Micro-, Meso- and Macro-Connectomics of the Brain

Kennedy, Henry; Essen, David C. Van; Christen, Yves

doi:10.1007/978-3-319-27777-6

Cited by 24 publications

(2 citation statements)

References 232 publications

(292 reference statements)

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“…First, the neurons that form the brain are very diverse morphologically [2] and dynamically [3]. Second, these neurons are connected to each other in extremely large numbers and forming very complex networks [4], whose structural characteristics are still mostly unknown. And third, brain dynamics are very irregular and complex [5,6].…”

Section: Introductionmentioning

confidence: 99%

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Escuain-Poole

García-Ojalvo

Pons

2018

Front. Appl. Math. Stat.

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Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary's model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and parameters of the neural masses and their interactions, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra-or extracranial, is shown over a wide variety of dynamical behaviours. Our results show potential towards future clinical applications of the method. Author SummaryTo completely understand brain function, we will need to integrate experimental information into a consistent theoretical framework. Invasive techniques as EcoG recordings, together with models that describe the brain at the mesoscale, provide valuable information about the brain state and its dynamical evolution when combined with techniques coming from control theory, such as the Kalman filter. This method, which is specifically designed to deal with systems with noisy or imperfect data, combines experimental data with theoretical models assuming Bayesian inference. So far, implementations of the Kalman filter have not been suited for non-invasive measures like EEG. Here we attempt to overcome this situation by introducing a model of the head that allows to transfer the intracranial signals produced by a mesoscopic model to the scalp in the form of EEG recordings. Our results show the advantages of using multichannel EEG recordings, which are extended in space and allow to discriminate signals produced by the interaction of coupled columns. The extension of the Kalman method presented here can be expected to expand the applicability of the technique to all situations where EEG 1/23 arXiv:1708.05282v1 [q-bio.NC] 1 Aug 2017 recordings are used, including the routine monitoring of illnesses or rehabilitation tasks, brain-computer interface protocols, and transcranial stimulation.

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Section: Introductionmentioning

confidence: 99%

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Escuain-Poole

García-Ojalvo

Pons

2018

Front. Appl. Math. Stat.

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“…Segregation refers to the specificity and modularity of brain organisation. Evidence for segregation of brain function can be found at every scale of investigation (Sporns, 2016), from single neurons that respond selectively to motion (e.g., Albright, 1984), to groups of neurons that form local processing units within sensory modalities (e.g., vision, audition and so on, Chiang et al, 2011), to macroscopic brain regions measured with fMRI that respond selectively to human faces (e.g., Kanwisher et al, 1997). By contrast, integration refers to how such specialised processing modules exchange information to produce perception and behaviour.…”

Section: From Functional Specialisation To Brain Networkmentioning

confidence: 99%

Characterisation of Functional Brain Networks underlying Cognitive Reasoning and Intelligence

Hearne¹

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Intelligence is a general mental capacity that encompasses the ability to plan, problem-solve, reason, think abstractly and comprehend complex relations between concepts. Measured via complex, multistep reasoning tasks, individual differences in intelligence scores are highly predictive of a number of crucial life outcomes. Despite many decades of research, however, the neural bases of intelligence and reasoning abilities are not well understood. Early neuroimaging and lesion studies suggested that regions of the prefrontal cortex are exclusively responsible for intelligent behaviour and higher cognitive reasoning abilities. More recently, however, investigators have conceptualized intelligence as relying upon a widely distributed network of interconnected nodes. The aim of my thesis was to investigate human reasoning and intelligence within this newer, network-centric framework. I conducted a series of functional magnetic resonance imaging (fMRI) experiments to characterize relevant networks in the brain, and related these networks to reasoning task performance, individual differences in intelligence, and the breakdown of reasoning ability amongst individuals with developmental anomalies in brain wiring.The thesis is organized into seven chapters. In the first chapter, I introduce the research question in the context of previous literature that has investigated the brain-basis of reasoning and intelligence by measuring brain activity using fMRI. In doing so, I discuss the origins of intelligence, why task complexity matters, as well as modern in-vivo brain imaging techniques and their biological basis. I then present the concepts of brain network organization, connectivity and graph theory as a relatively untapped avenue to explain higher cognitive reasoning. Armed with this new framework for understanding the brain, I discuss a number of studies that have used this approach to inform the key questions investigated in this thesis.In Chapter 2 I employed an individual-differences approach to investigate the relationship between 'resting-state' baseline functional networks and commonly used measures of intelligence.Previous work has implicated a fronto-parietal network of brain regions that support higher cognitive reasoning, formalized in the parieto-frontal theory of intelligence. However, existing evidence for the fronto-parietal theory is mixed, at least with respect to findings from connectivity studies. To address this issue, I undertook a quantitative summary of previous literature and performed an empirical analysis of data from a large cohort of individuals (N = 317) sourced from the Human Connectome Project. I found that connectivity between the default-mode and fronto-parietal networks best explained individual differences in intelligence.In Chapter 3 I investigated the relationship between default-mode and cognitive control networks during a complex reasoning task. Participants completed a modified version of the classic Wason Selection Task, a measure of relational reasoning, in which the num...

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Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities

Yeh

Jones

Liang

et al. 2020

Magnetic Resonance Imaging

116

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Diffusion MRI‐based tractography is the most commonly‐used technique when inferring the structural brain connectome, i.e., the comprehensive map of the connections in the brain. The utility of graph theory—a powerful mathematical approach for modeling complex network systems—for analyzing tractography‐based connectomes brings important opportunities to interrogate connectome data, providing novel insights into the connectivity patterns and topological characteristics of brain structural networks. When applying this framework, however, there are challenges, particularly regarding methodological and biological plausibility. This article describes the challenges surrounding quantitative tractography and potential solutions. In addition, challenges related to the calculation of global network metrics based on graph theory are discussed.Evidence Level: 5Technical Efficacy: Stage 1

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Micro-, Meso- and Macro-Connectomics of the Brain

Cited by 24 publications

References 232 publications

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Characterisation of Functional Brain Networks underlying Cognitive Reasoning and Intelligence

Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities

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