Dynamics of Neural Systems: From Intracellular Transport in Neurons to Network Activity

Neurodegenerative diseases such as Alzheimer’s disease (AD) are all results of neurons losing their normal functionality. However, the exact mechanics of neurodegeneration remains obscure. Most of the knowledge about this class of diseases is obtained by studying late stage patients. Therefore, the mechanism proceeding the late stages of such diseases are less understood. Better understanding of respective mechanisms can help developing in early diagnostic tools and techniques to enable more effective treatment methods. Analyzing the dynamics of neural systems can be the key to discover the underlying mechanisms, which lead to neurodegenerative diseases. The dynamics of neural systems can be studied in different scales. At subcellular level, dynamics of axonal transport plays an important role in AD. In particular, anterograde axonal transport conducted by kinesin-1, known conventionally as kinesin, is essential for maintaining functional synapses. The stochastic motion of kinesin in the presence of magnetic nanoparticles is studied. A novel reduced-order-model (ROM) is constructed to simulate the collective dynamics of magnetic nanoparticles that are delivered into cells. The ROM coupled with the kinesin model allows the quantification of the decrease in processivity of kinesin and in its average velocity under external loads caused by chains of magnetic nanoparticles. Changes in the properties of transport induced by perturbations have the potential to decipher normal transport from impaired transport in the state of disease. In single-cell level analysis, Ca2+ transients in ASH neuron of C. elegans model organism is studied in the context of biological conditions such as aging and oxidative stress. A novel mathematical model is established that can describe the unique Ca2+ transients of ASH neuron in C. elegans including its “on” and “off” response. The model provides insight into the mechanism that governs the observed Ca2+ dynamics in ASH neuron. Hence, the proposed mathematical model can be utilized as a tool that offers explanation for changes induced by aging or oxidative stress in the neuron based on the observed Ca2+ dynamics. Network level analysis of neurons does not require methods of extremely high spatial and temporal resolution compared to the analysis in subcellular and cellular level. Yet, malfunction in smaller scales can manifest themselves in dynamics of larger scales. In particular, impairment of synaptic connections and their dynamics can jeopardize the normal functionality of the brain in pathological conditions such as AD. The impact of synaptic deficiencies is investigated on robustness of persistence activity (essential for working memory, which is adversely affected by AD) in excitatory networks with different topologies. Networks with rich-clubs are shown to have higher robustness when their synapses are impaired. Hence, monitoring changes in the properties of the neural network can be utilized as a tool to detect defects in synaptic connections. Moreover, such defects are shown to be more devastating if they occur in synapses of highly active neurons. Impairments of synapses in highly active neurons can be directly linked to subcellular processes such as depletion of synaptic resources. Using stochastic firing rate models, the parameters that govern synaptic dynamics are shown to influence the capability of the model to possess memory. The decrease in the release probability of synaptic vesicles, which can be caused by loss of axonal transport, is shown to have a detrimental effect on memory represented by the firing rate of population models.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145921/1/mirzakh_1.pd

Connectomics of extrasynaptic signalling: applications to the nervous system of Caenorhabditis elegans

Connectomics – the study of neural connectivity – is primarily concerned with the mapping and characterisation of wired synaptic links; however, it is well established that long-distance chemical signalling via extrasynaptic volume transmission is also critical to brain function. As these interactions are not visible in the physical structure of the nervous system, current approaches to connectomics are unable to capture them. This work addresses the problem of missing extrasynaptic interactions by demonstrating for the first time that whole-animal volume transmission networks can be mapped from gene expression and ligand-receptor interaction data, and analysed as part of the connectome. Complete networks are presented for the monoamine systems of Caenorhabditis elegans, along with a representative sample of selected neuropeptide systems. A network analysis of the synaptic (wired) and extrasynaptic (wireless) connectomes is presented which reveals complex topological properties, including extrasynaptic rich-club organisation with interconnected hubs distinct from those in the synaptic and gap junction networks, and highly significant multilink motifs pinpointing locations in the network where aminergic and neuropeptide signalling is likely to modulate synaptic activity. Thus, the neuronal connectome can be modelled as a multiplex network with synaptic, gap junction, and neuromodulatory layers representing inter-neuronal interactions with different dynamics and polarity. This represents a prototype for understanding how extrasynaptic signalling can be integrated into connectomics research, and provides a novel dataset for the development of multilayer network algorithms.This work was supported by the Medical Research Council (MRC)

Neuronal control of sleep in Caenorhabditis elegans

Sleep is crucial for all organisms with a nervous system. Amongst other functions, it is required for energy allocation, higher brain functions and the control of physiological processes. Sleep-active neurons have previously been identified in many species. These neurons act as the motor of sleep as their depolarization causes inhibition of wakefulness circuits and leads to sleep induction. However, how these sleep-active neurons get regulated and how exactly they are involved in molecular pathways for the benefits of sleep remains unclear. In this study I focused on the neuronal component of sleep regulation in the model organism Caenorhabditis elegans. In C. elegans the ring interneuron RIS functions as single sleep active neuron. First, I aimed to identify a neuronal circuit that regulates RIS activity. I found that RIS is controlled by the command interneuron PVC through a positive feedback loop. The interneurons PVC and RIM act together to activate RIS and sleep is most likely induced at the transition from forward to reverse locomotion. While RIS activity and hence sleep gets regulated by the nervous system, I could also show through pan-neuronal imaging that the control is reciprocal and RIS depolarization directly inhibits nervous system activity. Next, I intended to design a stand-alone device for optogenetic long-term experiments: the OptoGenBox. Optogenetics is a method in which through genetically knocked-in actuators and light, for instance, individual neurons can get de- or hyperpolarized. Implementation of the OptoGenBox was successful and I could show that long-term optogenetic sleep inhibition by hyperpolarization of RIS leads to a reduced longevity of arrested first larval stage worms. Lastly, I investigated the functions of sleep in C. elegans. Selected health span assays and investigation of synaptic changes did not reveal further functions of sleep. To better assess sleep benefits, strains, in which RIS was either constantly de- or hyperpolarized through genetically knocked-in ion channels, were generated and characterized. Constant de- as well as hyperpolarization of RIS led to a reduction in sleep but diverging longevity effects in the arrested first larval stage. In conclusion, sleep in C. elegans is highly controlled by the nervous system and sleep induction is not only dependent on sleep-active neurons but furthermore wake-active circuits that activate sleep neurons. As sleep is evolutionary conserved, these circuits are most likely also existent in organisms with more complex nervous systems such as mammals. The OptoGenBox as well as the here presented new RIS manipulated worm strains present potent tools to further investigate neuronal circuits and protective pathways downstream of the sleep neuron RIS.2021-10-2

The Drosophila Larval Locomotor Circuit Provides a Model to Understand Neural Circuit Development and Function

From Frontiers via Jisc Publications RouterHistory: collection 2021, received 2021-03-24, accepted 2021-06-09, epub 2021-07-01Publication status: PublishedIt is difficult to answer important questions in neuroscience, such as: “how do neural circuits generate behaviour?,” because research is limited by the complexity and inaccessibility of the mammalian nervous system. Invertebrate model organisms offer simpler networks that are easier to manipulate. As a result, much of what we know about the development of neural circuits is derived from work in crustaceans, nematode worms and arguably most of all, the fruit fly, Drosophila melanogaster. This review aims to demonstrate the utility of the Drosophila larval locomotor network as a model circuit, to those who do not usually use the fly in their work. This utility is explored first by discussion of the relatively complete connectome associated with one identified interneuron of the locomotor circuit, A27h, and relating it to similar circuits in mammals. Next, it is developed by examining its application to study two important areas of neuroscience research: critical periods of development and interindividual variability in neural circuits. In summary, this article highlights the potential to use the larval locomotor network as a “generic” model circuit, to provide insight into mammalian circuit development and function

The effect of network transitions on spontaneous activity and sycnhrony in devloping neural networks

Connectivity patterns of developing neural circuits and the effects of its dynamics on network behavior, particularly the emergence of spontaneous activity and synchrony, are not clear. We attempt to quantify anatomical connectivity patterns of rat cortical cultures during different stages of development. By culturing the networks on dishes embedded with micro electrode arrays, we simultaneously record electrical activity from multiple regions of the developing network and monitor its electrical behavior, particularly its tendency to fire spontaneously and to synchronize under certain conditions. We investigate possible correlations between changes in the network connectivity patterns and spontaneous electrical activity and synchrony. Cocultures showed a higher degree of synchrony than primary cultures. Networks with cancer cells, besides failing to synchronize, produced seizure-like events. We expect these results to elucidate the effect of connectivity on network behavior and hence to provide insight into the effects of various disease states on network properties. Such information could be used to diagnose such states

인간 뇌의 구조적 커넥톰: 뇌의 구조적 네트워크 발달에 관한 분석 및 시뮬레이션

학위논문 (박사)-- 서울대학교 대학원 : 뇌인지과학과, 2015. 8. 이상훈, Marcus Kaiser.우리는 거시적으로는 뇌의 회백질 (grey matter)이 백질 (white matter)의 축삭다발 (axon bundles)을 통해 어떤 식으로 연결되어 있는지를, 미시적으로는 신경 세포들의 축삭 (axons)과 가지돌기 (dendrites)가 어떻게 연결되어 있는지를 그래프 (graph) 이론으로 자연스럽게 표현하고 연구할 수 있습니다. 이렇게 뇌 속의 신경세포들 연결 지도와 같은 것을 커넥톰 (connectome) 이라고 부릅니다. 이렇게 뇌의 연결 지도를 그래프 (혹은 네트워크)로 표현함으로써 우리는 여러 종들의 뇌를 같은 틀 (framework)에서 연구할 수 있습니다. 그러나 뇌는 위상적인 특질뿐만 아니라 뇌가 실재하는 물리적인 공간 (embedding space)과 대사 비용 (metabolic cost)의 제한을 받습니다. 따라서 위상적인 (topological) 특질들과 공간적인 (spatial) 특질들을 동시에 고려해야 합니다. 이 특질들은 뇌가 발달하면서 많은 변화를 보이게 됩니다. 본 연구에서는 뇌가 발달할 때 뇌의 연결 지도가 어떻게 조직이 되고 또 재편성 되는지 미시적, 거시적 관점을 통하여 알아보았습니다. 여기서 미시적 관점은 신경 세포간 시냅스 연결을 말하고 거시적 관점은 뇌의 각 영역간 연결을 확산텐서영상 (Diffusion Tensor Imaging, DTI)을 통해 알아보는 것을 말합니다. 더불어 두 단계 커넥톰 발달 가설 (Two-stage connectome maturation hypothesis)을 통하여 이 두 가지 관점을 연결 시킴과 동시에 건강한 뇌의 커넥톰과 병리적 뇌의 커넥톰이 어떻게 다르게 발달 할 수 있는 지에 대한 예측과 새로운 관점을 제시하였습니다.A brain can be represented as a graph (network) comprised of sets of nodes and edges called connectome, which is a natural representation of a brainneurons form synapses with dendrites and axons to transfer neural signals and regions of grey matter are interconnected to each other through axon bundles in white matter. Moreover, by representing a brain as a network, we can study brains from other species in the same framework using this graph-theoretical approach. A brain, however, is also constrained by other factors such as its embedding space and metabolic cost. Therefore, spatial characteristics as well as topological traits are important. These topological and spatial characteristics of a brain change during development. In this work, I investigated how brain network develops over age in micro and macro scales to find organising and re-organising principles during developmentmicroscopic scale refers to the synaptic connectivity between neurons and macroscopic scale refers to whole brain fibre tract connectivity between brain regions constructed from diffusion tensor imaging technique. Furthermore, I propose two-stage connectome maturation hypothesis by connecting both scales, which would elucidate principles of healthy and pathological brain development.I. Introduction 1 0.1. Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. Methodological background 5 1.1. Network analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.1. Topological properties . . . . . . . . . . . . . . . . . . . 5 1.1.1.1. Graph theoretical approach . . . . . . . . . . . 5 1.1.1.2. Edge density . . . . . . . . . . . . . . . . . . . 6 1.1.1.3. Global and local efficiency . . . . . . . . . . . . 6 1.1.1.4. Modularity . . . . . . . . . . . . . . . . . . . . 7 1.1.1.5. Within-module strength and Participation coefficient . . . . . . . . . . . . . . . . . . . . . . 8 1.1.2. Spatial properties . . . . . . . . . . . . . . . . . . . . . . 9 1.2. Diffusion Magnetic Resonance Imaging (Diffusion MRI) . . . . 9 1.2.1. Diffusion Tensor Imaging(DTI) . . . . . . . . . . . . . . 9 1.2.2. White Matter Tractography . . . . . . . . . . . . . . . . 14 1.2.3. Network construction . . . . . . . . . . . . . . . . . . . . 16 1.2.4. Correction for artefacts of DW images . . . . . . . . . . 17 II. Microscopic neuronal network 19 2. Developmental time windows for axon growth influence neuronal network topology 21 2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2. Methods and materials . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.1. Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.2. Comparison of growth scenarios . . . . . . . . . . . . . . 29 2.2.3. Validation of our model prediction with C. elegans connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.2.4. Statistical analysis . . . . . . . . . . . . . . . . . . . . . 32 2.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.3.1. Topological and spatial properties . . . . . . . . . . . . 33 2.3.1.1. Out-degree distribution . . . . . . . . . . . . . 33 2.3.1.2. Local efficiency . . . . . . . . . . . . . . . . . . 34 2.3.1.3. Connection probability . . . . . . . . . . . . . 34 2.3.1.4. Bidirectional connections . . . . . . . . . . . . 37 2.3.1.5. Connection length distribution and Axon length 39 2.3.1.6. Comparison of our model predictions with C. elegans connectivity . . . . . . . . . . . . . . . 41 2.3.2. Morphological properties . . . . . . . . . . . . . . . . . . 42 2.3.2.1. Potential synapses and established synapses . . 42 2.3.3. Simulation results with dendritic development. . . . . . 44 2.3.4. Partially overlapping time windows . . . . . . . . . . . . 45 2.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.6. Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 2.6.1. Different connection patterns between serial and parallel growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 2.6.2. Results for all conditions . . . . . . . . . . . . . . . . . . 55 2.6.3. Comparisons of local efficiency and connection probability from C. elegans data with the model predictions. . . . . 55 2.6.4. The effect on connection lengths when neurons change their position during development. . . . . . . . . . . . . 63 2.6.5. Simulation parameters . . . . . . . . . . . . . . . . . . . 65 2.6.5.1. Input parameters . . . . . . . . . . . . . . . . . 66 2.6.5.2. Output variables . . . . . . . . . . . . . . . . . 67 III. Macroscopic brain network 71 3. Preferential detachment during human brain development: Age and sex-specific structural connectivity in Diffusion Tensor Imaging (DTI)-data 73 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 3.2. Methods and materials . . . . . . . . . . . . . . . . . . . . . . . 77 3.2.1. DTI-Data . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.2.2. Data pre-processing and network construction . . . . . . 78 3.2.3. Network analysis . . . . . . . . . . . . . . . . . . . . . . 80 3.2.3.1. Modular membership assignment . . . . . . . . 81 3.2.4. Edge group analysis . . . . . . . . . . . . . . . . . . . . 83 3.2.5. Individual edge analysis . . . . . . . . . . . . . . . . . . 84 3.2.6. Statistical analysis . . . . . . . . . . . . . . . . . . . . . 85 3.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.3.1. Age effect for both genders . . . . . . . . . . . . . . . . 87 3.3.1.1. Connectedness . . . . . . . . . . . . . . . . . . 87 3.3.1.1.1. Streamline count vs. Edge density . . 87 3.3.1.1.2. Thick vs. Thin . . . . . . . . . . . . . 87 3.3.1.2. Small-world topology and long-distance connectivity . . . . . . . . . . . . . . . . . . . . . . . 88 3.3.1.2.1. Efficiency and small-world topology . 88 3.3.1.2.2. Short vs. long-distance connectivity . 88 3.3.2. Modular organisation . . . . . . . . . . . . . . . . . . . . 89 3.3.2.1. Modularity and module membership assignment 89 3.3.2.2. Within-module strength and Participation coefficient . . . . . . . . . . . . . . . . . . . . . . 91 3.3.2.3. Within vs. between module analysis . . . . . . 92 3.3.2.4. Individual edge analysis . . . . . . . . . . . . . 92 3.3.2.5. Sex-specific age-related changes . . . . . . . . . 93 3.3.2.6. Differences independent of age . . . . . . . . . 95 3.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 3.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 3.6. Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 3.6.1. Overall anatomical changes in brain volumes . . . . . . 114 IV. Two-stage connectome maturation 121 4. Two-stage connectome maturation: Establishment and refinement of functional modules 123 4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 4.2. Less is more? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 4.3. Pruning and protracted remodelling throughout development . 126 4.4. Abnormal pruning and imbalance between excitation and inhibition 128 4.5. Macroscopic scale of pruning . . . . . . . . . . . . . . . . . . . 128 4.6. Abnormal macroscopic pruning . . . . . . . . . . . . . . . . . . 130 4.7. Two-stage Connectome Maturation . . . . . . . . . . . . . . . . 131 4.8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 V. General discussion and outlook 137 5. Discussion and outlook 139 5.1. Summary and context . . . . . . . . . . . . . . . . . . . . . . . 139 5.2. Methodological consideration . . . . . . . . . . . . . . . . . . . 142 5.2.1. Constructing networks from DWI . . . . . . . . . . . . . 142 5.2.1.1. Definition of nodes and edges . . . . . . . . . . 142 5.2.1.2. Definition of weights . . . . . . . . . . . . . . . 143 5.2.2. Diffusion MRI: DTI and beyond . . . . . . . . . . . . . 144 5.2.2.1. Other methods for fibre orientation estimation 145 5.2.2.2. Optimal parameter values and models . . . . . 146 5.3. Future outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 ReferencesDocto

State Dependent Regulation of the Neural Circuit for C. Elegans Feeding

Rhythmic muscular contractions are essential for many different behaviors, from locomotion to respiration. These behaviors are modulated by changes in the external environment, such as temperature shifts and presence of predators, and by internal states, such as hunger and sleep. The roundworm Caenorhabditis elegans feeds on bacteria through rhythmic contraction and relaxation of its pharynx, a neuromuscular pump innervated by a nearly independent network of just 20 neurons. Feeding rate is modulated by many environmental and physiological factors, but feeding generally persists throughout the life of the worm, ceasing only during sleep. The mechanisms by which the pharyngeal nervous system controls feeding during wake and sleep are poorly understood. I used optogenetics, genetics, and pharmacology to define the cholinergic pharyngeal circuitry that regulates feeding rate during wake, and then used similar approaches to examine how feeding is inhibited during sleep. I identified a four-neuron circuit that regulates feeding rate during wake and found that it is degenerate, meaning that multiple different classes of neurons can stimulate feeding in a similar manner. I also found that feeding quiescence is generated by distinct mechanisms during two behaviorally indistinguishable sleep states: cholinergic motor neurons are inhibited during stress-induced sleep, while the muscle is directly inhibited during developmentally timed sleep. Thus, as in mammals and despite its behavioral homogeneity, sleep in C. elegans is not a physiologically homogenous state. These results provide insight into the function of a highly conserved neural circuit that generates robust rhythmic behavior, and illustrate how this circuit can be altered in different ways to produce the same behavioral output during two distinct sleep states

Structure of complex networks: Quantifying edge-to-edge relations by failure-induced flow redistribution

The analysis of complex networks has so far revolved mainly around the role of nodes and communities of nodes. However, the dynamics of interconnected systems is commonly focalised on edge processes, and a dual edge-centric perspective can often prove more natural. Here we present graph-theoretical measures to quantify edge-to-edge relations inspired by the notion of flow redistribution induced by edge failures. Our measures, which are related to the pseudo-inverse of the Laplacian of the network, are global and reveal the dynamical interplay between the edges of a network, including potentially non-local interactions. Our framework also allows us to define the embeddedness of an edge, a measure of how strongly an edge features in the weighted cuts of the network. We showcase the general applicability of our edge-centric framework through analyses of the Iberian Power grid, traffic flow in road networks, and the C. elegans neuronal network.Comment: 24 pages, 6 figure

Elucidating the mechanisms and dynamics of olfactory memory formation in Caenorhabditis elegans

Learning and memory are essential for survival—organisms must respond to their environment and update their knowledge accordingly. Memories undergo multiple stages before being stored long term: acquisition, consolidation, and maintenance. Various factors contribute to long-term memory formation, including sleep, synaptic plasticity, and molecular mechanisms. Sleep is thought to drive memory consolidation by promoting synaptic plasticity. However, the exact molecular, cellular, and synaptic mechanisms occurring during sleep to drive memory maintenance are not well known. To achieve single-cell and synaptic resolution of memory in a genetically-amenable organism, I utilized C. elegans and an odor spaced-training long-term memory paradigm. I show that sleep post training is required to consolidate the odor memory. I found that one specific interneuron (AIY) is required for long-term odor memory maintenance, and that synapses between that interneuron and one olfactory sensory neuron (AWC) are decreased after the long-term memory is formed, only in animals that slept. In addition, I found that the human transient receptor potential vanilloid (TRPV) channel osm-9, an ortholog of human TRPV5 and TRPV6, is required specifically for memory consolidation in order to establish this long-term olfactory memory, independent of sleep. Understanding the mechanisms underlying how learning and memory are mediated is important to understand how organisms learn from their environment to ensure survival, as well as what happens when these processes go awry, such as in the disease context. Expanded CGG repeat RNAs from the fragile X mental retardation 1 (FMR1) gene are present in the brain of patients with fragile X and fragile-X tremor/ataxia syndrome (FXTAS). We show that when human FMR1 with these expanded RNA repeats is expressed in C. elegans olfactory sensory neurons, that it blocks short-term odor memory. Behavioral plasticity perturbations are also observed in FXTAS patients, such as altered prepulse inhibition. We find that this aberrant plasticity requires the miRNA-Argonaute pathway. Thus, these studies may help to understand the molecular and cellular mechanisms and dynamics of how neural plasticity drives learning and memory, and what happens molecularly in diseases that limit plasticity

The anatomical distance of functional connections predicts brain network topology in health and schizophrenia.

The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive "pruning" of short-distance functional connections in schizophrenia.PEV is supported by the Medical Research Council (grant number MR/K020706/1). This work was supported by the Neuroscience in Psychiatry Network (NSPN) which is funded by a Wellcome Trust strategy award to the University of Cambridge and University College London. ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline; he holds stock in GSK.This is the final published version. It first appeared at http://onlinelibrary.wiley.com/doi/10.1111/jcpp.12365/full