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.

Chemical intolerance (CI) is a term that refers to the surprisingly common phenomenon of persons getting ill from everyday chemicals. Although seemingly similar to asthma and allergies, CI sufferers do not react to exposures with increased histamine release. CI neither conforms to toxicological dose-response relationships as sufferers react to very low concentrations of chemicals assumed to be harmless. In addition, no particular chemical can be tied to any particular set of symptoms as in the case of other kinds of toxic injuries. The two overreaching goals of this thesis were to empirically investigate important hypotheses regarding CI, and to develop a theoretical framework that integrates previous theories of CI into a coherent whole.There are four empirical studies in this thesis. Utilizing event-related potentials (ERPs), magnitude estimations of perceived intensity, detection tests and functional magnetic resonance imaging (fMRI), the studies provided support for the following hypotheses: (1) persons with self-reported CI sensitize to olfactory and chemosomatosensory stimuli, whereas non-intolerant individuals habituate; (2) sensitization in CI is similar in terms of brain activation patterns to both non-clinical sensitization and other unexplained illnesses such as fibromyalgia; (3) persons with CI have an attention bias to chemical exposures, reflected by problems with withdrawing attention from such stimuli; (4) measures of peripheral hyperreactivity are correlated with chemosensory ERP measures; but failed to corroborate (5) the reactions of women resemble those found in persons with CI to a greater degree than the case in men.Three major theories of CI are also discussed. The neural sensitization theory describes CI as pathological and non-immunological increases in neural responsiveness. The conditioning theory describes CI as the result of basic associative learning mechanisms. The neurogenic inflammation theory describes CI as proliferation of sensory c-fibers and inflammatory responses carried to several parts of the body through axon reflexes and release of inflammatory mediators. The main point of the theoretical synthesis is that the theories offer different and complementary perspectives on CI, rather than presenting conflicting ontologies. With an integrated perspective, infected debates whether CI is a psychological or organic illness can hopefully be avoided.Finally, the unexplained characteristics of CI, the empirical findings and the theoretical accounts are described within the theoretical framework of signal detection theory. Several features of CI, e.g. sensitization and peripheral hyperreactivity, are described in terms of applying a low criterion (ß). / Kemisk intolerans, det vill säga att få symtom av vardagliga lukter, är ett förvånansvärt vanligt problem. Trots att åkomman i många avseenden liknar astma och allergi, reagerar de drabbade inte med exempelvis ökad histaminfrisättning. Kemisk intolerans överensstämmer inte heller med toxikologiska dos-responsförhållanden, eftersom de drabbade blir sjuka av väldigt låga koncentrationer av luktämnen. Enskilda kemikalier kan inte kopplas till en karaktäristisk symtombild, vilket är vanligt vid andra typer av toxikologiska skador. I denna avhandling har jag två mål. För det första undersöker jag viktiga hypoteser om kemisk intolerans. För det andra erbjuder jag ett teoretiskt ramverk där jag integrerar tidigare teorier om kemisk intolerans till en sammanhängande helhet.Den empiriska delen av avhandlingen består av fyra forskningsstudier. Baserat på händelserelaterade hjärnpotentialer (ERPs), magnitudestimationer av upplevd styrka, detektionstest samt funktionell magnetresonansavbildning (fMRI) stöder studierna följande hypoteser: (1) personer med självrapporterad kemisk intolerans sensitiserar till olfaktoriska och kemosomatosensoriska stimuli, medan icke-intoleranta individer habituerar; (2) med avseende på hjärnaktiveringsmönster liknar sensitisering hos kemiskt intoleranta det mönster man finner både i icke-klinisk sensitisering och i exempelvis fibromyalgi; (3) personer med kemisk intolerans har en benägenhet att uppmärksamma kemisk exponering, vilket reflekteras i en oförmåga att ignorera sådana stimuli; (4) mått på perifer hyperreaktivitet korrelerar med kemosensoriska ERP-mått. Hypotesen att (5) kvinnors reaktioner på kemosensoriska stimuli liknar de man kan finna hos de kemiskt intoleranta i större utsträckning än vad fallet är för män, stöds däremot inte.Tre teorier om kemisk intolerans diskuteras. Den neurala sensitiseringsteorin beskriver intoleransen som en patologisk ökning av neural aktivitet. Betingningsteorin beskriver kemisk intolerans som ett resultat av grundläggande associativa inlägningsmekanismer. Slutligen beskriver teorin om neurogen inflammation intoleransen som en förhöjd aktivering av c-fiberaktivitet och ökade inflammatoriska processer. Huvudargumentet i den teoretiska sammanfattningen är att dessa teorier erbjuder komplementära perspektiv på kemisk intolerans. Med ett integrerat perspektiv kan förhoppningsvis infekterade debatter om huruvida kemisk intolerans är en psykologisk eller organisk åkomma undvikas.De oförklarade egenskaperna av kemisk intolerans, de empiriska fynden, samt de teoretiska förklaringarna beskrivs slutligen inom ett teoretiskt ramverk som utgår från signaldetektionsteorin. Flera egenskaper hos kemisk intolerans beskrivs i termer av ett förändrat eller lågt satt kriterium (ß).

Motion-selectivity in human visual cortex comprises a number of different cortical loci including V1, V2, V3A, V3B, hV5/MT+ and V6 (Wandell et al., 2007). This thesis sought to investigate the specific functions of V3A and sub-divisions of hV5/MT+ (TO-1 and TO-2) by using transcranial magnetic stimulation (TMS) to transiently disrupt cortical activations within these areas during psychophysical tasks of motion perception. The tasks were chosen to coincide with previous non-human primate and human neuroimaging literature; translational, radial and rotational direction discrimination tasks and identification of the position of a focus of expansion. These results assert that TO-1 and TO-2 are functionally distinct subdivisions of hV5/MT+, as we have shown that both TO-1 and TO-2 are responsible for processing translational motion direction whilst only TO-2 is responsible for processing radial motion direction. In ipsilateral space, it was found that TO-1 and TO-2 both contribute to the processing of ipsilateral translational motion. Taken in a wider context, further results also suggested that these areas may form part of a network of cortical areas contributing to perception of self-motion (heading/egomotion), as TO-2 was not found to be responsible for processing the position of the central focus of expansion (imperative for self-direction). Instead, area V3A has been implicated as functionally responsible for processing this attribute of vision. Overall it is clear that TO-1, TO-2 and V3A have specific, distinct functions that contribute towards both parallel and serial motion processing pathways within the human brain.

This thesis provides a novel dynamic large-scale network perspective on brain activity of human sleep based on the analysis of unique human neuroimaging data. Specifically, I provide new information based on integrating spatial and temporal aspects of brain activity both in the transitions between and during wakefulness and various stages of non-rapid-eye movement (NREM) sleep. This is achieved through investigations of inter-regional interactions, functional connectivity (FC), between activity timecourses throughout the brain. Overall, the presented findings provide new important whole-brain insights for our current understanding of sleep, and potentially also of sleep disorders and consciousness in general. In Chapter 2 I present a robust global increase in similarity between the structural connectivity (SC) and the FC in slow-wave sleep (SWS) in almost all of the participants of two independent fMRI datasets. This could point to a decreased state repertoire and more rigid brain dynamics during SWS. Chapter 2 further identifies the changes in FC strengths between wakefulness and individual stages of NREM sleep across the whole-brain fMRI network. I report connectivity in posterior parts of the brain as particularly strong during wakefulness, while connections between temporal and frontal cortices are increased in strength during N1 and N2 sleep. SWS is characterised by a global drop in FC. In Chapter 3 I take advantage of rare MEG recordings of NREM sleep to show, for the first time, the feasibility of constructing source-space FC networks of sleep using power envelope correlations. The increased temporal information of MEG signals allows me to identify the specific frequencies underlying the FC differences identified in Chapter 2 with fMRI. The beta band (16 – 30 Hz) thus stands out as important for the strong posterior connectivity of wakefulness, while a range of frequency bands from delta (0.25 – 4 Hz) to sigma (13 – 16 Hz) all appear to contribute to N2-specific FC increases. Consistent with the fMRI results, slow-wave sleep shows the lowest level of FC. Interestingly, however, the MEG signals suggest a fronto-temporal network of high connectivity in the alpha band, possibly reflecting memory processes. In Chapter 4 I expand the within-frequency FC analysis of Chapter 3 to explore potential cross-frequency interactions in the MEG FC networks. It is shown that N2 sleep involves an abundance of frequency cross-talk, while SWS includes very little. A multi-layer network approach shows that the gamma band (30 – 48 Hz) is particularly integrated in wakefulness. Chapter 5 addresses the identified MEG FC findings from the perspective of traditional spectral sleep staging. By correlating temporal changes in spectral power at the sensor level to fluctuations in average FC, a specific type of transient events is found to underlie the strong N2-specific coupling in static FC values. Lastly, in Chapter 6 I make the leap out of the constraints of traditional low-resolution sleep staging, and extract dynamic states of FC from fMRI timecourses in a completely unsupervised fashion. This provides a novel representation of whole-brain states of sleep and the dynamics governing them. I argue that data-driven approaches like this are necessary to fully characterise the spatiotemporal principles underlying wakefulness and sleep in the human brain.