Age-associated changes in rich-club organisation in autistic and neurotypical human brains

Macroscopic structural networks in the human brain have a rich-club architecture comprising both highly inter-connected central regions and sparsely connected peripheral regions. Recent studies show that disruption of this functionally efficient organisation is associated with several psychiatric disorders. However, despite increasing attention to this network property, whether age-associated changes in rich-club organisation occur during human adolescence remains unclear. Here, analysing a publicly shared diffusion tensor imaging dataset, we found that, during adolescence, brains of typically developing (TD) individuals showed increases in rich-club organisation and inferred network functionality, whereas individuals with autism spectrum disorders (ASD) did not. These differences between TD and ASD groups were statistically significant for both structural and functional properties. Moreover, this typical age-related changes in rich-club organisation were characterised by progressive involvement of the right anterior insula. In contrast, in ASD individuals, did not show typical increases in grey matter volume, and this relative anatomical immaturity was correlated with the severity of ASD social symptoms. These results provide evidence that rich-club architecture is one of the bases of functionally efficient brain networks underpinning complex cognitive functions in adult human brains. Furthermore, our findings suggest that immature rich-club organisation might be associated with some neurodevelopmental disorders

Network communities of dynamical influence

Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus when key elements are stimulated. We present a technique for identifying these key elements by investigating the relationships between a system’s most dominant eigenvectors. This approach reveals the most effective vertices for leading a network to rapid consensus when stimulated, as well as the communities that form under their dynamical influence. In applying this technique, the effectiveness of starling flocks was found to be due, in part, to the low outdegree of every bird, where increasing the number of outgoing connections can produce a less responsive flock. A larger outdegree also affects the location of the birds with the most influence, where these influentially connected birds become more centrally located and in a poorer position to observe a predator and, hence, instigate an evasion manoeuvre. Finally, the technique was found to be effective in large voxel-wise brain connectomes where subjects can be identified from their influential communities

Subcortical contributions to large-scale network communication

Higher brain function requires integration of distributed neuronal activity across large-scale brain networks. Recent scientific advances at the interface of subcortical brain anatomy and network science have highlighted the possible contribution of subcortical structures to large-scale network communication. We begin our review by examining neuroanatomical literature suggesting that diverse neural systems converge within the architecture of the basal ganglia and thalamus. These findings dovetail with those of recent network analyses that have demonstrated that the basal ganglia and thalamus belong to an ensemble of highly interconnected network hubs. A synthesis of these findings suggests a new view of the subcortex, in which the basal ganglia and thalamus form part of a core circuit that supports large-scale integration of functionally diverse neural signals. Finally, we close with an overview of some of the major opportunities and challenges facing subcortical-inclusive descriptions of large-scale network communication in the human brain

Hippocampal Contributions to the Large-Scale Episodic Memory Network Predict Vivid Visual Memories

† The first two authors contributed equally to this work. Abstract A common approach in memory research is to isolate the function(s) of individual brain regions, such as the hippocampus, without addressing how those regions interact with the larger network. To investigate the properties of the hippocampus embedded within large-scale networks, we used functional magnetic resonance imaging and graph theory to characterize complex hippocampal interactions during the active retrieval of vivid versus dim visual memories. The study yielded 4 main findings. First, the right hippocampus displayed greater communication efficiency with the network (shorter path length) and became a more convergent structure for information integration (higher centrality measures) for vivid than dim memories. Second, vivid minus dim differences in our graph theory measures of interest were greater in magnitude for the right hippocampus than for any other region in the 90-region network. Moreover, the right hippocampus significantly reorganized its set of direct connections from dim to vivid memory retrieval. Finally, beyond the hippocampus, communication throughout the whole-brain network was more efficient (shorter global path length) for vivid than dim memories. In sum, our findings illustrate how multivariate network analyses can be used to investigate the roles of specific regions within the large-scale network, while also accounting for global network changes

Age-Related Changes in Human Anatomical and Functional Brain Networks

Thesis (Ph.D.) - Indiana University, Psychological and Brain Sciences, 2015i) The first component characterizes age-related changes in specific connections. We find that functional connections within and between intrinsic connectivity networks (ICNs) follow distinct lifespan trajectories. We further characterize these changes in terms of each ICN’s “modularity” and find that most ICNs become less modular (i.e. less segregated) with age. In anatomical networks we find that hub regions are disproportionately affected by age and become less efficiently connected to the rest of the brain. Finally, we find that, with age stronger functional connections are supported by longer (multi-step) anatomical pathways for communication. ii) The second component is concerned with characterizing age-related changes in the boundaries of ICNs. To this end we used a multi-layer variant of modularity maximization to decompose networks into modules at different organizational scales, which we find exhibit scale-specific trends with age. At coarse scales, for example, we find that modules become more segregated whereas modules defined at finer scales become less segregated. We also find that module composition changes with age, and specific areas associated with memory change their module allegiance with age. iii) In the final component we use generative models to uncover wiring rules for the anatomical brain networks. Modeling network growth as a spatial penalty combined with homophily, we find that we can generate synthetic networks with many of the same properties as real-world brain networks. Fitting this model to individuals, we show that the parameter governing the severity of the spatial penalty weakens monotonically with age and that the overall ability to reproduce realistic connectomes for older individuals suffers. These results suggest that, with age, additional constraints may play an important role in shaping the topology of brain structural networks

A Biosymtic (Biosymbiotic Robotic) Approach to Human Development and Evolution. The Echo of the Universe.

In the present work we demonstrate that the current Child-Computer Interaction paradigm is not potentiating human development to its fullest – it is associated with several physical and mental health problems and appears not to be maximizing children’s cognitive performance and cognitive development. In order to potentiate children’s physical and mental health (including cognitive performance and cognitive development) we have developed a new approach to human development and evolution. This approach proposes a particular synergy between the developing human body, computing machines and natural environments. It emphasizes that children should be encouraged to interact with challenging physical environments offering multiple possibilities for sensory stimulation and increasing physical and mental stress to the organism. We created and tested a new set of computing devices in order to operationalize our approach – Biosymtic (Biosymbiotic Robotic) devices: “Albert” and “Cratus”. In two initial studies we were able to observe that the main goal of our approach is being achieved. We observed that, interaction with the Biosymtic device “Albert”, in a natural environment, managed to trigger a different neurophysiological response (increases in sustained attention levels) and tended to optimize episodic memory performance in children, compared to interaction with a sedentary screen-based computing device, in an artificially controlled environment (indoors) - thus a promising solution to promote cognitive performance/development; and that interaction with the Biosymtic device “Cratus”, in a natural environment, instilled vigorous physical activity levels in children - thus a promising solution to promote physical and mental health

The modular structure of brain functional connectivity networks: a graph theoretical approach

Complex networks theory offers a framework for the analysis of brain functional connectivity as measured by magnetic resonance imaging. Within this approach the brain is represented as a graph comprising nodes connected by links, with nodes corresponding to brain regions and the links to measures of inter-regional interaction. A number of graph theoretical methods have been proposed to analyze the modular structure of these networks. The most widely used metric is Newman's Modularity, which identifies modules within which links are more abundant than expected on the basis of a random network. However, Modularity is limited in its ability to detect relatively small communities, a problem known as ``resolution limit''. As a consequence, unambiguously identifiable modules, like complete sub-graphs, may be unduly merged into larger communities when they are too small compared to the size of the network. This limit, first demonstrated for Newman's Modularity, is quite general and affects, to a different extent, all methods that seek to identify the community structure of a network through the optimization of a global quality function. Hence, the resolution limit may represent a critical shortcoming for the study of brain networks, and is likely to have affected many of the studies reported in the literature. This work pioneers the use of Surprise and Asymptotical Surprise, two quality functions rooted in probability theory that aims at overcoming the resolution limit for both binary and weighted networks. Hereby, heuristics for their optimization are developed and tested, showing that the resulting optimal partitioning can highlight anatomically and functionally plausible modules from brain connectivity datasets, on binary and weighted networks. This novel approach is applied to the partitioning of two different human brain networks that have been extensively characterized in the literature, to address the resolution-limit issue in the study of the brain modular structure. Surprise maximization in human resting state networks revealed the presence of a rich structure of modules with heterogeneous size distribution undetectable by current methods. Moreover, Surprise led to different, more accurate classification of the network's connector hubs, the elements that integrate the brain modules into a cohesive structure. In synthetic networks, Asymptotical Surprise showed high sensitivity and specificity in the detection of ground-truth structures, particularly in the presence of noise and variability such as those observed in experimental functional MRI data. Finally, the methodological advances hereby introduced are shown to be a helpful tool to better discern differences between the modular organization of functional connectivity of healthy subjects and schizophrenic patients. Importantly, these differences may point to new clinical hypotheses on the etiology of schizophrenia, and they would have gone unnoticed with resolution-limited methods. This may call for a revisitation of some of the current models of the modular organization of the healthy and diseased brain

Passions before passivity, actions after self-certainty : binding the philosophy and neuroscience of affects

This thesis examines the turn to affect in both philosophy and neurobiology beginning in the 1990s. Both fields shared themes of a return to emotional aspects of the body; a rapprochement between natural sciences and humanities; and rethinking of causality, intentionality, identity and temporality. Yet the field remains contentiously divided. Disputes arise mainly from differences in understanding of key terms (notably between affect and emotion) and the place of the intentional subject within expanded, flattened conceptions of agency, causality and the animate/inanimate, differences ultimately between implications in and overcomings of past metaphysics of coupled opposites and the philosophy of the subject. Implication because conceptions of affect have been historically dominated by the active and passive understood as a doing and being done to; affects then become quantitative, external impositions disrupting purely self-present subjects requiring philosophies of defence that privilege sameness over difference. Whereas overcomings posit a pure activity or passivity, simultaneities of active and passive, or a non-temporal ‘before’ prior to activity/passivity. This thesis explores the alternative possibility that ‘active/passive’ never really translated the Greek ποιεῖν/πάσχειν that is its root and root of affect as translation of πάθος. The thesis is in two parts: in philosophy, I uncover a broader sense of πάσχειν as bindings of implicit differences prior to any explicit separation of agent and patient. Meanwhile, in contemporary neuroscience, action is being redefined through ‘prediction processing’ theories where error as the difference between world and an organism’s implicit models of that world motivates action. Affective neurobiology then describes this radical contingency of expectation and actuality in specifically affective terms as the organism in its self-difference. I conclude by binding the radical transformations in active and passive each turn effects to understand affect still as a pairing of active/passive but where these terms signify not an oppositional agent acting on patient, but as the binding of contingent, implicit differences with their making explicit through the affections of error in the organism’s necessary difference and togetherness with world