Axonal morphometry of hippocampal pyramidal neurons semi-automatically reconstructed after in vivo labeling in different CA3 locations

MRI-based axis-referenced morphometric model corresponding to lamellar organization for assessing hippocampal atrophy in dementia

Research on the local hippocampal atrophy for early detection of dementia has gained considerable attention. However, accurately quantifying subtle atrophy remains challenging in existing morphological methods due to the lack of consistent biological correspondence with the complex curving regions like the hippocampal head. Thereby, this article presents an innovative axis-referenced morphometric model (ARMM) that follows the anatomical lamellar organization of the hippocampus, which capture its precise and consistent longitudinal curving trajectory. Specifically, we establish an "axis-referenced coordinate system" based on a 7 T ex vivo hippocampal atlas following its entire curving longitudinal axis and orthogonal distributed lamellae. We then align individual hippocampi by deforming this template coordinate system to target spaces using boundary-guided diffeomorphic transformation, while ensuring that the lamellar vectors adhere to the constraint of medial-axis geometry. Finally, we measure local thickness and curvatures based on the coordinate system and boundary surface reconstructed from vector tips. The morphometric accuracy is evaluated by comparing reconstructed surfaces with those directly extracted from 7 T and 3 T MRI hippocampi. The results demonstrate that ARMM achieves the best performance, particularly in the curving head, surpassing the state-of-the-art morphological models. Additionally, morphological measurements from ARMM exhibit higher discriminatory power in distinguishing early Alzheimer's disease from mild cognitive impairment compared to volume-based measurements. Overall, the ARMM offers a precise morphometric assessment of hippocampal morphology on MR images, and sheds light on discovering potential image markers for neurodegeneration associated with hippocampal impairment.

Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint

We present a quantitative strategy to identify all projection neuron types from a given region with statistically different patterns of anatomical targeting. We first validate the technique with mouse primary motor cortex layer 6 data, yielding two clusters consistent with cortico-thalamic and intra-telencephalic neurons. We next analyze the presubiculum, a less-explored region, identifying five classes of projecting neurons with unique patterns of divergence, convergence, and specificity. We report several findings: individual classes target multiple subregions along defined functions; all hypothalamic regions are exclusively targeted by the same class also invading midbrain and agranular retrosplenial cortex; Cornu Ammonis receives input from a single class of presubicular axons also projecting to granular retrosplenial cortex; path distances from the presubiculum to the same targets differ significantly between classes, as do the path distances to distinct targets within most classes; the identified classes have highly non-uniform abundances; and presubicular somata are topographically segregated among classes. This study thus demonstrates that statistically distinct projections shed light on the functional organization of their circuit.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Combinatorial quantification of distinct neural projections from retrograde tracing

Comprehensive quantification of neuronal architectures underlying anatomical brain connectivity remains challenging. We introduce a method to identify distinct axonal projection patterns from a source to a set of target regions and the count of neurons with each pattern. A source region projecting to n targets could have 2n-1 theoretically possible projection types, although only a subset of these types typically exists. By injecting uniquely labeled retrograde tracers in k target regions (k < n), one can experimentally count the cells expressing different color combinations in the source region. The neuronal counts for different color combinations from n-choose-k experiments provide constraints for a model that is robustly solvable using evolutionary algorithms. Here, we demonstrate this method's reliability for 4 targets using simulated triple injection experiments. Furthermore, we illustrate the experimental application of this framework by quantifying the projections of male mouse primary motor cortex to the primary and secondary somatosensory and motor cortices.

Unsupervised classification of brain-wide axons reveals neuronal projection blueprint

Long-range axonal projections are quintessential determinants of network connectivity, linking cellular organization and circuit architecture. Here we introduce a quantitative strategy to identify, from a given source region, all "projection neuron types" with statistically different patterns of anatomical targeting. We first validate the proposed technique with well-characterized data from layer 6 of the mouse primary motor cortex. The results yield two clusters, consistent with previously discovered cortico-thalamic and intra-telencephalic neuron classes. We next analyze neurons from the presubiculum, a less-explored region. Extending sparse knowledge from earlier retrograde tracing studies, we identify five classes of presubicular projecting neurons, revealing unique patterns of divergence, convergence, and specificity. We thus report several findings: (1) individual classes target multiple subregions along defined functions, such as spatial representation vs. sensory integration and visual vs. auditory input; (2) all hypothalamic regions are exclusively targeted by the same class also invading midbrain, a sharp subset of thalamic nuclei, and agranular retrosplenial cortex; (3) Cornu Ammonis, in contrast, receives input from the same presubicular axons projecting to granular retrosplenial cortex, also the purview of a single class; (4) path distances from the presubiculum to the same targets differ significantly between classes, as do the path distances to distinct targets within most classes, suggesting fine temporal coordination in activating distant areas; (5) the identified classes have highly non-uniform abundances, with substantially more neurons projecting to midbrain and hypothalamus than to medial and lateral entorhinal cortex; (6) lastly, presubicular soma locations are segregated among classes, indicating topographic organization of projections. This study thus demonstrates that classifying neurons based on statistically distinct axonal projection patterns sheds light on the functional organizational of their circuit.

Cannabidiol modulates excitatory-inhibitory ratio to counter hippocampal hyperactivity

Cannabidiol (CBD), a non-euphoric component of cannabis, reduces seizures in multiple forms of pediatric epilepsies, but the mechanism(s) of anti-seizure action remain unclear. In one leading model, CBD acts at glutamatergic axon terminals, blocking the pro-excitatory actions of an endogenous membrane phospholipid, lysophosphatidylinositol (LPI), at the G-protein-coupled receptor GPR55. However, the impact of LPI-GPR55 signaling at inhibitory synapses and in epileptogenesis remains underexplored. We found that LPI transiently increased hippocampal CA3-CA1 excitatory presynaptic release probability and evoked synaptic strength in WT mice, while attenuating inhibitory postsynaptic strength by decreasing GABAARγ2 and gephyrin puncta. LPI effects at excitatory and inhibitory synapses were eliminated by CBD pre-treatment and absent after GPR55 deletion. Acute pentylenetrazole-induced seizures elevated GPR55 and LPI levels, and chronic lithium-pilocarpine-induced epileptogenesis potentiated LPI's pro-excitatory effects. We propose that CBD exerts potential anti-seizure effects by blocking LPI's synaptic effects and dampening hyperexcitability.

Combinatorial quantification of distinct neural projections from retrograde tracing

Comprehensive quantification of neuronal architectures underlying anatomical brain connectivity remains challenging. We introduce a method to identify the distinct axonal projection patterns from a source to a set of target regions and the count of neurons with each pattern. For a source region projecting to n targets, there are 2n - 1 theoretically possible projection types, although only a subset of these types typically exists. By injecting uniquely labeled retrograde tracers in k regions (k < n), one can experimentally count the cells expressing different combinations of colors in the source region1,2. Such an experiment can be performed for n choose k combinations. The counts of cells with different color combinations from all experiments provide constraints for a system of equations that include 2n - 1 unknown variables, each corresponding to the count of neurons for a projection pattern. Evolutionary algorithms prove to be effective at solving the resultant system of equations, thus allowing the determination of the counts of neurons with each of the possible projection patterns. Numerical analysis of simulated 4 choose 3 retrograde injection experiments using surrogate data demonstrates reliable and precise count estimates for all projection neuron types. We illustrate the experimental application of this framework by quantifying the projections of mouse primary motor cortex to four prominent targets: the primary and secondary somatosensory and motor cortices.

Parallel Pathways Provide Hippocampal Spatial Information to Prefrontal Cortex

Long-range synaptic connections define how information flows through neuronal networks. Here, we combined retrograde and anterograde trans-synaptic viruses to delineate areas that exert direct and indirect influence over the dorsal and ventral prefrontal cortex (PFC) of the rat (both sexes). Notably, retrograde tracing using pseudorabies virus (PRV) revealed that both dorsal and ventral areas of the PFC receive prominent disynaptic input from the dorsal CA3 (dCA3) region of the hippocampus. The PRV experiments also identified candidate anatomical relays for this disynaptic pathway, namely, the ventral hippocampus, lateral septum, thalamus, amygdala, and basal forebrain. To determine the viability of each of these relays, we performed three additional experiments. In the first, we injected the retrograde monosynaptic tracer Fluoro-Gold into the PFC and the anterograde monosynaptic tracer Fluoro-Ruby into the dCA3 to confirm the first-order connecting areas and revealed several potential relay regions between the PFC and dCA3. In the second, we combined PRV injection in the PFC with polysynaptic anterograde viral tracer (HSV-1) in the dCA3 to reveal colabeled connecting neurons, which were evident only in the ventral hippocampus. In the third, we combined retrograde adeno-associated virus (AAV) injections in the PFC with an anterograde AAV in the dCA3 to reveal anatomical relay neurons in the ventral hippocampus and dorsal lateral septum. Together, these findings reveal parallel disynaptic pathways from the dCA3 to the PFC, illuminating a new anatomical framework for understanding hippocampal-prefrontal interactions. We suggest that the representation of context and space may be a universal feature of prefrontal function.SIGNIFICANCE STATEMENT The known functions of the prefrontal cortex are shaped by input from multiple brain areas. We used transneuronal viral tracing to discover multiple prominent disynaptic pathways through which the dorsal hippocampus (specifically, the dorsal CA3) has the potential to shape the actions of the prefrontal cortex. The demonstration of neuronal relays in the ventral hippocampus and lateral septum presents a new foundation for understanding long-range influences over prefrontal interactions, including the specific contribution of the dorsal CA3 to prefrontal function.

Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization

Quantifying the population sizes of distinct neuron types in different anatomical regions is an essential step towards establishing a brain cell census. Although estimates exist for the total neuronal populations in different species, the number and definition of each specific neuron type are still intensively investigated. Hippocampome.org is an open-source knowledge base with morphological, physiological and molecular information for 122 neuron types in the rodent hippocampal formation. While such framework identifies all known neuron types in this system, their relative abundances remain largely unknown. This work quantitatively estimates the counts of all Hippocampome.org neuron types by literature mining and numerical optimization. We report the number of neurons in each type identified by main neurotransmitter (glutamate or GABA) and axonal-dendritic patterns throughout 26 subregions and layers of the dentate gyrus, Ammon's horn, subiculum and entorhinal cortex. We produce by sensitivity analysis reliable numerical ranges for each type and summarize the amounts across broad neuronal families defined by biomarkers expression and firing dynamics. Study of density distributions indicates that the number of dendritic-targeting interneurons, but not of other neuronal classes, is independent of anatomical volumes. All extracted values, experimental evidence and related software code are released on Hippocampome.org.

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.

BEAN: Interpretable and Efficient Learning With Biologically-Enhanced Artificial Neuronal Assembly Regularization

Deep neural networks (DNNs) are known for extracting useful information from large amounts of data. However, the representations learned in DNNs are typically hard to interpret, especially in dense layers. One crucial issue of the classical DNN model such as multilayer perceptron (MLP) is that neurons in the same layer of DNNs are conditionally independent of each other, which makes co-training and emergence of higher modularity difficult. In contrast to DNNs, biological neurons in mammalian brains display substantial dependency patterns. Specifically, biological neural networks encode representations by so-called neuronal assemblies: groups of neurons interconnected by strong synaptic interactions and sharing joint semantic content. The resulting population coding is essential for human cognitive and mnemonic processes. Here, we propose a novel Biologically Enhanced Artificial Neuronal assembly (BEAN) regularization to model neuronal correlations and dependencies, inspired by cell assembly theory from neuroscience. Experimental results show that BEAN enables the formation of interpretable neuronal functional clusters and consequently promotes a sparse, memory/computation-efficient network without loss of model performance. Moreover, our few-shot learning experiments demonstrate that BEAN could also enhance the generalizability of the model when training samples are extremely limited.

Influence of Neuronal Morphology on the Shape of Extracellular Recordings With Microelectrode Arrays: A Finite Element Analysis

OBJECTIVE

Measuring neuronal cell activity using microelectrode arrays reveals a great variety of derived signal shapes within extracellular recordings. However, possible mechanisms responsible for this variety have not yet been entirely determined, which might hamper any subsequent analysis of the recorded neuronal data.

METHODS

To investigate this issue, we propose a computational model based on the finite element method describing the electrical coupling between an electrically active neuron and an extracellular recording electrode in detail. This allows for a systematic study of possible parameters that may play an essential role in defining or altering the shape of the measured electrode potential.

RESULTS

Our results indicate that neuronal geometry, neurite structure, as well as the actual pathways of input potentials that evoke action potential generation, have a significant impact on the shape of the resulting extracellular electrode recording and explain most of the known variations of signal shapes.

CONCLUSION

The presented models offer a comprehensive insight into the effect of geometrical and morphological factors on the resulting electrode signal.

SIGNIFICANCE

Computational modeling complemented with experimental measurements shows much promise to yield meaningful insights into the electrical activity of a neuronal network.

Modelling unitary fields and the single-neuron contribution to local field potentials in the hippocampus

Key points

We simulate the unitary local field potential (uLFP) generated in the hippocampus CA3, using morphologically detailed models.

The model suggests that cancelling effects between apical and basal dendritic synapses explain the low amplitude of excitatory uLFPs.

Inhibitory synapses around the soma do not cancel and could explain the high‐amplitude inhibitory uLFPs.

These results suggest that somatic inhibition constitutes a strong component of LFPs, which may explain a number of experimental observations.

Abstract

Synaptic currents represent a major contribution to the local field potential (LFP) in brain tissue, but the respective contribution of excitatory and inhibitory synapses is not known. Here, we provide estimates of this contribution by using computational models of hippocampal pyramidal neurons, constrained by in vitro recordings. We focus on the unitary LFP (uLFP) generated by single neurons in the CA3 region of the hippocampus. We first reproduce experimental results for hippocampal basket cells, and in particular how inhibitory uLFP are distributed within hippocampal layers. Next, we calculate the uLFP generated by pyramidal neurons, using morphologically reconstructed CA3 pyramidal cells. The model shows that the excitatory uLFP is of small amplitude, smaller than inhibitory uLFPs. Indeed, when the two are simulated together, inhibitory uLFPs mask excitatory uLFPs, which might create the illusion that the inhibitory field is generated by pyramidal cells. These results provide an explanation for the observation that excitatory and inhibitory uLFPs are of the same polarity, in vivo and in vitro. These results suggest that somatic inhibitory currents are large contributors to the LFP, which is important information for interpreting this signal. Finally, the results of our model might form the basis of a simple method to compute the LFP, which could be applied to point neurons for each cell type, thus providing a simple biologically grounded method for calculating LFPs from neural networks.

In conclusion, computational models constrained by in vitro recordings suggest that: (1) Excitatory uLFPs are of smaller amplitude than inhibitory uLFPs. (2) Inhibitory uLFPs form the major contribution to LFPs. (3) uLFPs can be used as a simple model to generate LFPs from spiking networks.

We simulate the unitary local field potential (uLFP) generated in the hippocampus CA3, using morphologically detailed models.

The model suggests that cancelling effects between apical and basal dendritic synapses explain the low amplitude of excitatory uLFPs.

Inhibitory synapses around the soma do not cancel and could explain the high‐amplitude inhibitory uLFPs.

These results suggest that somatic inhibition constitutes a strong component of LFPs, which may explain a number of experimental observations.

Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

A computational model for how the fast afterhyperpolarization paradoxically increases gain in regularly firing neurons

The gain of a neuron, the number and frequency of action potentials triggered in response to a given amount of depolarizing injection, is an important behavior underlying a neuron's function. Variations in action potential waveform can influence neuronal discharges by the differential activation of voltage- and ion-gated channels long after the end of a spike. One component of the action potential waveform, the afterhyperpolarization (AHP), is generally considered an inhibitory mechanism for limiting firing rates. In dentate gyrus granule cells (DGCs) expressing fast-gated BK channels, large fast AHPs (fAHP) are paradoxically associated with increased gain. In this article, we describe a mechanism for this behavior using a computational model. Hyperpolarization provided by the fAHP enhances activation of a dendritic inward current (a T-type Ca²⁺ channel is suggested) that, in turn, boosts rebound depolarization at the soma. The model suggests that the fAHP may both reduce Ca²⁺ channel inactivation and, counterintuitively, enhance its activation. The magnitude of the rebound depolarization, in turn, determines the activation of a subsequent, slower inward current (a persistent Na⁺ current is suggested) limiting the interspike interval. Simulations also show that the effect of AHP on gain is also effective for physiologically relevant stimulation; varying AHP amplitude affects interspike interval across a range of "noisy" stimulus frequency and amplitudes. The mechanism proposed suggests that small fAHPs in DGCs may contribute to their limited excitability. NEW & NOTEWORTHY The afterhyperpolarization (AHP) is canonically viewed as a major factor underlying the refractory period, serving to limit neuronal firing rate. We recently reported that enhancing the amplitude of the fast AHP (fAHP) in a relatively slowly firing neuron (vs. fast spiking neurons) expressing fast-gated BK channels augments neuronal excitability. In this computational study, we present a novel, quantitative hypothesis for how varying the amplitude of the fAHP can, paradoxically, influence a subsequent spike tens of milliseconds later.

Effect of Morphologic Features of Neurons on the Extracellular Electric Potential: A Simulation Study Using Cable Theory and Electro-Quasi-Static Equations

Microelectrode arrays serve as an indispensable tool in electro-physiological research to study the electrical activity of neural cells, enabling measurements of single cell as well as network communication analysis. Recent experimental studies have reported that the neuronal geometry has an influence on electrical signaling and extracellular recordings. However, the corresponding mechanisms are not yet fully understood and require further investigation. Allowing systematic parameter studies, computational modeling provides the opportunity to examine the underlying effects that influence extracellular potentials. In this letter, we present an in silico single cell model to analyze the effect of geometrical variability on the extracellular electric potentials. We describe finite element models of a single neuron with varying geometric complexity in three-dimensional space. The electric potential generation of the neuron is modeled using Hodgkin-Huxley equations. The signal propagation is described with electro-quasi-static equations, and results are compared with corresponding cable equation descriptions. Our results show that both the geometric dimensions and the distribution of ion channels of a neuron are critical factors that significantly influence both the amplitude and shape of extracellular potentials.

Symmetric spike timing-dependent plasticity at CA3-CA3 synapses optimizes storage and recall in autoassociative networks

CA3–CA3 recurrent excitatory synapses are thought to play a key role in memory storage and pattern completion. Whether the plasticity properties of these synapses are consistent with their proposed network functions remains unclear. Here, we examine the properties of spike timing-dependent plasticity (STDP) at CA3–CA3 synapses. Low-frequency pairing of excitatory postsynaptic potentials (EPSPs) and action potentials (APs) induces long-term potentiation (LTP), independent of temporal order. The STDP curve is symmetric and broad (half-width ∼150 ms). Consistent with these STDP induction properties, AP–EPSP sequences lead to supralinear summation of spine [Ca²⁺] transients. Furthermore, afterdepolarizations (ADPs) following APs efficiently propagate into dendrites of CA3 pyramidal neurons, and EPSPs summate with dendritic ADPs. In autoassociative network models, storage and recall are more robust with symmetric than with asymmetric STDP rules. Thus, a specialized STDP induction rule allows reliable storage and recall of information in the hippocampal CA3 network.

STDP is dependent on the timing of pre- and post-synaptic activity. Here, the authors describe a symmetric STDP induction rule at CA3-CA3 synapses, which induces LTP over a broad range of paring intervals. Modelling suggests that this STDP rule may enhance storage capacity and pattern completion in the CA3 cell network.

A 3-D admittance-level computational model of a rat hippocampus for improving prosthetic design

Hippocampal prosthetic devices have been developed to bridge the gap between functioning portions of the hippocampus, in order to restore lost memory functionality in those suffering from brain injury or diseases. One approach taken in recent neuroprosthetic design is to use a multi-input, multi-output device that reads data from the CA3 in the hippocampus and electrically stimulates the CA1 in an attempt to mimic the appropriate firing pattern that would occur naturally between the two areas. However, further study needs to be conducted in order to optimize electrode placement, pulse magnitude, and shape for creating the appropriate firing pattern. This paper describes the creation and implementation of an anatomically correct 3D model of the hippocampus to simulate the electric field patterns and axonal activation from electrical stimulation due to an implanted electrode array. The activating function was applied to the voltage results to determine the firing patterns in possible axon locations within the CA1.

Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis

BACKGROUND

The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns.

RESULTS

Using all possible branching topologies for comparison we show that the relative abundance of short patterns of up to three bifurcations, together with overall tree size, effectively capture the local branching patterns of neurons. Dendrites and axons display broadly similar topological motifs (over-represented patterns) and anti-motifs (under-represented patterns), differing most in their proportions of bifurcations with one terminal branch and in select sub-sequences of three bifurcations. In addition, pyramidal apical dendrites reveal a distinct motif profile.

CONCLUSIONS

The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.

AMPK links cellular bioenergy status to the decision making of axon initiation in neurons

Neuronal polarization begins by the selection of a single minor neurite that subsequently undergoes rapid extension until reaching a formidable length. To ensure that the highly active growth can be sustained by a sufficient energy supply, neurons are supposed to sense their energy status prior to initiating polarization. Our recent work shows that the bioenergy sensor, AMPK, plays a crucial role in the regulation of axon initiation. Activation of AMPK to mimic energy-lacking conditions results in a halt in axon selection and growth, leading to a loss of neuronal polarization.

Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology

The branching structures of neurons are a long-standing focus of neuroscience. Axonal and dendritic morphology affect synaptic signaling, integration, and connectivity, and their diversity reflects the computational specialization of neural circuits. Altered neuronal morphology accompanies functional changes during development, experience, aging, and disease. Technological improvements continuously accelerate high-throughput tissue processing, image acquisition, and morphological reconstruction. Digital reconstructions of neuronal morphologies allow for complex quantitative analyses that are unattainable from raw images or two-dimensional tracings. Furthermore, digitized morphologies enable computational modeling of biophysically realistic neuronal dynamics. Additionally, reconstructions generated to address specific scientific questions have the potential for continued investigations beyond the original reason for their acquisition. Facilitating multiple reuse are repositories like NeuroMorpho.Org, which ease the sharing of reconstructions. Here, we review selected scientific literature reporting the reconstruction of axonal or dendritic morphology with diverse goals including establishment of neuronal identity, examination of physiological properties, and quantification of developmental or pathological changes. These reconstructions, deposited in NeuroMorpho.Org, have since been used by other investigators in additional research, of which we highlight representative examples. This cycle of data generation, analysis, sharing, and reuse reveals the vast potential of digital reconstructions in quantitative investigations of neuronal morphology.

Updating the lamellar hypothesis of hippocampal organization

Andersen et al. (1971) proposed that excitatory activity in the entorhinal cortex propagates topographically to the dentate gyrus, and on through a "trisynaptic circuit" lying within transverse hippocampal "slices" or "lamellae." In this way, a relatively simple structure might mediate complex functions in a manner analogous to the way independent piano keys can produce a nearly infinite variety of unique outputs. The lamellar hypothesis derives primary support from the "lamellar" distribution of dentate granule cell axons (the mossy fibers), which innervate dentate hilar neurons and area CA3 pyramidal cells and interneurons within the confines of a thin transverse hippocampal segment. Following the initial formulation of the lamellar hypothesis, anatomical studies revealed that unlike granule cells, hilar mossy cells, CA3 pyramidal cells, and Layer II entorhinal cells all form axonal projections that are more divergent along the longitudinal axis than the clearly "lamellar" mossy fiber pathway. The existence of pathways with "translamellar" distribution patterns has been interpreted, incorrectly in our view, as justifying outright rejection of the lamellar hypothesis (Amaral and Witter, 1989). We suggest that the functional implications of longitudinally projecting axons depend not on whether they exist, but on what they do. The observation that focal granule cell layer discharges normally inhibit, rather than excite, distant granule cells suggests that longitudinal axons in the dentate gyrus may mediate "lateral" inhibition and define lamellar function, rather than undermine it. In this review, we attempt a reconsideration of the evidence that most directly impacts the physiological concept of hippocampal lamellar organization.

AMPK signaling in neuronal polarization: Putting the brakes on axonal traffic of PI3-Kinase

Neuronal polarization, the process by which neurons form multiple dendrites and an axon from the soma, is the first critical step in the formation and function of neural networks. Polarization begins with the rapid extension of a single neurite to produce an axon of impressive size and complex geometry, while the remaining sister neurites differentiate into dendrites. The extensive biosynthesis required to produce an axon therefore necessitates coordination with cellular energy status to ensure an ample energy supply. Our recent work shows that activity of the AMP-activated protein kinase (AMPK), the bio-energy sensor responsible for maintaining cellular energy homeostasis in all eukaryotic cells, plays an important role in the initiation of axonal growth. AMPK phosphorylates the cargo-binding light chain of the Kif5 motor protein, leading to dissociation of the phosphatidylinositol 3-Kinase (PI3K) from the motor complex. The mislocation of PI3K, which is normally enriched at the axonal tip for extension and differentiation, results in a lack of neurite specification and neuron polarization. These findings reveal a link between cellular bioenergy homeostasis and neuron morphogenesis, and suggest a novel cellular mechanism underlying the long-term neurological abnormalities as a consequence of bioenergy deficiency during early brain development.

Automatic reconstruction of neural morphologies with multi-scale tracking

Neurons have complex axonal and dendritic morphologies that are the structural building blocks of neural circuits. The traditional method to capture these morphological structures using manual reconstructions is time-consuming and partly subjective, so it appears important to develop automatic or semi-automatic methods to reconstruct neurons. Here we introduce a fast algorithm for tracking neural morphologies in 3D with simultaneous detection of branching processes. The method is based on existing tracking procedures, adding the machine vision technique of multi-scaling. Starting from a seed point, our algorithm tracks axonal or dendritic arbors within a sphere of a variable radius, then moves the sphere center to the point on its surface with the shortest Dijkstra path, detects branching points on the surface of the sphere, scales it until branches are well separated and then continues tracking each branch. We evaluate the performance of our algorithm on preprocessed data stacks obtained by manual reconstructions of neural cells, corrupted with different levels of artificial noise, and unprocessed data sets, achieving 90% precision and 81% recall in branch detection. We also discuss limitations of our method, such as reconstructing highly overlapping neural processes, and suggest possible improvements. Multi-scaling techniques, well suited to detect branching structures, appear a promising strategy for automatic neuronal reconstructions.

Deterministic and stochastic neuronal contributions to distinct synchronous CA3 network bursts

Computational studies have suggested that stochastic, deterministic, and mixed processes all could be possible determinants of spontaneous, synchronous network bursts. In the present study, using multicellular calcium imaging coupled with fast confocal microscopy, we describe neuronal behavior underlying spontaneous network bursts in developing rat and mouse hippocampal area CA3 networks. Two primary burst types were studied: giant depolarizing potentials (GDPs) and spontaneous interictal bursts recorded in bicuculline, a GABA(A) receptor antagonist. Analysis of the simultaneous behavior of multiple CA3 neurons during synchronous GDPs revealed a repeatable activation order from burst to burst. This was validated using several statistical methods, including high Kendall's coefficient of concordance values for firing order during GDPs, high Pearson's correlations of cellular activation times between burst pairs, and latent class analysis, which revealed a population of 5-6% of CA3 neurons reliably firing very early during GDPs. In contrast, neuronal firing order during interictal bursts appeared homogeneous, with no particular cells repeatedly leading or lagging during these synchronous events. We conclude that GDPs activate via a deterministic mechanism, with distinct, repeatable roles for subsets of neurons during burst generation, while interictal bursts appear to be stochastic events with cells assuming interchangeable roles in the generation of these events.

Bioenergy sensing in the brain: the role of AMP-activated protein kinase in neuronal metabolism, development and neurological diseases

Bioenergy homeostasis constitutes one of the most crucial foundations upon which other cellular and organismal processes may be executed. AMP-activated protein kinase (AMPK) has been shown to be the key player in the regulation of energy metabolism, and thus is becoming the focus of research on obesity, diabetes and other metabolic disorders. However, its role in the brain, the most energy-consuming organ in our body, has only recently been studied and appreciated. Widely expressed in the brain, AMPK activity is tightly coupled to the energy status at both neuronal and whole-body levels. Importantly, AMPK signaling is intimately implicated in multiple aspects of brain development and function including neuronal proliferation, migration, morphogenesis and synaptic communication, as well as in pathological conditions such as neuronal cell death, energy depletion and neurodegenerative disorders.

Neuromantic - from semi-manual to semi-automatic reconstruction of neuron morphology

The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Non-homogeneous stereological properties of the rat hippocampus from high-resolution 3D serial reconstruction of thin histological sections

Integrating hippocampal anatomy from neuronal dendrites to whole system may help elucidate its relation to function. Toward this aim, we digitally traced the cytoarchitectonic boundaries of the dentate gyrus (DG) and areas CA3/CA1 throughout their entire longitudinal extent from high-resolution images of thin cryostatic sections of adult rat brain. The 3D computational reconstruction identified all isotropic 16 μm voxels with appropriate subregions and layers (http://krasnow1.gmu.edu/cn3/hippocampus3d). Overall, DG, CA3, and CA1 occupied comparable volumes (15.3, 12.2, and 18.8 mm(3), respectively), but displayed substantial rostrocaudal volumetric gradients: CA1 made up more than half of the posterior hippocampus, whereas CA3 and DG were more prominent in the anterior regions. The CA3/CA1 ratio increased from ∼0.4 to ∼1 septo-temporally because of a specific change in stratum radiatum volume. Next we virtually embedded 1.8 million neuronal morphologies stochastically resampled from 244 digital reconstructions, emulating the dense packing of granular and pyramidal layers, and appropriately orienting the principal dendritic axes relative to local curvature. The resulting neuropil occupancy reproduced recent electron microscopy data measured in a restricted location. Extension of this analysis across each layer and subregion over the whole hippocampus revealed highly non-homogeneous dendritic density. In CA1, dendritic occupancy was >60% higher temporally than septally (0.46 vs. 0.28, s.e.m. ∼0.05). CA3 values varied both across subfields (from 0.35 in CA3b/CA3c to 0.50 in CA3a) and layers (0.48, 0.34, and 0.27 in oriens, radiatum, and lacunosum-moleculare, respectively). Dendritic occupancy was substantially lower in DG, especially in the supra-pyramidal blade (0.18). The computed probability of dendrodendritic collision significantly correlated with expression of the membrane repulsion signal Down syndrome cell adhesion molecule (DSCAM). These heterogeneous stereological properties reflect and complement the non-uniform molecular composition, circuit connectivity, and computational function of the hippocampus across its transverse, longitudinal, and laminar organization.

Potential Synaptic Connectivity of Different Neurons onto Pyramidal Cells in a 3D Reconstruction of the Rat Hippocampus

Most existing connectomic data and ongoing efforts focus either on individual synapses (e.g., with electron microscopy) or on regional connectivity (tract tracing). An individual pyramidal cell (PC) extends thousands of synapses over macroscopic distances (∼cm). The contrasting requirements of high-resolution and large field of view make it too challenging to acquire the entire synaptic connectivity for even a single typical cortical neuron. Light microscopy can image whole neuronal arbors and resolve dendritic branches. Analyzing connectivity in terms of close spatial appositions between axons and dendrites could thus bridge the opposite scales, from synaptic level to whole systems. In the mammalian cortex, structural plasticity of spines and boutons makes these “potential synapses” functionally relevant to learning capability and memory capacity. To date, however, potential synapses have only been mapped in the surrounding of a neuron and relative to its local orientation rather than in a system-level anatomical reference. Here we overcome this limitation by estimating the potential connectivity of different neurons embedded into a detailed 3D reconstruction of the rat hippocampus. Axonal and dendritic trees were oriented with respect to hippocampal cytoarchitecture according to longitudinal and transversal curvatures. We report the potential connectivity onto PC dendrites from the axons of a dentate granule cell, three CA3 PCs, one CA2 PC, and 13 CA3b interneurons. The numbers, densities, and distributions of potential synapses were analyzed in each sub-region (e.g., CA3 vs. CA1), layer (e.g., oriens vs. radiatum), and septo-temporal location (e.g., dorsal vs. ventral). The overall ratio between the numbers of actual and potential synapses was ∼0.20 for the granule and CA3 PCs. All potential connectivity patterns are strikingly dependent on the anatomical location of both pre-synaptic and post-synaptic neurons.