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Wydział Anglistyki Niniejsza dysertacja bada problem neuronalnych korelatów przetwarzania fonologicznego języka ojczystego i języka obcego u polskich użytkowników języka angielskiego. W celu wskazania struktur i funkcji mózgu odpowiedzialnych za przetwarzanie fonologiczne stworzono zestaw eksperymentów, które przeprowadzono na grupie średniozaawansowanych polskich użytkowników języka angielskiego. Korzystając z metody rezonansu magnetycznego sprawdzono hipotezę zakładającą zrózżnicowanie w reprezentacji procesów fonologicznych w mózgu, w zależności od przetwarzanego języka oraz stopnia zaawansowania użytkownika. Zestaw zawierał cztery eksperymenty z zadaniami w języku polskim i angielskim, takimi jak słuchanie pasywne, Test Pamięci Roboczej Sternberga, test fluencji słownej, oraz Test Leksykalny dla Zaawansowanych Uczniów Języka Angielskiego. Na eksperymenty złożyły się również trzy eksperymenty nie oparte na żadnych zadaniach. Wyniki wraz z dokładną dyskusją sugerują model przetwarzania drugiego języka, który składa się z modułów związanych z zadaniami językowymi zbudowanych ze współgrających ze sobą sieci mózgowych. W związku z tym, niniejsza dysertacja wnosi nowe odkrycia do dziedzin językoznawstwa i neuronauki, poszerzając nasze zrozumienie procesów przetwarzania języka w mózgu. The present thesis investigates the issue of neural basis for phonological processing of native and non-native speech in Polish-English bilinguals. In order to pinpoint the structures and functions that are relevant to phonological processing, a set of experiments was devised on a group of moderately proficient Polish-English bilinguals. Using the method of Magnetic Resonance Imaging, and hypothesizing that there will be differences in the representation of phonological processes in the brain, dependent on the language and the level of proficiency in the language, four task-based experiments in both Polish and English, including listening to stories, Sternberg Working Memory Task, Verbal Fluency Task, and Lexical Test for Advanced Learners of English, and three task-independent experiments were conducted. The results of the thesis, along with the detailed discussion suggest a model of phonological processing in L2, which consists of different task-related modules of interacting brain networks. As such, the present thesis contributes to both fields of linguistics and neuroscience, broadening the understanding of how the brain processes language.

In 2001, 16.6 million deaths globally were due to cardiovascular diseases (CVD); this figure will increase to 25 million by 2025. The two leading causes of death worldwide are cardiovascular coronary heart disease (which causes heart attack and heart failure) and cerebrovascular disease (which causes stroke). The direct and indirect costs of CVD are high: enormous health care costs and productivity/income losses. Of all global deaths from CVD, 65 percent occur in developing countries. This will increase to 75 percent by 2025. By then, cardiovascular disorders will be the biggest cause of lost disability-adjusted life years (DALYs) worldwide, and the second leading cause of DALY loss in developing countries. In developing countries, cardiovascular diseases predominantly affect people of working age (30-64 years). Death and disability in middle age has major social and economic consequences Prevention or treatment of risk factors for CVD is effective and sustainable in the long run. The risk of CVD can be reduced quickly and substantially with successful preventive practices. This also has a favorable impact on other non-communicable diseases (NCDs) that share the same risk factors. Treatment of established CVD is expensive and resource intensive. Unregulated private health systems tend to direct a large proportion of resources to costly cardiovascular technologies available only to the wealthy few.

Auditory-verbal hallucinations (AVHs) are frequent and disabling symptoms, which can be refractory to conventional psychopharmacological treatment in more than 25% of the cases. Recent advances in brain imaging allow for a better understanding of the neural underpinnings of AVHs. These findings strengthened transdiagnostic neurocognitive models that characterize these frequent and disabling experiences. At the same time, technical improvements in real-time functional magnetic resonance imaging (fMRI) enabled the development of innovative and non-invasive methods with the potential to relieve psychiatric symptoms, such as fMRI-based neurofeedback (fMRI-NF). During fMRI-NF, brain activity is measured and fed back in real time to the participant in order to help subjects to progressively achieve voluntary control over their own neural activity. Precisely defining the target brain area/network(s) appears critical in fMRI-NF protocols. After reviewing the available neurocognitive models for AVHs, we elaborate on how recent findings in the field may help to develop strong a priori strategies for fMRI-NF target localization. The first approach relies on imaging-based “trait markers” (i.e., persistent traits or vulnerability markers that can also be detected in the presymptomatic and remitted phases of AVHs). The goal of such strategies is to target areas that show aberrant activations during AVHs or are known to be involved in compensatory activation (or resilience processes). Brain regions, from which the NF signal is derived, can be based on structural MRI and neurocognitive knowledge, or functional MRI information collected during specific cognitive tasks. Because hallucinations are acute and intrusive symptoms, a second strategy focuses more on “state markers.” In this case, the signal of interest relies on fMRI capture of the neural networks exhibiting increased activity during AVHs occurrences, by means of multivariate pattern recognition methods. The fine-grained activity patterns concomitant to hallucinations can then be fed back to the patients for therapeutic purpose. Considering the potential cost necessary to implement fMRI-NF, proof-of-concept studies are urgently required to define the optimal strategy for application in patients with AVHs. This technique has the potential to establish a new brain imaging-guided psychotherapy for patients that do not respond to conventional treatments and take functional neuroimaging to therapeutic applications.

A nested ice flow model was developed for eastern Dronning Maud Land to assist with the dating and interpretation of the EDML deep ice core. The model consists of a high-resolution higher-order ice dynamic flow model that was nested into a comprehensive 3-D thermomechanical model of the whole Antarctic ice sheet. As the drill site is on a flank position the calculations specifically take into account the effects of horizontal advection as deeper ice in the core originated from higher inland. First the regional velocity field and ice sheet geometry is obtained from a forward experiment over the last 8 glacial cycles. The result is subsequently employed in a Lagrangian backtracing algorithm to provide particle paths back to their time and place of deposition. The procedure directly yields the depth-age distribution, surface conditions at particle origin, and a suite of relevant parameters such as initial annual layer thickness. This paper discusses the method and the main results of the experiment, including the ice core chronology, the non-climatic corrections needed to extract the climatic part of the signal, and the thinning function. The focus is on the upper 89% of the ice core (appr. 170 kyears) as the dating below that is increasingly less robust owing to the unknown value of the geothermal heat flux. It is found that the temperature biases resulting from variations of surface elevation are up to half of the magnitude of the climatic changes themselves.

The bioavailability of methylmercury (MeHg) in the brains of orange spotted groupers, captured from four creeks of Mahshahr embayment was measured. Then the effects of this pollutant on the regulation of gene expression, acetylcholinesterase transcript levels was chosen in order to monitor the amounts of methylmercury concentrations in the creeks, and the fluctuations of mRNA expressions in the brain and their effect on fish health. Fishes were collected from Zangi, Ghanam, Marymous and Petrochemical Creeks, and their brains were removed by dissection. In parallel with these experiments some fishes were exposed to methylmerucry chloride in the Fisheries center and the amount of their gene expression was assessed via Real-Time PCR method. The lethal concentration of methylmerucry causing the mortality of half of the fish population after 96 hr (LC50-96) was assessed and gene expression of sub-lethal concentration (more and less than 10% of LC50-96) were analyzed. Gene expression studies revealed that the most polluted creek was the Petrochemical Creek, and the least polluted one was Marymous Creek. This regulation was assessed by the effect of MeHg on the gene expression, meaning the more gene expression, the less polluted and vice versa. From this study we concluded that acetylcholinesterase gene expression can serve as a biomarker of the effect of methylmercury, which can provide a good estimation of the amount of methylmercuric availability in the brain of Epinephelus coioides and its effect on the brain neurotransmission pathway. Published

Background: Adequate and timely identification of depression is essential to improve patient care. A potential method to achieve this is by using neuroimaging. Many neuroimaging studies have revealed widespread abnormalities in brain structure and function in patients with depression, but in most studies only single neuroimaging modalities were used. Links between abnormalities in brain structure and function need to be therefore further explored in order to define diagnostic and therapeutic applications. Methods: A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was conducted. Results: Out of 2,516 articles, only 14 studies were eligible to be included. These studies combined structural and functional neuroimaging methods in depressed patients compared to controls. Four studies reported a negative relationship between brain structure and function within the default mode network: reduced gray or white matter integrity in depressed patients compared to healthy controls was associated with enhanced neural activity or connectivity. The other studies reported positive relationships (two studies), mixed relationships (two studies), or no link (six studies) between structural and functional brain abnormalities. Conclusion: This systematic literature review revealed no robust relationship between abnormalities in brain structure and function in patients with depression. Remarkably, only 14 studies could be included and four of these suggested enhanced default mode network connectivity associated with reduced structural brain integrity. In the ongoing development of the diagnostic and treatment applications of neuroimaging, large-scale studies that combine structural with functional neuroimaging are required to determine the relationship between structural and functional abnormalities in depression.

In daily life, moral judgments are embedded in dynamic, complex, and contextualized environments. As we reason about morally right or wrong behaviors, our personal history shapes how we judge who did what to whom, where, when, and why. Yet, surprisingly little is known about how individual differences in moral dispositions modulate shared neural response patterns when processing increasingly complex moral scenarios. Consequently, we herein examine brain-behavior-trait coherence in moral cognition across three datasets of increasing naturalistic complexity. Applying intersubject representational similarity analysis, we demonstrate how between-subject variability in moral dispositions modulates similarity in neural responses when processing decontextualized moral vignettes, auditory movie summaries, political attack advertisement, soap opera clips, and full-length movies. Our approach highlights how brain-behavior-trait relationships during moral cognition are shaped by paradigm choice, and provides a reference for conducting research at the intersection of socio-moral cognition, communication science, and naturalistic neuroimaging.

Multimodal neuroimaging using EEG and fMRI provides deeper insights into brain function by improving the spatial and temporal resolution of the acquired data. However, simultaneous EEG-fMRI inevitably compromises the quality of the EEG and fMRI signals due to the high degree of interaction between the two systems. Fluctuations in the magnetic flux flowing through the participant and the EEG system, whether due to movement within the magnetic field of the scanner or to changes in magnetic field strength, induce electrical potentials in the EEG recordings that mask the much weaker electrical activity of the neuronal populations. A number of different methods have been proposed to reduce MR artifacts. We present an overview of the most commonly used methods and an evaluation of the methods using three sets of diverse EEG data. We limited the evaluation to open-access and easy-to-use methods and a reference signal regression method using a set of six carbon-wire loops (CWL), which allowed evaluation of their added value. The evaluation was performed by comparing EEG signals recorded outside the MRI scanner with artifact-corrected EEG signals recorded simultaneously with fMRI. To quantify and evaluate the quality of artifact reduction methods in terms of the spectral content of the signal, we analyzed changes in oscillatory activity during a resting-state and a finger tapping motor task. The quality of artifact reduction in the time domain was assessed using data collected during a visual stimulation task. In the study we utilized hierarchical Bayesian probabilistic modeling for statistical inference and observed significant differences between the evaluated methods in the success of artifact reduction and associated signal quality in both the frequency and time domains. In particular, the CWL system proved superior to the other methods evaluated in improving spectral contrast in the alpha and beta bands and in recovering visual evoked responses. Based on the results of the evaluation study, we proposed guidelines for selecting the optimal method for MR artifact reduction.

The human cerebral cortex comprises many functionally distinct areas that represent different information about the world. It has been challenging to map these areas efficiently. In this dissertation, I present a new approach that addresses this problem. In chapter one, I present a novel voxelwise encoding model based on Tikhonov regression. I discuss the theoretical basis for Tikhonov regression, demonstrate a computationally efficient method for its application, and show several examples of how Tikhonov regression can improve predictive models for fMRI data. I also show that many earlier studies have implicitly used Tikhonov regression by linearly transforming the regressors before performing ridge regression. In chapter two, I present a critique of an alternative method used to study brain representations called representational similarity analysis. I show that this method makes strong assumptions about the relationship between representational models and brain responses. I also show that representational similarity analysis can lead to incorrect conclusions when used to compare representational models. In chapter three, I present a rich paradigm for efficient non-invasive functional brain mapping. In this paradigm, subjects watch interesting short films while their brain activity is measured. Multiple feature spaces are used to model the brain responses to the short films. Each feature space constitutes a hypothesis about the type of representations that might be important for brain regions involved in watching, listening, and understanding the short films. The novel voxelwise encoding model developed in chapter one is then used to find the most predictive feature spaces across the cortical surface and also to recover maps that capture how the individual feature spaces are represented within cortical regions. The results suggest a high degree of homogeneous selectivity for feature spaces across large regions of the cortical surface within individual subjects. These patterns are highly consistent across all subjects. Finally, I explore the functional organization of the middle temporal cortex and show that the visual feature spaces can capture novel functional subdivisions in this region.

Timing is an essential component of human actions, and is the foundation of any sort of sequential behavior, from picking up a glass to playing an instrument or dancing. Because of this, our understanding of how we represent time in the brain (i.e., the human timing system) critically relies on basic research on simple behaviors. Perception of temporal regularities is central to a wide range of basic actions, but also underpins abilities unique to humans such as the creation of complex musical scores. This dissertation is an in-depth examination of endogenously and exogenously guided timing behavior, and how context is a critical component of understanding rhythmic entrainment in humans. We previously validated “gold standard” functional magnetic resonance imaging (fMRI) findings on action-based timing behavior using functional near infrared spectroscopy (fNIRS) (Rahimpour et al., 2020). In particular, we observed significant hemodynamic responses in cortical areas in direct relation to the complexity of the behavior being performed. To do so, we probed multiple levels of contextual influence on action-based timing behavior and patterns of cortical activation as measured using fNIRS. Our findings highlighted several distinct, context-dependent parameters of specific timing behaviors. Here we further interrogate human timing abilities by introducing variations of our original experimental design, observing that subtle contextual variations have a significant impact on the degree of rhythmic entrainment given the presence/absence of metronomic input. We used electroencephalogram (EEG) to further validate our fNIRS findings, demonstrating that single trial neurobiological activity can be used to predict whether behavior is exogenously or endogenously guided. We also found that patterns of neural activity correspond to differential use of the internal timing system, and that specific differences in neural activity correlate with accuracy of action-based timing behavior. These findings emerged from our use of a novel deep learning approach to extract person-specific, neural-based features as predictors of behavioral performance. Finally, we examined whether fNIRS and EEG produced similar localization information, finding that the influence of training factors on cortical localization must be accounted for to make such comparisons.