Browsing by Author "Laakso, Ilkka, Prof., Aalto University, Department of Electrical Engineering and Automation, Finland"

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  • Characterization of open issues in low-frequency computational dosimetry
    (2021) Soldati, Marco
     School of Electrical Engineering | Doctoral dissertation (article-based)
    Two international organizations, namely the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Institute of Electrical and Electronics Engineers International Committee on Electromagnetic Safety (IEEE ICES), have established exposure criteria and safety limits for human protection to electromagnetic fields. In the low-frequency range, both organizations recognize that the main adverse health effects are represented by the induction of retinal phosphenes, the alteration of synaptic activity and the stimulation of nerves. On this basis, the exposure limits were derived from threshold data of internal electric fields with the purpose of avoiding such adverse effects. Since direct measurement of the induced electric field is not feasible, both the standard and guidelines have introduced limits for external electric and magnetic field strengths. In this context, computational dosimetry was used to relate the internal induced quantities with the external field strengths. However, low-frequency dosimetry suffers from various sources of error and uncertainty.The main aim of the present thesis is to lessen such uncertainty, as well as further characterize computational artifacts in the evaluation of the induced electric fields. Investigations were carried out using state-of-the-art methods based on physiological measurements, high-resolution realistic anatomical models, individualized electric field computations and biological axon models. Several open issues affecting low-frequency dosimetry have been characterized with the aim of producing quantitative data useful for the harmonization and revision of current exposure standard and guidelines. Our findings showed a large margin of safety in the current exposure limits established by both international organizations. In this regard, the obtained results represent a solid basis for deriving safety levels that offer acceptable protection for the human population without being overly conservative. In addition, the present work improves the reliability of human exposure assessment at low frequencies.
  • Feasibility of body electrical loss analysis in detection of abdominal visceral fat
    (2018) Blomqvist, Kim
     School of Electrical Engineering | Doctoral dissertation (article-based)
    Objective: An excessive amount of abdominal visceral fat has found to be linked to metabolic and cardiovascular health much more than the body mass index (BMI). However, a cost-effective and accurate estimation of abdominal visceral fat accumulation in individuals and groups is hard. This work explores the feasibility of a novel bioimpedance-based method, named as body electrical loss analysis (BELA), aimed at estimating abdominal visceral fat that is in good agreement with estimates obtained using magnetic resonance imaging (MRI). Approach: During the BELA measurement, the subject's abdomen is covered with a radio frequency coil comprising a tunable high-Q LC resonator. No contact electrodes are attached to the subject standing within the coil. The time-varying magnetic field produced by the coil induces so-called eddy currents in the abdomen. The induced currents oppose the applied magnetic field, and from the coil's point of view power is lost, i.e., the coil is loaded. Lean body tissue that is electrically more conductive than fatty tissue loads the coil more. The loading effect is also heavily dependent on the radial distance of the tissue. So instead of just measuring the losses at some resonant frequency, the novel idea was to measure the rate of loss as a function of frequency. This was expected to have a strong relationship with the internal conductivity of the abdomen, i.e., indicating its level of adiposity. Main results: A promising correlation (r = 0.86 with SEE = 29.7 cm2) was found between the loss changing rate measured at the height of umbilicus +5 cm and the visceral fat area obtained with MRI in nine subjects recruited from laboratory personnel and acquaintances. However, the results obtained with the improved prototype in a clinical trial (17 males, 21 females, age 20–68 years, BMI 19.6–39.4 kg/m2, and waist circumference 78–128 cm) led to considerably weaker correlation, r = 0.61 with SEE = 59.30 cm2. Instead, BELA was found to correlate strongly with the circumference of abdominal wall muscles estimated from the waist circumference and the abdominal subcutaneous fat layer thickness measured with the abdominal impedance measurement (r = 0.86–0.90). The relationship with the subcutaneous fat area was found to be weak in both studies (r < 0.5), as was expected. Significance: Although the results do not suggest proposing the BELA in its current form for the prediction of abdominal visceral fat, the novelty of the method is considered a help to guide future studies on bioimpedance-based methods. The strong correlation of BELA with the circumference of abdominal wall muscles suggests its potential in other applications of body composition. The difference between the BELA and the widely used bioimpedance analysis (BIA) is that the BELA relies on the measurement of electrical loss only. No statistical gender, age, or anthropometry is used in the BELA.
  • Improved transcranial magnetic stimulation protocols to locate brain activations
    (2024) Matilainen, Noora
     School of Electrical Engineering | Doctoral dissertation (article-based)
    Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique used in clinical treatment and research. It is a technique that provides essential information about brain activity and function, as well as effective treatment for certain neurological disorders. The use of TMS is however still limited by several fundamental uncertainties. For example, it remains uncertain which forms of stimulation are required to elicit specific responses. In addition, the TMS procedure itself can be time consuming and is prone to errors. This summary offers new knowledge of how TMS parameters affect neurostimulation and what they stimulate. Publication I examines the effect of the TMS inter-pulse interval (IPI) on motor evoked potential (MEP) amplitude in active and resting muscles. Previous research has shown that MEP amplitudes are significantly influenced by IPI in resting muscles, with shorter intervals generally leading to decreased amplitudes. This study, however, reveals that active muscle contraction during TMS eliminates the modulating effect of IPI, allowing the use of shorter IPIs which speeds up TMS procedures. Publication II investigates the accuracy of a three-point navigated TMS, still a commonly used approach for neuronavigation. The findings reveal that errors in landmark pointing can significantly impact the accuracy of coil positioning and the induced electric field, highlighting the importance of minimizing such errors in TMS research. Publication III explores the use of computational dosimetry to predict the optimal coil positioning and to estimate motor threshold values in TMS. While the study shows promising results in predicting optimal coil locations, the accuracy of predicting hotspots is slightly less than the hypothetical target of 1 cm. Nevertheless, the method is possibly useful in clinical practise, offering potential improvements in the speed and reliability of TMS hotspot-finding procedures. Publication IV contributes to TMS localization and investigates the differences between posteroanterior (PA) and anteroposterior (AP) coil current directions. The study suggests that PA-TMS primarily activates the precentral gyrus, while AP-TMS is more likely to activate the postcentral gyrus, with both directions showing a higher likelihood of white matter activation. Together, these four studies contribute to a deeper understanding of TMS mechanisms, the optimization of stimulation protocols, and improved accuracy in TMS procedures, with implications for both research and clinical applications.
  • Individualized Computational Modeling of Transcranial Direct Current Stimulation
    (2020) Mikkonen, Marko
     School of Electrical Engineering | Doctoral dissertation (article-based)
    Different psychiatric and neurologic illnesses are a great burden to our society. These kinds of disorders are often treated with pharmaceuticals, regardless of a wide variety of side-effects, poor suitability for many patients and high costs. During the recent decades, non-invasive brain stimulation (NIBS) has risen as a viable treatment alternative to the use of drugs. In NIBS, the state of the brain is affected via electric currents either induced by magnetic fields, or applied directly via electrodes on the scalp. One such method is called transcranial direct current stimulation (tDCS), where a small direct current is applied non-invasively to the brain via electrodes placed onto the scalp. This has, for instance, been shown to be a potential treatment for depression. There is, however, a significant flaw with tDCS in terms of variable efficacy between different patients (inter-individual variability). This arises partially from the dosimetry of tDCS. The tDCS dose is commonly estimated based on the input current, which can be easily set to be the same for a group of subjects. However, multiple studies have pointed out that although the ingoing current is kept the same, the electric field experienced by the brain varies between subjects due to anatomic factors. As it is highly impractical to measure the electric fields in the brain during the stimulation in order to use them as a dose measure, computational modeling therefore remains the only viable way of studying them.In this doctoral thesis comprising five peer-reviewed journal articles, the inter-individual variability of tDCS electric fields is studied using anatomically realistic head models in finite element analysis (FEA). The aim of this thesis is to shed light onto the causes of this variation, as well as to provide evidence to support the viability of using these predicted electric fields as a dosimetric parameter for tDCS. Publication I presents an approach that lowers the computational costs of tDCS electric field predictions using the finite element method. In Publication II, we present a connection between transcranial magnetic stimulation (TMS) motor thresholds and the predicted tDCS electric fields, and in Publication V a connection between predicted electric field normal components and the outcome of tDCS. Publication III and Publication IV study the determinants of the inter-individual variability in tDCS electric fields, and show that body position affects the tDCS electric fields and the focality of the electric field montage used has an effect on the inter-individual variability of those tDCS electric fields. These results provide new information on the causes of inter-individual variability and offer possible approaches to better take it into account with tDCS. Additionally, these results provide further links to connect the FEA-predicted electric fields into physiologically measurable quantities related to NIBS thus giving further support for the value of using these models in the study of tDCS.