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Summary of Main Findings: The PASTeUR plug-and-play pTX package is extended to enable slab-selective Universal Pulses excitations. Their performance is demonstrated on 3D EPI and GRE sequences. Results showed improved flip-angle homogeneity resulting in a higher SNR, tSNR, as well as better anatomical images.

OBJECTIVE: International guidelines recommend early screening for identification of patients who are at risk of long-term cognitive impairments after cardiac arrest. However, information about predictors is not provided. A systematic review of the literature was performed to identify early predictors of long-term cognitive outcome after cardiac arrest. METHODS: Scopus and PubMed were systematically searched to identify studies on early predictors of long-term cognitive outcome in patients after cardiac arrest. The population included adult cardiac arrest survivors and potential early predictors were demographics, early cognitive screening scores, imaging measures, electroencephalographic measures, and levels of blood biomarkers. Two investigators reviewed studies for relevance, extracted data and assessed risk of bias. RESULTS: Five articles were included. Risk of bias was assessed as low or moderate. Most detected longterm cognitive impairments were in the domain of memory. Coma duration (2 studies), early cognitive impairments by the self-developed clinical Bedside Neuropsychological Test Battery (BNTB) screener (2 studies), and high S-100B levels on day 3 (2 studies) were the most prominent identified determinants of cognitive impairment on the group level. On the individual patient level, a score on the BNTB of ≤ 94.5 predicted cognitive impairments at 6 months after cardiac arrest (1 study without external validation). Studies on brain imaging and electroencephalography are lacking. CONCLUSION: Early bedside cognitive screening can contribute to prediction of long-term cognitive impairment after cardiac arrest. Evidence is scarce for S-100B levels and coma duration and absent for measures derived from brain imaging and electroencephalography.

The objective of this thesis was to create an EEG-based monitoring solution concept for consumers for everyday environments. This was done in collaboration with a project between Satakunta University of Applied Sciences (SAMK) and Tampere University (TAU). The thesis followed the Design Thinking method as a roadmap, which was found to be suitable but not without its limitations. Through user focus, collaboration, flexibility, and iteration a complex problem was turned into an innovative solution. Lack of structure, resources, and motivation were identified as the main drawbacks of the method. The data was collected from a workshop and three focus group meetings. The arousal measurement concept was the main outcome from the ideation workshop. The first focus group outcome included the user interface (UI) prototype I, and head piece design specifications. The focus group II outcome included the first handmade prototype and an updated version with buttons to give a better adjustment, and UI prototype II. After focus group III, the initial design was changed to a modular EEG product design concept on a fabric, and UI design III and a user flowchart were created. The solution had two components. The modular biosensors would collect real time EEG data and present it to the users in an arousal curve. UI biofeedback curve would show diminishing, optimal and increasing arousal levels in real time and alarm users when they are not in a desired range (sleep, apathy, optimal level, stress, anxiety, panic). The curve would monitor the user’s brain activity status to help maintain optimal arousal levels. Arousal monitoring biosensors and the software could be used for multiple purposes and applied in different use cases including sleep, stress, anxiety, or panic attacks, allowing a longer period of monitoring brain activity, which may not be possible in a laboratory setting. Depression, anxiety, and stress-related conditions are increasing. The primary risk to employee welfare has been found to be psychological, with mental health and stress being the top two causes of long-term absenteeism. Remote monitoring could lessen the cost of hospitalizations to community and reduce pressure on healthcare system. EEG for everyday environments could also allow users to monitor their wellbeing and adjust activities accordingly. More research needs to be done to validate these concepts.

Transkraniaalinen magneettistimulaatio (TMS) on kajoamaton menetelmä, jolla voidaan stimuloida neuroneita aivokuorella. TMS:n yhdistäminen funktionaaliseen magneettikuvantamiseen (fMRI) mahdollistaa TMS:n vaikutusten tutkimisen aivojen aineenvaihdunnan muutoksia kuvaamalla. Tämä informaatio on tarpeellista uusien sovellusten ja jo olemassa olevien TMS-hoitojen kehittämisessä, sekä turvallisuuden optimoinnissa. TMS– fMRI-yhdistelmän suurimpia haasteita on kuitenkin mekaaninen rasitus, joka kohdistuu keloihin fMRI-laitteen sekä virtapulssien luoman magneettikentän summana syntyvien voimien seurauksena. Nämä voimat voivat aiheuttaa kelojen rikkoutumisia ja muita turvallisuusriskejä. Tämän pro gradu -tutkielman tavoitteena oli tutkia ja vertailla monipaikkaisten TMS-kelojen (mTMS) mekaanisia ominaisuuksia ulkoisessa magneettikentässä elementtimenetelmää käyttäen, sekä rakentaa ja testata simulaatioiden pohjalta erilaisia kelaratkaisuja. Laskennallisissa mallinnuksissa käytettiin kuutta potentiaalista materiaalia rotille suunnitellun mTMS-kelan suojakuorelle ja tutkittiin kahden yleisesti käytetyn virtapulssimuodon sekä kelojen eri aktivointiyhdistelmien vaikutusta kelan suojakuoren jännitysjakaumiin. Ihmisille suunnitellulla kelamallilla tutkittiin kelan tason suuntauksen vaikutusta jännitysjakaumiin suhteessa ulkoiseen magneettikenttään. Mallinnusten tulokset lisäävät ymmärrystä kelan suojakuoren jännitysjakaumista stimulaation aikana ja jälkeen. Tulosten perusteella löydettiin yksi lupaava materiaali, 30 % lasikuiduilla täytetty polyamidi, kelan suojakuorta varten. Lopuksi tämä pro gradu -työ antoi ehdotuksia nykyisen TMS-kelasuunnittelun parantamiseksi. Transcranial magnetic stimulation (TMS) is a non-invasive method for stimulating cortical neurons in the brain. Combining TMS with functional magnetic resonance imaging (fMRI) shows the effects of TMS through the changes in brain metabolism. This information is necessary for developing new applications of TMS and for improving the efficacy and safety of the existing treatments. The biggest setback in current TMS–fMRI technology arises from the mechanical forces formed on the transducer as the interplay of the magnetic fields from the MRI and the changing current in the coil, leading to breakage of the transducers and additional safety risks. The objective of this Thesis was to assess and compare the mechanical stresses on multi-locus TMS (mTMS) transducers inside a high-field fMRI bore using finite element modeling, and to build and test transducer options based on the simulation results. For a transducer design for rats, six different coil former materials and three mTMS coil combinations were simulated with two commonly used current waveforms. In addition, the effect of the transducer orientation relative to the magnetic field was modeled with a transducer designed for humans. Our results show that the current pulses ran through the coils produce shock waves on the coil formers, leading to regions of maximum stresses that depend on the time instant. The intensity and location of the maximum stresses depends on the current waveform and coil combination used. Based on the results, 30% glass-fiber filled polyamide was found to be the most durable material. This Thesis provides novel insights for more durable TMS coil designs.

Alcohol use disorder (AUD) is a highly prevalent, chronic and relapsing disorder estimated to affect over 100 million people worldwide. Chronic alcohol exposure has been shown in animal models to increase both neural and systemic markers of inflammation. Alcohol- induced inflammation has been linked both to chronic alcohol-seeking and to the behavioral and neurotoxic effects of alcohol. However, the literature on inflammatory signaling and AUD is overwhelmingly preclinical, and it is unknown if this relationship can be extrapolated to clinical samples. Therefore, translation to clinical samples is necessary. In humans, addiction is often conceptualized as a reward deficit disorder, and brain activation in response to reward stimuli has been shown to be negatively associated with inflammation. However, associations between AUD, inflammation, and reward sensitivity have not yet been established. The dissertation studies presented herein combine behavioral and biological methods to elucidate this relationship. Chapter 1 consists of an investigation into the clinical and neural correlates of individuals who self-reported their primary motivation for drinking as either reward (i.e. positive reinforcement) or relief (i.e. negative reinforcement), finding differences between the groups on clinical measures of AUD severity and neural activation to visual alcohol cues in reward- associated brain regions. Chapter 2 investigates the effects of ibudilast, a neuroimmune modulatory medication in development for AUD treatment. Ibudilast was found to reduce visual alcohol cue-elicited functional connectivity within reward-related brain circuitry, and this attenuation was correlated with reductions in alcohol consumption. Chapter 3 explores the relationship between alcohol and monocyte production of intracellular cytokines following in vitro stimulation with lipopolysaccharide (LPS), finding that AUD was associated with enhanced sensitivity to the cellular LPS inflammatory challenge. Finally, Chapter 4 presents a brief argument for the use of LPS as a translational tool to experimentally explore the role of inflammation in clinical samples of AUD. Taken together, these findings seek to elucidate biological mechanisms related to reward response and inflammation in AUD. These studies provide clinical and neurobiological data on the relationship between alcohol use and inflammation, and may inform precision medicine and targeted inflammatory medication development for individuals with AUD.

The P300 speller is a common brain–computer interface (BCI) application designed to allow patients with neuromuscular disorders such as amyotrophic lateral sclerosis (ALS) produce text output through the detection of P300 signals in their electroencephalogram (EEG) signals. The standard P300 speller relies on the detection of signals evoked by visual stimuli, usually consisting of rows and columns highlighted in a grid of characters. Since the visual field is substantially larger than these stimuli, adjacent flashes to the attend characters may cause false positive signals and lead to erroneous output. While previous work has tried to address this issue by limiting the number of adjacent stimuli, no attempts have been made to account for these adjacency false positives in the classifier or utilize information from adjacent flashes to optimize the system. In this study we added a bias to the target character detection based on adjacent flashes and created a new probability model to improve the accuracy and speed of classification. We tested our adjacency classifier in both the standard P300 paradigm (we call it SWLDA method in the following content) and in conjunction with natural language processing. The new algorithm was evaluated offline on a dataset of 69 healthy subjects, which showed increases in speed and accuracy when compared to standard classification methods. On population level, LDA classifier shows a significant improvement, but the improvement for NLP is not significant. However, for some subject in both algorithms the enhancement of information transfer rate is substantial. As for LDA classifier, 57.4% subjects’ peak ITR increases of more than 5 bits per minute, 30.9% subjects’ peak ITR increases more than 10 bits per minutes, and 8.8% subjects’ peak ITR increases more than 15 bits per minutes. As for NLP, of the subjects, 21.7% had peak ITR increases of more than 5 bits per minute, 10.1% subjects’ peak ITR increased more than 10 bits per minutes, and 5.8% subjects’ peak ITR increased more than 15 bits per minutes. Therefore, incorporating adjacent fleshes can potentially provide a better communication system for some users.

This article reviews recent developments in designing and testing new types of materials which can be: (i) placed around the body for in vivo imaging, (ii) be integrated into a conventional RF coil, or (iii) form the resonator itself. These materials can improve the quality of MRI scans for both in vivo and magnetic resonance microscopy applications. The methodological section covers the basic operation and design of two different types of materials, namely high permittivity materials constructed from ceramics and artificial dielectrics/metasurfaces formed by coupled conductive subunits, either in air or surrounded by dielectric material. Applications of high permittivity materials and metasurfaces placed next to the body to neuroimaging and extremity imaging at 7 T, body and neuroimaging at 3 T, and extremity imaging at 1.5 T are shown. Results using ceramic resonators for both high field in vivo imaging and magnetic resonance microscopy are also shown. The development of new materials to improve MR image quality remains an active area of research, but has not yet found significant use in clinical applications. This is mainly due to practical issues such as specific absorption rate modelling, accurate and reproducible placement, and acceptable size/weight of such materials. The most successful area has been simple “dielectric pads” for neuroimaging at 7 T which were initially developed somewhat as a stop-gap while parallel transmit technology was being developed, but have continued to be used at many sites. Some of these issues can potentially be overcome using much lighter metasurfaces and artificial dielectrics, which are just beginning to be assessed.

© 2022. The Author(s). It has been well-documented that brain regions related to a task are activated during the task performance. We investigated whether brain activity and functional connectivity during the rest period are affected by the preceding task. Participants performed visual search tasks with three search conditions, which were followed by a rest period. During the rest period, participants were asked to look at the display that did not show any visual stimuli. In the result, brain activity in occipital and superior parietal regions would be deactivated by the preceding task during the rest period after visual search tasks. However, the activity of the inferior frontal gyrus during the rest period, which is also part of the attention network, was not affected by the brain activity during the preceding visual search task. We proposed a new model for explaining how the cognitive demands of the preceding visual search task regulate the attention network during the rest period after the task. In this model, the cognitive demand changes with task difficulty, which affects the brain activity even after removing the visual search task in the rest phase. publishersversion Peer reviewed

Methods for the analysis of neuroimaging data have advanced significantly since the beginning of neuroscience as a scientific discipline. Today, sophisticated statistical procedures allow us to examine complex multivariate patterns, however most of them are still constrained by assuming inherent linearity of neural processes. Here, we discuss a group of machine learning methods, called deep learning, which have drawn much attention in and outside the field of neuroscience in recent years and hold the potential to surpass the mentioned limitations. Firstly, we describe and explain the essential concepts in deep learning: the structure and the computational operations that allow deep models to learn. After that, we move to the most common applications of deep learning in neuroimaging data analysis: prediction of outcome, interpretation of internal representations, generation of synthetic data and segmentation. In the next section we present issues that deep learning poses, which concerns multidimensionality and multimodality of data, overfitting and computational cost, and propose possible solutions. Lastly, we discuss the current reach of DL usage in all the common applications in neuroimaging data analysis, where we consider the promise of multimodality, capability of processing raw data, and advanced visualization strategies. We identify research gaps, such as focusing on a limited number of criterion variables and the lack of a well-defined strategy for choosing architecture and hyperparameters. Furthermore, we talk about the possibility of conducting research with constructs that have been ignored so far or/and moving toward frameworks, such as RDoC, the potential of transfer learning and generation of synthetic data.

Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.