Consistency and variability in functional localisers

Much of research surrounding reading difficulties is based solely on behavioral analyses.A child’s early and correct diagnosis is imperative to insure that they receive adequate resourcesand support which can severely affect equal opportunities for success in the future. Afterstudying different methods of diagnosis for reading difficulties, we saw most of these tests couldnot assert significant differences between children with and without reading difficulties. Thiscontributes to diagnoses being heavily reliant on behavioral observations. Since statisticallysignificant distinction can’t be made with behavioral tests, researchers are pushed to exploredifferent approaches.In order to explore more concrete approaches to diagnoses, we then looked at fMRI datato determine if we could accurately attribute functional localizations in the brain to responses towords and pictures. Through this, researchers aimed to use specific neuroanatomical images todetermine how the brain activity of a person without reading difficulties corresponds to wordandimage-processing. We compared data between two runs of the same subject as well asbetween subjects in order to determine consistency of the study. Although we could not findconsistency between subject, we were able to explore possible ways to optimize fMRI studies forfuture diagnoses. We hope to one day optimize the data collection system in order to use thelocalization of brain activity to diagnose children with reading difficulties in an unbiased,systematic way

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

Spatial and temporal characteristics of error-related activity in the human brain

A number of studies have focused on the role of specific brain regions, such as the dorsal anterior cingulate cortex during trials on which participants make errors, whereas others have implicated a host of more widely distributed regions in the human brain. Previous work has proposed that there are multiple cognitive control networks, raising the question of whether error-related activity can be found in each of these networks. Thus, to examine error-related activity broadly, we conducted a meta-analysis consisting of 12 tasks that included both error and correct trials. These tasks varied by stimulus input (visual, auditory), response output (button press, speech), stimulus category (words, pictures), and task type (e.g., recognition memory, mental rotation). We identified 41 brain regions that showed a differential fMRI BOLD response to error and correct trials across a majority of tasks. These regions displayed three unique response profiles: (1) fast, (2) prolonged, and (3) a delayed response to errors, as well as a more canonical response to correct trials. These regions were found mostly in several control networks, each network predominantly displaying one response profile. The one exception to this “one network, one response profile” observation is the frontoparietal network, which showed prolonged response profiles (all in the right hemisphere), and fast profiles (all but one in the left hemisphere). We suggest that, in the place of a single localized error mechanism, these findings point to a large-scale set of error-related regions across multiple systems that likely subserve different function

ACC Neuro-over-Connectivity Is Associated with Mathematically Modeled Additional Encoding Operations of Schizophrenia Stroop-Task Performance

Functional magnetic resonance imaging at 7.0 Tesla was undertaken among Schizophrenia participants (Sz), and clinical (major mood disorder; MDD) and healthy controls (HC), during performance of the Stoop task. Stroop conditions included congruent and incongruent word color items, color-only items, and word-only items. Previous modeling results extended to this most widely used selective-attention task. All groups executed item-encoding operations (subprocesses of the item encoding process) at the same rate (performance accuracy being similarly high throughout), thus displaying like processing capacity; Sz participants, however, employed more subprocesses for item completions than did the MDD participants, who in turn used more subprocesses than the HC group. The reduced efficiency in deploying cognitive-workload capacity among the Sz participants was paralleled by more diffuse neuroconnectivity (Blood-Oxygen-Level-Dependent co-activation) with the anterior cingulate cortex (ACC) (Broadman Area 32), spreading away from this encoding-intensive region; and by less evidence of network dissociation across Stroop conditions. Estimates of cognitive work done to accomplish item completion were greater for the Sz participants, as were estimates of entropy in both the modeled trial-latency distribution, and its associated neuro-circuitry. Findings are held to be symptom and assessment significant, and to have potential implications for clinical intervention

Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging

In der angewandten Statistik können Regressionsmodelle mit hochdimensionalen Koeffizienten auftreten, die sich nicht mit gewöhnlichen Computersystemen schätzen lassen. Dies betrifft unter anderem die Analyse digitaler Bilder unter Berücksichtigung räumlich-zeitlicher Abhängigkeiten, wie sie innerhalb der medizinisch-biologischen Forschung häufig vorkommen. In der vorliegenden Arbeit wird ein Verfahren formuliert, das in der Lage ist, Regressionsmodelle mit hochdimensionalen Koeffizienten und nicht-normalverteilten Zielgrößen unter moderaten Anforderungen an die benötigte Hardware zu schätzen. Hierzu wird zunächst im Rahmen strukturiert additiver Regressionsmodelle aufgezeigt, worin die Limitationen aktueller Inferenzansätze bei der Anwendung auf hochdimensionale Problemstellungen liegen, sowie Möglichkeiten diskutiert, diese zu umgehen. Darauf basierend wird ein Algorithmus formuliert, dessen Stärken und Schwächen anhand von Simulationsstudien analysiert werden. Darüber hinaus findet das Verfahren Anwendung in drei verschiedenen Bereichen der medizinisch-biologischen Bildgebung und zeigt dadurch, dass es ein vielversprechender Kandidat für die Beantwortung hochdimensionaler Fragestellungen ist.In applied statistics regression models with high-dimensional coefficients can occur which cannot be estimated using ordinary computers. Amongst others, this applies to the analysis of digital images taking spatio-temporal dependencies into account as they commonly occur within bio-medical research. In this thesis a procedure is formulated which allows to fit regression models with high-dimensional coefficients and non-normal response values requiring only moderate computational equipment. To this end, limitations of different inference strategies for structured additive regression models are demonstrated when applied to high-dimensional problems and possible solutions are discussed. Based thereon an algorithm is formulated whose strengths and weaknesses are subsequently analyzed using simulation studies. Furthermore, the procedure is applied to three different fields of bio-medical imaging from which can be concluded that the algorithm is a promising candidate for answering high-dimensional problems

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

Neural Field Theory of Nonlinear Wave-Wave and Wave-Neuron Processes

Biophysical processes related to the modulation of cellular mechanisms occur due to either presynaptic or postsynaptic effects. These processes involve some physiological phenomena whose different dynamics can potentially be distinguished via traces they leave in the power spectra of brain activity and/or connectivity fluctuations. Systematic expansion of NFT equations in terms of nonlinear response functions is formulated in Chapter 2 to enable a wide variety of nonlinear wave-wave and wave-neuron processes. This theory helps to handle neural quantities such as firing rates, neural field, soma voltage, threshold, and coupling strength, along with their steady state values and perturbations. Many physiological processes such as facilitation, habituation, and refractoriness can be interpreted as the consequences of neural feedbacks that allow presynaptic and postsynaptic firing rates to modulate firing thresholds or synaptic strengths at a given location. NFT is used to analyze such feedback processes to determine their signatures, which are measurable through fluctuations in the power spectra of brain activity in Chapter 3. Depending on the feedback processes, these signatures include either enhancement or reduction of low-frequency activities, effects near the alpha resonance (enhancements and/or resonance splitting), and the appearance of new resonances. Physiological phenomena related to nonlinear feedback processes can potentially be identified and distinguished by means of these different spectral signatures. The spatiotemporal power spectra of connectivity fluctuations are also analyzed via NFT in Chapter 4, which also show distinctive features. Some spectral signatures result from the contributions from discrete spatial modes to the frequency power spectra depending on feedbacks. Some of these appear to be characteristic of just one feedback type and can potentially be used as diagnostics in experiments. Some ideas for future work are mentioned in Chapter 5

Biomedical Signal and Image Processing

Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based

Examining the potential clinical value of curcumin in the prevention and diagnosis of Alzheimer\u27s disease

Curcumin derived from turmeric is well documented for its anti-carcinogenic, antioxidant and anti-inflammatory properties. Recent studies show that curcumin also possesses neuroprotective and cognitive-enhancing properties that may help delay or prevent neurodegenerative diseases, including Alzheimer’s disease (AD). Currently, clinical diagnosis of AD is onerous, and it is primarily based on the exclusion of other causes of dementia. In addition, phase III clinical trials of potential treatments have mostly failed, leaving disease-modifying interventions elusive. AD can be characterised neuropathologically by the deposition of extracellular β amyloid (Aβ) plaques and intracellular accumulation of tau-containing neurofibrillary tangles. Disruptions in Aβ metabolism/clearance contribute to AD pathogenesis. In vitro studies have shown that Aβ metabolism is altered by curcumin, and animal studies report that curcumin may influence brain function and the development of dementia, because of its antioxidant and anti-inflammatory properties, as well as its ability to influence Aβ metabolism. However, clinical studies of curcumin have revealed limited effects to date, most likely because of curcumin’s relatively low solubility and bioavailability, and because of selection of cohorts with diagnosed AD, in whom there is already major neuropathology. However, the fresh approach of targeting early AD pathology (by treating healthy, pre-clinical and mild cognitive impairment-stage cohorts) combined with new curcumin formulations that increase bioavailability is renewing optimism concerning curcumin-based therapy. The aim of this paper is to review the current evidence supporting an association between curcumin and modulation of AD pathology, including in vitro and in vivo studies. We also review the use of curcumin in emerging retinal imaging technology, as a fluorochrome for AD diagnostics