1,721,236 research outputs found
Optimising network modelling methods for fMRI
Get PDFThe activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, functional connectivity (FC) in fMRI has been shown to be predictive of behavioural traits and psychiatric and neurological conditions.
Many studies estimate FC by calculating the average correlation across an entire fMRI scanning session data (static FC). A major goal of such neuroimaging studies is to develop predictive models to analyze the relationship between whole-brain FC patterns and behavioural traits. However, there is no single widely accepted standard pipeline for analyzing static FC. We proposed six steps for designing FC-based predictive models. This standardized framework for estimating static FC should accelerate both congruence and scientific progress within the neuroscientific community.
However, there is also increasing interest in studying the time-varying nature of FC in fMRI (time-varying FC). Typically, methods that measure time-varying FC have several limitations that bias the estimation of time-varying FC, to appear more stable over time than it actually is. We designed a new method, MAGE, that offers a potential explanation and a solution for the homogeneity of time-varying FC seen in existing approaches. This new framework for estimating time-varying FC showed that FC is not static over time and fluctuates on a sub-minute time-scale during resting-state fMRI.
This work also investigates the clinical applications of both static and time-varying FC. We found that MAGE estimated time-varying FC can provide a more precise understanding of how brain networks are related to cognitive abilities, both in health and disease. Lastly, we demonstrated thatMAGE can reliably capture the time-varying changes in FC across a range of behavioral and cognitive tasks, overcoming the limitations of
most popular existing methods
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Modelling microstructure and function of cortical layers with MRI
Get PDFThe cerebral cortex is a thin sheet of grey matter surrounding the brain. It is particularly well developed in primates, especially in humans, and it is thought to play a key role in all cognitive functions. Cortical structure and microstructure has been studied for over a century, and modern imaging methods such as MRI have accelerated research on its function. The cortex has a well characterised layered structure, anywhere between 3 to 6 layers, and sometimes with more differentiated layers. Cortico-cortical connections are also organised in a laminar fashion, and it has been theorised that these well established anatomical facts have important consequences for brain functional organisation. Most of what we know about the cortex comes from invasive, ex vivo, histological studies. However, understanding how cortical structure and connectivity relate to cortical function requires in vivo non-invasive methods. Advances in high resolution, and usually high field, fMRI are beginning to address this need, but sub-millimetre MRI is hard to achieve, SNR-poor, and still falls short of resolving layers as the cortex can be as thin as 1 millimetre.
In this thesis, we present new non-invasive MRI-based methods to study cortical laminar structure and connectivity. We introduce a new functional MRI technique, based on inversion recovery, that can modulate the measured signal in a laminar fashion through exploiting the variation of longitudinal relaxation across cortical layers. We also introduce two new modelling approaches to infer laminar connectivity in resting state time series: an extension of structural equation modelling tailored to the new inversion-recovery acquisition method, applied to resting state data, and an extension of dynamic causal modelling to cortical layers, accounting for effects of venous blood draining and applied to task fMRI. Finally, we introduce an approach to combining ultra-high resolution whole brain microscopy with high resolution MRI data for in vivo histology
State-switching models of human brain activity using recurrent neural networks
Get PDFIt has been shown that spatiotemporal dynamics of neuronal activity can be well described using state-related behaviour, comprising a discrete set of reoccurring quasi-stable states associated with distinct patterns of spatial and functional connectivity. Most methods of analysis will either assume stationarity of these states, as in ICA; or constrain the dynamics to be Markovian, as in hidden Markov models (HMMs). These tools lack the capability to explicitly model the higher order temporal dependencies that can occur over timescales of various scales.
In this thesis, we introduce a model that combines probabilistic state-space models with recurrent neural networks (RNNs), enabling us to relax the Markovian constraint of HMMs and learn temporal features of the data occurring over longer timescales. The model takes the form of a recurrent state-switching network, which models the uncertainty in time-varying state labels via discrete random variables. We introduce a variational Bayesian framework for computationally efficient inference of the model that also generalises to a variety of time series models.
Using simulations, data taken from the resting state magnetoencephalography (MEG) scans of 55 participants, and data taken from the MEG recordings of a face-viewing task undertaken by 19 participants, we demonstrate that we can reliably infer a set of states that fits the data better than where the Markovian constraint is enforced, however we do not see significantly different temporal behaviour emerging. We additionally demonstrate that unlike the Markovian model, the recurrent model can internally represent the temporal dynamics of the data
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Investigating the role of APOE-ε4, a risk gene for Alzheimer's disease, on functional brain networks using magnetoencephalography
Get PDFAlzheimer's disease (AD) is developing into the single greatest healthcare challenge in the coming decades. The development of early and effective treatments that can prevent the pathological damage responsible for AD-related dementia is of utmost priority for healthcare authorities. The role of the APOE-ε4 genotype, which has been shown to increase an individual's risk of developing AD, is of central interest to this goal. Understanding the mechanism by which possession of this gene modulates brain function, leading to a predisposition towards AD is an active area of research. Functional connectivity (FC) is an excellent candidate for linking APOE-related differences in brain function to sites of AD pathology. Magnetoencephalography (MEG) is a neuroimaging tool that can provide a unique insight into the electrophysiology underpinning resting-state networks (RSNs) - whose dysfunction is postulated to lead to a predisposition to AD. This thesis presents a range of methods for measuring functional connectivity in MEG data. We first develop a set of novel adaptations for preprocessing MEG data and performing source reconstruction using a beamformer (chapter 3). We then develop a range of analyses for measuring FC through correlations in the slow envelope oscillations of band-limited source-space MEG data (chapter 4). We investigate the optimum time scales for detecting FC. We then develop methods for extracting single networks (using seed-based correlation) and multiple networks (using ICA). We proceed to develop a group-statistical framework for detecting spatial differences in RSNs and present a preliminary finding for APOE-genotype-dependent differences in RSNs (chapter 5). We also develop a statistical framework for quantifying task-locked temporal differences in functional networks during task-positive experiments (chapter 6). Finally, we demonstrate a data-driven parcellation and network analysis pipeline that includes a novel correction for signal leakage between parcels. We use this framework to show evidence of stationary cross-frequency FC (chapter 7)
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