Integrated Population Genomic Analysis and Numerical Simulation to Estimate Larval Dispersal of Acanthaster cf. solaris Between Ogasawara and Other Japanese Regions

The estimation of larval dispersal on an ecological timescale is significant for conservation of marine species. In 2018, a semi-population outbreak of crown-of-thorns sea star, Acanthaster cf. solaris, was observed on a relatively isolated oceanic island, Ogasawara. The aim of this study was to assess whether this population outbreak was caused by large-scale larval recruitment (termed secondary outbreak) from the Kuroshio region. We estimated larval dispersal of the coral predator A. cf. solaris between the Kuroshio and Ogasawara regions using both population genomic analysis and simulation of oceanographic dispersal. Population genomic analysis revealed overall genetically homogenized patterns among Ogasawara and other Japanese populations, suggesting that the origin of the populations in the two regions is the same. In contrast, a simulation of 26-year oceanographic dispersal indicated that larvae are mostly self-seeded in Ogasawara populations and have difficulty reaching Ogasawara from the Kuroshio region within one generation. However, a connectivity matrix produced by the larval dispersal simulation assuming a Markov chain indicated gradual larval dispersal migration from the Kuroshio region to Ogasawara in a stepping-stone manner over multiple years. These results suggest that the 2018 outbreak was likely the result of self-seeding, including possible inbreeding (as evidenced by clonemate analysis), as large-scale larval dispersal from the Kurishio population to the Ogasawara population within one generation is unlikely. Instead, the population in Ogasawara is basically sustained by self-seedings, and the outbreak in 2018 was also most likely caused by successful self-seedings including possible inbreeding, as evidenced by clonemate analysis. This study also highlighted the importance of using both genomic and oceanographic methods to estimate larval dispersal, which provides significant insight into larval dispersal that occurs on ecological and evolutionary timescales.

Meaning and structural dynamics in poetry: a computational perspective

This work addresses Sri Aurobindo’s mantric poem, Savitri, with a computational linguistics approach. This is one of the longest poems ever written in English. We build the connectivity matrix between all main word pairs and analyse its structure. Concepts emerge as directions that better explain the variance of the data in the hyperspace of words. When projected to the low dimensional space of concepts, the vector of attention as the reader moves through the text shows a large correlation across sections of the poem, thus acting the future and the past over again. These findings suggest that the mathematical structure of Savitri is and reflects a substrate for the author’s main ideas, facilitating the reader’s understanding of the poem’s meaning via its long-range dynamical correlations. Acknowledging an irreducible essence to poetry, future studies on the relationship between words and sounds, and sounds and ideas may provide invaluable hints of the origin of language and its intimate relationship with the evolution of human consciousness.

Common mechanisms of attractorless oscillatory regimes in radioengineering models of brain thalamocortical network

This work aims to show that long transient processes in mesascale models of thalamocortical brain network can appear in very general case, in particular for different number of elements in the ensemble (different level of detalization) and different initial phase of external driving, with these regimes surviving at small variations of number and structure of couplings. Methods. Thalamocortical brain networks are modelled using electronic circuit realized using computer SPICE eluating software. FitzHugh – Nagumo analog generator is used as a single circuit element. Results. Long quasiregular and nonregular oscillation processes with stationary amplitude were shown to occur in ensembles of 14, 28 and 56 model FitzHug – Nagumo generators. The dependency of transient process length on the external driving initial phase and particular coupling matrix structure was studied. Conclusion. The proposed electronic models of thalamocortical system were proved to reproduce the pathological regimes of brain activity in similar way despite the number of elements in the circuit, connectivity matrix and initial driving phase.

Fully Distributed Control for a Class of Uncertain Multi-Agent Systems with a Directed Topology and Unknown State-Dependent Control Coefficients

To address the control of uncertain multi-agent systems (MAS) with completely unknown system nonlinearities and unknown control coefficients, a global consensus method is proposed by constructing novel filters and barrier function-based distributed controllers. The main contributions are as follows. Firstly, a novel two-order filter is designed for each agent to produce informational estimates from the leader, such that a connectivity matrix is not used in the controller's design, solving the difficultly caused by the time-varying control coefficients in a MAS with a directed graph. Secondly, combined with the novel filters, barrier functions are used to construct the distributed controller to deal with the completely unknown system nonlinearities, resulting in the global consensus of the MAS. Finally, it is rigorously proved that the consensus of the MAS is achieved while guaranteeing the prescribed tracking-error performance. Two examples are given to verify the effectiveness of the proposed method, in which the simulation results demonstrate the claims.

Neurotransmission and neuromodulation systems in the learning and memory network of Octopus vulgaris

The vertical lobe (VL) in the octopus brain plays an essential role in its sophisticated learning and memory. Early anatomical studies suggested that the VL is organized in a fan-out fan-in connectivity matrix comprising only three morphologically identified neuron types; input axons from the superior frontal lobe (SFL) innervating en passant millions of small amacrine interneurons (AMs) which converge sharply onto large VL output neurons (LNs). Recent physiological studies confirmed the feedforward excitatory connectivity: a glutamatergic synapse at the first SFL-to-AM synaptic layer and a cholinergic AM-to-LNs synapse. SFL-to-AMs synapses show a robust hippocampal-like activity-dependent long-term potentiation (LTP) of transmitter release. 5-HT, octopamine, dopamine and nitric oxide modulate short- and long-term VL synaptic plasticity. Here we present a comprehensive histolabeling study to better characterize the neural elements in the VL. We generally confirmed glutamatergic SFLs and cholinergic AMs. Intense labeling for NOS activity in the AMs neurites fitted with the NO-dependent presynaptic LTP mechanism at the SFL-to-AM synapse. New discoveries here reveal more heterogeneity of the VL neurons than previously thought. GABAergic AMs suggest a subpopulation of inhibitory interneurons in the first input layer. Clear GABA labeling in the cell bodies of LNs supported an inhibitory VL output yet the LNs co-expressed FMRFamide-like neuropeptides suggesting an additional neuromodulatory role of the VL output. Furthermore, a group of LNs was glutamatergic. A new cluster of cells organized in a deep nucleus showed rich catecholaminergic labeling and may play a role in intrinsic neuromodulation. In situ hybridization and immunolabeling allowed characterization and localization of a rich array of neuropeptides and neuromodulatores, likely involved in reward/punishment signals. This analysis of the fast transmission system, together with the newly found cellular elements helps integrate behavioral, physiological, pharmacological and connectome findings into a more comprehensive understanding of an efficient learning and memory network

Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here we recorded the activity from ~40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven network model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine, unveils ~200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. From this, we mathematically derived an interregional functional connectivity matrix, which is conserved across individual animals and correlates well with structural connectivity. This novel, assembly-based generative model of brain-wide neural dynamics enables physiology-bound perturbation experiments in silico.

[W]hat Lies Beneath: Using Latent Networks to Improve Spatial Predictions

Spatial interdependencies commonly drive the spread of violence in civil conflict. To address such interdependence, scholars often use spatial lags to model the diffusion of violence, but this requires an explicit operationalization of the connectivity matrices that represent the spread of conflict. Unfortunately, in many cases, there are multiple competing processes that facilitate the spread of violence making it difficult to identify the true data-generating process. We show how a network-driven methodology can allow us to account for the spread of violence, even in the cases where we cannot directly measure the factors that drive diffusion. To do so, we estimate a latent connectivity matrix that captures a variety of possible diffusion patterns. We use this procedure to study intrastate conflict in eight conflict-prone countries and show how our framework enables substantially better predictive performance than canonical spatial-lag measures. We also investigate the circumstances under which canonical spatial lags suffice and those under which a latent network approach is beneficial.

Case Report: Disruption of Resting-State Networks and Cognitive Deficits After Whole Brain Irradiation for Singular Brain Metastasis

Introduction: Long-term survivors of whole brain radiation (WBRT) are at significant risk for developing cognitive deficits, but knowledge about the underlying pathophysiological mechanisms is limited. Therefore, we here report a rare case with a singular brain metastasis treated by resection and WBRT that survived for more than 10 years where we investigated the integrity of brain networks using resting-state functional MRI.Methods: A female patient with a left frontal non-small cell lung cancer (NSCLC) brain metastasis had resection and postoperative WBRT (30.0 in 3.0 Gy fractions) and stayed free from brain metastasis recurrence for a follow-up period of 11 years. Structural magnetic resonance imaging (MRI) and amino acid [O-(2-[18F]fluoroethyl)-L-tyrosine] positron emission tomography (FET PET) were repeatedly acquired. At the last follow up, neurocognitive functions and resting-state functional connectivity (RSFC) using resting-state fMRI were assessed. Within-network and inter-network connectivity of seven resting-state networks were computed from a connectivity matrix. All measures were compared to a matched group of 10 female healthy subjects.Results: At the 11-year follow-up, T2/FLAIR MR images of the patient showed extended regions of hyper-intensities covering mainly the white mater of the bilateral dorsal frontal and parietal lobes while sparing most of the temporal lobes. Compared to the healthy subjects, the patient performed significantly worse in all cognitive domains that included executive functions, attention and processing speed, while verbal working memory, verbal episodic memory, and visual working memory were left mostly unaffected. The connectivity matrix showed a heavily disturbed pattern with a widely distributed, scattered loss of RSFC. The within-network RSFC revealed a significant loss of connectivity within all seven networks where the dorsal attention and fronto-parietal control networks were affected most severely. The inter-network RSFC was significantly reduced for the visual, somato-motor, and dorsal and ventral attention networks.Conclusion: As demonstrated here in a patient with a metastatic NSCLC and long-term survival, WBRT may lead to extended white matter damage and cause severe disruption of the RSFC in multiple resting state networks. In consequence, executive functioning which is assumed to depend on the interaction of several networks may be severely impaired following WBRT apart from the well-recognized deficits in memory function.

Inter-species cortical registration between macaques and humans using a functional network property under a spherical demons framework

Systematic evaluation of cortical differences between humans and macaques calls for inter-species registration of the cortex that matches homologous regions across species. For establishing homology across brains, structural landmarks and biological features have been used without paying sufficient attention to functional homology. The present study aimed to determine functional homology between the human and macaque cortices, defined in terms of functional network properties, by proposing an iterative functional network-based registration scheme using surface-based spherical demons. The functional connectivity matrix of resting-state functional magnetic resonance imaging (rs-fMRI) among cortical parcellations was iteratively calculated for humans and macaques. From the functional connectivity matrix, the functional network properties such as principal network components were derived to estimate a deformation field between the human and macaque cortices. The iterative registration procedure updates the parcellation map of macaques, corresponding to the human connectome project’s multimodal parcellation atlas, which was used to derive the macaque’s functional connectivity matrix. To test the plausibility of the functional network-based registration, we compared cortical registration using structural versus functional features in terms of cortical regional areal change. We also evaluated the interhemispheric asymmetry of regional area and its inter-subject variability in humans and macaques as an indirect validation of the proposed method. Higher inter-subject variability and interhemispheric asymmetry were found in functional homology than in structural homology, and the assessed asymmetry and variations were higher in humans than in macaques. The results emphasize the significance of functional network-based cortical registration across individuals within a species and across species.

MULTIMODAL HUMAN BRAIN LONGITUDINAL PARCELLATION ACROSS LIFESPAN

Subdividing the human brain into several functionally distinct and spatially contiguous areas is important to understand the amazingly complex human cerebral cortex. However, adult aging is related to differences in the structure, function, and connectivity of brain areas, so that the single population subdivision does not apply to multiple age groups. Moreover, different modalities could provide affirmative and complementary information for the human brain subdivision. To obtain a more reasonable subdivision of the cerebral cortex, we make use of multimodal information to subdivide the human cerebral cortex across lifespan. Specifically, we first construct a population average functional connectivity matrix for each modality of each age group. Second, we separately calculate the population average similarity matrix for the cortical thickness and myelin modality of each age group. Finally, we fuse these population average matrixes to obtain the multimodal similarity matrix and feed it into the spectral clustering algorithm to generate the brain parcellation for each age group.