The dissertation discusses various node identification techniques as well as their downstream effects on network characteristics using task-activated fMRI data from two working memory paradigms: a verbal n-back task and a visual n-back task. The three node identification techniques examined within this work include: a group-aggregated approach, a subject-specific approach, and a voxel wise approach. The first chapters highlight crucial differences between group-aggregated and subject-specific methods of isolating nodes prior to undirected functional connectivity analysis. Results show that the two techniques yield significantly different network interactions and local network characteristics, despite having their network nodes restricted to the same anatomical regions. Prior to the introduction of the third technique, a chapter is dedicated to explaining the differences between a priori approaches (like the previously introduced group-aggregated and subject-specific techniques) and no a priori approaches (like the voxel wise approach). The chapter also discusses two ways to aggregate signal for node representation within a network: using the signal from a single voxel or aggregating signal across a group of neighboring voxels. Subsequently, a chapter is dedicated to introducing a novel processing pipeline which uses a data driven voxel wise approach to identify network nodes. The novel pipeline defines nodes using spatial temporal features generated by a deep learning algorithm and is validated by an analysis showing that the isolated nodes are condition and subject specific. The dissertation concludes by summarizing the main takeaways from each of the three analyses as well as highlighting the advantages and disadvantages of each of the three node identification techniques. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

In this research two functional magnetic resonance tomography experiments were conducted to identify core regions of language comprehension processes. The focus of the studies was on inferencing, i.e. the activation of information which has not been explicitly mentioned in a given utterance but which is somehow implied because of general world knowledge. The research strategy was two-fold. First, text materials were used which allowed to isolate inference processes from more basic language processes. Second, two tasks verification in Experiment 1 and recognition in Experiment 2 were assigned to the participants to selectively enhance or attenuate processing at different levels of representation. In both experiments a network of brain areas was found to be active during language comprehension including areas all along the left superior temporal sulcus, the left lateral and medial prefrontal areas, as well as the right anterior temporal lobe and the posterior cingulate cortex. The results of Experiment 1 indicated that the dorsomedial prefrontal cortex was most prominently associated with inferencing in the context of the verification task. As expected, activity in this region was attenuated in Experiment 2 during recognition. No indications were found that the right hemisphere plays a particular role for inferencing as has been suggested by some authors. The results of both experiments are discussed with respect to the neuroimaging literature on language comprehension and with respect to recent approaches to memory systems in the brain particularly the episodic memory system. Finally, a functional neuroanatomical model of inferencing is sketched.

Cette thèse présente de nouvelles méthodes d’apprentissage structuré et parcimonieux sur les graphes, ce qui permet de résoudre une large variété de problèmes d’imagerie cérébrale, ainsi que d’autres problèmes en haute dimension avec peu d’échantillon. La première partie de cette thèse propose des relaxation convexe de pénalité discrète et combinatoriale impliquant de la parcimonie et bounded total variation d’un graphe, ainsi que la bounded `2. Ceux-ci sont dévelopé dansle but d’apprendre un modèle linéaire interprétable et on démontre son efficacacité sur des données d’imageries cérébrales ainsi que sur les problèmes de reconstructions parcimonieux.Les sections successives de cette thèse traite de la découverte de structure sur des modèles graphiques “undirected” construit à partir de peu de données. En particulier, on se concentre sur des hypothèses de parcimonie et autres hypothèses de structures dans les modèles graphiques gaussiens. Deux contributions s’en dégagent. On construit une approche pour identifier les différentes entre des modèles graphiques gaussiens (GGMs) qui partagent la même structure sous-jacente. On dérive la distribution de différences de paramètres sous une pénalité jointe quand la différence des paramètres est parcimonieuse. On montre ensuite comment cette approche peut être utilisée pour obtenir des intervalles de confiances sur les différences prises par le GGM sur les arêtes. De là, on introduit un nouvel algorithme d’apprentissage lié au problème de découverte de structure sur les modèles graphiques non dirigées des échantillons observés. On démontre que les réseaux de neurones peuvent être utilisés pour apprendre des estimateurs efficacaces de ce problèmes. On montre empiriquement que ces méthodes sont une alternatives flexible et performantes par rapport aux techniques existantes. / This dissertation presents novel structured sparse learning methods on graphs that address commonly found problems in the analysis of neuroimaging data as well as other high dimensional data with few samples. The first part of the thesis proposes convex relaxations of discrete and combinatorial penalties involving sparsity and bounded total variation on a graph as well as bounded `2 norm. These are developed with the aim of learning an interpretable predictive linear model and we demonstrate their effectiveness on neuroimaging data as well as a sparse image recovery problem.The subsequent parts of the thesis considers structure discovery of undirected graphical models from few observational data. In particular we focus on invoking sparsity and other structured assumptions in Gaussian Graphical Models (GGMs). To this end we make two contributions. We show an approach to identify differences in Gaussian Graphical Models (GGMs) known to have similar structure. We derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. We then show how this approach can be used to obtain confidence intervals on edge differences in GGMs. We then introduce a novel learning based approach to the problem structure discovery of undirected graphical models from observational data. We demonstrate how neural networks can be used to learn effective estimators for this problem. This is empirically shown to be flexible and efficient alternatives to existing techniques.

No description available.

This systematic review aims to investigate the effects of aerobic exercise on the neuroal basis of memory functions in healthy elderly individuals. The search was conducted in accordance with PRISMA and covered three electronic databases, namely PubMed, Scopus, and Web of Science, for peer-reviewed published, and original research. Six studies met the inclusion criteria and were included in the review. The studies utilized various behavioral or cognitive tasks related to memory, including the Sternberg Working Memory Task, Spatial Memory tests, and neuroimaging techniques such as magnetic resonance imaging (MRI). This systematic review suggests that aerobic exercise can improve memory in healthy elderly individuals, including spatial, working, and short- and long-term memory. As revealed by neuroimaging techniques, memory function improvement was accompanied by changes in brain structure and function in memory-processing regions. These findings provide evidence that aerobic exercise can improve the neurological basis of memory function in healthy elderly individuals. The beneficial effects of aerobic exercise on memory have significant implications for the aged population. Memory loss is a common and often debilitating issue in older adults, and the ability to recall and learn new information is crucial for maintaining independence and quality of life. Therefore, aerobic exercise is a promising intervention to improve memory function in healthy elderly individuals.

The human cerebral cortex, the outermost layer of the brain, is typically considered in imaging studies to consist of grey matter (GM), with white matter (WM) lying below it. With better imaging techniques, a third tissue type, found between GM and WM, can be identified. This layer contains myelinated axons and is found in the cortex, thus we call it intracortical myelin (ICM), or myelinated grey matter (GMm). We examined the cortical thickness measurements in female patients with bipolar I or II disorders (BD) versus healthy controls. Previous studies have only examined the thickness of the entire cortex, the GM. We developed a processing pipeline and a statistical tool for examining the ICM thickness between two groups. Results show that there are potential differences in GMm between BD and control groups. Further regional and statistical analysis is required to identify the regions of greatest difference, and to confirm significant differences between BD and control groups. / Thesis / Master of Science (MSc)

Background: Ayahuasca is a South American psychoactive brew that contains Dimethyltryptamine (DMT) and monoamine oxidase inhibitors (MAOIs). Research has experienced a resurgence of interest in exploring the potential of these substances in the last decade. Thus, the aim of this review was to systematically review studies that used a placebo-controlled design to explore the neural correlates and psychedelic experience induced by DMT and ayahuasca. Method: The search was conducted using the Web of Science and Scopus databases to select studies published between January 2000 and February 2022 that used neuroimaging techniques and recruited healthy participants. Thus, 7 papers were selected. Results: Ayahuasca alters electrical activity in the brain by decreasing spectralpower in all EEG frequency bands, predominantly in the alpha band. DMT caused a spatially widespread decrease in alpha bands and a more modest decrease in beta bands. Ayahuasca caused an increase in the flow of information in the brain from posterior regions to more frontal regions and an increase in scores in all the Hallucinogen Rating Scale (HRS) subscales. Ayahuasca decreased connectivity in the Default Mode Network (DMN) and increases connectivity between DMN and the salience network. Conclusion: Ayahuasca and DMT can reliably produce profound changes in perception, emotions, and sense of self. Moreover, the decrease in the alpha band, the alteration of information flow between posterior and frontal regions, and the decrease in connectivity in the DMN could be the keystone understanding the neural correlates and the psychedelic experience induced by DMT andayahuasca.

京都大学 / 新制・論文博士 / 博士(医学) / 乙第13554号 / 論医博第2283号 / 新制||医||1067(附属図書館) / (主査)教授 村井 俊哉, 教授 永井 洋士, 教授 井上 治久 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

Schizophrenia (SCZ) is characterized by a disconnect from reality that manifests as various clinical and cognitive symptoms, as well as consistent neurobiological abnormalities. However, unique sex-related differences have been observed regarding clinical presentation that imply separate brain substrates. The present study characterized deep-brain morphology using shape features to understand whether the neurobiology of schizophrenia varies as a function of sex. This study analyzed multi-site archival data from 1,579 male (M) and 836 female (F) participants with SCZ, as well as 1,934 male and 1,828 female healthy controls (CON) from twenty-four cross-sectional study samples from the ENIGMA Schizophrenia Workgroup. Harmonized shape analysis protocols were applied to each site's data independently for bilateral caudate, putamen, globus pallidus, accumbens, amygdala, hippocampus, and thalamus obtained from T1-weighted structural MRI scans. Four separate contrasts were conducted: 1) Schizophrenia-Male/Control-Male; 2) Schizophrenia-Female/Control-Female; 3) Schizophrenia-Male/Schizophrenia-Female; 4) Control-Male/Control-Female. For contrasts 1 & 2, mass univariate meta-analyses revealed more-concave-than-convex shape differences for the hippocampus, amygdala, accumbens, and thalamus, with more-convex-than-concave differences in the putamen and pallidum (d = -0.30 to 0.30, SE = 0.03 to 0.10, p<0.05) in SCZ for both male and female group comparisons. More extensive patterns of deformation were noted in right hippocampus and right thalamus for SCZ women. Contrasts 3 & 4 revealed more-concave-than-convex shape differences in the thalamus, pallidum, putamen, and amygdala among females compared to males, with mixed findings in the hippocampus and caudate in both SCZ and CON contrasts (d = -0.30 to 0.20, SE = 0.03 to 0.09, p<0.05). Pattern and extent of deformation was greater in dorsal, ventral, and lateral aspects of putamen, thalamus, amygdala, and pallidum in SCZ. Findings are consistent with prior volume-based analyses in SCZ, as well as earlier studies on sex differences in the brain. Shape patterns reveal more extensive abnormalities in SCZ women relative to SCZ men that could aid in our understanding of clinical expression and treatment response differences between men and women.

Purpose: Arterial stiffening poses an increased risk of cerebrovascular diseases, cognitive impairments, and even dementia as cardiac pulsations reach further into the brain causing white matter hyperintensities and microbleeds. Therefore it is of interest to obtain methods to estimate and map cardiac related pulsatility in the brain. Improvements of functional magnetic resonance imaging (fMRI) sequences is potentially allowing detection of rapid physiological processes in the echo-planar imaging (EPI) signal in the brainthrough a higher sampling rate. Specifically in this thesis, estimation and localization of cardiac pulsation and respiration is conducted through analysis of resting state data obtained with a multiband EPI sequence that permits whole brain imaging at a shorter repetition time (TR) than conventional EPI. The origin of these physiological signals are likely a mixture of inflow and compartment volume shifts during the cardiac- and respiratory cycles. As the amount of physiologically related signal in the multiband sequence used at the Biomedical Engineering Dept. R&amp;D, Umeå University Hospital is unknown, the aim of this project is to find and map cardiac pulsatility and respiration for future research. Methods: Multiband fMRI data from 8 subjects was used, collected in a 3 Tesla scanner using a 32-channel head coil. The physiological signals were estimated through an algorithm that was developed to down-sample and temporally shift copies of simultaneous recordings of pulse and respiration. These signals were obtained using the scanner’s built-in pulse oximeter and a respiration belt. The shifted copies were voxel-wise, and slice by slice, correlated to the fMRI data using Pearson correlation. The time shift yielding maximum mean correlation within the brain was, for each slice, used to create statistical maps for significant voxels to show the localization and magnitude of correlation for cardiac pulsation andrespiration. Results: Many voxels around and nearby larger vessels and ventricles were highly correlated with the down-sampled, time shifted signals of the cardiac pulsation for all subjects. The cardiac pulsation maps resembled cerebral vasculature and were mostly localized around the Circle of Willis, brainstem, and the ventricles. Respiration signal was also highly correlated, and spatially located at the sides of the brain although mostly concentrated at the parietal- and occipital lobes. Conclusion: The results demonstrated that many voxels in the brain were highly correlated with cardiac pulsation and respiration using multiband EPI, and the statistical maps revealed distinct patterns for both of the physiological signals. This method and results for mapping cardiac related pulsatility, and respiration could be used for future research in order to better understand cerebral diseases and impairments, and alsoto improve fMRI filtering. Keywords: Arterial stiffness, Functional magnetic resonance imaging, Resting state, Multiband, CardiacPulsation, Respiration, Correlation analysis / Syfte: Arteriell förstyvning medför en ökad risk för cerebrovaskulära sjukdomar, kognitiva störningar och till och med demens då hjärtpulsationer når längre in i hjärnan orsakar vit materia hyperintensiteter och mikroblödningar. Av detta skäl är det därför av intresse att ta fram metoder för att estimera och kartlägga hjärtrelaterad pulsationer i hjärnan. Förbättringar av funktionella magnetresonanstomografi (fMRI) sekvenser kan möjliggöra detektering av snabba fysiologiska processer i den eko-planära (EPI) signalen i hjärnan genom en högre samplingsfrekvens. Specifikt i denna uppsats, utförs en skattning och lokalisering av hjärtpulsation och respiration genom analys av ’resting state’ data erhållet av en multiband-EPI sekvens som tillåter bildgivning av hela hjärnan med en kortare repetitionstid (TR) än konventionell EPI. Ursprunget avdessa fysiologiska signaler är sannolikt från en blandning av flöde- och volymsförändringar under hjärt- och respirationscyklerna. Då mängden av fysiologiskt relaterad signaler i multiband sekvensen, som används på Biomedicinska avdelningen, FoU Umeå Universitetssjukhust, är okänd så är målet med projektet att hitta och kartlägga hjärtpulsation och respiration för framtida forskning. Metod: Multiband fMRI data från 8 personer användes, insamlade från en 3 Tesla scanner med en 32-kanals huvudspole. De fysiologiska signalerna uppskattades genom en algoritm som utveckades för att sampla ned och tidsförskjuta kopior av simultant tagna signaler av puls och respiration. Dessa signaler samlades in med skannerns inbyggda pulsoximeter och andningsband. De förskjutna kopiorna var voxelvis, snitt för snitt, korrelerade med fMRI datat med användning av Pearson-korrelation. Det tidsskift somför varje snitt resulterade i maximal medelkorrelation i hjärnan användes för att skapa statistiska kartor, med endast signifikanta voxlar, för att visa var och hur mycket korrelation av hjärtpulsation och respiration som finns. Resultat: Många voxlar runt och nära större kärl och ventriklar var för alla personer starkt korrelerade medde samtidigt tagna, och tidsförskjutna signalerna av hjärtpulsation. Pulsationskartorna liknade cerebral vaskulatur och var mestadels lokaliserade kring Willis ring, hjärnstammen och ventriklar. Respirationssignalen var även starkt korrelerad och lokaliserad på sidorna av hjärnan, mestadels koncentrerat vid parietal- och occipital loberna. Slutsats: Resultaten visade att många voxlar i hjärnan var starkt korrelerade med hjärtpulsation och respiration vid användning av multiband EPI, och de statistiska kartorna avslöjade distinkta mönster för de båda fysiologiska signalerna. Den framtagna metoden och dess resultat för kartläggning av hjärtrelaterade pulsationer och respiration kan användas i framtida forskning i syfte att bättre förstå cerebrala sjukdomar och nedsättning, även för att förbättre fMRI filtrering. Nyckelord: Arteriell förstyvning, Funktionell magnetresonanstomografi, Resting state, Multiband, Hjärtpulsation, Andning, Korrelationsanalys