1,720,981 research outputs found
Rehabilitating the addicted brain with transcranial magnetic stimulation
No full textSubstance use disorders (SUDs) are one of the leading causes of morbidity and mortality worldwide. In spite of considerable advances in understanding the neural underpinnings of SUDs, therapeutic options remain limited. Recent studies have highlighted the potential of transcranial magnetic stimulation (TMS) as an innovative, safe and cost-effective treatment for some SUDs. Repetitive TMS (rTMS) influences neural activity in the short and long term by mechanisms involving neuroplasticity both locally, under the stimulating coil, and at the network level, throughout the brain. The long-term neurophysiological changes induced by rTMS have the potential to affect behaviours relating to drug craving, intake and relapse. Here, we review TMS mechanisms and evidence that rTMS is opening new avenues in addiction treatments
Brain and Human Body Modelling 2021
Full text linkThis open access book describes modern applications of computational human modelling to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. This is an Open Access book
Brain and Human Body Modelling 2021
Full text linkThis open access book describes modern applications of computational human modelling to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. This is an Open Access book
Hierarchical Bayesian aspects of distributed neuromagnetic source models
Full text linkMagnetoencephalography (MEG) enables noninvasive measurements of cerebral activity with excellent temporal resolution, but localising the neural currents generating the extracranial magnetic fields admits no unique solution. By imposing some mathematical constraints on the currents, reasonable solutions to this electromagnetic inverse problem can be obtained.
In this work, we adopt the statistical formulation of the inverse problem in which the constraints are encoded as Bayesian prior probabilities. The prior is combined with a statistical MEG observation model via Bayes' theorem to yield the posterior probability of the unknown parameters, that is the currents, given the MEG data and modeling assumptions. Apart from the currents, the prior probability density may contain further parameters which are subject to uncertainty. These parameters are not related directly to the MEG observations and are called second-level parameters or hyperparameters, giving the model a hierarchical structure.
The thesis considers hierarchical generalisations of the classical Minimum-Norm and Minimum-Current Estimates (MNE and MCE). The MNE and MCE are distributed source reconstruction methods from which the former is known to produce spatially diffuse distributions and the latter more focal. The here studied extensions of the MNE and MCE prior structures allow more general and flexible modeling of distributed sources with properties in between MNE and MCE.
The first two studies included in this thesis involve more theoretical Bayesian analyses on the properties of the hierarchical distributed source models and the resulting inverse estimates. The latter two studies focus on validation of the models with empirical MEG data, practical analyses and interpretation of the inverse estimates.Magnetoenkefalografia (MEG) mahdollistaa pään ulkopuolelta tapahtuvan aivotoimintojen mittaamisen hyvällä ajallisella tarkkuudella, mutta nämä magneettikentät synnyttävien aivokudoksen sähkövirtojen paikallistaminen vaatii ns. sähkömagneettisen käänteisongelman ratkaisun, joka ei ole yksikäsitteinen. Jos virtakonfiguraatioille asetetaan sopivia matemaattisia rajoitteita, on kuitenkin mahdollista löytää käyttökelpoisia ratkaisuja tähän käänteisongelmaan.
Tässä työssä käänteisongelmaa lähestytään tilastollisesti, ja matemaattiset rajoitteet muotoillaan Bayesilaisittain a priori todennäköisyyksinä. Tämä priorijakauma yhdistetään tilastollisen MEG-havaintomallin kanssa, jolloin saadaan Bayesin teoreeman avulla tuntemattomien parametrien eli virtakonfiguraatioiden a posteriori -jakauma, joka kertoo eri virtakonfiguraatioden todennäköisyydet, annettuna havaittu data sekä tehdyt mallioletukset. Virtojen lisäksi priorijakaumaan saattaa liittyä muita tuntemattomia suureita, jotka sisältävät epävarmuutta. Nämä parametrit eivät kytkeydy suoraan MEG-mittauksiin, joten ne ovat siis sähkövirtoihin verrattuna seuraavalla mallitasolla. Näitä priorin parametreja kutsutaan hyperparametreiksi, ja mallilla on hierarkinen rakenne.
Väitöskirjassa tutkitaan klassisten miniminormi- ja minimivirtaestimaattien hierarkisia yleistyksiä. Miniminormi- ja minimivirtaestimaatit ovat lähdejakaumamalleihin liittyviä menetelmiä, joista ensimmäinen tuottaa paikallisesti varsin laajalle levineitä ja jälkimmäinen fokaalimpia käänteisongelman ratkaisuja. Näiden menetelmien tässä työssä tutkitut laajennukset mahdollistavat myös yleisempien ja joustavampien, ominaisuuksiltaan miniminormi- ja minimivirtaoletusten väliin sijoittuvien lähdejakaumien mallintamisen.
Kaksi ensimmäistä osatyötä keskittyvät esitettyjen hierarkisten Bayesilaisten lähdejakaumamallien sekä niiden tuottamien käänteisongelman ratkaisujen teoreettiseen tutkimiseen. Kahdessa jälkimmäisessä osatyössä pyritään validoimaan menetelmät käyttäen mitattua MEG dataa, sekä selventämään näiden hierarkisten käänteisongelman ratkaisujen käytännön merkitystä ja tulkintaa.reviewe
Brain and Human Body Modeling 2020
Full text linkThis open access book describes modern applications of computational human modeling in an effort to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. Describes computational human body phantom construction and application; Explains new practices in computational human body modeling for electromagnetic safety and exposure evaluations; Includes a survey of modern applications for which computational human phantoms are critical
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Electrically Small Dipole Antenna Probe for Quasi-static Electric Field Measurements
No full textThe thesis designs, constructs, and tests an electrically small dipole antenna probe for the measurement of electric field distributions induced by a transcranial magnetic stimulation (TMS) coil. Its unique features include high spatial resolution, large frequency band from 100 Hz to 300 kHz, efficient feedline isolation via a printed Dyson balun, and accurate mitigation of noise. Prior work in this area is thoroughly reviewed. The proposed probe design is realized in hardware; implementation details and design tradeoffs are described. Test data is presented for the measurement of a CW capacitor electric field, demonstrating the probe’s ability to properly measure conservative electric fields caused by a charge distribution. Test data is also presented for the measurement of a CW solenoidal electric field, demonstrating the probe’s ability to measure non-conservative solenoidal electric fields caused by Faraday’s law of induction. Those are the primary fields for the transcranial magnetic stimulation. Advantages and disadvantages of this probing system versus those of prior works are discussed. Further refinement steps necessary for the development of this probe as a valuable TMS instrument are discussed
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Development of the Boundary Element Fast Multipole Method for Quasistatic Electromagnetic Modeling of the Brain
No full textIn this thesis, several algorithmic improvements of the Boundary Element Fast Multipole Method (BEM-FMM) for quasistatic electromagnetic modeling of multi-tissue anatomical human models have been suggested and implemented. These improvements include:
- Fast solid-angle approach for neighbor E-field integral calculations – FMM implementation
- Fast cubatures for neighbor potential and E-field integral calculations – FMM implementation
In addition, several pre/post-processing improvements of the modeling pipeline have been suggested and implemented. They include:
- Automated detection and removal of coincident faces for meshes with duplicated boundaries;
- Approach for automated volumetric labeling for BEM problems with large surface meshes;
The application examples discussed in this thesis include:
- Simulation of the MIDA head model (with 11 M triangular surface elements and 100+ tissue compartments)
- Transcranial magnetic stimulation, transcranial electrical stimulation, and electroencephalography/magnetoencephalography modeling toolkits with the BEM-FMM
Appropriate MATLAB scripts are given in the text. The corresponding BEM-FMM modeling toolkits, along with the documentation and application examples (MATLAB platform), are available at the following locations:
Transcranial Magnetic Stimulation: https://tmscorelab.github.io/TMS-Modeling-Website/
Transcranial Electrical Stimulation: https://tmscorelab.github.io/TES-Modeling-Website/
EEG/MEG Forward Solver: https://tmscorelab.github.io/EEG_MEG-Modeling-Website/
The software should run as is on Windows systems running MATLAB r2019a or newer
Brain and Human Body Modeling 2020
Full text linkThis open access book describes modern applications of computational human modeling in an effort to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. Describes computational human body phantom construction and application; Explains new practices in computational human body modeling for electromagnetic safety and exposure evaluations; Includes a survey of modern applications for which computational human phantoms are critical
Brain and Human Body Modeling 2020
No full textThis open access book describes modern applications of computational human modeling in an effort to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. Describes computational human body phantom construction and application; Explains new practices in computational human body modeling for electromagnetic safety and exposure evaluations; Includes a survey of modern applications for which computational human phantoms are critical
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