneous constructs through layer-by-layer assembly. This strategy can be employed to construct vasculature with different diameters and lengths, composed of human umbilical vein endothelial cells and smooth muscle cells. These constructs can also induce controllable vascular network formation. Overall, the findings of this work extend the capabilities of acoustic cell assembly into 3D space, offering advantages including innovative, flexible, and precise patterning, and displaying great potential for the manufacture of various artificial tissue structures that duplicatein vivofunctions.

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Tallman and colleagues (this issue) report fMRI findings in support of the classic view of memory consolidation over its main challenger, the multiple trace theory. The present commentary highlights some of the obstacles facing any fMRI study of memory consolidation and notes which challenges were tackled by Tallman and colleagues and which challenges might be insurmountable.

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The cell surface can be engineered with synthetic DNA for various applications ranging from cancer immunotherapy to tissue engineering. However, while elegant methods such as click conjugation and lipid insertion have been developed to engineer the cell surface with DNA, little effort has been made to systematically evaluate and compare these methods. Resultantly, it is often challenging to choose a right method for a certain application or to interpret data from different studies. In this study, we systematically evaluated click conjugation and lipid insertion in terms of cell viability, engineering efficiency, and displaying stability. Cells engineered with both methods can maintain high viability when the concentration of modified DNA is less than 25-50 µM. However, lipid insertion is faster and more efficient in displaying DNA on the cell surface than click conjugation. The efficiency of displaying DNA with lipid insertion is 10-40 times higher than that with click conjugation for a large range of DNA concentration. However, the half-life of physically inserted DNA on the cell surface is 3-4 times lower than that of covalently conjugated DNA, which depends on the working temperature. While the half-life of physically inserted DNA molecules on the cell surface is shorter than that of DNA molecules clicked onto the cell surface, lipid insertion is more effective than click conjugation in the promotion of cell-cell interactions under the two different experimental settings. The data acquired in this work are expected to act as a guideline for choosing an approximate method for engineering the cell surface with synthetic DNA or even other biomolecules.

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Engineering of the cell plasma membrane using functional DNA is important for studying and controlling cellular behaviors. However, most efforts to apply artificial DNA interactions on cells are limited to external membrane surface due to the lack of suitable synthetic tools to engineer the intracellular side, which impedes many applications in cell biology. Inspired by the natural extracellular vesicle-cell fusion process, we have developed a fusogenic spherical nucleic acid construct to realize robust DNA functionalization on both external and internal cell surfaces via liposome fusion-based transport (LiFT) strategy, which enables applications including the construction of heterotypic cell assembly for programmed signaling pathway and detection of intracellular metabolites. This approach can engineer cell membranes in a highly efficient and spatially controlled manner, allowing one to build anisotropic membrane structures with two orthogonal DNA functionalities.

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Memory is a dynamic process that is based on and can be altered by experiences. Integrating memories of multiple experiences (memory integration) is the basis of flexible and complex decision-making. However, the mechanism of memory integration in neural networks of the brain remains poorly understood. In this study, we built a recurrent spiking network model and investigated the neural mechanism of memory integration before a decision is made (retrospective memory integration) at the neural circuit level. Our simulations suggest that retrospective memory integration accompanies reconfiguration of neural cell assemblies. Additionally, partially blocking neural network plasticity leads to failure of memory integration. These findings can potentially guide the experimental investigation of the neural mechanism of retrospective memory integration and serve as the basis for developing new artificial intelligence algorithms.

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