Filters

2016 - 201922020 - 20212

More filters

20224
Reset filters

Settings

End date: 2022 × Start date: 2022 × Start date: 2010 × Type: Article × Subject: Brain ×

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Local delivery to malignant brain tumors: potential biomaterial-based therapeutic/adjuvant strategies
    (Cambridge : RSC, 2021) Alghamdi, Majed; Gumbleton, Mark; Newland, Ben
    Glioblastoma (GBM) is the most aggressive malignant brain tumor and is associated with a very poor prognosis. The standard treatment for newly diagnosed patients involves total tumor surgical resection (if possible), plus irradiation and adjuvant chemotherapy. Despite treatment, the prognosis is still poor, and the tumor often recurs within two centimeters of the original tumor. A promising approach to improving the efficacy of GBM therapeutics is to utilize biomaterials to deliver them locally at the tumor site. Local delivery to GBM offers several advantages over systemic administration, such as bypassing the blood-brain barrier and increasing the bioavailability of the therapeutic at the tumor site without causing systemic toxicity. Local delivery may also combat tumor recurrence by maintaining sufficient drug concentrations at and surrounding the original tumor area. Herein, we critically appraised the literature on local delivery systems based within the following categories: polymer-based implantable devices, polymeric injectable systems, and hydrogel drug delivery systems. We also discussed the negative effect of hypoxia on treatment strategies and how one might utilize local implantation of oxygen-generating biomaterials as an adjuvant to enhance current therapeutic strategies. © 2021 The Royal Society of Chemistry.
  • Thumbnail Image
    Item
    Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis
    (San Francisco, California, US : PLOS, 2016) Deliano, Matthias; Tabelow, Karsten; König, Reinhard; Polzehl, Jörg
    Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.
  • Thumbnail Image
    Item
    More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses
    (San Francisco, California, US : PLOS, 2016) Schildknecht, Konstantin; Tabelow, Karsten; Dickhaus, Thorsten
    Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family at this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data.
  • Thumbnail Image
    Item
    Frequency spectrum recurrence analysis
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Ladeira, Guênia; Marwan, Norbert; Destro-Filho, João-Batista; Davi Ramos, Camila; Lima, Gabriela
    In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.