Improving the reliability of transcranial magnetic stimulation targeting

Aino Nieminen

Improving the reliability of transcranial magnetic stimulation targeting

Julkaisun otsikon käännös: Transkraniaalisen magneettistimulaation kohdistamisluotettavuuden parantaminen

Aino Nieminen

Neurotieteen ja lääketieteellisen tekniikan laitos

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Aivojen hermosoluja voidaan aktivoida paikallisesti transkraniaalisen magneettistimulaation (TMS) avulla. TMS:ää käytetään hermoverkkojen dynamiikan tutkimukseen, aivotoimintojen kartoitukseen sekä neurologisten sairauksien hoitoon. TMS:ssä aiheutetaan sähkökenttäpulssi aivoihin. Sähkökentän huippukohta kohdistetaan neuronavigaatiosysteemin avulla, joka näyttää stimulaatiokelan asettelun sekä sähkökentän aivokuvan päällä. Löytääkseen sopivan stimulaatiokohdan ja -suunnan, TMS:n käyttäjä aktivoi aivoja eri parametrein ja seuraa niistä aiheutuvia vasteita, kuten lihasnykäisyjä liikeaivokuorta stimuloitaessa. Tämä kartoitusprosessi on kuitenkin käyttäjäriippuvaista ja hidasta. TMS:n navigointisysteemitkään eivät ole virheettömiä. Epätarkkuudet ja eroavaisuudet TMS-menetelmissä voivat johtaa vaihteleviin tuloksiin, joita on hankala tulkita ja vertailla. Tämän väitöskirjan tavoitteena oli lisätä TMS:n kohdistustarkkuutta kehittämällä automatisoituja kartoitusmenetelmiä. Osatyössä 1 esitellään algoritmi optimaalisen stimulaatiopaikan ja -suunnan määrittämiseen. Algoritmilla etsittiin automaattisesti sellaiset stimulaatioparametrit, joiden avulla saatiin suurimmat lihassähkökäyrällä mitatut vasteet. Osatyössä 2 käytettiin samanlaista algoritmia optimaalisen stimulaatiosuunnan etsintään, mutta lihasvasteiden sijaan takaisinkytkentäsignaalina oli aivosähkökäyrä. Automatisoidut TMS-prosessit vaativat ei-manuaalisen tavan TMS-parametrien säätöön. Osatöissä 1 ja 2 stimulaatiosuuntaa ja -paikkaa säädettiin sähköisesti kaksikelaisen monipaikka-TMS:n avulla, jossa käytetään samanaikaisesti kahta päällekkäin olevaa stimulaatiokelaa. Päällekkäin olevien kelojen määrää lisäämällä voidaan kasvattaa sähköisesti ohjattavaa parametrialuetta. Tällainen toteutettiin osatyössä 3 viiden kelan systeemikokonaisuutena, joka mahdollisti käden edustusalueen automaattisen kartoituksen etukäteen valituilla stimulaatiopaikoilla ja -suunnilla. Osatyön 4 tavoitteena oli analysoida TMS-navigointiin liittyviä virheitä. Työn simulaatiot osoittivat, että navigaatiovirheiden suuruus riippuu käytetyistä menetelmistä. TMS-käyttäjä voi kuitenkin varmistaa luotettavan TMS-navigoinnin suorittamalla siihen liittyvät valmistelut huolella sekä valitsemalla navigointiin tarkimmat saatavilla olevat menetelmät. Tässä väitöskirjassa kehitetyt automatisointimenetelmät mahdollistavat TMS:n käytön nopealla ja käyttäjäriippumattomalla tavalla lisäten TMS:n luotettavuutta. Takaisinkytkentäalgoritmit luovat pohjaa TMS-hoitojen reaaliaikaiseen mukauttamiseen eli siihen, että TMS:ää säädetään hoidon aikana mitattujen vasteiden perusteella. Yhdessä tarkan navigointijärjestelmän kanssa, automatisoidun reaaliaikaisen säätämisen odotetaan lisäävän TMS-hoitojen tehokkuutta ja tuovan apua aivosairauksista kärsiville potilaille.

Julkaisun otsikon käännös	Transkraniaalisen magneettistimulaation kohdistamisluotettavuuden parantaminen
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Ilmoniemi, Risto, Vastuuprofessori Nieminen, Jaakko, Ohjaaja Vaalto, Selja, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-64-0820-0
Sähköinen ISBN	978-952-64-0821-7
Tila	Julkaistu - 2022
OKM-julkaisutyyppi	G5 Tohtorinväitöskirja (artikkeli)

Tutkimusalat

transkraniaalinen magneettistimulaatio
lihassähkökäyrä
aivosähkökäyrä
neuronavigaatio
automaattinen säätö
Bayesilainen optimointi

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-64-0821-7

OpenUrl-saatavuus

Koko teksti

Aalto NeuroImaging Infrastructure
Veikko Jousmäki (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility
Science-IT
Mikko Hakala (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Siteeraa tätä

@phdthesis{ada540bedc524e04afd157edb7de111d,

title = "Improving the reliability of transcranial magnetic stimulation targeting",

abstract = "Neurons in the human brain can be locally activated with transcranial magnetic stimulation (TMS). TMS is applied to study brain dynamics, to map cortical functions, and to treat neurological and psychiatric disorders. In TMS, an electric field is induced in the brain. The location of the electric field maximum is targeted with a neuronavigation system that shows the coil placement and the peak electric field on a brain image. To find a suitable stimulation location and orientation, the TMS operator gives pulses with varying stimulus parameters and observes the responses, such as muscle twitches upon stimulating the motor cortex. This mapping procedure is, however, operator-dependent and slow. In addition, TMS navigation systems are not error-free. Inaccuracies and variation in TMS methods can cause varying results that may be difficult to interpret and compare. This Thesis aimed at increasing the reliability of TMS targeting by developing automated targeting methods. Publication 1 presents an algorithm to automatically identify the best stimulation location and orientation in the motor cortex, i.e., the stimulus parameters that maximize the motor responses measured with electromyography. In Publication 2, a similar closed-loop approach was applied to search for the optimal stimulus orientation but now with electroencephalography as a feedback signal. Automated procedures require a way for non-manual adjustment of TMS parameters. In Publications 1 and 2, the stimulus location or orientation was electronically controlled with a 2-coil multi-locus TMS, which concurrently drives two overlapping coils. By adding more coils into the system, one can extend the electronically adjustable parameter space. This was realized in Publication 3 with a 5-coil device, which enabled a demonstration of an automated mapping of the hand motor area with predefined stimulus locations and orientations. Publication 4 aimed at analyzing errors related to TMS navigation systems. The simulations showed that the magnitudes of the errors in navigation depend on the methods employed. The operator can ensure reliable TMS navigation by performing related preparations carefully and by selecting the most accurate methods for navigation. The automated procedures developed in this Thesis allow operating TMS in a fast and user-independent way, increasing the reliability of TMS. Moreover, the closed-loop algorithms pave the way for adaptive TMS treatments, i.e., the real-time adjustment of the TMS delivery with observed effects so that the clinical outcome is maximized. This, together with accurate navigation, is expected to increase the efficacy of TMS therapy, bringing help to patients suffering from brain dysfunctions.",

keywords = "transcranial magnetic stimulation, electromyography, electroencephalography, neuronavigation, closed-loop, Bayesian optimization, transkraniaalinen magneettistimulaatio, lihass{\"a}hk{\"o}k{\"a}yr{\"a}, aivos{\"a}hk{\"o}k{\"a}yr{\"a}, neuronavigaatio, automaattinen s{\"a}{\"a}t{\"o}, Bayesilainen optimointi, transcranial magnetic stimulation, electromyography, electroencephalography, neuronavigation, closed-loop, Bayesian optimization",

author = "Aino Nieminen",

year = "2022",

language = "English",

isbn = "978-952-64-0820-0",

series = "Aalto University publication series DOCTORAL THESES",

publisher = "Aalto University",

number = "77",

address = "Finland",

school = "Aalto University",

}

TY - THES

T1 - Improving the reliability of transcranial magnetic stimulation targeting

AU - Nieminen, Aino

PY - 2022

Y1 - 2022

N2 - Neurons in the human brain can be locally activated with transcranial magnetic stimulation (TMS). TMS is applied to study brain dynamics, to map cortical functions, and to treat neurological and psychiatric disorders. In TMS, an electric field is induced in the brain. The location of the electric field maximum is targeted with a neuronavigation system that shows the coil placement and the peak electric field on a brain image. To find a suitable stimulation location and orientation, the TMS operator gives pulses with varying stimulus parameters and observes the responses, such as muscle twitches upon stimulating the motor cortex. This mapping procedure is, however, operator-dependent and slow. In addition, TMS navigation systems are not error-free. Inaccuracies and variation in TMS methods can cause varying results that may be difficult to interpret and compare. This Thesis aimed at increasing the reliability of TMS targeting by developing automated targeting methods. Publication 1 presents an algorithm to automatically identify the best stimulation location and orientation in the motor cortex, i.e., the stimulus parameters that maximize the motor responses measured with electromyography. In Publication 2, a similar closed-loop approach was applied to search for the optimal stimulus orientation but now with electroencephalography as a feedback signal. Automated procedures require a way for non-manual adjustment of TMS parameters. In Publications 1 and 2, the stimulus location or orientation was electronically controlled with a 2-coil multi-locus TMS, which concurrently drives two overlapping coils. By adding more coils into the system, one can extend the electronically adjustable parameter space. This was realized in Publication 3 with a 5-coil device, which enabled a demonstration of an automated mapping of the hand motor area with predefined stimulus locations and orientations. Publication 4 aimed at analyzing errors related to TMS navigation systems. The simulations showed that the magnitudes of the errors in navigation depend on the methods employed. The operator can ensure reliable TMS navigation by performing related preparations carefully and by selecting the most accurate methods for navigation. The automated procedures developed in this Thesis allow operating TMS in a fast and user-independent way, increasing the reliability of TMS. Moreover, the closed-loop algorithms pave the way for adaptive TMS treatments, i.e., the real-time adjustment of the TMS delivery with observed effects so that the clinical outcome is maximized. This, together with accurate navigation, is expected to increase the efficacy of TMS therapy, bringing help to patients suffering from brain dysfunctions.

AB - Neurons in the human brain can be locally activated with transcranial magnetic stimulation (TMS). TMS is applied to study brain dynamics, to map cortical functions, and to treat neurological and psychiatric disorders. In TMS, an electric field is induced in the brain. The location of the electric field maximum is targeted with a neuronavigation system that shows the coil placement and the peak electric field on a brain image. To find a suitable stimulation location and orientation, the TMS operator gives pulses with varying stimulus parameters and observes the responses, such as muscle twitches upon stimulating the motor cortex. This mapping procedure is, however, operator-dependent and slow. In addition, TMS navigation systems are not error-free. Inaccuracies and variation in TMS methods can cause varying results that may be difficult to interpret and compare. This Thesis aimed at increasing the reliability of TMS targeting by developing automated targeting methods. Publication 1 presents an algorithm to automatically identify the best stimulation location and orientation in the motor cortex, i.e., the stimulus parameters that maximize the motor responses measured with electromyography. In Publication 2, a similar closed-loop approach was applied to search for the optimal stimulus orientation but now with electroencephalography as a feedback signal. Automated procedures require a way for non-manual adjustment of TMS parameters. In Publications 1 and 2, the stimulus location or orientation was electronically controlled with a 2-coil multi-locus TMS, which concurrently drives two overlapping coils. By adding more coils into the system, one can extend the electronically adjustable parameter space. This was realized in Publication 3 with a 5-coil device, which enabled a demonstration of an automated mapping of the hand motor area with predefined stimulus locations and orientations. Publication 4 aimed at analyzing errors related to TMS navigation systems. The simulations showed that the magnitudes of the errors in navigation depend on the methods employed. The operator can ensure reliable TMS navigation by performing related preparations carefully and by selecting the most accurate methods for navigation. The automated procedures developed in this Thesis allow operating TMS in a fast and user-independent way, increasing the reliability of TMS. Moreover, the closed-loop algorithms pave the way for adaptive TMS treatments, i.e., the real-time adjustment of the TMS delivery with observed effects so that the clinical outcome is maximized. This, together with accurate navigation, is expected to increase the efficacy of TMS therapy, bringing help to patients suffering from brain dysfunctions.

KW - transcranial magnetic stimulation

KW - electromyography

KW - electroencephalography

KW - neuronavigation

KW - closed-loop

KW - Bayesian optimization

KW - transkraniaalinen magneettistimulaatio

KW - lihassähkökäyrä

KW - aivosähkökäyrä

KW - neuronavigaatio

KW - automaattinen säätö

KW - Bayesilainen optimointi

KW - transcranial magnetic stimulation

KW - electromyography

KW - electroencephalography

KW - neuronavigation

KW - closed-loop

KW - Bayesian optimization

M3 - Doctoral Thesis

SN - 978-952-64-0820-0

T3 - Aalto University publication series DOCTORAL THESES

PB - Aalto University

ER -

Improving the reliability of transcranial magnetic stimulation targeting

Abstrakti

Tutkimusalat

Pääsy asiakirjaan

OpenUrl-saatavuus

Sormenjälki

Laitteet

Aalto NeuroImaging Infrastructure

Science-IT

Siteeraa tätä