O objetivo deste trabalho é apresentar os métodos originais em Bioinformática desenvolvidos para a análise estatística na interpretação dos dados de duas técnicas baseadas em imagens: a técnica de microarranjos de DNA e a técnica de ressonância magnética funcional. O interesse principal é abordar essas técnicas experimentais quando enfrenta-se uma situação clara de amostras escassas, isto é, quando existem relativamente poucas observações experimentais do fenômeno estudado, sendo a análise individual/personalizada o representante extremo desta situação, que tem que ser resolvida. Para tanto, opta-se pelo uso da Inferência Bayesiana no contexto da Teoria da Decisão sob Incerteza, implementada computacionalmente sob o arcabouço dos Sistemas de Suporte à Decisão. Ambas as tecnologias estudadas produzem dados complexos, baseados na interpretação das diferenças entre imagens obtidas da resposta do sistema a um estímulo e da resposta numa situação controle. O resultado deste trabalho é o desenvolvimento de dois sistemas de suporte à decisão, chamados HTself e Dotslashen, para a análise de dados de microarranjos e ressonância magnética funcional, respectivamente; e de seus métodos matemáticos/computacionais subjacentes. Os sistemas desenvolvidos extraem conhecimento racional de bancos-de-dados normativos, através de modelos matemáticos específicos, contornando então o problema de amostras escassas. Finalmente, neste trabalho são descritas aplicações a problemas reais, para destacar a utilidade dos sistemas de suporte à decisão desenvolvidos nas áreas de Biologia Molecular e Neuroimagem Funcional. / The goal of this work is to present the novel Bioinformatics methods that were developed aiming the statistical analysis of two image-based techniques: DNA microarrays and functional magnetic resonance imaging. The main interest is to approach these experimental techniques in small sample size situations, i.e., when there are relatively few experimental observations of the phenomena of interest, for which the case of single subject/datum analysis is its most extreme. In order to approach these problems we chose to use Bayesian Inference in the context of the Decision Theory under Uncertainty, computationally implemented under the Decision Support Systems framework. Both technologies produce complex data, based on the interpretation of differences between images from the response to a given stimulus and the control situation. The result of this work is the development of two decision support systems, called HTself and Dotslashen, to analyze microarray and functional magnetic resonance imaging data, respectively; and the underling mathematical and computational methods. These systems use the rational knowledge from normative databases implemented in specific mathematical models, overcoming the problem of small sample size. Finally, in this work it is described applications to real problems in order to stress the utility for Molecular Biology and Functional Neuroimaging of the developed decision support systems.

The ability of the brain to change and form new neuropathways after brain injury is remarkable. The current study investigates the brains ability to form new pathways for language processing following traumatic brain injury (TBI), specifically a left temporal lobectomy. Two subjects participated in this study; one participant with TBI and one age-matched control. Sentence stimuli consisted of four types: semantically correct, semantically incorrect, syntactically correct, and syntactically incorrect. Participants underwent a fMRI scan while the auditory stimuli were presented in four blocks. Participants were asked to record if the sentence was correct or incorrect by pressing the corresponding button. It was found that reaction times for both the participant with TBI and the control were longer for the incorrect conditions. The participant with TBI generally had longer reaction times compared to the control participant and had more errors. During the fMRI scans, patient movement occurred. The block design was not set up to account for movement. Due to this factor, imaging results are questionable. While there were differences between the participant with TBI and the control participant, these differences are expected to be much larger in someone with this degree of brain injury. It is recommended for further studies to be conducted in this area with a revised block design to account for patient movement.

Remembering previously experienced events can happen as a result of an effortful retrieval attempt. At other occasions, a memory can enter our minds without any apparent effort – or, indeed, intention - to retrieve. Although it has long been appreciated that retrieval from episodic memory is intertwined with cognitive control, the neural mechanisms of memory-control interactions remain unclear. In this thesis I have used functional magnetic resonance imaging (fMRI) and scalp-recorded event-related potentials (ERP) to study the neural basis of episodic retrieval at varying levels of cognitive control. The dorsolateral prefrontal cortex (dlPFC) has been suggested to support a cognitive control mechanism (context processing) which is relevant during various situations that demand maintenance of current goals and rules. Although increased dlPFC recruitment with increasing context processing demands has been demonstrated during episodic retrieval, there are relatively few studies directly comparing the engagement of dlPFC during episodic retrieval with that during other task domains. In Study I, context processing demands were amplified in episodic retrieval, auditory attention and emotion regulation tasks. This led to overlapping dlPFC recruitment in the first two domains and a divergent reliance on ventromedial prefrontal cortex in the emotion domain. Thus, when selection between competing representations needs to be carried out in accordance with the currently relevant goals and task rules, the episodic memory system interacts with domain-general cognitive control mechanisms. Studies II and III explored the reactive nature of retrieval-specific control mechanisms: can we flexibly switch between semantic and episodic retrieval based on the information extracted from a retrieval cue? This was studied using a recognition memory task where the relevant information could with equal probability be supplied by the semantic or the episodic memory system. The fMRI results (Study II) showed that the brain activation during the ‘episodic’ but not the ‘semantic’ trials was expressed in the right prefrontal cortex. As the order of trials was unpredictable, the corresponding changes in brain activation might be evoked by differences in early cue-trace interactions. An event-related potential study (Study III) with the same experimental protocol as in Study II showed that neural processing corresponding to the two trial types diverged as early as in the time window 100-140 ms post-cue onset, thus highlighting the importance of early cue-trace matching in the selection of further retrieval processing. Study IV explored incidental episodic retrieval. Although this form of retrieval is a common experience in everyday life and a disturbing symptom in some psychiatric conditions, it is not clear how such spontaneous expressions of memory are initiated and to what extent the prefrontal cortex is engaged. The fMRI results showed, consistent with Study I, that dlPFC is specifically associated with the intention to retrieve, independently of success. Retrieval success engaged similar networks for incidentally as well as intentionally retrieved memories, comprising the hippocampus, precuneus, ventrolateral PFC, and the anterior cingulate cortex. Collectively, the fMRI and ERP results indicated that incidental retrieval was initiated by early (< 200 ms) oldness estimation carried out on the semantic information extracted from the retrieval cues. Taken together, the results of this thesis indicate that episodic retrieval can be initiated via two routes:  a bottom-up input rising early during the cue processing, and a top-down input provided by the cognitive control processes mediated by the prefrontal cortex.

Functional Magnetic Resonance Imaging (fMRI) is one of the best techniques for neuroimaging and has revolutionized the way to understand the brain functions. It measures the changes in the blood oxygen level-dependent (BOLD) signal which is related to the neuronal activity. Complexity of the data, presence of different types of noises and the massive amount of data makes the fMRI data analysis a challenging one. It demands efficient signal processing and statistical analysis methods.  The inference of the analysis is used by the physicians, neurologists and researchers for better understanding of the brain functions.      The purpose of this study is to design a toolbox for fMRI data analysis. It includes methods to detect the brain activity maps, estimation of the hemodynamic response (HDR) and the connectivity of the brain structures. This toolbox provides methods for detection of activated brain regions measured with Bayesian estimator. Results are compared with the conventional methods such as t-test, ordinary least squares (OLS) and weighted least squares (WLS). Brain activation and HDR are estimated with linear adaptive model and nonlinear method based on radial basis function (RBF) neural network. Nonlinear autoregressive with exogenous inputs (NARX) neural network is developed to model the dynamics of the fMRI data.  This toolbox also provides methods to brain connectivity such as functional connectivity and effective connectivity.  These methods are examined on simulated and real fMRI datasets.

Women are twice as likely as men to suffer from depression and anxiety disorders and have an increased risk of onset during periods associated with hormonal changes, such as the postpartum period and the menopausal transition. Furthermore, some women seem more sensitive to normal hormone fluctuations across the menstrual cycle, since approximately 3-5% suffers from premenstrual dysphoric disorder (PMDD). Why these disorders are so common in women has not been established but there is a probable involvement of the ovarian hormones. The aim of this thesis was to investigate the effect of the ovarian hormones on the female brain during different reproductive states using psychological tests known to affect brain activity in different ways. Paper one examined the effect of the ovarian hormones on prepulse inhibition (PPI) on the acoustic startle response (ASR) and comprised cycling women and postmenopausal women. The cycling women had lower levels of PPI compared to postmenopausal women and postmenopausal women with moderate estradiol levels had lower PPI compared to postmenopausal women with low estradiol levels. Paper two examined the effect of anticipation and affective modulation on the ASR in women with PMDD and healthy controls. Women with PMDD have an increased modulation during anticipation of affective pictures compared to healthy controls during the luteal phase of the menstrual cycle. Paper three examined brain activity during response inhibition among women with PMDD and healthy controls by the use of a Go/NoGo task and fMRI. Women with PMDD displayed a decreased activity in the left insula during follicular phase and an increased activity during the luteal phase compared to controls. Paper four comprised women in the postpartum period and non-pregnant controls to examine brain activity during response inhibition. While this study revealed decreased activity at 4 weeks postpartum compared to 48 hours postpartum we cannot ascertain the role of the ovarian steroids, since none of the significant brain areas correlated with ovarian steroid or neurosteroid serum concentrations. The results of this thesis demonstrate that the ovarian hormones, or at least various hormonal states, have a probable impact on how the female brain works.

Magnetic resonance imaging (MRI) is a very versatile imaging modality which can be used to acquire several different types of images. Some examples include anatomical images, images showing local brain activation and images depicting different types of pathologies. Brain activation is detected by means of functional magnetic resonance imaging (fMRI). This is useful e.g. in planning of neurosurgical procedures and in neurological research. To find the activated regions, a sequence of images of the brain is collected while a patient or subject alters between resting and performing a task. The variations in image intensity over time are then compared to a model of the variations expected to be found in active parts of the brain. Locations with high correlation between the intensity variations and the model are considered to be activated by the task. Since the images are very noisy, spatial filtering is needed before the activation can be detected. If adaptive filtering is used, i.e. if the filter at each location is adapted to the local neighborhood, very good detection performance can be obtained. This thesis presents two methods for adaptive spatial filtering of fMRI data. One of these is a modification of a previously proposed method, which at each position maximizes the similarity between the filter response and the model. A novel feature of the presented method is rotational invariance, i.e. equal sensitivity to activated regions in different orientations. The other method is based on bilateral filtering. At each position, this method averages pixels which are located in the same type of brain tissue and have similar intensity variation over time. A method for robust correlation estimation is also presented. This method automatically detects local bursts of noise in a signal and disregards the corresponding signal segments when the correlation is estimated. Hence, the correlation estimate is not affected by the noise bursts. This method is useful not only in analysis of fMRI data, but also in other applications where correlation is used to determine the similarity between signals. Finally, a method for correcting artifacts in complex MR images is presented. Complex images are used e.g. in the Dixon technique for separate imaging of water and fat. The phase of these images is often affected by artifacts and therefore need correction before the actual water and fat images can be calculated. The presented method for phase correction is based on an image integration technique known as the inverse gradient. The method is shown to provide good results even when applied to images with severe artifacts.

A novel method is presented for analyzing fMRI data, which relies on probabilistic estimates of microanatomically defined regions in individual fMRI volunteers. Postmortem structural and cytoarchitectonic information from the Julich/Dusseldorf group in Germany is aligned to the high-resolution structural MR images of functional MRI volunteers. This is achieved using nonlinear registration, which is applied only to the region of interest. The registered postmortem datasets are then combined into probability maps for microanatomically defined regions that are tailored to the anatomy of individual fMRI volunteers. These are then used as weighted spatial filters on functional MR data. In this thesis, three regions of the primary auditory cortex (located on Heschl's gyrus) have been targeted, and the analysis method is used to explore how these three areas respond to different kinds of sound. Regions Te1.0 and Te1.2 both demonstrate pitch sensitivity, consistent with published observations of the functional response of homologous regions in nonhuman primates. Area Te1.1 displayed sensitivity to both noise and pitch, consistent with the theory that it is homologous with the microanatomically similar area CM in nonhuman primates. Furthermore, the custom probability maps are much less diffuse and anatomically more precise than previous versions generated using the same postmortem data, and therefore permit a more sensitive and anatomically precise analysis of functional activity. This method could be applied to any other microanatomically defined region that has been characterized in the Julich postmortem dataset. / Thesis (Master, Computing) -- Queen's University, 2008-09-26 19:50:54.582

Background: Functional magnetic resonance imaging (fMRI) can require extensive preprocessing to minimize noise and maximize signal. There is evidence suggesting that fixed-subject preprocessing pipelines, the current standard in fMRI preprocessing, are suboptimal compared to individual-subject pipelines. Aim: We sought to test if individual-subject preprocessing pipeline optimization, compared to fixed, resulted in stronger and more reliable brain-patterns in episodic recognition. Methodology: 27 young healthy controls were scanned via fMRI while performing forced-choice episodic recognition. Several sets of fMRI preprocessing pipelines were tested and optimized in a fixed and individual-subject manner, using methods outlined by Churchill et al. (2011). Results: Individual-subject pipeline optimization, compared to fixed, significantly increased reproducibility, significantly increased the detection of positively and negatively activated voxels, and resulted in a brain-pattern with significant correlation to a task behavioral measure. Conclusions: Individual-subject pipeline optimization, compared to fixed, led to stronger and more reliable brain-patterns that are significantly correlated with behavior.

Background: Functional magnetic resonance imaging (fMRI) can require extensive preprocessing to minimize noise and maximize signal. There is evidence suggesting that fixed-subject preprocessing pipelines, the current standard in fMRI preprocessing, are suboptimal compared to individual-subject pipelines. Aim: We sought to test if individual-subject preprocessing pipeline optimization, compared to fixed, resulted in stronger and more reliable brain-patterns in episodic recognition. Methodology: 27 young healthy controls were scanned via fMRI while performing forced-choice episodic recognition. Several sets of fMRI preprocessing pipelines were tested and optimized in a fixed and individual-subject manner, using methods outlined by Churchill et al. (2011). Results: Individual-subject pipeline optimization, compared to fixed, significantly increased reproducibility, significantly increased the detection of positively and negatively activated voxels, and resulted in a brain-pattern with significant correlation to a task behavioral measure. Conclusions: Individual-subject pipeline optimization, compared to fixed, led to stronger and more reliable brain-patterns that are significantly correlated with behavior.

Die funktionelle Magnetresonanztomographie (fMRT) mit einer Feldstärke von 3 T ist in der prächirurgischen Nutzen-Risiko-Evaluation von Patienten mit Hirntumoren in bzw. im Bereich funktionell bedeutsamer Regionen, wie beispielsweise in Nachbarschaft zum Sulcus centralis, gut etabliert. Das Konzept der Neuroplastizität umfasst unter anderem Mechanismen zur zerebralen kortikalen Reorganisation nach Hirnschädigung. Ziel der vorliegenden prospektiven fMRT-Verlaufsstudie ist die Evaluation der noch wenig bekannten längerfristigen funktionellen Veränderungen des Gehirns nach neurochirurgischer Intervention. Zu diesem Zwecke wurden 14 Patienten mit Hirntumoren innerhalb oder in der Nähe des primären motorischen Cortex (MI) in die Studie eingeschlossen, welche sich einer neurochirurgischen Behandlung unterzogen. Bei 12 der Patienten wurde sowohl prä- als auch postoperativ eine funktionelle Bildgebung (fMRT) anhand des motorischen Paradigmas des unimanuellen und bimanuellen Fingertappens in einem 3 T MRT-Scanner durchgeführt. Wegen Bewegungsartefakten konnten lediglich 9 der Patienten in die weitere Auswertung eingeschlossen werden. Als Kontrollgruppe diente eine einmalige Untersuchung von neun gesunden Probanden. An längerfristigen Reorganisationsmustern konnten bei Patienten ohne Handparese sowohl die Rekrutierung der geschädigten als auch der intakten Hemisphäre des kortikalen motorischen Netzwerkes aufgezeigt werden. Tumorwachstum im Bereich des supplementär-motorischen Areals (SMA) ging mit einer bilateralen Rekrutierung der rostralen Portion des SMA (SMAr) einher. Die postoperative Reorganisation des motorischen Netzwerkes umfasste unter kontraläsionalen Fingertappen eine Lateralisierung der Aktivierung der SMAr zur nicht betroffenen Hemisphäre. Diese war umso ausgeprägter je größer das Tumorvolumen oder je näher der Tumor zur SMAr gelegen war. Demnach kann eine Dysfunktion der ipsilateralen SMAr präoperativ durch eine bilaterale und postoperativ durch eine kontraläsionale Rekrutierung kompensiert werden.