Connectivity Measures for In Vitro Neuronal Cell Networks

In this thesis, different connectivity measures are reviewed in detail in order to investigate what kind of information they provide, what are the advantages and limitations of them. Based on the literature review comparison, we selected three methods; Phase Lock Value (PLV), generalized Partial Directed Coherence (gPDC) and Transfer Entropy (TE). The selected methods were tested and evaluated with the data from human embryonic stem cell derived neuronal cell (hESC) networks which are cultured on MEAs. The analysis is divided into two parts: simulated connectivity signal studies and real MEA data analysis.The simulation study indicates that PLV method correctly recognized the connections, while gPDC provided unreliable results. TE provided the most detailed results only with few inaccuracies. Based on the simulation results, TE and PLV seem potential for further research on MEA signals. However, incoherent results were obtained in real MEA data analysis. For example, PLV claimed connections between signals measured from different wells. Based on the results, further research is needed in order to assess whether the incoherencies are influenced by the measurement environment, the methods themselves, or by the quality problem of signals in 6-well MEA

Sampling Rate Effects on Resting State fMRI Metrics

AbstractLow image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p Abstract Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning

Sampling Rate Effects on Resting State fMRI Metrics

Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p &lt; 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning

Aivotoiminnan ajallisen kytkeytymisen tutkiminen nopealla toiminnallisella magneettikuvauksella

AbstractAn amazing amount of activity is continuously occurring in the brain in multiple temporal and spatial scales even in the absence of explicit environmental outputs or inputs; this is called the resting-state, or spontaneous brain activity. It is now widely known that spontaneous brain activity, measured using resting state functional magnetic resonance imaging (fMRI) of the blood oxygen level dependent (BOLD) signal, is dominated by very low frequencies (VLFs; less than 0.1 Hz).Spatial correlations within VLF spontaneous brain activity result in what is widely referred to as functional connectivity, and the associated functionally connected regions are known as resting-state networks (RSNs). Conventional functional connectivity analyses such as seed-based analysis and independent component analysis (ICA), have revealed that spontaneous activity vary in different tasks and in some diseases, but also in a resting state in healthy subjects. However, conventional functional connectivity analyses have not addressed the temporal dimension of brain communication, that is, the propagation of information flow between brain regions.By studying temporal lags in the brain, it has recently been established that spontaneous BOLD fluctuations consist of reproducible patterns of whole brain activity propagation and these patterns are markedly altered as a function of brain state, whether pathological or physiological. In this thesis, we utilised fast magnetic resonance encephalography (MREG) imaging data and provided a comprehensive analysis approach, dynamic lag analysis (DLA), to study probabilistic patterns of information flow between brain regions. Our temporal analyses revealed new patterns in the way slow signals propagate between functional brain regions, and suggested that information flow is aberrant in autism spectrum disorder (ASD) and type 1 narcolepsy with cataplexy compared with neurotypical individuals.Our findings offer a glimpse into the principles that govern brain activity and potentially open a much broader line of research into the integrated functioning of the human brain. A deeper understanding of brain dynamics offers comprehensive views of brain physiology and potentially help to detect the sensitive biomarkers of some pathologies in the future.Original papersOriginal papers are not included in the electronic version of the dissertation.Raatikainen, V., Huotari, N., Korhonen, V., Rasila, A., Kananen, J., Raitamaa, L., Keinänen, T., Kantola, J., Tervonen, O., & Kiviniemi, V. (2017). Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data. NeuroImage, 148, 352–363. https://doi.org/10.1016/j.neuroimage.2017.01.024Self-archived versionRaatikainen, V., Korhonen, V., Borchardt, V., Huotari, N., Helakari, H., Kananen, J., Raitamaa, L., Joskitt, L., Loukusa, S., Hurtig, T., Ebeling, H., Uddin, L. Q., & Kiviniemi, V. (2019). Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder. Autism Research, 13(2), 244–258. https://doi.org/10.1002/aur.2218Self-archived versionJärvelä, M., Raatikainen, V., Kotila, A., Kananen, J., Korhonen, V., Uddin, L. Q., Ansakorpi, H., & Kiviniemi, V. (2020). Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy. Cerebral Cortex Communications, 1(1). https://doi.org/10.1093/texcom/tgaa073Self-archived versionTiivistelmäAivot ovat monimutkainen järjestelmä, jossa on käynnissä jatkuvasti valtava määrä aktiivisuutta monissa avaruudellisissa ja ajallisissa ulottuvuuksissa. Tätä toimintaa kutsutaan aivojen spontaaniksi tai lepotila-aktiivisuudeksi. Toiminnallisen magneettikuvauksen (TMK) tutkimusten kautta tiedämme nykyään, että aivotoiminnan spontaania aktiivisuutta dominoivat erittäin hitaat vaihtelut.Spontaanin aivotoiminnan erittäin hitaiden vaihteluiden avaruudellisia korrelaatioita kutsutaan yleisesti toiminnalliseksi liittyvyydeksi, ja näitä toiminnallisesti liittyneitä alueita aivojen lepotilahermoverkostoiksi. Perinteiset toiminnallisen liitettävyyden analyysien kautta tiedämme nykyään, että spontaani aivotoiminta huojuu tietyissä tehtävissä, jossain sairauksissa, mutta myös terveillä koehenkilöillä. Perinteiset analyysit eivät kuitenkaan ole keskittyneet aivotoiminnan ajallisiin vaihteluihin eli kuinka informaatio aivoalueiden välillä ajallisesti etenee.Aivan hiljattain on aivotoiminnan ajallisen kytkeytymisen tutkimuksista saatu selville, että veren happipitoisuudesta riippuvan (engl. blood oxygen level dependent, BOLD) signaalin spontaaneissa vaihteluissa on tunnistettavissa toistettavia ajallisia kytkeytymisiä. Ne vaihtelevat riippuen aivotoiminnan fysiologisesta tilasta tai patologisista prosesseista. Tässä väitöskirjatutkimuksessa kehitimme uuden analyysimenetelmän, dynaamisen viiveanalyysin (engl. dynamic lag analysis, DLA), ja hyödynsimme nopeaa magneettiresonanssienkefalogrammi (MREG) kuvantamista tutkiaksemme informaation kulkua ihmisillä eri toiminnallisten aivoalueiden välillä. Ajallisen analyysimenetelmän avulla löysimme uusia mekanismeja aivotoiminnan hitaiden vaihteluiden ajallisessa välittymisessä aivoalueiden välillä. Havaitsimme lisäksi, että aivotoiminnan ajallinen kytkeytyminen on poikkeava autismin kirjon oireyhtymässä ja tyypin 1 narkolepsiassa verrattuna terveisiin koehenkilöihin.Tuloksemme tarjoavat pilkahduksen mielenkiintoista uutta tietoa aivotoiminnan kytkeytymismekanismeista, ja voivat olla pohjana uudelle aivojen ajallisen kytkeytymisen tutkimushaaralle. Lisäksi aivojen ajallisen kytkeytymisen parempi ymmärrys tarjoaa uutta tärkeää tietoa ihmisaivojen fysiologiasta, mikä voi puolestaan auttaa löytämään tehokkaita biomarkkereita joidenkin patologisten tilojen tunnistamiselle tulevaisuudessa.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Raatikainen, V., Huotari, N., Korhonen, V., Rasila, A., Kananen, J., Raitamaa, L., Keinänen, T., Kantola, J., Tervonen, O., & Kiviniemi, V. (2017). Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data. NeuroImage, 148, 352–363. https://doi.org/10.1016/j.neuroimage.2017.01.024Rinnakkaistallennettu versioRaatikainen, V., Korhonen, V., Borchardt, V., Huotari, N., Helakari, H., Kananen, J., Raitamaa, L., Joskitt, L., Loukusa, S., Hurtig, T., Ebeling, H., Uddin, L. Q., & Kiviniemi, V. (2019). Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder. Autism Research, 13(2), 244–258. https://doi.org/10.1002/aur.2218Rinnakkaistallennettu versioJärvelä, M., Raatikainen, V., Kotila, A., Kananen, J., Korhonen, V., Uddin, L. Q., Ansakorpi, H., & Kiviniemi, V. (2020). Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy. Cerebral Cortex Communications, 1(1). https://doi.org/10.1093/texcom/tgaa073Rinnakkaistallennettu versioAcademic dissertation to be presented with the assent of the Doctoral Training Committee of Health and Biosciences of the University of Oulu for public defence in Auditorium 7 of Oulu University Hospital, on 14 May 2021, at 12 noonAbstract An amazing amount of activity is continuously occurring in the brain in multiple temporal and spatial scales even in the absence of explicit environmental outputs or inputs; this is called the resting-state, or spontaneous brain activity. It is now widely known that spontaneous brain activity, measured using resting state functional magnetic resonance imaging (fMRI) of the blood oxygen level dependent (BOLD) signal, is dominated by very low frequencies (VLFs; less than 0.1 Hz). Spatial correlations within VLF spontaneous brain activity result in what is widely referred to as functional connectivity, and the associated functionally connected regions are known as resting-state networks (RSNs). Conventional functional connectivity analyses such as seed-based analysis and independent component analysis (ICA), have revealed that spontaneous activity vary in different tasks and in some diseases, but also in a resting state in healthy subjects. However, conventional functional connectivity analyses have not addressed the temporal dimension of brain communication, that is, the propagation of information flow between brain regions. By studying temporal lags in the brain, it has recently been established that spontaneous BOLD fluctuations consist of reproducible patterns of whole brain activity propagation and these patterns are markedly altered as a function of brain state, whether pathological or physiological. In this thesis, we utilised fast magnetic resonance encephalography (MREG) imaging data and provided a comprehensive analysis approach, dynamic lag analysis (DLA), to study probabilistic patterns of information flow between brain regions. Our temporal analyses revealed new patterns in the way slow signals propagate between functional brain regions, and suggested that information flow is aberrant in autism spectrum disorder (ASD) and type 1 narcolepsy with cataplexy compared with neurotypical individuals. Our findings offer a glimpse into the principles that govern brain activity and potentially open a much broader line of research into the integrated functioning of the human brain. A deeper understanding of brain dynamics offers comprehensive views of brain physiology and potentially help to detect the sensitive biomarkers of some pathologies in the future.Tiivistelmä Aivot ovat monimutkainen järjestelmä, jossa on käynnissä jatkuvasti valtava määrä aktiivisuutta monissa avaruudellisissa ja ajallisissa ulottuvuuksissa. Tätä toimintaa kutsutaan aivojen spontaaniksi tai lepotila-aktiivisuudeksi. Toiminnallisen magneettikuvauksen (TMK) tutkimusten kautta tiedämme nykyään, että aivotoiminnan spontaania aktiivisuutta dominoivat erittäin hitaat vaihtelut. Spontaanin aivotoiminnan erittäin hitaiden vaihteluiden avaruudellisia korrelaatioita kutsutaan yleisesti toiminnalliseksi liittyvyydeksi, ja näitä toiminnallisesti liittyneitä alueita aivojen lepotilahermoverkostoiksi. Perinteiset toiminnallisen liitettävyyden analyysien kautta tiedämme nykyään, että spontaani aivotoiminta huojuu tietyissä tehtävissä, jossain sairauksissa, mutta myös terveillä koehenkilöillä. Perinteiset analyysit eivät kuitenkaan ole keskittyneet aivotoiminnan ajallisiin vaihteluihin eli kuinka informaatio aivoalueiden välillä ajallisesti etenee. Aivan hiljattain on aivotoiminnan ajallisen kytkeytymisen tutkimuksista saatu selville, että veren happipitoisuudesta riippuvan (engl. blood oxygen level dependent, BOLD) signaalin spontaaneissa vaihteluissa on tunnistettavissa toistettavia ajallisia kytkeytymisiä. Ne vaihtelevat riippuen aivotoiminnan fysiologisesta tilasta tai patologisista prosesseista. Tässä väitöskirjatutkimuksessa kehitimme uuden analyysimenetelmän, dynaamisen viiveanalyysin (engl. dynamic lag analysis, DLA), ja hyödynsimme nopeaa magneettiresonanssienkefalogrammi (MREG) kuvantamista tutkiaksemme informaation kulkua ihmisillä eri toiminnallisten aivoalueiden välillä. Ajallisen analyysimenetelmän avulla löysimme uusia mekanismeja aivotoiminnan hitaiden vaihteluiden ajallisessa välittymisessä aivoalueiden välillä. Havaitsimme lisäksi, että aivotoiminnan ajallinen kytkeytyminen on poikkeava autismin kirjon oireyhtymässä ja tyypin 1 narkolepsiassa verrattuna terveisiin koehenkilöihin. Tuloksemme tarjoavat pilkahduksen mielenkiintoista uutta tietoa aivotoiminnan kytkeytymismekanismeista, ja voivat olla pohjana uudelle aivojen ajallisen kytkeytymisen tutkimushaaralle. Lisäksi aivojen ajallisen kytkeytymisen parempi ymmärrys tarjoaa uutta tärkeää tietoa ihmisaivojen fysiologiasta, mikä voi puolestaan auttaa löytämään tehokkaita biomarkkereita joidenkin patologisten tilojen tunnistamiselle tulevaisuudessa

Connectivity Measures for In Vitro Neuronal Cell Networks

In this thesis, different connectivity measures are reviewed in detail in order to investigate what kind of information they provide, what are the advantages and limitations of them. Based on the literature review comparison, we selected three methods; Phase Lock Value (PLV), generalized Partial Directed Coherence (gPDC) and Transfer Entropy (TE). The selected methods were tested and evaluated with the data from human embryonic stem cell derived neuronal cell (hESC) networks which are cultured on MEAs. The analysis is divided into two parts: simulated connectivity signal studies and real MEA data analysis.The simulation study indicates that PLV method correctly recognized the connections, while gPDC provided unreliable results. TE provided the most detailed results only with few inaccuracies. Based on the simulation results, TE and PLV seem potential for further research on MEA signals. However, incoherent results were obtained in real MEA data analysis. For example, PLV claimed connections between signals measured from different wells. Based on the results, further research is needed in order to assess whether the incoherencies are influenced by the measurement environment, the methods themselves, or by the quality problem of signals in 6-well MEA

Dynamic lag analysis of human brain activity propagation:a fast fMRI study

Abstract An amazing amount of activity is continuously occurring in the brain in multiple temporal and spatial scales even in the absence of explicit environmental outputs or inputs; this is called the resting-state, or spontaneous brain activity. It is now widely known that spontaneous brain activity, measured using resting state functional magnetic resonance imaging (fMRI) of the blood oxygen level dependent (BOLD) signal, is dominated by very low frequencies (VLFs; less than 0.1 Hz). Spatial correlations within VLF spontaneous brain activity result in what is widely referred to as functional connectivity, and the associated functionally connected regions are known as resting-state networks (RSNs). Conventional functional connectivity analyses such as seed-based analysis and independent component analysis (ICA), have revealed that spontaneous activity vary in different tasks and in some diseases, but also in a resting state in healthy subjects. However, conventional functional connectivity analyses have not addressed the temporal dimension of brain communication, that is, the propagation of information flow between brain regions. By studying temporal lags in the brain, it has recently been established that spontaneous BOLD fluctuations consist of reproducible patterns of whole brain activity propagation and these patterns are markedly altered as a function of brain state, whether pathological or physiological. In this thesis, we utilised fast magnetic resonance encephalography (MREG) imaging data and provided a comprehensive analysis approach, dynamic lag analysis (DLA), to study probabilistic patterns of information flow between brain regions. Our temporal analyses revealed new patterns in the way slow signals propagate between functional brain regions, and suggested that information flow is aberrant in autism spectrum disorder (ASD) and type 1 narcolepsy with cataplexy compared with neurotypical individuals. Our findings offer a glimpse into the principles that govern brain activity and potentially open a much broader line of research into the integrated functioning of the human brain. A deeper understanding of brain dynamics offers comprehensive views of brain physiology and potentially help to detect the sensitive biomarkers of some pathologies in the future.Tiivistelmä Aivot ovat monimutkainen järjestelmä, jossa on käynnissä jatkuvasti valtava määrä aktiivisuutta monissa avaruudellisissa ja ajallisissa ulottuvuuksissa. Tätä toimintaa kutsutaan aivojen spontaaniksi tai lepotila-aktiivisuudeksi. Toiminnallisen magneettikuvauksen (TMK) tutkimusten kautta tiedämme nykyään, että aivotoiminnan spontaania aktiivisuutta dominoivat erittäin hitaat vaihtelut. Spontaanin aivotoiminnan erittäin hitaiden vaihteluiden avaruudellisia korrelaatioita kutsutaan yleisesti toiminnalliseksi liittyvyydeksi, ja näitä toiminnallisesti liittyneitä alueita aivojen lepotilahermoverkostoiksi. Perinteiset toiminnallisen liitettävyyden analyysien kautta tiedämme nykyään, että spontaani aivotoiminta huojuu tietyissä tehtävissä, jossain sairauksissa, mutta myös terveillä koehenkilöillä. Perinteiset analyysit eivät kuitenkaan ole keskittyneet aivotoiminnan ajallisiin vaihteluihin eli kuinka informaatio aivoalueiden välillä ajallisesti etenee. Aivan hiljattain on aivotoiminnan ajallisen kytkeytymisen tutkimuksista saatu selville, että veren happipitoisuudesta riippuvan (engl. blood oxygen level dependent, BOLD) signaalin spontaaneissa vaihteluissa on tunnistettavissa toistettavia ajallisia kytkeytymisiä. Ne vaihtelevat riippuen aivotoiminnan fysiologisesta tilasta tai patologisista prosesseista. Tässä väitöskirjatutkimuksessa kehitimme uuden analyysimenetelmän, dynaamisen viiveanalyysin (engl. dynamic lag analysis, DLA), ja hyödynsimme nopeaa magneettiresonanssienkefalogrammi (MREG) kuvantamista tutkiaksemme informaation kulkua ihmisillä eri toiminnallisten aivoalueiden välillä. Ajallisen analyysimenetelmän avulla löysimme uusia mekanismeja aivotoiminnan hitaiden vaihteluiden ajallisessa välittymisessä aivoalueiden välillä. Havaitsimme lisäksi, että aivotoiminnan ajallinen kytkeytyminen on poikkeava autismin kirjon oireyhtymässä ja tyypin 1 narkolepsiassa verrattuna terveisiin koehenkilöihin. Tuloksemme tarjoavat pilkahduksen mielenkiintoista uutta tietoa aivotoiminnan kytkeytymismekanismeista, ja voivat olla pohjana uudelle aivojen ajallisen kytkeytymisen tutkimushaaralle. Lisäksi aivojen ajallisen kytkeytymisen parempi ymmärrys tarjoaa uutta tärkeää tietoa ihmisaivojen fysiologiasta, mikä voi puolestaan auttaa löytämään tehokkaita biomarkkereita joidenkin patologisten tilojen tunnistamiselle tulevaisuudessa

Performance evaluation of a new automated skin flash method for radiotherapy of breast cancer

In volumetric modulated arc therapy of breast cancer, an outer margin for the target is needed. This study evaluated the clinical and planning target volume coverage and surface dose using an automated skin flash method compared to the virtual bolus method. Body outline changes of 4, 8 and 12 mm were simulated in 20 breast cancer patients. Median V95 and V90 clinical target volume coverage and surface dose remained at 90.1 %, 98.7 % and 85.7 % or above with the automated skin flash method. The median results of automated skin flash method were close to virtual bolus method