Adaptive filtering for removing nonstationary physiological noise from resting state fMRI BOLD signals

fMRI is used to investigate brain functional connectivity after removing nonneural components by General Linear Model (GLM) approach with a reference ventricle-derived signal as covariate. Ventricle signals are related to low-frequency modulations of cardiac and respiratory rhythms, which are nonstationary activities. Herein, we employed an adaptive filtering approach to improve removing physiological noise from BOLD signals. Comparisons between filtering approaches were performed by evaluating the amount of removed signal variance and the connectivity between homologous contralateral regions of interest (ROIs). The global connectivity between ROIs was estimated with a generalized correlation named RV coefficient. The mean ROI decrease of variance was -52% and -11%, for adaptive filtering and GLM, respectively. Adaptive filtering led to higher connectivity between grey matter ROIs than that obtained with GLM. Thus, adaptive filtering is a feasible method for removing the physiological noise in the low frequency band and to highlight resting state functional networks

Singular Spectrum Analysis and Adaptive Filtering: A Novel Approach for Assessing the Functional Connectivity in fMRI Resting State Experiments

Functional Magnetic Resonance Imaging (fMRI) is used to investigate brain functional connectivity at rest after filtering out non-neuronal components related to cardiac and respiratory processes and to the instrumental noise of MRI scanner. These components are generally removed at their fundamental frequencies through band-pass filtering of the Blood-Oxygen-Level-Dependent (BOLD) signal (low-frequency band – LFB: 0.01–0.10 Hz) while General Linear Model (GLM) is usually employed to suppress slow variations of physiological noise in the LFB, using a signal template derived from non-neuronal regions (e.g. brain ventricles). However, these sources of noise exhibit a non-stationary nature due to the intrinsic time variability of physiological activities or to the nonlinear characteristics of MRI scanner drifts: at present, the standard procedure (band-pass filtering and GLM) does not take into account these noise properties in the processing of BOLD signal. This thesis proposes the joint usage of two methods (Singular Spectrum Analysis – SSA – and adaptive filtering) that takes advantage of their statistical and time flexibility features, respectively. Indeed SSA is a nonparametric technique capable of extracting amplitude and phase modulated components against a null hypothesis of autocorrelated noise, while the adaptive filter removes the noise correlated to a reference signal, exploiting its non-stationary properties. The novel procedure (SSA and adaptive filtering) was applied to eight resting state recordings and compared to the standard procedure. The functional connectivity between homologous contralateral regions was then estimated in the LFB using a multivariate correlation index (the RV coefficient) and assessed on preselected grey (GM) and white matter (WM) regions of interest (ROIs). A corrected version of the RV coefficient for the number of voxels was developed and used to compare the functional connectivity estimates obtained by the standard procedure (using all available voxels) and from the novel procedure based on the voxel time courses with significant SSA components in the LFB (active voxels). The adaptive filtering showed a greater reduction of noise compared to GLM (average signal variance decrease in all ROIs: −43.9% vs. −10.1%), using a non-stationary noise template obtained from brain ventricles signals in the LFB. The functional connectivity quantified by the RV coefficient and estimated on the active voxels identified by SSA showed a higher contrast between GM and WM regions with respect to the standard procedure (35% vs. 28%). These results suggest that SSA and adaptive filtering may be a feasible procedure for properly removing the physiological noise in the LFB of BOLD signal and for highlighting resting state functional networks

State-space modeling and estimation for multivariate brain signals

Brain signals are derived from underlying dynamic processes and interactions between populations of neurons in the brain. These signals are typically measured from distinct regions, in the forms of multivariate time series signals and exhibit non-stationarity. To analyze these multi-dimensional data with the latent dynamics, efficient statistical methods are needed. Conventional analyses of brain signals use stationary techniques and focus on analyzing a single dimensional signal, without taking into consideration the coherence between signals. Other conventional model is the discrete-state hidden Markov model (HMM) where the evolution of hidden states is characterized by a discrete Markov chain. These limitations can be overcome by modeling the signals using state-space model (SSM), that model the signals continuously and further estimate the interdependence between the brain signals. This thesis developed SSM based formulations for autoregressive models to estimate the underlying dynamics of brain activity based on measured signals from different regions. The hidden state and model estimations were performed by Kalman filter and maximum likelihood estimation based on the expectation maximization (EM) algorithm. Adaptive dynamic model time-varying autoregressive (TV-AR) was formulated into SSM, for the application of multi-channel electroencephalography (EEG) classification, where accuracy obtained was better than the conventional HMM. This research generalized the TV-AR to multivariate model to capture the dynamic integration of brain signals. Dynamic multivariate time-varying vector autoregressive (TV-VAR) model was used to investigate the dynamics of causal effects of one region has on another, which is known as effective connectivity. This model was applied to motor-imagery EEG and motortask functional magnetic resonance imaging (fMRI) data, where the results showed that the effective connectivity changes over time. These changing connectivity structures were found to reflect the behavior of underlying brain states. To detect the state-related change of brain activities based on effective connectivity, this thesis further developed a novel unified framework based on the switching vector autoregressive (SVAR) model. The framework was applied to simulation signals, epileptic EEG and motor-task fMRI. The results showed that the novel framework is able to simultaneously capture both slow and abrupt changes of effective connectivity according to the brain states. In conclusion, the developed SSM based approaches were effective for modeling the nonstationarity and connectivity in brain signals

Monitoring Attentional State with Functional Near Infrared Spectroscopy

Functional Near Infrared Spectroscopy (fNIRS) is a technique for quantifying hemodynamic activity in the brain. Its portability allows application in real world operational contexts. The ability to distinguish levels of task engagement in safety-critical situations is important for detecting and preventing attentional performance decrement. We therefore investigated whether fNIRS can be used to distinguish between high and low levels of task engagement during the performance of a selective attention task, and validated these results using functional magnetic resonance imaging (fMRI) as a gold standard. Participants performed the multi-source interference task (MSIT) while we recorded brain activity with fNIRS from two brain regions. One was a key region of the “task-positive” network, which is associated with relatively high levels of task engagement. The second was a key region of the “task-negative” network, which is associated with relatively low levels of task engagement (e.g., resting and not performing a task). Using activity in these regions as inputs to a multivariate pattern classifier, we were able to predict above chance levels whether participants were engaged in performing the MSIT or resting. Classifier input features were selected from an array of probe channels at each of the two locations based on the fit to a model of expected task activity, or on training data. Standard linear regression was implemented with both static and adaptive general linear models to remove concurrently measured physiological noise. Two types of models were used to process the fNIRS signals. One employed knowledge of the task being performed to determine the system’s best capability. The other did not, for a realistic characterization. We were also able to replicate prior findings from fMRI indicating that activity in “task-positive” and “task-negative” regions is negatively correlated during task performance. Finally, data from both companion and simultaneous fMRI experimental trials verified our assumptions about the sources of brain activity in the fNIRS experiment, established a upper bound on classification accuracy expectations for response to the MSIT, and served to validate our fNIRS classification results. Together, our findings suggest that fNIRS could prove quite useful for monitoring cognitive state in real-world settings.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108861/1/angelarh_1.pd

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate 15 (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demon- 20 strate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architec- 25 ture is modulated by local blood oxygen level-dependent activity and a-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. 30 Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be 35 potentially useful as a predictor for learning and neural rehabilitation

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Functional MRI data analysis : Detection, estimation and modelling

Ph.DDOCTOR OF PHILOSOPH

Relating Spontaneous Activity and Cognitive States via NeuroDynamic Modeling

Stimulus-free brain dynamics form the basis of current knowledge concerning functional integration and segregation within the human brain. These relationships are typically described in terms of resting-state brain networks—regions which spontaneously coactivate. However, despite the interest in the anatomical mechanisms and biobehavioral correlates of stimulus-free brain dynamics, little is known regarding the relation between spontaneous brain dynamics and task-evoked activity. In particular, no computational framework has been previously proposed to unite spontaneous and task dynamics under a single, data-driven model. Model development in this domain will provide new insight regarding the mechanisms by which exogeneous stimuli and intrinsic neural circuitry interact to shape human cognition. The current work bridges this gap by deriving and validating a new technique, termed Mesoscale Individualized NeuroDynamic (MINDy) modeling, to estimate large-scale neural population models for individual human subjects using resting-state fMRI. A combination of ground-truth simulations and test-retest data are used to demonstrate that the approach is robust to various forms of noise, motion, and data processing choices. The MINDy formalism is then extended to simultaneously estimating neural population models and the neurovascular coupling which gives rise to BOLD fMRI. In doing so, I develop and validate a new optimization framework for simultaneously estimating system states and parameters. Lastly, MINDy models derived from resting-state data are used to predict task-based activity and remove the effects of intrinsic dynamics. Removing the MINDy model predictions from task fMRI, enables separation of exogenously-driven components of activity from their indirect consequences (the model predictions). Results demonstrate that removing the predicted intrinsic dynamics improves detection of event-triggered and sustained responses across four cognitive tasks. Together, these findings validate the MINDy framework and demonstrate that MINDy models predict brain dynamics across contexts. These dynamics contribute to the variance of task-evoked brain activity between subjects. Removing the influence of intrinsic dynamics improves the estimation of task effects