46,797 research outputs found
Optimizing Experimental Design for Comparing Models of Brain Function
This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work
Evaluating true BCI communication rate through mutual information and language models.
Brain-computer interface (BCI) systems are a promising means for restoring communication to patients suffering from "locked-in" syndrome. Research to improve system performance primarily focuses on means to overcome the low signal to noise ratio of electroencephalogric (EEG) recordings. However, the literature and methods are difficult to compare due to the array of evaluation metrics and assumptions underlying them, including that: 1) all characters are equally probable, 2) character selection is memoryless, and 3) errors occur completely at random. The standardization of evaluation metrics that more accurately reflect the amount of information contained in BCI language output is critical to make progress. We present a mutual information-based metric that incorporates prior information and a model of systematic errors. The parameters of a system used in one study were re-optimized, showing that the metric used in optimization significantly affects the parameter values chosen and the resulting system performance. The results of 11 BCI communication studies were then evaluated using different metrics, including those previously used in BCI literature and the newly advocated metric. Six studies' results varied based on the metric used for evaluation and the proposed metric produced results that differed from those originally published in two of the studies. Standardizing metrics to accurately reflect the rate of information transmission is critical to properly evaluate and compare BCI communication systems and advance the field in an unbiased manner
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
An electroencephalography (EEG) based brain activity recognition is a
fundamental field of study for a number of significant applications such as
intention prediction, appliance control, and neurological disease diagnosis in
smart home and smart healthcare domains. Existing techniques mostly focus on
binary brain activity recognition for a single person, which limits their
deployment in wider and complex practical scenarios. Therefore, multi-person
and multi-class brain activity recognition has obtained popularity recently.
Another challenge faced by brain activity recognition is the low recognition
accuracy due to the massive noises and the low signal-to-noise ratio in EEG
signals. Moreover, the feature engineering in EEG processing is time-consuming
and highly re- lies on the expert experience. In this paper, we attempt to
solve the above challenges by proposing an approach which has better EEG
interpretation ability via raw Electroencephalography (EEG) signal analysis for
multi-person and multi-class brain activity recognition. Specifically, we
analyze inter-class and inter-person EEG signal characteristics, based on which
to capture the discrepancy of inter-class EEG data. Then, we adopt an
Autoencoder layer to automatically refine the raw EEG signals by eliminating
various artifacts. We evaluate our approach on both a public and a local EEG
datasets and conduct extensive experiments to explore the effect of several
factors (such as normalization methods, training data size, and Autoencoder
hidden neuron size) on the recognition results. The experimental results show
that our approach achieves a high accuracy comparing to competitive
state-of-the-art methods, indicating its potential in promoting future research
on multi-person EEG recognition.Comment: 10 page
Optimizing Stimulation and Analysis Protocols for Neonatal fMRI
The development of brain function in young infants is poorly understood. The core challenge is that infants have a limited behavioral repertoire through which brain function can be expressed. Neuroimaging with fMRI has great potential as a way of characterizing typical development, and detecting abnormal development early. But, a number of methodological challenges must first be tackled to improve the robustness and sensitivity of neonatal fMRI. A critical one of these, addressed here, is that the hemodynamic response function (HRF) in pre-term and term neonates differs from that in adults, which has a number of implications for fMRI. We created a realistic model of noise in fMRI data, using resting-state fMRI data from infants and adults, and then conducted simulations to assess the effect of HRF of the power of different stimulation protocols and analysis assumptions (HRF modeling). We found that neonatal fMRI is most powerful if block-durations are kept at the lower range of those typically used in adults (full on/off cycle duration 25-30s). Furthermore, we show that it is important to use the age-appropriate HRF during analysis, as mismatches can lead to reduced power or even inverted signal. Where the appropriate HRF is not known (for example due to potential developmental delay), a flexible basis set performs well, and allows accurate post-hoc estimation of the HRF
Improving treatment of glioblastoma: new insights in targeting cancer stem cells effectively
Glioblastoma is the most common primary malignant brain tumour in the adult population.
Despite multimodality treatment with surgery, radiotherapy and chemotherapy, outcomes are
very poor, with less than 15% of patients alive after two years. Increasing evidence suggests
that glioblastoma stem cells (GSCs) are likely to play an important role in the biology of this
disease and are involved in treatment resistance and tumour recurrence following standard
therapy.
My thesis aims to address two main aspects of this research area: 1) optimization of methods
to evaluate treatment responses of GSCs and their differentiated counterparts (non-GSCs),
with a particular focus on a tissue culture model that resembles more closely the tumoral
niche; 2) characterization of cell division and centrosome cycle of GSCs, investigating possible
differences between these cells and non-GSCs, that would allow the identification of targets
for new therapeutic strategies against glioblastomas.
In the first part of my project, I optimized a clonogenic survival assay, to compare sensitivity of
GSCs and non-GSCs to various treatments, and I developed the use of a 3-dimentional tissue
culture system, that allows analysis of features and radiation responses of these two
subpopulations in the presence of specific microenvironmental factors from the tumoral niche.
In the second part, I show that GSCs display mitotic spindle abnormalities more frequently
than non-GSCs and that they have distinctive features with regards to the centrosome cycle. I
also demonstrate that GSCs are more sensitive than non-GSCs to subtle changes in Aurora
kinase A activity, which result in a rapid increase in polyploidy and subsequently in senescence,
with a consistent reduction in clonogenic survival. Based on these findings, I propose that
kinases involved in the centrosome cycle need to be explored as a novel strategy to target
GSCs effectively and improve outcomes of glioblastoma patients
The shuffle estimator for explainable variance in fMRI experiments
In computational neuroscience, it is important to estimate well the
proportion of signal variance in the total variance of neural activity
measurements. This explainable variance measure helps neuroscientists assess
the adequacy of predictive models that describe how images are encoded in the
brain. Complicating the estimation problem are strong noise correlations, which
may confound the neural responses corresponding to the stimuli. If not properly
taken into account, the correlations could inflate the explainable variance
estimates and suggest false possible prediction accuracies. We propose a novel
method to estimate the explainable variance in functional MRI (fMRI) brain
activity measurements when there are strong correlations in the noise. Our
shuffle estimator is nonparametric, unbiased, and built upon the random effect
model reflecting the randomization in the fMRI data collection process.
Leveraging symmetries in the measurements, our estimator is obtained by
appropriately permuting the measurement vector in such a way that the noise
covariance structure is intact but the explainable variance is changed after
the permutation. This difference is then used to estimate the explainable
variance. We validate the properties of the proposed method in simulation
experiments. For the image-fMRI data, we show that the shuffle estimates can
explain the variation in prediction accuracy for voxels within the primary
visual cortex (V1) better than alternative parametric methods.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS681 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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