Typical and aberrant functional brain flexibility: lifespan development and aberrant organization in traumatic brain injury and dyslexia

Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8–60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity

Prediction error dependent changes in brain connectivity during associative learning

One of the fundaments of associative learning theories is that surprising events drive learning by signalling the need to update one’s beliefs. It has long been suggested that plasticity of connection strengths between neurons underlies the learning of predictive associations: Neural units encoding associated entities change their connectivity to encode the learned associative strength. Surprisingly, previous imaging studies have focused on correlations between regional brain activity and variables of learning models, but neglected how these variables changes in interregional connectivity. Dynamic Causal Models (DCMs) of neuronal populations and their effective connectivity form a novel technique to investigate such learning dependent changes in connection strengths. In the work presented here, I embedded computational learning models into DCMs to investigate how computational processes are reflected by changes in connectivity. These novel models were then used to explain fMRI data from three associative learning studies. The first study integrated a Rescorla-Wagner model into a DCM using an incidental learning paradigm where auditory cues predicted the presence/absence of visual stimuli. Results showed that even for behaviourally irrelevant probabilistic associations, prediction errors drove the consolidation of connection strengths between the auditory and visual areas. In the second study I combined a Bayesian observer model and a nonlinear DCM, using an fMRI paradigm where auditory cues differentially predicted visual stimuli, to investigate how predictions about sensory stimuli influence motor responses. Here, the degree of striatal prediction error activity controlled the plasticity of visuo-motor connections. In a third study, I used a nonlinear DCM and data from a fear learning study to demonstrate that prediction error activity in the amygdala exerts a modulatory influence on visuo-striatal connections. Though postulated by many models and theories about learning, to our knowledge the work presented in this thesis constitutes the first direct report that prediction errors can modulate connection strength

Approaches to Understanding the Function of Intrinsic Activity and its Relationship to Task-evoked Activity in the Human Brain

Traditionally neuroscience research has focused on characterizing the topography and patterns of brain activation evoked by specific cognitive or behavioral tasks to understand human brain functions. This activation-based paradigm treated underlying spontaneous brain activity, a.k.a. intrinsic activity, as noise hence irrelevant to cognitive or behavioral functions. This view, however, has been profoundly modified by the discovery that intrinsic activity is not random, but temporally correlated at rest in widely distributed spatiotemporal patterns, so called resting state networks (RSN). Studies of temporal correlation of spontaneous activity among brain regions, or functional connectivity (FC), have yielded important insights into the network organization of the human brain. However, the underlying fundamental relationship between intrinsic and task-evoked brain activity has remained unclear, becoming an increasingly important topic in neuroscience. An emerging view is that neural activity evoked by a task and the associated behavior is influenced and constrained by intrinsic activity. Additionally, intrinsic activity may be shaped in the course of development or adult life by neural activity evoked by a task through a Hebbian learning process. This thesis aims to reveal correspondences between intrinsic activity and task-evoked activity to better understand the nature and function of intrinsic brain activity. We measured in human visual cortex the blood oxygen level dependent (BOLD) signal with fMRI to analyze the multivoxel activity patterns and FC structures of intrinsic activity, and compare them to those evoked by natural and synthetic visual stimuli. In chapter 1, we review previous evidence of an association between intrinsic and task-evoked activity across studies using different experimental methods. Two experimental strategies from the literature were adapted to our own experiments. First, from anesthetized animal studies of intrinsic activity in visual cortex, we set out to measure macro-scale multi-voxel patterns of spontaneous activity fluctuations as they relate to visually driven patterns of activity (Chapters 2 and 4). Second, from inter-subject correlation studies of visual activity driven by natural stimuli, we measure relationships between intrinsic and evoked activity, specifically in relation to their topographic similarity at the network level (Chapter 5). In Chapter 2 to 4, we establish a multivariate-pattern analysis (MVPA) approach to evaluate patterns of intrinsic and task-evoked activity. The main idea is that patterns of activity induced by behaviorally relevant stimuli over long periods of time would be represented in spontaneous activity patterns within the same areas. To test the idea, in Chapter 2, we compare the overall degree of pattern similarities between resting-state activity patterns, frame-by-frame (framewise), and visual-stimulus evoked activity patterns for natural (face, body, scenes, man-made objects) and synthetic (phase and position scrambled) object images during low-level detection task. We found that the variability, not the mean, of pattern similarity was significantly higher for natural than synthetic stimuli in visual occipital regions that preferred particular stimulus categories. Chapter 3 extends the static categorical pattern similarity measure of Chapter 2 into a temporal correlation measure. We built pattern-based FC matrices for different stimulus categories (e.g. a face specific multivoxel pattern) in regions that preferred particular stimulus categories (e.g. FFA, STG), and showed that the occurrence of a specific categorical pattern generalizes across category specific regions. These pattern-based FCs resemble that of resting-state FC of the same regions supporting that resting state patterns are related to category-specific stimulus-evoked multivoxel activity patterns. In Chapter 4, we repeat the analysis used in Chapter 2 with language stimuli. Language stimuli (alphabetic letters and English words) are interesting as they are learned through intensive training as kids learn to read. Therefore, they represent a non-natural category of stimuli that is, however, highly trained in literate individuals. The visual stimuli used in Chapter 2 to 4 are designed specifically for a laboratory environment that does not correspond to realistic ecological environments. In Chapter 5, to overcome this limitation, we use the more naturalistic visual experience of movie-watching and compare the whole-brain FC network structure of movie-watching and of resting-state. We show the whole-brain FC structure evoked by movie-watching is partly constrained by the resting network structure. In conclusion, our experiments show that the link between intrinsic activity and task-evoked activity is not only limited to inter-regional interactions (as in regular resting-state FC), hence potentially reflecting anatomical connectivity or modulations of excitability between cortical regions, but extends to multivoxel patterns that carry information about specific stimulus categories. This result supports the notion that intrinsic activity constrains task-evoked, not only in terms of topography or activation levels, but also in terms of the information states that are represented in cortex

High-Density Diffuse Optical Tomography During Passive Movie Viewing: A Platform for Naturalistic Functional Brain Mapping

Human neuroimaging techniques enable researchers and clinicians to non-invasively study brain function across the lifespan in both healthy and clinical populations. However, functional brain imaging methods such as functional magnetic resonance imaging (fMRI) are expensive, resource-intensive, and require dedicated facilities, making these powerful imaging tools generally unavailable for assessing brain function in settings demanding open, unconstrained, and portable neuroimaging assessments. Tools such as functional near-infrared spectroscopy (fNIRS) afford greater portability and wearability, but at the expense of cortical field-of-view and spatial resolution. High-Density Diffuse Optical Tomography (HD-DOT) is an optical neuroimaging modality directly addresses the image quality limitations associated with traditional fNIRS techniques through densely overlapping optical measurements. This thesis aims to establish the feasibility of using HD-DOT in a novel application demanding exceptional portability and flexibility: mapping disrupted cortical activity in chronically malnourished children. I first motivate the need for dense optical measurements of brain tissue to achieve fMRI-comparable localization of brain function (Chapter 2). Then, I present imaging work completed in Cali, Colombia, where a cohort of chronically malnourished children were imaged using a custom HD-DOT instrument to establish feasibility of performing field-based neuroimaging in this population (Chapter 3). Finally, in order to meet the need for age appropriate imaging paradigms in this population, I develop passive movie viewing paradigms for use in optical neuroimaging, a flexible and rich stimulation paradigm that is suitable for both adults and children (Chapter 4)

Dynamics of large-scale electrophysiological networks: a technical review

For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography / electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity