Organization Of Neuronal Networks Research Articles

Prolactin blood concentration relies on the scalability of the TIDA neurons’ network efficiency in vivo

Our understanding and management of reproductive health and related disorders such as infertility, menstrual irregularities, and pituitary disorders depend on understanding the intricate sex-specific mechanisms governing prolactin secretion. Using exvivo experiments in acute slices, in parallel with invivo calcium imaging (GRIN lens technology), we found that dopamine neurons inhibiting PRL secretion (TIDA), organize as functional networks both in and exvivo. We defined an index of efficiency of networking (Ieff) using the duration of calcium events and the ability to form plastic economic networks. It determined TIDA neurons' ability to inhibit PRL secretion invivo. Ieff variations in both sexes demonstrated TIDA neurons' adaptability to physiological changes. A variation in the number of active neurons contributing to the network explains the sexual dimorphism in basal [PRL]blood secretion patterns. These sex-specific differences in neuronal activity and network organization contribute to the understanding of hormone regulation.

Fractured columnar small-world functional network organization in volumes of L2/3 of mouse auditory cortex.

The sensory cortices of the brain exhibit large-scale functional topographic organization, such as the tonotopic organization of the primary auditory cortex (A1) according to sound frequency. However, at the level of individual neurons, layer 2/3 (L2/3) A1 appears functionally heterogeneous. To identify if there exists a higher-order functional organization of meso-scale neuronal networks within L2/3 that bridges order and disorder, we used in vivo two-photon calcium imaging of pyramidal neurons to identify networks in three-dimensional volumes of L2/3 A1 in awake mice. Using tonal stimuli, we found diverse receptive fields with measurable colocalization of similarly tuned neurons across depth but less so across L2/3 sublayers. These results indicate a fractured microcolumnar organization with a column radius of ∼50 µm, with a more random organization of the receptive field over larger radii. We further characterized the functional networks formed within L2/3 by analyzing the spatial distribution of signal correlations (SCs). Networks show evidence of Rentian scaling in physical space, suggesting effective spatial embedding of subnetworks. Indeed, functional networks have characteristics of small-world topology, implying that there are clusters of functionally similar neurons with sparse connections between differently tuned neurons. These results indicate that underlying the regularity of the tonotopic map on large scales in L2/3 is significant tuning diversity arranged in a hybrid organization with microcolumnar structures and efficient network topologies.

Immunoglobulin superfamily member 3 is required for the vagal neural crest cell migration and enteric neuronal network organization

The immunoglobulin (Ig) superfamily members are involved in cell adhesion and migration, complex multistep processes that play critical roles in embryogenesis, wound healing, tissue formation, and many other processes, but their specific functions during embryonic development remain unclear. Here, we have studied the function of the immunoglobulin superfamily member 3 (IGSF3) by generating an Igsf3 knockout (KO) mouse model with CRISPR/Cas9-mediated genome engineering. By combining RNA and protein detection methodology, we show that during development, IGSF3 localizes to the neural crest and a subset of its derivatives, suggesting a role in normal embryonic and early postnatal development. Indeed, inactivation of Igsf3 impairs the ability of the vagal neural crest cells to migrate and normally innervate the intestine. The small intestine of Igsf3 KO mice shows reduced thickness of the muscularis externa and diminished number of enteric neurons. Also, misalignment of neurons and smooth muscle cells in the developing intestinal villi is detected. Taken together, our results suggest that IGSF3 functions contribute to the formation of the enteric nervous system. Given the essential role of the enteric nervous system in maintaining normal gastrointestinal function, our study adds to the pool of information required for further understanding the mechanisms of gut innervation and etiology behind bowel motility disorders.

Exploiting volume electron microscopy to investigate structural plasticity and stability of the postsynaptic compartment of central synapses.

Volume reconstruction from electron microscopy datasets is a tool increasingly used to study the ultrastructure of the synapse in the broader context of neuronal network and brain organization. Fine modifications of synapse structure, such as activity-dependent dendritic spine enlargement and changes in the size and shape of the postsynaptic density, occur upon maturation and plasticity. The lack of structural plasticity or the inability to stabilize potentiated synapses are associated with synaptic and neuronal functional impairment. Mapping these rearrangements with the high resolution of electron microscopy proved to be essential in order to establish precise correlations between the geometry of synapses and their functional states. In this review we discuss recent discoveries on the substructure of the postsynaptic compartment of central excitatory synapses and how those are correlated with functional states of the neuronal network. The added value of volume electron microscopy analyses with respect to conventional transmission electron microscopy studies is highlighted considering that some limitations of volume-based methods imposed several adjustments to describe the geometry of this synaptic compartment and new parameters-that are good indicators of synapses strength and activity-have been introduced.

How Functional Connectivity Measures Affect the Outcomes of Global Neuronal Network Characteristics in Patients with Schizophrenia Compared to Healthy Controls.

Modern computational solutions used in the reconstruction of the global neuronal network arrangement seem to be particularly valuable for research on neuronal disconnection in schizophrenia. However, the vast number of algorithms used in these analyses may be an uncontrolled source of result inconsistency. Our study aimed to verify to what extent the characteristics of the global network organization in schizophrenia depend on the inclusion of a given type of functional connectivity measure. Resting-state EEG recordings from schizophrenia patients and healthy controls were collected. Based on these data, two identical procedures of graph-theory-based network arrangements were computed twice using two different functional connectivity measures (phase lag index, PLI, and phase locking value, PLV). Two series of between-group comparisons regarding global network parameters calculated on the basis of PLI or PLV gave contradictory results. In many cases, the values of a given network index based on PLI were higher in the patients, and the results based on PLV were lower in the patients than in the controls. Additionally, selected network measures were significantly different within the patient group when calculated from PLI or PLV. Our analysis shows that the selection of FC measures significantly affects the parameters of graph-theory-based neuronal network organization and might be an important source of disagreement in network studies on schizophrenia.

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

In neuroscience, the structural connectivity matrix of synaptic weights between neurons is one of the critical factors that determine the overall function of a network of neurons. The mechanisms of signal transduction have been intensively studied at different time and spatial scales and both the cellular and molecular levels. While a better understanding and knowledge of some basic processes of information handling by neurons has been achieved, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor the electrical activity of a large number of neurons in real time. The analysis of the data related to the activities of individual neurons can become a very valuable tool for the study of the dynamics and architecture of neural networks. In particular, advances in optical imaging techniques allow us to record up to thousands of neurons nowadays. However, most of the efforts have been focused on calcium signals, that lack relevant aspects of cell activity. In recent years, progresses in the field of genetically encoded voltage indicators have shown that imaging signals could be well suited to record spiking and synaptic events from a large population of neurons. Here, we present a methodology to infer the connectivity of a population of neurons from their voltage traces. At first, putative synaptic events were detected. Then, a multi-class logistic regression was used to fit the putative events to the spiking activities and a penalization term was allowed to regulate the sparseness of the inferred network. The proposed Multi-Class Logistic Regression with L1 penalization (MCLRL) was benchmarked against data obtained from in silico network simulations. MCLRL properly inferred the connectivity of all tested networks, as indicated by the Matthew correlation coefficient (MCC). Importantly, MCLRL was accomplished to reconstruct the connectivity among subgroups of neurons sampled from the network. The robustness of MCLRL to noise was also assessed and the performances remained high (MCC>0.95) even in extremely high noise conditions (>95% noisy events). Finally, we devised a procedure to determine the optimal MCLRL regularization term, which allows us to envision its application to experimental data.

Monosynaptic rabies virus tracing from projection-targeted single neurons

A single neuron integrates inputs from thousands of presynaptic neurons to generate outputs. Circuit tracing using G-deleted rabies virus (RVΔG) vectors permits the brain-wide labeling of presynaptic inputs to targeted single neurons. However, the experimental procedures are complex, and the success rate of circuit labeling is low because of the lack of validation to increase the accuracy and efficiency of monosynaptic RVΔG tracing from targeted single neurons. We established an efficient RVΔG tracing method from projection target-defined single neurons using TVA950, a transmembrane isoform of TVA receptors, for initial viral infection. Presynaptic neurons were transsynaptically labeled from 80 % of the TVA950-expressing single starter neurons that survived after infection with EnvA-pseudotyped RVΔG in the adult mouse brain. We labeled single neuronal networks in the primary visual cortex (V1) and higher visual areas, namely the posteromedial area (PM) and anteromedial area (AM), as well as the single neuronal networks of PM-projecting V1 single neurons. Monosynaptic RVΔG tracing from projection-targeted single neurons revealed the input–output organization of single neuronal networks. Single-neuron network analysis based on RVΔG tracing will help dissect the heterogeneity of neural circuits and link circuit motifs and large-scale networks across scales, thereby clarifying information processing and circuit computation in the brain.

Characteristics of Complex Networks in Neural Networks

The complex network characteristics of the neuronal functional network composing of individual neurons have been rarely studied, within even fewer recording the number of neurons. In this paper, we use a multi-electrode recording spike trains datasets recorded from slice cultures of rodent somatosensory cortex in monkeys. The number of neurons in a single recording process reached several hundred. The entire recording time was divided into non-overlapping time segments to construct a functional network over different times. We analyze the small world characteristics, the modular network and community structures of neuronal functional networks. The network characteristics are analyzed to determine the changing process with the evolution of different neuronal functional networks. We proposed a new similarity coefficient S to compare the similarity indexes between the community structures. We found that neuronal networks have small world and modularity characteristics in common, when compared to a random network. The network characteristics are stronger when the networks retain a small number of edges. These findings contribute to the study of the neuronal network organization of individual neurons.

Functional Weight of Somatic and Cognitive Networks and Asymmetry of Compensatory Mechanisms: Collaboration or Divergency among Hemispheres after Cerebrovascular Accident?

The human brain holds highly sophisticated compensatory mechanisms relying on neuroplasticity. Neuronal degeneracy, redundancy, and brain network organization make the human nervous system more robust and evolvable to continuously guarantee an optimal environmental-related homeostasis. Nevertheless, after injury, restitution processes appear dissimilar, depending on the pathology. Following a cerebrovascular accident, asymmetry, within- and across-network compensation and interhemispheric inhibition are key features to functional recovery. In moderate-to-severe stroke, neurological outcome is often poor, and little is known about the paths that enable either an efficient collaboration among hemispheres or, on the contrary, an antagonism of adaptative responses. In this review, we aim to decipher key issues of ipsilesional and contralesional hemispheric functioning allowing the foundations of effective neurorehabilitation strategies.

Chapter 3 - Anatomy and cytoarchitectonics of the human hypothalamus

Due to the complexity of hypothalamic functions, the organization of the hypothalamus is extremely intricate. This relatively small brain area contains several nuclei, most of them are ill-defined regions without distinct boundaries; these nuclei are often connected with each other and other distant brain regions with similarly indistinct pathways. These hypothalamic centers control numerous key physiological functions including reproduction, growth, food intake, circadian rhythm, behavior, and autonomic balance via neural and endocrine signals. To understand the morphology of the hypothalamus is therefore extremely important, though it remains a stupendous task due to the complex organization of neuronal networks formed by the various neurotransmitter and neuromodulator systems.

Age-Related EEG Features of Bursting Activity During Anesthetic-Induced Burst Suppression.

Electroencephalographic (EEG) Burst Suppression (BSUPP) is a discontinuous pattern characterized by episodes of low voltage disrupted by bursts of cortical synaptic activity. It can occur while delivering high-dose anesthesia. Current research suggests an association between BSUPP and the occurrence of postoperative delirium in the post-anesthesia care unit (PACU) and beyond. We investigated burst micro-architecture to further understand how age influences the neurophysiology of this pharmacologically-induced state. We analyzed a subset of EEG recordings (n = 102) taken from a larger data set previously published. We selected the initial burst that followed a visually identified “silent second,” i.e., at least 1 s of iso-electricity of the EEG during propofol induction. We derived the (normalized) power spectral density [(n)PSD], the alpha band power, the maximum amplitude, the maximum slope of the EEG as well as the permutation entropy (PeEn) for the first 1.5 s of the initial burst of each patient. In the old patients >65 years, we observed significantly lower (p < 0.001) EEG power in the 1–15 Hz range. In general, their EEG contained a significantly higher amount of faster oscillations (>15 Hz). Alpha band power (p < 0.001), EEG amplitude (p = 0.001), and maximum EEG slope (p = 0.045) all significantly decreased with age, whereas PeEn increased (p = 0.008). Hence, we can describe an age-related change in features during EEG burst suppression. Sub-group analysis revealed no change in results based on pre-medication. These EEG changes add knowledge to the impact of age on cortical synaptic activity. In addition to a reduction in EEG amplitude, age-associated burst features can complicate the identification of excessive anesthetic administration in patients under general anesthesia. Knowledge of these neurophysiologic changes may not only improve anesthesia care through improved detection of burst suppression but might also provide insight into changes in neuronal network organization in patients at risk for age-related neurocognitive problems.

Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics

Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network’s activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.

SPARC: A Comprehensive Single Neuron Molecular Phenotype Map of the Right Atrial Ganglionated Plexus in the Pig Heart

As part of the NIH SPARC program efforts to study the intrinsic cardiac nervous system, we developed the first comprehensive molecular phenotyping data on the right atrial ganglionated plexus (RAGP) within the intrinsic cardiac nervous system of the pig. We collected hundreds of single neurons from the RAGP and assayed these for expression of a wide range of genes relevant to neuronal functions. The sinoatrial node (SAN) of 4 pigs were injected with FastBlue, a retrograde tracer, labeling neurons projecting to the SAN. The RAGP of those pigs were then collected and sectioned. Both SAN‐projecting and non‐SAN‐projecting single neurons in RAGP were collected through laser capture microdissection (LCM), allowing for spatial mapping of all collected samples. Over 400 single neurons were collected (n=4 animals) and each neuron was assayed for over 300 genes using high throughput microfluidic qPCR using BioMark. Neuronal phenotypes were distinguished by multivariate analysis showing patterns of network activity between multiple genes. Interestingly, these phenotypes show no spatial preferences within the RAGP. Our results revealed extensive combinatorial expression of neurotransmitters across the RAGP neuronal phenotypes. Additionally, there was a large overlap in expression profiles of SAN‐projecting and non‐SAN‐projecting neurons, without any single gene or module acting as a distinguishing marker between the connectionally labeled groups. Previous studies have focused on cholinergic and catecholaminergic processes, showing evidence for extensive protein expression of cholinergic markers in RAGP. Our findings are in stark contrast to these results and demonstrate high gene expression correlation between cholinergic and catecholaminergic markers such as tyrosine hydroxylase (Th) and choline acetyltransferase (ChAT) in single neurons. This finding suggests that cells are poised for a rapid shift in neurotransmitter signaling as well as the possibility for post transcriptional regulation of neuron phenotype plasticity. Our present findings significantly expand the list of neuromodulatory peptides and their receptors known to be expressed in RAGP. For example, neuropeptide Y and somatostatin showed distinct co‐expression patterns with their respective receptors with significant non‐overlapping expression of transmitters and cognate receptors across single neurons. Taken together, our results reveal a complex organization of neuronal networks within RAGP‐SAN complex that is not entirely governed by connectivity or spatial location, with combinatorial patterns of local paracrine networks with potential influence on cardiac function.Support or Funding InformationSupported by the NIH Common Fund SPARC Program award OT2 OD023848 and NIH NHLBI Multiscale Modeling award U01 HL133360.

Waveform Plasticity under Entrainment to 12-h T-cycles in Drosophila melanogaster: Behavior, Neuronal Network, and Evolution.

A crucial property of circadian clocks is the ability to regulate the shape of an oscillation over its cycle length (waveform) appropriately, thus enhancing Darwinian fitness. Many studies over the past decade have revealed interesting ways in which the waveform of rodent behavior could be manipulated, one of which is that the activity bout bifurcates under environments that have 2 light/dark cycles within one 24-h day (LDLD). It has been observed that such unique, although unnatural, environments reveal acute changes in the circadian clock network. However, although adaptation of waveforms to different photoperiods is well studied, modulation of waveforms under LDLD has received relatively less attention in research on insect rhythms. Therefore, we undertook this study to ask the following questions: what is the extent of waveform plasticity that Drosophila melanogaster exhibits, and what are the neuronal underpinnings of such plasticity under LDLD? We found that the activity/rest rhythms of wild-type flies do not bifurcate under LDLD. Instead, they show similar but significantly different behavior from that under a long-day LD cycle. This behavior is accompanied by differences in the organization of the circadian neuronal network, which include changes in waveforms of a core clock component and an output molecule. In addition, to understand the functional significance of such variations in the waveform, we examined laboratory selected populations that exhibit divergent eclosion chronotypes (and therefore, waveforms). We found that populations selected for predominant eclosion in an evening window (late chronotypes) showed reduced amplitude plasticity and increased phase plasticity of activity/rest rhythms. This, we argue, is reflective of divergent evolution of circadian neuronal network organization in our laboratory selected flies.

From dynamics to links: a sparse reconstruction of the topology of a neural network

Abstract One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity and how information processing occurs in the neural circuits. At the cellular and molecular level, mechanisms of signal transduction have been studied intensively and a better knowledge and understanding of some basic processes of information handling by neurons has been achieved. In contrast, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor electrical activity of a large number of neurons in real time. Then, the qualitative and quantitative analysis of the spiking activity of individual neurons is a very valuable tool for the study of the dynamics and architecture of the neural networks. Such activity is not due to the sole intrinsic properties of the individual neural cells but it is mostly the consequence of the direct influence of other neurons. The deduction of the effective connectivity between neurons, whose experimental spike trains are observed, is of crucial importance in neuroscience: first for the correct interpretation of the electro-physiological activity of the involved neurons and neural networks, and, for correctly relating the electrophysiological activity to the functional tasks accomplished by the network. In this work, we propose a novel method for the identification of connectivity of neural networks using recorded voltages. Our approach is based on the assumption that the network has a topology with sparse connections. After a brief description of our method, we will report the performances and compare it to the cross-correlation computed on the spike trains, which represents a gold standard method in the field.

Neuronal network maturation differently affects secretory vesicles and mitochondria transport in axons

Studying intracellular dynamics in neurons is crucial to better understand how brain circuits communicate and adapt to environmental changes. In neurons, axonal secretory vesicles underlie various functions from growth during development to plasticity in the mature brain. Similarly, transport of mitochondria, the power plant of the cell, regulates both axonal development and synaptic homeostasis. However, because of their submicrometric size and rapid velocities, studying the kinetics of these organelles in projecting axons in vivo is technically challenging. In parallel, primary neuronal cultures are adapted to study axonal transport but they lack the physiological organization of neuronal networks, which in turn may bias observations. We previously developed a microfluidic platform to reconstruct a physiologically-relevant and functional corticostriatal network in vitro that is compatible with high-resolution videorecording of axonal trafficking. Here, using this system we report progressive changes in axonal transport kinetics of both dense core vesicles and mitochondria that correlate with network development and maturation. Interestingly, axonal flow of both types of organelles change in opposite directions, with rates increasing for vesicles and decreasing for mitochondria. Overall, our observations highlight the need for a better spatiotemporal control for the study of intracellular dynamics in order to avoid misinterpretations and improve reproducibility.

MRI in the Study of Animal Models of Stroke.

Stroke consists of the loss of cerebral functions resulting from the interruption of blood supply to a region of the brain, and represents the second cause of death and the leading cause of major disability in adults in Europe. Stroke is a very active field of research at preclinical and clinical levels, and Magnetic Resonance Imaging (MRI) is one of the most powerful tools that scientist and clinicians have for the study of the onset, evolution and consequences of this devastating disease, as well as for the monitoring of the success of available treatments, or for the development of novel therapeutic strategies.MRI can tackle the study of stroke from different points of view, and at scales ranging from subcellular to systems biology level. Magnetic resonance spectroscopy (MRS) allows the noninvasive measurement of the levels of principal metabolites in the brain, and how they change during the course of the disease, or in response to therapy. Glutamate, in particular, is very important in the field of stroke. Several anatomical MR techniques allow the characterization of the lesion volumes, the formation of cytotoxic and vasogenic edema, changes in cerebral blood flow and volume, structural changes in gray and white matter, the obtaining of the vascular architecture and status, etc. At functional level, diverse modalities of functional MRI (fMRI) allow the assessment of the alteration in the function and organization of neuronal networks of the subject under study, as a consequence of the disease or in response to treatment. Finally, emerging imaging modalities that include temperature and pH mapping of the brain, imaging by chemical exchange saturation transfer effect (CEST), all of them closely related to tissue status, or the use of contrast agents for the targeting of tissue in theranostic approaches or for cell tracking studies in cell-based therapies, etc., are only a few examples of the power and versatility of MRI as a definitive tool for the study of stroke.In this work we will set our focus on preclinical imaging of stroke models, emphasizing the most commonly used imaging modalities in a stroke-dedicated research laboratory. However, advanced techniques will be briefly discussed, providing references to specialized literature for more advanced readers. Thus, the aim of this chapter consist in the description of a simple imaging protocol for the study of the most important and common aspects of stroke in a research laboratory.

Fully implantable, battery-free wireless optoelectronic devices for spinal optogenetics.

The advent of optogenetic tools has allowed unprecedented insights into the organization of neuronal networks. Although recently developed technologies have enabled implementation of optogenetics for studies of brain function in freely moving, untethered animals, wireless powering and device durability pose challenges in studies of spinal cord circuits where dynamic, multidimensional motions against hard and soft surrounding tissues can lead to device degradation. We demonstrate here a fully implantable optoelectronic device powered by near-field wireless communication technology, with a thin and flexible open architecture that provides excellent mechanical durability, robust sealing against biofluid penetration and fidelity in wireless activation, thereby allowing for long-term optical stimulation of the spinal cord without constraint on the natural behaviors of the animals. The system consists of a double-layer, rectangular-shaped magnetic coil antenna connected to a microscale inorganic light-emitting diode (μ-ILED) on a thin, flexible probe that can be implanted just above the dura of the mouse spinal cord for effective stimulation of light-sensitive proteins expressed in neurons in the dorsal horn. Wireless optogenetic activation of TRPV1-ChR2 afferents with spinal μ-ILEDs causes nocifensive behaviors and robust real-time place aversion with sustained operation in animals over periods of several weeks to months. The relatively low-cost electronics required for control of the systems, together with the biocompatibility and robust operation of these devices will allow broad application of optogenetics in future studies of spinal circuits, as well as various peripheral targets, in awake, freely moving and untethered animals, where existing approaches have limited utility.

New developments in epilepsy management

Abstract The management of epilepsy has developed rapidly in the 21st century, with scientific and technological development providing better means of studying brain structure and function and improving outcome. Since the human genome was sequenced, molecular genetic technologies have developed at an astounding pace, accelerating discovery of disease-causing genes, giving insight into biological pathways and mechanisms underlying epilepsy and, paving ideas for more targeted therapies. In addition, increasing sophistication in structural and functional neuroimaging; and advances in neurophysiological techniques have improved our understanding of brain development, the organization of neural function and neuronal networks; contributing to increasing success in surgical and other non-pharmacological therapies. We highlight key recent developments of which paediatricians treating children with epilepsy should be aware.

Mesoscale Architecture Shapes Initiation and Richness of Spontaneous Network Activity.

Spontaneous activity in the absence of external input, including propagating waves of activity, is a robust feature of neuronal networks in vivo and in vitro The neurophysiological and anatomical requirements for initiation and persistence of such activity, however, are poorly understood, as is their role in the function of neuronal networks. Computational network studies indicate that clustered connectivity may foster the generation, maintenance, and richness of spontaneous activity. Since this mesoscale architecture cannot be systematically modified in intact tissue, testing these predictions is impracticable in vivo Here, we investigate how the mesoscale structure shapes spontaneous activity in generic networks of rat cortical neurons in vitro In these networks, neurons spontaneously arrange into local clusters with high neurite density and form fasciculating long-range axons. We modified this structure by modulation of protein kinase C, an enzyme regulating neurite growth and cell migration. Inhibition of protein kinase C reduced neuronal aggregation and fasciculation of axons, i.e., promoted uniform architecture. Conversely, activation of protein kinase C promoted aggregation of neurons into clusters, local connectivity, and bundling of long-range axons. Supporting predictions from theory, clustered networks were more spontaneously active and generated diverse activity patterns. Neurons within clusters received stronger synaptic inputs and displayed increased membrane potential fluctuations. Intensified clustering promoted the initiation of synchronous bursting events but entailed incomplete network recruitment. Moderately clustered networks appear optimal for initiation and propagation of diverse patterns of activity. Our findings support a crucial role of the mesoscale architectures in the regulation of spontaneous activity dynamics.SIGNIFICANCE STATEMENT Computational studies predict richer and persisting spatiotemporal patterns of spontaneous activity in neuronal networks with neuron clustering. To test this, we created networks of varying architecture in vitro Supporting these predictions, the generation and spatiotemporal patterns of propagation were most variable in networks with intermediate clustering and lowest in uniform networks. Grid-like clustering, on the other hand, facilitated spontaneous activity but led to degenerating patterns of propagation. Neurons outside clusters had weaker synaptic input than neurons within clusters, in which increased membrane potential fluctuations facilitated the initiation of synchronized spike activity. Our results thus show that the intermediate level organization of neuronal networks strongly influences the dynamics of their activity.