Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

A systematic review and meta-analysis of neuroimaging studies examining synaptic density in individuals with psychotic spectrum disorders

BACKGROUND

Psychotic disorders have long been considered neurodevelopmental disorders where excessive synaptic pruning and cortical volume loss are central to disease pathology. We conducted a systematic review of the literature to identify neuroimaging studies specifically examining synaptic density across the psychosis spectrum.

METHODS

PRISMA guidelines on reporting were followed. We systematically searched MEDLINE, Embase, APA PsycINFO, Web of Science and The Cochrane Library from inception to December 8, 2023, and included all original peer-reviewed articles or completed clinical neuroimaging studies of any modality measuring synaptic density in participants with a diagnosis of psychosis spectrum disorder as well as individuals with psychosis-risk states. The NIH quality assessment tool for observational cohort and cross-sectional studies was used for the risk of bias assessment.

RESULTS

Five studies (k = 5) met inclusion criteria, comprising n = 128 adults (psychotic disorder; n = 61 and healthy volunteers; n = 67 and specifically measuring synaptic density via positron emission tomography (PET) imaging of the synaptic vesicle glycoprotein 2 A (SV2A). Three studies were included in our primary meta-analysis sharing the same outcome measure of SV2A binding, volume of distribution (VT). Regional SV2A VT was reduced in psychotic disorder participants in comparison to healthy volunteers, including the occipital lobe (Mean Difference (MD)= -2.17; 95% CI: -3.36 to -0.98; P < 0.001 ), temporal lobe (MD: -2.03; 95% CI: -3.19 to -0.88; P < 0.001 ), parietal lobe (MD:-1.61; 95% CI: -2.85 to -0.37; P = 0.01), anterior cingulate cortex (MD= -1.47; 95% CI: -2.45 to -0.49; P = 0.003), frontal cortex (MD: -1.16; 95% CI: -2.18 to -0.15; P = 0.02), amygdala (MD: -1.36; 95% CI: -2.20 to -0.52, p = 0.002), thalamus (MD:-1.46; 95% CI:-2.46 to -0.46, p = 0.004) and hippocampus (MD= -0.96; 95% CI: -1.59 to -0.33; P = 0.003).

CONCLUSIONS

Preliminary studies provide in vivo evidence for reduced synaptic density in psychotic disorders. However, replication of findings in larger samples is required prior to definitive conclusions being drawn.

PROSPERO

CRD42022359018.

Cortical grey matter volume differences in children with developmental coordination disorder compared to typically developing children

Introduction

The cause of Developmental Coordination Disorder (DCD) is unknown, but neuroimaging evidence suggests that DCD may be related to altered brain development. Children with DCD show less structural and functional connectivity compared to typically developing (TD) children, but few studies have examined cortical volume in children with DCD. The purpose of this study was to investigate cortical grey matter volume using voxel-based morphometry (VBM) in children with DCD compared to TD children.

Methods

This cross-sectional study was part of a larger randomized-controlled trial (ClinicalTrials.gov ID: NCT02597751) that involved various MRI scans of children with/without DCD. This paper focuses on the anatomical scans, performing VBM of cortical grey matter volume in 30 children with DCD and 12 TD children. Preprocessing and VBM data analysis were conducted using the Computational Anatomy Tool Box-12 and a study-specific brain template. Differences between DCD and TD groups were assessed using a one-way ANOVA, controlling for total intracranial volume. Regression analyses examined if motor and/or attentional difficulties predicted grey matter volume. We used threshold-free cluster enhancement (5,000 permutations) and set an alpha level of 0.05. Due to the small sample size, we did not correct for multiple comparisons.

Results

Compared to the TD group, children with DCD had significantly greater grey matter in the left superior frontal gyrus. Lower motor scores (meaning greater impairment) were related to greater grey matter volume in left superior frontal gyrus, frontal pole, and right middle frontal gyrus. Greater grey matter volume was also significantly correlated with higher scores on the Conners 3 ADHD Index in the left superior frontal gyrus, superior parietal lobe, and precuneus. These results indicate that greater grey matter volume in these regions is associated with poorer motor and attentional skills.

Discussion

Greater grey matter volume in the left superior frontal gyrus in children with DCD may be a result of delayed or absent healthy cortical thinning, potentially due to altered synaptic pruning as seen in other neurodevelopmental disorders. These findings provide further support for the hypothesis that DCD is related to altered brain development.

Altered resting-state brain function in endurance athletes

Previous research has confirmed significant differences in regional brain activity and functional connectivity between endurance athletes and non-athletes. However, no studies have investigated the differences in topological efficiency of the brain functional network between endurance athletes and non-athletes. Here, we compared differences in regional activities, functional connectivity, and topological properties to explore the functional basis associated with endurance training. The results showed significant correlations between Regional Homogeneity in the motor cortex, visual cortex, cerebellum, and the training intensity parameters. Alterations in functional connectivity among the motor cortex, visual cortex, cerebellum, and the inferior frontal gyrus and cingulate gyrus were significantly correlated with training intensity parameters. In addition, the graph theoretical analysis results revealed a significant reduction in global efficiency among athletes. This decline is mainly caused by decreased nodal efficiency and nodal local efficiency of the cerebellar regions. Notably, the sensorimotor regions, such as the precentral gyrus and supplementary motor areas, still exhibit increased nodal efficiency and nodal local efficiency. This study not only confirms the improvement of regional activity in brain regions related to endurance training, but also offers novel insights into the mechanisms through which endurance athletes undergo changes in the topological efficiency of the brain functional network.

Fenfluramine increases survival and reduces markers of neurodegeneration in a mouse model of Dravet syndrome

OBJECTIVE

In patients with Dravet syndrome (DS), fenfluramine reduced convulsive seizure frequency and provided clinical benefit in nonseizure endpoints (e.g., executive function, survival). In zebrafish mutant scn1 DS models, chronic fenfluramine treatment preserved neuronal cytoarchitecture prior to seizure onset and prevented gliosis; here, we extend these findings to a mammalian model of DS (Scn1a+/- mice) by evaluating the effects of fenfluramine on neuroinflammation (degenerated myelin, activated microglia) and survival.

METHODS

Scn1a+/- DS mice were treated subcutaneously once daily with fenfluramine (15 mg/kg) or vehicle from postnatal day (PND) 7 until 35-37. Sagittal brain sections were processed for immunohistochemistry using antibodies to degraded myelin basic protein (D-MBP) for degenerated myelin, or CD11b for activated (inflammatory) microglia; sections were scored semi-quantitatively. Apoptotic nuclei were quantified by TUNEL assay. Statistical significance was evaluated by 1-way ANOVA with post-hoc Dunnett's test (D-MBP, CD11b, and TUNEL) or Logrank Mantel-Cox (survival).

RESULTS

Quantitation of D-MBP immunostaining per 0.1 mm2 unit area of the parietal cortex and hippocampus CA3 yielded significantly higher spheroidal and punctate myelin debris counts in vehicle-treated DS mice than in wild-type mice. Fenfluramine treatment in DS mice significantly reduced these counts. Activated CD11b + microglia were more abundant in DS mouse corpus callosum and hippocampus than in wild-type controls. Fenfluramine treatment of DS mice resulted in significantly fewer activated CD11b + microglia than vehicle-treated DS mice in these brain regions. TUNEL staining in corpus callosum was increased in DS mice relative to wild-type controls. Fenfluramine treatment in DS mice lowered TUNEL staining relative to vehicle-treated DS mice. By PND 35-37, 55% of control DS mice had died, compared with 24% of DS mice receiving fenfluramine treatment (P = 0.0291).

SIGNIFICANCE

This is the first report of anti-neuroinflammation and pro-survival after fenfluramine treatment in a mammalian DS model. These results corroborate prior data in humans and animal models and suggest important pharmacological activities for fenfluramine beyond seizure reduction.

PLAIN LANGUAGE SUMMARY

Dravet syndrome is a severe epilepsy disorder that impairs learning and causes premature death. Clinical studies in patients with Dravet syndrome show that fenfluramine reduces convulsive seizures. Additional studies suggest that fenfluramine may have benefits beyond seizures, including promoting survival and improving control over emotions and behavior. Our study is the first to use a Dravet mouse model to investigate nonseizure outcomes of fenfluramine. Results showed that fenfluramine treatment of Dravet mice reduced neuroinflammation significantly more than saline treatment. Fenfluramine-treated Dravet mice also lived longer than saline-treated mice. These results support clinical observations that fenfluramine may have benefits beyond seizures.

Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.

Genetically encoded phosphatidylserine biosensor for in vitro, ex vivo and in vivo labelling

BACKGROUND

The dynamics of phosphatidylserine in the plasma membrane is a tightly regulated feature of eukaryotic cells. Phosphatidylserine (PS) is found preferentially in the inner leaflet of the plasma membrane. Disruption of this asymmetry leads to the exposure of phosphatidylserine on the cell surface and is associated with cell death, synaptic pruning, blood clotting and other cellular processes. Due to the role of phosphatidylserine in widespread cellular functions, an efficient phosphatidylserine probe is needed to study them. Currently, a few different phosphatidylserine labelling tools are available; however, these labels have unfavourable signal-to-noise ratios and are difficult to use in tissues due to limited permeability. Their application in living tissue requires injection procedures that damage the tissue and release damage-associated molecular patterns, which in turn stimulates phosphatidylserine exposure.

METHODS

For this reason, we developed a novel genetically encoded phosphatidylserine probe based on the C2 domain of the lactadherin (MFG-E8) protein, suitable for labelling exposed phosphatidylserine in various research models. We tested the C2 probe specificity to phosphatidylserine on hybrid bilayer lipid membranes by observing surface plasmon resonance angle shift. Then, we analysed purified fused C2 proteins on different cell culture lines or engineered AAVs encoding C2 probes on tissue cultures after apoptosis induction. For in vivo experiments, neurotropic AAVs were intravenously injected into perinatal mice, and after 2 weeks, brain slices were collected to observe C2-SNAP expression.

RESULTS

The biophysical analysis revealed the high specificity of the C2 probe for phosphatidylserine. The fused recombinant C2 proteins were suitable for labelling phosphatidylserine on the surface of apoptotic cells in various cell lines. We engineered AAVs and validated them in organotypic brain tissue cultures for non-invasive delivery of the genetically encoded C2 probe and showed that these probes were expressed in the brain in vivo after intravenous AAV delivery to mice.

CONCLUSIONS

We have demonstrated that the developed genetically encoded PS biosensor can be utilised in a variety of assays as a two-component system of C2 and C2m2 fusion proteins. This system allows for precise quantification and PS visualisation at directly specified threshold levels, enabling the evaluation of PS exposure in both physiological and cell death processes.

Selective connectivity enhances storage capacity in attractor models of memory function

Autoassociative neural networks provide a simple model of how memories can be stored through Hebbian synaptic plasticity as retrievable patterns of neural activity. Although progress has been made along the last decades in understanding the biological implementation of autoassociative networks, their modest theoretical storage capacity has remained a major constraint. While most previous approaches utilize randomly connected networks, here we explore the possibility of optimizing network performance by selective connectivity between neurons, that could be implemented in the brain through creation and pruning of synaptic connections. We show through numerical simulations that a reconfiguration of the connectivity matrix can improve the storage capacity of autoassociative networks up to one order of magnitude compared to randomly connected networks, either by reducing the noise or by making it reinforce the signal. Our results indicate that the signal-reinforcement scenario is not only the best performing but also the most adequate for brain-like highly diluted connectivity. In this scenario, the optimized network tends to select synapses characterized by a high consensus across stored patterns. We also introduced an online algorithm in which the network modifies its connectivity while learning new patterns. We observed that, similarly to what happens in the human brain, creation of connections dominated in an initial stage, followed by a stage characterized by pruning, leading to an equilibrium state that was independent of the initial connectivity of the network. Our results suggest that selective connectivity could be a key component to make attractor networks in the brain viable in terms of storage capacity.

A perspective on molecular signalling dysfunction, its clinical relevance and therapeutics in autism spectrum disorder

Intellectual disability (ID) and autism spectrum disorder (ASD) are neurodevelopmental disorders that have become a primary clinical and social concern, with a prevalence of 2-3% in the population. Neuronal function and behaviour undergo significant malleability during the critical period of development that is found to be impaired in ID/ASD. Human genome sequencing studies have revealed many genetic variations associated with ASD/ID that are further verified by many approaches, including many mouse and other models. These models have facilitated the identification of fundamental mechanisms underlying the pathogenesis of ASD/ID, and several studies have proposed converging molecular pathways in ASD/ID. However, linking the mechanisms of the pathogenic genes and their molecular characteristics that lead to ID/ASD has progressed slowly, hampering the development of potential therapeutic strategies. This review discusses the possibility of recognising the common molecular causes for most ASD/ID based on studies from the available models that may enable a better therapeutic strategy to treat ID/ASD. We also reviewed the potential biomarkers to detect ASD/ID at early stages that may aid in diagnosis and initiating medical treatment, the concerns with drug failure in clinical trials, and developing therapeutic strategies that can be applied beyond a particular mutation associated with ASD/ID.

Dynamics of phase oscillator networks with synaptic weight and structural plasticity

We study the dynamics of Kuramoto oscillator networks with two distinct adaptation processes, one varying the coupling strengths and the other altering the network structure. Such systems model certain networks of oscillatory neurons where the neuronal dynamics, synaptic weights, and network structure interact with and shape each other. We model synaptic weight adaptation with spike-timing-dependent plasticity (STDP) that runs on a longer time scale than neuronal spiking. Structural changes that include addition and elimination of contacts occur at yet a longer time scale than the weight adaptations. First, we study the steady-state dynamics of Kuramoto networks that are bistable and can settle in synchronized or desynchronized states. To compare the impact of adding structural plasticity, we contrast the network with only STDP to one with a combination of STDP and structural plasticity. We show that the inclusion of structural plasticity optimizes the synchronized state of a network by allowing for synchronization with fewer links than a network with STDP alone. With non-identical units in the network, the addition of structural plasticity leads to the emergence of correlations between the oscillators' natural frequencies and node degrees. In the desynchronized regime, the structural plasticity decreases the number of contacts, leading to a sparse network. In this way, adding structural plasticity strengthens both synchronized and desynchronized states of a network. Second, we use desynchronizing coordinated reset stimulation and synchronizing periodic stimulation to induce desynchronized and synchronized states, respectively. Our findings indicate that a network with a combination of STDP and structural plasticity may require stronger and longer stimulation to switch between the states than a network with STDP only.

Small Nucleus Accumbens and Large Cerebral Ventricles in Infants and Toddlers Prior to Receiving Diagnoses of Autism Spectrum Disorder

Early interventions for autism spectrum disorder (ASD) are increasingly available, while only 42-50% of ASD children are diagnosed before 3 years old (YO). To identify neuroimaging biomarkers for early ASD diagnosis, we evaluated surface- and voxel-based brain morphometry in participants under 3YO who were later diagnosed with ASD. Magnetic resonance imaging data were retrospectively obtained from patients later diagnosed with ASD at Boston Children's Hospital. The ASD participants with comorbidities such as congenital disorder, epilepsy, and global developmental delay/intellectual disability were excluded from statistical analyses. Eighty-five structural brain magnetic resonance imaging images were collected from 81 participants under 3YO and compared with 45 images from 45 gender- and age-matched nonautistic controls (non-ASD). Using an Infant FreeSurfer pipeline, 236 regionally distributed measurements were extracted from each scan. By t-tests and linear mixed models, the smaller nucleus accumbens and larger bilateral lateral, third, and fourth ventricles were identified in the ASD group. Vertex-wise t-statistical maps showed decreased thickness in the caudal anterior cingulate cortex and increased thickness in the right medial orbitofrontal cortex in ASD. The smaller bilateral accumbens nuclei and larger cerebral ventricles were independent of age, gender, or gestational age at birth, suggesting that there are MRI-based biomarkers in prospective ASD patients before they receive the diagnosis and that the volume of the nucleus accumbens and cerebral ventricles can be key MRI-based early biomarkers to predict the emergence of ASD.

Childhood exercise predicts response inhibition in later life via changes in brain connectivity and structure

Participation in exercise during early life (i.e., childhood through adolescence) enhances response inhibition; however, it is unclear whether participation in exercise during early life positively predicts response inhibition in later life. This historical cohort study was designed to clarify whether participation in exercise (e.g., structured sports participation) during early life predicts response inhibition in adulthood and if so, to reveal the brain connectivity and cortical structures contributing to this association. We analyzed data derived from 214 participants (women = 104, men = 110; age: 26‒69 years). Results indicated that participation in exercise during childhood (before entering junior high school; ≤ 12 years old) significantly predicted better response inhibition. No such association was found if exercise participation took place in early adolescence or later (junior high school or high school; ≥ 12 years old). The positive association of exercise participation during childhood with response inhibition was moderated by decreased structural and functional connectivity in the frontoparietal (FPN), cingulo-opercular (CON), and default mode networks (DMN), and increased inter-hemispheric structural networks. Greater cortical thickness and lower levels of dendritic arborization and density in the FPN, CON, and DMN also moderated this positive association. Our results suggest that participation in exercise during childhood positively predicts response inhibition later in life and that this association can be moderated by changes in neuronal circuitry, such as increased cortical thickness and efficiency, and strengthened inter-hemispheric connectivity.

Growth strategy determines the memory and structural properties of brain networks

The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of synaptic density observed in the brain. We prove that the existence of a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, such that the resulting network can recover stored memories. Moreover, we show that intermediate synaptic densities provide optimal developmental paths with minimum energy consumption, and that ultimately it is the transient heterogeneity in the network that determines its evolution. These results could explain why the pruning curves observed in actual brain areas present their characteristic temporal profiles and they also suggest new design strategies to build biologically inspired neural networks with particular information processing capabilities.

Creative destruction: Sparse activity emerges on the mammal connectome under a simulated communication strategy with collisions and redundancy

Signal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in "information spreading" (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes: a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. Results hold on two networks of the monkey cortex and one of the mouse whole-brain. We also find evidence that activity is lower and more sparse for empirical networks compared to degree-matched randomized networks under IS, suggesting that brain network topology supports IS-like routing strategies.

Novel Therapeutic Approach for Excitatory/Inhibitory Imbalance in Neurodevelopmental and Neurodegenerative Diseases

Excitatory/inhibitory (E/I) balance, defined as the balance between excitation and inhibition of synaptic activity in a neuronal network, accounts in part for the normal functioning of the brain, controlling, for example, normal spike rate. In many pathological conditions, this fine balance is perturbed, leading to excessive or diminished excitation relative to inhibition, termed E/I imbalance, reflected in network dysfunction. E/I imbalance has emerged as a contributor to neurological disorders that occur particularly at the extremes of life, including autism spectrum disorder and Alzheimer's disease, pointing to the vulnerability of neuronal networks at these critical life stages. Hence, it is important to develop approaches to rebalance neural networks. In this review, we describe emerging therapies that can normalize the E/I ratio or the underlying abnormality that contributes to the imbalance in electrical activity, thus improving neurological function in these maladies.

Development and plasticity of the corpus callosum

The corpus callosum (CC) connects the cerebral hemispheres and is the major mammalian commissural tract. It facilitates bilateral sensory integration and higher cognitive functions, and is often affected in neurodevelopmental diseases. Here, we review the mechanisms that contribute to the development of CC circuits in animal models and humans. These species comparisons reveal several commonalities. First, there is an early period of massive axonal projection. Second, there is a postnatal temporal window, varying between species, in which early callosal projections are selectively refined. Third, sensory-derived activity influences axonal refinement. We also discuss how defects in CC formation can lead to mild or severe CC congenital malformations.

Association of Polychlorinated Biphenyls and Organochlorine Pesticides with Autism Spectrum Disorder in Jamaican Children

BACKGROUND

Polychlorinated biphenyls (PCBs) and organochlorine (OC) pesticides are suspected to play a role in autism spectrum disorder (ASD).

OBJECTIVES

To investigate associations of PCBs and OC pesticides with ASD in Jamaican children and explore possible interaction between PCBs or OC pesticides with glutathione S-transferase (GST) genes (GSTT1, GSTM1, GSTP1) in relation to ASD.

METHODS

Participants included n=169 age- and sex-matched case-control pairs of Jamaican children 2-8 years old. Socioeconomic status and food frequency data were self-reported by the parents/guardians. Blood from each participant was analyzed for 100 PCB congeners and 17 OC pesticides and genotyped for three GST genes. PCBs and OC pesticides concentrations below the limit of detection (LoD) were replaced with (LoD/√2). We used conditional logistic regression (CLR) models to assess associations of PCBs and OC pesticides with ASD, individually or interactively with GST genes (GSTT1, GSTM1, GSTP1).

RESULTS

We found inverse associations of PCB-153 [adjusted MOR (95% CI) = 0.44 (0.23-0.86)] and PCB-180 [adjusted MOR (95% CI) = 0.52 (0.28-0.95)] with ASD. When adjusted for covariates in a CLR the interaction between GSTM1 and PCB-153 became significant (P < 0.01).

DISCUSSION

Differences in diet between ASD and typically developing control groups may play a role in the observed findings of lower concentrations of PCB-153 and PCB-180 in individuals with ASD than in controls. Considering the limited sample size and high proportion of concentrations below the LoD, these results should be interpreted with caution but warrant further investigation into associations of PCBs and OC pesticides with ASD.

Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases

The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved. Importantly, by analyzing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models.

How Memory Conforms to Brain Development

Nature exhibits countless examples of adaptive networks, whose topology evolves constantly coupled with the activity due to its function. The brain is an illustrative example of a system in which a dynamic complex network develops by the generation and pruning of synaptic contacts between neurons while memories are acquired and consolidated. Here, we consider a recently proposed brain developing model to study how mechanisms responsible for the evolution of brain structure affect and are affected by memory storage processes. Following recent experimental observations, we assume that the basic rules for adding and removing synapses depend on local synaptic currents at the respective neurons in addition to global mechanisms depending on the mean connectivity. In this way a feedback loop between "form" and "function" spontaneously emerges that influences the ability of the system to optimally store and retrieve sensory information in patterns of brain activity or memories. In particular, we report here that, as a consequence of such a feedback-loop, oscillations in the activity of the system among the memorized patterns can occur, depending on parameters, reminding mind dynamical processes. Such oscillations have their origin in the destabilization of memory attractors due to the pruning dynamics, which induces a kind of structural disorder or noise in the system at a long-term scale. This constantly modifies the synaptic disorder induced by the interference among the many patterns of activity memorized in the system. Such new intriguing oscillatory behavior is to be associated only to long-term synaptic mechanisms during the network evolution dynamics, and it does not depend on short-term synaptic processes, as assumed in other studies, that are not present in our model.

The Protracted Maturation of Associative Layer IIIC Pyramidal Neurons in the Human Prefrontal Cortex During Childhood: A Major Role in Cognitive Development and Selective Alteration in Autism

The human specific cognitive shift starts around the age of 2 years with the onset of self-awareness, and continues with extraordinary increase in cognitive capacities during early childhood. Diffuse changes in functional connectivity in children aged 2-6 years indicate an increase in the capacity of cortical network. Interestingly, structural network complexity does not increase during this time and, thus, it is likely to be induced by selective maturation of a specific neuronal subclass. Here, we provide an overview of a subclass of cortico-cortical neurons, the associative layer IIIC pyramids of the human prefrontal cortex. Their local axonal collaterals are in control of the prefrontal cortico-cortical output, while their long projections modulate inter-areal processing. In this way, layer IIIC pyramids are the major integrative element of cortical processing, and changes in their connectivity patterns will affect global cortical functioning. Layer IIIC neurons have a unique pattern of dendritic maturation. In contrast to other classes of principal neurons, they undergo an additional phase of extensive dendritic growth during early childhood, and show characteristic molecular changes. Taken together, circuits associated with layer IIIC neurons have the most protracted period of developmental plasticity. This unique feature is advanced but also provides a window of opportunity for pathological events to disrupt normal formation of cognitive circuits involving layer IIIC neurons. In this manuscript, we discuss how disrupted dendritic and axonal maturation of layer IIIC neurons may lead into global cortical disconnectivity, affecting development of complex communication and social abilities. We also propose a model that developmentally dictated incorporation of layer IIIC neurons into maturing cortico-cortical circuits between 2 to 6 years will reveal a previous (perinatal) lesion affecting other classes of principal neurons. This "disclosure" of pre-existing functionally silent lesions of other neuronal classes induced by development of layer IIIC associative neurons, or their direct alteration, could be found in different forms of autism spectrum disorders. Understanding the gene-environment interaction in shaping cognitive microcircuitries may be fundamental for developing rehabilitation and prevention strategies in autism spectrum and other cognitive disorders.

Longitudinal Task-Related Functional Connectivity Changes Predict Reading Development

Longitudinal studies suggest developmentally dependent changes in lexical processing during reading development, implying a change in inter-regional functional connectivity over this period. The current study used functional magnetic resonance imaging (fMRI) to explore developmental changes in functional connectivity across multiple runs of a rhyming judgment task in young readers (8–14 years) over an average 2.5-year span. Changes in functional segregation are correlated with and predict changes in the skill with which typically developing children learn to apply the alphabetic principle, as measured by pseudoword decoding. This indicates a developmental shift in the proportion of specialized functional clusters is associated with changes in reading skill and suggests a dependency of reading development on changes of particular neural pathways, specifically decreases in transitivity is indicative of greater network integration. This work provides evidence that characteristics of these pathways, quantified using graph-theoretic metrics, can be used to predict individual differences in reading development.

A distributed algorithm to maintain and repair the trail networks of arboreal ants

We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithm's success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

Concurrence of form and function in developing networks and its role in synaptic pruning

A fundamental question in neuroscience is how structure and function of neural systems are related. We study this interplay by combining a familiar auto-associative neural network with an evolving mechanism for the birth and death of synapses. A feedback loop then arises leading to two qualitatively different types of behaviour. In one, the network structure becomes heterogeneous and dissasortative, and the system displays good memory performance; furthermore, the structure is optimised for the particular memory patterns stored during the process. In the other, the structure remains homogeneous and incapable of pattern retrieval. These findings provide an inspiring picture of brain structure and dynamics that is compatible with experimental results on early brain development, and may help to explain synaptic pruning. Other evolving networks—such as those of protein interactions—might share the basic ingredients for this feedback loop and other questions, and indeed many of their structural features are as predicted by our model.

How structure and function coevolve in developing brains is little understood. Here, the authors study a coupled model of network development and memory, and find that due to the feedback networks with some initial memory capacity evolve into heterogeneous structures with high memory performance.

Network Design and the Brain

Neural circuits have evolved to accommodate similar information processing challenges as those faced by engineered systems. Here, we compare neural versus engineering strategies for constructing networks. During circuit development, synapses are overproduced and then pruned back over time, whereas in engineered networks, connections are initially sparse and are then added over time. We provide a computational perspective on these two different approaches, including discussion of how and why they are used, insights that one can provide the other, and areas for future joint investigation. By thinking algorithmically about the goals, constraints, and optimization principles used by neural circuits, we can develop brain-derived strategies for enhancing network design, while also stimulating experimental hypotheses about circuit development and function.

Local Regulation of Trail Networks of the Arboreal Turtle Ant, Cephalotes goniodontus

This study examines how an arboreal ant colony maintains, extends, and repairs its network of foraging trails and nests, built on a network of vegetation. Nodes are junctions where a branch forks off from another or where a branch of one plant touching another provides a new edge on which ants could travel. The ants' choice of edge at a node appears to be reinforced by trail pheromone. Ongoing pruning of the network tends to eliminate cycles and minimize the number of nodes and thus decision points, but not the distance traveled. At junctions, trails tend to stay on the same plant. In combination with the long internode lengths of the branches of vines in the tropical dry forest, this facilitates travel to food sources at the canopy edge. Exploration, when ants leave the trail on an edge that is not being used, makes both search and repair possible. The fewer the junctions between a location and the main trail, the more likely the ants are to arrive there. Ruptured trails are rapidly repaired with a new path, apparently using breadth-first search. The regulation of the network promotes its resilience and continuity.

Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks

Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that depends only on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules, long-term potentiation and long-term depression, can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both by simulation and analytically, how different forms of edge-weight-update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.

Structural Plasticity Denoises Responses and Improves Learning Speed

Despite an abundance of computational models for learning of synaptic weights, there has been relatively little research on structural plasticity, i.e., the creation and elimination of synapses. Especially, it is not clear how structural plasticity works in concert with spike-timing-dependent plasticity (STDP) and what advantages their combination offers. Here we present a fairly large-scale functional model that uses leaky integrate-and-fire neurons, STDP, homeostasis, recurrent connections, and structural plasticity to learn the input encoding, the relation between inputs, and to infer missing inputs. Using this model, we compare the error and the amount of noise in the network's responses with and without structural plasticity and the influence of structural plasticity on the learning speed of the network. Using structural plasticity during learning shows good results for learning the representation of input values, i.e., structural plasticity strongly reduces the noise of the response by preventing spikes with a high error. For inferring missing inputs we see similar results, with responses having less noise if the network was trained using structural plasticity. Additionally, using structural plasticity with pruning significantly decreased the time to learn weights suitable for inference. Presumably, this is due to the clearer signal containing less spikes that misrepresent the desired value. Therefore, this work shows that structural plasticity is not only able to improve upon the performance using STDP without structural plasticity but also speeds up learning. Additionally, it addresses the practical problem of limited resources for connectivity that is not only apparent in the mammalian neocortex but also in computer hardware or neuromorphic (brain-inspired) hardware by efficiently pruning synapses without losing performance.

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance.