Neural mechanisms of resistance to peer influence in early adolescence

During the shift from a parent-dependent child to a fully autonomous adult, peers take on a significant role in shaping the adolescent’s behaviour. Peer-derived influences are not always positive, however. Here we explore neural correlates of inter-individual differences in the probability of resisting peer influence in early adolescence. Using functional magnetic-resonance imaging (fMRI), we found striking differences between 10-year old children with high and low resistance to peer influence in their brain activity during observation of angry hand-movements and angry facial expressions: compared with subjects with low resistance to peer influence, individuals with high resistance showed a highly coordinated brain activity in neural systems underlying perception of action and decision making. These findings suggest that the probability of resisting peer influence depends on neural interactions during observation of emotion-laden actions

Dance and emotion in posterior parietal cortex: a low-frequency rTMS study

Background: The neural bases of emotion are most often studied using short non-natural stimuli and assessed using correlational methods. Here we use a brain perturbation approach to make causal inferences between brain activity and emotional reaction to a long segment of dance. <p>Objective/Hypothesis: We aimed to apply offline rTMS over the brain regions involved in subjective emotional ratings to explore whether this could change the appreciation of a dance performance.</p> <p>Methods: We first used functional magnetic resonance imaging (fMRI) to identify regions correlated with fluctuating emotional rating during a 4-minutes dance performance, looking at both positive and negative correlation. Identified regions were further characterized using meta-data interrogation. Low frequency repetitive TMS was applied over the most important node in a different group of participants prior to them rating the same dance performance as in the fMRI session.</p> <p>Results: FMRI revealed a negative correlation between subjective emotional judgment and activity in the right posterior parietal cortex. This region is commonly involved in cognitive tasks and not in emotional task. Parietal rTMS had no effect on the general affective response, but it significantly (p<0.05 using exact t-statistics) enhanced the rating of the moment eliciting the highest positive judgments.</p> <p>Conclusion: These results establish a direct link between posterior parietal cortex activity and emotional reaction to dance. They can be interpreted in the framework of competition between resources allocated to emotion and resources allocated to cognitive functions. They highlight potential use of brain stimulation in neuro-æsthetic investigations.</p&gt

Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses.

International audienceThe aim of group fMRI studies is to relate contrasts of tasks or stimuli to regional brain activity increases. These studies typically involve 10 to 16 subjects. The average regional activity statistical significance is assessed using the subject to subject variability of the effect (random effects analyses). Because of the relatively small number of subjects included, the sensitivity and reliability of these analyses is questionable and hard to investigate. In this work, we use a very large number of subject (more than 80) to investigate this issue. We take advantage of this large cohort to study the statistical properties of the inter-subject activity and focus on the notion of reproducibility by bootstrapping. We asked simple but important methodological questions: Is there, from the point of view of reliability, an optimal statistical threshold for activity maps? How many subjects should be included in group studies? What method should be preferred for inference? Our results suggest that i) optimal thresholds can indeed be found, and are rather lower than usual corrected for multiple comparison thresholds, ii) 20 subjects or more should be included in functional neuroimaging studies in order to have sufficient reliability, iii) non-parametric significance assessment should be preferred to parametric methods, iv) cluster-level thresholding is more reliable than voxel-based thresholding, and v) mixed effects tests are much more reliable than random effects tests. Moreover, our study shows that inter-subject variability plays a prominent role in the relatively low sensitivity and reliability of group studies

Model-based segmentation for improved activation detection in single-subject functional Magnetic Resonance Imaging studies

Functional Magnetic Resonance Imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and regions having different intensities of activation. Our suggested approach is evaluated in realistic two- and three-dimensional simulation experiments as well as on multiple datasets. Finally, the value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients.Comment: 20 pages, 9 figures, 1 tabl

COMPREHENSIVE FRAMEWORKS FOR DECISION MAKING SUPPORT IN MEDICAL EQUIPMENT MANAGEMENT

Throughout medical equipment life cycle, hospitals need to take decisions on medical equipment management based upon a set of different criteria. In fact, medical equipment acquisition, preventive maintenance, and replacement are considered the most important phases, accordingly a properly planned management for these issues is considered a key decision of medical equipment management. In this thesis, a set of frameworks were developed regarding acquisition, preventive maintenance, and replacement to improve management process of medical equipment. In practice, quality function deployment was proposed as a core method around which the frameworks were developed

Background-Source separation in astronomical images with Bayesian Probability Theory

In this work a new method for the detection of faint, both point-like and extended, astronomical objects based on the integrated treatment of source and background signals is described. This technique is applied to public data obtained by imaging methods of high-energy observational astronomy in the X-ray spectral regime. These data are usually employed to address current astrophysical problems, e.g. in the fields of stellar and galaxy evolution and the large-scale structure of the universe. The typical problems encountered during the analysis of these data are: spatially varying cosmic background, large variety of source morphologies and intensities, data incompleteness, steep gradients in the data, and few photon counts per pixel. These problems are addressed with the developed technique. Previous methods extensively employed for the analysis of these data are, e.g., the sliding window and the wavelet based techniques. Both methods are known to suffer from: describing large variations in the background, detection of faint and extended sources and sources with complex morphologies. Large systematic errors in object photometry and loss of faint sources may occur with these techniques. The developed algorithm is based on Bayesian probability theory, which is a consistent probabilistic tool to solve an inverse problem for a given state of information. The information is given by a parameterized model for the background and prior information about source intensity distributions quantified by probability distributions. For the background estimation, the image data are not censored. The background rate is described by a two-dimensional thin-plate spline function. The background model is given by the product of the background rate and the exposure time which accounts for the variations of the integration time. Therefore, the background as well as effects like vignetting, variations of detector quantum efficiency and strong gradients in the exposure time are being handled properly which results in improved detections with respect to previous methods. Source probabilities are provided for individual pixels as well as for correlations of neighboring pixels in a multi-resolution analysis. Consequently, the technique is able of detecting point-like and extended sources and their complex morphologies. Furthermore, images of different spectral bands can be combined probabilistically to further increase the resolution in crowded regions. The developed method characterizes all detected sources in terms of position, number of source counts, and shape including uncertainties. The comparison with previous techniques shows that the developed method allows for an improved determination of background and source parameters. The method is applied to data obtained by the ROSAT and Chandra X-ray observatories whereas particularly the detection of faint and extended sources is improved with respect to previous analyses. This lead to the discovery of new galaxy clusters and quasars in the X-ray band which are confirmed in the optical regime using additional observational data. The new technique developed in this work is particularly suited to the identification of objects featuring extended emission like clusters of galaxies

Relating macroscopic measures of brain activity to fast dynamic neuronal interactions

The aim of this thesis was to find a systematic relationship between neuronal synchrony and firing rates, that would enable us to make inferences about one given knowledge of the other. Functional neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), are sensitive to changes in overall population synaptic activity, that can be interpreted in terms of rate coding for a particular stimulus or task. Characterising the relationship between synchrony and firing rates would facilitate inferences about fast neuronal interactions on the basis of macroscopic measures such as those obtained by fMRI. In this thesis, we used computer simulations of neuronal networks and fMRI in humans to investigate the relationship between mean synaptic activity and fast synchronous neuronal interactions. We found that the extent to which different neurons engage in fast dynamic interactions is largely dependent on the neuronal population firing rates and vice versa, i.e. as one metric changes (either activity or synchrony), so does the other. Additionally, as a result of the strong coupling between overall activity and neuronal synchrony, there is also a robust relationship between background activity and stimulus-evoked activity: Increased background activity increases the gain of the neurons, by decreasing effective membrane time constants, and enhancing stimulus-evoked population activity through the selection of fast synchronous dynamics. In concluding this thesis, we tested and confirmed, with fMRI in humans, that this mechanism may account for attentional modulation, i.e. the change in baseline neuronal firing rates associated with attention, in cell assemblies selectively responding to an attended sensory attribute, enhances responses elicited by presentation of that attribute

Improving nonlinear search with Self-Organizing Maps - Application to Magnetic Resonance Relaxometry

Quantification of myelin in vivo is crucial for the understanding of neurological diseases, like multiple sclerosis (MS). Multi-Component Driven Equilibrium Single Pulse Observation T1 and T2 (mcDESPOT) is a rapid and precise method for determination of the longitudinal and transverse relaxation times in a voxel wise fashion. Briefly, mcDESPOT couples sets of SPGR (spoiled gradient-recalled echo) and bSSFP (fully balance steady-state free precession) data acquired over a range of flip angles (α) with constant interpulse spacing (TR) to derive 6 parameters (free-water T1 and T2, myelin-associated water T1 and T2, relative myelin-associated water volume fraction, and the myelin-associated water proton residence time) based on water exchange models. However, this procedure is computationally expensive and extremely difficult due to the need to find the best fit to the 24 MRI signals volumes in a search of nonlinear 6 dimensional space of model parameters. In this context, the aim of this work is to improve mcDESPOT efficiency and accuracy using tissue information contained in the sets of signals (SPGR and bSSFP) acquired. The basic hypothesis is that similar acquired signals are referred to tissue portions with close features, which translate in similar parameters. This similarity could be used to drive the nonlinear mcDESPOT fitting, leading the optimization algorithm (that is based on a stochastic region contraction approach) to look for a solution (i.e. the 6 parameters vector) also in regions defined by previously computed solutions of others voxels with similar signals. For this reason, we clustered the sets of SPGR and bSSFP using the neural network called Self Organizing Map (SOM), which uses a competitive learning technique to train itself in an unsupervised manner. The similarity information obtained from the SOM was then used to accordingly suggest solutions to the optimization algorithm. A first validation phase with in silico data was performed to evaluate the performances of the SOM and of the modified method, SOM+mcDESPOT. The latter was further validated using real magnetic resonance images. The last step consisted of applying the SOM+mcDESPOT to a group of healthy subjects ( ) and a group of MS patients ( ) to look for differences in myelin-associated water fractions values between the two groups. The validation phases with in silico data verified the initial hypothesis: in more the 74% of the times, the correct solution of a certain voxel is in the space dictated by the cluster which that voxel is mapped to. Adding the information of similar solutions extracted from that cluster helps to improve the signals fitting and the accuracy in the determination of the 7 parameters. This result is still present even if the data are corrupted by a high level of noise (SNR=50). Using real images allowed to confirm the power of SOM+mcDESPOT underlined through the in silico data. The application of SOM+mcDESPOT to the controls and to the MS patients allowed firstly obtaining more feasible results than the traditional mcDESPOT. Moreover, a statistically significant difference of the myelin-associated water fraction values in the normal appearing white matter was found between the two groups: the MS patients, in fact, show lower fraction values compared to the normal subjects, indicating an abnormal presence of myelin in the normal appearing white matter of MS patients. In conclusion, we proposed the novel method SOM+mcDESPOT that is able to extract and exploit the information contained in the MRI signals to drive appropriately the optimization algorithm implemented in mcDESPOT. In so doing, the overall accuracy of the method in both the signals fitting and in the determination of the 7 parameters improves. Thus, the outstanding potentiality of SOM+mcDESPOT could assume a crucial role in improving the indirect quantification of myelin in both healthy subjects and patient

The form pathways in the visual brain.

The perception of visual forms is crucial for humans for successful interactions with the environment. This process occurs automatically, and its outcome is reflected in the inferences and decisions we constantly make. The focus of this thesis is on how the brain handles different aspects of the perception of forms. To study this in normal human individuals, experiments were performed using functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and psychophysical methods. This thesis first discusses experiments designed to unravel the mechanisms of form construction, i.e. those from which all the component parts of a single form are assembled. Results suggest that the construction of very simple forms occurs in intermediate visual areas in a parallel and recursive process, with an increase in brain activity with increments in form complexity. A further experiment was performed to study how regularities or known characteristics of images, and the brain responses they elicit, will contribute to explain current percepts. Results from this experiment are consistent with a model where images with learnt attributes activate more strongly anterior visual areas and images with random patterns cause higher activations in earlier visual areas, probably due to top-down signals that reduce activity when it is possible to explain the causes of the sensory stimulation. Finally, it shows differences in the evoked neural activity when forms are either detected or classified, relating these processes to the activity generated in early visual areas. Based on the results of these experiments, a mechanism of top-down and bottom-up interactions between visual areas in the human brain is discussed in the context of the perception of forms