As a non-invasive method, functional MRI (fMRI) has been widely used for human brain mapping. Although many applications have been done, there are still some critical issues associated with fMRI. Perfusion-weighted fMRI (PWI) with exogenous contrast agent suffered from the problems of recirculation, which could contaminate the cerebral blood flow (CBF) estimation and make its ability of prediction "tissue-at-risk" in debate. We propose a rapid and effective method that combines matched-filter-fitting (MFF) and ICA where ICA was used for regions with a prolonged TTP and MFF was utilized for the remaining areas. The calculation of cerebral hemodynamics afterwards demonstrates that the proposed method may lead to a more accurate estimation of CBF. The extent to which CBF is reduced in relationship to normal values has been utilized as an indicator to discern ischemic injury. However, despite the well known difference in CBF between gray and white matter, relatively little attention has been given as to how CBF may be differently altered in gray and white matter during ischemia due to the inability to accurately separate gray and white matter. To this end, we propose a robust clustering method for automatic classification of perfusion compartments. The method is first to apply a robust principal component analysis to reduce dimension and then to use a mixture model of multivariate T distribution for clustering. Our results in ischemic stroke patients at the hyperacute phase show the clear advantage over the conventional technique. BOLD fMRI, as a feasible and preferred method for developmental neuroimaging, is seldom conducted in pediatric subjects and therefore the information about brain functional development in the early age is somewhat lacking. To this end, this dissertation also focuses on how functional brain connectivity may be present in pediatric subjects in a sleeping condition. We propose a statistical method to delineate frequency-dependent brain connectivity among brain activation regions, and an automatic procedure combined with spatial ICA approach to determine the brain functional connectivity. Our results suggest that functional connectivity exists as young as two weeks old for both sensorimotor and visual cortices and that functional connectivity is highly age-dependent. / Dissertation

Previous studies have indicated that persons with autism spectrum disorder have distinct cerebella, striatum, and an impaired ability to anticipate implicit learning sequences; also, previous research indicates anatomic connections among these regions. Investigating distinctions in connectivity and impairments in the ability to anticipate implicit sequences linked to ASD would help clarify some of the core deficits associated with the disorder. This dissertation sought to explore differences in functional connectivity among the cerebellum, thalamus, and striatum. This dissertation also sought to determine if an impaired ability to anticipate implicit sequences is associated with ASD. Twelve ASD participants and 11 control participants were scanned using an MRI while engaged in a modified serial reaction task. The findings indicate that the cerebellum and the striatum are functionally connected and the thalamus mediates this connection. The results indicate that ASD participants have stronger connections than the control, and ASD participants demonstrated some impairments in learning. However, there was not enough evidence to link ASD to an impaired ability to anticipate implicit sequences. This dissertation recommends that future studies consider the roles that these distinct connections play in symptoms of ASD. / text

Successful recognition memory decisions depend on mnemonic and decision making processes that are computed by multiple, distributed brain areas. However, little is known about what computations these areas perform or how these areas are connected. Here, I collected behavioral and functional magnetic resonance imaging data from humans during the performance of an old-new recognition memory task with retrospective confidence judgments. Across runs, choice bias was successfully manipulated by providing rewards for correct responses that were either symmetric (equal reward for hits and correct rejections) or asymmetric (one response worth more than the other). Successful recognition memory was associated with activation in anterior prefrontal, parahippocampal, posterior cingulate, and parietal cortex. Resting state functional connectivity demonstrated that these brain areas are organized into two distinct networks. The first network includes parahippocampal cortex and angular gyrus. The second network includes lateral prefrontal cortex and intraparietal sulcus. The hippocampal-cortical network was most active during old vs. new decisions, did not differentiate hits from false alarms, and was differentially active during low confidence old and new judgments. In contrast, while the frontoparietal network was robustly activated by hits, it was not activated during either false alarms or low confidence old judgments. Thus, these two distinct networks can be distinguished by their relative connectivity to the medial temporal lobe vs. lateral prefrontal cortex and their responses during uncertain old judgments and errors. The choice bias manipulation had opposing effects on the parietal components of these networks, which further suggests these networks make distinct contributions to mnemonic decision making. / Psychology

Attention allows us to select important aspects of incoming sensory information while filtering out irrelevant information. It has crucial significance in understanding neurophysiological, emotional and behavioral outcomes. The research reported here focused on one central question: how do attentional manipulations influence various stages of cognition and emotion to result in goal-directed behavior? In six experiments, I used behavioral and functional Magnetic Resonance Imaging (fMRI) measures to investigate the impact of attention on visual recognition, mood and self-regulation. The results showed that attention influences functional connectivity between body-selective visual processing areas in occipito-temporal cortex. Changes in the scope of attention were also found to influence mood and self-regulation. Broadening attentional focus improves mood and self-regulation. Narrowing attentional focus impairs mood and self-regulation. Self-regulation was also aided through the pre-engagement of attentional inhibition. This diverse set of methodologies and experimental paradigms provides converging evidence that attention influences goal-relevant functional connections to facilitate visual processing, promotes fluency of information to result in better mood and prioritizes goal-relevant representations to result in successful self-regulation. / NSERC

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.

Emotional intelligence (EI) is defined as an individual's capacity to accurately perceive, understand, reason about, and regulate emotions, and to apply that information to facilitate thought and achieve goals. Although EI plays an important role in mental health and success in academic, professional and social realms, the neurocircuitry underlying this capacity remains poorly characterized, and no study to date has yet examined the relationship between EI and intrinsic neural network function. Here, in a sample of 54 healthy individuals (28 women, 26 men), we apply independent components analysis (ICA) with dual regression to functional magnetic resonance imaging (fMRI) data acquired while subjects were resting in the scanner to investigate brain circuits (intrinsic resting state networks) whose activity is associated with greater self-reported (i.e. Trait) and objectively measured (i.e. Ability) EI. We show that higher Ability EI, but not Trait EI, is associated with stronger negatively correlated spontaneous fMRI signals between the basal ganglia/limbic network (BGN) and posterior default mode network (DMN), and regions involved in emotional processing and regulation. Importantly, these findings suggest that the functional connectivity within and between intrinsic networks associated with mentation, affective regulation, emotion processing, and reward are strongly related to ability EI.

No description available.

Understanding how communication between brain areas evolves to support dynamic function remains a fundamental challenge in neuroscience. One approach to this question is functional connectivity analysis, in which statistical coupling measures are employed to detect signatures of interactions between brain regions. Because the brain uses multiple communication mechanisms at different temporal and spatial scales, and because the neuronal signatures of communication are often weak, powerful connectivity inference methodologies require continued development specific to these challenges. Here we address the challenge of inferring task-related functional connectivity in brain voltage recordings. We first develop a framework for detecting changes in statistical coupling that occur reliably in a task relative to a baseline period. The framework characterizes the dynamics of connectivity changes, allows inference on multiple spatial scales, and assesses statistical uncertainty. This general framework is modular and applicable to a wide range of tasks and research questions. We demonstrate the flexibility of the framework in the second part of this thesis, in which we refine the coupling statistics and hypothesis tests to improve statistical power and test different proposed connectivity mechanisms. In particular, we introduce frequency domain coupling measures and define test statistics that exploit theoretical properties and capture known sampling variability. The resulting test statistics use correlation, coherence, canonical correlation, and canonical coherence to infer task-related changes in coupling. Because canonical correlation and canonical coherence are not commonly used in functional connectivity analyses, we derive the theoretical values and statistical estimators for these measures. In the third part of this thesis, we present a sample application of these techniques to electrocorticography data collected during an overt reading task. We discuss the challenges that arise with task-related human data, which is often noisy and underpowered, and present functional connectivity results in the context of traditional and contemporary within-electrode analytics. In two of nine subjects we observe time-domain and frequency-domain network changes that accord with theoretical models of information routing during motor processing. Taken together, this work contributes a methodological framework for inferring task-related functional connectivity across spatial and temporal scales, and supports insight into the rapid, dynamic functional coupling of human speech.

Background: Recently there has been a revitalization in research on classic psychedelic substances. This class of drugs has been found to produce intense and profoundly meaningful experiences, and offers a unique opportunity to study the neural correlates of the sense of self. The objective of this research was to systematically review the effects of classic psychedelics on spontaneous brain activity, as measured on three electrophysiological modalities: spectral analysis, signal diversity, and functional connectivity. Method: We searched Pubmed to identify papers in English, published between January 1990 to May 2021, where electrophysiological methods were used to evaluate the effects of classic psychedelics in healthy individuals during non-task resting states. Results: Sixteen papers were included. Classic psychedelic substances generally decrease spectral power in most frequency bands, mainly in the alpha range, increase signal diversity, and decrease the flow of information throughout the brain. Conclusion: Decreases in alpha power, increased signal diversity, and decreases in default mode network activity might be important neural correlates of the psychedelic state. However, inconsistencies in the results and heterogeneity in study design are some of the limitations that have to be considered when interpreting these results.

The ability to attentively filter sensory information and manipulate it in working memory is critical for our ability to interact with the world. Although primary and secondary sensory cortical areas have been well-studied, frontal lobe contributions to sensory attention and working memory remain under-investigated. This dissertation investigates the topography and network organization of sensory-biased and multi-sensory regions in the healthy human brain using functional magnetic resonance imaging (fMRI). First, this research developed a series of functional connectivity analyses of data from the Human Connectome Project to validate and extend recently localized auditory-biased network structures, transverse gyrus intersecting the precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS), and visual-biased network structures, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), in lateral frontal cortex (LFC). Results replicated the original findings and extended them by revealing five additional bilateral LFC regions, including middle inferior frontal sulcus (midIFS) and frontal operculum (FO), differentially connected to either the visual- or auditory-biased networks. Due to inter-subject anatomical variability, identification of sPCS, tgPCS, iPCS and cIFS depends critically on within-subject analyses. Next, this work demonstrated that an individual’s unique pattern of resting-state functional connectivity can accurately identify their specific pattern of working memory (WM) and attention task activation in LFC using “connectome fingerprinting” (CF). CF predictions were superior to group-average predictions and matched the accuracy of within-subject task-based functional localization. This research developed and validated methods that use intrinsic functional connectivity information to perform functional brain analyses on highly idiosyncratic brain regions. Finally, a combined auditory, tactile and visual WM study revealed the joint organization of sensory-biased and multi-sensory regions within individual subjects. Hypothesized visual-biased midIFS and auditory-biased FO regions were functionally confirmed for the first time. Several bilateral tactile-biased regions, premotor dorsal, premotor ventral, anterior middle frontal gyrus, middle insula, postcentral sulcus, posterior middle temporal gyrus and pre-supplemental motor area, abutting previously described visual- and auditory-biased regions were identified. Several multi-sensory WM regions, recruited in each stimulus modality, were observed to partially overlap with visual-biased regions. Intrinsic functional connectivity analyses revealed that regions segregate into networks largely based upon their modality preferences. / 2020-06-14T00:00:00Z