Seizure-onset mapping based on time-variant multivariate functional connectivity analysis of high-dimensional intracranial EEG : a Kalman filter approach

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (< 60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach

Time-varying effective EEG source connectivity: the optimization of model parameters*

Adaptive estimation methods based on general Kalman filter are powerful tools to investigate brain networks dynamics given the non-stationary nature of neural signals. These methods rely on two parameters, the model order p and adaptation constant c, which determine the resolution and smoothness of the time-varying multivariate autoregressive estimates. A sub-optimal filtering may present consistent biases in the frequency domain and temporal distortions, leading to fallacious interpretations. Thus, the performance of these methods heavily depends on the accurate choice of these two parameters in the filter design. In this work, we sought to define an objective criterion for the optimal choice of these parameters. Since residual- and information-based criteria are not guaranteed to reach an absolute minimum, we propose to study the partial derivatives of these functions to guide the choice of p and c. To validate the performance of our method, we used a dataset of human visual evoked potentials during face perception where the generation and propagation of information in the brain is well understood and a set of simulated data where the ground truth is available

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

Overlapping connectivity patterns during semantic processing of abstract and concrete words revealed with multivariate Granger Causality analysis

Unlike concrete, nouns refer to notions beyond our perception. Even though there is no consensus among linguists as to what exactly constitutes a concrete or abstract word, neuroscientists found clear evidence of a "concreteness" effect. This can, for instance, be seen in patients with language impairments due to brain injury or developmental disorder who are capable of perceiving one category better than another. Even though the results are inconclusive, neuroimaging studies on healthy subjects also provide a spatial and temporal account of differences in the processing of abstract versus concrete words. A description of the neural pathways during abstract word reading, the manner in which the connectivity patterns develop over the different stages of lexical and semantic processing compared to that of concrete word processing are still debated. We conducted a high-density EEG study on 24 healthy young volunteers using an implicit categorization task. From this, we obtained high spatio-temporal resolution data and, by means of source reconstruction, reduced the effect of signal mixing observed on scalp level. A multivariate, time-varying and directional method of analyzing connectivity based on the concept of Granger Causality (Partial Directed Coherence) revealed a dynamic network that transfers information from the right superior occipital lobe along the ventral and dorsal streams towards the anterior temporal and orbitofrontal lobes of both hemispheres. Some regions along these pathways appear to be primarily involved in either receiving or sending information. A clear difference in information transfer of abstract and concrete words was observed during the time window of semantic processing, specifically for information transferred towards the left anterior temporal lobe. Further exploratory analysis confirmed a generally stronger connectivity pattern for processing concrete words. We believe our study could guide future research towards a more refined theory of abstract word processing in the brain

Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

Recent studies on motor imagery (MI)-based brain computer interaction (BCI) reported that the interaction of spatially separated brain areas in forms of functional or effective connectivity leads to a better insight of brain neural patterns during MI movements and can provide useful features for BCIs. However, existing studies suffer from unrealistic assumptions or technical weaknesses for processing brain signals, such as stationarity, linearity and bivariate analysis framework. Besides, volume conduction effect as a critical challenge in this area and the role of subcortical regions in connectivity analysis have not been considered and studied well. In this thesis, the neurophysiological connectivity patterns of healthy human brain during different MI movements are deeply investigated. At first, an adaptive nonlinear multivariate statespace model known as dual extended Kalman filter is proposed for connectivity pattern estimation. Several frequency domain functional and effective connectivity estimators are developed for nonlinear non-stationary signals. Evaluation results show superior parameter tracking performance and hence more accurate connectivity analysis by the proposed model. Secondly, source-space time-varying nonlinear multivariate brain connectivity during feet, left hand, right hand and tongue MI movements is investigated in a broad frequency range by using the developed connectivity estimators. Results reveal the similarities and the differences between MI tasks in terms of involved regions, density of interactions, distribution of interactions, functional connections and information flows. Finally, organizational principles of brain networks of MI movements measured by all considered connectivity estimators are extensively explored by graph theoretical approach where the local and global graph structures are quantified by computing different graph indexes. Results report statistical significant differences between and within the MI tasks by using the graph indexes extracted from the networks formed particularly by normalized partial directed coherence. This delivers promising distinctive features of the MI tasks for non-invasive BCI applications

Shedding light into the brain: Methodological innovations in optical neuroimaging

Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are non-invasive techniques used to infer stimulus-locked variations in human cortical activity from optical variations of near-infrared light injected and subsequently detected at specified scalp locations. Relative to functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), these optical techniques are more portable, less invasive and less sensitive to motion artifacts, making them ideal to explore brain activity in a variety of cognitive situations, and in a range of populations, including newborns and children. FNIRS and DOT measure stimulus-locked hemodynamic response in the form of changes in oxy- (HbO) and deoxy- (HbR) hemoglobin concentration taking place in specific areas. This signal is however structurally intertwined with physiological noise owing to cardiac pulsations, respiratory oscillations and vasopressure wave. Furthermore, the absolute magnitude of hemodynamic responses is substantially smaller than these non-informative components of the measured optical signal, and has a frequency which largely overlaps with that of the vasopressure wave. Thus, recovering the hemodynamic response is a challenging task. Several methods have been proposed in the literature to try to reduce physiological noise oscillations and recover the hemodynamic response, but none of them has become a common standard in the optical signal processing pipeline. In this thesis, a novel algorithm, devised to overcome a large subset of drawbacks associated with the use of these literature techniques, is presented and validated. Reduced sensitivity to motion artifacts notwithstanding, the optical signal must always be assumed as contaminated by some form of mechanical instability, most prominently during signal acquisitions from pathological (e.g., stroke patients) or difficult (e.g., newborns) populations. Several techniques have been proposed to correct for motion artifacts with the specific aim of preserving contaminated measures as opposed to rejecting them. However, none of them has become the gold standard in the optical signal processing pipeline, and there are currently no objective approaches to choose the most appropriate filtering technique based on objective parameters. In fact, due to the extreme variability in shape, frequency content and amplitude of the motion artifacts, it is likely that the best technique to apply is data-dependent and, in this vein, it is essential to provide users with objective tools able to select the best motion correction technique for the data set under examination. In this thesis, a novel objective approach to perform this selection is proposed and validated on a data-set containing a very challenging type of motion artifacts. While fNIRS allows only spectroscopic measurements of hemoglobin concentration changes, DOT allows to obtain 3D reconstructed images of HbO and HbR concentration changes. To increase the accuracy and interpretability of DOT reconstructed images, valuable anatomical information should be provided. While several adult head models have been proposed and validated in this context, only few single-ages head models have been presented for the neonatal population. However, due to the rapid growth and maturation of the infant's brain, single-age models fail to capture precise information about the correct anatomy of every infant's head under examination. In this thesis, a novel 4D head model, ranging from the preterm to the term age, is proposed, allowing developmental neuroscientists to make finer-grained choices about the age-matched head model and perform image reconstruction with an anatomy as similar as possible to the real one. The outline of the thesis will be as follows. In the first two chapters of this thesis, the state of the art of optical techniques will be reviewed. Particularly, in chapter 1, a brief introduction on the physical principles of optical techniques and a comparison with other more common neuroimaging techniques will be presented. In chapter 2, the components of the measured optical signal will be described and a brief review of state of the art of the algorithms that perform physiological noise removal will be presented. The theory on which optical image reconstruction is based will be reviewed afterwards. In the final part of the chapter, some of the studies and achievements of optical techniques in the adult and infants populations will be reviewed and the open issues and aims of the thesis will be presented. In chapters 3, 4 and 5, new methodologies and tools for signal processing and image reconstruction will be presented. Particularly, in chapter 3, a novel algorithm to reduce physiological noise contamination and recover the hemodynamic response will be introduced. The proposed methodology will be validated against two literature methods and results and consequent discussion will be reported. In chapter 4, instead, a novel objective approach for the selection of the best motion correction technique will be proposed. The main literature algorithms for motion correction will be reviewed and the proposed approach will be validated using these motion correction techniques on real cognitive data. In chapter 5, instead, a novel 4D neonatal optical head model will be presented. All the steps performed for its creation will be explained and discussed and a demonstration of the head model in use will also be exhibited. The last part of the thesis (chapters 6, 7 and 8) will be dedicated to illustrate three distinct examples of application of the proposed methodologies and tools on neural empirical data. In chapter 6, the physiological noise removal algorithm proposed in chapter 3 will be applied to recover subtle temporal differences between hemodynamic responses measured in two different areas of the motor cortex in short- vs. long- duration tapping. In chapter 7, the same algorithm will be applied to reduce physiological noise and recover hemodynamic responses measured during a visual short-term memory paradigm. In both chapters, cognitive results and a brief discussion will be reported. In chapter 8, instead, the neonatal optical head model proposed in chapter 5 will be applied to perform image reconstruction with data acquired on a healthy full term baby. In the same chapter, the importance of motion artifact correction will be highlighted, reconstructing HbO concentration changes images before and after the correction took place

Multichannel Characterization of Brain Activity in Neurological Impairments

Hundreds of millions of people worldwide suffer from various neurological and psychiatric disorders. A better understanding of the underlying neurophysiology and mechanisms for these disorders can lead to improved diagnostic techniques and treatments. The objective of this dissertation is to create a novel characterization of multichannel EEG activity for selected neurological and psychiatric disorders based on available datasets. Specifically, this work provides spatial, spectral, and temporal characterizations of brain activity differences between patients/animal models and healthy controls, with focus on modern techniques that quantify cortical connectivity, which is widely believed to be abnormal in such disorders. Exploring the functional brain networks in these patients can provide a better understanding of the pathophysiology and brain network integrity of the respective disorders. This can allow for the assessment of neural mechanism deficits and possibly lead to developing a model for enhancement in the biology of neural interactions in these patients. This unique electrophysiological information may also contribute to the development of target drugs, novel treatments, and genetic studies. Moreover, the outcomes not only provide potential biomarkers for the diagnosis of respective disorders but also can serve as biofeedback for neurotherapy and also development of more sophisticated BCIs

Best practices for fNIRS publications

The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers