An experimental platform for stochastic analyses of single serotonergic fibers in the mouse brain

The self-organization of the serotonergic matrix, a massive axon meshwork in all vertebrate brains, is driven by the structural and dynamical properties of its constitutive elements. Each of these elements, a single serotonergic axon (fiber), has a unique trajectory and can be supported by a soma that executes one of the many available transcriptional programs. This "individuality" of serotonergic neurons necessitates the development of specialized methods for single-fiber analyses, both at the experimental and theoretical levels. We developed an integrated platform that facilitates experimental isolation of single serotonergic fibers in brain tissue, including regions with high fiber densities, and demonstrated the potential of their quantitative analyses based on stochastic modeling. Single fibers were visualized using two transgenic mouse models, one of which is the first implementation of the Brainbow toolbox in this system. The trajectories of serotonergic fibers were automatically traced in the three spatial dimensions with a novel algorithm, and their properties were captured with a single parameter associated with the directional von Mises-Fisher probability distribution. The system represents an end-to-end workflow that can be imported into various studies, including those investigating serotonergic dysfunction in brain disorders. It also supports new research directions inspired by single-fiber analyses in the serotonergic matrix, including supercomputing simulations and modeling in physics.

Predicting the distribution of serotonergic axons: a supercomputing simulation of reflected fractional Brownian motion in a 3D-mouse brain model

The self-organization of the brain matrix of serotonergic axons (fibers) remains an unsolved problem in neuroscience. The regional densities of this matrix have major implications for neuroplasticity, tissue regeneration, and the understanding of mental disorders, but the trajectories of its fibers are strongly stochastic and require novel conceptual and analytical approaches. In a major extension to our previous studies, we used a supercomputing simulation to model around one thousand serotonergic fibers as paths of superdiffusive fractional Brownian motion (FBM), a continuous-time stochastic process. The fibers produced long walks in a complex, three-dimensional shape based on the mouse brain and reflected at the outer (pial) and inner (ventricular) boundaries. The resultant regional densities were compared to the actual fiber densities in the corresponding neuroanatomically-defined regions. The relative densities showed strong qualitative similarities in the forebrain and midbrain, demonstrating the predictive potential of stochastic modeling in this system. The current simulation does not respect tissue heterogeneities but can be further improved with novel models of multifractional FBM. The study demonstrates that serotonergic fiber densities can be strongly influenced by the geometry of the brain, with implications for brain development, plasticity, and evolution.

High-resolution spatiotemporal analysis of single serotonergic axons in an in vitro system

Vertebrate brains have a dual structure, composed of (i) axons that can be well-captured with graph-theoretical methods and (ii) axons that form a dense matrix in which neurons with precise connections operate. A core part of this matrix is formed by axons (fibers) that store and release 5-hydroxytryptamine (5-HT, serotonin), an ancient neurotransmitter that supports neuroplasticity and has profound implications for mental health. The self-organization of the serotonergic matrix is not well understood, despite recent advances in experimental and theoretical approaches. In particular, individual serotonergic axons produce highly stochastic trajectories, fundamental to the construction of regional fiber densities, but further advances in predictive computer simulations require more accurate experimental information. This study examined single serotonergic axons in culture systems (co-cultures and monolayers), by using a set of complementary high-resolution methods: confocal microscopy, holotomography (refractive index-based live imaging), and super-resolution (STED) microscopy. It shows that serotonergic axon walks in neural tissue may strongly reflect the stochastic geometry of this tissue and it also provides new insights into the morphology and branching properties of serotonergic axons. The proposed experimental platform can support next-generation analyses of the serotonergic matrix, including seamless integration with supercomputing approaches.

Brain serotonergic fibers suggest anomalous diffusion-based dropout in artificial neural networks

Random dropout has become a standard regularization technique in artificial neural networks (ANNs), but it is currently unknown whether an analogous mechanism exists in biological neural networks (BioNNs). If it does, its structure is likely to be optimized by hundreds of millions of years of evolution, which may suggest novel dropout strategies in large-scale ANNs. We propose that the brain serotonergic fibers (axons) meet some of the expected criteria because of their ubiquitous presence, stochastic structure, and ability to grow throughout the individual's lifespan. Since the trajectories of serotonergic fibers can be modeled as paths of anomalous diffusion processes, in this proof-of-concept study we investigated a dropout algorithm based on the superdiffusive fractional Brownian motion (FBM). The results demonstrate that serotonergic fibers can potentially implement a dropout-like mechanism in brain tissue, supporting neuroplasticity. They also suggest that mathematical theories of the structure and dynamics of serotonergic fibers can contribute to the design of dropout algorithms in ANNs.

Reflected fractional Brownian motion in one and higher dimensions

Fractional Brownian motion (FBM), a non-Markovian self-similar Gaussian stochastic process with long-ranged correlations, represents a widely applied, paradigmatic mathematical model of anomalous diffusion. We report the results of large-scale computer simulations of FBM in one, two, and three dimensions in the presence of reflecting boundaries that confine the motion to finite regions in space. Generalizing earlier results for finite and semi-infinite one-dimensional intervals, we observe that the interplay between the long-time correlations of FBM and the reflecting boundaries leads to striking deviations of the stationary probability density from the uniform density found for normal diffusion. Particles accumulate at the boundaries for superdiffusive FBM while their density is depleted at the boundaries for subdiffusion. Specifically, the probability density P develops a power-law singularity, P∼r^{κ}, as a function of the distance r from the wall. We determine the exponent κ as a function of the dimensionality, the confining geometry, and the anomalous diffusion exponent α of the FBM. We also discuss implications of our results, including an application to modeling serotonergic fiber density patterns in vertebrate brains.

Serotonergic Axons as Fractional Brownian Motion Paths: Insights Into the Self-Organization of Regional Densities

All vertebrate brains contain a dense matrix of thin fibers that release serotonin (5-hydroxytryptamine), a neurotransmitter that modulates a wide range of neural, glial, and vascular processes. Perturbations in the density of this matrix have been associated with a number of mental disorders, including autism and depression, but its self-organization and plasticity remain poorly understood. We introduce a model based on reflected Fractional Brownian Motion (FBM), a rigorously defined stochastic process, and show that it recapitulates some key features of regional serotonergic fiber densities. Specifically, we use supercomputing simulations to model fibers as FBM-paths in two-dimensional brain-like domains and demonstrate that the resultant steady state distributions approximate the fiber distributions in physical brain sections immunostained for the serotonin transporter (a marker for serotonergic axons in the adult brain). We suggest that this framework can support predictive descriptions and manipulations of the serotonergic matrix and that it can be further extended to incorporate the detailed physical properties of the fibers and their environment.

Serotonergic Axons as 3D-Walks

Experimental and theoretical research suggests that serotonergic axons (fibers) can be modeled as random walks or stochastic processes. This rigorous approach can support descriptive methods and dynamic control of the ascending reticular activating system, at the level of individual fiber trajectories.

A stochastic approach to serotonergic fibers in mental disorders

Virtually all brain circuits are physically embedded in a three-dimensional matrix of fibers that release 5-hydroxytryptamine (5-HT, serotonin). The density of this matrix varies across brain regions and cortical laminae, and it is altered in some mental disorders, including Major Depressive Disorder and Autism Spectrum Disorder. We investigate how the regional structure of the serotonergic matrix depends on the stochastic behavior of individual serotonergic fibers and introduce a new framework for the quantitative analysis of this behavior. In particular, we show that a step-wise random walk, based on the von Mises-Fisher probability distribution, can provide a realistic and mathematically concise description of these fibers. We also consider other stochastic models, including the fractional Brownian motion. The proposed approach seeks to advance the current understanding of the ascending reticular activating system (ARAS) and may also support future theory-guided therapeutic approaches.

Some Galeomorph Sharks Express a Mammalian Microglia-Specific Protein in Radial Ependymoglia of the Telencephalon

Ionized calcium-binding adapter molecule 1 (Iba1), also known as allograft inflammatory factor 1 (AIF-1), is a highly conserved cytoplasmic scaffold protein. Studies strongly suggest that Iba1 is associated with immune-like reactions in all Metazoa. In the mammalian brain, it is abundantly expressed in microglial cells and is used as a reliable marker for this cell type. The present study used multiple-label microscopy and Western blotting to examine Iba1 expression in the telencephalon of 2 galeomorph shark species, the swellshark (Cephaloscyllium ventriosum) and the horn shark (Heterodontus francisci), a member of an ancient extant order. In the swellshark, high Iba1 expression was found in radial ependymoglial cells, many of which also expressed glial fibrillary acidic protein. Iba1 expression was absent from most cells in the horn shark (with the possible exception of perivascular cells). The difference in Iba1 expression between the species was supported by protein analysis. These results suggest that radial ependymoglia of the elasmobranchs may be functionally related to mammalian microglia and that Iba1 expression has undergone evolutionary changes in this cartilaginous group.

Serotonin in Space: Understanding Single Fibers

All brain regions contain fibers that store and release 5-hydroxytryptamine (5-HT, serotonin). Since these fibers often do not have well-defined trajectories, most studies have focused on their overall densities, a measure that can be associated with local 5-HT tone in heathy and diseased brains. However, the observed fiber densities are the consequence of the behavior of single fibers. Evidence is presented as to why understanding single-fiber trajectories is important for basic and clinical neuroscience. In particular, serotonergic fibers can be viewed as natural, readily detectable stochastic probes that sample the invisible microarchitecture of brain tissue.

A receptor-based analysis of local ecosystems in the human brain

Background

As a complex system, the brain is a self-organizing entity that depends on local interactions among cells. Its regions (anatomically defined nuclei and areas) can be conceptualized as cellular ecosystems, but the similarity of their functional profiles is poorly understood. The study used the Allen Human Brain Atlas to classify 169 brain regions into hierarchically-organized environments based on their expression of 100 G protein-coupled neurotransmitter receptors, with no a priori reference to the regions’ positions in the brain’s anatomy or function. The analysis was based on hierarchical clustering, and multiscale bootstrap resampling was used to estimate the reliability of detected clusters.

Results

The study presents the first unbiased, hierarchical tree of functional environments in the human brain. The similarity of brain regions was strongly influenced by their anatomical proximity, even when they belonged to different functional systems. Generally, spatial vicinity trumped long-range projections or network connectivity. The main cluster of brain regions excluded the dentate gyrus of the hippocampus. The nuclei of the amygdala formed a cluster irrespective of their striatal or pallial origin. In its receptor profile, the hypothalamus was more closely associated with the midbrain than with the thalamus. The cerebellar cortical areas formed a tight and exclusive cluster. Most of the neocortical areas (with the exception of some occipital areas) clustered in a large, statistically well supported group that included no other brain regions.

Conclusions

This study adds a new dimension to the established classifications of brain divisions. In a single framework, they are reconsidered at multiple scales—from individual nuclei and areas to their groups to the entire brain. The analysis provides support for predictive models of brain self-organization and adaptation.

Serotonin 5-HT4 receptors modulate the development of glutamatergic input to the dorsal raphe nucleus

The dorsal raphe nucleus (DRN) is a major serotonin (5-hydroxytryptamine, 5-HT)-producing region in the central nervous system. It receives glutamatergic inputs from several brain regions, which are reciprocally modulated by serotonergic signals. We investigated whether serotonin 5-HT₄ receptors (5-HT₄Rs) play a role in the development of glutamatergic control of the DRN, with an emphasis on cortical inputs. Double-label immunohistochemistry and confocal microscopy were used to quantify vesicular glutamate transporter 1 (vGluT1)-immunoreactive terminals in the DRN of mice with a null-mutation in the 5-HT₄R gene. We found no significant change in the overall density of vGluT1-positive terminals in homozygous and heterozygous mice, but heterozygous mice had a significantly higher density of vGluT1-positive terminals contacting serotonergic neurons. These results suggest that altered 5-HT₄R expression may affect the development of cortical glutamatergic control of the DRN.

Serotonin dynamics in and around the central nervous system: is autism solvable without fundamental insights?

Altered serotonin (5-hydroxytryptamine, 5-HT) signaling has been implicated in some developmental abnormalities of autism spectrum disorder (ASD). However, the presumed role of 5-HT in ASD raises new questions in fundamental neuroscience. Specifically, it is not clear if the current piecemeal approach to 5-HT signaling in the mammalian body is effective and whether new conceptual approaches may be required. This review briefly discusses 5-HT production and circulation in the central nervous system and outside of it, especially with regard to ASD, and proposes a more encompassing approach that questions the utility of the "neurotransmitter" concept. It then introduces the idea of a generalized 5-HT packet that may offer insights into possible links between serotonergic varicosities and blood platelets. These approaches have theoretical significance, but they are also well positioned to advance our understanding of some long-standing problems in autism research.

Functional associations among G protein-coupled neurotransmitter receptors in the human brain

Background

The activity of neurons is controlled by groups of neurotransmitter receptors rather than by individual receptors. Experimental studies have investigated some receptor interactions, but currently little information is available about transcriptional associations among receptors at the whole-brain level.

Results

A total of 4950 correlations between 100 G protein-coupled neurotransmitter receptors were examined across 169 brain regions in the human brain using expression data published in the Allen Human Brain Atlas. A large number of highly significant correlations were found, many of which have not been investigated in hypothesis-driven studies. The highest positive and negative correlations of each receptor are reported, which can facilitate the construction of receptor sets likely to be affected by altered transcription of one receptor (such sets always exist, but their members are difficult to predict). A graph analysis isolated two large receptor communities, within each of which receptor mRNA levels were strongly cross-correlated.

Conclusions

The presented systematic analysis shows that the mRNA levels of many G protein-coupled receptors are interdependent. This finding is not unexpected, since the brain is a highly integrated complex system. However, the analysis also revealed two novel properties of global brain structure. First, receptor correlations are described by a simple statistical distribution, which suggests that receptor interactions may be guided by qualitatively similar processes. Second, receptors appear to form two large functional communities, which might be differentially affected in brain disorders.

Brain growth trajectories in mouse strains with central and peripheral serotonin differences: relevance to autism models

The genetic heterogeneity of autism spectrum disorders (ASDs) suggests that their underlying neurobiology involves dysfunction at the neural network level. Understanding these neural networks will require a major collaborative effort and will depend on validated and widely accepted animal models. Many mouse models have been proposed in autism research, but the assessment of their validity often has been limited to measuring social interactions. However, two other well-replicated findings have been reported in ASDs: transient brain overgrowth in early postnatal life and elevated 5-HT (serotonin) levels in blood platelets (platelet hyperserotonemia). We examined two inbred mouse strains (C57BL/6 and BALB/c) with respect to these phenomena. The BALB/c strain is less social and exhibits some other autistic-like behaviors. In addition, it has a lower 5-HT synthesis rate in the central nervous system due to a single-nucleotide polymorphism in the tryptophan hydroxylase 2 (Tph2) gene. The postnatal growth of brain mass was analyzed with mixed-effects models that included litter effects. The volume of the hippocampal complex and the thickness of the somatosensory cortex were measured in 3D-brain reconstructions from serial sections. The postnatal whole-blood 5-HT levels were assessed with high-performance liquid chromatography. With respect to the BALB/c strain, the C57BL/6 strain showed transient brain overgrowth and persistent blood hyperserotonemia. The hippocampal volume was permanently enlarged in the C57BL/6 strain, with no change in the adult brain mass. These results indicate that, in mice, autistic-like shifts in the brain and periphery may be associated with less autistic-like behaviors. Importantly, they suggest that consistency among behavioral, anatomical, and physiological measures may expedite the validation of new and previously proposed mouse models of autism, and that the construct validity of models should be demonstrated when these measures are inconsistent.