Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used

Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used

Reliability of resting brain networks using FMRI

Resting state FMRI studies on human subjects are primarily focused on elaborating effects of resting state brain networks on task induced paradigm and to check consistency of these networks between different groups of populations. Recent studies have shown consistency of RSFC networks within same subjects with intra-site and intra-session variation. The primary objective of this study was to check consistency of resting state networks between sites and between different groups of people in spite of change in scanning parameters and population. A total of 437 subjects resting state FMRI data from six different sites were collected varying in age group from 21 to 40 years, with scanning parameters varying from site to site. All the data was pre-processed in exactly similar fashion to reduce effects of site variation and to make group comparison feasible. It was hypothesized that in spite of variation in scanning parameters and population differences, cross-correlation values of time series between 97 ROIs chosen in the brain, should be consistent. To compare resting state connectivity measures, scatter plots of cross-correlation co-efficient between ROIs across sites were used. The investigation demonstrated a strong correlation between cross correlation values for pair of ROIS between sites. These findings suggest reliability and consistency of resting state brain networks between sites

An Uncertainty Visual Analytics Framework for Functional Magnetic Resonance Imaging

Improving understanding of the human brain is one of the leading pursuits of modern scientific research. Functional magnetic resonance imaging (fMRI) is a foundational technique for advanced analysis and exploration of the human brain. The modality scans the brain in a series of temporal frames which provide an indication of the brain activity either at rest or during a task. The images can be used to study the workings of the brain, leading to the development of an understanding of healthy brain function, as well as characterising diseases such as schizophrenia and bipolar disorder. Extracting meaning from fMRI relies on an analysis pipeline which can be broadly categorised into three phases: (i) data acquisition and image processing; (ii) image analysis; and (iii) visualisation and human interpretation. The modality and analysis pipeline, however, are hampered by a range of uncertainties which can greatly impact the study of the brain function. Each phase contains a set of required and optional steps, containing inherent limitations and complex parameter selection. These aspects lead to the uncertainty that impacts the outcome of studies. Moreover, the uncertainties that arise early in the pipeline, are compounded by decisions and limitations further along in the process. While a large amount of research has been undertaken to examine the limitations and variable parameter selection, statistical approaches designed to address the uncertainty have not managed to mitigate the issues. Visual analytics, meanwhile, is a research domain which seeks to combine advanced visual interfaces with specialised interaction and automated statistical processing designed to exploit human expertise and understanding. Uncertainty visual analytics (UVA) tools, which aim to minimise and mitigate uncertainties, have been proposed for a variety of data, including astronomical, financial, weather and crime. Importantly, UVA approaches have also seen success in medical imaging and analysis. However, there are many challenges surrounding the application of UVA to each research domain. Principally, these involve understanding what the uncertainties are and the possible effects so they may be connected to visualisation and interaction approaches. With fMRI, the breadth of uncertainty arising in multiple stages along the pipeline and the compound effects, make it challenging to propose UVAs which meaningfully integrate into pipeline. In this thesis, we seek to address this challenge by proposing a unified UVA framework for fMRI. To do so, we first examine the state-of-the-art landscape of fMRI uncertainties, including the compound effects, and explore how they are currently addressed. This forms the basis of a field we term fMRI-UVA. We then present our overall framework, which is designed to meet the requirements of fMRI visual analysis, while also providing an indication and understanding of the effects of uncertainties on the data. Our framework consists of components designed for the spatial, temporal and processed imaging data. Alongside the framework, we propose two visual extensions which can be used as standalone UVA applications or be integrated into the framework. Finally, we describe a conceptual algorithmic approach which incorporates more data into an existing measure used in the fMRI analysis pipeline

Studying spontaneous brain activity with neuroimaging methods and mathematical modelling

The study of spontaneous brain activity using functional Magnetic Resonance Imaging (fMRI) is a relatively young and rapidly developing field born in the mid-nineties. So far, sufficiently solid foundations have been established, mainly in validating the neuronal origin of a significant component of observed low-frequency fluctuations in the 'resting state' fMRI signal. Nevertheless, the field is still facing several major challenges. This thesis first reviews the current state of knowledge and subsequently proceeds to present original research results that are directed towards overcoming these challenges. The first challenge stems from the indirect nature of the fMRI recordings, obscuring the interpretation in terms of the underlying neuronal activity. Two investigations related to this are presented. First, I show that increased head-movement, epiphenomenal to altered states of consciousness, can lead to spurious increases in low-frequency fluctuations in fMRI signal. This may adversely affect inferences on the underlying neurophysiological processes. Second, I demonstrate a direct electrophysiological correlate of increased synchronisation of fMRI activity in areas of the much studied default-mode network. By directly studying electrophysiological correlates of fMRI-based functional connectivity, this study took a pioneering approach to confirming the biological validity of the fMRI functional connectivity concept. Another widely debated question within the field is the optimal method for extracting relevant information from the extreme volumes of neuroimaging data. I present an investigation providing insights and practical recommendations for this question, based on assessing the interdependence information neglected by the commonly used linear correlation for fMRI functional connectivity studies. The results suggest that in typical resting state data, the nonlinear contributions to instantaneous connectivity are negligible. The third major challenge of the field is the integration of the experimental evidence into theoretical models of spontaneous brain activity. In the last part of this thesis, such models are discussed in detail, focusing on the two crucial features of observed spontaneous brain activity: functional connectivity and low-frequency fluctuations. Two specific mechanisms for emergence of the latter are proposed, depending either on the local synchronisation dynamics or the regulatory action of particular neuromodulators. The thesis concludes with discussion of the questions arising from the presented results in the context of the most recent development in the wider field

Studying spontaneous brain activity with neuroimaging methods and mathematical modelling

The study of spontaneous brain activity using functional Magnetic Resonance Imaging (fMRI) is a relatively young and rapidly developing field born in the mid-nineties. So far, sufficiently solid foundations have been established, mainly in validating the neuronal origin of a significant component of observed low-frequency fluctuations in the 'resting state' fMRI signal. Nevertheless, the field is still facing several major challenges. This thesis first reviews the current state of knowledge and subsequently proceeds to present original research results that are directed towards overcoming these challenges. The first challenge stems from the indirect nature of the fMRI recordings, obscuring the interpretation in terms of the underlying neuronal activity. Two investigations related to this are presented. First, I show that increased head-movement, epiphenomenal to altered states of consciousness, can lead to spurious increases in low-frequency fluctuations in fMRI signal. This may adversely affect inferences on the underlying neurophysiological processes. Second, I demonstrate a direct electrophysiological correlate of increased synchronisation of fMRI activity in areas of the much studied default-mode network. By directly studying electrophysiological correlates of fMRI-based functional connectivity, this study took a pioneering approach to confirming the biological validity of the fMRI functional connectivity concept. Another widely debated question within the field is the optimal method for extracting relevant information from the extreme volumes of neuroimaging data. I present an investigation providing insights and practical recommendations for this question, based on assessing the interdependence information neglected by the commonly used linear correlation for fMRI functional connectivity studies. The results suggest that in typical resting state data, the nonlinear contributions to instantaneous connectivity are negligible. The third major challenge of the field is the integration of the experimental evidence into theoretical models of spontaneous brain activity. In the last part of this thesis, such models are discussed in detail, focusing on the two crucial features of observed spontaneous brain activity: functional connectivity and low-frequency fluctuations. Two specific mechanisms for emergence of the latter are proposed, depending either on the local synchronisation dynamics or the regulatory action of particular neuromodulators. The thesis concludes with discussion of the questions arising from the presented results in the context of the most recent development in the wider field

Neuronal and Hemodynamic Functional Connectivity in the Awake Mouse

Resting State functional Magnetic Resonance Imaging (rs-fMRI) has revealed brain-wide correlation patterns throughout the human brain, interpreted as Functional Connectivity. Dynamic Functional Connectivity (DFC) has recently expanded on this technique via sliding window correlation analysis, revealing moment-to-moment changes in functional connectivity across an imaging session. However, the meaning of these transitions in terms of neural activity and behavior are not well understood.In this work, I utilized Dynamic Functional Connectivity analytical techniques in conjunction with Wide Field Optical Mapping (WFOM) in the awake, freely behaving mouse. I hypothesized that neural and hemodynamic activity observed with WFOM would exhibit similar transitions between functional connectivity states as reported by fMRI DFC studies. I also explored whether changes in functional connectivity would correspond to changes in behavior. Simultaneous neural and hemodynamic activity was collected using WFOM from five freely behaving head-fixed Thy1-jRGECO1a mice. Behavioral metrics of movement, whisking and pupillometry were acquired simultaneously. Raw neuroimaging data were dimensionally reduced to representative time courses across the dorsal surface of the cortex for each subject utilizing a semi-supervised clustering technique. Functional Connectivity analysis revealed rich spatiotemporal structures within neural and hemodynamic activity, which were consistent across imaging sessions and subjects. I observed broad changes in Functional Connectivity metrics during rest, locomotion, and transitional epochs between the two by directly comparing windows captured during these epochs. It was also observed that Functional Connectivity metrics immediately following locomotion offset could be distinguished from periods of sustained rest. Similar to human fMRI studies, a distinct increase in bilateral connectivity of anterior lateral prefrontal cortex was observed, which became significantly less synchronized with posterior brain regions during sustained periods of rest. I next used an unsupervised clustering technique on the same data to test if these properties could be observed in an indirect manner. This approach has been previously used in numerous human fMRI studies, and contextualized this work to human fMRI studies. A sliding window was used to calculate moment-to-moment Functional Connectivity maps across each imaging session. These dynamic correlation maps were clustered into multiple states, which could then be used to calculate the most representative state for any given epoch. Unsupervised clustering revealed strikingly similar dynamic states to our previous observations. These dynamic states also exhibited independent distributions of behavioral activity both in neural and hemodynamic models, leading us to conclude that there is not only a meaningful link between Functional Connectivity in neural and hemodynamic activity, but that behavioral shifts largely drive these changes. My findings provide strong evidence that Dynamic Functional Connectivity has neural origins, and hemodynamic responses are able to depict correlation patterns that tracks rapid changes in behavior and internal brain states such as the level of arousal or alertness. Future studies are necessary to further investigate this speculation, but this offers an excellent framework to better understand the rich, dynamic properties of brain activity