1,720,969 research outputs found
Statistical causality in the EEG for the study of cognitive functions in healthy and pathological brains
Full text linkUnderstanding brain functions requires not only information about the spatial localization of neural activity, but also about the dynamic functional links between the involved groups of neurons, which do not work in an isolated way, but rather interact together through ingoing and outgoing connections. The work carried on during the three years of PhD course returns a methodological framework for the estimation of the causal brain connectivity and its validation on simulated and real datasets (EEG and pseudo-EEG) at scalp and source level. Important open issues like the selection of the best algorithms for the source reconstruction and for time-varying estimates were addressed. Moreover, after the application of such approaches on real datasets recorded from healthy subjects and post-stroke patients, we extracted neurophysiological indices describing in a stable and reliable way the properties of the brain circuits underlying different cognitive states in humans (attention, memory). More in detail: I defined and implemented a toolbox (SEED-G toolbox) able to provide a useful validation instrument addressed to researchers who conduct their activity in the field of brain connectivity estimation. It may have strong implication, especially in methodological advancements. It allows to test the ability of different estimators in increasingly less ideal conditions: low number of available samples and trials, high inter-trial variability (very realistic situations when patients are involved in protocols) or, again, time varying connectivity patterns to be estimate (where stationary hypothesis in wide sense failed). A first simulation study demonstrated the robustness and the accuracy of the PDC with respect to the inter-trials variability under a large range of conditions usually encountered in practice. The simulations carried on the time-varying algorithms allowed to highlight the performance of the existing methodologies in different conditions of signals amount and number of available trials. Moreover, the adaptation of the Kalman based algorithm (GLKF) I implemented, with the introduction of the preliminary estimation of the initial conditions for the algorithm, lead to significantly better performance. Another simulation study allowed to identify a tool combining source localization approaches and brain connectivity estimation able to provide accurate and reliable estimates as less as possible affected to the presence of spurious links due to the head volume conduction. The developed and tested methodologies were successfully applied on three real datasets. The first one was recorded from a group of healthy subjects performing an attention task that allowed to describe the brain circuit at scalp and source level related with three important attention functions: alerting, orienting and executive control. The second EEG dataset come from a group of healthy subjects performing a memory task. Also in this case, the approaches under investigation allowed to identify synthetic connectivity-based descriptors able to characterize the three main memory phases (encoding, storage and retrieval). For the last analysis I recorded EEG data from a group of stroke patients performing the same memory task before and after one month of cognitive rehabilitation. The promising results of this preliminary study showed the possibility to follow the changes observed at behavioural level by means of the introduced neurophysiological indices
Measuring Connectivity in Linear Multivariate Processes With Penalized Regression Techniques
Full text linkThe evaluation of time and frequency domain measures of coupling and causality relies on
the parametric representation of linear multivariate processes. The study of temporal dependencies among
time series is based on the identification of a Vector Autoregressive model. This procedure is pursued
through the definition of a regression problem solved by means of Ordinary Least Squares (OLS) estimator.
However, its accuracy is strongly influenced by the lack of data points and a stable solution is not always
guaranteed. To overcome this issue, it is possible to use penalized regression techniques. The aim of this
work is to compare the behavior of OLS with different penalized regression methods used for a connectivity
analysis in different experimental conditions. Bias, accuracy in the reconstruction of network structure
and computational time were used for this purpose. Different penalized regressions were tested by means
of simulated data implementing different ground-truth networks under different amounts of data samples
available. Then, the approaches were applied to real electroencephalographic signals (EEG) recorded from
a healthy volunteer performing a motor imagery task. Penalized regressions outperform OLS in simulation
settings when few data samples are available. The application on real EEG data showed how it is possible
to use features extracted from brain networks for discriminating between two tasks even in conditions of
data paucity. Penalized regression techniques can be used for brain connectivity estimation and can be
exploited for the computation of all the connectivity estimators based on linearity assumption overcoming
the limitations imposed by the classical OLS
Editorial: Use of neuroimaging techniques for the prevention, assessment, and treatment of mood disorders
No full textEEG-based indices as outcome measures for a memory rehabilitation treatment in stroke patients
No full textThe efficacy of cognitive rehabilitation treatments after stroke is routinely assessed by means of neuropsychological tests battery. More evidences indicate that the neuroplasticity phenomena which occurs after stroke can be characterized by investigating brain networks changes. Despite the efforts in the field, a complete description of connectivity patterns characterizing different phases of cognitive recovery in stroke patients is still missing. In this work, we proposed a combined approach of advanced methodologies for effective connectivity estimation and graph theory for defining EEG-based descriptors able to: i) characterize the brain processes at the basis of a memory rehabilitation treatment and ii)support its clinical evaluation. We derived neurophysiological indices from a previous study on healthy subjects and then we used them as outcome measures of a rehabilitation treatment on stroke-patients.The efficacy of cognitive rehabilitation treatments after stroke is routinely assessed by means of neuropsychological tests battery. More evidences indicate that the neuroplasticity phenomena which occurs after stroke can be characterized by investigating brain networks changes. Despite the efforts in the field, a complete description of connectivity patterns characterizing different phases of cognitive recovery in stroke patients is still missing. In this work, we proposed a combined approach of advanced methodologies for effective connectivity estimation and graph theory for defining EEG-based descriptors able to: i) characterize the brain processes at the basis of a memory rehabilitation treatment and ii)support its clinical evaluation. We derived neurophysiological indices from a previous study on healthy subjects and then we used them as outcome measures of a rehabilitation treatment on stroke-patients
Estimating brain connectivity when few data points are available: Perspectives and limitations
No full textMethods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in which current methods fail, due to the lack of enough data points. We tested the performances of this new approach, in comparison with the classical approach based on ordinary least squares (OLS), by means of a simulation study implementing different ground-truth networks, under different network sizes and different levels of data points. Simulation results showed that the new approach provides better performances, in terms of accuracy of the parameters estimation and false positives/false negatives rates, in all conditions related to a low data points/model dimension ratio, and may thus be exploited to estimate and validate estimated patterns at single-trial level or when short time data segments are available
Detecting brain network changes induced by a neurofeedback-based training for memory function rehabilitation after stroke
No full textThe efficacy of rehabilitative interventions in stroke patients is routinely assessed by
means of a neuropsychological test battery. Nowadays, more evidences indicate that the
neuroplasticity which occurs after stroke can be better understood by investigating
changes in brain networks. In this pilot study we applied advanced methodologies for
effective connectivity estimation in combination with graph theory approach, to define
EEG derived descriptors of brain networks underlying memory tasks. In particular, we
proposed such descriptors to identify substrates of efficacy of a Brain-Computer
Interface (BCI) controlled neurofeedback-based intervention to improve cognitive
function after stroke. EEG data were collected from two stroke patients before and after
a neurofeedback-based training for working memory deficits. We show that the
estimated brain connectivity indices were sensitive to different training intervention
outcomes, thus suggesting an effective support to the neuropsychological assessment in
the evaluation of the changes induced by the BCI-based rehabilitative intervention
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
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