Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations

For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously -- elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala, and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow time scales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding "feeder" cortical regions show unstable, rapidly fluctuating dynamics likely crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.Comment: 35 pages, 6 figure

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Successful object encoding induces increased directed connectivity in presymptomatic early-onset Alzheimer's disease

Background: Recent studies report increases in neural activity in brain regions critical to episodic memory at preclinical stages of Alzheimer’s disease (AD). Although electroencephalography (EEG) is widely used in AD studies, given its non-invasiveness and low cost, there is a need to translate the findings in other neuroimaging methods to EEG. Objective: To examine how the previous findings using functional magnetic resonance imaging (fMRI) at preclinical stage in presenilin-1 E280A mutation carriers could be assessed and extended, using EEG and a connectivity approach. Methods: EEG signals were acquired during resting and encoding in 30 normal cognitive young subjects, from an autosomal dominant early-onset AD kindred from Antioquia, Colombia. Regions of the brain previously reported as hyperactive were used for connectivity analysis. Results: Mutation carriers exhibited increasing connectivity at analyzed regions. Among them, the right precuneus exhibited the highest changes in connectivity. Conclusion: Increased connectivity in hyperactive cerebral regions is seen in individuals, genetically-determined to develop AD, at preclinical stage. The use of a connectivity approach and a widely available neuroimaging technique opens the possibility to increase the use of EEG in early detection of preclinical AD.Postprint (author's final draft

Brain circuits involved in self-paced motion: the influence of 0.1 Hz waves

The neural mechanisms behind human voluntary motion are not fully characterized yet, in spite of numerous research studies. Slow ( 0.1 Hz) brain oscillations are known to have a powerful modulatory effect on several cognitive and physiological phenomena, including free movement. This study is based on fMRI data acquired from 25 young, healthy subjects. The tasks were: rest, self-paced motion, motion paced by a periodic 0.1 Hz stimulus. The temporal resolution was finer than standard fMRI protocols (TR=871 ms). After preprocessing, the signal from brain regions of interest was extracted, and functional connectivity was computed between brain regions using wavelet phase coherence. Complementarily, effective connectivity was measured using Granger causality. The final output was Phase-Locking (PL) and Granger Causality (GC) matrices reflecting inter-regional phase coherence and causal interactions, respectively, around 0.1 Hz. Using the GraphVar toolbox, inter-task and inter-group comparisons were performed. In inter-task comparisons PL matrices showed encouraging results unlike GC matrices. Pairs of regions for which PL differs significantly between rest and self-paced movement were identified. These include mainly the Postcentral gyrus, Putamen, the Anterior Cingulum, the Precentral gyrus, the Calcarine, the Lingual and the Insula (all in the left hemisphere). Topological changes in the brain wiring were identified across the tasks by computing the node degree and global efficiency. Inter-group comparisons took into account the inter movement interval and the coupling between BOLD and heart rate beatto-beat interval signals and showed changes in brain activity depending on the regularity of movement intervals and specific connectivity patterns for neural BOLD oscillations, respectively. This methodological approach allowed to make a contribution towards the characterization of the functional connectivity of brain circuits related to voluntary motor behavior

Time resolved functional brain networks : a novel method and developmental perspective

Functional neuroimaging has helped elucidating the complexity of brain function in ever more detail during the last 30 years. In this time the concepts used to understand how the brain works has also developed from a focus on regional activation to a network based whole brain perspective (Deco et al., 2015). The understanding that the brain is not just merely responding to external demands but is itself a co-creator of its perceived reality is now the default perspective (Buzsáki and Fernández-Ruiz, 2019). This means that the brain is never resting and its intrinsic architecture is the basis for any task related modulation (Cole et al., 2014). As often in science, understanding and technological advances go hand in hand. For the advancement of the functional neuroimaging field during the last decade, methods that are able to track, capture and model time resolved connectivity changes has been essential (Lurie et al., 2020). This development is an ongoing process. Part of the work presented in this thesis is a small contribution to this collective endeavor. The first theme in the thesis is time resolved connectivity of functional brain networks. This theme is present in Study I which presents a novel method for analysis of time resolved connectivity using BOLD fMRI data. With this method, subnetworks in the brain are defined dynamically. It allows for connectivity changes to be tracked from time point to time point while respecting the temporal ordering of the data. It also provides relational properties in terms of differences in phase coherence between simultaneously integrated networks and their gradual change. The method can be used see how whole brain connectivity configurations recure in quasi-cyclic patterns. Finally, the method is able to estimate flexibility and modularity of individual brain areas. The method is applied in Study III in order to understand how premature birth effects flexibility and modularity of intrinsic functional brain networks. Beyond the purely scientific endeavor to understand how the brain creates cognition, consciousness, perception and supports motor function, neuroimaging research has also been helpful in elucidating normal brain development and neurodevelopmental disorders. The second theme in this thesis is brain development in extremely preterm born children at school age. This theme is the focus of Study II & III. Study II investigates the prevalence of discrete white matter abnormalities at school age in children born extremely preterm and the relationship to neuro-motor outcome. The prevalence of white matter abnormalities was high but there was no relationship to an unfavorable outcome. Also, a longitudinal association to neonatal white matter injury was seen. While discrete white matter abnormalities were not correlated to quantitative measures of white matter volume and white matter integrity, neonatal white matter injury was associated with lower volume and integrity at age 8- 11 years. Moreover, neonatal white matter injury was associated with lower processing speed at 12 years. The third and final study investigated flexibility and modularity as well as lateralization of intrinsic networks in children born extremely preterm at age 8-11 years. No significant differences in either flexibility or modularity was seen for any intrinsic network after correcting for multiple comparisons. However, at the level of individual brain areas, preterm children showed decreased flexibility in both the basal ganglia and thalamus. Also, children born extremely preterm had a decreased level of lateralization in most networks

Spatio-temporal Principles of Infra-slow Brain Activity

In the study of systems where basic laws have eluded us, as is largely the case in neuroscience, the simplest approach to progress might be to ask: what are the biggest, most noticeable things the system does when left alone? Without any perturbations or fine dissections, can regularities be found in the basic operations of the system as a whole? In the case of the brain, it turns out that there is an amazing amount of activity even in the absence of explicit environmental inputs or outputs. We call this spontaneous, or resting state, brain activity. Prior work has shown that spontaneous brain activity is dominated by very low frequencies: the biggest changes in brain activity happen relatively slowly, over 10’s-100’s of seconds. Moreover, this very slow activity of the brain is quite metabolically expensive. The brain accounts for 2% of body mass in an adult, but requires 20% of basal metabolic expenditure. Remarkably, the energy required to sustain brain function is nearly constant whether one is engaged in a demanding mental task or simply out to lunch. Furthermore, work over the past three decades has established that the spontaneous activities of the brain are not random, but instead organized into specific patterns, most often characterized by correlations within large brain systems. Yet, how do these correlations arise, and does spontaneous activity support slow signaling within and between neural systems? In this thesis, we approach these questions by providing a comprehensive analysis of the temporal structure of very low frequency spontaneous activity. Specifically, we focus on the direction of travel in low frequency activity, measured using resting state fMRI in humans, but also using electrophysiological techniques in humans and mice, and optical calcium imaging in mice. Our temporal analyses reveal heretofore unknown regularities in the way slow signals move through the brain. We further find that very low frequency activity behaves differently than faster frequencies, that it travels through distinct layers of the cortex, and that its travel patterns give rise to correlations within networks. We also demonstrate that the travel patterns of very low frequency activity are highly dependent on the state of the brain, especially the difference between wake and sleep states. Taken together, the findings in this thesis offer a glimpse into the principles that govern brain activity