Spatiotemporal dynamics of low frequency fluctuations in bold fMRI

Traditional fMRI utilizes blood oxygenation level dependent (BOLD) contrast to map brain activity. BOLD signal is sensitive to the hemodynamic changes associated with brain activity, and gives an indirect measure of brain activity. Low frequency fluctuations (LFFs) have been observed in the BOLD signal even in the absence of any anesthetic agent, and the correlations between the fluctuations from different brain regions has been used to map functional connectivity in the brain. Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. The research presented in this document investigates the spatiotemporal dynamics of the BOLD fluctuations to identify common spatiotemporal patterns within a scan. First, the presence of a visually detectable spatiotemporal propagation pattern is demonstrated by utilizing single-slice data with high spatial and temporal resolution. The pattern consists of lateral-medial propagation of BOLD signal, demonstrating the presence of time-varying features in spontaneous BOLD fluctuations. Further, a novel pattern finding algorithm is developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm is applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consists of waves of high signal intensity, propagating in a lateral-medial direction across the cortex, replicating the results obtained using visual observation. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the "default-mode" (e.g., posterior cingulate and anterior medial prefrontal cortices) and the "task-positive" (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points is also observed. The pattern finding algorithm is shown to be reasonably insensitive to the variation in user-defined parameters, and the results are consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.Ph.D.Committee Chair: Keilholz, Shella; Committee Member: Hu, Xiaoping; Committee Member: Jaeger, Dieter; Committee Member: Sathian, Krish; Committee Member: Schumacher, Eri

Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship

Analysis of resting-state neurovascular coupling and locomotion-associated neural dynamics using wide-field optical mapping

Understanding the relationship between neural activity and cortical hemodynamics, or neurovascular coupling is the foundation to interpret neuroimaging signals such as functional magnetic resonance imaging (fMRI) which measure local changes in hemodynamics as a proxy for underlying neural activity. Even though the stereotypical stimulus-evoked hemodynamic response pattern with increased concentration of oxy- and total-hemoglobin and decrease in concentration of deoxy-hemoglobin has been well-recognized, the linearity of neurovascular coupling and its variances depending on brain state and tasks haven’t been thoroughly evaluated. To directly assess the cortical neurovascular coupling, simultaneous recordings of neural and hemodynamic activity were imaged by wide-field optical mapping (WFOM) over the bilateral dorsal surface of the mouse brain through a bilateral thinned-skull cranial window. Neural imaging is achieved through wide-field fluorescence imaging in animals expressing genetically encoded calcium sensor (Thy1-GCaMP). Hemodynamics are recorded via simultaneous imaging of multi-spectral reflectance. Significant hemodynamic crosstalk was found in the detected fluorescence signal and the physical model of the contamination, methods of correction as well as electrophysiological verification are presented. A linear model between neural and hemodynamic signals was used to fit spatiotemporal hemodynamics can be predicted by convolving local fluorescence changes with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Beyond confirming that the resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying neural activity, the patterns of bilaterally symmetric spontaneous neural activity observed by WFOM emulate the functionally connected networks detected by fMRI. This result provides reassurance that resting-state functional connectivity has neural origins. With the access to cortical neural activity at mesoscopic level, we further explore the cortical neural representations preceding and during spontaneous locomotion

Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits

Accumulating evidence from whole brain functional magnetic resonance imaging (fMRI) suggests that the human brain at rest is functionally organized in a spatially and temporally constrained manner. However, because of their complexity, the fundamental mechanisms underlying time-varying functional networks are still not well under-stood. Here, we develop a novel nonlinear feature extraction framework called local space-contrastive learning (LSCL), which extracts distinctive nonlinear temporal structure hidden in time series, by training a deep temporal convolutional neural network in an unsupervised, data-driven manner. We demonstrate that LSCL identifies certain distinctive local temporal structures, referred to as temporal primitives, which repeatedly appear at different time points and spatial locations, reflecting dynamic resting-state networks. We also show that these temporal primitives are also present in task-evoked spatiotemporal responses. We further show that the temporal primitives capture unique aspects of behavioral traits such as fluid intelligence and working memory. These re-sults highlight the importance of capturing transient spatiotemporal dynamics within fMRI data and suggest that such temporal primitives may capture fundamental information underlying both spontaneous and task-induced fMRI dynamics.Peer reviewe

On the estimation of brain signal entropy from sparse neuroimaging data

Multi-scale entropy (MSE) has been recently established as a promising tool for the analysis of the moment-to-moment variability of neural signals. Appealingly, MSE provides a measure of the predictability of neural operations across the multiple time scales on which the brain operates. An important limitation in the application of the MSE to some classes of neural signals is MSE’s apparent reliance on long time series. However, this sparse-data limitation in MSE computation could potentially be overcome via MSE estimation across shorter time series that are not necessarily acquired continuously (e.g., in fMRI block-designs). In the present study, using simulated, EEG, and fMRI data, we examined the dependence of the accuracy and precision of MSE estimates on the number of data points per segment and the total number of data segments. As hypothesized, MSE estimation across discontinuous segments was comparably accurate and precise, despite segment length. A key advance of our approach is that it allows the calculation of MSE scales not previously accessible from the native segment lengths. Consequently, our results may permit a far broader range of applications of MSE when gauging moment-to- moment dynamics in sparse and/or discontinuous neurophysiological data typical of many modern cognitive neuroscience study designs

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7–13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activ- ity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific con- tributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field