Topographic electrophysiological signatures of FMRI Resting State Networks

Background: fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG. Methodology/Principal Findings: In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band. Conclusions/Significance: Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.

Shifted Coupling of EEG Driving Frequencies and fMRI Resting State Networks in Schizophrenia Spectrum Disorders

INTRODUCTION: The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state networks (RSNs). Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates) and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN) and left-working-memory network (LWMN). METHODS: Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n = 11) and matched healthy controls (n = 11) using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps. RESULTS: The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls. CONCLUSIONS: By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling. SIGNIFICANCE: The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations), arise.

When the systems fail to communicate. Cerebral resting state networks in schizophrenia spectrum disorders.

There have been numerous attempts to reveal the neurobiological basis of schizophrenia spectrum disorders. Results however, remain as heterogeneous as the schizophrenia spectrum disorders itself. Therefore, one aim of this thesis was to divide patients affected by this disorder into subgroups in order to homogenize the results of future studies. In a first study it is suggested that psychopathological rating scales should focus on symptoms-clusters that may have a common neurophysiological background. The here presented Bern Psychopathology Scale (BPS) proposes that alterations in three wellknown brain systems (motor, language, and affective) are largely leading to the communication failures observable on a behavioral level, but also - as repeatedly hypothesized - to dysconnectivity within and between brain systems in schizophrenia spectrum disorders. The external validity of the motor domain in the BPS was tested against the objective measure of 24 hours wrist actigraphy, in a second study. The subjective, the quantitative, as well as the global rating of the degree of motor disorders in this patient group showed significant correlations to the acquired motor activity. This result confirmed in a first step the practicability of the motor domain of the BPS, but needs further validation regarding pathological brain alterations. Finally, in a third study (independent from the two other studies), two cerebral Resting State Networks frequently altered in schizophrenia were investigated for the first time using simultaneous EEG/fMRI: The well-known default mode network and the left working memory network. Besides the changes in these fMRI-based networks, there are well-documented findings that patients exhibit alterations in EEG spectra compared to healthy controls. However, only through the multimodal approach it was possible to discover that patients with schizophrenia spectrum disorders have a slower driving frequency of the Resting State Networks compared to the matched healthy controls. Such a dysfunctional coupling between neuronal frequency and functional brain organization could explain in a uni- or multifactorial way (dysfunctional cross-frequency coupling, maturational effects, vigilance fluctuations, task-related suppression), how the typical psychotic symptoms might occur. To conclude, the major contributions presented in this thesis were on one hand the development of a psychopathology rating scale that is based on the assumption of dysfunctional brain networks, as well as the new evidence of a dysfunctional triggering frequency of Resting State Networks from the simultaneous EEG/fMRI study in patients affected by a schizophrenia spectrum disorder.

Génération et reconnaissance de rythmes au moyen de réseaux de neurones à réservoir

Les fichiers sons qui accompagne mon document sont au format midi. Le programme que nous avons développés pour ce travail est en language Python.

Prognóstico de falhas baseado em redes neurais com estados de eco

Tese (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2015.

Modeling resting-state functional networks when the cortex falls asleep: local and global changes.

The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network.

Changes in resting-state functionally connected parieto-frontal networks after videogame practice

Neuroimaging studies provide evidence for organized intrinsic activity under task-free conditions. This activity serves functionally relevant brain systems supporting cognition. Here, we analyze changes in resting-state functional connectivity after videogame practice applying a test–retest design. Twenty young females were selected from a group of 100 participants tested on four standardized cognitive ability tests. The practice and control groups were carefully matched on their ability scores. The practice group played during two sessions per week across 4 weeks (16 h total) under strict supervision in the laboratory, showing systematic performance improvements in the game. A group independent component analysis (GICA) applying multisession temporal concatenation on test–retest resting-state fMRI, jointly with a dual-regression approach, was computed. Supporting the main hypothesis, the key finding reveals an increased correlated activity during rest in certain predefined resting state networks (albeit using uncorrected statistics) attributable to practice with the cognitively demanding tasks of the videogame. Observed changes were mainly concentrated on parietofrontal networks involved in heterogeneous cognitive functions.

Increased resting state functional connectivity in the default mode network in recovered anorexia nervosa.

Functional brain imaging studies have shown abnormal neural activity in individuals recovered from anorexia nervosa (AN) during both cognitive and emotional task paradigms. It has been suggested that this abnormal activity which persists into recovery might underpin the neurobiology of the disorder and constitute a neural biomarker for AN. However, no study to date has assessed functional changes in neural networks in the absence of task-induced activity in those recovered from AN. Therefore, the aim of this study was to investigate whole brain resting state functional connectivity in nonmedicated women recovered from anorexia nervosa. Functional magnetic resonance imaging scans were obtained from 16 nonmedicated participants recovered from anorexia nervosa and 15 healthy control participants. Independent component analysis revealed functionally relevant resting state networks. Dual regression analysis revealed increased temporal correlation (coherence) in the default mode network (DMN) which is thought to be involved in self-referential processing. Specifically, compared to healthy control participants the recovered anorexia nervosa participants showed increased temporal coherence between the DMN and the precuneus and the dorsolateral prefrontal cortex/inferior frontal gyrus. The findings support the view that dysfunction in resting state functional connectivity in regions involved in self-referential processing and cognitive control might be a vulnerability marker for the development of anorexia nervosa.

Time course based artifact identification for independent components of resting-state FMRI

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

fMRI of Emotion

Recent brain imaging work has expanded our understanding of the mechanisms of perceptual, cognitive, and motor functions in human subjects, but research into the cerebral control of emotional and motivational function is at a much earlier stage. Important concepts and theories of emotion are briefly introduced, as are research designs and multimodal approaches to answering the central questions in the field. We provide a detailed inspection of the methodological and technical challenges in assessing the cerebral correlates of emotional activation, perception, learning, memory, and emotional regulation behavior in healthy humans. fMRI is particularly challenging in structures such as the amygdala as it is affected by susceptibility-related signal loss, image distortion, physiological and motion artifacts and colocalized Resting State Networks (RSNs). We review how these problems can be mitigated by using optimized echo-planar imaging (EPI) parameters, alternative MR sequences, and correction schemes. High-quality data can be acquired rapidly in these problematic regions with gradient compensated multiecho EPI or high resolution EPI with parallel imaging and optimum gradient directions, combined with distortion correction. Although neuroimaging studies of emotion encounter many difficulties regarding the limitations of measurement precision, research design, and strategies of validating neuropsychological emotion constructs, considerable improvement in data quality and sensitivity to subtle effects can be achieved. The methods outlined offer the prospect for fMRI studies of emotion to provide more sensitive, reliable, and representative models of measurement that systematically relate the dynamics of emotional regulation behavior with topographically distinct patterns of activity in the brain. This will provide additional information as an aid to assessment, categorization, and treatment of patients with emotional and personality disorders.

The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.