Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures—based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.

Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Although the sensorimotor and associative frontostriatal circuits are known to mediate arbitrary visuomotor mappings, the underlying corticocortico dynamics remain elusive. Here, we take a novel approach exploiting gamma-band neural activity to study the human cortical networks and corticocortical functional connectivity mediating arbitrary visuomotor mapping. Single-trial gamma-power time courses were estimated for all Brodmann areas by combing magnetoencephalographic and MRI data with spectral analysis and beam-forming techniques. Linear correlation and Granger causality analyses were performed to investigate functional connectivity between cortical regions. The performance of visuomotor associations was characterized by an increase in gamma-power and functional connectivity over the sensorimotor and frontoparietal network, in addition to medial prefrontal areas. The superior parietal area played a driving role in the network, exerting Granger causality on the dorsal premotor area. Premotor areas acted as relay from parietal to medial prefrontal cortices, which played a receiving role in the network. Link community analysis further revealed that visuomotor mappings reflect the coordination of multiple subnetworks with strong overlap over motor and frontoparietal areas. We put forward an associative account of the underlying cognitive processes and corticocortical functional connectivity. Overall, our approach and results provide novel perspectives toward a better understanding of how distributed brain activity coordinates adaptive behaviors.

Understanding the causes of the Sino-Japanese disjunctions in plant taxa has been a central question in eastern Asian biogeography, with vicariance or long-distance dispersal often invoked to explain such patterns. Diabelia Landrein (Caprifoliaceae; Linnaeoideae) comprises four shrubby species with a Sino-Japanese disjunct distribution. The species diversification time within Diabelia, covering a long geological history of the formation process of the Sino-Japanese flora, dated back to the middle Oligocene, therefore, Diabelia would be an ideal model to elucidate the biogeographic patterns of Sino-Japanese disjunctions with climate fluctuation. In this study, we analyzed complete plastome sequence data for 28 individuals representing all four species of Diabelia. These 28 plastomes were found to be highly similar in overall size (156 243-157 578 bp), structure, gene order, and content. Our phylogenomic analysis of the plastomes supported a close relationship between Diabelia ionostachya (Nakai) Landrein & R.L. Barrett var. wenzhouensis (S.L. Zhou ex Landrein) Landrein from eastern China and Diabelia spathulata (Siebold & Zucc.) Landrein var. spathulata from Japan. Diabelia serrata (Siebold & Zucc.) Landrein was identified as sister to a population of Diabelia sanguinea (Makino) Landrein from Tochigi in central Japan and D. spathulata Landrein, from Toyama, central Japan. Most Diabelia lineages were estimated to have differentiated 8-28 Mya. Our results indicate that two independent vicariance events could explain the disjunction between Japan and Korea in the mid to late Miocene, and between Zhejiang and Japan in the early Miocene.

The Sino-Japanese Floristic Region (SJFR) is a key area for plant phylogeographical research, due to its very high species diversity and disjunct distributions of a large number of species and genera. At present, the root cause and temporal origin of the discontinuous distribution of many plants in the Sino-Japanese flora are still unclear. Diabelia (Caprifoliaceae; Linnaeoideae) is a genus endemic to Asia, mostly in Japan, but two recent discoveries in China raised questions over the role of the East China Sea (ECS) in these species' disjunctions. Chloroplast DNA sequence data were generated from 402 population samples for two regions ( rpl 32- trn L, and trn H- psb A) and 11 nuclear microsatellite loci were screened for 549 individuals. Haplotype, population-level structure, combined analyses of ecological niche modeling, and reconstruction of ancestral state in phylogenies were also performed. During the Last Glacial Maximum (LGM) period after the Tertiary, Diabelia was potentially widely distributed in southeastern China, the continental shelf of the East China Sea and Japan (excluding Hokkaido). After LGM, all populations in China have disappeared except those in Zhejiang which may represent a Glacial refuge. Populations of Diabelia in Japan have not experienced significant bottleneck effects, and populations have maintained a relatively stable state. The observed discontinuous distribution of Diabelia species between China and Japan are interpreted as the result of relatively ancient divergence. The phylogenetic tree of chloroplast fragments shows the characteristics of multi-origin evolution (except for D. sanguinea ). STRUCTURE analysis of nuclear Simple Sequence Repeat (nSSR) showed that the plants of the Diabelia were divided into five gene pools: D. serrata, D. spathulata, D. sanguinea, D. ionostachya ( D. spathulata var. spathulata -Korea), and populations of D. ionostachya var. ionostachya in Yamagata prefecture, northern Japan. Molecular evidence provides new insights of Diabelia into biogeography, a potential glacial refuge, and population-level genetic structure within species. In the process of species differentiation, ECS acts as a corridor for two-way migration of animals and plants between China and Japan during glacial maxima, providing the possibility of secondary contact for discontinuously distributed species between China and Japan, or as a filter (creating isolation) during glacial minima. The influence of the ECS in speciation and biogeography of Diabelia in the Tertiary remains unresolved in this study. Understanding origins, evolutionary histories, and speciation will provide a framework for the...

Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co‐occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open‐access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local‐to‐regional datasets to openly release data. We thus present sPlotOpen, the largest open‐access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co‐occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot‐level data also include community‐weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01–40,000 m². Time period and grain 1888–2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot‐level records. Software format Three main matrices (.csv), relationally linked.

Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients’ seizures. These key parameters together with their personalized model determine a given patient’s EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non–seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy.

Objective The virtual epileptic patient (VEP) is a large‐scale brain modeling method based on virtual brain technology, using stereoelectroencephalography (SEEG), anatomical data (magnetic resonance imaging [MRI] and connectivity), and a computational neuronal model to provide computer simulations of a patient's seizures. VEP has potential interest in the presurgical evaluation of drug‐resistant epilepsy by identifying regions most likely to generate seizures. We aimed to assess the performance of the VEP approach in estimating the epileptogenic zone and in predicting surgical outcome. Methods VEP modeling was retrospectively applied in a cohort of 53 patients with pharmacoresistant epilepsy and available SEEG, T1‐weighted MRI, and diffusion‐weighted MRI. Precision recall was used to compare the regions identified as epileptogenic by VEP (EZVEP) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZC). In 28 operated patients, we compared the VEP results and clinical analysis with surgical outcome. Results VEP showed a precision of 64% and a recall of 44% for EZVEP detection compared to EZC. There was a better concordance of VEP predictions with clinical results, with higher precision (77%) in seizure‐free compared to non‐seizure‐free patients. Although the completeness of resection was significantly correlated with surgical outcome for both EZC and EZVEP, there was a significantly higher number of regions defined as epileptogenic exclusively by VEP that remained nonresected in non‐seizure‐free patients. Significance VEP is the first computational model that estimates the extent and organization of the epileptogenic zone network. It is characterized by good precision in detecting epileptogenic regions as defined by a combination of visual analysis and EI. The potential impact of VEP on improving surgical prognosis remains to be exploited. Analysis of factors limiting the performance of the actual model is crucial for its further development.