Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity

Functional brain networks are well described and estimated from data with Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance estimators. Comparing functional connectivity of subjects in two populations calls for comparing these estimated GGMs. Our goal is to identify differences in GGMs known to have similar structure. We characterize the uncertainty of differences with confidence intervals obtained using a parametric distribution on parameters of a sparse estimator. Sparse penalties enable statistical guarantees and interpretable models even in high-dimensional and low-sample settings. Characterizing the distributions of sparse models is inherently challenging as the penalties produce a biased estimator. Recent work invokes the sparsity assumptions to effectively remove the bias from a sparse estimator such as the lasso. These distributions can be used to give confidence intervals on edges in GGMs, and by extension their differences. However, in the case of comparing GGMs, these estimators do not make use of any assumed joint structure among the GGMs. Inspired by priors from brain functional connectivity we derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. This leads us to introduce the debiased multi-task fused lasso, whose distribution can be characterized in an efficient manner. We then show how the debiased lasso and multi-task fused lasso can be used to obtain confidence intervals on edge differences in GGMs. We validate the techniques proposed on a set of synthetic examples as well as neuro-imaging dataset created for the study of autism

Computational Brain Connectivity Mapping: A Core Health and Scientific Challenge

International audienceOne third of the burden of all the diseases in Europe is due to problems caused by diseases affecting brain. Although exceptional progress have been obtained for exploring the brain during the past decades, it is still terra-incognita and calls for specific efforts in research to better understand its architecture and functioning. To take up this great challenge of modern science and to solve the limited view of the brain provided just by one imaging modality, this article advocates the idea developed in my research group of a global approach involving new generation of models for brain connectivity mapping and strong interactions between structural and functional connectivities. Capitalizing on the strengths of integrated and complementary non invasive imaging modalities such as diffusion Magnetic Resonance Imaging (dMRI) and Electro & Magneto-Encephalography (EEG & MEG) will contribute to achieve new frontiers for identifying and characterizing structural and functional brain connectivities and to provide a detailed mapping of the brain connectivity, both in space and time. Thus leading to an added clinical value for high impact diseases with new perspectives in computational neuro-imaging and cognitive neuroscience

Brain networks under attack : robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research

Identification of autism spectrum disorder using deep learning and the ABIDE dataset

The research was supported by CAPES, Brazilian Ministry of Education (Projeto ACERTA CAPES/OBEDUC 0898/2013; number 23038.002530/2013-93Peer reviewe

Brain Activity During Paired and Individual Mindfulness Meditation: A Controlled EEG Study

Objective: In this study, we evaluated brain electroencephalographic (EEG) activity in healthy participants during the performance of paired and individual mindfulness meditation (MM). We hypothesized that EEG activity is differentially affected by meditation in pairs compared to individual meditation. Methods: A total of 20 healthy female university students (mean age 19.54 years, SD =1.53) with no prior experience in MM participated in this study. All participants had to perform a 5-minute MM task together and individually while the other participant was in rest or performing a concentration task (control condition). To exclude social interaction as main factor, participants were separated from their research partner by an opaque screen while instructions were given through headphones. Brain electroencephalographic (EEG) activity from each individual student was measured during all conditions. Results: The main findings indicate that left-frontal alpha and theta spectral EEG power was significantly higher during the paired MM condition compared to individual MM and control condition. Conclusions: This controlled MM study demonstrates differences in brain activity between practicing mindfulness in pairs compared to practicing it individually. We conclude that the increased alpha and theta EEG power during paired MM may be associated with social facilitation or the activation of theory of mind. The results invite further reflection on interpersonal communication and mindfulness

Brain Activity During Paired and Individual Mindfulness Meditation: A Controlled EEG Study

Objective: In this study, we evaluated brain electroencephalographic (EEG) activity in healthy participants during the performance of paired and individual mindfulness meditation (MM). We hypothesized that EEG activity is differentially affected by meditation in pairs compared to individual meditation. Methods: A total of 20 healthy female university students (mean age 19.54 years, SD =1.53) with no prior experience in MM participated in this study. All participants had to perform a 5-minute MM task together and individually while the other participant was in rest or performing a concentration task (control condition). To exclude social interaction as main factor, participants were separated from their research partner by an opaque screen while instructions were given through headphones. Brain electroencephalographic (EEG) activity from each individual student was measured during all conditions. Results: The main findings indicate that left-frontal alpha and theta spectral EEG power was significantly higher during the paired MM condition compared to individual MM and control condition. Conclusions: This controlled MM study demonstrates differences in brain activity between practicing mindfulness in pairs compared to practicing it individually. We conclude that the increased alpha and theta EEG power during paired MM may be associated with social facilitation or the activation of theory of mind. The results invite further reflection on interpersonal communication and mindfulness