Patients with Amyotrophic lateral sclerosis (ALS) often experience cognitive impairment that accompanies degeneration of the motor system. A valuable tool for assessing cognitive control over behaviour is the antisaccade task which requires: 1) inhibition of the automatic response to look towards an eccentric visual stimulus (prosaccade) to instead 2) redirect gaze in the opposite direction of the stimulus (antisaccade). Psychometric tests were used to quantify the degree of impairment, while eye tracking, functional magnetic resonance imaging (fMRI) and structural MRI were combined to identify the neural correlates of cognitive impairment in ALS. We predict ALS patients will have executive dysfunction and grey matter loss in executive and oculomotor control areas that will affect antisaccade performance and will alter the corresponding brain activation. ALS patients and age-matched controls participated in a rapid-event-related fMRI design with interleaved pro- and antisaccade trials. Catch trials (no stimulus presented after instructional cue to prepare pro- or antisaccade) allowed us to discern the preparatory period from the execution period. ALS patients were biased towards automatic saccade responses, and had greater difficulty with antisaccades relative to controls in terms of correct and timely responses. We found that worsened antisaccade performance in ALS correlated with the degree of cognitive impairment. Generally, we found trends of increased brain activation during the preparatory period of antisaccades in ALS patients compared to controls in most oculomotor areas; meanwhile few differences were seen during execution. Structural analyses revealed ALS patients had decreased grey matter thickness in frontotemporal and oculomotor regions such as the frontal and supplementary eye fields (FEF, SEF) and the dorsolateral prefrontal cortex (DLPFC). These findings suggest that loss of structural integrity and executive dysfunction may elicit compensation mechanisms to improve functional and behavioural performance. Despite this compensation, ALS patients still performed worse on antisaccades than controls. Further investigation to expand the current data set should improve our ability to assuredly identify the neural correlates of cognitive decline in ALS, and may provide a model system to use for critical evaluation of future therapies and interventions for ALS. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2011-09-22 14:20:39.704

This master’s thesis deals with functional magnetic resonance and monitoring of the effect of acquisition acceleration methods on the quality of functional images and observed BOLD signal. The basic principles of magnetic resonance imaging, the explanation of the specifics of functional magnetic resonance and the formation and scanning of BOLD signal are described here. Subsequently, there is the definition of fMRI experiment and description of sequences used for fMRI, focusing on aquisition acceleration techniques. The influence of sequence parameters on image quality and the data processing methods are explained aftewards. The practical part describes the parameters of used sequences, the acquisition procedure and the task for the subject during aquisition. Data from 26 healthy volunteers were obtained and analyzed afterwards. Based on this, the differencesbetween the different sequence variants were evaluated and the initial assumption that the multi-echo acquisition yields better results with faster measurements than single-echo was confirmed.

L’imagerie de l’activité neuronale (AN) permet d’étudier le fonctionnement normal et pathologique du cerveau humain, en plus d’aider au diagnostic et à la planification d’interventions neurochirurgicales. L’électroencéphalographie (EEG) et l’imagerie par résonance magnétique fonctionnelle (IRMf) comptent parmi les modalités d’imagerie fonctionnelle les plus utilisées en recherche et en clinique. Plusieurs éléments de la structure cérébrale peuvent toutefois influencer les signaux mesurés, de sorte qu’ils ne reflètent pas uniquement l’AN. Il importe donc d’en tenir compte pour bien interpréter les résultats, surtout lorsqu’on compare des sujets à l’anatomie cérébrale très différente. En outre, la maturation, le vieillissement et certaines pathologies s’accompagnent de changements structurels du cerveau. Ceci complique l’analyse de données longitudinales et la comparaison d’un groupe cible avec un groupe contrôle. Or, notre compréhension des interactions structure-signal demeure incomplète et très peu d’études en tiennent compte. Mon projet de doctorat a consisté à étudier les impacts de la structure cérébrale sur les signaux d’EEG et d’IRMf ainsi qu’à explorer des pistes de solution pour s’en affranchir. J’ai d’abord étudié l’effet de l’amincissement cortical dû au vieillissement sur la désynchronisation liée à l’événement (« event-related desynchronization » - ERD) en EEG. Les résultats ont mis en lumière une relation linéaire négative entre l’ERD et l’épaisseur corticale, ce qui a permis de corriger les signaux par régression. J’ai ensuite étudié l’impact de la présence de veines sur la réponse BOLD (blood-oxygen-level dependent) mesurée en IRMf suite à une stimulation visuelle. Ces travaux ont démontré que la densité veineuse locale, qui varie fortement d’une région et d’un sujet à l’autre, corrèle positivement avec l’amplitude et le délai de la réponse BOLD. Finalement, j’ai adapté une technique de classification de données visant à améliorer la détection des régions du cortex activées en IRMf. Cette méthode permet d’éviter plusieurs problèmes de l’analyse classique en IRMf, de réduire l’impact de la structure cérébrale sur les résultats obtenus et d’établir des cartes d’activité cérébrale contenant plus d’information. Globalement, ces travaux contribuent à l’amélioration de notre compréhension des interactions structure-signal en EEG et en IRMf, ainsi qu’au développement de méthodes d’analyse réduisant leur impact sur l’interprétation des données en termes d’AN. / Abstract : Imaging neural activity allows studying normal and pathological function of the human brain, while also being a useful tool for diagnosis and neurosurgery planning. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are some of the most commonly used functional imaging modalities, both in research and clinic. Many aspects of cerebral structure can however influence the measured signals, so that they do not only reflect neural activity. Taking them into account is therefore of import to correctly interpret results, especially when comparing subjects displaying large differences in brain anatomy. In addition, maturation, aging as well as some pathologies are associated with changes in brain structure. This acts as a confounding factor when analysing longitudinal data or comparing target and control groups. Yet, our understanding of structure-signal relationships remains incomplete and very few studies take them into account. My Ph.D. project consisted in studying the impacts of cerebral structure on EEG and fMRI signals as well as exploring potential solutions to mitigate them. In that regard, I first studied the effect of age-related cortical thinning on event-related desynchronization (ERD) in EEG. Results allowed identifying a negative linear relationship between ERD and cortical thickness, enabling signal correction using regression. I then investigated how the presence of veins in a region impacts the blood-oxygen-level dependent (BOLD) response measured in fMRI following visual stimulation. This work showed that local venous density, which strongly varies across regions and subjects, correlates positively with the BOLD response amplitude and delay. Finally, I adapted a data clustering technique to improve the detection of activated cortical regions in fMRI. This method allows eschewing many problematic assumptions used in classical fMRI analyses, reducing the impacts of cerebral structure on results and establishing richer brain activity maps. Globally, this work contributes to further our understanding of structure-signal interactions in EEG and fMRI as well as to develop analysis methods that reduce their impact on data interpretation in terms of neural activity.

La complexité de la connaissance humaine est révèlée dans l'organisation spatiale et temporelle de la dynamique du cerveau. Nous pouvons connaître cette organisation grâce à l'analyse des signaux dépendant du niveau d'oxygène sanguin (BOLD), lesquels sont obtenus par l'imagerie par résonance magnétique fonctionnelle (IRMf). Nous observons des dépendances statistiques entre les régions du cerveau dans les données BOLD. Ce phénomène s' appelle connectivité fonctionnelle (CF). Des modèles computationnels sont développés pour reproduire la connectivité fonctionnelle (CF). Comme les études expérimentales précédantes, ces modèles assument que la CF est stationnaire, c'est-à-dire la moyenne et la covariance des séries temporelles BOLD utilisées par la CF sont constantes au fil du temps. Cependant, des nouvelles études expérimentales concernées par la dynamique de la CF à différentes échelles montrent que la CF change dans le temps. Cette caractéristique n'a pas été reproduite dans ces modèles computationnels précédants. Ici on a augmenté la non-linéarité de la dynamique locale dans un modèle computationnel à grande échelle. Ce modèle peut reproduire la grande variabilité de la CF observée dans les études expérimentales. / The complexity of human cognition is revealed in the spatio-temporal organization of brain dynamics. We can gain insight into this organization through the analysis of blood oxygenation-level dependent (BOLD) signals, which are obtained from functional magnetic resonance imaging (fMRI). In BOLD data we can observe statistical dependencies between brain regions. This phenomenon is known as functional connectivity (FC). Computational models are being developed to reproduce the FC of the brain. As in previous empirical studies, these models assume that FC is stationary, i.e. the mean and the covariance of the BOLD time series used for the FC are constant over time. Nevertheless, recent empirical studies focusing on the dynamics of FC at different time scales show that FC is variable in time. This feature is not reproduced in the simulated data generated by some previous computational models. Here we have enhanced the non-linearity of local dynamics in a large-scale computational model. By enhancing this non-linearity, our model is able to reproduce the variability of the FC found in empirical data.

Le but de ce travail de thèse a été de caractériser in vivo chez l’Homme, la connectivité cérébrale sur son versant fonctionnel, par le biais de deux techniques: la stéréoélectroencéphalographie (SEEG) et l’IRM fonctionnelle (IRMf) de repos. Ces travaux se sont intégrés dans le cadre du bilan préchirurgical des épilepsies du lobe temporal pharmacorésistantes, dont le but est de déterminer la zone épileptogène à réséquer pour traiter ces patients. Alors que plusieurs études en électrophysiologie ont montré que durant les crises, il existait une synchronisation anormalement élevée entre les structures impliquées dans les processus épileptogènes, aucune donnée de connectivité n’était disponible en période intercritique. Pourtant, la période intercritique est le siège d’anomalies interictales enregistrées en EEG, de profonds remaniements morphologiques, et est associée à des troubles cognitifs. Nous apportons avec ce travail, grâce au recueil et au traitement de signaux SEEG et IRMf enregistrés durant la période intercritique, de nouvelles connaissances i) sur l’organisation de la connectivité fonctionnelle basale (CFB) chez les sujets sains ; ii) sur les altérations de la CFB chez des groupes de patients mais également au niveau individuel ; iii) sur le rapport entre ces anomalies de CFB et les troubles cognitifs observés chez ces patients ; iv) enfin, sur les différences et les similitudes de la CFB évaluée par SEEG et IRMf chez les mêmes sujets, ouvrant ainsi de nouvelles perspectives dans la compréhension des relations entre le signal BOLD et le signal EEG. / The aim of this thesis was to characterize the Human brain functional connectivity in vivo based on signals recorded using stereoelectroencephalography (SEEG) and resting-state functional MRI (fMRI). This work was conducted during the presurgical assessment of drug resistant temporal lobe epilepsy, which aims at determining the epileptogenic zone to be removed to treat these patients. While several electrophysiological studies have shown high synchronization between structures involved in the epileptogenic process during seizure, no similar connectivity data was available during inter-critical period. However, the interictal period is characterized by spikes recorded on EEG, morphological alterations and cognitive impairment. By analyzing fMRI and SEEG signals recorded during the interictal period, this work provides new insights into, i) basal functional connectivity (BFC) organization in healthy subjects, ii) BFC alterations in patients groups but also at the individual level, iii) the relationship between these BFC abnormalities and cognitive impairment observed in these patients; iv) the differences and similarities of BFC evaluated by SEEG and fMRI in the same subjects, thus opening up new perspectives in better understanding of relationships between BOLD and SEEG signal coupling.

This master´s thesis deals with processing and analysis of data, acquired from experimental examination performed with functional magnetic resonance imaging technique. It is an oddball type experimental task and its goal is an examination of cognitive functions of the subject. The principles of functional magnetic resonance imaging, possibilities of experimental design, processing of acquired data, modeling of a response of organism and statistical analysis are described in this work. Furthermore, particular parts of preprocessing and analysis are carried out using real data set from experiment. The main goal of this work is suggestion and realization of model, which enables advanced categorization of stimuli, considering the type of previous rare stimulus and the number of frequent stimuli within them. With its in-depth categorization, this model enables detail exploration of cerebral processes, associated mainly with attention, memory, expectancy or cognitive closure. The second point of that work is an evaluation of models of hemodynamic response, which are applied in statistical analysis of data from fMRI experiment. Comparison of basis functions, the models of hemodynamic response to experimental stimulation used for general linear model, is performed in this work. The result of this comparison is an evaluation of detection efficiency of activated voxels, false positivity rate and computational and user difficulty.

This thesis deals with functional magnetic resonance imaging (fMRI), in particular with dynamic causal modelling (DCM) as one of the methods for effective brain connectivity analysis. It has been studied the effect of signal coordinates selection, which was used as an input of DCM analysis, on its results based on simulated data testing. For this purpose, a data simulator has been created and described in this thesis. Furthermore, the methodology of testing the influence of the coordinates selection on DCM results has been reported. The coordinates shift rate has been simulated by adding appropriate levels of various types of noise signals to the BOLD signal. Consequently, the data has been analyzed by DCM. The program has been supplemented by a graphical user interface. To determine behaviour of the model, Monte Carlo simulations have been applied. Results in the form of dependence of incorrectly estimated connections between brain areas on the level of the noise signals have been processed and discussed.

This diploma thesis deals with an analysis of subject’s movement during measurements with funcional magnetic resonance imaging (fMRI). It focuses on methods of a movement artifacts detection and their removal in fMRI images. Thesis deals with metrics which are used for the movement rate of measured subjects evaluation. Metrics and a correction of movement are implemented into the programme in MATLAB. Comparison of subjects suffering from Parkinson’s disease with a group of healthy control was carried out. Tresholds of individual metrics were suggested and a criterion for the removal of subjects with high movement rate was determined.

<p>    </p> <p>Cerebrovascular diseases, such as stroke, constitute the most common life-threatening neurological disease in the United States. To support normal brain function, maintaining adequate brain perfusion (i.e., cerebral blood flow (CBF)) is important. Therefore, it is crucial to assess the brain perfusion so that early intervention in cerebrovascular diseases can be applied if abnormal perfusion is observed. The goal of my study is to develop metrics to measure the brain perfusion through modeling brain physiology using resting-state and task-based blood-oxygenation-level- dependent (BOLD) functional MRI (fMRI). My first and second chapters focused on deriving the blood arrival time using the resting-state BOLD signal. In the first chapters, we extracted the systemic low-frequency oscillations (sLFOs) in the fMRI signal from the internal carotid arteries (ICA) and the superior sagittal sinus (SSS). Consistent and robust results were obtained across 400 scans showing the ICA signals leading the SSS signals by about 5 seconds. This delay time could be considered as an effective perfusion biomarker that is associate with the cerebral circulation time (CCT). To further explore sLFOs in assessing dynamic blood flow changes during the scan, in my second chapter, a “carpet plot” (a 2-dimensional plot time vs. voxel) of scaled fMRI signal intensity was reconstructed and paired with a developed slope-detection algorithm. Tilted vertical edges across which a sudden signal intensity change took place were successfully detected by the algorithm and the averaged propagation time derived from the carpet plot matches the cerebral circulation time. Given that CO₂ is a vasodilator, controlling of inhaled CO₂ is able to modulate the BOLD signal, therefore, as a follow-up study, we focused on investigating the feasibility of using a CO₂ modulated sLFO signal as a “natural” bolus to track CBF with the tool developed from the second chapter. Meaningful transit times were derived from the CO₂-MRI carpet plots. Not only the timing, the BOLD signal deformation (the waveform change) under CO₂ challenge also reveals very useful perfusion information, representing how the brain react to stimulus. Therefore, my fourth chapter focused on characterizing the brain reaction to the CO₂ stimulus to better measure the brain health using BOLD fMRI. Overall, these studies deepen our understanding of fMRI signal and the derived perfusion parameters can potentially be used to assess some cerebrovascular diseases, such as stroke, ischemic brain damage, and steno-occlusive arterial disease in addition to functional activations. </p>

Abstract Neuropsychiatric illnesses usually become overtly manifest in adolescence and early adulthood. A critical long-term aim is to be able to prevent the development of such illnesses, which requires instruments to identify subjects at high risk of illness and to offer them effective interventions. There is an indisputable need for more sophisticated methods to enable more precise detection of adolescents and young adults who are at high risk of developing psychosis. Abnormal function in brain networks has been reported in people with schizophrenia and other psychotic disorders. Similar abnormalities have been found also in people at risk for developing psychosis, but it is not known whether this applies also to spontaneous resting state activity in young people with a familial risk for psychosis. We conducted resting-state functional MRI (R-fMRI) in 72 (29 male) young adults with a history of psychosis in one or both parents (FR) but without psychosis themselves, and 72 (29 male) similarly healthy control subjects without familial risk for psychosis. Both groups in the Oulu Brain and Mind study were drawn from the Northern Finland Birth Cohort 1986. All volunteers were 20–25 years old. Parental psychosis was established using the Care Register for Health Care. R-fMRI data was pre-processed using independent component analysis (ICA). A dual regression technique was used to detect between-group differences with p &lt; 0.05 threshold corrected for multiple comparisons at voxel level. FR subjects demonstrated significantly decreased activity compared to control subjects in the default mode network and in the central executive network and increased activity in the cerebellum. The findings clarify previously controversial literature on the subject. The finding suggests that abnormal activity in these brain networks in rest may be associated with increased vulnerability to psychosis. The findings maybe helpful in developing more precise methods for detecting young people at highest risk for developing psychosis. / Tiivistelmä Psykoottisiin häiriöihin sairastutaan yleensä nuoruudessa tai varhaisaikuisuudessa. Psykoositutkimuksen tavoitteena on löytää uusia menetelmiä, joiden avulla kyettäisiin tunnistamaan suurimmassa psykoosiriskissä olevat nuoret, jotta heille voitaisiin tarjota sairautta ennaltaehkäiseviä hoitokeinoja. Skitsofreniaan ja muihin psykoottisiin häiriöihin sairastuneilla on havaittu aivotoiminnan poikkeavuuksia. Samankaltaisia aivotoiminnan poikkeavuuksia on havaittu myös nuorilla, jotka ovat vaarassa sairastua psykoosiin. Toistaiseksi on ollut epäselvää, onko psykoosiin sairastuneiden henkilöiden lapsilla aivohermoverkkojen toiminnan poikkeavuuksia lepotilassa. Suoritimme aivojen lepotilan MRI-tutkimuksen (R-fMRI) 72:lle (29 miestä) nuorelle aikuiselle, joiden jompikumpi vanhempi oli sairastunut psykoosin sekä 72:lle (29 miestä) nuorelle aikuiselle, joiden vanhemmat eivät olleet sairastaneet psykoosia. Molemmat tutkimusryhmät tässä Oulu Brain and Mind -tutkimuksessa olivat Pohjois-Suomen 1986 syntymäkohortin jäseniä. Tutkittavat olivat 20–25 vuoden iässä. Lepotilan toiminnallinen magneettikuvaus suoritettiin 1.5 Teslan Siemensin magneettikuvantamislaitteella. Tutkimuskohteiksi valittiin lepotilan toiminnallinen aivohermoverkko, toiminnan ohjauksesta vastaava aivohermoverkko ja pikkuaivot. Kuvantamisdataan sovellettiin itsenäisten komponenttien analyysia aivohermoverkkojen määrittämistä varten. Ryhmien välisen eron havaitsemiseen käytettiin ei-parametristä permutaatiotestiä, joka kynnystettiin tilastollisesti merkitsevään tasoon (p &lt; 0.05). Lepotilan oletushermoverkossa ja toiminnanohjauksesta vastaavassa aivohermoverkoissa havaittiin vähäisempää aktiivisuutta ja pikkuaivoissa kohonnutta aktiivisuutta perinnöllisessä psykoosiriskissä olevilla nuorilla aikuisilla verrattuna verrokkeihin. Tutkimustulokset selkeyttivät aiempaa ristiriitaista kirjallisuutta tutkimusaiheesta. Tutkimuksessa havaittujen aivoalueiden poikkeava toiminta lepotilassa voi liittyä kohonneeseen psykoosin puhkeamisriskiin. Tutkimuslöydösten avulla voidaan todennäköisesti edesauttaa parempien kuvantamismenetelmien kehittämistä suurimmassa psykoosiriskissä olevien nuorten tunnistamiseen.