Biostatistical modeling and analysis of combined fMRI and EEG measurements

The purpose of brain mapping is to advance the understanding of the relationship between structure and function in the human brain. Several techniques---with different advantages and disadvantages---exist for recording neural activity. Functional magnetic resonance imaging (fMRI) has a high spatial resolution, but low temporal resolution. It also suffers from a low-signal-to-noise ratio in event-related experimental designs, which are commonly used to investigate neuronal brain activity. On the other hand, the high temporal resolution of electroencephalography (EEG) recordings allows to capture provoked event-related potentials. Though, 3D maps derived by EEG source reconstruction methods have a low spatial resolution, they provide complementary information about the location of neuronal activity. There is a strong interest in combining data from both modalities to gain a deeper knowledge of brain functioning through advanced statistical modeling. In this thesis, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. This method builds upon a newly developed mere fMRI activation detection method. In general, activation detection corresponds to stimulus predictor components having an effect on the fMRI signal trajectory in a voxelwise linear model. We model and analyze stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression. For mere fMRI activation detection, the predictor consists of a spatially-varying intercept only. For EEG-enhanced schemes, an EEG effect is added, which is either chosen to be spatially-varying or constant. Spatially-varying effects are regularized by different Markov random field priors. Statistical inference in resulting high-dimensional hierarchical models becomes rather challenging from a modeling perspective as well as with regard to numerical issues. In this thesis, inference is based on a Markov Chain Monte Carlo (MCMC) approach relying on global updates of effect maps. Additionally, a faster algorithm is developed based on single-site updates to circumvent the computationally intensive, high-dimensional, sparse Cholesky decompositions. The proposed algorithms are examined in both simulation studies and real-world applications. Performance is evaluated in terms of convergency properties, the ability to produce interpretable results, and the sensitivity and specificity of corresponding activation classification rules. The main question is whether the use of EEG information can increase the power of fMRI models to detect activated voxels. In summary, the new algorithms show a substantial increase in sensitivity compared to existing fMRI activation detection methods like classical SPM. Carefully selected EEG-prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio

fMRI activation detection with EEG priors

The purpose of brain mapping techniques is to advance the understanding of the relationship between structure and function in the human brain in so-called activation studies. In this work, an advanced statistical model for combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings is developed to fuse complementary information about the location of neuronal activity. More precisely, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. I.e., we model and analyse stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression with either a spatially-varying or constant EEG effect. Spatially-varying effects are regularized by intrinsic Markov random field priors. Inference is based on a full Bayesian Markov Chain Monte Carlo (MCMC) approach. Whether the proposed algorithm is able to increase the sensitivity of mere fMRI models is examined in both a real-world application and a simulation study. We observed, that carefully selected EEG--prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio

Biostatistical modeling and analysis of combined fMRI and EEG measurements

The purpose of brain mapping is to advance the understanding of the relationship between structure and function in the human brain. Several techniques---with different advantages and disadvantages---exist for recording neural activity. Functional magnetic resonance imaging (fMRI) has a high spatial resolution, but low temporal resolution. It also suffers from a low-signal-to-noise ratio in event-related experimental designs, which are commonly used to investigate neuronal brain activity. On the other hand, the high temporal resolution of electroencephalography (EEG) recordings allows to capture provoked event-related potentials. Though, 3D maps derived by EEG source reconstruction methods have a low spatial resolution, they provide complementary information about the location of neuronal activity. There is a strong interest in combining data from both modalities to gain a deeper knowledge of brain functioning through advanced statistical modeling. In this thesis, a new Bayesian method is proposed for enhancing fMRI activation detection by the use of EEG-based spatial prior information in stimulus based experimental paradigms. This method builds upon a newly developed mere fMRI activation detection method. In general, activation detection corresponds to stimulus predictor components having an effect on the fMRI signal trajectory in a voxelwise linear model. We model and analyze stimulus influence by a spatial Bayesian variable selection scheme, and extend existing high-dimensional regression methods by incorporating prior information on binary selection indicators via a latent probit regression. For mere fMRI activation detection, the predictor consists of a spatially-varying intercept only. For EEG-enhanced schemes, an EEG effect is added, which is either chosen to be spatially-varying or constant. Spatially-varying effects are regularized by different Markov random field priors. Statistical inference in resulting high-dimensional hierarchical models becomes rather challenging from a modeling perspective as well as with regard to numerical issues. In this thesis, inference is based on a Markov Chain Monte Carlo (MCMC) approach relying on global updates of effect maps. Additionally, a faster algorithm is developed based on single-site updates to circumvent the computationally intensive, high-dimensional, sparse Cholesky decompositions. The proposed algorithms are examined in both simulation studies and real-world applications. Performance is evaluated in terms of convergency properties, the ability to produce interpretable results, and the sensitivity and specificity of corresponding activation classification rules. The main question is whether the use of EEG information can increase the power of fMRI models to detect activated voxels. In summary, the new algorithms show a substantial increase in sensitivity compared to existing fMRI activation detection methods like classical SPM. Carefully selected EEG-prior information additionally increases sensitivity in activation regions that have been distorted by a low signal-to-noise ratio

Zum Zusammenhang zwischen Rational-Emotiver Theorie und Attributionstheorie: Irrationale Gedanken als Determinanten depressogener Ursachenzuschreibungen und maladaptiver Emotionen

Die Rational-Emotive Theorie (RET) nach Ellis sowie die Attributionstheorien betrachten Kognitionen als notwendige und hinreichende Bedingungen bestimmter Emotionen. Zudem beinhalten beide Theoriekomplexe spezifische Darstellungen von Kognitionen, die der psychischen Gesundheit abträglich sind: Diese sind in der RET durch irrationale Gedanken („ich muss unbedingt...“) in der Attributionstheorie insbesondere durch den depressogenen Attributionsstil gekennzeichnet. Auf der Grundlage dieser theoretischen Gemeinsamkeiten überprüft eine experimentelle Fragebogenstudie mittels unterschiedlicher Szenarien in permutierten Darbietungen, inwiefern irrational („ich muss unbedingt...“) und rational („ich möchte gerne...“) denkenden Stimuluspersonen unterschiedliche Emotionen, Kausalattributionen und Verhaltensweisen zugeschrieben werden. Es zeigt sich, dass adaptive Emotionen bei rational denkenden und maladaptive Emotionen bei irrational denkenden Personen vermutet werden. Hinsichtlich der Dimensionen Stabilität, Lokation und Globalität ergeben sich für irrational im Unterschied zu den rational Denkenden signifikante und konsistente depressogene Attributionsmuster. Zudem wird bei den irrationalen Denkern tendenziell eine stärkere wahrgenommene Kontrollierbarkeit vermutet. Bezüglich zukünftiger Verhaltensweisen werden überwiegend bei rational denkenden Personen produktive Verhaltensresultate vermutet. Zusätzlich erhobene Einschätzungen bestätigen eine höhere Funktionalität adaptiver Emotionen

Frageformateffekte bei der Beantwortung von Fragebögen: Der Einfluss des gegebenen Zeitrahmens bei offenen Häufigkeitsfragen auf das Antwortverhalten

Empirical research confirms that respondents asked to report behavioral frequencies use formal features of the question format to form their answer: E.g. assessing frequency reports by means of scales is influenced by a tendency towards the middle range of the scale. Therefore literature about questionnaire design recommends the use of open response question formats where the respondent gives a number. At this the question includes a special temporal interval, the answer should be given for. In this study the possible influence of this specified interval is examined. A college sample reported behavioral frequencies of different fields of activities. The temporal interval was manipulated between-subjects (week, month, quarter, semester). A systematic bias could be detected for all items: The shorter the temporal interval the higher the behavioral frequencies reported by the respondents. Recommendations regarding questionnaire design are discussed.Empirische Befunde belegen, dass bei der Erfassung von Verhaltenshäufigkeiten durch Verhaltenshäufigkeiten das Frageformat starke Auswirkungen auf das Antwortverhalten hat: Eine Erfassung mittels Antwortskalen führt beispielsweise aufgrund der Tendenz zur Mitte zu verzerrten Antworten. Die Literatur zum Fragebogendesign empfiehlt daher ein offenes Frageformat, bei dem der Respondent frei eine Zahl angeben kann. Die Frage enthält dabei ein bestimmtes Zeitintervall, für das die Antwort gegeben werden soll. In der vorliegenden Studie wird überprüft, ob dieses Intervall ebenfalls Auswirkung auf die berichtete Verhaltenshäufigkeit hat. Eine studentische Stichprobe berichtete im offenen Frageformat über Verhaltenshäufigkeiten hinsichtlich ihrer sozialen Aktivitäten, der Nutzung universitärer Angebote und ihres Studienalltags. Between-subjects wurden die identischen Fragen mittels unterschiedlicher Zeitintervalle (Woche, Monat, Quartal, Semester) erfasst. Es zeigte sich für alle Items ein konsistenter und systematischer Bias in der Form, dass bei kürzerem Zeitintervall signifikant höhere Verhaltenshäufigkeiten berichtet werden. Empfehlungen zur Erfassung von Verhaltenshäufigkeiten werden abgeleitet

Predictors of RSV LRTI Hospitalization in Infants Born at 33 to 35 Weeks Gestational Age: A Large Multinational Study (PONI)

Preterm infants are at high risk of developing respiratory syncytial virus (RSV)-associated lower respiratory tract infection (LRTI). This observational epidemiologic study evaluated RSV disease burden and risk factors for RSV-associated LRTI hospitalization in preterm infants 33 weeks+0 days to 35 weeks+6 days gestational age not receiving RSV prophylaxis

Odd Ratios of Risk Factors for RSV-Associated Hospitalization in Multivariable Analysis (Selected by Backward Selection).

<p>ICU/PICU = neonatal/pediatric intensive care units; non-hsCHD = non-hemodynamically significant congenital heart disease; OR = odds ratio; RSV = respiratory syncytial virus. *On 1 October. ^†At birth.</p

Predictive Models of 4 and 6 Prognostic Factors for Hospitalization Due to RSV-Associated LRTI With Optimism-Corrected Performance Value.

<p>Predictive Models of 4 and 6 Prognostic Factors for Hospitalization Due to RSV-Associated LRTI With Optimism-Corrected Performance Value.</p

Study Flow Diagram.

<p>LRTI = lower respiratory tract infection; RSV = respiratory syncytial virus.</p