A Network Neuroscience Approach to Typical and Atypical Brain Development.

Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions

A unifying framework for measuring weighted rich clubs.

Network analysis can help uncover meaningful regularities in the organization of complex systems. Among these, rich clubs are a functionally important property of a variety of social, technological and biological networks. Rich clubs emerge when nodes that are somehow prominent or 'rich' (e.g., highly connected) interact preferentially with one another. The identification of rich clubs is non-trivial, especially in weighted networks, and to this end multiple distinct metrics have been proposed. Here we describe a unifying framework for detecting rich clubs which intuitively generalizes various metrics into a single integrated method. This generalization rests upon the explicit incorporation of randomized control networks into the measurement process. We apply this framework to real-life examples, and show that, depending on the selection of randomized controls, different kinds of rich-club structures can be detected, such as topological and weighted rich clubs.J.A. is supported by the NIH-Oxford-Cambridge Scholarship Program. P.P. is employed by Queen Mary University of London. M.R. is supported by the NARSAD Young Investigator and Isaac Newton Trust grants. E.T.B. is employed half-time by the University of Cambridge, UK, and half-time by GlaxoSmithKline (GSK). P.E.V. is supported by the Medical Research Council (grant number MR/K020706/1).This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/srep0725

A generative network model of neurodevelopmental diversity in structural brain organization.

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition

Low-dimensional morphospace of topological motifs in human fMRI brain networks.

We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs, and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC of the motif distribution is correlated with efficiency and inversely correlated with transitivity. Hence this axis approximately conforms to the well-known economical small-world trade-off between integration and segregation in brain networks. Finally, we show that the economical clustering generative model proposed by Vértes et al. (2012) can approximately reproduce the motif morphospace of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualize the relationships between network properties and to investigate generative or constraining factors in the formation of complex human brain functional networks

Generative models of the human connectome

The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.Comment: 38 pages, 5 figures + 19 supplemental figures, 1 tabl

Peripheral Immune Cell Populations Associated with Cognitive Deficits and Negative Symptoms of Treatment-Resistant Schizophrenia.

BACKGROUND: Hypothetically, psychotic disorders could be caused or conditioned by immunological mechanisms. If so, one might expect there to be peripheral immune system phenotypes that are measurable in blood cells as biomarkers of psychotic states. METHODS: We used multi-parameter flow cytometry of venous blood to quantify and determine the activation state of 73 immune cell subsets for 18 patients with chronic schizophrenia (17 treated with clozapine), and 18 healthy volunteers matched for age, sex, BMI and smoking. We used multivariate methods (partial least squares) to reduce dimensionality and define populations of differentially co-expressed cell counts in the cases compared to controls. RESULTS: Schizophrenia cases had increased relative numbers of NK cells, naïve B cells, CXCR5+ memory T cells and classical monocytes; and decreased numbers of dendritic cells (DC), HLA-DR+ regulatory T-cells (Tregs), and CD4+ memory T cells. Likewise, within the patient group, more severe negative and cognitive symptoms were associated with decreased relative numbers of dendritic cells, HLA-DR+ Tregs, and CD4+ memory T cells. Motivated by the importance of central nervous system dopamine signalling for psychosis, we measured dopamine receptor gene expression in separated CD4+ cells. Expression of the dopamine D3 (DRD3) receptor was significantly increased in clozapine-treated schizophrenia and covaried significantly with differentiated T cell classes in the CD4+ lineage. CONCLUSIONS: Peripheral immune cell populations and dopaminergic signalling are disrupted in clozapine-treated schizophrenia. Immuno-phenotypes may provide peripherally accessible and mechanistically specific biomarkers of residual cognitive and negative symptoms in this treatment-resistant subgroup of patients

Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy.

Abnormalities of tau protein are central to the pathogenesis of progressive supranuclear palsy, whereas haplotype variation of the tau gene MAPT influences the risk of Parkinson disease and Parkinson's disease dementia. We assessed whether regional MAPT expression might be associated with selective vulnerability of global brain networks to neurodegenerative pathology. Using task-free functional magnetic resonance imaging in progressive supranuclear palsy, Parkinson disease, and healthy subjects (n = 128), we examined functional brain networks and measured the connection strength between 471 gray matter regions. We obtained MAPT and SNCA microarray expression data in healthy subjects from the Allen brain atlas. Regional connectivity varied according to the normal expression of MAPT. The regional expression of MAPT correlated with the proportionate loss of regional connectivity in Parkinson's disease. Executive cognition was impaired in proportion to the loss of hub connectivity. These effects were not seen with SNCA, suggesting that alpha-synuclein pathology is not mediated through global network properties. The results establish a link between regional MAPT expression and selective vulnerability of functional brain networks to neurodegeneration.Medical Research Council (Grant IDs: G1100464, MR/K020706/1, G0700503), Wellcome Trust (Grant ID: 103838), National Institute for Health Research Cambridge Biomedical Research Centre, Beverley Sackler fellowship scheme, NARSAD Young Investigator Award, Isaac Newton TrustThis is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neurobiolaging.2016.09.00

Topological Isomorphisms of Human Brain and Financial Market Networks

Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular – more highly optimized for information processing – than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets

Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning.

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.This work was supported by grants to ZK from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), the Leverhulme Trust (RF-2011-811 378), the Alan Turing Institute (TU/B/000095), the Wellcome Trust (205067/Z/16/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2011-290011, AEW from the Wellcome Trust (095183/Z/10/Z) and the [European Community's] Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA-2012-316746, PT from Engineering and Physical Sciences Research Council (EP/L000296/1), PEV from the MRC (MR/K020706/1)

Natural Language Processing markers in first episode psychosis and people at clinical high-risk.

Funder: MQ: Transforming Mental Health; Grant(s): MQF17_24Recent work has suggested that disorganised speech might be a powerful predictor of later psychotic illness in clinical high risk subjects. To that end, several automated measures to quantify disorganisation of transcribed speech have been proposed. However, it remains unclear which measures are most strongly associated with psychosis, how different measures are related to each other and what the best strategies are to collect speech data from participants. Here, we assessed whether twelve automated Natural Language Processing markers could differentiate transcribed speech excerpts from subjects at clinical high risk for psychosis, first episode psychosis patients and healthy control subjects (total N = 54). In-line with previous work, several measures showed significant differences between groups, including semantic coherence, speech graph connectivity and a measure of whether speech was on-topic, the latter of which outperformed the related measure of tangentiality. Most NLP measures examined were only weakly related to each other, suggesting they provide complementary information. Finally, we compared the ability of transcribed speech generated using different tasks to differentiate the groups. Speech generated from picture descriptions of the Thematic Apperception Test and a story re-telling task outperformed free speech, suggesting that choice of speech generation method may be an important consideration. Overall, quantitative speech markers represent a promising direction for future clinical applications.SEM was supported by the Accelerate Programme for Scientific Discovery, funded by Schmidt Futures, a Fellowship from The Alan Turing Institute, London, and a Henslow Fellowship at Lucy Cavendish College, University of Cambridge, funded by the Cambridge Philosophical Society. PEV is supported by a fellowship from MQ: Transforming Mental Health (MQF17_24). This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), the UK Medical Research Council (MRC) and the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London