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Objective To characterize the presence and nature of discrete behavioral phenotypes and their correlates in a cohort of youth with new and recent onset focal and generalized epilepsies (YWE). Methods The parents of 297 YWE (age 8–18; n = 183) and typically developing participants (n = 107), completed the Child Behavior Checklist. The 8 behavior problem scales were subjected to hierarchical clustering analytics to identify behavioral subgroups. Factors examined included: demographics, neuropsychological data, history of academic problems, epilepsy characteristics, parental IQ and education, cortical thickness, daily executive functioning, and number of lifetime-to-date DSM-IV diagnoses. Results Hierarchical clustering identified three behavioral phenotypes which included: no behavioral complications (Cluster 1, 67% of YWE), internalizing problems (Cluster 2, 11% of YWE), and combined internalizing and externalizing problems (Cluster 3, 22% of YWE). These behavioral phenotypes were characterized by orderly differences in the factors analyzed. Cluster 1 was most similar to controls across most metrics while Cluster 3 was the most abnormal. Epilepsy syndrome was not a predictor of cluster membership. Conclusion YWE fall into 3 distinct behavioral phenotypes associated with a variety of co-occurring features and comorbidities. This approach identifies important phenotypes of behavior problem presentations and their accompanying factors that serve to advance clinical and theoretical understanding of the behavioral complications of children with epilepsy and the complex conditions with which they co-occur.

Older adults frequently experience a decrease in balance control that leads to increased numbers of falls, injuries and hospitalization. Therefore, evaluating older adults’ ability to maintain balance and examining new approaches to counteract age-related decline in balance control is of great importance for fall prevention and healthy aging. Non-invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) have been shown to beneficially influence motor behavior and motor learning. In the present study, we investigated the influence of tDCS applied over the leg area of the primary motor cortex (M1) on balance task learning of healthy elderly in a dynamic balance task (DBT). In total, 30 older adults were enrolled in a cross-sectional, randomized design including two consecutive DBT training sessions. Only during the first DBT session, either 20 min of anodal tDCS (a-tDCS) or sham tDCS (s-tDCS) were applied and learning improvement was compared between the two groups. Our data showed that both groups successfully learned to perform the DBT on both training sessions. Interestingly, between- group analyses revealed no difference between the a-tDCS and the s-tDCS group regarding their level of task learning. These results indicate that the concurrent application of tDCS over M1 leg area did not elicit DBT learning enhancement in our study cohort. However, a regression analysis revealed that DBT performance can be predicted by the kinematic profile of the movement, a finding that may provide new insights for individualized approaches of treating balance and gait disorders.

Infectious diseases are omnipresent and in the research field of epidemiology the emergence, incidence, distribution, persistence and possible control of diseases are of special interest. Research in experimental evolution can be crucial to get further insights in these subjects and to better understand infectious diseases and its dynamics. We experimentally studied the eco-evolutionary effects on infectious disease dynamics in a coevolving host-virus system consisting of the asexual reproducing, unicellular green algae Chlorella variabilis and its hostspecific dsDNA Virus, the Chlorovirus Pbcv-1. We established a novel system of two connected batch cultures (patches) to ascertain whether and how ecological and evolutionary dynamics might interfere in a spatial structured system. After infection of the algae population, the population density decreases rapidly, whereas the virus population density increased. Due to lack of hosts the virus populations decreased over time and the algae populations recovered slowly after some time of infection (25.87 ± 2.99 days), followed by a repeated decrease of algae population and an increase of virus population. Using time-shift experiments, we tested whether and when resistance of algae to virus evolved, or vice versa whether and when the virus counter adapted to the host. The time-shift experiments showed a rapid evolution of resistance of algae populations within approximately four days after infection with virus. Most importantly, our study revealed that spatial structure has a profound impact on the eco-evolutionary effects and therefore on the infectious disease dynamics in natural populations. In this context spatial heterogeneity or patchiness, which is common in nature, can have a major influence on the infectious disease dynamics.

Transcranial alternating current stimulation (tACS) is a form of noninvasive brain stimulation and is capable of influencing brain oscillations and cortical networks. In humans, the endogenous oscillation frequency in sensorimotor areas peaks at 20 Hz. This beta-band typically occurs during maintenance of tonic motor output and seems to play a role in interhemispheric coordination of movements. Previous studies showed that tACS applied in specific frequency bands over primary motor cortex (M1) or the visual cortex modulates cortical excitability within the stimulated hemisphere. However, the particular impact remains controversial because effects of tACS were shown to be frequency, duration and location specific. Furthermore, the potential of tACS to modulate cortical interhemispheric processing, like interhemispheric inhibition (IHI), remains elusive. Transcranial magnetic stimulation (TMS) is a noninvasive and well-tolerated method of directly activating neurons in superficial areas of the human brain and thereby a useful tool for evaluating the functional state of motor pathways. The aim of the present study was to elucidate the immediate effect of 10 min tACS in the β-frequency band (20 Hz) over left M1 on IHI between M1s in 19 young, healthy, right-handed participants. A series of TMS measurements (motor evoked potential (MEP) size, resting motor threshold (RMT), IHI from left to right M1 and vice versa) was performed before and immediately after tACS or sham using a double-blinded, cross-over design. We did not find any significant tACS-induced modulations of intracortical excitation (as assessed by MEP size and RMT) and/or IHI. These results indicate that 10 min of 20 Hz tACS over left M1 seems incapable of modulating immediate brain activity or inhibition. Further studies are needed to elucidate potential aftereffects of 20 Hz tACS as well as frequency- specific effects of tACS on intracortical excitation and IHI.

Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding; 2) enhance diagnostic performance; and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader.

Bei kürzer werdenden Produktzyklen, zunehmend zahlreichen Alternativen, sich beschleunigender Entwicklung des technischen Fortschritts, sich sättigenden, teils inkohärenten Märkten wird es immer wichtiger, die richtigen Innovationen zur rechten Zeit am richtigen Platz bereit zu haben. D.h. nicht nur, den künftigen Kundenwunsch zu erkennen, sondern auch, gewappnet zu sein gegenüber wirtschaftlichen und technologischen Diskontinuitäten. Zentrale Aufgabe der Strategischen Planung in Unternehmungen ist es demnach, mögliche Zukünfte in Wirtschaft, Gesellschaft und Technologie zu antizipieren, daraus relevante zukünftige Produkt- und Technologieentwicklungen "vorherzusehen" und sowohl rechtzeitig entsprechende Weichenstellungen vorzunehmen als auch Alternativlösungen vorzubereiten. Das Futuring kennt hierzu eine Vielzahl an eigenen Einzelmethoden und Verfahren oder adaptierten Methoden aus anderen Bereichen wie z.B. dem Innovations- und Technologiemanagement. Sie ähneln einander oft und zeigen fast immer die gleiche prinzipielle Abfolge: Problemabgrenzung, Datensammlung, Entwurf der Zukunftsbilder, Bewertung sowie Entscheidungsfindung und Umsetzung.